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ergy Systems | Center for Environmental Policy | University of Florida

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Proceedings

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Emergy Synthesis 2: Theory and Applications of the Emergy Methodology

Contributors, Acknowledgments, Introduction

1. Energy Hierarchy and Transformity in the Universe

Howard T. Odum

2. Mathematical Formulation of the Maximum Em-Power Principle

Corrado Giannantoni

3. Spatial Modeling of Empower and Environmental Loading

Mark T. Brown

4. The Correlation Between GDP And Both Energy Use And Emergy Use

Jae-Young Ko and Charles A. S. Hall

5. Environmental Accounting for The Saemangeum Tideland Reclamation Project

Suk Mo Lee, Woo Suk Kim, and Ji Ho Son

6. From Emergy Analysis to Public Policy: Soybean in Brazil

E. Ortega , M. Miller, M.H. Anami, P.R. Beskow

7. Sustainability Assessment of Slash-And-Burn and Fire-Free Agriculture in Northeastern

Pará, BrazilRodrigues, G.S.; Kitamura, P.C.; Sá, T.D. de A.; Vielhauer, K.

8. Environmental and Economic Aspects of Agro-forestry and Agricultural Systems in

Chiapas, Mexico

Hugo A. Guillén Trujillo

9. Emergy Evaluation of Building Operation in Thailand

Vorasun Buranakarn

10. Emergy and Life-Cycle Assessment of Steel Production in Europe

Silvia Bargigli and Sergio Ulgiati

11. Emergetic and Exergetic Analysis of a Combined Cycle Power Plant

Simone Tonon and Alberto Mirandola

12. On the Rationale of the Transformity Method

Dennis Collins

13. Mathematics for Quality. Living and Non-living Systems

Corrado Giannantoni

14. Transformities from Ecosystem Energy Webs with the Eigenvalue Method

Howard T. Odum and Dennis Collins

15. Emergy Analysis of the Prehistoric Global Nitrogen Cycle

Daniel E. Campbell

16. Spectral Transformities for Solar Radiation Reaching the Earth

David R. Tilley

17. Transformity and Simulation of Microbial Ecosystems

Howard T. Odum

18. Emergy Perspectives on the Argentine Economy Throughout the Twentieth Century

Cecilia Ferreyra and Mark T. Brown

19. Emergy Evaluation of a Common Market Economy: MERCOSUR Sustainability

Mark T. Brown, Cecilia Ferreyra and Eliana Bardi

20. Student Empower: A Teaching Exercise

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p://cep.ees.ufl.edu/emergy/conferences/ERC02_2001/proceedings.shtml[31.07.2013 21:29:56]

Elisabeth C. Odum and Howard T. Odum

21. Emergy Comparison of Ethanol Production in Brazil: Traditional Versus Small Distillery

With Food and Electricity Production

Ortega, E., Ometto A.R., Ramos P.A.R., Anami M.H., Lombardi G., Coelho, O.F.

22. Evaluation of a Coal Gasification Process Towards Hydrogen Production: An Integrated

Assessment

Marco Raugei, Silvia Bargigli, and Sergio Ulgiati

23. Fertilizer Co-Products as Agricultural Emternalities: Quantifying Environmental Services

Used in Production of Food

Sherry Brandt-Williams and Gonzague Pillet

24. The Energy Basis Of A Subtropical Wetland Mesocosm

C. Streb, E. Biermann, S. Lutz, P. Kangas and W. Adey

25. A Note on the Uncertainty in Estimates of Transformities Based on Global Water

Budgets

Daniel E. Campbell

26. Dynamic Emergy Simulation of Soil Genesis and Techniques for Estimating

Transformity Confidence Envelopes

Matthew Cohen

27. Emergy Evaluation for Sustainable Development Strategy of Fisheries Resources in

Darién, Panama

John McLachlan-Karr

28. An Environmental Accounting of Water Resources Production System in the Samoggia

Creek Area Using Emergy Method

Laura Fugaro, Nadia Marchettini, Ilaria Principi

29. Emergy Evaluation Of The "Emternalities" In Non-Industrialized Regions: The Case of

Two Mountain Communities in Italy

Federico M. Pulselli, Riccardo M. Pulselli, Maria P. Picchi

30. Emergy Assessment of Incineration and Landfilling of Municipal Solid Waste in Italy

Niccolucci V., Panzieri M., Porcelli M. , Ridolfi R.

31. Social Structure and Ecotourism Development on Bonaire

Thomas Abel

Citation

Brown, M.T. H.T. Odum, D. Tilley and S. Ulgiati. 2003. (eds) Emergy Synthesis 2: Theory and Applications of the Emergy Methodology.

Proceedings of the 2nd biennial emergy conference held at Gainesville, FL September 2001. The Center for Environmental Policy, University of

Florida, Gainesville. 432 pages.

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© 2003-2013 Emergy Systems | Center for Environmental Policy | University of Florida

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Theory and Applications of the

Emergy Methodology2EMERGY

SYNTHESIS



Howard T. Odum

1924 - 2002

On the Occasion of the Memorial Service for H T Odum, October 17, 2002

Thank you, all of you, for coming. It is a wonderful tribute to HT Odum that so many people have comeso far. In some ways you are a testament to his transformity and continuing empower.

I would especially like to recognizeBetty Odum, HT’s wife and collaborator for the past 28 years

HT’s daughter Ann Odum HT’s second daughter Mary Logan, her husband Todd, and their daughter Kelsey

and the Trimmer family… Ruth and her husband Burton and their children Quilla and Luke Pete and his son Eryan and daughter Tanya Kathy and her husband Dan, and their children Elliot, Anthony and Kristana Morris and his wife Amy, and their children Russell and Maya

Finally, Joanie Breeze, HT’s secretary and keeper of institutional memory for the past 30 years.

My father used to tell a story “on me” whenever he wanted to remind me of my beginnings. This particularevent occurred when I was quite small on some beach in south Florida. It seems we were walking along

picking our way through seaweed and shells as we often did. Me following him, trying to place my feetsquarely in his foot prints, and not being particularly successful at this for want of longer legs and bettercoordination. I called after him… “Daddy, don’t walk so big”

ii



Now fast forward to 1970. A group of Architecture students and I were sitting in a geography class presidedover by Josh Dickinson. He enters the room and announces with much excitement that the University just hired H.T. Odum. We looked quizzical. Who? I mutter. Who’s that? Famous last words.

Fast forward again several months when in one of his rst lectures on campus, HT Odum ended with…

“Let’s draw a systems diagram of some system.” I jumped up and said, “ Draw a city.” As that cityunfolded before us on the board I was hooked. I saw, for the rst time, how the pieces t and meaning

and causality replace the endless stream of compartmentalized bits and pieces of knowledge I had beengiven up to that point.

Thus began a 32 year odyssey for me. Working with HT Odum, I was often overwhelmed by the sizeof his steps. We were friends and colleagues at times, but more often than not mentor and student. Weteamed together to teach ecological engineering, energy analysis and ecological economics. We ran theThursday afternoon Systems Seminar together. On occasion we created and explored together. We trav-eled together. What a trip it was. Through it all I was mindful that he, more than anyone else, seemedto knew his place in the biosphere. It seemed that he was not only part of it, he was fully aware of and

perfectly in tune with it.

Today we are gathered here to, in some way, pay tribute to and recognize an individual who had a largerthan life presence and impact on many of us. An individual who often walked alone but with many ofus following along behind as best we could. A man who left an incredible legacy; a pathway of ideas,teaching, and ethical conduct that while not so easy to follow at times, is non-the less well marked.

Over the past several months I have been fortunate to witness an outpouring of letters and emails frommany people who knew HT and some who didn’t know him directly but who knew of him.

There were several themes in the letters and emails tributes to his ideas/intellect, to his compassion, generosity, and kindness,

and nally his teaching.

So I would like to share with you some of what was said to HT in his nal days, and what has been writ-ten since his passing by his friends associates and former students.

Concerning his ideas and intellect…

Hardly a day goes by without my invoking some theory of yours, either directly or

indirectly in my daily agenda. It’s amazing to me how the business of the world ts so

neatly into models you have developed.

History will judge your legacy, but for me you are one of the greatest minds of the

20th century.

I’m just one of the many, of course, who have been inuenced by your genius and

enthusiasm. You have been unique. I think no one has made better use of their time on

this earth, I’m so glad that your life impinged on mine.

The themes of Environment, Power and Society, are with us more than ever, as the worldtries to address “sustainability” or “global change,” or “energy/resource shortages,”

or as war drums beat for maintaining oil supply lines.

iii



Concerning his compassion, generosity and kindness with others...

I am so glad our paths crossed and am thankful for your generosity – a different kind

of sharing and mentoring than any I had ever experienced before.

I was extremely grateful for the generosity and kindness you showed me when it came

to my own efforts at research. This encouragement has inspired me to continue my own

work, and to try to be generous and kind to my colleagues as well.

I must thank you once more for all the incredible help and personal example of courage

and independent thinking you’ve given me and my colleagues. I will always treasure

your eloquent and powerful voice, uninchingly speaking and ghting for the truth.

How often you were the lone voice for allowing ecological self-organization to proceed

amidst the chorus of “scientic” control freaks! You never lost sight and comprehen-

sion of the big picture that was necessary to model and understand nature. The details

always came later, if they were needed at all.

Concerning his Teaching…

It was in 1992 when I came across your text Systems Ecology that I rst got to know

your ideas. Oh what a symphonic melody to me. Computer simulation and natural

systems, what better match for my interest.

What was most important to your graduate students, however, was your classroom

lectures. They were so intellectually challenging yet satisfying, and so much fun! ….

I certainly wished that your critics had a chance to sit through a semester in one of your great classes. Just about everyone would be completely taken in by your energy,

good humor and especially incredibly integrated and exciting view of how the world

works. I still operate in all that I do professionally on what I learned in those wonder-

ful classes.

Jack Ewel has written an obituary for the Southeastern Biologist that captures several very interestingand personal qualities of HT. I’d like to quote a few lines from it…

Those familiar with H. T. Odum’s scholarship primarily through his contributions of the

past 20 years may not know that he was also an outstanding student of natural history.

He knew the microbial soups of springs and microcosms, the mollusks and crustaceansof inshore marine communities, the plankton and benthic fauna of cypress swamps, and

the complex biota of the rain forest. But organisms were always more than binomials,

and if students were told what it was, they were inevitably told what it did.

H. T. Odum had an uncanny ability to turn one’s world around 180 degrees, more often

than not converting doomsday into optimism. If the topic was acid rain, he wanted to

know where all the bases were; if it was non-indigenous species, he heralded the new

services being provided; and if it was devegetation by sacred cows, he pointed to their

role as draft animals and the fuel value of their dung.

Highly principled, H. T. Odum always stuck to his convictions, even when the easier

path often would have been to mount the bandwagon of peers. On the one hand, he was

a erce though friendly competitor: they are still looking for opponents’ croquet balls

iv



he sent sailing off court at many a croquet match, and pity the poor graduate student,

especially any large male, who ended up across from him on the volleyball court. On the

other hand, he went out of his way to foster the underdog, especially those whose oppor-

tunities had been limited by factors beyond their control. To say that he was a gentleman

vastly understates his genteelness; to tag him a scholar is too small a label for one of

science’s greatest minds. One of H. T. Odum’s dicta was that, in order to understand a

system, you had to understand the next larger system of which it was a part

H.T. Odum was an extraordinary individual. His love of teaching, his creative and imaginative way ofviewing the biosphere, his grasp of so many different elds of science, and his drive and unbounded

energy have left many students, colleagues, and associates awestruck. His unique way of understandingthe biosphere and human’s place within it, his gift to us all, will endure and expand as it is more fullyunderstood by this and succeeding generations. He leaves us a legacy of books, scientic publications,

and even a movie or two, but far beyond these tangible remnants of his scientic inquiry is the devotion

to his students, close associates, family and to human kind. In his own words in the Prosperous WayDown, his most recent book with Betty, he said “As sometimes attributed to past cultures, people may

nd glory in being an agent of the earth.” H.T. Odum was an agent of the earth, striving always to teachgood stewardship and a profound respect for the cycles and hierarchies of the biosphere.

There is no doubt that HT Odum’s foot print was quite large (a size 13 if my memory serves me), andin many respects impossible to ll. On the other hand he has made it easy for us to follow, for with

prints as large as his, we cannot help but nd our place within them.

Mark T. Brown

Gainesville, FL

v



EMERGY SYNTHESIS 2:Theory and Applications of the Emergy Methodology

Proceedings from the Second Biennial Emergy Analysis Research Conference,

Gainesville, Florida, September, 2001.

Edited by

Mark T. BrownUniversity of Florida


Associate EditorsHoward T. Odum



David Tilley

University of Maryland

College Park, Maryland

Sergio Ulgiati

University of Siena

Siena, Italy

December 2003

The Center for Environmental PolicyDepartment of Environmental Engineering Sciences

University of FloridaGainesville, FL

The Center for Environmental PolicyP.O. Box 116450

vi



University of FloridaGainesville, FL 32611-6450Fax (352) 392-3076

ISBN: 0-9707325-1-1

Center for Environmental Policy

This book may be purchased for $20 from:The Center for Environmental Policy

vii


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Contributors ............................................................................................................................................ xi

Acknowledgments ................................................................................................................................... xiv

Introduction ............................................................................................................................................ xvi

1. Energy Hierarchy and Transformity in the Universe ...................................................................... 1

Howard T. Odum

2. Mathematical Formulation of the Maximum Em-Power Principle .............................................15

Corrado Giannantoni

3. Spatial Modeling of Empower and Environmental Loading ........................................................35

Mark T. Brown

4. The Correlation Between GDP And Both Energy Use And Emergy Use ................................... 51


5. Environmental Accounting for The Saemangeum Tideland Reclamation Project ..................... 61


6. From Emergy Analysis to Public Policy: Soybean in Brazil .........................................................77

E. Ortega , M. Miller, M. H. Anami, and P.R. Beskow

7. Sustainability Assessment of Slash-and-Burn and Fire-Free Agriculture

in Northeastern Pará, Brazil ........................................................................................................... 95

G. S. Rodrigues, P. C. Kitamura, T. D. de A. Sá, and K. Vielhauer

8. Environmental and Economic Aspects of Agro-forestry and

Agricultural Systems in Chiapas, Mexico .................................................................................... 109


9. Emergy Evaluation of Building Operation in Thailand .............................................................. 123

Vorasun Buranakarn

10. Emergy and Life-Cycle Assessment of Steel Production in Europe ......................................... 141 Silvia Bargigli and Sergio Ulgiati

11. Emergetic and Exergetic Analysis of a Combined Cycle Power Plant ....................................157


12. On the Rationale of the Transformity Method ...........................................................................171

Dennis Collins

13. Mathematics For Quality. Living and Non-living Systems .......................................................185

Corrado Giannantoni

CONTENTS



ix

14. Transformities from Ecosystem Energy Webs with the Eigenvalue Method .......................... 203


15. Emergy Analysis of the Prehistoric Global Nitrogen Cycle ......................................................221

Daniel E. Campbell

16. Spectral Transformities for SolarRadiation Reaching the Earth .............................................241 David R. Tilley

17. Transformity and Simulation of Microbial Ecosystems ............................................................ 249

Howard T. Odum

18. Emergy Perspectives on the Argentine Economy Throughout ................................................ 271

the Twentieth Century


19. Emergy Evaluation of a Common Market Economy: .............................................................. 283 MERCOSUR Sustainability

Mark T. Brown, Cecilia Ferreyra and Eliana Bardi

20. Student Empower: A Teaching Exercise ................................................................................... 293


21. Emergy Comparison of Ethanol Production in Brazil: Traditional ........................................301

Versus Small Distillery With Food and Electricity Production

E. Ortega, A.R. Ometto, P.A.R. Ramos, A.H. Anami, G. Lombardi, and O.F. Coelho

22. Evaluation of a Coal Gasication Process Towards Hydrogen ................................................ 313 Production: An integrated assessment.


23. Fertilizer Co-Products as Agricultural Emternalities: ............................................................. 327

Quantifying Environmental Services Used in Production of Food


24. The Energy Basis of a Subtropical Wetland Mesocosm ............................................................339


25. A Note on the Uncertainty in Estimates of Transformities Based ........................................... 349 on Global Water Budgets

Daniel E. Campbell

26. Dynamic Emergy Simulation of Soil Genesis and Techniques .................................................355

for Estimating Transformity Condence Envelopes

Matthew Cohen

27. Emergy Evaluation for Sustainable Development Strategy ..................................................... 371

of Fisheries Resources in Darién, Panama

John McLachlan-Karr



x

28. An Environmental Accounting of Water Resources Production .............................................387

System in the Samoggia Creek Area Using Emergy Method


29. Emergy Evaluation of the “Emternalities” In Non-Industrialized ......................................... 397

Regions: The case of two mountain communities in Italy


30. Emergy Assessment of Incineration and Landlling of Municipal ......................................... 409

Solid Waste in Italy

V. Niccolucci , M. Panzieri, M. Porcelli, and R. Ridol

31. Social Structure and Ecotourism Development on Bonaire ......................................................421

Thomas Abel



xi

Contributors

THOMAS ABEL, National Taipei University, Taipei, Taiwan [[email protected]]

W. ADEY, Botany Department, NMNH, Smithsonian Institution, Washington, D.C. 20742, USA

M. H. ANAMI, School of Food Engineering (FEA), Unicamp, CP6121, Campinas, SP, Brazil

ELIANA BARDI, Department of Environmental Engineering Sciences, Box 116350, University ofFlorida, Gainesville, Florida, USA [[email protected]]

SILVIA BARGIGLI, Department of Chemistry, University of Siena, Siena, Italy [[email protected]]

P. R. BESKOW, DTAISER / CCA, UFSCar, Araras, SP, Brasil.

E. BIERMANN, Biological Resources Engineering Department, University of Maryland, CollegePark, Maryland 20742, USA

SHERRY BRANDT-WILLIAMS, USEPA/ORD/NHEERL/AED, Narrangansett, RI, 02882, USA[[email protected]]

MARK T. BROWN, Department of Environmental Engineering Sciences, Box 116350, University ofFlorida, Gainesville, Florida 32611-6350, USA [[email protected]]

VORASUN BURANAKARN, Faculty of Architecture, Chulalongkorn University, Phayatai, Wangmai,

Bangkok 10330 Thailand [[email protected]]

DANIEL E. CAMPBELL, USEPA, National Health and Environmental Effects Laboratory, AtlanticEcology Division, Narragansett, Rhode Island, 01879, USA [[email protected]]

O. F. COELHO, School of Food Engineering, State University of Campinas - Unicamp,Campinas, SP, Brazil [[email protected]]

MATTHEW COHEN, Department of Environmental Engineering Sciences, Box 116350, Uni-versity of Florida, Gainesville, Florida 32611-6350, USA [ [email protected]]

DENNIS COLLINS, Department of Mathematics, University of Puerto Rico, Box 9018, Mayaguez,Puerto Rico 00681 [[email protected]]

T. D. de A. SÁ, Embrapa Eastern Amazon, Belém PA, Brazil

CECILIA FERREYRA, National Institute of Agricultural Technology, Argentina[[email protected]]

LAURA FUGARO, Department of Chemical and Biosystems Sciences, University of Siena, via A.Moro; 53100 Siena, Italy

CORRADO GIANNANTONI, ENEA - “National Agency for New Technology, Energy and the Envi-ron ment”, Energy Department - Research Centre of Casaccia, S. Maria di Galeria, 00060Rome, Italy [[email protected]]



xii

HUGO A. GUILLÉN TRUJILLO, Department of Engineering, Universidad Autónoma de Chiapas,Chiapas, Mexico [[email protected]]

CHARLES A.S. HALL, SUNY, College of Environmental Sciences and Forestry, Syracuse, NY13210, USA [[email protected]]

PAT KANGAS, Biological Resources Engineering Department, University of Maryland, College Park,Maryland 20742 USA [[email protected]]

WOO SUK KIM, Department of Environmental Engineering, Pukyong National University, 599-1,DAE YEON 3 DONG, NAM KU, PUSAN 608-737, Korea [[email protected]]

P. C. KITAMURA, Embrapa Environment, Caixa Postal 069, Jaguariúna (SP), Brazil - DEP 13820-000

JAE-YOUNG KO, Coastal Ecology Institute, Louisiana State University, Baton Rouge, Louisiana70803, USA [[email protected]]

SUK MO LEE, Department of Environmental Engineering, Pukyong National University, 599-1, DAEYEON 3 DONG, NAM KU, PUSAN 608-737, Lorea [[email protected]]

G. LOMBARDI, School of Engineering, University of Sao Paulo - USP Sao Carlos, SP, Brazil[[email protected]]

JOHN McLACHLAN-KARR, Department of Environmental Engineering Sciences, Box 116450, Uni-versity of Florida, Gainesville, Florida 32611-6450, USA [[email protected]]

NADIA MARCHETTINI, Department of Chemical and Biosystems Sciences, University of Siena, viaA. Moro; 53100 Siena, Italy

M. MILLER, School of Food Engineering (FEA), Unicamp, CP6121, Campinas, SP, Brazil

ALBERTO MIRANDOLA, Department of Mechanical Engineering, University of Padova, 1, Via Vene-zia 35131 Padova, Italy [[email protected]]

V. NICCOLUCCI, Department of Chemical and Biosystems Sciences and Technology, UniversityofSiena, Via Ettore Bastianini, 12-53100 Siena, Italy and A.R.C.A. (Associazione Ricerca eConsulenza Ambientale) [[email protected]]

ELIZABETH C. ODUM, Santa Fe Community College, Gainesville, Florida 32606, USA[[email protected]]

HOWARD T. ODUM, Department of Environmental Engineering Sciences, Box 116350, Uni-versity of Florida, Gainesville, Florida, USA

A. R. OMETTO, School of Engineering, University of Sao Paulo - USP Sao Carlos, SP, Brazil[[email protected]]

ENRIQUE ORTEGA, School of Food Engineering (FEA), Unicamp, CP6121, Campinas, SP, Brazil[[email protected]]


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M. PANZIERI, Rina (Registro Italiano Navale) S.p.A. via Volturno 10/12, 50019 Sesto Fiorentino,Flo- rence, Italy and A.R.C.A. (Associazione Ricerca e Consulenza Ambientale)

MARIA PICCHI, Department of Chemical and Biosystems Sciences and Technologies, Via della Di-ana 2/A, University of Siena, 53100 Siena, Italy

GONZAGUE PILLET, ECOSYS(R), Inc. Applied Economics & Environmental Economics, CH 1227,Geneva, Switzerland [[email protected]]

M. PORCELLI, Department of Chemical and Biosystems Sciences and Technology, UniversityofSiena, Via Ettore Bastianini, 12-53100 Siena, Italy and A.R.C.A. (Associazione Ricerca eConsulenza Ambientale) [[email protected]]

ILARIA PRINCIPI, Department of Chemical and Biosystems Sciences, University of Siena, via A.Moro; 53100 Siena, Italy [[email protected]]

FEDERICO PULSELLI, Department of Chemical and Biosystems Sciences and Technologies, Viadella Diana 2/A, University of Siena, 53100 Siena, Italy [[email protected]]

RICCARDO PULSELLI, Department of Chemical and Biosystems Sciences and Technologies, Viadella Diana 2/A, University of Siena, 53100 Siena, Italy

P. A. R. RAMOS, School of Mechanical Engineering, Polytechnic Institute - IPSJAE Havanna, Cuba

[[email protected]]

MARCO RAUGEI, Department of Chemistry, University of Siena, Siena, Italy

[[email protected]]

R. RIDOLFI, Department of Chemical and Biosystems Sciences and Technology, Universityof Siena,Via Ettore Bastianini, 12-53100 Siena, Italy and A.R.C.A. (Associazione Ricerca e Consu-

lenza Ambientale)

GERALDO STACHETTI RODRIGUES, Embrapa Environment, Caixa Postal 069, Jaguariúna (SP),Brazil - DEP 13820-000 [[email protected]]

JI HO SON, Department of Environmental Engineering, Pukyong National University, 599-1, DAEYEON 3 DONG, NAM KU, PUSAN 608-737, Lorea [[email protected]]

C. STREB, Biological Resources Engineering Department, University of Maryland, College Park,Mary land 20742, USA

DAVID R. TILLEY, Biological Resources Engineering and Natural Resources Management, Univer-sity of Maryland, College Park, Maryland 20742, USA [[email protected]]

SIMONE TONON, Department of Mechanical Engineering, University of Padova, 1, Via Venezia -35131 Padova, Italy [[email protected]]

SERGIO ULGIATI, Department of Chemistry, University of Siena, Siena, Italy [[email protected]]

K. VIELHAUER, Embrapa Eastern Amazon, Belém PA, Brazil



Acknowledgements

xiv

I wish to express my gratitude to all who have helped in so many ways to make this conference a successand whose hard work has made these long awaited proceedings possible. Neither would have occurredwithout the help and dedication of so many people. The organizing committee; Howard Odum, DavidTilley, and Sergio Ulgiati, were instrumental in providing support at all stages of production. Their time,energy, ideas, and commitment are greatly appreciated. Students in our Systems Ecology Program at theUniversity of Florida greatly facilitated conference events and welcomed all participants to our campus.Carol Binello, my partner, once again was invaluable in dedicating much of her time formatting all themanuscripts for printing. I am forever indebted to her! In the end, Eliana Bardi was instrumental in put-ting the nishing touches on the document and, as in the past, did it with her wonderful sense of timing

and good humor.

The participants in our conference are due a special thanks for sharing their work and providing a forum

for exchanging ideas, information, and their passion for emergy synthesis. I also want to thank the fol-lowing reviewers of papers in this volume, who devoted much time and energy to their reviews (hopefullywe have not left anyone out!), Donald Adolphson, Brigham Young University, Provo, UT Robert Beyers, University of South Alabama, Mobile, AL

Bhavik R. Bakshi, The Ohio State University, Columbus, OH Sherry Brandt-Williams, USEPA, Narragansett, RI

Vorasun Buranakarn, Chulalongkorn University, Bangkok, ThailandDaniel E. Campbell, USEPA, Narragansett, RI

Matthew Cohen, University of Florida, Gainesville, FL Dennis Collins, University of Puerto Rico, Puerto Rico

Vito Comar, Univesidade Estadual de Mato Grosso do Sul, Dourados, BrazilSteven Doherty, .Slippery Rock University, Slippery Rock,PABrian Fath, Towson University, Towson, MD

Ben Fusaro, Florida State University, Tallahassee, FL Corrado Giannantoni, ENEA, Rome, Italy Alan Hodges, University of Florida, Gainesville, FL Norbert Jaworski, Sanford, FL

Kim Jones, Texas A& M University—Kingsville, Kingsville, TXDaesoek Kang, Korea Maritime Institute, Soul, KoreaPat C. Kangas, University of Maryland, College Park, MA

Klyde Kiker, University of Florida, Gainesville, FL Robert King, Austin, TX

Robert Knight, Gainesville, FL Dolores Krausche, Gainesville, FL

Jae Young Ko, Louisiana State University, Baton Rouge, LACharlotte Lagerberg, Swedish Univ. of Ag. Sci., Sweden

David Lambert, University of North Florida, Jacksonville, FLFrancois Levarlet, Institut français de l’environnement, Paris, FranceDavid Maradan, University of Geneva, SwitzerlandJay Martin, Ohio State University, Columbus OH

Guy McGrane, Millers Creek, North CarolinaKozo Mayumi, University of Tokushima, Japan Mike Murry-Hudson, HOORC, Maun, Botswana Enrique Ortega, DEA-FEA-Unicamp, Campinas, Brazil



Murray Patterson, Massey University, Palmerston, New ZealandGonzague Pillet, University of Fribourg, SwitzerlandDavid Pimentel, Cornell University, Ithaca, NYMarco Raugei, University of Siena, Italy

Geraldo Rodriguez, EMBRAPA Environment, Jaguariúna, BrazilTorbjorn Rydberg, Swedish University of Agricultural Sciences, Uppsala, SwedenEnrico Sciubba, University of Roma, ItalyJi Ho Son, Pukyong National University, Pusan, KoreaDavid R. Tilley, University of Maryland, College Park, MA

Dennis Swaney, Cornell University, Ithaca, NYAlberto Vega, Miami, FL

Finally, I am at a loss for words, and I know I speak for everyone represented in this volume, in expressingmy profound gratitude and appreciation to H.T. Odum for his constant intellectual stimuli and dedicationover the years. His passion will always provide inspiration to our lives.

There is a quote from one of HT’s chapters in Charlie Hall’s “Maximum Power” which somehow hasstruck a cord with me and has meaning far beyond its surface. It is as follows:

“Truth” is a state of mind in which there is no contradiction. A person perceives his idea as true because he has heard no contradiction. The less one knows, the easier it is to be

dogmatic and to be sure that what one knows is true. We tend to defend dogmatically astrue the things we are taught, whereas the things we learn from experience and experimentstend to be properly couched in some-times-contradictory reality.

HT, you are missed beyond words…Mark T. BrownGainesville, FL

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Introduction

Emergy Synthesis

The name for this series of books that result from our conferences reects our commitment to building

understanding rather than dissection knowledge. Synthesis is the act of combining elements into coherentwholes. Emergy synthesis is a “top-down” approach to quantitative policy decision making and evalu-ation. Rather than dissect and break apart systems and build understanding from the pieces upward,emergy synthesis strives for understanding by grasping the wholeness of systems. Emergy is the amountof energy of one form used directly and indirectly to make something. Emergy is context driven. Itis a systems concept, and cannot be fully understood outside a systems context. Scienceman (1987)coined the phrase “energy memory” which was shortened to emergy as a means of providing a namefor a quantitative concept that was based on energy, but different from energy. The theory of emergy isgrounded in the understanding that not all forms of energy are the same and that heat, as a measure of

energy, is inadequate to describe the ability to do work, especially complex work. Emergy recognizesthat there are quality differences to energies of different form. While a calorie is a calorie, is a calorie,no matter how it is derived, a calorie of sunlight and a calorie of energy from food cannot support thesame types of work.

Emergy is a universal measure of work expressed on a common basis. This work results in energy trans-formations that when viewed in totality are interconnected webs of energy ow. All energy transforma-tions of the geo-biosphere can be arranged in an ordered series to form an energy hierarchy (Odum, 1988,1996). The hierarchies are dissipative, in that at each progressive level less and less energy is availableas a result of dissipation in previous levels. For example, in the hierarchy of the biosphere, many joules

of sunlight are required to make a joule of organic matter, many joules of organic matter to make a jouleof fuel, several joules of fuel are required to make a joule of electric power, and so on. Since differentkinds of energy are not equal in their contribution to processes, work is made comparable by expressingeach form of energy in units of one form. In all, emergy synthesis generates a deeper understanding ofhow one might apply principles of self-organization and maximum power at every scale in the hierarchyof the universe.

Denitions

The following paragraphs contain denitions and a brief explanation of emergy concepts. A more com-plete introduction is given elsewhere (Odum 1996).

Emergy is the availability of energy (exergy) of one kind that is used up in transformations directly and

indirectly to make a product or service. The unit of emergy is the emjoule, a unit dened as the available

energy of one form. For example, sunlight, fuel, electricity, and human service are all different forms ofenergy, but can be put on a common basis by expressing them all in terms of the emjoules of solar energythat is/was required to produce them. In this case the emergy is expressed in units of solar emergy andcalled solar emjoules (abbreviated sej). Most frequently, solar emergy is used, although other units havebeen used, such as coal emjoules or electrical emjoules.

Unit Emergy Values are calculated based on the emergy required to produce them. There are three types

of unit emergy values as follows:Transformity is dened as the emergy per unit of available energy (exergy). For example, if4000 solar emjoules are required to generate a joule of wood, then the solar transformity of thatwood is 4000 solar emjoules per joule (abbreviated sej/J). Solar energy is the largest but most

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dispersed energy input to the earth. By denition, the solar transformity of sunlight absorbed

by the earth is 1.0.

Specic emergy is the unit emergy value of matter dened as the emergy per mass, usually

expressed as solar emergy per gram (sej/g). Solids may be evaluated best with data on emergyper unit mass rather than as emergy per unit exergy. Because energy is required to concentratematerials, elements and compounds not abundant in nature have higher emergy/mass ratios whenfound in concentrated form since more work was required to concentrate them, both spatiallyand chemically.

Emergy per unit money is a unit emergy value used to convert money payments into emergyunits. Since money is paid to people for their services and not to the environment, the contribu-tion to a process represented by monetary payments is the emergy that people purchase with themoney. The amount of resources that money buys depends on the amount of emergy supportingthe economy and the amount of money circulating. An average emergy/money ratio in solaremjoules/$ can be calculated by dividing the total emergy use of a state or nation by its gross

economic product. It varies by country and has been shown to decrease each year. This emergy/money ratio is useful for evaluating service inputs given in money units where an average wagerate is appropriate.

Emergy accompanying a ow of something (energy, matter, information, etc.) is calculated using a unit

emergy value. The ow expressed in its usual units is multiplied by the emergy per unit of that energy or

material. For example, the ow of a fuel input to a process, in joules per time, can be multiplied by the

transformity of that fuel (emergy per unit energy in solar emjoules/joule), or the mass of a material inputcan be multiplied by its specic emergy (emergy per unit mass in solar emjoules/gram). The emergy of a

storage is calculated by multiplying the storage quantity in its usual units by its unit emergy value.

Unit emergy values can be thought of as a kind of efciency measure, since they relate all the inputs to

an output. The lower the transformity or specic emergy the more efcient the conversion. It follows

from the second law that there are minimum unit emergy values for processes which are consistent withmaximum power operations. While we don’t have a way to calculate them directly, we use the lowesttransformity found in long-operating systems as an approximation.

Empower is a ow of emergy (i.e. emergy per time). In most evaluations and under most circumstances,

emergy ows are usually expressed in units of solar empower (solar emjoules per time).

The Emergy Research Agenda

The focus of the research agenda of most of the participants in the biennial emergy conferences is onmerging science and policy, coalescing principles and data from basic research in ecology, energy systems,systems ecology, ecological economics, and ecological engineering into an understanding of human andenvironmental systems.

Environmental accounting and the concept of emergy accounting, the major research interests of mostconference participants, is an important aspect of environmental policy, relating natural resources and theun-monied contributions of the environment to the economy. The research of the conference participants isat the forefront of worldwide research into quantifying contributions of environmental systems, ecological

impacts of economic development, and the role and importance of energy in the affairs of humans. Ourresearch has spanned such global problems as population carrying capacity, greenhouse emissions, andmaterial uxes in conventional and renewable energy production systems. We have applied our work in

quantifying environmental services and natural capital in regional and national economies and calculating

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environmental carrying capacity of regions. Our research has resulted in the development of quantitative methods for addressing questions of energypolicy and natural resource management throughout the world, helping developing nations understand theirresource issues and to evaluate alternative solutions. In all, through combined research efforts of manyresearchers throughout the world, we have developed a holistic approach to understanding humanity’splace in the biosphere. As a result we have developed a network of scientists and students that has leadto a rich, global interchange of knowledge. These biennial conferences are important in that regard, forthey allow us to get together and discuss where we have been and where we are going. Papers presentedhere are often the beginning of very important advances in our methods and applications, but even moreimportant they often present new theory, concepts, and principles. The growth of our network and theresulting global exchange is reinforced by having these conferences every two years.

An Introduction to Emergy Synthesis 2

In this volume 31 papers are presented that resulted from the Second Biennial Emergy Conference held

in Gainesville, Florida in September 2001. The conference occurred just nine days after the September11th terrorist attacks on the World Trade Center and the Pentagon. As a result, a number of participantscould not attend the conference. We, however, have broken with tradition and published all papers thatwere scheduled to be presented.

The papers span both theory and applications of the emergy methodology reecting the interests of the

50 scientists who conducted the research. Few papers are seldom wholly theoretical or applied, reecting

an interesting aspect of our science: emergy theory continues to mature, and with each application oftensome new theoretical wrinkle emerges. It also reects the inherent philosophy of most of the participant,

that theory is often best derived from application. As we learn more through observation of systems and

their organization, synthesize what we know, and apply our understanding to other systems, we extendand rene the general theory. It is this information cycle…the convergence of information from applica-tion of the accounting methods and current concepts, principles and theories, that results in new theorywhich in turn is followed by further application. This information cycle is what makes emergy synthesisrather than emergy analysis.

Mark T. BrownGainesville, FL

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http://slidepdf.com/reader/full/2nd-biennial-emergy-research-conference 21/481 xix






Edited by



Associate Editors

Howard T. Odum



David Tilley



Sergio Ulgiati

University of Siena

Siena, Italy

December 2003



Gainesville, FL


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Chapter 1. Energy Hierarchy and Transformity in the Universe

1

Energy Hierarchy and Transformity in the Universe

Howard T. Odum

ABSTRACT

Many properties of the universe discovered in recent years by progress in astrophysics and

cosmology are found consistent with the principles of energy hierarchy developed for other scales of

time and space. Energy and mass are converged and concentrated by autocatalytic self organization of

aggregates of matter, galaxies, stars, and black holes. As energy passes through energy transformation

series, concentrations at centers increase, energy ows decrease, territories of support increase,

intervals between feedback pulses to lower levels increase, and intensity of episodes of recycles of mass

and energy increase. Emergy (spelled with an “m”) puts structures and processes on a common basis.

Transformities (emergy/energy) indicate position of each form of energy of the universe in the universal

energy hierarchy. High energy radiation is returned to background radiation by a dispersing cascade

of absorption by matter and reradiation. The red shift increase with distance may be attributed to the

greater mass and gravity of centers of systems of increasing scale. The second law of thermodynamics

accounts for energy transformations at each scale of size and time, except at the lowest level where self

organization of background radiation and background materials can form an “equilibrium” hierarchy.Using estimates of density of the background energy and matter, a preliminary calculation is made of the

sun’s share of the universe’s resources, deriving transformities of the sun, earth, and life in units of the

universe’s background emergy.

INTRODUCTION

Many of the properties of science, including environment and economy, appear to t the

concepts of energy hierarchy proposed as an energy law (Odum, 1996, 2001). This paper considers the

ways the energy hierarchy principles may apply to the larger scale of astronomy and the universe. If theexpectations of energy hierarchy are found in space, the generality of energy hierarchy concepts can be

extended, perhaps also helping to choose among theories in astrophysics. In this paper, the expectations

of the energy hierarchy are compared with the observations of cosmic structure and processes. On this

large scale, where gravity is the major force, let’s see how the principle of self organization for maximum

empower develops units, spatial convergence, divergence, and recycle of matter and energy. As in earlier

studies of other realms, drawing a hierarchical energy systems diagram is a useful methodology for

organizing facts about energy and matter while considering theories.

A preliminary “ecological view” of the universe was presented to a Swedish Academy audience

in Stockholm in 1989 (Odum, 1995). Using conventions of the energy systems language, astronomical

components were diagrammed from left to right in order of increasing scale, concentration and turnover

time with recycle of matter and energy. This paper assembles additional evidence and arguments, andillustrates with a numerical example.



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Energy Hierarchy Concepts as Used on Earth

In common language, the word “hierarchy” means that many units at one level contribute to a

few units at a higher level, with the high level feeding controls back to those at the lower level. A military

organization is an example. Because of the second law, any energy transformation uses many calories

of available energy of one kind to generates a few calories of available energy of another kind. Hence,

energy transformations are an energy hierarchy. When there are several transformations in series, the

network has many levels of energy hierarchy.

The energy hierarchy concept was developed by generalizing from ecological food chains (Odum,

1971, 1976, 1988, 1996) and offered as an energy law — one that follows from the second law and Lotka’s

principle of self organizing for maximum power, offered as the fourth energy law (1922a, 1922b):

An energy transformation is a work process that converts one or more kinds of available energy

into a different type of available energy.

Emergy (spelled with an “m”) is the available energy (exergy) of one kind required to be used

up previously, directly and indirectly, to generate the inputs for an energy transformation. Unitsof emergy are emjoules, emcalories, or emergs of one form of energy. On earth, solar energy

is used as the common denominator (solar emjoules, solar emcalories, or solar emergs). This

paper uses ergs (10-7 joules) for energy and emergs for emergy.

All energy transformations can be arranged in a series, and the position of an energy ow in the

series is marked by the transformity. Transformity is the emergy required in transformations

divided by the energy in the transformed product. Numerically, transformities are the same

whether expressed in emjoules, emcalories, or emergs. (transformity = emjoule/joule = emcalorie/

calorie = emerg/erg). Transformity (quotients) can be calculated from energy ows or from

accumulated energy storages. On earth, if available energies are expressed as solar emergy, the

transformities are greater than one.

The ow of usable available energy through a network is power. The ow of emergy is called

empower. Empower = emergy ow per time.

The energy hierarchy concepts can be visualized with energy systems diagrams (Odum, 1967,

1971, 1983, 1996) that separate the scales with small fast turnover units on the left and items of larger

scale of space and time on the right (Figure 1). Figure 1a is a network of energy transformations, which is

aggregated into a linear series in Figure 1b. Emergy ow from the left is constant and conserved through

the transformations to the right. Available energy decreases with each transformation step from left to

right, but the transformity increases. In Figure 1c, available energy ow is plotted as a function of theincreasing transformity on logarithmic coordinates. This plot is an energy hierarchy spectrum.

The higher the transformity, the more available energy of another kind was required to make it.

According to the energy hierarchy concepts, transformations that survive the natural selection processes

of self organization reinforce their supporting network with a feedback of its energy output even though

its energy ow is less. Commensurate reinforcement with less energy is possible because the systems

concentrate outputs spatially (Figure 1d) and accumulate the products and deliver their feedback actions

in pulses (1e).

In terms of Lotka’s principle, each transformation that survives self organization is organized to

help maximize its power while reinforcing the network. However, the high level transformation processes

(lower power ow on the right) are just as important as the low level processes (higher power ows onthe left). Maximum power might be misunderstood to mean giving priority to low level processes. In

Figure 1b the empower is the same through the whole series. Therefore, Lotka’s principle is claried by

stating it as the principle of self organization for maximum empower.



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Spatial Convergence

From observation and theory, the series of energy transformations in an energy hierarchy

converge their transformed energies to more concentrated centers, even as the total energy transformeddecreases (Figure 1c). One of the reasons for this is that reinforcement feedbacks needed to prevail in self

organization can be commensurate with what was required in their formation if they concentrate in area.

Figure 1. Summary of concepts of energy hierarchy. (a) Network of energy ows connecting energy transformations

in order of declining energy ow; (b) web aggregated as a series with energy ows in steady state; (c) empower

and transformity of series with constant empower; (d) increasing size of centers and transformities; (e) decreasing

frequency and delivery time of pulses.



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Centers and the supporting territories of these centers increase with successive transformations along the

series from left to right (Figure 1d). An example of spatial hierarchy generated by self organization is

the vivid pattern of night lights of cities and towns as seen from satellite.

Accumulation and Pulsing

As suggested by pulses shown in Figure 1e, units higher in the energy hierarchy (higher

transformity) have longer periods of accumulating energy storage but sharper pulses in their feedback

actions. Examples are the energy feedbacks of carnivores, storms, governments, and earthquakes to their

areas. By storing longer and concentrating their impact from smaller concentrated centers in shorter

times, the lesser energy of higher units can have enough impact to reinforce their supporting energy

transformation chain (a design that ts the maximum empower principle). The pulsing increases in

period and intensity along the series of increasing scale from small scale molecular oscillations to large

scale earthquakes (Figure 1e).

Energy Quality Increase

Although the total energy ow is less, the high transformity energy ows to the right are more

concentrated, have more effect per unit, are more exible in their uses, and in these senses are higherquality. In other words, after self organization, energy ow of higher transformity requires more for its

support and has more effect.

Hierarchy in the Universe

With energy hierarchy concepts in mind, let’s consider next the characteristics of the universe

that appear to t the energy hierarchy model. That the stars and galaxies of the universe are hierarchically

organized has long been recognized. Astronomy and astrophysics texts have many plots of energy and

mass distribution that are hierarchical. (Jastrow, 1967, Lerner, 1991). For example, many stars areorganized around larger gravitational centers, and these in turn on a larger scale are organized around

even larger gravitational centers.

Autocatalytic Units of Space

In the vast realm of space, stars and other units that self organize are gravity produced, as described

in astronomy textbooks (Caroll and Ostlie, 1996; Chaisson and McMillan, 1998). Under the pull of gravity,

units of matter condense, storing energy and developing structure. The resulting increased gravity captures

more material. The potential energy of mass falling inward together is concentrated and transformed into

heat and energy of rotation. When the gravity and temperature are high enough, fusion reactions convert

the mass of hydrogen into energy, turning such units into light emitting stars. Subsequently, there are

sequences of succession not unlike that in ecosystems. All units send out radiant energy which disperses,losing concentration, thus degrading consistent with the second law. Some mass is dispersed outward

with heated gas. When diagrammed with energy language symbols (Figure 2), the autocatalytic units of

space are not unlike the consumers of ecosystems which also have growth cycles and pulses.

Pulsing

The self organization of stars is known to operate mechanisms involving sequences of nuclear

reaction, centrifugal force, concentration by gravity, and thermal expansion that accumulate the conditions

to cause explosive mass expulsion (i.e. supernovae). Periods of energy accumulation followed by pulsing

outows are known or have been proposed for most of the universe’s objects ranging from proto-star

aggregates and stars to the quasars and black holes of galaxies and clusters of larger scale structure.

Mass

Ninety percent of the mass detected may be ordinary matter, with the rest in the more concentrated,

light-emitting, stars, black holes, etc. There are many small stars and fewer large stars. Plots on logarithmic



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coordinates show nearly straight line relationship of star numbers and star mass. Going from smaller to

larger scale, the size and mass of stars increase as their numbers decrease. The turnover times increase,

and the territories of inuence increase, with increasing distances between centers. The patterns are

similar to the energy hierarchy concepts shown in Figure 1.

Energy Hierarchy

The energy stored in objects, their temperature and rotation, increase with size and mass. As shown

on Hertzsprung-Russell plots of light intensity and temperature, denser stars have higher temperatures

and brighter light emission, depending on the stages of development. The cosmic rays reaching the earth

made up of high energy particles and high frequency light suggest the hierarchical energy distributions

of their sources. There are many rays with lesser energy and fewer with higher energies.

Figure 3a shows the distribution of radiant energy in the universe on a log scale as a function ofthe wavelength and energy of photons. Also see Hoyle et al. (2000, p 304). Energy quantity decreases

but energy concentration (per photon) increases from left to right. With larger scale to the right, the

energy required (emergy) increases as does the potential impact of photons in interactions with matter.

In a more recent presentation, Henry (1999) plots intensity of diffuse background radiation as a function

Figure 2. Design of autocatalytic consumers of space which produce structure and storage using the properties of

gravity (modied from Odum, 1983, page 157).



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of frequency, a plot in which the area of the graph is proportional to energy. Elsewhere in this volume,

Tilley (2002) estimates transformity values for photons increasing with frequency.

The most abundant energy is the background radiation that ts the spectrum of emission according

to Planck’s law for blackbodies at 2.7 degrees Kelvin (Figure 3b). The universe is uniformly lled with

this low quality radiation and with matter that can absorb and reradiate these photons. For the most part,

this background matter and energy may be in radiation exchange equilibrium while contributing resources

to and receiving recycle from the higher energy systems.

AN ENERGY SYSTEMS THEORY OF THE UNIVERSE

Next, lets consider the way the principles of self organization for maximum power and energy

hierarchy (Figure 1) may account for the structure and functions of the universe. How can a fairly uniform

distribution of background energy and matter generate and sustain the fantastic forms and variety in the

heavens?

Energy Hierarchy within the Maxwell-Boltzmann Distribution

A gas in an insulated container at constant temperature has the kinetic energy of its molecules

distributed according to the exponential shaped Maxwell-Boltzmann distribution (Figure 4a). The shape

of this curve was often explained as the result of the velocities of molecules being distributed at random,

according to the bell-shaped Gaussian curve. Kinetic energy is proportional to the square of the molecular

velocities. When the velocities are Gaussian, the energy distribu≠tion is exponential. This is an anarchical

Figure 3. Spectrum of radiant energy in the universe (from Kolb and Turner, 1986). (a) Radiant energy distribution

with wave length; (b) spectrum of background radiation.



-7-


view of nature, based on the randomness idea.

An alternate view recognizes that the energy distribution is hierarchical because of self organization

principles. From the much larger scale of view, a human sees constant patterns and calls it an equilibrium.

Many molecules at low kinetic energy, by their interactions, generate a fewer number at higher energy

and velocity. A large number of these generate a fewer number at an even higher energy level, etc. If

there is a constant percent at each level to generate those at the next, the distribution is mathematically

exponential. Nothing random is needed or implied. If only a sector of the process is viewed on the tiny

scale of the molecules, many small energies build a few higher velocity molecules while their average

momentum is decreased. In other words, when viewed on a smaller scale, the second law appears.

The distribution is consistent with operation of the maximum empower principle within anequilibrium gas. There is a natural selection of the design that generates energy hierarchy. Using the

Maxwell Demon metaphor, Brillouin (1962) suggested that there was not enough energy in molecules to

support a process of concentrating energy against the gradient. However, the self organizational process

converges the energy of many molecules to support a few of higher energy (Figure 4b) and consequently

Figure 4. Hierarchy of energy in the Maxwell-Boltzmann distribution of a gas at equilibrium. (a) Energy distribution;

(b) natural selection of a converging design that concentrates energy (Maxwell demon); (c) energy systems view of

closed cycle energy ows.



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provides more energy for the selection process. In other words, the natural selection of self organization

for maximum power is a kind of Maxwell demon. Figure 4c is an energy systems summary of the energy

hierarchy of a gas in equilibrium (as viewed from a larger scale).

The Self Organization of Available Energy from Equilibrium

The Maxwell-Boltzmann distribution shows how a smooth uniform distribution of matter and

energy generates a hierarchical “lumpy” distribution of matter and energy. Once there is some mass

concentration, its elds of gravity can evolve into autocatalytic units (Figure 2b) and grow (the classical

explanation of star formation). Once there is a mass center, background radiation can help raise its

temperature because of the spherical geometry of incoming radiation. Self organization may reinforce

and select spiral organization and rotation as typically maximizing power transformations and feedback

efciency. Once there are units of higher mass and energy, these can be self organized to converge on

higher level centers that have higher concentration and territory of inuence, as in any energy consumption

chain (Figure 1).

Energy System Diagram of the Universe

Using energy systems language, Figure 5 models the universe showing principal structures,

energy, and mass ows. The diagram separates populations of different scale and position in the energy

transformation hierarchy from left to right. The background radiation and dilute matter are the lowest

concentrations on the far left. These contribute energy and gravity-concentrated mass into matter

aggregates. The aggregates condense into higher energy, visible units to the right. For each level of

increased scale (galaxies, galaxy clusters, etc.), there is a more intensive center (smaller and denser because

of the increased gravity). From left to right, nuclear reactions generate elements of higher atomic weightbut in lesser quantities. The larger the territory of organization the higher in energy hierarchy is its center

unit. To the far right are black holes with enough gravitational mass to draw in light energy and inhibit

outbound radiation. Mechanisms are known or proposed to generate high energy radiation pulses and

emissions even by black holes. Hoyle et al. (2000) suggest quasars at the center of clusters are important

in pulsing recycle of energy and matter.

The diagram is not a program of development stages. However, development that starts without

much initial structure will form the units on the left before those on the right because of the shorter storages

and turnover times. Like ecological “succession,” the whole system starts with low energy, rst growing

little organized “weeds,” next adding hierarchy and diversity, culminating in the largest scale organization

with most intense centers on the right. The evolution of some stars to become higher energy centers of

galaxies is analogous to local cities that emerge as dense emergy centers of states and nations because of

their locations. Like ecosystems and cities, large scale stellar consumption uses up available energy and

fuels (nuclear fuel), recycles matter, and completes a sequence of growth and decline. In ecosystems, large

scale catastrophic mechanisms often destroy the structures, restarting the growth sequence. By analogy

we might expect some black holes to explode as “little bangs.” Observations in space are detecting more

and more explosive energy dispersals (Schilling, 1999).

For repeating sequences to be sustainable, matter and energy have to be recycled. Some

mecha≠nism is required to regenerate the hydrogen and other matter used up in nuclear reactions. Perhaps

energy is used to regenerate hydrogen in black holes, as has been suggested. With a universal view,

one would expect different areas to be in different stages of the development sequence. Having many

populations of units in each category constitutes a steady state on the average.



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Alternate Theories of the Cosmos

The literature of cosmology is rich with alternative theories that scientic observation and

measurement may not yet be adequate to chose. Kragh (1999) reviews in detail many ideas and

mechanisms involved in big bang and steady state theories. Both views include efforts to explain the

observed hierarchical properties. If the energy hierarchy principles apply to the universe (Figure 5), its

predictions may help understand observations.

Radiation Dispersal and Red Shifts

Consistent with the energy hierarchy concept of organization (Figure 1), light dispersing out

from a center is a recycle of energy that loses concentration by diverging. Light is also being absorbedby space matter and dark bodies and reradiated at different and lower frequencies cascading down to

background (to the left in Figure 5).

Because of the nature of energy in atoms and molecules, light emitted from space objects is in

Figure 5. Energy systems model of energy hierarchy in the universe separating populations of structures and processes

according to their scale and energy transformations increasing from left to right.



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characteristic wavelengths. Light from moving objects arrives with a changed frequency, the Doppler

effect. Doppler effects are observed in rotating galaxies, with blue shifts of light from stars moving toward

the earth and red shifts for those moving away. However, most of the starlight viewed from earth has its

wavelengths shifted toward lower frequency (longer wavelength), a shift to the red. The most popular

interpretation attributes most of the red shifts to an expanding universe accelerating away.

The “tired light theory” (Zwicky, 1929) explains some red shifts as a loss of energy to gravitational

pull of intergalactic matter or other mechanisms. Since light maintains constant speed in space, loss of

energy requires a shift to a lower energy frequency, a red shift. Recall the wave characteristics of light

that are demonstrated by passing light through a slit and observing its spread. Perhaps diverging light on

the large scales of space red shifts in proportion to its loss of concentration?

Light is known to act as a mass, exerts pressure, and its rays are bent by gravity. The larger the

mass and gravity of the light source, the larger is the red shift (large shifts from quasar light for example).

This relationships might be expected if gravity of the emitting center removes energy from light traveling

away (a mechanism of energy efciency ?). More distant objects have larger red shifts. This follows

from the energy hierarchy concepts. The larger the scale of an organized pattern, the further away is

its center from an earth observer and the more concentrated is its mass-causing red shift. As evidence

of hierarchy, there are many small red shifts and few large red shifts (Hoyle et al., 2000). Perhaps allthese mechanisms (Doppler effect, energy divergence, and source gravity) contribute to the red shifts we

observe from earth.

Universe and the Second Law

If you include the energy converted from mass to heat in nuclear reactions as a major part of

the fuel, the autocatalytic consumer units of space (Figure 5) follow the second law (Figure 2). They

transform abundant energy and mass of lesser kinds into smaller quantities of higher intensity energy and

mass, with an overall degrading of energy and mass, and an increase in entropy. However, the available

energy for most structures and processes is generated by the self organization of energy hierarchy at the

bottom near-equilibrium, as already explained (Figure 4). When phenomena are viewed on each scale

of time and space except the lowest background, the second law applies. On each scale, there is steady

state only if summed over a long enough time to average out the pulsing.

When viewed as a whole with a long scale of time, the universe could be like the bottom scale,

a constant average pattern of structure, mass and energy ows in which the second law merges with

the equilibrium concept (Figure 4). The energy hierarchy concept seems to make the big bang theory

unnecessary, although much of what is discussed in this paper could also apply to a post-big bang

regime.

Equations of Scale

The concepts of energy hierarchy and maximum empower predict equations and designs for each

scale, even though the mechanisms for production, autocatalysis, and reinforcement may differ. Evenon earth, similar models and quantitative relationships apply when changing from one scale to another,

providing you change space, time, and transformity. For examples, the same computer simulation models

can be used for microbes and cities, after the units of time, space, and energy are all changed by the same

factor (Odum, 1983). Einstein’s relativity equations are a means to represent phenomena that involve

changing scale of time and space in the realm of gravity and light. Transformities, empower density and

other emergy indices may be a simpler way to understand the similarities and differences with scale.

THE UNIVERSE’S EMERGY CONTRIBUTION TO EARTH

In order to start the quantitative application of energy hierarchy concepts to space, a preliminarycalculation is made in Figure 6. Emergy ows, emergy storages and transformities are estimated for earth,

the sun, and its share of the universe hierarchy. No doubt the numerical values may change as better

data are obtained for the numbers used. Because this is a thermodynamic overview calculation, it is not



-11-


necessary to know all the details of the many mechanisms that may be involved.

In the emergy evaluations of phenomena on earth, emergy was evaluated in units of solar

insola≠tion, which is for earth the most abundant but lowest quality energy source at the base of the

earth energy networks. Emergy was evaluated in solar emjoules (x 107 ergs/joule = solar emergs).

The hierarchical position of energy of various processes on earth was indicated by the transformities in

units of solar emjoules per joule (solar emergs/erg). In an analogous way to the calculation for the solar

system, we are trying here to express everything in emergy of the most abundant, lowest quality energy,

the background radiation with emergy units labeled as Universal emergs (abbreviated Uemergs). Then

the more concentrated energy ows and storages in space can be characterized by universal transformities

with the units Uemergs/erg). These calculations view the background radiation as part of a dynamic

system that continuously draws background energy into the hierarchical network and regenerates it from

the system as recycle.

To use the transformity concept to place cosmic objects in the universal hierarchy, all kinds of

Figure 6. An emergy evaluation of sun and earth based on the sun’s share of the universe. (a) Sketch of the series

of transformations evaluated; (b) bars representing the energy ows between stages.



-12-


energy need to be expressed in emergy units of one kind. To avoid fractions, transformities are given

in terms of the most abundant, lowest quality emergy, the background radiation, even though that is not

the immediate source of energy for the nuclear operations at higher levels. If we are using emergy of

background radiation as our base, then by denition this energy has a universal transformity of 1 Uemerg/

erg.

In order to quantitatively relate the background radiation and background matter to the sun

and its planet earth, let’s estimate the part of the universe which has been the sun’s share of low energy

support. The sun is a star with about half of its supply of hydrogen for fusion used up. The calculation

in Figure 6 was made for 9 billion years, the time when the sun has completed its life cycle (twice the

present age). Perhaps a billion years can be added for the total time required to collapse background

resources into a sun.

Most of the sun’s radiant energy results from the nuclear conversion of its matter (hydrogen

used in fusion), but that process builds on the emergy in the previous accumulations and transformaions.

We calculated the volume the sun’s mass would have if dispersed back to the concentration of the most

dilute background matter (reciprocal of matter density 3 x 1031 g/cm3, Carroll and Ostlie, p. 1228). This

is the volume of the universe’s low quality resources that would be required to form the sun. This volume

was used to estimate the sun’s share of the universal background radiation at the base of and somewhatcaught up in the evolution process (Note A, Figure 6).

The energy converted by nuclear reaction from the original dilute matter also comes from the

lowest level in the universe, the dispersed hydrogen. The dispersed matter has the energy equivalence

of its mass that is later converted into energy (called mass-energy after Chaisson and McMillan (1997)).

Therefore, before concentration it is given the same transformity as background radiation with a value

of 1 Uemergs/masserg.

The sun’s share of background energy, gravitational potential, and background matter energy are

shown as the bar on the left. The total output of solar radiation emitted in the sun’s life is in the center

bar, and the total solar insolation received in the earth’s biosphere is on the right.

In the successive transformations from the dilute matter and energy to more concentrated units,

the universal emergy passes from input to output, but the total quantity of transformed energy decreases.

Therefore, the transformity increases as cosmic bodies form. The more processes of concentration and

transformation, the higher the transformity. Transformities increase from left to right in Figure 5. In

Figure 6 the resource emergy (A + B + C) was divided by the concentrated energy ows for the sun D

and earth E, respectively, to estimate their universal transformities (center of Figure 6). More rened

calculations might include the mass expulsions during concentration processes, the pulsed emissions of

sun spots, and other details of the succession.

Life and the Biosphere

At rst thought, it might seem strange to nd the cool, low energy planet earth at a higher level ofenergy quality and transformity than the intense energy of the sun. However, the earth is an information

center with very high solar transformity of life, estimated earlier as 1032 solar insolation emjoules/

joule (Odum 1996 p. 229). Life required the planet, its appropriate orbit, its appropriate star, and the

large quantities of matter and energy processed over time for its development. Expressed as emergy

of universal background in Figure 6, life may have a universal transformity of 5.1 x 10 44 Uemerg/erg,

obtained by multiplying the universal emergy per erg of earth insolation 5.1 x 1012 Uemerg/solar erg by

1032 solarerg/life erg.

SUMMARY AND CONCLUSION

The concepts of energy hierarchy developed for many scales on earth are consistent with many

of the observed phenomena of the universe. An energy systems model with hierarchical networks helps

explain how energy and matter can be produced and recycled through each level in a cycle of evolution and



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pulsing. If the cosmos includes units at different stages of the cycle, the energy hierarchy organization is

compatible with a universe in steady state, although concepts are not inconsistent with the self organization

after a big bang. Recognizing the pulsing paradigm that is part of the energy hierarchy theory, we often

describe the spatial pattern of energy hierarchy on earth as a Christmas tree with ashing lights, an analogy

that may be appropriate in a universe with many areas of alternating efuorescence.

The transformity concept can be extended to the universe by dividing the required emergy of

background energy and mass-energy by the energy of processes and storages of heavenly bodies. A

tentative calculation of emergy and transformity of the solar system and the earth provides a numerical

example. In principle at least, the example shows how to put the solar transformities calculated for earth

environment and economy on a universal basis.

Acknowledgment

Work on this paper was aided by suggestions and criticism in collaborative discussion with Dr. Dolores

Krausche, Florida Center for Engineering Education, Gainesville, FL

REFERENCES

Allen, C.W. 1981. Astrophysical Quantities. Athlone Press, Univ. of London, U.K.

Brillouin, L. 1962. Science and Information Theory, 2nd Ed. Academic Press, New York, 351 pp.

Carroll, B.W. and D.A. Ostlie. 1996. An Introduction to Modern Astrophysics. Addison-Wesley, Reading,

Massachusetts.

Chaisson, E. and S. McMillan. 1998. Astronomy, 3rd Ed. Prentice Hall, Upper Saddle River, New

Jersey, 470 pp.

Charlier, C.V.T. 1908. Archiv fur Mat. Phys. 4 (1).Henry, R.C. 1999. Diffuse background Radiation. Astrophysical Journal, 516:L49-L52.

Hoyle, F.G. Burbidge and J.V. Narlikar. 2000. A Different Approach to Cosmology. Cambridge University

Press, New York, 357 pp.

Jastrow, R. 1969. Red Giants and White Dwarfs. New American Library, Signet, 241 pp.

Kolb, E.W. and M.S. Turner. 1986. The Early Universe. Addison-Wesley, Reading, Massachusetts.

Kragh, H. 1999. Cosmology and Controversy, The Historical Development of Two Theories of the

Universe. Princeton Univ. Press, Princeton, New Jersey, 500 pp.

Lerner, E.J. 1991. The Big Bang Never Happened. Times Books, Random House, New York, 466 pp.

Lotka, A.J. 1922a. Contribution to the energetics of evolution. Proc. Natl. Acad. Sci. 8:147-151.

Lotka, A.J. 1922b. Natural selection as a physical principle. Proc. Natl. Acad. Sci. 8:151-154.

Odum, H.T. 1967. Biological circuits and the marine systems of Texas. pp. 99-157 in Pollution and

Marine Ecology., ed. by T.A. Olson and F.J. Burgess. Interscience, John Wiley, New York.

Odum, H.T. 1971. Environment, Power and Society. John Wiley, New York, 331 pp.

Odum, H.T. 1976. Energy quality and carrying capacity of the earth. Tropical Ecology 16(1):1-8.

Odum, H.T. 1983. Systems Ecology. John Wiley, New York, 644 pp.

Odum, H.T. 1988. Self organization, transformity, and information. Science 242:1132-1139.

Odum, H.T. 1995. Self organization and maximum power. pp. 311-364 in Maximum Power, ed. by

C.A.S. Hall. Univ. Press of Colorado, Boulder.

Odum, H.T. 1996. Environmental Accounting, Emergy and Decision Making. John Wiley, New York,

373 pp.

Odum, H.T. 2001. An energy hierarchy law for biogeochemical cycles. pp. 235-248 in Emergy Synthesis,Proceedings of the Emergy Conference of 1999, ed. by M.T. Brown. Center for Environmental

Policy, 424 Black Hall, PO Box 116450, Univ. of Florida, Gainesville.



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Schilling, G. 1999. Watching the universe’s second biggest bang. Science 283:2003-2004.

Tilley, D. R. 2002. Spectral transformities for solar radiation reaching the earth. Proceedings of the

Emergy Conference of 2001, ed. by M.T. Brown. Center for Environmental Policy, 424 Black

Hall, PO Box 116450, Univ. of Florida, Gainesville..

Zwicky, F. 1929. On the red shift of spectral lines through interstellar space. Proc. Natl. Acad. Sci.

15:773-779.






Edited by



Associate Editors

Howard T. Odum



David Tilley



Sergio Ulgiati

University of Siena

Siena, Italy

December 2003



Gainesville, FL


vi



2

Mathematical Formulation of the Maximum

Em-Power Principle

Corrado Giannantoni

ABSTRACT

After having briefly recalled the main results shown in the previous Conference (a rigorousdefinition of Emergy in mathematical terms and a general formulation of the Emergy Balance equation)the paper presents an extremely general mathematical formulation of the Maximum Em-Power Principle.Such a formulation allows us to analyze in particular (but not exclusively): i) in what sense the “maximum”

flow has to be understood, not only in steady state conditions but also in stationary and variableconditions; ii) what reciprocal role Transformity and Exergy play in maximizing such a flow; iii) in what

sense such an extremely General Principle can be interpreted (but only reductively) as a traditional Thermodynamic Principle.

The last aspect enables us to point out the different and wider presuppositions of the M. Em-P. Principle (as a more general “Thermodynamic” Principle) in comparison with the other Thermodynamic

Principles (especially the First and the Second ones) and furnishes a clear answer to the (apparently)contradictory assertions between the Maximum Em-Power Principle and the Minimum Action Principle. In addition, such a mathematical formulation throws new light on the thermodynamic concepts of order and disorder, and paves the way to a better understanding of the so-called Fifth Principle.

1. INTRODUCTION

This paper expressly deals with the mathematical formulation of the Maximum Em-Power Principle. To this purpose it is worth starting from the basic results already achieved and presented at theFirst Emergy Research Conference (Giannantoni, 2000a), which constitute the most correct

presuppositions obtained for such a possible formulation. Let us recall two of them very synthetically: ageneral mathematical definition of Emergy and the structure of the global Emergy Balance Equation.i) A rigorous and general definition of Emergy, valid in whatever variable conditions, can be

given by the following expression

Em t Ex d

t

eq

∗ ⋅

−∞

= ∫ ( ) ( )τ τ (1.1)

where Exeq ( )τ is defined as

Ex c x y z x y z ex x y z d V eq

D

( ) ( , , , ) ( , , , ) ( , , , )

( )

τ τ ρ τ τ τ

= ⋅ ⋅∗

∫ 3

(1.2)



and is the instantaneous equivalent Exergy Power used up during the process of generating aspecific product.Eqs. (1.1) and (1.2) translate, in mathematical terms, the general definition of Emergy given by

H. T. Odum as ´the total solar equivalentavailable Energy directly and indirectly used up to generate aspecific form of Energy (or product)ª. In fact, the coefficient which appears in Eq. (1.2) is a dimensional

structural factor (whose dimensions are sej/J, that is solar emergy joules per Joule) and depends, amongother things, on co-injection or co-production factors. It is thus defined in such a way as to summarize

all the rules of Emergetic Algebra (this is the reason for the term “equivalent”); D∗

( )τ is the Domain of

integration which defines the quantity of the considered matter, is the mass density, is the specificExergy, while Newton’s “dot” notation in Eq. (1.1) stands for the total derivative with respect to time.

Therefore, when we assume a Lagrangian perspective and steady state conditions, Eqs. (1.1)and (1.2) define the traditional and usual concept of Emergy, while under whatever variable conditionsthey define Emergy in its widest and most general conception.

ii) A general Global Balance Equation for any System made up of n sub-systems can be

written as follows

α α γ γ ∂

∂ β β j

j

m

j j k

k

n

k k m D l l l

l

p

E m u u u ut

A t E m ys

∗

=

∗

=

∗ ∗

=∑ ∑ ∑⋅ ⋅ ( ) + ⋅ ⋅ ( ) = + ⋅ ⋅ ( )

1 1

1 2

1

. .

, ,.., ( )Φ (1.3)

where sej

sec

Φk mu u u

∗ ( )1 2, , .., is the “equivalent ” Source Term1 relative to the k - sub-System,

which is expressed as a linear combination of all the real sub-System contributions

Φ Φk m kr

r

n

r u u u∗ ∗

=

( ) = ⋅∑1 2

1

, ,.., λ (1.4),

while )(t D s A is the Global Accumulation Term due to all the distinct contributions of the n sub-

systems, thus it is given by an appropriate sum of equivalent accumulation terms, each one (in

turn) expressed as a linear combination of all the real sub-System contributions

A A A A A A D t i i

i

n

D D D D i i

i

n

ij D

j

n

s i n j( ) , ,..,= ⋅ ⋅ ( ) = ⋅ ⋅ ⋅∗

=

∗ ∗

=

∗

=∑ ∑ ∑δ δ δ δ ε

1 1 11 2 (1.5)

where

, are the co-injection and co-production coefficients for each sub-System

are their associated re-normalization factors (referred to the Whole System), iδ are the pertinent “weights” in the corresponding sub-system balance equation

, ∗iδ are the associated re-normalization factors

∗kr λ , ∗

ijε are the specific incidence coefficients.

Moreover, it is worth recalling that the considered Global Balance Equation (1.3), in additionto accounting for input and output quantities as a global result of the different

co-injective or co-productive sub-System structures, contemporarily accounts for threedistinct additional contributions:

- Accumulation Terms, amplified by both the productive and connective structure of the System;- Source Terms, characterized by similar ( productive and connective) amplification effects;



- Circulating Emergy Flow, which quantifies the Flow of Information through feed-back pathways.

The latter quantity, if Eq. (1.3) is already structured in its standard form2, may be defined as

E m E m y E m ycirc l l l

l

p

l

l

p. . .

= ⋅ ⋅ ( ) − ( )∗

= =∑ ∑β β 1 1

(1.6)

and corresponds to the‘ Flow of Information through the feed-back pathways of the System.On the basis of such presuppositions we will firstly try to answer a fundamental question.

2. WHAT EXACTLY IS MAXIMUM?

This is certainly not a trivial question. In fact the general Global Balance Equation (1.3), whichis valid for any Complex System, may be written in several forms by organizing the different terms insuch a way as they could appear, according to specific exigencies, either on the first or on the second

side of the equation. Let us consider some of these forms.In addition to Eq. (1.3) which, restructured as follows through Eq. (1.6), may be denominatedas form A

A)

α α γ γ ∂

∂ j

j

m

j j k

k

n

k k m D circ l

l

p

E m u u u ut

A t E m E m ys

∗

=

∗

=

∗

=∑ ∑ ∑⋅ ⋅ ( ) + ⋅ ⋅ ( ) = + + ( )

1 1

1 2

1

. . .

, ,.., ( )Φ (2.2)

we could also consider other main forms, for instance the following ones:

B)

γ γ ∂

∂ α α k

k

n

k k m D circ l

l

p

j

j

m

j ju u ut

A t E m E m y E m us

∗

=

∗

=

∗

=∑ ∑ ∑⋅ ⋅ ( ) = + + ( ) − ⋅ ⋅ ( )

1

1 2

1 1

Φ , ,.., ( ). . .

(2.3)

C)

γ γ α α ∂

∂ k

k

n

k k m j

j

m

j j l

l

p

D circu u u E m u E m yt

A t E ms

∗

=

∗ ∗

= =∑ ∑ ∑⋅ ⋅ ( ) + ⋅ ⋅ ( ) − ( ) = +

1

1 2

1 1

Φ , ,.., ( ). . .

(2.4)

D)

γ γ α α ∂

∂ k

k

n

k k m j

j

m

j j l circ

l

p

Du u u E m u E m y E mt

A t s

∗

=

∗ ∗

= =∑ ∑ ∑⋅ ⋅ ( ) + ⋅ ⋅ ( ) − ( ) − =

1

1 2

1 1

Φ , ,.., ( ). . .

(2.5)

which do not evidently exhaust all the various other possibilities.If we now consider the form A as the reference structure in order to formulate the M. Em-P. P.,

this assumption is equivalent to saying that “the System tends to maximize the sum of input andautogenerated Emergy flows (first side) in order to maximize the rate of accumulated Emergy together with circulating and output Emergy flows (second side)”.

Analogously, if we take the other considered forms as reference structure, the pertainingformulation of the M. Em-P. P. would respectively assert that:



B) “The System tends to maximize the autogenerated Emergy flow (first side) in order tomaximize the rate of accumulated Emergy, together with circulating and output Emergy flows, at net of the equivalent input one (second side)”;

C) “The System tends to maximize the autogenerated Emergy plus the net input/output Emergyflows (first side) in order to maximize the rate of accumulated and circulating Emergy in the system(second side)”;

D) “The System tends to maximize the autogenerated Emergy plus the net input/output Emergyflows at net of circulating Emergy flow (first side), in order to maximize the rate of accumulated Emergyin the system (second side)”.

Such different possible statements allow us to reformulate the initial question in a more correctform: what is the mathematical structure that is potentially more suitable to best translate Odum’sformulation of the Principle under consideration?

We will now try to show that form B is the most suitable starting structure in order to formulatesuch a general reference principle for self-organizing systems which is known as Maximum Em-Power Principle.

3. USEFUL EMERGY AND PROCESSED EMERGY

In many publications H.T. Odum repeatedly asserts that “Em-power” has to be understood as“useful

Emergy power”. This could lead us to think (erroneously) that the Emergy Balance in form A isthe one which is more suitable to represent such a concept, especially if we consider the terms which

appear on the second side of Eq. (2.2). But it is also evident that, if the System has all the Source

Terms Φk mu u u∗ ( )1 2, ,.., equal to zero, it is intrinsically unable to organize input resources, so that it

simply transforms the equivalent input Emergy flow into other forms of flow, without any incremental

Emergy contribution that might be really useful to improve either its internal structure or its externalEmergy products. It is not a self-organizing system at all, but it is only a mechanical transducer . In fact,

under such conditions, all the pertinent co-production coefficients ( β l ) are all equal to 1, as well as the

corresponding re-normalization factors ( ∗l β ). Thus Eq. (1.6) implies that

E m E m y E m ycirc l l l

l

p

l

l

p. . .

= ⋅ ⋅ ( ) − ( ) =∗

= =∑ ∑β β

1 1

0 (3.1).

Consequently: a) In steady state conditions such a system merely transfers input Emergy flowdirectly into output; b) In variable conditions input Emergy flow is simply transformed into the rate of

Accumulated Emergy and output Emergy flow, but without showing any organizing phenomenon (itacts like a mass anchored to a spring and contemporarily subjected to an external forcing energy source).In addition, if the output flow is persistently equal to zero, the system reduces to an Emergy storage(which evidently cannot be defined as a self-organizing system).

We may thus conclude that a self-organizing System (in its most meaningful sense) is only theone that “manages” input resources by increasing them by an additional positive and net contributionwhich is the only really useful one to increase both its rate of Accumulated and Circulating Emergy flowand its output Emergy flows.

In other words, what is really “useful” is not simply an input/output Emergy transfer, but thenet contribution given by the internal generation process of Emergy which, through an appropriateorganization of input resources, finalizes everything to a net increase of Emergy both in terms of rate of

Accumulation, Circulation and output Production.It should now be clear that the Balance Equation in form B is the one which is specifically

indicated to express Odum’s findings. At the same time, if we take into consideration that, apart from the



Emergy Source Term, all the other forms of Emergy are involved in such a (re)organization process, wemay consequently name them by a comprehensive term such as “processed” Emergy flows (obviouslyeach flow is understood as accounted for with its own specific algebraic sign).

We can now propose the researched formulation of the M. Em-P. Principle.

4. MATHEMATICAL FORMULATION OF THE MAXIMUM EM-POWER PRINCIPLE

Eq. (2.3) can be at first re-organized in a more synthetic and compact form. In fact suchcharacteristics, as already pointed out in Giannantoni (2000a), are particularly necessary in order toclearly answer the question as to whether the Maximum Em-Power Principle is really a ThermodynamicPrinciple or not. At the same time it is also desirable for such a formulation to be structured in this wayfor the following additional reasons:

to facilitate its comparison with the structure of the other Thermodynamic Principles (especiallythe First and the Second ones);

to include the most general possible conditions pertaining to very Complex Systems, concerning both their time evolution behavior and space structure organization.The latter consideration especially refers to the fact that, if we want to study a living organism

(e.g. a plant or an animal) or even an organ (e.g., the liver or the brain) as a whole, the presence of billions and billions of cells suggests we consider a continuous system rather than a discrete one madeup of n parts. This implies that Eq. (2.3) becomes more general if re-written in terms of integrals insteadof summations. However, even if formulated in such a way, it can always be easily reduced into discreteterms, if necessary. And even if there might be some discontinuity conditions, they can always be dealtwith in the frame of Lebesques’ theory of integrals (Kolmogorov and Fomin, 1980), which undoubtedlycovers all the cases usually considered in Literature on the subject.

Under such assumptions, Eq. (2.3) can be re-written in the following synthetic and compactform

Γ ϕ v D t

v

D t

d V d

dt em d V

∗ ∗=∗ ∗∫ ∫ 3 3

( ) ( )

(4.1)

where∗vϕ = the “equivalent ” Source Term per unit volume (see Eq.(1.4) in discrete terms);

Γ = the local structural amplification and re-normalization factor (corresponding to the

product of the coefficients ∗k γ and k γ in the case of discrete form (see Eq. (2.3)), which also accounts

for the structural variations with time;

++∗ ++=wvqv

emememem mvv ,,, (4.2)

in which

exC em mv ??= ρ , is the Emergy per unit volume associated to the mass

(thus transportable by mass flows)3+

qvem

,= the Emergy per unit volume associated to heat source terms

+wvem , = the Emergy per unit volume associated to work source terms4.



Under such conditions the Maximum Em-Power Principle may be mathematically formulatedas follows

Γ ϕ v D t

v

D t

d V d

dt em d V Max

∗ ∗= →∗ ∗∫ ∫ 3 3

( ) ( )

, )()( t S t D U ∀ ∗ (4.3)

that is valid for any Domain ( ∗ D ) belonging to Universal Space ( )(t S U ). Such a principle, in the lightof the previous considerations, may be verbally formulated as follows: “Every System tends to organizeits internal structure to generate progressively increasing spring-Emergy levels in order to maximize theflow of processed (or “useful”) Emergy”.

5. GENERAL CONSIDERATIONS ON THE MATHEMATICAL

FORMULATION OF THE MAXIMUM EM-POWER PRINCIPLE

Formulation (4.3) may be seen as constituted by three parts:

1st

part:d

dt em d V Maxv

D t

∗

∗∫ →

3

( )

, ∀ ⊆∗ D t S t U ( ) ( ) (5.1)

which corresponds to the usual definition of the Principle in terms of phenomenological effects:“Every System tends to maximize the flow of processed (or “useful”) Emergy”;2nd part:

Γ ϕ v D t

d V Max∗

∗∫ →

3

( )

, ∀ ⊆∗ D t S t U ( ) ( ) (5.2)

which points out the internal causes of such effects: “Every System tends to organize its internal structurefor progressively increasing spring-Emergy levels”;

3rd part:

Γ ϕ v D t

v

D t

d V d

dt em d V Max

∗ →

∗= →∗ ∗∫ ∫ 3 3

( ) ( )

, ∀ ⊆∗ D t S t U ( ) ( ) (5.3)

which emphasizes that its mathematical structure is a logical consequence of the first two mentioned parts and points out the existing direct relationship between internal causes (5.2) and phenomenological

effects (5.1). The symbol =→ has been specifically introduced to emphasize the versus of the equivalence

which goes from causes to effects.5 Thus an alternative verbal formulation which is able to include boththe first and the second side of Eq. (5.3) could be the following one: “Every System tends to maximizeits internal structure level in order to maximize its Spring-Emergy Flow which, in turn, maximizes theflow of processed Emergy”. In addition it is also worth pointing out that Eq. (4.3) (or Eq. (5.3)) expressesa tendency Principle. This implies that:

i) the symbol Max has to be considered, in general, as an Extremum;

ii) thus in the long run, Eq. (4.3) can also be written as follows

Γ ϕ v D t

v

D t

d V d

dt em d V

∗ ∗= ≥∗ ∗∫ ∫ 3 3

0

( ) ( )

, ∀ ⊆∗ D t S t U ( ) ( ) (5.4)

where the sign equal holds only when the maximum is actually achieved;iii) for very complex systems the dynamic behaviour is usually controlled by very high timeconstants, so that the slope of the increasing trend could be so slow that, in a given time-spacewindow of analysis, it would be possible to assume that



Γ ϕ v D t

v

D t

d V d

dt em d V

∗ ∗= ≅∗ ∗∫ ∫ 3 3 0

( ) ( )

(5.5).

It is also worth mentioning that the previous expression (4.3) constitutes the formulation of theM. Em-P. P. in its general version that could also be said in a weak sense. In fact it is also possible to

consider another and more cogent definition (in a strong sense) as followsd

dt d V

d

dt em d V v

D t

v

D t

Γ ϕ ∗ ∗= ≥∗ ∗∫ ∫ 3

2

2 3 0

( ) ( )

, ∀ ⊆ ⊂∗ ∗ D t S S t U ( ) ( ) (5.6)

which is valid however only for particular subsets ( S ∗ ) of Universal Space S t U ( ) .On the basis of the previous results we can now analyze the relationship between the M. Em-P.

Principle and the other Thermodynamic Principles.

6. THE SECOND PRINCIPLE IN THE LIGHT OF THE M. EM-P. PRINCIPLE

As an introductory aspect let us consider a simple example: a Complex System, made up of n

discrete sub-systems, in steady state conditions. In this case Eq. (2.3) may be re-written as follows

( ). .

11 1

+ ⋅ ⋅ ⋅ ( ) = ⋅ ⋅ ( )∗ ∗

=

∗

=∑ ∑ϕ α α β β j

j

m

j j l l l

l

p

E m u E m y (6.1)

In fact, on the basis of Eqs. (1.3) and (1.4), the comprehensive “equivalent ” Source Term ( Φ∗ )

can be expressed in terms of the equivalent input Emergy flow, through an equivalent specific amplification

(or generative) efficiency ϕ ∗ :

Φ Φ∗ ∗

=

∗ ∗ ∗

=

= ⋅ ⋅ ( ) = ⋅ ⋅ ⋅ ( )∑ ∑γ γ ϕ α α k

k

n

k k m j

j

m

j ju u u E m u1

1 2

1

, ,..,.

(6.2)

Such a procedure, which is always possible on the basis of the n Emergy Balance equations

describing the n considered sub-systems (see Giannantoni, 2001d), becomes particularly simple in

steady state conditions (Giannantoni, 2000a).

If we now assume, for the sake of simplicity, that the System has just one input, we get

( ). .

1

1

+ ⋅ ( ) = ⋅ ⋅ ( )∗ ∗

=

∑ϕ β β E m u E m yl l l

l

p

(6.3)

If (for ulterior simplicity) we assume equal co-production coefficients ( β l ), equal

renormalization factors ( ∗l β ) and their product (

l l β β ?∗ ) equal to 1, we have

Tr y Tr u Ex u

Ex yl

l

l

p( ) ( ) ( )= ⋅ ( )

( )⋅ +

=

∗

∑1

1 ϕ (6.4).

Eq. (6.4) has a fundamental importance because it shows that the Transformity of any outputquantity is an amplification of the input Transformity as a consequence of two distinct reasons:

i)the dissipation of Exergy due to the Second Thermodynamic Principle ( Ex y Ex ul

l

p

( ) < ( )=

∑1

);



ii) the amplification factor )1( ∗+ϕ , which indicates the global gain specifically due to the

generation of Emergy on behalf of internal Source Terms.

But what is really now worth pointing out is that, for very Complex Systems characterized bya lot of internal co-generation and/or interaction processes, the contribution given by the amplification

factor )1( ∗+ϕ is generally much higher than the one due to Exergy losses. In addition, what is even

more important is that the amplification due to Emergy Source Terms is in-dependent from the other one: the former can also be present both in ideal Systems (that is even if Exergy losses had been absent)and in real Systems (even if characterized by an increase of Exergy).

This result can be considered as a first indication of the fact that the M. Em-P. P. is independent from the Second Principle and, what is even more important, it indicates the effective reason for theincreasing order in self-organizing Systems, whereas the (habitually) associated loss of Exergy constitutesonly a concomitant circumstance.

Such a conclusion can be easily (and more rigorously) drawn by starting from a very general point of view, that is by starting from the general formulation (4.3) of the M. Em-P. Principle (as we will

see in par. 9). Let us now consider another important aspect: how it is possible to obtain the traditionalExergy Balance Equation from the one that expresses the M. Em-P. Principle. If, in fact, we consider Systems described only in terms of a traditional Thermodynamic approach (that is by neglecting anyEmergy Source term, although ever present), our description reduces either to the classical ExergyBalance Equation or the Energy Balance Equation, according to the specific distinct assumptionsconsidered in the two different mentioned cases.

The Exergy Balance Equation may be obtained by remembering that the irreversibility flow

terms (mainly due to internal, but also to external losses) which can be expressed as

d

dt

em d V

t

C ex d V C ex v d S v irr

D t

irr

D t

irr rn

D t

,

( ) ( ) ( )

∗ ∗ ∗

∂

= ∂

∂

⋅ ⋅ + ⋅ ⋅ ⋅∗ ∗ ∗

∫ ∫ ∫ 3 3 2ρ ρ (6.5)

are generally neglected in Emergy Analysis on the basis of the assumption that their specific co-productioncoefficients are equal to zero (see Giannantoni, 2000a). If, vice versa, we continue to consider suchcontributions, but we distinguish between their conceptual existence and the fact that their pertinent

local co-production coefficients are everywhere numerically equal to zero ( c x y zirr ( , , , )τ = 0 ) (ib.),

we may recognize that the Exergy Balance Equation can be thus easily obtained from the MaximumEm-Power Principle by assuming that all the co-injection and co-production factors are equal to 1. Such

an assumption corresponds to considering independent inputs and all the outputs as splits. Under these

conditions, and without any Emergy Source Term ( Φ∗ = 0 ), Eq. (4.3) becomes, in explicit terms

+ + + ∂

∂ ⋅ + ⋅ ⋅+

⋅+

⋅

∂

∗ ∗

∂∗ ∗ ∗ ∗∫ ∫ ∫ ∫ w d V w d S

t ex d V ex v d S v p

D t

s

D t

irr

D t

irr rn

D t

,

( ) ( ) ( ) ( )

3 2 3 2ρ ρ

03 2 3 2

= ∂∂

⋅ − ⋅ + ⋅ − ⋅ + ⋅ + +∗ ∗ ∗ ∗∫ ∫ ∫ ∫

∂

+⋅

+⋅

∂t

h Ts d V h Ts v d S q d V q d S kz

D t

kz rn

D t

v

D t

s

D t

ρ ρ θ θ ( ) ( )

( ) ( ) ( ) ( )

which exactly expresses the Exergy Balance Equation, usually written as follows

(6.6)



q d V q d S v

D t

s

D t

+⋅

+⋅

∂

+ +∗ ∗∫ ∫ θ θ

3 2

( ) ( )

∂

∂ ⋅ − ⋅ + ⋅ − ⋅ =∗ ∗∫ ∫

∂t

u Ts d V h Ts v d S kz

D t

kz rn

D t

ρ ρ ( ) ( )

( ) ( )

3 2

+ + − ∂

∂

⋅ ⋅ − ⋅ ⋅+⋅

+⋅

∂

∗ ∗

∂∗ ∗ ∗ ∗

∫ ∫ ∫ ∫ w d V w d S

t

ex d V ex v d S v

D t

s

D t

irr

D t

irr rn

D t

3 2 3 2

( ) ( ) ( ) ( )

ρ ρ (6.7)6

where

θ is the generalized Carnot coefficient.

7. THE FIRST PRINCIPLE IN THE LIGHT OF THE M. EM-P. PRINCIPLE

As far as the relationship between the First Principle and the M. Em-P. P. is concerned, we mayeasily obtain the mathematical formulation of the former from that of the latter by following a

methodological procedure analogous to the one followed in the case of the Second Principle. The onlydifference now is that the First Principle does not take into consideration what happens inside the System(which is in fact considered as a “black box”), but only accounts for quantities measured on the Systemfrontier as simple additive contributions. Under such conditions, the local co-injection and co-production

factors ),,,( τ z y xcirr due to the various irreversibilities, are assumed to be equal to zero in the whole

volume not because it is assumed that they do not contribute to the Balance Equation (like in the case of Emergy Balance), but because they are not “seen” in the assumed balance perspective. If then, in addition,

we do not consider any form of Emergy (although ever present), that is Φ∗ = 0 , we can easily get, in

explicit terms

0 3 2 3 2= ∂∂ ⋅ ⋅ + ⋅ ⋅ + + +∗ ∗ ∗ ∗∫ ∫ ∫ ∫

∂

+

⋅

+

⋅

∂t

h d V h v d S q d V q d S kz

D t

kz rn

D t

v

D t

s

D t

ρ ρ ( ) ( ) ( ) ( )

w d V w d S v p

D t

s

D t

,

( ) ( )

+⋅

+⋅

∂

+∗ ∗∫ ∫ 3 2

which is exactly the Energy Balance Equation, usually written as follows

∂

∂ ⋅ ⋅ + ⋅ ⋅ = + +∗ ∗ ∗ ∗∫ ∫ ∫ ∫ ∂

+⋅

+⋅

∂t en d V en v d S q d V q d S

D t

rn

D t

v

D t

s

D t ρ ρ 3 2 3 2

( ) ( ) ( ) ( )

w d V w d S v nc

D t

s nc

D t

,

( )

,

( )

+⋅

+⋅

∂

+∗ ∗∫ ∫ 3 2 (7.2)7.

It is now really important to point out that the methodological procedure previously followed inorder to obtain both the Exergy Balance Equation and (analogously) the Energy Balance one from themathematical formulation of the Maximum Em-Power Principle cannot be thought of as a deduction of the former equations from the latter, but rather as a “reduction” of the latter to the former. The considered

Principles in fact always remain in-dependent from each other. The procedure followed only shows inwhat reductive perspective (and associated limiting assumptions) the M. Em-P. P. can be thought of as being “equivalent” to the two basic well-known traditional Thermodynamic Principles.

(7.1)



Now, before analyzing the Third Thermodynamic Principle in the light of the M. Em-P. Principle,it is worth dealing with the Minimum Action Principle, because it is closely related to the First Principle.

8. THE MINIMUM ACTION PRINCIPLE IN THE LIGHT OF THE

MAXIMUM EM-POWER PRINCIPLE

The Minimum Action Principle8 is a well-known in Classical Mechanics. It deals with Systemscharacterized by kinetic and potential Energy, in the absence of any dissipative process. Under such

conditions it states that, for each System, there exists a function L q q t ( , , )⋅

defined as

L q q t T U ( , , )⋅

= − (8.1)

which satisfies the following condition

δ δ L q q t dt T U dt

t

t

t

t

( , , )⋅

⋅ = −( ) ⋅ =∫ ∫ 1

2

1

2

0 (8.2)

where

q = set of geometrical coordinates

q⋅ = set of their time derivatives

T = kinetic Energy

U = potential Energy

and the symbol δ represents the variation of the first order (often simply named as variation) of the

integral when the function )(t q is replaced by )()( t qt q δ + , where )(t qδ is a function which is

sufficiently small in the whole time interval [ ]21 , t t . The Minimum Action Principle is very useful to

describe the time-space evolution of a Complex System made up of n parts. In this case, and in the(usual) hypothesis of time uniformity, it is perfectly equivalent to the Energy Conservation Principle. In

fact, in these conditions, the Lagrangian function L q q t ( , , )⋅

does not depend explicitly on the time and

Eq. (8.2) can be re-written as follows (Landau and Lifchitz, 1969)

δ L q q t dt L

q

d

dt

L

qq dt

t

t

t

t

( , , )⋅

⋅⋅ = ∂

∂ −

∂

∂

⋅ ∂ ⋅ =∫ ∫

1

2

1

2

0 (8.3).

The equations of motion

∂

∂ −

∂

∂=⋅

L

q

d

dt

L

q0

(8.4)

derived from Eq. (8.3) imply, as a main consequence, that one particular integral of the first order (called Energy) is constant

const U T En =+= (8.5).



In other words the Minimum Action Principle describes the time-space evolution of a System(see Eqs. (8.4)) which involves a continuous transformation between kinetic and potential Energycontributions (see Eq. (8.2)), but in such a way as to satisfy the condition that their sum is at any timekept constant (Eq. (8.5)). Consequently the Maximum Em-Power Principle is by no means in contrastwith such a principle: in fact it not only confirms the Minimum Action Principle, but also adds something

more. As far as the former aspect is concerned, what we said in par. 7 may be considered as beingalready sufficient. As far as the latter is concerned, we should remember that the Minimum ActionPrinciple describes the System only in terms of additive contributions of mechanical Energy, whereasthe Maximum Em-Power Principle is able to describe phenomena in which the whole is more than its

parts. As an example we can consider two elements that generate a new entity described by a binary function: in this case the Emergy output, obtained as a solution of a differential equation of fractional order (see Giannantoni, 2001d), is higher than the input Emergy of its corresponding components. Thecase of a photon (considered as made up of a positron and an electron (Giannantoni 2000b, 2001a,b) andthe examples analyzed in Giannantoni (2001d) can be significantly explanatory. From a general point of

view however, if we consider an isolated System Σ• , we can write

Γ Σ

Σϕ vt

d V d dt

Em Max∗ = →∗∫ 3

( )

(8.6).

Consequently, by remembering (see Ref. [10]) that we can always write the System Emergy as

Em Tr ExΣ Σ Σ= ⋅ (8.7),

in the long run we have

d

dt Em

d

dt Tr Ex Tr

d

dt ExΣ Σ Σ Σ Σ= ⋅ + ⋅ ≥ 0 (8.8)

Consequently, there are two different possibilities: a) if we analyze the System in terms of onlyEnergy additive contributions (like in the case of the Minimum Action Principle), there are no Emergy

Source Terms. At the same time this implies that the System Transformity ΣTr is constant and Eq. (8.8)

is valid with the sign equal, that is

d

dt ExΣ = 0 (8.9)

which evidently corresponds to the Energy Conservation Principle for non-dissipative systems; b) but we will immediately see (in the next paragraph) that Emergy may generally be increasing

even if Eq. (8.9) holds. This means that the Maximum Em-Power Principle, as a Quality Principle,adherently contemplates the co-existence of the Minimum Action Principle (see Eq. (8.8)), which can

consequently still be considered as being a quantitatively independent Principle.

9. ORDER AND DISORDER IN THE LIGHT OF THE M. EM-P. PRINCIPLE

The M. Em-P. Principle also throws new light on the relationship between order and disorder inthe Universe. To this purpose it is useful to start from the application of Eq. (4.3) to the Whole Universe,thought of as an isolated System (such an application will also clearly illustrate the result syntheticallymentioned in par. 6). Under such conditions we have (see also Eq. (8.6))

Γ ϕ vS t

U d V d

dt Em Max

U

∗ = →∫ 3

( )

(9.1).



Consequently, by remembering (see Giannantoni 2000a) that we can always write theUniverse Emergy as

Em Tr ExU U U = ⋅ (9.2)

and by taking the total derivative, we can then obtain (in analogy to E. (8.8))

d

dt Em

d

dt Tr Ex Tr

d

dt ExU U U U U = ⋅ + ⋅ (9.3).

If we now consider that in the long run we may use Eq. (5.4), we can write

1 1

Tr

d

dt Tr

Ex

d

dt Ex

U

U

U

U ⋅ ≥ − ⋅ (9.4)

which, on the basis of Eq. (6.4), is always valid in the following form

1 1

Tr

d

dt Tr

Ex

d

dt Ex

U

U

U

U ⋅ >> − ⋅ (9.5).

If then, on the basis of the Second Principle, we assert thatlim lim ( )

t t U

t

t

S Ex d Max→+∞ →+∞

⋅

= − →∫ ∆0

τ τ (9.6)

this implies that

( )− =d

dt ExU 0 lim

t →+∞ (9.7).

Consequently Eq. (9.5) enables us to assert that Tr U is always increasing (according to a

logarithmic trend) even in these extreme conditions, because it is always

1 0Tr

d dt

Tr U

U ⋅ >>+×♦t

lim (9.8).

This result continues to be true in the light of the Third Thermodynamic Principle too.In fact, if we formulate the Third Principle in terms of Exergy variations, that is

lim limT T

S Ex

T 0 00 00

0→ →

= − =∆ ∆

(9.9)

(where 0T is the absolute Temperature of the Universe), we are able to assert that, even if the Exergy

variations are infinitesimal of higher order with respect to T 0, this fact does non imply any superior

limit to the increasing Universe Transformity U Tr . So that the M. Em-P. Principle allows us to conclude

that: i) not only does the meta-mechanical order (represented by U Tr ) increase much more rapidly than

the mechanical disorder (expressed by the increase of Entropy); ii) but even when the latter is near its

maximum, there is always a possible increase of U Tr (even in the presence of an extremely limited

availability of residual Exergy), because most of the increase in U Tr (as already shown) is independent

from Exergy variations, which always represent only a concomitant circumstance.



10. THE SO-CALLED FIFTH PRINCIPLE IN THE LIGHT OF THE

MAXIMUM EM-POWER PRINCIPLE

The results achieved in the previous paragraph allow us to show that the hierarchical order of the Universe (the so-called Fifth Principle) can also be “obtained” on the basis of the M. Em-P. Principle,

but such process of “deduction” is substantially different from a traditional deductive process. Such aPrinciple in fact cannot be thought of as being a mathematical corollary to the Maximum Em-Power Principle, but as a sort of crowning of the former, because it presents a higher Quality content in itsconclusive assertions.

The Fifth Principle in fact asserts that “Energy flows of the Universe are organized in Energytransformation hierarchy. Position in the Energy hierarchy is measured with Transformities” (Odum,1994b). Alternatively, it can also be enunciated as follows: “The Universe is hierarchically organizedand a manifestation of Energy. Transformity is a measure of the hierarchy of Energy” (ib.).

The latter of the two quoted equivalent versions is especially indicated for our considerations.In fact, if we consider the Universe as being a Whole, which is (only ideally) repeatedly sub-dividedinto two different parts, and in such a perspective we first apply Eq. (9.1) to the whole System

Γ Γ ϕ ϕ v v

D t D t

d V d V d

dt Em Em Max

∗ ∗+ = +( ) →∗∗∫ ∫ 3 3 1 2

21 ( )( )

, D t D t S t U 1 1

∗ ∗∪ ≡( ) ( ) ( ) 10.1)

and contemporarily we apply the same equation to thetwo distinct parts (e.g. in a discrete form, such as Eq. (2.3)), it is easy to show that: i) while the global

Transformity of the Universe ( U Tr ) is generally increasing (see also par. 9), ii) the rates of the Accumulated

Emergy pertaining to the two sub-systems are not identical: in fact they depend on different EmergySource distributions and generally different Emergy interchange flows. If we then consider the

corresponding equations that express such sub-system variations in terms of Transformity and Exergy(in analogy to Eq. (9.3) which is valid for the whole System), we can easily conclude that, even if (byhypothesis) the sub-system Transformities are originally equal, in general they progressively tend todiffer with time. This evidently shows that there is a hierarchy between the Transformities pertaining tothe two sub-systems each time considered. Consequently there is a distribution of Transformities in theUniverse, which describes the content of information (or the degree of organization) of all the various possible subsystems considered. Such a distribution is fundamentally due to two distinct reasons: thedistribution of Emergy Sources (main cause), which is also responsible for Emergy interchange flows,and the distribution of Exergy variations (as concomitant effects) associated to Energy transformations.So that, on the basis of considerations similar to those that have led us to Eq. (6.4), we can also assertthat the effects due to Emergy Sources are those which are generally dominant and, above all, independent

from those which are due only to the above-mentioned associated circumstances. Consequently, thehierarchical organization logically derived from Exergy variations (generally proportional to a time-

space scale) is thus only a basic (or a first order ) distribution (as a consequence of the Second Principle).In fact this basic effect is contemporarily modulated by a superimposed hierarchical distribution due toSpring-Emergy Sources (Fourth Principle), which is generally much more influent than the former. Thisallows us to reach the following general conclusions: i) the hierarchical organization tendency of theUniverse is generally increasing ; ii) it is essentially based on the Forth Principle, but its statementshows an increased Quality content so that such a “de-ductive” process could be more appropriatelynamed (by means of a neologism) as an “over-deduction” ; iii) the Fifth Principle is thus a substantial

step ahead with respect to the traditional hierarchy of the various well-known Thermodynamic Principles.

It Represents a new jump of Quality in such a hierarchy and, consequently, an explicit harmoniouslyadherent crowning of the M. Em-P. Principle.



In the light of the previous considerations we could thus reformulate such a Principle as follows:“The Universe is hierarchically organized. Its organization order level has a generally increasing tendencyand is a manifestation of both Emergy Source distribution and Energy transformations. Transformity isa comprehensive measure of such a dynamic (or, rather, thermodynamic?) hierarchy”.

At this stage, as a consequence of all the previous paragraphs, we may ask the following (and,in a certain sense, already anticipated) basic question:

11. IS THE MAXIMUM EM-POWER PRINCIPLE A THERMODYNAMIC

PRINCIPLE?

In order to answer this question in a complete and correct way, we have to deal with another really preliminary question: as to whether Emergy is a state variable or not.

To this purpose we may re-structure Eq. (4.1) in an appropriate way or, to simplify further,we may re-structure Eq. (2.3) in the following form;

d

dt C ex d V u u u E m u E m y D t

k k

n

k k m j j

m

j j q w l l l q wl

p

ρ γ γ α α β β ⋅ = ⋅ ( ) + ⋅ ( ) − ⋅ ( )∗∫ ∑ ∑ ∑∗

=

∗ ∗

=

∗

=3

11 2

1 1( )

.

, ,

.

, ,, , ..,

' '

Φ (11.1),

where the last two terms refer to input and output Emergy contributions associated to heat and work flows (in fact mass flow contributions are included in the term on the left side of the equation).

Equation (11.1), if thought of as belonging to the complete set of balance equations (mass,momentum, Energy, Exergy, etc.)describing the behavior of the System, contributes (directly or in its

integrated form) to define State x t −

( ) of the System, which satisfies the required fundamental conditions

of uniqueness, causality, consistency and separability (see Appendix 1). This implies that Emergy, defined by Eqs. (1.1) and (1.2), satisfies (through Eq. (11.1)) all the properties required in order to be a statevariable.

Furthermore we have also shown that, under specific assumptions, the well-knownThermodynamic Principles can be obtained from the M. Em-P. Principle. Such a process, however, as previously stated, should not be considered as a deduction of the former from the latter, but rather as asort of “reduction” of the latter to the former. In other words such a procedure gives us results which arestrictly equivalent to the classical formulations only in quantitative terms, but not in Quality terms: infact there is always the presence of dimensional factors characteristic of Emergetic Algebra that retaintheir specific meaning even if they are assumed to be equal to 1. This means that we are able to obtainthe above-mentioned Classical Principles (in their quantitative version) by accounting for only thatfraction of Emergy which corresponds merely to the mechanical relationship between the different parts of the System: this can be either the part which can be integrally transformed into mechanical work

(in the case of the Second Principle) or the part which pertains to every Energy relationship, in itsvarious forms, independently on the reciprocal and complete transformability of one form into another (in the case of the First Principle). In other words the M. Em-P. Principle never loses its capability of accounting for that meta-mechanical relationship (that is: beyond the mere mechanical aspect) whichalways characterizes physical phenomena, the presence of which is always recalled by the associateddimensional factors, though (or even if) we forget or neglect its presence when accounting for onlymechanical entities.

This fact should already be sufficient to assert that the M. Em-P. P. is not a ThermodynamicPrinciple (in the usual sense of the term), because in actual fact it is much more. In fact it cannot be,strictly speaking, referred to as Thermodynamic because it does not deal only with the meretransformability of heat into mechanical work or transformability of mechanically equivalent Energy

from one form into another. On the other hand it can still be termed as “Thermodynamic” (in a moregeneral sense) if we recognize that there are forms of “work” that go beyond the mechanical aspects(this is why they have been previously named meta-mechanical), found especially in living systems,



human systems, information, artificial intelligence and so on. There are in fact many forms of “Energy”(understood in its widest meaning) capable of sustaining and driving physical phenomena. All of themcan be object of analysis by means of the Maximum Em-Power Principle: the only necessary requisite istheir observability, describability and measurability.

12. CONCLUSIONS

By starting from a rigorous mathematical definition of Emergy (Eqs. (1.1), (1.2)) and a GeneralEmergy Balance Equation appropriately restructured (Eq. (2.3)), we have shown that: i) A mathematicalformulation of the Maximum Em-Power Principle can be stated in the form given by Eq. (4.3); ii) As afirst result, such a formulation contributes to a clear and rigorous definition of the meaning of “useful”(or “processed”) Emergy; iii) The real novelty, however, of such a formulation, apart from themathematical aspect, is its constitution in three fundamental parts: causes, effects, and their relationshipunderstood as being a

common tendency; iv) As atendency Principle, the M. Em-P. P. can also be formulated both in a weak sense (as a general

principle) and in a strong sense (in particular cases); v) If understood as a general principle, the givenmathematical formulation allows us to enlighten the previous and well-known Thermodynamic Principles,without ever losing its Quality properties, even if it is applied according to a “reduction” procedure. Infact we have shown, in particular, that: vi) The First and the Second Principles can be obtained(independently from each other) from the given mathematical formulation of the Maximum Em-Power Principle; vii) Such a “derivation” however cannot be thought of as being a deduction of the former Principles from the latter, but rather as a “reduction” of the latter to the former; viii) The three consideredPrinciples in fact always remain in-dependent from each other. The procedure followed only shows inwhat reductive perspective (and associated limiting assumptions) the M. Em-P. P. can be thought of as being “equivalent” to the two basic well-known traditional Thermodynamic Principles; ix) The Maximum

Em-Power Principle, as a Quality Principle, adherently contemplates the co-existence of the MinimumAction Principle, so that this can still be considered as being an independent (though only quantitative)Principle; x) The mathematical formulation of the M. Em-P. Principle also allows us to see the relationship

between order and disorder in a new light: in fact, the meta-mechanical order (represented by Tr )

increases much more rapidly than the mechanical disorder (expressed by the increase in Entropy); in

addition, most of the increase in Tr is independent from Exergy variations, which always represent only

a concomitant circumstance; xi) This clearly shows how the M. Em-P. Principle, even if it accounts for Exergy dissipations (which are ever present in any process), is conceptually independent from the SecondPrinciple (which only deals with such concomitant circumstances); xii) The Fifth Principle, vice versa,although “obtainable” from the mathematical formulation of the Maximum Em-P. Principle, cannot be

considered as a mathematical corollary to the latter, but as an exceeding Quality crowning of the MaximumEm-P. Principle; xiii) The order in the Universe (asserted by such a Principle), hierarchically associatedto different time-space scales and basic Exergy variations, is in fact mainly characterized by asuperimposed ordering distribution fundamentally due to the Spring-Emergy Sources, when these areseen as part of a Whole; xiv) We also demonstrated that Emergy has all the properties of a state variable;xiv) This paved the way to a clear assertion in favor of the M. Em-P. P. as a Thermodynamic Principle: both when it is understood in the “reductive” traditional sense and in the new much more general Thermo-dynamic perspective opened by the concept of Emergy; xvi) As far as the last aspect is concerned, wealso emphasized the widest perspective of such a Principle in dealing with observable, describable andmeasurable phenomena. Consequently, in this context, it has an extremely wide generality and an almostunlimited practical application; xvii) Finally, the mathematical formulation of M. Em-P. Principle enabledus to show that such a Principle, when seen in the light of the hierarchical progressive increase inQuality content, in the successive passages from the traditional Thermodynamic Principle to the FifthPrinciple, represents a new fecund starting point rather than an important point of arrival , especially



because of the above-mentioned properties which have been clearly pointed out as consequence of itsmathematical formulation.

To conclude this paper it is worth pointing out that such a mathematical formulation of theMaximum Em-Power Principle is not only the result of a persistent effort constantly gazing forth to givean elegant form to this Quality Principle: in other words, a quality language for a quality principle. It isalso, and at the same time, a sort of hymn to Quality.

Quality, in fact, which always plays such a fundamental role in Emergy Analysis, cannot be,strictly speaking, “derived”. It can only be recognized, described, accepted. It cannot be derived in anycase whatsoever, because it is fundamentally, by itself and in itself, in-derivable. Quality in fact isalways emerging , generative, primary. It simply appears: shows itself, presents itself, reveals itself, andit is always source of astonishment, fascination, charm. It certainly has a foundation: this is given by thequality of the presuppositions from which it originates; but the “process of emerging spring genesis” isthe one which always remains, specifically, in-derivable.9

REFERENCES

Brown M. T., 1993. Workshop on Emergy Analysis. Siena, 20-25 September.Gaggioli R. A., 1988. Available Energy and Exergy. Int. Journal of Applied Thermodynamics, Vol. 1

(No. 1-4), pp. 1-8.Giannantoni C., 1998. Environment, Energy, Economy, Politics and Rights. Proceedings of Advances in

EnergyStudies, Porto Venere, Italy, May 27-31. Ed. MUSIS, Rome, pp. 541-558.Giannantoni C., 1999.

Integrated Approach to the Analysis of Investments by Means of Three Synthetic Economic Indicators: Energetic, Exergetic and Emergetic DCF (Discounted Cash Flow). International Conferenceon Indices and Indicators of Sustainable Development. St. Petersburg, Russia, July 11-16.

Giannantoni C., 2000a.Toward a Mathematical Formulation of the Maximum Em-Power Principle. Proceedengs of t h eFirst Biennial Emergy Analysis Research Conference. Univ. of Florida, Gainesville (USA), p.155-169.

Giannantoni C., 2000b. Multiple Bifurcation as a Solution of a Linear Differential Equation of Fractional Order. International

Congress of “Qualitative Theory of Differential Equations”.Siena (Italy), September 18-20.Giannantoni C., 2001a.

Advanced Mathematical Tools for Energy Analysis of Complex Systems. Proceedings of the InternationalWorkshop on “Advances in Energy Studies”. Porto Venere (Italy), May 23-27, 2000. Ed. SGE,Padua.

Giannantoni C., 2001b. The Problem of the Initial Conditions and Their Physical Meaning in Linear Differential Equations of Fractional Order. Third Workshop on “Advanced Special Functionsand Related Topics in Differential Equations” - June 24-29 – Melfi (Italy). To be published byElsevier Science.

Giannantoni C., 2001d. Mathematics for Quality: in Living and Non-Living Systems. Second EmergyEvaluation and Research Conference. Gainesville (Florida, USA), September 20-22, 2001.

Kolmogorov A. N. and Fomin S. V., 1980. Elements of the Theory of Functions and Functional Analysis.Ed. MIR, Moscow.

Krasnov M. L., Makarenko G. I., and Kisele A. I., 1984. Variational Calculus. Ed. MIR, Moscow.

Landau L. and Lifchitz E., 1969. MÈcanique. Ed. MIR, Moscow.Odum H. T., 1994a.



Ecological and General Systems. An Introduction to Systems Ecology. Re. Edition. University PressColorado.

Odum H. T., 1994b. Environmental Accounting. Environ. Engineering Sciences. University of Florida.Odum H. T., 1994c.

Ecological Engineering and Self-Organization . Ecological Engineering. An Introduction toEcotechnology. Edited by Mitsch W. and Jorgensen S. J Wiley & Sons, Inc..

Odum H. T., 1994d. Self Organization and Maximum Power. Environ. Engineering Sciences. Universityof Florida.

Odum H. T.,1995a. Public Policy and Maximum Empower Principle. Net EMERGY Evaluation of Alternative Energy Sources. Lectures at ENEA Headquarters, May 24.

Odum H. T., 1995b. Energy Systems and the Unification of Science. From Maximum Power. The Ideasand Applications of H. T. Odum. C. A. S. Hall, Editor. University Press Colorado.

Ruberti A. and Isidori A., 1969.Theory of Systems. Ed. Siderea, Rome.

Szargut J., Morris D. R. and Steward F. R., 1988. Exergy Analysis of Thermal, Chemical and Metallurgical Processes. Hemisphere Publ. Corp., USA.

APPENDIX 1. EMERGY AS A STATE VARIABLE

The problem can be seen in the most general context of Dynamic Systems Theory. In such acontext, in fact, the approach is simplified because Emergy Analysis is specifically oriented at describingwhat happens

inside the System (it is not a “black box” analysis), so that we can immediately start from aSystem description which corresponds to the input-state-output approach. Thus we first consider the set

of differential equations that describe the Complex System under consideration, that is: continuity,momentum, Energy, Exergy, etc., and Emergy Balance Equations written for each sub-System. Under such conditions it is very easy to identify those variables that are potentially able to play the role of statevariables. One of those could be Total Emergy (obviously related, as we already know, to the specificEmergy of each sub-system).

If we then consider the classical input-state-output mathematical representation of the Systemin the pertinent form given through the state transition function

where ψ : ( ) ,TxT x X x U X ∗

− − −→ (A.1)

and the output transformation function

y t t x t u t − − −

=( ) ( , ( ), ( ))η where η : Tx X xU Y − − −

→ (A.2)

where u t −

( ) , )(t x−

, )(t y−

are the input , state and output vectors respectively such as−−

U t u )( ,

−− X t x )( , −−

Y t y )( (while−U ,

− X ,

−Y are the input, state and output spaces respectively), we can easily

verify that Eq. (11.1) satisfies the above-mentioned conditions such as (see Ruberti and Isidori, 1969):a) uniqueness and causality

ψ ψ ( , , ) ( , , ), ,

t t x u t t x ut t t t

0 0 1 0 0 2

0 0− − [ ) − − [ )=, ,

(A.3)



∗∀ )(),( 0 TxT t t , −−

X x0 , ∀ ∈

− − −u u U

1 2, :

u ut t t t

1 2

0 0− [ ) − [ )

=, ,

that is: identical inputs lead the System to the same and unique final state value; b) consistency

−−−

= 0000 ),,,( xu xt t ψ (A.4)

∀ ∈t T 0

, ∀ x X 0− −

∈ , ∀ ∈− −u U

that is: the transition into the initial state coincides with the initial state value;c) separability

ψ ψ ψ ( , , ) ( , , ( , , ) ), , ,

t t x u t t t t x u ut t t t t t

0 0 1 1 0 00 0 1 1− − [ ) − − [ ) − [ )

=, , , (A.5)

∀ ∈ ∗( , ) ( )t t TxT 0

, ∀ ∈t t t 1 0

( , ) , ∀ ∈− − x X 0 , ∀ ∈

− [ ) −u U

t t 0 ,

that is: the state evolution can be analyzed by means of a finite sequence of successive steps.It is also worth adding that the property of separability has already been considered as a

fundamental assumption in order to define a reference level of Emergy (e.g., Solar Emergy). In fact sucha property immediately allows us to write

Em t Em Ex d t

t

eq

∗ ⋅

= + ∫ ( ) ( )0

0

τ τ (A.6)

(see also Giannantoni, 2000a, Eq. (2.3)).

(Footnotes)

1 Such a concept, already introduced in Giannantoni (2000a), will be analyzed in detail in thecompanion paper (Giannantoni, 2001d) presented in this Conference, especially as far as its profoundmeaning and wide consequences are concerned.

2 In this form each input and internal contribution to the System has its correspondingeffectdirectly and exclusively expressed in terms of output quantities. Under these conditions we always

have β β l l

∗ ⋅( ) ≥ 1 , for l = 1,2,Ö p.

3 For the sake of generality, in open systems the specific mass Exergy is defined as

gzv sT hex ++−= 2

02

1)( , that is it includes the kinetic and potential terms.

4 The apex “+” indicates a positive quantity when furnished by the System, while its algebraicsign specifies the exact versus. Obviously we may use, if needed, all the consistent transformations

such as −+ +=− A A and so on. Such a convention is particularly useful for a successive comparison

with the Second Principle formulated in terms of Exergy Balance Equation (see par. 6). Generallyspeaking we could say that the convention suggests the perspective according to which one shouldevaluate the actual versus of each considered flow.



Moreover, the term pertaining to work is understood as including a specific contribution

due to pressure work such as∂

∂∗∫

p

t d V

D t

3

( )

in order to have the same expression for specific mass

Exergy (ex) both in the volume and surface integrals and in the case of both a Eulerian and Lagrangian

description. At the same time the System is assumed to be subjected to conservative force fieldswhich are assumed to be constant in time (as usually happens).

5 Even if such a symbol is not adopted in writing the following equations, it will always beunderstood as being substituted by the convention according to which the equations will be (generally)written: the left side will represent the causes and the right side will represent the correspondingeffects. Such a physical-mathematical convention ( from left to right ) is in some way analogous to thetopological convention adopted in Emergy Analysis System Diagrams.

6 The difference between the presence of gzvhhkz ++= 2

2

1 in Eq. (6.6 ) and of

gzvuukz ++= 2

21 in Eq. (6.7) depends on the fact that the term

?+ pvw ,

includes the pressure work

mentioned in note 4, whereas the term?+

vw does not.

7 The difference between Eq. (7.1) (in which there is gzvhhkz ++= 2

2

1) and Eq. (7.2)

(in which there is gzvuuen kz ++== 2

2

1) depends on the fact that the terms

?+ncvw ,

and?+nc sw ,

account for all the contributions due to non-conservative forces, including those due to pressurework (apart from the term mentioned in note 4, which is now unnecessary).

8 The Hamilton Principle, generally known as The Least -Action Principle, has been hererenamed by making use of the adjective Minimum (even if less correct) only to stress the (apparent)contrast with Maximum. On the other hand the distinction between Minimum and Extremum isunessential to the establishment of the equations of motion (8.4).

9 A significantly explicative example is appropriately given by the above-mentioned“passages” (or, rather, over-deductions) from the traditional thermodynamic Principles to the FourthThermodynamic Principle and then, from the latter, to the Fifth Principle.






Edited by



Associate Editors

Howard T. Odum



David Tilley



Sergio Ulgiati

University of Siena

Siena, Italy

December 2003



Gainesville, FL


vi



-35-

Chapter 3. Spatial Modeling of Empower and Environmental Loading

3Spatial Modeling of Empower and Environmental Loading

Mark T. Brown

ABSTRACT

Empower (emergy per time: sej yr-1, sej da-1) and empower density (emergy per time per unit area:

sej yr-1 ha-1; sej da –1 m-2) of land uses and processes have a spatial dimension. Empower and empower

density are related to intensity of human activity and could be used to evaluate environmental support,

carrying capacity, and environmental buffers. Two environmental support areas: a Renewable Support Area (RSA) and a Synchronal Support Area (SSA) are dened and calculated for different land use types.

The two environmental support areas are used to calculate a upper and lower bound to carrying capacity.

The RSA is based on renewable emergy and represents the lower bound as it calculates the area neces-

sary to provide inputs to a process solely for renewable sources. The SSA is the upper carrying capacity

as it is based on present conditions and the intensity of development that matches local conditions and

is therefore competitive.

An index of environmental loading, the Environmental Loading Ratio (ELR) is shown to vary over

space and used to calculate buffer dimensions. Empower and ELR are both a function of space. As the

area over which they a re calculated increases, empower and environmental loading decrease. Several

methods of spatially dening empower, empower density and environmental loading are explored.

INTRODUCTION

The impacts from human uses of landscapes might be related to the intensity of use. The more

intense land is utilized for urban or agricultural uses, the greater the potential for off-site impacts on sur-

rounding natural ecological systems. Consider, for instance, the two extremes of full development on

the one hand and completely natural on the other. A fully developed landscape, dominated by high-energy

land uses, may have few if any functional, natural ecological systems. At the other extreme, a natural

landscape, one with no agricultural or urban development, would probably have intact ecological systems

and processes. Landscapes in most regions of the globe fall somewhere between these two extremes in a

gradient extending from completely natural to highly disturbed. They are composed of some developed

areas but also have some natural ecological communities. Landscapes might be characterized by their

intensity of human uses using empower density, or the emergy use per time per unit area (sej yr-1 m-2).

Most landscapes are composed of patches of developed land and patches of wildlands, or un-

developed lands that remain within a developed landscape mosaic. While not directly converted, often

wildlands experience cumulative secondary assaults that originate in developed areas and that spread

outward into surrounding and adjacent undeveloped lands. The more developed a landscape, the greater

the intensity of assaults. The systems diagram in Figure 1 illustrates some of the assaults originating in

developed lands that are experienced by surrounding and adjacent wildlands. They come in the form of

air- and water-born pollutants, physical damage, changes in the suite of environmental conditions (like

changes in groundwater levels or increased ooding), or combinations of all of them. Pathways fromthe developed lands module on the right carry nutrients and toxins that affect surface and ground water

which in turn negatively affect terrestrial and marine and aquatic systems. Other pathways interact directly



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with the biomass and species of wildlands decreasing viability and quantity of each. Pathways that affect

the inow and outow of surface and groundwater may alter hydrologic conditions, which in turn, may

negatively affect ecological systems.

In this paper we discuss spatial modeling of empower density and environmental loading.

Spatial dynamics of environmental loading and empower might be used to evaluate carrying capacity,

to determine areas necessary for environmental support of processes, and to develop a quantitative basis

for establishing buffers around developed lands as a means of minimizing their impacts on surrounding

ecological systems

METHODS

Empower Density

Empower is the emergy use per time (sej yr-1). Empower density is emergy use per time perarea. For a land use or process depicted in Figure 2, the nonrenewable empower density is as follows;

NREmpD(j)

= (F + NR) / area (1)

where, NREmpD

(j) = Nonrenewable empower density of land use, or process (j),

F = Purchased emergy (sej/time) NR = Nonrenewable emergy use (sej/time)

area = Area over which the empower is averaged (m2 or ha)

Wastes

Investment$

Surface & Gd.Waters

Image

Goods

CummulativeImpacts

Marine & AquaticEcosystems

Ter rest r i a lEcosystems

BSpp.

B

Spp.

Sed.

N & P Tox .

Assets

People

$

Markets

$

P e o p l e

Water,Fuels,Elec.Storms

Tox.

N & P

Sed.Rain

Wind

Sunlight

Developed Lands

Surface& Gd. Water

Landscape Unit or Watershed

E v a p

o t r a n s

p i r a t i o n

Figure 1. Systems diagram illustrating some of the assaults originating in developed lands that are experienced

by wildlands surrounding and adjacent to developed lands.



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Empower density is a relative measure of the intensity of activity. Rural areas have empower

densities of 1 to 10 E11 sej yr-1m-2. Industrial agriculture and extractive industries have empower densities

of from 10 to 1000 E11 sej yr-1

m-2

, while major urban centers are characterized by empower densities inexcess of 1000 to 100,000 E11 sej yr-1m-2. Renewable empower density is the renewable emergy use

per unit area per unit time. The average global renewable empower density is about 0.2 E11 sej yr-1m-2.

On land, where geobiospheric processes converge, the renewable empower density is between 0.5 and

5 E11 sej yr-1m-2. Table 1 gives typical empower densities for several land based human dominated

systems and the average for the Florida landscape without humans.

Environmental Loading Ratio

Recall that environmental loading was dened by the ratio of nonrenewable emergy to renewable

emergy for a process or landscape unit and has been termed the “Environmental Loading Ratio (ELR)(Odum 1996, Brown and Ulgiati,1997). The ELR is calculated using the empower of renewable and

nonrenewable emergy as follows:

ELR = NR / R (2)

where;

ELR = Environmental Loading Ratio

NR = Nonrenewable empower (sej yr-1)

R = Renewable empower (sej yr-1)

ELR’s have been calculated for a variety of landscape units ranging in scale from the entire globe

(Brown and Ulgiati, 1999) to countries (see for instance, Brown and McClanahan, 1996; or Brown

Table 1. Typical non-renewable empower densities for human dominated land uses

Local Non-

renewable

sources

Environmental

Systems

Economic Use

N R LocalRenewable

Sources

R

Purchased Resources

Services

Yield

F

Y

Environmental Loading Ratio =(F+NR)/R

where:

R = Renewable emergy,

NR = Nonrenewable emergy, and

F = Purchased emergy

Figure 2. Diagram summarizing the calculation of nonrenewable empower density for a land use or process.



-38-


__________________________________________________________________________________

_

Type Empower Density Source

(sej yr-1 m-2)

__________________________________________________________________________________

_

Agricultural lands 2 - 10 E 11 after Brandt-Williams (2001)

Residential land uses 1 – 10 E 13 Brown, (1980)

Commercial land uses 3 - 30 E 13 Brown, (1980)

Highways 3 – 5 E 13 after Parker, (1998)

Renewable empower density of 5 – 6 E 10 after Bardi, (2002)

natural landscape (Florida)

__________________________________________________________________________________

_

To express as sej yr-1 ha-1 multiply empower density by 1.0 E4

et al. 1995) to land based processes such as in agriculture (Brandt-Williams, 2000) or technological process

like electrical production (Ulgiati and Brown, 2001). Typically undeveloped areas have ratios less than

1.0. developing regions have ratios of between 1/1 to 4/1, and developed economies have ratios greater

than 5/1 (Brown et al. 1995). For individual processes or economic activities that require small area,

environmental loading can be quite large. Industrial agriculture can have ELRs of 10-100 /1 while intense

economic activities and highly urbanized areas can have ratios greater than 1000/1 (Odum, 1996).

Environmental Support Areas

Renewable Support Area

In a recent publication (Brown and Ulgiati, 2002) we suggested that a lower limit to carrying

capacity could be expressed as land area required to support an economic activity solely on a renewable

base. We called this required area, “renewable support area.” It is derived by dividing the total emergy

input to a process (or land use) by the average renewable empower density of the region in which it is

located as follows:

SA(r)

= (F + NR) / REmpD(r)

(3)

where,

SA(r) = Renewable Support Area (m

2

) REmpD(r)

= renewable empower density of region (sej yr-1m-2)

F = purchased inputs (sej yr-1)

NR = non-renewable inputs (sej yr-1)

The result of the calculation is the necessary area of the surrounding region that would be required

if the economic activity were using solely renewable emergy inputs from the landscape.

Synchronal Support Area

Synchronal Support Area (SSA) is dened as the area necessary to lower the ELR of a process

or land use to the ELR characteristic of the region in which the process or land use is embedded. SSA is

calculated using the average annual ux of renewable emergy per year per unit area of landscape. For themost part, when evaluating land based activities, the renewable empower ux that is used by the develop-

ment is “planar” in origin (ie it is uniformly distributed across the land [rain or sunlight for instance],

rather than a point source) Renewable emergy/area is derived from an analysis of the regional economy.



-39-


To determine the SSA for a process or land use, rst the ELR for the region is calculated (Eq 1) and then

the following simple equivalent proportion is constructed:

ELR(r)

= ELR(d.)

(4)

where:

ELR(r)

= environmental loading ratio of the region,

ELR(d.)

=environmental loading ratio of the development = [F + NR] / R*(5)

and

F and NR are as given in Equation 4.

The ELR(d)

is the loading ratio that is necessary to equal that of the region, thus the R* in Equation 6 is

the required amount of renewable emergy necessary to lower the ELR of the development to that of the

region. The equation is solved as follows:

R* = [F+ NR] / ELR(r)

(6)

Once the quantity, (R*), is known, the Synchronal Support Area (SSA) is calculated as follows:

SSA = R* / REmpD(r)

(7)

If we assume a circular SSA, then the radius of the SSA can be calculated as follows:

Area of Circle = 3.14 * r2

and Area of Circle = SSA, then;

(8)

and substituting Equation 7 for R*,

(9)

Finally, to determine the distance “r” (assuming a circular support area) required for the renew-able support area we combine the equation for area of a circle with Equation 3, as follows:

(10)

and if we assume the regional ELR is 8/1 as is characteristic of the USA (Odum, 1996), and using Equa-

tion 10 the radius of the Synchronal Support Area becomes:

(11)



-40-


Spatial Decay of Empower

Since empower density is a function of area, as the distance from a process or land use increases the non-

renewable empower density decreases. The actual decrease with distance from source in nonrenewable

empower density, assuming a circular decay, is given as follows:

(12)

where,

NREd(d)

= nonrenewable empower density at distance d

F = nonrenewable purchased emergy of land use or process j

NR = nonrenewable empower of land use or process j

d = distance from centroid of land use or process

RESULTS

Empower and Area

Figure 3a and b are graphs of empower versus area. Figure 3a illustrates the effect of increasing

distance from a source on empower density (assuming a circular area around a empower source) and Fig-

ure 3b illustrates the effect of ever increasing area on total renewable empower. Nonrenewable empower

density that results from one hectare of a typical medium density single family residential development

is shown in Figure 3a with the graph that declines from about 2.2 E13 to about 8.0 E 10 sej yr-1 m-2. The

horizontal axis is distance from the edge of the development. The sloping curve is based on the assump-tion that the area is circular around the development, so that at a distance of about 25 meters from the

edge of the development the area is equal to a little over 2 hectares (one hectare of development and one

hectare in the surrounding area). At 43 meters from the development the area is about 3 hectares and so

on. Since the area increases empower density declines.

Shown in Figure 3b, is the accumulated renewable empower with increasing area, based on a

renewable empower density of 5.0 E 14 sej yr-1 ha-1 (5.0 E10 sej yr-1 m-2 ). Since renewable empower in

the average landscape is spatially dened, that is, it is a “planar source” inowing on an areal basis, as

the area increases the cumulative empower from renewable sources increases. The assumption used to

generate the graph is that the area represents a circular area around the one hectare development.

It is important to note that the two graphs have different units. The Y axis in Figure 3a is em-

power density (sej yr-1 ha-1) while the Y axis in Figure 3b is empower ( sej yr-1). The graphs illustrate thatnonrenewable empower density from a source (like a residential development) decreases as the area used

to calculate the density function increases…nothing new here, However, an important point results from

the observation, non-the-less, as it provides some interesting implications for calculating support areas

and buffers between developments and sensitive ecological systems. Nonrenewable empower density

declines with increasing area and cumulative renewable empower increases.

Decay of Empower With Distance

The intensity of empower can be thought of as decreasing with distance from the source. Figure

4 shows two different concepts of empower decay. In Figure 4a, the decay of empower from a linearland use, such as a highway is a function of the distance. The empower of the source is assigned to the

edges of the land use. In this case there are 20 edge cells so each is assigned 1/20th of the empower of



-41-


Figure 3. Graphs of nonrenewable empower density as a function of increasing area(a) and renewable empower

as a function of increasing area (b) .



-42-


the land use, and then decayed at the rate of 1/d. The graph in Figure 4a shows decay of empower from

a typical 2 lane highway with linear decay.

A second decay function results when the empower source is thought of as a point source (Fig-

ure 4b). In this case the decay is a function of the distance squared (3.14*d2) since it radiates out in all

directions. Shown in Figure 5a and b are graphs of empower density verses distance from the source for

a highway and the three general land uses (agriculture, residential and commercial)

Environmental Loading Ratio With Distance

Since the Environmental Loading ratio (ELR) is a function of both non renewable and renewable

empower (see Eq 2), it changes over space as a result of the increase in total renewable empower that

is factored into its calculation. As more and more area is included, the ratio decreases because the total

amount of renewable empower increases, as shown in Figure 3b. Figure 6 shows the decrease in ELR

with distance from the source for the three general land uses given above. Environmental Loading Ratio

in this example is calculated by dividing the empower of the land use by the renewable empower.

The horizontal dashed line on the graph represents an ELR of 8.0 (the ELR of developed land-

scape). The intersection of each graph line with the 8.0 ELR line represents the distance away from the

land use where the ELR of the development is equal to that of a developed region. This assumes a circular

area around the land use and therefore distance is equal to the radius of the circular support region.

Environmental Support Regions

The area of environmental support can be calculated using the method describe in equations 3 –

11. It is possible to calculate a renewable empower support area which is the area required to provide a

renewable empower base equal to the nonrenewable empower use of a land use (Eq. 3). It is also possible

to calculate a “synchronal support area” for different land uses using the ELR for the region (Eq.7) Table 2 lists nonrenewable empower densities for a number of typical land uses. Units of em-

power are sej per year per square meter (sej yr-1 m-2) and vary three orders of magnitude, from a low of

d b

Linear Decay - (NR/d )

Point Source

Circular Decay (NR/3.14d2)

d2

Figure 4. Two different concepts of empower decay, the decline in empower from a linear source is a function of

distance, while the decline in empower from a point source is a function of the distance squared.

d dd2



-43-


Figures 5. Graphs of empower density verses distance from the source for: (a) linear element like a two lane high-

way, and (b) the three general land use types (agriculture, residential and commercial)



-44-


about 0.7 E11 sej yr-1 m-2 for passive recreation to a high of over 2.9 E13 sej yr-1 m-2 for central business

districts. Natural lands and water have no nonrenewable empower density. The second and third columns

in the table give the renewable support area and environmental support area for each of the land uses

expressed as area of support per area of land use (hectares/hectares). Renewable support areas vary from

about 1.1/1 for low intensity recreation to 4900/1 for central business district. The synchronal support

areas for land uses vary from a low of 0.1/1 for low intensity recreation to a high of about 612/1. In all,

the synchronal support areas represent the amount landscape required to achieve a ELR of 8/1 for each

hectare of the given land use types.

Spatial Simulation of the ELR

Spatial simulations of environmental loading reveal the extent of support regions. Using land

use GIS data from the Florida Keys as an example, spatial simulations of empower and ELR were con-

ducted. Figure 7 shows a detail of Kudjoe Key, an island in the Florida Keys, about 22 miles east of

Key West. The light gray is land, darker areas are urban development, and white areas are water. The

overseas highway is visible as the east west line in the map. The developed areas consist of residential,

commercial and roadways of differing empower densities (see Table 2), but have been given the same

color value for illustration purposes.

Figure 8 shows the spatial simulation of empower density for the Cudjoe Key area using a decay

function that is based on distance squared. To obtain the nal map of empower density, each land use

area is simulated separately and the resulting spatial empowers are added together. Figure 9 is a map ofspatial ELR’s for the Cudjoe Key area. The ELR’s range from <1 to 315/1. As would be expected, the ELR

within the developed areas is quite high from 30/1 to as high as 315/1. The lightest gray area represents

the extent of the renewable support area, while the edge given by the 8/1 contour represents the extent of

the environmental support area.

Figure 6. The decrease in Environmental Loading Ratio with distance from the source for the three general land

uses. The ndashed horizontal line is the ELR for a developed landscape.



-45-


Table 2. Non-Renewable Empower Density by land use types and environmental support areas

(after Brown and Vivas, 2002)

__________________________________________________________________________________

__

Non-Renewable Renewable Synchronal

Land Use Empower Density Support Area Support Area

(E11 sej yr-1 m-2) (hectares/ hectare)

__________________________________________________________________________________

_

Natural Open lands and waters 0

Recreational / Open Space (Low-intensity) 0.7 1.1 0.1

Woodland Pasture (with livestock) 0.8 1.3 0.2

Pasture (without livestock) 1.7 2.9 0.4

Low Intensity Pasture (with livestock) 3.3 5.6 0.7

Citrus 4.4 7.3 0.9

High Intensity Pasture (with livestock) 4.7 7.8 1.0

Row crops 10.7 17.9 2.2Single Family Residential (Low-density) 107.7 179.5 22.4

Recreational / Open Space (High-intensity) 123.0 205.0 25.6

High Intensity Agriculture (Dairy farm) 134.9 224.9 28.1

Single Family Residential (Med-density) 217.5 362.5 45.3

Single Family Residential (High-density) 237.2 395.3 49.4

Mobile Home (Medium density) 274.8 458.0 57.3

Highway (2 lane) 308.0 513.3 64.2

Low Intensity Commercial 375.8 626.3 78.3

Institutional 404.2 673.7 84.2

Highway (4 lane) 502.0 836.7 104.6

Mobile Home (High density) 508.7 847.8 106.0

Industrial 521.1 868.4 108.6

Multi-family Residential (Low rise) 739.2 1231.9 154.0

High Intensity Commercial 1266.1 2110.2 263.8

Multi-family Residential (High rise) 1282.5 2137.5 267.2

Central Business District (Average 2 stories) 1615.0 2691.7 336.5Central Business District (Average 4 stories) 2940.1 4900.2 612.5

_____________________________________________________________________

DISCUSSION

Spatial Empower Density

Empower density is a function of area and as such the area over which empower is averaged

determines intensity. As more and more area is included, empower density decreases. It is often appropri-

ate to express empower density of a particular land use or process only using the area it occupies, as this

gives some indication of the intensity of use, especially when compared with other land uses or processes.

When calculating empower density in this way, a unit area is often used, such as m2 or hectare. However

no land use is separated from its surrounding lands and so the empower when considered spatially could

be much lower depending on the amount of area included.

It might be possible to use nonrenewable empower density as a means of tting land uses withindeveloping landscapes in such a way that the do not have negative effects between them. It has long

been suggested (see Brown, 1980) that incompatibilities between land uses of differing emergy intensity

could be solved by separating them with natural buffers. The separation distance could be determined



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as the distance necessary for the empower density of the higher intensity land use to be lowered so that

it equaled the empower density of the lower.

Buffers and Spatial Empower

The differences between developed lands and natural areas are signicant, and the more in-

tensely developed, the greater the differences. The gradient in intensity of noise, waste, temperature, light,structure, and activity from undeveloped to developed lands can be signicant. Often, it is quite apparent

that the “quality” of an ecosystem immediately adjacent to developed areas is not as good as one that is

some distance away. Adjacent ecosystems are often depopurate of wildlife, have accumulated garbage,

yard wastes and other jetsam and otsam from developed area, and suffer from increase re frequency,

cutting etc. In all, these impacts result in erosion of environmental quality of ecological systems adjacent

to developed areas. The degree of impact is strongly correlated to the intensity of the development and

distance separating the ecological community from developed areas.

Buffers between urban lands and natural ecosystems might be calculated based on empower

density. Using the average empower of the region as the goal, buffer distances could be designed as the

distance necessary for the nonrenewable empower density of land uses to be lowered so that they equal

the average empower density of the region. Particularly sensitive ecosystems or wildlands could be

protected if the buffer width was equal to the distance required to lower non renewable empower density

to that of the renewable empower density of the landscape.

Figure 7. Developed land use including commercial, residential and transportation, on Cudjoe Key, an island in

the Florida Keys, about 22 miles east of Key West.



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Spatial Environmental Loading

Nearly all productive processes of humanity involve the interaction of nonrenewable emergies

with the renewable emergies of the environment and as a result the environment is “loaded”.

Environmental Loading is a function of space as well. Consider, for example, point sources of pollution

disperse outward from the source, much like ripples in a lake. Impacts near the source, are greater thanthose experienced farther away. An Environmental Loading Ratio calculated as a static spatial ratio (ie

assumed equal through out a spatial unit) does not recognize this fact. While an ELR of a land use or

process calculated using only the area it occupies can reveal important information about potential load

on the environment, it does not recognize the spatial character of off-site impacts.. It should be obvious

that lands closer to an empower source are more affected than lands farther away.

Spatial change in the environmental loading ratio can be used to determine relative environmental

impacts. As the distance from a empower source increases, the environmental loading ratio decreases.

at distances where the ELR matches that of the regions as a whole it might be said that impacts for a

land use or process are no greater than those within the region as a whole. At distances where the ELR

is equal 1/1, it might be said that there is no net effect.

Figure 8. Spatial simulation of empower density for the Cudjoe Key area using a decay function that is based on

distance squared.

Empower Density

(E11 sej*yr-1 *m-2)



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Carrying Capacity and Environmental Support Areas

Spatial calculation of ELR can be used to dene environmental support areas and may be a way

of spatially dening carrying capacity. Two environmental support areas are proposed in this paper: aRenewable Support Area (RSA) and a Synchronal Support Area (SSA). The RSA denes the amount of

land that would be necessary to provide the emergy required by a land use or process from renewable

sources of the particular region. It is related to carrying capacity in that it is calculated from the average

renewable emergy inputs to a region by determining how much area is required to sequester sufcient

inputs to provide the total emergy requirements of a land use or process. Calculating a renewable carrying

capacity assumes that all emergy requirements will be derived from renewable sources. Since renewable

emergy sources are areal based, carrying capacity becomes area required . T Carrying capacity calculated

in this way may be a predictor of long term sustainability.

A second approach to carrying capacity is related to the “tness” of development within a local

economic and environmental system. This second approach is based on the intensity of development,

which has been termed environmental loading. The intensity of development in relation to existing condi-

tions may be critical in predicting its effect and its short term sustainability (Brown et al. 1995, Ulgiati

et al. 1996, Brown and Ulgiati 1997). If a development’s intensity is much greater than that which is

Figure 9. Map of spatial simulation of Environmental Loading Ratio for the Cudjoe Key area.

Environmental Loading Ratio



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characteristic of the surrounding region, on average, the development has greater capacity to alter exist-

ing social, economic, and ecologic patterns (Brown 1980, Odum 1980). If it is similar in intensity it is

more easily integrated into existing patterns. This second method of evaluating carrying capacity uses a

ratio of non-renewable emergy to renewable emergy, called an Environmental Loading Ratio (ELR) and

provides an upper limit to carrying capacity.

Combined, the two approaches provide lower and upper bounds respectively, to carrying capac-

ity of local environments for economic developments. In the rst case, the renewable emergy carrying

capacity assumes that all resources sustaining an economic endeavor must come from the local renewable

resource base. In the second case, the average environmental loading ratio is used to determine how much

of the local environment is matched with economic enterprises under current conditions and suggests

new development should maintain a similar intensity so as not to alter current local cultural, economic,

and environmental patterns.

SUMMARY

Empower and empower density are spatial phenomena varying with distance from the source of

empower. Spatial simulations of empower and environmental loading provide a means of tting land useswithin a developing landscape matrix, spatial carrying capacity, and environmental buffers for sensitive

lands.

Renewable support areas of urban and agricultural land uses vary from a low of 1.1/ 1 for the

lowest intensity uses like passive recreation to nearly 5000/1 for highly urbanized uses like the central

business district. The synchronal support area varies form a about 0.1/1 for passive recreation to a highof over 600/1 for central business districts.

Spatial simulations of empower and environmental loading ratio can be used to provide a spatial

dimension to potential environmental impact.

REFERENCES

Brandt-Williams. S. 2001. Emergy of Agricultural Systems. Folio # 4 of the handbook of Emergy Evalu-ation. Center for environmental Policy, University of Florida, Gainesville.

Brown, M. T. 1980. “Energy Basis for Hierarchies in Urban and Regional Systems.” Ph.D. dissertation,Department of Environmental Engineering Sciences, University of Florida, Gainesville.

Brown, M.T. and S. Ulgiati 2001. Emergy Measures of Carrying Capacity to Evaluate Economic Invest-ments. Population and Environment Vol 22:5 pp 471-501.

Brown, M.T. and S. Ulgiati.1999. Emergy evaluation of natural capital and biosphere services. AMBIO.Vol.28 No.6, Sept. 1999.

Brown, M.T. and S. Ulgiati. 1997. Emergy Based Indices and Ratios to Evaluate Sustainability:Monitoring technology and economies toward environmentally sound innovation. Ecological

Engineering 9:51-69Brown, M.T. and T. McClanahan 1996. Emergy Analysis Perspectives for Thailand and Mekong River

Dam Proposals. Ecological Modeling 91:pp105-130Brown, M.T. H.T. Odum, R.C. Murphy, R.A. Christianson, S.J. Doherty. T.R. McClanahan, and S.E. Ten-

nenbaum. 1995 Rediscovery of the World: Developing an Interface of Ecology and Economics.In C.A.S Hall (ed) Maximum Power. University Press of Colorado Press. P.O. Box 849, Niwot,CO 80544, pp.216 - 250

Brown, M.T. and M.B. Vivas. 2002. Landscape Development Intensity Index. working paper. Centerfor Wetlands, University of Florida, Gainesville. 15 p.

Odum, H.T., 1996. Environmental Accounting: Emergy and environmental decision making. John Wiley,New York. 370 p.

Parker, N. 1998. Spatial simulation of nutrient dynamics in the St. Marks River Basin. MS Thesis. De-partment of Environmental Engineering Sciences University of FloridaUlgiati, S. and M.T. Brown. 2001. Emergy Evaluations and Environmental Loading of Alternative Elec-

tricity Production Systems. Journal of Cleaner Production 10:335-348



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Edited by



Associate Editors

Howard T. Odum



David Tilley



Sergio Ulgiati

University of Siena

Siena, Italy

December 2003



Gainesville, FL


vi


http://slidepdf.com/reader/full/2nd-biennial-emergy-research-conference 76/481-51-

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Chapter 4. The Correlation Between GDP And Both...

4The Correlation Between GDP And Both Energy Use

And Emergy Use


ABSTRACT

What humans call wealth is generated principally by the use of commercial (i.e. purchased) energy

to transform raw materials from nature’s own energy ows into goods and services. We have examinedthe relation between commercial energy use and GDP of economic output for 17 different countries over

the last 35 years. We nd that in almost all cases there is a nearly one to one correlation between com-

mercial energy input and economic output over time for each country, and a less precise but still strong

correlation among different countries for a given year. On the other hand there is a much poorer relation

between GDP and total emergy used by those countries. As believers, fundamentally, in the importance

of all energy inputs and of correcting them for quality we must ask “Why is this so”? If much of this

wealth is produced by natural energies, one would think that there might be a much stronger correlation

with total emergy than with just that part that enters the market. We offer three hypotheses as to why

this correlation might not be strong: 1) economic prices used to generate GDP measures are somehow

missing the services of nature 2) the services of nature are in fact worth little to the economy and 3) sinceGDP accounting does not include the value of the upgrading of raw elements to natural resources, which

is done by natural energy ows, this economic step is not included in GDP and therefore would not be

expected to add to the correlation.

INTRODUCTION

A long standing question in economics is “where does wealth come from”? One answer, derived

from the classical economists including Adam Smith, David Ricardo and Karl Marx, is labor. They be-

lieved that it was the action of workers that changed raw materials of low value to items of higher value

that people wanted sufciently to pay for in what we call now nal demand. While this issue is consideredless explicitly (to our knowledge) by neoclassical economists they would probably answer that wealth is

generated as a response to market-driven prices, in turn a response to the intersection of supply and demand

curves. Hall et al. (1986) have proposed that wealth comes from nature, that it is the free energy services

of nature that do most of the work in generating wealth (for example, distilling fresh water from salt and

delivering it to the tops of mountains, or concentrating ores through hydrogeobiological processes, or

making fossil fuels, or simply maintaining and refreshing the milieu in which we can live) that allow us

to tap them with very little energy relative to either their actual energy content or their utility and value

to humans. This perspective is similar to that of Howard Odum’s long standing perspective of the value

of natural energy ows to human cultures.

We have been engaged for some time in reexamining the relation of the production of wealth

for humans (as imperfectly measured by GNP (Gross National Product) or GDP (Gross Domestic Prod-

uct), see e.g. Hall et al. 1986 for some of the imperfections) and the use of energy (e.g. Cleveland et al.

1984; Ko et al. 1998; Tharakian et al. 2001). We have been struck by the frequent very high correlations



Chapter 4. The Correlation Between GDP And Both....

(r-square value > 0.9) for most nations between GDP and energy use over time. In our analyses “energy

use” means the use of what we will call here commercial energy: oil, natural gas, coal, primary electricity

(electricity generated from hydroelectric or nuclear sources), and traditional energy (biomass). In our

calculations we multiply the primary electricity energies by a factor of 2.6, reecting the conversion ef -

ciency of turning fossil fuels into electricity (38% thermal efciency), so that all are in, roughly, “fossil

fuel equivalents”.

An obvious question is: why don’t we use a comprehensive emergy analysis for such comparisons,

where it is obvious that there are many other inputs to the economy besides commercial energy, including

of course sunlight for photosynthesis, the water cycle, winds and so on, geological and tidal energies, and

other inputs. We consider this a reasonable question and put forth the hypothesis that “Including other

inputs in addition to commercial energies as inputs to economies will lead to higher correlations between

economic production and energy use.”

METHODS

Our approach is very simple. We had already undertaken analysis of the relation of commercial

energy use and GDP for 17 nations over (about) 30 years as part of other projects. We did not have thetime or ability to undertake comprehensive emergy analysis for each of these 17 countries, especially

considering the quite varied terrain and resources of each. But we reasoned that most sources of emergy

are from the sun, that rain is the most important input to ecosystems (Odum, personal communication)

and that the sunlight use should not be double counted (Odum 1996: pp.51-52), so that we simply read

the mean annual rainfall data (Table 1), and corrected it for its transformity (Table 2). We then added

this to the commercial energy input. While we acknowledge that this does not include all energy inputs

to these economies, it does include the most important, and if there are other important inputs (i.e. tidal,

wind, river water, or whatever), they might emerge as obvious residual patterns. If the approach looks

promising then much more detail might be added later. If it does not look promising then perhaps there

are some important concepts that might emerge from these relatively quick and dirty results.

We used constant GDP in 1987 US dollars as an indicator of economic outputs of the countries.

The data used were for the period: 1970-1996. The 17 countries used in the analysis included: Argentina,

Brazil, Columbia, Costa Rica, India, Kenya, Korea (south), Malaysia, Mexico, Netherlands, Nigeria,

Philippines, Senegal, Thailand, United States, Venezuela, Zambia. These nations were chosen to reect

a variety of relatively rich and relatively poor nations from each of the three main continents: Africa, the

Americas and Asia. The transformities used for analysis are given in Table 2. The full spreadsheet of

numbers is available from the rst author.

Table 1. Sources of data used for analyses.

A. Energy consumption:

1. Multiple years of UN Statistical energy yearbook for 1970-1995 2. IEA. 2000. International Energy Annual for 1996

B. Rainfall:

Atlas of the World. 1968. Time Life Books and Rand McNally.

C. Economic output: Gross Domestic Product (GDP) in expressed as constant 1987 U.S. dollars: World

Resource Institute (WRI). 1998. Databases 1998-99.

D. Quality corrected energy (QCE) consumption is derived as: Total energy consumption = solids +

liquids + gas + quality corrected primary electricity + traditional energy. Quality correction is

done by applying 38% thermal efciency.

E. Emergy calculation: The parameters including transformity values are from Odum, H.T. 1996.

Environmental Accounting. John Wiley & Sons. New York.F. Efciency: calculated as economic output over quality corrected energy consumption or emergy

in put. Odum, H.T. 1996. Environmental Accounting. John Wiley & Sons. New York.

____________________________________________________________________



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__________Table 2. Transformities and calculation of total emergy input.

A. Rainfall-chemical: the rainfall data are converted into chemical energy by multiplying 15,444

sej/J (Odum, p.186)

B. Solid energy: the UN data * coal transformity , 39,800 sej/J (Odum, p.186)

C. Liquid : the UN data * transformity of petroleum products, 66,000 sej/J (Odum, p.186)

D. Gas: the UN data * transformity of gas, 48,000 sej/J (Odum, p.186)

E. Primary electricity: the UN data * transformity of electricity use, 159,000 sej/J (Odum,p.186)

F. Traditional energy: the UN data * transformity of rainforest wood, transported and chipped,

4.4*104 sej/J, (Odum, p.308)

We tested the proposed hypotheses by two approaches: 1) time series intra-national analysis, and

2) cross-sectional international analysis. We rst undertook time series analyses for each country by

regressing the 35 year record of GDP for each year vs on the one hand commercial energy use for each

year, and, on the other, total emergy use. Our second analysis was to regress GDP vs. both commercial

energy use and emergy use for all nations for one year. We did this for 1970, 1980 and 1990. The data

were normalized for per-square kilometer economic output and energy input.The economic and energy intensity of the Netherlands, South Korea, and United States

dominated the correlations so that we undertook the analysis both with and without these three nations

included (For display purposes, the values of economic output, quality-corrected energy and emergy of

the Netherlands, South Korea, and United States were reduced by 100, 10, and 10 times respectively for

Figure 1 to Figure 6.)

Figure 1. QCE use vs. GDP of selected countries for 1970




Figure 2. Emergy use vs. GDP of selected countries for 1970




c--


Figure 4. Emergy use vs. GDP of selected countries for 1980





RESULTS

Our analysis does not support our hypothesis that adding in additional transformity-corrected

natural energy ows to commercial energy inputs improves the correlation between energy inputs and

economic outputs, at least as measured by standard economic criteria. More specically, in nearly all

cases there is a lower correlation between GDP and emergy compared to GDP and commercial energy.

Specics follow.

Time Series Intra-National Analyses

The results of the time series analyses show that 13 of 17 nations had a higher correlation coef -

cient with just the commercial energy use, 3 nations had a (slightly) higher correlation with the total

emergy input, and one had an equal t (Table 3).

Cross-Sectional International Analyses

The relatively large intensities of economies of South Korea, the Netherlands, and the US have

generated high levels of r-square values for 1970, 1980, and 1990 by dominating the statistical ts for both

energy use and emergy use (Figures 1-6). We conducted the same analyses without the three countries,

which decreases the r-square values. However, the degrees of decrease are different between the twomethods. The energy use analysis without the three countries had higher r-square values over the emergy

analysis without the three countries for the same periods.

Figure 6 . Emergy use vs. GDP of selected countries for 1990



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Table 3. Results of emergy and quality corrected energy consumption analysis for selected countries

__________________________________________________________________________________

_

Country EMERGY Quality-Corrected Energy Analysis

Equation R-Square Equation R-Square

__________________________________________________________________________________

_

Argentina GDP = 4.964 M + 1.379 0.718 GDP = 0.026 Q + 58.23 0.735

Brazil GDP = 4.242 M – 382.7 0.589 GDP = 0.0243 Q + 92.84 0.598

Columbia GDP = 5.271 M – 81.96 0.591 GDP = 0.0312 Q - 1.208 0.592

Costa Rica GDP = 6.156 M – 3.644 0.904 GDP = 0.0397 Q + 0.7804 0.909

India GDP = 5.234 M – 153.0 0.957 GDP = 0.0250 Q + 26.12 0.968

Kenya GDP = 3.691 M- 11.90 0.922 GDP = 0.0178 Q + 0.1442 0.922

Korea, South GDP = 6.159 M + 15.49 0.959 GDP = 0.0365 Q + 16.30 0.968

Malaysia GDP = 6.600 M – 28.67 0.974 GDP = 0.0362 Q + 5.901 0.964

Mexico GDP = 3.452 M + 5.641 0.966 GDP = 0.0207 Q + 42.53 0.968

Netherlands GDP = 11.65 M + 25.34 0.805 GDP = 0.0622 Q + 31.66 0.773Nigeria GDP = 2.465 M – 16.11 0.491 GDP = 0.0138 Q + 11.92 0.526

Philippines GDP = 2.915 M + 2.612 0.801 GDP = 0.0170 Q + 15.36 0.817

Senegal GDP = 7.961 M – 7.449 0.768 GDP = 0.0420 Q + 1.385 0.774

Thailand GDP = 6.020 M – 30.80 0.980 GDP = 0.0318 Q + 5.564 0.978

United States GDP = 10.42 M – 1111 0.729 GDP = 0.0570 Q -560.9 0.753

Venezuela GDP = 1.527 M + 15.30 0.784 GDP = 0.0086 Q + 30.82 0.791

Zambia GDP = 0.09255 M + 1.724 0.091* GDP = 0.00066 Q + 2.080 0.134**

__________________________________________________________________________________

_

•

*P value = 0.127; ** P value = 0.0598; *** P value for the other cases = 0.0001. • M stands for eMergy and Q for Quality-corrected energy consumption.

• Emergy in 1022 sej; Quality corrected energy in petajoules; GDP in billion 1987 US dollars.

It is interesting that the gap of R-square values between the emergy use and energy use is reduced

over time, probably because the relative portion of rainfall energy to the total emergy use of the countries

has reduced over time.

Efciency

Another interesting statistic is energy and emergy efciency over time. There has been a greatdeal of interest in improving energy efciency from many quarters because it allows us to have our cake

of afuence and our eating of it (increased economic consumption) too, and because if offers an explicit

and “doable” goal for engineers, environmentalists and others. However we see no clear improvement in

commercial energy-based efciency for the 17 countries analyzed, although some countries have increased

and some have decreased efciency over time (Figure 7). On the other hand the emergy analysis indicates

an improvement in efciency for all countries (Figure 8). This is perhaps an artifact of the observation

that because commercial energy input is low, there is a very large quantity of solar-based energy input,

which remains constant as both GDP and commercial energy are added.

DISCUSSION

The failure of our original hypothesis to be supported, despite our original assumption that it

would be, supports the concept that what we account for as wealth, at least in these 17 countries, depends




far more on our ability to exploit nature than on the base of nature itself. This may be because there is

a far smaller variation in solar inputs to nations than in either wealth generated or in commercial energyinputs.

Our analysis should not be construed, however, as weakening the argument for the importance

of nature in generating wealth, or in emergy analysis for numerically accounting for it. It simply says that

given the way that we account for wealth, or possibly the way that we price it, there is a much stronger

relation between what we call GDP to our exploitation of nature rather than the activity of nature itself.

This would seem to support the concept that we need to be much more comprehensive in including the

dollar value of nature’s total services in our estimates of GDP.

Reviewers suggested that there maybe good economic reasons as to why market value and emergy

are not especially closely correlated. Since only purchased energies (i.e. fossil fuel, etc.) enter into market

transactions, and not solar, tidal or rain energies (except through rent for land) there is no particular reason

they should be well correlated. Yet these other energies contribute to human and ecosystem well-beingoutside of markets. Thus emergy assessments may measure not only the total of market value of consumer

surplus (i.e. that part of value to the consumer derived in part from exploitation of nature’s free energies),

but also non-market economic production (such as food production not sold in markets) and possibly

even some externalities or their alleviation. Perhaps then, emergy is indeed a more complete assessment

of value. We leave that judgment up to the reader.

CONCLUSION

We do not nd that emergy is as good a predictor of wealth generation as measured by conven-

tional means (i.e. GDP) as is total commercial energy use. Because we certainly do believe that all ows

Figure 7. Quality corrected energy efciency of selected countries, 1970-96



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of nature are important for generating wealth we think that the ability of humans to generate wealth is

more a function of humans’ ability to exploit a resource base rather than the resource base itself. Whythis is so we can only speculate on.

REFERENCES

Brown, M.T., H.T. Odum, R.C. Murphy, R. A. Christianson, S.J. Doherty, T. R. McClanahan, and S. E.

Tennenbaum. 1995. Rediscovery of the world: developing an interface of ecology an econom

ics, pp. 216-250. In C.A. S. Hall (Ed.). Maximum Power University Press of Colorado.Niwot,

Colorado.

Cleveland, C.J., R. Costanza, C.A.S. Hall and R. Kaufmann. 1984. Energy and the United States econo-

my: a biophysical perspective. Science 225:890-897Hall, C.A.S., C.J. Cleveland, and R. Kaufmann. 1986. Energy and Resource Quality: The Ecology of the

Economic Process John Wiley & Sons. New York.

Ko, J-Y, C. A. S. Hall, and L. G. L. Lemus. 1998. Resource use rates and efciency as indicators of

regional sustainability: An examination of ve countries. Environmental Monitoring and As-

sessment 51:571-593.

Tharakian, P. T, Kroeger and C. A. S. Hall. 2001. Twenty ve years of industrial development: a study

of resource use rates and macro-efciency indicators for ve Asian countries. Environmental

Figure 8. Emergy based energy efciency of national economy for selected countries, 1970-96






Edited by



Associate Editors

Howard T. Odum



David Tilley



Sergio Ulgiati

University of Siena

Siena, Italy

December 2003



Gainesville, FL


vi



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Chapter 5. Environmental Accounting for The Saemangeum Tideland...

5

Environmental Accounting for The Saemangeum TidelandReclamation Project


ABSTRACT

The conicts between environmental protection and economic development are becoming

increasingly important in environmental decision making of a Saemangeum tideland reclamation projectin South Korea. A science-based evaluation system is now needed to compare both the environmental

values and economic values with a common measure. The emergy concept was used to evaluate the

contributions of the proposed Saemangeum tideland to the regional economy. The original system

before reclamation receives more free environmental emergy plus a small inow from the surroundings,

resulting in the production of 5.04 E8 Em$/yr. After the development of economic uses, the new system

receives less environmental emergy and more emergy ow from the larger economy. The production of

the developed alternative is 4.49 E8 Em$/yr. The production of sheries before development is 3.72 E7

Em$/yr, and the production of agriculture and water supply after developments is 4.02 E8 Em$/yr. In

addition, the environmental cost and benet of storages after reclamation are 2.87 E8 Em$ and 6.81 E8

Em$ respectively. The construction cost is 3.94 E9 Em$. Based on the results of environmental accounting, it will take around 17.1 years to compensate

the net environmental cost of 3.55 E9 Em$ by the net production of 2.08 E8 Em$/yr. Among existing and

planned developments, emergy accounting can compare productivity with potentials. If those systems

prevail the production of more emergy and the more efcient utilization, then the systems with greater

useful emergy ow would be better and more likely to continue. The South Korean government should

stop the Saemangeum reclamation project and remove the partially constructed seawall, and restore the

tideland as coastal wetlands.

INTRODUCTION

Korea’s west sea tidal at is one of the world’s ve major tidal-at regions with the East coast of

Canada, the East coast of America, the North Sea in Europe, and the North-east coast of South America.

Most major tidal ats in Korea are located near the estuaries of major rivers, such as the Kum, Young-

sang, and Han Rivers. The Man-kyung River and Dong-jin River have deposited silt into Saemangeum,

the largest tidal at in Korea (Ministry of Agriculture & Forestry, 1991).

Recently, South Korea’s largest wetland is threatened by reclamation. The Saemangeum Tideland

Reclamation Project (STRP) was rst announced by the government in 1986, and is the largest of many

reclamation projects in South Korea. It commenced in 1991 and it is scheduled to be completed in 2011.

The 4 billion dollar project aims to reclaim 40,100 ha of tidal at by constructing a 33 km seawall to

reclaim agricultural land and create a water supply reservoir (Ministry of Agriculture & Forestry, 2000).A cost-benet ratio was estimated between 1.4 and 2.9 (greater than one means a net benet) by the Ag-

ricultural and Rural Infrastructure Corporation in Korea. However, this report neglected the effect of the

wetland loss due to the reclamation. A study conducted by the Korea’s non-governmental environmental



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organizations estimated a cost-benet ratio between 0.06 and 0.16. The study also strongly suggested that

the reclaimed farmland might not be as economically efcient as the existing tidal ats in the long term

(Green Korea United, 2000). Despite the strong oppositions and protests from various environmental and

civilian groups, the Saemangeum Tideland Reclamation Project (STRP) is under construction.

Various methods of environmental accounting have been used to estimate both the market and

non-market components of the value of ecosystem services (Costanza et al., 1997). Yet these attempts

fail from a scientic perspective, since they are based on “willingness-to-pay” and in no way reect the

values of the services and natural capital of ecosystem. For over a century theorists have sought ways of

relating resource limitations to economic-environmental systems, often using energy as a common metric.

This approach had limited success because different kinds of available energy are not equivalent.

Therefore a science-based evaluation system is now necessary to estimate both the environmental and

economic values with a common measure. To estimate the environmental and economic use, this study

applied emergy analysis to evaluate the STRP.

Emergy, a measure of real wealth, is available energy of one kind previously required directly

and indirectly to make a product or service. Emergy analysis transforms commodities, services, and

environmental work of different types to a common basis (Odum, 1996).

Expressing emergy in emdollars (Em$) indicates the part of the gross economic product due toa ow of emergy. The emdollar value of something helps people visualize its public policy importance.

Calculating the emergy of storages and processes provides a new scale for evaluating environment, re-sources, human service, information, and alternatives for development.

If emergy evaluation is able to identify environmental management plans that maximize eco-

nomic vitality, the result will include less trial and error in selecting urgent plans, improve efciencies,

innovation with fewer failures, and more rapid adoption to change. By applying an emergy analysis of the

Saemangeum project, this study might suggest a better way to arrive at public policies on management

of resources and environment.

The goal of this paper is to evaluate the emergy of the Saemangeum tidal at system before

and after reclamation, and perform the environmental accounting of the energy ows, production, and

storages by comparing the environmental costs and benets in emdollars.

METHODS

Location of Study Area

Saemangum tidal at covers the Man-kyung and Dong-jin river estuaries, located midway along

South Korea’s west coast (centered at approximately 35∞ 50’ N, 126∞ 45’ E) between the cities of Kunsan

and Kimje, stretching 35km from north to south and 30 km from west to east (Fig. 1).

Emergy Analysis

A more detailed explanation of the methodology of emergy analysis is presented in Odum, 1996.

The principle steps for the emergy analyses shown in this study are briey described below.

Systems Diagrams

Saemangeum tidal at systems before and after reclamation were diagrammed for emergy analysis

using the energy systems language (Odum, 1971). The principle variables, sources, and processes in the

Saemangeum tideland before and after reclamation were illustrated in the diagrams. The main ows and

storages of these diagrams were evaluated in the emergy table.



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Data Collection

The information required to quantify the emergy ows and the storages in the systems diagrams

was obtained from published literature, technical reports, and yearly statistical references. Data sources

and energy calculations are included in the emergy table’s appendix.

Emergy Tables

Emergy evaluation tables were generated from the systems diagrams. Each energy input required

for the system before and after reclamation was multiplied by its appropriate transformity (or emergy per

unit) to calculate its emergy contribution in solar emjoules per year (sej/year).

126 30’E 126 45’E

36 00’N

0 0

0

0

35 30’N

Fig.1. Location of the Saemangeum tideland in Korea. Ag. is proposed agricultural land in Saemangeum tideland reclamation project.

Seawall

Ag.

Ag.

Ag.

Ag.

Reservoir

N

Figure 1. Location of the Saemangeum tideland in Korea. Ag. is proposed agricultural land in Saemangeum

tideland reclamation project.



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Environmental Accounting

Once the total emergy budget was obtained by summing all the ows and storages in the emergy

tables, the environmental accounting of the energy ows, economic production, environmental storagesand construction costs was performed by comparing the environmental costs and benets in emdollars.

RESULTS

Original System of Saemangeum Tideland

Fig. 2 illustrates the system diagram for the original use of the Saemangeum tideland before the

reclamation project. The total emergy inow (8.32 E20 sej/yr) is 87% from renewable sources including

tide, river, and wave energies (Table 1). The total emergy of economic production is 1.16 E 20 sej/yr

including production of shellshes, shes, and seaweeds. The total emergy of storages is 7.15 E21 sejincludes mud and sediment, phytoplankton, benthos, and shes.

Tide Seawater,

Larvae, Fish,

Nutrients

Water

Harvestconsumers

High

of diversity

Waves

Rain

$

G & S

Market

River,

Sediments,

Organic matter,

Nutrients

B

Sediment,

ShellfishCultures

Fisheries&

Sun,Wind

Original use

Sediment,

O.M.,N,P

M

detritus

Fig.2. System diagram of Saemangeum tideland before reclamation. O.M. is organic

matter; N is nitrogen; P is phosporus; B is biomass in primary production; M is

microorganism; G & S is goods and services.

$

$

Primary

production

SAEMANGEUM

Figure 2. System diagram of Saemangeum tideland before reclamation. O.M. is organic matter, N is nitrogen, P

is phosphorus, B is biomass in primary production, M is microorganism, G & S is goods and services.



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Table 1. Emergy evaluation for ows, production, and storages before reclamation.

__________________________________________________________________________________

_

1. Flows

No. Raw units Transformity Solar Macro-economic

(sej/unit) emergy value(Emdollar)

(sej/yr)

_________________________________________________________________________________

_

RENEWABLE SOURCES

1 Sunlight 6.92E+17 J 1 6.92E+17 4.19E+05

2 Wind, kinetic energy 5.73E+14 J 1496 8.58E+17 5.20E+05

3 Rain, chemical 2.35E+15 J 7435 1.74E+19 1.06E+07

4 Tide 2.36E+16 J 16842 3.98E+20 2.41E+08

5 Waves 2.67E+15 J 30550 8.17E+19 4.95E+07

6 River, chemical 6.33E+15 J 48459 3.07E+20 1.86E+08

SUM OF RENEWABLE SOURCES 7.22E+20 4.38E+08

PURCHASED INPUT

7 Goods & Services 6.65E+07 $ 1.65E+12 1.10E+20 6.65E+07

SUM 8.32E+20 5.04E+08

_________________________________________________________________________________

_

2. Economic production



(sej/yr)

_________________________________________________________________________________

_

PRODUCTION

8 Shellshes 7.26E+13 J 8.10E+05 5.88E+19 3.56E+07

9 Seaweeds 5.34E+12 J 1.10E+04 5.87E+16 3.56E+04

10 Fishes 1.50E+12 J 1.60E+06 2.41E+18 1.46E+06

SUM 6.15E+20 3.72E+07

_________________________________________________________________________________

_3. Storages



(sej/yr)

_________________________________________________________________________________

_

INDIGENOUS STORAGES

11 Mud and sediment 9.26E+16 J 3509 3.25E+20 1.97E+08

12 Phytoplankton 9.10E+06 g 6.59E+12 6.00E+19 3.63E+07



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13 Benthos 1.80E+14 J 2.90E+05 5.21E+19 3.16E+07

14 Fishes 2.23E+13 J 1.60E+06 3.57E+19 2.16E+07

SUM 4.73E+20 2.87E+08

_________________________________________________________________________________

_

Footnotes can be found at end of chapter.

System after Reclamation Project

Fig. 3 illustrates the system diagram for the new use of the Saemangeum tideland after recla-mation project. The development will decrease the total emergy ow from 8.32 E20 to 7.41 E20 sej/yr

(Table 2). It will diminish the environmental emergy from 7.22 E20 to 5.14 E20 sej/yr. The total emergy

of economic production will increase from 1.16 E20 to 6.64 E20 sej/yr by the agricultural production and

water supply. The total emergy of environmental storage before development (4.73 E20 sej) will disap-

pear and the total emergy of environmental storage after development (1.12 E21 sej) will be built by the

inow for the construction of agricultural land and water supply reservoir.

Comparison of Emergy Indices between Original and New Uses

Table 3 shows several emergy indices and ratios calculated from the emergy tables before and

after reclamation (Table 1 and 2). These indices may serve as a measure of environmental decision mak-

ing to compare the original and new uses.

Emergy yield ratio (EYR) for original and new uses is 7.58 and 3.81, meaning that the new use

Soil

Agricultural land

consumers

Highdiversity

Rain

$

G & S

Market

River,

Sediments,

O.M., N, P

Sun,Wind

B

Earthcycle

Water supply

Outflow to the sea

New use

O.M.,N,P

$

$

Water supply reservoir

SAEMANGEUM

Figure 3. System diagram ofSaemangeum region after reclamation. O.M. is orgnaic matter; N is nitrogen; P is

phosphorus; B is biomass in primary production; M is microorganism; G & S is goods and services.



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will decrease the contribution of original use to the main economy. Emergy investment ratio (EIR) for

original and new uses is 0.15 and 0.36, meaning that the new use will cause a high intensity of economic

development. Environmental loading ratio for original and new use is 0.15 and 0.44, meaning that the

new use will cause a high loading of the environment. Sustainability index (SI) for original and new uses

is 49.90 and 8.63, meaning that the new use will be indicative of systems that consume a relatively large

percentage of total emergy in the form of nonrenewable emergy.

Table 2. Emergy evaluation for ows, production, and storages after reclamation.___________________________________________________________________________________1. Flows No. Item Raw units Transformity Solar Economic (sej/unit) emergy value (sej/yr) (Emdollar)___________________________________________________________________________________

RENEWABLE SOURCES

1 Sunlight 6.92E+17 J 1 6.92E+17 4.19E+05 2 Wind, kinetic energy 5.73E+14 J 1496 8.58E+17 5.20E+05

3 Rain, chemical 2.35E+15 J 7435 1.74E+19 1.06E+07

4 River, chemical 1.00E+16 J 48459 4.87E+20 2.95E+08

5 Earth cycle 2.83E+14 J 34377 9.73E+18 5.90E+06

SUM OF RENEWABLE SOURCES 5.14E+20 3.11E+08

NONREMEWABLE SOURCE

6 Top soil 4.24E+14 J 7.40E+04 3.27E+19 1.98E+07

PURCHASED INPUT

7 Goods & Service for agri. 9.90E+07 $ 1.65E+12 1.63E+20 9.90E+07

8 Cost for water management 1.88E+07 $ 1.65E+12 3.09E+19 1.88E+07

SUM OF PURCHASED INPUT 1.94E+20 1.18E+08

SUM 7.41E+20 4.49E+08___________________________________________________________________________________2. Economic ProductionNo. Item Raw units Transformity Solar Economic (sej/unit) emergy value

(sej/yr) (Emdollar)__________________________________________________________________________________

_

PRODUCTION

9 Water supply 4.93E+15 J 48459 2.39E+20 1.45E+08

10 Agricultural production 2.12E+15 J 2.00E+05 4.25E+20 2.58E+08

SUM 6.64E+20 4.02E+08___________________________________________________________________________________3. Storages

No. Item Raw units Transformity Solar Economic (sej/unit) emergy value (sej/yr) (Emdollar)__________________________________________________________________________________



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_

INDIGENOUS STORAGES

11 Freshwater 1.58E+15 J 48459 7.66E+19 4.64E+07

12 Soil 1.42E+16 J 7.40E+04 1.05E+21 6.35E+08

SUM 1.12E+21 6.81E+08

___________________________________________________________________________________


Table 3. Comparison of emergy indices for original use and new use.

__________________________________________________________________________________

_Item Name of index Expression Original use New use Unit

__________________________________________________________________________________

_

1 Renewable emergy ow R 7.22E+20 5.14E+20 sej/yr 2 Nonrenewable emergy ow N - 3.27E+19 sej/yr

3 Flow of imported emergy F 1.10E+20 1.94E+20 sej/yr

4 Total emergy inows R+N+F 8.32E+20 7.41E+20 sej/yr

5 Total emergy used, U F+N+F 8.32E+20 7.41E+20 sej/yr

6 Renew % (R/U)*100 86.81 69.36

7 Emergy yield ratio (EYR) (R+N+F)/F 7.58 3.81

8 Emergy investment ratio (EIR) F/(R+N) 0.15 0.36

9 Environmental loading ratio (ELR)(F+N)/R 0.15 0.44

10 Sustainability index (SI) EYR/ELR 49.90 8.63

_________________________________________________________________________________

__

Environmental Accounting

Table 4 shows the environmental cost and benet from the changes of energy ows, economic

production, and environmental storages and construction cost after the Saemangeum tideland reclama-

tion.

The total environmental cost of energy ows in emdollars is 3.59 E8 Em$/yr, which includes

lost renewable sources such as tide and goods and services for agriculture and water management of ar-

ticial freshwater reservoir. The total environmental benet of energy ow in emdollars is 2.01 E8 Em$/

yr, including additional river water from Kum River, land formation, and reduction of goods and services

for original use. The net environmental cost of energy ow in emdollars is 1.58 E8 Em$/yr.The economic production of original use in emdollars is 3.71 E7 Em$/yr, including sheries

production. The economic production of new use in emdollars increases as much as 3.66 E8 Em$/yr ac-

cording to the plan of the Korean government.

The evaluation by the Korean government omits the very large emergy storage of tideland, be-

ing diverted to the agricultural land and freshwater reservoir. Including the net benet of environmental

storage (3.94 E8 Em$) the total net cost of environmental storages and construction cost in emdollars

becomes 3.55 E9 Em$.

DISCUSSION

In recent years there has been increasing interest in alternative valuing techniques that can

recognize and incorporate ecological values into public policy in decision-making framework. Many



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development projects have been judged according to their money costs and benets. A monetary cost-

benet method is not even a good predictor of economic success of the proposed development, because

it doesn’t evaluate whether the new resources to be processed are large net contributors or not. To judge

success of future production, evaluating alternative development and compensation of the net environ-mental cost based on environmental accounting is required to determine the overall benets and cost to

the whole system of environment and economy.

Among existing and planned developments, emergy accounting can compare productivity with

potentials. If these systems prevail the production of more emergy and the more efcient utilization, then

the systems with greater useful emergy ow would be better and more likely to sustain. For comparing

alternative systems, the existing and proposed alternatives were compared with the regional potential

based on the regional emergy investment ratio, IR (4.4 of Korea) (Lee et al., 2000).

Table 4. Environmental accounting of energy ows, economic production, and environmental storages

and construction cost for the Saemangeum tideland reclamation.

__________________________________________________________________________________

_

No. Item Environmental Cost Environmental Benet (Em$/yr) (Em$/yr) ___________________________________________________________________________________

RENEWABLE SOURCE

1 Tide 2.41E+08

2 River, chemical 1.09E+08

3 Earth cycle 5.90E+06

NONRENEWABLE SOURCE

4 Top soil 1.98E+07

PURCHASED INPUT5 Goods & Services for sheries 6.65E+07

6 Goods & Services for agriculture 9.90E+07

7 Cost for water management 1.88E+07

Total (Em$/yr) 3.59E+08 2.01E+08

Net cost (Em$/yr) 1.58E+08

Economic productionNo. Item Original use (Em$/yr) New use (Em$/yr)

PRODUCTION8 Shellshes 3.56E+07

9 Seaweeds 3.56E+04

10 Fishes 1.46E+06

11 Water supply 1.45E+08

12 Agricultural production 2.58E+08

Total (Em$/yr) 3.71E+07 4.03E+08

Cost-benet (Em$/yr) 3.66E+08

Environmental storages and construction costNo. Item Environmental Cost Environmental Beneft



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INDIGENOUS STORAGES

13 Mud and sediment 1.97E+08

14 Phytoplankton 3.63E+07

15 Benthos 3.16E+07

16 Fishes 2.16E+07

17 Freshwater 4.64E+07

18 Soil 6.35E+08

CONSTRUCTION COST

19 Total cost for construction 3.94E+09

Total (Em$) 4.23E+09 6.81E+08

Net cost (Em$) 3.55E+09

___________________________________________________________________________________

As shown in Fig. 4, the original production, P1 is 5.04 E8 Em$/yr and the new production, P2 is 4.49

E8 Em$/yr. A particular development can be compared with the intensity of development that is predicted

while assuming the input of environmental resources according to the regional investment ratio, IR.Emergy ow P3 is the sum of the emergy of F (1.18 E8 Em$/yr) and the environmental emergy I3 (equal

to F/IR). The emergy of alternative investment, P3 is 1.44 E8 Em$/yr, which means that the developed

alternative for agriculture production and water supply would still be more productive than regional

average industrialization. A development can also be compared with the economic matching that could

result if all the original environmental emergy ow I1 (4.38 E8 Em$/yr) was retained and matched with

feedback emergy according to the regional investment ratio, IR. Fig. 4 shows the potential (2.36 E9

Em$/yr) if development could be revised to restore the original environmental contributions while also

combining these with compatible economic development attracted according to the emergy investment

ratio. This comparison shows that the economic vitality of the Saemangeum tideland system might be

Environ.

Resource

Imports

from Main

Economy

New Use

Original Use

to beDiverted

Alternate

Potential

I

F

F1

I1

I2

I1

I3F2

P1= 504

P2= 449

P3 = 144

P4 = 2,360

I1 * IR

Old : P1 = I1 + F1

New : P2 = I2 + F2

Potential : P4 = I1 + I1 * IR

Alternate : P3 = F2 +F2IR

Em$

E6

Figure 4. Evaluating alternative development. P1 is the production from original use, P2 is the production fromnew uses, P3 is the output of the alternative use of the economic input if it is matched with other environmental re-

sources (I3) according to the prevailing Emergy investment ration, IR, P4 is the output of the potential full use of the

environmental input if it is matched with economic input according to the regional Emergy investment ratio IR.



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increased about ve times by a more holistic pattern of development.

Based on the results of environmental accounting (Table 4), compensation period can be writ-

ten:

CP = Cs / (Bp - Ce)

where CP is the compensation period (yr), Cs is the net costs of environmental storages and

construction cost between original and new uses (Em$), Bp is the net benets of the economic productionbetween original and new uses (Em$/yr), Ce is the net costs of energy ow between original and new

uses (Em$/yr).

It would take about 17.1 years to compensate the net environmental cost of 3.55 E9 Em$ by the

difference (2.08 E8 Em$/yr) between the increase of the economic production (3.66 E8 Em$/yr) and the

decrease of the environmental net cost (1.58 E8 Em$/yr) after reclamation.

CONCLUSIONS

Tidelands are an asset belonging to present and future generations. They deserve conservation to

benet future generations. Under the name of development and the social traps of market value, we are

now damaging resources which future generations can efciently use and enjoy their potentials. We have

a duty to hand down resources for future generation.

For the sustainable use of tidelands, the South Korean government should stop the Saemangeum

reclamation project and remove the partially constructed seawall, and restore the tideland as coastal

wetlands.

REFERENCES

Costanza, R., et. al. 1997. The value of the world’s ecosystem services and natural capital. Nature 387:

253-260

Fisheries Research and Development Agency. 1995. Annual Report of Oceanographic Observations. TheGovernment of the Public of Korea. Pusan. 495pp. (In Korean).

Green Korea United. 2000. Saemangeum Tideland Reclamation Project. Korea’s Non-Governmental

Environmental Organizations. Seoul. http://www.greenkorea.org.

Korea Meteorological Administration. 1992. Meteorological Statistical Yearbook. The Government of

the Public of Korea. Seoul. 244pp. (In Korean).

Kwon, S. K., et al. 1998. Regional and Environmental Engineering. Hyangmun-sa. Seoul. 422pp. (In

Korean).

Lee, S. M. and H. T. Odum. 1994. Emergy Analysis Overview of Korea. J. of the Korean Environmental

Sciences Society Vol. 3: 165-175.

Lee, S. M., J. H. Son and D. S. Kang. 2000. Evaluation of Korea’s Sustainable Development by the Sys-tems Ecology (?): Emergy Analysis of Korea’s Natural Environmental and Economic Activity.

J. of the Korean Environmental Sciences Society 9: 449-454. (In Korean)

Ministry of Agriculture, Forestry & Fisheries. 1988a. The prediction of ecological variation of shery

stock at Saemangeum area, Korea. The Government of the Public of Korea. Kyonggi-do. 551pp.

(In Korean).

Ministry of Agriculture, Forestry & Fisheries. 1988b. The evaluation of natural environment variation at

Saemangeum area. The Government of the Public of Korea. Kyonggi-do. 407pp. (In Korean).

Ministry of Agriculture, Forestry & Fisheries. 1991. A proposal for the Saemangeum Tideland Reclama-

tion Project. The Government of the Public of Korea. Kyonggi-do. 217pp. (In Korean).

Ministry of Agriculture, Forestry & Fisheries. 1994. The investigation of compensation of sheries impact

at Saemangeum Tideland Reclamation Project (5-6). The Government of the Public of Korea.Kyonggi-do. 745pp. (In Korean).

Ministry of Agriculture, Forestry & Fisheries. 1994. The investigation of compensation of sheries impact



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at Saemangeum Tideland Reclamation Project (7-13). The Government of the Public of Korea.

Kyonggi-do. 495pp. (In Korean).

Ministry of Agriculture & Forestry. 2000. Saemangeum Tideland Reclamation Project. The Government

of the Public of Korea. Kyonggi-do. http://www.maf.go.kr.

Odum, H. T. 1971. An energy circuit language for ecological and social system, its physical basis. In

System Analysis and Simulation in Ecology. B. Pattern, Ed. Academic Press. New York. Vol.

2. 139-211.

Odum, H. T. 1996. Environmental Accounting: Emergy and Environmental Decision Making. John Wiley

& Sons. New York. 370pp.

Ofce of Hydrographic Affairs Republic of Korea. 1995. Tide Tables Vol. 1. Inchon. 250pp.

Rural Development Corporation. 1992. The investigation of environmental management at Saemangeum

Tideland Reclamation Project. 205pp. (In Korean).

Rural Nutrition Institute. 1991. Food Composition. Rural Development Administration. Seoul. 295pp.

(In Korean).



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Appendix A. Footnote to Table 1

RENEWABLE SOURCE

1 Solar Energy

System area = 4.01E+08 m2

(Ministry of Agriculture, Forestry & Fisheries, 1991)

Insolation = 2.47E+09 J/m2/yr (Korea Meteorological Admin

istration, 1992)

Albedo = 0.3(% given as decimal) (Lee and Odum,1994)

Energy(J) = (System area)(average of insolation)(1-albedo)

= (_ m2)(4.19E+09 J/m2/yr)(1-0.3)

= 6.92E+17 J/yr

2 Wind Energy

Wind gradient = 0.0028 m/sec/m (Korea Meteorological Administration, 1992)

Energy(J) = (height)(density)(diffusion coefcient)(wind gradient)(area) = (1000 m)(1.23 kg/m3)(4.7 m3/m/sec)(3.154E+07 sec/yr)

(0.0028 m/sec/m)2(_ m2)

= 5.73E+14 J/yr

3 Rain, Chemical

Rainfall = 1.18 m/yr (Rural Development Corporation, 1992)

Gibbs free energy = 4.94 J/g (Lee and Odum, 1994)

Energy(J) = (area)(rainfall)(Gibbs free energy)

= (_ m2)(1.18 m/yr)(1E+06 g/m3)(4.94 J/g)

= 2.35E+15 J/yr4 Tidal Energy

Area elevated = 4.01E+08 m2

Tide height = 4.00E+00 m

(Ofce of Hydrographic Affairs Republic of Korea, 1995)

Density = 1.03E+03 kg/m3

(Fisheries Research and Development Agency, 1995)

Tides/year = 7.30E+02

(Ofce of Hydrographic Affairs Republic of Korea, 1995)

Energy(J) = (Area elevated)(0.5)(tides/yr)(mean tidal range)2

(density of seawater)(gravity)

= (_ m2)(0.5)(_ /yr)(_ m)2(_ kg/m3)(9.8 m/s2)

= 2.36E+16 J/yr

5 Waves

Shore length = 3.00E+04 M (estimated)

Density = 1.03E+03 kg/m3

Depth = 1.90E+00 m


Energy(J) = (shore length)(1/8)(density)(gravity)(height squared)(velocity)

= (_ m)(1/8)(1.03E+03 kg/m3

)(9.81 m/sec2

)(_ m)2

[((9.81 m2/sec)*( _m)) 0.5]*(3.154 E+07 sec/yr)

= 2.67E+15 J/yr

6 River, Chemical



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Volume ow = 1.51E+09 m3/yr (Saemangeum Project Investigation

Committee, 2000b)

Water supply = 2.28E+08 m3/yr

(Saemangeum Project Investigation Committee, 2000b)

Energy(J) = (Volume ow - water supply)(density)(Gibbs free energy)

= (_ m3/yr)(1 E+06 g/m3)(4.93 J/g) = 6.33E+15 J/yr

PURCHASED INPUT

7 Goods & Services

G. & S. for purchased inputs = 6.65E+07 $/yr

(Saemangeum Project Investigation Committee, 2000c)

Emergy/Money ratio = 1.65E+12 sej/$ (Son, 1999)

PRODUCTION

8 Shellshes

Yield = 2.09E+07 kg/yr(Ministry of Agriculture, Forestry & Fisheries, 1994a)

Energy(J) = (_ kg/yr)(1 E+03 g/kg)(3.474 E+03 J/g) (Rural Nutrition

Institute, 1991)

= 7.26E+13 J/yr

9 Seaweeds

Yield = 4.85E+06 kg/yr


Energy(J) = (_ kg/yr)(1 E+03 g/kg)(1.102 E+03 J/g) (Rural Nutrition

Institute, 1991)

= 5.34E+12 J/yr

10 Fishes

Yield = 3.18E+05 kg/yr

(Ministry of Agriculture, Forestry & Fisheries, 1994a)

Energy(J) = (_ kg/yr)(1 E+03 g/kg)(4730 J/g) (Rural Nutrition Institute,

1991)

= 1.50E+12 J/yr

I NDIGENOUS STORAGE

11 Mud and sediment

Tidal at area = 2.08E+08 m2

(Saemangeum Project Investigation Committee, 2000a)

Energy(J) = (1.47 g/cm3)(1 E06 cm3/m3)(2.01 g OM/100 g sed)(_ m2)(_ m)

(3.6 kcal/g)(4186 J/kcal) (You, 2000)

= 9.26E+16 J

12 Phytoplankton

Concentration of chl.a = 4.715 mg/m^3


Ratio of algal biomass to chlorophyll-a =0.01 g algae/mg chl.a (U.S. EPA, 1985)

Quantity = (_ mg chl.a/m3) (0.01 g algae/mg chl.a)(_ m2)(_ m)

= 9.10E+06 g dry-wt13 Benthos

Quantity = 5.17E+07 kg



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Energy (J) = (_ kg)(1 E+03 g/kg)(3.474 E+03 J/g)

= 1.80E+14 J

14 Fishes

Quantity = 2.44E+02 kg/10000m2

(Ministry of Agriculture, Forestry & Fisheries, 1994a)Energy(J) = (_ kg/10,000 m2)(_ m2)(1 E+03 g/kg)(4730 J/g)

= 2.23E+13 J

Appendix B. Footnote to Table 2RENEWABLE SOURCE

1 Solar Energy System area = 4.01E+08 M2

Insolation = 2.47E+09 J/m2/yr

Albedo = 0.3 (% given as decimal)

Energy(J) = (System area)(average of insolation)

= (_ m2)(4.19E+09 J/m2/yr)(1-0.3)

= 6.92E+17 J/yr

2 Wind Energy

Wind gradient = 0.0028 m/sec/m

Energy(J) = (height)(density)(diffusion coefcient)(wind gradient)(area)

= (1000 m)(1.23 kg/m3)(4.7 m3/m/sec)(3.154E+07 sec/yr)

(0.0028 m/sec/m)2(_ m2)= 5.73E+14 J/yr

3 Rain, Chemical

Rainfall = 1.18E+00 m/yr

Gibbs free energy = 4.94 J/g

Energy(J) = (area)(rainfall)(Gibbs free energy)

= (_ m2)(1.27 m/yr)(1E+06 g/m3)(4.94 J/g)

= 2.35E+15 J/yr

4 River, Chemical

Volume ow = 1.51E+09 m3/yr

(Saemangeum Project Investigation Committee, 2000c)Volume of inow water = 5.20E+08 m3/yr (Ministry of Agriculture & Forestry, 2000)

Energy(J) = (volume ow + inow water )

(density)(Gibbs free energy)

= (_ m3/yr)(1E+06 g/m3)(4.94 J/g)

= 1.00E+16 J/yr

5 Earth cycle

Land area = 2.83E+08 m2

Energy(J) = (_ m2)(1E+06 J/m2/yr) (Lee and Odum, 1994)

= 2.83E+14 J/yr

NONRENEWABLE SOURCE

6 Top soil

Area of dry paddies = 2.83E+08 m2



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Erosion rate = 1.48E+03 g/m2/yr (Kwon, 1998)

Soil loss = 4.19E+11 g/yr

Energy(J) = (_ g/yr)(0.07 gOM/g sed)(3.6 kcal/g)(4186 J/kcal)

= 4.42E+14 J/yr

PURCHASED INPUT

Operational inputs

7 Total cost for the operation

Total cost = 9.90E+07 $/yr (Saemangeum Project Investigation

Committee, 2000c)

8 Cost for water management

Total cost = 1.88E+07 $/yr (Saemangeum Project Investigation

Committee, 2000c)

PRODUCTION

9 Water supply

Yearly water supply = 1.00E+09 m3/yr (Ministry of Agriculture & Forestry, 2000)

Energy(J) = (_ m3/yr)(1000 kg/m3)(4.93 E+3 J/kg)

= 4.93E+15 J/yr10 Agricultural production

Productoion = 1.40E+05 ton (Ministry of Agriculture & Forestry, 2000)

Energy (J) = (_ ton)*(1 E6 g/ton)*(3.625 kcal/g)*(4186J/kcal)

= 2.12E+15 J/yr

INDIGENOUS STORAGE

11 Freshwater

The amount of stock = 3.21E+08 m3 (Saemangeum Project Investigation

Committee, 2000c)

Energy(J) = (_ m3)(1000 kg/m3)(4.93 E+3 J/kg)

= 1.58E+15 J/yr

12 Soil

Land area = 2.83E+08 m2 (Saemangeum Project Investigation

Committee, 2000a)

Top soil = (_ m2)(0.05 m) (estimated)

Energy(J) = (_ m2)(0.05 m)(10.0E+08 J/m3) (Odum, 1996)

= 1.42E+16 J






Edited by



Associate Editors

Howard T. Odum



David Tilley



Sergio Ulgiati

University of Siena

Siena, Italy

December 2003



Gainesville, FL


vi



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Chapter 6. From Emergy Analysis to Public Policy: Soybean in Brazil

6From Emergy Analysis to Public Policy:

Soybean in Brazil

E. Ortega , M. Miller, M. H. Anami, P.R. Beskow

ABSTRACT

An emergy analysis was accomplished to compare the main technological options of soybeancultivation in Brazil: (a) “chemical inputs and machinery intensive”, (b) “herbicide and no tillage”, (c)

“ecological-traditional” and (d) “modern organic enterprise”. The effect of farm size on sustainability,

protability and job density was evaluated, by comparing emergy, economic and social indices of small

farms (30 ha, ecological), medium farms (300 ha, chemical; 300 ha organic enterprise) and large farms

(3000 ha, herbicide-no tillage). The results were extrapolated to encompass the whole country with one

stand-alone production type to build agricultural scenarios that allow us to visualize the environmental

and social impacts. Doing so it has been possible to demonstrate that small ecological-organic producers

have the greatest renewability and protability per area and the smallest environmental impact and the

minimum dependence on industrial inputs. They use more labor per hectare, basically family members.

Therefore, at time of great need of jobs and low monetary resources, the best option is the small ecological-organic family farm because it offers rural jobs with an acceptable pattern of life under sustainable

parameters. The research lead us to make the following suggestions: to avoid the use of the herbicide-no

tillage option because it increases rural exodus and promotes more wealth concentration; to give support

to ecological-organic family operated systems due to its multi-purpose benec characteristics; to study

and discuss public policy to x prices and incentives to farmers that preserve nature and recycle materials

and to tax producers that damage the environment and do not generate jobs.

INTRODUCTION

The aim of this research is to compare the main four methods for soybean production used in Brazil.

To accomplish this objective we have analyzed: (1) the traditional ecological-organic method used by

farmers of European origin in the Southern States of Brazil; (2) the chemical method corresponding to the

“green revolution”, promoted in the 70´s; (3) the herbicide and no tillage model, the “new green revolution”

introduced in the last decade (that can use transgenic seeds, now forbidden) and, (4) the “organic modern

enterprise”, a new rural system now appearing and adopted in medium and big size farms.

METHODS

As a rst step, we prepared a traditional economic report with monetary ows. After that, all thephysical, biological and monetary inputs of the agricultural systems studied were converted into emergy

ows (emergy = necessary Joules of solar energy to produce a product or a service, abbreviated sej).



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The Emergy Flows Table lead to the calculation of Emergy Indices and to make an Ecological Diagnosis

(Odum, 1996). Farm inputs data relative to years 1999, 2000 and 2001 were obtained from yearbooks

and contacts with farmers.

Figure 1 shows the energy ows involved. Corresponding emergy values were calculated taking intoaccount the amounts of natural resources, material inputs and services involved in each type of production

(Ortega, Miller & Anami, 2000; Miller & Ortega, 2001). Tables 9 and 10 contain data of inputs per hectare

of soybean production in the period of one year.

To evaluate the impact on the country, the data with the inputs corresponding to usual yields of

each soybean production system analyzed has been extrapolated to encompass the whole area cultivatedwith soybean in Brazil (12.6 million ha in 1999 and 13.6 million in 2000).

RESULTS AND DISCUSSION

Considerations regarding economic and social aspectsEconomic Inputs

As materials we consider: seeds, limestone, fertilizers, inoculating agent, pesticides, herbicide, fuels,

machinery depreciation; as services we consider: manpower, administration, transport, cleaning and drying

costs, taxes, insurance, social security and land leasing. The manpower data used have been expressed in

terms of number of hours of work/ha/year. As shown in Table 2, chemical and herbicide/no tillage systemsuse 50% and 100% more materials (almost 0,9 and 1.8 billion of US dollars, respectively), than the ecologic

and organic systems. This money is driven abroad, as most of these materials are imported.

Price

Local

environmental

services

Nitrogen from

atmosphere

(eroded soil & biota,

chemicals, people)

Sun,wind &

rain

Losses

Nutrients

from soil

Regional

biodiversity Forest

reserve

Product

Fines/subsidy

Value

Transport

& drying

Soybeanculture

$

People

Soil,

biodiversity

people

Fines/subsidy

$ Sales

Prices

Losses Recovering

Degraded energy

Environmental

services

Wastes

Materials& services

Value

Figure 1. Energy systems diagram of soybean production system



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Manpower

Table 3 shows that the greatest percentage of ecological farm expenses is in unqualied humanlabor, almost 47% of the value expended with services. That can be explained by the fact that ecological

farms are, in its majority, small and the labor is of family origin. The organic farms use a lot of labor but

organic enterprises may be energy intensive and do not incorporate labor. There are almost US$ 600 million

more in hard work labor expenses in the biological options than in the chemical ones. An estimate of the

number of jobs that could be generated with this amount of money can be easily made. If a rural workerearns US$ 100 a month, it means US$ 1200 per year; dividing US$ 600 million by US$ 1200, it leads to

500 000 new jobs in agriculture. In the conventional agriculture (chemical) and also in the new technology

based on no-tillage and herbicide (and transgenic seeds, now forbidden) the expenses are mainly due to

the administrative labor, since weeds destruction is done chemically or by machine.

Economic and social indices

Surprisingly the largest economic prot per hectare is obtained by the ecological option (Table4). The protability of ecological and organic farms (1.17 and 1.12) is considerably larger than that forchemical (0.35) and herbicide (0.14) farming. The reason is that some chemical inputs are expensive and

ecological and organic products achieve better prices. But the prot per farm is greater for the organic,

chemical and herbicide options due only to their bigger size.The data do not permit to compare the annual income per family because only one of the systems

is based on family production; the other two are usually business enterprises. The productivity per labor

Table 3. Manpower in the soybean production systems expressed in US$ x 106 /year.

Items Ecological Organic Chemical Herbicide

Unqualied hand labor 520.6 359.0 11.5 1.8

Qualied hand labor 12.6 20.2 453.6 252.4

Administrative labor 46.8 46.8 46.8 46.8

Technical assistance 108.8 108.8 21.8 31.6

Total labor 688.8 534.8 533.6 332.5

Source: FNP 1999, Agrorgânica 2000, Terra Preservada, 2002.

Table 2. Inputs for the four systems of soy production expressed in US$ x 109 /year.

30 ha 300 ha 300 ha 3 000 ha

Ecological Organic Chemical Herbicide

Family Farm Enterprise Enterprise Enterprise

Materials 1.79 1.83 2.73 3.62

Services 1.22 1.06 1.11 9.14

Economy feedback 3.01 2.90 3.84 4.53

Source: FNP, 1999; Agrorgânica, 2000; Terra Preservada, 2002.



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index is not a convenient ratio to consider because the problem is not of labor productivity but of input

productivity and employment per area. Instead, workability and output/input ratios that favor biological

systems could be considered (Table 4).

Considerations about technological and political dependenceIn the system of herbicide/no tillage there exists a larger dependence of external inputs, mainly of

transgenic seeds and herbicides. This leads to a loss of autonomy of producers and country in relation

to technology and prices xed abroad. Rural workers and small and medium farmers will have troublesto keep their work, their income will decrease and, as a result, agricultural properties will be bought and

controlled by a few big owners with big prots. Although these big producers have low productivity per

hectare their properties size ensures them high income. Besides that, the chemical and herbicide basedsystems depend on external inputs. The largest value of sustainability corresponds to the ecological and

organic systems. These systems use fewer resources from economy and more natural renewable resources,

which guarantee its sustainability. They ensure the survival of the producer throughout the time and the

preservation of the biodiversity. If government could support ecological and organic options the country’s

balance of trade could be improved each year in almost US$ 2.0 billion!

Considerations regarding emergy indices (Tables 5 and 6)Transformity (Tr)

Tr = Y / Ep = S (Emergy used) / energy of main product = S (Ji * Tr

i) / Ep

Table 4. Soybean indicators. Data: FNP 1999, Agrorgânica, 2000, Terra Preservada, 2002.

Economic indices Ecological Organic Chemical Herbicide(30 ha) (300 ha) (300 ha) (3000 ha)

Production (kg/ha/year) 1920 1920 2240 2240

Price (US$/kg) 0.250 0.235 0.170 0.170

Sales (US$/ha) 480.00 451.20 380.80 380.80

Costs (US$/ha) 221.11 213,09 282.60 333.22

Net income (US$/ha/year) 258.89 238,11 98.2 47.58

Return ratio = sales / costs 2.17 2,12 1.35 1.14

Protability = (sales- costs) / costs 1.17 1,12 0.35 0.14Farm area (ha) 32.5 300 300 3000

Farm annual net income (US$/year) 8 414 71 433 29 461 142 742

Farm month net income (US$/month) 701 5 953 2 455 11 895

Work hours/ha/year 147.0 103.2 75.1 40.5

Workers / ha 0.0503 0.0353 0.0257 0.0139

Prod/Worker (kg soy/worker) 38 139 54 326 87 118 161 501

Output/Input (kg /kg) 92 728 93 082 45 479 46 381



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0.47

0.35

1.171.12

0.14

0.23

0.43

0.26

0.00

0.20

0.40

0.60

0.80

1.00

1.20

1.0 2.0 3.0 4.0

log (size of farm)

Profit per unit of area

Renewability

The transformity is the inverse value of the system efciency for a specic product. Transformityvalues vary from 80 000 to 110 000. The transformity values of ecological and organic options (88 146

and 81 957, respectively) are lower than of herbicide/no tillage and chemical options (103 904 and 111

527); this means that biological systems are more efcient.

Renewability (R/Y)

This ratio measures the sustainability of the system, because it represents the proportion of all theresources used that are renewable. As renewable resources we consider: rain, nutrients captured from air

(nitrogen) and soil rocks (minerals), products and services obtained from the farm area under preservation

(at least 20% of total area according to Brazilian law). Usually ecological farmers keep this forest area

and take benet of it. Agricultural enterprises, as a general rule, do not obey the law and are not stronglyforced to do that.

The chemical system renewability is slightly bigger (0.23) than for herbicide/no tillage (0.21) but

both are much lower than the values for ecological and organic options (0.46 and 0.42). In the organic

option, almost half of the natural resources come from renewable sources, which give to this system a

higher autonomy. This index would be even larger if the methodology could consider the purchased manure

as a renewable resource and not as a non-renewable input obtained from the urban economy.

Net Emergy Yield Ratio (EYR=Y/F)The EYR ratio typical values for agricultural products vary from 1 to 4. The lowest value is one,

which happens when nature inputs are null (R+N = 0). The difference above the minimum value measuresthe cost-free contribution of the environment to production.

Figure 2. Renewability and Protability vs. size of farm



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Table 6. Emergy indices

Emergy Indices Ecological Organic Chemical Herbicide

Transformity (Tr, sej/J) 88 146 81 957 103 904 111 527

Net Emergy Yield Ratio (EYR) 1.92 1.78 1.74 1.31

Emergy Investment Ratio (EIR) 1.09 1.27 1.35 3.25Environmental Loading (ELR) 1.19 1.40 3.40 3.70

Renewability (R) 0.46 0.42 0.23 0.21

Emergy Exchange Ratio (EER) 1.45 1.35 2.51 2.69

Table 5. Aggregated emergy ows

Emergy Flows (sej/ha/year) Ecological Organic Chemical Herbicide

Renewable resources (R) 1.18E+15 9.98E+14 8.04E+14 8.08E+14Non renewable resources (N) 5.34E+13 5.34E+13 6.98E+14 8.60E+13

Nature contribution (I) 1.23E+15 1.05E+15 1.50E+15 8.94E+14

Material inputs (M) 1.07E+15 1.09E+15 1.75E+15 2.72E+15

Services (S) 2.68E+14 2.47E+14 2.80E+14 1.87E+14

Feedback from Economy (F) 1.34E+15 1.34E+15 2.03E+15 2.90E+15

Total emergy incorporated (Y) 2.57E+15 2.39E+15 3.54E+15 3.80E+15

Table 7. Indicators for agriculture reform public policy.

Public Policy Indices Ecological Organic Chemical Herbicide

Country soybean area (ha) 1.36 E+07 1.36 E+07 1.36 E+07 1.36 E+07

Size of farm (ha) 32.5 300 300 3000

Number of farms (ideal value) 418 462 45 333 45 333 4 533

Workers / 1000 ha 50.3 35.3 25.7 13.9

Jobs 684 658 480 658 29 808 7 452

Job index 5.5 4.0 2.2 1.0



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Figure 5. Hectares of soybean cultivation in RS, PR, and MT

0

50000

100000

150000

200000

250000

Ecological Organic Chemical Herbicide

N u m b e r o f j o b s

Figure 4. Employment created by each soybean option at national level

2756775

3853225

1440000

0

2000000

4000000

6000000

small farms medium farms large farms

A

r e a ( h e c t a r e s )



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environment has a relatively larger contribution than the economy (goods and services), having lower

costs and being more competitive. This ratio gives a clear vision of the difference between the systems

in relation to the investment needed for production.

The herbicide/no tillage value is high (3.25), thus demonstrating an economically fragile agriculture

due to its dependence on purchased inputs from foreign regions. The chemical option has an intermediate

value (1.35). Ecological agriculture shows the lowest value (1.09). The small ecological family farm

uses nature resources (free) instead of economy resources (expensive) having lower need of external

investment and lower production costs. The organic option demands more economy inputs than the

ecological option (1.27).

Environmental Loading Rate (ELR = (N + F) / R)Chemical and herbicide/no tillage methods (3.40 and 3.70) produce great environmental damage.

Biological agriculture instead has lower values (1.19 and 1.40), which conrms great use of naturalrenewable resources by ecological and organic production techniques; practically the same quantity of

emergy from renewable sources than from non-renewable, producing reduced environmental impact.

Emergy Exchange Ratio (EER)

All four options give more energy to the buying system than to the producing system. The worstin terms of emergy exchange is the herbicide based system (2.69), followed by the chemical (2.51), the

ecological (1.45), and nally by the organic system (1.35). The emergy per dollar ratio of Brazil wasconsidered in the evaluation. If the Europe’s ratio were used the results would be three times worse. This

means that neither the buyers nor the government take into account the nature’s work.

Considerations regarding size of propertiesThe chemical option, if adopted, could sustain 30 000 rural jobs in the whole country. If herbicide

option becomes dominant, manpower would decrease to 7 500 workers and a lot of people would migrate

to cities. With ecological systems in the whole country there would be almost 685 000 jobs in family

farms producing in multi-purpose agriculture. In case of organic enterprises the number of jobs could be480 000 (Table 7).

The results were plotted to make evident some facts. Figure 2 shows that renewability results are

favorable to biological systems. The renewability of a small ecological farm is almost twice the value of a

conventional medium property and three times the value of a big herbicide/no tillage property. The medium

organic property has also good results in both parameters but can use machinery instead of labor. The results

for protability per hectare also indicate better values for small and medium size biological systems. Themarket pays more (twice) for organic or ecological soybean destined for human consumption.

Figure 3 makes clear that to make prots comparable to biological systems the chemical intensiveoptions must be of greater scale. The scale factor overwhelms other parameters (efciency, renewability)and is responsible for big incomes in some areas of Brazil.

Figure 4 reveals an astonishing trend; the labor in agriculture decreases in direct proportion to theuse of industrial inputs. Generally, in areas where agricultural systems are energy-intensive a huge exodus

takes place and serious urban problems are created, besides that property and wealth concentrates in less

number of individuals.

Figure 5 shows the soybean area cultivated in Paraná, Rio Grande do Sul and Mato Grosso,responsible for the main part of national production (Man Yu, 1993; Farias, 1996; Roessing, 1996). Thedistribution of area according to the type of farm make evident that the small family farms are responsible

for almost a third part of production.

CONCLUSIONS

The best option for Brazil is an agricultural system based on small family properties that use

ecological-organic cultivation. It allows the farmer an acceptable life quality and a proper use of natural



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resources, moderate use of economy resources and recycling of many materials. According to recent studies

(Veiga, 2002) the Brazilian counties with small ecological farms have the highest human development

index (HDI) and can produce for internal and external markets with productivity equivalent to conventionalchemical systems. Just as a remark, Altieri (1998) says that conventional systems show a decrease ofproductivity with time due to destruction of soil organic matter stocks and biological activity; this loss can be

so intensive that the productivity becomes lower than that of ecological- organic production systems.

The suggested public policies are:

(a) Not allow use of the herbicide/direct planting option in soybean production due to its

social, environmental and political negative impacts.

(b) Implement agrarian land ownership reform and agriculture restructuring programs

with enough investments in public infrastructure to give support to organic family

operated systems.

(c) Establish incentives to promote preservation of nature and recycling;

(d) Impose taxes on producers that damage the environment or destroy jobs to induce

changes to organic or ecological farming, agrarian reform and natural area

preservation enforcement.

(e) Establish certication of soybean producers to induce proper prices for each kind ofproduction systems

Acknowledgement

We thank Anita Kacenelenbogen Guimarães for her kind and careful revision of our

manuscript.

REFERENCES

AGRORGÂNICA, 2000. Comunicação pessoal. Capanema, Paraná.ALTIERI, M., 1998; Agroecologia: a dinâmica produtiva da agricultura sustentável. Porto Alegre:

Ed. UFRGS, 1998. 110p.AHRENS, S; 1997. Manejo de recursos orestais no Brasil; Conceitos, realidades e perspectivas, in

Curso de Manejo orestal sustentável 1, 1997, Curitiba , Paraná, Tópicos em manejo orestalSustentável, Colombo, Embrapa – CNPF (Documentos, 34), 253p.

BUCKMANN, H. O. BRADY, N. C., 1983, Natureza e propriedade dos solos, Rio de Janeiro, FreitasBastos, p. 25.

DÖBEREINER, J, 1999, A importância da fxação biológica de nitrogênio par a agricultura

sustentável, http://geocities.com/thetropics/cabana/4792/xacaodenitrogênio.htm,

FNP, CONSULTORIA & COMÉRCIO, 1999. Agrianual 1999. Anuário da Agricultura Brasileira. São

Paulo: Argos Comunicação. 521p.FNP, CONSULTORIA & COMÉRCIO, 2000. Agrianual 2000. Anuário da Agricultura Brasileira. São

Paulo: Argos Comunicação. 546p.IPT - CEFER, 1980. Manual de Fertilizantes, São Paulo - SP, IPT/CEFER, p 22 – 23.KIEHL, E. J; 1985. Fertilizantes Orgânicos, Piracicaba, São Paulo, Editora Agronômica Ceres Ltda.,

492p.

MAN YU, C. 1993. Tipifcação e caracterização dos produtores rurais do Estado do Paraná, IAPAR,Londrina, PR.

MELLO, F.A.F.; BRASIL S., M.O.C.; ARZOLLA, S.; SILVEIRA, R.I.; COBRA N., A.; KIEHL, J.C.;1983. Fertilidade do Solo, Nobel, São Paulo, 400p.

MILLER, M.; ORTEGA, E., 2001. Análise ecossistêmica e emergética da produção transgênica,

convencional e orgânica de soja. (Estudo de impacto sócio-ambiental). Relatório nal de



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pesquisa de iniciação cientíca - CNPq/ PIBIC/ Unicamp. Campinas, SP, 28 de julho de 2001. http://www.unicamp.br/fea/ortega/soja/soja-br.htm (4/09/2001):

ODUM, H.T., 1996. Environmental Accounting: Emergy and environmental decision making. New

York-USA: John Wiley & Sons Inc., 370p.ORTEGA, E. 1999. Indicadores de sustentabilidade dos agrossistemas de acordo com a Metodologia

Emergética. Campinas: Unicamp, 14p. In Gusman, J.M. (editor) Indicadores de sustentabilidadede sistemas agrícolas. Embrapa-Meio Ambiente. In printing.

ORTEGA, E.; MILLER, M., 2000. Software para comparação ecossistêmica, energética e econômica desoja: (a) Orgânica, (b) Agroquímica, (c) Herbicidas - Plantio Direto (transgênica) em Herramientasde Calculo en Ingeniería de Alimentos. Universidade Politécnica de Valência, 2000.

ORTEGA, E.; MILLER, M.; ANAMI, M.H., 2000. Avaliação ecossistêmica - emergética de processos

agrícolas e agroindustriais. Estudo de caso: a produção de soja. I Seminário Internacional

de Agroecologia do Rio Grande do Sul, EMATER-RS, Porto Alegre, 21 de novembro de 2000.

http://www.unicamp.br/fea/ortega/portoalegre/portoalegre.htm (4/09/2001):

ROESSING, A.C. 1996. Soja: Aspectos Econômicos e Contribuição para o Crescimento Brasileiro.

In: Soja: Suas Aplicações. MEDSI (Editora Médica e Cientíca Ltda.) Rio de Janeiro, 256p.

SILVA, J. N. M., 1997; Manejo de orestas de terra rme da Amazônia brasileira, in: Curso de Manejoorestal sustentável, 1997, Curitiba, Paraná, Tópicos em manejo orestal sustentável, Embrapa- CNPF, 253p.

TERRA PRESERVADA, 2002. Comunicação pessoal. Capanema, Paraná.VEIGA, J. E., 2002. Cidades Imaginárias. O Brasil é menos urbano do que se calcula. Ed. Autores

Associados, 2002, 304 p. http://gipaf.cnptia.embrapa.br/itens/publ/colunas.html (4/07/2002):

CalculationsAccording to Brazilian law part of farm area must be destined to preserve nature with economic use (in

Amazon region: forested areas 80%; savannas 35%; in all other areas 20%). We multiply the inputs by a

factor (put beside the value of ow) because each input affects in different proportion the area of farm.For emergy and monetary calculations the quantity considered is the product of input times area factor.

Transformity references

1. Odum. H. T. 1996. Environmental Accounting. Emergy and Decision Making. John Wiley.N.Y.

2. Value estimated by authors.3. Brown. M; & Arding J. 1991. Transformities working paper. Center for Wetlands. Univ. of

Florida.4. Coelho. O.F.; Ortega. E.; Comar. V. 1997. Balanço de Emergia do Brasil

(1981.1989.1996).



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Table 8. Agronomical techniques:

__________________________________________________________________________________

_

Ecological Organic Conventional No tillage

_____________________________________________________________________

Seedbed Plowing and Plowing and Plowing and No seedbedpreparation harrowinging harrowing harrowing preparation, use

of herbicides to

clear elds from

weeds.

___________________________________________________________________________

Application Only those Only those Highly Highly solubleof fertilizers permitted for permitted for soluble chemical

organic organic chemical products

production production products___________________________________________________________________________

Weeds Hand clearing Mechanized and Utilization of Utilization ofcontrol manual weeding pre-seedling pre-seedling

herbicides herbicides __________________________________________________________________________________

_

Plague Utilization of Utilization of Utilization of Utilization of

control natural products natural products insecticides insecticides

(bugs) (biological (biological

control) and control) traps andchemical control chemical control

on the borders of on the borders of

planted area planted area

__________________________________________________________________________________

_

Harvest Made with help Mechanized Mechanized Mechanizedof manual

threshers

__________________________________________________________________________________

_

Additional explanations of agricultural processes used in soybean cultivation:___________________________________________________________________________________Plowing Digging furrows in the soil, before seeds are planted___________________________________________________________________________________Harrowing Breaking lumps of earth up___________________________________________________________________________________Border of planted area Area not harvested for organic production; generally planted with

shrubs to prevent from contamination by chemical productsutilized in neighboring areas___________________________________________________________________________________Pre-seedling herbicides Herbicides applied before seedling of crops and weeds____________________________________________________________________________________



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Table 9. Economic inputs and services; proportion factor according with area affected.

Note Flows Units Ecol. Org. Chem. Herb.

Materials (Economy resources)

M1 Farmer seeds kg/ha/y 10 0.8 10 0.8 0 0.8 0 0.8

M2 Certied seeds kg/ha/y 70 0.8 70 0.8 70 0.8 85 0.8M3 Transgenic seeds kg/ha/y 0 0.8 0 0.8 0 0.8 0 0.8

M4 Limestone kg/ha/y 0 0.8 0 0.8 1000 0.8 1000 0.8

M5 Nitrogen fertilizer kg/ha/y 0 0.8 0 0.8 0 0.8 0 0.8

M6 Phosphate fertilizer kg/ha/y 150 0.8 150 0.8 150 0.8 250 0.8

M7 Potassium fertilizer kg/ha/y 50 0.8 50 0.8 150 0.8 100 0.8

M8 Inoculating agent kg/ha/y 1 0.8 1 0.8 1.7 0.8 1.7 0.8

M9 Herbicides kg/ha/y 0 0.8 0 0.8 4.3 0.8 8.3 0.8

M10 Insecticides kg/ha/y 1 0.8 1 0.8 1.8 0.8 1.8 0.8M11 Formicides kg/ha/y 0 0.8 0 0.8 1 0.8 1 0.8

M12 Fungicides kg/ha/y 0 0.8 0 0.8 0.2 0.8 0.2 0.8

M13 Petroleum fuels kg/ha/y 30 0.8 40 0.8 80 0.8 40 0.8

M14 Steel (depreciation) kg/ha/y 1.3 0.8 2.7 0.8 2.7 0.8 2.7 0.8

M15 Manure (20% humidity) kg/ha/y 2667 0.8 2667 0.8 0 0.8 0 0.8

Services (Economy resources)

S1 Hard worker manpower hours/ha/y 145 0.8 100 0.8 3.2 0.8 0.5 0.8

S2 Operator manpower hours/ha/y 2 0.8 3.2 0.8 71.9 0.8 40 0.8

S3 Administrative labor US$/ha/y 4.3 0.8 4.3 0.8 4.3 0.8 4.3 0.8

S4 Technical assistance US$/ha/y 10 0.8 10 0.8 2 0.8 2.9 0.8

S5 Accounting labor US$/ha/y 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8

S6 Trips costs US$/ha/y 0.4 0.8 0.4 0.8 0.4 0.8 0.4 0.8

S7 Governmental taxes US$/ha/y 9.5 0.8 9.5 0.8 13.6 0.8 13.6 0.8

S8 Circulating capital costs US$/ha/y 2.95 0.8 2.95 0.8 2.95 0.8 2.95 0.8

S9 Insurance costs US$/ha/y 1 0.8 1 0.8 0.59 0.8 1.0 0.8

S10 Transport to storage cost US$/ha/y 6.8 0.8 6.8 0.8 6.8 0.8 6.8 0.8

S11 Drying & storage cost US$/ha/y 14.31 0.8 14.31 0.8 14.31 0.8 14.31 0.8S12 Social security taxes US$/ha/y 12.8 0.8 12.8 0.8 13.6 0.8 13.6 0.8

S13 Land leasing US$/ha/y 0 0.8 0 0.8 0 0.8 0 0.8

Sources:Chemical and Herbicide options: FNP, 1999. Organic: Agrorgânica, 2000 and FNP, 1999.* Value estimated by authors (weight of tractors and area of use).

Services (Econ. resources) Ecol. Org. Chem. Herb.

S20 Government subsidy US$/ha/y 0 0.8 0 0.8 0 0.8 0 0.8

S21 Efuent treatment US$/ha/y 0 0.8 0 0.8 20 0.8 10 0.8

S22 Risk & health treatment US$/ha/y 10 0.8 10 0.8 20 0.8 50 0.8Source: value of externality estimated by authors (to be conrmed in future studies).



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Table 10. Environmental inputs, services and output, proportion factor according with area affected.


Renewable Natural Resources

R1 Rain 106 kg/ha/y 1.5 1.0 1.5 1.0 1.5 1.0 1.5 1.0

R2 Nutrients from rocks kg/ha/y 10 1.0 10 1.0 1 1.0 3 1.0R3 Nitrogen (atmosphere) kg/ha/y 181 0.8 181 0.8 181 0.8 181 0.8

R4 Sediments (rivers) kg/ha/y 0.5 0.2 0.5 0.2 0.5 0.2 0.5 0.2

R5a Forest products: seeds kg/ha/y 10 0.2 5 0.2 0 0.2 0 0.2

R5b Forest products: food kg/ha/y 100 0.2 50 0.2 0 0.2 0 0.2

R5c Forest products: biomass kg/ha/y 2000 0.2 1000 0.2 0 0.2 0 0.2

R6a Forest services: water kg/ha/y 12 1.0 6 1.0 0 1.0 0 1.0

R6b Forest services: leisure US$/ha/y 3.3 1.0 1.65 1.0 0 1.0 0 1.0

R6c Forest biological control US$/ha/y 50 1.0 25 1.0 0 0.8 0 0.8Sources:R1: IBGE, 2001.R2: value estimated by authors based on Buckmann (1983) and IPT (1986).R3: value estimated by authors based on Dobereiner (1999).R4: general value estimated by authors.R5a: value estimated by authors based on Silva (1997) and Ahrens (1997).R5b, R5c: values estimated by authors.R6a, R6b: values estimated by authors.R6c: value estimated by authors based on annual cost of pesticides per hectare of other options.

The farmers that adopted chemical and herbicide options generally do not obey legislation that demand

to preserve 20% of area as forest. Organic producers need the benets of forest and usually preserve it .


Non Renewable Natural Resources

N1 Soil loss kg/ha/y 1000 0.8 1000 0.8 12500 0.8 1500 0.8

N2 Biodiversity loss kg/ha/y 0 0.8 0 0.8 100 0.8 19 0.8

Sources:N1: Correia, L. http://www.cnps.embrapa.br/search/planets/coluna14/coluna14.html (23/10/2001).N2: value estimated by authors.

Production data Units Ecologic Organic Chemical Herbicide

P1 Soybean production kg/ha/y 1920 1920 2240 2240

P2 Price US$/kg 0.250 0.235 0.170 0.170

P3 Sales US$/ha/y 480 451.2 380.8 380.8

P4 Humidity (water/soybean) kg/kg 0.18 0.18 0.18 0.18

P5 Conversion factor kcal/kg 4428 4428 4428 4428

P6 Conversion factor J/kcal 4186 4186 4186 4186

P7 Energy of Product J/ha/y 2.9E+10 2.9E+10 3.4E+10 3.4E+10

P8 Emergy of Sales sej/ha/y 1.78E+15 1.7E+15 1.4E+15 1.4E+15

Sources: Chemical and Herbicide options: FNP, 1999. Organic: Agrorgânica, 2000 and FNP, 1999.



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Table 11. Table of emergy ows expressed in 1013 sej/ha/y

Note Flows Units sej/ unit R. Ecologic Organic Chemical Herbicide

Renewable Resources 117.7 99.8 80.4 80.8

R1 Rain kg/ha/y 9.10E+07 1 13.7 13.7 13.7 13.7R2 Nutrients from rocks kg/ha/y 1.71E+12 1 1.7 1.7 0.2 0.5

R3 Nitrogen (atmosphere) kg/ha/y 4.60E+12 1 66.6 66.6 66.6 66.6

R4 Sediments (rivers) kg/ha/y 1.71E+12 1 0.02 0.02 0.02 0.02

R5a Forest products: seeds kg/ha/y 1.48E+12 1 0.3 0.1 0 0

R5b Forest products: food kg/ha/y 4.50E+11 1 0.9 0.5 0 0

R5c Forest prod.: biomass kg/ha/y 3.69E+11 1 14.8 7.4 0 0

R6a Forest services: water kg/ha/y 5.50E+08 1 0.001 0 0 0

R6b Forest services: leisure US$/ha/y 3.70E+12 4 1.2 0.6 0 0R6c Forest biological control US$/ha/y 3.70E+12 4 18.5 9.3 0 0

Non Renewable Resources 5.3 5.3 69.8 8.6

N1 Soil loss kg/ha/y 6.67E+10 1 5.3 5.3 66.7 8.0

N2 Biodiversity loss kg/ha/y 3.90E+11 1 0 0 3.1 0.6

Total Natural Resources 123.0 105.2 150.3 89.4

Materials (Econ. resources) 99.5 102.5 177.7 201.6

M1 Certied seeds kg/ha/y 1.00E+12 1 0.8 0.8 0 0

M2 Certied seeds kg/ha/y 1.00E+12 1 5.6 5.6 5.6 7M3 Transgenic seeds kg/ha/y 1.00E+13 2 0 0 0 0.0

M4 Limestone kg/ha/y 1.00E+12 1 0 0 80.0 80.0

M5 Nitrogen fertilizer kg/ha/y 3.80E+12 1 0 0 0 0.0

M6 Phosphate fertilizer kg/ha/y 3.90E+12 1 46.8 46.8 46.8 78.0

M7 Potassium fertilizer kg/ha/y 1.10E+12 1 4.4 4.4 13.2 8.8

M8 Inoculating agent kg/ha/y 3.18E+13 1 2.5 2.5 4.3 4.3

M9 Herbicides kg/ha/y 1.48E+13 3 0 0 5.1 9.8

M10 Insecticides kg/ha/y 1.48E+13 3 1.2 1.2 2.1 2.1

M11 Formicides kg/ha/y 1.48E+13 3 0 0 1.2 1.2

M12 Fungicides kg/ha/y 1.48E+13 3 0 0 0.2 0.2

M13 Petroleum fuels kg/ha/y 2.76E+12 1 6.6 8.8 17.7 8.8

M14 Steel (depreciation) * kg/ha/y 6.70E+12 1 0.7 1.4 1.4 1.4

M15 Manure (20% humid) kg/ha/y 1.45E+11 2 30.9 30.9 0 0

Services (Econ. resources) 26.2 24.1 28.6 24.0

S1 Hard worker manpower hour/ha/y 6.28E+11 1 7.3 5.0 0.2 0

S2 Manpower (operator) hour/ha/y 1.88E+12 1 0.3 0.5 10.8 6.0

S3 Administrative labor US$/ha/y 3.70E+12 4 1.3 1.3 1.3 1.3



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S4 Technical assistance US$/ha/y 3.70E+12 4 3.0 3.0 0.6 0.9

S5 Accounting labor US$/ha/y 3.70E+12 4 0.2 0.2 0.2 0.2

S6 Trips costs US$/ha/y 3.70E+12 4 0.1 0.1 0.1 0.1S7 Governmental taxes US$/ha/y 3.70E+12 4 2.8 2.8 4.0 4.0

S8 Circulating capital costs US$/ha/y 3.70E+12 4 0.9 0.9 0.9 0.9

S9 Insurance costs US$/ha/y 3.70E+12 4 0.3 0.3 0.2 0.3

S10 Transport to storage cost US$/ha/y 3.70E+12 4 2.0 2.0 2.0 2.0

S11 Drying & storage cost US$/ha/y 3.70E+12 4 4.2 4.2 4.2 4.2

S12 Social security taxes US$/ha/y 3.70E+12 4 3.8 3.8 4.0 4.0

S13 Land leasing US$/ha/y 3.70E+12 4 0 0 0 0

Externalities (services) 3.0 3.0 11.8 17.8

S20 Government subsidy US$/ha/y 3.70E+12 4 0 0 0 0S21 Efuent treatment US$/ha/y 3.70E+12 4 0.0 0.0 5.9 3.0S22 Risk & health treatment US$/ha/y 3.70E+12 4 3.0 3.0 5.9 14.8

Economy Feedback 125.7 126.6 206.3 225.6Total Economy Feedback 128.7 129.6 218.1 243.4

Total Emergy 251.7 234.7 368.4 332.8

Table 11 continued



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Table 12. Table of monetary ows in US$/ha/year

Note Flows Units US$/unit Ecologic Organic Chemical Herbicide

M1 Farmer seeds kg/ha/y 0 0.00 0.00 0.00 0.00

M2 Certied seeds kg/ha/y 0.29 16.24 16.24 16.24 19.72

M3 Transgenic seeds kg/ha/y 0.34 0.00 0.00 0.00 0.00

M4 Limestone kg/ha/y 0.02 0.00 0.00 16.00 16.00

M5 Nitrogen fertilizer kg/ha/y 0.18 0.00 0.00 0.00 0.00

M6 Phosphate fertilizer kg/ha/y 0.40 48.00 48.00 48.00 80.00

M7 Potash fertilizer kg/ha/y 0.18 7.20 7.20 21.60 14.40

M8 Inoculating agent kg/ha/y 0.46 0.37 0.37 0.63 0.63

M9 Herbicides kg/ha/y 15.00 0.00 0.00 51.60 99.60

M10 Insecticides kg/ha/y 10.33 8.26 8.26 14.88 14.88

M11 Formicides kg/ha/y 8.44 0.00 0.00 6.75 6.75

M12 Fungicides kg/ha/y 11.59 0.00 0.00 1.85 1.85

M13 Petroleum fuels kg/ha/y 0.35 8.40 11.20 22.40 11.20

M14 Steel (depreciation) * kg/ha/y 0.45 0.47 0.97 0.97 0.97

M15 Manure (20% humidity) kg/ha/y 0.02 42.67 42.67 0.00 0.00

Material Inputs 131.61 134.92 200.92 266.00

S1 Hard worker manpower hour/ha/y 0.33 38.28 26.40 0.84 0.13S2 Manpower (operator) hour/ha/y 0.58 0.93 1.48 33.35 18.56

S3 Administrative labor US$/ha/y 1.00 3.44 3.44 3.44 3.44

S4 Technical assistance US$/ha/y 1.00 8.00 8.00 1.60 2.32

S5 Accounting labor US$/ha/y 1.00 0.64 0.64 0.64 0.64

S6 Trips costs US$/ha/y 1.00 0.32 0.32 0.32 0.32

S7 Governmental taxes US$/ha/y 1.00 7.60 7.60 10.88 10.88

S8 Circulating capital costs US$/ha/y 1.00 2.36 2.36 2.36 2.36

S9 Insurance costs US$/ha/y 1.00 0.80 0.80 0.47 0.80S10 Transport to storage cost US$/ha/y 1.00 5.44 5.44 5.44 5.44

S11 Drying & storage cost US$/ha/y 1.00 11.45 11.45 11.45 11.45

S12 Social security taxes US$/ha/y 1.00 10.24 10.24 10.88 10.88

S13 Land leasing US$/ha/y 1.00 0.00 0.00 0.00 0.00

Services 89.50 78.17 81.68 67.22

Economic cost 221.11 213.09 282.60 333.22






Edited by



Associate Editors

Howard T. Odum



David Tilley



Sergio Ulgiati

University of Siena

Siena, Italy

December 2003



Gainesville, FL


vi



production; and(iii) early (post-mulching) soil fertilization coupled with crop rotation/association that

allow the development of one additional harvest cycle per five-year period.

The fire-free agricultural management system should bring about, on the one hand, economicimprovements to the farmers, by allowing agricultural intensification without soil degradation; and onthe other, betterment of environmental quality and natural resources conservation, resulting in importantsocial benefits to the local communities. These advantages are expected due to the possibility of cultivatingthe land for two consecutive years instead of only one, followed by just three fallow years instead of four or five. Besides, the enriched secondary forest yields usable wood materials, being also economicallyattractive, whereas the conventional slash-and-burn system remains economically unproductive duringthe whole fallow period.

There are also drawbacks associated with the fire-free management system. Most of the benefitsare manifested in the long run, and are only partially perceived in monetary terms. Fire-free managementinvolves higher investment costs due to the mechanic mulching operation and fertilizer application, neededto compensate the delayed release of nutrients from the mulch, as compared with the prompt nutrient

release from the ashes returned to the soil in the slash-and-burn system (Kato & Kato, 1999). A researchchallenge resides in assessing the balance between the environmental and social (as well as some private) benefits, and the private costs to the farmer, of the fire-free agricultural management system proposed bythe SHIFT-Capoeira project. A system’s ecology approach based on emergy evaluation (Odum, 1996) has been proposed as an option to carry out this comparative assessment (Rodrigues et al., 2001). The mainadvantage of applying this method is that it allows consideration of all needed resources, inputs, andflows using solar energy units as a common currency, facilitating the comparison of management practiceswith contrasting resource inputs and final product outputs. This paper reports on the findings of such anevaluation.

METHODS

The first step for the emergy evaluation of both the conventional slash-and-burn and the proposedfire-free agricultural production was the overall delimitation and characterization of “typical shiftingcultivation systems” (Table 1). This was accomplished by constructing emergy flow diagrams of the production systems using system’s language symbols (Figures 1 and 2; Christianson, 1986). Severalassumptions were made in order to compose the typical slash-and-burn and fire-free management systemsupon which the diagrams were drawn, as follows:

Table 1. Typical small landholder’s shifting cultivation agricultural systems in Northeastern Pará State(Brazil), showing activities in a yearly basis ___________________________________________________________________________________ Slash-and-burn production system Fire-Free production system

___________________________________________________________________________________

Year 1 Slash-and-burn, Sow corn, Plant Cut/chop/mulch/fertilize, Sow corn, Plant cassava,

cassava, Harvest corn, Cultivate Plant seedlings, Harvest corn, Cultivate

Year 2 Harvest cassava, Fallow Sow 2nd corn crop, 2nd cultivation, Harvest corn,

Harvest cassava

Year 3 Fallow Fallow

Year 4 Fallow Fallow

Year 5 Fallow, Back to year 1 Harvest charcoal, Harvest firewood, Harvest timber,

Back to year 1_____________________________________________________________________



i. The systems are driven by energy inputs from natural and man-made sources on a similar basis

of natural resources, represented by fallow and cropland;ii. These land uses are interchanged into each other according to management practices, that is,

cropland becomes fallow land by abandonment, and fallow land is converted into cropland either by slash-and-burn or slash-and-mulch;

iii. The renewable energy flows derived from nature used up in production, including sunlight, rainwater, and winds can be summed up as transpiration associated with primary production; whilethe losses of soil organic matter and nutrients in the ashes resulting from burning are used up in production as nonrenewable inputs;

iv. Money (flow expressed by dashed lines) is exchanged for the harvest (yield) to pay for labor,services, and man-made resources;

v. The main differences between the production systems studied are represented by the use of mechanized slash-and-mulch operation, fertilizer input, and secondary vegetation enrichment,which result in production of wood materials in addition to crops; as opposed to slash-and-burn,for which no market inputs are needed and no marketable production from the secondaryvegetation is obtained.

Based on the systems diagrams, emergy evaluation tables were formulated with all inputs, flows,and outputs of the systems. Data on inputs and flows for one hectare (ha) of these typical systems on ayearly basis were obtained from the reports of the SHIFT-Capoeira project, on information offered directly by project researchers, and from selected references, as cited in the notes to each Table. Finally, systems’ performances were evaluated using ratios and indices derived from these flows, as proposed by Ulgiati et

al. (1995) and Odum (1996).

land shift

landabandonment

seedingTranspirat ion

S un

W ind

Ra i n

f a l l o wland

cropland

Money

Capoeira(secondary forest)

Crops

phyto-mass

nut r i -ents

Goodsand

Services

Ma r ke t

PRODU C T I ON

Corn grain,

cassava tubers

runoffSlash-and-burn agriculture

Slashandburn

Ashes

Labor

Figure 1. System diagram of the slash-and-burn agricultural production system




Slash-and-burn Agricultural Production

The diagram representing the slash-and-burn system presented in Figure 1 shows the importantemergy flow associated with the burning operation and Table 2 summarizes the data. This flow represents

24% of the total yield in this system, and as much as 46% when only the nonrenewable flows (those thatdiffer between the systems) are considered (Table 2). This large contribution is provided by the capacityof the secondary vegetation to replenish these storages during the fallow period, without any additionalnonrenewable resource depletion or input from the economy. The Environmental Loading Ratio (sum of purchased and nonrenewable inputs by renewable inputs – a measure of environmental impact) for theslash-and-burn system is, thus, relatively small (1.04; Table 5), even when compared to organic agricultural production in Brazil (1.75) (Comar, 2000).

Table 2. SHIFT - Capoeira PROJECT SUSTAINABILITY ASSESSMENT

___________________________________________________________________________________

EMergy Evaluation Table of the Slash-and-burn Production System Unit Solar Solar Em$

Note Item Unit Data EMERGY EMERGY Value

(units/yr) (sej/unit) (E13 sej/yr) ($/yr) ___________________________________________________________________________________ RENEWABLE RESOURCES1 Sun J 6.94E+13 1 7 82 Rain J 1.13E+11 1.80E+04 204 2433 Wind J 5.19E+09 1.50E+03 1 14 Et J 4.19E+10 1.82E+04 76 91

NONRENEWABLE STORAGES5 Net Topsoil losses J 3.62E+09 7.38E+04 27 326 Nutrient loss by burning J 3.66E+14 37 44Sum of free inputs (sun, rain, wind omitted) 140 167PURCHASED INPUTSOperational inputs7 Fuel J 0.00E+00 6.60E+04 0.00 0.008 Phosphate g P 0.00E+00 1.78E+10 0.0 0

9 Labor J 2.01E+09 8.10E+04 16 19

10 Services $ 0.00E+00 8.40E+12 0 0

Sum of purchased inputs 16 19PRODUCTION AND TRANSFORMITIES

11 Corn kg/yr 8.60E+02 1.68E+12

12 Cassava kg/yr 5.60E+03 2.57E+11

13 Charcoal kg/yr 0.00E+00

14 Firewood kg/yr 0.00E+00

15 Timber kg/yr 0.00E+00

16 Total Yield g dry 2.43E+06 5.94E+08 156 186

weight

17 Production J 4.47E+10 3.22E+04 ___________________________________________________________________________________

Footnotes given at the end of this chapter.



The yield in this system is represented by one corn grain and one cassava crop, per five year

shifting cultivation cycle, which go to the market in exchange for money to pay for the labor employed. No additional interactions with the market occur, as no fuels or machinery or fertilizers are used. Evenwith such small inputs, the specific emergy of the production (or its transformity) is comparable to thatobtained in other agricultural systems, when the total weight of produce harvested in the five-year cycleis considered in a yearly basis. Thus, due to the non-intensive use of resources, the Empower Density for the slash-and-burn production system was only 1.56E+15 sej/ha/yr, which corresponds to approximatelyone eighth of that observed in grain corn production in Florida (Brandt-Williams, 2001), or one-fourth of that observed in organic agriculture in Brazil (Comar, 2000).

Most important for policy considerations regarding the slash-and-burn agriculture in NortheasternPará is the very low Emergy Investment Ratio of this system (sum of purchased by non-purchased inputs,

0.12; Table 5). It will be difficult to convince small landholders to change a traditional management practice that, even with a relatively small Empower Density (that corresponds to total yield) brings sucha surplus on investment. With such small investment ratio, the Emergy Yield Ratio of the slash-and-burnsystem is considerably high (9.56), some six-fold higher that observed in organic agriculture in Brazil(Comar, 2000). Consequently, and confirming the historical trend of centuries of shifting cultivation inthe region, a high Emergy Sustainability Index is obtained for this system, as perceived by the smalllandholders.

Fire-free Agricultural Production

The diagram representing the fire-free production system presented in Figure 2 shows a prominent

emergy flow associated with the mechanized mulching operation. In addition to labor, fuels and machineryare obtained as services, and at least phosphate fertilizer is needed to compensate for the immobilizationof nutrients in the soil microbial biomass developed on the mulch (Kato & Kato, 1999). The emergy

land shift

landabandonment

slashand

mulch

Fuel &machinery

S un

W ind

Ra i n

f a l l o wland

cropland

Money

Capoeira(secondary forest)

Crops

phyto-mass

nutr i -ents

Goodsand

Services

Ma rket

PRODUCT ION

Corn grain,

cassava tubers,

timber, charcoal,

firewood

runoffFire-free agriculture

Transpirat ion seeding

Labor

Fe r t i l i z e r

Figure 2. System diagram of the fire-free agricultural production system



flows supporting a typical fire-free agricultural system are summarized in Table 3. These inputs from theeconomy are paid for with the output of wood products from the secondary vegetation, besides grain cornand cassava obtained in two crops instead of only one, which is made possible by the input of fertilizers.

Even though a similar erosion rate was considered for both agricultural production systemsstudied, slightly larger emergy expenditure related to soil loss occurs in the fire-free as compared with theslash-and-burn system, due to a greater soil carbon content in the former. As there are no nutrients lost in burning, however, the total environmental contribution for fire-free agricultural production is a littlesmaller. On the other hand, 15% of the total yield in this system is represented by the input of purchasedfertilizer. The total expenditure in purchased inputs reaches almost 40% of the emergy yield.

Table 3. SHIFT - Capoeira PROJECT SUSTAINABILITY ASSESSMENT

_______________________________________________________________________________

EMergy Evaluation Table of the Fire-Free Production System

Unit Solar Solar Em$ Note Item Unit Data EMERGY EMERGY Value

(units/yr) (sej/unit) (E13 sej/yr) ($/yr) _______________________________________________________________________________ RENEWABLE RESOURCES

1 Sun J 6.94E+13 1 7 8

2 Rain J 1.13E+14 1.80E+04 204 243

3 Wind J 3.90E+12 1.50E+03 1 1

4 Et J 4.19E+10 1.54E+04 76 91

NONRENEWABLE STORAGES

5 Net Topsoil losses J 4.70E+09 7.38E+04 35 41

6 Nutrient loss by J 0.00E+00 0 0 burning

Sum of free inputs (sun, rain, wind omitted) 111 132

PURCHASED INPUTS

Operational inputs

7 Fuel J 8.11E+05 6.60E+04 0.01 0.01

8 Phosphate g P 1.50E+04 1.78E+10 26.7 32

9 Labor J 2.65E+09 8.10E+04 21 26

10 Services $ 2.50E+01 8.40E+12 21 25

Sum of purchased inputs 69 82

PRODUCTION AND TRANSFORMITIES

11 Corn kg/yr 1.60E+03 1.05E+12

12 Cassava kg/yr 1.04E+04 1.62E+11

13 Charcoal kg/yr 0.00E+00 #DIV/0!

14 Firewood kg/yr 6.90E+03 2.44E+11

15 Timber kg/yr 0.00E+00 #DIV/0!

16 Total Yield g dry weight 1.04E+07 1.62E+08180 214

17 Production J 1.81E+11 9.29E+03 ________________________________________________________________________________

Footnotes given at the end of this chapter.



This dependency of the fire-free management on external nonrenewable resources results in alarger Environmental Loading Ratio than the slash-and-burn system, and an Emergy Yield Ratio threetimes smaller. Hence, even when one considers the larger marketable weight of produce obtained with thetwo consecutive corn and cassava harvests and the wood products collected after the fallow period in thissystem, the sustainability, or the surplus perceived by the farmer in relation to the production effort, isquite smaller. This is expressed in the smaller transformity of the produce (the real wealth perceived bythe farmer) obtained in the fire-free system. In other words, even with a larger Empower Density (larger total yield), the specific emergy (sej/kg) of the corn or the cassava in the fire-free system is almost 60%smaller. Also, the larger Emergy Investment Ratio of the fire-free system causes a smaller profitability.With an emergy dollar value in total production corresponding to Em$214.00/ha/yr (sej/$ ratio of 8.4E+12for the Brazilian economy – Odum, 1996) and a market expenditure of Em$82.00, the profitability of thefire-free system reaches Em$132.00/ha/yr, as compared with Em$167.00/ha/yr in the slash-and-burnsystem.

These results imply that, from a policy-making point of view, additional motivations must beoffered to farmers if they are to consider altering their traditional management practices toward fire-freemanagement. First, the fire-free agricultural production system must include alternative, environmentally

cost effective ways of providing the services and resources needed for production. For instance, promotingcollectivism in the ownership and utilization of machinery, and biological means of providing phosphorusafter mulching, could drastically reduce costs. Second, additional value could be perceived from theresources managed within the system (e.g., the organic matter incorporated into the soil) and transferredfrom society to the farmers, compounding incentives that could not be obtained when practicing slash-and-burn.

Carbon Sequestration and Incentives for Fire-free Management

The profuse literature on the costs and benefits of fire-free agricultural management points out

advantages perceived in different scales, and by various actors involved in societal interest in productionand conservation in the Amazon and elsewhere. One such benefit is the sequestration of carbon in the soilorganic matter, which could contribute to mitigate the emission of greenhouse gases to the atmosphere promoted by burning. The sequestered carbon could be tradable in the market of environmental commoditiesand pollution permits being forwarded by the proposed Kyoto Agreement on Global Change (Kitamura& Rodrigues, 2000).

In order to perform an evaluation of the potential contribution of the fire-free production systemto carbon sequestration, a somewhat different perspective is needed. Some important storages and flowsthat occur within the boundaries of the fire-free and the slash-and-burn systems simultaneously toagricultural production were not included in the previous analysis, due to the systems delimitation presentedin Figures 1 and 2. For example and in conformity with theory, the large emergy flow contained in the

mulch was not computed in the fire-free production system, because it is not consumed (for it is actuallystored within the system), and therefore is not a contribution to production. However, if the buildup of soil organic matter (SOM – Table 4) could be regarded as an additional product of the system, tradable inan “environmental commodity market,” radical changes would occur in the systems’ performance indices.

A comparison of the slash-and-burn and the fire-free management systems including the buildupof SOM as a marketable commodity is presented in Table 5. This table summarizes the systems’ indices,now including the changes of stored SOM (Table 4) as additional production. As a result of fallow (7 year period, Vlek et al., 1999), as much as 158 T/ha of SOM is accumulated in the fire-free system, a 42 T/ha increase over the non fallow land. In emergy terms this storage buildup would represent thelargest product of the system, and would increase the Emergy Yield Ratio sevenfold. The slash-

and-burn system would benefit as well under this carbon sequestration compensation scenario, because even when the above ground portion of the secondary vegetation is burned, some surplus below ground organic matter is accumulated (approximately 7 T/ha, Vlek et al., 1999). An



Emergy Yield Ratio twice as large would be obtained in this system when considering SOM buildup as production.

Quite expectedly, under this scenario the farmer would perceive an enormous advantage,for compensation would be offered for a resource still in place within the system. The total

emergy dollar value of the fire-free production in this case would amount to Em$1,340.00/ha/yr, up from Em$132.00 when only the normal crops and wood materials made up the output of the system. The emergy dollar profit of the slash-and-burn system would reach Em$382.00/ha/yr, also a considerable improvement. In both cases, of course, the sustainability index is largelyincreased.

This hypothetical analysis suggests that in a carbon sequestration compensation scenario,in which society at large would assist with incentives for environmental conservation in agricultureand forestry, fire-free management practices would be greatly advantageous, and could contributeto improve the sustainability of land use in the Amazon.

CONCLUSIONThe emergy analysis performed on the basis of the defined typical slash-and-burn and fire-free

agricultural production systems showed that while production in the former is primarily based on freeenvironmental inputs, the latter is highly dependent on purchased inputs. This difference is crucial, andmakes it advantageous to the farmers to rely on burning to clear and fertilize the land for planting. Significantefficiency improvements would be required in the mulching operation, as well as in nutrient fixation/recovery, to make the fire-free production system competitive.

Evaluations of actual farms practicing these different management systems should be carriedout to check for possible feedback reinforcements used by farmers to improve efficiency. Also, theengagement of farmers in special social arrangements that might foster the collective use of resources

and equipment should receive special attention. Finally, a local community-wide evaluation could shedlight onto other, off farm advantages, both environmental and economic, that could compensate for the

Table 4. Emergy evaluation of carbon sequestration in slash-and-burn and fire-free agricultural

management systems.

________________________________________________________________________________

Carbon Sequestration Assessment SLASH-AND-BURN production

SOM buildup

SOM content (g/ha, 0-100cm) = (g C/g soil)*(soil density * 1E6 cm3/m2 * 10000 m2/ha)SOM in control soil= 1.16E+08 (Vlek et al., 1999)

SOM in topsoil (g C/g soil)= 0.008 (Vlek et al., 1999)

SOM in topsoil (g/ha)= 1.23E+08

Energy cont./g organic= 5.40 kcal/g

Annual energy: 2.45E+10 (7 yr fallow study)

Carbon Sequestration Assessment FIRE-FREE production

SOM buildup

SOM content (g/ha, 0-100cm) = (g C/g soil) * (soil density* 1 E6 cm3/m2 * 10000 m2/ha)SOM in topsoil (g C/g soil)= 0.0104 (Vlek et al., 1999)

SOM in topsoil (g/ha)= 1.58E+08

Annual energy: 1.37E+11 (7 yr fallow study) __________________________________________________________________



increased resources demanded by fire-free agriculture.

ACKNOWLEDGEMENTS

The first author is indebted to FUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DESÃO PAULO (FAPESP) for a post-doctoral research grant. This study is part of the SHIFT-program,which is supported by Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq, ProjectENV-25), Fundo Estadual de Ciência e Tecnologia do Pará-FUNTEC e Programa Piloto para a Proteçãodas Florestas Tropicais do Brasil, Subprograma de Ciência e Tecnologia- PPG/7 Brazil, and theBundesministerium fur Biuldung und Forschung (BMBF) / Deutsches Zentrum fur Luft- und Raumfahrt(DLR, Project 01LT0012), Germany.

REFERENCES

Table 5. Summary emergy evaluation of the slash-and-burn and the fire-free agricultural management

systems including soil organic matter buildup as production. Refer to Tables 2 and 3 for

details, and Table 4 for soil organic matter buildup evaluation.

____________________________________________________________________

Carbon Sequestration Assessment

Production System - Slash-and-burn Fire-free

Solar Em$ Solar Em$

EMERGY Value EMERGY Value

(E13 sej/yr) ($/yr) (E13 sej/yr) ($/yr) __________________________________________________________________________________ Renewable Inputs 76 91 76 91

Nonrenewable storages 64 76 35 41

Soil organic matter (SOM) buildup 181 216 1014 1207

Purchased inputs 16 19 69 82Production (not accounting for SOM 156 186 180 214

buildup)

Production (SOM buildup is included as 337 401 1194 1422

production)

INDICES SYSTEM’S RESULTS

% Renewable 0.49 0.42

Environmental Loading Ratio 1.04 1.36

Emergy Investment Ratio 0.12 0.62

Emergy Yield Ratio 20.68 17.27

Emergy Yield Ratio excluding SOM buildup 9.56 2.60

Nonrenewable/Renewable 0.83 0.81

Empower Density 3.37E+15 1.19E+16

Emergy Sustainability Index 19.80 12.68

____________________________________________________________________



Bastos, T. X.; & Pacheco, N. A. 1999. Características agroclimatólogicas do município Igarapé-Açu.Anais do Seminário sobre Manejo da Vegetação Secundária para a Sustentabilidade daAgricultura Familiar da Amazônia Oriental. Belém. 51-58.

Block, A.; Lucke, W.; Denich, M.; Vlek, L. G. 1999. The newly developed bush chopper “Tritucap” in

field test, research on working capacity and working quality under capoeira conditions. Anaisdo Seminário sobre Manejo da Vegetação Secundária para a Sustentabilidade da AgriculturaFamiliar da Amazônia Oriental. Belém. 109-111.

Brandt-Williams, S. L. 2001. Folio #4: Emergy of Florida Agriculture. Handbook of Emergy Evaluation:A Compendium of Data for Emergy Computation Issued in a Series of Folios. Center for Environmental Policy, University of Florida, Gainesville. 40p.

Brown, M. T.; & Bardi, E. 2001. Folio #3: Emergy of Ecosystems. Handbook of Emergy Evaluation: ACompendium of Data for Emergy Computation Issued in a Series of Folios. Center for Environmental Policy, University of Florida, Gainesville.

Comar, V. 2001. Emergy evaluation of organic and conventional horticultural production in Botucatu,São Paulo State, Brazil. In Emergy Synthesis: theory and Applications of the Emergy

Methodology. 181-195.Christianson, R. A. 1986. Simulating land rotation in plantations at Jari, Brazil. In Energy Systems Overview

of the Amazon Basin. Odum, H.T.; Brown, M. T.; & Christianson, R. A. Center for Wetlands,University of Florida, Gainesville. 153-159.

Holscher, D.; Ludwig, B.; Moller, R. F.; Folster, H. 1997. Dynamic of soil chemical parameters in shiftingagriculture in the eastern Amazon. Agriculture, Ecosystems and Evironment, 66:153-163.

Jonsson, A. 2000. Energy balance of a traditional and a modified land use system in the Eastern Amazon basin, Brazil, as a case study. M. Sc. Thesis, Georg-August University, Gottingen, Germany.93p.

Jonsson, A.; Vielhauer, K.; Denich, M.; & Vlek, P. L. G. 1999. Energy balance of traditional and modified

land-use systems in the Eastern Amazon basin, Brazil, as a case study. Anais do Seminário sobreManejo da Vegetação Secundária para a Sustentabilidade da Agricultura Familiar da AmazôniaOriental. Belém. 66-68.

Kato, M. S. A.; & Kato, O. R. 1999. Preparo de área sem queima, uma alternativa para a agricultura dederruba e queima da Amazônia Oriental: aspectos agroecológicos. Anais do Seminário sobreManejo da Vegetação Secundária para a Sustentabilidade da Agricultura Familiar da AmazôniaOriental. Belém. 35-37.

Kato, M. S. A.; Kato, O. R.; Denich, M.; Vlek, P. L. G. 1999. Disponibilidade de fósforo em sistema demulch, no Nordeste Paraense. Anais do Seminário sobre Manejo da Vegetação Secundária paraa Sustentabilidade da Agricultura Familiar da Amazônia Oriental. Belém. 116-119.

Kitamura, P. C.; & Rodrigues, G. S. 2000. Avaliação de tecnologia e manejo para o desenvolvimento

sustentável da agricultura familiar. PPG-7 Meeting. Belém.Odum, H. T. 1986. Energy analysis overview of Brazil. In Energy Systems Overview of the Amazon

Basin. Odum, H.T.; Brown, M. T.; & Christianson, R. A. Center for Wetlands, University of Florida, Gainesville. 64-81.

Odum, H. T. 1996. Environmental Accounting. Emergy and Environmental Decision Making. John Wiley/ Sons, Inc. New York, 370p.

Rodrigues, G. S. ; Kitamura, P. C.; Meyer, Leandro, F. F. ; Denich, M.; Sá, T. D. de A. 2001. Integrationof information on fallow systems toward supporting public policies. Proceedings of the German-Brazilian Workshop on Neotropical Ecosystems, Hamburg, in press.

Sommer, R.; Vlek, P. L. G.; Folster, H.; Sa, T. D. de A. 1999. Slash-and-mulch to reduce nutrient losses in

shifting cultivation in the eastern Amazon. Anais do Seminário sobre Manejo da VegetaçãoSecundária para a Sustentabilidade da Agricultura Familiar da Amazônia Oriental. Belém. 80-82.



Ulgiati, S.; Brown, M. T.; Bastianoni, S.; & Marchettini, N. 1995. Emergy-based indices and ratios toevaluate the sustainable use of resources. Ecological Engineering, 5:519-531.

Vielhauer, K.; & Sa, T. D. de A. 1999. Efeito do enriquecimento de capoeira com árvores leguminosasde rápido crescimento para a produção agrícola no Nordeste Paraense. Anais do Semináriosobre Manejo da Vegetação Secundária para a Sustentabilidade da Agricultura Familiar daAmazônia Oriental. Belém. 27-34.

Vlek, P. L. G.; Sommer, R.; Vielhauer, K.; & Serrao, A. 1999. The carbon and nutrient balance under slash-and-burn agriculture and its alternatives in the Amazon. Mimeographed, 19p.

Footnotes to Table 2

1 Sun, J

Annual energy = (Avg. Total Annual Insolation kcal/cm2/yr)(Area)(1-albedo)

Insolation: 1.95E+02 kcal/cm2/yr (Odum et al, 1986)

Albedo: 0.15Annual energy: 6.94E+13

2 Rain, J (mm/yr)(Area)(1E6g/m3)(4.94J/g)(1 - runoff)

mm/yr 2470 (Bastos & Pacheco, 1999)

Runoff coefficient: 7.00E-02

Annual energy: 1.13E+11

3 Wind, J

Density of air = 1.30E+00 Kg/m3

Average wind velocity = 1.40E+00 mps Bastos & Pacheco, 1999

Geostrophic wind = 2.33E+00 mps Observed wind is about .06 of geostrophic windDrag coefficient = 1.00E+-3

Annual energy = (area)(air density)(drag coeff.)(velocity^3)

(___m^2)(1.3 kg/m^3)(1.00E-3)(___mps)(3.14E7 s/yr)

Annual energy = 5.19E+09 J/yr

4 Evapotranspiration, J (g/m2)(J/g)(area)

Et: 848 mm/yr 8.48E+05 g/m2 (Bastos & Pacheco, 1999)


5 Net Topsoil loss J (erosion rate * SOM)(5.4 kcal/g)(4186 J/kcal)

Erosion rate = 2000 g/m2/yr

% organic in soil = 0.0080 (Vlek et al., 1999)


6 Nutrient loss by burning, JLost C, N, P, K, Ca (43.4T/ha DM, Sommer et al., 1999) * transformity (Odum

1996)

Total nutrient loss (J/ha) =(g/ha N, P, K, Ca*%retained*J/g)/5yr

Total Biomass Loss (J/ha) = g C*3.6Kcal/g*4186J/kcal


7 Fuel, J per ha (diesel, machinery operation) (gal/ha*1.51E5 J/gal /5yr)

Gallons: 0.00E+00

Total energy: 0.00E+00



8 Phosphate fertilizer (g)

Annual consumption: 0.00E+00 (Kato & Kato, 1999; Kato et al, 1999)

9 Labor, J (pers-hours/ha/yr)*(3500 kcal/day)*(4186J/Cal)

pers-hours: 1.37E+02 (Jonsson et al, 1999; Jonsson, 2000)

* Adapted from passion fruit/cassava crops - includes slash/burning, cultivating-2/5, harvesting 2/5


Transformity: 8.10E+04 (uneducated labor - Odum, 1996)

10 Services, $ per ha * Estimated cost for machinery operation

$/yr: 0.00E+00 (Vielhauer, 2000 - personnal communication)

Production (kg/ha/yr) All production figures for 5 years production cycle

11 Corn (4300 kg in 5 years) 8.60E+02 (Vielhauer & Sa, 1999; Kato & Kato, 1999; Vielhauer, 2000 –

personal com)

12 Cassava (28,000 5.60E+03 (Vielhauer & Sa, 1999; Kato & Kato, 1999; Vielhauer, 2000 -

kg in 5 years) personal com)13 Charcoal 0.00E+00

14 Firewood 0.00E+00 (Vlek et al, 1999; Vielhauer, 2000 – personal communication)

15 Timber 0.00E+00

16 Total Yield * as total energy investment for production

Dry weight =

Corn 7.48E+05 13% humidity, 13.6% protein, 7.9% fat, 78,5% carbohydrates

Cassava 1.68E+06 70% humidity, 9% protein, 1% fat, 90% carbohydrates (assume =

potato)

Charcoal 0.00E+00 2% humidity, twice energy content of firewood assumedFirewood 0.00E+00 15% humidity, 4.0kcal/g (Brown & Bardi, 2001)

Timber 0.00E+00 15% humidity, 4.0kcal/g

17 Product in Joules * as total energy in product

protein at 24KJ/g, fat at 39KJ/g, carbohydrates 17KJ/g (Brown & Bardi, 2001)

Energy content = 4.47E+10 J

Footnotes to Table 3

Notes * only those items different from table 1a.

5 Net Topsoil loss J (erosion rate * SOM)(5.4 kcal/g)(4186 J/kcal)

Erosion rate = 2000 g/m2/yr

% organic in soil = 0.0104 (Vlek et al., 1999)


6 Nutrient loss by burning, J

Total nutrient loss (J/ha)= (g/ha N, P, K, Ca*%retained*J/g)/5yr

Total Biomass Loss (J/ha) = g C*3.6Kcal/g*4186J/kcal

Annual energy: 0.00E+007 Fuel, J per ha (diesel, machinery operation) (gal/ha*1.51E5 J/gal /5yr)

Gallons: 5.37E+00 (Block et al, 1999)




8 Phosphate fertilizer (g)

Annual consumption: 1.50E+04 (Kato & Kato, 1999; Kato et al, 1999)

9 Labor, J * (pers-hours/ha/yr)*(3500 kcal/day)*(4186J/Kcal)

pers-hours: 1.81E+02 (Jonsson et al, 1999; Jonsson, 2000)

* Adapted from passion fruit/cassava crops - includes mulching, growing seedlings, cultivating-2/5,

harvesting 4/5


Transformity: 8.10E+04 (uneducated labor - Odum, 1996)

10 Services, $ per ha * * Estimated cost for machinery operation

$/yr: 2.50E+01 (Vielhauer, 2000 - personnal communication)

Production (kg/ha/yr) All production figures for 5 years production cycle

11 Corn (4,000 kg in each of 2 harvests in 5 years) 1.60E+03(Two crops) (Vielhauer & Sa, 1999; Kato &

Kato, 1999; Vielhauer, 2000 - personal com.)12 Cassava (26,000 kg in each of 2 1.04E+04 (Two crops) (Vielhauer & Sa, 1999; Kato & Kato, 1999;

harvests in 5 years) Vielhauer, 2000 - personal com.)

13 Charcoal 0.00E+00

14 Firewood (34,500 kg in 5 years) 6.90E+03 (Vlek et al, 1999; Vielhauer, 2000 – personal

communication)

15 Timber 0.00E+00

16 Total Yield * as total energy investment for production

Dry weight =

Corn 1.39E+06 13% humidity, 13.6% protein, 7.9% fat, 78,5% carbohydratesCassava 3.12E+06 70% humidity, 9% protein, 1% fat, 90% carbohydrates (assume = potato)

Firewood 5.87E+06 15% humidity, 4.0kcal/g (Brown & Bardi, 2001)

17 Product in Joules * as total energy in product

protein at 24KJ/g, fat at 39KJ/g, carbohydrates 17KJ/g (Brown & Bardi, 2001)

Energy content = 1.81E+11 J






Edited by



Associate Editors

Howard T. Odum



David Tilley



Sergio Ulgiati

University of Siena

Siena, Italy

December 2003



Gainesville, FL


vi



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Chapter 8. Environmental and Economic Aspects of Agro-forestry...

8Environmental and Economic Aspects of Agro-forestry and

Agricultural Systems in Chiapas, Mexico


ABSTRACT

Since traditional cost-benet analysis does not include environmental variables in consideration,

the concept of emergy was used to measure all direct and indirect energy to produce a product. In

emergy analysis, environmental, social and economic variables are included and indexes are calculated for comparison for several systems. In this paper, emergy and nancial ratios are used to determine

the sustainability of agro-forestry and agricultural systems in Chiapas, Mexico. Emergy and economic

analysis are calculated for: 1) 400 hectares in forest extraction, 2) 1 hectare in shaded coffee cultivation,

and 3) 1 hectare for industrialized sugar cane production.

Wood extraction from tropical forests is the system with less dependence on external inputs

(purchased/free=0.41) and with a high protability (net revenue/cost=1.93). Industrialized sugar cane

production is one of the most productive activities in the region (net revenue/cost=1.5). Coffee cultivated

under the shade is a sustainable agroforestry system because it maintains part of the original forest.

However, the use of chemical on coffee production makes coffee economically unattractive. (net revenue/

cost=0.47).

INTRODUCTION

Agro-forestry and agro-industrial systems need to be evaluated using both a emergy and nancial

basis to give a better perspective of sustainability in the long term. To evaluate these systems the investment

and environmental loading ratios are used to determine the environmental and economic aspects of agro-

forestry and agricultural systems in Chiapas, Mexico. The net revenue/cost is used as a nancial indicator

of the protability of the system. Although coffee grown in full sun yields around 3,450 kg per hectare,

the shade coffee is an important function for conservation of forest cover.

Systems Description

Agro-forestry System

The system consists of 400 hectares of highland tropical rain forest located at Felipe Angeles,

Cintalapa, Chiapas, Mexico. The characteristic species found in this tropical region are: Nectandra globosa,

Brosimum alicastum, Hyperbaena mexicana, Persea schiedeana, Guarea chichon. The climate is warm

and humid with abundant rains in summer. The mean annual temperature and precipitation are 24.4oC and

1,833.6 mm, respectively. There is a steep topography with slopes that oscillate from 10 to 45%.

The extraction system consists mainly on the use of wood forest resources of tropical species,

such as: Platymiscium dimorphandrum, Cedrella odorata, Guarea glabra, and Brosimum alicastrum,

among others. The trees to be cut must have a minimum diameter breast height of 45 cm. No more than

35% of standing trees with this diameter can be cut in one cycle. The four hundred hectares were divided

in 12 stands of 33.33 hectares, with a cutting cycle of every 12 years. Based on a previous inventory, it

was determined that with these extraction quotas, the system can be sustainable in the long term (Borja

et al., 2001).



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Shaded Coffee Cultivation System

Thirty percent of the coffee grown in Mexico is cultivated in Chiapas. However, this year, the

protability of coffee production has been low because the international markets are saturated. Selected

for the study was a hectare of fertilized shade coffee located in the municipality of La Concordia, Chiapas,

Mexico. The area and crop is representative of many regions of the state. The farming system that was

chosen for this study is locally know as “especializado” which is a monoculture of Arabic coffea. The

characteristics are: 1) high yield, 2) densities bigger than 1,200 plants per hectare, 3) specialized shade,

4) two to three fertilizations per year, and 5) systematic prunning (Zavala García et al., 2001). This coffee

is cultivated preferably in a warm, humid climate with abundant rains and grows best between 500 and

1,500 meters above sea level. The plants that provide shade for this coffee are: Acacia virginalis, Cajanus

indicus, Paternal inga, Inga edulis, Eugenia jambos, among others. The useful life of a coffee plant varies

between 15 and 25 years depending on the conservation and farming systems. The production average

per hectare of coffee is 563.5 kg.

Sugar Cane Industrialized Cultivation System

Sugar cane is one of the most important agro-industrial systems cultivated in the central regionof Chiapas. Values used in this study correspond to averaged data of 3,686 hectares of sugar cane for

the varieties Mex-57473 and Mex-69290 (Castillejos Núñez et al., 2001). Sugar cane is cultivated in

the municipalities of Venustiano Carranza and Las Rosas, Chiapas, Mexico, at mean elevation of 600

meters above sea level. The climate is warm and humid and has an annual mean temperature of 25.3oC

and annual mean precipitation is 1,218 mm with most rain during the summer. Vegetation is scarce since

previously tropical rain forest has been replaced by savanna vegetation. Dams and irrigation systems have

been built to provide water to the elds during the dry seasons. In 1999, 3,500 m3 of water were supplied

through distribution channels. It is estimated that 50% of distributed water is lost due to deciency and

bad operation of the irrigation system. Sugar cane production has been increased from 50 tons per hectare

up to 130 tons by using improved technologies. The mean annual productions is of 95 tons for hectare.

METHODS

Several different tools of analysis were used to address questions of sustainability of productive

activities. Emergy analysis of the ows of energy, materials and labor were used to evaluate productive

activities. Financial analysis was used to evaluate the economic benets of productive activities.

Information for each system was collected from experts in the region.

Emergy Analysis

The emergy analysis methodology is a top down systems approach and is designed to evaluate

the ows of energy and materials of systems in common units that enable one to compare environmentaland economic aspects of systems (Brown and Murphy, 1994; Odum, 1996). The rst step in each of

the emergy analyses was to construct a system diagram to organize thinking and relationships between

components and pathways of exchange and resource ow. The second step was to construct an emergy

analysis table from the diagram (see Appendix). The emergy ows were aggregated (Figure 1) into

environmental inputs (R and N), purchased feedbacks (M and S), and output products (Y). The nal step

involved calculating several emergy indices (see Figure 1) that related emergy ows of the economy with

those of the environment, and allowed for the prediction of economic viability and carrying capacity.

The investment ratio (IR) is the ratio of emergy of purchased inputs (economic inputs) to emergy

of free inputs (renewable and nonrenewable) derived from local sources. The name is derived from the

fact that it is a ratio of “invested” emergy to resident emergy. The investment ratio is a dimensionlessnumber; the bigger the number the greater the amount of purchased emergy per unit of resident emergy. The

environmental loading ratio (ELR) is a measure of potential impact or “loading” a particular development

activity can have on its environment (Brown et al., 1992). It is the relationship of purchased emergy



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(M and S) plus resident nonrenewable emergy (N) to resident renewable emergy (R). This ratio can be

used as an indicator of the appropriate level of development of the alternatives. Nearly all productive

processes of humanity involve the interaction of nonrenewable resources with renewable sources from the

environment. Low ELRs indicate relatively small “loading” on the ecosystem support base, while high

ELRs reect greater potential impact. The ELR, an index of environmental loading, reects the potential

environmental strain or stress of a development when compared to the same ratio for the region.

Financial Analysis

The economic appraisal suggested by Kiker and Lynne (1995) consists of the following steps:

1) to establish the socioeconomic boundaries associated with the object under study, 2) to identify the

ows of important resources and outputs within and across the boundary, 3) to quantify the ows of

resources and outputs within and across the boundary in multiple units, 4) to identify the benets and

costs associated with the alternative, 5) to quantify the monetary benets and costs, and 6) to compare

benets and costs. The revenue/cost ratio is calculated in monetary terms.

RESULTS AND DISCUSSIONThe wood extraction system used in the tropical rain forest has a purchased to free ratio of 0.41

(Table 1) indicating its minimum dependence on external inputs such as chemicals, machinery or imported

services. Seventy percent of its total emergy comes from the rain (251x1015 sej/yr, Table 2). For long term

sustainability, all the area under forest is required and the wood extraction quota of 28.3 m3 per hectare

per year should not be surpassed (Borja et al., 2001). This system is the most protable of the three

analyzed systems, indicated by the net revenue/cost ratio of 1.93. This means that for each dollar that

local people invest, they make USD1.93 as net prot. The corn shifting cultivation system was even more

protable (net revenue/cost=2.32) than the forest extraction system with less required area for cultivation.

However, the corn cultivation system requires cutting the forest and fallow lands are allowed to regenerate

only for ve years with succession forest. To conserve tropical rain forest results more convenient theforest extraction system; however, at present, it has been observed (Borja et al., 2001; Guillén Trujillo,

1998) that this system is not sustainable for the following reasons: 1) increasing colonization pressures,

R

NM S

Y

$SYSTEM

Figure 1. Summary Diagram Showing the Main Flows of Emergy and Materials



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2) diminishing tropical forests for corn cultivation with chemical and cattle grazing, and 3) surpassing

wood extraction quotas by local people due to cash needed, corruption with wood companies and lack of

law enforcement by local and federal authorities.

On the opposite side, industrialized systems depend mainly on external sources (M and S), such

as materials, machinery, fertilizers, etc; and services brought to the local areas, instead of free renewable

and nonrenewable resources (R and N). The industrialized sugar cane system had a purchased to free

ratio of 16.26 indicating its strong dependence on external inputs, mainly fertilizers (9.8E15 sej/year)

accounting for 34 percent of its total emergy (Table 3). Although at the present it is one of the most

productive activities (net revenue/cost=1.5), the main disadvantage of promoting this system is that

tropical rain forest has to be completely cut.

A system that can be considered in an intermediate point of forest substitution and forest

conservation is coffee planted under shade. This system depends on the partially maintained forest for

its cultivation as opposed to coffee planted in completely open areas. Coffee planted under shade usually

substitutes around 50 percent of the original tropical rain forest, maintaining part of the biodiversity of

native ecosystems. Trees that provide good shade and x nitrogen are planted, maintaining a forest cover.

Organic coffee cultivation is usually proposed by conservation organizations as a system to be managed

in the transition zone between protected areas and agricultural elds. However, the systemanalyzed in this study is a very common system that uses chemicals with a high purchased to free ratio

(9.35, Table 1 and 4), making local people vulnerable to external markets.

Table 1. Emergy and Financial Ratios for Evaluating Productive Activities in Chiapas (2001).

__________________________________________________________________________________

_

Agroforestry Corn Shifting Corn Shaded Industrialized

(400 hectares) Cultivation cultivation coffee sugar cane

(12.5 hectares) with (1 hectare) (1 hectare)

(4) chemicals

(1 hectare)

(5)

__________________________________________________________________________________

_

Emergy ratios:

Purchased/free 0.41 0.74 5.68 9.35 16.26

(investment ratio)

Nonrenewable/renewable 0.03 0.10 5.98 7.74 18.27

Service/free 0.39 0.72 1.58 6.01 2.06

Service/resource 0.39 0.70 0.31 1.39 0.14Developed/environment 0.44 0.87 8.14 19.85 20.89

(environmental loading ratio)

Emergy Use (1E+15 sej) 360.34 22.27 8.70 15.39 28.54

Transformity (1E+05 sej/J) 0.38 3.64 2.38 9.14 0.21

Financial ratios:

Net revenue/cost 1.93 2.32 1.05 0.47 1.50

Total revenue (USD) (1,2) 1,175,169 664 448 1,263 2,970

Total costs (USD) (3) 400,615 200 219 858 1,186

Net revenue (USD) 774,554 464 229 405 1,784

Government subsidy (USD) 0.00 126 50.53 52.63 36.84

__________________________________________________________________________________

_



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Furthermore, labor requirements for coffee shaded cultivation are very high, compared to the

other productive systems, because of 1) shade management, 2) sloping conditions of cultivating plots

(commonly bigger than 45%), 3) coffee plants maintenance, and 4) production activities such as cutting,

washing, drying, etc. The service to free, and the service to resource ratios of 6.01 and 1.39, respectively,indicate the strong contribution of labor accounting for almost sixty percent of the total emergy.

Presently, due to the low price of coffee at the international market, the intensive labor required

for production, and the lack of mechanisms for small farmers to add value to the coffee production, coffee

cultivation using chemicals is not a good economic alternative. This is indicated by a low net revenue/cost

ratio of 0.47 compared to the other productive activities with ratios greater than one. The state government

has been giving subsidies to small farmers to overcome the economic crisis. Unfortunately, one of the

immediate effects of the lack of protability in the shaded coffee cultivation, as it has been observed in

some regions of Chiapas, is that people are switching their coffee plots into other less sustainable uses

of land such as corn cultivation or cattle grazing, impacting dramatically the conservation of tropical

rain forest. Further studies should address the sustainability of organic coffee cultivation under shade to

evaluate its protability.In addition to the three systems (forest extraction, coffee and sugar cane production) analysed in

this work, two other traditional cultivation systems, previously evaluated by Guillén Trujillo (1998), are

included for comparison purposes: 1) corn shifting cultivation in 12.5 hectares (2.5 hectares cultivated

per year with a rotation cycle of 5 years), and 2) a one hectare corn cultivation using chemical fertilizers

(see Table 1).

Overall, the purchased to free ratio and the environmental loading ratio (developed/environment)

increase as systems import resources from external markets. The systems with ratios lower than one are

those that depend mainly on free renewable resources such as corn shifting cultivation and forest extraction

from tropical rain forest.

CONCLUSIONS

Wood extraction from tropical forests is the system with less dependence on external inputs and

with a high protability. However, its long term sustainability is threatened by shrinking tropical rain

forest and wood over-exploitation. The analyzed system (400 hectares) should be maintained within an

extraction quota of 28.3 m3 / hectare/year and assure that it is not surpassed.

Industrialized sugar cane production is one of the most economically productive activities

analysed in this study. However, this system has a strong external dependence and substitutes native

ecosystems.

Coffee cultivated under shade is a sustainable agroforestry system because it maintains part of

the original forest. However, coffee planted using chemicals is not economically attractive (net revenue/cost=0.47). Further studies are required to evaluate organic coffee production under shade.

Finally, one can conclude that industrialized systems (corn, coffee and sugar cane planted with

chemicals) require less area with greater dependence on external resources. On the other hand, systems

with extended areas such as wood extraction from forests and organic coffee planted under shade will

maintain a forest cover. However, these systems are threatened by population pressure. Agro-forestry

systems should be promoted as much as possible as productive alternatives where local conditions are

adequate for their implementation. More comprehensive studies that include other variables, such as water

and soil pollution by chemicals, should be done to gain a better perspective of the systems.

REFERENCES

Borja Texocotitla A.M., F.R. Arroyo and J.R. Ramos Moreno. 2001. Emergy Evaluation of Wood

Extraction in Tropical Rain Forest in Felipe Angeles, Cintalapa, Chiapas. Environmental



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Valuation Short Course. Department of Engineering. Autonomous University of Chiapas.

Mexico

Brown, M.T., P. Green, A. Gonzalez, and J. Venegas. 1992. Emergy Analysis Perspectives, Public

Policy Options, and Development Guidelines for the Coastal Zone of Nayarit,

Mexico. Volume 1 and 2. Report to the Cousteau Society and the Government ofNayarit, Mexico. Center for Wetlands and Water Resources. University of Florida.

USA.

Brown, M.T. and R.C. Murphy. 1994. Emergy Analysis Perspectives on Ecotourism, Carrying

Capacity, and Sustainable Development. Center for Wetlands. University of Florida.

U.S.A.

Castillejos Núñez J., M. de J. Méndez González, A. W. Rosales Valencia, A. Jonapá González and J.

Martínez Pale. 2001. Emergy Evaluation of One Hectare Sugar Cane with Chemicals in

Echpoiná (Module 01), Water District 059 Río Blanco, Venustiano Carranza, Chiapas.

Environmental Valuation Short Course. Department of Engineering, Autonomous

University of Chiapas. Mexico.

Guillén Trujillo, Hugo A. 1998. Sustainability of Ecotourism and Traditional Agricultural Practices inChiapas, Mexico. University of Florida. Dissertation. U.S.A.

Kiker, C.F. and G.D. Lynne. 1995. Wetland Values and Valuing Wetlands. In: C. B. Coultas and Y. P.

Hieh (eds). Intertidal Marshes of Florida’s Gulf Coast, St. Lucy Press, Boca Raton,

FL.

O’Brien, K.L. 1994. Tropical Deforestation and Climate Change in the Selva Lacandona: How Strong

are the Links? Conservation International Chiapas. Mexico.

Odum, H. T. 1996. Environmental Accounting: Emergy and Environmental Decision Making. John

Wiley and Sons, Inc, New York.

Zavala García, J.G., A. Zavala García and H.M. Sansebastían García. 2001. Emergy Evaluation of

One Hectare of Coffee Cultivated under Shade with Chemicals in La Concordia,Chiapas. Environmental Valuation Shorcourse. Department of Engineering,

Autonomous University of Chiapas. Mexico.

APPENDIX

Table 2. Emergy Evaluation of a 33 Hectare Agroforestry System, Mexico (2001)_____________________________________________________________________________

Trans- Solar Emdollar

Note Item Raw Units formity Emergy Value

1E+15(units/yr) (sej/unit) (sej/yr) (US$/yr)*

_____________________________________________________________________________

RENEWABLE RESOURCES:

1 Sunlight 2.62E+16J 1.00E+00 26.22 13,947

2 Rain, chemical 1.63E+13J 1.54E+04 250.97 133,494

NONRENEWABLE RESOURCES:

3 Soil erosion 6.78E+10J 7.37E+04 5.00 2,658

OPERATIONAL INPUTS:

4 Seeds 4.88E+08J 3.64E+05 0.18 95

5 Fert. and Pesticides 0.00E+00g 1.40E+10 0.00 0

6 Tools 7.34E+02USD 1.88E+12 1.38 7347 Oil 1.07E+10J 6.60E+04 0.71 377

8 Gasoline 2.23E+10J 6.60E+04 1.47 784

9 Human labor 2.11E+10J 4.77E+06 100.63 53,529



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YIELDS:

10 Wood extracted 9.51E+12J 3.79E+04 360.34 191,671

______________________________________________________________________________


Table 3. Emergy Evaluation of a Hectare Sugar Cane Production System, Mexico (2001).

___________________________________________________________________________



1E+15

(units/yr) (sej/unit) (sej/yr) (US$/yr)*

___________________________________________________________________________


1 Sunlight 6.56E+13 J 1.00E+00 0.07 35

2 Rain, chemical 3.67E+10 J 1.54E+04 0.57 3013 Irrigated water 4.68E+10 J 2.79E+04 1.30 694


3 Soil erosion 4.75E+09 J 7.37E+04 0.35 186

OPERATIONAL INPUTS:

4 Seeds 3.52E+10 J 3.64E+05 12.80 6,808

5 Fert. and Pesticides 7.00E+05 g 1.40E+10 9.80 5,213

6 Machinery and Tools 4.13E+02 USD 1.88E+12 0.78 413

7 Water Struc. Maint. 1.58E+01 J 1.88E+12 0.03 16

8 Fuels 1.09E+09 J 6.60E+04 0.07 38

9 Human labor 7.15E+08 J 4.77E+06 3.41 1,814

YIELDS:

10 Sugar Cane 1.39E+12 J 2.05E+04 28.54 15,182____________________________________________________________________________


Table 4. Emergy Evaluation of a Hectare Specialized Monoculture Coffee Production System,

Mexico (2001)._____________________________________________________________________________



1E+15

(units/yr) (sej/unit) (sej/yr) (US$/yr)*


1 Sunlight 5.27E+13J 1.00E+00 0.05 28

2 Rain, chemical 4.79E+10J 1.54E+04 0.74 393


3 Soil erosion 1.02E+10 J 7.37E+04 0.75 399

OPERATIONAL INPUTS:

4 Seedling 7.02E+01 USD 1.88E+12 0.13 705 Fert. and Pesticides 3.25E+05 g 1.40E+10 4.56 2,423

6 Machinery and Tools 1.45E+02 USD 1.88E+12 0.27 145



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7 Human labor 1.88E+09 J 4.77E+06 8.95 4,758

YIELDS:

8 Coffee 1.68E+10 J 9.14E+05 15.39 8,188____________________________________________________________________________


Footnotes to Table 1.

1) USD = 9.5 Mexican pesos, 2001

2) Land opportunity cost and family labor are not included in any system.

3) Total revenue and costs does not include government subsidies

4) Corn Shifting cultivation (12.5 hectares) is the system analyzed for Corozal in Guillén Trujillo

(1998).

5) Corn cultivation with chemicals is the TMF-MC (Palenque) system evaluated in Guillén Trujillo

(1998).


The system includes 400 ha of tropical rain forest under wood extraction

Total area in the system= 400 hectares

Rotation period= 12 years

Total area extrated per year = 33.333 hectares

* EMergy/$= 1.88E+12 Mexico sem/$ ratio (sej/ USD,1994)

1 SOLAR ENERGY:

Land Area = 4.00E+06 m^2

Insolation = 1.80E+02 Kcal/cm^2/yr World Ener. Data Sheet, avg.

Mexico. Albedo = 0.13 (% given as decimal) est: trop. rain forest; O’Brien, 1994

Energy(J) = (area)*(avg insolation)*(1-albedo)

= (____m^2)*(____Kcal/cm^2/

y)*(E+04cm^2/m^2)*

(1-0.13)*(4186J/kcal)

= 2.62E+16

2 RAIN, CHEMICAL POTENTIAL ENERGY:


Rain (land) = 1.83 m/yr Data for Santa Maria, Cintalapa, Chiapas

Transp. rate = 0.45 (est. as 45% of total rain)

Energy (land) (J)= (area)(trans)(rainfall)(Gibbs no.)

= (____m^2)*(____m)*(1000kg/m^3)*(4.94E+03J/

kg)

Total energy (J) = 1.63E+13

3 TOPSOIL:

Soil loss = 3.00E+00 tons/ha/yr avg. Area exposed to

erosion = 3.33E+01 hectares

Energy(J) = (soil ero.ton/yr)*(ha.)*(0.03 organic)*(1E6 g/ton)*(5.4 Kcal/g)(4186

J/Kcal)

= 6.78E+10

4 SEEDS

Seeds = 1.00E+00 kg/ha



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Energy(J) = (seeds kg/yr)*(ha)*(1E3g/kg)*(3.5 Kcal/g)*(4186 J/Kcal)

= 4.88E+08

5 FERTILIZERS AND PESTICIDES

Fert. and Pest.= 0.00E+00

6 TOOLS: Note: These costs are for the 33.3 hectares

machetes = 4.00E+00 USD

sawchains = 7.18E+02 USD

axes = 1.20E+01 USD

Total = 7.34E+02 USD

7 OIL

Oil = 1.20E+01 liters/ha/yr

Energy(J) = (lts/ha/yr)*(ha)/(234 lt/barrel)*(6.28E9 j/barrel)

= 1.07E+10

8 GASOLINEGasoline = 2.40E+01 liters/ha/yr

Energy(J) = (lts/ha/yr)*(ha)*(2.79E7j/lts)

= 2.23E+10

9 HUMAN LABOR:

Labor = 1800 days for management of total area (33 hectares)

Total = 1800 days

Energy(J) = (days) (8 hrs/day) (350 kcal/hr) (4186 J/kcal)

= 2.11E+10

11 WOOD EXTRACTION:

Wood = 9.35E+02 m3/yr in 33 has.

Energy(J) = (m3/yr)*(1E+06 cm3/m3)*(10176J/cm3)

= 9.51E+12 j

Transformity = 3.79E+04 sej/j

RATIOS FOR EVALUATING RESOURCES:

Purchased/free (M+S)/(R+N) 0.408

Nonrenewable/renewable (N+M)/R 0.035

Service/free S/(N+R) 0.393

Service/resource S/(R+N+M) 0.387Developed/environmental (N+M+S)/R 0.436

R= Free renewable EMergy of Environmental Inputs

Rain, chemical 2.51E+17 sej/yr



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The system includes one hectare of irrigated sugar cane production system

Total area in the system= 1 hectare

Data averaged for 3,686 hectares available in the district

* EMergy/$ = 1.88E+12 Mexico sem/$ ratio (sej/ USD,1994)

1 SOLAR ENERGY:


Insolation = 1.80E+02 Kcal/cm^2/yr World Ener. Data Sheet, avg. Mexico.

Albedo = 0.13 (% given as decimal) est: trop. rain forest; O’Brien, 1994


= (____m^2)*(____Kcal/cm^2/

y)*(E+04cm^2/m^2)*

(1-0.13)*(4186J/kcal)

= 6.56E+13

2 RAIN, CHEMICAL POTENTIAL ENERGY:Land Area = 1.00E+04 m^2

Rain (land) = 1.65 m/yr Data for year 1995


Energy (land) (J) = (area)(trans)(rainfall)(Gibbs no.)

= (____m^2)*(____m)*(1000kg/m^3)*(4.94E+03J/kg)


3 IRRIGATED WATER

Volume = 9.47E+03 m^3

Energy (J) = (water volume, m3/yr) (1000 kg/m3) (4940 J/kg)

= 4.68E+10

Note: Tr used is equivalent to physical stream energy, Tab. C.3, Odum, 1996.

4 TOPSOIL:

Soil loss = 7.00E+00 tons/ha/yr avg.

Energy(J) = (soil ero.ton/yr)*(ha.)*(0.03 organic)*(1E6 g/ton)*(5.4 Kcal/

g)(4186 J/Kcal)

= 4.75E+09

5 SEEDS

Seeds = 2400 kg/haEnergy(J) = (seeds kg/yr)*(ha)*(1E3g/kg)*(3.5 Kcal/g)*(4186 J/Kcal)

= 3.52E+10

6 FERTILIZERS AND PESTICIDES

Triple 17 = 5.00E+05 one aplication per year g/ha

Urea = 2.00E+05 one aplication per year g/ha

Fert. and Pest = 7.00E+05 g/ha

7 MACHINERY AND TOOLS: Note: These costs are for one hectare

machinery (tractors) = 4.32E+01 US$

machetes = 7.00E+01 US$

pumps = 2.00E+02 US$

shovels = 1.00E+02 US$



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Total = 4.13E+02 US$

8 OPERATIONAL COSTS FOR WATER STRUCTURE MAINTENANCE:

Operational costs = 1.58E+01 USD ($150 mexican pesos 1999; $9.5MEX/USD)

NOTE: These costs include operation, conservation and administration of the water structure for

irrigation.

The government subsidy equivalent to the same amount is not included in this analysis

9 FUELS

Diesel = 3.90E+01 lts/ha/yr

Energy = (lts/ha/yr)*(ha)*(2.79E7 j/lts)

= 1.09E+09

10 HUMAN LABOR:

Labor = 61 days (one hectare)

Total = 61 days


= 7.15E+08

11 YIELD:Sugar cane production = 9.50E+01tons/ha/yr Average of 3,686 hectares of the district

Energy(J) = (Production Tons)*(1E+06 g/ton)*(3.5kcal/g)*(4186 J/kcal)

= 1.39E+12 J/yr






Service/resource S/(R+N+M) 0.136

Developed/environmental (N+M+S)/R 20.888

R= Free renewable EMergy of Environmental Inputs

Water, irrigated 1.30E+15 sej/yr

R= 1.30E+15 sej/yr

N=Free nonrenewable resource Emergy from the local environment

Soil erosion= 3.50E+14 sej/yr

N= 3.50E+14 sej/yr

M=Purchased EMergy of materials brought to the system

Seeds= 1.28E+16 sej/yr

Fert. and Pesticides=9.80E+15 sej/yr

Tools= 7.77E+14 sej/yr

Water structure maint. = 2.97E+13 sej/yrFuels = 7.18E+13 sej/yr

The system includes a hectare of specialized monoculture coffee production system

Total area in the system= 1 hectare

Span life is 15 years; pine forest ecosystem; height between 830-1,300 meters above sea level

Location: Custepequez, La Concordia, Chiapas, Mexico

* EMergy/$ = 1.88E+12 Mexico sem/$ ratio (sej/ USD,1994)

USD = 9.50E+00 Mexican pesos

1 SOLAR ENERGY:


Insolation = 1.80E+02 Kcal/cm^2/yr World Ener. Data Sheet, avg. Mexico.



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Albedo = 0.30(% given as decimal) local data


= (____m^2)*(____Kcal/

cm^2/y)*(E+04cm^2/m^2)*

(1-0.13)*(4186J/kcal)

= 5.27E+13

2 RAIN, CHEMICAL POTENTIAL ENERGY:


Rain (land) = 2.15 m/yr local data


Energy (land) (J) = (area)(trans)(rainfall)(Gibbs no.)

= (____

m^2)*(____m)*(1000kg/m^3)*(4.94E+03J/kg)


3 TOPSOIL:

Soil loss = 1.50E+01 tons/ha/yr avg. 18% soil slope

Energy(J) = (soil ero.ton/yr)*(ha.)*(0.03 organic)*(1E6 g/ton)*(5.4 Kcal/

g)(4186 J/Kcal)

= 1.02E+10

4 SEEDLINGS

Seedlings = 7.02E+01 USD5 FERTILIZERS AND PESTICIDES

Fertilizers = 3.20E+05 g/ha

Pesticides = 5.40E+03 g/ha

Fert. and Pest. = 3.25E+05 g/ha

6 MACHINERY AND TOOLS:

tools = 9.81E+01 USD Note: Tools were estimated as 15% of total labor

per hectare

pumps = 4.74E+01 USDTotal = 1.45E+02 USD

7 HUMAN LABOR:

Labor = 160 days (one hectare)

Total = 160 days


= 1.88E+09

11 YIELD:

Coffee production = 1.15E+00 tons/ha/yr The average in Chiapas is 0.563 tons/ha/yr

Energy(J) = (Production Tons)*(1E+06 g/ton)*(3.5kcal/g)*(4186 J/kcal)



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= 1.68E+10 J/yr






Service/resource S/(R+N+M) 1.387

Developed/environmental (N+M+S)/R 19.855

R = Free renewable EMergy of Environmental Inputs

Rain, chemical 7.38E+14 sej/yr

R = 7.38E+14 sej/yr

N = Free nonrenewable resource Emergy from the local environment

Soil erosion = 7.50E+14 sej/yr

N = 7.50E+14 sej/yr

M = Purchased EMergy of materials brought to the system

Seedlingss = 1.32E+14 sej/yr

Fert. and Pesticides = 4.56E+15 sej/yr

Machinery and Tools= 2.73E+14 sej/yr



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Edited by



Associate Editors

Howard T. Odum



David Tilley



Sergio Ulgiati

University of Siena

Siena, Italy

December 2003



Gainesville, FL


vi



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Chapter 9. Emergy Evaluation of Building Operation in Thailand

9Emergy Evaluation of Building Operation in Thailand

Vorasun Buranakarn

ABSTRACT

Sixty percent of the energy budget in Thailand is consumed by building operation, 40 percent for commercial building and roughly 20 percent for residential. Thailand buildings mostly use electricity

for air-conditioning system. Two kinds of air-conditioning systems were compared: split-type and water

chiller. Transformities range from 4 to 61 E+12 sej/m2 for split-type system and 104 to 232 E+12 sej/

m2 for water chiller system. Yield ratios are closed to 1. Emergy Investment ratios range from 5 to 53

and 157 to 244 while Environmental Loading ratios are 6 to 54 and 173 to 434. Emergy Sustainable

Index numbers are very small fraction about two orders of magnitude. Percent of renewable resources

are very low since only water is used by people in a small amount. The results show that building is one

consumer on the very top of energy hierarchy. Among two groups of mechanical system, there are 2 to

31 E+9 sej/m2-hr and 52 to 109 E+9 sej/m2-hr of input to the people in building.

INTRODUCTION

Buildings consume about 60 percent of the energy budget in Thailand with approximately 40

percent for commercial building and 20 percent for residential. Generally, a building’s expected life

would be between 30 to 50 years. Mechanical systems in buildings have about half the life of the building

structure, needing major repair or to be overhauled.

Most buildings in Thailand use electrical energy for air-conditioning systems. It is believed that

electricity is clean energy and easy to use. Today, more appliances in the Thai market require electricity

as an energy source while some, like the stove and oven, could use lower quality energy in the energy

hierarchy. The energy is used to cool people, usable space, and building material. Cooling load neededfor people is about one-third of the total while the rest is used to cool the building itself (Boonyatikarn,

1999).

The objective of this paper is to nd out how the buildings behave. The results may provide

owner, designers, engineers, architects, and contractors in the construction industry a better understand-

ing of building characteristics as well as impact to nature. Building names and data sources are kept

condential since this is proprietary information.

METHODS

Ten high-rise buildings (8 ofce buildings and 2 condominiums) in Bangkok, Thailand, named as

A-J, were selected according to data availability. Annual Data of each were collected. Operation inputs are

water, fuel, chemical, electricity, operation expenses, and labor. Wastewater is the only signicant output



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from the building system. Services as labor input was evaluated separately from operation expenses.

Standard emergy evaluation procedures were used (Odum 1996). Data were compared to square

meter of building area. The principle ows, sources and processes required for building operation were

illustrated in the diagram (Figure 1). The main ows and storage included in the diagram were calculated

in the emergy tables. Emergy per building area (square meter) reects consumption of building. For

emdollar values, the transactions have been used to convert from Baht to US dollar. Then, the emdollars

per square meter were calculated and analyzed.

Ratios and emergy indices are used as follows:

Emergy per building area (sej/m2)

Energy Yield Ratio: EYR = Y/F

Emergy Investment Ratio: EIR = F/(N+R)

Emergy Sustainability Index: ESI = (Y/F) / ((N+F)/R)

Environmental Loading Ratio: ELR = (N+F)/R

Renewability: R = (R/Y)*100

Emdollar (em$/m2)

Operation cost ($/m2

)Energy consumption (J/m2)

RESULTS

Figure 1. System diagram of building operation.



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The results illustrated two groups of building operation. The rst group, packaged air-conditioning

system, includes buildings A, B, C, D, I and J. The second group, chiller system, includes buildings E,

F, G and H. Emergy per building area in the rst group range between 4 to 61 E+12 sej/m2 while the

second group is 104 to 232 E+12 sej/m2 (Table 1). Other key emergy ratios are also shown in Table 1. A

comparison between these ratios is illustrated in Figure 2.

Table 1. Emergy indices summary of annual building operation in Thailand (1999-2001).____________________________________________________________________________________________

Ofce Building Condominium US

Group 1 Group 2 Group 1 Average

A B C D E F G` H I J

1.13E9

Building hight (Floor), 37-Fl 20-Fl 19-Fl 24-Fl 17-Fl 35-Fl 17-Fl 24-Fl 8-Fl 15-Fl

Conditioned areas (sq.m.), 35500 43066 21576 1868 12875 95000 8000 18494 3520 8000

(Total building area; sq.m.) (35500) (4306)(21576) (1868) (12875)(49356)(12061) (40532) (8800)

(20000)

______________________________________________________________________________________________________

__

Emergy per building area with services 4 9.8 6 61 104 184 218 232 26 35 354

(E+12 sej/sq.m.)

Emergy per building area without 2.6 4.3 4 33 104 184 215 229 23 29 N/A

services (E+12 sej/sq.m.)

Emergy per area per hour 2 5 3 31 52 92 109 116 13

18

(E+9 sej/sq.m.-hr) *

Energy Yield Ratio: 1.12 1.06 1.19 1.05 1.00 1.00 1.01 1.01 1.02 1.04

EYR, Y/F

Emergy Investment Ratio: EIR, 8 17 5 19 244 244 157 97 53 25

F/(N+R)

Emergy Sustainability Index: 0.14 0.06 0.23 0.06 0.004 0.004 0.006 0.005 0.02 0.04

ESI, (Y/F) / [(N+F)/R]

Environmental Loading Ratio: ELR, 9 21 6 23 310 434 173 213 54 28(N+F)/R

Renewability: 9.94 4.54 14.43 4.15 0.32 0.23 0.57 0.47 1.82 3.41

R = (R/Y)*100

Building age (year) 5 3 7 5 7 5 9 9 3 2 N/A

Year of data collected 2000 2001 2001 2000 2001 2000 2001 2001 1999 1999 1992

Emdollar (Em$/sq.m.) 5 11 7 70 118 209 248 263 30 40

Operation & labor expenses 3.38 7.43 5 64.2 0.68 0.36 4.5 3.6 6.7 11.5 N/A

(E+4 $/sq.m.)

Energy consumption: fuel & electricity 2.59 3.89 3 12 353 586 730 779 66.9 79.8 0.22

(E+10 J/sq.m.)

______________________________________________________________________________________________________

__

* Calculation based on 8 working hours a day, 5 days a week, 50 weeks a year

Buildings, like consumers, require energy and materials to operate its systems. Routine checkup

and maintenance schedules are needed to keep the system performing as designed. Mechanical systems

in each building are different. Some ofce buildings have central air-conditioning with chillers to cool

all spaces. Some buildings were designed as individual air-conditioning units as sprit-type, for instance,

and each rental ofce unit pays for energy costs separately. Therefore, energy consumption results of

buildings in this paper are signicantly different.

The energy yield ratio (EYR) is very low (close to 1) since there is a high use of inputs from

economic rather than natural resources. The building system is designed to cool spaces.



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Figure 2. Comparison of emergy indices.

DISCUSSIONEmergy per building area of the second group (central air-conditioning with chillers) are lower

than consumption in the United States, where the average building operation was 354E+12 sej/m2 in

1992 (data calculated from Statistical Abstract, 1996, Table no. 931, p.588, see appendix B). Emergyper building area of construction in the United States is 1.82 E+15 sej/m 2 (Buranakarn, 1998), or about

ve times less than operation.

The emergy investment ratio (EIR) shows different types of building system. Most buildings

use electricity as a major energy input. Therefore, renewable and non-renewable inputs are a small por-

tion to the total energy used.

The environmental loading ratio (ELR) is high since only water is used in the system. Feed-

back is a variable factor to environmental loading ratio. Electricity is various according to mechanical

system while operation and labor are quite constant. Since environmental loading ratio is very high and

energy yield ratio closes to 1, the emergy sustainability index (ESI) is very low. A building is in the top

of system hierarchy like humans. Since renewable input in building operation is very low compared to

yield, renewability (%R) is a very small number.Emdollar values (Em$/m2) represent the economic value to the evaluated system. More feedback

in building operation causes higher emdollar per square meter. The rst building group (packaged air-

Emergy indices

0

10

20

30

4050

60

70

A C B D I J

Buildings in group 1 (Package air-conditioning system)

Ew (E12 sej/m2)

EIR

ESI

ELR

conditioned area(E+4 m2)

Emergy indices

0

50

100

150

200

250

300

350

400

450

500

E F G H

Buildings in Group 2 (Chiller system)

Ew (E12 sej/m2)

EIR

ESI

ELR

conditioned area(E+4 m2)



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conditioning system) has less than a hundred emdollar per square meter while the second group (central

air-conditioning with chillers) ranges between 200 to 300 emdollar per square meter. Operation and labor

expenses ($/m2) are shown as management costs. Generally, it is less than ten thousand dollars per square

meter per year.

The second group (central air-conditioning with chillers) of sample buildings is a common design

in Thailand, especially high-rise buildings. Since electricity is a major ow, alternative energy sources,

such as solar collector, wind or biogas, used to produce electricity may change the emergy budget (sej).

The research results also support energy conservation policy in Thailand to reduce electricity consump-

tion since electricity mostly produced by imported lignite and gasoline.

For evaluating output of services in ofce building, emergy per square meter should be included

as investment cost for each business. Emergy per building area of each building could be recalculated

as emergy ow per area per hour (see Table 1) or per personal space. Those numbers might account for

some hidden cost in the buildings.

Energy consumption as fossil fuel (J/m2) of each building is required by building code. In

most cases, energy consumption of buildings are only applied after designed. After constructed, energy

monitoring is not required by regulation. The emergy evaluation method using in this paper would be

one alternative to evaluate environmental cost or building design of building industry as a current systemsustainability trend.

REFERENCEBoonyatikarn, Soontorn. 1999. Energy conservation Design Technique for A Better Life Quality. (in Thai)

Chulalongkorn University Press.

Buenl, Andres A. 2000. Sustainaable Use of Potable Water in Florida: an Emergy Analysis of Water

Supply and Treatment Alternatives. Proceedings of the First Biennial Emergy Analysis Research

Conference, Emergy Synthesis: Theory and Applications of The Emergy Methodology. The Center

for Environmental Policy, Department of Environmental Engineering Sciences, University of

Florida, Gainesville, FL, USA. pp.107-118.Buranakarn, Vorasun. 2000. Energy and Environmental inputs in Building Operation

(Condominium Case Study, Bangkok, Thailand). Sarasart Satapad Journal, Faculty of Architecture,

Chulalongkorn University, Thailand.

Buranakarn, Vorasun. 1998. Evaluation of Recycling and Reuse of Building Materials Using The Emergy

Analysis Method. Ph.D. Dissertation, University of Florida, USA.

Crowther, P. 1999. Design for Disassembly to Recover Embodied Energy. Plea’99 Sustaining the Future:

Energy-Ecology-Architecture. Proceeding of the Plea’99 Conference, Brisbane, Australia,

September 22-24, 1999. Vol. 1. pp.95-100.

Hansen, Klaus, Hanne Krogh, and Jorn Dinesen. Environmental Assessment of Building Projects Based

Upon A Life Cycle Approach. Sustainable Construction: Proceeding of the First International

Conference of CIB TG 16 , November 6-9, 1994, Tampa, Florida, USA. Center for Constructionand Environment, M.E. Rinker Sr. School of Building Construction, College of Architecture,

University of Florida. 1994. pp. 203-212.

Odum, H.T. May 2000. Handbook of Emergy Evaluation: A Compendium of Data for Emergy Computation

Issued in A Series of Folios. Folio #2: Emergy of Global Processes. Center for Environmental

Policy, Environmental Engineering Sciences, University of Florida, Gainesville, USA.

Odum, H.T., Mark T. Brown and Sherry Brandt-Williams. May 2000. Handbook of Emergy Evaluation: A

Compendium of Data for Emergy Computation Issued in A Series of Folios. Folio #1: Introduction

and Global Budget. Center for Environmental Policy, Environmental Engineering Sciences,

University of Florida, Gainesville, USA.

Odum, Howard T. 1996. Environmental Accounting: Emergy and Environmental Decision Making. JohnWiley & Sons, Inc.

Statistical Abstract of the United States 1996. U.S. Census Bureau. (Data accessed November 26, 1997,

on the World Wide Web at http://www.census.gov/prod/2/gen/96statab/96statab.html)



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APPENDIX

Appendix A. Calculation table of building A-J.

Table A. Ofce building 37 Floors (35500 m2) in 2000 *

Note Item Units Input Emergy Emergy Em$** (1.00E+04) per unit (1 E 14sej)

(sej/unit)

1 Water J 17784000.00 8.06E+04 143 16297

2 Fuel J 1424081.16 1.11E+05 16 1794

3 Electricity J 7776000.00 2.92E+05 227 25830

4 Operation expenses $ 6.00 8.80E+11 528 60000

5 Labor/guard $ 6.00 8.80E+11 528 60000

6 Building area (ofce) with services m2 3.55 4.06E+12 1443 163921

without services m2 3.55 2.58E+12 915 103921

7 Waste water J 14820000.00 9.73E+05 1443 163921

* Date are collected for a year of operation in 2000 (construction completed in 1995). 37 oors ofce

building, Bangkok, Thailand.

** Solar emergy in column 3 divided by8.80E+11sej/$ for US in 2000 (from Buenl, 2000)

Footnotes:

1 Water (3000 m3/mo)(12 mo/yr)(1 ton/m3)(1000000 g/ton)(4.94 J/g)

= 1.7784E+11 JTransformity = 48000 Sej/J (Odum, 1996, p.120)

= 80640 Sej/J corrected by factor of 1.68

2 Fuel (30 liters/mo)(12 mo/yr)(1/1000 m3/l)(6.29 bbl/m3)(6289000000 J/bbl)

(diesel) = 14240811600 J

Transformity = 66000 Sej/J (Odum, 1996, p. 308)


3 Electricity (1800 kWh/mo)(12 mo/yr)(3600000 J/kWh)

= 77760000000 JTransformity = 174000 Sej/J (Odum, 1996, p. 305)


4 Operation expenses (200000 baht/mo)(12 mo/yr2543)

= 2400000 Baht 40 baht/US$

= 60000.00 $

Transformity = 8.80E+11 Sej/$ for US in 2000

5 Labor/guard management fee 200000 bahts/mo (44 employees)

12 guards, 10 technicals, 1 engineers, 1 manager, 19 ofce employees, 4 building staffs(200000 baht/mo)(12 mo/yr2543)

= 2400000 Baht 40 baht/US$



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= 60000.00 $


6 Building area (ofce) 35500 m2 total

(product) (35500 m2 total)

= 35500 m2

7 Waste water (2500 m3/mo)(12 mo/yr)(1 ton/m3)(1000000 g/ton)(4.94 J/g)

= 1.482E+11 J

Table B. Ofce building 20 Floors (42266 m2) in 2001 *

Note Item Unit Input Emergy Emergy Em$**

(1.00E+04) per unit (1 E 14sej)

(sej/unit)

1 Water J 23712000.00 8.06E+04 191 217292 Fuel J 3797549.76 1.11E+05 42 4785

3 Electricity J 12960000.00 2.92E+05 379 43051


5 Additional construction $ 8.89 8.80E+11 782 88889

6 Labor/guard $ 26.67 8.80E+11 2347 266667

7 Building area (ofce)

with services m2 4.31 9.78E+12 4210 478453


8 Waste water J 20748000.00 2.03E+06 4210 478453



** Solar emergy in column 3 divided by 8.80E+11sej/$ for US in 2001 ( from Buenl, 2000)

Footnotes:


= 2.3712E+11 J


(diesel) = 37975497600 J


= 1.296E+11 J


= 2400000 Baht 45 baht/US$

= 53333.33 $


5 Additional construction 800 m2 additional conference room (4 million baht) completed in March

2001

(4000000 Baht/yr 2544)

= 4000000 Baht 45 baht/US$



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= 88888.89 $



(1000000 baht/mo)(12 mo/yr2544)

= 12000000 Baht 45 baht/US$

= 266666.67 $

7 Building area (ofce) 42266 m2 total with 800 m2 additional (conference room)

(product) (42266 m2 total)+(800 m2 addional)

= 43066 m2


= 2.0748E+11 J

Table C. Ofce building 19 Floors (21576 m2) in 2001 *


(1.00E+04) per unit (1 E 14sej)

(sej/unit)

1 Water J 23712000.00 8.06E+04 191 21729

2 Fuel J 1898774.88 1.11E+05 21 2392

3 Electricity J 4752000.00 2.92E+05 139 15785

4 Others $ 0.25 8.80E+11 22 2500


6 Repair $ 0.16 8.80E+11 14 1556

7 Labor/guard $ 5.33 8.80E+11 469 53333


with services m2 2.16 6.14E+12 1326 150629


9 Waste water J 20748000.00 6.39E+05 1326 150629



** Solar emergy in column 3 divided by8.80E+11 sej/$ for US in 2001 (from Buenl, 2000)

Footnotes:


= 2.3712E+11 J


(diesel) = 18987748800 J


= 47520000000 J

4 Others Sealance (rubber) 100000 baht (contract in 2000 (2543))

= 100000 Baht 40 baht/US$

= 2500.00 $



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= 2400000 Baht 45 baht/US$

= 53333.33 $

6 Repair (70000 Baht/yr 2543)

(general) = 70000 Baht 45 baht/US$

= 1555.56 $

7 Labor/guard management fee 200000 bahts/mo (26 employees) 6 technicals, 14 ofce

employees, 6 building staffs

(200000 baht/mo)(12 mo/yr2544)

= 2400000 Baht 45 baht/US$

= 53333.33 $

8 Building area (ofce) 21576 m2 total(product) (21576 m2 total)

= 21576 m2


= 2.0748E+11 J

Table D. Ofce building 24 Floors (1868 m2) in 2000 *


(1.00E+04) per unit (1 E 14sej)

(sej/unit)

1 Water J 5928000.00 8.06E+04 48 5432

2 Fuel J 949387.44 1.11E+05 11 1196

3 Electricity J 1296000.00 2.92E+05 38 4305


5 Labor/guard $ 6.00 8.80E+11 528 60000


with service m2 0.19 6.17E+13 1152 130934


7 Waste water J 5928000.00 1.94E+06 1152 130934



** Solar emergy in column 3 divided by 8.80E+11sej/$ for US in 2000 (from Buenl, 2000)

Footnotes:


= 59280000000 J


(diesel) = 9493874400 J




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= 12960000000 J

4 Operation expenses insurance, pest control, tel., lift maintainance, cleaning fee, garbage

fee, etc.

(200000 baht/mo)(12 mo/yr2543)

= 2400000 Baht 40 baht/US$

= 60000.00 $



4 guards, 5 technicals, 1 manager, 5 ofce employees, 2 building staffs

(200000 baht/mo)(12 mo/yr2543)

= 2400000 Baht 40 baht/US$

= 60000.00 $

6 Building area (ofce) 1868 m2 total(product) (1868 m2 total)

= 1868 m2


= 59280000000 J

Table E. Ofce building 17 Floors (12875 m2) in 2001 *


(1.00E+04) per unit (1 E 14sej)

(sej/unit)

1 Water J 5335200.00 8.06E+04 43 4889

2 Fuel J 1044326.18 1.11E+05 12 1316

3 Electricity J 453600000.00 2.92E+05 13260 1506777


5 Labor/guard $ 0.43 8.80E+11 38 4267


with services m2 1.29 1.04E+14 13392 1521782

without services m2 1.29 1.04E+14 13354 15175157 Waste water J 5335200.00 2.51E+07 13392 1521782



** Solar emergy in column 3 divided by8.80E+11 sej/$ for US in 2001 (using 2000 from Buenl,

2000)

Footnotes:


= 53352000000 J


(diesel) = 10443261840 J



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= 4.536E+12 J



= 4533.33 $



(16000 baht/mo)(12 mo/yr2544)


= 4266.67 $

6 Building area (ofce) 12875 m2 total


= 12875 m2


= 53352000000 J

Table F. Ofce building 35 stories (95000 m2) in 2000 *


(1.00E+04) per unit (1 E 14sej)

(sej/unit)

1 Water J 49737402.00 8.06E+04 401 45578

2 Fuel J 28006929.48 1.11E+05 311 35289

3 Fertilizer g 913.10 6.38E+09 583 66241

4 Chemical g 1793.00 6.38E+08 114 13007

5 Concrete g 28800.00 2.42E+09 6967 791738

6 Paint g 1277.00 2.55E+10 3261 370562

7 Others J 300.00 3.53E+04 0.0011 0.12

8 Electricity J 5537519856.00 2.92E+05 161873 18394634

9 Electrical components $ 2.75 1.80E+12 494 56175


11 Repair $ 1.24 8.80E+11 109 1240012 Labor/guard $ 2.57 8.80E+11 226 25660


with services m2 9.49 1.84E+14 174411 19819378


14 Waste water J 33553962.00 5.20E+07 174411 19819378



** Solar emergy in column 3 divided by 8.80E+11 sej/$ for US in 2000 (Buenl, 2000)

Footnotes:

1 Water Average toilets 4490.25 m3/mo, A/C 3900 m3/mo

(8390.25 m3/mo)(12 mo/yr)(1 ton/m3)(1000000 g/ton)(4.94 J/g)



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= 4.97374E+11 J

2 Fuel running generatorand re pump weekly

(diesel) (590 liters/mo)(12 mo/yr)(1/1000 m3/l)(6.29 bbl/m3)(6289000000 J/bbl)

= 2.80069E+11 J

3 Fertilizer ammonium phosphate 18-46-0 = 3656 kg/yr, ammonia (urea) 46-0-0 = 5475 kg/yr

(9131 kg/yr)(1000 g/kg)

= 9131000 g

Transformity = 3.80E+09 Sej/g (Odum, 1996, p.310)

= 6.38E+09 Sej/g corrected by factor of 1.68

4 Chemical ph paper 10 kg/yr, softener 13000 kg/yr, anti-rust & anti-bateria 2600

kg/yr, cleaning agent 2320 kg/yr

(17930 kg/yr)(1000 g/kg)

= 17930000 gTransformity = 3.80E+08 Sej/g (Brown et al., 1992, Table A-1)


5 Concrete 2400 m2 50000 baht (assume 0.05 m. thick)

(2400 m2)(0.05 m.)(2400 kg/m3)(1000 g/kg)

= 288000000 g

Transformity = 1.44E+09 Sej/g (Buranakarn, 1998, Table 3-2)


6 Paint 12770 m2 1090000 baht (10 m2/gallon, 10 kg/gal)

(12770 kg/yr)(1000 g/kg)

= 12770000 g

Transformity = 1.52E+10 Sej/g (Buranakarn, 1998, Table C-12)


7 Others Sealance (rubber) 2000 m2 700000 baht

assume 10 m2/gal, 15 kg/gal

((2000 m2)/(10m2/gal))(15 kg/gal)(1000 g/kg)

= 3000000 g

(3000000 g)(4.94 J/g)= 14820000 J

Transformity = 2.10E+04 Sej/J (Odum et al., 1983, Table 3.1, p.40-45)

= 3.53E+04 Sej/J corrected by factor of 1.68

8 Electricity (1281833.3 kWh/mo)(12 mo/yr)(3600000 J/kWh)

= 5.53752E+13 J

9 Electrical components Transformer repairation 1100000 baht

= 1100000 Baht 40 baht/US$

= 27500.00 $

Transformity = 1.07E+12 Sej/$ for US in 2000 (Projected from

Odum, 1996, Table D.1, p. 313-315)

= 1.7976E+12 Sej/$ corrected by factor of 1.68



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10 Operation expenses insurance, pest control, tel., lift maintenance, cleaning fee, garbage

fee, etc.

(323754.62 baht/yr2542)

= 323754.62 Baht 40 baht/US$

= 8093.87 $

= 1.07E+12


11 Repair (496000 Baht/yr 2543)

(machine) = 496000 Baht 40 baht/US$

= 12400.00 $

12 Labor/guard management fee 1026400 bahts/mo (96 employees)42 guards, 15

technicals, 2 engineers, 3 part-time, 1 manager, 22 clean, 4 secretary,

1 PR, 2 policemen, 4 others

(1026400 baht/yr2543)

= 1026400 Baht 40 baht/US$

= 25660.00 $

13 Building area (ofce) 94908 m2 total (49356 m2 rentable area)


= 94908 m2

14 Waste water Average 5660.25 m3/mo

(5660.25 m3/mo)(12 mo/yr)(1 ton/m3)(1000000 g/ton)(4.94 J/g)

= 3.3554E+11 J

Table G. Ofce building 17 Floors (12061 m2) in 2001 *


(1.00E+04) per unit (1 E 14sej)

(sej/unit)

1 Water J 12448800.00 8.06E+04 100 11408

2 Fuel J 949387.44 1.11E+05 11 1196

3 Electricity J 583200000.00 2.92E+05 17048 19372844 Operation expenses $ 0.40 8.80E+11 35 4000

5 Labor/guard $ 3.20 8.80E+11 282 32000


with services m2 0.80 2.18E+14 17476 1985888


7 Waste water J 12448800.00 1.40E+07 17476 1985888



** Solar emergy in column 3 divided by 8.80E+11 sej/$ for US in 2001 ( from Buenl, 2000)



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Footnotes:


= 1.24488E+11 J


(diesel) = 9493874400 J


= 5.832E+12 J



= 4000.00 $



(120000 baht/mo)(12 mo/yr2544)= 1440000 Baht 45 baht/US$

= 32000.00 $

6 Building area (ofce) 12061 m2 total (8000 m2 operation area)

(product) (8000 m2 operation area)

= 8000 m2


= 1.24488E+11 J

Table H. Ofce building 24 Floors (40532 m2) in 2001 *


(1.00E+04) per unit (1 E 14sej)

(sej/unit)

1 Water J 24897600.00 8.06E+04 201 22815

2 Fuel J 1424081.16 1.11E+05 16 1794

3 Electricity J 1439177760.00 2.92E+05 42070 4780687


5 Labor/guard $ 5.33 8.80E+11 469 53333


with services m2 1.85 2.32E+14 42873 4871963


7 Waste water J 24897600.00 1.72E+07 42873 4871963



** Solar emergy in column 3 divided by 8.80E+11 sej/$ for US in 2001 (from Buenl, 2000)

Footnotes:




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= 2.48976E+11 J


= 14240811600 J


= 1.43918E+13 J

4 Operation expenses insurance, pest control, tel., lift maintenance, cleaning fee, garbage fee, etc.

(50000 baht/mo)(12 mo/yr2544)


= 13333.33 $



(200000 baht/mo)(12 mo/yr2544)

= 2400000 Baht 45 baht/US$ = 53333.33 $

6 Building area (ofce) 40532 m2 total (18494 m2 operational area)


= 18494 m2


= 2.49E+11 J

Table I. Condominium 8 Floors (8800 m2) in 1999 *


(1.00E+04) per unit (1 E 14sej)

(sej/unit)

1 Water J 2092584.00 8.06E+04 17 1918

2 Fuel J 23734.69 1.11E+05 0.26 30

3 Chemical g 16.00 6.38E+08 1.02 116

4 Electricity J 23518080.00 2.92E+05 687 78123

5 Electrical components g 0.22 1.13E+10 0.24 28

6 Operation expenses $ 0.85 8.80E+11 75 85207 Repair & Renovation $ 0.17 8.80E+11 15 1676

8 Labor/guard $ 1.51 8.80E+11 133 15080


with services m2 0.35 2.64E+13 928 105490


* Date are collected for 3.5 years of operation(since construction completed). This evaluation uses only

in 1999.

** Solar emergy in column 3 divided by 8.80E+11 sej/$ for US in 1999 (using 2000 from Buenl,

2000)

Footnotes:




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= 20925840000 J

2 Fuel (0.5 liters/mo)(12 mo/yr)(1/1000 m3/l)(6.29 bbl/m3)(6289000000 J/bbl)

(Benzine) = 237346860 J

Transformity = 66000 Sej/J (Odum, 1996, p. 308)

= 110880 Sej/J corrected by factor of 1.68 (Odum et al., 2000)

3 Chemical (160 kg/yr)(1000 g/kg)

= 160000 g

Transformity = 3.80E+08 Sej/g (Brown et al., 1992, Table A-1)

= 638400000 Sej/g corrected by factor of 1.68 (Odum et al., 2000)

4 Electricity monthly max. 6045 kwh, min 4843 kwh =>average 5444 kwh/mo

(5444 kWh/mo)(12 mo/yr)(3600000 J/kWh)

= 2.35181E+11 J

5 Electrical components (6 lightbulbs/mo)(122 mo)(30 g/unit)

= 2160 g

Transformity = 6700000000 Sej/g using machinery (Odum et al, 1987, Table 1, p.

4)

= 11256000000 Sej/g corrected by factor of 1.68 (Odum et al., 2000)

6 Operation expenses insurance, pest control, tel., lift maintenance, cleaning fee, garbage fee, etc.

= 323754.62 Baht/yr 2542 38 baht/US$

= 8519.86 $


7 Repair & Renovation = 63700 Baht/yr 2542 38 baht/US$

= 1676.32 $

8 Labor/guard management fee 573050 baht, security fee 214214 baht

= 573050 Baht/yr 2542 38 baht/US$

= 15080.26 $

9 Common area 40 % of total oor area is common area

(product) (8800 m2 total)(0.4)

= 3520 m2



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Table J. Condominium 15 Floors (20000 m2) in 1999 *


(1.00E+04) per unit (1 E 14sej)

(sej/unit)

1 Water J 11856000.00 8.06E+04 96 10864

2 Fuel J 949387.44 1.11E+05 11 1196

3 Chemical g 59.00 6.38E+08 4 428

4 Electricity J 62912160.00 2.92E+05 1839 208983

5 Electrical components g 1.33 1.13E+10 1.50 170


7 Repair & Renovation $ 0.50 8.80E+11 44 4987

8 Labor/guard $ 5.79 8.80E+11 510 57916


with services m2 0.80 3.51E+13 2807 318929 without services m2 0.80 2.87E+13 2297 261013

* Date are collected for 2 years of operation(since construction completed). This evaluation uses only

in 1999.

** Solar emergy in column 3 divided by 8.80E+11 sej/$ for US in 1999 ( from Buenl,

2000)

Footnotes:


= 1.1856E+11 J


(diesel) = 9493874400 J

3 Chemical (590 kg/yr)(1000 g/kg)

= 590000 g

4 Electricity monthly max. 15526 kwh, min 13600 kwh =>average 14563 kwh/mo

(14563 kWh/mo)(12 mo/yr)(3600000 J/kWh)

= 6.29122E+11 J

5 Electrical components (37 lightbulbs/mo)(122 mo)(30 g/unit)

= 13320 g

6 Operation expenses insurance, pest control, tel., lift maintenance, cleaning fee, garbage

fee, etc.

= 1306600 Baht/yr 2542 38 baht/US$

= 34384.21 $


7 Repair & Renovation = 189500 Baht/yr 2542 38 baht/US$ = 4986.84 $



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8 Labor/guard management fee 1540800 baht, security fee 660000 baht

= 2200800 Baht/yr 2542 38 baht/US$

= 57915.79 $

9 Common area 40 % of total oor area is common area

(product) (20000 m2 total)(0.4)

= 8000 m2

Appendix B. Calculation table of average building operation in US (1992).

Data from Statistical Abstract of the United States 1996.

Total area of this building is 1.09E+4 sq.ft. or 9.81E+02 sq.m.

1 Fuel For US Commercial building in 1992 (Statistical Abstract, 1996, Table no. 931, p.588)

(1,247 trillion BTUs)/(12,399 million sq.ft. total building area in US)

[(1,247E+12 BTUs)(1054 J/Btu)]/(12,399E+6 sq.ft.)= 1.06E+8 J/sq.ft.

= 1.18E+09 J/sq.m. (11.11 sq.ft/sq.m.)

Transformity 66000 Sej/J (Odum, 1996, p. 308)

110880 Sej/J corrected by factor of 1.68 (Odum et al., 2000)

= 1.31E+14 Sej/sq.m.

2 Electricity For US Commercial building in 1992 (Statistical Abstract, 1996, Table no. 931, p.588)

(704 trillion BTUs)/(12,399 million sq.ft. total building area is US)

[(704E+12 BTUs)(1054 J/Btu)]/(12,399E+6 sq.ft.)= 5.98E+7 J/sq.ft.

= 6.64E+08 J/sq.m. (11.11 sq.ft/sq.m.)


292320 Sej/J corrected by factor of 1.68 (Odum et al, 2000)

= 1.94E+14 Sej/sq.m.

3 Natural gas For US Commercial building in 1992 (Statistical Abstract, 1996, Table no. 931,

p.588)

(388 trillion BTUs)/(12,399 million sq.ft. total building area in US)

[(388E+12 BTUs)(1054 J/Btu)]/(12,399E+6 sq.ft.)= 3.298E+7 J/sq.ft.

= 3.67E+08 J/sq.m. (11.11 sq.ft/sq.m.)


80640 Sej/J corrected by factor of 1.68 (Odum et al., 2000)

= 2.96E+13 Sej/sq.m.

36.85 %Fuel

Total 3.54E+14 Sej/sq.m. 54.81 %Electricity

1.13E+9 Sq.m. 8.34 %Natural gas






Edited by



Associate Editors

Howard T. Odum



David Tilley



Sergio Ulgiati

University of Siena

Siena, Italy

December 2003



Gainesville, FL


vi



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Chapter 10. Emergy and Life-Cycle Assessment of Steel Production...

10Emergy and Life-Cycle Assessment of

Steel Production in Europe

Silvia Bargigli and Sergio Ulgiati

ABSTRACT

The object of this study is the industrial production of steel as building and structural material, in

three cases: primary steel (100% from iron ores), secondary steel (100% from steel scrap recycling) anda weighted mix of the two. The analysis is carried out by means of an integrated approach, using mass,

energy, exergy and emergy accounting, and in accordance with the environmental quality requirements

issued by the International Standardization Ofce (norms EN ISO 14041/97, EN ISO 14042/98 and

updates). The “added value” of the integrated approach is discussed in the paper.

A set of efciency and environmental sustainability indicators for each kind of steel is calculated

(material intensities, oil equivalent, exergy efciency, specic emergy and transformity).

Specic emergy (seJ/g) is used as an indicator of donor-side “quality”, dened as the environmental

support needed to produce one unit of mass of the nished product.

The conversion from specic emergy (seJ/g) to transformity (seJ/J) is performed by dividing by

the specic exergy of each product ow (Odum 1996, p. 302; Szargut et al., 1988, p. 7). This translates

into values of the transformities that are dependent on both donor-side and user-side quality factors. New

relationships were found and are discussed in this paper.

INTRODUCTION

This paper presents a multi-criteria evaluation of the steel production process from ore mining

to rened steel. Due to the relevance of steel in modern society, steel-making was extensively studiedby several investigators (Andersen et al. 2001, Macedo Costa et al. 2001, Worrell et al. 2001, Price etal. 2002, Tapani Makkonen et al. 2002, Ozawa et al. 2002). However, previous investigations alwaysfocused on one aspect of the evaluation (economic, energetic, material, etc), and therefore several goodbut somehow incomplete results are available and performance indicators are not always consistent andcomparable. The goal of this paper is to offer a multi-criteria multi-scale evaluation. This was obtainedby analyzing the process according to different complementary approaches, applied simultaneously atappropriate space-time scales and integrated for synergic results.

The subject of the analysis is steel for building purposes, which is produced world wide in hugequantities, but is of lower quality (i.e. different physico-chemical properties compared to the stainlesssteel or other special kinds of steel alloys). Generally, steel obtained by scrap recycling is called secondarysteel, while steel produced starting from iron ores is called primary steel.

In this work, the production of primary steel, secondary steel and a weighted mix of the two wasconsidered and compared. Moreover, in the process of evaluating the nal product a signicant amount

of step-by-step information was found regarding the intermediate products. In order to maintain the dataas homogeneous as possible, both spatially (European standards) and temporally (mid-nineties) most ofthem are taken from the Wuppertal Institute database (Merten T. et al., 1995; Manstein C., 1996; StillerH., 1999) and are carefully integrated by means of other literature data.



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THE INTEGRATED APPROACH

The analysis is carried out in accordance to the environmental quality requirements issued by theInternational Standardization Ofce (norms EN ISO 14041/97, EN ISO 14042/98 and updates).

Four different methods are applied in this paper accounting for ows of mass, energy, exergy and

emergy within an integrated framework (Figure 1), as suggested by Ulgiati (2000). A set of efciencyand environmental sustainability indicators for each kind of steel is calculated.In short, the evaluation includes the following steps :

• A preliminary Life-Cycle Assessment, as dened by the European Committee for Standardization(CEN, 1997; CEN, 1998) was performed. The evaluation required information and assumptionson all the sub-processes involved, directly and indirectly, in making the nal product, from oremining to rened steel including some recycling of scraps.

• An inventory of mass ows directly and indirectly involved in the production of the nal productand the intermediate products was performed as a preliminary requirement of all upstream and

downstream evaluations. A properly assessed balance between mass input and output safeguardsthe LCA from oversights and missing emission ows (Ayres, 1995). It also allows for thecalculation of intensive and extensive mass-based indicators (Total Material Requirement, TMR,or “Ökologische Rucksack” and Material Intensity Factors, MI, according to Hinterberger andStiller, Wuppertal Institute, 1998). Indicators of material requirement at local as well as largerscales integrate the well-known indicators of material emissions as measures of environmentaldisturbance (see below). These indicators cumulatively account for the overall material inputwhich humans use, move or take away while generating products and services. Consequently,they can be used as a direct measure of the exploitation of natural resources (soil excavation,water withdrawal, biotic material degradation, etc.) and an indirect measure of environmental

impact (ecosystem stress, alterations of local climate, loss of biodiversity). Mass indicators alsohighlight those production phases that are characterized by the most intensive use of materialsand may therefore suggest improvements to avoid misuse. In order to increase the transparency ofthe results, these were presented both including and excluding the so-called ecological backpackof electricity.

• Intensive indicators of airborne emissions (CO2, NO

x, SO

2, etc) were also calculated for each

step of the investigated processes.

• A conventional energy evaluation of input and output ows (according to IFIAS, 1974;Herendeen, 1998) was performed at all steps of the production chain. Figures of the energy cost

of the intermediate products were generated and compared. Input ows were multiplied by theiroil equivalent conversion coefcients available in scientic literature (Boustead and Hancock,1979) to yield the energy value assigned to each ow as well as the total energy input to thesub-process. The energy cost of the product is then calculated as the ratio of the total energy ininput (expressed as crude oil equivalent energy) to the mass of the output. The energy efciency(input energy/mass of product) of the whole steel production chain is assessed, together withpartial efciencies for the main steps of the process (iron ores, pellets, sinter, pig iron, raw steeletc..).

• An exergy evaluation of input and output ows was performed in order to better highlight thethermodynamic efciency of the selected production processes (according to Szargut et al.,1988; Szargut, 1998), as well as to provide an estimate of the potential harm to the environmentassociated with their outputs (Ayres, 1998). As input exergy ows are then multiplied by asuitable transformity value and translated into emergy ows, the exergy accounting step is also



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F i g u r e

1 .

D i a g r a m o

f t h e I n t e g r a t i o n o f S e v e r a l A p p r o a c h e s a t D i f f e r e n t S c a l e s o f A p p l i c a t i o n .



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the basis for the following emergy evaluation.

• A measure of the environmental support required by the investigated processes (sometimes calledecological footprint) was performed by means of the “emergy accounting” approach (Odum,1988, 1996). The way emergy is calculated (tracing back to the solar exergy source) expands thescale of the assessment in space and time. It generates an evaluation of the relationship betweenthe system and the environment, focusing on the supply of resources and free ecosystem servicesto the investigated process (dilution of pollutants, cooling, etc).

THE PROCESS OF STEEL MAKING

This work focuses on the production of steel for building purposes, starting from ore mining tosteel nishing. Waste material recycling or disposal as well as emission abatement are not dealt with in thispaper, since the related evaluation is still in progress. However, Brown and Buranakarn (2000) performedan interesting emergy evaluation of the recycling of building materials, which also includes steel products.Figures 2 and 3 show the Emergy System Diagram and the production steps that were analyzed.

Most iron ores are extracted by surface mining worldwide. In evaluating the mining phase boththe extraction of direct shipping iron ores (with an iron content of more than 50% w/w) and lower gradeiron ores (with an iron content of less than 30% w/w) were considered. Nevertheless, as the amount ofoverburden (i.e. abiotic part of soil and rocks dug during the mining operations) has only a minor naturalfunction, we choose not to include it in the calculation.

Higher-grade iron ores only need to be crushed. Iron ore nes, however, must rst be agglomerated,which implies reforming them into lumps of suitable size by “sintering”. Instead, lower grade iron oresneed to be upgraded (concentrated) before smelting. The upgraded ores are in the form of a very ne

Figure 2. Energy System Diagram of the Steel Production Process Investigated in the Present Study.



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powder that is physically unsuitable for blast furnace use. They have a much smaller particle size than orenes and cannot be agglomerated by sintering. Concentrates must be agglomerated by “pelletizing”.

Generally ore concentration takes place in the countries where ores are extracted, while thefollowing production steps take place in the importing countries, where pellets and higher grade ironores are shipped to be processed according to the required quality characteristics. Sintering is usuallydone at the furnace site.Sinter, pellets and iron ore lumps, together with coke, are the charge of the blast furnace (assumed relativepercentages are shown in gure 3).

The output of the blast furnace is molten pig iron. Slag, dust and gases are produced as well. Pigiron at this point contains too much carbon to be used directly as steel; therefore it enters a new process,the Basic Oxygen Furnace (BOF), where its carbon content is lowered to under 2% w/w.

An alternative way to manufacture steel is to recycle iron and steel scraps, melting them in an

Electric Arc Furnace (EAF). The EAF output, as well as the steel coming from the Basic Oxygen Furnace,must then be rened and nished. Both EAF steel and BOF steel may then converge in given proportionsto yield the required mix. A proportion of 17% EAF steel – 83% BOF steel was chosen according toMerten T. et al. (1995), which reects the average German rate of steel scrap recycling in the nineties(see gure 3 for percentages).

RESULTS

The nal results of the multi-criteria analysis of the three types of steel are shown in Table 1.Details on calculations (input raw data, source of data, calculation procedures) are too extensive for

inclusion in this paper, however they are available on request. As far as the material intensity factors areconcerned, they are higher for primary steel compared to secondary steel and the mix of the two. Resultsare signicantly affected by the inclusion of cooling water in the “ecological backpack” of electricity.

Figure 3. Flow Diagram of the Steel Production Process Investigated in the Present Study. (Numbers indicate the

mass per cent of the input contributing to the production process).



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Table 1. Summary of nal results of the analysis for the three different types of steel (outputs).

Indicators UNITS Primary Secondary Steel mix

Cumulative mass indicators

Material intensity, abiotic factor g(abiotic(ç)

)/g (output) 6.68 2.81 6.02

Material intensity, water factor g(water)

/g (output)

12.62 5.49 11.41

Material intensity, air factor g(air)

/g (output)

2.15 0.56 1.88

Material intensity, biotic factor g(biotic

(çç))/g

(output)0.03 0.00 0.02

Cumulative airborne emissions (global scale)

CO2

gCO2/g

output2.89 0.78 2.53

H2O (vapor) gH

2O/g

output0.89 0.24 0.78

NOx (as NO2 equivalents) gNO2/g output 0.01 0.003 0.01SO

2gSO

2/g

output0.04 0.01 0.04

VOC gVOC/goutput

2.28E-04 6.13E-05 2.00E-04

Particulate gparticulate

/goutput

1.09E-03 2.94E-04 9.58E-04

Cumulative energy indicators

Oil equivalent goil

/goutput

0.91 0.25 0.80

Cumulative exergy indicators

Exergy efciency Joutput

/Jinput

(°)

0.29 0.56 0.32

Cumulative emergy indicators

without labor and services (1)

Specic emergy seJ/g output 3.69E+09 6.04E+08 3.16E+09

Transformity seJ/Joutput 5.20E+05 8.51E+04 4.46E+05

with labor and services (2)

Specic emergy seJ/g output 4.30E+09 1.22E+09 3.78E+09

Transformity seJ/Joutput 6.06E+05 1.71E+05 5.32E+05

(ç) = in the abiotic cathegory are included all the abiotic inputs directly and indirectly involved in theproduction of the nal products (3 types of steel). (Examples are fuels, overburden, chemicals, plantstructures etc.) Same consideration s can be done for yhe water and air cathegories.(çç) = in the biotic cathegory are included all the biotic inputs directly and indirectly involved in the

production of the nal products (3 types of steel). (Examples are biomass and the organic part ofsoil previously existing at the mining and plant sites.)

(°) = quotient of the exergy content of the main output (steel) divided by the sum of the exergy contentof all the inputs.

(1) The input emergy includes direct environmental ows, fuels and electricity, goods, raw minerals.(2) The input emergy also includes the emergy ow supporting labor and services. This is globallyevaluated by means of the market price of the nal products.



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Water is a dominant material input to electricity production (both for steam generation and cooling).Therefore, processes where electricity plays a major role show higher water material intensities if coolingwater is accounted for. The water factor is higher for secondary steel, due to the high amount of electricityused for scrap recycling. The inuence of cooling water on the ecological backpack of the electricity and,consequently, on the steel nal material intensity factors is shown and compared in Table 2.

In the present analysis, however, the ecological backpack of electricity does not include cooling

water to better underline the direct water consumption in the different production steps of the 3 types ofsteel (primary, secondary and the mix).

Referring only to the results for the steel mix and without accounting for cooling water, it appearsthat abiotic, water, air, and biotic factors are respectively 31.1%, 58.8%, 9.7% and 0.1% of a total materialintensity of 19.33 g/g

steel. Instead, if indirect electricity-related factors are accounted for, results become

respectively 12.9%, 83.0%, 4.0%, and 0.04%. In both cases only abiotic (mainly fuel and machinery) andwater are relevant (and maybe limiting) factors, while air and abiotic factors are clearly negligible.

The energy cost per unit of product expressed in grams of crude oil per gram of steel is 0.91 forprimary steel and a low 0.25 for secondary steel from scraps. The advantage of recycling is particularlyevident. We have calculated a weighed average of 0.80 g

oil/g

steel for steel mix, in good agreement with

available literature data (Boustead and Hancock, 1979). Cumulative airborne emissions, calculated step bystep, are dominated by carbon dioxide, water vapor and nitrogen oxides, as it was expected for a processmainly based on fossil fuel combustion. Lower emissions from secondary steel production conrm theadvantage of recycling.

The cumulative exergy efciency is also shown in Table 1. Results are highly dependent on thenumber of transformation steps, and are therefore better for secondary steel than for primary steel or themix. Calculations include not only the exergy of fuels and heat ows, but also the exergy of materialinputs and outputs. Relevant exergy drops characterize those steps where combustion plays a major role,as it clearly appears from Table 3, where the exergy efciency of each step as well as the changes ofcumulative exergy efciency are shown.

As regards the emergy analysis, results for each production step of the three types of steel are

shown in Table 4. Transformities of primary steel (6.1E09 seJ/g) match quite well with other valuesavailable in literature (1.78E09 seJ/g - H.T. Odum, 1996 p.186; 2.77E09 seJ/g - Haukoos, 1995; 5.35E09seJ/g - Buranakarn, 1998 p.142). We calculated a transformity of 1.7E09 seJ/g for secondary steel, veryclose to the value calculated by Buranakarn (1998, p.142), 4.41E09 seJ/g. Unfortunately, the latter is theonly transformity value of secondary steel available in literature.

The transformities and the specic emergies (i.e. the quotient of emergy divided by mass) ofthe three types of steel are highly dependent on the transformity assigned to the steel scraps processedin the Electric Arc Furnace to produce the secondary steel. In fact, scraps are recycled material fromthe decommissioning phase of buildings or other steel products; however, they do not carry the sameinformation content as the manufactured products, but only the information content of the raw material. Thisis mainly because they are usually pressed (unshaped) and transported to the recycling facility where they

are processed for reuse. From this point of view steel scrap inputs can be seen as a feedback coming froma split of the nal steel output (see Figure 2), and therefore, in order to avoid double counting, they mustbe assigned an emergy content equal to zero, except for the preparation and transportation contributions,for which no data were available but the price of scraps and which were considered negligible in thisstudy. All the other possible processes after steel nishing and before the decommissioning phase thatcan produce high transformity composite materials should not be accounted for in the evaluation of theemergy content of scraps, as their contribution is lost in the recycling phase. According to the considerationsabove, results show again a net advantage of recycling.

Specic emergies and transformities of primary steel were calculated with and without accountingfor labor and services, the inclusion of which affects, although not dramatically, the nal result (+14.2%),

as it is generally expected for primary production processes. The opposite is true in the EAF step, whereservices associated to scrap recycling increase the specic emergy by 50.5%. Here services are more

relevant as we are no longer dealing with a primary process in a strict sense.The specic emergy (seJ/g) of each kind of steel increases step by step, as expected, due to



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Table 2 .Inuence of the electricity “ecological backpack” (i.e. material intensity factors ) on the nalsteel material intensity factors.

Process Material Intensity Factors

Units g(abiotic)

/unit g(water)

/unit g(air)

/unit g(biotic)

/unit Tot Mat Int.

Primary Steel using electricity

data (1) gsteel

6.68 12.62 2.15 0.03 21.48

Primary Steel using electricity

data (2) gsteel

6.68 37.98 2.15 0.03 46.83

Secondary Steel using

electricity data (1) gsteel

2.81 5.49 0.56 0.00 8.86

Secondary Steel using

electricity data (2) gsteel 2.81 41.83 0.56 0.00 45.20

Steel Mix using electricity

data (1) gsteel

6.02 11.41 1.88 0.02 19.33

Steel Mix using electricity

data (2) gsteel

6.02 38.63 1.88 0.02 46.56

Electricity (Europe avg) w/out

cooling water (1) kWh 2086 5855 369 n.d. 8310

Electricity (Europe avg)

w/cooling water (2) kWh 2086 57227 369 n.d. 59681

(1) Ecological backpack of electricity not including the cooling water as a input for the electricity production.Data refer to the European mix in the nineties (our calculation based onManstein C., Wuppertal Papers Nr. 51. Feb.1996).

(2) Ecological backpack of electricity including the cooling water as a input for the electricity production.Data refer to the European mix in the nineties (our calculation based onManstein C., Wuppertal Papers Nr. 51. Feb.1996).

n.d.= not determined.

additional emergy input and decreased amount of output (Figure 4 shows this behavior in the case of steelmix production). When ows characterized by very different specic emergy converge, the specic emergyof the nal product averages accordingly. This is the case, for example, of the product ows from the BasicOxygen Furnace and the Electric Arc Furnace converging into the steel rening and nishing steps.

The whole chain of processes from ore mining to steel nishing is characterized by increasedpurity of the iron ow. The process chain starts with iron ore, which is in quasi-equilibrium with the earthcrust and is therefore assigned a very low specic exergy (Szargut et al., 1988). Instead, pig iron at blast

furnace exit as well as nal rened steel have a very high specic exergy (Szargut et al., 1988), expressingtheir thermodynamic distance from crustal concentration, assumed as the reference level. The increasedvalue of the specic exergy reects in principle a higher user-side quality, due to modied chemical



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F i g u r e 4 . V

a l u e s o f t h e S p e c i c E m e r g y a l o n g

t h e S t e e l P r o d u c t i o n C h a i n . T h e c a

s e o f S t e e l M i x ( 8 3 % B O F s t e e l , 1 7 % E A F s t e e l ) . S p e c i c e m e r g y i s t h e q u o t i e n t o f

e m e r g y d i v i d e d b y m a s s . ( F o r c o n s e q u e n t i a l i t y b e t w e e n p h a s e s s e e F i g . 3 ; s i m b o l s * a n d * * a r e e x p l a i n e d i n T a b l e 3

a n d 4 f o o t n o t e s ) .



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and technological characteristics in the course of the process. Since transformities (seJ/J) are denedas the ratio of total emergy to the available energy (exergy) of the product, they can be calculated fromspecic emergy values (seJ/g) divided by the specic exergy (J/g) of the product itself. This translatesinto a decrease of step-by-step transformities (Table 4), an unexpected result that deserves attentionand further investigation within the framework of the emergy theory. In principle, transformities shouldincrease with increasing donor-quality and hierarchical position of the item. A preliminary explanationof this result might be found in the use of exergy, instead of energy or mass, to quantify the product ow.In fact, exergy quanties the user-side quality, i.e. the amount of work that can be theoretically extractedfrom the product by means of a reversible process. Transformities calculated by using exergy expressthe emergy invested per unit of reversible work that is potentially supplied to the user. Instead, when theproduct ow is quantied by using rst-law concepts (heat content or mass used to quantify the product),transformities express the amount of environmental work supporting the process (donor-side quality),without considering its user-side quality. Transformities calculated in such a way show the expectedincrease, paralleling the increase of specic emergies. These two different pictures are not in contradiction,but call for a deeper investigation about the numeraire to be used to quantify matter and energy ows as

well as about the concept of quality itself 1.

THE ADDED VALUE OF INTEGRATED APPROACHES

A “code of practice” for biophysical analyses and emergy accounting was proposed in the 1st

Emergy Research Conference (Ulgiati, 2000), based on several common features of the different methodsof evaluation. Synergic effects and added values are likely to emerge from integration of approaches. LCAanalysts are also trying to develop a common accounting procedure, in order to make results consistent andcomparable (CEN, 1997, 1998 and updates). Finally, a general drive towards integration of approaches canbe recognized worldwide (among others: Valero, 1998, 2000; Tonon et al., 2000; Sciubba, 2000). Qualityof data, problems of scales and boundary, goals and scope of the investigation are all crucial factors that

may affect the nal results and require a process to be investigated from several complementary pointsof view.

In short, quantifying direct and indirect ows of matter and energy to and from a system permitsthe construction of a detailed picture of the process itself as well as of its relation with the surroundingenvironment. Processing these data in order to calculate material and energetic intensities makes it possibleto compare the process output with other products of competing processes. Results may differ dependingon the goal, the boundary, the time scale, and the technology and may suggest different optimizationprocedures. If the analyst is able to provide converging results as well as to explain divergences at theappropriate scales of the investigation, the process can be more easily understood. Conclusions are alsoreinforced and become more easily acceptable for research, application and policy strategies.

According to the results of the present investigation (some of which are shown in Tables 1- 4):a. The quantied emissions at local and global scales offer a picture of the environmentaldisturbance due to process releases. They also call for proper evaluation of environmentalimpacts in each sub-process site.

b. The quantied direct and indirect energy inputs lead to the assessment of the true energycost of a product, very often hidden by the relatively low economic cost of fossil energysources;

c. The calculated material intensities measure the environmental pressure of the systemdue to the withdrawal of matter at scales other than local and may suggest some kindsof material input (e.g.: water) as limiting factors;

d. Exergy efciency values for the different production steps help to focus on those sub-

processes where irreversibilities mainly occur and where technical improvements areneeded;

e. The calculation of the transformity of each product contributes a measure of direct andindirect environmental services supporting the process, also accounting for the previous



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history of each input (time becomes a quantied input to the process). Transformitieshelp quantify the global process efciency, the ecological footprint of the process, therenewability and “hierarchical position” of the product at the thermodynamic scale ofthe biosphere. Finally, the fraction of transformity that is due to the emergy value oflabor and services claries the amount of environmental work supporting labor withinthe economic process, which is very often disregarded in economic as well as LCA andenergy analyses.

CONCLUSIONS

A new set of efciency and sustainability indicators were calculated on the basis of average,published data on the steel production process, in order to build a process database that accounts forseveral approaches. The validity of the previous available data and indicators based on single approacheswas checked and mostly conrmed. The nal result of the investigation presented in this paper is acomprehensive mass, energy, exergy and emergy evaluation of the whole chain of processes from oremining to rened steel. Data can be in turn used in any other process where steel for building purposes

is a relevant input.The use of different methods has highlighted the different characteristics of the investigated

system. The environmental/energetic advantage of steel scraps recycling was univocally underlined byall the applied methods. It clearly appears that each method only focuses on some special aspect of theinvestigated case and therefore an integrated picture may help understanding the multifaceted dynamicsof the process itself. Finally, when several approaches are integrated, new theoretical problems emerge,contributing to check the internal consistency of each approach.

EndNote1. Quality is not a universally agreed concept. In this paper we assign a higher user-side quality (measuredby its exergy) to nal products characterized by higher chemical purity. Instead, Odum points out thata “chemically pure product has much available energy that can accelerate corrosion. Perhaps goodsystems should evolve so that their nal products do not tend to corrode or react with the environment.A better system might have organized to produce a less reactive product.” (H.T. Odum, 2002, personalcommunication).

REFERENCES

Andersen J. P. and Hyman B., (2001) Energy and material ow model for the US steel industry. Energy

26: 137-159.

Ayres R.U. 1995. Life Cycle Analysis: A critique. Resources, Conservation and Recycling, 14: 199-223.

Ayres R.U. and Masini A., 1998. Waste Exergy as a Measure of Potential Harm. In: Advances in EnergyStudies. Energy Flows in Ecology and Economy. Ulgiati S., Brown M.T., Giampietro M.,Herendeen R.A., and Mayumi K. (Eds). Musis Publisher, Roma, Italy; pp. 113-128.

Brown M.T., Buranakarn V., 2000. Emergy Evaluation of Material Cycles and Recycle Options. In:

Emergy Synthesis. Theory and Applications of the Emergy Methodology. Proceedings of the FirstBiennial Emergy Analysis Research Conference, Brown M.T., Brandt-Williams S., Tilley D.,and Ulgiati S. (Eds), The Center for Environmental Policy, University of Florida, Gainesville,USA, pp.141-154.



-154-


Buranakarn V., 1998. Evaluation of recycling and reuse of building materials using the emergy analysis

method. A dissertation presented to the Graduate School of the University of Florida, in partialfullment of the requirements for the degree of Doctor of Philosophy. Available at the Centerfor Wetlands, University of Florida, Gainesville, FL, pp.258.

Boustead I., Hancock G.F.,1979. Handbook of Industrial Energy Analysis, Wiley and Sons, New York,pp. 422.

CEN (1997), Environmental management – Life cycle assessment – Principles and framework (EN ISO14040:1997) European Committee for Standardization, June 1997, Brussels. pp.16.

CEN (1998), Environmental management – Life cycle assessment – Goal and scope denition andinventary analysis (EN ISO 14041:1998) European Committee for Standardization, October1998, Brussels. pp.27.

IFIAS, 1974 International Federation of Institutes for Advances Study. Energy Analysis Workshop on

Methodology and Conventions. Stockholm. M. Slesser editor.

Haukoos S. D., 1995. Sustainable architecture and its relationship to industrialized building. A thesis

presented to the Graduate School of the University of Florida, in partial fullment of therequirements for the degree of Master of Science in Architectural Studies. Available at the Centerfor Wetlands, University of Florida, Gainesville, FL, pp.275.

Herendeen R.A., 1998. Embodied Energy, embodied everything… now what? In: Advances in Energy

Flows in Ecology and Economy. Ulgiati S., Brown M.T., Giampiero M., Herendeen R.A., andMayumi K. (Eds). Musis Publisher, Roma, Italy; pp. 13-48.

Hinterberger F. and Stiller H., 1998. Energy and Material Flows. In: Advances in Energy Flows in Ecology

and Economy. Ulgiati S., Brown M.T., Giampiero M., Herendeen R.A., and Mayumi K. (Eds).Musis Publisher, Roma, Italy; pp. 275-286.

Kakela P.J. 1978. Iron ore: Energy, Labor, and Capital Changes with Tecnology. Science v.202.

Macedo Costa M., Schaeffer R. and Worrell E., Exergy accounting nad material ows in steel productionsystems. Energy 26 (2001) 363-384.

Manstein C., 1996. Das Elektrizitaetsmodul im MIPs-Konzept, Wuppertal Paper n. 51.

Merten T., Liedtke C., Schmidt-Bleek F., 1995. Materialintaensitatnalysen von Grund-, Werk- undBaustoffen (1). Wuppertal Papers Nr. 27.

Odum H.T., 1996. Environmental Accounting: Emergy and Environmental Decision Making. Wiley andSons, New York, pp.370.

Ozawa L., Sheinbaum C., Martin N., Worrell E. and Price L., (2002) Energy use and CO2 emissions inMexico’s iron and steel industry. Energy 27: 225-239.

Price L., Sinton J., Worrell E., Phylipsen D., Xiulian H. and Ji L., (2002) Energy use and carbon dioxideemissions from steel production in China. Energy 27: 429-446.

Sciubba E., 2000. On the Possibility of Establishing a Univocal and Direct Correlation Between MonetaryPrice and Physical Value: The Concept of Extended Exergy Accounting. In: Advances in Energy

Studies. Exploring Supplies, Constraints, and Strategies. Ulgiati S., Brown M.T., Giampietro M.,Herendeen R.A., and Mayumi K. (Eds). SGE Publisher, Padova, Italy; Pp. 617-633.

Stiller H., 1999. Material Intensity of Advanced Composite Materials. Wuppertal Papers Nr. 90.

Szargut J., Morris D.R., and Steward F.R., 1988. Exergy analysis of the thermal, chemical and metallurgical

processes, Hemisphere Publishing Corporation, London.



-155-


Szargut J., 1998. Exergy analysis of the thermal processes; Ecological Cost, In: Proceedings of theInternational Workshop “Advances in Energy Studies – Energy Flows in Ecology and Economy”,Ulgiati S., Brown M.T., Giampietro M., Herendeen R.A., and Mayumi K. (Eds). Musis Publisher,Roma, Italy; pp. 77-98.

Tapani Makkonen H., Heino J., Laitila L., Hiltunen A., Poylio E. and Harkki J., (2002) Optimization of

steel plant recycling in Finland: dusts, scales and sludge. Resource, Conservation and Recycling 35: 77-84.

Tonon S., Brown M.T., Luchi F., Mirandola A., Stoppato A. And Ulgiati S., 2000. Integration ofThermodynamic, Economic and Environmental Parameters for the Evaluation of Energy Systems.In: Advances in Energy Studies. Exploring Supplies, Constraints, and Strategies. Ulgiati S.,Brown M.T., Giampietro M., Herendeen R.A., and Mayumi K. (Eds). SGE Publisher, Padova,Italy; Pp. 635-647.

Ulgiati S., 2000. Energy, Emergy and Embodied Exergy: diverging or converging approaches? In: Emergy Synthesis. Theory and Applications of the Emergy Methodology. Proceedings of the FirstBiennial Emergy Analysis Research Conference, Brown M.T., Brandt-Williams S., Tilley D.,and Ulgiati S. (Eds), The Center for Environmental Policy, University of Florida, Gainesville,USA, pp.15-32.

Valero A., 1998. Thermoeconomics as a Conceptual Basis for Energy-Ecological Analysis. In: Advances

in Energy Studies. Energy Flows in Ecology and Economy. Ulgiati S., Brown M.T., GiampietroM., Herendeen R.A., and Mayumi K. (Eds). Musis Publisher, Roma, Italy; p.415-444.

Valero A., 2000. Exergy Accounting: Capabilities and Drawbacks. In: Advances in Energy Studies.

Exploring Supplies, Constraints, and Strategies. Ulgiati S., Brown M.T., Giampietro M.,Herendeen R.A., and Mayumi K. (Eds). SGE Publisher, Padova, Italy; Pp. 663-677.

Worrell E., Price L. and Martin N., (2001) Energy efciency and carbon dioxide emissions reductionopportunities in the US iron and steel sector. Energy 26: 513-536.



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Edited by



Associate Editors

Howard T. Odum



David Tilley



Sergio Ulgiati

University of Siena

Siena, Italy

December 2003



Gainesville, FL


vi



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Chapter 11. Emergetic and Exergetic Analysis of a Combined ...

11Emergetic and Exergetic Analysis of a Combined

Cycle Power Plant


ABSTRACT

The market of new power plants is currently dominated by new and large power plants based

on combined gas-steam cycles running on natural gas. The advantages of these plants are a very highelectrical efciency, a short pay-back time, a fast construction time and less environmental impact com-

pared with conventional steam power plants.

A typical large combined plant of the current generation has been studied and assessed by means

of detailed exergy and emergy analyses. The exergy method focuses one’s attention on the energy conver-

sion process performances in exploiting fuel energy, while the emergy one evaluates system efciency in

exploiting resources at a larger time and space scale.

The combination of both analyses supplies comprehensive and effective information to evaluate

sustainability of technological choices and improvements. This represents an added value in the decision-

making process and is the result of a larger effort for integration of different energy theories in system

analysis.

Different alternatives for the steam condensing system of the power groups have also been ex-amined since a different use of environmental resources (air, water) is involved. The base plant congu -

ration concerns the use of air coolers while the rst alternative uses wet cooling towers and the second

a traditional owing-water condenser.

The comparison of the results with conventional oil and coal steam power plants and an hy-

droelectric power plant has been carried out. Some indications about the importance of construction

and operation emergy inputs are then inferred, in order to extend results beyond thermoelectric power

plants.

INTRODUCTION

The market for power generation in Italy is changing because of new laws which partially

privatized the electric system. Many corporations are now entering the market and the national power

company (ENEL) is forced to sell power plants for a total of 15000 MW in order to decrease its generating

capacity, which must be less than 50% of the total national capacity.

Many companies are currently considering the option of new power plants, either on sites that

were already used for old power plants, or on new sites. As stated by GRTN [2001], 95% of all proposals

for new connections of power plants to the grid are combined cycle power plants running on natural gas

(NGCC). Manufacturers of gas turbines for power generation currently face demands that far exceed their

production capacity.

In the past, other types of analyses2 that have been performed showed that the main advantages

of these plants include high electrical efciency, a short pay-back time, a fast construction time and less



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Chapter 11. Emergetic and Exergetic Analysis of a Combined...

environmental impact compared with conventional steam power plants.

The great attention toward these power plants prompted the analysis presented in this paper. Regardless of

their popularity, exergy and emergy analysis proposed here provide further and complementary information

about plant performances. Shortly, exergy analysis leads to the evaluation of the irreversibilities connected

with the process while emergy analysis supplies information about the use of environmental resources,in particular with regard to their nature (renewable or non-renewable, local or imported).

DESCRIPTION OF THE PLANT

The power plant investigated is a recently introduced thermoelectric combined cycle plant running on

natural gas. The plant is popular in new installations. The plant, which has a net electrical output of 735 MW,

is composed of two identical groups of generating equipments plus auxiliary supporting equipment.

Each power group consists of a gas turbine (GT), a heat recovery steam generator (HRSG), a

steam turbine (ST) and a steam condenser that in this conguration is an air cooler (AC); the simplied

process ow diagram of each group is described in Figure 1.

The gas turbine supplies mechanical power to drive both compressor and generator. Gasesdischarged from the GT feed the HRSG to take advantage of the considerable energy content. Sub-critical

steam generated by the HRSG supplies a steam turbine to provide further mechanical power. The ST and

the GT are mechanically connected to the generator that supplies the electric power. The steam exhausted

from the turbine is condensed in an air cooler.

Each power group is installed in a building to facilitate maintenance and reduce the noise; the

corresponding air cooler is close to each building.

The gas turbine under investigation belongs to the newest generation of commercial large heavy-

duty gas turbines and is one of the most powerful currently on the market. It is equipped with Dry Low

NOx combustor that represents the best available technology for reduction of NOx and CO emission.

Flue gases are discharged from the stack at an average temperature of 100 °C with a concentration of

NOx and CO of 25 and 10 ppm, respectively.

The HRSG that is coupled to the GT produces steam at three pressure levels with an intermediate

reheat in order to maximize the efciency of the steam cycle.

Figure 1. Simplied process ow diagram of the power plant



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Table 1. Plant features.

_____________________________________________________

Natural Gas Combined Cycle Plant

_____________________________________________________

Location ItalyNumber of power groups 2

Area directly involved [m2] 80000

Expected plant lifetime [years] 30

Expected construction times [years] 2

Net electric power in nominal conditions [MW] 735

Expected yearly operation [h] 8000

Yearly electric energy produced [GWh] 5881Net Efciency [%] 54.0

_______________________________________________________

Plant auxiliary includes natural gas compressors, circulation and feed-water pumps, power electric

system, water demineralization plant, waste-water treatment system, re protection system, cooling towers

for auxiliaries, electric substation, various buildings for maintenance, control room, etc.

The main features of the overall plant are presented in Table 1. The total area required for the

installation of the plant is approximately 400 m by 200 m and includes all the components previously

described. The consumption of water is low and is that required for cooling the generators and theauxiliaries, the demineralized water for the boilers and for general uses. Water is obtained by means of

a well located within the area of the plant. The total amount of water required by the plant at nameplate

conditions is 36.1 m3/h.

Table 2. Energy and Exergy ows

__________________________________________________________________________________

_

Subsystem Flow [MW] Energy Exergy

Gas Turbines Thermal power input 681 704

Mechanical power 255 255

Steam Turbines Thermal power input (from Gas Turbine) 426 193

Mechanical power 122 122

Air Coolers Thermal power 220 18.9

Stacks Exhausted gases 64.3 7.3

Auxiliaries Electric power 4.5 4.5

OVERALL PLANT Thermal input power 1363 1409

Net electric power 735 735

E A C H G

R O U P



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_________________________________________________________________________________

__

Data required by the analyses have been drawn from a prior feasibility study. The gas turbine

and the steam cycle have been studied using the program DIMAP (Lazzaretto et al. 1998).

ENERGY AND EXERGY ANALYSIS The energy and exergy analysis of the plant have been carried out with regard to standard

environmental conditions (p = 1 atm and T = 15 C) and 8000 hours of operation per year.

The analysis has been performed considering all the physical components involved in the

power unit (heat exchangers, turbines, etc.), but results presented here are only for the main subsystems

such as gas turbines, steam turbines, air coolers, stacks and auxiliaries. Losses of power due to the

generators and the power transformers have been considered in the calculation of the net electric power

provided by the plant.

EMERGY ANALYSIS

The emergy analysis of the plant has been performed considering the plant itself, so that all thecomponents directly involved in the energy production process are included as well as the process of

natural gas production and extraction. By including the process of extraction of natural gas in the system,

it is accounted for as a non-renewable resource locally available. A system diagram drawn according to

Odum [1996] is proposed in Figure 2.

The analysis of the construction has been carefully studied in order to get a detailed description

of the materials involved. Almost all the components have been considered in detail (gas turbines and their

ttings, steam turbines and their ttings, electric generator, air coolers, heat exchangers, HRSG casing,

chimneys, pipes, valves, bypass, tanks, water demineralization system, auxiliary boiler, compressors, noise

absorbing box, HP and LP pumps). All the electric system except the power transformers was considered

as one entity. In addition, all the civil works have been considered and divided into major categories (e.g.,

inert, concrete, steelwork, pre-built structures, fences and insulation). Labor hours for the installation

of the components have been also extensively estimated. Services for manufactured components were

Sun,

Environmental Power groups

Natural

Aux systems

Purchased

inputs

Labour

Yield

F

Y

F

Extraction

S Services

N

R

$, _

Figure 2. Emergy system diagram



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estimated by considering the total emergy associated with the cost of the plant through the emergy intensity

in Emdollars.

Table 3. Emergy ows for the NGCC

______________________________________________________________________________

Construction Operation Total

1 E18 seJ, except where noted

______________________________________________________________________________

Renewable inputs R 0.0 33.5 33.5

Non-renewable inputs N 2,072.0 2,072.0

Purchased inputs F 7.0 0.4 7.4

Direct Labor FL 1.1 4.1 5.1

Services S 14.6 286.4 300.9

Yield Y 22.7 2,396.0 2,419.0

Electrical output (1 E15 J) E 21.2 21.2

______________________________________________________________________________

The analysis of the operation includes the solar radiation over the plant site, the oxygen requiredfor natural gas combustion, the heat and pollutant dispersion by the air coolers and the stacks, various water

uses, natural gas and services for its extraction and processing, used chemicals as well as maintenance

works and labor.

All the emergy ows for construction and operation of the plant are diagrammed in Figure 2

and quantitatively presented in Table 3. Construction emergy shown in the table is reported as annual

ows.

The largest emergy ow is that associated with natural gas. Second largest is the ow of the

services for plant equipment manufacture and fuel extraction and processing. This is due to the nature of

the system (an industrial process plant) that relies almost entirely on purchased inputs.

All the transformities used in the calculation of the emergy ows reect the estimate of 9.44x1024

seJ/yr3 as the solar empower base of the globe.

INDICATORS AND COMPARISON WITH OTHER POWER PLANTS

Based upon the evaluation of the main energy, exergy and emergy ows involved4, some

indicators of performance have been calculated. Among the various energy indicators, the 1st Law and

the 2nd Law efciency to get the products (electric energy) from natural gas have been evaluated. An

additional indicator eraw

(eq. 1) based on First Law analysis has been evaluated in order to qualify the use

of raw (non-renewable) resources as fuels (if present) in the process of energy conversion (see Mirandola

et al. 2000). This indicator is related to the amount of raw energy saved for not using fossil fuels to getthe same products. Its numerical value can range between h (no renewable energy used) and • (best use,

no raw energy used at all).

Energy and exergy indicators proposed here have been evaluated for the whole plant to allow

the comparison of various power plant performances. They are also useful in complementing the

information provided by the emergy indicators (also calculated at plant scale).

Concerning the emergy evaluation, indicators used to determine plant performances have been

evaluated according to Ulgiati et al. [1996]. Indicators calculated for the plant under investigation are

presented in Table 5 and are compared with those of other types of power plants previously evaluated

(Tonon [1998], Brown and Ulgiati [1998]), which are described in Table 4.



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From the analysis of the results presented in Table 5, it must be pointed out that the high 1st and

2nd Law efciency values of the NGCC, when compared to those of the other thermoelectric plants, are

obtained because a thermal cycle different from the conventional steam cycle (Rankine cycle) is employed.

This cycle better exploits the thermal content of natural gas. A 2nd Law efciency greater than 50% is an

important technological breakthrough, but it is going to be overcome by newer NGCCs, that are reported

to have a 1st Law efciency of 60%5.

The steam power plant running on oil and the one running on coal have the lowest energy and

exergy efciencies among the plants considered, but these plants are almost 20 years old. Nevertheless,

they signicantly express the present state-of-the-art of several power plants in Italy.

Concerning the emergy indicators, the transformity of electric energy for the plant under

investigation is the lowest among the thermoelectric power plants considered. Hydroelectric has the

lowest overall.

Table 4. Main features of the other power plants evaluated.

__________________________________________________________________________________

_ Thermoelectric 4 x 320 MWe groups, running on oil, sub-critical steam cycle. Condensers

running on oil are cooled with seawater. NOx and SO2 abatement equipment not installed

at the time of investigation.

Thermoelectric 4 x 330 MWe groups, running on coal and oil, sub-critical steam cycle.

running on coal Condensers are cooled with seawater. NOx and SO2 abatement equipment

not installed at the time of investigation.

Hydroelectric 2 Pelton turbines on vertical axis (45 MWe). The related water system holds

a natural productiveness of 140 GWh on a yearly basis

___________________________________________________________________________________

As noted before, the emergy associated with the fuel, in general for most of the thermoelectric power

plants6, is far greater than that of other inputs, which makes them negligible (97.1% of the total emergy

requirement when services are considered, 84.4% when services for fuel supply are not included). Hence,

the transformity of electric energy when services are accounted for can be approximated as shown in

eq. 27.

For this reason, the high electric efciency of the plant under investigation implies a low

transformity (1.14 x105 calculated with services).

Eq. 3 can be reasonably assumed valid for many thermoelectric power plants. Transformity

for electricity has also been re-calculated considering the efciency of the next generation commercial

combined cycles (60%) and turns out to be 0.82x105 seJ/J, a value that is somewhat comparable to that

of some power plants running on renewable resources.

This suggests there might exist a limit in the transformity of electricity (for power plants usingboth renewable and non-renewable fuels) thus conrming previous ndings8. In fact, even power plants

running on renewable resources involve a considerable amount of non-renewable resources for plant

construction.



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Environmental Loading Ratio (ELR), by reporting the ratio between imported resources and

local available ones, is usually very high for process plants, in particular for the plant under investigation

but also for all the thermoelectric plants that have been considered.

The emergetic efciency measured by EYR for the NGCC is high, greater than that of the otherthermoelectric plants, indicating that local resources (natural gas included, since the process for extrac-

tion, processing and supply has been considered) are well exploited by means of the driving ows. Its

value is close to that of the hydroelectric power plants and leads to a good sustainability (EIS) from the

emergetic point of view.

ANALYSIS OF ALTERNATIVE CONDENSING SYSTEMS

Two alternative solutions to condense the steam discharged by the steam turbine have been

evaluated by means of exergetic and emergetic indicators. The rst one involves the use of wet cooling

towers that are installed instead of the air coolers, while the second one is a traditional system with acondenser fed with owing water.

The system based on cooling towers includes various identical modules (12), where circulation of

air is activated by blowers. The structure is mainly made of steel except for the lling, made of plastic.

The use of wet cooling towers allows the condensation pressure to be reduced to 0.04 bar (vs.

0.08 bar with air coolers), so that more power can be obtained from the Low Pressure stage of the steam

turbine (129.3 vs. 122 MW). Thus, the total efciency of the plant is increased. Moreover, the thermal

power of the cooling towers is lower than that of the air coolers.

The cooling towers are generally less expensive than air coolers of the same thermal power, even if they

require more energy to run additional auxiliaries. On the other hand, because of the evaporation process

that takes place in the cooling tower, the water requirement is greater (additional 1000 m3/h, half of which

evaporates) and additional chemicals are required for the treatment of wastewater before its discharge. In

the present emergy evaluation, the same installation cost of components were considered for all cases.

Table 5. Evaluated indicators

__________________________________________________________________________________

NGCC Oil T.E. Coal T.E. HydroE.

__________________________________________________________________________________

1st Law efciency, h [%] 54.0 38.4 35.1 71.6

Raw energy conversion efciency, eraw

0.540 0.384 0.351 2.86

2nd Law efciency, hex

[%] 52.2 38.1 33.6 63.0

Total Emergy Requirement, TER [seJ] 2.42x1021 4.76x1021 4.18x1021 2.46x1019

W/o services 1.00x105 2.35x105 1.51x105 0.59x105

Transformity, Tr [seJ/J]

W/ services 1.14x105 2.65x105 1.60x105 0.62x105

Emergy Yield Ratio, EYR 7.72 2.56 4.99 7.65

Environmental Loading Ratio, ELR 71.27 96.92 57.65 0.45Emergy Index of Sustainability, EIS 0.108 0.026 0.087 16.9

Emergy Density, ED [seJ/m2] 7.56x1015 2.88x1015 2.03x1015 1.59x1013

__________________________________________________________________________________



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Table 6. Indicators for alternative condensing systems ________________________________________________________________

Air Cooler Cooling Towers Water Condenser

________________________________________________________________

h [%] 54.0 54.9 55.0

eraw

0.540 0.549 0.550

hex

[%] 52.2 53.1 53.2

TER [seJ] 2.42x1021 2.42x1021 2.42x1021

Tr S [seJ/J] 1.00x105 0.99x105 0.98x105

Tr [seJ/J] 1.14x105 1.13x105 1.12x105

EYR 7.72 7.66 7.73

ELR 71.27 71.35 71.25

EIS 0.108 0.107 0.109

ED [seJ/m2] 7.56x1015 7.57x1015 7.56x1015

_ __________________________________________________________________

As noted previously (see Eq. 3), the improved efciency of the plant produces a lower transformity,

since the emergy input of natural gas, which is far greater than the other emergy inputs, remains the same,

but electrical output is increased. Even if ELR and EIS for the three congurations have similar values,

due to the increased use of water, decision makers are usually skeptical toward these solutions.

The second alternative to condense the exhausted steam is the use of traditional condenser where

heat is exchanged with a ow of water. In this case the condensation pressure remains the same while

the power at the auxiliaries is slightly less (thus efciency is slightly increased). From the emergy point

of view, the variations affect essentially the ow of renewable resources due to the large amount of water

required (73300 m3/h).

It must be observed that even if the values of the emergy indicators are similar, the relative

difference between them is signicant, even if transformities are valid within a margin of error.

Consequently, the emergy indicators, ELR and EIS, report the increased use of renewable resources. As

already pointed out, this solution, as well as the previous one considered, involves some concerns because

of the relevant use of water.

CONCLUSIONS

The interest for large combined cycle power plants is high. The analyses performed conrmed

that these plants have great potential if introduced widely in the power generation market.

An effective analysis has been performed through energy, exergy and emergy indicators in

order to supply comprehensive and complementary information. Energy and exergy analyses reported

process performances in exploiting the fuel and have been used in quantifying process irreversibilities

and eventually to account for each component. At plant scale, energy and exergy indicators have shown

the great technological breakthrough of the plant when compared to the last generation of thermoelectric

power plants. Emergy analysis has proved to be effective in providing information on how resources at the

scale of the biosphere have been used. In particular, the time scale is increased so that resources for plant



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and equipment construction have been also accounted for, thus supplying a complete picture of resources

required during all the life cycle.

The comparisons are valid regardless of information on the accuracy of the transformities used since

the same transformities were used for all power plant types. However, a more effort should be given to

calculating accurate values of transformities.

The combined cycle power plant investigated has the lowest transformity among the thermoelectric

plants that have been considered, which means that less environmental resources are required to produce

the same amount of electrical energy. Also, EYR, ELR and EIS indicators demonstrated the potential

benets of the NGCC compared to other thermoelectric power plants.

A comparative analysis of different systems to condense the exhausted steam recommends water

condensers. If they are not available (places where no large amount of water such as rivers and sea is

available), the solution of using wet cooling towers gives some advantages over steam condensation

provided by air coolers. Energy and exergy efciencies are higher and transformity is lower than that

of the conguration with air coolers. This means that, even if the relevant use of water may cause some

concerns among decision-makers because of environmental fears, the use of air coolers brings about a

greater use of environmental resources, because of the reduced energy efciency (mainly fuel, that is a

non-renewable resource).Integration of exergy and emergy analyses in evaluating NGCC power plants and alternatives for

their steam condensing system has been useful in understanding the relations between process performances

and global system sustainability. When compared to other thermoelectric power plants, NGCC proved to

be effective from both points of view.

ACKNOWLEDGEMENTS

The authors wish to thank Prof. Sergio Ulgiati, University of Siena, Italy, and Prof. Mark. T. Brown,

University of Florida, for helpful discussions about the emergy analysis. Financial support to this work

was supplied by MIUR (Italian Ministry of University and Research), which is gratefully acknowledgedby the authors.

REFERENCES

Bejan, A., Tsatsaronis, G., Moran M.J., 1996, Thermal Design and Optimization, John Wiley and Sons,

New York.

Brown, M.T., Ulgiati, S. 1997. Emergy-based Indices and Ratios to Evaluate Sustainability: Monitoring

Economies and Technology toward Environmentally Sound Innovation. Ecological Engineering

9: 51-69.

Brown, M.T., Ulgiati, S. 1998. Emergy Evaluation of the Environment: Qualitative Perspectives onEcological Footprints. Proc. of Advances in Energy Studies: Energy Flows in Ecology and Economy,

Porto Venere: 223-240.

Brown, M.T., Ulgiati, S. 2001. Emergy Evaluations and Environmental Loading of Electricity Production

Systems. in press.

GRTN (Italian Council for Electrical Power Grid Management). 2001. April 2000 – March 2001: relation

on the activity. GRTN ed. Rome

Lazzaretto, A., Macor, A., Mirandola, A., Stoppato, A., and Donatini, F. 1995. DIMAP, a Modular Computer

Code for the Thermodynamic, Exergetic and Thermoeconomic Simulation of Energy Systems. Proc.

of the Winter Annual Meeting of the ASME, AES 35: 119-126.

Lazzaretto, A., Macor, A., Mirandola, A., Stoppato, A. 1998. Potentialities and Limits of ExergoeconomicsMethods in the Design, Analysis and Diagnosis of Energy Conversion Plants. Proc. of Advances in

Energy Studies: Energy Flows in Ecology and Economy, Porto Venere: 515-530.

Mirandola, A., Stoppato, A., Tonon, S., 2000, An Integrated Approach to the Assessment of Energy



-166-


Conversion Plants, International Journal of Applied Thermodynamics 3: 111- 119.

Odum, H.T. 1996. Environmental Accounting. Emergy and Environmental Decision-Making. John Wiley

& Sons Inc.

Odum, H.T., Brown, M.T., Brandt-Williams, S. 2000. Handbook of Emergy Evaluation – Folio #1

Introduction and Global Budget. Center for Wetlands, University of Florida. Gainesville.

Odum, H.T. 2000. Handbook of Emergy Evaluation – Folio #2 Emergy of Global Processes. Center for

Wetlands, University of Florida. Gainesville.

Tonon, S. 1998. Model for evaluation of energy systems by means of environmental, energy and economic

parameters (in Italian). MSc Thesis. University of Padova.

Tonon, S., Brown, M.T., Luchi, F., Mirandola, A., Stoppato, A., Ulgiati, S. 2000. Integration of

Thermodynamic, Economic and Environmental Parameters for the evaluation of Energy Systems.

Proc. of 2nd Int. Workshop Advances in Energy Studies: Exploring supplies, constraints and strategies,

Porto Venere: 635-648.

Ulgiati, S., Brown, M.T., Bastianoni, S. and Marchettini, N. 1996. Emergy Based Indices and Ratios to

Evaluate Sustainable Use of Resources. Ecological Engineering 5: 497-517.

Ulgiati, S., Brown, M.T. 1998. Modelling patterns of sustainability in natural and man-made ecosystems.

Ecological Modelling 108: 23-36.Ulgiati, S., Bargigli, S., Raugei, M., Tabacco, A.M. 2001. Emergy Evaluation of Atmospheric Oxygen

and Nitrogen. Proc. of 2nd Emergy Research Conference. In press

Literature Cited in Appendices

Buenl. A. A. 2001. Sustainable Use of Drinking Water in Florida: an Emergy Analysis of Water Supply

and Treatment Alternatives. Ph.D. Thesis, University of Florida

EPA, Environmental Protection Agency, 1971. National Primary and Secondary Ambient Air Quality

Standards. Federal Register, V.36, n.84, p.8187. Part II. Washington, 1971.

Haukoos D.S., 1994. An emergy analysis of various construction materials. Class report ENV 6905,Environmental Engineering Science, under Dr. Mark T. Brown supervision.

Lapp C.W, 1991. Emergy analysis of the nuclear power system in the United States. Class report, EES

6916, Environmental Engineering Sciences, under Dr. H.T. Odum supervision.

Lyons G.J., Lunny F. and Pollock H.P., 1985. A procedure for estimating the value of forest fuels. Biomass,

8: 283-300.

Odum E.P., 1969. Fundamentals of Ecology. Second edition. W.B. Saunders, Philadelphia, P.A.

Odum H.T., 1996. Environmental Accounting. Emergy and environmental decision making. Wiley &

Sons, Inc., New York, 370 pp.

Tabet E., 1983. Fonti di energia e sanità pubblica. Medicina - Riv. E.M.I. 3: 245-258.

Ulgiati S. and Brown M.T., 2001, Emergy Accounting of Human-Dominated, Large-Scale Ecosystems, in

Thermodynamics and Ecological Modelling. S.E. Jørgensen Editor. Lewis Publishers, Boca Raton,

FL, USA; 63-113.

Ulgiati S., Odum H.T. and Bastianoni S., 1994a. Emergy analysis, environmental loading and sustainability.

An emergy analysis of Italy. Ecological Modelling 73: 215-268.

Ulgiati S., Cassano M. and Pavoletti M., 1995. Bridging nature and the economy. The energy basis of growth

and development in Italy. European Community, Environmental Research Programme. Area III,

Economic and Social Aspects of the Environment. Final Report, Contract No.EV5V-CT92-0152.



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ENDNOTES1Corresponding author2Several economic, environmental and energetic analyses have been performed by different subjects.

For example, consider EU studies reported at http://europa.eu.int/comm/energy_transport/atlas/

homeu.html.3As a result of some discussions at the previous Emergy Conference (September 1999), it was proposed

and accepted to change the value of global emergy base from 9.44 1024 seJ/yr to 15.83 1024 seJ/yr.

In such a way all the transformities are increased by a factor 1.68, still keeping the same relations.4See table 2 and table 3.5General Electric, Siemens Westinghouse; sources reported here are not listed in the references.6Comparison with results presented in Brown and Ulgiati, [4].7Since the transformity is dened as the “available energy (exergy)” (Odum [9]), it should be evaluated

using the specic exergy of fuel instead of the LHV. The slight difference between these two values

for fossil fuels makes the approximation reasonable.8Brown and Ulgiati, results of an unpublished work.

Table A1. Emergy accounting of the construction phase of a NGCC in Matera, Italy based on expected

life, but reported on annual basis.___________________________________________________________________________________

Solar Ref. Solar

Item Unit Amount Transformity for Emergy

(sej/unit) Transf. E18 sej__________________________________________________________________________________

_

POWER GROUPS

Gas turbines [g] 1.00E+09 2.77E+09 [1] 2.77Steam turbines [g] 4.00E+08 2.77E+09 [1] 1.11

Electric generator [g] 9.00E+08 2.77E+09 [1] 2.49

Gas turbines ttings [g] 2.00E+08 2.77E+09 [1] 0.55

Steam turbines ttings [g] 2.00E+08 2.77E+09 [1] 0.55

Installation [years] 9.38E+01 4.98E+16 [2] 4.67

AIR COOLERS

Air coolers [g] 4.40E+09 2.77E+09 [1] 12.19


HEAT RECOVERY STEAM GENERATOR

Heat exchangers [g] 4.68E+09 2.77E+09 [1] 12.96

Casing [g] 7.00E+08 2.77E+09 [1] 1.94

Chimneys [g] 2.30E+08 2.77E+09 [1] 0.64


AUXILIARIES

Pipes, valves, bypass [g] 8.00E+08 2.77E+09 [1] 2.22


Steelwork [g] 3.00E+08 2.77E+09 [1] 0.83


Tanks [g] 4.00E+07 2.77E+09 [1] 0.11


Water demineralization system [g] 3.50E+07 2.77E+09 [1] 0.10Installation [years] 1.46E+00 4.98E+16 [2] 0.07

___________________________________________________________________________________



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Table A1 (Continued)___________________________________________________________________________________

Solar Ref. Solar

Item Unit Amount Transformity for Emergy

(sej/unit) Transf. E18 sej ____

_______________________________________________________________________________ Various pipes, valves [g] 4.40E+08 2.77E+09 [1] 1.22


Aux boiler [g] 1.50E+08 2.77E+09 [1] 0.42

Installation [years] 2.08E+00 4.98E+16 [2] 0.10Compressors [g] 1.51E+07 2.77E+09 [1] 0.04

Installation [years] 6.25E-01 4.98E+16 [2] 0.03

Sound absorbing box [m2] 3.01E+07 1.50E+09 [3] 0.05

Sound absorbing box casing [g] 3.44E+07 2.77E+09 [1] 0.10

Installation [years] 6.25E-01 4.98E+16 [2] 0.03

Feeding HP pumps [g] 1.16E+08 2.77E+09 [1] 0.32

LP pumps [g] 2.80E+07 2.77E+09 [1] 0.08CIVIL WORKS

Inerts [g] 1.26E+11 1.00E+09 [4] 125.60

Concrete [g] 1.66E+10 5.08E+08 [5] 8.44Steelwork [g] 8.41E+08 2.77E+09 [1] 2.33

Pre-built [g] 7.56E+06 5.08E+08 [5] 0.00

Fences [g] 7.78E+08 5.08E+08 [5] 0.40

Insulation [g] 1.04E+08 1.50E+09 [3] 0.16

Installation [years] 1.41E+02 4.98E+16 [2] 7.00ELECTRIC SYSTEMS

Steel [g] 4.69E+08 2.77E+09 [1] 1.30

Copper [g] 1.88E+08 2.00E+09 [6] 0.38Aluminum [g] 2.81E+08 9.36E+10 [7] 26.30

Installation [years] 4.98E+01 4.98E+16 [2] 2.48TRANSFORMERS

Steel [g] 5.94E+08 2.77E+09 [1] 1.65

Copper [g] 8.38E+07 2.00E+09 [6] 0.17

Oil [J] 7.26E+12 6.60E+04 [8] 0.48

VARIOUS

Installation cooling towers [years] 2.40E+00 4.98E+16 [2] 0.12

Installation DCS [years] 4.17E+00 4.98E+16 [2] 0.21

Installation I&C [years] 1.56E+01 4.98E+16 [2] 0.78

Installation aux systems [years] 5.21E+01 4.98E+16 [2] 2.59

Engineering [years] 5.21E+01 7.47E+16 [9] 3.89

Commissioning [years] 4.17E+01 4.98E+16 [2] 2.08

Electric energy [J] 1.80E+13 1.60E+05 [10] 2.88



-169-


Diesel for local work [J] 2.68E+12 6.60E+04 [8] 0.18

Diesel for large transportation [J] 8.04E+11 6.60E+04 [8] 0.05

Diesel for general transportation [J] 4.69E+12 6.60E+04 [8] 0.31

Water [lt] 2.40E+07 2.43E+08 [12] 0.006

RENEWABLE

Solar radiation [J] 4.42E+15 1 [11] 0.004

SERVICES

Additional services for

plant manufacture [Mld lit] 5.50E+02 7.94E+17 [13] 436.70__________________________________________________________________________________

_

Table A2. Emergy accounting of the operation of a NGCC in Matera, Italy reported on annual basis.

____________________________________________________________________________________________

Solar Ref. Solar

Item Unit Amount Transformity forEmergy

(sej/unit) Transf E18 sej

__________________________________________________________________________________

_________ RENEWABLE

Solar radiation (average) [J] 1.77E+15 1.00E+00 [11] 0.00

Heat to the air by the Air Coolers [J] 1.46E+14 1.50E+03 [14] 0.22

Heat to the air by the Chimneys [J] 4.26E+13 1.50E+03 [14] 0.06Oxygen for combustion [g] 3.28E+12 9.29E+06 [15] 30.43

Oxygen for NG comb. . [g] 3.28E+11 9.29E+06 [15] 3.04

Pollutants released to the air [J] 2.53E+10 1.50E+03 [14] 0.00GOODS

Natural gas [J] 3.92E+16 4.80E+04 [16] 1,883.52

Add NG for fuel processing [J] 3.92E+15 4.80E+04 [16] 188.35

Water for Services (DEMI+general) [lt] 9.36E+07 2.43E+08 [12] 0.02

Water for Cooling Towers (Delta) [lt] 1.95E+08 2.43E+08 [12] 0.05

Acid solution for DEMI plant [g] 6.99E+07 2.92E+09 [17] 0.20

Basic solution for DEMI plant [g] 2.66E+07 2.37E+09 [18] 0.06

Chemical for boilers [g] 7.00E+06 2.50E+09 [19] 0.02Chemical for wastewater treatment [g] 5.00E+05 2.50E+09 [19] 0.00

MAINTENANCE

Expenses for maintenance [Mld lit 98] 5.50E+00 7.94E+11 [13] 0.00Labour from outside [years] 7.00E+01 2.49E+16 [18] 1.74

LABOUR

Graduated [years] 3.00E+00 7.47E+16 [8] 0.22Technical [years] 2.20E+01 4.98E+16 [2] 1.10

Base technicians [years] 4.00E+01 2.49E+16 [18] 1.00

SERVICES

Services for fuel supply, proc. & extrac [Mld lit] 3.55E+02 7.94E+08 [13] 282.00

ENERGY REQUIRED BY THE PLANT

Self-consumed electricity [J] 0 1.60E+05 [10] -



-170-


PRODUCTS

Electricity [J] 2.12E+16 -___________________________________

________________________________________________________ REFERENCES FOR

TRANSFORMITIES (TABLE A1 and A2)

[1] Houkoos, 1994 [11] Odum, 1996

[2] Ulgiati, 1996 [12] Buenl, 2001[3] Ulgiati, [13] Ulgiati, 1996

[4] Houkoos, 1994 [14] Odum, 1996

[5] Houkoos, 1994 [15] Ulgiati

[6] Lapp, 1991 [16] Odum, 1996

[7] Ulgiati, [17] Tonon, 2001 (estimation)

[8] Odum, 1996 [18] Tonon, 2001 (estimation)

[9] Ulgiati, 1996 [19] Tonon, 2001 (estimation)

[10] Odum, 1996 [20] Ulgiati, 1996

Table A3. Emergy ows for NGCC based on use of air coolers

___________________________________________________________________

Construction Operation Total %

1 E18 seJ, except where noted

___________________________________________________________________

Direct Renewable input R1 0.0 334.7 0.3 1.4%

Indirect Renewable input R2 0.4 0.0 0.0%

Renewable input R 335.1 0.3 1.4%

Non-Renewable input N 20,720.0 20,720.0 85.7%

Purchased input F 70.5 3.6 74.0 0.3%

Direct Labour FL 10.7 40.6 51.3 0.2%

Services (indirect labor) S 145.6 2,864.0 3,009.0 12.4%

Yield R+N+F 226.7 23,960.0 24,190.0 100.0%

____________________________________________________________________






Edited by



Associate Editors

Howard T. Odum



David Tilley



Sergio Ulgiati

University of Siena

Siena, Italy

December 2003



Gainesville, FL


vi



12

On the Rationale of the Transformity Method

Dennis Collins

ABSTRACT

In terms of transformities, this paper presents some over-simplified or “inorganic” differential-

equation models for ecological networks that nonetheless have some desirable mathematical properties.

It is hoped that these models may provide a platform or springboard for creating more realistic or “or-

ganic” models based on transformities.

INTRODUCTION

From networks, the transformities of H.T. Odum or quality-equivalent mechanisms (QEM’s ) of

M. Patterson can be calculated. These represent essential qualities or “trophic levels” of the system.

Also, systems of differential or difference equations can be developed to describe the system. A kind of

inverse problem is whether or not the system of differential equations can be written completely in terms

of the transformities. For example, a “normalized” Lotka-Volterra predator-prey system with equilibrium

point (herbivores, carnivores) = ( x, y) = (8, 1) might be written:

x’ = x(1 - y)

y’ = y( - 8 + x).

Here the fact that eight herbivores units support one carnivore unit might roughly indicate a

transformity of 8 to 1 for carnivores versus herbivores. The transformity “8” then represents the essential

parameter of the differential equations. Also, the point (8, 1) is an equilibrium point of the system, since

x’ = 0 and y’ = 0 at x = 8, y = 1. This paper presents an “inorganic” solution, that is, a solution that has only

linear slope functions for the differential equations system, to the above problem. This model—which

may be of primarily mathematical interest—roughly corresponds to a system of tanks which more-or-less

gradually fill up to the required equilibrium values. A more realistic solution would require non-linear

slope functions as the Lotka-Volterra model above that contains the risk of death (( x, y) = (0, 0) equilibrium)

as well as life. The model obtained in this paper for the predator-prey system would be the following: x’ = 8 - x

y’ = 1/8 x - y,

which also has equilibrium point (1, 8).

Then this paper will deal with a limited version of the emergy-transformity ecological models of

H.T. Odum. The models here are digraphs (or directed graphs) with two types of nodes (or vertices): 1)

Production nodes (here rectangles but in Odum’s work, rectangles rounded in the direction of the output)

combine various commodities or food to produce one or more output commodities or life forms. 2)

Commodity-storage nodes (here circles but tank-shaped in Odum’s work) receive branches from processes

that produce the given commodity and branch out to production nodes or processes that use the commodity

as input. The digraph is bipartite in the sense that branches from production nodes can only connect to

storage nodes, and branches from commodity nodes can only connect to production nodes. This method

agrees with Patterson’s later work. Roughly, the storage nodes are capacitive and the production nodes

are dissipative or resistive.



The discussion will cover three other types of model besides the digraph model (1.) discussed

above: 2. Transformity models to calculate transformity; 3. Flow models to calculate steady-state

flows; 4. Dynamic or differential equation models involving the storage nodes. There is a limited

duality between the models 2. and 3. developed by Mikulecky and other previous writers. The amount

stored in the tank nodes is not important in the steady-state theory, i.e., until the dynamic model 4.

The theory will be worked out for four digraph models (A, B, C, and D in Figure 1). The models

will illustrate the “battle for uniqueness” within the approaches.

A

B

C

D

30

301

30

15

Z*1 Z*2

130

152

30

82

30

83

30

1

S

S

S

S

100

1001

200

20

Z*1

1100

102

200

2Z*2

100

14

100

1005

Z*33

100

12

1

2

3

1

2

3

4

5

4

5

6 6 7300

5

600

10

Z*6

100

1

500

9

Z*5

100

1

500

9

100

5

100

5

400

10

300

5

400

10

Z*3

Z*4

100

5

100

5100

100

100

100

100100

100

100

100100

1

2

1

2

100

20

100

50

100

20

10050

3

1505

50

10

3

4

150

5

50

10

5

4

Z*1

Z*2

Z*3

Z*4

By-product

150

5

50

10

5

10010

Z*1

Z*2

100

1

Feedback Loop

"Commodity

Unification"

3

Figure 1. Four energy networks in which emergy flow is written above the pathways and energy flows below. The

ratios are transformities by definition. A, pipeline topology; B, system with a split and feedback; C, web with

commodity unification; D, web with co-product branch.



The Transformity Model

“Emergy” or “energy memory” measures energy required to produce something. Thus, emergy

is never forgotten in the digraph flow process, unlike energy, which, although it is conserved, is mostly

wasted in production processes, so that the useful energy flow is constantly decreasing from input to

output of a production process. Only useful energy flow is accounted for in these models. Thus, all nodessatisfy “emergy in = emergy out,” and storage nodes also satisfy (in this approximation) “energy in =

energy out,” but the production nodes satisfy “useful energy in > useful energy out” due to dissipative

losses of the second law of thermodynamics.

Every branch in the steady state process thus has an emergy flow and an energy flow assigned to

it. In the diagrams (Figure 1, A to D), the emergy flow is put above the branch and the energy flow below

the branch. The ratio of the emergy flow divided by the energy flow is called the transformity. It is

assumed that the transformity of every branch from a given storage node is the same, since it reflects the

flow of the same commodity. Thus, this identical transformity can be assigned to the storage node from

which the commodity flows and also to the commodity itself. Knowing the transformities of the

commodities plus the energy flow into each process allows the calculation of the emergy into each process

or production node by summing “transformity x energy flow” (= emergy flow) over the branches enteringa given production node.

The transformity model is simply the system of linear equations stating “emergy in = emergy

out” for each production node in terms of the transformity of each commodity involved, i.e., each emergy

term is represented by a product (called a constitutive relation) “emergy flow of given commodity =

transformity energy flow.” Thus, the number of equations is equal to the number of production nodes,

and the number of variables is equal to the number of storage nodes. Transposing all terms to the left

results in equations in terms of a commodity energy flow matrix J and a transformity column vector T:

J T = O

where the 0 is the column of zeros.

Table 1. Template for Evaluating Transformities with Mathematica with Values of Energy

Flows of Transformation Processes Entered in the Matrix m.



A BASIC program to solve for the transformities by an eigenvalue method was developed by

the author and presented at a colloquium of H.T. Odum on July 2, 1997. A similar method is included in

the 1998 paper of Patterson. A Mathematica version (Table 1) is used to approximate the transformities

in the above programs (AT-DT). See Collins and Odum (2001) for explanation and applications to

ecosystems. Further applications were reported in this volume (Odum and Colliins, 2002).

The matrices for the four examples are given in the programs AT, BT, CT, DT (Table 2) to solve

for transformity.

If there is more than one product of the process node (by-product case), then additional equations

could be added describing the partition of emergy coming out of the production node. This case occurs in

example D in Figure 1.

Table 2. Transformities Calculated by the Mathematica Program in Table 1 for the Four Systems in

Figure 1.

__________________________________________________________________________

System Matrix J* Error Transformities

Vector T

__________________________________________________________________________ A [30 -15 0 1181, 232, -2.8E-15 x1 = 1

0 15 -8] x2 = 2

x3 = 3.75

B [100 -20 1 0 0 10408, 194, 52, 1.95, x1 = 1

0 10 -2 5 0 -3.1E-15 x2 = 10

0 10 0 -5 0 x3 = 100

0 0 1 0 -1] x4 = 20

x5 = 100

x1 = 1

C [100 -5 0 0 0 0 0 30059, 281, 177, 42, x2 = 20

100 0 -5 0 0 0 0 28, 1.36, -4 E-15 x3 = 20

100 0 0 -10 0 0 5 x4 = 40

0 5 0 0 -1 0 0 x5 = 100

0 0 5 10 0 -9 0 x6 = 55.6

0 0 0 0 1 9 -10] x7= 60

Pattersons Method (omit “Commodity Unification” in Fig. 1)

C [100 -5 0 0 0 30059, 281, 177, 42, x1 = 1

100 0 -5 0 0 28, 1.36, -4 E-15 x2 = 15.8100 0 0 -10 5 x3 = 22.1

0 5 0 0 -1 x4 = 39.8

0 0 5 10 -9] x5 = 57

D [100 -20 0 0 0 21747, 3642, 956, x1 = 1

100 0 -50 0 0 504, -107 E-14 x2 = 5

0 20 50 -5 -10 x3 = 2

0 0 0 5 -30] x4 = 30

x5 = 5

__________________________________________________________________________

* Energy flows under transformity columns: x1, x2, x3,etc



The Flow Model

This paper is concerned with the inverse problem of calculating the steady-state flows, knowing

the transformities. There is a problem because there are many more branches than nodes, so that there are

more branch flows to calculate than there are node transformities to calculate them. As a consequence,

the m transformity equations 1] must be “expanded” to cover the equation “emergy in = emergy out” asan equation over all branches. Of course, the flow and transformity variables have been interchanged in

a kind of duality. Further, to specify the branch flows, two more sets of equations must be added: 2]

commodity node equations stating “energy in = energy out” for each commodity node, and 3] “recipe” or

proportion equations stating the proportion of energy required for each production node. These equations

have the form in terms of a transformity matrix TF and flow vector JF:

TF . JF = 0

If there are k branches, the above equations will uniquely solve for the energy flow in each branch.

These equations are presented and solved in the programs AF, BF, CF, and DF (Table 3) by the same

program. However, as in the Gauss elimination method or its unique row-reduced echelon form

Figure 2. Differential equations for the four systems in Figure 1, where zi is the flow from process i to storage i.



Table3.E

nerg

yFlowsCal cu

latedbyth e

M a t h e m

a t i c a

PrograminT

able

1forth

eFourSystem

sinFigu

re1

________

__________

___________

___________

__________

___________

___________

___________

__________

___________

__________

Syst

em

Ma

trix*

Error

Transfo

rmities

Energy

Fl o

w

Vector

Ve cto

r

______

___________

___________

__________

___________

___________

__________

___________

___________

__________

___________

_

A

[30

- 15

0

1

181,232,

-2.8E-15

x1

=1

30

0

15

-8]

=J

x2=2

=T

z1*

=15

x3=

3.75

z2*=8

AlternateMe

thod

A

[1

-2

0

0

9

14

,19,3.9,-4.4

E-16

x1=1

30

0

2

-3.750=

TF

x2

=2

z1*=15

=J F

0

0

3.7

5-30]

x3=3.75

z2*

=8

x4=3

0

1

B

[1

-10

100

0

0

0

0

0

2

0050,

10500,500,5

2,

x1=1

1 00

0

0

0

10

0

0

20

-100

5

.5,1.6,1.39,-1

.33E-13

x2=10

z1*=20

0

0

0

0

10-20

0

0

1

0

0

0

1

-1

0

0

0

=T

F

10

=J F

1

0-100

0

0

0

0

0

10

0

0

0

1

0

0

-2

0

x3=100

z2*= 2

0

1

0

-1

-1

0

0

0]

x4=

20

5

x5

=100

z3*=5

C

[1

0

0-20

0

0

0

0

0

0

0

2

4415,7107,

100

0

1

0

0-20

0

0

0

0

0

0

4

556,181

3,376,

100

0

0

1

0

0-40

0

0

0

0

0

3

31,

67,2.98,

99.8

0

0

0

20

0

0-100

0

0

0

0

1

.0,0.99,0.0

011

z1*=5

0

0

0

0

20

40

0

-55.60

0

0

z2*= 5

0

0

0

0

0

0100

55.5- 6

0

0

0

z3*

=9.97

=J F

0

0

0

0

0

0

1

1

-1

0

0

=T

F

z4*=+1

0

0

0

0

0

0

0

0

1

-1

-1

z5*=

8.97

0

0

0

0

1

1

0

-1

0

0

0

z6*=9.98

0

0

1

0

0

0

0

0

0-20

0

5. 0

0

0

0

0

0

0

9

-1

0

0

0

5.0



Table3E

nergyFlows(continued)

__________________________________________________________

_________________________

_________________________

___

System

Matrix*

Error

Transformities

EnergyFlow

Vector

Vector

__________________________________________________________

_________________________

_________________________

___

1

-1

0

0

0

0

0

0

0

0

0

0

1

-1

0

0

0

0

0

0

0

0]

__________________________________________________________

_________________________

_________________________

___

System

MatrixTF*

Error

Tran

sformities

EnergyFlowJ F

__________________________________________________________

_________________________

_________________________

___

Patterson

’smethod

C

[1

0

0

-15.8

0

0

0

0

0

0

6862,3795,3662,441,

100

- >58.8

0

1

0

0

-22.1

0

0

0

0

0

238,91,3.0,1.01,1.00

100

- >59.2

0

0

1

0

0

-39.8

0

0

+57.6

0

0.0027

99.8

- >59.6

0

0

0

15.8

0

0-57.6

0

0

0

z1*=5

- >3.68

0

0

0

0

22.1+39.8

0

-57.6

0

0

z2*=5

- >2.68

0

0

0

0

0

0

1

1

-1

-1

z3*=9.97

- >5.82

0

0

0

0

1

1

0

-1

0

0

z4*=+1

- >1.0

0

0

1

0

0

0

0

0

-20

0

z5*=8.97

- >5.05

0

0

0

0

0

0

9

-1

0

0

No

deeliminated

9.98

1

-1

0

0

0

0

0

0

0

0

5.0

- >2.98

0

1

-1

0

0

0

0

0

0

0

5.0

- >3.07

Recipefo

rsplittingstudy

D

[1

0

-5

0

0

0

9

57,32,5.8,2.1,

z1*=20

0

1

0

-5

0

0

1

.7,6.7E-15

z2*=50

0

0

5

2

-30

-5

z3*=4

=J F

1

-1

0

0

0

0

z4*=10

0

0

2.5-1

0

0

z5*=1

0

0

0

0

2

-1]

z6*=2

__________________________________________________________

_________________________

_________________________

___

*Energy

flowsarrangedundertransform

itycolumns:x1,x2,x3,x4,etc.



generalization, the flows not included in the original transformity equations can gradually be expressed

in terms of the distinct process-to-dominant-commodity flows and eliminated from the flow equations

(cf. pp. 134-145 in Mikulecky). Keeping track of the relations, all flows can be derived from the original

transformity equations, written in terms of the reduced set of flow variables, and with the transformities

determining the coefficient matrix (for the most part, except proportionality constants).

The Dynamic Model

Now the equilibrium flows satisfy the J T = 0 of the Transformity Model section above, so that

if the flow equations are changed into differential equations for the rate of change of flow in the commodity

nodes, then an equilibrium point of these equations will be the steady state flows.

This creates—albeit artificially—a dynamic system which has as its equilibrium limit the steady-

state flows. The creation of such systems is a stated goal of the network thermodynamics of Mikulecky

(cf. pp. 14 and 78). Although the method described above is still what the network thermodynamics

wishes to avoid, because it merely changes the equilibrium status into a system of differential equations,

nonetheless it may give some indication of how to set up a correct dynamical system for the flows involved.

The method differs from the electrical models in that the transformity typically goes up as one follows the

emergy “current,” whereas the voltage potential goes down. It still holds, however, that the sum of

transformity changes around a closed loop is zero, similar to the Kirchhoff voltage law.

The method above appears to differ somewhat from Odum’s version in that he regards emergy

as being used up when a commodity follows a loop back to itself (cf. p. 89 in Odum’s book), whereas here

the “emergy in = emergy out” always holds, although the emergy may be diluted so as to approximate

Odum’s approach. For example, if energy is spent on water to purify it, and later the water is dumped into

an untreated water storage, in this theory the water of the storage would increase its average emergy level

somewhat (perhaps a small epsilon) above its previous value. Of interest is how other Odum symbols

(such as the consumption symbol) should be related to emergy as treated here. Observe that since “emergy

in = emergy out” in the steady-state model, emergy can cycle without building up. It may be that one canignore the cycles and leave them out of the analysis as I believe Odum claims, but, as in the case of

electrical systems where steady state current cycles driving appliances are a major interest, this procedure

may also leave out the most important topics of analysis.

To test most dynamic differential equation properties, it is necessary to have an n x n system.

Such a system can be obtained by gradually eliminating branch flows, as explained above. However,

there is still some doubt as to which variables to include, and results can change somewhat, depending on

these decisions.

Examples of systems of differential equations for the four systems A to D are given in Figure 2

(AE to DE). After dividing by the transformity (according to the “constituitive relation”), the coefficient

in each equation of the variable differentiated is -1, which goes toward Odum’s purpose of making the

dynamics comparable regardless of energy scale.

Ordinarily it takes an entire matrix R to determine a system of linear differential equations, say

dz/dt = Rz. Here the matrix R is determined by only the one vector of transformities T, plus perhaps some

other constants involved in reducing the flow system to an n x n system. In this sense the vector T is like

the infinitesimal generator of a continuous group representation, which can depend on only as many

parameters as the group (say 3), even though the matrix has many more components (say 3 x 3 = 9). The

paper by Terrell gives conditions under which a matrix can be considered to be generated by the one

vector of coefficients of its characteristic polynomial. The systems of differential equations can be tested

for different properties of control theory, such as complete controllability. The property of complete

controllability depends on whether or not the vectors b, Rb, R 2, R 3 b,...,R n-1 b are linearly independent,

where b is the input vector. Complete controllability apparently implies the storage node flows can bedriven to arbitrary amounts. The first example is completely controllable using the input



1

030

-2 0

2 -3.75

-1 0

2 -1 /3 .75and matrix or

The dynamic systems set up in AE to DE, which may be written

dz/dt = f = Az + b

can be easily solved numerically by programs such as Mathematica and various properties calculated,

such as complete controllability.

Stability

The stability of linear systems of differential equations of the above type is discussed in Example

A8, p. 27 (Higashi and Burns, 1991). Such a system is asymptotically stable if the real parts of the

eigenvalues of the system matrix are all negative.

CONJECTURE: Any non-trivial system derived as above is asymptotically stable.

The reason is that the differential equations describe the energy flows zi, which are alwaysdecreasing by the second law of thermodynamics. The eigenvalues are calculated for a couple of the

cases (BE real parts -1.38, -1.38, -.239, and CE real parts -1.39, -1.39, -1, -1, -1, -.206), and seen to have

negative real parts. An example output run of the dynamical system (“inverse method”) for case B is

given in Figure 3.

Sensitivity

The systems of differential equations can be rewritten in terms of the transformities (Figure 4).

Now the transformities play the role of parameters, and it is possible to study the sensitivity of the steady-

state flow vector (called x* in Higashi and Burns, pp. 27-28) as a function of these parameters. Theequation expressing the sensitivity is given as

x*

pk

= -A-1

pk

f

In the notation above, x is replaced by z, and p by T.

Here the vector f represents the vector of differential equations.

For example in case BE, the sensitivity of steady-state value z* with respect to T1 is calculated

as follows:

x*= A-1

T

f

T= -

-1-1 5 0

1/20 -1 1/5

1/4 0 -1

100(-1/T12) + (-1/T1

2)T2z2

2

(z*,T)

z12T2z1

2T3

-2 -10 -2

-1/5 -2 -2/5

-1/2 -5/2 -3/2

-2

1/10

1/2

=

-2

0

0

. Here z* = 2

5

20

and T = 100

20

10

= -



(a)

(b)

(c)

Figure 3. Mathematica output plots over time of the equations derived for system B of Figure 1. (a) z1; (b) z2;

(c) z3.



DISCUSSION

There are several versions of how to compute transformity at present, including that discussed

in Prof. Odum’s book and that (the QEM) of Prof. Patterson. It is important to understand how these

methods differ, since the success of transformity in applications will probably depend on whether or not

different researchers can agree on values obtained. Fortunately the method is robust to the extent that

many different approaches may give approximately the same result.

For example, Patterson’s method seems to differ from Odum’s in that Patterson does not require

a “commodity unification node” to combine commodities produced by different processes, and therefore

with different transformities. The consequence is a certain approximation to the results obtained in this

paper, as illustrated by the two versions of Example C in Figure 1.

Figure 4. Differential equations for the four systems in Figure 1 in “normal form.”



Another example of a difference between Odum’s method and Patterson’s is in the treatment of

by-products. Prof. Odum’s method, although consistent, has the mathematical problem that the emergy

function is discontinuous when emergy disappears after completing a feedback loop. One way of handling

this problem (Patterson’s criticism) is to eliminate the feedback loops and work only with feedforward

paths. If feedback loops include only the emergy of the source, they can be omitted in Odum’s procedure

to avoid double counting. The emergy of a closed loop is regarded as constant. However, where feedback

energy has an independent source, some feedback of emergy is allowed without instability, and increases

the transformity as expected. Also (part of Odum’s theory), some increase of emergy at a node due to co-

products (which may be termed “synergy”) is allowed without instability, although this increase would

violate the “emergy in = emergy out” rule of this study. Mathematically, the matrices a = mt *m, which

appear in the calculation of transformities, represent a wider class beyond “singular M-matrices” that

have a positive eigenvector (Berman and Plemmons, 1994). A mathematical study up to 3 x 3 matrices is

available as an appendix to this paper but is not included here. A condition (1 - su - pv - qt - svq - upt >

0, where u, v and s, t and p, q represent the feedback proportions from the three processes to the other two

processes) for stability is given in this appendix. Some “synergy” (say u = 1 and v = 1, so that u + v = 2

> 1) is allowed provided some of the other feedback coefficients are small.

In general, the approach here is that energy flow recipes into a process must be prescribed as part of thedefinition of the process (as in a cookbook), and emergy flows of by-products must be estimated.

The question of what to do in the case of default of information is partly handled by Odum, and

also may be considered part of Patterson’s method. The author’s view of the above approach may change

on consideration of more complicated examples.

CONCLUSION

Transformities may have a value in describing the differential equations of a network as well as

indicating rough “trophic levels,” or levels of complexity. The problem of working out this relationship

for non-linear systems such as the Lotka-Volterra system remains open; however this paper presentscertain approaches which may be applied to the general problem. Roughly it is believed that the nonlinear

systems can be studied by the process of Hopf bifurcation (Borrelli and Coleman, 1998). Although the

models of this paper are stable, the stability of the linearization is not necessary for stable limit cycles to

exist in supercritical Hopf bifurcations. This extends that fact that logistic growth defined by a nonlinear

slope function x’ = x (a - bx)) is limited by the saturation value x = a/b even though the linearized model

x’ = ax shows exponential or unstable growth. These considerations may be relevant to Hannon’s unstable

material flow models (Hannon, 1986), that is, if the correct nonlinear differential equations were obtained,

the instability of the linear versions would not be a problem. Although the over-simplified models of this

paper may be mainly of mathematical interest, unless the linear models are studied for their deficiencies,

the nonlinear theory remains on an unstable footing.

Finally, the author would like to thank Prof. H.T. Odum for his many discussions and work on

this paper. His awesome ability to point the way to solutions of ecological problems will be almost

impossible to replace should he not recover from his present illness. Also, should this paper be included

in the proceedings of the 2001 conference, it will be due to the tireless efforts of Prof. Mark Brown and

Ms. Joan Breeze.

REFERENCES

Berman, A. and R.J. Plemmons. 1994. Non-negative Matrices in the Mathematical Sciences. Siam,

Philadelphia, Pennsylvania.

Borelli, R.L. and C.S. Coleman. 1998. A Modeling Perspective. John Wiley, New York, pp. 490-494.Collins, D. and H.T. Odum. 2001. Calculating transformities with an eigenvector method. pp. 265-280

in Emergy Synthesis, ed. by M.T. Brown. Center for Environmental Policy, Univ. of Florida,

Gainesville.



Hannon, B. 1986. Ecosystem control theory. J. Theor. Biol. (1986) 121, 417-437.

Higashi, E.M. and T.P. Burns. 1991. pp. 26-27 in Theoretical Studies of Ecosystems: The Network

Perspective. Cambridge U. Press, Cambridge, U.K.

Mikulecky, D.C. 1991. Network thermodynamics: A unifying approach to dynamic nonlinear living

systems. pp. 71-99 in Theoretical Studies of Ecosystems: The Network Perspective, by E.M.

Higashi and T.P. Burns. Cambridge U. Press, Cambridge, U.K.

Odum, H.T. 1996. Environmental Accounting. John Wiley, New York, 370 pp.Odum, H.T. and D. Collins. 2002. More transformities for ecosystem energy webs with the eigenvalues

method. Proc. of Emergy conference of 2001. Center for Environmental Policy, Univ. of Florida,

Gainesville.

Patterson, M. 1998. Understanding energy quality in ecological and economic systems: a brief explanation

of the QEM. pp. 257-274 in Advances in Energy Studies, Porto Venere, Italy, 26/30 May, 1998.

Musis, Rome, Italy.

Terrell, W.J. 1999. Some fundamental control theory I: controllability, observability, and duality. American

Mathematical Monthly 106(8):705-719.






Edited by



Associate Editors

Howard T. Odum



David Tilley



Sergio Ulgiati

University of Siena

Siena, Italy

December 2003



Gainesville, FL


vi



13

MATHEMATICS FOR QUALITY.

LIVING AND NON-LIVING SYSTEMS

Corrado Giannantoni

ABSTRACT.

The traditional Differential Calculus often shows its limits when it tends to describe living Systems.These Systems in fact present such a richness of characteristics that are, in the majority of cases, muchwider than the description capabilities of the usual differential equations.This suggests we extend our concept of “derivative” in order to include at least fractional order derivatives. This simple step is already sufficient to give differential bases to the rules of Emergy Algebra(e.g., co-products, splits, etc.) and introduces a new class of functions (the “binary” functions) whichare able to show how a “co-generated” product can be something more than the sum of the co-generating elements. This result, in turn, implies that the evaluation of a derivative can be determined in-dependently

from the evaluation of its lower order derivatives. This enables us to introduce a different perspective inanalyzing living Systems: we can follow their evolution from the very beginning, in their “rising”, intheir “incipient” act of being born, which is aptly described by the so-called “incipient” (or “spring”)derivative.

Such an approach results not only as being more adequate to describe the specific processes typical of Life, but presents particularly interesting mathematical characteristics: i) the solutions to differential equations are always explicit (with enormous advantages of calculation) and, above all, ii) such solutions

show a sort of “persistence of form” in the product generated with respect to the agents of the generating process. This mathematical aspect shows a profound analogy with genetic processes: every being retains genetic characteristics of its ancestors, even though, at the same time, it constitutes one new and solebeing.

Even if the paper is especially finalized to the description of self-organizing living Systems, some specific examples devoted to non-living System will also be mentioned. In fact, what is much more surprising is that such an approach is even more valid (than the traditional one) to describe non-living

Systems too (internal structure of the photon, precessions of Mercury, etc.).

1. INTRODUCTION. MATHEMATICS FOR SELF-ORGANIZING SYSTEMS

The Analysis of Complex Systems requires us to make use of

all those mathematical tools able to describe their time-space evolution as being even more

strictly related to their intimate specific structure. In particular, if we want to analyze self-organizing

Systems through the Maximum Em-Power Principle (Giannantoni, 2001c), this requires the knowledgeof the mathematical structure of the Emergy Source Terms, in order to account for the increase in Quality

corresponding to the different hierarchical structural levels of the analyzed Systems. At the same time it



would be desirable for all the rules of Emergy Algebra, in addition to their logical bases, to be explicitly

derived on the basis of the generative processes which happen inside the System. As we will see, both

such aspects are not only strictly related to each other, but they can also be thoroughly analyzed by

means of a new mathematical approach: the Intensive Fractional Differential Calculus. In such a way

both the intimate structure of Emergy Sources and the rules of Emergy Algebra can be contemporarily

derived (under extremely general dynamic conditions) from a unique and innovative mathematical point

of view.

However this (in general) cannot be considered sufficient in itself. In fact “generative” systems

(such as living systems) require a more appropriate mathematical language to describe their spring-activity, in addition to the specific characteristics of the generated “product”: the persistence of the formof its ancestors’ genetic characteristics (although the generated being contemporarily constitutes something

new). To this purpose, a generalization of the above-mentioned approach, suggested by one of its basic

results (that is the independence of each derivative from the sequence of lower order derivatives), allows

us to introduce the even more general differential perspective termed Incipient Differential Calculus (or

Prior Operator Differential Calculus). The fundamental advantages of such a generalized mathematical

approach will be shown through the analysis of some examples pertaining to self-organizing systems,

by means of the solution of Riccati’s and Abel’s equations which can now be solved in explicit terms, both with constant and variable coefficients. We will also show how such an approach can allow us to

face both old and new problems, in Classical and Quantum Mechanics respectively , by drawing some

surprising and unexpected conclusions concerning non-living systems too.

Let us now start from the mathematical description of the Emergy Source Terms which appear

in the mathematical formulation of the Maximum Em-Power Principle (Giannantoni, 2001c).

2. INTENSIVE FRACTIONAL DIFFERENTIAL CALCULUS AS A BASIC

“LANGUAGE”

Let us consider the following linear differential equation of the first order with constantcoefficients

(2.1)

which contains a derivative of order Ω in addition to the traditional derivative of order one.

If we take into account that the fractional derivative of order Ω of the exponential function e t α has

two distinct values defined as follows (see Appendix 1)

d

dt e e e

t t t

1 2 1

2

1 2

/

/

α α α α α = = ± ⋅ (2.2)

it is possible to express the general solution to Eq. (2.1) by means of two distinct functions

( ) f t c e c et t

1 11 12112

122

= +α α (2.3) ( ) f t c e c et t

2 21 22212

222

= +α α (2.4).

The function ( ) f t 1 is carried out by assuming the former structure in Eq. (2.2), that is ,

whereas ( ) f t 2 corresponds to the structure − ⋅α α

e t

, while the pertinent exponential coefficientsare derived from the two associated characteristic equations (Giannantoni 2000b, 2001a,b). We may

dF t

dt A

d

dt F t B F t

( )+ ⋅ ( ) + ⋅ =

1 2

1 2 0

/

/ ( )

+ ⋅α α e

t



now observe that, although the derivative ( )d

dt F t

1 2

1 2

/

/ presents two distinct values, it conceptually

constitutes one sole entity. In addition, it does not imply any particular assumption about the order of

the sequence of the signs (+/- or -/+), so that the functions ( ) f t 1 and ( ) f t 2 should have couples of

corresponding exponents, in principle interchangeable, according to the order of consideration of such

signs. The general solution ( ) F t may thus be written in a compact form as follows

F t f t

f t

c

ce

c

ce

t t

( ) = ( )

( )

=

⋅ +

⋅

1

2

11

22

12

21

11

22

212

21

2α

α

α

α (2.5),

where, by convention, the upper exponents refer to the function (corresponding to the choice of the sign

“plus”) whereas the lower exponents refer to the function (corresponding to the choice of the sign

“minus”). On the basis of Eq. (2.5), the comprehensive solution may be named as a “binary” function,

not only because it is made up of two distinct functions, but also (and especially) because the twocomponents are so strictly related that they form one sole entity.

In spite of this consideration, the initial conditions for the function (solution to Eq. (2.1)) can be given,

in principle, without any particular restriction, for instance as follows

F f

f 0

10

20

( ) =

(2.6) and F

f

f

1 2 10

1 2

20

1 20

/

/

/

( )( )

( )( ) =

(2.7),

conditions which only require the prefixed up/down convention of signs to be respected.

3. EMERGY SOURCE TERMS AND “BY-PRODUCTS” IN THE LIGHT OFTHE RACTIONAL CALCULUS

An equivalent mathematical model (chosen from several possible) for a co-production processª,

in steady state conditions, has already been analyzed in Giannantoni (2000a). In that case (see Fig.1) we

showed that the Emergy Flow Balance could be written as follows

E m u u E m y E m y⋅ ⋅ ⋅

( ) + ( ) = ( ) + ( )Φ 1 2

(3.1)

Figure. 1- Equivalent mathematical model for a co-production process

E m y⋅

( )2

( )u

E m y⋅

( )1 E m u⋅ ( )



where

E m y E m y⋅ ⋅

( ) = ( )1 2 (3.2) and Φ u E m u( ) = ( )

⋅ (3.3),

whereas now, in variable conditions, we have to take the Emergy Accumulation Term into account, so

that Eq. (3.1) becomes

E m u ut

A E m y E m y D

⋅ ⋅ ⋅( ) + ( ) = + ( ) + ( )Φ

∂

∂ 1 2 (3.4)

where the term t A D ƒ ƒ / represents the “local” variation (in the Eulerian sense) of the Accumulated

Emergy on behalf of the considered System.

In writing the previous equation we simply assume that the analyzed System represents a co-productive

process only on the basis of the relational structure of its pertinent outputs, that is without considering its internal productive structure, which, in any case, does not “appear” explicitly in Eq. (3.4). So that, in

order to reach a possible mathematical description of its internal productive structure, we may now

compare Eq. (3.4) with a fractional differential equation, written in a unique variable (already thoughtof as a flow, for simplicity of notation), whose homogeneous part is similar to Eq. (2.1), that is

C dEm t

dt A

d

dt Em t B Em t Em u t ⋅ + ⋅ + ⋅ = [ ]

( )( ) ( ) ( )

/

/

1 2

1 2 (3.5).

As a first result it is then easy to show that, if we assume the output Emergy Flow to be proportional to

the accumulated Emergy (as is usual in physical and biological systems)

Em k A D= ⋅ (3.6)

it follows that, for C k = A = 1 and B = 2 (3.7),

Eq. (3.5) presents a solution in the variable which is, in principle, a binary function

Em t Em y t

Em y t ( )

( ( ))

( ( ))=

1

2

(3.8)

whose “components” are the two un-known output functions. We will now show in detail, through the

analysis of the explicit solution, that the definition of “co-products” directly derives from the inner

structure of the System and that the “Source Term” is strictly related to the derivative of order 1/2.1

The explicit solution of Eq. (3.5) is formally given by (see Giannantoni, 1995)

Em t t D Em u t Em D t

k k

k

( ) ( , ) [ ( ( )) ]= ⋅ + ( )−

−

=∑ϕ δ 1

0

2

1

2

0

1

(3.9)

where ϕ ( , ) ( )t D C D A D B= ⋅ + ⋅ +1

2 2

1

2 (3.10)

is a second order polynomial operator in the basic operator 2

1

D , while D is a differential operator

simply defined as follows (by means of the Dirac delta function )(t δ )(ib.)



)()0()()( t f t f dt

d t Df δ −= (3.11)

which allows us to directly introduce the associated initial conditions (generally considered separately)

into the formal solution (3.9). Such a solution can also be given in explicit terms

Em t k t Em u t Em D t d k k

k

t

( ) ( , ) [ ( ( )) ]= ⋅ + ( ) ⋅ −

=∑∫ τ δ τ

0

21

2

0

1

0

(3.12)

where

),( τ t k is the solving kernel of Eq. (3.5), which always has anexplicit form (see par. 6).

The formal structure of Eq. (3.9) allows us to draw the following main conclusions:

i) the operator 1),( − Dt ϕ is the one which characterizes the generative action of the System;

ii) in the absence of any external input that acts as a forcing action (that is ( ) )0)( =t u Em and specific

initial conditions (different from zero), it represents only a generative potentiality;iii) in the case of given initial conditions (and in the absence of any external forcing action), it defines

the specific evolution of the System, which expresses its intrinsic potentialities by taking into account

the actually given boundary conditions;

iv) even in the presence of a given external forcing action ( ))(t u Em , the operator 1),( − Dt ϕ continues

to express its specific originality by “filtering” such an external occasional condition and by modulating

it in such a way as to transform it into something new. In fact, even if the input Emergy ( ))(t u Em is a

“monadic” function (see par. 5), the latter is trans-formed by 1),( − Dt ϕ into a binary function.

This clearly illustrates, through this new mathematical “language”, the well-known experimental

evidence that the outputdiversification is often related to an increase in the structural level of the System (e.g., let us

think of biodiversity).

The same considerations can also be drawn by starting from the explicit solution (3.12).

The combined consideration of Eqs. (3.9) and (3.12) also allows us to clarify (in mathematical terms)

the fundamental distinction between effective causes, concomitant (or accidental) circumstances and

associated external conditions already dealt with in reference to the mathematical formulation of the

Maximum Em-Power Principle (Giannantoni, 2001c).

Moreover, the direct comparison between Eqs. (3.4) and (3.5), yields

Φ u A d dt

Em t ( ) = − ⋅1 2

1 2

/

/ ( ) (3.13)

which shows how the Source Term ( )uΦ can be directly related to the term containing the derivative of

order Ω. Such a result will become much more meaningful in the light of the Prior Operator Differential

Approach (introduced later on, in par. 6), according to which the operator2/1

2/1

dt

d is the one which

“generates” (as a prior operator) the “bifurcation” of )(t Em . For the moment it is sufficient to point

out that the fractional derivative of order Ω is responsible for such a new form of “bifurcation” (understood

as a “generation” of a “binary” function), which well illustrates one of the fundamental basic processes



through which a System can self-organize its structure and yield co-products of a higher physical

“Quality”. Such a “Quality”, in turn, can be hierarchically associated to the higher ordinality (binary,

ternary, and so on) of the output multiple functions of the System.

4. IRREDUCIBILITY OF THE PROBLEM TO ORDINARY DERIVATIVES.

“SPLITS” AS “DUAL” FUNCTIONS

Alternatively, one could think of interpreting the given “co-production” process in terms of twodistinct output functions which satisfy the same differential equation, even if they are subjected to

different initial conditions.

Let us consider, for instance, the following equation which, potentially, could be apt to describe the

process under consideration:

C d

dt

Em t

Em t B

d

dt

Em t

Em t A

Em t

Em t Em u t ⋅

( )( )

+ ⋅

( )( )

+ ⋅

( )( )

= ( )

2

2

1

2

1

2

1

2

( ) (4.1)

with the following well-posed initial conditions

Em

Em

Em

Em

1

2

10

20

0

0

( )( )

=

(4.2) ,

Em

Em

Em

Em

1

2

10

20

0

0

'

'

'

'

( )( )

=

(4.3).

The general solution of the associated homogeneous equation is then given by

Em t

Em t

C

C e

C

C e

t t 1

2

11

22

12

21

1 2( )( )

=

⋅ +

⋅λ λ

(4.4)

where the exponents and are the solutions of the unique characteristic equation and the coefficients are

defined by the conditions (4.2) and (4.3).

At a first glance such a solution could seem “similar” to the expression given by Eq. (2.5) and

this fact would consequently suggest we analyze the possibility of reinterpreting the behavior, previously

described in terms of one fractional differential equation, as being possibly describable by this differential

equations of integer order. If we thus impose identical initial conditions for both problems, that is

F Em

Em0

10

20

( ) =

(4.5) , F

Em

Em

1 2 10

20

0 /

'

'

( )( ) =

(4.6),

it could seem possible for us to interpret the function expressed by Eq. (2.5) as a compact solution to Eq.

(4.1), with the pertinent initial conditions (4.2) and (4.3). That is it might be thought possible for us to

write

F t f t

f t

Em t

Em t ( ) =

( )( )

=

( )( )

1

2

1

2

(4.7).

In reality there is a profound conceptual difference between the last member of Eq. (4.7) and

the function , so that such a particular relationship holds only under very particular specific conditions.

In fact, the “couple” of functions and constitutes a mathematical entity which is something more than

a simple vector made up of the two components ( )t Em1 and ( )t Em2

. On the one hand, in fact, ( )t Em1

and ( )t Em2 satisfy the same Eq. (4.1) independently. The corresponding initial conditions may or may

not be related, and this implies that it is possible to think that the two functions are in reality solutions totwo distinct differential problems (Giannantoni 2000b, 2001a,b). On the other hand the function is the

sole solution to the unique differential problem represented by Eq. (2.1) (completed by its associated



initial conditions): its sub-division in two distinct equations is just a preliminary and artificial procedure

in order to evaluate the two functions and, according to an arbitrary prefixed sequence of signs (+/- or

-/+); such a distinction in fact is afterwards re-composed through the assumption of the compact form

(2.5) which expresses the sole, comprehensive and general solution. Moreover, the fact that the derivative

of order Ω is one sole entity (although giving rise to two distinct results) does not imply, in particular,

that the initial conditions (2.6) and (2.7) necessarily specialize as follows

F f

f 0

0

0

( ) =

(4.8) F

f

f

1 2 0

1 2

0

1 20

/

/

/

( )( )

( )( ) = +

−

(4.9).

This is because the definition of a “binary” function depends only on the type of the unique

differential equation. More precisely, on its basic fractional derivative of order 1/2. In fact, even if such

previous conditions are not satisfied, the solution of the fractional differential equation does not lose its

unity (as a binary function). It might “degenerate” into a “dual” function, that is a function made up of

two independent “monadic”2 functions (extrinsically related, as in the case of a vector), only if we

decide beforehand to analyze the trend of such (supposedly independent) solutions exclusively in terms

of ordinary derivatives (that is on the basis of a particular perspective, preliminarily chosen, whichimplicitly excludes other possibilities). In fact: a fractional differential problem is never reducible to anordinary differential problem without losing its specific intrinsic characteristics.

This also implies that the behavior described by one

fractional differential equation univocally characterizes (and consequently defines) a co-

production process, whereas a traditional vector differential equation is only able to describe (by means

of its solutions as “dual” functions) a split process. Consequently, this implies that the two distinct

processes (co-production and split, respectively) can never be confused , because their intrinsic definition

is now based on the intimate structure of each System, which is unequivocally described in differential

terms, profoundly (because essentially) different: fractional basic derivatives and integer basic derivatives,

respectively.

5. REDUCIBILITY OF NON-LINEAR PROBLEMS TO

LINEAR DIFFERENTIAL EQUATIONS OF FRACTIONALORDER

If we now consider the same equation (2.1) with the following initial conditions

F f

f 0

0

0

( ) =

(5.1) F

f H t

f H t

1 2 0

1 2

0

1 20

/

/

/

( )( )

( )( ) = + ( )

− ( )

(5.2)

where is the Heaviside function, we obtain the solution still structured in the form (2.5) where t h e

pertinent “binomial” coefficients are given by

c

c

f f H t

f f H t

11

22

0 12 0

1 2

12 11

0 12 0

1 2

12 11

=

− ( )−

+ ( )−

( )

( )

α

α α

α

α α

/

/ (5.3)

c

c

f f H t

f f H t

12

21

0 11 0

1 2

12 11

0 11 0

1 2

12 11

=

− + ( )−

− − ( )−

( )

( )

α

α α

α

α α

/

/ (5.4).

Such a solution corresponds to a “binary” function whose branches, although coinciding for ,

give rise to a “bifurcation” for (see Giannantoni 2000b, 2001a,b).

Such a bifurcation should not be understood as being made up of two distinct and independent functions,

but as a unique binary function (in the sense previously specified) which always remains the samemathematical entity both if its “branches” coincide or diverge.



This simple example shows how the new concept of fractional derivative and the associated

new class of functions (the “binary” functions) enable us to re-interpret a bifurcation (generally associated

to a non-linear differential problem) as a solution of a linear fractional differential equation in the basic

derivative of order 1/2. This also points out (from a more general point of view) the interest of such an

approach in Emergy Analysis of Complex Systems. It is sufficient in fact to think of those wide classes

of equations that can be derived through a linear combination of an increasing number and order of different fractional basic derivatives, contemporarily present in the same equation (or systems of

equations), which may be at the same time characterized by an arbitrary number of “modes”3. This

consequently allows us to assert that: “a wide class of phenomena whose behavior is generally thought of as “non-linear” could in actual fact be linear , if analyzed in terms of intensive fractional differential equations”.

Let us now consider the above-mentioned generalization of the previous approach, which is

fundamentally based on the direct priority of differential operators, a methodology which is particularly

indicated when describing living Systems.

6. GENERATIVE SYSTEMS AND PERSISTENCE OF FORM

The characteristics of living Systems as generative Systems stimulated us to look for more

adequate mathematical concepts able to describe their peculiar generative processes. This led us to the

new concept of derivative we are going to present, elaborated in turn as a generalization of the fractional derivative previously shown (and synthesized in Appendix 1).

Such a different perspective starts from the consideration of the fact that the traditional definition

of the derivative of a function

( ) f t given in Mathematical Analysis

lim

∆

∆

∆t t f t

→ ( )

0 (6.1)may be considered as being an a posteriori definition (e.g., let us think of the definition of velocity). In

fact, although it is usually read from left to right, it is vice versa interpreted from right to left. In other

words its meaning is based on a reverse priority of the order of the three elements that constitute its

definition: i) the concept of function (which is assumed to be a primary concept); ii) the incremental

ratio (of the supposedly known function); iii) the operation of limit (referred to the result of the previous

two steps).

Now we may ask: what happens if we interpret the sequence of symbols in expression (6.1)

according to the same order as they are written (that is from left to right)?

Such a direct perspective gives rise to a new concept of derivative (indicated byd

d t

∼

∼ and defined in

Appendix 2) which can be named “incipient” (or “spring”) because of some special characteristics that

will be illustrated through the derivative of the exponential function )(t eϕ , which now gives

d

d t e

d

d t t e e

n

n

t

n

t

n

t

∼

∼

∼

∼

∼

∼

=

⋅ =

⋅ϕ ϕ ϕ ϕ ϕ ( ) ( ) ' ( )( ) (6.2).

Such a result is always formally different from the one obtainable through traditional ordinary

derivatives, even when both results coincide numerically (that is, for any order derivative, if



β α ϕ += t t )( , otherwise, if )(t ϕ is a non-linear function, only in the case of first-order derivative).

Consequently the adopted symbology reminds us of the main differences: i) the resulting expression

refers to a virtual evolution, which may also become a real evolution, but only in dependence on particular

boundary conditions; ii) the comprehensive structure of Eq. (6.2) reminds us that the obtained result is

due to a “generating process”, the virtual (evolutive) possibilities of which are delineated in terms of its

intrinsic genetic characteristics∼

n)( 'ϕ , which are essentially due to both the generator d

d t

n

n

∼

∼ (understood

as a prior “operator”) and the “fertile” co-operation of the considered function‘ e t α ; iii) thus the final

result represents an evolutive modality which is completely new with respect to the original function: it

is not seen now as a “necessary” consequence (as in the case of operators interpreted a posteriori) but,

because of the a priori interpretation of operators, it is conceived as an “adherent” consequence of its

“generation” modalities: all the various functions resulting from the “generating process” represented

by Eq. (6.2), for N n , are a similar to harmonic evolutions which are in “resonance” (as in a “musicalchord”) with the original function and at the same time with each other.

7. EXAMPLES OF APPLICATION TO LIVING AND NON-LIVING SYSTEMS

We may start from the consideration that some very simple self-organization‘ processes pertaining

to Living Systems can be described by the simplest non-linear equation represented by Riccati’s equation.

H. Odum devoted many pages to the description of such processes (e.g., Odum 1994a, chap. 9). What it

is here worth pointing out is that the most general Riccati equation with variable coefficients, if understood

now in terms of incipient derivatives,

)()()()()( 2 t P t f t Rt f t Q

t d

df =++∼

∼

(7.1)

always has an explicit solution. This equation, in fact, through the substitution (Davis, 1960)

y t f t R t

df

d t ( )

( ) ( )= ⋅

∼

∼1

(7.2),

reduces to a second-order linear differential equation with variable coefficients

0)()()()( 2'

2

2

=−−− ∼

∼∼

∼

∼

t y PRt y

t d

d QR Rt y

t d

d R (7.3).

The advantage of the incipient derivatives is due to the fact the all the linear differential equations,

of any integer or fractional order, now have an explicit solution. In particular, with reference to Eq.

(7.3), the incipient derivative is now able to solve the secular problem of giving an explicit solution to a

second order linear differential equation with variable coefficients

0)()()()()( 012

2

=++

∼∼

∼

∼∼

∼

∼

t f t at f t d

d t at f

t d

d (7.4)

with its well-posed initial conditions



f f k

k

( )( )

∼ ∼

=0 0for 1,0=k (7.5)

by means of finite terms and quadratures. If we remember in fact the property of the “incipient” derivative

of an exponential function such as e t ϕ ( ) (see Eq. (A2.4) in Appendix 2) and substitute such an expression

into Eq. (7.4), we obtain

ϕ ϕ ' '( ) ( ) ( ) ( )

∼ ∼

+

+ =t a t t a t

2

1 0 0 (7.6)

which is an algebraic equation (with variable coefficients) in the un-known function ϕ ' ( )∼

t .

If )(t i

∼α ( 2,1=i ) are the two solutions to Eq. (7.6), we can easily obtain two independent solutions

ϕ i t ( ) to the initial equation (7.4) through the following “incipient” integrals4

ϕ α i

t

it u du∼ ∼ ∼ ∼

= ∫ ( ) ( )0

1

( 2,1=i ) (7.7)

so that the general solution to Eq. (7.4) is given by

f t c ei

i

u dut

i

( ) ( )=

=

∫ ∑∼ ∼ ∼

1

2

0

1

α (7.8)

where the constant coefficients ci are defined by means of the initial conditions (7.5).

If, differently, the solution

)(1

t

∼

α

has a multiplicity 21

=ν , an additional independent

“incipient” integral is given by

y t e d t u du

t i

20

1 1

( ) ( )

= ∫ ∼

∫ ∼

∼ ∼ ∼

ξ α ξ (7.9).

Such explicit solutions enable us to extend the results achieved for basic self-organizing systems,

describable by a Riccati equation with constant coefficients to more Complex Living Systems whose

behavior is described by Abel’s non-linear equations (e.g. Odum 1994a, p.151) of any order , with variablecoefficients. Such equations, as it is well-known, are structured as

d

d t f t F t f t A t f t k

k

nk

∼

∼

∼ ∼

=

∼= = ⋅∑( ) [ , ( )] ( ) [ ( )]

0

(7.10)

where )](,[ t f t F ∼

is a polynomial of integer order n in )(t f ∼

. Eq. (7.10) in fact constitutes a

generalization of Riccati’s equation (with variable coefficients) and it can now be analogously solved in

explicit terms, if previously reduced to a linear equation through an appropriate transformation of variable

(Davis, 1960).

What is important to underline is that the solutions obtained by means of this differential approach

coincide exactly with the traditional solutions to differential equations when these are available in finiteterms and quadratures, that is: in the case of linear differential equations (of any integer order) with

constant coefficients and first order equations with variable coefficients. In all the other cases the



traditional solutions which are obtained, for instance through expansion series, gradually differ from the

explicit solutions obtained in terms of incipient derivative. In other words traditional solutions present a sort of “drift ”, which is even more marked according to the increasing order of the involved derivatives.

As an example of such a “shifting” we can mention a well-known problem of Celestial

Mechanics: the precession of the planets (especially Mercury). In fact, on the basis of Newton’s Laws,

the classical variation ( cϕ ∆ ) of the angular anomaly per each revolution is given by the followingexpression (Landau and Lifchitz, 1969)

∆ϕ cr

r M r dr

m E U M r = ⋅

− −∫ 22

2

2

( / )

( ) ( / )min

max

(7.11)

which, however, vanishes after a number of nm /2π revolutions (where m and n are integer numbers)

because the central force field is characterized by a potential energy which is proportional to r /1 (ib.).

If vice versa we re-interpret the basic differential equation of the angular anomaly in terms of incipient derivatives

d

d t

d

d t

2

2

2

2 0

∼

∼

∼∼

∼

∼+ ⋅

=ϕ ϕ (7.12),

we may search for an approximate solution to non-linear Eq. (7.12) through two successive linearization

processes: the first one based on the approximation of the sole first-order “incipient” derivative, the

second one also based on the approximation of the second-order “incipient” derivative. The corresponding

solutions to the two linearized versions of Eq. (7.12) are given respectively by the following expansion

series

( ) ( ) .....6

1

2

1 32

1 +∆+∆−∆=∆

∼

ccc ϕ ϕ ϕ ϕ (7.13)

∆ ∆ ∆ ∆

ϕ ϕ ϕ ϕ ∼

= −

+

+

2

2 3

2

1

3 2c

c c ..... (7.14).

Consequently, an estimation of the real secular precession can be given respectively by

( ) ( ) ...6

1

2

1 32

11sec, +∆−∆=∆−∆=∆∼

ccc ϕ ϕ φ ϕ ϕ = 86.12’’ (7.15)

and ∆ ∆ ∆ ∆ ∆

ϕ ϕ φ

ϕ ϕ sec, ...2 2

2 3

2

1

3 2= − =

−

+

∼

c

c c

= 43.06’’ (7.16)

The latter result, in particular, which is in almost perfect agreement with the most recent available data5,

has still to be considered as an approximation, because it is both the result of a linearization process and

in addition its value presents a rather marked sensitivity to the initial data. In any case whatsoever the

shown solution process based on the incipient derivatives suggests a profound reflection not only

concerning Classical and Relativistic Mechanics and their reciprocal relationship, but also the reciprocal

influence between physical “Models” and mathematical “Methods”. In fact, the obtained results indicate

a well-defined line of tendency: Newton Laws could be still considered as being substantially adequateto describe such an effect which has been (up to now) inexplicable in terms of Classical Mechanics (in

fact it was explained by Relativity, but only on the basis of a different physical model). This implies that

the defect could be attributed more to the mathematical language adopted rather than to an intrinsicdeficiency in Newton’s Laws. At the same time it is worth pointing out that Eq. (7.12), which has been

assumed as a basic starting point, has to be considered as being only a preliminary approximation of the



physical reality. In fact, the “binary” functions introduced in par. 2, suggest we describe the whole

System made up of Sun and Mercury as a “binary” System. Such a physical model should be thus more

appropriate to describe (still in terms of a priori fractional derivatives) the phenomenon under

consideration, not only from a quantitative point of view, but from the Quality point of view, especially

because of the associated required modifications of the concepts of space and time which are consequently

involved.

The above-mentioned “drift” effects are also fundamental to re-interpret the well-known

mathematical aspect named as “deterministic chaos”. For the sake of brevity, this will only be mentioned

here. What, on the contrary, is worth pointing out is that the introduction of the incipient derivatives

does not imply a deterministic perspective but, on the contrary, suggests a new perspective: adherentism,

understood as a dynamic evolution in which every “product” generated, besides representing a substantial

novelty, is always “faithful” to the presuppositions of its generation, that is it is always in consonancewith its “ancestors’ genetic characteristics” (see Giannantoni, 2001c).

As a concluding example, we can here recall the preliminary model of the “creation of a positron-

electron couple” presented in Giannantoni (2000b, 2001a,b) and there analyzed in terms of intensivefractional derivatives. In fact, if the same basic equations that describe the model are now re-interpreted

in terms of fractional spring -derivatives, the considered model is able to show how such a kind of “creation” should be understood: not as an ex nihilo creation, but as a “creative” (in the sense of “original”)

response of the gamma ray to the external concomitant circumstance (in this case, an elastic interaction),

in order to maintain its binary entity, in spite of the adverse external conditions. The gamma ray, by

“filtering” and “modulating” such an external input, transforms into something else which is

phenomenologically new: a positron and an electron which, although distinguishable between themselves,

at the same time always remain describable by the same binary function. The persistence of the form of

the binary function which contemporarily represents both of them, indicates that they cannot even be

considered as being already present as such in the gamma ray, but as being generated as a consequence

of a re-organization of its intimate structure.

8. CONCLUSIONS

The previous considerations and corresponding explicative examples should have clearly shown

how the basic presuppositions of the traditional mathematical approach to living Systems is rather restrictive and somewhat reductive. In particular, the consideration of

differential equations of only integer order (generally adopted to describe such Systems) is rather limiting

with respect to the much wider variety of their biodiversity characteristics. Vice versa, the introduction

of an intensive concept of fractional derivative and, above all, the concept of incipient differential

equations (of integer and fractional order) can represent a valid approach to describe and analyze the

spring-dynamism of such Systems. In fact, we have shown that: i) fractional derivatives generate a new

class of functions (“binary”, “ternary” functions, and so on) that are able to describe the new realitygenerated by a given process as being one sole entity; ii) they enable us to express, in explicit form, the

Emergy Source Terms which appear in the mathematical formulation of the Maximum Em-Power

Principle; iii) they also enable us to distinguish unequivocally “by-products” from “splits”, simply on

the basis of the differential operators that describe the intimate structure of the concerned process; iv) to

re-interpret a simple (or multiple) bifurcation, generally thought of as being described by a non-linear differential equation of integer order, as a solution to a linear differential equation in the fractional derivative of order Ω (or higher); v) fractional derivatives also suggest that more general differential

equations, structured as a linear combination of an ever increasing number of different fractional basicderivatives, in turn characterized by an arbitrary number of “modes”, may be able to describe wide

classes of phenomena whose behavior is generally thought of as being “non-linear”; vi) if then suchequations are interpreted in terms of incipient derivatives, they enable us to analyze in explicit terms the

dynamic behavior of more complex self-organizing Systems described by Riccati’s and Abel’s non-



Giannantoni C., 2001c. Mathematical Formulation of the Maximum Em-Power Principle. Second Biennial

International Emergy Conference. Gainesville (Florida, USA), September 20-22.

Kolmogorov A. N. and Fomin S. V., 1980. Elements of the Theory of Functions and Functional Analysis.

Ed. MIR, Moscow.

Kransov M. L., 1983. Ordinary Differential Equations. Ed. MIR, Moscow.

Landau L. and Lifchitz E., 1969. MÈcanique. Ed. MIR, Moscow.

Odum H. T., 1988.

Self-Organization, Transformity and Information. Science, v. 242, pp. 1132-1139, November 25.

Odum H. T., 1994a. Ecological and General Systems. An Introduction to Systems Ecology. Re. Edition.

University Press Colorado.

Odum H. T., 1994b. Environmental Accounting. Environ. Engineering Sciences. University of Florida.

Odum H. T., 1994c. Ecological Engineering and Self-Organization. Ecological Engineering. An

Introduction to Ecotechnology. Edited by Mitsch W. and Jorgensen S. J Wiley & Sons, Inc..

Odum H. T., 1994d. Self Organization and Maximum Power. Environ. Engineering Sciences. University

of Florida.

Odum H. T.,1995a. Public Policy and Maximum Empower Principle. Net EMERGY Evaluation of

Alternative Energy Sources. Lectures at ENEA Headquarters, May 24.Odum H. T., 1995b. Energy Systems and the Unification of Science. From Maximum Power. The Ideas

and Applications of H. T. Odum. C. A. S. Hall, Editor. University Press Colorado.

Odum H. T. and Odum E. C., 2001. A Prosperous Way Down. University Press Colorado.

Oldham K. B. and Spanier J., 1974.

The Fractional Calculus. Theory and Applications of Differentiation and Integration to Arbitrary Order .Academic Press, Inc., London.

Ruberti A. and Isidori A., 1969. Theory of Systems. Ed. Siderea, Rome.

Smirnov V. 1976. Cours de MathÈmatiques SupÈrieurs. Vol. I, II, III, IV, V. Ed. MIR, Moscow.

Szargut J., Morris D. R. and Steward F. R., 1988.

Exergy Analysis of Thermal, Chemical and Metallurgical Processes. Hemisphere Publ. Corp., USA.

Footnotes

1 The following explanation, which is

always valid in terms of “incipient” derivatives (see par. 6), is also valid in terms of intensive fractional

derivatives, because the linear equations here considered are with constant coefficients, and their solutionsare consequently based on the exponential function as a “hinge” function. Such an explanation, however,

here introduced only for the sake of simplicity and clarity (and not for generality), should be already

thought of in terms of incipient derivatives, and consequently as a generalized explanation.2 That is each one can be thought of as a solution of an independent ordinary differential equation.3 The number of modes is defined as the ratio between the order of the differential equation and the order

of the basic fractional derivative (Giannantoni 2000b, 2001a).

4 Such “incipient” integrals will not analyzed here for the sake of space. What it is vice versa worth

pointing out is their property of “absolute locality” (or absolute absence of “non-locality”), which is

closely related to the “absence of drift” in the explicit solutions mentioned few lines ahead.5 Astronomical measurements give 42.6’’± 0.9’’ per century. The value predicted by Relativity Theory

is 43.0’’ per century (Landau and Lifchitz, 1969).



APPENDIX 1. INTENSIVE FRACTIONAL DERIVATIVE

Its definition is given (see Giannantoni 2000b, 2001a) through a decompositon-recomposition procedure

initially based on an extension of the traditional definition of derivative of order n to any rational number

q Q :

( ) ( ) ( )d

dt f t

t f t

q

q t

t

q

q=

−♦

lim∆

∆

∆0

1δ (A1.1)

where the Newton expansion of the second side introduces a linear operator δ ∆t

n such as

( ) ( )δ ∆ ∆t

n f t f t n t = + (A1.2).

Such a definition, if applied to the function ( ) f t e t = α , gives

d e

dt

q

k e

t e

q

k e

t

q t

qt

k

t

q k t

k

q

t

t

k t q k t

k

q

α

α

α

α δ

=−( )

( )

⋅ =−( )

=→

−

=

∞

→

+ −( )[ ]

=

∞∑ ∑lim lim∆

∆

∆

∆

∆ ∆0

0

0

0

1 1

=−( )

( )

⋅ = −( )

⋅ = ⋅→

−( )

=

∞

→

∑lim lim∆

∆

∆

∆

∆ ∆t

k t q k

k

q

t

t

t q

q

t q t

q

k e

t e

e

t e e

0

0

0

11

α

α

α

α α α (A1.3)

which, for q = 1 2/ , provides the same result used in solving Eq. (2.1). The procedure shown by Eq.

(A1.3) can be easily extended to any analytical function ( ) f t : the series of passages shown in the previous

example defines a technique of “re-composition” of the Newton binomial expansion, that contextually

isolates a particular operator , whose limit is finally applied to the considered function. This may be

termed as an intensive definition because it does not necessarily require the convergence of the concerned

expansion series. In addition, for the most habitual conditions, such a procedure is extremely simplified

(see Giannantoni 2000b, 2001a).

Here it is worth pointing out two fundamental aspects concerning the previous definition, in particular

with respect to the definition commonly used in the traditional Fractional Calculus:

i) the intensive definition here recalled presents a multiplicity of results similar to the case of the roots of

a complex number, for example

( )d

dt e e e

m n

m n

t m n t

i

m t /

/

/1 11

? ?= = ε ( i n= 1 2, ,..., ) (A1.4)

where ε i is the i-th root of 1; ii) the exponential function e

t , apart from such a multiplicity of values, is

an invariant with respect to any order of fractional derivation.

This latter property is the one that allows the exponential function to play that “hinge” role in solving

fractional differential equations which is similar to the one that it plays in the case of ordinary differential

equations. The former property, on the other hand, is the one which introduces that variety of solutions,

typical of fractional differential equations, which enables us to analyze some well-known behavior,such as a bifurcation, in a different perspective (as shown in the text).



APPENDIX 2. “INCIPIENT” OR “SPRING” DERIVATIVE (OF INTEGER

AND FRACTIONAL ORDER)

Its definition, first referred to any integer n , is given by

d

d t f t

t f t

n

n t

n

∼

∼→

∼∼

∼

∼

( ) = ⋅ −

⋅ ( )∼ +

lim:∆ ∆0 0

1δ (A2.1)

where the symbol∼

δ represents an “operator” that generates a translation of a function, that is

δ ∼ ∼

( ) = +

f t f t t ∆ (A2.2),

which has the following characteristics: i) the time variation

∆

∼

t

can also be real, but in general it is

understood as being virtual (and the associated symbol ∆∼

reminds us of such an assumption); ii) the

symbol ( )δ ∼ f t is not only the representation of the second side of Eq. (A2.2), because the “operator”

δ ∼ is prior with respect to ( ) f t : it is the one that originates such a virtual translation; iii) the “operator”

δ ∼ may be thus better named as “generator” because, according to definition (A2.2), it “acts” as generator

of a translation; iv) the name “generator” also reminds us that it acts in combination with something

else: δ ∼ is in fact the prior “principle”, ( ) f t is the posterior “principle”, and f t t +

∼∆ is what “rises”

from the combination of both. Such a result (or “product”) is something new, but at the same time it

retains the main genetic characteristics of its generating “principles”.

Analogous considerations can be made with respect to the “operator”δ ∼

∼

∼

−

1

∆ t

n

.

Finally, the operation of limit (∼

♦∆ +∼

00:

limt

) is here also considered as a prior operator with respect to those

that follow it in Eq. (A2.2) but, at the same time, it is posterior to the very primary operation: the

passage from the time t , initially prefixed, to the virtual time

t t t ∼∼∆+=δ (A2.3)

as a consequence of a virtual translation generated by the “generator” δ ∼. Such an operation is represented

by the symbol ∆∼

t : 0 0♦ + to remind us that our concept of “limit” is a “ spring-concept ”: it is the

“source” of what rises as a consequence of an infinitesimal virtual variation, immediately after a given

time t , which in turn activates the sequence of the successive “generators” in its “spring-perspective”.



Definition (A2.1) also implies

d

d t e

d

d t t e e

n

n

t

n

t

n

t

∼

∼

∼

∼

∼

∼

=

⋅ =

⋅ϕ ϕ ϕ ϕ ϕ ( ) ( ) ' ( )( ) (A2.4)

so that, for any function f t e f t ( ) ln ( )= , we consequently have

d

d t f t

d

d t e

f t

f t e

f t

f t f t t f t

n

n

n

n

f t

n

f t

n

f

n∼

∼

∼

∼

∼∼

∼∼

∼= =

⋅ =

⋅ =

⋅( )

( )

( )

( )

( )( ) ( ) ( )

ln ( )

'

ln ( )

'

β (A2.5),

where the factor of similarity )(t f

∼β (generally depending on time) acts as a new “generator” in Eq.

(A2.5), similarly to )(' t ∼

ϕ in Eq. (A2.4). In general it can be either a scalar quantity or a vector or even

a matrix. This evidently depends on the function under consideration.

Eq. (A2.5) can be easily extended to any Qq and applied to the most common functions in

Mathematical Analysis (such as, for instance, analytical functions).






Edited by



Associate Editors

Howard T. Odum



David Tilley



Sergio Ulgiati

University of Siena

Siena, Italy

December 2003



Gainesville, FL


vi



-203-

Chapter 14. Transformities from Ecosystem Energy Webs ...

14Transformities from Ecosystem Energy Webs

with the Eigenvalue Method


ABSTRACT

This paper describes a general procedure to compute solar transformities (ratios of emergy/

energy) in ecosystems, applying the minimum-Eigenvalue method to previously published sets of data

on energy ows. A tutorial with examples explains ve steps: (a) drawing energy systems diagram

with energy ows on the pathways, (b) writing emergy equations for each transformation, (c) entering

energy ow values in a transformity-transformation matrix, (d) adding transformities to connect the data

set to global solar emergy, and (e) using a specic computer software to calculate the transformities.

Software templates are provided for making the calculation with MATHEMATICA and with TRUEBASIC.

Transformities of biomass and other structural storages were computed from production data, whereas only

the higher transformities for inter-compartment transfers were obtained for the energy networks without

production data. Solar transformities obtained with these methods are included for Cone Spring (Iowa)

groundwater meadow, an oyster reef, the Tabonuco rain forest, and dwarf cloud forest in the mountains

of Puerto Rico.

INTRODUCTION

Transformity, the ratio of emergy/energy, measures the position of an energy ow or storage in

the energy hierarchy of a network. This paper uses the minimum Eigenvalue method (Patterson, 1983;

Collins and Odum, 2000) to determine transformities from published data sets. By making calculations in

solar emergy units (solar transformities), networks of all kinds become comparable, even social networks.

Solar transformities indicate the position of any energy ow or storage in the complex hierarchy of energy

networks of the earth. Tables of solar transformities are also useful in emergy-emdollar evaluations of

alternatives for development of the economy and conservation that recognize the work of nature. Basedon our experience with these calculations, a tutorial is provided to help future evaluations.

Emergy is the energy of one kind previously used up directly and by indirect pathways in

transformations required to generate a product or service. Transformity is the emergy per unit energy.

Transformity increases with scale of time and space and marks the position of something in the universal

hierarchy of energy that results when energy transformations are connected in series in a branching web

with intermittent pathways, which is the way they are found in nature. See Odum, 1996, for concepts

and examples.

The Patterson-Collins method of evaluating transformities from emergy equations was used to

show the positions of species and other components of ecological systems in the universal energy hierarchy

(Patterson, 1983, Collins and Odum, 2000). The program in MATHEMATICA was used for the matrix

calculations as presented at the last conference (Collins and Odum, 2000). To make the application moreportable, a program in TRUEBASIC (True BASIC, Inc., Hartford, Vermont) is also offered.



-204-


Energy network diagrams are usually highly aggregated simplications with only a few pathways

often arbitrarily selected based on the observers concepts, available data, and scale of interest. The

Patterson-Collins method sorts a set of emergy transformation equations without requiring a knowledge

of all the pathways and branches. However, we recommend assembling the data with an energy systems

diagram rst to help avoid error and to insure that each kind of energy ow is connected by at least one

transformation pathway to the rest of the network.

Figure 1a is an example of a network of energy transformations which includes the several kinds

of inter-unit connections: transformations in simple series, branches which are splits of the same kind of

energy, branches which are two different kinds of energy, and transformations using two or more inputs.

In order to express results in solar emergy units (solar emjoules), at least one energy transformation

pathway is needed that connects solar emergy as in Figure 1a.

x1 x2

x4

x3

x3

x3x6

x7

2 x 107 100 10

Flows in Joules per time

x5

0.01

0.1

0.2

18

2 SolarEmergy

x's are transformit ies to be calculated

spl i t

x8

0.02(a) Energy Systems Diagram

x1*2 107 = x2*100

(b) Emergy equations for the Energy Transformations

x2*100 = x3*10

x3*8 = x4*1

x3*2 =x5*0.2

x3*8 = x8*0.02

x4*1 = x6*.1

x5*0.2 + x6*0.1 = x7*0.01

(c) Transformity-Transformation Matrix

x1 x2 x3 x4 x5 x6 x7 x8

2E7 -100 0 0 0 0 0 00 100 -10 0 0 0 0 0

0 0 8 -1 0 0 0 0

0 0 2 0 -0.2 0 0 0

T r a n s f o r m a t i o n s

Transformit ies

_______________________________________

0 0 8 0 0 0 0 -0.02

0 0 0 1 0 -1 0 0

0 0 0 0 0.2 0.1 -0.01 0

_______________________________________

Figure 1. An energy system network and the emergy equations of its energy transformations. (a) Diagram; (b)

emergy equations; (c) transformity-transformation network.



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OUTLINE OF THE METHOD: A PROCEDURE TUTORIAL

The following is a tutorial on using the Eigenvalue method of calculating transformities from

data on energy transformations.

1. Assemble data by writing energy ow values on an energy network diagram. Figure 1 is a very simpleexample which has several energy transformations, one with a split of one kind of energy output, one

with coproducts of two different kinds of energy output, and one with two inputs. The process of putting

numbers on the pathways helps clarify which pathways are splits or junctions of the same kind of energy

and which are different kinds of energy. For the purpose of these calculations, the energy systems diagram

need not represent the whole ecosystem. Only the pathways with data need to be shown. However, for

the calculations to succeed, every pathway with data needs to connect directly or indirectly with all the

others. In other words, there must be no gap in the network.

In some networks, the energy ows are all consistent, referring to one ecosystem with values given in

the same time units and referring to the same area. Such data can be represented with numbers on the

pathways. However, the Eigenvalue method does not require that energy data in each transformation row

be consistent. Where an energy transformation line (equation) is not part of the same ecosystem data, it

is represented in the gure with an arrow along the path. See, for example, the microbe transformations

in Figure 8.

2. Write emergy equations for each energy transformation. Emergy is conserved in energy transformations

which do not involve feedbacks and loops. Each pathway of input emergy and output emergy is represented

by the energy ow times the transformity. Transformities are the unknowns in this procedure and are

designated by x’s (x1, x2, x3, etc.). An emergy equation has the emergy of one or more inputs (summed)

set equal to emergy of one output. For example, in Figure 1:

x5*0.2 + x6*0.1 = x7*0.01

Figure 2. Template for the commercial program MATHEMATICA with matrix of data entered from the system in

Figure 1. Calculate by entering SHIFT & RETURN.



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3. Next, prepare a table of these emergy equations with one line for each transformation in the system.

For example, Figure 1b has the emergy equations for the system in Figure 1a. Each line in the list has

the energy ows in and out of the transformation.

4. Next, put the input and output energy ows in a transformity-transformation matrix (table) in which

transformities are the columns and designated with x’s (x1, x2, x3, etc.). Each line contains the one or

more input energy ows with plus signs (no sign meaning “plus”), and the output energy ow is given a

minus sign. Figure 1c is the transformity-transformation matrix for the system in Figure 1a.

5. If the data do not include an energy transformation row connecting solar emergy, add a line with the

solar transformity from other sources of at least one of the kinds of energy already in the matrix. For

example, suppose there is a compartment for plant biomass, but none for the solar emergy required. If

x1 = 1x2 = 200,000

x4 = 1.6 x 107

x5 = 2.0 x 107

x6 = 1.6 x 108

x7 = 2.0 x 109

Solar Transformities

x1 x2

x4

x3x3

x3x6

x7

2 x 107 100 10

Flows in Joules per time

x5

0.01

0.1

0.2

18

2 SolarEmergy

x's are transformities to be calculated

split

x8

0.02(a) Systems diagram with energy flows

x8 = 8.0 x 108

x3 = 2.0 x 106

(b) Output of MATHEMATICA Calculation

Out[24]//MatrixForm=

Out[30]=

Out[33]//MatrixForm=(c)

Figure 3. Result of the calculating transformities in the matrix of Figure 2. (a) System network and energy ows;

(b) matrix as printed out followed by minimum Eigenvalues; (c) transformities calculated.



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the solar transformity of plant biomass is known from other studies to be 2000 solar emjoules per one

joule, add a line to the matrix with 2000 in the column x1 for solar emergy and -1 in the column for the

biomass x2.

6. Next, load the software program to make the calculation (example, MATHEMATICA with its template

in Figure 3). At the top of the calculation template, type in the values of the matrix from step #4. For

example, the matrix of the simple example (Figure 1c) was entered in the template in Figure 3. Or make

the calculations with TRUEBASIC, as described in the next section.

7. Run the program. For the MATHEMATICA template, hold down the SHIFT key and type ENTER.

Figure 4 shows the result with a list of transformities at the bottom. The program will calculate the

transformities, expressing them in units (emjoules) of the lowest quality energy in the matrix.

8. Running the program with the transformation matrix in Figure 2 yields the transformities in Figure 3

in solar emjoules/joule. Figure 4 contains another example of transformity calculations. The biomass

diagrams of Figure 4a were connected to solar emergy with an assumed solar transformity of 10,000 sej/J

(Figure 4d). Then the data were entered into the transformation-transformity matrix of the MATHEMATICAtemplate. Figure 4e has the printout of the matrix and the resulting transformities.

It is not necessary to put all the data of an ecosystem into one large matrix. The solar transformity

determined in one set of equations was used as a line item to another set of equations so that the second

set could be linked. After transformities were tabulated, the positions of units in the systems diagrams

can be adjusted so that items are in order of increasing transformity from left to right.

CALCULATING EIGENVALUES WITH TRUEBASIC

In the Appendix Table is the listing of program EIGNTRAN.tru, which makes the same matrix

calculation as the MATHEMATICA template. TRUEBASIC can be obtained from True BASIC, Inc, 1523

Maple Street, Hartford, VT, 05047-0501. Phone 1-800-436-2111. http://www.truebasic.com.

Copy EIGNTRAN.tru to your hard disk. Load TRUEBASIC. Use its File Menu to open

EIGNTRAN.tru. Enter the rows of the transformity-transformation matrix. Run the program.

ECOSYSTEMS AND COMPARTMENTS

Self organization appears to form systems of similar design as they join inputs from smaller scale

and transformity to those of the next scale. The energy hierarchy in ecosystems has been represented

with energy systems diagrams since 1966 (Watt, 1968; Holling, 1973). Symbols representing the parts

and processes of ecosystems are arranged so that the turnover time, territory, and transformity increase

from left to right (Figures 1-11). Values of energy ow are often represented on the pathways of these

systems. Then transformities can be calculated for the ows with one of several methods. Common

patterns of energy ow and transformation are observed in the self organization of ecological systems of

many kinds. Partly this is due to real similarities and partly due to similar concepts used by people who

assemble the network information. Larger organisms with larger areas of support and inuence have

higher transformities.

For any population or other aggregated compartment it is necessary to distinguish the smaller

transformity of biomass production and storage from the higher transformity of the output transfer toother units in the network. All may be of interest. To explain the differences and ways of calculation,

Figure 4 has energy ows and storages for one compartment box at steady state (inows equal outows

and storage-structures constant). The word storage-structure refers to a compartment’s storage, which is



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100

100

Storage-Structure

10

32

5

500

Unassimilated

2073

Production

Autocatalyt ic Feedback

Depreciat ion

Flow in Kilojoules per day =

= Storage-structure in Kilojoules

Input

Inter-boxoutputs:

2

EnergyTransformationProcess orProcesses

Used energy,not available

20

100

Storage-Structure

10

2

500

Production

20

(a)

(b)

(c)

Respiration = f + d

f

d

78

Figure 4. One compartment of an environmental system at steady state showing energy ows, storages, and conceptsoften used to represent structure and processes. Controlling inputs from other boxes are not included in this example.

(a) Compartment without interior details; (b) compartment showing structural storage and related energy ows; (c)

compartment simplied with values for production and storage only;



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100

Storage-Structure

10

32

5

500

Unassimilated

2070

Production

Autocatalytic

Feedback

Depreciation

(d)

SolarEmergy

1 x 106

x1 x2x3

x4

x5

x6

x7

x1 Solar

x2 Foodx3 Unassimilated

x4 Production

x5 Feedback

x6 Depreciations

x7 Transfer out

Transformities(e)

Food

Out[24]//MatrixForm=

Out[30]=

Figure 4. continued. (d) diagram with solar emergy connection added; (e) results of transformity calculation.

often a combination of functioning structure and stocks of stored substance.

Figure 4a represents an energy transformation where the details are aggregated. Many published

studies of ecosystem networks use data aggregated so that the only ows given are inputs, outputs, and

interunit transfers. When data are used from networks aggregated in this way, only the interunit ows

are available to evaluate. A box compartment may be for a population of a species, but in other cases

may be for a single organism or structure.

Other sets of data show more of the ows within each box compartment. Figure 4b has inputs,

production of storage, storage, autocatalytic feedback, depreciation, and transfer to other compartments.Transformity of stored structures can be determined by multiplying inow emergy by the turnover time

and dividing by the stored energy. Because output transfers come from stored-structure, they represent

the accumulation of more emergy and have higher transformities than the energy ows of productive

input. Most data sets do not offer such details.

Figure 4c is a simplication of the numerical data to include inputs, output transfers, and only

the production that generates the storage. The production numbers are written within the autocatalytic

loop. If only the energy in the structural-storage is known, the production to storage can be estimated by

dividing the storage by its turnover time (replacement time). For example, the production energy ow

in Figure 4c could be obtained from Figure 4b by dividing the storage 500 J by the turnover time of 50

days to obtain 10 J/day.

Energy ow of production may also be calculated as the sum of the outows from storage

and the respiration. Respiration may be known or calculated from the storages using published data on

metabolism per unit weight. In Figure 4c respiration is the sum of pathways feedback f and depreciation



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d . Energy ows may be estimated from data given in carbon use per time by multiplying by the energy/

carbon ratio for that type of process.From Figure 4e the transformity of the production to biomass storage was 100,000 sej/J. To

verify the emergy of the biomass storage, multiply the input empower 1 x 106 sej/day by the turnover time

of 50 days to obtain the biomass emergy storage = 5 x 107 sej. The transformity of the storage = emergy

stored/energy stored = (5 x 107 sej)/(500 J) = 100,000 sej/J in agreement with the matrix calculation. The

transformity of the biomass passed out to other units was larger, 500,000 sej/J.

The detritus concept has been used in ecology for a half century. Detritus is a pool of organic

matter of many kinds and qualities with contributions from every level in biological food chains. Many

ecosystem networks are drawn with a miscellaneous pool (the detritus) that receives left-overs from other

compartments. Having a pool with organisms at many levels may increase system stability. Included are

dead organisms, faecal pellets of unassimilated food, and organic secretions. For example, in Figure 4d,the unassimilated outow is a different kind of energy from the transfer of storage-structure and has a

different transformity. Flows into detritus might not be lumped together if the point of view of the analyst

were on a smaller scale. Sometimes the bacterial and other living micro-organisms which constitute a

Figure 5. Calculations using the energy systems diagram of Cone Spring ecosystem found in published literature.

(a) Energy diagram and energy ows; (b) transformity-transformation matrix; (c) transformities calculated.

x1 x2 x3 x4 x5 x6 x7 x8

x1 Sun

x2 Photosynthatex3 Plants

x4 Litterx5 Detritus

x6 Bacteria

x7 Detrit ivores

x8 Carnivores

Carni-vores

Detrit i-vores

Bacteria

Detritus

Plants

Kcal/m2/y r

Su n

Litter

x1 x2

x4

x5x8

x6

x3 x7

(a) Cone Springs System

11184

300

8881

860

5205

2309

75

255

1600

370

167

200

635

(b) Matrix in Output

(c) Solar Transformities

Out[2]//MatrixForm=

Out[8]=



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good part of detritus are grouped separately from the dead organic matter.

RESULTS

The transformity calculation procedures were used to evaluate other well known sets of energydata on ecosystems starting with Cone Spring and including a South Carolina Oyster reef, the tropical rain

forest in Puerto Rico, and the Baltic Sea. For each set of ecosystem, data transformity computations were

made and reported with a gure that includes the systems diagram, the energy ow values on the pathways

or on adjacent arrows, the transformity-transformation matrix, and the resulting transformities.

Cone Spring

The Cone Spring system (Dames and Patten, 1981) has been used by many authors to introduce

various energy network calculations and concepts (Wulff, Field, Mann and others, 1989). The system is an

example of the coarse aggregation of Figure 4a. Figure 5 shows an energy systems diagram, transformity-

transformation matrix, and resulting transformities. These transformities are for inter-unit transfers and

not for the production or storages.

Note the higher transformity of the “bacteria” compared to the organic pool. Many networks

Detritus

Deposit

Oysters,

Filter-

feeders

Predators

DepositFeeders

Micro-

biota

GlobalSources

EstuarineProduction

Phytopl.

Organics

15.8

2000

1000

8.2

7.3

0.640.17

0.51

0.66

1.21

1.9

1.21

0.05

41.5

6.2Kilocalories/m2/day

69.2

24.1Meio-

fauna

2.4

16.3

2,075,000

22.3

4.24

4.9

2.4

.68

10.9610.44

x1 = Solar emergy

x3 = Oyster yield

x2 = Phytoplank-organics

x4 = Detritus Deposit

x5 = Predator yield

x6 = Meiofauna yield

x8 = Microbiota yield

x7 = Deposit Feeder yield

Transformities

.

x1 x2 x3 x4 x5 x6 x7 x8Out[24]//matrixForm=

Out[30]=

Figure 6. Energy systems diagram and transformity calculations for the oyster reef system (Dame and Patten,

1981) used for other network analysis studies (Higashi and Burns, 1991).



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E a r t h

S u n ,

R a i n ,

W i n d 1

5 , 7

0 0

s e m K c a l

P g r o s s

L e a v e s

4 4

L

a b i l e

K c a l o r i e s / m 2 / d a y 1

3 1

x 1

x 2

8 7 p

h l o e m

6

B r a n .

F r u i t

B o l e s

R o o t s

O l d S o i l

O r g a n i c s

L i t t e r

A n i m a l s

2 . 4

0 . 8

6 1 . 4

6

5

T h e N u m b e r w i t h i n

t h e a u t o c a t a l y t i c l o o p

i s t h e p r o d u c t i o n

e n e r g y f l o w .

1 0

7 . 8

0

. 8

0 . 3

4 . 6

9 . 0

7 . 0

2 . 3

2 . 2

0 . 6

0 . 7

0 . 9

R o o t l e t s

1 2 . 4

1 . 0

0 . 4

0 . 1

0 . 1

0 . 6

0 . 1

0 . 1

4 . 5

0 . 5

R t l t .

B r a n c h e s

x 3

x 4

x 1 S o l a r e m e r g y

x 2 L a

b i l e p h o t o s y n t h a t e

x 3 L e

a v e s

x 5

F r u i t

x 4 B r a n c h e s

x 6 B

o l e s

x 7 R o

o t l e t s

x 8 L a

r g e r o o t s

x 9 A

n i m a l s

x 1 0 L

i t t e r u s e d

x 1 1 O

l d s o i l o r g a n i c s

S o l a r

T r a n s f o r m i t i e s

x 1

x 2

x 3

x 4

x 5

x 6

x 7

x 8

x 9

x 1 0

x 1 1

x 5

x 6

x 8

x 7

x 9

x 9

x 1 1

7 . 8 2 . 4

0 . 2

- 0 .

8

- 0 .

3

- 6

- 0 .

5

0 . 1

6

4 . 6

0 . 8

O u t [ 8 ] =

2 . 4

6 7 3 8 x 1 0 1 0 ,

5 9 5 8 . 9

4 ,

3 4 9 . 3

7 7 ,

8 6 . 5

5 4 8 , 1

9 . 5

3 7 9 ,

2 . 1

2 5 9 ,

0 . 9

8 0 8 9 9 ,

0 . 4

6 2 2 0 7 ,

0 . 1

7 3 4 5 9 ,

0

. 0 9 5 8 5 7 8 ,

5 . 2

3 1 4 6 x 1 0 -

6 )

1 1 6 9 l .

1 1 9 9 0 . 8

9 5 9 2 . 6

1

1 2 2 7 0 . 5

2 8 1 9 8 . 3

1 2 4 9 2 . 3

1 5 4 9 0 5 .

x 1 0

F i g u r e 7 .

E n e r g y s y s t e m s d i a g r a m a n d

t r a n s f o r m i t y c a l c u l a t i o n s f o r c o m p

a r t m e n t s o f t h e T a b o n u c o r a i n f o r e s t i n t h e L u q u i l l o M o u n t a i n s o f P u e r t o R i c o , w i t h

e n e r g y o w s d e r i v e d f r o m O d u m ( 1 9 7 0 -

F i g u r e 2 , p a g e I - 1 9 3 ) . H e a t s i n k s

o m i t t e d .



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SolarEmergy Forest

VegetationLitter

Fungi

x4

10,5571.0

2449

147

10.1 1.0

10557 -1 0 0

0 2449 0 -147

0 10.1 -1 0

x1 x2 x3 x4

Transformation-transformity Matrix

x1 x2

x3

Microbes

x1 solar emergy = 1

x2 Litter = 10,557

x3 Microbes = 106,626

x4 Fungi = 175,878

Transformities

-147 -10 0

Figure 8. Energy transformations and transformities of microbes in the Tabonuco rain forest. Heat sinks omitted.

are aggregated so that the bacteria are regarded as part of the detritus, but the Cone Spring data reect an

attempt towards separation. Here the name bacteria is applied to the large scale cluster of microbiological

processes, not to individual bacteria which are on a smaller scale. Aggregating small fast turnover items

into clusters which have longer existence and larger scale increases the transformity. People studying

ecosystems on a macroscale often aggregate the small components into groupings that may be real, but

confusion is generated if these larger entities are named by the tiny items on smaller scale within.

Oyster Reef

Another much studied energy network is the South Carolina intertidal oyster reef depicted in

Figure 6 (Dame and Patten, 1981). In the rst line, the input of phytoplankton and particulate organics

was given a solar transformity of 50,000 sej/J, a plausible value base on some other studies, so that the

matrix could be based on solar emergy. High values were obtained for the animal categories, as might

be expected where there was a high concentration of biomass.

Tabonuco Rainforest in Puerto Rico

Transformities were calculated for several sets of energy ow data for the mid altitude rain forests

of the Tabonuco type in the Luquillo Mountains of eastern Puerto Rico. Energy ows in main categoriesof forest production in the Tabonuco forest at mid elevation were obtained from Odum (1970) with the

results in Figure 7. Here, estimates of production were used to obtain transformities of the structural



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storages (i.e. leaves, branches, roots, etc.), whereas only the transfer ows were obtained from the Cone

Spring and Oyster data sets.

Data on microbial processes in the Tabonuco forest were evaluated in Figure 8, even though

a microbial network model with energy ows was not available. Three fragments of information on

the energy transformations leading to microrganisms were combined in a matrix calculation to obtain a

transformity for bacteria and one for fungi, both considered as aggregated categories.

In Figure 9, fragments of energy transformation data on several animals in the Tabonuco forest

were assembled in a transformation-transformity matrix and transformities calculated. Energy ows for

several animals were obtained from Reagan and Waide (1996).

Eln Cloud Forest of the Luquillo Mountains

A cloud forest of dwarf trees covered with epiphytes with dense mats of roots is found on mountain

tops above 1000 m in the Luquillo rain forest. Almost continuously in the clouds, transpiration and solar

insolation are both small. Data on the Eln Cloud Forest of the mountain top from Weaver and Murphy

(1990) were analyzed in Figure 10. Very high transformities were found, consistent with the very slow

growth rates known from many studies there.

SUMMARY

The examples in this paper show how the Eigenvalue method can estimate transformitiesof systems by combining data from different sources with previously determined transformities.

Transformities of interpopulation ows result from typical network data, but transformities for the

SolarEmergy Organics

x5

x1 x2 x3 x4


x5 x6

x1 x2

x4

x1 Solar emergy = 1

x2 Gross product = 1,200

x3 Net org. product = 1,690

x5 Coqui frogs = 86,207

Transformities

x6 Tody bird = 4,462,450

x4 Arthropods = 6,903

x3 Arthropodsas rain

1200

kcal/m2/day

131 93

9.3

Coqui Frogs

x6

Tody Bird

5.1

1.0

0.0066

2.5x10-5

Figure 9. Energy transformations and transformities of some animals of the Tabonuco rainforest. To change

kcal to kJ, multiply by 4.18. Heat sinks omitted.



-215-


populations can be obtained where there are energy transformation data of production and growth. The

method is synthetic when it describes the energy transformation hierarchy of a system with a set of

transformities. With or without preconceived ideas about network connections, the hierarchy emerges

from the energy transformation data of the parts.

ACKNOWLEDGMENT

This study is part of the Long Term Ecological Research Program 2001 on the tropical montane rainforest

in Puerto Rico, coordinated by Prof. J. Zimmerman.

REFERENCES

Collins, D. and H.T. Odum. 2000. Calculating transformities with an Eigenvector Method. pp. 265-

280 in Emergy Synthesis, ed. by M.T. Brown, Center for Environmental Policy, University of

Florida, Gainesville, 328 pp.

Dames, R.F. and B.C. Patten. 1981. Analysis of energy ow in an intertidal oyster reef. Marine Ecol.

Progress Series, Baruch, South Carolina, 5:115-124.

Higashi, M. and T.P. Burns, eds. 1991. Theoretical Studies of Ecosystems, the Network Perspective.

Cambridge Univ. Press, Cambridge, United Kingdom, 364 pp.Holling, C.S. 1973. Resilience and stability. Ann. Rev. Ecol. Systems 4, 1-23.

Odum, H.T. 1970. In Odum, H.T. and R.F. Pigeon. A Tropical Rain Forest, Division of Technical

SolarEmergy

x5

x1 x2 x3 x4


x5 x6

x1x2

x4

x1 solar emergy = 1

x2 Mountain rain = 50,000

x3 Leaves = 7,487,030

x5 Wood = 110,404,000

Transformities

x6 Litter = 73,290,900

x4 Roots = 90,889,600

x3as Rain

kcal/m2/day

x6

GlobalProcess

Mountain Rain

DwarfForest

Leaves

5 x 104

1000

6.68

Litter

Roots

Wood SoilOrganics

Decomp.

55.8

17

1.7

29.5

2

606

x7

x7

x7 Soil Organics = 75,866,100

Figure 10. Energy transformations and transformities of vegetation of the dwarf cloud forest on the top of the Lu-

quillo Mountains of Puerto Rico. Heat sinks omitted.



-216-


Information, TID 24270, Atomic Energy Commission, Clearinghouse for Federal Scientic and

Technical Information, Springeld, Virginia, 1660 pp.

Patterson, M. 1983. Estimations of the quality of energy sources and uses. Energy Policy 2(4):346-

359.

Reagon, D.P. and R.B. Waide. 1996. The Food Web of a Tropical Forest. Univ. of Chicago Press,

Chicago, Illinois, 616 pp.

Watt, K.E.F. 1968. Ecology and Resource Management. McGraw-Hill, New York.

Weaver, P.L. and P.G. Murphy. 1990. Forest structure and productivity in Puerto Rico’s Luquillo

Mountains. Biotropica 22:69-82.

Wulff, F., J. Field, and K. Mann, eds. 1989. Flow Analysis in Marine Ecosystems. Springer, New

APPENDIX TABLE

Program EIGNTRAN.tru for Calculating Transformites from

Energy Transformation Data

20 ! EIGNTRAN.tru

30 ! Dennis Collins, Dept of Mathematics, Univ of Puerto Rico, Mayaguez

40 OPTION BASE 0

110 ! Program in TRUEBASIC for Calculating Transformities from Energy

Equations

120 !Enter Transformation-transformity matrix as rows of DATA starting in line 4000

190 ! least squares min length solution of Ax = b

210 ! by singular value decomposition and generalized inverse

230 ! enter dimensions of augmented matrix (A b) on line 3000

250 ! enter augmented matrix (A b) row-by-row starting on line 3010

270 read m, n

290 let n = n-1

310 ! n is number of columns of matrix A within program, so change

330 ! n = n-1 in line 290

350 dim a(0,0)

360 mat redim a(m, n+1)

370 dim v(0,0)

380 mat redim v(n, n)

382 dim tv(0)

383 mat redim tv(n)

400 dim z(0)410 mat redim z(n)

420 dim z1(0)

430 mat redim z1(n)

450 dim b(0,0)

460 mat redim b(n, n)

470 dim c(0,0)

480 mat redim c(m, n)

500 dim d(0,0)

510 mat redim d(n, m)

520 dim e(0,0)

530 mat redim e(n, m)

550 dim x(0)

560 mat redim x(n)



-217-


Appendix Table (continued)

580 read t

600 for i = 1 to m

620 for j = 1 to n+1

640 read a(i, j)

660 print a(i, j); “ “;

680 next j

700 print

720 next i

740 for i = 1 to n

760 for j = 1 to n

780 let v(i, j) = 0

800 next j

820 let v(i, i) = 1

840 next i

860 let c1 = n*(n-1)/2880 for j = 1 to n-1

900 for k = j+1 to n

920 let p = 0

930 let q = 0

940 let r = 0

960 for i = 1 to m

980 let p = p+a(i, j)*a(i, k)

1000 let q = q+a(i, j)*a(i, j)

1020 let r = r+a(i, k)*a(i, k)

1040 next i

1060 if q >= r then goto 1150

1100 let c_t = 0

1110 let s = 1

1130 goto 1290

1150 if q*r = 0 then goto 1490

1190 if (p*p)/(q*r) < t then goto 1490

1230 let q = q-r

1240 let v_t = sqr(4*p*p+q*q)

1260 let c_t = sqr((v_t+q)/(2*v_t))

1270 let s = p/(v_t*c_t)

1290 for i = 1 to m1310 let r = a(i, j)

1320 let a(i, j) = r*c_t+a(i, k)*s

1340 let a(i, k) = -r*s+a(i, k)*c_t

1360 next i

1380 for i = 1 to n

1400 let r = v(i, j)

1410 let v(i, j) = r*c_t+v(i, k)*s

1430 let v(i, k) = -r*s+v(i, k)*c_t

1450 next i

1470 goto 1510

1490 let c1 = c1-1

1510 next k

1530 next j



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1550 if c1 > 0 then goto 860

1590 for j = 1 to n

1610 let q = 0

1630 for i = 1 to m

1650 let q = q+a(i, j)*a(i, j)

1670 next i

1690 let q = sqr(q)

1700 let z(j) = q

1720 if q < t then goto 1820

1760 for i = 1 to m

1780 let a(i, j) = a(i, j)/q

1800 next i

1820 next j

1840 print

1860 print “matrix U with Ut*U = I”1880 for i = 1 to m

1900 for j = 1 to n

1920 print a(i, j); “ “;

1940 next j

1960 print

1980 next i

2000 print

2020 print “singular values”

2040 for j = 1 to n

2060 print “z(“; j; “)= “; z(j)

2080 if abs(z(j)) < t then goto 2140

2120 let z1(j) = 1/z(j)

2140 next j

2160 print

2180 print “orthogonal matrix V”

2200 for i = 1 to n

2220 for j = 1 to n

2240 print v(i, j); “ “;

2260 next j

2280 print

2300 next i2320 print

2321 let sv=v(1,n)

2322 for j=1 to n

2323 If abs(v(j,n))<abs(sv) then let sv=v(j,n)

2324 next j

2325 for j=1 to n

2326 let tv(j)=v(j,n)/sv

2327 print tv(j)

2328 next j

2329 print

2340 for i = 1 to n

2360 for j = 1 to n

2380 let b(i, j) = z(i)*v(j, i)



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2400 next j

2420 next i

2440 print “check A= U*S*Vt”

2460 for i = 1 to m

2480 for j = 1 to n

2500 let c(i, j) = 0

2520 for k = 1 to n

2540 let c(i, j) = c(i, j)+a(i, k)*b(k, j)

2560 next k

2580 print c(i, j); “ “;

2600 next j

2620 print

2640 next i

2660 print

2680 for i = 1 to n2700 for j = 1 to m

2720 let d(i, j) = z1(i)*a(j, i)

2740 next j

2760 next i

2780 print “generalized inverse= V*(S+)*Ut”

2800 for i = 1 to n

2820 for j = 1 to m

2840 let e(i, j) = 0

2860 for k = 1 to n

2880 let e(i, j) = e(i, j)+v(i, k)*d(k, j)

2900 next k

2920 print e(i, j); “ “;

2940 next j

2960 print

2980 next i

3000 print

3020 print “least squares min length solution”

3040 for i = 1 to n

3060 for j = 1 to m

3080 let x(i) = x(i)+e(i, j)*a(j, n+1)

3100 next j3120 print “x(“; i; “)= “; x(i)

3140 next i

3152 ! dimensions of matrix next: rows, columns

3160 data 3, 4

3180 data .000001

4000 data 100,1,-11,0

4020 data 100,2,-21,0

4030 data 100,0,-1,0

5000 end



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Edited by



Associate Editors

Howard T. Odum



David Tilley



Sergio Ulgiati

University of Siena

Siena, Italy

December 2003



Gainesville, FL


vi



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Chapter 15. Emergy Analysis of the Prehistoric Global Nitrogen Cycle

15Emergy Analysis of the Prehistoric Global Nitrogen Cycle

Daniel E. Campbell

ABSTRACT

In this analysis of the prehistoric global nitrogen cycle, relationships between the emergy per unit

mass and the mass concentration of nitrogen were examined. A monotonic increase in the emergy per

unit mass (the specic emergy) as a function of increasing material concentrations is predicted by energyhierarchy principles and was observed in the data. This pattern is expected when energy transformation

is accompanied by an increase in the concentration of material in higher transformity units. Among

nitrogen species in the troposphere a different pattern was observed, where transformity and the emergy

per unit mass are greater for the more reactive but lower concentration nitrogen species. In this case,

lightning discharges create nitrogen species of higher transformity by breaking the stable triple bond

of diatomic nitrogen and increasing its chemical reactivity. The nitrogen ows through living systems

are arranged in an energy capturing order-disorder loop upon which a trophic hierarchy is built. The

emergy per unit mass increases as energy is transformed and nitrogen is concentrated through the trophic

web. Planetary nitrogen ows through living systems are large compared to geochemical uxes and as a

consequence they have lower specic emergies. In general living systems have greater mass concentrations

of nitrogen which supports the hypothesis that emergent properties of an element or elements ,e.g., life,

arise when a threshold of mass concentration is exceeded. Energy principles and the data on global

nitrogen ows indicate that the specic emergy of pathway ows may approach a minimum value as the

system approaches a state of maximum empower.

INTRODUCTION

The distribution of energy and materials within the global ecosystem can be understood by

examining the network of energy transformations that gives rise to these patterns. Odum (1996) showed

that the generation of hierarchal organizations follows as a consequence of the properties of energy being

transformed in a manner that maximizes power in a network. He proposed this principle as a fth law ofthermodynamics that explains the ubiquity of hierarchical patterns in the universe. The maximum power

principle was rst put forward as a fourth law of thermodynamics (sensu lato) by Lotka (1922a&b). The

fourth law (as modied by Odum 1996) states that network designs which maximize empower prevail in

competition with alternatives. Such designs develop positive feedback loops that capture and use more

energy, building a network structure that maximizes empower or emergy per unit time. When positive

feedbacks of energy develop from multiple levels of organization, e.g., as in the organization of cities, the

eddy structure of the ocean, the trophic webs of the sea, etc., a hierarchy is established. Competition among

alternative system designs releases a goal seeking mechanism inherent in the structure of hierarchical

networks that becomes the means to maximize empower. (Campbell 2001).

The properties of hierarchies of energy transformation are, in part, determined by the secondlaw of thermodynamics, because the quantity of available energy or exergy must decrease with each

transformation according to the second law of thermodynamics, whereas the total emergy input to

the network which is required to accomplish each successive transformation is not diminished. The



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transformity of a network ow is the ratio of these two quantities. For example, the emergy required to

produce the ow along a pathway in solar equivalent joules (sej) is divided by the actual energy moving

on the pathway to obtain the transformity of that pathway. The energy hierarchy (fth?) law (Odum 1996)

states that ows of energy in the universe are organized into hierarchies by energy transformation and that

position in a hierarchy is measured by transformity. General patterns of organization are expected as a

consequence of the this law, e.g., it implies that hierarchies will have many low transformity units with

small support areas distributed throughout a given space with progressively fewer higher transformity

units that have progressively larger support areas and thus are more widely distributed in space as they

Macroscopic mini-model of the Global Nitrogen Cycle

as two coupled order-disorder loops.

(1) Outer geochemical loop

(2) Inner biochemical loop

(3) Coupling from (2) to (1) by dentrification

(4) Coupling from (1) to (2) by nitrogen fixation

Org.matter

Solar

Radiation

Earth

DeepHeat

Tidal

Energy

N2N2O

NH3

NOx Water

NH4

NOx

Animals

Carbon

N

Plants

Carbon

N

NH4

NOx

Biosphere

Troposphere

Crust

Carbon

N

N Sediments

N

Lithosphere

x

MicrobesN

X

X

X

X

X

X

X

X

N

Coal

N

TomantleFrommantle

Stratosphere

x x(1)

(1)

(1)

(1)

(1)(1)

(2)

(2)

(2)

(2)

(3)

(4)

(2)(1)

(1)

X X X

S

E

G

(3)

Earth

Figure 1. An overview energy systems model of the global nitrogen cycle diagramed as two coupled order-disorder

loops. The outer geochemical loop (1), shown as dark gray lines, is coupled to the inner biochemical loop (2), shown

as black lines, through denitrication (3) and nitrogen xation (4), shown as dashed lines.



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require more energy for their support.

Odum (2000a) observed that when a self-organizing process (one with positive feedbacks)

converges and concentrates energy in centers, materials are also concentrated. He proposed a

thermodynamic principle to account for this observation and suggested it as a possible a sixth law of

thermodynamics. This principle states that the coupling of matter ows in biogeochemical cycles to the

hierarchy of energy transformations explains the widely observed skewed (log normal) distribution of

materials with concentration (Aherns, L.H. 1954). Aherns (1954) proposed the log normal distribution

of the elements as a law of geochemistry based on his observations on the concentrations of 20 elements

in igneous rocks. Emergy researchers have obtained a fairly good understanding of the operation of the

proposed fourth and fth laws in ecosystems (Odum 1971a, Brown 1981, Fontaine 1981, Odum 1994,

Hall 1995, Campbell 2000a); however, the operation of the sixth law has not been thoroughly investigated.

An investigation of the biogeochemical cycle of lead in relation to the energy hierarchy (Odum 2000c)

was the primary empirical study used to demonstrate this law. Additional, information on phosphorus

from Brandt-Williams (1999) was used to conrm patterns observed for lead. In this paper I examined

the prehistoric global nitrogen cycle to look for patterns in the emergy per unit mass of nitrogen called

specic emergy by Ulgiati in this volume. Graphs of the specic emergy of nitrogen species as a function

of concentration and other plots were compared with expectations derived from the energy hierarchyprinciples mentioned above.

A CONCEPTUAL MODEL OF THE GLOBAL NITROGEN CYCLE

The nitrogen cycle is complex which may explain why most representations of this global cycle

have focused on uxes between compartments (Delwiche, 1970; Soderlund and Svensson,1976; Jaffe

1992; Stedman and Shetter,1983; Galloway, 1998) or budgets for individual species, e.g., ammonia

(Schlesinger and Hartley,1992) rather than on developing overview models that capture the structure

and function of the global nitrogen network as a system. Energy systems language is a tool designed forthe analysis of networks and the simplication of complexity. The biogeochemical cycle of nitrogen can

be represented as two coupled order-disorder cycles or Michaelis-Menton loops (Odum 1994). Figure

1 shows an overview of the global nitrogen cycle diagramed using the energy systems language (Odum

1971b, 1994). Both cycles are driven by the earth’s three primary energy sources; solar radiation, the

earth’s deep heat, and the gravitational attraction of the sun and moon. By denition, disordered forms

are either chemically simpler or less concentrated than the ordered forms of nitrogen.

The outer cycle (Figure 1) shows geochemical nitrogen ows moving from a dilute form in the

rocks of the lithosphere (53 gN m-3) to N2, a more concentrated form (963 gN m-3) in the atmosphere.

In a prehistoric steady state condition, before human agricultural and industrial activities altered global

nitrogen ows, most of the nitrogen added annually to the atmosphere eventually made its way back to

the lithosphere (Stedman and Shetter 1983). The available energy of nitrogen species concentrated inthe atmosphere and in the primordial ocean interacted with the external energy sources to the earth, as

well as other material storages, to develop a second inner cycle of nitrogen circulating through a network

of biochemical interactions. This inner cycle (Figure 1) moves nitrogen from the relatively disordered

inorganic species, e.g., ammonia, nitrate, nitrite, to more complex forms, e.g., amino acids and proteins

present in living organisms. Living systems build trophic hierarchies that further concentrate nitrogen as

a consequence of successive energy transformations. The exterior geochemical cycle and the interior

biochemical cycle of nitrogen are marked (1) and (2), respectively, in Figure 1. These two cycles are

coupled from (2) to (1) through the process of denitrication (3) in Figure 1. Nitrogen xation (4)

completes the coupling by establishing a connection from cycle (1) to cycle (2). After an undetermined

number of passages through the biosphere, a nitrogen atom will eventually be deposited in sedimentswhere it is eventually returned to the crust in the formation of sedimentary rocks.



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A Brief Account of the Evolution of the Nitrogen Cycle

It may be instructive to consider the evolution of the global nitrogen cycle as outlined by Burns

and Hardy (1975) to see how these two coupled cycles may have come into being. The upward arm of the

outer cycle marked (1) is driven by the geochemical nitrogen uxes from crustal outgassing and volcanic

eruptions that have supplied N2

and NH3

to the atmosphere since the rocks of the primordial earth rst

solidied. Schlesinger (1997) points out that the atmosphere of the earth was probably dominated by

N2an other moderately reduced gases when life rst developed. After a long period of abiotic organic

chemical evolution (Oparin 1953), perhaps facilitated by the introduction of amino acids in meteors

(Schlesinger 1997), concentrations of nitrogen and other elements necessary for life in the oceans of the

primordial earth are thought to have been exploited by self-organizing, biochemical processes culminating

in the development of cells with nitrogen as one of the key constituents. Death and decomposition of

cellular life forms, most probably by autolysis, produced ammonia which closed the primordial inner

loop. These primitive early life forms were anaerobic organisms dependent on the most accessible and

abundant nutrients to support their metabolism. Ammonia is considered to be the primary nitrogen

compound supporting early life forms because of the almost universal capacity of organisms to utilize it.

The primordial reducing environment of the earth would have supported an easy interchange betweenthe disordered forms of nitrogen in the environment and the more ordered forms of nitrogen in living

organisms (Burns and Hardy 1975).

The evolution of the nitrogen cycle cannot be separated from the development of the other global

biogeochemical cycles, i.e., carbon, oxygen, phosphorus, sulfur, etc. upon which life depends (Deevey

1970). The development of photosynthesis was an event of particular importance to the cycling of all the

biogeochemical elements because it produced increasing concentrations of oxygen in the atmosphere,

which promoted the chemical synthesis of oxidized forms of the other elements. Increased concentrations

of oxygen in the troposphere, produced by photosynthesis, allowed the oxidation of large quantities of

ammonia to nitrate. As nitrate accumulated in the environment, life developed the capacity to use this

additional energy source through assimilatory nitrate reduction and denitrication (the reduction of

nitrate to a gaseous form, most commonly N2 or N

2O). With the development of denitrication as a

major pathway, coupling the biological nitrogen cycle to the geochemical one, diatomic nitrogen could

be indenitely sustained in the atmosphere along with oxygen. Nitrogen xation may have developed as

reactive oxygen accumulated in the atmosphere and ammonia become scarce (Burns and Hardy 1975).

Alternatively, the earliest organisms may have had limited supplies of nitrogen available for protein

synthesis (Schlesinger 1997) and nitrogen xation may have developed in anaerobic conditions as a

means of increasing available nitrogen supplies in the primordial ocean by drawing on the large storage of

atmospheric nitrogen. There is little evidence available to x the time at which biological nitrogen xation

developed (Schlesinger 1997) , but in either case, it acted as a feedback pathway capable of alleviating

any nitrogen shortage in the biosphere that might prevent the biosphere as a whole from obtaining the

optimum loading of nitrogen (Odum and Pinkerton 1955) needed to develop maximum empower in theglobal biogeochemical network.

METHODS

Energy systems language (Odum 1971b, 1994) was used to diagram the global nitrogen cycle

(Figures 1 and 2). The model of the prehistoric global nitrogen cycle (Figure 2) was evaluated using

information from Stedman and Shetter (1983) and other references including four additional evaluations

of the global nitrogen cycle performed between 1970 and 1983 (see Table 1). I made several assumptions

in addition to those made by Stedman and Shetter (1983) to allow a reasonable estimate of prehistoricnitrogen ows through the network shown in Figure 2. First, global nitrogen ows were assumed to have

reached a steady state condition prior to intervention by mankind. This steady state assumption was used

to balance all storages in the network given initial values from Stedman and Shetter (1983) and the other



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sources and calculations documented in Table 1. Second, the emergy per unit mass for all storages and

ows in the model was calculated based on the fact that the emergy around a completely interconnected

closed loop is a constant. The global network of nitrogen ows (Figure 2) was assumed to approximate

a completely interconnected closed loop, and therefore, the emergy driving these ows is the emergy

supplied to the earth by its independent energy sources, i.e., solar radiation, the earth’s deep heat, and the

gravitational attraction of the sun and moon (Odum 1996, Campbell 2000b). The emergy required to place

a gram of nitrogen in storage was determined by multiplying the annual emergy input to the earth by the

turnover time of the storage in years, since this is the time required to completely replace the material

stored. This value was divided by the mass of the storage to give the specic emergy of the storage in sej

g-1. The specic emergy of ows was determined by dividing the annual emergy inow to the earth (sej)

by the annual nitrogen ux in grams owing along a pathway in the global network

RESULTS

The results of this study consist of (a) a conceptual model of the global nitrogen cycle (already

presented), (b) a model of nitrogen ows through a four compartment global model (Figure 2) and an

evaluation of that model for prehistoric conditions, (c) an analysis of relationships between the specicemergy of network storages and the degree to which nitrogen has been concentrated in those storages and

(d) an analysis of the relationship between the magnitude of nitrogen uxes and the specic emergy of

those uxes. The results from (c) and (d) above will be compared to the results expected from the energy

hierarchy principles described earlier and in Odum (2000a).

An energy systems model of the global network of nitrogen storages and ows is shown in Figure

2 and further documented in Table A-1(given as an Appendix) . Item labels in the table are also found

on the appropriate storage or ow in the diagram. The three external forcing functions driving ows of

nitrogen through the global network were evaluated in emergy units. The global system is modeled using

four large compartments: (1) The lithosphere includes the crust where nitrogen is stored in igneous rocks,

sedimentary rocks, and fossil fuel; and the mantle with its stored nitrogen. (2) The troposphere has nitrogenstored as diatomic nitrogen, N2; ammonia gas, NH

3; nitrous oxide, N

2O; and organic nitrogen. In addition,

the nitrogen stored as nitric oxide, NO, and nitrogen dioxide, NO2 in the troposphere are combined into

the NOx variable and ammonia and NO

x in the aqueous phase are shown as storages of the ammonium

ion, NH4

+ and nitric acid, HNO3

-. (3) The oceans contain nitrogen stored in plant biomass, animal biomass,

sediments, dissolved organic matter or DON, particulate organic matter or PON; as well as, storages of

diatomic nitrogen, N2, ammonium, NH

4, nitrous oxide, N

2O, and NO

x that are dissolved in seawater. (4)

The land compartment contains nitrogen stores in plant and animal biomass, litter, soil organic matter,

soil microbes; as well as nitrogen present in the inorganic pool including insoluble and soluble forms.

The links between compartments are shown as thicker lines and intra-compartment links are designated

with thinner lines. Gray lines indicate the ow of used energy to the heat sink. Over seventy uxes within

and among compartments have been identied and documented in Table A-1.The primary connections between the four major compartments of the model are as follows: Out-

gassing and volcanic eruptions, J1, is the major nitrogen ow linking the lithosphere to the troposphere.

The lithosphere is also linked to the land systems through weathering of sedimentary and igneous rocks,

J2. Nitrogen returns to the lithosphere when ocean sediments are subducted into the earth, J

60, and

transformed into sedimentary rock. The troposphere is linked to the ocean and land compartments through

the wet and dry deposition of NH3, J

21, J

22, J

23, and NO

x, J

13, J

14. Also, there is a net ux of N

2O, J

7, from

the troposphere to the stratosphere and a return ow of N2, J

64, and NO

x, J

65, from the stratosphere to the

troposphere. The ow of N2 is produced by the decomposition of N

2O, either by direct photolysis or

through reaction with photolytically activated oxygen atoms (Anderson 1983). In addition, N2 and N

2O

are returned to the atmosphere from the land and ocean compartments through denitrication, J27, J29, J6,J16,and land and ocean systems take-up atmospheric N2 through biological nitrogen xation, J

57, J

26, J

28. In

addition to the uxes already mentioned ammonia volatilization from soils, J20

, and animal excreta, J18

,

link the land compartment with the troposphere and water runoff carries several nitrogen species from



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There is a small net loss of nitrogen from the stratosphere to space that has not been considered here. All

storages and ows within the global nitrogen network are evaluated and documented in Table A-1.

Figure 3 shows the result of plotting the ratio of the earth’s annual emergy inow to the grams

of nitrogen found in selected global storages versus the concentration of nitrogen in those same storages,

as determined by the mass of nitrogen per unit volume of the substrate. The pattern found was the one

described by Odum (2000a) with the emergy to mass ratio monotonically increasing from left to right

as a function of concentration as expected for a hierarchical network of interactions. Figures 4, 5, and

6 show plots of the emergy necessary to place a gram of nitrogen in a given storage as a function of

the concentration of nitrogen (gN’-3 substrate). The emergy per gram for material in the compartments

of the lithosphere is plotted in Figure 4 as a function of the concentration of nitrogen per cubic meter of

the substrate, e.g., the mass of nitrogen contained in a cubic meter of coal was determined to nd the x

coordinate of the point labeled coal in Figure 4. The pattern made by the four points in Figure 4 increases

monotonically from left to right as in Figure 3, but the slope of the curve in this plot abruptly increases

between the second and third points. This occurs because the specic emergy of nitrogen in sedimentary

rocks is an order of magnitude larger than the specic emergy of diatomic nitrogen gas in the troposphere.

This difference is attributable to the differences between the turnover times of the two storages.

The specic emergy of the nitrogen stored in various forms in the troposphere versus theconcentration of those forms in gN per cubic meter of air is shown in Figure 5. In this case the pattern

is different, displaying a line of points that progressively decrease in magnitude from left to right. This

pattern is in part produced by lightning acting on individual molecules of highly unreactive diatomic

nitrogen to break its triple bond and create nitrogen species of greater chemical reactivity. The point on

the far right is relatively unreactive diatomic nitrogen which has the lowest emergy per unit mass.

Figure 6 shows a plot of the specic emergy and mass concentration of components in the

oceans compartment. The specic emergy of the components plotted in this gure fall into two groups:

(1) those that are part of the nitrogen cycling through the inner loop (solid triangles) and (2) those that

are a part of the outer geochemical loop (open circles). The land compartment components of the inner

biochemical loop are not plotted, because they show a similar pattern to that seen in the oceans. The

pattern shown in Figure 6, and repeated with somewhat different values by the biological components

of the land compartment; is not, at rst, recognizable as belonging to either of the two patterns identied

through examination of Figures 4 and 5. Both the land and ocean components show that ammonia has

the lowest specic emergy of all the nitrogen species examined and that plants and plant derived organic

matter have a specic emergy similar to but slightly higher than ammonia. The specic emergy of

animals is 5 to10 times greater than that of plants, but nitrogen is only slightly more concentrated in the

animal form. The oxidized forms of nitrogen, i.e., NOx, have greater specic emergy and slightly higher

concentration than ammonia in both land and ocean systems. These relationships are shown in aggregate

form in Table 1 where the specic emergies of the various forms of nitrogen are determined based on

their annual global throughput.

Figure 7 plots the log of the material throughput of the components in the ocean compartmentagainst the log of the specic emergy of the ux. Two distinct groups of points are shown on the material

ow plot reecting the two groups of components distinguished in Figure 6. Greater mass ux occurs

for lower specic emergy components within each group, as well as for both groups together. The two

coherent groups that appear in this plot may reect the different larger system properties that control

nitrogen ow in the biochemical and in the geochemical loops of the global nitrogen cycle.

DISCUSSION

The results of this study can be interpreted and understood by rst considering the properties of theenergy transformation process responsible for the creation of the hierarchy upon which a set of observations

was made. If the observed set of observations is from a hierarchical series of energy transformations,

transformity will always accurately dene the position of any unit in the hierarchy. The results of this



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study indicate that the specic emergy may also accurately dene the position of a unit in the hierarchy

(Figure 7). However, the relationships between specic emergy and mass concentration examined in

this study (Figures 3,4,5) show that increased concentration of mass does not always follow the increase

of specic emergy seen in higher transformity units of a hierarchy. For there to be a relation between

the specic emergy and the mass concentration of an element, mass must be concentrated as part of the

energy transformation process. The data presented here show that the concentration of mass accompanies

energy transformation in many hierarchies that increase the energy stored in higher transformity units by

increasing the mass of those units, e.g., turbulent eddies in the ocean, food webs in the sea, cities, etc.

But energy transformation can increase the energy per unit in a different manner, e.g., by increasing the

chemical reactivity or the velocity of a molecule rather than by concentrating mass, and as a result the

positive correlation between the concentration of a material and its specic emergy is not observed (Figure

5). In Figure 5, the specic emergy and the transformity of nitrogen has been increased by changing the

chemical reactivity of the element rather than by concentrating its mass. The concentrations of nitrogen

species found in the troposphere are also determined by nitrogen ows to and from the land and ocean

compartments, (see particularly the values of NH3 (g), NH

4 (aq) and organic N in Figure 5) and by the

solubility and reactivity of the various nitrogen species themselves (more reactive species have lower

residence times). In general, the fundamental assumption that mass is concentrated in higher transformity

units does not apply to hierarchies of molecular velocity or chemical reactivity such as those found in

the gaseous state. However, the specic emergies of nitrogen species in the troposphere are consistent

with the energy hierarchy principle (5th law) when plotted as the number of molecules in a volume of air

versus the emergy per gram of each species. These observations support the view that the principle ofmass concentration with energy transformation (proposed 6th law) may be a corollary of the more general

energy hierarchy principle (5th law).

10 100 1000 10000 100000 1000000

Concentration in gN per cubic meter of substrate

1.00E+1

1.00E+3

1.00E+5

1.00E+7

1.00E+9

1.00E+11

1.00E+13

A n n u a l E m

e r g y F l u x p e r g N i n S t o r a g e ( s e j / g )

Mantle

Animals

Phytoplankton

Land PlantsCoal

Soil

Ocean Sediments

N2

Sedimentary Rock

Figure 3. The ratio of the earth’s annual emergy inow to the grams of nitrogen stored in various components of the

global network plotted against the mass of nitrogen per unit volume of substrate.



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Because mass is commonly concentrated as the means of concentrating energy in higher

transformity units, the energy hierarchy principle explaining mass concentration in biogeochemical

systems can be expected to have broad applicability and considerable explanatory power in interpreting

patterns in nature. For example, Odum (2000a) showed that a simple relation between the emergy per unit

mass and concentration is expected for free elements such as lead that act largely outside the constraints

supplied by a more complex structure. When an element is only a part of a more complex structure the

relationship between specic emergy of the element and its mass concentration will be shaped by the

properties of the complex system of which it is a part. The pattern of specic emergy of nitrogen with

its mass concentration in the ocean (Figure 6) and land compartments of the global model (Figure 2) is

to a large degree determined by the unique manner in which living systems use nitrogen and the other

biologically active elements to capture energy and build organized structures. For example, the points

plotted as triangles in Figure 6 show the pattern that living systems have imposed on the specic emergy

and mass concentrations of nitrogen species in the ocean. The initial energy capturing step for the

ecosystem converts relatively disordered elements, C, N, P, S, O, etc. into more ordered organic matter.

The specic emergies of the ordered and disordered forms of nitrogen around the loop are approximately

the same implying that the nitrogen in plant matter and in ammonia are of similar quality and will

have limited energy barriers to free exchange between the two forms. The relatively free exchange ofnitrogen between its ordered and disordered forms may have deep evolutionary roots as noted by Burns

and Hardy (1975). In contrast, the nitrogen in plant and organic matter in the oceans is approximately

a million times more concentrated than the ammoniacal nitrogen in seawater. This extreme increase

in mass concentration of the nitrogen in plant matter over that found in the sea is a consequence of the

unique structural and functional properties of living matter which are dissipative systems that exist far

from the equilibrium conditions that govern the disordered forms of nitrogen in the sea. Ammonia is

10 100 1000 10000 100000


1.00E+10

1.00E+11

1.00E+12

1.00E+13

1.00E+14

1.00E+15

E m e r g y p e r g N ( s e j / g )

Coal

Mantle Troposphere

Sedimentary Rock

Figure 4. The emergy required to place a gram of nitrogen in the designated storages of the lithosphere as a function

of the concentration of nitrogen per cubic meter of substrate.



-230-


the disordered form of lowest specic emergy in both ocean (3.66E9 sej g-1) and land (3.8E9 sej g-1)

compartments and in the global system as a whole (Table 2). The further transformation of energy captured

by this initial order-disorder loop of cycling elements is used to build hierarchical networks in which the

mass of the constituent elements is hypothesized to become more concentrated in higher transformity

network components. The nitrogen data from this study supports this hypothesis at a highly aggregated

level because nitrogen is somewhat more concentrated in oceanic animals than it is in phytoplankton.

The specic emergy of nitrogen in the higher trophic levels represented by oceanic animals is about and

order of magnitude higher than that found in phytoplankton as predicted by the energy transformationand mass concentration principle. Oxidized forms of nitrogen appear in slightly higher concentration

than ammonia but with specic emergy an order of magnitude higher than ammonia. These forms must

be synthesized from ammonia by nitrifying bacteria or reduced to ammonia through assimilatory nitrate

reduction before being used by plants. The additional energy transformations required to make or utilize

NOx may account for the increase in its specic emergy (1.8E10 sej g-1 in the oceans and 3.4E10 sej g-1

on land) over that of ammonia.

Figure 7 shows that ows through the storages in the inner biochemical loop are two orders of

magnitude greater than the outer geochemical ows. The high concentrations of nitrogen in inner loop

components and the increased magnitude of nitrogen ow through this loop indicate that nitrogen is being

used in a dynamically different manner by the living systems of the inner loop and that the hierarchicalorganization of nitrogen ows in these systems is differentiated from, although still connected to, the slow,

low ow nitrogen cycle of the outer loop. The accumulation and cycling of nitrogen and other elements

by autocatalytic living systems has produced unique organizational properties, e.g., greater empower ow

1E-10 1E-8 1E-6 0.0001 0.01 1 100 10000

Concentration in gN per cubic meter of air

1.00E+10

1.00E+11

1.00E+12

1.00E+13

E m

e r g y p e r g N ( s e j / g )

N2

N2O

NH3 (g)

NOX (aq)

NH4 (aq)

NOX (g)

Organic N

Figure 5. The emergy required to place a gram of nitrogen in the designated storages in the troposphere compartment

plotted against the mass concentration of nitrogen per cubic meter of air.



-231-


and more structural complexity, than are possible in a system driven by geochemical processes alone.

One might conclude that a distinguishing property of living systems is the ability to accumulate and cycle

high concentrations of the biologically active elements or ,alternatively, that life is an emergent property

of the biologically active elements and their compounds when concentrations exceed a threshold.

The Carnot ratio from thermodynamics implies that there is a maximum thermodynamic efciency

that can be obtained through design improvements for any real production process. This observation has

been combined with the maximum empower principle to deduce that there is a minimum transformity for

any storage or ow that indicates that the item is being produced at the optimum efciency for maximum

power (Odum1996). If mass ux commonly follows energy ow the lower specic emergy of the inner

loop ows shown in Figure 7 may be a result of the development of a design to maximize empower in

earth’s biogeochemical network; i.e., the ows of energy and mass are highest for a given emergy base

in the design that is operating at the optimum loading for maximum power (Odum and Pinkerton 1955).

Thus, a maximum mass ux per unit of available emergy may be associated with the thermodynamic

minimum transformity for a network operating at maximum power. The patterns of specic emergy and

global nitrogen ows in the living systems of the oceans and land observed in this study were consistentwith design criteria that result in maximizing empower on earth.

0.0001 0.01 1 100 10000 1000000


1.00E+9

1.00E+10

1.00E+11

1.00E+12

E

m e r g y p e r g N ( s e

j / g )

NH4

NOX

N2O N2

Animals

Sediments

PON, DON, Phytoplankton

Figure 6. The emergy required to place a gram of nitrogen in the designated storages of the oceans compartment

plotted as a function of the mass concentration of nitrogen per cubic meter of substrate. Nitrogen storages that are

part of the inner biochemical loop are indicated with a solid triangle, whereas, nitrogen storages that are part of

the outer geochemical loop are shown as open circles. In this case N 2 and N

2O are the concentrations of these gases

dissolved in seawater.



-232-


ACKNOWLEDGMENTS

I thank Jim Latimer, Giancarlo Cicchetti, Cathy Wigand, Curt Norwood, Dennis Swaney, and

Norbert Jaworski for reviewing the manuscript. I also thank H.T. Odum without whose insight and

inspiration this work would not have been possible. Although the research described in this article has

been funded by the U.S. Environmental Protection Agency, it has not been subjected to Agency review.

Therefore, it does not necessarily reect the views of the Agency. This paper is contribution number AED-

02-017 of the Atlantic Ecology Division, National Health and Environmental Effects Research Laboratory,

Ofce of Research and Development, United States Environmental Protection Agency.

REFERENCES

Anderson, L.G. 1983. Fate of nitrogen oxides in urban atmospheres, pp. 371-409. In: Swartz, S.E. (ed)

Trace Atmospheric Constituents: Properties, Transformations, and Fates. John Wiley, New

York.

Aherns, L.H. 1954. The lognormal distribution of the elements (a fundamental law of geochemistry andits subsidiary). Geochimica et Cosmochimica Acta 5:49-73.

Bidigare, R.R., 1983. Nitrogen excretion by marine zooplankton, pp. 385-409. In: Carpenter, E.J., Capone,

1.00E+9 1.00E+10 1.00E+11 1.00E+12 1.00E+13

Log specific emergy (sej/g)

10

100

1000

10000

L o g m

a t e r i a l f l o w

( T g N / y )

NH4, Phytoplankton, DON

PONNO3

Animals

N2

N2O

Sediment N

Figure 7. A plot of the log of nitrogen uxes through the components in the oceans compartment as a function of the

log of the specic emergy of the ux.



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D.G. (eds) Nitrogen in the Marine Environment . Academic Press, New York.

Brandt-Williams, S. 1999. Evaluation of Watershed Control of Two Central Florida Lakes: Newnan’s

Lake and Lake Weir. Ph.D. Dissertation, Environmental Engineering Sciences, University of

Florida, Gainesville. 257 pp.

Brown, M.T. 1981. Energy basis for hierarchies in urban and regional systems, pp. 517-534. In Mitsch,

W.J.,Bosserman, R.W.,Klopatek, J.M. (eds) Energy and Ecological Modelling.. Proceedings

of a Symposium sponsored by the International Society for Ecological Modelling, Elsevier

Scientic, New York.

Burns, R.C., Hardy, R.W.F. 1975. Nitrogen Fixation in Bacteria and Higher Plants. Springer-Verlag,

New York. 189 pp.

Butler, E.I., Corner, E.D.S., Marshall, S.M. 1970. On the nutrition and metabolism of zooplankton. VII.Seasonal survey on nitrogen and phosphorus excretion by Calanus in the Clyde sea area. J. mar.

biol. Ass. U.K. 50:525-560.

Cailleux, A. 1968. Anatomy of the Earth, (Translated from the French by J. M. Stuart). World University

Library, McGraw-Hill, New York.

Campbell, D. E. 2000a. Using energy systems theory to dene, measure, and interpret ecological integrity

and ecosystem health. Ecosystem Health 7:181-204.

Campbell D. E. 2000b. A revised solar transformity for tidal energy received by the earth and dissipated

globally: Implications for Emergy Analysis. In: Brown, M.T. (ed) Emergy Synthesis. Proceedings

of the First Biennial Emergy Analysis Research Conference, The Center for Environmental

Policy, Department of Environmental Engineering Sciences, Gainesville, FL.

Campbell, D.E. 2001. Proposal for including what is valuable to ecosystems in environmental assessments.

Environmental Science & Technology 35(14): 2867-2873.

Codispoti, L. A. 1983. Nitrogen in upwelling systems, pp. 513-564. In: Carpenter, E.J., Capone, D.G.

Table 1. The specic emergy of global nitrogen ows based on the annual throughput of the various

species. The annual solar emergy input to the earth is 15.83E24 sej y-1 (Odum 2000b)

____________________________________________________________________

Item

Nitrogen Flow, Tg y-1

S p e c i f i cEmergy, sej g-1

_________________________________________________________________________________

Ammonia (NH3and NH

4)

6688a

2.37E9

Particulate organic nitrogen

6543b

2.42E9

Dissolved organic nitrogen

5250c

3.02E9

NOx ( NO, NO

2and NO

3)

1358d



-234-


(eds) Nitrogen in the Marine Environment . Academic Press, New York.

Considine, D.M. (ed) 1976. Van Nostrand’s Scientic Encyclopedia. Van Nostrand Reinhold Company,

New York.

Deevey, E.S. 1970. Mineral Cycles. Scientic American 223(3):149-158..

Delwiche, C.C. 1970. The Nitrogen Cycle. Scientic American 223(3):137-146.

Fontaine, T.D. 1981. A self-designing model for testing hypotheses of ecosystem development, pp. 281-

291. In Progress in Ecological Engineering and Management by Mathematical Modeling 1.

Second International conference on the State of the Art in Ecological Modelling. Copenhagen,

Denmark.

Galloway, J.N. 1998. The global nitrogen cycle: changes and consequences. Environmental Pollution

102(S1):15-24.

Hall, C.A.S. (ed) 1995. Maximum Power. The Ideas and applications of H.T. Odum. University of

Colorado Press, Niwot. 454 pp.

Jaffe, D.A. 1992. The nitrogen cycle, pp. 263-284. In: Butcher, S.S., Charlson, R.J., Orians, G.H., and

Wolfe, G.V. (eds) Global Biogeochemical Cycles. Academic Press, San Diego, CA.

Krause, H.R. 1962. Investigation of the decomposition of organic matter in natural waters. FAO Fish.

Biol. Rep. No. 34, pp. 19.Lotka, A.J. 1922a. Contribution to the energetics of evolution. Proc. Natl. Acad. Sci. 8:147-151.

Lotka, A.J. 1922b. Natural selection as a physical principle. Proc. Natl. Acad. Sci. 8:152-154.

Lui, S.C., Cicerone, R.J., Donahue, T.M. Chambers, W.L. 1977. Sources and sinks of atmospheric N2O

and possible ozone reduction due to industrial xed nitrogen fertilizers. Tellus 29:251-263.

Odum, H.T. 1971a. Environment, Power, and Society. Wiley, New York. 331 pp.

Odum, H.T. 1971b. An energy circuit language for ecological and social systems, its physical basis, pp.

139-211. In: Patten, B. (ed) Systems Analysis and Simulation in Ecology, Vol. 2. Academic Press,

New York.

Odum, H.T. 1994. Ecological and General Systems: an Introduction to Systems Ecology (Revised Edition).

University of Colorado Press, Niwot, CO.

Odum, H.T. 1996. Environmental Accounting: Emergy and Environmental Decision Making. John Wiley

and Sons, NY. 370 pp.

Odum, H.T. 2000a. An energy hierarchy law for biogeochemical cycles, pp. 235-248. In: Brown, M.T.

(ed) Emergy Synthesis. Proceedings of the First Biennial Emergy Analysis Research Conference,

The Center for Environmental Policy, Department of Environmental Engineering Sciences,

Gainesville, FL.

Odum, H.T. 2000b. Handbook of Emergy Evaluation . Folio #2 Emergy of Global Processes. Center for

Environmental Policy, Environmental Engineering Sciences, University of Florida, Gainesville,

FL. 30 pp.

Odum, H.T. (ed) 2000c. Heavy Metals in the Environment. Using Wetlands for Their Removal. Lewis

Publishers, Boca Raton. 326 pp.Odum, H.T.; Pinkerton, R.C. 1955. Time’s speed regulator: The optimum efciency for maximum power

output in physical and biological systems. American Scientist 43:321-343.

Oparin, A.I. 1953. Origin of Life. Dover Publications ,Inc. New York. 270 pp.

Parsons, T.R., Tagahashi, M. 1973. Biological Oceanographic Processes. Pergamon Press, Oxford.

Pierrou, U. 1975. The global phosphorus cycle, pp.75-88. In: Svensson, B.H. & Soderlund, R.

(eds) Nitrogen, Phosphorus, and Sulfur – Global Cycles. SCOPE Report 7. Ecol. Bull. 22,

Stockholm.

Prather, M., Derwent, R., Ehhalt, D., Fraser, P., Sanhueza, E., Zhou, X. 1995. Other trace gases and

atmospheric chemistry, pp. 77-118. In: Houghton, J.T., Meira, L.G., Filho, J.B., Hoesung Lee,

Callander, B.A., Haites, E., Harris, N., Maskell, K. Climate Change 1994. Cambridge University

Press, Cambridge.

Rayleigh, L., 1939. Nitrogen, argon, and neon in the earth’s crust with applications to cosmology. Proc.

Roy. Soc. London, Ser. A 170:451-464.



-235-


Rosswall, T. 1976. The internal nitrogen cycle between microorganisms, vegetation and soil, pp. 157-167.

In: Svensson, B.H. & Soderlund, R. (eds) Nitrogen, Phosphorus, and Sulfur – Global Cycles.

SCOPE Report 7. Ecol. Bull. 22, Stockholm.

Schlesinger, W.H. 1997. Biogeochemistry: An Analysis of Global Change, 2nd edition . Academic Press,

San Deigo, CA. 588 pp.

Schlesinger, W.H.& Hartley. A.E., 1992. A global budget for atmospheric NH3

. Biogeochemistry 15:191-

211.

Soderlund, R. & Svensson, B.H. 1976. The Global Nitrogen Cycle, pp. 23-73. In: Svensson, B.H. &

Soderlund, R. (eds) Nitrogen, Phosphorus, and Sulfur – Global Cycles. SCOPE Report 7. Ecol.

Bull. 22, Stockholm.

Smith, R. (ed) 1999. Encyclopedia of Geology. Fitzroy Dearborn Publishers, Chicago.

Stedman, D.J., Shetter, R.E. 1983. The global budget of atmospheric nitrogen species, pp. 411-454. In:

Swartz, S.E. (ed) Trace Atmospheric Constituents: Properties, Transformations, and Fates. John

Wiley, New York.

Ulgiati, S. et al. 2002. This volume.



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Table A-1. Denitions, values, and sources for the external forcing functions, nitrogen storages and ows

shown in Figure 2, a diagrammatic representation of the global nitrogen cycle prior to the beginning of

the industrial age.

__________________________________________________________________________________

Item Denition Value Source

_________________________________________________________________________________

__

External forcing functions, units sej y-1

Solar Radiation Emergy of the annual planetary insolation. 3.93E24 Odum (2000b)

Earth Deep Heat Emergy of annual heat ux from earth. 8.06E24 Odum (2000b)

Gravitation Emergy supplied by the gravitational

attraction of the sun and moon. 3.84E24 Odum (2000b)

Storages, units Tg nitrogen (1 Tg = 1012 g)

Mantle Nitrogen in the earth’s mantle 8.15E10 Cailleux (1968)1

Crust Nitrogen in igneous rock of the crust. 2.0E8 Considine(1976) 2

Sed. Rock Nitrogen in sedimentary rock in the crust. 1.4E9 Considine(1976) 3

Troposphere

N2

Diatomic nitrogen gas in the troposphere. 3.9E9 Stedman & Shetter (1983)

N2O Nitrous oxide in the troposphere. 1400 Stedman & Shetter (1983)

NOX (g) NO and NO

2 in the troposphere 0.21 Stedman & Shetter (1983)

NH3 (g) Ammonia gas in the troposphere. 0.3 Stedman & Shetter (1983)

NOX (aq) NOX in aqueous phase mostly as HNO3. 0.1 Stedman & Shetter (1983)NH4 (aq) NH

4 in the aqueous phase 0.6 Stedman & Shetter (1983)

Organic N Organic nitrogen in the troposphere. 1 Soderlund & Svensson (1976)

Oceans

N2

Diatomic nitrogen dissolved in the sea. 2.2E7 Delwiche (1970)

N2O Nitrous oxide gas dissolved in the sea. 200 Soderlund & Svensson (1976)

NH4 Nitrogen in the sea as ammonium ion. 7000 Soderlund & Svensson (1976)

NOX Nitrogen in the sea as nitrate and nitrite. 5.75E5 Soderlund & Svensson (1976)

Plants Nitrogen in phytoplankton. 300 Soderlund & Svensson (1976)

Animals Nitrogen in marine animals. 170 Soderlund & Svensson (1976)

DON Nitrogen as dissolved organic nitrogen. 5.3E5 Sod er lu nd & Sv en ss on

(1976)PON Nitrogen as particulate organic nitrogen. 2.4E4 Soderlund & Svensson (1976)

OM Nitrogen in the sediment organic matter. 5.4E5 Burns & Hardy (1975)

Land

Plants Nitrogen in land plants. 1.35E4 Soderlund & Svensson (1976) 4

Litter Nitrogen in plant litter. 2800 Soderlund & Svensson (1976) 4

Organic matter Nitrogen in soil organic matter. 3.24E5 Soderlund & Svensson (1976) 4

Microbes Nitrogen in bacteria and fungi. 500 Soderlund & Svensson (1976)

Animals Nitrogen in land animals. 216 Soderlund & Svensson (1976)

Soluble N Soluble nitrogen in soil. 1000 Burns & Hardy (1975)5



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Insoluble N Nitrogen bound with inorganic matter. 1.0E5 Burns & Hardy (1975)

Table A-1. Continued

__________________________________________________________________________________

Item Denition Value Source

___________________________________________________________________________________

Global Nitrogen Flows, units Tg y-1

Lithosphere

J1

Nitrogen out-gassing and volcanic gases. 1 Jaffe (1992)

J2

Nitrogen leaving the crust by weathering. 4 Smith(1999)6

J2’

Nitrogen from weathering that is soluble 3 Split 3:1 soluble: insoluble

J2”

Nitrogen in weathered particulate matter 1 Split 3:1 soluble: insoluble

J3

Nitrogen entering the crust from the mantle 2 Balances nitrogen losses

J4

Nitrogen in sedimentary rocks going into 3 By difference to balance sed.

fossil fuel formation and anatexis rock storageJ

59Nitrogen in subducted organic matter 5 Burns & Hardy (1975)7

J60

Nitrogen entering in subducted sediments 6 Stedman & Shetter (1983)8

J61

Nitrogen in crust reabsorbed into the mantle 2 Assume it balances

crystallization

J62

Nitrogen incorporated into fossil fuel 1 By difference9

J63

Nitrogen in subducted inorganic matter 1 A s s u m e i t b a l a n c e s

weathering

Troposphere

J5

Net ux N2O from denitrication at sea. 3 Prather et al. (1995)

J6 Net ux N2O from denitrication on land. 6 Prather et al. (1995)J7

Loss by diffusion to the stratosphere. 9.3 Stedman & Shetter (1983)

J8

Gain from oxidation of NOx by ozone 0.3 Stedman & Shetter (1983)

J9

Lightning and combustion 5 Stedman & Shetter (1983)

J10

NH3 oxidized to NO

x0.3 Stedman & Shetter (1983)

J11

NOx evolved from soil microbes 5 Balances NO

x (g) storage

J12

NOx dissolving in aqueous phase 5 Stedman and Shetter (1983)

J13

NOx (g) deposited in dry deposition 5 Stedman & Shetter (1983)10

J13’

NOx (g) deposited by dry deposition to sea 1.2 Stedman & Shetter (1983)10

J13”

NOx (g) deposited y dry deposition 3.8 Stedman & Shetter (1983)10

J14

Wet deposition of NOx

5 All dissolved rains out.

J14’ Wet deposition of NOx to sea 1.2 Schlesinger & Hartley (1992)11

J14”

Wet deposition of NOx to land 3.8 Schlesinger & Hartley (1992)11

J15

Net ux of N in N2 from oceanic xation

and denitrication 9 Stedman & Shetter (1983)

J16

Net ux of N in N2 from terrestrial xation

and denitrication 8 Stedman and Shetter (1983)

J17

NH3 solution in aqueous phase 22 By difference, balances inows

J18

NH3 volatilization from wild animals 3 Soderlund &Svensson (1976)

J19

Ammonia volatilization from the sea 13 Schlesinger & Hartley (1992)

J20

Ammonia volatilization from soil 10 Schlesinger & Hartley (1992)

J21 NH3 dry deposition on land 5 Schlesinger & Hartley (1992)



-238-


J22

NH4 rain out on the land 16 Schlesinger & Hartley (1992)

J23

NH4 rain out on the ocean 6 Schlesinger & Hartley (1992))

Table A-1. Continued

__________________________________________________________________________________

Item Denition Value Source_________________________________________________________________________________

__

J24

DON aerosolized in sea foam 10 Soderlund & Svensson (1976)

J25

DON rain out on land 10 Soderlund &Svensson (1976)

J64

N2 return ow from stratosphere 8.3 Assumed to balance N

2O ux

J65

NOx return ow from stratosphere 1 Stedman & Shetter (1983)

Biosphere: Oceans

J26

Nitrogen xation in water column 30 Stedman & Shetter (1983)12

J27

Denitrication in water column to N2

19.5 Stedman and Shetter (1983)

J28

Sediment N xation 10 Soderlund & Svensson (1976)

J29 Sediment denitrication producing N2 11.5 Burns & Hardy (1975)13 J

29’Sediment denitrication 15 Burns & Hardy (1975)14

J30

N2O from sediment denitrication 3.5 Stedman & Shetter (1983)13

J31

Conversion of NOx to N

2O 19 By difference, bal. N

2O tank

J32

Uptake of NOx by plants 857 So de rl un d & S ve ns so n

(1976)15

J33

NOx in runoff 5 Stedman & Shetter (1983)

J34

Ammonia converted to NOx in nitrication 869 By difference, bal. NO

x tank

J35

Ammonia uptake by plants 3440 S o d e r l u n d & S v e n s s o n

(1976)15

J36 Ammonia in runoff 0.5 Stedman & Shetter (1983)J37

Ammonication of DON 4088 By difference, bal. NH4 tank

J38

Plant production of PON 2862 By difference, balances plant

st.

J39

Plant production of DON 635 Parsons & Tagahashi (1973)16

J40

Plants grazed by animals 800 Parsons & Tagahashi (1973)17

J41

PON produced by animals 445 Parsons & Tagahashi (1973) 18

J42

DON produced by animals 127 Parsons & Tagahashi (1973)19

J43

NH4 excreted by animals 228 Parsons & Tagahashi (1973)20

J44

PON in runoff 3 S o d e r l u n d & S v e n s s o n

(1976)21

J45 PON settling out 10 Burns & Hardy (1975)J

46PON converted to DON 3330 By difference, to balance DON

J47

DON in runoff 6 S o d e r l u n d & S v e n s s o n

(1976)21

Biosphere: Land

J48

N in Litter produced by land plants 1900 Soderlund & Svensson (1976)

J49

Nitrogen uptake in NPP 2200 Soderlund & Svensson (1976)

J50

Nitrogen consumed by land animals 300 By difference on plant biomass

J51

Nitrogen in feces and mortality 297 By difference, animal biomass

J52

N in litter decaying to organic matter 1700 Soderlund & Svensson (1976)

J53 N leached from litter 497 By difference, balances litter



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st.

J54

N in Organic matter consumed by microbes 3200 Rosswall (1976)

J55

N returned to organic matter by microbes 1503 Rosswall (1976)

J56

N remineralized by microbes 1807 Soderlund & Svensson (1976)

J57

Nitrogen xation 110 Stedman & Shetter (1983)

J58

Insoluble nitrogen in runoff 1 Runoff balances the tank

__________________________________________________________________________________

Notes to Table A-1

1 Cailleux (1968) 4.075E15 Tg mass of mantle times 20 ppm N from Rayleigh (1939)2 9.8E24 g Considine (1976) X 20 ppm from Illinois State Survey web site, http://www.sws.uiuc.edu/

nitro/detail.asp?lpg=areas&type=geosphere.3 Use Considine (1976) to get volume and mass of the crust in relation to igneous volume to get weight

of sedimentary rocks X 500 ppm N, (200ppm to 4000 ppm from Illinois State Survey web site).

This checks with estimate in Stedman and Shetter (1983).4 Times 1.08 to reect pre-industrial conditions from Stedman and Shetter (1983)5 Estimated as 1% of insoluble inorganic nitrogen.6 Split 3:1 between sedimentary and igneous rock.7 Decreased by half for prehistoric conditions and checked with turnover time of crustal rocks.8 Chosen from range to balance prehistoric runoff.9 T he storage shown is coal only. The nitrogen in oil and gas should be added.10 Dry deposition is assumed to be about equal to wet deposition under prehistoric conditions.11 Wet deposition is split 0.75 to land and 0.25 to sea.12 Minus 10 Tg xed by sediment from Soderlund and Svensson (1976)13 Lui et al. (1977) N

2

O : N2

=.23 in denitrication.14 A 5 Tg recycle from sediments exceeds xation by 515 Codispoti (1983) estimated new nitrogen supplies 25% of total production in world oceans.16 Exudation of DOC is 15% of total xed carbon assume DON is exuded in a similar manner.17 Transfer efciency is 10-20% per trophic level, assume 15% for rst level and 20% for 2 succeeding

levels, 18.6%18 Fraction fecal is 0.375 from Butler et al. (1970) + fraction mortality that is particulate, 0.50 to 0.8,

average 0.675 from Krause (1962).



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Edited by



Associate Editors

Howard T. Odum



David Tilley



Sergio Ulgiati

University of Siena

Siena, Italy

December 2003



Gainesville, FL


vi



16

Spectral Transformities for SolarRadiationReaching the Earth

David R. Tilley

ABSTRACTSolar transformities were computed for the solar irradiance spectrum at the top of the Earth’s

atmosphere, assuming a model in which the sun radiates as a blackbody with a temperature of 6000 K.

Conventionally, the solar transformity (ST) of sunlight reaching the surface of the Earth’s atmosphere

has been defined as one. Here, the ST of irradiance at the median wavelength, 794 nm, is defined as one.

This prevents double-counting and presumes that emergy splits when allocated to each wavelength of

radiation. Based on the assumption that the energy from the Sun’s thermonuclear reactions that produced

ultraviolet radiation also produced visible light and infrared radiation, the ST for a specific wavelength

was computed using the cumulative sum of energy at longer wavelengths. The ST of infrared radiation

ranged between 0.1 and 1.3 sej/J, less than the range of solar transformities for visible light (1.0 – 2.0 sej/

J). The ultraviolet spectrum (100 – 400 nm) had solar transformities between 2.0 and 1.7 x104 sej/J. X-

ray radiation (0.225 – 0.45 nm) was found to have solar transformities between 1.3x106 and 1.5x108 sej/

J.

INTRODUCTIONSolar radiation arrives at the surface of the Earth in a spectrum of wavelengths ranging from X-

ray’s to radiowaves. Each wavelength is unique in the sense that it possesses a distinct ability to do work

and effect system transformations. For example, a Joule of x-ray radiation can be much more damaging

to animal tissue than a Joule of infra-red radiation. On Earth visible radiation drives photosynthesis,

infra-red radiation provides heat to drive plant transpiration, and ultraviolet increases the risks of genetic

damage.

To date, since most applications of emergy synthesis have been on earthly systems, the most

often used basis for emergy calculations has been solar energy received at the surface of the Earth (Odum

1996). The solar transformity of this solar radiation was defined to be 1.0 sej/J (Odum 1996). Thus, the

solar transformity of all energies created directly and indirectly from solar radiation have solar transformities

greater than one. However, the solar radiation received at the surface of the Earth is not homogeneous,

rather it is a distribution of wavelengths which range over 15 orders of magnitude in length from X-ray to

radiowaves. Intuitively, qualitative differences among the forms of radiation are apparent. Quantification

of these differences can be accomplished by calculating spectral solar transformities, since transformity

is a measure of the location an energy form occupies in the universal energy hierarchy (Odum 1996).

Planck’s Law indicates that hot blackbodies not only radiate more power than cold ones, they do

so at shorter wavelengths. Wien’s Law says that the wavelength (nm) at which the maximum amount of

radiation is emitted by a blackbody is a constant (2 897 000 000 K nm) divided by temperature (K). Thus,

higher temperatures have shorter peak wavelengths. Fig. 1 shows that the entire irradiance spectra is



larger, shifts to shorter wavelengths, and has a peak at shorter wavelengths as blackbody temperature

increases. The solar irradiant power distribution can be approximated as a blackbody with a temperature

slightly less than 6000K (Fig. 2).

Fig. 3 shows a typical solar irradiance spectra reaching the surface of the Earth. The difference

between the spectral irradiance at the top of the atmosphere (Fig. 2) and the surface of the Earth (Fig. 3)

is due to absorption by various chemical compounds, such as O2, O

3, H

2O and others. The general

distribution of radiation is only altered by this absorption; there is no transformation of wavelengths.

Solar spectral transformities were developed based on the principle that the creation of low-

energy, long-wavelength (low frequency) radiation requires less solar emergy to create than high-energy,

short-wavelength (high frequency) radiation. This assumption is supported by the evidence given from

Planck’s Law and Wien’s Law. Thus, the solar transformity of a wavelength is inversely proportional to

its length (i.e., short wavelength has high transformity, long wavelength has low transformity).

Understanding the qualitative differences of wavelengths in quantitative terms, which can be accomplished

by identifying their solar transformities, may lead to a better general understanding of the effects and

potential of each wavelength. One wonders also whether the wavelength-specific radiative absorption of

chemical compounds and elements could be employed to estimate their solar transformities. If this were

found valid, it would provide an independent means of doing so.

Figure 1. Log-log plot of blackbody irradiance in units of Watts per square meter per nanometer of wavelength as

a function of wavelength and frequency as given by Planck’s Law. Higher temperature blackbodies generate more

irradiance in total and at shorter wavelengths (higher frequency).



Figure. 2. Solar spectral irradiance in units of Watts per square meter per nanometer of wavelength received at the

top of Earth’s atmosphere compared to that produced by a blackbody at 5777K according to Planck’s Law (solid

line). Measured data from Howard et al. 1960

Figure. 3. Solar spectral irradiance reaching the surface of the Earth as measured at noon on July 31, 2000 near

Port Mansfield, Texas, USA (Ahmed 2000).



Since the specific internal solar processes responsible for creating particular wavelengths of

radiation is not well understood, a proxy for the solar emergy required to create specific wavelengths of

radiation was needed. Solar irradiance measured across the spectrum was taken to be indicative of the

amount of solar energy of one kind required to create the power of a specific waveband. This assumption

was key to be able to calculate solar spectral transformities with known data.

METHODOLOGYBy definition, the solar transformity (ST) of solar radiation reaching the surface of the Earth is

one solar emjoule per Joule (1 sej/J) (Odum 1996). The solar transformity of an energy form is defined as

the total direct and indirect emergy required to create the energy form divided by the energy available in

that form. In the case of the specific wavelengths of solar radiation, the particular source energy is not

well known. Therefore, a proxy of a wavelength’s solar emergy was required to estimate its solar

transformity. The solar emergy of a wavelength (λλλλλ) of solar radiation was approximated as the cumulative

energy from the longest wavelength (4000 nm was used since radiation from longer wavelengths accounts

for much less than 1% of total solar irradiance) to the wavelength under consideration. Consequently, the

solar transformity of a specific wavelength was this proxied solar emergy divided by the power deliveredat λλλλλ.

This methodology causes the transformity of the longest wavelength considered (i.e., 4000 nm)

to be 1 sej/J. To set the solar transformity of the Sun’s median wavelength (794 nm) equal to 1 sej/J and

all other wavelength transformities (STλ

) relative to this median, each initial estimate of a wavelength

transformity was divided by the wavelength transformity of the median wavelength. Normalizing to the

median assumes that the solar emergy required to create a specific wavelength is independent of other

processes that were required to generate all other wavelengths of the same process. This is analogous to

the rule for splitting incoming emergy in emergy algebra (Brown and Herendeen, 19__), in which multiple

outputs from a single process share, based on some allocation principle, the incoming emergy.

Equations

To derive the set of STλ

the first step was to calculate a solar transformity for each wavelength

that was not normalized to the solar transformity of the median wavelength (Eq. 1).

non-normalized STλλλλλ

= E

λλλλλ / e

λλλλλ(1)

where,

Eλλλλλ = solar irradiance integrated from λλλλλ

0 to λλλλλ (W m-2)

=; λλλλλ0 = 4000 nm (2)

and

eλλλλλ = solar irradiance at λλλλλ (W m-2) (3)

Thus, the longest wavelength measured in the solar irradiance spectrum (4000 nm), had a non-

normalized solar transformity of 1 sej/J. To normalize the non-normalized solar transformity so that the

solar transformity of 794 nm was 1 sej/J, Eq. 4 was used.

eλ

λ

λ

0

∑



STλλλλλ

= (non-normalized STÎ) / (non-normalized ST

λλλλλ=794) (4)

Where,

non-normalized STλλλλλ=794

= Eλλλλλ=794

/ eλλλλλ=794

(5)

and

Eλλλλλ=794

= solar power integrated from λλλλλ0 to λλλλλ=794, the median wavelength for solar radiation

(W m-2)

=eλ

λ

λ

0

=

∑794

; λλλλλ0

= 4000 nm (6)

and

eλλλλλ=794

= solar power at the median wavelength, λλλλλ (W m-2)

(7)

Fig. 4 gives a graphical explanation of the preceding set of equations. The cumulative solar

irradiance (E) divided by the wavelength specific irradiance (e) is the non-normalized solar transformity

of energy delivered in that wavelength. Dividing the non-normalized solar transformity by the non-

normalized solar transformity of the median wavelength, normalized the wavelength specific solar

transformity so that the transformity of the median wavelength was one.

RESULTS AND DISCUSSIONFig. 4c presents the spectral solar transformities for the distribution of radiation that reaches the

top of the Earth’s atmosphere. Near-infrared radiation (4000 – 700 nm) had the lowest set of solar

transformities and x-ray had the highest, while solar transformities for visible and UV were between the

two (Fig. 4c). Although the ends of the spectral distribution possessed distinctly different solar transformities, the great majority of the Sun’s wavelengths had solar transformities near one sej/J. At the

lowest end of the spectrum, 4000 nm had a solar transformity of 0.1 sej/J. Visible solar transformities

were between 1 and 2 sej/J (Fig. 5). UV wavelengths ranged from 2 to 20 000 sej/J and x-ray’s were

between 100 000 and 10 000 000 sej/J (Fig. 5). Thus, high transformities were computed for high emergy

UV radiation as expected for radiation that is rare in the solar spectrum. X-ray radiation, which represents

an even smaller fraction of the total solar power distribution, was found to have the highest solar transformity

of all radiation wavelengths evaluated (Fig. 5). Accordingly, its ability to do work (i.e., effect system

performance) is best captured when it interacts with an energy or material of similar solar transformity.

For example, to effectively block the transmission of x-ray radiation, a heavy metal such as lead, which

has a high transformity, is required. Also, x-ray radiation finds utility in x-ray fluorescence spectrometry,

which is used to detect heavy metals.Since the solar transformities of the great majority of solar radiation was found to be very near

1.0 sej/J, there appears to be no need to correct any methodologies used previously to evaluate emergy

flows. According to the new solar transformities presented here, there will be little, if any, error in not

accounting for the spectral solar transformities.

What is interesting about the new solar transformities is that the different wavebands (NIR, VIS,



Figure 4. Cumulative distribution of solar energy received at top of Earth’s atmosphere (A), which served as a

proxy for solar emergy required to generate power in spectral wavebands (B). Division of solar emergy in A by

power in B gave solar spectral transformity (C).



UV, x-ray) have significantly different solar transformities, which corresponds to their differing abilities

to interact with other energies and substances. For example, visible radiation had a significantly lower

solar transformity than UV radiation, which corresponds to the role of visible light in driving photosynthesis

(i.e., a productive process) whereas UV radiation can damage genetic material (i.e., a destructive process).

These clear distinctions in transformity support the concept that energies possessing different abilities

have different solar transformities, by orders of magnitude. Additionally, the solar transformity of

wavelengths coincides with the type of light absorption, i.e., high solar transformity wavelengths are

required to transition electronically, whereas lower solar transformity wavelengths will only provide

vibrational (e.g., infrared) or rotational transitions (e.g., microwaves) (Harris 1991).

One conjecture put forth here was that the solar transformity of an element or chemical compound

could be related to the solar transformity of the wavelength at which it absorbs power, as when it switches

from its ground electronic state to its excited electronic state. For example, nitric oxide (NO) absorbs

radiation over a portion of the UV spectrum (225 – 230 nm). Can it be inferred that the solar transformityof NO is 100 sej/J since that is approximately the average solar transformity of those wavelengths (see

Fig. 5)? Likely it is not quite that simple. How about if 100 micrograms of NO per cubic meter of air (ug

m-3) absorbs 2000 Joules of solar radiation with a wavelength of 225 nm. Since the solar transformity of

225 nm is approximately 100 sej/J, the total solar emergy absorbed is 200 000 sej. Would the solar

emergy per gram of NO be 200 000 sej divided by 100 ug (2 E9 sej/g)? It may be the case that the emergy

required to excite the molecule, divided by the new energy level of the molecule, was the only solar

Figure. 5. Solar spectral transformities of radiation received at top of Earth’s atmosphere over the near-infrared to

x-ray range as a function of wavelength and frequency.



transformity that could be calculated in this manner, which tells us nothing of the solar transformity of the

actual molecule. Another problem with this method occurs when a molecule absorbs in multiple bands

of the wavelength spectrum (a common property). Chlorophyll a is one of many such molecules; it

absorbs strongly at 425 nm and 660 nm. How is its solar transformity related to the solar transformity of

the two wavelengths? For now the question remains unanswered, but determining the solar transformitiesof irradiance was the first step toward being able to address the question.

Future work can explore the relationship between solar spectral transformity and solar transformity

of absorbing compounds. Such relationships could prove useful in determining safe radiation exposure

levels for humans in flight and in space, rating the risks of damage to satellite electronics,

telecommunications and power transmission, and calculating the solar transformity of chemical compounds

and elements based on solar emergy absorption.

REFERENCES

Ahmed, M., 2001. Spectral reflectance patterns of wetland vegetation along a water quality gradient in a

self-organizing mesohaline constructed wetland in south Texas. M.S. thesis, Texas A&M University—

Kingsville. 80 pp.

Brown M.T. and R.A. Herendeen, 1996. Embodied energy analysis and EMERGY analysis: a comparative

view. Ecological Economics 3(19):219-235

Harris, D.C., 1991. Quantitative Chemical Analysis Third edition. W.H. Freeman and Co., New York.

782 pp.

Howard, J.N., J.I.F. King, and P.R. Gast, 1960. Thermal Radiation, in Handbook of Geophysics. Macmillan,

New York.

Odum, H.T., 1996. Environmental Accounting: Emergy and environmental decision making . John Wiley,

New York. 370 pp.






Edited by



Associate Editors

Howard T. Odum



David Tilley



Sergio Ulgiati

University of Siena

Siena, Italy

December 2003



Gainesville, FL


vi



1

Transformity and Simulation of Microbial Ecosystems

Howard T. Odum

ABSTRACT

Evaluating emergy and transformity in models of microbiological systems raises questions about scales,organic aggregates, the transformity of microbes, and the concept of detritus. Microbial networks were

studied with simulation models for EXTEND and TRUEBASIC. The paper explains an intellectual

approach to complex systems in which verbal models are diagrammed with object-oriented energy systems

symbols (program EXTEND) connected on screen for simulation without thinking about mathematical

equivalents. New object-oriented blocks were programmed for microbes of different scale. In addition to

the passive computation of emergy and transformity in dynamic simulation, these models include

transformity ratios in growth equations to control the efficiency of access to organic energy aggregates of

different scales. Availability of sunlight decreases with transformity and scale of producers. Data on

microbes and organic matter in marshes were used to estimate the transformity for a microbe’s share of

global emergy. The exponential growth of autocatalytic consumers levels off as their increasing scale

and transformity diminishes their efficiency in using energy aggregates of small scale. Transformities from simulation are compared with those calculated with other methods. A plot of energy and transformity

represents detritus on the scale of energy hierarchy.

INTRODUCTION

This paper uses emergy calculations and computer simulations to understand the transformity of

microorganisms, their populations, and their relation to solar insolation, non-living organic substance,

and scale. Between the size of a solar photon and the plant and animal structures visible to the naked eye

are realms of several orders of magnitude. Solar transformity, which has been recognized as one of themeasures of scale, ranges from one for incident solar energy to a thousand or more for woody structures.

But when emergy evaluations are made of larger ecosystems, the microbiological components are often

aggregated so that higher transformities result, sometimes combined in the pool of soil organic matter or

the pool of organic matter in aquatic ecosystems that is often called detritus.

Many micro-organism cells are hardly larger than solar photons (less than 1 micron = 1 micrometer

= 10-6 meter), whereas the wave length of invisible light is 0.4 to 0.8 microns). A priori, considering the

tiny size, one would expect such cells to have very small transformities. However, tiny organisms cannot

long survive except as part of a population that is sustained by group processes and reproduction.

There is a hierarchy of energy transformation in any population of organisms (Odum, 1983,

1996). Many small eggs, larvae, or seedlings develop and eventually generate a few large adult trees or

animals. Thus, transformity increases along the successive growth stages, since more energy is used in

the early stages, as required to support the few units at the top. The population as a whole is represented

by its genome of inherited information and the environmental emergy required to sustain its life cycles.



Similarly, microbial population and the organic structures that support it may be expected to have a

higher transformity than its individual microbes.

When dynamic simulation models are assembled using object-oriented blocks, it is possible to

connect a higher organism such as a fish population with small energy flows per area directly to the large

energy inflow of sunlight for that area. The result is an unrealistic rapid growth of fish. Such models

dramatize the dependence of energy transformation efficiency on the scale of organic aggregates, which

is not recognized in those models. We already know from energy and emergy evaluations of real systems

that follow the laws of energy hierarchy that transferring energy from one scale to another requires a large

consumption of available energy. Energy of sunlight to support a fish has several levels of energy

transformation, each involving a concentration from its distribution on one scale to a more concentrated

center of less energy of the next higher scale.

Many of our simulation models (Odum, 1996; Odum and Odum, 2000) have included a compu-

tation of the emergy and transformity of the flows and storages of systems models, but these calculations

were passive, not set up to affect the equations for the main state variables and processes. Since transformity

is a measure of scale, simulation equations can be written so that inter-scale energy transformations are

controlled by transformity. But what are the appropriate transformities of the microbiota on the small

scale? How are these different from transformities of microbial populations and the microbes in organicdetritus pools in larger scale view?

This papers uses sample calculations and computer simulations to help understand the trans-

formities of micro-organisms and the several scales on which their populations operate and are interpreted.

Transformity and Microbial Scales

Scales of Micro-Organisms

Aquatic ecosystems of the oceans and large lakes contain a microscopic world of tiny plants and

animals in sizes ranging over 5 orders of magnitude (0.2 micrometers to 2 millimeters). Summarizingdecades of research on the diverse, microscopic life in the clear, low nutrient waters of the ocean and

large lakes, Caron and Swanberg (1990) reviewed knowledge of the species, their physiological roles,

and their ecological relationships. This is sometimes called a protistan microworld.

In general, the smaller the species is, the smaller is its area of support and influence, the faster it

processes energy, and the quicker its biomass is replaced. Some idea of the organization of these ecosystems

can be understood by drawing the network of organic energy flows that connect photosynthetically

producing units and consuming units, with scale increasing from the small-fast on the left to the larger-

slower species on the right (Figure 1). The photosynthetic organisms are shown with bullet-shaped

“producer” symbols. The consumer organisms are shown with the hexagon shaped symbols. These

organisms are connected by their food pathways to form ecosystems. The network has producers and

consumers of different sizes from 0.2 micrometers (mm) to 2000 micrometers.

Both the producers and the consumers have designs in which the energy stored and materials

released are fed back (to the left) to augment (multiply) the energy intake. In this way, the design is

sustained because the feedbacks from growth and reproduction increase the intake of energy.

Transformity and Scale

The ecosystem runs on sunlight energy transformed by the photosynthesis of the algae and other

protists into organic matter that feeds the microscopic consumers of many kinds. As the sun’s energy is

captured and transformed into organic matter, it is concentrated spatially as it is passed to the right along

the food web. The amount of potential energy available to support life is used up by the processes of

conservation and concentration, being degraded into used heat energy that is shown passing out the bottom of the diagram in Figure 1. Thus, the quantity of energy decreases in each step (Figure 2), but the

quality and concentration of the converted energy increases at each step. A useful index of the increased



energy quality is the quotient of solar energy incoming divided by the energy outflow. This ratio is called

the solar transformity. It increases with each step through the energy web, as shown with some order-of-

magnitude values in Figure 2.

Protistan networks are examples of the fundamental energy hierarchy of the universe, in which

many Calories of energy on one level are required to make a few Calories of higher quality energy at

another level. Thus, a chain of such transformations has a step down in the flow of energy in each

conversion. In a protistan energy web of the ocean, these energy transformations occur within individual

organisms of each species, all based on abundant but low concentration solar energy.

The first step in photosynthesis captures photons of light that are very broadly dispersed in time

and space. Regardless of the size of the photosynthetic organism, the first step involving chlorophyll

Figure 1. Energy systems diagram of a Protistan web with components from left to right in order of scale

(size and territory). Bullet-shaped symbols are producers; hexagon shaped symbols are consumers;

hexagon symbols within the producers represent metabolic functions of organism physiology; shaded symbol are pigmented units carrying out photosynthetic production with sunlight.

Bacteria

Organics

200-2000 µm

20-200 µm

2-20 µm

0.2-2 µm

Sun

Faecal Pellets

Dead Organics

2-20 µm

20-200 µm

200-2000 µm

>2000 µm

Metazoa

Used Energy



Figure 2. Components, energy flow, and transformity of photosynthetic organisms of different size running

on sunlight. (a) Examples of microbes arranged by scale increasing from the left; (b) energy flows

decreasing with each transformation to larger scale; (c) solar transformities (= emergy/energy) measuring

energy concentration and quality for each scale.

10,000

100,000

100,000

10,000

1

=

Sun

Bacteria

Synechococcus

Zooplankton

(a) Scale of Organisms

0.2-2 2-20 20-200 200-2000

0.1

Size, µm: >2000

Organics

Large Diatoms

Examples

Foraminifera

Protista

Dinoflagellates

Nannochloris

Prorocentrum

Chloroplasts



organelles is at the low quality end of the energy hierarchy (shaded structure in Figures 1 and 2a). However,

the species range in size over 5 orders of magnitude. The long producer symbols in Figures 1 and 2 show

how the larger photosynthetic organisms include more energy transformation steps than the short ones.

The larger (longer) producers accumulate and concentrate organic matter, although there is less energy

remaining in the whole population after the series of transformations. The larger photosynthetic organisms

have more consumption steps within their body. Consumer functions operate respiration:

organic matter + oxygen —> carbon-dioxide + water

the same process in the stand-alone consumers, that utilize the smaller photosynthetic organisms.

Concept and Transformity of Detritus

Ogawa et al. (2001) summarize bacterial production of detritus. Microorganisms and their or-

ganic energy sources are adsorbed or attracted to surfaces. Labile substances are metabolized, building

microbial structure, not only the individual cells, but also the larger organic film structures, many of

which pass into the environment as films, particles, and other units of larger size and turnover time.

Viruses are important in converting the individual microbes into more resistant organic matter. Some of these fall downward in aquatic ecosystems, sometimes called an organic “snow.” Much of the organic

matter in the sea is in the form of less-labile organic structure, with surfaces that act autocatalytically to

support the active microbes. In the process, organic carbon fixed by photosynthesis is recycled in a loop

through consumers.

Producer cells as they age become dead organic matter. Consumers eating food egest unassimi-

lated organic remnants. For example, zooplankton capturing smaller algae have unassimilated organic

matter that is discarded back into the water as faecal pellets. Such particulate organic matter is concentrated

enough to support bacteria. The diagram in Figure 1 shows some organic matter particles coming from

all the independent consumers. The pool of organic matter with its components of different transformity

is often called detritus, a nutritive mixture found in most if not all ecosystems.Flow into the detritus pool of coastal waters of Louisiana were calculated by Bahr, Day and

Stone (1982) and the transformities evaluated by several means (Tennenbaum, 1988, Collins and Odum,

2001). In Figure 3 these have been plotted on a power-transformity plot, which shows a wide range of

transformities and energy quality in contributions to that transformity pool. How much of the range in

this example is real and how much is caused by approximations in the data is not known. If detritus

contributions are in proportion to the energy hierarchy, a decrease of quantity with higher quality is to be

expected. However, it might be reasoned that higher levels in food chains aggregate and leave more

organics.

Evaluations of detritus inputs yield relatively high transformities (105-107 solar sej/J), as high

as the animals that generate them. This is consistent with the high concentrations of the organic matter as

it is released. Detritus as observed in a microscope is partly composed of bacterial and other livingmicro-organisms engaged in its use and decomposition (2-25% of biomass). What is the transformity of

these microbial cells considered separately on their small scale?

Microbial Share of Global Empower

For the most part, the earth is covered with a thin blanket of organic detritus in the oozes of lake,

estuaries, deep columns of the open oceans, sea bottoms, and in the soils of the land. The microbes are

known to be associated with particles, where a good part of their consumption is of dissolved organic

substances, partly adsorbed and otherwise associated with the particles. Some of the consumption process

is carried out by extra-organismic enzymes.

Using George Knox’s recent review of literature on the composition and processes of detritus(Knox, 2001), order of magnitude calculations were made of microbes per unit area and their turnover

times in Figure 4. Data on weights and numbers of microbes at the surface of salt marsh from P.A. Rublee

(1982) were used. How typical the organic detritus over salt marsh is of the other ecosystems remains to



be tabulated. For our purposes microbes are considered by size and metabolism, although species withquite different DNA histories may be involved together.

Figure 4 shows the stock of detritus, and within, the 1% that is living microbes. The global daily

emergy budget was divided by the area of the earth to show the emergy supporting a square meter on the

average. Biomass estimates of detritus and microbes were put on a common basis of kilocalories per

square meter and metabolic rates on kilocalories per square meter per day. As shown in the figure, a

microbe’s share of the global emergy is tiny, with a small solar transformity of 265, possibly appropriate

to the small scale of single microbes considered as individuals.

A population of organisms has higher transformities than its individuals because of the greater

emergy that has to support life cycles, interactions, dispersal mechanisms, organic structures, and particles

developed as part of microbe functions. As already explained (Figure 3), detritus as a higher level structure

has even higher transformities, reflecting the valuable components that develop over longer periods inwhich the component microbes are turning over more frequently.

Transformity in Simulation

Dynamic computer simulation usually shows the temporal patterns and indices that are the con-

sequence of models and their numerical values of storage and flow which have been assigned. In addition

to plots of the main state variables and processes, we have been adding emergy-transformity equations to

simulation programs for some time so that the programs plot emergy, empower, and transformity also

(Odum and Petersen, 1995; Tilley, 1999; Odum and Odum, 2001). Equations for an autocatalytic consumer unit are shown in Figure 5. (Examples are microbes, animals, parts of plants, fuel burning, engines,

cities, etc.)

Figure 3. Characteristics of the components of detritus of Louisiana coastal waters from Bahr, Day, and

Stone (1982) plotted on a graph of annual energy flow and solar transformity.

0

x 106 semkcal/kcal

50

1

2

3

1 0 1 2

k i l o c a l o r i e s

p e r

y e a r

10

Detr i tus



The emergy in a flow is the product of transformity and the energy flow. Three simulation

equations are used for emergy storage (Figure 5b). While the energy in a storage is increasing, the

emergy is the sum of the input emergy minus those transferred for use. Emergy is constant if the storedenergy is constant. Transformity of storage is the sum of emergy accumulated divided by the energy

accumulated. A storage accumulates energy and emergy when the inflows are greater than the outflows.

If there is a transfer of the storage out of the system, its share of energy and emergy goes with it. Some

storage drains away due to the second energy law. When a simple model of storage is approaching a

balance between its inflows and outflows, it is in the range where fluctuations of the smaller scale part of

the system determine the actual storage level of energy, emergy, and its transformity. In other words, in

the more complex real world, emergy and transformity stop increasing before the simple model would

level off. If the model doesn’t include the small scale part of the network, it is convenient to add a limit

(1% in the example of Appendix C) to stop accumulating emergy in the zone that would be controlled by

the smaller scale.Emergy decreases when energy of storage diminishes, however it happens. Rate of emergy

decline is the product of storage transformity and rate of energy decrease. These previous emergy-

transformity simulations are “passive,” since they do not affect the rest of the dynamic simulation.

Figure 4. Possible role of micro-organisms in processing dissolved and particulate organic matter as

part of detritus. Values were adapted from Rublee (1982) as reviewed by Knox (2001). See Notes.

= _________________ ___ = 265 sekcal/kcal

GlobalDai ly

Empower

sekcal/m2 /day

EcosystemCover of the

GeoBiosphere

20,162

20,162 sekcal/m2 /day

76 kcal/m2 /daySolar Transformity of Microbes

Layer 0 to 0.2 m

32,000 kcal/m2

MicrobeBiomass

1015 individ. /m2

318kcal/m2

Microbe Replacement Time = 4 days

760kcal/m2 /day

Detr i tus

76 kcal/m2 /day

MicrobeProduct ion



However, transformity is a measure of the hierarchical position of an energy flow or storage and

is an indicator of what other kinds of energy can interact. A corollary of the energy hierarchy concept is

that energies on one scale with one transformity self organize to interact with one or two levels above and

below, so that they amplify. But energies with very different transformities cannot interact to effectively

use each other. A bass fish cannot grow on sunlight. The difficulty in converting solar energy with low

transformity into electric power or other high quality energy in one step, may be explained by the very

different scales of technology and solar energy. Carnivores can eat smaller animals but cannot use solar

energy efficiently without intermediate transformations that cascade and concentrate. Many dynamic

simulation models do not have these scale characteristics built in. Transformities of organisms are dynamic

properties because they change value when the organism grows and changes its scale of support and

influence.

In this paper, however, we add the ratio of the food transformity to the consumer transformity to

control the efficiency of productive energy transformations (Figure 5c).

(Transformity of energy source Q)/(Transformity of consumer A) = (Tr Q/Tr A)

Thus, these simulation programs not only calculate emergy and transformities but use the trans-formities to control the efficiencies of each component according to its scale and that of its energy supply.

The factor of 10 is multiplied so that the quotient’s effect on the use of an appropriate energy source one

scale smaller will be neutral. In other words, (10*Tr Q/Tr A) = 1.

In addition, the higher the consumer the more inherited structure and mechanistic programs it

has for operating as the center of a larger territory. For example, larger birds have the structures to feed

over larger areas. Since it takes available energy to operate these concentrating mechanisms, more of

their energy is diverted from net growth. The larger units have lower net production efficiencies.

As shown in Figures 1 and 2, producers range from tiny nanoplankton size up to plant cells or

colonies visible to the eye. As shown in Figure 2, the larger producer units have more successive energy

transformations built into their organic structure, with less net production output at the end of the interior

food chain. In order to represent these differences in producers in simulation models that explore

relationships of different networks and connections of sizes, each producer unit is characterized by Eff,

an efficiency factor, that is inherently smaller in the producer species with larger units.

In this paper, two software programs, EXTEND and TRUEBASIC, are used to simulate energy

systems models, their emergy and transformity. They use transformity ratio and the internal efficiency

factor EFF to control each species energy transformation role in the network.

Verbal-connectivity Thinking with EXTEND

One of the purposes of this paper is to show how verbal thinking can generate simpler overview

comprehension of systems networks and the consequences of the designs by arranging simulation tofollow from network diagrams without mathematical thinking. With the program EXTEND it is easy to

program object-oriented symbols so that when connected on the computer screen, they automatically set

up the mathematical relationships for simulation. At least for some people, this fosters a higher level

comprehension of network designs (Figure 6-10). See our book “Modeling for All Scales” (Odum and

Odum, 2000) for discussion of alternative approaches from mind to models. Although not expensive, this

book has a CD that contains the program EXTEND, version 4. Figure 6 illustrates symbol use that shows

the relationships with interior pathways. Figures 8 and 9, with solid-shaded symbols, allow the mind to

focus on the larger scale.

A demonstration example of transformity control is provided using the Simulation software

EXTEND (Imagine That, 6830 Via del Oro, Suite 230, San Jose, California 95119. Web Site http://www.imaginethatinc.com). With EXTEND users may make their own blocks which are programmed to

simulate systems when the icons are connected on screen and given calibration values. When blocks are



connected, they share information including their transformities. The blocks are stored in “library” files

with the extension .lix, and the assembled systems models are saved with the extension .mox. Examples

of simulation models and their output in EXTEND are given in Figures 6-8. Details on loading and

running EXTEND are in Appendix A.

Passive Simulation of Emergy and Transformity

The equations for simulation of emergy and transformity of a structural storage in a dynamic

simulation are given in Figure 5b. The simulation of the consumer and its growth is not affected, but the

additional equations and the connected plotters for transformity, emergy, and/or transformity providegraphs of these properties. Chapters 11 and 19 in “Modeling for all Scales” (Odum and Odum, 2000)

explain several simulation models with passive emergy evaluation, including models of international

economic exchange.

A

Q

J1J2

K4*Q*A*

K1

K2*A

K5*AMaterials

Byproduct

To Other Units

Store-Structure

dA/dt = K1*Q*A - K2*A - J1 - J2

K0*Q*A Energy

Source withTransformity TrQ

EA

Assets Equation: dA/dt = (10*TrQ /TrA)*K1*Q*A - K2*A - J1 - J2

(b) Passive Rate Equations for Emergy Store EA:

When DA/dt > 0, : dEA /dt = Tr

Q*K

0*Q*A - Tr

A*J

1 - Tr

A*J

2Where TrA = EA /A

When DA/dt = 0, : dEA /dt = 0

When DA/dt < 0, : dEA /dt = TrA*dA/dt

(a) Dynamic Simulation Model

(c) Transformity Ratio (TrQ /TrA) Controlling Use Efficiency:

Emergy Stor ing Equat ion:

When DA/dt > 0, dEA /dt = (10*TrQ /TrA)*Trq*K0*Q*A - TrA*J1 - TrA*J2

Figure 5. Energy systems diagram and equations for the “transform consumer” unit used for simulations

in Figure 6. The “transform consumer” block is an autocatalytic consumer with one energy input from

the left, a flow of byproducts (example: unassimilated food) from the right connector (org. flow), a flow of

materials released (example: nutrients) from the connector at the lower right, and two connectors for

pathways of energy transfer to other units. A is the quantity of assets (biomass) stored. Growth is

proportional to the ratio of input transformity to that of the assets (10*Tr Q /Tr A ) = 1, thus representing

the degree of concentration of the organic matter (whether dissolved, colloidal, small particles, large

biomass).



Simulating Cross-scale Efficiency with Transformity

In this paper, EXTEND models are given that use blocks with the energy intake and conversion

controlled by the transformity ratio. The demonstration model in Figure 6a (ConsTr.mox) has a consumer

growing on unlimited quantity of energy (large and constant concentration). But the ability to use that

energy depends on the scale and packaging. The blocks of the model are in the library file (MicrLib.lix).

The consumer block in the middle had its script written with the equations of Figure 5c, including the

transformity ratio and EFF factors for control of energy conversion efficiency.

The consumer block “Transform consumer” is connected to two plotters on the right, an ar-

rangement that causes two graphs to plot, the quantity of energy in the stored assets and its transformity

(Figures 6b and 6c). The block on the left supplies a constant force source (constant concentration) with

a dialog box in which you can type the strength of the source Q and its transformity Tr Q.

Demonstration of Transformity Control with Simulation

If adequate energy is available at the appropriate concentration, an autocatalytic consumer and a

constant force source generate exponential growth (Figure 6b). If however, the energy source is not in

concentrated packages appropriate to the consumer, it will have lower transformity, the transformity

quotient will be too small, and the unit does not grow. The simulation model in Figure 6 was run with Eff held constant to study the effect of transformity source on the growth of one species. The initial run

(Figure 6b and 6bc) was run with the transformity in the dialog box of the source set at 1000. When the

consumer is higher in the hierarchy, initial transformity is large (1000 to 100,000); it does not grow when

the energy source transformity is 200 or less. Increasing the concentration of the food energy (higher

transformity) or decreasing the scale of the consumer start (lower starting transformity) will restore growth.

Another Consumer block in the MicrLib.lix library is named “Transformity consumer” and is

the same as the “Transform consumer” except its icon is colored solid, hiding the pathways and

mathematical relationships. You can substitute this block in the model and hide the complexity of details.

Highlight and delete one block, copy the new one from the library, and connect pathways as before. This

system is already assembled as model ConsTran.mox.

Transformity in Producers of Different Scales

Whereas the reception of solar energy photons is inherently small scale because of the low

concentration of solar photons, the size of the living organisms that carry out photosynthesis range from

microns to meters. Figure 1 showed how the small and low concentrations of dispersed producers are

connected to a food chain of consumers forming an energy hierarchy that converges and concentrates

with increasing transformity. The larger producers on a larger scale have more steps of energy processing

and concentrating within single individuals. More of the concentrating, storing and increasing transformity

occurs within their structures, which are the center of larger territory of support and influence. With more

energy transformation steps required, the energy stored in producer biomass is less. In other words, the

efficiency of net production is less, but the transformity is greater.To represent the producers of different size appropriately in simulation models, these differences

in efficiency and transformity have to be included in the equations and their calibration. Two changes

were made in the producer of Figure 7 to adjust to scale.

Inherent Efficiency Factor in Producers

Eff, the fractional efficiency factor already used in consumers (Figure 5) is included in the net

production term of producers (Figure 7c). Larger producers have larger territories from which they draw

their nutrients and scattered light. The larger the producer, the lower the Eff term.

Rates and Turnover with Scale

The larger scale producers develop larger biomass and slower turnover times. In the simulationmodel, increasing the value of the biomass used for calibration is one of the adjustments required to fit

flows and storages to scale (Figure 7d).



EXTEND Simulation Comparing Producers of Different Scale

Three producer blocks (Nanoprod, Microprod, and Mesoprod) were programmed for EXTEND,

each representing a different scale. Each uses the same model and equations of Figure 7 except for thetwo changes. The efficiency concentrating factor EFF was increased, with values 1.0 ,0.5 and 0.25,

respectively. The calibration biomass was also increased with values 1, 10, and 100 g/m2.

Figure 6. Simulation of autocatalytic consumer growth with energy sources of different transformity

with programmed blocks for EXTEND. (a) Screen view of assembled model; (b) exponential growth of

stored assets when energy concentration (transformity) is large enough relative to the concentration and

scale (transformity) of consumers; (c) plot of increasing transformity with growth.



A model (ProdCons.mox) was assembled with three blocks (Figure 8a), each running on a sepa-

rate source-limited (renewable) solar energy source. The simulation results show the larger the scale of

the unit, the more storage-structure developed, and the longer the growth time required (Figures 8b). The

larger the scale the higher the resulting transformity (Figure 8c).

These blocks are versatile in generating oxygen as well as organic growth. Their consumption

process uses oxygen. The blocks can be used to simulate the day-night sequences. However, in Figure 8

carbon-dioxide and oxygen were not connected. These blocks were programmed with a substitution of

equations so that they would operate with or without the input of carbon-dioxide and with or without the

input of oxygen.

Figure 7 . Model of producers preprogrammed in computer simulation blocks for EXTEND. (a) Energy

systems diagram and equations; (b) equations to simulate emergy and transformity without affecting the

dynamic relationships in a; (c) efficiency adjustments to recognize more energy transformations within

one structure; (d) change of the biomass used in calibration as a way of adjusting rates to change scale.

R

R = Ji - K0*R*B and R = Ji /( 1 + K0*B)

GrossProduction

Ji

K0*R*B

SunB

Biomass

Ne tProduction

CO 2 = NOxygen = X

Resp.

dB/dt = Knp*R*N*B - Kr*B*X - Kg*B - J1 - J2

Kp*R*B

OrganicDetritus

Kg*B

Kr*B*XKnp*R*N*B

J1 J2

(a) Dynamic Simulation Model

(b) Passive Rate Equations for Emergy Storage EB

EB

When DB/dt > 0dEB/ dt = TrR*K0*R*B + TrN*K 2*R*N*B + TrX*Kx*B*X - TrB*J1 - TrB*J2

Where TrB = EB /B and TrR, Tr N, TrX, TrB are Transformities

When DB/dt = 0,: dEB /d t = 0

When DB/dt < 0,: dEB /d t = TrB*dB/dt

Biomass Equation: dB/dt = Eff * Knp*R*N*B - Kr*B*X - Kg*B - J1 -J2

(c) Equations Use Eff to Adjust Energy Conversion to Scale:

Where Ef f = eff iciency, reduced by energy used to concentrate biomass

Calibration BiomassBc

Kg

KrKnp

Increasing BC Reduces Rate Coefficients (K's) and Turnover Time

(d) Calibration of Biomass Adjusts Rates to Scale,



EXTEND Simulation Comparing Consumers of Different Scales

Three consumer blocks like that in Figure 6 were made to represent consumers adapted to oper-

ate at increasing scales by reprogramming the “consumer transformity” block with larger biomass, and

higher inherent transformity adjusted by making the Eff factors less. These blocks have solid color icons

and were labeled: ConsNano, ConsMicr, and ConsMeso. All their calibrations were the same except for

the biomass calibration to adjust scale (1, 10, 100 g/m2). Figure 9 uses pictorial icons of pig farm wastes

for the energy source, and an artistic sketch of a waste pile to represent a storage. When these blocks

were run in parallel, each with its own steady energy source (source concentrations similar), the storages

that developed and the transformities of the storages increased with the scale adjustments in the same

order as expected (Figure 9b and c). Each of these blocks were given two input consumption and production

functions and connectors so they could be used in branching energy networks.

Simulating Microbial Networks with EXTEND

With producer and consumer blocks containing the properties of scale (calibration biomass and

the transformity control ratio), network models can be assembled to understand the interplay. For example,

Figure 10 shows an EXTEND model MicrWeb.mox with two producers (small scale Nanoprod and larger

scale MesoProd) and two consumers (small scale ConsNano and larger scale ConsMicr). Simulationswere run with different combinations:

With the two producers both using and competing for the sunlight with transformity of 1 (no

consumers connected), the small scale producer prevails, the other going extinct. Biomass and transformity

of the nano-producer is small.

Adding the small scale consumer to use the nano-producer depletes its population, and the larger

producer prevails with larger biomass, transformity, and longer growth time. However, connecting the

small scale consumer to use both producers returns dominance to the small producer.

When the larger scale consumer is connected to the small producer or to both, it reduces the

steady state stock of the small producer, but not enough to displace its dominance over the larger producer.

When the small scale consumer was connected to the small scale producer and the large scale

consumer connected to the large scale consumer, some oscillations were observed, and small changes incoefficients cause change in the prevailing population.

Emergy Simulation with TRUEBASIC

Energy systems models are readily simulated with some form of BASIC. Our book “Modeling

for all Scales” has a hundred programs for simulating energy systems models with QUICK BASIC and

CHIPMUNK BASIC. Chapter 11 explains the simulation of emergy and transformity in BASIC software.

Because TRUEBASIC is simpler, inexpensive, and available for all PC and Mac computers, we have

converted several hundred programs to TRUEBASIC. This paper includes TRUEBASIC programs that

use transformity ratio for the control of energy flows. Appendix B has more details on the use of

TRUEBASIC, and Appendix C contains listing of the program.

Consumer -Food Transformity (Program TrCons.tru)

A TRUEBASIC program for relating autocatalytic growth to the transformity of energy sources

is supplied in Appendix C for simulating the growth equation in Figure 11a. Varying the transformity of

the energy source determined whether there is growth or not, as already explained with Figure 6 using

EXTEND. As a unit grows in storage, it diminishes its turnover time, increases its transformity—and

thus increases its scale. Larger dimensions require more concentrated food packages of larger transformity

and scale. Autocatalytic growth on a constant force (unlimited energy) source normally accelerates in

exponential growth (Figure 11b). However, when energy available is only moderate, a unit with thetransformity control ratio levels its own growth because its increasing size, concentration, and transformity

makes its former dilute energy source no longer sufficient for further growth. Note the example in Figure

11c, which was simulated with the TRUEBASIC program ScaleExp.tru (Appendix C).



Figure 8. Use of EXTEND to compare three producers modules that operate at different scales. (a)

Model showing units receiving identical energy sources; (b) growth of producer storages; (c) accompanying

transformities of storage.



Figure 9. Using EXTEND to compare consumers of three different scales. (a) Model with each consumer

supplied a similar pool of renewed energy; (b) comparison of growth; (c) comparison of transformity of

storage.

(a)

(b)

(c)



SUMMARY

The microbial components of the environment include individual microbes, populations, microbial

associations with particles, and complex combinations of living cells and detrital storages. The

transformities apparently range from 100 for a single cell to 106 for the aggregates, as they are viewed

from the human scale. Transformity measures the concentration of organic matter and microbiota into

particles and packages, and thus the availability as an energy source to units of larger scale. The ratio of

transformities of input to that of consumer can be used in computer simulation programs to represent the

scale effects. Knowing the total available organic matter is not enough. Computer programs that

continuously compute the transformities may use them to represent concentration and scale effects on

energy flow in networks.

Figure 10. EXTEND model use to compare different connections between producers and consumers.



Figure 11. Autocatalytic growth model for study of transformity effects in TRUEBASIC. (a) Energy

systems diagram and equations; (b) typical exponential growth; (c) growth limited by loss of energy

source due to increasing scale.

Emergy

Transformity

Biomass

(c) Growth Levels Because of Increasing Transformity

Time

Time

Emergy

Transformity

Biomass

(b) Exponent ial Growth on Constant Force Energy Source

EmergyEnergyStores

Constant Force F

Unl imited Energy Source

Transferto Other User

dQ/dt = (10*Sts /St)*K1*F*Q - K4*Q - K5*Q

where Sts = Source Transformity

St = Store Transformity = Em/Q

K4

K1K0

K5

Sts

Q

St

(a)



REFERENCES

Bahr, C.M., J.W. Day and J.H. Stone. 1982. Energy cost-accounting of Louisiana fishery production.

Estuaries 5:209-215.

Caron, D.A. and N.P. Swanberg. 1990. The ecology of plankton. Aquatic Sciences 3(2,3):147-180.

Collins, D. and H.T. Odum. 2001. Calculating transformities with an Eigenvector Method. pp. 265-280in Emergy Synthesis, ed. by M.T. Brown. Center for Environmental Policy, University of Florida,

Gainesville, Florida, 328 pp.

Knox, G.A. 2001. The Ecology of Seashores. CRC Press, Boca Raton, Florida, 555 pp.

Odum, H.T. 1996. Envioronmental Accounting, Emergy and Decision Making. J. Wiley, New York,

370 pp.

Odum, H.T. and N. Petersen. 1995. Simulating and Evaluation with Energy Systems Blocks. Ecol.

Modeling 93:155-173.

Odum, H.T. and E.C. Odum. 2000. Modeling for All Scales, An Introduction to Simulation. Academic

Press, San Diego, California, 458 pp.

Ogawa, H., Y. Amagai, I. Kolke, K. Kaiser, and R. Benner. 2001. Production of refractory dissolved

organic matter by bacteria. Science 292:917-919.Rublee, P.A. 1982. Estuarine comparisons, bacteria and microbial distribution in estuarine sediments.

pp. 159 in Estuarine Comparisons, ed. by C. Kennedy. Academic Press, New York.

Tennenbaum, S.E. 1988. Network Energy Expenditures for Subsystem Production. M.S. Thesis, Envi-

ronmental Engineering Sciences, University of Florida, Gainesville, 131 pp.

Tilley, D.R. 1999. Emergy Basis of Forest Systems. Ph.D. Dissertation, Environmental Engineering

Sciences, University of Florida, Gainesville, 298 pp.

NOTES: CALCULATIONS MADE FOR FIGURE 4

Global solar empower of the whole geobiosphere 15.84 x 1024 sej/yr for area of earth, 5.12 1014 m2 or

3.08 x 1010 sej/m2/yr (8.44 x 107 sej/m2/day) = 20,162 semkcal/m2/day.

Energy of microbe biomass to 20 cm (Rublee, 1982 in Knox, 2001)

(31.83 g carbon/m2)(2 g dry/g C)(5 kcal/g dry) = 318.3 kcal/m2

Detritus to 20 cm where microbes are 1% of organic biomass energy

Biomass: (31.83 g C/m2)(2 g dry/g C)(100) = 6366 g dry/m2

Energy: (318.3 kcal/m2/microbes)(100 kcal detritus/kcal microbes)

= 31,830 kcal/m2

Number of microbes:

(20 cm3/cm2)(5 x 109 ind/cm3)(1 E4 cm2/m2) = 1 E 15 ind/m2

Energy per individual microbe:

(318. kcal/m2)/(1.0 x 1015 microbes/m2) = 3.18 x 10-13 kcal/individual

Stored emergy in bacteria to 20 cm

(4.1 days)(2.02 E4 semkcal/m2/day) = 82,820 semkcal/m2

Emergy/individual:

82,820 semkcal/m2)/(1 E15 ind/m2) = 8.28 x 10-11 semkcal/indTransformity of microbe stock:

(82,820 semkcal/m2)/(318.3 kcal/m2) = 260 semkcal/kcal



Detritus metabolism = consumption of microbial processes

1.0 mg oxygen/g detritus/hour (0.7 to 1.4)

(1.0 E-3 g O2/g detritus/hr)(24 hr/day)(5 kcal/g)(6366 g detritus/m2)

= 764 kcal/m2/day

Assumed efficiency of conversion to microbe live biomass = 10%

Microbial production = (764 kcal/m2/day)(0.10) = 76.4 kcal/day

Turnover time of microbes: (318 kcal/m2)/(76.4 kcal/m2/day)= 4.2 days

Turnover time of detritus: 32,000 kcal/m2)/(764 kcal/m2/day) = 42 days

Solar transformity of microbes: (20,162 sekcal/m2/day)/(76 kcal/m2/day) =265 sekcal/kcal

Solar transformity of detritus: (20,162 sekcal/m2/day)/(760 kcal/m2/day) = 2650 sekcal/kcal

APPENDIX A: USING EXTEND TO ASSEMBLE AND RUN A SIMULATION

1. Load EXTEND to your hard drive and use its Installer. After installation, double click on its icon to

Open EXTEND.

2. To bring up a model that is already assembled, use the FILE menu to locate its name, and double click

to open. As it loads, a library of blocks may be loaded first, after which the model appears on the screen.

However, the program may ask you to locate the appropriate library file. Locate the folder and click on

the appropriate library for this model. After that library is loaded, the model that uses these blocks will

appear. You can use the LIBRARY menu to see the name of the library that has been loaded. You can seeits contents in a strip of icons by releasing the mouse on the “open library window” command.

3. To assemble your own system of blocks on a model screen, use the file menu to open a NEW worksheet

(model screen). To get the blocks which are to form the system, move the mouse pointer to access OPEN

in the Library menu. Find the folder with the appropriate library and click to install that library. Use the

mouse and the LIBRARY menu to open the list of blocks. One after another, release the mouse button on

each block to be placed on the screen for the model. Include a plotter icon. Use the mouse to arrange the

icons in order of energy hierarchy, with the plotter icon to the right.

4. The small squares attached to each icon are connectors to transfer data from one block to others in your

system. The output connectors (black boxes) on the right of a block send flows out. The input connectors

(white boxes) on the left take flows in. Connect the icons with pathways representing the flows of energy,

materials, and information. When the mouse is held over a connector box, it becomes a “pen” for drawing.

Draw a line with your mouse held down to connect the icons. The line goes from the output box of one

icon to the input box of the next icon. The line will be dashed until there is a good connection. For items

to be plotted as output graphs, connect a line from output connector to a plotter input. The plotter has four

input connectors; it can keep track of four sets of changes in different units and draw four lines on the

graph.

5. Most of the icon blocks have numerical values to set. These may already be set in the model you

opened. You can double click on each icon to see the DIALOG BOX in which the values are indicated.You can retype these numbers to make changes. For a new model you will probably calibrate the model

by entering new numbers.



6. Use the RUN menu to select the time span for the run and the iteration interval (dt). Then RUN the

program. A graph appears for each plotter that you have used. To read the values in output graphs, move

the mouse across the graph, which moves a hair line indicating its position. The numerical values at each

position are plotted on the top line of the table just below the output graph.

7. Various adjustments can be made to label and color the lines on the graph. Click on the graph control

tools at the top of the window of the simulation graph. Plot lines can be assigned to a second vertical

scale on the left. If you want to rescale a graph (quantity on vertical coordinate or time on horizontal

coordinate), click on the graph number and type in the new number in the box which appears.

8. To print, choose PRINT in the FILE menu, select what you want to print (program, output graph, etc.),

and click on the Print button. (Caution: if you want to print only the graph and not the table, be sure the

box for Plot Data Tables is not checked. If it is, you will get hundreds of numbers.)

9. To save a new model, select SAVE AS in the FILE menu. To save something you have already saved

with the same name, select SAVE in the FILE menu. To close without saving changes, select Close under the File menu.

10. To copy a graph, click on the graph to select it and choose Copy Plot from the Edit menu. The graph

will automatically go into the Clipboard. From there you can paste it into any word processor, draw, or

paint program.

APPENDIX B: USING TRUEBASIC

To program and simulate in a simple classical way, instructions are supplied here for using TRUEBASIC,an inexpensive program available for all the versions of PC WINDOWS and Macintosh. (TRUEBASIC,

Inc., Hartford, Vermont, 05047-0501; http://www.truebasic.com). The commands are simpler than

QUICKBASIC, and programs fit both Windows and Macintosh.

Use its installer on the TRUEBASIC DISK to put TRUEBASIC on your hard drive. Open TRUEBASIC

by clicking on its icon. Use its FILE menu and the NEW command to open a blank screen (“edit screen”).

Type in the program. Run the program with the RUN command in the RUN menu.

To load a program that has already been saved, use the FILE menu and the OPEN command. Then type

the name of the program in the dialog box provided (for example type EMTANK.tru). The “edit” screen

appears with the program. Run the program from the RUN menu.

The result of the run appears in the “output” screen. To save and print the result, copy that screen to

clipboard and paste into WORD or into a drawing program. Or, screen dump by pressing PRINT SCREEN

on the PC or press the keys APPLE-SHIFT-3, which puts the picture into a hard disk file “Picture1”. Call

up and print the picture with a drawing program or simple text.

If the output screen is called up with the WINDOWS Menu before the run, it can be sized smaller with the

mouse and shifted to the side. Thereafter, the output graph will fit within the adjusted box when it runs.

After a run, it is necessary to click on the output screen to get back to other operations. To go to other

programs such as WORD without exiting TRUEBASIC, type NEW in its dialog box. It will open a blank screen, and also allow you to go to other programs. After a run, you have to click in the output screen,

before you can do anything else.



To transfer a program from Macintosh to Windows or vice versa, use clipboard to copy program to

WORD on a PC formatted disk. Move the disk to the PC, open TRUEBASIC and use the clipboard to

copy from WORD to a TRUEBASIC screen. To transfer from PC, use a clipboard to copy the TRUEBASIC

program to PC Word on a PC disk. Move the disk to the Macintosh, open the TRUEBASIC on the

Macintosh and copy the WORD listing to a TRUEBASIC screen.

To convert Line commands that are not boxes (without B), a QUICKBASIC Line command such as: 60

LINE (0,90)-(240,90),3 plots a line from the coordinates in the first parenthesis to the one in the next

parenthesis, followed by a 3 to indicate a color. In TRUEBASIC, use the PLOT command to indicate the

coordinates of the points at the start and end of the line, with a semicolon in between which instructs the

program to draw a line between the points: 60 PLOT 0, 90; 240, 90. The line will have the color of the

last color instruction line above it.

Colors of the graphics are designated with the SET COLOR command followed by the name of the color

in quotes. SET COLOR “green”. That color will prevail until the program comes to another SET COLOR

statement. If there is no color statement then the color is black. Instead of the name of the color you canuse its number. Colors available are: 0 = black, 1 = blue, 2 = green, 3 = cyan, 4 = red, 5 = magenta, 6 =

brown, 7 = white, and 14 = yellow. Some colors that are available only by using numbers are: 8 = gray,

9 = bright blue, 10 = bright green, 11 = bright cyan, 12 = bright red, 13 = bright magenta, 15 = bright

white.

APPENDIX C: MODEL IN TRUEBASIC FOR RELATING

AUTOCATALITIC GROWTH

TO TRANSFORMITY

10 Option Nolet

20 ! ScaleExp.tru

25 ! Model of Exponential growth and transformity

30 Clear

40 Set Color “Black”

50 Set Window -30, 310, -30, 310

60 Box Lines 0, 300, 0, 90

63 Box Lines 0,300,100,190

67 Box Lines 0,300,200,300

70 Q = 1080 St = 5.0! Starting Transformity Of Storage

90 Em = St*Q

100 F =3 ! External Source, Constant Force Type (5 Is Exponential)

110 Sts = 1! Solar Transformity Of The Source

115 K0 = .7

120 K1 = .07

130 K4 = .05

140 K5 = .02! Outflow Which Carries Avalble Energy And Emergy

150 Dt = 0.1

160 St0 = .5 !Scaling Factor For Transformity

170 T0 = .3! Scaling Factor For Time180 Q0 = 3! Scaling Factor For Storage

190 Em0 = 100 !Transformity For Emergy Storage



300 !Start Iteration With Plotting

310 Set Color “Green” ! For Storage

320 If Q/Q0 < 90 Then Plot T/T0, Q/Q0

330 Set Color “Blue” ! For Transformity

340 If St/St0 < 200 Then

345 Plot T/T0, 100 + St/St0 ! Transformity Of Storage

350 End If

355 Set Color “Red” ! For Emergy

360 If (Em/Em0) < 300 Then

370 Plot T/T0, 200 + Em/Em0

375 End If

380 Dq = ((10*Sts)/St)*K1*F*Q -K4*Q - K5*Q ! Storage Equation

390 If Dq > .01 Then

400 Dem = Sts*((10*Sts)/St)*K0*F*Q - K5*Q ! Emergy Equation

410 Else

420 Dem = St*Dq

430 End If 440 Q = Q+Dq*Dt

450 If Q < .001 Then Q = .001

460 Em = Em + Dem*Dt

470 St = Em/Q

480 T = T+Dt

490 If T/T0 < 300.0 Then Goto 300

500 End






Edited by



Associate Editors

Howard T. Odum



David Tilley



Sergio Ulgiati

University of Siena

Siena, Italy

December 2003



Gainesville, FL


vi



18

Emergy Perspectives on the Argentine Economy

Throughout the Twentieth Century


ABSTRACT

Governments around the world evaluate economic performance using macroeconomic indicators, such as GDP. In this study emergy accounting was used to assess the economy of Argentina during the

20th century. Emergy evaluations of the Argentine economy for five time periods (1900-1929, 1930-1943,

1944-1975, 1976-1989, and 1990-1995) were conducted. Results were compared with evaluations of

other countries. Argentina started the century relying mostly on the use of renewable energy, with

nonrenewable energy increasing its importance as the economy developed.

The relative position of Argentina with regard to its international debt in emergy terms was also

evaluated. In emergy terms, Argentina had already paid its external debt by 1985. In 1996, the accumulated

emergy value of total debt services represented 2.9 times the emergy of the total debt stocks. The unfair

terms of trade for Argentina and many developing countries are at the heart of the external debt issue.

Results of an analysis of emergy terms of trade showed the influence of macroeconomic policies on

sustainability. As an exporter of commodities (oil, minerals, agricultural products), Argentina is providing buyers more emergy than she receives in exchange. Changing them should be the base for a realistic

strategy towards the solution of this issue.

INTRODUCTION

Governments around the world evaluate their countries’ economic performance using

macroeconomic indicators, such as the gross domestic product, or GDP (Hecht, 1999). GDP is a measure

of the value of final goods and services produced by labor and other resources located within a particular

country (Kearl, 1993).

During the last few decades there has been increasing concern regarding use of GDPmeasurements. One of the main issues is that GDP overestimates total production since it includes dealing

with unwanted side effects of production, such as environmental protection and remediation (Daly and

Cobb, 1989; Kearl, 1993; Hecht, 1999). Another problem is that natural capital consumption is assimilated

completely as income. Therefore, timber exports based on unsustainable rates of deforestation will not

only increase one country’s income but also fail to reflect the destruction of its natural capital assets.

Finally, environmental goods and services consumed directly without exchange in economic markets are

not included in standard national income accounts (Daly and Cobb, 1989; Folke et al ., 1994; Hecht,

1999).

Numerous approaches have been proposed to improve the GDP measurements, or to replace it

with other indicators (Daly and Cobb, 1989; Repetto et al ., 1989; Hecht, 1999). In this study Emergy

Accounting, was used to assess the sustainability of Argentina during the 20th century.



Stages of Argentina’s Development

Five stages of the Argentine economic development were analyzed, following the model proposed

by Veganzones & Winograd (1997):

i. 1900-1929: The Golden Age of Argentine Growth.

ii. 1930-1943: The World Depression and Destabilization of the Argentine Model.

iii. 1944-1975: Import Substitution and Increasing Economic and Political

instability.

iv. 1976-1989: The Attempt to Liberalize the Economy, the Debt Crisis and Extreme

Macroeconomic Volatility.

v. 1990-1995: Hyperinflation and Change in the System. Return of Sustainable

Growth?

The reliance on economic interpretation for the selection of the study periods acknowledged the

interdependence of natural capital and economic development. Moreover, the selection was made with

the purpose of attaining another perspective of Argentina’s economic history incorporating an emergyaccounting point of view. A detailed description of each of the five periods can be found in Veganzones

and Winograd (1997). A synthesis of their perspective follows.

Environmental

Systems

Economy

Renewable

SourcesR

N

Non-

renewables

Yield (Y) = R+N +I

%Renew = R/(R+N+I)

Nonrenewable to Renewable Ratio = (N+I)/R

Emergy Yield Ratio (EYR)= Y/(N+I)

Environmental Loading Ratio (ELR) = (N+I)/R

Emergy Sustainability Ratio = EYR/ ELR

Emergy Money Ratio (EMR) = (R+N+I)/ GDP

M a t e r i a l R e c y c l e

Argen t i n a

Degraded Energy

World

Economy

Imports (I)

Exports (E)

GDP

Figure 1. main emergy flows driving an economy and summary of calculated emergy indices



1900-1929: The Golden Age of Argentine Growth - The rapid growth that had begun in the early

1880s continued in the 1900-29 period which was closely linked to rising exports and investment

. Agriculture was the most important sector in the economy. State intervention in the economy

was limited and the country was heavily dependent on the free flow of merchandise and capital.

1930-1943: The World Depression and Destabilization of the Argentine Model - The 1930s

crisis revealed the fragility of the development model chosen by Argentina and awareness of

this fragility led the country’s leaders in 1943 to adopt an import-substitution policy.

1944-1975: Import Substitution and Increasing Economic and Political Instability - Against a

background of chronic and accelerating inflation, economic and political instability arising

primarily from existing policies, led to loss of control of the economy by the early 1970s . The

1975 breakdown marked the definitive limit of the import-substitution regime.

1976-1989: The Attempt to Liberalize the Economy, the Debt Crisis and Extreme Macroeconomic

Volatility. - By 1981 the military government’s mishandling of its stabilization programs and

economic liberalization policy had plunged the country into a serious crisis of unprecedentedlength. Liberalization was gradually reintroduced by the Radical Government elected in 1983,

but resulted in a high degree of instability and demonetisations and two bouts of hyperinflation:

one in 1989, under the Radicals; and another in 1990, during the Peronist government

1990-1995: Hyperinflation and Change in the System. Return of Sustainable Growth? - In the

relatively short period from 1990 to 1995, economic performance was exceptional compared to

the two preceding decades. Profound economic reforms were undertaken that set in motion a

change in growth strategy. Liberalization of the economy was completed and stabilization was

achieved. .

METHODS

Standard methods for the evaluation of national economies (See Odum, 1996) were used to

evaluate Argentina’s economy for the five time periods. Emergy evaluation tables were constructed and

data were obtained from various literature sources. Most of the data for the historic evaluation of Argentina

were obtained from published sources that account for the evolution of the country throughout the century

analyzed (Azar, 1977; BAC, 1982; INDEC, 1945). Data corresponding to more recent years were also

obtained from databases maintained from public institutions of Argentina, such as the Ministry of Economy

(MECON) and the National Institute of Statistics (INDEC), and from international and foreign

organizations, such us the Food and Agricultural Organization (FAO) and the Energy Information

Administration (EIA) of the United States.

Emergy Analysis Tables

Emergy evaluation tables were constructed for each time period. Emergy driving the Argentine

economy comes from three main sources: renewable inputs of biospheric emergy (outside sources),

imported, nonrenewable sources (purchased goods, fuels, services), and indigenous nonrenewable energy

sources (soils, wood, fuels harvested from within each country). These inputs were evaluated for each of

the five time periods, along with emergy balance of payments resulting from international trade. Mean

values derived from yearly data for the inflows and exports of materials, resources and energy were used

for each time period. All flows across the Argentine boundary –inputs and exports- were expressed in physical units then converted to emergy using published transformities. Data series for the 20th century

were used to obtain the annual average inflows and outflows for each evaluation period. In case of missing



data, averages from available years were used. Emergy flows of the national economy for each time

period were aggregated and indices of sustainability calculated as summarized in Figure 1.

Emergy and Money

Calculation of the Emergy Money Ratio (EMR) used Gross Domestic Product (GDP) and thetotal emergy used. GDP was expressed in constant dollars in order to reduce distortions caused by inflation.

This information, along with population data, was obtained from the work by Maddison (1995), which

provided a comprehensive economic database for fifty-six countries over the period from 1820 to 1992,

among them Argentina. Maddison used the Geary-Khamis approach to transform annual GDP levels into

a common unit, 1990 dollars. A detailed description of the methodology, as well as the information

sources used for Argentina’s GDP estimations can be found in Maddison (1995).

International Debt

An emergy perspective of the international debt of Argentina in the 1990’s was undertaken.

Dollar value of international loans and debt payments were converted to emergy and compared. Emergy

value of international loans was calculated by multiplying the monetary value of the loans by the global

EMR for each year (Brown and Ulgiati, 1999). The emergy value of Argentina’s debt service was evaluated

using the annual monetary payments multiplied by the national emergy money ratio for each year.

RESULTS

A summary of emergy use the Argentine economy during the 20th century is given in Table 1.

The total emergy budget of Argentina increased about 20% throughout the century, while nonrenewable

emergy use increased nearly 1400%. Imported emergy declined during the century by almost 50% from

a high of 11.9 E22 sej*yr -1 to 6.4 E22 sej*yr -1 An emergy signature of Argentina is given in Figure 2,showing the dominance that renewable emergy has in the economy. Emergy associated with chemical

potential energy of rainfall is the largest renewable emergy inflow to the economy of Argentina. A detail

of the emergy signature showing only the nonrenewable emergy is given in Figure 3. There was a dramatic

increase in the fuels and electric emergy use in the economy and a nearly equal decrease in services. The

decline in the emergy value of services was driven in part by the marked increase in fuel use.

Table 1. Energy use in Argentina for the period 1900 – 1995

Period Total Emergy Renew (R) NonRenew (N) Imported (I)

(E 22 sej/yr)

1900-1929 40.0 26.8 1.3 11.9

1930-1943 39.4 26.8 2.8 9.8

1944-1975 42.9 26.8 7.0 9.2

1976-1989 47.9 26.8 17.5 6.21990-1995 48.3 26.8 19.2 6.4



Figure 2. Emergy signature of Argentina’s economy for the period 1900 - 1996

Figure 3. Detail of emergy signature of Argentina showing only the non-renewable emergy flows for

the period 1900 - 1996



The emergy value of production in four main sectors of the economy of Argentina is given in

Figure 4. Agriculture and livestock production were the dominate sectors of the economy for the first

seven decades of the century. While fuels gained in importance overtaking agriculture and livestock in

1975, the production in these two sectors still increased throughout the century. In all, agricultural and

livestock production increased nearly 300% during the century. Beginning about 1975, fuels gained in

importance and contributed about 1.4 times the emergy to the overall economy compared to Livestock

and about 4 times the emergy when compared to agricultural production.

Table 2 lists several indices of emergy use, including the Emergy Money Ratio (EMR). The

EMR decreased by over 85% from 14.1 E12 sej/4 to 1.9 E12 sej/$. While in theory dollars of GDP werederived from a study that reported that they were adjusted for inflation and that reported values were

constant 1990 dollars, the steep decline in the EMR is ample evidence that the so called constant dollars

are not constant as inflation has been about 85% throughout the century. The decline in EMR is a measure

of inflation, since as the EMR gets smaller, the buying power declines. Or to put it another way, as the

EMR decreases, the amount of emergy that can be purchased with a dollar declines. To purchase the

same amount of emergy in the 1990’s would require 7 times the money it did in the earliest period of the

century. Overall, there was a decreasing trend in the ratio of emergy use to the GDP (86%), a consequence

of increasing participation of human activities in the emergy flows of the country. Per capita fuel use increased more than 400%, however total emergy use per capita decreased

about 70%, reflecting the steady increase in population. While fuel use increased, the relatively small

amount of fuel use per capita did not offset the fact that the increasing population shared an ever decreasing portion of the renewable emergy driving the economy. The decrease in emergy per capita reflects a

continuous trend of lower standards of living declining about 25% since the early 1970’s.

Figure 4. Emergy value of production in four main sectors of Argentina’s economy during the 20th

century



Emergy Indices of Sustainability

The emergy indices of sustainability for Argentina during the 1900’s are summarized in Table 3.

Per cent renewable emergy decreased from about 68% to 55% during the century; a decline of nearly

20%. The EYR, measuring the productivity of the economy per unit nonrenewable input, more than

doubled during the century. The same trend is observed for the ELR, which relates nonrenewable emergy

use to renewable use and is a relative measure of the load on the environment due to economic activity.

Combined however, the increase in EYR and the increased ELR cancel each other and there was essentially

no differences in EIS among the different periods considered.

Exports and Emergy Trade Balances

Argentina’s exports during the 1900’s are given in Figure 5. showing the overwhelming

dominance of the agricultural sector. The emergy in exported agricultural products accounted for nearly

90% of all exports for the first three quarters of the century and had only recently been eclipsed by oil and

Table 2. Comparison of indices of solar emergy use in Argentina during the 20t

century,

Period Total emergy

used(E+23 sej/yr)

Emergy/money

ratio (EMR)(E+12 sej/$)

Emergy use

per unit area(E+11 sej/m2)

Emergy use

per person(E+16 sej/p)

Fuel use per

person(E+14 sej/p)

1900-1929 4.0 14.1 1.4 5.1 7.91930-1943 3.9 7.3 1.4 2.9 10.0

1944-1975 4.3 3.5 1.6 2.1 22.8

1976-1989 4.8 2.2 1.7 1.7 26.6

1990-1995 4.9 1.9 1.8 1.5 33.2

Table 3. Emergy indices for Argentina during the period 1900 - 1995

Period EMERGY Indices

%Ren EYR ELR EIS

1900-1929 67 3.4 0.5 6.8

1930-1943 68 4.0 0.5 8.5

1944-1975 66 4.4 0.5 8.4

1976-1989 56 8.1 0.9 9.1

1990-1995 55 8.2 1.0 8.4

Percent Renewable= R / (R + N + I)EmergyYield Ratio (EYR)= (R + N + I) / (N + I)

Environmental Loading Ratio (ELR)= (F + N) / R Emergy Index of Sustainability (EIS)= EYR / ELR



Figure 5. Argentina’s exports of main sectors of the economy during the twentieth century

Figure 6. Ratio of emergy in imports to emergy in exports for Argentina during the twentieth century.



mineral exports. In 1996, crude oil dominated exports from the energy sector, followed by gasoline, gas

oil, and liquid gas (Consejo Tecnico de Inversiones [CTI], 1996). Exported emergy per dollar of crude oil

in Argentina was approximately 6.5 times larger than the exported emergy per dollar of agricultural

products during that year (Ferreyra, 2001). The emergy exported in agroindustrial products (1.75E+12

sej/USD) for each dollar received is lower than average commodities in the economy, while the emergy

exported in crude oil (11.3E+12 sej/USD) per dollar received is about 5 times more than average

commodities. The average USA emergy money ratio during the late 1990’s was about 1.0 E 12 sej/$.

Thus in balanced monetary trades with the USA, Argentina loses about 1.75/1.0 when trading agricultural

products and about 11.3/1.0 when trading crude oil for an average basket of commodities from the USA.

Argentina’s emergy trade balance, or the ratio of imports to exports, is shown in Figure 6 Through

the first 3/4’s of the century, the trade balance was positive, reflecting the fact that Argentina imported

fuels and exported agricultural products. However beginning in the late 1970’s Argentina’s exports were

increasingly dominated by crude oil and minerals, which have very high emprices.

Emergy and International Debt

Issues surrounding international debt might be exacerbated by emergy terms of trade betweendeveloped economies (lending countries) and developing economies (borrowing countries). Table 4

provides a monetary and emergy accounting of Argentina’s international debt. The first column in the

table lists the years from 1980 to 1996. The second two columns give the global EMR and Argentina’s

EMR respectively. Values given under the two columns labeled “Total Debt’ give the yearly debt in

constant dollars, and the emergy value of that debt using the global EMR. Emergy value of the debt is

obtained by multiplying the dollar value by the global EMR. The next two columns under the heading

“Debt Service” list the dollar payments on the debt and the emergy equivalent of the payments. The

emergy values of debt service was obtained by multiplying the dollar payments by Argentina’s EMR.

Table 4.. Dollar and emergy accounting of Argentina’s international debtYear Global EMR a Argentina EMR Total Debt Deb t Serv ice Accum. Debt Service

(1E 12 sej/$) (1E 12 sej/$) (E6 USD) b

(E18 sej) (E6 USD) b

(E18 sej) (E6 USD) (E18 sej)

1980 1.5 2.52 27157 40736 4182 10539 4182 10539

1983 1.38 2.37 45920 63370 6805 16148 10987 26687

1984 1.34 2.32 48857 65468 5197 12078 16184 38765

1985 1.3 2.28 50945 66229 6089 13852 22273 52617

1986 1.28 2.23 52450 67136 7323 16301 29596 68918

1987 1.27 2.18 58458 74242 6244 13593 35840 82511

1988 1.26 2.13 58741 74014 5023 10689 40863 93200

1989 1.25 2.08 65257 81571 4357 9058 45220 102259

1990 1.24 2.03 62233 77169 6161 12507 51381 114765

1991 1.24 1.98 65403 81100 5545 10985 56926 125750

1992 1.22 1.93 68345 83381 5003 9666 61929 135416

1993 1.19 1.88 70576 83985 6556 12345 68485 147761

1994 1.15 1.83 77434 89049 6693 12275 75178 160036

1995 1.1 1.79 83536 91890 9692 17300 84870 177336

1996 1.07 1.74 93841 100410 14021 24340 98891 201676(a)

Source: Brown and Ulgiati, 1999.(b) Source: European Parliament, 1999.



REFERENCES

Azar, C.L. 1977. Argentina: Evolución Económica 1915-1976 . Buenos Aires: Fundación Banco de Boston.BAC (Banco de Análisis y Computación). 1982. Relevamiento Estadístico de la Economía Argentina

1900-1980. Buenos Aires: BAC.

Brown, M.T. 1998. Environmental Accounting: Emergy Perspectives on Sustainability, in IICA/

PROCISUR (ed.), Valoración Económica en el Uso de los Recursos Naturales y el Medio

Ambiente. Montevideo: Procisur.

Brown, M.T. and S. Ulgiati. 1999. Emergy Evaluation of the Biosphere and Natural Capital. Ambio

28(6): 486-492.

Consejo Técnico de Inversiones (CTI ). 1996. The Argentine Economy. Buenos Aires: CTI.

Daly, H.E. and J.B. Cobb, Jr. 1989. For the Common Good: Redirecting the Economy towards Community,

the Environment, and a Sustainable Future. Boston: Beacon Press.

European Parliament. 1999. Trade Relations between the European Union and Latin America. Statistical

Reference Series. Luxembourg: European Parliament.

Ferreyra, M.C. 2001 Emergy perspectives on the Argentine economy and food production systems of the

rolling pampas during the twentieth century. MS Thesis, College of Natural Resources andEnvironment, University of Florida, Gainesville,

Folke, C., M. Hammer, R. Costanza and A. Jansson. 1994. Investing in Natural Capital-Why, What, and

How?, in C. Folke et al . (eds.), Investing in Natural Capital: The Ecological Economics Approach

to Sustainability. Washington: Island Press.

Hecht, J.E. 1999. Environmental Accounting: Where We Are Now, Where We Are Heading. Resources

14(135): 14-17.

INDEC (Instituto Nacional de Estadisticas y Censos). 1945. Comercio Exterior Argentino. Buenos Aires:

INDEC.

INDEC (Instituto Nacional de Estadisticas y Censos). . 1982. Comercio Exterior Argentino. Buenos

Aires: INDEC.

INDEC (Instituto Nacional de Estadisticas y Censos). . 1996. Comercio Exterior Argentino. Buenos

Aires: INDEC.

Kearl, J.R. 1993. Principles of Macroeconomics. Lexington, MA: D.C. Heath and Company.

Maddison, A. 1995. Monitoring the World Economy 1820-1992. Paris: OECD

MERCOSUR. 1998. Anexo Estadístico. Mercosur Journal 2(10): 50-73.

Odum, H.T. 1996. Environmental Accounting. Emergy and Environmental Decision Making . New York

: John Wiley & Sons, Inc.

Repetto, R., W. Magrath, M. Wells, C. Beer and F. Rossini. 1989. Wasting Assets: Natural Resources in

the National Income Accounts. Washington, D.C.: World Resources Institute.

Veganzones, M. and C. Winograd. 1997. Argentina in the 20th Century. An Account of Long-Awaited

Growth. Paris: OECD.






Edited by



Associate Editors

Howard T. Odum



David Tilley



Sergio Ulgiati

University of Siena

Siena, Italy

December 2003



Gainesville, FL


vi



Emergy Evaluation of a Common Market Economy:

MERCOSUR Sustainability

Mark T. Brown, Cecilia Ferreyra and Eliana Bardi,

ABSTRACT

Emergy Accounting, is used to explore perspectives on sustainability of the economies of

MERCOSUR countries individually, as well as collectively. Emergy evaluations of Argentina, Bolivia,

Brazil, Chile, Paraguay and Uruguay for c.1998 were completed and compared.

The analysis showed significant asymmetries within MERCOSUR, identifying four different types

of economies: (1) Bolivia, relying mostly on renewable resource flows; (2) Uruguay, Paraguay, and

Chile, with an increasing participation of purchased and nonrenewable resource use; and (3) Chile and

Argentina and (4) Brazil, with the highest percentage of nonrenewable resource use.

While Bolivia had the highest renewable resource base, all indicators suggest that it has the

lowest sustainability. Paraguay and Uruguay enjoy the highest sustainability indices, but Paraguay

suffers from serious emergy trade deficits with its trading partners and the world economy in general. Emergy balance of payments resulting from international trade was also evaluated. Trade be-

tween MERCOSUR countries and globally was evaluated using an emergy measure of buying power of

currencies and an Emergy Exchange Ratio. All MERCOSUR countries when trading globally are at a

disadvantage, exporting more value per dollar than they import. Within the MERCOSUR, Argentina

benefits most from trade with other member countries when trade is evaluated in emergy terms while

Paraguay suffers in all trades with partner nations, exporting more emergy than it imports. One key to

sustainable common markets may be equitable trade measured in emergy rather than money.

INTRODUCTION

In 1991 Argentina, Brazil, Paraguay and Uruguay formed a customs union called the Common

Market of the South (Figure 1). The union, commonly known as MERCOSUR, is the fourth largest

integrated market after the North American Free Trade Area, the European Union, and Japan (Partenariat,

2001). MERCOSUR entered free trade agreements with Chile and Bolivia in 1996 and 1997, respec-

tively. With a population of 200 million and a GDP around US$ 1 trillion (Table 1), MERCOSUR has

considerable market power and influence over trade (Connolly and Gunther, 1999; Brazilian Embassy in

London, 2000).

Like other regional trade agreements, MERCOSUR has generated a fair amount of debate

(Connolly and Gunther, 1999). One of the main concerns is the potential impact of the integration on the

natural resource base of the countries. The MERCOSUR Treaty recognized conservation of the environ-

ment as one of its main objectives. However, Godynas (1997) notes “MERCOSUR norms do not have a

supra-national legal status, requiring an independent approval by each of the countries separately. As

each country takes its environmental measures in isolation, consensus has not been reached on an envi-

19



ronmental strategy for the common market”. A Framework Accord on the Environment of MERCOSUR

was approved in March 2001. According to Valente, (2001), reactions to the document have been wide

ranging, some supporting its pragmatism and others considering it a major reversal. The accord stressescooperation on sustainability issues as long as it does not interfere with much-needed economic develop-

ment. Notwithstanding the importance of legal agreements and coordination, quantitative measures of

sustainability are a prerequisite to the design of appropriate environmental regional policies.

In this paper Emergy Accounting, of the economy and environmental contributions of the

MERCOSUR countries was used to explore perspectives on sustainability and to highlight trade issues

between countries within common markets. Insights gained may apply to issues of globalization where

some economies by virtue of their emergy intensity and the buying power of their currency can strip

resources from smaller economies with higher renewable resource bases but lower buying power.

METHODSThe approach taken in this study consisted of an emergy evaluation of MERCOSUR members

(Argentina, Brazil, Paraguay, and Uruguay) and associates (Chile and Bolivia) for 1991, 1994, and 1997.

Figure 1. Map of the Southern Common Market. (Source: Foreign Office, 2001) (Source: Brazilian Embassy in

London, 2000)



Table 1. Emergy flows supporting economies of MERCOSUR countries (c 1998)

___________________________________________________________________________________

Renewable (R) Nonrenewable (NR) Purchased (F) Total Use (G)

(E 22 sej/yr) (E 22 sej/yr) (E 22 sej/yr) (E 22 sej/yr)

___________________________________________________________________________________

Bolivia 1.82 0.08 0.04 1.94

Paraguay 3.82 0.27 0.75 4.84

Uruguay 1.96 0.3 0.82 3.08

Chile 12.1 10.1 5.78 28.0

Argentina 19.4 19.7 6.14 45.2

Brazil 68.7 88.3 22.2 179.2

MERCOSUR 107.8 118.8 35.7 262.23

USA(1998)* 106.5 421.5 201.5 729.5

World (1995)** 944 2046 — 2990

___________________________________________________________________________________

* from Rodrigues (2002)** from Brown and Ulgiati (1999)

Emergy driving MERCOSUR economies comes from three main sources: renewable inputs of biospheric

emergy (outside sources), imported, nonrenewable sources (purchased goods, fuels, services), and indig-

enous nonrenewable energy sources (soils, wood, and fossil fuels harvested or used from within each

country). These inputs were evaluated for each year, along with emergy balances of payments resulting

from international trade. Data were obtained form the literature.

The general methodology for emergy evaluations has been explained in numerous publications

(Odum, 1996; Brown and Ulgiati, 1997) and thus only very brief methods are given here. The first step

is to construct systems diagrams that are a means of organizing the analysis and elucidating relationships

between components and pathways of exchange and resource flow. The second step is to construct

emergy evaluation tables directly from the diagrams. The final step involves calculating several emergy

indices (Brown and Ulgiati, 1996; Ulgiati and Brown, 1995, Ulgiati et al. 1994) that relate emergy flows

of the economy with those of the environment, and are used to predict economic viability, carrying capac-

ity and over all sustainability. Expressing the economy in emergy terms and relating the emergy to

monetary flows provides insights related to international flows of money and resources leading to evalu-

ation of sustainability of trade.

The flows of emergy and money within each economy, once evaluated were further aggregated

into main flows supporting the economy as indicated in Figure 2. Several ratios were calculated from the

aggregated emergy flows as discussed next.

Evaluating Economic Sustainability

Three principles are used to judge “fitness” and sustainability of economies. An economy is

more sustainable when (1) it results in at least a minimum of 5.0 E15 sej/capita/yr (the global average per

capita emergy use), (2) a larger percentage of its total emergy inputs come from renewable sources: and

(3) the economy minimizes the “load” on the environment. Several indices were calculated from the

emergy evaluations and used to judge sustainability and fitness, they are: Per capita Emergy use (sej./

capita/yr), Percent Renewable (%Ren), the Environmental Loading Ratio (ELR), and the Emergy

Sustainability Index (ESI). Two additional indices, the Emergy / Money Ratio (EMR) and the Emergy

Exchange Ratio (EER), provide links to the monetary economy and provide an index of sustainable trade,respectively. All of these indices are shown in Figure 1 and further defined in Appendix A.



Emergy, Money and International Trade

When comparisons are made following an emergy analysis, it is sometimes easier to express the

emergy in more familiar terms. Emergy was converted to dollars of buying power ( or “emdollars”, the

term used to describe emergy buying power), based on an average ratio of emergy per dollar of Gross

Domestic Product (GDP) in the local economy. The abbreviation for emdollars is $Em to distinguish

them from currency dollars.

The emergy /money ratio (EMR; units are sej/$) was calculated for each economy by dividing

the total emergy use in the economy by its GDP, expressed as US dollars. It is a measure of buying power,

in essence quantifying the average amount of emergy that circulates in the economy for every dollar that

circulates. Emergy flows were converted to emdollars by dividing emergy by the emergy / money ratio.

In like manner, monetary flows in the economy were converted to emergy by multiplying them by the

emergy /money ratio for that economy

International trade was evaluated by multiplying the dollars of trade by the EMR for the respec-

tive economy as indicated in Figure 3. The emergy trade advantage that results from export of a com-

modity and import of another is the dollars received for the export multiplied by its emprice (the emergy

per dollar of the commodity) divided by the dollars spent on an export times its emprice. Emergy Ex-change Ratio (EER) of two economies was calculated as the ratio of the two trading partners’ EMR s.

R e n e w a b l eE n e r g i e s

R u r a l U s e

Urban Use

F u e l s ,

M i n e r a l s Go od s

I m p o r t

S e r v i c e

M a r k e t sN 2

N 1

N 0

Ru ra l E nv .S y s t e m s

P 1

$

P 1 EG D P

$

$

B

N 2

$

N o n -

r e n e w a b l eE x p o r t s

Di rect Export

P 2

P 2 I

F G

I

Figure 2. Pathways for evaluating the overall energy use of a state or a nation (adapted from Odum,1996,).

Renewable emergy sources = R, Non renewable emergy use = N 0 + N

1 , Purchased emergy = F + G + P

2 I.



GD PR

World

EconomyExports

GD PR

Exports

Imports

Imports

Country A

Country B

Exports

Imports

GDPaR

(Eb)

GDPbR

Country A

Country B

Ia

(Ea)

Ib Pb

Sb

Pa

Sa

Country A:

Emergy/Money Ratio (EMRa) = Total Emergy Use / GDPa

Trade advantage = (Pb * Empb) / (Sb * Empa)

Emergy Exchange Ratio (EERa) = EMRa / EMRb

Country B:

Emergy/Money Ratio (EMRb) = Total Emergy Use / GDPb

Trade advantage = (Pa * Empa) / (Sa * Empb)

Emergy Exchange Ratio (EERb) = EMRb / EMRa

Empa

Empb

Figure 3. Trade between nations. Emergy money ratio of an economy is total emergy use divided by the GDP of the

country, Trade advantage of country A is the money paid for an imported commodity (P b ) times the emprice (Emp

b )

of the commodity divided by the money received for an exported commodity (S b )times its emprice (Empa ). The Emergy Exchange Ratio of each economy is the EMR of the “home country divided by the EMR of the trading

partner.




Total Emergy Use

A summary of major emergy flows supporting the economies of the MERCOSUR countries is

given in Table 1 (note that while only Argentina, Brazil, Paraguay and Uruguay are members of

MERCOSUR, while and Bolivia and Chile are associate members, we have reported results for all six

countries). For comparative purposes, the 1998-emergy flows for the US and the 1995 values for the

World economy are also presented. The analysis reflects the differences among MERCOSUR members

and associates. The six countries can be grouped into four groups based on the relative proportions of

emergy inflows from renewable and non-renewable sources: 1. Bolivia: whose economy relies mostly on

renewable resources, 2. Uruguay and Paraguay: whose economies are dominated by renewable emergy

inputs, but also have flows of non-renewables and purchased emergy, 3) Chile and Argentina with over

half of their total inputs from nonrenewable and purchased emergy inflows, and 4) Brazil whose economy

is more dominated by non-renewables and purchased inflows, but still relatively low compared with that

of more developed economies, like the USA.It is obvious that Brazil dominates the MERCOSUR economy having over 68% of the total

emergy flows within the market. Because of its size relative to the rest of the countries, Brazil contributes

nearly 64% of the renewable emergy base of the market countries, and nearly 72% of the nonrenewable

and purchased emergy use. Argentina and Chile combined contribute about 29% of the renewable emergy

base of the market economy and about 27% of the nonrenewable and purchased emergy use. In total,

these three countries account for nearly 93% of the renewable emergy base and about 99% of the pur-

chased and nonrenewable emergy use.

Total emergy use in the MERCOSUR economy in 1998 was about 2.6 E 24 sej/yr while the US

economy was nearly 3 times as large having a total emergy use of about 7.3 E24 sej/yr. Total emergy use

in the MERCOSUR economy amounted to less than 10% of the total global emergy use.

Emergy Indices of Sustainability

The percentage of an economy that is contributed by renewable sources is a good indicator of

long term sustainability. Should fossil fuels decrease in availability economies with larger portions of

their emergy budgets supported by renewable sources are less likely to exhibit serious dislocations of

populations. Table 2 gives renewable percent for the countries of the MERCOSUR. On average, the

countries of MERCOSUR have over 41% of the economies supported by renewable sources; in contrast

to the USA, which has only about 15% of its resource base from renewable sources. In the short run,

competitive advantage is won by economies with low percent renewable resource base and so economic

sustainability in the short run is highest for those economies with high nonrenewable use. Bolivia withabout 94% of total emergy inputs from renewable sources, and therefore an economy that is relatively

sustainable in the long run, is not competitive in the short run.

Emergy per capita may give insight into the well-being of populations. The global average

emergy per capita of about 5 E15 sej/capita/yr (Table 2) might be used as a ‘cut-off” for current population’s

well-being. Countries with per capita emergy use less than the global average are probably not providing

an adequate resource base for their populations. Per capita emergy use in Bolivia is about half the global

average., and is less than 10% of that in the USA. Of all the MERCOSUR countries, Chile enjoys the

highest per capita emergy use, followed by Argentina. Average per capita emergy use in all MERCOSUR

countries is about twice that of the global average and about 40% that of the USA.

The ELR, EYR and ESI for each of the countries of MERCOSUR are given in Table 2. In

general, the indices indicate that the countries of the MERCOSUR are intermediate in their load on theenvironment (ELRs of abut 1.4/1) , and their effective use of environmental emergy (EYRs equal to about

1.7/1). The ESI is a ratio that relates the emergy yield to society to the load on the environment that is

required to obtain that yield. On the average the ESI for the MERCOSUR countries is about 1.2, while for



Table 2. Emergy indices of the MERCOSUR countries (c 1998)

___________________________________________________________________________________

Population Percent Emergy/capita ELR EYR ESI

(E 6 people) Renewable

___________________________________________________________________________________

Bolivia 7.8 94% 2.49E+15 0.07 16.17 245.2

Paraguay 5.2 79% 9.31E+15 0.27 4.75 17.8

Uruguay 3.2 64% 9.63E+15 0.57 2.75 4.8

Chile 14.8 43% 1.89E+16 1.31 1.76 1.3

Argentina 35.1 43% 1.29E+16 1.33 1.75 1.3

Brazil 166.6 38% 1.08E+16 1.61 1.62 1.0

MERCOSUR 232.7 41% 1.1E+16 1.43 1.70 1.2

USA(1998)* 281.4 15% 2.59E+16 5.85 1.17 0.2

World (1995)** 5900 32% 5.07E+15 2.17 1.46 0.7

___________________________________________________________________________________


the USA it is about 0.2 The very high ESI for Bolivia is indicative of an economy that has large renew-

able emergy flows with very small nonrenewable emergy matching. Paraguay and Uruguay both have

ESIs that indicate good sustainability since environmental loads are relatively low and their yields to

society are relatively high. While the yields in Chile, Argentina and Brazil are about the same as those in

Paraguay and Uruguay, the loads on the environment are higher, thus the ESI for these three countries is

much lower.

Emergy, Money, and International TradeTable 3 gives several monetary indices for the MERCOSUR countries: Gross Domestic Prod-

uct (GDP) in billions of US dollars, the Emergy Money Ratio (EMR), and the Emergy Exchange Ratio

(EES). The GDP of the entire MERCOSUR economy is about 10% of the US economy and about 3.4%

of the global economy.

World EMR in 1995 was is about 1.1E 12 sej/$, while the USA had an EMR in 1998 of 0.73/1.

The countries of MERCOSUR have EMRs considerably higher than those of the Global economy and

the USA. These are average values, but serve instructive purposes. Paraguay has the highest emergy/

money ratio, nearly ten times that of the USA and about 1.5 times that of Chile, the next highest EMR

within the MERCOSUR. Argentina has the lowest EMR (1.52 E 12 sej/$). The average EMR for

MERCOSUR is 2.8 E12 sej/$The final column in Table 3 lists the global Emergy Exchange Ratio for the MERCOSUR coun-

tries. The global EER is the ratio of the Global EMR to each country’s EMR. Relative buying power of

a currency is in its ratio to another currency’s buying power. MERCOSUR countries all have Global

EESs less than 1.0/1 The lowest is Paraguay (0.14/1), which in essence means that on average, Paraguay

is at a 7 to 1 disadvantage when trading on the global market. Obviously this is not the case for every

trade, but reflects the average conditions. Argentina has the highest global EES, but losses about 1.4 to 1

on average in global trading.

Since the relative buying power of a currency when compared to another country’s currency is

in the ratio of the two countries EMRs, the USA with the lowest EMR enjoys considerable advantage. In

essence, since the EMR ratios between Paraguay and the USA are about 10 to 1, it suggests that the USA

dollar spent in Paraguay will purchase ten times the average emergy the same dollar would purchase inthe US.. In Chile, the ratio is about 6.8 to 1 The trade advantage with all MERCOSUR countries is in the

USA’s favor ranging from 10 to 1 (Chile) to a low of about 2.1 to 1 (Argentina).



Table 3. Emergy, monetary and trade indices of the MERCOSUR countries (c 1998)

___________________________________________________________________________________

Total Use (G) GDP EMR Global EER

(E 22 sej/yr) (E 9 US $) (E12 sej/$)

___________________________________________________________________________________

Bolivia 1.94 6.40 3.03 0.36

Paraguay 4.84 6.30 7.68 0.14

Uruguay 3.08 12.20 2.52 0.44

Chile 27.98 54.90 5.10 0.22

Argentina 45.24 297.70 1.52 0.73

Brazil 179.2 556.80 3.22 0.34

MERCOSUR 262.28 934.30 2.81 0.39

USA(1998)* 729.5 9937.00 0.73 1.50

World (1995)** 2990 27100.00 1.10 1.00

___________________________________________________________________________________


Within MERCOSUR, trade advantage follows the asymmetry of the economies. Table 4 gives

internal EERs, or trade advantages/disadvantages of the MERCOSUR countries. Trade relationships are

read as a trade “from” along the top row, “to” in columns below. The EER for trade from Bolivia to

Paraguay is advantageous to Bolivia at about 2.53 to 1. Further, Bolivia is at a disadvantage when trading

with Uruguay since the EER is less than 1.0. Paraguay’s trade with other countries of MERCOSUR is

always disadvantageous to Paraguay. Argentina on the other hand, enjoys trade advantages with all

MERCOSUR countries, ranging from 5 to 1 with Paraguay to about 1.66 to 1 with Uruguay.The EER is calculated on the EMRs of two economies and as such represents the average con-

ditions for trade. Any particular commodity that is traded can have significant variation from the aver-

age. For instance, the emergy per dollar of crude oil exported from Argentina in 1996 was about 6.5 times

the emergy per dollar in soybeans (1.13E+13 sej/US$ and 1.75E+12 sej/US$, respectively). Thus, as

Argentina’s exports shift from mainly agricultural products towards emergy-rich minerals and oil (Ferreyra

and Brown, this volume), its trade balance can get even worse.

Table 4. Internal trade advantage/disadvantage (internal EER) of MERCOSUR countries

___________________________________________________________________________________

FROM Bolivia Paraguay Uruguay Chile Argentina Brazil

TO

Bolivia 1.00 0.39 1.20 0.59 1.99 0.94

Paraguay 2.53 1.00 3.04 1.51 5.06 2.39

Uruguay 0.83 0.33 1.00 0.50 1.66 0.78

Chile 1.68 0.66 2.02 1.00 3.35 1.58

Argentina 0.50 0.20 0.60 0.30 1.00 0.47

Brazil 1.06 0.42 1.27 0.63 2.12 1.00

___________________________________________________________________________________



SUMMARY

The members and associate members of MERCOSUR exhibit large differences in their emergy

use and emergy signatures. As a result indices of sustainability are mixed. In general, the countries of

MERCOSUR are more sustainable within their boarders than the global average sustainability. Their

ELRs are lower, their EYRs are higher, and their ESIs are higher (if only by a small margin) With theexception of Bolivia the emergy per capita of each country is higher. By far, Bolivia has the lowest

current sustainability, but because of its lower overall development status, it has very high percent of its

total economy that is derived from renewable sources. Yet its per capita empower is lower than the global

average, leading one to surmise that currently Bolivia is unsustainable as far as provision of basic re-

quirements to its population is concerned. The highest emergy per capita is found in Chile and Argentina.

All things considered, Paraguay, has the best overall sustainability. Its emergy per capita is

about twice the world average, it has a very low ELR and relatively high EYR, and a high ESI. Unfor-

tunately however, its trade advantages are quite low, suggesting that it exports much more emergy than it

imports from trading partners. It is our belief that such trade situations can exist for short periods of time,

but if trade is to be sustainable, it must be balanced.

An Emergy Trade Policy

MERCOSUR, the Common Market of the South, represents a great opportunity for the develop-

ment of South America. The Emergy evaluation of the countries of MERCOSUR revealed significant

asymmetries among its members and associates. Regional policies concerning the trade between

MERCOSUR countries should account for these differences. Balancing trade between nations using

money does not lead to equitable trade, on the contrary, it causes disparities where the economies with the

highest emergy use per dollar of GDP lose in trading relationships with countries having lower ratios. An

interesting approach to create fair trade and therefore to make it more advantageous for all countries

involved would be to balance trade using emergy. In other words, balance the emergy in exchanges

instead of the money. As it now stands, Paraguay stands to lose on each average trade transaction with all

other members and associate members.

REFERENCESBrazilian Embassy in London. 2000. Mercosul. Online, (http://www.brazil.org.uk).

Brown, M.T. 1998. Environmental Accounting: Emergy Perspectives on Sustainability, in IICA/

PROCISUR (ed.), Valoracion Economica en el Uso de los Recursos Naturalesy elMectio

Ambiente. Montevideo: Procisur.

Brown, M.T. and S. Ulgiati. 1999. Emergy Evaluation of the Biosphere and Natural Capital. Ambio

28(6): 486-492.Connolly, M. and J. Gunther. 1999. Mercosur: Implications for Growth in Member Countries. Current

Issues in Economics and Finance 7(5); 1-6.

Ferreyra, C. and M.T. Brown. 2002. Emergy Perspectives on the Argentine Economy throughout the

Twentieth Century. Proceedings of the Second Biennial Emergy Research Conference. The Center

for Environmental Policy, University of Florida., Gainesville:

Gudynas, E. 1997. Sustainable Development Issues in Mercosur. Bridges between Trade and Sustainable

Development 4(1)). Online, (http://www.ictsd.org/html/bridgesl-4.htm)

Odum, H.T. 1996. Environmental Accounting. Emergy and Environmental Decision Making. New York:

John Wiley & Sons, Inc.

Partenariat. 2001. The Mercosur. Partenariat Union Europea-Mercosur. Online, (http://

www.mercopartenariat.org)Rodrigues, G.S. 2002. SAMeFrame: Sustainability Assessment Methodology Framework. Working

paper, Center for Environmental Policy, University of Florida, Gainesville



Valente, M. 2001. Mercosur: Integration, Environment, and Pragmatism. Tierramerica. Online. 8

(http://www.tierramerica.net)

APPENDIX A

Further discussion and definitions can be found in Odum, 1996; Brown and Ulgiati, 1997; Ulgiati

et al. 1995

Definitions

Emergy / Money Ratio (EMR). The ratio of total emergy flow in the economy of a region or

nation to the GDP of the region or nation. The EMR is a relative measure of purchasing power when the

ratios of two or more nations or regions are compared.

Emergy Exchange Ratio (EER). The ratio of emergy exchanged in a trade or purchase (what isreceived to what is given). The EER is always expressed relative to one or the other trading partners and

is a measure of the relative trade advantage of one partner over the other.

Environmental Loading Ratio. (ELR) The ratio of nonrenewable and imported emergy use to

renewable emergy use. The ELR is an index of how much “load” is placed on the local environment of a

r process, region, or country.

Emergy per capita. The ratio of total emergy use in the economy of a region or nation to the total

population. Emergy per capita can be used as a measure of potential, average standard of living of the

population.

Emergy Sustainability Index (ESI). The ratio of the Emergy Yield Ratio to the Environmental

Loading Ratio. It measures the contribution of a resource or process to the economy per unit of environ-

mental loading. In the case of a regional or national economy the ESI relates the national yield ratio to the

national environmental loading ratio.

Emergy Yield Ratio (EYR). The ratio of the emergy yield from a process to the emergy costs.

The ratio is a measure of how much a process will contribute to the economy. In the case of a regional or

national economy, the EYR is the ratio of total emergy used by society to the nonrenewable and pur-

chased emergies.

Emprice – the emprice of a commodity is the emergy one receives for the money spent. Its units

are sej/$.

Percent renewable emergy (%Ren). The ratio of renewable emergy to total emergy use. In the

long run, only processes with high %Ren are sustainable.



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Chapter 20. Student Empower: A Teaching Exercise

20Student Empower: A Teaching Exercise


ABSTRACT

The purpose of this exercise is to give students a realistic personal perspective on emergy and

emdollar evaluation. From the model of a student “footprint”, students ll out a table of items that they use.

Included is the value of their college education as well as environment, gasoline, natural gas, electricity,

water, food, and goods and services. From these quantities and their transformities they calculate the

emergy and em$ of each item. Using the totals they answer questions which help them evaluate theirlifestyles. For example, results from using the exercise in several college and university classes suggest

that information inputs (college education) were half or more the students’ total emergy use, goods and

energy inputs amounted to about one quarter of the inputs, whereas environmental inputs amounted to

less than 10% of inputs.

INTRODUCTION

Calculating emergy and em$ of their personal life gives students an idea of the meaning of these

concepts. Each student calculates the emergy supporting his or her life and education. Then questions are

asked to help their interpretations. Figure 1 is a systems diagram of a student’s life. It is called a StudentFootprint.

Interesting inferences can be drawn from the results of the 45 students and the comparisons

Figure 1. Systems diagram of emergy ows to a student.



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of their emergy use to that of other categories of people. The results reinforce the emergy concept that

information is very high quality. This exercise refers to a systems overview study of the University of

Florida. In that study (Odum,1996) the highest emergy values were in the knowledge of the faculty and

students.

METHODS

The student adds personal information to that in Table 1.

One of the largest inputs is that from the college or university (item 8). To evaluate the

emergy coming to a student from the university, data from the University of Florida were used (Odum

1996). Therefore, the ratio of Santa Fe Community College’s annual budget to the University of Florida

budget was used as the ratio of emergy use per student at that college to the emergy use calculated in the

University of Florida study. For 2000 this proportion was: SFCC 50 E6 $ for 11,000 students / UF 650 E6

$ for 35,000 students = 0.25. Therefore, the information inputs item in the community college exercise

was calculated as 25% of that of the university.

Table 1. Example Emergy Use Per Student

__________________________________________________________________________________

_

Item Quantity in Joules per Transformity Emergy/ Em$/student/

ordinary units unit b sej/J c student year e

per day a E13 sej/day d

__________________________________________________________________________________

_

1 Environment (sun, 1.02 E13 J 1 sej/J 1.02 3723.0

wind, rain, land2. Gasoline for car 1.71 gallons 1.5 E8 J/gal 6.6 E4 sej/J 1.69 6168.5

3. Natural gas house heat .23 therms 1.054 E8 4.8 E4sej/J 0.116 423.4

4. Electricity 11.36 KWH 3.6 E6 1.7 E5 ej/J 0.695 2536.8

5. Water 150 gallons 1.7 E4 J/gal 7 E5 sej/J 0.179 653.4

6. Food 2300 4186 J/ 5 E5 sej/J 0.481 1755.7

kilocalories kilocalorie

7. Goods and services 39.68 $ 1.0 E12 sej/$ 3.97 14491.0

8. Information inputs 1 student 1.8 E14 18.0 65700.0

sej/student

9. Total per day 26.15 E13

sej/day10. Total per year 9544.8 E13 95448.

sej/year em$/year

__________________________________________________________________________________

_

a. Data for students

b. See footnotes to Table A-1 in Appendix

c. Odum 1996 p. 308

d. Multiply columns 2, 3, 4

e. Divide column 4 by emergy/money ratio (1 E12 sej/$)



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RESULTS

Table 1 is an example of the completed exercise. The last two columns contain the annual

emergy contributions to a student and their emdollar equivalent respectively.

This exercise has been given to two University of Florida classes and ve Santa Fe CommunityCollege classes (two online). Table 2 summarizes the results.

Table 2. Results of use of the exercise in upper division classes: data for other populations included.

_______________________________________________________________________________

Category Number of students Average emergy use per person per year

E15 sej

_______________________________________________________________________________

Graduate 6 88.4University 19 94.1

College 27 47.1

Florida (state) 27.0*

U.S. 28.0**

World 4.0**

_______________________________________________________________________________

* 2000, recent calculation

** 1990, Odum 1998 p. 360

DISCUSSION

The results show how important information is when comparing university and college

students with the general public. When these students become part of the general public, this high emergy

information will not be an automatic part of their lives. An interesting further study would be the large

difference between the university and college information inputs. A disturbing but expected contrast is

the average emergy use per person in the United States compared to the world average.

This exercise was the result of college faculty member’s interest in creating a project in which

each student must explore his or her own lifestyle. Other purposes are to help the students become aware

of their energy basis, teach them concepts of emergy evaluation, and give them practice in doing emergy

calculations. They enjoyed researching and coming to conclusions about themselves.

Class Discussion of the Questions in the College Classes

The rst question was to calculate the proportion of the total they paid for. It was designed to

reinforce the idea that money only pays for services, not the energy in gasoline and electricity.

In answer to the questions about reducing their emergy use, many thought they were living

about as conservatively as they could. Others suggested cutting down on their heat and air conditioning,

and the use of their cars.

It can be seen from a comparison of the Table Item 8 and the Totals that the information inputs

(college education) were half or more of the students’ total use (See Table 2). They were surprised. They



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hadn’t thought much about the immediate current value of their education.

Another interesting revelation was that their emergy use was higher than that of the average

person. They were also surprised that it was their college education that was giving them the higher value.

In the discussion students wondered what their emergy use would be after they left college.

This exercise has now been successfully used in courses in ecology, biology, environmental

science, and human values. Different questions can be asked which refer specically to the different

course subjects.

The exercise in the Appendix is ready to use in class. It can also be found on the web site:

http://inst.sfcc.edu/~natsci/biology/odum/emergystu.htm.

REFERENCES

Odum,H.T..1996. Environmental Accounting. John Wiley, NYC.

Odum,H.T., E.C.Odum, M.T.Brown. 1998. Environment and Society in Florida. CRC Press,

Boca Raton, FL.



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APPENDIX

Student Empower

A Teaching Exercise

ANNUAL EMERGY SUPPORTING A STUDENT

The usual measure of standard of living is based on dollar ow. This exercise uses emergy

which includes the unpaid work of nature and people as well as the quantities you pay for.

To get started on the questions you need to review and be sure you understand: emergy,

transformity, solar emjoules, Joules, sej/J and em$. Chapters in the text, Environment and Society in

Florida (Odum,1998) which will be helpful are: 7, 31, 35, 37 and Appendix B.

The diagram in Figure A-1 shows the contributions to the life of a person who is a college

student. It includes the input and output ows. Whether you are a full-time or part-time student, these

ows determine your emergy use. Note eight inows that cross into a student’s life.

To calculate your personal emergy use, ll out Table A-1 which has line items for each inputin the diagram. Put the emergy you use from nature together with the emergy you use for utilities, food,

fun and education.

Table A-1 is set up for you to ll in the quantities for the eight inows. For each quantity describe

how you calculated it and show your calculations.

Figure A-1. Systems diagram of emergy ows to a student.



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Table A-1. Emergy Use Per Student

_____________________________________________________________________Note Item Quantity in Joules per Transformity Emergy/ Em$/

student/

ordinary units unitb

sej/Jc

student/day yeare

per daya E13 sejd

__________________________________________________________________________________

_

1 Environment (sun, 1.02 E13 J 1 sej/J 1.02 3723.0

wind, rain, land

2. Gasoline for car ___ gallons 1.5 E8 J/gal 6.6 E4 sej/J

3. Natural gas house heat ___ therms 1.054 E8 4.8 E4 sej/J

J/therm

4. Electricity ___ KWH 3.6 E6 1.7 E5 sej/J

J/KWH

5. Water ___ gallons 1.7 E4 J/gal 7 E5 sej/J

6. Food ___ 4186 J/ 5 E5 sej/J

kilocalories kilocalorie

7. Goods and services ___ $ 1.0 E12 sej/$

8. Information inputs 1 student 1.8 E14

sej/student

9. Total per day E13

sej/day

10. Total per year sej/year em$/year

__________________________________________________________________________________

_

* Annual emergy divided by the emergy/money ratio (= 1 E12 sej/$)

(a) Romer, R.R. 1984. Energy Facts and Figures. Spring Street Press, Amherst, MA.

(b) Odum, H.T., E.C.Odum, M.B.Brown. 1998. Environment and Society in Florida. CRC Press, Boca

Raton, FL.

(c ) Odum, H.T..1996. Environmental Accounting.1996. John Wiley, NYC.

Footnotes:

1. Environmental footprint: Florida area (ref.b p.396) 1.4E11 m2 / 15E6 people = 9.3E3 m2 / person

Environmental emergy in Florida (ref.b p.315):

559 E20 sej/yr / 1.4 E11 m2 / 365 d/yr = 1.09E9 sej/m2/day1.09E9 sej/m2/d * 9.3 E3 m2/person = 1.02 E13 sej/person/day

2. Gasoline for car (ref.a p.7): 3.8 liters/gal * 900g/liter / 1000g/kg * 44E6 J/kg = 1.5 E8 J/gallon



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3. Natural gas for house heat and AC: 100 CF (cubic feet) = 1 therm = 1 E8 J (ref.a p.7)

4. Electricity: 3.6 E6 J/KWH (ref.a p.7)

5. Water: 1 gallon = 1.7 E4 J (ref.a p.7)

6. Food: Calorie = kilocalorie 4186J/kcal (ref.a p.7)

7. Goods and services: average dollars paid out per day for food, rent, utilities, gasoline, books, tuition,

recreation, clothes (from job, loans, grants, family).

8. Information in college education: UFL 225 E19 sej/yr / 35,000 students / 365 d/yr = 1.8E14 sej/

student/day (ref.c p.234);

First you gure out the quantity of each item in ordinary units for an average day (second

column). Second, you multiply that number by the Joules per unit (third column). Then you multiply that

number by the transformity in sej/J (solar emjoules per Joule)(fourth column). This is the quantity of solar

emergy in a Joule of that ow. Write this nal quantity in the fth column. Add all the quantities in the

sixth column and you will have the total of your average emergy use per day. In the last column emergy

values can be expressed in their emdollar equivalent by dividing by the emergy-dollar ratio. Next you

can answer the questions (in emergy or em$ units).

Follow these instructions to complete the table.

1. The environment of sun, wind, rain, and land that supports an average person per day is estimated. Since

the transformity is 1 sej/J (solar emjoules per Joule), you don’t need to calculate anything in this row.

2. Your transportation is estimated as your use of gas for your car. You can estimate the gallons you use

per week and divide by 7. Put that number in the second column. To get the solar emergy of these gallons,

multiply the quantity of gallons by the Joules per gallon and the transformity (next two columns).

3. For natural gas, electricity and water, you need to check your utility bill. Natural gas is measured in

therms or CF (cubic feet). Since your bill is probably monthly, divide by the number of days. Try to

estimate an average month, considering summer and winter. If you do not use natural gas, put 0.

4. Electricity is measured in kilowatt hours (KWH).

5. Water is measured in gallons.

6. Estimate how many kilocalories (this is listed on packages as Calories) of food you eat every day

on the average. For non-packaged foods check nutrition books or the Web. Remember Calories (with a

capital C) are kilocalories.

7. For goods and services estimate how much you spend per day for everything: food, rent, utilities,gasoline, books, tuition, recreation, clothes. This includes money spent on you if your family pays for

your tuition or you have loans or grants. It may be easiest to estimate your yearly costs and divide by

365. The Transformity column uses the emergy-dollar ratio for the U.S. in 2000.

8. The data for the emergy in college student education was calculated in a University of Florida study.

9. Total Emergy. Be sure all the quantities are calculated to E13. Add all the items in the fth column.

This is your average daily emergy use.

10. Em$. Calculate your emergy use per year and then divide by the emergy/money ratio of the year

2000 (1 E12 sej/$).



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Questions

1. What percentage of your total emergy value is paid for in money? (Show your calculation.)

2. In a situation where there was less energy and you had to cut 1/3 from your total use (cut waste to

maintain your most useful energy):

a. What would you cut?

b. How would that change your lifestyle?

c. If you cut your education, how would that affect your future and your part in society?

3. How does your emergy use per person compare with the average emergy use per person:

a. in Florida? (2000: 27 E15 sej per person per year.) b. in the United States (1990: 28 E 15 sej per person per year)

c. in the world (1990: 4 E15 sej per person per year)

Explain the differences.






Edited by



Associate Editors

Howard T. Odum



David Tilley



Sergio Ulgiati

University of Siena

Siena, Italy

December 2003



Gainesville, FL


vi



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Chapter 21. Emergy Comparison of Ethanol Production in Brazil...

21Emergy Comparison of Ethanol Production in Brazil:

Traditional Versus Small Distillery With Food and

Electricity Production

Ortega, E., Ometto A. R., Ramos P.A.R.;

Anami, M.H., Lombardi G., Coelho, O.F.

ABSTRACT

The Conventional Production Model (CPM) of ethanol production in Brazil is characterized by

extensive sugarcane plantations (between 20,000 and 30,000 ha) that are burned before harvest, use of

large quantities of agro-chemicals, and seasonal jobs. An alternative, the Medium size Integrated and

Diversied Ethanol Distillery (MIED) project involves 2 060 ha to produce ethanol, electricity, and food

(meat, milk, leather and cereals) with permanent jobs. These two systems have been compared using

emergy analysis and the results were favorable to the MIED project. Some features prove the advantages

of the MIED proposal over CPM: better waste recycling, creation of permanent jobs at the lowest cost

possible to maintain or establish populations in rural areas, contribution to electric power generation.

This is an important issue considering that the area covered by sugarcane in Brazil is 5 million ha.

INTRODUCTION

The twentieth century was characterized by a huge growth of industry and transportation basedon petroleum that led to important environmental and public health hazards. It took many years to discoveroil in Brazil. During World War II the University of São Paulo (USP) created a commission to promotethe production of alternative fuels, among them ethanol which then was produced as by-product in thesugar industry. Some years after, the studies about ethanol production promoted a large agro-industrial

activity based on solar energy and industrial inputs.The expansion of the Ethanol Agribusiness started in 1970, with support from the BrazilianEthanol Production Program (Pró-Álcool) that subsidized the installation of hundreds of new large-scale distilleries. Ethanol was used mainly as a motor fuel. Although it has a lower caloric value thangasoline, a larger thermodynamic combustion efciency and better environmental qualities (1, 2) offsetthis. In the decades of 1980 and 1990 the ethanol production moved from hydrated motor fuel (92%)to absolute alcohol (99%) used as gasoline additive (up to 20%) in replacement for toxic lead products.Sugarcane areas, as well as sugar and ethanol industries, increased very fast. Nowadays, after 30 years ofdepreciation, the Pró-Álcool Program demands equipment substitution and reactivation. New economictrends and improvements in ethanol technology allow the expansion of its use in car engines and demandfor increase of its production. It is the right time to discuss the several proposals to implement this new

Pró-Álcool stage. The electrical energy shortage in the State of São Paulo may put an additional pressureon Pró-Álcool for new objectives (Electricity Production) as well as public policies with concerns forhuman employment and agrarian reform.



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Table 1. MIED Emergy Analysis Table___________________________________________________________________________________ Energy Transformity Emergy Inputs Mass Flows%

Values Units Money sej/ha/y__________________________________________________________________________________

_

Natural Resources (I) 1.69E+15 22.68Renewable (R) 1.36E+15 18.23

Rain on land 1.20E+00 m3/m/y 5.94E+10 J/ha/y 1.82E+04 sej/J 1.08E+15 14.48Nutrients (nature) 1.80E+01 kg/ha/y 1.80E+01 kg/ha/y 3.00E+12 sej/kg 5.40E+13 0.72Nitrogen (atmosphere) 7.00E+00 kg/ha/y 7.00E+00 kg/ha/y 4.61E+12 sej/kg 3.23E+13 0.43Biological control - forest 6.80E-01 t/ha/y 3.84E+09 J/ha/y 2.46E+04 sej/J 9.45E+13 1.27Groundwater - irrigation 5.00E-01 m3/ha/y 2.47E+06 J/ha/y 1.10E+05 sej/J 2.72E+11 0.00Biodiversity gain 2.00E+03 kg/ha/y 2.24E+09 J/ha/y 4.43E+04 sej/J 9.90E+13 1.33Nonrenewable (N) 3.32E+14 4.45

Soil loss 1.82E+02 J/ha/y 4.11E+09 J/ha/y 7.38E+04 sej/J 3.03E+14 4.06People loss 1.80E-02 p/ha 2.89E+07 J/ha/y 1.00E+06 sej/US$ 2.89E+13 0.39Economy Resources (F) 5.77E+15 77.32

Agricultural production 1.41E+15 18.93

Materials (M) 8.29E+13 1.11Seedlings 4.24E+01 kg/ha/y 4.24E+01 kg/ha/y 1.47E+12 sej/kg 6.23E+13 0.83Crop Protection 3.00E+00 L/ha/y 1.99E+00 kg/ha/y 1.48E+12 sej/kg 2.95E+12 0.04Equipment 9.85E+00 kg/ha/y 9.85E+00 kg/ha/y 1.80E+12 sej/kg 1.77E+13 0.24

Services (S) 1.33E+15 17.81Unqualied labor 6.31E-03 p/ha/y 2.66E+07 J/ha/y 7.66E+05 sej/J 2.04E+13 0.27Qualied labor 9.71E-04 p/ha/y 3.20E+06 J/ha/y 7.66E+06 sej/J 2.45E+13 0.33Fuel (diesel) 6.05E+02 L/ha/y 1.90E+10 US$/ha/y 6.60E+04 sej/US$ 1.25E+15 16.79Maintenance 6.39E+00 US$/ha/y 6.39E+00 US$/ha/y 3.70E+12 sej/US$ 2.37E+13 0.32Taxes and rates 2.14E+00 US$/ha/y 2.14E+00 US$/ha/y 3.70E+12 sej/US$ 7.93E+12 0.11

Cattle production 6.20E+14 8.31

Materials (M) 5.87E+13 0.79Livestock purchase 3.13E+00 kg/ha/y 2.33E+07 J/ha/y 1.73E+06 sej/J 4.03E+13 0.54Milky factory 2.35E+00 US$/ha/y 2.35E+00 US$/ha/y 3.70E+12 sej/US$ 8.68E+12 0.12Corrals 1.42E+00 US$/ha/y 1.42E+00 US$/ha/y 3.70E+12 sej/US$ 5.24E+12 0.07Slaughterhouse 5.50E-01 US$/ha/y 5.50E-01 US$/ha/y 3.70E+12 sej/US$ 2.04E+12 0.03Fermentation tanks 6.47E-01 US$/ha/y 6.47E-01 US$/ha/y 3.70E+12 sej/US$ 2.39E+12 0.03Leather tanner center 3.20E-02 US$/ha/y 3.20E-02 US$/ha/y 3.70E+12 sej/US$1.18E+11 0.00Services (S) 5.61E+14 7.52Unqualied labor 3.40E-03 p/ha/y 1.43E+07 J/ha/y 7.66E+05 sej/J 1.10E+13 0.15Qualied labor 9.71E-04 p/ha/y 3.20E+06 J/ha/y 7.66E+06 sej/J 2.45E+13 0.33Animal husbandry 1.14E+02 US$/ha/y 1.14E+02 US$/ha/y 3.70E+12 sej/US$4.21E+14 5.63 Maintenance 9.59E+00 US$/ha/y 9.59E+00 US$/ha/y 3.70E+12 sej/US$ 3.55E+13 0.48 Taxes and rates 1.89E+01 US$/ha/y 1.89E+01 US$/ha/y 3.70E+12 sej/US$ 6.99E+13 0.94Industrial production 2.98E+15 39.91

Materials (M) 1.82E+14 2.44Chemical inputs 4.09E+01 l/ha/y 3.27E+01 kg/ha/y 3.80E+12 sej/kg 1.24E+14 1.66Equipment - infrastructure 7.16E+00 kg/ha/y 7.16E+00 kg/ha/y 6.70E+12 sej/kg 4.80E+13 0.64Construction 2.69E+00 US$/ha/y 2.69E+00 US$/ha/y 3.70E+12 sej/US$ 9.97E+12 0.13

Services (S) 2.80E+15 37.47Unqualied labor 1.41E-02 p/ha/y 5.94E+07 J/ha/y 7.66E+05 sej/J 4.55E+13 0.61



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Qualied labor 5.34E-03 p/ha/y 1.76E+07 J/ha/y 7.66E+06 sej/J 1.35E+14 1.81Administration labor 3.40E-03 p/ha/y 1.12E+07 J/ha/y 5.00E+06 sej/J 5.60E+13 0.75Laboratory labor 2.43E-03 p/ha/y 8.00E+06 J/ha/y 5.00E+07 sej/J 4.00E+14 5.36

Biological control - laboratory 6.80E-01 t/ha/y 5.10E+02 J/ha/y 2.46E+04 sej/J 1.25E+07 0.00Manufacturing 4.61E+02 US$/ha/y 4.61E+02 US$/ha/y 3.70E+12 sej/US$ 1.71E+15 22.86Maintenance 9.06E+01 US$/ha/y 9.06E+01 US$/ha/y 3.70E+12 sej/US$ 3.35E+14 4.49Taxes and rates 3.22E+01 US$/ha/y 3.22E+01 US$/ha/y 3.70E+12 sej/US$ 1.19E+14 1.60

Complementary Services 7.59E+14 10.17Waste treatment and recycling 1.74E+02 US$/ha/y 1.74E+02 US$/ha/y 3.70E+12 sej/US$ 6.42E+14 8.60Equipment - infrastructure 3.16E+01 US$/ha/y 3.16E+01 US$/ha/y 3.70E+12 sej/US$ 1.17E+14 1.57

Total Emergy (Y) 7.46E+15 100.00____________________________________________________________________________________________ The data were obtained from Ramos (6), Lanzotti, Ortega, Guerra (9) and Ortega & Miller (10).

The Conventional Production Model (CPM) adopted in Brazil is generally based on a planta-tion system with extensive use of agricultural land, biodiversity destruction by use of single-culturetechniqueswith intensive use of fertilizers, pesticides, water and re (5). Guivant comments (4) thatsince 1985 the world’s agricultural productivity has declined due to environment degradation: soil lossdue to erosion, salt deposition, acid rain, leaching, etc. Elliot and Colle comment that somehow thisrepresents nature’sanswer to capitalistic agricultural systems with land concentration and intensive use

of machinery, agro-chemicals and fossil fuels (3). According to World Resources (7), during the last 50years, approximately 66% of the world’s agricultural soils were degraded. This motivates the search fora cleaner production model for the Pró-Álcool System.

Recently, the agricultural and industrial activities of the CPM were analyzed (5) to identify theenvironmental impacts they cause. Major objections to the Pró-Álcool Program are: (a) culture basedsolely on sugarcane; (b) large-scale production techniques, often resulting in job losses; and (c) pollutionof water sources by farm and distillery efuents. To overcome these problems, new approaches have beenproposed, and these are embodied in the MIED concept. The MIED project is based on 40 years researchat several institutions within USP (ESALQ, EP, IF, IPT, IPAI, EESC) that now gain the collaboration ofUnicamp (Brazil) and “Jose Antonio Echeverria” Polytechnic Institute of Havana, Cuba.

CPM environmental and social impacts

Among CPM environmental impacts, some stand out for the magnitude of their negativeexternalities: the practice of burning cane areas before the harvest, the intensive use of pesticides, theexcessive mechanical tillage of soil, the exploitation of rural workers (low wages and extenuating work), theconcentration of land and income through unfair land tenancy, the inadequate use of distillery efuents.

The Sugar-Industrial Complex continuously tends to amplify its area in order to increase economicprots. Evidently this leads to larger plantations and fewer owners. The CPM system is limited to workaround 120 to 167 days a year when based only on sugarcane as the raw material. The agricultural labor,

generally from other regions, is concentrated during cane harvest. Finding parallel activities betweencrops could minimize these problems. The transportation of cane over long distances (50 kilometers mean)increases production costs; this is done in trucks with two trailers on sinuous roads, thus deteriorating

Table 1 continued. MIED Emergy Analysis Table___________________________________________________________________________________ Energy Transformity Emergy Inputs Mass Flows%



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highways and increasing the risk of accidents.

Agricultural and industrial research conducted in Brazil (1, 2, and 6) has resulted in MIED propos-als, an innovative approach to ethanol production. Through the effective integration of the processes thatconstitute the biomass-based chain of ethanol production, the MIED system becomes feasible. The maincontribution of the MIED will be to avoid the social and economical problems associated with large-scaleagro-industrial complexes, allowing the activities to take place on a scale that is suited to labor intensiveoperation and small entrepreneurs investment capabilities or rural cooperatives. The MIED concept maybe implemented at various capacities, ranging from 20 000 to 80 000 liters/day.

If we compare solely the industrial systems (MIED of 2000 ha and CPM of 20 000 ha) both ofthem reveal similar economic indices even rather close emergy indices. The difference appears when weconsider the size of the region affected. In an area of 20 000 ha, as used by CPM, the MIED project couldtake only a tenth for ethanol and electricity production and the rest of it, 18 000 ha, could be used with

diverse agricultural projects, cattle, aquaculture, natural forest, forest plantations; those complementaryprojects would lead to better ecological and social indices (more jobs/hectare). As result, instead of “asea of cane without people” we would observe a patched area with biodiversity. Nevertheless, for practi-cal matters, in this study we compare only the industrial systems. In Figure 1 we show the energy ows

diagram of MIED project and in Figure 2 the aggregated ows diagram.A MIED system producing 20 000 liters/day of ethanol (90% concentration) for use in motor

cars would have the following elements: (a) total area of 2 060 ha, 1 670 ha for raw materials (780 ha ofsugarcane and 890 ha of sweet sorghum that will supply the distillery when cane is not available) and 390ha for other uses, (b) a whole cane handling system, billet feeding system, juice extraction system withdiffuser, distillery with fermentation tanks and distillation columns; (c) a 60-80 atm boiling house and

turbo generator; (d) a bio-gas digester; (e) bagasse hydrolysis unit, cattle feeding lots and slaughterhouse;(f) industrial operation during 10 or 11 months/year; (g) preservation of, at least, 20% of the agriculturalland as Legal Reserve (LR), and preservation of Permanent Protected Areas ( PPA) to meet the Brazilian

Figure 1. Energy ows diagram of Medium size Integrated Ethanol Distillery (MIED)



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law.The MIED outputs, besides 20 000 liters/day of ethanol, would be: (a) 26 200 MWh/y of electricity

from the combustion of sugarcane and sorghum bagasse; (b) bio-gas from fermentation of distillery wastesand cattle manure to produce enough steam to run the distillery (3 500 m3/day); (c) rich fertilizer efuentfrom the bio-gas digester, suitable for use on the complex’s elds. The internal consumption of 24 tonsper day of silage fodder will permit semi-connement of cattle to supply meat for a population of 10 000people; (d) 2 500 liters/day of milk; (e) 700 calves per annum; (f) a range of other crops or vegetables(0.5 t/ha) that can be grown on the sorghum land (grain productivity: 2 000 and 3 000 kg/ha) during the8 months of the year between sorghum crops.

Some advantages of MIED are: (a) no burns; (b) production of food; (c) obeisance to lawsconcerning preservation of forested areas; (d) use of motors adapted to vaporized ethanol; (e) attractionof thousands of people without hope in the cities back to the countryside; (f) an alternative option topetroleum thermoelectric plants.

MIED innovates in operational procedures and use of modern technologies. It offers a newvision for the Sugar Industrial Complex: small and integrated systems distributed throughout the country.

Figure 2. Aggregated Diagram of Emergy Flows



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It is genuinely a revolution over the historically used conventional production models based on bigplantations. The agricultural and technical issues are proved and well understood, and it is clear that thereal challenge in implementing MIED project lies in setting up institutional arrangements and regulatoryframework that would ensure successful making of rural projects. The MIED proposal could incorporatein the future organic and agro-ecological agricultural techniques. It is felt that its potential benets wouldfar outweigh the costs. Anyway, creative solutions in agriculture and renewable energy are much neededand recommended by Agenda 21.

Table 2. MIED x CPM indices___________________________________________________________________________________ MIED CPM___________________________________________________________________________________Ethanol Capacity 20,000 L Ethanol/day 200,000 L Ethanol/dayTotal Area (hectare) 2,060 ha 25,490 haEmergy ows

R (sej/ha/year) 1.36E+15 1.36E+15N (sej/ha/year) 3.32E+14 8.27E+14I = R + N 1.69E+15 2.19E+15M (sej/ha/year) 3.24E+14 7.95E+15S (sej/ha/year) 5.34E+15 3.50E+15F = M + S 5.66E+15 1.15E+16Y = I + F 7.35E+15 1.36E+16Emergy indicesEYR =Y/F 1.30 1.19EIR = F/I 3.34 5.24ELR = (N+F)/R 4.41 9.03%R = R/Y 18.50 9.97

Q total (J/ha/y) 1.95E+11 2.99E+11Tr =Y/Q total 3.76E+04 4.57E+04Tr =Y/Q sugar - 96,036Tr =Y/Q ethanol 86,486 214,421Tr =Y/Q electricity 118,569 4,442,078ProductsEthanol (J/ha/y) 8.50E+10 6.36E+10Electricity (J/ha/y) 6.20E+10 3.07E+09Sugar (J/ha/y) - 1.42E+11Bagasse (J/ha/y) - 9.00E+10Vegetables (J/ha/y) 3.21E+09 -Milk (J/ha/y) 3.20E+10 -

Meat (j/ha/y) 4.39E+09 -Leather (J/ha/y) 8.68E+09 -Yeast (J/ha/y) 6.31E+07 -Qualied Work (sej/ha/y) 1.84E+15 2.62 E+15Unqualied Work (sej/ha/y) 7.69E+13 2.36E+14Protability 1.05 1.12___________________________________________________________________________________

Environmental and sustainable development benefts

This approach to ethanol production fulls the requirements for nancing of instruments suchas the Clean Development Mechanism (UNO´s Kyoto Protocol). It is envisaged that MIED complexes



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could be constructed to provide the basis for decentralized rural development nodes in areas suitable forsugar cane production. The ethanol produced could be used for transportation purposes and productionof ethanol-based gel-fuel suitable for domestic use. In addition, once fuel cells become available, it couldbe used for power generation at the individual household level.

The MIED project would provide good quality jobs and a technical solution to allow the possibilityof sustainable development. There are further possibilities of productive activities derived from otheroutputs such as electricity, agricultural products and cattle. In the current global predicament of risingpetroleum prices and climate change concerns, it offers a good and elegant solution to create rural jobsand access to affordable and convenient sources of energy.

Further investigation is required for better understanding of the behavior of the model, but it isfelt that MIED system may give signicant benets in countries where climate and soil resources couldmake the adoption of this kind of agro-industrial system possible.


Global Transformity: Tr = (R+N+M+S) / (Sum of energies produced)As basis for comparison we used the transformity of the system and not of a specic product. We

considered the total emergy captured and the sum of all the energies of sold products (ethanol, electricity,vegetables, milk, meat and leather). The transformity values are 45,660 sej/J (CPM) and 37,600 sej/J(MIED). They reveal that MIED project has better system efciency.

Table 3. Human Labor___________________________________________________________________________________

First Method* Second Method** Third Method***

Human Labor (sej/J ) Tr Year Tr Year Tr Year ___________________________________________________________________________________Brazil

Unqualied Labor 7.66E+05 2001 3.57E+06 2001 - -Qualied Labor 7.66E+06 2001 - - - -

USA

Unqualied Labor 2.62E+06 2001 8.08E+06 2001 8.90E+06 1996Qualied Labor 1.57E+07 2001 - - 2.46E+07 1996

___________________________________________________________________________________

* The rst method is based on emergy content of minimal salary in each country (sej/ year) divided by

one worker metabolism (Joules/year).

** In the second method the values are obtained dividing the annual emergy of the country (sej/year)by the total population metabolism (J/ year).

*** The third method values are obtained multiplying the energy expended by a human being by thetransformity of that person’ considering education and experience (Odum, 1996).

Net Emergy Yield Ratio: EYR = Y/F Net emergy yield ratio values obtained for the two systems are satisfactory when compared withother agro-industrial systems in Brazil. Both systems deliver free energy to the surrounding economy. The



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services and takes advantage of its integration concept.

Emergy Investment Ratio: EIR = F/I The emergy investment ratio of MIED project is smaller (3.34) than the EIR value for CPM

(5.24), still this is a reasonable value when compared to EIR values of other industrial activities, whichmay vary between 0.8 and 40. The mean value for this ratio in USA is 8.0 (Odum, 1996).

Renewability: (R%) = 100 (R/Y) The renewability of the MIED project is bigger (18.5%) than the value of CPM (10%); stillthese low values of renewability express that these options will not survive in social systems withoutpetroleum subsidies. The MIED project makes more and better recycling and uses less chemicals inputs.MIED´s renewability index could be improved when we consider the same affected region (20 000 ha).As we mentioned before, the MIED project could be planned as a tenth part of a bigger, more diversied

agro-ecological rural system with less use of non-renewable resources.

Environmental Loading Ratio: ELR = (N+F)/RThe ELR value of the MIED project is better (4.41) than the value for the CPM system (9.03).

It is due to efcient treatment of efuents, better recycling with animal participation. If organic andagro-ecological procedures were incorporated as well as water treatment the ELR ratio of MIED wouldbe improved.

Social and Economic Analysis

Proftability:

The MIED system has a slightly lower protability (1.05) than the new model of CPM (1.12).MIED’s protability is affected by national and international policy concerning agricultural prices, taxesand subsidies. The discussion about public policy to promote more sustainable rural systems has alreadybegun in Brazil. In order to implement more sustainable systems progressively at national level, researchmust be done about commercial trade and international prices in order to consider equitably producers andconsumers. Regional planning should benet this kind of ethanol system production due to its ecologicaland social advantages.

Labor:

Jobs in the conventional system (CPM) are based on manpower exploitation; with low salariesin manual harvest and offer of work for only a short period of time (seasonal jobs), hence the job qualityis very low. Although the work is hard it involves women and sometimes children. Payment to workers

is based on the amount of cane cut and collected. So, the laborers are forced to work very hard, receivinga small amount of money that sometimes allows only paying food. MIED project would offer better andstable jobs for a longer period of time or more work hours if this system would be adopted in a regional

scale.

CONCLUSIONS

This is the rst emergy analysis prepared for a medium-scale, diversied and integrated ethanolproduction system and may induce reections on how to proceed in the future to establish self-sufcientrural systems that could also absorb people from cities. Its renewability needs to be improved with an

appropriate mixture of agricultural and forest activities. This article has made a special effort to includethe Forest inputs in the MIED system in order to value this important element within this system. Theproject also needs to be reformulated from the perspectives of regional planning, employment and agrarian

value for MIED (1.30) is slightly larger than that for CPM system (1.19), because it uses environmental



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reform. The MIED transformities for ethanol and electricity showed the systems good efciency incomparison with other production systems producing electricity and ethanol. The CPM system showedto be efcient only to sugar production.

ACKNOWLEDGEMENT

We thank Anita Kacenelenbogen Guimarães for her kind and careful revision of ourmanuscript.

REFERENCES

CORSINI R. (1988). Mini Usinas Integradas. Exposição Comparativa. Anais SINERGE, vol. 1. Brasil.pp. 154-162.

CORSINI R. (1999). Viabilidade econômica de Mini Usina de álcool Integrada. Monograa. EESC,Brasil.

ELLIOT, E. T. e COLE, C.V. (1989). A perspective on agro-ecosystem

VIOLA, E. J.; LEIS, H.R; SCHERER-WARREN, I.; GUIVANT, J.S.; VIEIRA, P.F.; KRISCKE,P.J. Meio Ambiente, Desenvolvimento e Cidadania: desaos para as Ciências Sociais 2.ed. São

Paulo, Cortez. Cap. 3, p.99-133.

OMETTO, A. R. (2000). Discussão sobre os fatores ambientais impactados pelo setor sucro-alcooleiroe a certicação sócio-ambiental. São Carlos. Dissertação (Mestrado) – Escola de Engenharia deSão Carlos, Universidade de São Paulo.

RAMOS, P.A.R. e LOMBARDI G. (2001). Viabilidade econômica do projetoconceptual para execução do projeto dimensional. Relatório de pesquisa FAPESP. Escola

Engenharia São Carlos, São Paulo, Brasil.

WORLD RESOURCES. World Resources 2000-2001 People and Ecosystems: The fraying Web ofLife Hardbound. United Nations Environment Program, World Bank World Resource Institute.

http://www.elsevier.com/homepage/sag/worldresources/agro.html (29/08/2000).

LANZOTTI, C.R.; ORTEGA, E. & GUERRA S.G.M, 2001. Emergy analysis and trends forethanol production in Brazil. Emergy Synthesis. Proceedings of First Reunion on Emergy andTransformity. Center of Environmental Policy. University of Florida.

ORTEGA, E. & MILLER, M ( 08/29/2001). Avaliação ecossistêmica - emergética de processosagrícolas e agroindustriais. Estudo de caso: a produção de soja. http://www.unicamp.br/fea/ortega/curso/ portoalegre/portoalegre.htm

ODUM, H.T. (1996). Environmental Accounting: Emergy and environmental decision-making. 370pages, John Wiley & Sons, Inc., New York, USA.

STAB (2001). Revista da Sociedade Brasileira de Técnico do Açúcar e do Álcool. São Paulo.

CALCULATIONS

1) Natural Resources

1.1) Renewable (R)

R1 Rain on land:

Rain: 1 200 mm/m2

/y. Gibbs’s free energy for water: 4 949 J/kg.1.2m3/ m2/y * 10 000 m2/ha * 1 000kg/ m3 * 4 940 J/kg = 5.93 E+10 J/ha/yR2 Nutrients from nature:The biochemical and physical weathering of soil will supply 18 kg/ha of P to plant nutrition. The plant



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needs 425 kg/ha/year of essential elements: 119 kg of N (nitrogen), 51 kg of P (phosphorus) and 276 kgof K (potassium). Part of the nutrients will be restored by irrigation of bio-fertilizer produced by digestionof industrial wastes, corresponding to 426 kg/ha/year of essential elements: 50 kg N/ha/y, 33 kg P/ha/yand 281 kg K/ha/y plus the residues of livestock, which correspond to 62 kg/ha of N.

R3 Nitrogen (atmosphere):

Microorganisms inoculated will supply up to 7 kg/ha/year through biological xation of nitrogen of theair.R-4 Biological control - ForestAccording to STAB (2001), without biological control there is a loss of 0.68 t /ha/y of sugar. Biologicalcontrol services are provided by natural forest reserve and also supplied by a laboratory. The forestcontributes with 25%. The caloric potential of organic matter is: 5400 kcal/kg * 4186 J/Kcal = 22.6E+06 J/kg.0.68 t/ha/y * 0.25 * 1 000 kg/t * 22.6 E+06 J/kg = 0.38 E+09 J/ha/y.R-5 Groundwater - irrigationIrrigated quantity: 0.5 m3/ha/y. Gibbs’s free energy for water: 4949 J/kg.0.500 m3/ha/y * 5 000 J/kg * 1 000 kg/m3 = 2.50 E+06 J/ha/y.

R-6 Biodiversity gain The growth of forest for this area was estimated to be close to values obtained from commercial forestsof pines or eucalyptus, almost 2 tons by hectare by year.2 tons * 1000kg/t * 5400 kcal/kg * 4186 J/kcal = 2.24 E+9 J/ha/y

1.2) Non-renewable (NR)

NR-1 Soil loss

The organic matter loss in soil with straw covering is around 182 kg/ha/y:182 kg/ha/y * 4 186 J/kcal * 5 400 kcal/kg = 4.11 E+09 J/ha/y.NR-2 People loss

To obey a new Brazilian law mechanical harvest will be adopted in sugar cane areas, resulting in a decrease

in jobs. A machine picks 300 t/day while a laborer picks an average of 7.5 t/day. We will have less thantwo operators for the whole area (2 600 ha). The loss is of 0.018 persons/ha.0.018 workers /ha * 3200 kcal /day (metabolic spends) * 4 186 J/kcal * 120 days/year =2.9 E+07 J/ha/y

2) Economic resources (F)

The calculations were split according to the activities accomplished in MIED: agriculture, livestockand industry.

2.1) Agriculture (sugar cane, sweet sorghum, grains and vegetables)

2.1.1) Materials (MA)

MA-1 Seedlings

144 kg of cane seedlings for 5 years, 10 kg of sweet sorghum seeds and 1.54 kg of seeds of grains andvegetables each year with a stock of 5 %: ((144/5 + 10 + 1.54) kg) * 1.05 = 4.1E+1kg/ha/y.MA-2 Crop ProtectionAverage use of herbicides: 3.0 liters/ha/y. Herbicide density: 0.8 kg/L. Agricultural area: 1 705 ha; totalarea: 2 060 ha.3.0 L/ha/y * (1 705 ha / 2 060 ha) * 0.8 kg/L = 1.99 kg/ha/y.MA-3 EquipmentTotal costs of agricultural equipment for crop production, harvest and transport: US$ 2 349226. Depre-ciation occurs in 30 years. Average price of steel in agricultural equipment: 3.86 US$/kg.

(2 349 226 US$ / 3.86 US$/kg) / (2 060 ha * 30 y) = 9.848 kg/ha/y

2.1.2) Services (SA)



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SA-1 Unqualied labor Thirteen workers (p) on the agricultural area of 2 060 ha. An unqualied worker spends 3200 kcal/day.1 year = 315 work days:((13 p/d)/ 2 060 ha) * 3 200 kcal/p/d * 4186 J/kcal * 315 d/y = 2.66 E+07 J/ha/y.SA-2 Qualied labor Two qualied workers in 2 060 ha. A qualied worker spends 2 500 kcal/d.((2 p/d) / 2 060 ha) * 2 500 kcal/p/d * 4 186 J/kcal * 315 d/y = 3.20 E+06 J/ha/ySA-3 Agricultural operationsAnnual expenses on agricultural operations: 761 851.38 US$/y; the total area is 2 060 ha.761 851.38 US$/y / 2 060 ha = 196.70 US$/ha/y.SA-4 MaintenanceThe maintenance of the agricultural machines is 6% of 219 469.48 US$/y.( 219 469.48 US$/y * 0.06) / 2 060 ha = 6.39 US$/ha/ySA-5 Taxes and ratesThe taxes of the agricultural area are calculated by adopting 2% of the agricultural sales, which is76.72 US$/ha/y.

76.72 US$/ha/y * 0.02 = 1.53 US$/ha/y. 2.2) Livestock

2.2.1) Materials (ML)

ML-1 Livestock purchasesThe number of cows is 3 438; a calf weighs 75 kg and, for reproduction, an annual replacement of 25% is needed; the equivalent heat energy of cattle is 7 438 596 J/kg, the area is 2 060 ha; the period ofdepreciation is 30 years.(3 438 * 75 kg) / (2 060 ha * 30 y) * 0.75 * 7 438 596 J/kg = 2.33 E+07 J/ha/y.ML-2 Milk factory

The total cost of the system is 145 000 US$.

(145 000 US$ / 2 060 ha * 30 y) = 2.346 US$/ha/y.ML-3 Corrals

The total cost of corrals is 87 587 US$.87 587 US$ / (2 060 ha * 30 y) = 1.417 US$/ha/y.ML-4 SlaughterhouseThe cost of slaughterhouse is 34 000 US$.34 000 US$ / (2 060 ha * 30 y) = 0.55 US$/ha/y.ML-5 Fermentation tanksThe fermentation tanks are destined to the production of animal feed. The total cost is US$ 40 000.40 000 US$ / (2 060 ha * 30 y) = 0.65 US$/ha/y.ML-6 Leather tanner centerThe total cost is US$ 1 978.1 978 US$ / (2 060 ha * 30 y) = 0.032 US$/ha/y.

2.2.2) Services (SL)

SL-1 Manual work labor Use the same calculation as for simple labor, SA-1. Seven workers.(7 p/d / 2 060 ha) * 3 200 kcal/p/d * 4 186 J/kcal * 315 d/y = 1.43 E+07 J/ha/y.SL-2 Qualied labor Use the same calculation as for qualied labor, SA-2. Two qualied workers.(2 p/d / 2 060 ha) * 2 500 kcal/p/d * 4186 J/kcal * 315 d/y = 3.20 E+06 J/ha/ySL-3 Animal husbandry

The total expenses for treating healthy cattle is 234 180.52 US$/y; the total area is 2 060 ha.234 180.52 US$/y / 2 060 ha = 113.68 US$/ha/y.



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SL-4 Maintenance

The annual expenses of maintenance is 9% of US$ 219 469.48219 469.48 US$/y * 0.09 / 2 060 ha = 9.59 US$/ha/y.SL-5 Taxes and ratesThe taxes for livestock are calculated as 2% of the annual sales of its products, which is 944.31 US$/ha/y.944,31 US$/ha/y * 0,02 = 18.89 US$/ha/y

2.3) Industry

2.3.1) Materials (MI)

MI-1 Chemical inputsThe volume of oil for the production of alcohol and electricity is 40.86 liters per hectare, for the wholearea. The density is 0.80 kg/L.40.86 L/ha * 0.80 kg/L = 32.7 kg/ha/y.MI-2 Equipment and infrastructureThe total quantity of steel in industrial equipment and infrastructure (with 30 years of depreciation) is7.16 kg/ha/y.MI-3 Civil and industrial constructionThe total expenditure for the civil and industrial construction is US$ 166 500, the total area is 2 060 ha;the period of depreciation is 30 years:166 500 US$ / (30 y * 2 060 ha) = 2.69 US$/ha/y.

2.3.2) Services (SI)

SI-1 Unqualied laborUse the same calculation as for simple labor, SA-1. Twenty-nine men working at the industrialcomplex.(29 p/d / 2 060 ha) * 3 200 kcal/p/d * 4 186 J/kcal * 315 d/y = 5.94 E+07 J/ha/y.SI-2 Qualied laborUse the same calculation as for qualied labor, SA-2. Eleven qualied workers.(11 p/d / 2 060 ha ) * 2 500 kcal/p/d * 4 186 J/kcal * 315 d/y = 1.76 E+07 J/ha/y.SI-3 Administration laborUse the same calculation as for qualied labor, SA-2. Seven ofcers.(7 p/d / 2 060 ha) * 2 500 kcal/p/d * 4 186 J/kcal * 315 d/y = 1.12 E+07 J/ha/y.SI-4 Lab laborUse the same calculation as for qualied labor, SA-2. Five technical workers on labs.

(5 p/d / 2 060 ha) * 2 500 kcal/p/d * 4 186 J/kcal * 315 d/y = 8.00 E+06 J/ha/y.SI-5 Biological control - laboratoryUse the same calculation as for biological control by forest, R-4. Associate rate for labs is 75% of thetotal biological control:0.68 t/ha/y * 0.75 * 100 000 kg/t * 8 807 344 J/kg = 4.49 E+09 J/ha/y.SI-6 ManufacturingAnnual expenditure on manufacture are US$ 950 013.34, the total area is 2 060 ha.950 013.34 US$/y / 2 060 ha = 461 US$/ha/y.SI-7 Maintenance

Annual expenditure on maintenance are 85 % of US$ 219 469.48, the total area is 2 060 ha.(219 469.48 US$ / 2 060 ha) * 0.85 = 90.56 US$/ha/y.SI-8 Taxes and ratesThe industrial taxes is 2% of the total sales of the industrial products, which is 1 610.56 US$/ha/y.






Edited by



Associate Editors

Howard T. Odum



David Tilley



Sergio Ulgiati

University of Siena

Siena, Italy

December 2003



Gainesville, FL


vi



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Chapter 22. Evaluation of a Coal Gasication Process towards Hydrogen Production...

Evaluation of a Coal Gasication Process towards Hydrogen

Production: An integrated assessment.


ABSTRACT

This paper explores the (energetic and exergetic) thermodynamic efciency as well as theenvironmental sustainability (mass ow accounting and emergy accounting) of syngas production from

several types of coal (lignite, anthracite, bituminous and sub-bituminous coals). Syngas production is

an intermediate step towards hydrogen production, accomplished by means of CO2 sequestration and

trace element removal.

The basic model investigated in this work is the Koppers-Totzek gasication process, the

performance of which is assessed by means of selected literature data. The task is performed by means

of an in-house developed software, adjusted to match the specicity of the investigation, according to

the environmental quality requirements issued by the International Standardization Ofce (norms ISO

14040/1997 to ISO 14043/2000).

A set of efciency and environmental sustainability indicators for each kind of coal used is

calculated, in order to provide useful operational suggestions for further research and policy. A comparison

with hydrogen production by steam reforming of natural gas and alkaline water electrolysis is also

performed. The results have shown a relative independence of the efciencies on the kind of feedstock

coal used, whereas the two environmental indicators exhibit opposite but equally marked trends, the

meaning of which is discussed in the paper. Calculated transformities for syngas range from 4.0E+04 to

7.5E+04, depending on the type of feedstock.

THE SYSTEM

The object of this paper is two-fold: (a) the analysis of the well known industrial processfor the gasication of coal, the so-called Koppers-Totzek process (Figure 1), applied to several kinds

of commercial coal; (b) the validation of an integrated multicriteria approach for performance and

sustainability assessment, to be used for further research on hydrogen fuel production.

Results are compared with those previously obtained on hydrogen production via steam reforming

of natural gas and via alkaline water electrolysis (Ulgiati et al., 2001; Figure 2) by means of the same

approach. Gasication and steam reforming are the two mainstream processes for the reforming of coal

and natural gas, respectively.

The main reactions taking place inside the gasier consist of the partial oxidation of coal in the

presence of oxygen and water vapour, according to the reactions:

C + H2O

CO + H2

C + _ O2 CO

The gaseous mixture in output from the gasier, rich in CO and H2, is called raw synthesis gas

22



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or “syngas”.

The Koppers-Totzek process is different from other xed or uidised bed gasication processesin that the pulverised coal is blown into a reaction chamber where a vortex is established and maintained

until the reaction is completed.

Syngas can be used as it is, as a medium heating value gaseous mixture. This is the case that is

discussed in this paper, and to which all the resulting indicators refer.

Alternatively, if the product of interest is pure hydrogen, syngas can be puried from the trace

elements (essentially S), and the carbon monoxide can be oxidised to carbon dioxide in the so-called shift

reactor, and eventually sequestered; the hydrogen gas can then be separated from the resulting mixture

with very good selectivity (> 97.5%). These last purication steps are not explicitly analysed in this paper.

However, since the heat generated by the gasication process is the main source of energy supporting

syngas purication, it can be reasonably assumed that the performance indicators per unit energy would

not change signicantly. Instead, the nal product after purication (hydrogen) would have a lower massper unit exergy content, resulting in higher values of the indicators per unit mass.

METHODS

The system boundaries only include the pre-heating of the feedstock fuel and water, the gasication

step and the rst cooling and ash removal step. The depreciation of the plant structure was not accounted

for, since it can be assumed to be negligible in all energy transformation processes, compared to the huge

ow of fuel processed. The output product is raw syngas, with a mean hydrogen content of 30%.

A thorough mass inventory supports four analysis procedures (carried out at local and global scales,

where appropriate): Mass Flow Accounting, Energy and Embodied Energy Analysis, Exergy Analysis, and

Emergy Synthesis, providing indicators of system performance respectively based on material, energy,exergy and emergy intensities. Due to the different spatial and time scales of application, each approach

is able to answer different questions. Integration of results offers a multicriteria multiscale picture of the

Figure 1. ESDs of syngas production.



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process under investigation. Further details on this integrated approach can be found in Ulgiati, 2000

and Ulgiati et al., 2001.

Material Intensity (MI) is dened as the total amount of materials that are directly or indirectly

required to provide a given ow or storage of energy or matter, and is calculated separately for the four

main environmental compartments: abiotic matter, biotic matter, water and air (Hinterberger and Stiller,

1998). They can be used as a direct measure of the exploitation of natural resources (soil excavation,

water withdrawal, biotic material degradation, etc.) and an indirect measure of environmental impact

(ecosystem stress, alterations of local climate, loss of biodiversity).

MI factors are an input-side measure of ecosystem disturbance, while an output-side point of view

is offered by the analysis of airborne, liquid and solid emissions. The latter can be calculated in several

ways, e.g. global CO2 emissions calculated directly from fossil fuel utilization and indirectly from Oil

Equivalents, and ashes and slugs deriving directly from the gasication process.

An Energy analysis of the system is then performed on the life cycle scale, where all inputs are accounted

for in terms of their embodied energy (Herendeen, 1998), which is usually expressed as Oil Equivalents.

Since the product of interest is an energy carrier, its energy intensity is better expressed in the form of a

life cycle energy efciency.

Exergy is dened as the amount of work obtainable when a certain amount of matter or energy isbrought to a state of thermodynamic equilibrium with the common components of the natural surroundings

by means of reversible processes, involving interaction only with the above-mentioned components of

Figure 2. ESDs of hydrogen production.



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the biosphere (Szargut et al., 1988). Since all real processes are irreversible, measures of delivered work

compared with the theoretical value provide indicators of exergy losses due to the irreversibility of a

process. All exergy efciencies are calculated in this paper with reference to the physical boundaries of

the system, thus indicating the actual thermodynamic efciency of the process.

Emergy is the amount of available energy (exergy) of one form that is directly or indirectly

required to provide a given ow or storage of exergy or matter (Odum, 1996). When inputs are measured

in units of solar exergy, the solar emergy is calculated, measured in solar emergy joules (seJ). Converting

ows of different nature into ows of one kind requires conversion coefcients called transformities

(seJ/J) and specic emergies (seJ/g). Emergy accounting acknowledges that different forms of energy

may require a different environmental support and may show different properties. If different energy or

exergy ows are summed without accounting for the convergence of environmental work supporting them,

their donor-side quality is not accounted for, and useful information on the relationship of the process

with the biosphere dynamics is lost.

Mass and energy inputs are rst translated into the corresponding exergy ows, and then converted

into emergy inputs by means of transformities. The linking of emergy and exergy ows contributes to

highlight the thermodynamic basis of the emergy approach.

The analyses were performed by means of an in-house developed software, adjusted to matchthe specicity of the investigation, according to the environmental quality requirements issued by the

International Standardisation Ofce (norms ISO 14040/1997 to ISO 14043/2000).

The production of the necessary steam, as well as feedstock coal drying, were analysed separately,

in order to calculate the correct values of the Material Intensity, Oil Equivalent and transformity for these

items. It was assumed that the fuel used for the production of the steam be the same kind of coal that is

used as feedstock in the gasier. Details of calculations are not shown in this paper, but are available on

request.


The comparison between the different kinds of coal that can be used as feedstock for the productionof syngas gave some interesting results. Moreover, the results of a previous analysis performed by this

Figure 3. Life cycle energy efciency of syngas.



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group on hydrogen gas production from steam reforming of natural gas and from water electrolysis are

also compared and discussed. Since all the indicators for water electrolysis are worse than those forsteam reforming because of the inevitable extra step of thermal electricity production, our attention will

be principally focused on the comparison with hydrogen from steam reforming.

Figures 3 and 4 show the energy and exergy efciencies of the gasication process for the

various kinds of feedstock coal used, expressed as J of syngas per J of invested Oil Equivalent on the life

cycle scale (HHV energy), and J of syngas per J of direct input (exergy), respectively. These life cycle

(energy) and process (exergy) efciencies are calculated from published data (Boustead and Hancock,

1979, Fan et al., 1983, Rosen, 1987, Szargut, 1988, Smil, 1991, Hall et al., 1992, Hydro-Chem web site,

2002), integrated with standard thermodynamic data. Both efciencies are shown to be relatively constant,

regardless of the kind of feedstock coal used.

Due to the need for comparing processes and results from different authors, the main goal of thispaper, standard Higher Heating Values and specic exergies were used throughout (e.g. for hydrogen,

HHV = 140,000 J/g and b0ch

= 120,000 J/g, respectively). Our results may therefore be slightly different

from those of the cited literature.

The lower efciencies calculated for hydrogen production with respect to syngas production

(Figures 5 and 6) can be explained at least in part by the requirement for a high purity output gas (>99%)

in the examined steam reforming plants.

As opposed to the comparatively constant efciencies, the Material Intensities of syngas (Figure 7)

exhibit a marked dependence on the kind of feedstock coal used, becoming worse and worse as the carbon

content of coal decreases (Table 1). This behaviour can be interpreted in the light of the larger water content

of the lower grade coals, as well as the increased material requirements in the coal mining phase. In this

regard, in the last fty years there has been a trend of increasing material requirement, as overburden, forthe extraction of low quality coal, as opposed to the relatively constant material requirement for higher

quality coals (Manstein, 1996). Focusing our attention on abiotic matter, and expressing the MIs on the

Figure 4. Exergy efciency of syngass.



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Figure 5. Comparison of Life Cycle Energy Efciencies.

Figure 6. Comparison of Exergy Efciencies.



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common basis of grams per MegaJoule of product, syngas is also shown to be more matter intensive than

hydrogen produced via steam reforming or electrolysis, by approximately an order of magnitude (Figure

8). It is important to underline that these results can be read as indicators of the global environmental

impact associated with the activities performed within the life cycle of the investigated processes.

In order to complement this information, the two main emissions of the analysed processes

are shown in Figure 9. Comparing the results for syngas production via coal gasication and hydrogen

production via steam reforming, hydrogen is favoured both from the point of view of CO2 emissions and

of solid emissions, which are negligible in the steam reforming process. Moreover, it is important to note

that these solid emissions, in the case of coal gasication, are mainly composed of ashes and coal tars,

which are rich in carcinogenic Polynucleated Aromatic Hydrocarbons (PAHs), and can cause serious

ecotoxycological problems to the environment in the area surrounding the plant if they are simply dumped

in a landll, as it was customary to do in the early days of coal gasication. Thus, the material, energetic

and emergetic costs of the proper treatment of these solid emissions should be accounted for in order for

the comparison between the two kinds of industrial processes to be equitable.

Solar transformities (Figure 10) show an increasing trend moving from lignite all the way up

to anthracite, which is largely determined by the increased specic exergies and transformities of the

different types of coal. In this regard, we made the assumption that the transformity of coal be directly

proportional to its carbon content, the latter being an indirect measure of its fossilisation stage. It is worth

noting that this increasing trend is partially smoothed by the larger amount of emergy needed for the

coal drying process in the case of lower quality coals, as well as by the larger emergy associated with

the removal of the overburden. However, the variability in this latter contribution to the transformity ofthe different kinds of coal was assumed to be negligible, based on the very low average energetic cost of

coal extraction (Boustead and Hancock, 1978, page 122).

Table 2 shows an example of the full emergy accounting procedure for syngas from one common

kind of feedstock coal (High Volatile A-bituminous). Feedstock coal is the largest emergy input by almost

two orders of magnitude (92% of total, also covering the energy costs of oxygen separation from air

and syngas purication). This explains the strong dependence of the transformity of syngas on that of

the type of coal used. It is also interesting to note that the second largest emergy input is process steam

(5% of total, which also includes the emergy needed for the coal drying process). Finally, services only

account for 2% of total emergy input, as is expected for a process mainly consisting of the conversion

of a primary energy source.

Table 3 shows calculated transformities and specic emergies for syngas and hydrogen compared

with raw and processed fossil fuels. The transformity of syngas falls approximately in the same range as

processed fossil fuels, while the transformity of hydrogen ranks one order of magnitude higher.

Table 1. Types of feedstock coal used (average values from literature).

________________________________________________________________

CoalType C% H2O Specic Exergy

(w/w) (w/w) (J/g)

________________________________________________________________

Lignite 36 43 14,900Sub-bituminous 42 28 17,100

High volatile

B-bituminous 55 13 23,700

High volatile

A-bituminous 62 13 26,100

Anthracite 75 4 30,800

_________________________________________________________________



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Table 2. Emergy accounting of syngas production from High Volatile A-bituminous Coal.

_____________________________________________________________________

Amount Solar Solar

# Item Unit transformity Emergy

(seJ/unit) (seJ)

__________________________________________________________________________________

_

Inputs

Material inputs

1 Coal (feedstock) J 2.65E+10 4.62E+04 1.22E+15

2 Oxygen J 7.43E+05 0.00E+00 0.00E+00

3 Water into the jacket J 1.05E+07 4.10E+04 4.32E+11

3a Water into the waste heat boiler J 6.86E+07 4.10E+04 2.81E+12

4 Process steam & coal drying J 1.57E+08 3.81E+05 6.01E+13

Economic inputs

5 Labour yr-pers 0.00E+00 2.50E+16 0.00E+00

6 Services $ 2.96E+01 1.00E+12 2.96E+13

TOTAL INPUTS seJ 1.32E+15

TOTAL INPUTS w/o Economic inputs seJ 1.29E+15

Products and by-productsRaw syngas is puried to H

2 in the downstream steps of the process, not investigated here. These steps

are driven by jacket steam and high pressure steam, thus the latter are not accounted for as usable end

outputs of the gasication process. Therefore, all input ows are assigned to the output syngas.

7 Raw synthesis gas J 2.02E+10 6.53E+04 1.32E+15

w/o Economic inputs 6.39E+04 1.29E+15

8 Jacket steam J 1.25E+08

9 High pressure steam J 1.45E+09

References for transformities:

Material inputs

1 Our estimate based on Odum H.T., 1996

2 By denition

3a Odum H.T., 1996

4 Our estimate – this work

Economic inputs

5 Our estimate based on selected european countries in the nineties

6 Our estimate based on selected european countries in the nineties



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Figure 7. Material Intensities of Syngas.

Figure 8. Comparison of Material Intensities (abiotic).



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Figure 9. Comparison of Main Emissions.

Figure 10. Solar Transformities of Syngas.



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Table 3. Comparative table of transformities of selected fuels.

__________________________________________________________________________________

_

Fuel Transformity Specic Emergy

(seJ/g) (seJ/g)

__________________________________________________________________________________

_

Raw Coal (mean) 4.00E+04 9.01E+08

Raw Natural Gas (mean) 4.80E+04 2.50E+09

Crude Oil 5.40E+04 2.16E+09

Diesel and gasoline 6.60E+04 2.85E+09

Rened natural gas 5.23E+04 2.72E+09

Syngas (mean) 5.25E+04 2.11E+09

Hydrogen from Steam Reforming of

Natural Gas (mean) 9.69E+04 1.16E+10

Hydrogen from Water Electrolysis 1.74E+05 2.09E+10__________________________________________________________________________________

_

Since transformity can be regarded as a measure of global (biosphere-scale) efciency for processes

delivering the same kind of output, these results indicate a better performance for syngas produced from

coal compared to hydrogen from natural gas, if the product of interest is the amount of work actually

obtainable (MJ of exergy delivered). This is mainly due to the lower transformity of the raw material used

as feedstock (coal instead of natural gas). It must however be noted that the results could be different if

the emergy cost of the environmental remediation of coal mining and the safe disposal of solid emissions

were taken into account (according to the procedure suggested by Ulgiati et al., 1995).

The higher transformity of hydrogen (cfr. also Figure 11) may also suggest a higher user-sidequality of this energy carrier, consistent with the fact that it can be converted into work with greater

efciency and lower emissions (e.g. in Fuel Cells).

Transformities give a clear indication of the necessary environmental support for the production

of the various kinds of syngas. They are very differentiated depending on the kind of feedstock coal

used, which reects its geological history, as well as its extraction and processing phase. It is however

interesting to note how this indicator shows an opposite trend with respect to the Material Intensities: the

transformities increase from the lower grade coals (e.g. lignite) to the higher grade ones (e.g. anthracite).

This can be easily interpreted if we keep in mind that transformity accounts for the “memory” of the

environmental resources that were used up in the past for the production of the inputs, which in the case

of coal goes back to millions of years, whereas MIs are strictly calculated within the time frame of the lifecycle of the investigated process. Thus, the two indicators give different answers to different questions:

MIs are a measure of present ecosystem disturbance associated with resource extraction and use, and

transformities are a measure of past global environmental support, as well as renewability. Thus, we are

left with two complementary pieces of information, incorporating the shift from local to global scales in

two different ways.

As a concluding remark, we would like to underline that evaluating comparable alternative

processes, when specic answers regarding different possible uses of resources in the space-time frame

of interest are sought, necessarily requires the adoption of a multi-criteria approach. It must be realised

that in virtually all cases there is no single “optimal” solution to all problems. Only an analysis based

on several complementary approaches can highlight the inevitable trade-offs that reside in alternative

scenarios, and thus enable a wiser selection of the option embodying the best compromise in the light ofthe existing economic, process and environmental conditions.



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ACKNOWLEDGEMENTS

Support to the research described in this paper was provided by the Italian ENEA - National

Agency for Energy and the Environment, research contract no.1033/TEA - ENEA, which the authors

gratefully acknowledge.

REFERENCES

Ayres R.U., Ayres L.W., Martinàs K., 1996. Eco-Thermodynamics: Exergy and Life Cycle Analysis.

INSEAD, Centre for the Management of Environmental Resources.Bargigli S., 2002. A Life Cycle Assessment of Natural Gas Extraction and Rening. Report to ENEA,

Italy, in press.

Boustead I., Hancock G.F., 1978. Handbook of Industrial Energy Analysis. John Wiley & Sons, New

York.

Fan L.T., Shieh J.H., Ishimi T., 1983. Practical Application of Process Systems Engineering to Energy

and Resource Conservation and Management. Computers & Chemical Engineering, 7, no.4,

pp.493-528.

Hall C.A.S., Cleveland C.J., and Kaufmann R., 1992. Energy and Resource Quality. The Ecology of the

Economic Process. Colorado University Press.

Hinterberger and Stiller, 1998. Energy and Material Flows. In: Advances in Energy Studies. Energy Flows

Figure 11. Comparison of solar transformities (without economic inputs)



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in Ecology and Economy, S. Ulgiati, M.T. Brown, M. Giampietro, R.A. Herendeen, K. Mayumi,

Eds., MUSIS Publisher, Italy, pp.275-286.

Hydro-Chem web site, 2002. http://www.proquip-corp.com/hydrochem/experience/plant_types.shtml

ISO, 1997-2000. Environmental Management – Life Cycle Assessment . International Standardisation

Ofce, Norms 14040 – 14043.

Manstein C., 1996. Das Elektrizitätsmodul im MIPS-Konzept. Wuppertal Papers, 51.

Odum H.T., 1996. Environmental Accounting: Emergy and Environmental Decision Making. John Wiley

& Sons, New York.

Rosen, 1987. An energy-exergy analysis of the Koppers-Totzek process for producing hydrogen from

coal. International Journal of Hydrogen Energy, 12, no.12, pp. 837-845.

Shieh J.H., Fan L.T., 1982. Estimation of Energy (Enthalpy) and Exergy (Availability) Contents in

Structurally Complicated Materials. Energy Sources, 6, no.1/2.

Smil V., 1991. General Energetics. Energy in the Biosphere and Civilization. John Wiley & Sons, New

York.

Stiller H., 1999. Material Intensity of Advanced Composite Materials. Wuppertal Papers, 90.

Szargut J., Morris D.R., Steward F.R., 1988. Exergy analysis of thermal, chemical, and metallurgical

processes. Hemisphere Publishing Corp., New York.

Ulgiati S., 2000. Energy, Emergy and Embodied Exergy: diverging or converging approaches? In: Emergy

Synthesis. Theory and Applications of the Emergy Methodology, M.T. Brown, S. Brandt-Williams,

D.Tilley, S.Ulgiati, Eds., Center for Environmental Policy, Gainesville, FL, USA, pp.15-32.

Ulgiati S., Bargigli S., Raugei M., Tabacco A.M., 2001. Life-Cycle and Environmental Impact Assessment

of Hydrogen and Fuel Cells. In: Advances in Energy Studies. Exploring Supplies, Constraintsand Strategies, S. Ulgiati, M.T. Brown, M. Giampietro, R.A. Herendeen, K. Mayumi, Eds., SGE

Publisher, Padova, Italy, pp.29-42.

Ulgiati S., Brown M.T., Bastianoni S., Marchettini N., 1995. Emergy-based indeces and ratios to evaluate

the sustainable use of resources. Ecological Engineering, 5, pp.519-531.



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Edited by



Associate Editors

Howard T. Odum



David Tilley



Sergio Ulgiati

University of Siena

Siena, Italy

December 2003



Gainesville, FL


vi



-327-

Chapter 23. Fertilizer Co-Products as Agricultural Emternalities...

23

Fertilizer Co-Products as Agricultural Emternalities:

Quantifying Environmental Services

Used in Production of Food


ABSTRACT The environmental consequences of processes necessary for food production are rarely factored

into crop evaluations. Byproducts created as part of an agricultural system inputs, prior to use of the main

product, and without further concentration or recycle, require additional fundamental environmental

services associated with eliminating their environmental impact, and fall within the food production

process. Emergy was used in this evaluation to determine a more realistic sustainability picture for the

food we eat and to numerically value a new set of services by suggesting the incorporation of these by-

products into emergy ratios presently incorporating only inputs actually used in the process. This study

also suggests the use of emternalities both as a terminology and methodology to evaluate sustainability

by using emergy to value a new set of non-traditional externalities. Integrating co-product emergy in the

Florida tomato evaluation increased total ows by 28%. Florida cabbage showed an 88% increase intotal emergy, oranges increased by 75% and watermelon increased by 55%. A comparison of renewable

emternalities to non-renewable emternalities illustrated an emergy decit of 6.3 E22 sej/yr to the state

of Florida from gypsum co-products associated with tomato, cabbage, watermelon and orange yields

in 1994. The emdollar value of services to the United States for treatment of diammonium phosphate

fertilizer produced in 2000 was valued at 564 billion emdollars.

INTRODUCTION

Agribusiness is an industry dependent on manufactured chemical inputs for high yields. For

every additive benecial to productivity, there are by-products requiring extensive environmental servicesto render them inert. Radioactive gypsum stacks, residual pesticides and increasing nutrient loading to

lakes and rivers are examples. The consequences of processes necessary for food production are rarely

factored into crop evaluations except for eld-scale studies looking at soil loss or nutrient leaching.

This paper presents a method for incorporating a larger scale perspective into agricultural commodities,

and deals with the fate of co-products.

Economic evaluations recognize the processing cost for these additives, but not the other fun-

damental environmental services – neither in crop production nor in the additive manufacture. While

these services are sometimes recognized as important externalities from the fertilizer process, difculty

in assessing loss or redirection of use of public commodities, such as estuaries, keeps them from being

broadly internalized1

(Pillet 2001; Pillet 1986; Koomey et al. 1997; Jordan 1995). Further, classic de-nitions of externalities typically deal with output products and not the environmental support required

1Internalized externality is a dollar amount that has been directly added to the cost of the nal product.



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prior to production (Pillet et al. 2000) – the long-term concentration of phosphate rock or photosynthetic

energy required for phosphate mining, for example . Looking at the next step in food production – the

eld – classic externalities might cover the loss of soil as an unvalued output, but the environmental

services prior to crop production are not typically considered externalities. Consequently, wastes pro-

duced just prior to use of the intended product would not be added into the cost of food production.

Even if the wastes from fertilizer processing were internalized as outowing process externalities, thisinput would be passed on to agribusiness (and to the consumer) as a purchased service rather than as a

renewable or non-renewable component. Using services in accounting rather than actual environmental

services obscures the details needed to complete an accurate long-term carrying capacity assessment

of crop production.

Emergy has been extensively used to account for environmental inputs to agriculture (Ulgiati

et al. 1994, Odum et al. 1987, and many others), and allows backtracking to the environmental source

and numeric valuation of each input. While direct inputs are adequately priced, the byproducts of

each input are usually omitted in calculating the emergy of the next recipient of the input, despite the

total cost to the larger scale hierarchy (Figure 1). Because these by-products are not used in the actual

production of a fruit or vegetable (the smaller inside box in Figure 1), the value of these environmental

services is not counted in their emergy ratios, despite the fact that these byproducts are unavoidable

and currently unrecyclable.

Multi-scale evaluations are a common systems theory practice (Odum 1994, Allen et al. 1982),

and are particularly important in a process as pervasive as agriculture and its supporting industries. Shift-

ing the system perspective beyond eld production incorporates environmental services not traditionally

included in economic or single crop emergy evaluations. Co-products created as part of the next system’s

inputs, and without further concentration or dispersion, designate additional fundamental environmental

services within the food production process. In this way, gypsum, an outowing waste product from

fertilizer production and requiring renewable environmental inputs to render harmless, becomes part of

the overall food process, and its environmental requirements become part of the inputs.

The evaluation’s scale also determines what components are non-renewable versus purchased.In Florida, for example, where agriculture, phosphate mining and fertilizer production often exist within

a single sub-basin, phosphate rock is no longer a purchased input to the agricultural process, but rather

represents a non-renewable extraction, with only a fraction of the rock retained within the resource area.

In fact, if viewed from a global perspective, phosphate rock is not a purchased input, but represents a

non-renewable with nite bounds of particular importance to feeding a burgeoning world population.

This differentiation between non-renewable and purchased is important in assessing the sustainability

of not just a watershed or the state of Florida.

In economic theory, emternalities can be viewed as the metaphorical counterpart of established

economic externalities. In both cases, private ownership is unclear, and both constitute ows not rendered

into currency values. However, emternalities designate unassessed infowing environmental contribu-

tions instead of unpriced outfowing impacts of economic processes. In Figure 1, pathways A, B, C, D

and E represent emternalities, while F minus H and L are externalities. Pathways F and I represent costs

included in traditional economic evaluations. Another difference is that externalities are internalized

according to preference-related, or user, methods, whereas emternalities are valued using emergy, a

thermodynamic donor methodology. Emternalities represent social and environmental inputs translated

into traditional emergy units of sej/J or sej/g, but a currency ratio of $/sej is used instead of the inverse

sej/$. This ratio is comparable to other price ratios more familiar to economists and consumers.

Several commodities have been evaluated using this concept (Pillet et al. 2000, Pillet 1995,

Pillet 1987, Pillet 1986, Pillet and Odum 1984), and the idea of using non-renewable emternalities as

a decit in agricultural evaluations has been studied at a national level (Pillet 1999). Emternalities

consider only renewable and non-renewable inputs, and the difference between the two values can beviewed as a method for assessing sustainability of a particular process. If the non-renewables are greater

than the renewables, the process is not sustainable. Emternalities have a further reaching applicability

than just environmental inputs – social inequality and displacement for example. This paper, however,



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Figure 1. Shifting agricultural perspectives beyond eld production incorporates wastes from production of

main eld inputs not traditionally included in economic evaluations. Components placed on the lines indicating

boundaries illustrate how components regarded as purchased inputs at one scale become non-renewables whenviewed at a larger scale if the resource is contained within a regional boundary.



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will focus on environmental services, and specically those associated with fertilizer production used

in production of food crops.

Processing phosphate rock into fertilizer produces gypsum, carbon monoxide, and hydrouoric

and sulfuric acid as co-products, or byproducts in traditional process terminology. The substances are

produced at the same time as the desired fertilizers, and therefore have the same emergy, by denition

(Odum 1996), as the fertilizer. Carbon monoxide is scrubbed during the process and is therefore accountedfor in the evaluation of fertilizer (Brandt-Williams 2001). Hydrouoric acid is recycled as feedstock

to other polymer processes and directly incorporated into an end use outside of this evaluation (Klein

1996). The emergy of reclamation of mined areas is not included for two reasons. Lost productivity

from high clay content overburden piles is theoretically short-term since reclamation is now required by

the state. This cost is indirectly passed on to the consumer through the purchase price of fertilizer.

Gypsum by itself could be incorporated into other uses, but gypsum produced from Florida phos-

phate has high radium and radon levels(O’Brien 1997, Lloyd 1985), a radioactive material – limiting

the recycle value. Large stacks of gypsum, supporting little life because of high acidity, now occupy

large areas of otherwise productive land, with sulfuric acid runoff into streams and estuaries with every

rain event.

Emergy is used in this study to evaluate these additional environmental services required for thefood we eat and to numerically value emternalities by suggesting the inclusion of co-products created

as part of the system inputs with the typical direct inputs. Emternalities are used to assess sustainability

by comparing environmental fractions for renewable and non-renewable inputs. Results are presented

at several different scales - eld or commodity (the tomato you actually eat), regional or state (all the

tomatoes eaten in the state of Florida), and national (all the tomatoes eaten in the U.S.) – to emphasize

the enormity of this issue.

METHODS

The existence and extent of by-product formation in fertilizer production was determined fromchemical equations and balances for the reactions taking place, starting with phosphate rock and ending

with diammonium phosphate fertilizer (Shreve 1945). The following chemical equations illustrate the

steps required to produce high-grade fertilizer from raw mined rock. Some of these are separate reac-

tions occurring in unit operations, but it is impossible to separate the production of gypsum and other

components from the production of phosphorus in a commercial form available to photosynthesis.

4[CaFCa4(PO

4)

3] + 14H

2SO

4 + 34H

2O --> 6[CaH

4(PO

4)

2] + 6H

2O + 4HF + 14(CaSO

4·2H

2O)

6[CaH4(PO

4)

2] + 6SiO

2 + 30C --> 12P + 12H

2O + 30 CO + 6CaSiO

3

12P + 15O2 --> 6P

2O

5

6P2

O5

+ 18H2

O --> 12H3

PO412H

3PO

4 + 24NH

3 --> 12(NH

4)

2HPO

4

In addition, standing gypsum recombines with water and atmospheric carbon dioxide to

reform highly concentrated sulfuric acid.

14(CaSO4 • 2H

2O) + 14 CO

2 --> 14CaCO

3 + 14H

2O + 14H

2SO

4

(gypsum) (sulfuric acid)

Services required for treatment were identied using systems diagramming (Figure 2).

This more detailed process diagram replaces the natural treatment process box in the larger hierarchy

(Figure 1). Fertilizer production creates acidic and radioactive gypsum piles that are not useable forvegetation. As rain falls on the piles and as residual water leaches through the pile, sulfuric acid is cre-

ated. This decreased pH creates an environment inhospitable to plants and some animals and dissolves

the supporting limestone causing a switch action in stress levels when insufcient water is available



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for dilution. Gypsum’s main detriment is the elimination of productivity in areas where stacks occupy

what was previously pine atwood, swamps or marsh areas and the liberation of sulfuric acid requiring

dilution above a pH that will not kill plants and wildlife as it moves through the landscape and into

streams and estuaries. Assumptions and sample calculations are presented for these two co-products.

An evaluation of fertilizer main products is presented in Folio #4, Brandt-Williams (2001), and the

emergy of this process is included in phosphate and nitrogen inputs in the original evaluation of each

Florida agricultural commodity.

Gypsum Treatment Data

Productivity is assumed lost for the rst 100 years of one half-life of 226Ra, the main isotope

present in dry gypsum (O’Brien 1997). The half-life is actually 1622 years (Summers 1975), but because

the sequestering of gypsum is a political decision based on human toxicology, 100 years was chosen as

a shorter political turnover time. This assumption is not without merit for two reasons: in 100 years, if

land shortage is an issue, legislation preventing recycle of radioactive gypsum in low exposure situations

may be removed; in less than 100 years, areas with large phosphate deposits and high fertilizer produc-

tion (typically coastal plains) may be back under seawater again; in 100 years, ecological engineering

alternatives may have remediated the loss of productivity. Gross primary productivity of 7.7 J/ha/yrwas used, assuming a mosaic of pine atwood ( 7.8 E11 J/ha/yr, Orrell 1998), forested wetlands (7.9

E11 J/ha/yr, averaged from Odum 2000), herbaceous wetlands (4.4 E11J/ha/yr, averaged from Odum

2000) and saltmarsh (10.5 E11 J/ha/yr, Odum 1996) prior to gypsum stacking.

Figure 2. Energy system diagram of environmental services required to either render fertilizer co-products harm-

less to surrounding environment or to account for lost productivity. This box represents the “natural treatment”

process contained within the system presented in Figure 1.



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An estimated volumetric proportion of 200 feet in height for a square mile stack (Klein 1996)

and a loose gypsum density of 60 lb/ft3 (averaged from 53 –64 lb/ft3, Chemical Engineering Handbook

1975) were used to determine the approximate stack geometry of a typical gypsum pile. The surface

area of land required to hold the specic quantity of gypsum produced during annual fertilizer produc-

tion was then calculated using volumetric formulas for a truncated pyramid, and using the ratio of total

stack volume to the volume of gypsum produced. The ratio of gypsum to fertilizer used on a crop wasdetermined by standard gram-mole equivalents from chemical equations presented above. The result-

ing surface area was used to calculate lost productivity.

Sulfuric Acid Treatment Data

A lower threshold pH of 4 is assumed as necessary for sustaining life in both terrestrial and

aquatic environments (Wilson et al. 1999; D’Cruz et al. 1998; Rowe et al. 1992). The amount of water

required to dilute H2SO

4 to this pH was determined by typical molarity equations2, using an ionization

potential of 2 H+. Loss of productivity from acid seepage is intermittent, and, because of rapid turnover

times of phytoplankton and benthic organisms most adversely impacted, was assumed to be short-term.

Further, since the emergy associated with this was less than 1% of the total, this component was notincluded in the evaluation, although at a much larger scale this might have greater impact. Loss of

productivity associated with possible community switches from mangrove to saltmarsh was also not

included, although with a more detailed spatial evaluation of the extent and kind of changes, the loss

could become a signicant value.

Emternalities as Sustainability Indicators

Emternalities are dened as the environmental resources used in the food production system,

both renewable and non-renewable. Composite emternalities add renewables and non-renewables

together. A composite ratio divides the total emternalities in a commodity by the total yield to society,

or (R + N)/Y, similar to other emergy investment ratios, and is useful in determining the percentage

of environmental inputs not being accounted for in the total value to society. The renewable fraction

ratio is indicative of relative chances for long-term success or suitability of a product for a particular

area. The lower the ratio, the less that commodity is making use of sustainable resources, or conversely,

the more services and additives are required to insure the plant outcompetes native ora and survives

consumption by native fauna.

Renewables can also be designated as positive values and non-renewables as negative, where

the positive designation indicates a currently unending source of energy and the negative reects the loss

to the nite amount of other environmental resources. A decit between renewables and non-renewables

indicates a need for a larger support area, and consequently a lack of regional sustainability.

RESULTS

Calculations of g-mole equivalents from the reactions producing fertilizer from phosphate rock

are presented in Table 1. These values were then used to determine total co-products linked with produc-

tion of specic crops, based on their respective fertilizer usage. An evaluation of tomatoes completed

without co-product emergy is presented in Table 2 (from Brandt-Williams 2001). Analysis of the same

crop, including gypsum and sulfuric acid co-products is presented in Table 3.

Incorporating co-product emergy in the Florida tomato evaluation increased total ows by

28%. Florida cabbage shows an 88% increase in total emergy, oranges a 75% increase, and watermelon

a 55% increase.A comparison of renewable emternalities to extracted and unavailable emternalities (Tables 4

2 log M*2 = 4; Water g = 1 g/ml * (H2SO

4 g * 1000 ml/l H

2O) / ( M * 98 g-mole H

2SO

4); M - molarity



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and 5) indicates the state of Florida operates at an emergy decit from gypsum co-products. Of the four

products used in this evaluation tomatoes, with a renewable fraction ratio of 0.03, are the least suitablefor Florida’s long term agricultural production, while citrus, with a renewable fraction ratio of 0.12,

appears to be a better t, and is also the commodity with the largest in-production acreage. However,

the high composite ratio for all of these commodities indicates that very little of their real value is ac-

counted for in traditional economics, since environmental services necessary for production account

for 40 to 70% of their total inputs.

Calculations for phosphate fertilizer use in Algeria (Table 6) exhibit emternalities of the same

order of magnitude as Florida commodities. Because water is scarce in this geographic region, both

irrigation and the co-product emternalities are assumed to be essentially non-renewable, and are there-

fore decits.

Total P2O

5 production gures for both the U.S. and Algeria (Table 7) required 1.17E23 and

4.6E19 sej/yr, respectively, in environmental services. This service was valued at 564 billion and 224million emdollars per year for each respective country.

Table 1. Ratios of co-products produced during fertilizer production to fertilizer used on crops

Substance Gram-moles Ratio for Ratio for Ratio for

produced DAP g used P g used N g used

DAP fertilizer 132 -- -- --Gypsum slurry 201 1.52 6.48 14.34

Sulfuric acid 98 0.74 3.16 7

Table 2. Emergy evaluation of Florida tomatoes without fertilizer co-products,

modied from Folio #4, Brandt-Williams (2001)

Item Inputs Solar Emergy

Per ha/yr E13 sej/ha/yr

RenewablesSun 5.93 E13 J 1

Evapotranspiration 6.02 E10 J 156

Non-renewables

Net topsoil loss 6.33 E7 J <1

Purchased

Fuel 7.37 E10 J 817

Electricity 0 0

Potash 1.39 E5 gK 26

Lime 3.29 E6 g 553

Pesticides 1.59 E5 g 401

Phosphate 4.60 E4 gP 170

Nitrogen 4.75 E4 gN 192

Labor 1.20 E9 J 16

Services 4.38 E3 $ 1199

Total emergy 3530



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DISCUSSION

For every gram of diammonium phosphate fertilizer produced, 1.5 grams of gypsum and 1

gram of sulfuric acid are produced as co-products. These specic byproducts have the potential forenvironmental loss and damage until transformed or diluted below a probable effects threshold. To

ignore these emternalities associated with food production because they are not considered direct in-

Table 3. Emergy evaluation of Florida tomatoes including environmental

services for fertilizer byproducts.

Item Inputs Solar Emergy

Per ha/yr E13 sej/ha/yr

Renewables

Sun 5.93 E13 J 1

Evapotranspiration 6.02 E10 J 156

Non-renewables

Net topsoil loss 6.33 E7 J <1

Lime 3.29 E6 g 553

Phosphate 4.60 E4 gP 170

Gypsum treatment 2.95E12* J 941

H2SO

4 treatment 2.51 E10 g H2O 45

Purchased

Fuel 7.37 E10 J 817

Electricity 0 0

Potash 1.39 E5 gK 26

Pesticides 1.59 E5 g 401

Nitrogen 4.75 E4 gN 192

Labor 1.20 E9 J 16

Services 4.38 E3 $ 1199

Total emergy 4516

Table 4. Florida crops – Emternalities assessed for an individual hectare and for total state

production*, then compared to total product emergy

Environmental Emergy Emternality Ratios

E15 sej/ha/yr E15 sej/FL/yr Composite Ratio Renewable Fraction Ratio

Crop (composite) (composite) (R + N) / Y R / Y

Tomato 18 406476 0.40 0.03

Citrus 9 1827494 0.61 0.12

Cabbage 15 56460 0.71 0.08

Watermelon 9 198555 0.55 0.08

* Acreage in production from 1994, Florida Statistical Abstract (Pierce 1995) and FAECM data (Fluck

1992).



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puts overlooks the benet of using emergy to evaluate the indirect costs of fertilizer inputs. While in

theory the land this gypsum is stored on is in private ownership (a fertilizer company as opposed tothe government of the United States), the benets of CO

2 uptake, O

2 production and overall organic

productivity belong to the public domain. It is true then that any conversion of property to unvegetated

uses should be counted as an emternality – parking garages and malls, for instance – but the growing

stacks of gypsum as agribusiness feeds a growing population are perhaps a more spectacular example

of loss of productivity.

It is important to note, that although the gypsum stacks become storages with time, the value

ascribed to them in this evaluation is the ow created during production of each gram of fertilizer used

to increase agricultural yields. Adding these ows to the total emergy ow of a commodity does not

violate emergy algebra. They have not been viewed from a user preference perspective, nor are they

double-counted from the fertilizer emergy (see Brandt-Williams 2001 for fertilizer evaluations). De-pending upon the region and the scale of analysis, these co-products do cross boundaries as inputs and

have been incorporated as such.

Current fertilizer inputs are not sustainable for the state of Florida or Algeria, as evidenced by

Table 6. Algerian fertilizer co-products per hectare per year for selected crops, with resulting

emternalities all non-renewable and therefore decits

Crop Irrigation water P2O5 applied Gypsum H2SO4 Emternalities m3 g/ha/yr produced produced Sej/ha/yr

Oranges 8000 6.0 E5 1.7 E6 9.7 E5 9.1 E15

Almonds 8000 3.5 E5 9.9 E5 5.6 E5 5.7 E15

Figs 8000 4.0 E5 1.1 E6 6.4 E5 6.4 E15

Olives 8000 3.0 E5 8.5 E5 4.8 E5 5.0 E15Note: data from Algerian Ministry of Agriculture

Table 5. Emternalities categorized to show potential for sustainability

Emergy Loss to State

Emternalities per Year

Crop Positive, Renewable Negative, Non-renew R - N

E13 sej/ha/yr E13 sej/ha/yr E19 sej/yearTomato 1561 644 -3360

Citrus 168 715 -1132

Cabbage 163 1337 -442

Watermelon 140 786 -1385

Table 7.Emdollar value for environmental services associated with annual production

numbers for phosphate fertilizer (P2O

5 is a DAP feedstock) in Algeria and the U.S.

P2O

5 Gypsum treatment emergy H

2SO

4 treatment emergy Em$/yr

Country g/yr sej/yr sej/yr

Algeria 5.0 E9 2.0 E20 4.6 E19 224 million

U.S.A. 1.3 E13 4.9 E23 1.2 E23 564 billionNote: data from McCoy (2001) and Algerian Ministry of Agriculture. Uses 1.08E12 sej/$ for 2000

US$.



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emternality decits. These decits demonstrate a high priority need for research focused on increasing

gypsum utility and/or reducing fertilizer dependency either through improved eld management or in-

creased dependence on natural fertilizers. Some of the problem might be alleviated simply by imposing

more stringent regulations leading to better stacking/storage of gypsum. These alternatives should be

carefully evaluated using emergy.

Americans consume about 92 pounds of tomatoes a year per person, either fresh or processed

(Lucier 2000). With a population of 275,562,673 (CIA 2000), this means 2.43 E21 sej/yr of environ-

mental services are used to treat co-products from tomato production alone. Corn production for the

U.S. requires another 1.1 E22 sej/year for environmental treatment of fertilizer co-products (estimated

from NASS 2000, Good 2000 and Herbert 1995). The emdollar value of services for these two crops

alone is over 11 billion em$/yr.

Total annual production of P2O

5 (a fertilizer and feedstock for DAP) in the U.S. in 1998 was

12.6 trillion kilograms (13,891,000 tons, McCoy 2001), and 5 million kilograms in Algeria (Algerian

Agriculture Ministry). Detrimental co-products from these fertilizers require 1.17E23 and 4.6E19 sej/yr,

respectively, in environmental services, or about 1.4% of the total emergy use per nation. This service

was valued at 564 brillion and 224 million U.S. emdollars per year for each respective country. Eventhe small island nation of Mauritius uses 3.74 E17 sej/yr to subsidize co-products from their tomato

consumption despite having a per capita income of less than a third of the U.S (CIA 2000; Govinden

1999).

Although sulfuric acid and lost productivity are two large and immediate environmental hazards,

there are other potential problems that should be added to a total evaluation of the modern commercial

food production system. As these stacks of gypsum grow, a concentration of other trace elements, rare

earth elements and uorine occurs as particles settle and sift due to size, some of which may reach lev-

els of environmental concern (Arocena et al. 1995). Further, while this study has focused on fertilizer

impacts, production of pesticides and herbicides has associated environmental issues, and excess or

poorly timed use of all three leads to other environmental impacts deserving of an emergy evaluationthat goes beyond traditional transformity calculations.

ACKNOWLEDGEMENTS

The authors thank H.T. Odum, Robert King and Enrique Ortega for their thoughtful reviews. The re-

search described herein was developed by the author, an employee of the U.S. Environmental Protection

Agency (EPA) on her own time. It was conducted independent of EPA employment and has not been

subjected to the Agency’s peer and administrative review. Therefore, the conclusions and opinions

drawn are solely those of the author and are not necessarily the views of the Agency.

REFERENCES

Allen T., Starr T., 1982. Hierarchy: Perspectives for Ecological Complexity. University of Chicago

Press.

Arocena J. M., Rutherford P. M. and Dudas M. J.1995. Heterogeneous distribution of trace elements and

uorine in phosphogypsum by-product, The Science of The Total Environment 162(2-3): 149-160.

Brandt-Williams S. 2001. Folio #4: Emergy of Florida Agriculture, in: Handbook of Emergy Evaluation:

A Compendium Of Data for Emergy Computation Issued in a Series of Folios, Gainesville, Center

for Environmental Policy.

Brown M and Bardi E, 2001. Folio #3: Emergy of Ecosystems, in: Handbook of Emergy Evaluation:

A Compendium Of Data for Emergy Computation Issued in a Series of Folios, Gainesville, Center

for Environmental Policy.

CIA 2000. Mauritius Economy, The World Fact Book 2000, Central Intelligence Agency, Washing-



-337-


ton.

CIA 2000. U.S. Economy, The World Fact Book 2000, Central Intelligence Agency, Washington.

D’Cruz L M; Wood C M, 1998. The inuence of dietary salt and energy on the response to low pH in

juvenile rainbow trout. Physiological Zoology 71 (6) 642-657.

D’Cruz L M; Dockray J J; Morgan I J; Wood C M, 1998. Physiological effects of sublethal acid expo-

sure in juvenile rainbow trout on a limited or unlimited ration during a simulated global warming

scenario. Physiological Zoology 71 (4): 359-376.

Ecosys®, Inc. 2000. Appréciation quantitative des externalités de l’agriculture suisse / Externalities in

Swiss Agriculture: An Assessment , Swiss Federal Ofce of Agriculture / Ofce fédéral de l’agriculture

(SFOA-OFAG), Berne, 162 pp. + 62 pp. Annexes

Fluck R. C., Panesar B. S. and Baird C. D., 1992b. Florida Agricultural Energy Consumption Model

(FAECM). Final Report to the Florida Energy Extension Service, Institute of Food and Agricultural

Sciences, University of Florida.

Good D. 2000. Weekly Outlook: Corn Consumption, Ace News 8/8/00, College of Agricultural, Con-

sumer and Environmental Sciences, University of Illinois, Urbana-Champaign.

Govinden N. 1999. Agricultural diversication: a survey on tomato consumption. Prosi 363.Herbert G.W., Ferguson R.B., Shapiro C.A., 1995. Fertilizer Suggestions for Corn, G74-174-A, Institute

of Agriculture and Natural Resources, University of Nebraska, Lincoln.

Jordan J. 1995. Incorporating externalities in conservation programs. Journal of American Water Works

Association 87 (6): 49-56.

Klein J. 1996. Gypsum nds ecological concerns stacked against it. The Business Journal 12/09/96.

Koomey J; Krause F.; 1997. Introduction to Environmental Externality Costs. In Kreith, F.; West, R.

(eds) CRC Handbook on Energy Efciency in 1997. CRC Press, Boca Raton, FL, 300 pp.

Lloyd G.M. 1985. Phosphogypsum: a review of the Florida Institute of Phosphate Research programs

to develop uses for phosphogypsum, FIPR pub. 01-000-035.

Lucier G., Biing-Hwan L., Allshouse J., and Kantor L. S., 2000. Factors affecting tomato consumption

in the United States, Vegetables and Specialties, Economic Research Service, USDA VGS-282

26-32.

McCoy M. 2001. Facts and Figures for the Chemical Industry. Chemical and Engineering News

79(26) 42-81.

National Agricultural Statistics Service (NASS) 2000. Production Report 9/12/00.

O’Brien R.S. 1997. Gamma doses from phospho-gypsum plaster board. Health Physics 72(1) 92-96.

Odum H.T. 2000. Heavy Metals in the Environment, Lewis Publishers, Boca Raton.

Odum H.T. 1996. Environmental Accounting, Wiley, New York.

Odum H.T., Odum E.C. and Blisett M., 1987. Ecology and Economy: “Emergy” Analysis and Public

Policy in Texas. Report to the Energy Systems in Texas and the United States Policy Research

Project and the Ofce of Natural Resources, Texas Department of Agriculture.Orrell J.J. 1998. Cross Scale Comparison of Plant Production and Diversity. MS Thesis. Department

of Environmental Engineering Sciences. University of Florida, Gainesville.

Pierce A. (Ed.), 1995. Florida Statistical Abstract . Univ. Press of Florida, Gainesville, FL.

Pillet G., 2001. L’efcace, le juste et l’écologique. Helbing and Lichtenhahn, Bâle.

Pillet G., 2000. Emternalities Vs. Externalities: Calling attention to the multiple non-commodity inputs

question. Joint OECD-USDA Workshop, Washington, D.C.

Pillet G., Maradan D., Zingg N., and Brandt-Williams S., 2000. Emternalities – Theory and Assessment,

in Brown M., Brandt-Williams S., Tilley D., and Ulgiati S. (eds) Emergy Synthesis: Theory and Ap-

plications of the Emergy Methodology, Center for Environmental Policy, Gainesville.

Pillet G. 1995. Case Study of the Role of the Environment in Geneva Vineyard Cultivation and Wine

Production, 1972-1986, in: Hall, C.A.S (ed.): Maximum Power, The Ideas and Applications of H.T.

Odum, Niwot, University Press of Colorado, 279-283






Edited by



Associate Editors

Howard T. Odum



David Tilley



Sergio Ulgiati

University of Siena

Siena, Italy

December 2003



Gainesville, FL


vi



-339-

Chapter 24. The Energy Basis Of A Subtropical Wetland Mesocosm

24

The Energy Basis Of A Subtropical Wetland Mesocosm


ABSTRACT

A major challenge in Ecology is to design experimental units (microcosms, mesocosms, enclo-

sures, exclosures, etc.) that adequately represent nature. One design approach is to match the energy

signature, or set of outside forcing functions of a system, between the experimental system and the natural

analog. To illustrate this approach, the energy basis of the Smithsonian Institution’s Florida Everglades

mesocosm in Washington DC is described as a design analysis. The Everglades mesocosm was built in

a greenhouse as a prototype for a portion of Biosphere 2 in 1987. It consists of a series of habitat tanks

connected along a salinity gradient. The mesocosm maintained a biota characteristic of the Everglades

for more than a decade and it served as an adequate model for the natural analog system. The energy

signature of the mesocosm is compared with data from south Florida by using emergy analysis. Tide,

waves, water circulation, precipitation, wind and temperature are maintained in the mesocosm through

the use of human labor, natural gas and electricity along with natural sunlight. Natural gas and electric-

ity dominate the mesocosm’s energy signature with inputs on the order of 10E16 solar emjoules/yr. The

natural energy signature of south Florida is three orders of magnitude lower than that of the mesocosmand is dominated by wave energy at 10E14 solar emjoules/yr. These results are discussed in relation to

the design, creation and restoration of ecosystems, with emphasis on mesocosm design.

INTRODUCTION

Modern ecological research is strongly dominated by the experimental method (Hairston 1989,

Resetarits and Bernardo 1998). A rst requirement of this method is that replicates exist so that manipula-

tions can be conducted and compared with controls that were not manipulated. Although it is possible to

employ a single system and replicate in time, most experiments involve multiple systems that are used as

simultaneous replicates. There are special situations where nature provides replicate systems such as inphytotelemata, rock outcrop vegetation, tide pools or oceanic islands (Beyers and Odum 1993). However,

in the absence of natural replicates, the human experimenter must devise them. Often articial structures

are used to isolate natural ecosystems into replicates or to create model ecosystems that serve as replicates.

Design of these structures requires an ecological engineering approach to varying degrees.

Because the replicates must represent the natural ecosystem, the inuences of the articial struc-

tures must be minimized. Many design issues must be considered in this regard depending on the type

of ecosystem (ie., forest, stream, mudat, etc.). These include choice of materials, the size, shape and

conguration of the experimental units and the role of possible edge effects caused by the structures. In

some cases it is possible to divide the natural ecosystem into replicate units, as in the case of construction

of exclosures or enclosures. These units utilize different kinds of materials in the form of cages or curtains

to create replicates. This approach has the advantage that the replicates can be good models of the naturalsystem since they are simply partitioned up pieces of the natural system itself. In other cases whole new

ecosystems must be created, usually microcosms or mesocosms, either because the experiments involve

manipulations that are not appropriate to be conducted in the natural ecosystem (ie., release of toxins) or



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because the experiment requires control over the environment that is not possible when done in nature.

The creation of new experimental systems is a signicant design challenge because of the complexity of

ecosystems. In this paper the use of energy signatures as an aid to design of created experimental systems

is described and illustrated for a case study of a mesocosm model of the Florida Everglades.

The energy signature of an ecosystem is the set of energy sources that affect it. Another term used

for this concept is forcing functions, or those outside causal forces that inuence system behavior and

performance. H. T. Odum (1971) suggested the use of the energy signature as a way of classifying

ecosystems based on a physical theory of energy as a source of causation in a general systems sense. A

fundamental aspect of the energy signature approach is the recognition that a number of different energy

sources affect ecosystems. Kangas (1990) reviewed the history of this idea in ecology. Sunlight was

recognized early in the history of ecology as the primary energy source of ecosystems because of its role

in photosynthesis at the level of the organism and by extrapolation in primary production at the level of the

ecosystem. Organic inputs were formally recognized as energy sources to ecosystems in the 1960s with

the development of the detritus paradigm, primarily in stream ecology (Cummins 1974) and in estuaries

(Odum 1980, Sibert and Naiman 1980). The terms autochonthous (sunlight driven primary production

from within the system) vs. allochthonous (detrital inputs from outside the system) were coined in the

1960s to distinguish between the main energy sources in ecosystems. Finally, in the late 1960s H. T.Odum introduced the concept of auxiliary energies to account for inuences on ecosystems from sources

other than sunlight and organic matter (Odum 1967). E. P. Odum (1971) provided a simple denition of

auxiliary energies as follows:

“Any energy source that reduces the cost of internal self-maintenance of the ecosystem,

and thereby increases the amount of other energy that can be converted to production, is

called an auxiliary energy ow or an energy subsidy.”

Tidal and wave energy are probably the most widely recognized auxiliary energies (Steever et al. 1976,

Leigh et al. 1987) but Nixon (1988) covers a variety of energies for aquatic ecosystems. Furthermore,

humans have developed technologies to harness some of these auxiliary energies (ie., wind, geothermal,

etc.) as alternatives to fossil fuels for input to the economy (see Isaacs and Schmitt 1980 for a marine

example).

The concept can be used to design microcosms by matching, as closely as possible, the energy

signature of the natural analog system with the microcosm under controlled conditions of the labora-

tory or eld setting. The most straight forward approach to this matching of energies is to develop the

microcosm in the eld where it is at least physically exposed to the same energy signature as natural

ecosystems. Examples are the pond ecosystem commonly used in ecotoxicology and in situ plastic bags

oated in pelagic systems such as limnocorrals. The challenge of matching energies is greater in the

lab. Signicant effort is usually taken to match sunlight with articial lighting whose intensity, spectral

distribution and timing can be controlled fairly easily. Simulating other kinds of auxiliary energy sourcesin microcosms requires more ingenuity and often detailed engineering, as can be seen for example in the

efforts to create realistic turbulence in pelagic microcosms (Sanford 1997).

Adey and Loveland (1998) have developed a systematic method for creating experimental aquatic

systems that includes a focus on simulating energy signatures. The purpose of this paper is to analyze

the energy signature of a mesocosm that has been shown to be a successful model of a real ecosystem

(Lange et al. 1994, Adey et al. 1996). H. T. Odum’s (1996) emergy analysis method is used to compare

energy signatures for the mesocosm and the real ecosystem in both actual energy and emergy units for

full perspective.

SITE DESCRIPTION



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The Everglades mesocosm was built in 1987 as a prototype for one of the biomes that was

contained in Biosphere 2 in Arizona. The system was closed in Fall 2000. It was located in a 30.5m

(100’) x 12.2m (40’) greenhouse on the grounds of the Smithsonian Institution’s horticultural complex in

Northeast Washington DC. The systems consisted of seven interlocking tanks, characterized by a gradi-

ent of habitats ranging from freshwater to saltwater (Figure 1). Detailed descriptions of the mesocosm

are given by Adey and Loveland (1998), Lange et al. (1994) and Adey et al. (1996). Engineering was

used to provide tides, waves, currents (with pumps), wind (with fans), temperature regimes (with water

chillers in summer and heaters in winter), freshwater (with a sprinkler system and a reverse osmosis unit)

and water quality management (with algal turf scrubbers). Biota was imported from southwest Florida

to seed the system and partial census found 369 species in the system in 1994-1995 (Adey et al. 1996).

Construction costs of the mesocosm were approximately 300,000$.

METHODS

H. T. Odum’s emergy analysis method was used to construct energy signatures for the Everglades

mesocosm in Washington DC and for a comparable area of the real Everglades in southwest Florida. Theenergy signatures are shown in Figure 2. The emergy analysis method requires separate calculations for

each energy source. Actual energies are either evaluated directly (ie., sunlight) or calculated based on

formulas for physical energy ow (ie., tide, waves, etc.). Although actual energy units for different kinds

of energy are the same, the energies differ in their ability to do work within ecosystems. Thus, a joule of

sunlight can not do the same amount of work as compared to a joule of electricity. Both can generate the

same amount of heat but they can not do the same amount of work, as in operating a pump to move water

or a fan to generate wind. The emergy analysis method is an accounting system that was developed to

convert actual energy units into work equivalent values that can be directly compared. This is done by

multiplying actual energies by scaling factors, called transformities, that account for abilities of different

kinds of energy to do work, rather than to generate heat. The calculated work equivalent values are inunits of emergy, or emjoules, which is the amount of one kind of energy required to make another kind of

energy. A base energy type is used for relating the different kinds of energies and solar is most often used

Figure 1: Plan view of the Everglades mesocosm in Washington, D.C.



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as a convention. In this case the transformities have units of solar emjoules per joule of actual energy and,

therefore, different energies are compared in terms of their equivalent solar emjoules. In this study the

transformity values given by Odum (1996) were used to calculate emergy values for energy signatures.

An energy signature for the Everglades in southwest Florida was developed by review of the

literature. This effort was aided by published energy signatures for nearby sites (DeBellevue et al. 1979,

Odum 1974, Odum and Hornbeck 1997). Calculations were made for sunlight, tide, wind, rain and wavesand these were scaled to the dimensions of the mesocosm so that direct comparisons could be made.

An energy signature for the Everglades mesocosm in Washington DC was developed based

Figure 2. Comparison of energy signatures: a. Everglades mesocosm in Washington, D. C., b. Everglades in

southwest Florida.



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on actual energies used to operate the system. Calculations were made for sunlight, labor, natural gas,

electricity and tap water. Actual energy values for labor, natural gas and electricity were taken from the

operating log of the mesocosm and water input was from Lange (1998).

RESULTS

Energy signature calculations for the Florida Everglades are shown in Table 1. Sunlight con-

tributed nearly 100% of the total actual energy budget but less than 1% of the total emergy budget. Con-

versely, waves contributed less than 1% of the total actual energy budget but 87% of the total emergy

budget. These results are not surprising since sunlight is a spatially extensive but dilute energy source

that provides heating and triggers molecular reactions in photosynthesis. Wave energy on the other hand

is spatially intensive and highly focused on the coastline that does work in shaping landforms. Another

way to conceptualize this dichotomy is that sunlight acts as a general driving force for the ecosystem

while wave energy and the other types act as auxiliary energies in providing specialized work functions.

The second most signicant emergy contribution came from chemical potential energy for rain. This

energy plays a special role in maintaining salinity patterns which are important in organizing the estuarine

ecosystems characteristic of the southwest Florida Everglades. Energy signature calculations for the Everglades mesocosm in Washington DC are shown in

Table 2. This is a very different energy signature which is dominated by high quality energies (ie., high

transformity) characteristic of human systems. These energy types are utilized through conventional en-

gineering to reproduce the natural Everglades energy signature inside the mesocosm greenhouse. Natural

gas is used in heating the greenhouse, electricity is used in a variety of ways, such as powering pumps

and fans, to provide the internal tides, waves and wind, tap water from the municipal supply is used for

maintaining water balances after treatment by reverse osmosis and human labor is used for overall system

maintenance, like a top predator in ecosystem control. Sunlight provides about 50% of the total actual

energy budget but its contribution to the total emergy budget approaches zero. Nearly equal contribu-

tions from natural gas and electricity dominate the emergy budget, which reects their important rolesin powering devices that create the temperature regimes along with water and air movements within the

greenhouse.

DISCUSSION

Although mesocosms are commonly used for conducting ecological experiments (Odum 1984,

Kangas and Adey 1996), there has been little formal analysis of principles for their design. The work

presented here describes an energy signature design approach that can be applied to any experimental

system. The principle states that to create an adequate experimental system, one must match the energy

signature of the experimental system to the natural system being modelled. This is a challenge which

requires various ecological engineering considerations (Adey and Loveland 1998). For the case study of the Florida Everglades, the mesocosm utilized three orders of magnitude

more emergy equivalents than the natural system being modelled (1.39 x 10E17 vs. 1.32 x 10E14). The

additional emergy for the mesocosm is needed to operate the engineering components that simulate the

natural energy signature. Less emergy would have been required if the mesocosm had been physically

built in south Florida rather than Washington DC, so that heating costs could have been minimized, but

this probably would not have changed the difference in emergy totals by an order of magnitude, due to

the large amounts of electricity utilized for purposes other than heating.

No similar comparison of energy signatures between an experimental system and the natural sys-

tem being modelled seems to have been made. Beyers and Odum (1993) constructed an energy signature

for the MERL (Microcosm Estuarine Research Laboratory) tanks used to model the pelagic system ofNarragansett Bay, Rhode Island. Their study considered sunlight, turbulence and nutrient uxes with both

actual energy and emergy calculations. These mesocosms are open to the atmosphere without articial

heating so additional energy input was not needed for this purpose. Labor inputs and electricity for pumps



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were not evaluated . No comparison was made for the natural bay energy signature though Nixon et al.

(1980) had made some actual energy comparisons. Nelson et al. (1993) also provided a partial actual

energy signature for Biosphere 2 with data on solar input and electricity for external support, heating and

cooling, but no comparisons were made with natural systems that were being modelled.

The results of the energy signature comparison between the Everglades mesocosm and the realEverglades given in this report have implications for restoration ecology. The real Everglades has been

dramatically altered by humans over the last century and large scale restoration is currently being attempted

(Cohn 1994, Dahm et al. 1995, Gunderson et al. 1995). The cost to maintain the Everglades model in

Washington DC was very large compared to the cost of maintaining the natural system in Florida. This

magnitude of cost is a constraint on restoration and will limit efforts to articially create habitat replace-

ments. Lange et al. (1994) discussed this issue in terms of compensatory mitigation policies for wetlands,

suggesting that the Everglades mesocosm represents a particularly high energy example of restoration.

Thus, one indirect implication of this study is that calculations of the emergy cost of replacement model

systems can serve to illustrate the high value of intact, natural ecosystems. Cost effective approaches are

needed to restore quality and quantity of water ows and many other aspects of the system.

Table 1. Energy signature evaluation for the Everglades in Southwest Florida for an area equivalent to

the Everglades Mesocosm.

_______________________________________________________________________

Energy Energy Transformity Emergy

(E8 J/year) (sej/J) (E12 sej/year)

_______________________________________________________________________

Sun (1) 26000 1 2.6

Wind (2) 3.6 1496 0.5

Tide (3) 2.1 16842 3.5

Rain (4) 6.1 18199 11.1

Waves (5) 37.5 30550 115

TOTAL 26049.3 132.7

_______________________________________________________________________

1) Average solar insolation for south Florida is approximately 7.00 x 10E9 J/m2/yr. (E. P. Odum 1971). Total solar

energy is (7.00 x 10E9 J/m2/yr.)(372.1 m2).2) Wind energy = (0.5)(density of air)(wind velocity^2)(eddy diffusion coefcient)(height of boundary layer).

Density = 1.2 x 10E-3 g/cm3. Wind velocity = 378.3 cm/sec (Ruttenber 1979). Eddy diffusion coefcient = 1 x

10E4 cm2/sec (Kemp 1977). Height of boundary layer = 1 x 10E4 cm). Area affected = 130.5 m2.3) Tidal energy = (0.5)(area elevated)(tides/yr.)(tidal height^2)(density of water)(gravitational acceleration). Area

= 60 m2. Tides/yr. = 706 (Odum 1996). Tidal height = 100 cm (Carter et al. 1973). Density = 1.025 g/cm3. Gravi-

tational acceleration = 980 cm/sec2.4) Chemical potential of rain = (area)(rainfall)(Gibbs free energy of water). Area = 90 m2. Rainfall at Ft. Myers,

FL = 1.37 m/yr. (Drew and Schomer 1984). Gibbs free energy = 4.94 J/g (Odum 1996).

5) Wave energy = (shore length)(1/8)(density of water)(gravitational acceleration)(wave height^2)(velocity) fromOdum (1996). Shore length = 3.1 m. Density = 1000 kg/m3. Gravitational acceleration = 9.8 m/sec2. Wave height

= 0.1 m (assumed). Velocity = (gravity x depth)^1/2, where depth = 1 m (assumed).



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REFERENCES

Adey, W. H. and K. Loveland. 1998. Dynamic Aquaria, Building Living Ecosystems. Second Edition.

Academic Press, San Diego, CA.

Adey, W. H., M. Finn, P. Kangas, L. Lange, C. Luckett and D. M. Spoon. 1996. A Florida Everglades

Mesocosm – model veracity after four years of self-organization. Ecological Engineering

6:171- 224.

Beyers, R. J. and H. T. Odum. 1993. Ecological Microcosms. Springer-Verlag, New York, NY.Carter, M. R, L. A. Burns, T. R. Cavinder, K. R. Dugger, P. L. Fore, D. B. Hicks, H. L. Revells and T.

W. Schmidt. 1973. Ecosystems analysis of the Big Cypress swamp and estuaries. U. S.

Environ mental Protection Agency, Atlanta, GA.

Cohn, J. P. 1994. Restoring the Everglades. BioScience 44:579-583.

Cummins, K. W. 1974. Structure and function of stream ecosystems. BioScience 24:631-641.

Dahm, C. N., K. W. Cummins, H. M. Valett and R. L. Coleman. 1995. An ecosystem view of the resto-

ra tion of the Kissimmee River. Restoration Ecology 3:225-238.

DeBellevue, E., H. T. Odum, J. Browder and G. Gardner. 1979. Energy analysis of the Everglades Na

tional Park. pp. 31-43. in: Proceedings of the First Conference on Scientic Research in the

National Parks, Vol. 1. R. M. Linn (ed.). National Park Service, U. S. Department of the Inte -rior. Washington DC.

Drew, R. D. and N. S. Schomer. 1984. An ecological characterization of the Caloosahatchee River/Big

Table 2. Energy signature evaluation for the Everglades mesocosm in Washington DC.

_______________________________________________________________________

Energy Energy Transformity Emergy

(E8 J/year) (sej/J) (E12 sej/year)

______________________________________________________________________

Sun (1) 20500 1 2.1

Tap Water (2) 2.5 18199 4.5

Labor (3) —— —— 4050

Electricity (4) 3350 174000 58300

Gas (5) 16000 48000 76800

TOTAL 39852.5 139156.6_______________________________________________________________________

1) Average insolation for Washington, DC is approximately 5.50 x 10E9 J/m2/yr. (E. P. Odum 1971). Total solarenergy is (5.50 x 10E9 J/m2/yr.)(372.1 m2).

2) Chemical potential energy of water added to the mesocosm = (volume)(density)(Gibbs free energy). Volume used

was 53,400 liters/yr. (Lange 1998). Density = 1000 g/liter. Gibbs free energy = 4.62 J/g (Odum 1996).3) Labor requirements for the mesocosm were 20 hours/week or 43.33 days/yr. multiplied by the Emergy use/person

of 9.35 x 10E13 sej/day (Odum 1996).

4) Based on power consumption and operational times of of all pumps, heaters, fans, etc., the total electrical use was3.35 x 10E11 Joules/yr. in the mesocosm.

5) Based on power consumption and operational time of gas heaters, the total gas use was 1.60 x 10E12 Joules/yr.

in the mesocosm.



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Cypress watershed. FWS/OBS-82/58.2. U. S. Dept. of the Interior, Washington, DC.

Gunderson, L. H., S. S. Light and C. S. Holling. 1995. Lessons from the Everglades. BioScience Sup-

ple ment 1995: S-66 - S-73.

Hariston, N. G., Sr. 1989. Ecological Experiments. Cambridge University Press, Cambridge, UK.

Isaacs, J. D. and W. R. Schmitt. 1980. Ocean energy: forms and prospects. Science 207:265-273.

Kangas, P. C. 1990. An energy theory of landscape for classifying wetlands. pp. 15-23. in: Forested

Wetlands, Vol. 15. Ecosystems of the World. A. E. Lugo, M. Brinson and S. Brown (eds.).

Elsevier, Amsterdam.

Kangas, P. and W. Adey. 1996. Mesocosms and ecological engineering. Ecological Engineering 6:1-5.

Kemp, W. M. 1977. Energy analysis and ecological evaluation of a coastal power plant. PhD. Disserta

tion, University of Florida, Gainesville, FL.

Lange, L.E. 1998. An Analysis of the Hydrology and Fish Community Structure of the Florida Ever

glades Mesocosm. MS Thesis, Marine-Estuarine-Environmental Sciences Program, Univer-

sity of Maryland, College Park, Maryland.

Lange, L., P. Kangas, G. Robbins and W. Adey. 1994. A mesocosm model of the Everglades: an ex-

treme example of Wetland Creation. pp. 95-105. in: Proceedings of the Twenty First Annual

Confer ence on Wetlands Restoration and Creation. F. J. Webb, Jr. (ed.). Hillsborough Com-munity Col lege, Plant City, FL.

Leigh, E. G., Jr. et al. Wave energy and intertidal productivity. Proceedings of the National Academy of

Science, U. S. A. 84:1314-1318.

Nelson, M., T. L. Burgess, A. Alling, N. Alvarez-Romo, W. F. Dempster, R. L. Walford and J. P. Allen.

1993. Using a closed ecological system to study Earth’s biosphere. BioScience 43:225-236.

Nixon, S. W. 1988. Physical energy inputs and the comparative ecology of lake and marine ecosys -

tems. Limnology and Oceanography 33:1005-1025.

Nixon, S. W., D. Alonson, M. E. Q. Pilson and B. A. Buckley. 1980. Turbulent mixing in aquatic micro

cosms. pp. 818-849. in: Microcosms in Ecological Research. J. P. Giesy, Jr. (ed.). U. S. Depart

ment of Energy, DOE Symposium Series 52. Washington DC.

Odum, E. P. 1971. Fundamentals of Ecology. W. B. Saunders Co., Philadelphia, PA.

Odum, E. P. 1980. The status of three ecosystem-level hypotheses regarding salt marsh estuaries: tidal

subsidy, outwelling, and detritus-based food chains. pp. 485-495. V. S. Kennedy (ed.). Aca

demic Press, New York, NY.

Odum, E. P. 1984. The mesocosm. BioScience 34:558-562.

Odum, H. T. 1967. Biological circuits and the marine systems of Texas. pp. 99-157. in: Pollution and

Marine Ecology. T. A. Olson and F. J. Burgess (eds.). John Wiley & Sons, New York, NY.

Odum, H. T. 1971. Environment, Power, and Society. John Wiley & Sons, New York, NY.

Odum, H. T. 1974. Energy cost-benet models for evaluating thermal plumes. pp. 628-649. in: Ther-

mal Ecology, Proceedings of a Symposium of the Savannah River Laboratory. W. Gibbons

and R. Sharitz (eds.). Division of Technical Information, U. S. Atomic Energy Commission,Washing ton DC.

Odum, H. T. 1996. Environmental Accounting, Emergy and Environmental Decision Making. John Wi-

ley & Sons, New York, NY.

Odum, H. T. and D. A. Hornbeck. 1997. Emergy evaluation of Florida salt marsh and its contribution

to economic wealth. pp. 209-230. in: Ecology and Management of Tidal Marshes. C. L.

Coultas and Y-P. Hsieh (eds.). St. Lucie Press, Delray Beach, FL.

Resetarits, W. J., Jr. and J. Bernardo. (eds.). 1998. Experimental Ecology. Oxford University Press,

New York, NY.

Ruttenber, A. J., Jr. 1979. Urban areas as energy ow systems. PhD. Dissertation, Emory University,

Atlanta, GA.

Sanford, L. P. 1997. Turbulent mixing in experimental ecosystem studies. Marine Ecology Progress Se

ries 161:265-293.

Sibert, J. R. and R. J. Naiman. 1980. The role of detritus and the nature of estuarine ecosystems. pp.



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311- 323. in: Marine Benthic Dynamics. K. R. Tenore and B. C. Coull (eds.). University of

South Carolina Press, Columbia, SC.

Steever, E. Z., R. S. Warren and W. A. Niering. 1976. Tidal energy subsidy and standing crop produc -

tion of Spartina alterniora. Estuarine and Coastal Marine Science 4:473-478.



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Edited by



Associate Editors

Howard T. Odum



David Tilley



Sergio Ulgiati

University of Siena

Siena, Italy

December 2003



Gainesville, FL


vi



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Chapter 25. A Note on the Uncertainty in Estimates of Transformities...

25A Note on the Uncertainty in Estimates of Transformities

Based on Global Water Budgets

Daniel E. Campbell

ABSTRACT

The chemical potential energy in rainfall is often the largest renewable emergy input to a given

land area. An average of ve determinations of the transformity of rainfall using global water budgetsshowed that 95% of the values may be expected to fall within 11.6% of the mean value of 18,100 sej J –1.

The transformity of water evapotranspired, calculated from four estimates, was 28,100 sej J –1 with 95%

of the determinations predicted to fall within 16.5% of the mean value. The transformity of the chemical

potential energy in rivers calculated from ve estimates was 50,100 sej J –1 with 95% of the values expected

to fall within 6.8% of the mean. Based on these data the precision with which global water budgets have

been determined allows the transformity of global water ows to be known within an average standard

deviation of 5.9± 2.5 % of their mean values.

INTRODUCTION

Documenting the uncertainty in the estimates of key transformities used in emergy evaluations

is an important step that helps build condence in the results of an analysis. Unfortunately this step is

seldom taken, because of the additional work it requires and the practical time and monetary constraints

under which most emergy analyses are performed. Determination of the renewable emergy supporting a

region often depends on the values assigned to the transformities of planetary water uxes. In this note

three global water budgets are presented and used to determine transformities for global precipitation,

precipitation over the continents, evapotranspiration, and runoff. Figure 1 shows one estimate of the

annual global uxes of water through the planetary network formed by the oceans, the atmosphere and

the continents. Odum et al. (1998) made an evaluation of global water ows to and from the continents

using data from L’vovich (1974). The three additional global water budgets evaluated in this note are

combined with Odum’s earlier studies to calculate average transformities for planetary uxes of chemical

potential energy in water ows and to document the uncertainty of these estimates. All transformities

determined in this note are expressed relative to the 9.26E24 sej y –1 planetary baseline (Campbell 2000,

Odum et al. 2000).

METHODS

The uxes of chemical potential energy in each water ow can be determined using the solute

concentration of sea water as the ground state (Odum 1996). If the solute concentration in the sea and in

plant tissue is approximately 35,000 ppm and the solute concentration of water in the atmosphere is 10

ppm (Odum 1996) then the Gibbs free energy, G, per gram of water transpired or precipitated is:



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Repeating this calculation using an average solute concentration of 150 ppm for river water (Odum

1996) gives 4.72 J g-1 for the chemical potential energy of river water relative to the sea. The tem-

perature at which G is determined, 288ºK, is approximately the average temperature of the earth

(Barry&Chorley 1992) . The value for the universal gas constant was taken from Weast (1981).

RESULTS

Table 1 shows the available data on the chemical potential energy uxes for each of four global

water uxes (1) rain over the surface of the earth,(2) rain over the continents, (3) evapotranspiration from

the continents to the atmosphere, and (4) runoff from the continents to the sea. The data from the three

additional budgets analyzed here, when rounded to three signicant gures, shows that the transformity

for the chemical potential energy of rain over the land lies between 17,000 sej J –1 and 19,700 sej J –1

with the average of three determinations being 18,100 sej J –1. This value is close to the value of 18,605

sej J –1 determined from data used by Odum (1996) and 17,711 sej J –1 derived from the values in Odum

et al. (1998) by using 4.74 J g –1 for G and multiplying by 0.98 to place the value on the 9.26E 24 sej y –1

baseline. Averaging the three new values for the transformity of precipitation over the continents with

the values given in Odum (1996) and Odum et al. (1998) and then rounding to three signicant gures

gives 18,100 ± 1070 sej J –1 as the average value and standard deviation for the transformity of the chemical

potential energy of rain over land. It is clear from this analysis that if the transformity of the chemical

Atmosphere

Oceans

Land

Planetary Web ProducingGlobal Water FlowsLand Albedo

Sea Albedo

3.93E24 sej y -1

4.07E24 sej y -1

Earth’s

Deep Heat

Evapotranspiration

Runoff

Precipitation

on Land

Precipitation

on the Sea

Evaporation

from the Sea

Solar

Radiation

1.26E24 sej y -1

GravitationalAttraction

40E12m3y-1

75E12m3y-1

431E12m3y-1

391E12m3y-1

115E12m3y-1

Figure 1. A diagram of the earth’s emergy sources driving global ows of water as precipitation, evaporation,

evapotranspiration, and runoff. Numbers on the diagram are from Oki (1999).



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potential energy in rain is a normally distributed random variable, and I have no reason to believe that it

is not, we can expect 68% of all determinations to fall within approximately 5.9% of the average value

of 18,100 sej J –1.

The transformity of water evapotranspired over the continents ranged from 26,000 sej J –1 to 31,500

sej J –1 with the average of the three new determinations giving a transformity of 28,400 sej J –1. This value

is close to the value of 27,333 sej J –1 calculated from the numbers in Odum et al. (1998) using 4.74 J g –1

for G and multiplying by 0.98 to place the value on the 9.26E 24 sej y –1 baseline. The three new determi-

nations of the transformity for the water evaporated and/or transpired over the continents were averaged

with the determination in Odum et al. (1998) to get an average value of 28,100 ± 2370 sej J –1. In

Table 1. Chemical potential energy uxes accompanying global water ows and their transformities

based on a solar emergy input to the earth of 9.26E24 sej y-1 given in Campbell (2000).

__________________________________________________________________Note Global Flux Water Flow Energy Flow Transformity*

m3 y-1 J y-1 sej J-1

_______________________________________________________________________________

1 Precipitation total globala 506E12 2.40E21 3861

1 Precipitation total globalb 423E12 2.01E21 4618

1 Precipitation total globalc 496E12 2.35E21 3938

2 Precipitation on landa 115E12 5.45E20 16988

2 Precipitation on landb 99E12 4.69E20 19733

2 Precipitation on landc 111E12 5.26E20 17598

2 Precipitation on landd

110E12 5.21E20 177602 Precipitation on lande 105E12 4.98E20 18605

3 Evapotranspirationa 71E12 3.37E20 27515

3 Evapotranspirationb 75E12 3.56E20 26037

3 Evapotranspirationc 62E12 2.94E20 31510

3 Evapotranspirationd 71E12 3.37E20 27515

4 Runoff a 40E12 1.90E20 49046

4 Runoff b 37E12 1.75E20 53023

4 Runoff c 40E12 1.90E20 49046

4 Runoff d 39E12 1.84E20 50304

4 Runoff e 40E12 1.90E20 49046

______________________________________________________________________________

* Transformity = solar emergy ÷ energy owa Global budget from Oki (1999)b Global budget from Peixoto (1993) and Peixoto&Kettani (1973)c Global budget of Baumgartner&Reichel (1975)d Global budget from L’vovich (1974) quoted in Odum et. al. (1998)e Global ows from Odum (1996)

Notes 1-3: (Water Flow m3 y-1) X 1.0E6 g m-3 X 4.74 J g –1 = chemical potential energy of the water

ow in precipitation and evapotranspiration. Divide the Solar emergy base for global processes by theenergy of the global water ow to get the transformity.

Note 4: Use the formula in Notes 1-3 but substitute 4.72 J g –1 for the value of the Gibbs free energy of

river water (150 ppm) relative to sea water.



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some recent papers, the value for the transformity of evapotranspiration over the land was assumed to

be approximately equal to the transformity of rain over land or approximately 18,100 sej J –1 (Odum

et al. 2000, Odum, 1996, Tilley 1999). When the water transpired is used to determine the planetary

emergy supporting primary production, this analysis indicates that 28,100 sej J –1 is the appropriate

transformity by which the joules of chemical potential energy in the water ux should be multiplied.

Based on these four values, we may expect that 68% of the determinations of the transformity of

evapotranspiration will fall within 8.4 % of the mean value.

The transformity of runoff from the continents to the sea ranged from 48,800 sej J –1 to 53,000

sej J –1 with the average of three determinations being 50,200 sej J –1. This value is close to the values of

49,542 sej J –1 and 50,525 sej J –1 calculated from the numbers in Odum (1996) and Odum et al. (1998),

respectively, using 4.74 J g –1 for G and multiplying by 0.98 to place the value on the 9.26E 24 sej y –1

baseline. The three new determinations of the transformity for the runoff from the continents to the sea

from Table 1 were averaged with the determinations in Odum (1996) and Odum et al. (1998) to obtain

an average value and standard deviation of 50,100 ± 1750 sej J –1. Based on these ve values, we may

expect that 68% of the determinations of the transformity of runoff from the continents will fall within

approximately 3.5 % of the mean value.

Based on the transformities for global water ows calculated from the three global water budgetsevaluated in this note and the existing transformities in Odum (1996) and Odum et al. (1998) , we can

be condent that 95% of the determinations of the transformity of rain over land will be within 11.6% of

the mean value of 18,100 sej J –1. Similarly, we expect that 95% of the time the transformity for evapo-

transpiration will fall within 16.5% of its mean value of 28,100 sej J –1 and the transformity of runoff

is expected to be within 6.8% of its mean value of 50,100 sej J –1 95% of the time. Taking the mean of

the coefcients of variation allows us to assign a precision for the determination of transformities from

estimates of global water ows of 5.9±2.5%.

DISCUSSION

The reported uncertainty in the estimation of the transformities for global water ows is de-

pendent on our ability to measure these ows in the global water budget. Differences in the estimates

of precipitation over land, runoff and evapotranspiration may arise from variability of these uxes over

time or from errors associated with the measurement of these ows. However, for a world in steadystate,

global precipitation must balance global evaporation over the course of a year and the total quantity of

water will remain the same from year to year. Peixoto& Kettani (1973) point out that in the past the

accuracy of estimations of the global water budget has been limited by the ability to directly measure

evapotranspiration. Using new methods of atmospheric modeling the water ux in the atmosphere has been

used to check the estimates of global evaporation (Peixoto& Kettani 1973). The data used to obtain the

budget of Peixoto& Kettani (1973) was obtained from global measurements made during the International

Geophysical Year (1958), the data used in Baumgartner and Reichel (1975) is from a global budget for

1973 and the data used by Oki (1999) is average data over the period from 1989 to1992 obtained from

the European Centre for Medium-range Weather Forecasts. Based on the quality and length of the data

input I believe that Oki’s estimates should be the most accurate approximations of average global ows.

As a rule of thumb, values for the transformities of global water ows may be considered to fall within

about 10% of the mean value 95% of the time with this uncertainty being largely a product of our ability

to measure global water ows.

ACKNOWLEDGMENTS

I thank Peg Pelletier, Glen Thursby, Cathy Wigand, Dave Tilley and H.T. Odum for reviewing

this manuscript. Although the research described in this article has been funded by the U.S. Environmental

Protection Agency, it has not been subjected to Agency review. Therefore, it does not necessarily reect



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the views of the Agency. This paper is contribution number AED-02-070 of the Atlantic Ecology Division,

National Health and Environmental Effects Research Laboratory, Ofce of Research and Development,

United States Environmental Protection Agency.

REFERENCES

Barry, R.G., Chorley,R.J. 1992. Atmosphere, Weather and Climate. 6th edition, Routledge, London. 392

pp.

Baumgartner, A., Reichel, E. 1975. The World Water Balance. Elsevier, Amsterdam, 179 pp.

Campbell, D.E. 2000. A revised solar transformity for tidal energy received by the earth and dissipated

globally: Implications for Emergy Analysis, pp. 255-264. In: Brown, M.T. (ed) Emergy Synthe-

sis. The Center for Environmental Policy, Department of Environmental Engineering Sciences,

University of Florida, Gainesville, FL.

L’vovich, 1974. World Water Resources and Their Future. Mysl’ Publishing, Moscow. (1979Translation

by the American Geophysical Union, Washington, DC)

Odum, H.T. 1996. Environmental Accounting: Emergy and Environmental Decision Making. John Wileyand Sons, NY. 370 pp.

Odum, H.T., Romitelli, S., Tighe, R. 1998. Evaluation of the Cache River and Black Swamp in Arkansas.

Center for Environmental Policy, Environmental Engineering Sciences, University of Florida,

Gainesville, FL. 128 pp.

Odum, H.T., Brown, M.T., Brant-Williams, S. 2000. Handbook of Emergy Evaluation: Folio #1 Introduc-

tion and Global Budget. Center for Environmental Policy, Environmental Engineering Sciences,

University of Florida, Gainesville. 16 pp.

Oki, T. 1999. The global water cycle, pp. 10-29. In: Browning, K.A., Gurney, R.J. (Eds) Global Energy

and Water Cycles. Cambridge University Press.

Peixoto, J.P., Kettani, M.A. 1973. Sci. Am. 228:46-61.

Peixoto, J.P. 1993. Atmospheric energetics and the water cycle, pp. 1-42. In: Rasche, E., Jacob, D. (eds) Energy and Water Cycles in the Climate System. Springer-Verlag, Berlin.

Tilley, D. R. 1999. Emergy Basis of Forest Systems. Ph.D. Dissertation, University of Florida, Gaines-

ville, FL. 296. pp.

Weast, R.C. 1981. CRC Handbook of Physics and Chemistry. CRC Press, Inc., Boca Raton, FL.



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Edited by



Associate Editors

Howard T. Odum



David Tilley



Sergio Ulgiati

University of Siena

Siena, Italy

December 2003



Gainesville, FL


vi



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Chapter 26. Dynamic Emergy Simulation of Soil Genesis and Techniques...

Dynamic Emergy Simulation of Soil Genesis and

Techniques for Estimating Transformity

Confdence Envelopes

Matthew Cohen

ABSTRACT

A systems model of topsoil genesis was developed to explore the dynamics of various components

of the soil system. The model was calibrated for long-term soil conditions in humid and semi-humid

tropical Africa, but the model structure is sufciently general to allow application to other climatic and

biotic regions. Of specic interest was the addition of emergy to the simulation to arrive at transformity

and specic emergy estimates for various internal components, including soil organic matter (SOM),

soil nutrients, soil cation exchange capacity (CEC), and soil structure. Standard emergy simulation

rules were applied to this multiple state variable system with one exception: material pathways that are

recycled to a base storage add emergy to that storage based on the current transformity of the material

in that storage. This allows material dispersal and re-concentration within a system without articial

transformity effects. The problem of simulating emergy in systems with recycled materials is explored

using a two-tank mini-model before the new techniques are applied to the soil genesis model.

The calibrated model provided estimates of transformity and specic emergy values for a tropical

savanna of 2.23E5 sej/J (SOM), 2.6E10 sej/gram (nutrients), 1.34E10 sej/gram (CEC), and 5.14E10 sej/J

(Soil Structure). A Visual Basic module was added to the model to allow random draws from probability

distributions for each parameter to be propagated through the model. Repeated cycles (~1000) of this

Monte Carlo simulation ultimately provide condence bounds for the computed transformities. Estimated

probability distributions for this model resulted in the following standard deviations around the mean

value reported above: 4.04E4 sej/J (SOM), 3.8E9 sej/gram (nutrients), 1.3E9 sej/gram (CEC) and 5.79E10

sej/J (soil structure). Data were also generated for tropical forest systems.

INTRODUCTION

Tropical agro-ecosystems are generally regions where signicant levels of soil loss occur (Pimentel

1979). This is due primarily to the convergence of intense tropical rainfall patterns, low levels of soil

subsidy (e.g. fertilizer, mulch) due to prevailing poverty, and large numbers of livestock reliant on small

land parcels. As a result of this convergence of circumstances in western Kenya, soil erosion continues

to be a formidable and charismatic problem (ICRAF, 2000). Within the context of an effort to study this

problem using emergy analysis, it became necessary to establish locally calibrated and highly reliable

transformity values for soil functional features.

Soil degradation in the tropics takes several forms. Erosion is the most charismatic manifestation,but soil structure losses due to excessive grazing/trampling, nutrient export as a result of low-input

agriculture, and oxidation of SOM due to tillage operations all represent a depreciation of the natural capital

that is central to the livelihoods of rural farmers. Emergy analyses have historically used soil organic

26



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matter (SOM) as the value-bearer for the entire soil system. However, due to the multiple degradation

mechanisms present in tropical Africa, it was deemed necessary to extend the valuations of soil to other

functional components.

The conceptualization of soil genesis patterns is largely due to the work of Hans Jenny in the 1940’s.

His forcing function heuristic model, that includes climate, organisms, topography (relief), parent material

and time (the cl.o.r.p.t. model), has been widely used as a template for quantitative assessments of the

process (Amundsen et al. 1994). To date, models of soil genesis fall into two broad categories: qualitative,

functional models, such as Jenny’s work, which generally lack explicit mathematical descriptions, and

quantitative mechanistic models (e.g. CENTURY – Parton et al. 1994 or ORTHOD – Hoosbeek and

Bryant 1994) that are extremely complex due to over-compartmentalization. The modeled described

herein represents an effort to greatly simplify the dynamics of soil formation without losing the essential

functional relationships.

METHODS

Emergy Simulation Models

Simulation models typically begin by diagramming the system of interest. Using the symbolic systems

language (Odum, 1984) components and sources are linked to create a visual pattern of the organization of

the system. Once a pictorial representation of the system has been achieved, the mathematical equations

for how the components and sources interact can be rigorously extracted and simulated.

Flows in the simulation model generally represent energy or materials. However, emergy values

can be simulated along with these ows. This allows the computation of transformity (or specic

emergy) values to be computed for each compartment within the system simultaneously. Standard rules

for simulation of emergy are shown in Figure 1. These rules (Odum, 1996; Tilley 1999) can be verbally

stated as follows: emergy accumulates in a storage as long as that storage is growing. Emergy is exported

Figure 1. Emergy dynamics rules for a single-tank model (after Tilley 1999, Odum 1996)



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only on ows from that storage that are used by other components in the system, of exported to the next

larger system. Any ow that represents 2nd Law dispersal carries no emergy. However, when a storage

begins to decline, emergy is lost along all pathways, including those that represent dispersal. In all cases,

transformity (or specic emergy) is the ratio of emergy to energy (or mass).

The application of these rules to systems with recycle pathways was explored through the simulation

of mini-models before application to more complex models designed to simulate soil genesis.

Confdence Interval Estimation

Transformity estimates represent static computations of a systems status in a single location.

As such, they should be accompanied by some estimate of the variance of the prediction. In general,

there are insufcient numbers of comprehensive studies of the same phenomenon in the same location

to allow meta-analysis of parallel studies. In this work, an alternative is proposed. Monte Carlo, orstochastic, simulation techniques applied to the parameter estimates in the model can allow uncertainty

to be propagated through the model. By assessing the nal status of the system after each simulation,

and compiling the results, variance estimates for desired information can be calculated. A diagram of

the process is shown in Figure 2, illustrating the critical difference between this method and standard

sensitivity analyses. Centrally, Monte Carlo simulations allow the interactive (or multivariate) effects of

uncertainty to be predicted, whereas sensitivity analysis evaluates univariate uncertainty. In this work,

the Monte Carlo process was written as a Visual Basic module that can be overlayed on any simulation

model (or emergy table) constructed in Microsoft Excel.

In all cases presented, mean and standard deviation estimates for each parameter were used to create

the probability density function from which random samples were drawn. Normal distributions werethroughout for illustrative purposes, but any continuous distribution (e.g. Weibull, Gamma, Log-normal

etc.) could be used. Random draws were based on the following function:

Z = m + s*sqrt(-2*log(U1))*cos(2*p*U2) (1)

Figure 2. Symbolic representation of the Monte Carlo simulation technique used in this work to dene condence

intervals for transformity values.



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where Z is a normally distributed random variable with mean m and variance s2, and U1 and U2

are random numbers between 0 and 1.

RESULTS

Emergy Simulation with Recycling

First, emergy simulation summary results from the simple single-tank model are presented in Figure

3. This chart shows the effects on steady state transformity, and the time required to reach steady state,

due to changes in the dispersal:ow (proportion dispersal) ratio. The greater the proportion of total inow

dispersed (ow J4 in Figure 1), the larger the transformity of the material at steady state and the longer

the time required to reach steady state. This is considered the expected behavior for emergy accumulating

in a storage (Tilley, 1999), and illustrates the importance of distinguishing between yield and dispersal

pathways in the system of interest.

The second model that was tested is only slightly more complicated than the single tank model used

to formulate the emergy simulation rules. In this model, shown in Figure 4, the two tanks represent some

low quality abundant resource (T) and an upgraded storage of the same material (Q). An example might

be nutrients in the soil (T) and nutrients bound in plants (Q). One energy source (S1) drives the upgrade

process, while another (S2) replenishes material dispersed from T. Export and dispersal pathways are

included for both storages.

A simulation model of this system was constructed to include the emergy rules previously

presented. However, as Figure 5a indicates, the transformity of the storage T, to which dispersed materialsow, behaves in an unexpected manner. Specically, the transformity value appears not to be a function of

the proportion of the inow that is dispersed, which is the expected behavior established using the single

Figure 3. Summary of single-tank emergy dynamics. Shown are the response of steady state transformity and the

time-to-steady-state to changes in the proportion of owing material that is dispersed (2nd Law Losses)



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tank model of emergy dynamics (Tilley, 1999 and Figure 1). The observed behavior indicates that steady

state transformity is a function of the ratio between inow and use (where use can exceed inow because

of the recycle pathway). This problem arises because it was previously assumed that no emergy owed

on the recycle pathway (and therefore, EmJ4 = 0), and, therefore, the emergy used to drive the upgrade

process (EmJ2) is not replaced when the material is recycled. There is, as shown, an articial decrease

in TrT such that at steady state, the value of TrT is an articial function of the inow:use ratio:

TrT = (S2/J2)*TrS2 (2)

where, TrS2 and TrT are the transformities of source S2 and storage T, S2 is the input from source

2 and J2 is the use (see ows in Figure 4). This implies that, if, for example, nutrient cycling is efcient

and use greatly exceeds input, the transformity of the nutrient storage will be considerably smaller than

the transformity of the inowing product, clearly an unrealistic situation.

The proposed resolution of this problem is to explicitly account for the emergy value of the recycled

materials. This model can be linked to a real system by expanding on the nutrient analogy. In the

environment, nutrients are recycled to some larger storage at some lower concentration than the upgraded

procuct. However, that concentration in the environment still represents a gradient from background states,implying that the storage has a transformity (i.e. it is higher quality). Therefore, in a material cycle, the

emergy on the recycle pathway should reect that quality, which can be computed by multiplying the

Figure 4. Simple two-tank model simulated to explore the dynamics of emergy in systems with material recycle

pathways. Recycle is shown as dispersal from high quality storage (Q), where energy is degraded but materials

are returned to their lower quality storage (T).



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Figure 5. Transformity Output for simulation of emergy with recycle. a) The response of steady state transformity to

changes in the proportion of ow dispersal for a variety of input-to-use ratios under the standard emergy rules applied

to the system in Figure 2. b) The response of steady state transformity to changes in the input-to-use ratio for a variety

of proportion dispersal levels using new network emergy rules (note the different x-axis from 5a). c) The response ofsteady state transformity to changes in proportion dispersal using network emergy rules (as in Figure 3).



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recycled material ow by the transformity of the destination storage (in the example model, Tr2*J4). We

continue to assume, however, that no emergy is drained from the upgraded stock by the dispersal ow.

Using this approach, hereafter called the network emergy rule, the results presented in Figure 5b

are achieved. The transformity of the storage is now dependant only on the ratio between the use and

dispersal (proportion dispersal), as in the single-tank model (Figure 1), and not on the inow to use (I:U)

ratio. Note that the x-axes in gures 5a and 5b are different, with the axis in gure 5b presenting the

ratio of inow to use, and the character of the response indicating the independence of transformity from

this ratio. Figure 5c shows the response of the steady state transformity to the change in the dispersal

proportion (as in Figure 3), which replicates exactly the functional behavior of the single tank storage.

Note that the geometry of the response is the same regardless of the input-to-use ratio, hence the presence

of only one line.

Topsoil Genesis

Model Confguration

Figure 6. Systems model of soil genesis in tropical grasslands and forests. Shown are the stocks of biomass (B),

detritus (litter), soil organic matter (OM), nutrients (N), clay cation exchange capacity (Clay/CEC) and soil struc-

ture (SS). Sources are sun, wind, rain, nutrients in rain (Nrain), and parent material (PMat) and associated owsof clay (Clay/CEC) and nutrients (Nmin). Flows labeled “ox” are oxidation pathways representing disperal and

material recycling.



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Figure 6 shows the diagram that was conceived to model topsoil genesis based on a review of the

literature (Jenny, 1941; Bryant and Arnold, 1994; Young 1976; Amundsen and Tandarich 1994; Nye and

Greenland, 1961; Bolker et al. 1997). The model, verbally, represents the use of three energy sources

– sun, rain, and geologic inputs – in the process of creating saprolite or topsoil. The components in the

model are vegetative biomass, detritus (litter), soil organic carbon (active and passive pools aggregated),

soil nutrients, cation exchange capacity and soil structure. The model is designed to represent soil

processes on a yearly basis, and model is simulated for 500 years. Several critical features of the model

construction should be noted:

1) All interactions in the diagram are multiplicative unless otherwise stated. In general,

associations between soil variables are understood only in a conceptual sense (e.g.

the cl.o.p.r.t. model of Jenny, 1941), and little quantitative rationale was found in the

literature for interactions that are more complex.

2) Biomass production is a function of sunlight, inltration, nutrient storage and soil structure.

It is not autocatalytic because of the long time increment.

3) There are three chemical oxidation pathways (i.e. organic matter dispersal). They are

drawn from the storages of biomass, litter and organic matter, and are linear functions

of those storages.4) Nutrient content and cation exchange capacity (CEC) are functions of time and parent

material quality. For the simulation results presented here, the quality of parent mate-

rial is considered constant. CEC is measured in standard units cmol/kg of mineral soil,

measured as the sum of exchangeable acidity and exchangeable bases during standard

titrations. Nutrient content cannot exceed CEC (i.e. 100% base saturation).

5) Nutrient, organic matter and CEC are all reduced through leaching. Nutrients, however,

Figure 7. Method for approximating the energy content of structure soil vis a vis parent material.



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are retained in the system by the existing exchange capacity of clay, and - of central

importance in highly weathered tropical soils - by the large effective exchange capacity

provided by organic material.

6) Soil structure is created by the growth of plants, and by the litter humication process,

which represents, among other organic consumption, the important activity of organ-

isms such as termites and earthworms.

7) Erosion is a quadratic function of runoff (transport capacity) and a negative exponential

function of vegetative cover (detachment capacity). Enrichment, the preferential en-

trainment of clay, silt, and organic material, is governed by an enrichment ratio in the

model of 2.5. This value, gleaned from the erosion modeling literature (Byne, 2000)

indicates that for each gram of soil eroded, the various enrichment components are

proportionally augmented 250%.

8) Soil structure is a unitless storage where 1 represents maximal structure and 0 represents

completely structureless soil (i.e. parent rock). This can be converted roughly to bulk

density by dividing by 0.8. In simulating emergy, the energy value of the soil structure

is dened as shown in Figure 7, where the vertical displacement of the soil (due to in-

creased pore space) offsets the center of gravity of the original rock material by someheight (h

diff ), which can be translated into gravitational potential energy. The use of bulk

density as a proxy for the nebulous quality “soil structure” ignores many of the inherent

complexities of a soil physical condition (particle size hierarchy, macropores, structural

shapes – Brady and Weil, 2002), but is the most quantitatively accessible measure of

the ability of a soil to perform hydrologic and biological function. Moreover, the as-

sociation between bulk density and other functional structural characteristics is widely

Figure 8. Simulated output of soil genesis under tropical savanna conditions. All stocks are shown on the left y-axis

except soil structure which is shown on the right y-axis.



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assumed (e.g. Biswas and Mukherjee, 1994). Furthermore, for the transformity to be

computed, the chosen measure of soil structure must be convertible to energy units,

which is the case for bulk density (using the method described in Figure 7) but is less

readily computable for other potential measures.

Simulation ResultsThe simulation results for the mass storage values are shown for one scenario, a tropical moist

savanna, in Figure 8. As shown, when all of the function soil components are absent (biomass, detritus,

SOM and soil structure) is takes long period of time for steady state to be reached. Several key features

should be noted:

1) SOM takes nearly 250 years to reach steady state, illustrating the value of that compo-

nent. Biomass and detritus peak after less than 20 years (typical of grasslands).

2) Nutrients and exchange capacity initially decrease rapidly (relative to their long turnover

times - ~1000 years) due to the lack of organic material to mitigate leaching effects,

but stabilize once that stock stabilizes.

3) Soil structure requires nearly as long as organic matter to reach steady state despite theinternal model assumption that bulk density can be created in 40 years. This reects

the intimate relationship between soil organic carbon and soil structure.

4) A steady state is reached in the system between the weathering rate, replenishing lost

nutrients and exchange capacity, and the losses due to leaching and erosion. This output

assumes a moderate quality parent material (30 cmol/kg clay and 50% base saturation).

However, tropical systems often have highly weathered parent material resulting in

lower quality geologic inputs, which in turn force ecosystems to be more efcient with

nutrients.

Figure 9. Emergy dynamics of the model under tropical savanna conditions. Biomass, litter and SOM transformitylevels are shown on the right y-axis while soil structure transformity and nutrient and CEC specic emergy levels

are shown in the left y-axis.



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Emergy Dynamics

When emergy is added to the model according the network emergy rule, transformity dynamics can

be explored. These are shown in Figure 9 for the tropical savanna. Table 1 shows the transformity and

specic emergy output for both forest and savanna ecosystems. Notable in the output:

1) Soil organic matter stabilizes at a transformity of 2.25E6 sej/J after 300 years. This value

is approximately 4 times larger than previous computations (Odum 1996; Buranakarn,

1997).2) Transformity values of nutrients and CEC were started at their anticipated steady-state

specic emergy values because of effects on the dynamics of the rest of system due to

the extremely long turnover times (~1500 years) of these stocks.

3) Soil structure has a transformity value (based on the energy calculation presented above)

of 3.55E10 sej/J. This is due to the low energy storage represented in the vertical dis-

placement, and the high environmental investment (and feedback potential).

Confdence Intervals

The Monte Carlo simulation technique was applied to the soil genesis model to explore the effects

of uncertainty on the transformity estimates. Table 2 shows a selection of the parameter means and

variances that were used for both forest and savanna simulations. The summary results are presented in

Table 3, which shows the mean transformity values for each component and error bars representing one

standard deviation from the mean. The probability distributions that were used to produce the computed

condence intervals were all normal, and based on literature values and estimates. The output (Table 3)

shows the relative condence in the transformity estimates for all components except soil structure, for

which the standard deviation is larger than the mean. Soil structure had a higher mean value in forest

soils (1.11E11 sej/J) than in savanna soils (5.14E10) both of which were higher than the computed value

of 3.55E10 using the dynamic emergy approach without error propagation (Table 1).

Table 1. Steady State Transformity and Specic Emergy Values predicted from model for savanna and

forest calibrations. __________________________________________________________________________________

Savanna Forest

Transformity Transformity

__________________________________________________________________________

Biomass (sej/J) 4.47E+04 4.71E+04

Litter (sej/J) 6.39E+04 6.83E+04

Soil Org. Matter (sej/J) 2.24E+05 2.24E+05

Nutrients (sej/g) 2.71E+10 2.71E+10

CEC/Clay (sej/g) 1.34E+10 1.34E+10

Soil Structure (sej/J) 3.55E+10 5.11E+10

__________________________________________________________________________



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DISCUSSION

The examination of emergy dynamics in the context of a system with material recycling is an ongoing

thread of research in emergy science. Buranakarn (1997) explored the emergetics of various recycling

schemes within the economy (e.g. paper, glass, aluminum) and proposed the emergy retrograding concept

that is central to this study. That is, when materials are recycled to a lower quality state they lose the

embodied information that represents the upgrade in quality. However, because the stocks are still highly

concentrated relative to the background states in the environment, the recycled material is not devoid of

emergy. Simply put, ows of materials to the left in systems models represent a decrease in the specic

emergy until the environmental base state is reached at which point the specic emergy is zero. Likewise,

with dynamic simulation it was necessary to make the assumption that recycled materials maintain some

emergy value; specically they take on the transformity of the storage to which they are owing. It should

be noted that while the distinction between dispersal and recycle is clear in this conceptualization, these

ows many be difcult to distinguish in real systems.

Exploration of the soil genesis model that was beyond the scope of this paper indicates that the

model structure is generally sound, and sufciently general to allow exploration of soil development

under a variety of climatic conditions. However, the literature revealed a relative paucity of numeric or

experimental data to validate the model. Those studies for which there were data were used to calibratethe model, making proper validation unfeasible. However, the model appears to conform well to the

conceptual models of soil development, and matched calibration data effectively. The addition of model

Table 2. Selected model parameter means and standard deviations as used in Monte Carlo simulation

for transformity variance estimation.

__________________________________________________________________________________

_

Mean Parameter Values Standard Deviation

Model Parameters SavannaForest Savanna Forest__________________________________________________________________________________

_

Veg Biomass (g/m^2/yr) 3700 12200 555 1830

Structural Litter (g/m^2/yr) 660 3700 99 555

Soil Organic Matter (g/m^2/yr) 5700 7000 855 1050

Clay (%) 30% 30% 6% 6%

CEC-Clay (cmol/kg) 30 30 9 9

Base Saturation (%) 50% 50% 10% 10%

Sunlight (J/yr) 1.83E+06 1.83E+06 9.15E+04 9.15E+04

Rain (m/yr) 1.10E+00 2.20E+00 5.50E-02 1.10E-01

Weathering Rate (g/m^2/yr) 0.014 0.024 0.0028 0.0048

Sunlight remainder (J/yr) 1.83E+05 1.83E+05 9150 9150

Runoff (m^3/m^2/yr) 1.10E-01 1.10E-01 0.022 0.022

Seepage (m^3/m^2/yr) 2.20E-01 4.40E-01 0.044 0.088

Biomass Production (g/m^2/yr) 900 1200 90 120

Biomass Dispersal (g/m^2/yr) 300 350 60 70

Litterfall (g/m^2/yr) 600 850 120 170

Soil Structure Creation (unit/yr) 0.05 0.05 0.015 0.015

CEC of Organic Matter (cmol/kg) 800 800 240 240

Transformity Sun (sej/J) 1.00E+00 1.00E+00 0.00E+00 0.00E+00Transformity Rain (sej/J) 3.10E+04 3.10E+04 9.30E+03 9.30E+03 ____________________________________________________________________________________________



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ows to represent agriculture, forestry, and pasture will allow exploration of the behavior of the soilresource, and associated costs of human management. However, it is important to recognize that the

system of soil genesis presented here ignores signicant formative factors in landscapes: specically the

accumulation of eroded sediments in topographically lower pedons. The resulting catena (topographic

soil sequence) has signicant implications on the types of ecosystems and human systems that can persist

at a site. By ignoring the larger spatial dimension of soil genesis (temporarily; there are plans to include

inowing sediments) there are risks of not adequately capturing the process.

One interesting feature of the emergy portion of the model output can be observed by comparing

Tables 1 and 3. Table 1 represents the calculation of transformity using the deterministic approach while

Table 3 shows the mean of the ~1000 Monte Carlo simulations that were done. For all stocks except soil

structure, the values are nearly identical reecting the stability of the model within the range of parameter

values and the overall linearity of the model structure. However, the signicant differences that exist

between predicted values for soil structure indicate that this portion of the model may have some inherent

bias or non-linearity that is unanticipated. The uncertainty in the soil structure parameters in the model,

due to the scant quantitative literature on the subject, also leads to extremely large standard deviation

estimates (i.e. greater than the mean value) around the predicted mean. This uncertainty should be reected

and propagated in any application of the transformity values presented here.

The implications of the Monte Carlo approach to propagating error can be widespread. First, the

addition of condence intervals to each transformity estimate will help silence critics of the approach that

suggest that the lack of statistical rigor compromises any conclusions. Second, and most importantly,

the output from each analysis can be assessed more effectively when, for example, competing policy

options have variance bounds. Since Monte Carlo techniques can be applied to any spreadsheet analysis,not just dynamic simulations, the potential to integrate this process into standard protocols is large, and

the method is remarkably simple. It is important to note, however, that this technique can only address

uncertainty with specied model parameters. It cannot address the larger questions of uncertainty that

arise with the manner in which the model is formulated. Uncertainty because of errors and omissions in

the diagram, a source of considerable potential error, remain unknown.

CONCLUSIONS

Three undertakings form the foundation of this research. The rst is an effort to understand and

devise rules for the simulation of emergy in systems that contain material cycles. This subset of systemsmodels is not adequately addressed by the current emergy dynamics rules. The second part of the

research was the description and simulation of a simplied soil genesis model to explore the dynamics

of the critical soil stocks and their transformities. Critical soil stocks were considered to be vegetative

Table 3. Transformity and Specic Emergy output from the model for two conditions (tropical forest

and savanna) along with standard deviation estimates.

_____________________________________________________________________________

_

Savanna Forest

Transformity Standard Deviation Transformity Standard Deviation_____________________________________________________________________________

_

Biomass (sej/J) 4.46E+04 1.24E+04 4.56E+04 6.50E+03

Litter (sej/J) 6.24E+04 8.94E+03 6.65E+04 9.90E+03

Soil Org. Matter (sej/J) 2.23E+05 4.04E+04 2.18E+05 3.86E+04

Nutrients (sej/g) 2.60E+10 3.80E+09 2.56E+10 4.29E+09

CEC/Clay (sej/g) 1.34E+10 1.30E+09 1.34E+10 1.88E+09

Soil Structure (sej/J) 5.14E+10 5.79E+10 1.11E+11 1.25E+11

_________________________________________________________________



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biomass, litter (unincorporated organic material), soil organic carbon, nutrients, cation exchange capacity

and soil structure. The third portion of the work was the application of error propagation tools to the

simulation. A Monte Carlo simulation approach was proposed not only for simulation models, but for all

emergy tables as a means for assessing the condence in the output and for enhanced ability to interpret

analysis results.

Central conclusions are:

1) Emergy dynamics in systems with material recycle pathways require the addition of

network emergy rules to the standard emergy simulation rules for realistic results. The

network emergy rule simply allows the material on a recycle (dispersal) pathway to

embody emergy. The specic emergy of the recycle ow is set at the level of the stor-

age to which the ow is directed.

2) The soil genesis model proposed herein appears to capture the behavior expected in

the system. Additional modules to allow human management and extractive harvest

(i.e. farming, forestry, animal grazing) will allow the dynamics and emergy costs to be

explored further.

3) Transformity estimates were made using the network emergy rules applied to the soil

genesis model. Values for soil structure were 3.35e10 sej/J based on an energy com-puting method that accounts for the gravitational potential energy that accrues when

saprolite is formed. Biomass and litter transformities were in the expected range, but

the transformity for soil organic matter was found to be 4 times larger than previously

computed.

4) The propagation of parameter uncertainty through the model was successfully under-

taken, and condence intervals for the transformity estimates were computed. Simple

overlay modules for Microsoft Excel spreadsheets were constructed that can be ap-

plied to any simulation model or emergy table for propagating error. Additionally, any

probability distribution can be used in this manner, though normal distributions were

assumed throughout.

5) The discrepancy between the predicted transformities using the deterministic simulation

approach versus those gleaned from the Monte Carlo simulation must be explained by

non-linearities in the model stucture, and potential instability in the model due to the

large uncertainty associated with the soil structure parameters.

REFERENCES

Amundsen, R. and J. Tandarich 1994. Factors of Soil Formation – A Fiftieth Anniversary

Retrospective – SSSA Special Publication #33. Soil Science Society of America, Madison WI

Barber, R.G. 1983. The Magnitudes and Sources of Soil Erosion in Some Humid and Semi-Arid Areas

of Kenya, and the Signicance and Soil Loss Tolerance Values in Soil Conservation in Kenya.In Soil and Water Conservation in Kenya: Proceedings of a Second National Workshop D.B.

Thomas and W.M. Senga (eds). Institute of Development Studies. Nairobi, KENYA

Biswas T.D. and S.K. Mukherjee 1994. Textbook of Soil Science – 2nd Edition. Tata – McGraw Hill.

New Delhi INDIA

Bolker, B.M., S.J. Pacala and W.J. Parton 1997. Linear Analysis of Soil Decomposition: Insights from

the CENTURY Model. Ecological Applications

Brady, N.C. and R.R. Weil 2002; The Nature and Properties of Soils – 13th Ed. Prentice Hall, Saddle

River, NJ

Buranakarn, V. 1997. Working Transformity Collection. University of Florida, Gainesville FL

Buranakarn, V. 1998. Evaluation of Recycling and Reuse of Building Materials Using EmergyAnalysis Methods. PhD Dissertation, University of Florida, Gainesville FL

Byne, W. 2000. Predicting Sediment Detachment and Channel Scour in the Process Based Planning

Model – ANSWERS2000. ME Thesis, Virginia Polytechnic Institute, Blacksburg VA



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Coleman D.C., J.M. Oades and G. Uehara (eds.) 1989. Dynamics of Soil Organic Matter in Tropical

Ecosystems. University of Hawaii Press. Honolulu HI

Hilborn, R. and M. Mangel 1997. The Ecological Detective: Confronting Models with Data. Princeton

University Press, Princeton NJ

Hoosbeek, M.R. and R.B. Bryant 1994. Developing and Adapting Soil Process Submodels for Use in

the Pedodynamic ORTHOD model. In Quantitative Modeling of Soil Forming Processes –

SSSA Special Publication #39, R.B. Bryant and R.W. Arnold (eds.). Soil Science Society of

America, Madison WI

Jenny, H. 1941. Factors of Soil Formation: A System of Quantitative Pedology. McGraw Hill, New

York NY

Nye, P.H and D.J Greenland 1960. The Soil Under Shifting Cultivation. Commonwealth Soils

Technology Community 51

Odum, H.T. 1996. Environmental Accounting: Emergy and Environmental Decision Making. John

Wiley and Sons, New York, NY

Parton, W., P. Woomer, and A. Martin 1994. Modeling Soil Organic Matter Dynamics and Plant

Productivity in Tropical Ecosystems. In The Biological Management of Tropical Soils

Fertility, P. Woomer and M. Swift (eds). Wiley-Sayce, San Francisco CASanchez, P.A., K.D. Shepherd, M.J. Soule, F.M. Place, R.J. Buresh, A.N. Izac, A.U. Mokwunye,

F.R. Kwesiga, C.G. Ndiritu and P.L. Woomer 1997. Soil Fertility Replenishment in Africa:

An Investment in Natural Capital in Replenishing Soil Fertility in Africa SSSA Special

Publication #51. R.J. Buresh, P.A. Sanchez and F. Calhoun (eds). Soil Science Society of

America. Madison WI

Tate, R.L. 1987. Soil Organic Matter: Biological and Ecological Effects. John Wiley and Sons. New

York NY

Tilley, D.R. 1999. Emergy Basis of Forest Systems. PhD Dissertation, University of Florida.

Gainesville FL

Young, A. 1976. Tropical Soils and Soil Survey. Cambridge University Press. Cambridge ENGLAND



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Edited by



Associate Editors

Howard T. Odum



David Tilley



Sergio Ulgiati

University of Siena

Siena, Italy

December 2003



Gainesville, FL


vi



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Chapter 27. Emergy Evaluation for Sustainable Development Strategy...

27

Emergy Evaluation for Sustainable Development Strategy ofFisheries Resources in Darién, Panama

John McLachlan-Karr

ABSTRACT

In order to evaluate the sheries development potential for Darién province, Panama, data

were collected during visits in 1998 for an emergy evaluation from a national perspective. The effect of

current sheries activities on the estuarine system was studied by comparing emergy indices of artisanal shing (mainly from dugouts) for n-sh and shrimp in the Gulf of San Miguel (emergy yield 1.9 E20 sej/

yr) with industrial trawler shrimp shing on the continental shelf (emergy yield 0.84 E20 sej/yr). The

emergy yield ratio (EYR) for artisanal sheries was 31/1 and trawlers 2.5/1. The difference in EYR of

the two competing activities illustrates the disparity in development between Darién and Panama City

(near where the trawlers are based). The difference in emergy investment ratio (EIR) 0.31 for Panama

and about 0.0031 for Darién explains why over 95% of the total catch is shipped to Panama City and

then overseas, where emergy/$ ratios are lower and most of the benets are accrued.

The high renewable emergy ows in the Gulf of San Miguel (142.8 E20 sej/yr) and continental shelf

(136 E20 sej/yr) and low emergy investment ratios (0.0004 for artisanal shing and 0.0025 for trawlers),

suggests unused economic potential. The ratios also predict more catch by trawlers from Panama City,

continuing the pattern of exporting Darién’s wealth and prolonging Darién’s status as the least developed

and poorest province in Panama. This analysis suggests that providing development incentives locally

at an intermediate emergy investment ratio (around 0.031) would attract more investment for a larger,

local shing eet and processing of the catch. The result might be a shing eet led by about 20 small,locally built, multipurpose, diesel-powered shing vessels, processing facilities, greater sustainable useof resources and greater benet to Darién.

INTRODUCTION

This paper was part of a larger effort by the Inter American Development Bank (IADB) and

the Government of Panama (GOP) to develop a sustainable development program for Darién Province.According to the terms of reference for the project, sustaining Darién’s economy and natural resources wasa principal development goal of the IADB. Darién Province is the least developed in Panama accordingto recent economic studies by the GOP (GOP, 1997). Most economic activity in the Province involvesnatural resource extraction and export, typical of underdeveloped regions worldwide. Activities of concernto the GOP, because of the long-term negative environmental impacts, include forestry, agriculture andshing (GOP, 1997).

The Pacic coast of Panama supports a major shrimp (for export) and n-sh shery. This

shery in Darién currently supports 477 artisanal shing vessels that, along with trawlers visiting from

outside of the Province, take about 1.1 E6 kg/yr of shrimp and a similar quantity of n-sh per year (see

results). An increasing amount of the catch is taken by visiting trawlers that are displacing the localshermen and with little benet to Darién. To reduce this conict, the GOP is proposing restricted areasto trawlers and closed shing seasons. At the same time, sheries exports are an important export earner

for Panama, and large investments in infrastructure have been undertaken in Panama City. To resolve



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these conicts, place sheries on a sustainable basis (for maximum empower) in Darién, Panama. In this

study, the identication of imports and recommendations for development of Darién’s marine resources

are based on Emergy methodology. Data were calculated by remote imagery, eld sampling, and from

published reports.

Figure 1. Map of the study area in Darién, Panama.



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BACKGROUND AND DESCRIPTION OF ESTUARINE SYSTEM OFDARIÉN

For thousands of years, human societies of Darién have been supported by the renewable ows

of energy and matter through natural systems including tides, freshwater, organic matter, and currents in

rivers, estuaries and the coastal zone. These resources support most of Darién’s inhabitants and economywith food, trade goods, recreational opportunities and waste disposal.Systems, with and without people, “self organize” to sustain the highest energy production and

use according to the maximum power principle (Odum, H.T., 1996). The maximum power principle meansthat each component of the entire system is positioned to use the maximum amount of the passing energyow and develop autocatalytic storages to maximize useful power transformations (Lotka, A., 1922). In

keeping with this principle, the maximum value of an estuarine/marine system may come by retaining asmuch as possible of the original values of the natural system while attracting more economic and publicinvestment to bring in more resources (Odum & Arding, 1991). When considering development options,it is necessary to evaluate the productive features of the natural system and match them with a compatibleeconomic system to maximize value of the ecosystem and its sustainable human economy.

The Gulf of San Miguel is a large, tropical estuarine system that dominates the geography ofeastern Panama (see Figure 1). It is a second order embayment in the Gulf of Panama lying between 7o and 10o N and 77o to 78.5o W, in the northerly part of the Equatorial Low Pressure Trough, and thus within

the inter-tropical trade wind convergence zone. The rainfall increases from 50 mm per month duringthe dry season to an average 275 mm per month for the monsoon rainy season, generally May throughDecember (Situacion Meteorologia, Panama, 1996).

Lowman (1970) describes the Gulf of San Miguel to be an estuarine, tributary, circulation

system with signicant hydraulic concentration over tidal ats and marginal bays of the main Gulf. The

embayment consists of a central scout channel up to 36 m deep, anked by semicircular bays generally

less than 12 m deep. For the purpose of this study, the Gulf’s estuarine system is bordered on the seaward

side by the 50 m bathymetric line where signicant mixing with water from the clear, cooler Colombiancurrent takes place. The continental shelf is the seaward area of Darién to the 200 m bathymetric lineand is not considered part of the estuary.

The tidal regime for the Gulf of San Miguel is semi-diurnal. Flood and ebb tide currents arequite fast and average 200 cm/sec on the ood tide and 230 cm/sec on the ebb tide. During ood tides,

the river ow loses some of its velocity, and as a result, much suspended material falls out of suspension

until resuspended by increased current velocities during the ebb tide. The salinity of the water in theGulf is greatly reduced by tidal mixing notably during the end of the wet season in October/November.Carbon levels vary within the Gulf from 21 to 40 mg carbon/m3 in the dry season and drop to between 0and 10 mg carbon/m3 during the wet season (Lowman, 1970).

Of a total of 280,000 hectares of forested area in the ve principle watersheds of the Gulf of San

Miguel, 40,684 hectares are mangrove forest. There are 4.07 E8 m2 of mangroves in the coastal system(GIS, Dames and Moore, 1998). The turnover of the standing crop of above ground mangrove biomassin Darién is slightly greater than one year (Golley et al., 1972). Average litterfall is given as 995 g/m2/yr (Sell, 1977) (see Table 1). Mangrove forest gives way to the Riverine or Cativale (Prioria copaifera),forest covering 23,386.3 hectares along the freshwater reaches.

Golley et al. (1972) report that nutrient inow from rivers into the estuary and coastal zone is

relatively low because of the tight cycling of nutrients in forested areas. Moreover, the short residencetime of water in the Gulf (average 46 days) and the large amount of suspended inorganic material prevents

the ecological benet of much of the nutrients from being incorporated into the estuarine ecosystem.

Although a proportion of the annual loading of organic matter accumulates in the estuary and over time

has built up a deep layer; much sediment, nutrients and organic matter is exported to the continental shelf.Here, it subsidizes production in the offshore food chains.The population density of Darién is about 4 persons/km2 (Panama en Cifras, 1996). The Dariénitas

(inhabitants of mainly mixed Spanish, Embera-Wamnan and African descent), mainly live on around the



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Gulf of San Miguel and its tributaries and are the main shing group. In 1996, Darién had 477 registered

artisanal shing boats over half of which operate in the Gulf (Ministerio de Vivienda, 1997). The principal

shing settlements include La Palma (administrative centre), Chepigana, Punta Alegre, Seteganti and

Garachine. In these towns, shing and allied industries such as boat/outboard repair and provisioning are

the main economic activities, although many Dariénita shermen are part-timers and increasingly they

compete for the commercial shrimp catch with large trawlers from Panama City. Trawlers visit the Gulf

because of declining catches of shrimp elsewhere in the Gulf of Panama (FAO/ODP/EP/91/07 Report).This study evaluates the Darién’s estuary/watershed as one system and continental shelf area as

another. It considers development options that maximize the economic/ecological system value of bothto help resolve the following concerns: a) conicts over shrimp stocks between local artisanal shermen

and the Panamanian industrial trawler eet, b) decline of offshore shrimp resources, c) lack of economic

development and investment in Darién sheries.

METHODOLOGY

First, data were collected for this report in 1998 by eld sampling, remote imagery and published

materials. Field sampling involved market and vessel surveys, interviews and limited net sampling.Remote imagery using satellite data (Landsat images) was used to calculate data (GIS) for system areas

and boundaries.The gulf and the continental shelf areas of Darién were separated for the purpose of this study

because sheries in the two zones are different. The Gulf of San Miguel is calmer and is traditionally

shed by local shermen in small inshore vessels. Only recently has this area been subject to shing by

non-Darién based trawlers that mostly shed the deeper and rougher continental shelf area of Darién.

This area is largely inaccessible to local shermen except for the very nearshore areas by shermen in

pursuit of n-sh. Physically, the continental shelf area is distinct from the gulf. The gulf was evaluated

including the wetland areas adjacent to the gulf.

Second, a systems diagram was drawn to help show the main processes of the estuarine/coastalsystem and visualize how complex interactions can develop cumulative impacts. The processes arerepresented with diagrams drawn with energy systems language. Circles outside the dened boundary

frame are sources of external resources, good and services. This approach retains the features of thelarge-scale interactions at the expense of the small-scale inuences and individual species and is the

appropriate level to make most policy decisions (Odum, H.T., 1996).Third, a list of the important sources, (external causes, external factors, forcing functions) is

made from the diagram. Then the principal component parts and ows are listed in an analysis table for

quantitative comparisons. Quantitatively measure of the different components of the system has beenmissing in many ecosystem models. In this study, the systems model ows are converted to a common

base (joules) to give them the same value. Each ow is then converted by its transformity, calculated

from previous studies (Odum & Arding, 1991, Odum, 1996). For example, the production of shrimpstarts with sunlight producing organic matter used by benthic invertebrates to grow, that are then eatenby shrimp to build storages of biomass, which go to support larger long-lived consumers such as snapper.

Table 1. Biomass of Two Wetland Forests in Darién (Source: Golley et al., 1972).___________________________________________________________________________________Compartment (kg/ha dry weight/year) Riverine Mangrove___________________________________________________________________________________Total living biomass 1,188,760 469,094

Total dead biomass 19,075 102,106



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Fourth, two emergy indices, investment ratio and yield ratio are applied to the aggregated calculationsso comparisons can be made and conclusions drawn. Emergy Investment Ratio is the emergy from theeconomy divided by the emergy free from environment. Emergy Yield Ratio is the emergy yield dividedby the emergy from the economy.

RESULTSThe systems diagram in Figure 2 shows that much of the estuarine productivity (sub-system on

the left) is the result of the converging contribution of several resources (see Table 2). The main ows

include river organic matter and nutrients (11.8 E20 sej/yr), tides (44.8 E20 sej/yr), river chemical potential

energy (82.1 E20 sej/yr), and the surge of freshwater during the wet season (1.33 E20 sej/yr). Theseows contribute to estuarine primary productivity and support large populations of n-sh, crustaceans

and bivalve mollusks as well as human economic activity. Commercial sheries are based on species of

Carangidae, Clupeoididae, Lutjanidae, Centropomidae, Serranidae, Penaeid shrimp species and bivalve

mollusks. In addition, there are important groups of euryhaline species of lesser commercial importanceincluding mullet (Mugilidae), catsh (Ariidae), grunts (Haemulidae), sharks and bobos (Polynemidae)

that inhabit the estuary and its tributaries. Some of the emergy in the Gulf is deposited and resuspended asne muds and organic matter throughout much of the estuary and some may be oxidized in the sediment.

The energy pulses of the system result in a pattern of inshore and offshore reproduction and inshorenursery growth.

Table 2 shows biogeographical Data for the Gulf of San Miguel Estuarine System and DariénContinental Shelf System. The Gulf of San Miguel occupies an area equal to about 13% of Darién province.A total of 1.06 m3/yr of runoff into the gulf is calculated from the watershed area of 1.1 E9 m2.

Table 3 shows annual EMERGY Flows of the Gulf of San Miguel and Darién ContinentalShelf.

The renewable empower calculated to ow through the Gulf of San Miguel was 143 E20 sej/

yr. Based on the area of the gulf of 2.2 E9 m

2

, this is the equivalent empower density of 7 E17 sej/yr/m2 or an emergy concentration about twenty-eight times greater per unit area than average for Panama(McLachlan-Karr, 1998 IDB unpublished report). The estimated Gulf shrimp catch of 1.1 E6 kg/yr (0.22

E20 sej/yr) is about 50% of the estimated sustainable yield for the total Panama Pacic coast.2 The n-sh

Table 2. Biogeographical Data for Gulf of San Miguel Estuarine System/Darién Continental Shelf System(from GIS data Dames and Moore, 1998, or as referenced)___________________________________________________________________________________Estuary Area of Gulf of San Miguel (not mangroves) = 2.2 E9 m2

Average inow from 5 watersheds into the Gulf = 857.4 m3/sec (Lowman, 1970)Water shed area = 1.1 E9 m2 Runoff total = 1.06 E9 m3/yr Runoff 44% of precipitation (Golley, 1969)

Mangrove area in Gulf = 2.8 E9 m2

Area of riverine forest 1.0 E8 m2

Area of mangroves (not Gulf) = 4.07 E8 m2 Shore line length Gulf = 30 kmAverage rainfall in Gulf at Jaque = 2.19 m/yrWater retention time in Gulf is about 46 days.

Area of Pacic shelf of Darién from mean high tide to the 50 m bathymetric line = 1.76 E9 m2 Shoreline length of coast (not Gulf) = 146 km

Area of continental shelf to 200 m line = 4.1 E9 m2

Darién area = 1.67 E11 m2

Area of estuary inside the trawler restricted zone 1.04 E9 m2



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yield is about 1.1 E6 kg (1.65 E20 sej/yr) or 25% of the Pacic coast catch. Over 95% of the combined

artisanal n-sh + shrimp yield (1.9 + E20 sej/yr) or about 1.3 % of the total combined emergy ow of

the estuary is exported to Panama City because of the higher prices there.Renewable emergy of 136 E20 sej/yr (estimate) is received by the continental shelf of Darién

(empower density of 3.3 E12 sej/m2/yr).3 (See Table 3.) The main ows of renewable emergy to the

continental shelf include chemical potential energy of freshwater from the short coastal rivers and streams(2.1 E20 sej/yr), tides (37.7 E20 sej/yr, and mixing energy and nutrients from the Colombia Current (17E20 sej/yr). Aerial surveys suggest that some emergy from the Gulf is deposited as ne muds and detritus

in the nearshore sediments of the continental shelf and the Colombian Current transports some of theorganic mater, sediment, and mixing energy further offshore particularly when the northerly currentsare strong. In the Gulf of Panama, the emergy ows mix with clear water, nutrient inows and mixing

energy from deep-water currents to increase primary production over a wide area. It supports the lessaccessible (deeper water shrimp) and pelagic sheries resources. Estimated annual shrimp catch in the

Gulf of Panama is 4.2 E6 kg (Panama en Chifras, 1996).4

DISCUSSIONFigures 3 and 4 show the aggregated emergy ows that support sheries in the Gulf of San

Miguel and Darién’s continental shelf. The renewable environmental inputs (I) from the left, purchasedinputs from the economy from the top (F) and the yield of product outow (Y) is on the lower right. Two

indices calculated from these gures are compared to give perspectives on the type and efciency of the

environmental use. The ultimate purpose of environmental accounting indices is to match the renewableows of the system for maximum sustainable contribution to the human economy.

The Emergy Yield Ratio (EYR) is the emergy of yield divided by the emergy of all the feed-backsfrom the economy (Y/F). The EYR of each system is a measure of its net contribution to the economybeyond its own operation. This index is important for economic activities since they must support more

than their own system (<1). The EYR for artisanal shing in the Gulf is 31 and the EYR for industrialtrawling on the Darién’s continental shelf about 2.5. While both activities make a net contribution to theoverall economy, the wide difference in the results reects the large disparity in economic development

between Darién and Panama. An EYR of 31 reects a low level of investment and is typical of very

undeveloped regions (Odum, 1996) that tend to export their wealth. It also signies that artisanal shing

N

PMangroves

Shade

Environ-mentalEnergies,Sun,Tide

Sed.,OrganicMaterial

ConsumersShrimps,Snappers

R<P

Yield

Gulf of San Miguel

Sun

R<P

N K.E.Turbidity

PAlgae

SedimentsOrganicMaterial

RShrimp,Fin-fish

Continental Shelf

Gulf of Panama

Yield

Migrations

FishingEffort

Tides,Winds,Waves,EstuaryWater

Anthro-pogenicNutrients

N1

Migrations

FishingEffort

R

Rivers,Organics,

SuspendedSolids,

Nutrients

Migration

Figure 2. Systems diagram of the estuarine Gulf of San Miguel and the adjacent continental shelf. Sed = Sediments,

N = Nutrients, P = Photosynthesis, R = Respiration, K.E. = Kinetic Energy of water.



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Table 3. Annual EMERGY Flows of the Gulf of San Miguel and Darién Continental Shelf (See Appendixfor Calculations)________________________________________________________________________ Raw Solar Units Transformity Emergy Emdollars*Note Item J, g, $ sej/unit1 E20 sej/yr E6 US Em$________________________________________________________________________Gulf of San Miguel1 Sun 9.85 E18 J 1 0.096 3.92 Wind 1.1 E17 J 663 .73 2 9.63 Rainfall 2.1 E16 J 6380 1.33 54

3a Rain Phosphate 2.1 E8 g 8.1 E9 .017 .73b Rain Nitrogen 7.1 E8 g 9 E8 .063 2.4

4 Tides .9 E17 J 23564 44.8 1819

5 Waves 5.6 E 12 J 25889 0.014 .57

6 Rivers Chemical

Potential 2 E17 J 41068 82.1 33337 Rivers Organic Load 1.9 E16 J 62400 11.8 479

7a Rivers Total N 9.9 E10 g 9 E8 .9 36.57b Rivers Total P 1.1 E10 g 8.1 E9 .9 36.59 Estuary Fishery Fuel 1.02 E14 J 53000 0.06 1.7

10 Estuary Fishery Goodsand Services 6.1 E5 $ 2.46 E12 .015 0.61

11 Estuary Shrimp Yield 5.5 E12 J est. 4 E6 0.22 47.1

12 Estuary Fin-sh Yield 5.5 E12 J est. 30 E6 1.65 81.6

Total Emergy Flow 146 5846

Continental Shelf 1 Sun 1.83 E19 J 1 0.18 7.32 Wind 2.1 E17 J 663 1.4 56.8

3 Rainfall 4.1 E16 J 6380 2.6 105.6

3a Rain Phosphate 4.1 E8 g 8.1 E9 0.03 1.2

3b Rain Nitrogen 1.6 E9 g 9 E8 0.01 0.4

4 Tides 1.6 E17 J 23564 37.7 1530

5 Waves 5.3 E 13 J 25889 0.014 .57

6 Rivers ChemicalPotential 5.2 E15 J 41068 2.1 85.3

7 Rivers Organic Load 4.8 E15 J 62400 3 1227a Rivers Total N 2.5 E10 g 9 E8 0.22 8.97b Rivers Total P 2.8 E9 g 8.1 E9 0.22 8.98 Current Energy 1.4 E14 J 8 E6 11.2 455

8a Deepwater Nutrient N 2.8 E11 g 9 E8 2.5 101.58b Deepwater Nutrient P 3.9E10 g 8.1 E9 3.2 1239 Trawler Fishery Fuel 1.3 E14 J 53000 0.07 2.8

10 Trawler Fishery Goodsand Services 1.1 E7 $ 2.46 E12 0.27 10.9

11 Trawler Fishery Yield 2.1 E13 J 4 E6 0.84 38.4

Total Emergy 65.4 2,725.0

Total Emergy Used = 6.5 from the shelf + 71 from the Gulf = 136.0 5,666.0

________________________________________________________________________* Solar emergy divided by 2.4 E12 sej/$.

For footnotes with calculations, see Appendix.



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makes a large net emergy contribution to the economy that purchases it. An EYR (2.5) is about the sameas the average calculated ratio for Panama (2.4) (McLachlan-Karr and Tilley unpub., 1998) and means the

industry is holding its own in the national economy, although a FAO/ODP/EP/91/07 study found that theprototypical trawler in Panama lost money in 1991. This suggests that the industrial eet may diversify

into other species.5 This also suggests that the trawler eet is overcapitalized and could be reduced by

about 20% for the benet of the national economy.

Energy investment ratio (EIR) is the purchased emergy (F) feedback from the economy dividedby the free emergy inow from the environment (I) F/I. This index measures the intensity of the economic

development and the loading on the environment. In highly developed countries like the U.S., theinvestment ratio tends to be 7 or higher. In Panama, the ratio was about 0.31 in 1997 (McLachlan-Karr,

1998). A low EIR signies that a large proportion of the population is rural and many of the resources

that people use are received free from the environment including food, energy, transportation, buildingmaterials and recreation. A reection of this is the use of high transformity energy such as electricity. In

1972, the kWh/inhabitant for Panama City was 695 and for Darién 23 (Proyecto de Desarrollo Integradode La Region Oriental de Panama, Darién No. 8 1976). From this, it is estimated that the overall emergy

investment ratio for Darién as a province would be at least 10 times lower than the overall Panama ratio

or around 0.03.The EIR for artisanal shing in Darién is 0.0004 and for industrial trawling is 0.0025. The

economic investment ratios in the two sheries are difcient by nearly two orders of magnitude, reecting

the inaccessability of Darién’s continental shelf and the small number of large vessels that sh this wide

area. A high EYR and low EIR for artisanal shing in Darién also means that the prices of the product

are competitive because an activity that receives a larger contribution free from the environment producesthe same goods for less (Odum, 1996).6 However, the industry does not attract many additional resourcesfor development and it also implies that it attracts investment at a level below what is possible. In otherwords, there is an unused potential available in the natural resources that can be usefully applied whenthey are combined with more economic inputs. A low EYR and low EIR (0.0025) for trawlers also reects

the much larger area in which they operate and limited number of target species.Without direct development assistance, the inputs used to increase the EIR in the Gulf are likely

to come from outside of Darién so as to process more output and more money. In Darién, too little moneycirculates to allow local nancing of larger shing vessels and processing facilities so the tendency will

be to increase investment from Panama City (or internationally). This is forecast by the difference in theemergy per dollar use. The emergy per dollar was calculated to be 2.4 E12 sej/yr for Panama in 1997

I142.8

Y1.9

F0.06

Production

EmergyYield Ratio = = 31Y

F

Emergy Investment Ratio = = 0.0004F

I

Figure 3. Aggregated diagram of emergy ows for sheries production in the Gulf of San Miguel. Fishery ProductsY = 0.22 E20 sej/yr. (shrimp) + 1.68 (n-sh) = 1.9 E20 sej/yr;

Environment I = 142.8 E20 sej/yr;

Economic Feedback F = 0.06 E20 sej/yr.



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(McLachlan-Karr, 1998). This is similar to West Germany with 2.5 E12 sej/$ in the 1980s (Odum, 1996)

and due to the intensity of economic activity associated with the Panama Canal, including the large bankingand free trade infrastructure and US military bases, in a small area. In rural Darién, the emergy use perdollar would be much higher. Because of this disparity, incursions into the Gulf by trawlers from PanamaCity are likely to increase and eventually displace many of the local shermen altogether. This loss of

wealth from Darién is very obvious to the local shermen who protest vehemently to the Government

of Panama. It is reected in the drift of Dariénitas away from the Gulf of San Miguel and to the urban

areas of Panama City.Trawlers from Panama City frequently enter the Gulf of San Miguel to catch the abundant, though

smaller, shrimp. Trawler beam trawl nets catch all sizes of shrimp (and sh) including the juveniles because

the nearby mangroves serve as a nursery area for many species in an estuary dominated by energy pulses.These pulses organize many species into a pattern of estuary reproduction/nursery and offshore growth.Other species follow an offshore growth and reproduction, estuary nursery area. Artisanal shermen take

less by-catch here because they use tangle nets that catch larger shrimp and adult estuarine sh.7 Goodpolicy for developing the Gulf’s sustainable regional shery is to exclude trawlers and establish protected

nursery areas supported by local management. The feasibility of this is under consideration with the

Environment Authority (INRENARE, 1996).8

Calculations suggest that the sustainable shrimp and n-sh yield may be higher than the current

catch because of the concentrated renewable emergy ows in the Gulf of San Miguel. 9 If economicincentives were created that would keep more of the catch in Darién, investments would be attracted tothis area and prices would drop. What is the level of investment and development in shing will make

the highest sustainable benets to Darién? Artisanal shing, even with outboard motors, has a too low

investment ratio to compete with the industrial eet or to attract much additional investment to develop

the local economy. Developments at a higher investment ratio before its time, (0.31, for example, thePanama ratio), however, would severely disrupt the local economy because:a) On the local scale, a subsidized development of an intensive activity ahead of general development ofthe region results in products being too expensive for local use, so the benets tend to be exported.

b) High levels of shing effort are required to repay initial loans. Loans are paid back by exports so

emergy is exported and target species may be over shed.

c) Benets may only be accrued to a few persons so all investment comes from outside the area, employment

I136

Y0.84

F0.34

Production

Emergy Yield ratio = = 2.5YF

Emergy Investment Ratio = = 0.0025FI

Figure 4. Aggregated diagram of ows for shrimp production by trawlers on Darién continental shelf. Environmental Inputs I = 71 E20 sej/yr (half of emergy from Gulf exported) + 65 E20 sej/yr on the shelf area = 136 E20 sej/yr;

Yield Y = 0.84 E20 sej/yr;

Economic Feedback (Fuels, Goods & Services) F = 0.34 E20 sej/yr.



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is based elsewhere and prots are exported.

d) If the benets are limited to a few, most shermen will not support community based resource

management initiatives.10

e) Developments at high EIRs requires more infrastructure such as port facilities, energy, goods andservices than can be supplied by the local economy so would likely fail.11

Between the two extremes of shing effort in the Gulf of San Miguel, there exists an appropriate

level of investment in shing that will increase wealth to the province (Figure 5). The best matching is

when the investment is around the EIR ratio for the region. This level is calculated as a function of thecompetitive level of investment in the local economy and renewable capability of the resource.

In Figure 5, examples of shing investment efciencies are: 1) Artisanal shery in Darién, 2) Gulf of

Panama large trawlers, 3) estimated most efcient level for shing in Gulf of San Miguel.

Figure 5 shows an intermediate point where the level of shing activity is commensurate with

Darién’s level of economic development. Financing proposed for Darién development by GovernmentAgencies may be best utilized to provide incentives to develop a moderate sized eet of wooden boats

(about 20 feet in length), diesel powered, multi-purpose using tangle nets, small beam trawls, traps andlines to sh year round and based in Darién. This would require nancial and logistical infrastructure for

catch processing. The vessels may be developed and built locally and cost an estimated $8,000/vessel(pers. comm. La Palma boat-builders corp.). Such vessels would replace some of the canoes/large dinghies

35 31 15 2.5 1

T r

a n s f o r m i t y

ThermodynamicMinimum

(1) Artisanal Fisheries

(3) Best forDarien?

(2) IndustrialTrawlers

0.00031ArtisanalFisheries

0.0031 0.031 0.31Panama

3.1Panama

City?Emergy Investment Ratio (EIR)

Energy Yield Ratio (EYR)

Figure 5. Examples of shing investment efciencies: (1) Artisanal shery in Darién; (2) Gulf of Panama large

trawlers (this study); (3) estimated most efcient level for shing in the Gulf of San Miguel.



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currently used and can be used to sh the shelf areas for shrimp when the weather allows. Such boats

may also convert to n-sh12, lobsters, or crabs when the Gulf and continental shelf areas are closed toshrimp shing. Anticipated benets include:

1. Greater employment in support industries such as: processing, energy generation, transportation,port infrastructure, provisioning and boat building and repair;2. Development of local infrastructure for processing and marketing for the benet of all shers

including artisanal shers;

3. Greater circulation of money within Darién and year-round employment;4. Daily landings mean a potentially a higher quality product through less handling and freezing

and greater local consumption of sh because prices should drop;

5. Local support for sheries management initiatives.

This ecological economic evaluation of sheries in Darién shows a large disparity in resource

use between trawler and artisanal shermen. The trend has been an increase in the Gulf of San Miguel

commercial catch with little benet to the people of Darién. The alternatives presented here represent

solutions that are sustainable because their contribution to the economy over the long term is greater.

ACKNOWLEDGMENTS

The author thanks the Inter American Development Bank, Government f Panama and the staff at Damesand Moore, Inc., Panama, for assistance in data collection.

REFERENCES

Alcaldía de Panamá. 1997. Dirección de Mercados Resultado en Encuesta Dueños de Botes Area del

Terraplén. Departamento De Asistencia Técnica.Decreto Ejecutivo No. 124 Por Medio del Cual se Dictan Disposiciones para Regular la Pesca de

Camaron.Golley, F. B., J. T. McGinnis, R. G. Clements, G. Child, and M. Duever. 1972. Mineral Cycling in a Tropcial Moist Forest Ecosystem. University of Georgia Press Athens, Georgia.Lowman. 1972. In Mineral Cycling in a Tropical Moist Forest Ecosystem. University of Georgia

Press, Athens, Georgia.Lugo, A, S. Brown and M. Brinson. 1988. Forested wetlands in freshwater and saltwater

environments. Limnology Oceanography 33.

Martin, J. H. 1969. Distribution of C, H, N, P, Fe, Mn, Zn, Ca, and Sc in Plankton samples Collected

off Panama and Columbia. Bioscience 19 No. 10.Martin, W. E., J. A. Duke, S. G. Bloom, and J. T. McGinnis. 1969. Possible effects of a Sea Level

Canal on the Marine ecology of the American Isthmian Region. BioEnvironmental andRadiological Safety Feasibility Studies Atlantic-Pacic Interoceanic Canal.

McLachlan-Karr, J. and D. Tilley. 1998. Emergy Evaluation of Panama in Unpublished Report,

Dames & Moore, Inc. to IADB Panama.Mendez, T. E. 1979. El Darién Imagen y Proyecciones. Ediciones Instituto Nacional de Cultura

Panama.Ministerio de Comercio e Industrias. 1997. Direccion General de Recursos Marinos.

Montero, J. R. and D. Lopez. 1988. Evaluation del Estado de Explotacion del Camaron Blanco enPanama y Recommendations para su Mejoramiento Memorias del Simposio Internacional de

los Recursos Vivos y Las Pesquerias en el Pacico Sudeste.

Muschett, I. 1974. Sobre la Composición Química y El Aporte Nutritivo de Los Ríos Lluvias



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Adyacentes al Golfo de Panama Thesis 1974.

Odum, H.T. and J. Arding. 1991. Emergy Analysis of Shrimp Mariculture in Ecuador. Coastal Resources Center, Narragansett, Rhode Island.Odum, H.T. 1997. Environmental Accounting. John Wiley and Sons, New York.Odum, H.T. and M. Munroe. 1993. Simulation of the Laguna Joyuda Ecosystem in Western Puerto

Rico. Acta Cientica.

OLDPESCA Grupo de Trabajo Fao/Oldepesca en Economia Y Planicación Presquera para America

Central. 1991.Organization of American States (OEA) Desarrollo Integrado de la Región Oriental del Darién. 1978.Panama en Chifres. 1992-6. Direccion de Estadística y Censo, Panama, 1997.Philomena, A.L. 1990. Shrimp Fishery: Energy Modeling as a Tool for Management. Ecological

Modeling.Programa Regional de Apoya al Desarrollo de la Pesca en el Istmo Centro-Americano PRADEPESCA.Proyecto de Desarrollo Integrado de La Region Oriental de Panama (Darién). 1975.

Situacion Meteorologia. 1995. Panama, Pamama.Smayda, T.J. 1966. A quantitative analysis of the phytoplankton of the Gulf of Panama III. Inter-

American Trop. Tuna Comm. Bull. 7.Templeton, W.L., J.M. Dean, D.G. Watson, Loftin, H.G. and L.A. Rancitelli. 1969. Final Report,

Freshwater Ecology BioEnvironmental and Radiological Safety Feasibility Studies, Atlantic- Pacic Interoceanic Canal. Battelle Memorial Institute.

APPENDIX: CALCULATIONS FOR LINE ITEMS IN SUMMARY TABLE 3

1. Sunlight absorbed at surface = (area)(insolation)(1-albedo)Insolation average =1.55 E2 kcal/cm-2/yrAlbedo = 0.31 (31% average annual absorbed for Darién Situacion Meteorologia, 1995))Estuary total = (2.2 E9 m-2)(155 E4 Kcal/m2/yr)(1-0.31)(4186 J/Kcal) = 9.85 E18 J/yr

Continental shelf = (4.1 E9 m2)(155 E4 cal/m2/yr)(1-0.31)(4186 J/Kcal) = 1.83 E19 J/year

2. Wind absorbed at surface (Kinetic energy)

Annual energy = (height)(density)(diffusion coefcient)(wind gradient)(area)

Average height of province = 471 m (OAS, 1978)

Density of air = 1.23 kg/m3

Diffusion coefcient = 2.25 m3/m2/sec (Situacion Meteorologia, 1995)Wind gradient = 0.58 E-3 m/sec/m (Situacion Meteorologia, 1995)Conversion = 3.154 E7 sec/yr

Estuary energy = (1000 m)(1.23 kg/m3)(2.25 m3/m2/sec)(.58E-3 m/sec/m)(3.154 E7 sec/yr)(2.2 E9 m2) =1.1 E17 J/yrContinental shelf = 2.1 E17 J/yr

3. Rain chemical potential energy (Annual) = (area)(rainfall)(Gibbs free energy)Rainfall Ave. Over shelf = 2.0 m/yr (Martin, et al., Battelle Memorial Institute, 1969).Rainfall Ave. Over estuary = 1.91 m/yr (Proyecto de Desarrollo Integrado de Darién, 1976)Gibbs energy = 4.94 J/g

Conversion 1 m3 H2O = 1 E6 gEstuary annual energy = 2.1 E16 J/yrContinental Shelf Annual energy = 4.05 E16 J/yr

Mangrove area annual energy = 3.3 E15 J/yr

3a. Phosphorus in rain annual amount = (area)(rainfall rate)(average concentration)(conversion)



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Concentration total P = 0.05 g/m3 (Golley et al., 1972)Annual input estuary = 2.1 E8 g/yrAnnual input continental shelf = 4.1 E8 g/yr

3b. Nitrogen in rain annual amount = (area)(rainfall rate)(average concentration)Concentration = 0.17 g/m3 NH3-N (Smayda, 1966)Estuary = 7.1 E8 g/yrContinental shelf = 1.6 E9 g/yr

4. Tidal energy (annual) = (area elevated)(0.5)(# tides/yr.)(ht)2(density)(gravity constant)# tides/yr = 706 tides /yr (US Army Corp of Engineers), estimated 50% of tidal energy absorbed.

Tide Height = 4.7 m average estuary at La Palma (Battelle Report, 1969)

Tide Height = 3.3 m average ave. shelf area (Battelle Report, 1969)Seawater Density 1.025 E3 kg/m3

Gravity constant 9.8 m/sec2

Annual energy estuary = 1.9 E17 J/yr (estuary)

Annual energy Shelf = 1.6 E17 J

5. Wave energy absorbed at the shore annual calculation = (shore length)(0.125)(density)(gravity) (height2)(velocity)(3.154 E7 sec/yr)

Gravity constant 9.8 m/sec2

Velocity (gravity constant)(assume depth at 10 m)^.5 = 9.9 m/sec

Wave height = 0.8 m (estimated average shelf, 0.5 m estuary)Continental shelf annual energy = (1.48 E5 m)(0.125)(1.025 kg/m3)(9.8 m/sec-1)(0.8 m)(0.8 m)(9.9 m/sec)(3.154 E7 sec/yr) = 3.7 E13 J/yr

Estuary annual energy = 5.6 E12 J/yr

Rivers and Streams6. River chemical potential energyAnnual energy = (volume of ow)(density of water)(G) where G is Gibbs free energy relative to

seawater = 138.8*ln(1 E6 - 450)/965000 = 4.88 J/g

4.16 E10 m3/yr for Gulf of San Miguel (Smayda, 1966)1.06 E9 m3/yr (estimate Continental shelf)Average dissolved solids = 4 g/l (Lugo et al., 1988)

Estuary Annual energy = (2.0 E11)(1 E6 g/m2) = 2.0 E17 JContinental shelf = 5.2 E15 J/yrOverall 450 mg/l Area = 1.09 E9 m2

7. Organic matter annual energy = (organic matter conc.)(volume of ow)(5 kcal/g)(4186 J/kcal)

Volume of ow = 4.16 E10 m3/yr for Gulf of San Miguel (Smayda, 1969)Vol. continental shelf = (ave. rainfall)(watershed area)(runoff coeff.) = 1.06 E9 m3/yrMangrove organic matter conc. (1021 g/m2/yr. Golley, 1969)(area of mangrove)/(area of estuary)= 21.0 g/m2/day + 0.8 g/m2/day = 21.8 g/m2/day from forestsStanding crop of litter riverine forest (Golley et al., 1972) = 140 g/m2/yrEstuary annual energy = 1.9 E16 JContinental shelf = 4.8 E15 J

Organic matter Mangrove biomass growth: 2.8g/m2/day (Snedaker, 1986) = 5.98 E16 J/yr

7a. Nitrogen in riversAverage NH3-N for rivers concentration = 0.17 g/m3 (Smayda, 1966)Annual energy Gulf = (0.17 g)(14g/at-g)(2.01 E10) = 9.9 E10 J



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Annual energy continental shelf = 2.5 E10 J

7b. Phosphorus in riversAverage P for all rivers 0.085 PPM (Golley et al., 1972)Annual energy Gulf = (0.085 mg/l)(1 g/m3/l)(31 g/g-at)(4.16 E10 m3/yr) = 1.1 E10 JAnnual energy continental shelf = 2.8 E9 J

8. Physical current energyThe Columbia current sweeps over the continental shelf of Gulf of Panama for 4 months at 35 cm/sec

and 25 cm/sec for 8 months (Smayda, 1966)Equation (4.1 E9 m2)(50 m average depth)(1.025 E3/kg/m3)(0.28m/sec)(0.28 m/sec) =1.65 E13JRate if replacement turnover from velocity and entry cross-section(0.28 m/sec)(3.15 E7 sec/yr)(50m)(100 E3 m)/(4.1 E9)(50) = 84 times per year

Energy absorbed = (1.65 E13)(84)(0.1) assuming 10% of energy absorbed = 1.4 E14 J

8a. Nutrients (Nitrogen) inowing from deepwater onto the shelf = 5.7

mg/m3

in dry season 0.67% of year. Wet 4.0 mg/m3

0.33% of year. Average = 5.14 with 40%exported (Odum, H., 1993)Energy = (0.6)(0.514 mg-at/m2/sec)(14 E-6 g/Nu-at)(3.154 E7 sec/yr) = 136 g/m2/yr(136 g/m2/yr)(.5)(4.1 E9) = 2.8 E11 g/yr

8b. Nutrients (phosphorus) inowing from deepwater on half of the area for the year.

Average concentration = 0.065 mg at/m2/s 40% exported (Carpenter and Capone, 1983)

*(0.6)(0.065)(31 E6 g P/mg ñat)(3.154 E7 s/yr) = 38 g/m2/yrAnnual energy continental shelf = 38 g/m2/yr (average)(.5)(4.1 E9 m2) = 3.9 E10 g/yr

9. Shrimp trawl fuel for 125 vessels (50% of the Pacic coast vessels est.)

7,500 gallons of diesel/lubricants per vessel per year. (OLDEPESCA Fao/Odp/Ep/91/07)

Total energy = (125)(7.5 E3 gal)(137 E6 J/gal) = 1.3 E14 J/yr

Artisanal shing fuel (outboard 20 to 40 hp)

Fuel used 20 gals/day 220 days per year (average shing days/yr per boat BID, 1975)

477 boats total registrations Darién (MICI, 1997). Half Pacic coast/half Caribbean (Est.).

100 boats full time shing 220 days a year. Total rest of boats = 69 at 220 days a year estimate.

Total fuel = (20 gal/trip)(220 days)(169 boats) = 7.4 E5 gals/yr

Total energy = (7.4 E5)(137 E6 J/gal) = 1.02 E14 J/yr

10. Goods and services (not fuel)

Shrimp trawl using average gures from all trawlers (OLDEPESCA Fao/Odp/Ep/91/07)= ($62, 000/year)(125 boats) + (2.5 E4 $ 10%depreciation per year)(125)

= $1.09 E7 $/yr in 1997Artisanal shing goods and services:

Boat value = $600Motor = $800Depreciation = $140/yr (10%)

Maintenance and Repairs = $200/yrCrew labor: (2)(2000) = $4000

Ice and food: $400/yr

Taxes: $20/yrInsurance: $40/yr (3%)

Total: $690/year/boat + Labor = $6.1 E5



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11. Trawl Shrimp Catch on the Darién Continental Shelf (9.35 E6 lbs.)(0.2 dry weight)(454 g/lb.)(6 kcal/g)(4186 J/kcal) = 2.1 E13 J/yr

Artisanal shing catch total = 137 lbs. per trip, (50% shrimp est.) (MICI, 1997), 220 days effective

shing (Projecto de Pesca Darién, 1975) = (169 boats)(220)(68.5lbs) = 2.5 E6 lbs./yr

Energy = (1.1 E6 kg)(0.2 dry wt.)(6 kcal/g)(4186 J/Kcal)(1E3 g/kg) = 5.5 E12 J/yr.

12. Fin sh Production in Gulf Estimated catch = 2.5 E6 lbs. (est.).

Energy = (1.1E6 kg)(0.2 dry wt.)(1E3g/kg)(6kcal/g)(4186 J/kcal) = 5.5 E12 J (Panama national n sh

catch of 1.04 E7 kg/yr (1996) and total sh exports value $21,838,000, Panama en Cifres, 1996).

ENDNOTES

1 Transformities from Odum and Arding: Emergy Analysis of Shrimp Mariculture in Ecuador, 19912 Montoro and Lopez (1989) give gures for the maximum sustainable catch of 2.04 E6 kg/year

for 40,000 days of shing for the current shrimp eet on the Panama/Pacic coast (around250 industrial trawlers) with a period of closed shing (4 months) to allow stocks to recover.

3 From Lowman (1970). For the purposes of this study, an estimated 50% of the Gulf’s emergy

is exported offshore.4 For trawlers working the shelf, there has been a decline in overall catch of commercial shrimp

species (Panama Chambre of Commerce Statistics, 1997).5 Good sheries policy for Panama may be to further limit licenses to foreign vessels shing in

its

territorial waters and allow the national industry to develop.6 Over 95% of the artisinal seafood production of Darién is exported to Panama City whereprices are higher (also AlcaldÌa de Panama, 1997) where the catch is processed and much is sold on international markets.7 Estimates of by-catch for trawlers operating in the Gulf of San Miguel range from 80 to 99%.8 Trawling in the Gulf is legally restricted beyond a certain point but difcult to enforce

(Decreto Ejecutivo No. 124, pers. comm. Panama Dept. Fisheries, 1998).

9 Organic matter production = (1 kg/m2/yr) Golley et al., 1972 (2.8 E9 m2) est. 0.1% org. matter transfer to consumer biomass = (4.8 E9 kg/yr)/.001 potential shrimp production (2.9 E6 kg/

yr). Turner (1985), gives commercial yield of adults per hectare of vegetated nursery in Ecuadoras 10kg. In the Gulf, this gives an estimated shrimp yield of 2.9 E6 kg/yr. = potential n-shery

production in Gulf of 1.9 E6 kg/yr.10 The establishment of locally based sheries management is designed to help local shermen

self regulate shing to sustainable levels. The marine protected area is hoped to reduce the



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Edited by



Associate Editors

Howard T. Odum



David Tilley



Sergio Ulgiati

University of Siena

Siena, Italy

December 2003



Gainesville, FL


vi



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Chapter 28. An environmental accounting of water resources production...

28

An environmental accounting of water resources productionsystem in the Samoggia creek area using emergy method


ABSTRACT

The purpose of this study was to evaluate the real value of water resources of a specic area

using emergy analysis. Emergy has the ability to calculate different types or quality of energy in one form, the solar emergy joule. Therefore, products and services are evaluated on the environmental efforts

necessary to generate them.

First part of the study is focused on an analysis of the natural resources. We evaluated the

emergy ow that supports local surface water, calculating the environmental inputs that are necessary to

sustain the Samoggia Creek. Groundwater reservoirs are also present in the area. Transformities reported

in literature are based on global processes. In the present study, to better evaluate local resources, we

suggest a new method for calculating them case by case. The new method considers as primary variables

the recharge time of the storage and the watershed area necessary for its direct rell.

The last part of the study consisted of an emergy analysis of the domestic water distribution

system of the six municipalities present in the area in order to underline the role of nonrenewable inputs

in the production of potable water. We consider all products and services necessary to extract water fromreservoirs, to treat it and to provide it to consumers.

Results obtained were translated in Emlire values through the emergy money ratio. By

transforming the solar emergy joules in economical values, Emlire, we obtained a value that takes into

account environmental works. The result, since it is not based on traditional economic rules, shows the

real value of water in different realities.

INTRODUCTION

The concept of sustainable development applied to water resources management should consider

water as the most precious resource in the whole planet. Water is essential not only for human primarynecessities and other activities that support our survival (e.g., agriculture) but, water is the essential

element to life on earth. Water is usually considered a “free” resource with little or no value because of

its availability.

Sustainability applied to water resources should consider efforts toward reducing pumping

from natural storage, toward reintroducing water as near as possible to its extracting zones and with

characteristics as similar as possible to natural ones. Most frequently, values of natural resources are

based upon their commercial value, not taking into account the environmental services necessary to make

them available for our needs.

In this study, in order to assess the environmental contribution, we apply emergy analysis,

rst to natural water sources and then to a potable water distribution system. This allows us to obtain a

comprehensive value of material and energy ows required to produce water, and make it accessible forhuman consumption.

The system we analysed for the evaluation of the sustainability of water resources management

is the area of the Samoggia River, in the Province of Bologna (Italy).



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METHODS

Description of the System

Samoggia creek mountain water basin extends over 29 km encompassing 170 km2 on the west

side of the Bologna province (central Italy). The area includes six municipalities and is characterized byhill and pre-apennine geography. In this area the Samoggia creek is not articially constrained. The area

is scarcely populated, dependent mainly on agriculture and not stressed by productive activities.

Water consumption in year 1999 was 9.05x106 m3 (3.54 x106 m3 (39%) for domestic use, 3.75

x106 m3 (41%) for industrial use and 1.76 x106 m3 (20%) for agriculture (Province of Bologna, 2000).

Samoggia creek itself and its minor tributaries supply most of agricultural water demand. Industry

satises its demand from a variety of small groundwater reservoirs within the system boundaries. The

most important groundwater resource in the area is located in the Bazzano municipality along the Panaro

River delta and is extracted only for domestic use.

System Diagrams and Emergy Table

The system is diagrammed by using energy systems language. Connection between natural and

articial system in the use and management of water are shown in Figure 1. The main ows and storages

and their variables are then evaluated and presented in the emergy tables.

Data reported in the tables were obtained from technical reports, literature, personal interviews

and, when necessary, from assumptions established in accordance with experts supervising the area.

Explanations of the assumptions are indicated in tables.

An accurate mass balance of the supply pipeline, and of the sand that covers it, was performed.

The results are listed in the emergy table.

Emergy tables, representing both natural system (surface and ground water) and the articial

Domesticuse

Wastewatertreatment

system

Rain

Importedwater

Domestic water

distributionsystem

Ground

water

Surface

water

Sunlight& Wind

Cement,

sand &pipelines

Fuels& electricity

Humanlabor

Chemicals

& machinery

Earthheat

Spring

water

Water

out

Agricultural

useIndustrial

use

Figure 1. Energy system diagram of Samoggia creek mountain basin area. Systems not evaluated in the emergy

analysis are colored in gray.



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one, were made listing inputs from the system diagrams. Each input was multiplied by its transformity

(or emergy-per-unit) in order to convert it in solar emergy joules. Transformities (or emergy-per-unit)

reported in the tables are taken from literature, calculated in the present work or calculated in a previous

work (Bastianoni, 2001) (full calculations are available from the authors).

Emmoney Evaluation

The nal part of the analysis consisted in translating ows of solar emjoule in economic value

by dividing the emergy value of environmental services by the emergy money ratio index. Due to the

unavailability of the total emergy ow through the area, the index used is referred to the Province of

Modena, a Province very similar to Bologna’s. The index represents the total emergy used in driving

the economy of the area in year 1999. The emergy values are expressed as emlire to provide units more

familiar with the public and to compare them with the commercial costs of the services in the area.


Figure 1 is an overview of the energy system diagram of the Samoggia creek mountain basin

area. Natural and articial systems are kept separated and their interaction is clearly demonstrated.

Emergy analysis was carried out rst on the natural ows and storage of water resources within the system

boundaries. The results obtained were then reported in the analysis of the potable water distribution

system.

Supercial Aquifer Analysis

The emergy evaluation of the Samoggia creek was achieved by calculating all the inputs necessary

to support the system: solar energy, rain, deep earth heating and spring water (Table 1). The watershed

covers 170 km2

, a semi-permeable surface that collects precipitation to the water stream. Emergy owsthrough the territory due to the Samoggia creek were calculated at 1.80x1019 sej/year.

The output of the system is the amount of water that ows every year in the river, 5.93x107 m3

(Table 1). The main emergy input to the territory is rain. This is because the amount of water owing in

the Samoggia creek mainly depends on precipitation collected by its watershed.

Table 1. Emergy evaluation of the Samoggia creek surface water (see footnotes for detailed calcula-

tions).___________________________________________________________________________________

Solar Emergy

Item Raw Unit Transformity Flow

unit (sej/unit) (sej/yr)___________________________________________________________________________________

1 Insolation 5.60x1017 J 1 5.60x1017

2 Precipitation 7.90x1014 J 1.82x104 1.44x1019

3 Earth heat 5.36x1014 J 6.01x103 3.22x1018

4 Spring water 1.09x1013 J 4.10x104 4.47x1017

Total emergy 1.80x1019

5 Surface water 5.93x1013 g 3.04x105

__________________________________________________________________________________

_



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Emergy per mass was found to be 3.04x105 sej/g, which is comparable to the value in Odum

2000 (4.00x105 sej/g) even though the two values were calculated at local and global scales.

Potable Water Distribution System

Water extractions within the area (7.15x105 m3) satisfy only 20% of the total domestic water uses

(3.52x106 m3). The local distribution network (707 km), which is a subsystem of the Bologna aqueduct

(6.3 km), directly provides the main part of the water demand of the area.

Emergy evaluation of the aqueduct supplying potable water in the Samoggia area is presented

in Table 2. Local extractions mainly depend on groundwater. The 2.00x105 m3 of surface water was

evaluated by applying emergy per mass value obtained in this analysis. To avoid double counting, only

20% of goods and services were included in the analysis, since the emergy per mass of imported water

(1.78x106 sej/g) from the Bologna aqueduct already includes such inputs.

The emergy per mass estimated at 2.16x106 sej/g, represents the emergy ow that supports

extraction, treatment and distribution of drinking water.

Em£ Evaluation

Emergy ows calculated in Tables 1 and 2 were translated into Em£ values by using the emergy

money ratio of the Modena Province in 1999 (7.26x108 sej/£). Results are shown in Table 3. The emvalue

of potable water per m3 is 2,973 Em£, a value that is almost seven times higher than supercial water (418

Em£/m3). The difference is due to the nonrenewable resources that are used to make water available for

human consumption. On the other hand, the large amount of water ow in the Samoggia river (5.93x107

m3/year), compared with domestic use (3.54x106 m3/year), results in a higher nal emlire value for surface

water (24.79 and 8.42 billions of Em£, respectively). In 1999 the price of drinking water in the area was 1,483 £ per cubic meter, about half of the

emvalue calculated in this study. Policy makers are presently discussing to revise such price in order to

reduce consumption of potable water.

Ground Water Resources Analysis

Hereafter we suggest a revised method for calculation of groundwater transformity, since

literature value (4.10x104 sej/J) (Brown and Arding, 1991) is based on global processes and not evaluated

locally.

Natural maintenance of a reservoir mainly depends on two parameters: precipitation on land and

water amount of supercial aquifers on the corresponding drainage area. Precipitation causes a rise of

water table, while supercial aquifers can both receive or confer water to the underground storage. Both

events occur with a time delay, as is shown in Figure 1, with small boxes on the pathways.

In overall water balances, the ow domain is considered to be vertical, from the soil surface to the

impermeable base of the groundwater reservoir (Boonstra, 1995). As a result water table level is dependent

on soil porosity and on the extension of the permeable area to which precipitation and supercial aquifer

ow. The last important variable for the formation and maintenance of a reservoir, is the time required to

ll it once extractions are carried out (turnover time of recharge), since natural rells occur with a time

delay.

Inputs of the system therefore are: rain on the drainage area, amount of water carried by the



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supercial stream in the area and sedimentary materials that ltrate water. Those inputs must be considered

for every year necessary to recharge the aquifer. The output is quantity of groundwater in the reservoir.

Table 2. Emergy evaluation of the system supplying potable water in the Samoggia area, Italy. See

footnotes for detailed calculations and text for full explanation.

___________________________________________________________________________________

Solar Emergy

Item Raw Unit Transformity ow

unit (sej/unit) (sej/yr)___________________________________________________________________________________

1 Water

Surface water 2.00x1011 g 3.04x105 6.08x1017

Ground water 2.54x1012 J 4.10x104 1.05x1017

Imported water

(Bologna aqueduct) 2.83x1012 g 1.78x106 5.03x1018

2 Pipeline

Polyethylene 1.58x106 g 5.87x109 9.30x1015

Asbestos cement 2.70x106 g 1.54x109 4.16x1015

Steel 7.06x106 g 4.65x109 3.28x1016

PVC 7.61x104 g 5.85x109 4.45x1014

Pig iron1. 62x106 g 2.65x109 4.29x1015

Other 4.22x105 g 3.91x109 1.65x1015

3 Electricity 1.98x1012 J 1.43x105 2.83x1017

4 Fuels

Gasoline 2.61x1011 J 6.60x104 1.72x1016

Diesel 6.47x1011 J 6.60x104 4.27x1016

5 Machinery 1.48x106 g 6.70x109 9.90x1015

6 Human Labour 5.28x109 J 7.38x106 3.90x1016

7 Tanks (concrete) 4.04x107 g 1.54x109 6.22x1016

8 Sand surface 4.23x108

g 1.00x109

4.23x1017

Total emergy 6.11x1018

9 Potable water 2.83x1012 g 2.16x106

__________________________________________________________________________________

_


Transformity, or emergy-per-mass, can be evaluated as:

If we consider different aquifers bearing the same amount of water we will likely obtain higher transformity

when:

x



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• Drainage area is larger and

• Turnover time of recharge is greater.

Therefore this method is strickly dependent on location and landscape characteristics.

Due to the unavailability of the required set of local data, only a theoretical approach is presented here.

Groundwater transformity used in the previous calculations is taken from literature.

Table 3. Em£ evaluation of the Samoggia creek and of local drinking water distribution facilities (see

footnotes for detailed calculations).

__________________________________________________________________________________

_

Samoggia creek Potable water supply system

__________________________________________________________________________________

_

Emergy ow (sej/year) 1.80x1019 6.11x1018

Emlire value (billions of Em£) 24.79 8.42

Value per m3 (Em£/m3) 418 2,973Current price in 1999 (£/m3) 1,483

__________________________________________________________________________________

_


Once the required set of data would be available we could apply the Em£ evaluation, as reported

in Table 3, in order to estimate the real value of groundwater resources and to compare it with commercial

value currently given to them.

CONCLUSIONS A deep knowledge of all materials and energy sources that support either natural or articial cycle

is fundamental for a sustainable management of water resources. Results demonstrate how the natural

cycle is strictly dependent on renewable environmental energy sources, and, on the other hand, how the

articial cycle is strictly dependent on nonrenewable resources. In this way reducing consumption of

drinking water means preventing waste of energy.

Preserving water storage from pollutants becomes more important if we consider the use of

nonrenewable resources in treatment process. Even though groundwater transformity has not been fully

calculated we expect it to be higher than that of surface water, a result mainly triggered by the time period

involved in the maintenance of the reservoir. Higher transformity would also be in accordance with the

higher quality of groundwater.Non renewable inputs required in producing drinking water can be reduced if:

1. the quality of extracted water is high (treatment processes will require less inputs);

2. leaks in distribution system are minimized (more output);

3. extractions are as close as possible to consumers (less distribution facilities).

REFERENCES

Bastianoni S., Fugaro L., Principi I. and Tiezzi E. 2001. Implementazione di un sistema di contabilità

ambientale su scala provinciale e intercomunale. Edited by Province of Bologna.

Bastianoni S., Marchettini N., Principi I. and Tiezzi E. 2000. Sviluppo di un modello di analisi emergetica

per il sistema elettrico nazionale. Final report for CESI edited by University of Siena.

Boonstra J. and Bhutta M. N. 1996. Groundwater recharge in irrigated agriculture: the theory and practice

of inverse modelling. Journal of hydrology, vol. 174, pp. 357-374.



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Odum H.T., Brown M.T. and Brandt-Williams S. 2000. Emergy of Global Processes Folio #1. Center for

Environmental Policy. University of Florida, Gainesville.

Odum, H.T. 1996. Environmental accounting. Emergy and environmental decision making. Wiley &

Sons, New York.

Province of Bologna. 2000. First Annual Report of the State of the Environmental in the Province of

Bologna. Italy.

SEABO (Province of Bologna Energy Supplying Company). 2000. Annual report. 1999. Bologna,

Italy.

Ulgiati S., Odum H.T. and Bastianoni S. 1994. Emergy use, environmental loading and sustainability. An

emergy analysis of Italy. Ecological Modelling, vol. 73, pp. 215-268.


1) Insolation

Solar insolation on Samoggia watershed = 1.70x108 [m2, watershed area] (Hydrological annals, 1978/79)x 4.12x109 [J/m2, insulation] (Geophysical observatory, Italy) x (1-0.2) [% albedo given as decimal] (Hen-

ning, 1989) = 5.60x1017 J. Solar transformity by denition 1 sej/J.

2) Rain

Precipitation on area = 1.70x108 m2 [m2, watershed area] (Hydrological annals, 1978/79) x 0.94 [m, mean

annual rainfall] (ARPA, environmental protection agency Italy; personal communication) x 4.94 [J/g,

Gibb’s free energy] = 7.90x1014 J. Solar transformity from Odum (1996).

3) Earth heat

Earth heat energy on area = 1.70x108 m2 [m2, watershed area] (Hydrological annals, 1978/79) x 3.15x106

[J/m2, heat ow per area] (Loddo and Mongelli, 1978) = 5.36x1014 J. Solar transformity from Odum

(1996).4) Spring water

Energy of spring water in river = 2.21x106 [m3, amount of water] (our estimate) x 1.00x106 [g/m3, water

density] x 4.94 [J/g, Gibb’s free energy] = 1.09x1013 J. Solar transformity groundwater from Brown and

Arding (1991).

5) River water

Quantity of water in the Samoggia creek = 1.88 [m3/s, mean annual river ow] (Hydrological annals,

1978/79) x 3.16x107 [s/yr, second per year] x 1.00x106 [g/m3, water density] = 5.93x1013 g.


1) Water

• Quantity of water extraction from surface aquifer = 2.00x105 [m3, amount of water] (Seabo, Prov-

ince

of Bologna Energy Supplying Company) x 1.00x106 [g/m3, water density] = 2.00x1011 g. Solar

emergy per mass resulting from calculations in this work.

• Quantity of water extraction from groundwater aquifer = 5.15x105 [m3, amount of water] (Seabo)

x 1.00x106 [g/m3, water density] x 4.94 [J/g, Gibb’s free energy] = 2.54x1012 J. Solar transformity

from Brown and Arding (1991).

• Quantity of water imported from the bologna aqueduct = 2.83x106 [m3, amount of water] (Seabo)

x 1.00x106 [g/m3, water density] = 2.83x1012g. Solar emergy per mass from Bastianoni et al.

(2001).

2) Pipeline

Brown M.T. and Arding J.E. 1991. Transformity Working Paper. Center for Wetlands, Environmental



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water resources = 20.2% of 7.84x106 g = 1.58x106 g. PVC Solar emergy per mass from Burana-

karn (1998).

• Asbestos cement mass in pipeline =2.85x104 [m, pipeline length] (Seabo) x 2.34x104 [g/m, mean

mass per meter of pipeline] (aqueduct mass balance) = 6.68x108 [g, mass of pipes] (aqueduct mass

balance) / 50 [yr, pipes lifetime] (our estimate) = 1.34x107 g. Amount of input referred to local

water resources = 20.2% of 1.34x107

g = 2.70x106

g. Solar emergy per mass for concretefrom Buranakarn (1998).

• Steel mass in pipeline =2.40x105 [m, pipeline length] (Seabo) x 7.28x103 [g/m, mean mass per me-

ter of pipeline] (aqueduct mass balance) = 1.75x109 [g, mass of pipes] (aqueduct mass balance)/ 50

[yr, pipes lifetime] (our estimate) = 3.49x107 g. Amount of input referred to local water resources

= 20.2% of 3.49x107 g = 7.06x106 g. Solar emergy per mass from Odum (1983).

• PVC mass in pipeline =1.15x104 [m, pipeline length] (Seabo) x 1.64x103 [g/m, mean mass per me-

ter of pipeline] (aqueduct mass balance) = 1.88x107 [g, mass of pipes] (aqueduct mass balance) / 50

[yr, pipes lifetime] (our estimate) = 3.77x105 g. Amount of input referred to local water resources

= 20.2% of 3.77x105 g = 7.61x104 g. Solar emergy per mass from Buranakarn (1998).

• Pig iron mass in pipeline =1.05x104 [m, pipeline length] (Seabo) x 3.82x104 [g/m, mean mass permeter of pipeline] (aqueduct mass balance) = 4.01x108 [g, mass of pipes] (aqueduct mass balance)

/ 50 [yr, pipes lifetime] (our estimate) = 8.02x106 g. Amount of input referred to local water

resources = 20.2% of 8.02x106 g = 1.62x106 g. Solar emergy per mass from Buranakarn

(1998).

• Unknown =2.10x104 [m, pipeline length] (Seabo) x 4.93x103 [g/m, mean mass per meter of aque

duct] (aqueduct mass balance) = 1.04x108 [g, mass of pipes] (aqueduct mass balance) / 50 [yr,

pipes lifetime] (our estimate) = 2.09x106 g. Amount of input referred to local water resources

= 20.2% of 2.09x106 g = 4.22x105 g. Solar emergy per mass evaluated as an averageof

aqueduct materials.

3) Electricity Electricity used = 9.80x1012 J (Seabo). Amount of input referred to local water resources = 20.2%

of 9.80x1012 J = 1.98x1012 J. Solar transformity from Bastianoni et al. (2000).

4) Fuel

• Energy content of gasoline used = 4.00x103 [l, fuel] (Seabo) x 734 (g/l, gasoline density) x

4.40x105 [J/g, net caloric value] = 1.29x1012 J. Amount of input referred to local water

resources = 20.2% of 1.29x1012 J = 2.61x1011 J. Solar transformity from Odum (1996).

• Energy content of diesel used = 9.00x103 [l, fuel] (Seabo) x 833 (g/l, diesel density) x 4.27x105 [J/g,

net caloric value] = 3.20x1012 J. Amount of input referred to local water resources = 20.2% of 3.20x1012

J = 6.47x1011 J. Solar transformity from Odum (1996).

5) Machinery• Steel content in cars = 9 [cars number] (Seabo) x 7.15x105 [g, car mass] (Seabo) x 0.95 [steel con

tent as percentage] (our estimate) / 15 [yr, lifetime] (our estimate) = 4.08x105 g.

• Steel content in trucks = 11 [trucks number] (Seabo) x 3.50x106 [g, truck mass] (Seabo) x 0.95

[steel content as percentage] (our estimate) / 15 [yr, lifetime] (our estimate) = 2.44x106 g.

• Steel content in pumps = 39 [pumps number] (Seabo) x 1.00x106 [g, pump mass] (Seabo) / 15 [yr,

lifetime] (our estimate) = 2.60x106 g.

• Steel content in tractors = 7 [tractors number] (Seabo) x 4.00x106 [g, tractor mass] (Seabo) / 15

[yr, lifetime] (our estimate) = 1.87x106 g.

• Total steel content of machinery = 4.08x105 g + 2.44x106 g + 2.60x106 g + 1.87x106 g = 7.32x106

g. Amount of input referred to local water resources = 20.2% of 7.32x106 g = 1.48x106 g. Solaremergy per mass from Brown and Arding (1991).

6) Human labor

Energy in human labor = 2.00x103 [h, working hours] (Seabo) x 5.23x105 [J/h, metabolism ener-



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gy] = 2.62x1010 J. Amount of input referred to local water resources = 20.2% of 2.62x1010 J = 5.28x109

J. Solar transformity from Ulgiati et al. (1994).

7) Tanks

Concrete in tanks = 1,00x1010 [g, concrete mass] (Seabo) / 50 [yr, lifetime] (our estimate) = 2.00x108 g.

Amount of input referred to local water resources = 20.2% of 2.00x108 g = 4.04x107 g. Solar emergy per

mass from Buranakarn (1998).

8) Sand surfaces

Sand mass used in pipeline = 2.10x1011 [g, sand] (estimated from pipeline mass balance) / 100 [yr, lifetime]

(our estimate) = 2.10x109 g. Amount of input referred to local water resources = 20.2% of 2.10x109 g =

4.23x108 g. Solar transformity from Odum (1996).

9) Potable water

Amount potable water distributed = 2.83x106 m3 (Seabo).


Samoggia creek:

Total economical value = 1.80x1019 [sej, Year emergy ow] (Table 1) x 7.26x108 [sej/£, Emergy money

ratio of Modena Province] (unpublished manuscript) = 24.79x109 Em£.

Economical value per m3 = 24.79x109 [Em£] / 5.93x107 [m3, Samoggia creek ow] (Hydrological annals,

1978/79) = 418 Em£/m3.

Potable water supply system:

Total economical value = 6.11x1018 [sej, Year emergy ow] (Table 2) x 7.26x108 [sej/£, Emergy money

ratio of Modena Province] (unpublished manuscript) = 8.42x109 Em£.

Economical value per m3 = 8.42x109 [Em£] / 2.83x106 [m3, drinking water distributed] (Seabo, 1999) =

2,973 Em£/m3.



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Edited by



Associate Editors

Howard T. Odum



David Tilley



Sergio Ulgiati

University of Siena

Siena, Italy

December 2003



Gainesville, FL


vi



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Chapter 29. Emergy Evaluation of the “Emternalities” in Non-Industrialized Regions...

29

Emergy Evaluation Of The “Emternalities” In Non-

Industrialized Regions: The case of two mountain

communities in Italy


ABSTRACT

A case study is presented in which the emergy evaluation is applied to two particular communities,called “Comunità Montana” (Mountain Community). A “Comunità Montana”, in general, is an

administrative association of municipalities located in a hill-mountain district characterized by some

peculiarities such as low density of population, difculties of communication and transportation, and

physical and natural obstacles for economic growth. However, at the same time, the mountain lands still

keep a priceless environmental heritage worthy of preservation.

The purpose of this analysis is to detect the factors of environmental loading even in such a

non-impacted area from an industrial point of view.

Emergy and emternality evaluations of all the inputs supporting the system are presented to give

a measure of the fundamental support of environment to a territorial system.

The results show that the environmental fraction sustaining both system is relevant (72% and

82% of total emjoules for the two communities); the economic valuation of this environmental fraction

(called emvalue) is very high due to the exploitation of local non-renewable resources; nally, their use

make the level of environmental stress higher than a typical non-industrialized mountainous area.

INTRODUCTION

Socio-economic reality of a mountainous area is atypical for several reasons: the low level of

population density and the propensity of the young people to leave and look for a job elsewhere, the

natural difculties in communication and transportation, limited industrial settlements, and many others.

This is a land characterized by fragile equilibria and physical constraints imposed by nature which seem

to jeopardize the principal goal for a modern society: economic growth. This paper considers the effect of

these constraints on the real wealth of a given population and attempts to answer this question: is economic

growth, measured by the trend of GNP, the real indicator of the state of a Nation or a region, when it does

not take into account all the elements without a market price, such as environmental resources?

In Italy, there are 350 administrative associations of municipalities called “Comunità Montane”

(Hill-Mountain Communities), whose purpose is to better face the difculties and to enjoy the subsidies to

development given by the national government and the European Union. The economy of these territories

is mainly based on agriculture and production of typical agro-alimentary products, on tourism both in

Summer and in Winter, and on a low level of manufacturing and craft. Activities are often dependent on

local resources and the environment plays a fundamental role in sustaining the population.

Our study is the result of one year of research in the territory of two communities called “ComunitàMontana del Metauro” and “Comunità Montana del Catria e Cesano” (in the course of this paper we will

say only Metauro and Catria for the sake of brevity) located in the Marche region, in the Center East of



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Italy. They are respectively 334.17 km2 (268.31 km2 classied as mountainous) and 226.54 km2 (208.38

km2 classied as mountainous) large. The inhabitants are 32,836 in Metauro and 13,860 in Catria.

The purpose of the investigation is to assess the use of resources, both manufactured and provided for free

by nature, inside the two systems, by means of emergy analysis. This enables us, rst, to determine the

relative weight of each class of used inputs, to offer some information and suggestions to decision makers

to make the anthropic system sustainable and, second, to detect the factors of environmental loading even

within such a non-industrially impacted region. Furthermore, we adopt the concept of “emternalities” to

assess the economic support of Nature to society always neglected in the traditional national (or local)

accounting systems.

METHODS

All evaluations presented in this paper are based on emergy methodology as a tool of environmental

(but not only environmental) accounting. The system is represented by an emergy diagram (Figure 1)

showing all the inputs supporting the system and all the interrelations between its internal components

and the external elements, such as the market.

The emergy of all inputs is calculated in terms of solar emjoules (sej) by means of suitable

transformities. In our evaluation we use the Environmental Loading Ratio, the Emergy Yield Ratio, the

Emergy Investment Ratio, Emergy Density and Emergy per Person to assess the sustainability of the use

of resources.

Emternalities are dened as “measure of the environmental fraction that is embodied in economic

products but which is not captured by commercial markets” (Pillet, G. et al., 1999). In the case of a

Figure 1. Emergy Diagram of the hill-mountain Communities



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Table 1. Emergy evaluation of local resources basis for the hill-mountain community of “Metauro”

__________________________________________________________________________________

_

Solar Solar

Input Raw units Unit Transformity Emergy

(sej/unit) (sej/yr)

_________________________________________________________________________________

__

Renewable Local Energy

Sunlight 1.33E+18 J/yr 1.00E+00 1.33E+18

Rain 2.76E+14 g/yr 8.99E+04 2.48E+19

Wind 1.12E+15 J/yr 1.50E+03 1.67E+18

Geothermal Heat 1.10E+15 J/yr 3.44E+04 3.79E+19

Non Renewable Local Energy

Ballast, sand 4.16E+11 g/yr 1.00E+09 4.16E+20

Limestone 2.42E+11 g/yr 1.00E+09 2.42E+20

Clay 1.44E+11 g/yr 1.00E+09 1.44E+20

Loss of Topsoil 3.98E+14 J/yr 6.25E+04 2.49E+19

Water 1.93E+12 g/yr 1.25E+06 2.42E+18

Table 2. Emergy evaluation of energy sources for the Hill-Mountain Community of “Metauro”

__________________________________________________________________________________

Solar Solar

Input Raw units Unit Transformity Emergy

(sej/unit) (sej/yr)

__________________________________________________________________________________

Imported Energy Sources

1 Rened Oil Products 2.29E+15 J/yr 6.60E+04 1.51E+20

2 Natural Gas 4.31E+14 J/yr 4.80E+04 2.07E+19

3 Electricity 3.42E+14 J/yr 2.00E+05 6.85E+19

Production Of Energy

4 Hydroelectric Energy 1.34E+14 J/yr 1.02E+05 1.37E+19____




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territory they could be dened as the environmental fraction which contributes to support the system and

its economic processes without leaving any footprint in the traditional economic accounting framework. In

this paper we calculate the total emternalities in terms of emergy and money (Euro, €), and the Emternality

Ratio.

RESULTS

Some tables and gures are presented to show the emergy accounting of each class of resources. Table

1 shows the emergy evaluation of local resources basis for the Mountain Community of Metauro. For

the same Community, the emergy evaluation of the energy sources is presented in Table 2. In Table 3 the

summary of imported goods and services for both Communities is shown. We present the percentage of

each class of input in Figure 2.Table 4 represents a summary of all principal results for both Communities:

emergy evaluation of each class of inputs; calculation of emergy-based indices and ratios; emergy

evaluation of emternalities.

DISCUSSION

Local resources play the main role in both communities. In fact, while the total emergy is 1.24•1021

sej/year for Metauro and 7.21•1020 sej/year for Catria, the rst community uses 8.91•1020 sej/year (71%)

and the second one 5.91•1020 sej/year (82%) of local emergy from endogenous sources.

As industry and manufacturing are not so developed, the two systems do not need large quantities

of resources imported from other systems, with the exception of energy sources such as electricity,

natural gas and fuels. These represent 68.4% of the total amount of imports for Metauro and 72.4% for

Catria. However, at the same time, in Metauro, 2.68•1019 sej of hydroelectric energy are produced, which

constitute 40% of the total system electricity consumption. Imported goods and services are small: 8.95%

of total emergy in Metauro and 5.01% in Catria. This could mean a low level of dependence upon othersystems or, in other words, it is not the case of systems that transfer loading factors elsewhere because

other ecosystems are not stressed by a heavy demand of resources.

Table 3. Summary of emergy values of Imported Goods and Services in the H-M Communities of

“Metauro” and “Catria e Cesano”

__________________________________________________________________________________

_

Metauro Catria

(sej/yr) (sej/yr)__________________________________________________________________________________

_

Imported Emergy of Agriculture, Livestock,

Hunting and Fishing 1.17E+19 4.06E+18

Imported Emergy of Minerals 4.91E+19 1.47E+19

Imported Emergy of Manufacturing and Craft 5.04E+19 1.73E+19

Total Imported Emergy 1.11E+20 3.60E+19



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The importance of local inputs is due to the high exploitation of non renewable resources. In both

systems there is a consolidated extraction activity of several materials whose emergy is especially relevant

because it takes a long geological time and a lot of environmental work to make them available while an

innitesimal historical time is enough for men to exploit them. In fact, to determine the renewability of

a given resource we should consider the time and the energy necessary to obtain it (transformity) as well

as the amount used in each period compared to the total available quantity.

The use of non-renewables make the Environmental Loading Ratio rather high for a typical

mountainous zone. Its value is 18.80 for Metauro and 14.57 for Catria and it is above the national average,

9.47 (Ulgiati, S. et al., 1994). The higher value in Metauro depends also on a more dynamic productive

system in general and on a better infrastructure than Catria’s. In spite of the last considerations, Emergy

Density is not so high (3.72•1012 sej/m2/year for Metauro and 3.17•1012 for Catria, compared to 4.20•1012

for Italy) reecting an agrarian tradition, only affected by the use of non-renewable local resources, and

a territory which is not a limiting factor in future development. The values of Emergy per Person are

Figure 2. Resources use in Catria and Metauro



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relevant for both communities, 3.78•1016 sej/person/yr for Metauro and 5.20•1016 sej/person/yr for Catria.

This indicator is representative of the standard of living in terms of availability of resources but it could

indicate also the average contribution of each inhabitant to sustainability (or unsustainability). However,

in these two cases, the high values depend on low population density in mountain regions rather than

on environmental stress. While the Emergy Yield Ratio (Metauro, 3.54 and Catria, 5.57) is higher than

the national value (1.65), due to the low dependence from external systems and economies, the Emergy

Investment Ratio underlines the use of imported inputs per unit of local natural resources. The low level

of the latter (0.39 for Metauro and 0.22 for Catria compared to the national value of 1.60) means that both

communities are good users of resources from the economic system because they purchase few inputs

from outside to exploit each unit of local resources (both renewable and non renewable).

Emternalities of Metauro and Catria are given by the sum of all independent environmental

sources, both free renewable and mined non-renewable ones. Hence, as shown in Table 4, they are

8.91•1020 sej/yr for Metauro and 5.91•1020 sej/yr for Catria, while the Composite Emternality Ratio, given

Table 4. Results of emergy evaluation and emternalities for Metauro and Catria

___________________________________________________________________________________

Metauro Catria

___________________________________________________________________________________

Renewable inputs sej/yr R 6.27E+19 4.63E+19

Non renewable inputs sej/yr N 8.28E+20 5.45E+20

Total local Emergy sej/yr (R+N) 8.91E+20 5.91E+20

Imported energy inputs sej/yr A 2.40E+20 9.34E+19

Imported goods & services sej/yr B 1.11E+20 3.60E+19

Total Imported Emergy sej/yr F=A+B 3.51E+20 1.29E+20

Total Emergy Used sej/yr U=R+N+F 1.24E+21 7.21E+20

Environmental Loading Ratio (N+F)/R 18.80 14.57

Emergy Density sej/m2 /yr U/area 3.72E+12 3.17E+12

Emergy per person sej/man/yr U/people 3.78E+16 5.20E+16

Emergy Yield Ratio U/F 3.54 5.57

Emergy Investment Ratio F/(N+R) 0.39 0.22

Emergy/GRP* Ratio sej/ € U/GRP 2.38E+12 3.27E+12

Emternalities sej/yr (R+N) 8.91E+20 5.91E+20

Renewable Emternality Ratio R/U 5.05% 6.42%

Composite Emternality Ratio (R+N)/U 71.73% 82.05%

Monergy € /sej GRP/U 4.21E-13 3.06E-13

EMvalues(R)

€ 2.64E+07 1.42E+07

EMvalues(R+N)

€ 3.75E+08 1.81E+08

* GRP: Gross Regional Product___________________________________________________________________________________




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by the ratio of total environmental inputs to total emergy, is respectively 72% and 82%. This result is the

consequence of the low propensity for industry (due to the geomorphology), and the large exploitation

of non-renewable local materials provided by nature indicates that the environment contributes to sustain

the two systems in a way that can not be neglected.

We use the monetary approach to emternalities using monergy, which is the ratio of GRP (Gross

Regional Product, expressed in Euro, which could be dened as the measure of the economic performance

at local level) to total emergy supporting the system, as proposed by Pillet et al. (2000). We can obtain the

GRP of the two communities by multiplying the GRP per capita of the Province of Pesaro (in which they

are located) by the number of their inhabitants. We have 522,233,273 Euro for Metauro and 220,433,462

Euro for Catria.

Monergy is respectively 4.21•10-13 €/sej and 3.06•10-13 €/sej. It is a tool to convert the emergy

of inputs in monetary terms. Hence the emvalues of the environmental fraction are 374,609,951 Euro for

Metauro and 180,855,149 Euro for Catria. The non renewable environmental fraction of the emvalues in

both the communities is very high as it is 93% (348,239,631 Euro) in the rst case and 92% (166,693,821

Euro) in the second one. Table 4 shows also the emvalues of renewable inputs which support the system.

Their weights are low and the measures in Euro are respectively 26,370,320 and 14,161,328, representing

a negligible amount in presence of any level of exploitation of local exhaustible resources.

CONCLUSION

The results of the analysis stress the importance of non-renewables, in particular the resources

from extractive activities. In case of territorial systems, rather than agroecosystems, the main role is played

by “negative” emternalities. They are negative because their use progressively diminishes the xed stock

inside the system even if they are input to the productive system, as well as the renewable and “positive”

ones.

The question should be considered from a different point of view. While we know the

environmental problems, such as exhaustion, uncertainty, irreversibility, pollution, etc., at the same time

we have to take into consideration social implications, such as local occupation, economic implications

of a sector, such as income levels, and other aspects such as uniqueness and scarcity of a given resource

etc.

From this perspective, the management of local ecosystems is a crucial point in terms of

sustainability, dened as the capacity of a system to maintain itself in the long run. This is more evident in

the case of non-industrialized systems such as a mountainous district where the environmental components

are more important to the real wealth of the people.

Finally, the assessment of emvalues underlines this key point in economic terms, involving also

the problem of the difference between the value of a given asset, calculated on the basis of environmental

dimensions, and its market price on the basis of individual preferences. It is not enough nor always

appropriate to increase in general the prices of environmental goods. Decision makers need support toassess the real value of resources to better manage the use of nature in a sustainable way. Monergy is a

way to give an alternative measure of the Gross Regional Product, taking into account environmental

inputs, using the same language of traditional economics.

REFERENCES

Brown, M.T., Arding, J. 1991. Transformity Working Paper. Center of Wetland, University of Florida.

Gainesville.

Bastianoni, S., Marchettini, N. 1995. Ethanol production from biomass: analysis of process efciency

and sustainability. Biomass and Bioenergy, Vol. 11, n. 5: 411-418.

Odum, H.T. 1991. Emergy and Biogeochemical Cycles. Ecological Physical Chemistry, 25-56. Ed. by C.

Rossi and E. Tiezzi. Elsevier Science Publisher. Amsterdam.



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Odum, H.T., 1992. Emergy and Public Policy. Part I-II. Environmental Engineering Sciences, University

of Florida, Gainesville, FL.


and Sons. New York.

Pillet, G., Maradan, D., Zingg, N., Brandt-Williams, S. 2000. Emternalities - Theory and assessment.

Proceedings of the 1st biennal Emergy Analysis Research Conference. Ed. by M. T. Brown.

Gainesville.

Ulgiati, S., Odum, H.T., Bastianoni, S. 1993. Emergy analysis of Italian agricultural system. The role of

energy quality and environmental inputs. Trends in Ecological Physical Chemistry. L. Bonati et

al. Eds, 187-215.

Ulgiati, S., Odum, H.T., Bastianoni, S. 1994. Emergy use, environmental loading and sustainability. An

emergy analysis of Italy. Ecological Modelling 73: 215-268.

Tiezzi, E. 1996 Fermare il tempo. Raffaello Cortina. Milano.

Tiezzi, E., Marchettini, N. 1999. Che cos’è lo sviluppo sostenibile. Donzelli Editore. Roma.

Tiezzi, E. and coworkers. 2000. Sviluppo di un modello di analisi emergetica per il sistema elettrico

nazionale. Final Report for CESI, Dept. of Chemical and Biosystems Sciences and Technologies,

University of Siena. Italy.Tiezzi, E. 2001. Tempi storici, tempi biologici. Venti anni dopo. Donzelli Editore. Roma.

Notes to Table 1

1 SUNLIGHT

Area of Community = 3.34E+08 m2

Incoming solar radiation = 4.99E+09 J/m2/yr (ASSAM 1999)

Total energy (J/yr) = (area)(insolation)(1-albedo)= 1.33E+18 J/yr

Transformity = dened as 1

2 RAIN

Area of Community = 3.34E+08 m2

Annual rainfall = 8.26E-01 m/yr (ASSAM 1999)

Water density = 1.00E+06 g/m3

Quantity (g/yr) = (area)(rainfall)(water density)

= 2.76E+14 g/yr

Transformity = 8.99E+04 se j/ g( Br ow n an d Ha rd in g

1991)

3 WINDArea of Community = 3.34E+08 m2

Boundary layer height = 1.00E+03 m

Density = 1.23E+00 kg/m3 (Odum 1996)

Diff. Coefficient = 1.51E+01 m2/s (Campbell

1998)

Speed of wind = 2.39E+00 m/s (ASSAM 1999)

Total energy (J/yr) = (area)(b.l.height)-1(density)(diff.coeff.)(speed)2(3.15E+07sec/yr)

= 1.12E+15 J/yr

Transformity = 1.50E+03 sej/J (Odum 1991)

4 GEOTHERMAL HEATArea of Community = 3.34E+08 m2

Heat ow = 2.50E-06 cal/cm2/sec (CNR 1999)

Total energy (J/yr) = (area)(heat ow)(1.00E+04cm2/m2)(3.15E+07sec/yr)(4.186J/



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cal) = 1.10E+15 J/yr


5 BALLAST, SAND

Mining production = 2.08E+05 m3/yr (M ar ch e Re g i on

1999)

Density = 2.00E+06 g/m3

Total production = (mining production)(density)

= 4.16E+11 g/yr

Transformity = 1.00E+09 sej/g (Odum 1996)

6 LIMESTONE

Mining production = 1.21E+05 m3/yr (M ar ch e Re g i on

1999)



= 2.42E+11 g/yr


7 CLAYMining production = 6.55E+04 m3/yr (M ar ch e Re g i on

1999)



= 1.44E+11 g/yr


8 LOSS OF TOPSOIL

Cultivated area = 2.19E+04 ha (Province of Pesaro

1999)

Speed of erosion = 8.00E+06 g/ha/yr (estimate)

% soil organic substance = 1.09E-01 (ASSAM)

Energy cont./ g org.subst. = 5.00E+00 kcal/g (Odum 1996)

Total energy (J/yr) = (area)(speed of erosion)(% org.)(En.content)(4186 J/

kcal) = 3.98E+14 J/yr

Transformity = 6.25E+04 se j/ J( Br ow n an d Ha rd in g

1991)

9 WATER

Consumption of water = 1.93E+06 m3/yr (MEGAS 1999)

Water density = 1.00E+06 g/m3

Total consumption = (consumption)(density)

= 1.93E+12 g/yr Transformity = 1.25E+06 sej/g (Bastianoni,

Marchettini 1995)

Notes to Table 2

1 ENERGY OF REFINED OIL PRODUCTS

Gasoline = 7.73E+14 J/yr(Ministry of Industry 1999)

Diesel = 7.85E+14 J/yr(Ministry of Industry 1999)

Fuel Oil = 7.02E+14 J/yr(Ministry of Industry 1999) LPG = 2.70E+13 J/yr(Ministry of Industry 1999)

Lubricants = 8.23E+11 J/yr(Ministry of Industry 1999)



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Total = 2.29E+15 J/yr


2 NATURAL GAS

Total consumption = 1.25E+07 m2/yr (MEGAS 1999)

Total energy = (consumption)(8200 kcal/m3)(4186 J/kcal)

= 4.31E+14 J/yr


3 ELECTRICITY

Total consumption = 9.51E+07 kWh/yr (Marche Reg ion

1999)

Total energy = (consumption)(3.60E+06 J/kWh)

= 3.42E+14 J/yr


4 PRODUCTION OF HYDROELECTRIC ENERGY

Total production = 3.72E+07 kWh/yr ( M a r c h e R e g i o n

1999)

Total energy = (production)(3.60E+06 J/kWh)= 1.34E+14 J/yr

Transformity = 1.02E+05 sej/J (Tiezz i et al.

2000)

Notes to Table 3

Table 3 is the summary of a large quantity of data about the import of goods in the Province of

Pesaro (in which the Communities are located) provided by the National Institute of Statistics (ISTAT)

for 1999. The data are collected at provincial level (expressed in terms of g or J ). They are imputed to theCommunities on the basis of weights, for example, the ratio of the number of employed in a given sector

(agriculture, mineral processing, metal processing, etc.) of a Community to the number of employed in

the same sector at provincial level.

Example of Imported Emergy of Agriculture

FRUIT

Quantity for the Province = 7.60E+08 g/yr ( I S T A T

1999)

Caloric value = 5.50E-01 kcal/g

Total energy for the Province = (raw quant.)(calor.value)(4186 J/kcal)

= 1.75E+12 J/yr

Weight of Metauro = 18.52%

Total energy for Metauro = 3.25E+11 J/yr

Transformity = 2.87E+05 sej/J (Ulgiati et al. 1993)

Emergy = 9.32E+16 sej

FRUIT


1999)

Caloric value = 5.50E-01 kcal/g

Total energy for the Province = (raw quant.)(calor.value)(4186 J/kcal)= 1.75E+12 J/yr

Weight of Catria = 8.18%

Total energy for Catria = 1.43E+11 J/yr



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Transformity = 2.87E+05 sej/J (Ulg ia ti et al .

1993)


Example of Imported Emergy of Minerals

NON METAL MINERALS


1999)

Weight of Metauro = 16.61%

Imported quantity for Metauro = (raw quant. for Prov.)(weight for Met.)

= 2.45E+10 g/yr



NON METAL MINERALS


1999) Weight of Catria = 4.96%

Imported quantity for Catria = (raw quant. for Prov.)(weight for Cat.)

= 7.33E+09 g/yr



Example of Imported Emergy of Manufacturing



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Edited by



Associate Editors

Howard T. Odum



David Tilley



Sergio Ulgiati

University of Siena

Siena, Italy

December 2003



Gainesville, FL


vi



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Chapter 30. Emergy Assessment of Incineration and Landlling...

30

Emergy Assessment of Incineration and Landlling ofMunicipal Solid Waste in Italy

Niccolucci V., Panzieri M., Porcelli M. , Ridol R.

ABSTRACT

Waste management is an important issue to solve as the assimilative capacity of the biosphere

becomes overloaded by human needs and activities. According to European guidelines, reduction ofwaste generation and recovery of energy and materials are strongly recommended for environmentally

sound waste management. In order to adopt a long-term sustainable strategy, it is necessary to assess

the validity of ways for extracting new “utility” from wastes, such as electricity.

We applied the emergy analysis approach to evaluate an incinerator and a managed landll located

in the center of Italy. We compared the electrical energy efciency (in terms of emergy units) and the

environmental impact of power production from municipal solid waste (MSW) for these two approaches

to assess their sustainability. Incineration and landlling require almost the same emergy investment

per ton of waste (1.27x1014 and 1.47x1014 sej/t-MSW/yr, respectively). Electrical energy recovery from

incineration is more efcient than landlling (4.76x109 and 2.49x1010 sej/t-MSW/yr per MWh, respectively).

These results suggest that incineration can provide more electrical power for the same amount of MSW

emergy input.

INTRODUCTION

In 1999 the average Italian production of Municipal Solid Waste (MSW) was more than 26 million

tons (about 465 kg/person). Recently, an appropriate management strategy for this large quantity of

material and energy has become one of the main concerns for national environmental policy (Bastianoni

et al., 1999). In the United Nation’s Agenda XXI document (1992), the majority of the world’s countries

have agreed to develop sustainable waste management based on reducing waste production, increasing

re-use, recycling waste nutrients, and recovering heat for electricity generation.In the past, most MSW was disposed of directly in landlls. Recently, because of regulations and

increasing cost of landlls, the alternatives of solid waste disposal have been investigated. The energy

conversion of MSW from incineration is becoming increasingly important. The available landll space

will be lled in a few years if another solution is not adopted (Clarke, 2000; Chang and Huang 2001).

In this paper, we evaluated the sustainability of waste management strategies in Italy: an incineration

process with electricity co-generation and a managed landll with biogas recovery for electrical generation.

We carried out an Emergy Analysis (EA) of each Municipal Solid Waste Management System (MSWMS)

taking into account all the inputs needed to manage solid wastes “from cradle to grave”.

We divided each MSWMS into three different phases:

1) collection,

2) treatment: incineration or managed landlling, 3) disposal of solid and liquid residues.



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For each of these phases we calculated the Emergy Investment (EI) dened as:

(1)

where:B is the solar emergy (sej/yr);

W is the amount of waste collected (ton/yr);

n is the number of times that a single item is required in the waste management life cycle.

We used EA to convert inputs, such as human labor, trucks, fuels, chemicals, and plant cost, to a

common unit (the equivalent solar energy) for comparison. We evaluated both the contribution of each

item to the three phases and the weight of each input to the total investment. In this way, it was possible

to evaluate the “critical step” which needs the highest investment in terms of emergy inow. Finally, we

divided the emergy investment by the electricity produced in order to measure and compare the efciency

of the two plants.

RESULTS and DISCUSSION

Managed Landll

The managed landll consists of impermeable liners and caps, leachate and gas collection systems.

In this plant, wastes are rst deposited in a dumping site, then are highly compacted and nally are covered

by a great quantity of clay and inert materials. These operations allow the biodegradation of wastes and the

resultant production of biogas (a mixture of CH4 and CO2). Once produced, biogas is collected and burnt

0

500

1000

1500

2000

1998 2008 2018 2028 2038 2048

years

m c / h

L F G

TheoreticalProduction

Biogas recovery

Figure 1. Biogas production vs. time



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to generate electricity. The efciency of this process depends on the methane percentage in the biogas. In

the rst 12 years we considered an average electricity production of 5907 MWh per year.

The landll covers 51000 m2 and has a capacity of 520000 m3. In 1999 it received 170,000 tons

of waste (115,000 tons from public collection and 55,000 tons from private one). At this rate it will be

totally lled in 3 years.

Since the kinetics of biogas production are slow, energy recovery in a managed landll occurs over

a long time (about 40 years), making it necessary to consider the lifetime of the plant for the emergy

analysis. In fact, there is a delay between the waste disposal and biogas recovery. In order to account for

the annual emergy investment required to manage the landll, we supposed a plant lifetime of 12 years,

because that is the time necessary to recover 80% of the total biogas production (Fig. 1). Therefore, theEmergy Investment accounts for the number of times (n) that each input item is repeated in 12 years. Table

1 summarizes the emergy analysis of the managed landll (Landll System) and contains two additional

columns, compared to Odum’s methodology, which specify the value of n for each item and the total

emergy investment. The processes of collection, treatment and disposal of MSWMS are diagrammed

in Figure 2.

The emergy investment calculated for the collection phase is 7.21x1012 sej/t-MSW/yr (Table 1).

The largest input is fuel consumption for trucks. The analysis of the collection is based on the public

case because of the lack of data for the private collection. Therefore, we consider that the public and

private collection have the same emergy investment. Anyway, the error coming from this assumption

is not very relevant because of the low importance of the collection phase. The waste treatment phase

requires more emergy than the other two phases, because materials for construction and management ofthe landll predominate.

The last phase, the chemical treatment of the leachate produced from wastes, has the lowest weight

Water Labor MachineryFuels Electricity ChemicalsMaterials

Total

Wastes

Collected

WASTE COLLECTION

MANAGED

LANDFILL

Wastes

collected

Leachate

Electricity

LEACHATE

TREATMENT

Market

$

Italy, 1999

Figure 2 : System diagram of MSWM by managed landfilling.

Figure 2. Systems diagram of MSWM by managed landlling



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Footnotes to Table 1 are given as Appendix 1



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Footnotes to Table 2 are given as Appendix 2



-414-


with respect to the others. In this phase we considered only the two main inputs, i.e. chemicals and

electricity, while human labor and cost plant are negligible.

The total emergy investment per ton of collected waste is 1.47x1014 sej/t MSW/yr while the emergy

investment per ton of waste to produce 1 MWh of electricity is 2.49x1010 sej/MWh/t MSW/yr.

Incinerator

The incinerator has three conveyors belts and burns an average 110,000 tons/yr of refuse. The heat

produced is thus converted into electricity by a turbine driven generator. This plant consists of a combustion

and post-combustion chambers, an electro-lter and other systems to clean up the fumes produced. Themain combustion residues are ash and dross: ash is transferred to a chemical neutralization treatment, while

dross is landlled. Besides a heat exchanger, the incinerator also has a magnetic separator for recovering

ferrous metals from the ashes.

In Figure 3 the energy system diagram of the MSWMS by incineration (Incineration System)

emphasizes that the main product of the process is power production while ash and dross can be considered

co-products. The emergy analysis of System 2 is presented in Table 2.

In 1999 the incinerator plant generated 2.66x104 MWh of energy from 108,000 tons of wastes

(slightly less than its capacity). The emergy investment for the collection phase is 1.97x1013 sej/t-MSW/

yr and, as with collection for landlling, fuel consumption was the most important input.

The waste treatment phase requires an high emergy investment in terms of electricity and plant cost.

For both incineration and landlling, the treatment phase is the more emergy demanding: for LandllingSystem, materials have a high emergy weight, while for Incineration System, plant construction and

electricity have a high emergy value.

Chemicals MachineryMaterialsWater Labor Electricity

Lubric.,

Fuel

Gas Nat.

Wastes

MANAGED

LANDFILL

ASHES

TREATMENT

WASTE COLLECTION

INCINERATION

Wastes

collected Ashes

Drosses

Figure 3 : System diagram of MSWM by incineration.

LEACHATE

TREATMENT

$

ElectricityMarket

Italy, 1999

Figure 3. System Diagram of MSWM by incineration



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The chemical disposal of solid and liquid residues (the disposal phase) requires the least amount

of emergy in both MSWMS. However, in Incineration System more dangerous residues (ash and dross)

are generated than in the Landlling System.

The total emergy investment per ton of collected waste is 1.27x1014 sej/t MSW/yr for the incinerator

and its emergy investment per ton of waste to produce 1 MWh of electricity is 4.76x109 sej/MWh/t MSW/

yr.

In the present emergy analysis we only evaluated the process in both MSWMs. We did not consider

the emergy of the waste because we have consider only the investment necessary to manage the waste.

Anyway, we think that the emergy of a product, when it becomes waste, should have a lower value than

its initial emergy content, because its emergy is not anymore consistent with its actual properties. In fact

waste leaves its own life cycle either because of consumer’s choice or because it has lost the properties

for what it was produced (Riva and Tiezzi 1997).

Sensitivity Analysis

The managed landll analysis shows that the highest emergy ow is from materials (line items 7

and 9 in Table 1). Due to this fact and since there is some controversy on how the emergy of materials

should be accounted for in emergy evaluations, we conducted a sensitivity analysis of incineration and

landlling using an alternative value for the emergy per mass of materials.

Before reporting the outcome of the sensitivity analysis we would like to explain why we chose

a value of 1.00x109 sej/g for the emergy per mass of materials. We know that in the emergy analysis a

ow, that is accounted in a process, have to be used up. This consideration is easy when you have to deal

with fuels but not so easy when you have to deal with materials. In fact, it is not so easy to determine the

“cradle” of a material, i.e. to state whether it is used up or not.

In the case of landlling we considered that the material used in the landll is not available again.

This assumption is due to the fact that the re-use of the materials in the landll is possible only when

the MSW in the landll is no longer dangerous. The land lled materials could therefore be re-used onlyafter a long time. Moreover, it is also difcult to think of a reuse of something that has been in contact

with wastes for this time.

Another consideration could carefully think about the real quality of these materials used in the

landll. In this case they were waste materials from other processes, their transformity, according to

emergy analysis, should have an higher value than the material itself if the waste material considered is

the nal co-product of a transformation process. Furthermore, we believe that the emergy per mass of a

material cannot have a lower value than the land cycle, which is 1.00x109 sej/g (Odum 1996). According

to these considerations, we think that the value of 1.00x109 sej/g for the materials could be the more

accurate (the primary case).

Others (D.R. Tilley, personal communication) suggest testing the effect of using a lower, but

plausible, emergy per mass (5.00x108 sej/g-granitic rocks, Odum 1996). Using this value changed the

Emergy Investment and the Emergy Investment per electricity produced for the landlling process from

1.48x1014 sej/t-MSW/yr to 0.80x1014 sej/t-MSW/yr and from 2.50x1010 sej/t-MSW/MWh/yr to 1.36x1010

sej/t-MSW/MWh/yr, respectively. This was a decrease of 45.8% in both the Emergy Investment and in

the Emergy Investment per electricity produced. This indicates that our conclusions are sensitive to the

emergy per mass used for materials.

The same sensitivity analysis was conducted for incineration. The Emergy Investment and the

Emergy Investment per electricity produced changed from 1.27x1014 sej/t-MSW/yr to 1.17x1014 sej/t-

MSW/yr and from 4.76x109 sej/t-MSW/MWh/yr to 4.39x109 sej/t-MSW/MWh/yr, respectively. For the

incineration analysis there is less sensitivity to emergy of materials—the decrease is only 7.8%—since

the main ow in the system is from to fuel consumption.



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It is important to notice that, in spite of the lower emergy per mass of materials, the incineration

process remains the most efcient one for producing electricity. What was effected in the sensitivity

analysis was the Emergy Investment. In the primary case, the Emergy Investment was almost the same

for incineration and landlling, however in the secondary case (i.e., lower emergy per mass for materials)

this index was twice as great for incineration as for landlling.

CONCLUSIONS

This paper compared the sustainability of landlling and incineration using the emergy methodology.

We evaluated the emergy of each input for the three main phases of both waste management systems

(collection, treatment and disposal). We determined that the treatment phase requires the highest emergy

for both alternatives. The main emergy investment for incineration was electricity and for landlling the

largest emergy inputs were for construction materials and management.

The emergy investment per ton of waste required to produce 1 MWh of electricity, a measure

of transformation efciency because it considers the total inputs, was greater for landlling than for

incineration. Thus, if electrical generation is a main goal, incineration is more appropriate than landlling.

Furthermore, incineration recovers energy instantaneously while landlling requires several years. Inaddition, incineration has the advantage of reducing the nal volume of wastes to less than 30% of the

initial volume and consequently the land surface area required for wastes disposal. Emissions from this

new type of incinerator are kept under strict control in order to prevent environmental problems, compared

with the old plants that released more dangerous substances.

REFERENCES

Bastianoni, S., Donati, A., Marchettini, N., Niccolucci, V., Porcelli, M. and Tiezzi, E. 1999. Ecological

modelling of waste management based on sustainability indicators. Annali di Chimica 89: 655-659.

Brown, M.T. and Harding, J.E. 1991. Transformities Working paper. Center for Wetlands, University of

Florida, Gainesville, FL.

Chang, M.B. and Huang, C.K. 2001. Characteristics of energy ow in municipal solid waste incinerator.

Journal of Environmental Engineering 127(1): 78-81.

Clarke, W.P. 2000. Cost-benet analysis of introducing technology to rapidly degrade municipal solid

waste. Waste Management and Research 18: 510-524.


and Sons. New York.

Riva, A. and Tiezzi, E. 1997. I riuti come risorsa sostenibile, in Gea Speciale (Rimini, Italy): 11:18.

Tiezzi, E., and coworkers. 2000. Emergy evaluation of the Piemonte region, Dept. of Chemical and

Biosystem Sciences, University of Siena, Italy.

Tiezzi, E., and coworkers. 2001. Sviluppo di un modello di analisi emergetica per il sistema elettrico

nazionale. Final report for CESI, Dept. of Chemical and Biosystem Sciences, University of Siena,

Italy.

Ulgiati, S., Odum, H.T. and Bastianoni, S. 1994. Emergy use, environmental loading and sustainability.

An emergy analysis of Italy”, Ecological Modelling 73: 215-268.

United Nation, 1992. Nations of the Earth Report . Vol. 1, United Nation, New York.



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APPENDIX 1: Notes to Table 1



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APPENDIX 2: Notes to Table 2



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APPENDIX 2: continued



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Edited by



Associate Editors

Howard T. Odum



David Tilley



Sergio Ulgiati

University of Siena

Siena, Italy

December 2003



Gainesville, FL


vi



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Chapter 31. Social Structure and Ecotourism Development on Bonaire

31Social Structure and Ecotourism Development on Bonaire

Thomas Abel

ABSTRACT

Emergy analysis was used to evaluate the impacts of recent ecotourism development on the

island of Bonaire, N.A. One portion of that research focused on transformations in social structure.

This paper will discuss the methods applied to this emergy analysis of socials structure. Comparison

and contrast is made to more conventional models of “culture” in systems ecology. Results suggest that political-ecological considerations should be incorporated into the modeling of human systems by emergy

researchers. This will result in researchers asking questions in different ways, and making signicantly

different policy recommendations.

INTRODUCTION

Energy Hierarchy and Sociocultural Self-Organization

It can be argued that human-ecosystem relationships co-evolve through the mutual development

of self-organizing autocatalytic processes that maximize empower in the system as a whole. In humanprehistory, the self-organization of human population, rudimentary technologies, social structural differ-

entiation, language and cultural models resulted in the capture of additional ecosystem energies for human

groups. Human hierarchies of power and specialization long ago rst emerged atop ecosystem food webs,

transforming them in the process. Archaic state societies used military power and new technologies to

manipulate stone, metal and water to reshape landscapes and to challenge large carnivores and herbivores

for old and new ecosystem energies. Humans literally reshaped the food webs that supported themselves,

and added new energies never before available to them.

More recently, with the emergence of fossil fuel use by modern states and world systems (Waller-

stein 1974), humans have added vast storages of additional energy to the biosphere. Additional energies,

as state above, do not simply build new human structure atop natural ecosystems, but simultaneously

transform natural systems in fundamental ways. For the small island of Bonaire in the south Caribbean

Sea, extensive fossil fuel use has arrived in only the last half century. With widespread fuel use accom-

panying ecotourism development has come the emergence of new political-ecological specialization and

hierarchy.

This paper will briey describe research that was conducted in the vein of systems ecology, ad-

dressed to the event of ecotourism development on the island of Bonaire (Abel 2000). There were four

spatial scales of analysis in the dissertation: the multinational scale (or world system scale, following

Wallerstein (1974)), the island scale, the inter-island economic scale, and the household-farm scale. The

focus of this paper is inter-island economic production and accompanying structural self-organization.

It will not address the transformations in island, multinational or household economic scales that accom-

panied ecotourism development. That analysis can be found in Abel (2000, 2001, 2002a, 2002b). Thepaper will conclude with a discussion of some unique features of the energy systems diagrams used in

this paper, and with comparison to more traditional energy systems diagrams of social structure.



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Bonaire Sociocultural System

The earth biosphere is an open thermodynamic system that is maintained ultimately by the sun,

earth deep-heat, and lunar gravity. Over evolutionary time, sociocultural systems have self-organized

with environmental systems in the biosphere. Sun, wind, rain, fuels, goods, services, etc., are emergy

sources that drive human-ecosystems and sociocultural systems on Bonaire and elsewhere.

Figure 1 is a highly aggregated context diagram of Bonaire’s sociocultural system. While its

focus is the human system, it is essential to identify the driving energies from other spatial-temporal

scales that make that system possible. The Bonaire sociocultural system is dependent upon environ-

mental production, geologic processes, ocean currents, imported fossil fuels, other goods and services,

nancial aid, loans, and other sources. These are identied in the context diagram and were evaluated in

the island-international scale analysis (Abel 2000, 2002b).

Figure 2 is a detailed view of Bonaire’s sociocultural system. The term “sociocultural system”

was chosen over “culture” because the later is often conceived as human symbolic behavior alone. The

term sociocultural system more precisely conveys the fact that humans have co-evolved an integrated

repertoire of symbolic behavior plus material assets, technologies, and social structure, each within a

language context. In Figure 2, these components are identied with separate storage symbols. They areassembled from left to right, suggesting differences in turnover time. They are joined by a single inter-

action symbol, indicating that no component is “prior,” each is potentially limiting, and all may amplify

production with autocatalytic feedback. In systems terms, they are co-products of humanity.

The storage of symbolic “culture” is here labeled “cultural models” to avoid confusion with the

Figure 1. Bonaire Sociocultural System and Support



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prior imprecise term, to accentuate the fact that symbolizing behavior is only one component within the

larger sociocultural system, and to draw our attention to recent social scientists’ contentions (Holland and

Quinn 1987) that symbolic culture is composed of countless cultural models that interact, sharing themesor postulates, that are constantly evolving or being re-negotiated, and that constitute their own system,

one that profoundly and fundamentally shapes the ways we see the world.

The prime focus of this paper is another of the storages in Figure 2, the storage of “social struc-

ture”. Social structure is a broad term that may refer to the political-economy, division of labor, or, in

a term of growing popularity, the political-ecology of a society. In ecosystem terms it is equivalent to

structural diversity. Diversity, division of labor, structure, these terms imply division between units. What

are the units? The researcher’s answer to this question fundamentally shapes the social science that they

will perform. For the current research, that answer is given below.

It is a principle of systems science that systems self-organize into energy transformation hierar-

chies. Figure 3 depicts a hierarchy from ve different perspectives. Figure 3b shows a typical hierarchy

that could be an ecosystem with plant producers on the left and animal consumers on the right, concentrating

food in a food web that is capped by one or several top carnivores. The energy that moves through that

web is highlighted in the Figure 3d bar graph, with energy amounts shrinking as they move up the web.

Figure 3e shows the emergy amounts, which by denition are equal at all levels in the web.

On Bonaire, human subsistence production is manifest in a web of market and non-market

production subsystems that form an energy transformation hierarchy as in Figure 3. Bonaire does not

possess every conceivable economic production subsystem. Figure 4 provides a way to depict the pro-

duction subsystems that do exist on Bonaire, and how they are related to one another. The features of

this drawing (compare to Figure 3b) depict the unique nature of the Bonaire web of political-ecological

production subsystems.

Figure 4 is an unusual systems diagram. Systems diagramming is normally used to simplifya complex network of interactions, in order to identify determinant ows and processes. Figure 4 is

intended instead to be thematic, to depict a pattern of ow and process.

As a model of a sociocultural system, Figure 4 has a number of other unique features. The indi-

Figure 2. Sociocultural System



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Figure 3. Energy Transformation Hierarchy. Adapted from (Odum 1996:23). (a) Spatial view of units and territories,

(b) Energy network including transformation and feedback, (c) Aggregation of energy networks into an energy chain,

(d) Bar graph of energy ows for the levels in an energy hierarchy, (e) Bar graph of emergy ows for the levels in thesame hierarchy. The emergy ow is the same at each pathway, but the energy ow decreases at each step.



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vidual units are household types. Each household contains the storages of a sociocultural system, assets

(including the house itself), people, a small division of labor, cultural models (specialized by education and

experience), and language (negotiated and reproduced by each household in interaction with others). In a

recursive or fractal sense, households are thought to be imbedded within a larger sociocultural system.

The units in this diagram represent household types and not individual households, of which

there are approximately 3,300 on Bonaire. Data for households and companies was collected during the

14-month eldwork through interviews and published sources. Two household surveys were conducted

by the author. Informal interviews were conducted with part-time farmers and a model was constructed

of farming on Bonaire. Formal interviews with owners and managers of companies were conducted.

Over 30 emergy analyses were performed. From those analyses the arrangement of households withcompanies are placed on Figure 4. Households with lower emergy ows are placed further on the left,

while those with large ows are placed on the right. This is analogous to ecosystem webs, in which spe-

cies that converge greater amounts of emergy are placed on the right, while species with more individuals

but less emergy converged per individual are placed on the left.

Figure 5 is an aggregated diagram of gure 4. This more conventional diagram groups processes

by energy ow into “trophic” levels. This highly simplied diagram makes visible the natural plant and

animal production that in part supports the Bonaire system. Notice that the Bonaire sociocultural system

has been broken into three lumps. This arrangement is based on emergy analyses in Abel (2000).

Figure 6 is a detailed look at the concept of political-ecological hierarchy produced by the own-

ership or control of asset storages. As is depicted in this diagram, labor households control very modest

storages of assets. Owner/manager households, by contrast, control large asset storages that can be used

to feedback and amplify production to themselves. This simple strategy, it is argued (Abel 2000), has co-

evolved with human populations through millennia, resulting in the emergence of sociocultural hierarchy

Figure 4. Bonaire’s Web of Political-Ecological Production



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in chiefdoms, archaic states, modern states, and world systems. In Figure 6, owner/manager households

are depicted that control vast storages of assets, which range from machines and factories to the legal

deeds and police/military apparatus that guarantee their private ownership. Money (dotted lines) moves

in counter-current to emergy ows.

It should be re-emphasized that there is an “informational” component included among the asset

storages of any household. By denition, “specialization” implies that individual laborers in state econo-

mies are specialized in their knowledge and skills. Those cultural models that compose that special training

are the informational component that helps to construct and maintain the web of social structure.

For contrast and comparison, a more conventional system diagram is presented in Figure 7.Systems diagramming from Odum (1983) is a very exible diagramming tool. Utilizing a handful of

fundamental objects, complex systems of many forms can be represented. Theory of hierarchy, scale,

convergence, and self-organization are embodied in a limited set of diagramming conventions that model-

ers apply across systems. While no conventions, per se, exist for fragmenting human activity into named

system objects, a review of several volumes by their pioneer (Odum 1983, 1996, Odum and Odum 2002)

yields some often repeated designs. A number of these informal conventions are here addressed and can

be found in Figure 7. The theory of social organization in this type of diagram is signicantly different

from the approach outlined in this paper. These differences are not simply differences of aggregation,

but encode a distinctively different model of sociocultural causality. Applications of one model or the

other to real-world problems will result in different explanations (and thus different recommendations to

policy makers, etc.). It is therefore essential that the underlying, implicit models be made explicit and

analyzed.

This is not a novel recommendation. Social science has undergone a “post-modern” revolution

Figure 5. Aggregated Political-Ecological System. This more conventional diagram groups processes by energy ow

into “trophic” levels. The arrangement of three sociocultural levels is based on emergy analyses in Abel (2000).



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in recent decades, in which all researchers have been challenged to critically examine their theoretical

assumptions, and the political-economic context of science more generally. Power, ethnicity, gender,class—these issues were mostly absent from the “functional” social theory that emerged in the 1950s-

1960s, which often viewed society as a superorganism of cooperating components. With this anatomist’s

or engineer’s model of society, parts are “functions” with names such as “industry” or “education”. Parts

may be criticized as functioning well or ill. There is no explanation for the appearance and persistence

of parts other than to provide their assigned function. People have a limited role in this model beyond

ne tuning these functions and consuming the many goods and services produced (as in Figure 7). This

analytic model has been largely abandoned today by sociologists and anthropologists, though it persists in

neoclassical economic theory whenever society is divided into economic “sectors”, and arguably remains

a dominant cultural model in American society.

In the political-ecological model described here, society is not a machine or an organism. It is

instead a system constructed of countless nearly identical parts. These parts are households of people.How does a sociocultural system produce the distinctive functions in a production hierarchy from identi-

cal parts?

In state societies, people are differentiated by specialized training, assets, race, gender, class,

power, ethnicity, and other features (Hawley 1988). The differentiation of people and households can

have a horizontal dimension, distinguished by function or identity, and a vertical dimension, distinguished

by power and class. In state societies, coercive power is exercised by state elites through their monopoly

of punitive and judicial assets. Other forms of power are produced in the market, such as the economic

power exerted by employers over employees. This characterization of social structure is a political-

economic model.

If the functionalist’s focus on economic production is adapted to a political-economic model, theresult is a political-ecological theory. In this political-ecological model described in this paper, house-

holds are organized in a hierarchy of convergence, in which natural resources, goods, and services are

converged into storages controlled by fewer individuals. In this model, punitive assets (courts, weapons,

Figure 6. Political-Ecological Structure of Households and Storages. This diagram uses symbols to represent the

hierarchy of households. Labor households on the left possess simple households and few additional assets like

personal automobiles. Owner/manager households own or control larger household assets, plus company assets,

which they use to feed-back and control other scales in the hierarchy. Dotted lines are money ows, which move in

counter-current to emergy ows.



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prisons) and production assets (mines, reneries, shipping and others) are concentrated in the hands of

few elites,. All assets in this model will function in a manifest sense to produce goods and services. In

a latent sense, furthermore, these assets feed-back for the maintenance or dynamic reproduction of the

household hierarchy that constitutes the system.

To be clear, political-ecological hierarchies in this model are argued to “function” ultimately in

the same sense as “functionalist” social models, to maximize empower. In other words, it is contended

that political-ecological models result in food being produced, services provided, goods manufactured,

wars fought, children educated, and so on. The difference is that those functions are provided (in state

societies) by the emergence of political-ecological production hierarchies. The result of either model is

the world we nd around us. Recommendations to policy makers, etc., however, may be very different.

While one approach might argue for greater efciency in industry, transportation, or security, another

would seek solutions in global social justice, or international trade equity.

Consider the US automobile dilemma, for example. The functionalist approach might argue

that buying luxury automobiles is wasteful behavior (Odum and Odum 2002:176). Recommendations

would be to legislate for smaller automobiles. If those recommendations go unheeded it can be claimed

that the political process is “irrational” or worse.

In contrast, from a political-ecological perspective, it can be argued that luxury automobiles con-tribute to the maintenance of the owner’s position in the existing political-ecological hierarchy. Second,

at a societal scale, growth in the US auto industry amplies production nation-wide, assuring the position

of economic elites and politicians within the political-ecological hierarchy.

This approach, therefore, allows us to understand the behavior of consumers and politicians as

rational. It also leads us to very different recommendations for action. Conspicuous spending, it would

appear, is ultimately the (rational) result of the continued political-military-nancial hegemony of the

US over its many peripheries. A system with nancial instruments that depend on growth, that demand

growth, must at a minimum, maintain a “region” of growth even as global production plateaus. The US

Figure 7 . A Regional System. Adapted from Odum (1983:537).



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appears to be using its superpower status to isolate itself and other core nations and to maintain growth

at a sub-global scale.

However, if global production is indeed plateauing due to diminishing returns from nite oil

supplies (Odum and Odum 2002), this sub-global growth can only be achieved to the detriment of other

nations. This is a precarious path to say the least. Global social injustice can only breed resentment,

which will surely be expressed violently by those affected nations. The global scale, it is argued by the

political-ecological model, is the scale at which to address solutions. Achieve global economic equity,

and the automobile dilemma, for instance, will take care of itself because regional and isolated growth

will be replaced by a global production slowdown.

To indicate the distance between the functionalist model and the political-ecological approach

proposed in this paper, Figure 8 superimposes the functionalist model on the Bonaire political-ecological

model. Labor from households located anywhere in the household web may contribute to the education

sector, the government sector, the religion sector, etc. Therefore, in this design, “education” has no clear

position in the Bonaire production hierarchy, nor does “government”, “religion”, transport”, “mining”,

“health” or any other function.

To summarize, Figure 9 depicts the general differences between the functionalist and political-

ecological perspectives. The major points are highlighted here:

1. Both depict social organization as an assemblage of units in an energy hierarchy.

2. In the political-ecology model (Figure 9b), units are households and the assets they control.

In the functionalist model (9a), units are storages of people, “information”, and abstract

Figure 8. Institutions in Web Diagram



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functions, often labeled economic sectors or “corporate functions” (Hawley 1986).

3. In the political-ecology model, households are organized in a web of production specializa-

tions, which distinguish them “functionally” (from top to bottom in a systems diagram). This

is analogous to the self-organization of species within a trophic level. Species morphologies

are analogous to the people and assets that compose a production specialization.

4. In the political-ecology model, households are organized in a power hierarchy (from left to

right in the diagram) by the unequal or differential control of productive assets and resources

by individuals or groups. This is analogous to the self-organization of trophic levels in an

energy hierarchy.

5. In the functionalist model (9a), people are not differentiated horizontally or vertically, not

by knowledge, assets, or power. Instead, abstracted functions are assembled in a hierarchy,

capped by a single tank of people and the cultural “information” they produce. There is

only a weak analogy here to ecosystem models because the latter are always assemblages

of actual species (and not the functions they might perform in an ecosystem), and because

“information” in ecosystems is usually represented by the ecosystem web itself.

CONCLUSIONS

The material nexus of sociocultural systems is ultimately anchored to individual persons or

households. In state societies, households differentiate themselves by the alliances they form, the assets

they control, and the technologies they command, all within an ecological and demographic context.

We in state societies do not easily see the complex web of power that was woven over millennia to

protect and defend the social inequality that constitutes the production hierarchy. We rarely perceive that

the extant hierarchy of economic “sectors”, occupations, and class privilege that surrounds us is a human

construction. However, unlike an ecosystem web, composed of disparate species, a human sociocultural

hierarchy of production is composed only of people and the assets they control. Private ownership and

control of those assets in state societies is defended by the power of professional militaries, a relativelyrecent cultural evolutionary phenomenon.

A social hierarchy that is constructed from people and their assets encodes a social model

different from the functionalist’s. Economic “functions” can still be represented, now as aggregations

of like owners and assets. But in addition, important and determinant (at some scale) aggregations of

ethnicity, gender, or class can be shown, with components organized within hierarchies maintained by

power. From this perspective, the activities of government elite’s or their agencies that are sometimes

labeled irrational or “pathological” (Holling et al. 1998) can become explicable. Agencies may in fact

have many “functions,” some explicit, but perhaps most are less visible and aligned to the reproduction

of political-ecological hierarchies.

A desirable approach to understanding sociocultural dynamics could be one that incorporates

at a theoretical level a model of ecosystems dynamics. Complex system theory applied to socioculturalsystems should give equal weight to understanding the dynamics of matter and energy in addition to the

“informational” components most commonly emphasized by social theorists or managers. Furthermore,

not only must emphasis be placed on the use of material resources by humans, but complex, thermody-

namic models of structure and function must be more broadly applied to theory of human social structure

and organizational dynamics, observed at local, regional and world systems scales.

REFERENCES

Abel, T. 2000. Ecosystems, Sociocultural Systems, and Ecological-Economics for Understanding De-

velopment: The Case of Ecotourism on the Island of Bonaire, N.A. Dissertation, University ofFlorida.

Abel, T. 2001. “Evaluating Ecotourism with Ecological Economics: A Case Study from Bonaire,” in Vi-



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sions, Missions and Misconceptions of Caribbean Tourism. Edited by C. Jayawardena. Kingston:

The University of the West Indies.

Abel, T. 2002a. Ecotourism and Household Self-Organization on Bonaire. ms.

Abel, T. 2002b. Systems Thinking Redux: The Case of Ecotourism on Bonaire. ms.

Holland, D., and N. Quinn. 1987. Cultural Models in Language and Thought. Cambridge: Cambridge

University Press.

Odum, H. T. 1983. Systems Ecology. New York: John Wiley.

Odum, H. T. 1996. Environmental Accounting: Emergy and Decision Making. New York: John Wiley.

Figure 9. Functionalist vs. Political-Ecological Model



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Proceedings

Chapters from the Proceedings are available here as individual PDF files. A paperback book

version of the Proceedings is also available. Please contact us for purchasing information.

Emergy Synthesis: Theory and Applications of the Emergy Methodology

Contributors, Preface, Acknowledgments

1. Emergy Synthesis: An Introduction

M.T. Brown, S. Brandt-Williams, D. Tilley, and S. Ulgiati

2. Energy, Emergy and Embodied Exergy: diverging or converging approaches?

Sergio Ulgiati

3. The Transformity of Riverine Sediments in the Mississippi Delta

Jay Martin

4. Emternalitie - Theory and Assessment

Gonzague Pillet, David Maradan, Nicole Zingg, and Sherry Brandt-Williams

5. Emergy Analysis of the New Bolivia-Brazil Gas Pipeline (Gasbol)

Maria Silvia Romitelli

6. Transformities and Exergetic Cost - A Discussion

J.T.V. Pereira and S.A. Nebra

1st Biennial Conference

September 1999



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