accounting for indirect land-use changes in the life cycle assessment of biofuel supply chains

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doi: 10.1098/rsif.2011.0769published online 30 March 2012J. R. Soc. Interface

Dawson, Robert Edwards, Adam J. Liska and Rick MalpasSusan Tarka Sanchez, Jeremy Woods, Mark Akhurst, Matthew Brander, Michael O'Hare, Terence P.assessment of biofuel supply chainsAccounting for indirect land-use change in the life cycle

References

ref-list-1

http://rsif.royalsocietypublishing.org/content/early/2012/03/27/rsif.2011.0769.full.html#This article cites 26 articles, 6 of which can be accessed free

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REVIEW

Accounting for indirect land-usechange in the life cycle assessmentof biofuel supply chains

Susan Tarka Sanchez1,*, Jeremy Woods2, Mark Akhurst3,

Matthew Brander4, Michael OHare5, Terence P. Dawson6,

Robert Edwards7, Adam J. Liska8 and Rick Malpas9

1Life Cycle Associates, LLC, 985 Portola Road, Portola Valley, CA 94028, USA2Porter Institute, Centre for Environmental Policy, Imperial College London,

London SW7 2AZ, UK3LCAworks, Imperial Consultants, 58 Princes Gate, Exhibition Road,

London SW7 2PG, UK4Ecometrica, Top Floor, Unit 3B, Kittle Yards, Edinburgh EH9 1PJ, UK

5Richard and Rhoda Goldman School of Public Policy University of California,University of California Berkeley, 2607 Hearst Avenue, Berkeley,

CA 94720-7320, USA6Geography: School of Environment, University of Dundee, Perth Road,

Dundee DD1 4HN, UK7Joint Research Centre, European Commission Institute for Energy Renewable Energies

Unit, TP 450, 21020 Ispra (VA), Italy8Department of Biological Systems Engineering, University of Nebraska-Lincoln,

203 L.W. Chase Hall, Lincoln, NE 68583-0726, USA9Projects and TechnologyRetail Fuels Technology Shell Research Ltd, Shell Technology

Centre Thornton, PO Box 1, Chester CH1 3SH, UK

The expansion of land used for crop production causes variable direct and indirect green-house gas emissions, and other economic, social and environmental effects. We analysethe use of life cycle analysis (LCA) for estimating the carbon intensity of biofuel productionfrom indirect land-use change (ILUC). Two approaches are critiqued: direct, attributionallife cycle analysis and consequential life cycle analysis (CLCA). A proposed hybridcombined model of the two approaches for ILUC analysis relies on first defining thesystem boundary of the resulting full LCA. Choices are then made as to the modellingmethodology (economic equilibrium or cause effect), data inputs, land area analysis,carbon stock accounting and uncertainty analysis to be included. We conclude thatCLCA is applicable for estimating the historic emissions from ILUC, although improve-

ments to the hybrid approach proposed, coupled with regular updating, are required, anduncertainly values must be adequately represented; however, the scope and the depth ofthe expansion of the system boundaries required for CLCA remain controversial. Inaddition, robust prediction, monitoring and accounting frameworks for the dynamic andhighly uncertain nature of future crop yields and the effectiveness of policies to reducedeforestation and encourage afforestation remain elusive. Finally, establishing compatibleand comparable accounting frameworks for ILUC between the USA, the EuropeanUnion, South East Asia, Africa, Brazil and other major biofuel trading blocs is urgentlyneeded if substantial distortions between these markets, which would reduce its applicationin policy outcomes, are to be avoided.

Keywords: consequential life cycle analysis; indirect land-use change;carbon intensity

*Author for correspondence ([email protected]).

J. R. Soc. Interface

doi:10.1098/rsif.2011.0769

Published online

Received 9 November 2011Accepted2 March 2012 1 This journal is q 2012 The Royal Society

on March 31, 2012rsif.royalsocietypublishing.orgDownloaded from
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1. INTRODUCTION

1.1. Background

Land-use change (LUC) from biofuels, and in fact anyform of new demand on land and its products, caninduce several economic, social and environmentaleffects. Direct greenhouse gas (GHG) emissions have

been shown to be associated with land conversionfrom its original state (forest, grassland, pasture, crop-land, degraded land, etc.) to an altered state thatresults from the production of biofuel feedstocks. Indir-ect land use change (ILUC) results in displacementeffects, including price-induced changes in global com-modity markets, that, in turn, also lead to land beingaltered from one state to another, with resultingchanges in GHG emissions and carbon stocks on thatland. Estimating an overall net ILUC GHG emissionsvalue for a specific biofuel involves complex modelling.A coupled modelling framework is needed to estimatethe impacts of the conversion of land between ecosys-

tem types and the resulting balance of carbon stocksover time, with associated storage or release of carbonand other GHG species [1 9].

Life cycle analysis (LCA) has been used for decadesto model system pollution and resource flows directlyattributed to the producer and relative to a functionalproduct unit. Life cycle inventory (LCI) is identified,and collated into the building blocks of inputs and out-puts, then translated into indicators about the productsystems potential impacts on the environment, onhuman health, and on the availability of naturalresources [10,11]. LCA has evolved in transportationfuel analysis to measure the energy and emission

impacts of advanced vehicle technologies and newtransportation fuels; the fuel cycle from wells towheels (WTWs) and the vehicle cycle through materialrecovery and vehicle disposal [12]. Current work in bio-fuels sustainability evaluation focuses on the GHGemissions compared with a fossil fuel baseline fromLCA estimates, and is divided between the use ofthe attributional versus consequential (ALCA andCLCA) approaches.

The objective of the ILUC modelling approach is toestimate climate-change impacts arising from changesto the net release of GHGs that, in turn, result fromthe substitution of one fuel for another. By comparing

one megajoule (MJ) of fuel with another, the result isthe difference in the physical global warming intensity(GWIp) values of the two fuels. The GWIp for eachfuel is the sum of its direct and indirect emissionsmeasured as grams CO2e MJ

21. In the followingpages, we assess the GWI of a fuel in the sense of itbeing a measure of carbon intensity (CI) notionallyapplicable to evaluating the climate effect of this phys-ical substitution.1 Multiple accounting systems ormetrics are required to measure (or estimate) specificcomponents of the GHG emissions of supply chainsthat can originate on different land types in different

regions. These supply chains also result in differentproducts, but affect the same markets.

The challenge in estimating land-use change effectsof biofuel expansion include a number of different mod-elling and biofuel scenario projection issues, such as:

data issues;

carbon accounting; multiple indirect effects; time treatment; and uncertainty in a wide range of factors.

Herein, the key assumptions, models employed, andinterpretation of results are analysed.

The attributional life cycle analysis (ALCA)approach provides information about the direct emis-sions from the production, consumption and disposalof a product, but does not consider indirect effects aris-ing from changes in the output of a product. ALCAgenerally provides information on the average unit ofproduct and is useful for consumption-based carbonaccounting. A further expansion of ALCA method-ologies includes the PAS 2050,2 in which stakeholderinput into the LCA is specified as a life cycle assessmentof the analysis: specification for the assessment of thelife cycle greenhouse gas emissions of goods and ser-vices, and to a large extent ISO 14044 EnvironmentalManagementLife Cycle AssessmentRequirementsand Guidelines.3 An ALCA informs comparisonsbetween the direct impacts of products, and is used toidentify opportunities for reducing direct impacts indifferent parts of the life cycle. The allocation methodis most often used in this approach to account for theimpacts of co-products.

Consequential life cycle analysis (CLCA) aims toprovide information about the net change in systememissions caused by a change in the level of productionof a product. CLCA is useful in trying to understandthe total GHG consequences from changing the levelof production for a product, and is therefore mostappropriate for policy appraisal. Co-products are trea-ted by system expansion in CLCA and are evaluatedon a similar spatial and temporal scale as biofuelproduction [14,15].

ALCA, therefore, measures environmental flowsfrom and to a system and its subsystems, while, in theCLCA method, these relevant flows change in responseto economic signals transmitted through the worldeconomy often far, both physically and causally, fromactivities directly associated with fuel use [16]. CLCAis highly dependent on projections of the future, andunderstanding of the past, and requires what-if scen-arios and proposed counterfactual circumstances. In

1This measure is a step along the way to, but not the same as, anadministrative CIa that should be assigned to the fuel in a policycontext. This difference is discussed in Hare et al. [13] and notfurther considered here.

2PAS 2050 is a publicly available specification that provides a methodfor assessing the life cycle GHG emissions of goods and services( jointly referred to as products); developed by the BSI Group (aglobal business services organization providing standards-basedsolutions in more than 150 countries).3ISO 14040:2006 describes the principles and framework for LCA,

including: definition of the goal and scope of the LCA, the LCIanalysis phase, the LCI assessment phase, the life cycleinterpretation phase, reporting and critical review of the LCA,limitations of the LCA, the relationship between the LCA phases,and conditions for use of value choices and optional elements.

2 Review. Indirect land-use change S. T. Sanchez et al.


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contrast to ALCA, the system boundary in CLCAexpands through consequential runs, to estimate mar-ginal products affected by a change in the physicalflows in the central life cycle. CLCA is currently themodel approach choice by regulators, and academicsto estimate effects such as ILUC and global marketeffects from biofuel production.

1.2. Defining the system boundary and life cycleinventory

Several parameters are involved in averaging datainputs. The initial choice in LCA is how to delimitthe system boundary and this choice will ultimatelyaffect what to estimate in the upstream versus down-stream of the biofuel production process, and furtherto indirect land use (e.g. elsewhere), or global marketeffects. All LCAs for fuels confine the boundary of thefuel production pathway to a manageable system.Figure 1 identifies the system boundary for the generalbiofuel production pathway.

The CLCA method requires choices that are often nottransparent in analyses. Table 1 separates these choicesand compares the data parameters. They include: the

definition of the system, the treatment of co-products,carbon (GHG) emission factors (EFs; inclusive of farm-ing practice), data uncertainty (including in the valuesused to assess each parameter), world market flux,predictions about the future trends in production tech-nology (including yield improvements) and estimates ofhistorical and changing land use and the carbon stocksof land types used to estimate EFs. While severalother issues are important, this paper evaluates onlyland-use change and how GHG emissions are calculatedby LCA approaches for estimating direct and indirectGHG emissions from LUC from biofuels [1719],and arising from new demand for biofuels. ALCA and

CLCA model the same process quite differentlyand the key difference between ALCA and CLCA isthe choice of boundary and whether the dynamics of asystem are considered or not.

The key drivers for choosing ALCA involves simplicityand the availability of average, as opposed to the mar-ginal, data needed for CLCA. ALCA analyses includeall the emissions under direct control of the sequence ofproduction process operators, at the production sites(farm, biorefinery, etc.), whereas the CLCA includes alleffects whether under the control of the operator or not(such as all significant indirect contributions thatchange global GHG concentrations). This leads to theargument that perhaps LCA cannot provide what it isultimately meant to calculate: the total impact of a pro-

duction system (or policy); followed by the questionaddressed in this review: does CLCA represent a moreaccurate sum of the consequences of the perturbation ofa system or of a policy? Our conclusion is that ALCAand CLCA are complementary, because both performdifferent functions in the assessment of real productionsystems, yet the indirect effects from biofuels are difficultto model, estimate and therefore project.

2. ATTRIBUTIONAL ANDCONSEQUENTIAL LIFE CYCLEANALYSES MODELS AND LINKAGES

Several authors have reviewed the utility of LCAmethods and models to analyse indirect effects, includingfuture temporal scenarios and impacts for biofuels out-side the immediate production chain [1,4,13,2024](M. OHare 2010, unpublished data). Methods vary forestimating land conversion and associated GHG impactsto combine agro-economic model output with spatial andtemporal data that estimate associated changes to thecarbon cycle. The combined model approach inCLCA involves econometrics models, although not allare publically available. These models can be separatedby geographical scope, treatment of time, partial orgeneral equilibrium, the type of analysis and estimates

of GHG emissions from land conversion.Models are based on different assumptions, internal

structures, datasets, emissions and criteria pollutantstracked, and limitations on the different fuel pathways.

direct

LUC

crop

emission

impacts

cultivation

and harvest

indirect

LUC

feedstock

handling,

transport

biofuel

production

storage,

blending,

transport

biofuel use

agricultural

inputs

co-

products

alternative

product

biogenic

CO2

biofuel pathway

displaced

power

electric

power

time

horizon

change in

carbon

stocks

Figure 1. System boundary for LCA inclusive of indirect effects.

Review. Indirect land-use change S. T. Sanchez et al. 3




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For example, the Greenhouse Gases, Regulated Emis-sions and Energy Use in Transportation (GREET)model developed by Wang et al. at Argonne NationalLaboratories includes more than 100 fuel productionpathways from various energy feedstocks and is designedwith stochastic simulations to model uncertainty.This model has been adapted to specific regional

fuel mixes, and mandates, such as the low carbon fuelstandard (LCFS) in California to estimate directemissions [25].

ILUC requires an expansion of the nested variablesfound in standard econometric models such as theGlobal Trade Analysis Project (GTAP). GTAP has,therefore, evolved from a classic econometric model andtransformed for biofuel modelling from a full bilateraltrade between world regions to accommodate biofuelILUC estimates for those regions. For example, GTAP-AEZ (Food and Agriculture Organization (FAO)/ Inter-national Institute for Applied Systems Analysis (IIASA)),involves production with intra- and inter-regional land

heterogeneity, represented by agro-ecological zoning(AEZs), that categorizes 18 different types of landwithin each region based on land characteristics (soiltype, rainfall, etc.). GHG emissions and sequestration

modifications for non-CO2 and different classificationsof emissions incorporate new detailed forest carbonstock data, and modelling of intensive and extensivecarbon management options are just some of theadditional variables considered in GTAP-AEZ(figure 2). The goal of this approach is to calibratemitigation responses to partial equilibrium (PE) model

responses, although there are limitations to this approach.The measurement of overall CI for a biofuel pathway

is often estimated as a single value, although it com-bines both direct and indirect effects. The CaliforniaAir Resources Board (CARB) designed the LCFS tobe based on the overall CI value of the fuel; withthe intention of incentivizing improved fuel pathwayswith the lowest CI. CARB provides separate CI valuesfor each feedstock (referred to as an ILUC riskadder), derived from the combined econometricmodel approach, including sequential runs of GTAP.GREET has been adapted further for the LCFS, e.g.CA-GREET, to model specific biofuel pathways

[27,28] for the direct emission estimates. The resultsare published as biofuel pathways on the CARB web-site. The US Environmental Protection Agency(EPA) uses a different approach for the renewable fuel

Table 1. Comparison of attributional and consequential analysis.

parameter questions asked/attributional LCA agricultural data/consequential LCA

questions asked what is the global warming potential measured bythe carbon intensity (CI) produced for an averageunit of product?

what is the consequential change in total emissionsas a result of a marginal change in production?

what is the CI for a specific fuel pathway?

approach calculate total direct (including directupstream)emissions from inputs and LCI vectors

model emissions associated with economic responseto output and price effects

data producer data inputs; using average data or defaultvalues

marginal data inputsprice elasticitiesproduct demand and supply curvesplus ALCA data

application ofresults

determine emissions associated with production of aspecific product

inform policy-maker or consumer of total emissionsand indirect effects (as much as possible) for apurchasing or policy decisiondetermine consumption-based emissions

system boundary system flows under direct or indirect control of theoperator

process flows within system boundary and outside ofboundary

boundary may be expanded to capture importantlocal effects

indirect effects include market, constrained resourceuse, substitution effect; ideally all consequences

treatment of co-products

allocation or substitution method substitution with second-order or indirectsubstitution effects including market-mediatedeffects

agricultural data average or marginal data historical and projections; Food and AgricultureOrganization of the United Nations statisticalservice; Food and Agricultural Policy ResearchInstitute; other outlook models

model approach spreadsheet or database models with interlinkedpathways and circular references

general equilibrium (GE) LCA flows; partialequilibrium (PE) (rebound effects); dynamic(improve understanding of marginal systemeffects). Separate, or combined with ALCAapproach

market effects

counted?

no (or with exogenous displacement factor) yes

non-marketindirect effects

generally no depends on approach

aAdapted from Tipper et al. [18].





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standard (RFS2), where direct and indirect CIvalues are combined to provide an overall value, andtherefore the indirect CI value is proprietary to EPA.Results are published as one value so you cannotdistinguish an ILUC value separately. One, or theother, combination is one of the options beingconsidered by the European Commission in the Renew-able Energy Directive to measure ILUC as a CI value,or score per biofuel pathway.

The double-counting approach is seemingly justifiedby categorizing emissions into direct and indirectcategories and using allocation for direct emissionsand substitution or system expansion for indirect emis-sions. However, this is better characterized as the root ofthe confusion rather than a methodological justification.

2.1. Sources of uncertainty related to indirectland-use change

ALCA provides an inventory of the emissions directlyassociated with the lifecycle of a productbut not thetotal system change in GHG emissions caused by a

change in the production of the product, i.e. it doesnot estimate the total impact of the policy, which iswhere CLCA has its application. Several sources ofuncertainty have been identified in LCA modellingrelated to this, although the primary debate revolvesaround the methodological approach of using ALCAand CLCA to model ILUC.

While the original focus of ILUC analysis wascentred on corn (maize) ethanol production most ofthe worlds current feedstocks for biofuel productionhave now been assessed using a wide range of modellingtechniques (economic and non-economic) includingsugarcane ethanol [6,29,30], wheat (e.g. [6,7,31]), palm

oil [6,7], etc. The model approaches discussed previouslycontinue to expand to include evaluation of specificpathways, in different regions of the world, from verydifferent feedstocks.

2.1.1. Data issuesThis section compares and contrasts the issues of datarequirements, availability and uncertainty for ALCAand CLCA. Table 2 summarizes our findings.

Several areas are identified in resulting CI valueswhen disaggregated. For example, working backwardsin the EPA analysis for RFS2, significant uncertain-ties are clearly identified (e.g. the levels of soil N2Oemissions resulting from nitrogen fertilizer use, chan-ges to carbon stocks of soils, how to account forco-products). EPA ran the RFS2 analysis through to2022, using baseline data on carbon stocks for a 4year period (this was later revised to 6 years), assuminga wide range of crop yields and technology improve-ments. EPAs analysis model approach includes allmajor emission changes, including land-use changeand non-LUC emissions. Data required for CLCAinclude many sources that are at present less well under-stood and less well documented, although EFs for otherindirect effects are beginning to be recognized as impor-tant for inclusion into overall CI values. Ongoing workestimating livestock emissions, rice cultivation, crop

switching and differences in on-farm energy and agri-chemical use are important not only to expand ourunderstanding of the range of emissions but also toidentify uncertainty and gaps for further work.

While it is likely that the quality of data sourcesrequired for CLCA will improve in the near future,it is critical to estimate uncertainty, particularly ina modelling approach that includes several defaultparameters and scaled-up historical datasets.

2.1.2. Hybrid indirect land-use change modelapproachesCombining ALCA and CLCA may have practical

advantages, i.e. ALCA is easier for reporting fuel sup-pliers, but it can be methodologically confused and,when it is, it produces neither truly direct nor indir-ect results. When combined with CLCA, this lack of

other polar, moist tropical, wet

tropical, moist

tropical, dry

polar, dry

boreal, moist

boreal, dry

tropical, montane

warm temperate, moist

warm temperate, dry

cool temperate, moist

cool temperate, dry

Figure 2. Agro-ecological zones (AEZs) of the world used to represent biophysical heterogeneity in the GTAP-AEZ modelapproach [26].





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Table2.(Continued.)

ALCA

CLCA

datauncertainty

thegreatestuncertaintiesininputdataarewheredirect,accuratemeasurementhasnot

yetbeen

carriedout,orisprohibitivelyexpens

ive,orwherevaluesvarysignificantly

overtim

e.Themoststrikingexampleistheun

certaintyofnitrousoxideemissions

fromsoils,whichcanvarydramaticallyfromregiontoregionandevenbetween

adjacentfields;whiledefaultvaluesareavailable,thereisstillconsiderable

uncertaintyinthesedefaultvalues[32].

Units

usedforreportingquantitiesoffuels

andmaterialsconsumedmayvaryamongindu

striesandregions,challengingthe

reportin

ganddocumentationinfuelLCAmod

els.Nonetheless,likequantities

shouldb

ereportedinaconsistentmanner(perhapsrepeatedinmetricunits)

type1inputs:

projectedsupplyanddemanddata:datainfluencedbymultiplefactors,manyof

whichareinterdependentandmaybeinfluencedbybiofuelexpansionitself

priceelasticitydata:basedonhistorictrends.Anystructuralchang

einworldtrade

couldinducechangesthatca

nnotbereflectedfromthehistoricdata

type2inputs:

cropyieldresponsetoincreaseindemandandprice:threefactorsaffectthisinput:

croptechnologypotential:newtechniquesinbreedingandselectionareconstantly

emerging.Therateofdev

elopmentandpotentialofnew,asyetunknown,

technologiesisimpossible

topredictaccurately

increasedfarminginputs:

difficulttoforecastandhighlyvariable.Fertilizeruseis

particularlydifficulttopr

ojectwhereverydifferentapplicationratesandmethods

arecommonwithinandb

etweenfarmsinthesameregionandthedatawould

alsobesourcedfromeconomicmodelswhichcannotreflectreal-timefarming

practice

farmpractices:thelikelyimpactofimprovedfarmingpracticesisalsouncertain

anddifficulttomodel.Th

emainareaofuncertaintyistheinterrelationship

betweenmarketfactorsandtheimplementationoftheimproved

practices

potentialofdisused,abandoned,marginalanddegradedland:significantpotential

forcroplandexpansionintot

histypeofland[33,34].Althoughmostmodelsdonot

yetfactorinthepotentialim

pactofbiofuelsexpansiononthistype

ofland,any

impactwillbestronglypolicy-driven.

Theinevitableuncertaintyab

outtheextent

andthedirectionofgovernm

entpoliciesindifferentworldregionsiscertaintherefore

toaddsignificantuncertaintytomodellingthisparameter.

(Seeaboveformore

detail)

impactofbiofuel-drivensust

ainabilitycriteriaonfarmingoutputs:

Europeanbiofuel

suppliersmustcomplywithstrictsustainabilitycriteria.

Thereare

asyetnodata

availableonthelikelyimpac

tsofthisrequirementanditisnotfactoredintoexisting

modellingcapability





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methodological clarity of the ALCA can double-countthe benefit of co-products. The problem arises if theALCA for the direct emissions allocates emissions toco-products, and the estimation of ILUC includes acredit or reduced net ILUC figure owing to co-productsubstitution effects, as the benefit of the co-productswill be counted twice. The result could over-estimate

the GHG savings from biofuels.If the total GHG consequences arising from biofuelproduction (total system change in emissions peradditional unit of biofuel produced) need to be esti-mated, then a CLCA should be undertaken. A pureCLCA treats co-products only once using a substitutionor system expansion.

2.1.3. Consequential life cycle analysis: accounting formultiple indirect green house gas emissionsThe US EPA has recognized that multiple significantindirect GHG emission sources may be consequentiallychanged by biofuel production. In the LCA methodologyemployed in RFS2, the EPA attempts to quantify amultitude of indirect changes in the USA and globalagricultural economies for several biofuel productionpathways, including changes in domestic and inter-national GHG emissions from farm inputs, land-usechange, rice methane and livestock. The EPA estimateschanges in these sectors using the Forestry and Agricul-tural Sector Organization Model (FASOM) and Foodand Agricultural Policy Research Institute (FAPRI)econ-omic models. The GREET model, along with EFs fromthe Intergovernmental Panel on Climate Change(IPCC), is then used to translate these agricultural andecosystem changes into GHG emissions. By estimatingindirect emissions in this manner, the EPA has tried tocomply with US legislation, while recognizing the vastglobal complexity in various significant emission sourcesthat are indirectly affected by biofuel production. Inaddition to the models above, the RFS2 methodologyalso uses data compiled from: CENTURY, DAYCENT,MOVES, FORCARB, NEMS and ASPEN4 to projectGHG emissions as a consequence of biofuel production.In total, at least eight highly complex models areemployed to quantify direct and indirect GHG emissionsfrom the corn ethanol life cycle.

In addition to land-use change, the aggregation ofmany other significant indirect emissions leads to agreat expansion in analytical complexity. Because theEPA estimates projected global changes in GHG emis-sions from all major agricultural sectors and ecosystemsfor 15 years into the future, it is clear that such anapproach that incorporates tens of thousands of par-ameters is likely to be associated with a large degree oferror. We recognize that econometric modelsdynamic,static or with different sectoral resolutionare by neces-sity uncertain and that CLCA compares a baselineagainst a projected alternative scenario that aims tocancel out a large part of the uncertainty; yet, each pro-jection associated with a sector change is associated

with an uncertainty, and multiplying sector projectionswill add to total uncertainty.

Consider the uncertainty in estimating indirect GHGemissions from corn production in the USA. Usingreasonable parameter values, these estimates haveranged from 118 gCO2e MJ

21 [35] to 13.9 gCO2e MJ21

[36], and have even ranged from 18.3 to 80.4 gCO2eMJ21 within a single study based on uncertainty ineconomic projections [37]. In the EPA analysis, 30000EFs are used to estimate emissions from land conversionalone. These EFs are one of two datasets included in the

EPAs partial error analysis, leading to a 95% confi-dence interval that is +28% of a mean value of30.1 gCO2e MJ

21. The Searchinger et al. [1] model con-tained no specific land supply structure for variouscountries, and models with plausible land conversionsupply curves appropriate for each country have notyet been published.

The issue is where to set the boundaries for CLCA, inparticular with respect to the large number of indirecteffects that can be considered in a system. Yet, it isclear that reasonable indirect effects that have similarmagnitudes must be accounted for in parallel in CLCA.The difficultly in deciding where to set these boundaries,

and the inadequacy of accounting for ILUC alone, can befurther illustrated by assessing the cumulative impact ofthree uncertain indirect effects included in the RFS2analysis when comparing US corn ethanol with gasolineproduced from Middle Eastern oil.

A recent estimate of ILUC emissions by Tyner et al.[36] provides a value of less than half of the estimateused by the EPA (table 3).

For livestock impacts, analysis suggests that livestockpopulations may decline more owing to biofuels andlivestock GHG emissions may be higher per unit,which together lead to greater indirect GHG savings

than the EPAs estimate [4]. Although ILUC associated with the extraction and

production of fossil fuels was discussed by the EPA,some have argued that other indirect effects

Table 3. Two alternative sets of estimates of the impact ofthree indirect emissions in CLCA.

selected indirect effectsEPA RFS2 [38]gCO2e MJ

21other estimatesgCO2e MJ

21

global indirect land-use change emissions

(ILUC)

30.1 13.9a

global livestock 20.28 247.5b

US military emissionsresulting fromsecuring MiddleEast oil

0 217.5c

sum of indirectemissions

29.8 251.1

aAdapted from Tyner et al. [36].bAdapted from Liska & Perrin [4] based on Searchingeret al. [1] and Steinfeld et al. [39].cAdapted from Liska & Perrin [40]: consequential reduction inUS military in the Middle East based on LCA of US military

and attribution of 20% (approx. $100B yr21) to oil security.

4See EPA docket for all models and approach: http://www.epa.gov/otaq/fuels/renewablefuels/regulations.htm.



http://www.epa.gov/otaq/fuels/renewablefuels/regulations.htmhttp://www.epa.gov/otaq/fuels/renewablefuels/regulations.htmhttp://www.epa.gov/otaq/fuels/renewablefuels/regulations.htmhttp://www.epa.gov/otaq/fuels/renewablefuels/regulations.htmhttp://www.epa.gov/otaq/fuels/renewablefuels/regulations.htmhttp://www.epa.gov/otaq/fuels/renewablefuels/regulations.htmhttp://www.epa.gov/otaq/fuels/renewablefuels/regulations.htmhttp://www.epa.gov/otaq/fuels/renewablefuels/regulations.htmhttp://www.epa.gov/otaq/fuels/renewablefuels/regulations.htmhttp://www.epa.gov/otaq/fuels/renewablefuels/regulations.htmhttp://www.epa.gov/otaq/fuels/renewablefuels/regulations.htmhttp://rsif.royalsocietypublishing.org/http://rsif.royalsocietypublishing.org/http://www.epa.gov/otaq/fuels/renewablefuels/regulations.htmhttp://www.epa.gov/otaq/fuels/renewablefuels/regulations.htmhttp://www.epa.gov/otaq/fuels/renewablefuels/regulations.htm


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associated with fossil fuels should be included.For example, GHG emissions, primarily arisingfrom ship and plane movements, associated withthe acquisition and defence of foreign oil for theUSA [40,41].

The net cumulative impact of these three indirecteffects within the full fuel cycle could amount toGHG emissions savings from corn ethanol as large as51 gCO2e MJ

21, which is more than 80 gCO2e MJ21

lower than the positive indirect GHG emissions thatthe EPA currently ascribes to US corn ethanol supply(29.8 gCO2e MJ

21).This highlights the large degree of uncertainty (both

in magnitude and direction) that exists in assessingmultiple complex indirect effects simultaneously inCLCA, and the fact that one indirect effect may beoffset by a series of other indirect effects. A recentreport by Ensus [42] also indicates that there appearsto be a structural bias in the way that equilibrium

(economic) models have been used leading to a ten-dency to over-estimate the scale of indirect emissionsfrom certain biofuel supply chains. These chains includewheat to ethanol, rape (canola) to biodiesel and corn(maize) to ethanol, i.e. those that produce protein-richco-products that can be used as animal feed.

If robust estimates of the overall net GHG emissionsfrom biofuel supply chains are required then it is likelythat the boundaries used in CLCA will need to beexpanded beyond quantifying ILUC emissions toencompass other significant drivers of indirect emis-sions. Currently, it remains unclear how to definethese boundaries consistently, but it is clear that effects

of similar magnitude should be analysed.

2.2. Other impacts: food

The consequences of significant land-use conversion tobiofuel crops may have major implications for food secur-ity, biodiversity and soil and water quality. Thedisplacement of existing land-use for biofuel production(biofuel crop area expansion) increases the pressure onother types of land use [43]. A key variable is the diet,especially the shares of meat and dairy, which exert alarge leverage on land use owing to pasture and feed.Developing the capability to quantify these impacts and

to include them in an LCA remains a major challenge.How the displaced activities, such as food production,are relocated will establish the magnitude of the impactof ILUC, which will be determined by the availability ofagricultural and uncultivated (e.g. set-aside, fallow andforests) land [22]. Recent modelling studies of climatechange impacts on global food production and undernour-ishment [44] has supported FAO predictions that foodproduction will have to increase by 70 per cent over thenext 40 years to feed the worlds growing population[45]. The FAO further stated that, with the worlds popu-lation expected to increase from the current 6.7 billion to9.1 billion by mid-century, if more land is not brought

into use for food production now, 370 million peoplecould be facing famine by 2050.

The Bioenergyand Food Security (BEFS) [46] projectfocuses on the management of the sector and provides an

example of how case studies such as those run by BEFSin Peru, Tanzania and Thailand can also be integratedinto a countrys food security monitoring system(FAO). The BEFS framework helps us to understandthe very complex issue of the linkages between bioenergyand food security and is intended to be diagnostic ratherthan prescriptive. It includes natural and social diagnos-

tic analyses, and economic assessment, and intends toact as a policy tool.Clearly, expanded biofuel production will have vari-

able positive or negative impacts on food production,and estimated impacts are contingent on regional policies,decisions and assumptions made both as external to andwithin the system boundary of the analysis. Biofuels com-pete for land and resources needed for food productionor, alternatively, they help to provide the investment ininfrastructure and the energy inputs needed to enhancethe productivity of food cropping, harvesting, processingand delivery. Approaches such as BEFS could augmentexisting policy frameworks by providing working

examples of the complexities of bioenergy and their lin-kages, and develop frameworks that are applicable inthe regional or localized context.

2.2.1. Carbon accounting and time treatmentEFs for land conversion are calculated based on carbonstock estimates and application of carbon stock account-ing methods, specifically the IPCC Agriculture Forestryand Other Land Use (AFOLU) methodology [47]. Highuncertainty in above- and below-ground carbon stock esti-mates is well known although the inclusion of thisuncertainty in overall EFs is variable in application by

different US policies for ILUC [37,4850]. There are twocentral issues: (i) how to estimate the type of land coverand area estimated to change and (ii) how to combine sev-eral separate EF estimates for each land type, carbon pooland applied stock change factor, i.e. conversion, reversionand associated management factors.

Currently, the existing estimates of ILUC have useddifferent carbon stocking and flux estimates andmethods to calculate EFs. In the Searchinger et al. [1]analysis, historical carbon stock estimates from the1990s were provided by Houghton & Hackler [48]using Woods Hole Research Center (WHRC) datasetsfor above- and below-ground biomass stocks separated

into 10 regions of the world. EFs were based on IPCC(tier 1) methods and then combined with GTAP, toprovide estimated areas and locations for the land pre-dicted to be converted to farmland as a result of theincreased commodity prices resulting from expandeddemand for feedstocks for biofuels. To formulate GHGEFs for different conversion types (e.g. forest to crop-land), these values include various assumptions aboutthe lands prior vegetation type and the release periodpost-conversion. The EPAs RFS2 EFs were estimatedby the Winrock team [50] and represent spatially expli-cit estimates for 314 key regions in 35 countries. TheEPA applied EFs to the satellite-based remote sen-

sing-mapped regions estimated to be converted owingto direct and indirect biofuel expansion in the futureand conducted individual model runs for biofuelscenarios in the PE model FAPRI.





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In addition to the potential one-off release of carbon,5

the calculation should also account for any subsequentuptake of carbon to the soils as a result of the crop, e.g.as a result of carbon sequestered in root systems and inthe above-ground biomass. Several publications report onthese data, particularly for released carbon [51]. However,it is well documented that above- and below-ground

carbon stocks are variable and, although model and dataimprovements are employed in more recent ILUC analyses[36], there is still a requirement to incorporate refined soilcarbon data inclusive of variance in carbon density basedon type, practice and measurements.

The updated EPA analysis for RFS2 includes severalupdated above-ground carbon estimates and now usesthe Harmonized World Soil Database (HWSD) forbelow-ground carbon estimates. Another differencebetween the CARB and EPA EFs is the treatment ofharvested wood products (HWPs), whereby Winrockdid not include an HWP factor. The CARB analysis,however, does account for harvested wood in the sensi-

tivity analysis by applying the IPCC default 90 per centoxidized carbon (e.g. 10% retained in wood products).Finally, the uncertainty analysis by EPA included aMonte Carlo analysis, while CARB used weightedaverages. The resulting EFs impact the overall CI differ-ently and, while this topic is not fully explored here,CARB and EPA are evaluating updates to ILUC esti-mates for feedstocks inclusive of ILUC factors appliedto the total CI.6

2.3. Treatment of biogenic carbon

The treatment of biogenic carbon is a complex issue that is

closely tied to the treatment of LUC. Several metrics arepossible for biogenic carbon and these are applied incon-sistently among fuel LCA models. Figure 3 shows theconcept graphically, comparing baseline fossil fuel withbiofuel. Biogenic carbon can be treated as neutral, i.e.carbon emissions from combustion at the vehicle tailpipeand along the fuel supply chain are assumed to be balancedout by prior or re-growth of the biomass and its associatedcarbon fixation from the atmosphere.

Alternatively, all carbon emissions are counted alongthe full fuel supply pathway including end use and theuptake of carbon during the crop growth. In most cases,biogenic carbon is treated as neutral for biofuel cropsbut positive (i.e. as a net emission) for biogas arisingfrom the anaerobic digestion of waste materials. Thetreatment of biogenic carbon from waste materialsrequires further examination owing to the various

possible alternative fates of the carbon, e.g. re-use throughrecycling into a range of products with differing half-livesof the embedded carbon, disposal and long-term seques-tration in landfills, disposal and rapid release asmethane in landfills, disposal, rapid release and captureof methane and use as fuel for heat and electricity (see

Brander et al. [21], for a comprehensive assessment ofthis issue). The quantitative amount of biogenic residuesand wastes currently used for biofuels is small, though,and, in consequence, any inconsistency in accounting forC storage owing to the life time of biogenic materialsused as products is comparatively small. Robust account-ing will require consistently distinguishing between fuelswhose use almost certainly assures quick recapture ofthe carbon released when they are burned (e.g. annualcrops and perennial grass feedstocks) and fuels whosecarbon may or may not be recaptured. Time accountingfor the carbon stocks and fluxes in longer rotation forestryand its multiple potential products is extremely complex

and accepted methodologies for doing so are only justemerging [52].As depicted in figure 3, emission sources may result

from changes in biofuel production rates. To highlightthe differences in LCA CI that can result from adoptingdifferent approaches to biogenic carbon, the LCFSGREET and Joint Research Centre (JRC) studies yieldsimilar results for starch (corn and wheat)-based fermen-tation ethanol pathways as they use similar (neutral)assumptions for biogenic carbon. By contrast, the twostudies produced strikingly similar emission results forwaste forestry wood, but yield different results forfarmed wood. The LCFS GREET calculates much

lower GHG emissions than the JRC study because thebiogenic carbon contained in the forest waste is deductedfrom the GHG emissions in the GREET model; the JRCstudy does not credit the biogenic carbon in the residues.JRC considers this to be consistent with the practice ofnot accounting for reductions in soil carbon by conven-tional cropping, and it has, at this time, estimated theeffect on long-term forest C stock to be low.

2.4. The time horizon

The warming potential of biofuel GHGs requires achoice of treatment of the period of release. LUC-

caused emissions typically occur at, or near, thebeginning of the production cycle. The time horizonused to calculate global warming potential (GWP) dif-fers in that the latter is used to estimate the useful life of

fossil fuel fuel

biofuel

atm. uptake production

LUC adder

Figure 3. Schematic showing fossil fuel and biofuelcycle emissions with fuel carbon (detailing fuel combustion,downstream in black).

5Searchinger etal. [1] assume all above-ground carbon is lost immediatelyupon conversionand 25 percent is assumedstored in theground. Furtheranalyses [36] and the EPA (RFS2 2009) account for forgone emissionsand differences in stored carbon. IPCC defaults are compiled estimatesfor soil and land-cover carbon profiles at a global scale with nospecification of geographical distribution. New approaches incomputable general equilibrium (CGE) modeling (GTAP-AEZ) usecarbon data down to the AEZ level and the new harmonized globalsoil carbon database combined with FAO data on carbon-harvestedwood stocks. For information, CGE models are a class of economic

models that use actual economic data to estimate how an economymight react to changes in policy, technology or other external factors.6Owing to decisions of the Cancun Conference in December 2010,developing countries will become subject to bi-annual GHG nationalreporting that takes into account LUC.





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biofuel production infrastructure. The choices madewhen combining ILUC emissions with direct emissionsusually requires a choice of amortization process; thisis analogous to the combination of capital and variablecosts of producing products. The GWP has been used inseveral analyses of LUC where the total indirect emis-sions from commencing fuel production are divided by

the total fuel produced during a predicted productionperiod, and this average is added to the direct emis-sions. This approach implicitly treats a unit GHGemission released today as though it has the same con-sequences as one released decades in the future [53].Considerations affecting the allocation of ILUC to aunit of fuel include:

Amortization period [54]. Some biofuels have muchlonger prospective production periods than others ongrounds of cost (e.g. sugar cane ethanol versus cornethanol; form factor/tractability) owing to different

harvesting and ratooning (rotation) cycles. Also,non-biofuel alternatives may displace them (i.e.various electric vehicles). For example, assuming a30 versus 100 year lifetime for a US corn ethanol pro-duction chain7 results in substantially different CIs.

The use of discounting. Although the employmentof a discounting factor to the time horizon is deba-table, all GHG emissions for fuels being comparedoccur at the same time for each fuel, and may beregarded as reasonable proxies for warming (as ofa given time, or accumulated over a long period,etc.). But if they are not, discharges need to be con-verted into warming (as a proxy for social cost) to

which a discount factor can be applied; this greatlychanges comparative GWP indexes between biofuelsand other fuels [54].

Analytical horizon. An analytical horizon extendinginto decades requires predictions about the expectedcultivation period and post-cultivation LUC,decisions on how post-cultivation LUC emissionsshould be credited, and assessment of the timevalue of benefits and costs. Benefit cost analysisbrings with it the need to settle on a reasonabledamage function and an appropriate discount rateas well. The UN Framework Convention on ClimateChange has decided to apply a 100 year time horizon

for its political decision making (e.g. Kyoto Protocol),whereas the US EPA considered both 30 and 100 yeartime horizons, finally using 30 years for RFS2. Policy-makers may find it appropriate to focus on morecertain, near-term climate impacts, in which case ashort horizon for fuel warming potential (FWP)8 issufficient. For short analytical horizons, discountinghas little effect and post-cultivation LUC occursbeyond the system boundary [54].

2.4.1. Can indirect land-use change uncertainty be trulycaptured?In summary, current model estimates of CI for biofuelsmay be biased downwards or upwards if not accuratelyincluding all indirect emissions (without much moreresearch, this issue willnotbe easily solved). These include:

the inability of economic models to recognize unma-naged, therefore un-priced and untraded, land;much of this land is high-carbon-stock forest, andforest has been a important source of cropland [55];

over-estimates of price-yield elasticities for crops [56]; the assumed production period over which ILUC

is amortized may be too long or too short forsome fuels;

decreases in livestock GHG emissions may offset alarge fraction of ILUC emissions.

To date, no models have presented a systematic descrip-tion of the uncertainty (ideally in the form of confidence

intervals or a probability density function (PDF))implied by the variation or uncertainty in their par-ameters. A meta-analysis of model and parameteruncertainty in ILUC estimates is presented in Plevin[57]; the important implications of this analysis are thewide error bands associated with any estimate of ILUCfor a given fuel, and the asymmetry of the PDF implied.In turn, this asymmetry suggests that the ILUC assignedto a fuel for administrative purposes by policy is probablynot the modal value of the pdf but something higher,depending on the cost of error in this assignment (thedifference between the real but unknown ILUC of a fueland the value inferred from a model or models and used

in policy implementation). A systematic treatment ofuncertainty in this context is a matter of ongoingresearch; OHare [58] sketches such an analysis as a pro-blem in decision theory. Instead of stepwise approachesof uncertainty, a corridor approach could be employedto estimate cumulated ranges of key assumptions in scen-arios, and in this way results can be derived withoutreferring to individual events of uncertainty [9].

3. DISCUSSION

ALCA and CLCA are used to answer different ques-

tions and therefore provide variable results whichmust be interpreted carefully. ALCA models directand upstream (vehicle tank-to-field) energy consump-tion and direct and upstream emissions throughout afuel supply pathway; this process poses unique con-straints within an analysis of a biofuel productionsystem that is inherently complex. ALCA allocatesenergy and emissions between the fuel and any co-products, and the results aim to reflect the averagetotal emissions associated with a unit of production.Allocation choices are as critical as system boundarychoices, as the value ascertained from energy (mass orcarbon) content versus substitution is divergent.

Through evaluation of scenarios, over time, and datathat can adequately capture a dynamic system, directemissions can be estimated over geographical areas.This is contrary to the CLCA analysis where land

7The 30 year time horizon was also used by Searchinger et al. [1] andPAS 2050.8FWP is defined as the ratio of the cumulative radiative forcingcaused by the life cycle GHG emissions from a biofuel relative to

that of its fossil substitute. Where discounting is desired, OHareet al. [54] propose an economic version of the FWP, defined as theratio of the net present values of the cumulative radiative forcingfrom the two fuels. Any positive discount rate magnifies theimportance of early emissions.





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intersects with an exchange of land elsewhere, and com-plex global commodities markets.

Therefore, CLCA is much larger in scope thanALCA and naturally is accompanied by uncertainty,owing to the complexity of the real world systems,their interconnections and the scope of the CLCA.This scope includes the total emissions from fuel pro-

duction, plus all indirect effects that cascade overtime, resulting from economic effects. CLCA includesemissions that are within the fuel pathways systemboundary plus those that are outside that boundary,e.g. anywhere in the world. Without adequate timeseries, and scenarios that are sensible, models can onlyaccomplish an isolated evaluation in ALCA, whereasthe results of a CLCA depend on a combination ofmodels and data sources used to calculate an overallCI value for an uncertain set of variables representinga complex orchestration of economic behaviour.

ILUC from biofuels has caused an intense globaldebate which developed over a relatively short time-

frame (under 4 years). It has focused on policiesthat have been developed as an impetus to changeand on what effects need to be measured in lieu ofrapidly changing biofuel policies. Using LCA tomodel direct effects within the production chain ata given place in time is inherently difficult. As thescope is expanded to include policy choices, additionaldata on global markets, rates of penetration and effec-tiveness of new technologies on those markets mustreflect the time scale of emissions (e.g. time horizon)resulting from an overall perturbation of global com-modity markets. Indirect analyses incorporate criticalchoices and can include such effects through partial

and general equilibrium modelling.

3.1. Recommendations

As they develop, biofuel mandates should benefit intheir presentation of expanded scenarios, rather thanjust intermediate results, which vary among fuel LCAmodels making the comparison, disaggregation anduses of these values very difficult. Measuring the indir-ect effect of one production cycle in one country andassuming a 1 : 1 displacement effect on another landmass elsewhere is not tenable. For example, cattle

stocking rates are much higher in South American pro-duction systems than for US soy production [59].However, multiple models and combined analysis resultsare incorporated into policies such as the LCFS andRFS2 in the USA, which aim to evaluate the hetero-geneity of indirect effects. This limitation can only beanalysed more carefully through several sequentialmodel runs, inclusive of a range of scenarios (e.g. elastici-ties on crop yields) and perhaps even a range of resultsrather than one final number (e.g. the risk adder usedin the LCFS framework). The European Union (EU)has taken a different approach whereby indirect effectsare modelled as reference scenarios, thereby focusing on

technical improvements by supplying a combination ofoff-limit areas, e.g. high carbon stock and biodiversityareas, and exemplifying regional cases where biofueloperators can benchmark improvements.

Perhaps more fundamentally, CLCA requires anti-cipating time-sensitive, nonlinear parameters (e.g. theeffectiveness of existing and future policies designed tocontrol and manage deforestation and afforestation,such as carbon policies focusing on reducing emissionsfrom deforestation and degradation), including linkswith livestock management policies that can be govern-

mental, industrial or non-governmental in nature or acombination, as with Brazils soy and livestock moratoria.Regulators and policy-makers should clearly dis-

tinguish between the best-available estimate of fuel CIfor use in purely physical substitution (MJ/MJ),GHG emission comparisons and the CI estimate thatshould be used in any given policy implementation[13]. Among the considerations separating these differentvalues of CI are the different time profiles of GHGemissions [54] and the asymmetry of the distribution ofthe CI value [5]. More generally, the uncertainty associ-ated with all, or any, estimates of ILUC is not random,nor is any best distribution estimate centred at zero; bio-

fuels policy should not implicitly act as though it is byignoring ILUC on grounds of uncertainty. Much resul-tant policy (economic, health and safety, environmentaland more) is made in the face of uncertainty andaccommodates it using a range of instruments.

Recommended improvements to the CLCA frameworkinclude methodological choices, and alignment of policiesthat differ in model approaches, in addition to parametricstandardization. Carbon stock calculations are criticalto evaluate, and update, and should include a more care-ful evaluation of the accounting of above- and below-ground biomass, inclusive of root measurement. Variousmetrics as applied to fuel LCA lead to the presentation

of widely varying WTW and well-to-tank results.9

Theinputs to fuel LCA models are often difficult to relate tooperational data and parameters with physical meaning.Fuel LCA models tend to deal with energy inputs andefficiency while real world plant operators may dealwith scf, barrels, kW, $ and many other units of com-merce. The result is that the input values to models(both WTW and LUC) are often distant cousins of thephysical parameter being measured.

In the absence of certainty on the magnitude ofILUC, more research and policy measures should befocused on mitigating the risk of ILUC. Potentialmeasures include investing in agricultural research

and development to increase yields of energy and foodcrops, protecting high-carbon-stock land, identifyingand cultivating degraded land (or, conversely, moreadequately matching crops to land productivity), anddisseminating best agronomic practices. Policy instru-ments should reflect the nature of this uncertaintyand target practical ways to encourage the privatesector to minimize GHG emissions and wider environ-mental impacts throughout the life cycle (ALCA) andpenalize damaging behaviour (through ALCA andCLCA). ILUC poses a serious challenge in this respect,primarily owing to the inability of suppliers of eitherbiofuels themselves or the feedstocks they are likely

9Lifecycle or well-to-wheels (WTW) emissions refer to the well-to-wheel and tank-to-wheel (TTW) emissions, therefore incorporatingwell, e.g. farming through to the fuel combustion process.





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to be made from to fully address indirect impacts.However, CLCA provides an opportunity to understandand manage macro-systems-level impacts, both positiveand negative, and modify policy as a result.

4. CONCLUSION

Substantial differences currently exist between thenature of the instruments being deployed betweenthe worlds major markets for biofuels, with divergentpriorities emerging between climate-change mitigationand adaptation, energy security, food security and ruraldevelopment. New tools are under development to under-stand, measure and monitor land use and land-usechange and the under- and over-lying carbon stocks.This paper has explored the potential use of a hybridizedapproach to estimate CLCA and ALCA impacts frombiofuels and has highlighted differences in approachbetween the US and EU policy-makers. In so doing, ithas classified the roles and opportunities for using

ALCA versus CLCA. This is an important novelapproach to hybridizing the two, in order to provide abetter understanding of the systemic consequences onthe global provisioning system of policies designed tostimulate one-specific market or production system.Work is urgently required to understand and standardizethe accounting frameworks before serious, and damaging,distortions are introduced to the trade in biomass for bio-fuels, for heat and electricity, and by extension to thefood-based commodity and emerging biomaterials andbiochemical markets.

We thank Richard Templer, Mairi Black, Nilay Shah and

Catherine Oriel from the Porter Alliance and ImperialCollege London for facilitating the Chatham House Land-Use Change Workshop and posing the question to ourgroup: Can the LCA model approach estimate the directand indirect effects of biofuels?. We also acknowledge MiaoGuo, Imperial College London, for her assistance as scribefor our group and Stefan Unnasch for supplying figures andreview of this manuscript. We appreciate the contribution ofthe Energy Biosciences Institute (EBI), the Porter Allianceand Chatham House, London, for sponsoring and holdingthe conference in London in May 2009.

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