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Entropy Debt: A Link to Sustainability?
Caroline von Schilling
B.Sc., University of Northern British Columbia, 2002
Thesis Submitted In Partial Fulfillment Of
The Requirements For The Degree Of
Master Of Science
In
Natural Resources and Environmental Studies
(Geography)
The University of Northern British Columbia
June 2009
© Caroline von Schilling, 2009
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ABSTRACT
An emerging science of sustainability seeks to better understand the relationships between
humans and the natural environment. Some of this research views human society through the
conceptual lens of open systems thermodynamics, and specifically, the theory of dissipative
structures. This theory holds that the 'structure' of open systems, i.e. complexity, requires a
constant throughput of energy, which 'dissipates' energy gradients in the environment
external to the system. This is the 'entropy debt' of system complexity. Entropy debt
provides this research with a conceptual opportunity to highlight and, using surrogate
measures, analyse the relationship between municipal 'structure', the energy drawn from the
natural environment required to maintain the structure, and its cost to the natural environment
of doing so. Five British Columbia municipalities were investigated to determine a) how the
theory of dissipative structures could effectively be operationalized, and b) if doing so could
elucidate systemic 'drivers' of anthropogenic environmental degradation.
ii
TABLE OF CONTENTS
ABSTRACT ii LIST OF TABLES iv LIST OF FIGURES v LIST OF FIGURES v ACKNOWLEDGEMENT vi INTRODUCTION 7
Research Questions 10 Objectives 11 Overview 11
Chapter ONE: Context 12 Introduction 12 Meaning & Measure in the Emerging Science of Sustainability 12 Open Systems Conceptual Framework Approaches 13
Chapter TWO: Municipalities As Dissipative Structures 27 Municipalities as Complex, Open Systems 27 Municipalities as Dissipative Structures 28
Chapter THREE: Methodology 33 Introduction 33 Systems Inquiry 33 Rationale for Conceptual Frameworks 36 Methodology 37
Chapter FOUR: Results 57 Introduction 57 Selecting the Municipalities 57 Final Data Sets 59 Comparing Energy Throughput Surrogates 63 Comparing Community Entropy Debt Surrogates 72 Comparing the Energy-Entropy Ratios 77 Linking the Data 84 Summary of Findings 100
Chapter FIVE: Discussion 103 Introduction 103 Discussing the Findings 103 Discussing the Analogical Model & Lessons Learned 114 Discussing the Conceptual Framework 123 How the Objectives Were Met 130 Opportunities for Further Research 131
CONCLUSION 132 REFERENCES 133 APPENDIX A - Concepts 143 APPENDIX B - Data Preparation 154 APPENDIX C - Data Tables 186 APPENDIX D - Complexity Variables 198
iii
LIST OF TABLES
Table 1. Summary of community system selection criteria 41 Table 2. Summary of the systems considered for study 43 Table 3. Summary of analogue model, corresponding dimensions, and preferred surrogates
47 Table 4. Summary of available data corresponding to the preferred data 52 Table 5. Summary of available data - sources investigated, selection criteria, and actions required 54 Table 6. Summary of1995 energy consumption data per municipality 59 Table 7. Summary of2004 energy consumption data per municipality 60 Table 8. Summary of1995 emissions data per municipality 61 Table 9. Summary of2004 emissions data per municipality 61 Table 10. Summary of change of community entropy debt, 1995 - 2004 and 1992-2002 62 Table 11. Municipal highlights of energy consumption for building space-heating 70 Table 12. Municipal highlights ofLangford and Oak Bay's per capita emissions, 1995-2004
75 Table 13. Esquimalt's proportionally high commercial sector per capita area source emissions/energy consumption, 1995 81 Table 14. Esquimalt's decreased emissions and increased energy consumption (per capita), 1995, 2004 82 Table 15. Estimating energy-entropy ratio using ordinal ranking 83 Table 16. Summary of key trends, municipal ranking & complexity surrogate variable 85 Table 17. Summary of community complexity surrogates linked to municipal patterns of energy consumption 101 Table 18. Summary of the general trends revealed in the energy consumption data 102
IV
LIST OF FIGURES
Figure 1. Map of the Capital Regional District 58 Figure 2. Periods of time represented by the selected data 63 Figure 3. Total 1995 fossil fuel and hydro-electricity consumption 64 Figure 4. Total 2004 fossil fuel and hydro-electricity consumption 65 Figure 5. Total fossil fuel and hydro-electricity consumption in 1995 by activity type 66 Figure 6. Total fossil fuel and hydro-electricity consumption in 2004 by activity type 67 Figure 7. Detailed breakdown of fuel type consumed per activity type in 1995 68 Figure 8. Detailed breakdown of fuel type consumed per activity type in 2004 69 Figure 9. All available 1995 criteria air contaminants (CAC), greenhouse gases (GHG), metals, and other emissions of point, area, and mobile sources 73 Figure 10. All available 2004 air, water, land emissions of greenhouse gases (GHG), criteria air contaminants (CAC), metals, and other of point, area, and mobile sources 74 Figure 11. Sensitive ecosystems lost between 1992 and 2002 76 Figure 12. 1995 energy consumption (GJ) vs. 1995 emissions (tonnes) 78 Figure 13. 2004 energy consumption (GJ) vs. 2004 emissions (tonnes) 79 Figure 14. 1995 and 2004 energy consumption (GJ) vs. 1995 and 2004 emissions (tonnes) 80 Figure 15. 1995 transportation energy consumption vs. 1996 population density; 2004 transportation energy consumption vs. 2001 population density 87 Figure 16. 1995 and 2004 transportation energy consumption vs. 2001 roadway length 88 Figure 17. 2004 transportation energy consumption vs. 2001 commuting distances 89 Figure 18. 1995 transportation energy consumption vs. 1996 % driving commuters; 2004 transportation energy consumption vs. 2001 % driving commuters 89 Figure 19. 1995 transportation energy consumption vs. 1996 % walkers, public transiters; 2004 transportation energy consumption vs. 2001 % walkers, public transiters 90 Figure 20. % change in transportation energy consumption (1995 to 2004) vs. % change population (1996 to 2001); % change in transportation energy consumption (1995 to 2004) vs. % change population density (1996 to 2001) 91 Figure 21. 1995 Residential space-heating energy consumption vs. 1996 population density; 2004 residential space-heating energy consumption vs. 2001 population density 93 Figure 22. 1995 Residential space-heating energy consumption vs. 1996 % composition of high-density dwellings; 2004 residential space-heating energy consumption vs. 2001 % composition of high-density dwellings 94 Figure 23. 1995 Residential space-heating energy consumption vs. 1996 % composition of low-density dwellings; 2004 residential space-heating energy consumption vs. 2001 % composition of low-density dwellings 95 Figure 24. % Change in residential space-heating consumption (1995-2004) vs. % change in population (1996-2001); % Change in residential space-heating consumption (1995-2004) vs. % change in population density (1996-2001) 96 Figure 25. % Change in residential space-heating consumption (1996-2004) vs. % change in percentage composition of newly constructed dwellings: total dwellings (1991-2001) 97 Figure 26. Loss of sensitive ecosystems vs. % change in population; loss of sensitive ecosystems (1992-2002) vs. % change in population density (1996-2001) 99 Figure 27. Loss of sensitive ecosystems (1992-2002) vs. percentage composition of area of sensitive ecosystems to total municipal land area 100
v
ACKNOWLEDGEMENT
I would like to extend a warm thank you to my thesis committee members, past and present, Dr. Darwyn Coxson, Dr. David Connell, Dr. Lorraine Lavallee, and Dr. Debra Straussfogel, for their time to review and provide guidance to this thesis. Additionally, their encouragement helped me meet the challenges of this thesis research.
This thesis research was funded by the Social Science and Humanities Research Council and the University of Northern British Columbia. Thank you for this funding support.
Thank you to the following individuals for facilitating this project by assisting with locating data sources or disseminating data (in no particular order): Bruce Greig, District Municipality of Central Saanich; Connie Nicolette, City of Langford; Nigel Beattie, District of Oak Bay; Jon Munn, City of Colwood; Sonja Zupanec, Township of Esquimalt; Sasha Angus, Greater Victoria Development Agency; Derek Hutcheson, Andy Liu, and Tom Watkins, Capital Regional District; Geoffrey Johnson, Program Analyst, Business Services Section, BC Environment; Karen Kirby, BC Stats; Rob Klein, Ministry of Agriculture; Tony Wakelin, Ministry of Water, Land, and Air Protection; Jan Kirkby, Environment Canada; Hans Mertins, Terasen Gas; Stephen Salter, Consultant; Patricia Taylor, Consultant; Peter Jackson, Professor, University of Northern British Columbia.
I would especially like to thank Dan Hrebenyk and Bryan McEwen, SENES Consulting Ltd. for kindly facilitating me with answers regarding the methods in which their air emissions and energy consumption data were grounded.
A final thank you is reserved for my fellow grad student friends and 'teachers' alike for helping to make my Master's education the stellar life experience it was.
vi
INTRODUCTION
The findings of the Millennium Ecosystem Assessment (MEA) are unequivocal;
humans are degrading the ecosystem services upon which they rely at an alarming rate. "The
changes that have been made to ecosystems have contributed to substantial net gains in
human well-being and economic development, but these gains have been achieved at
growing costs in the form of the degradation of many ecosystem services . . . " (MEA, 2005,
p. 1). In 2001, upon the recommendation of United Nations Secretary-General Kofi Annan,
over 1,300 experts worldwide began research on the impact of human activity on the world's
ecosystems and on the impact of the world's changing ecosystems on human well-being.
Conducting research to inform sustainable practices, that is, to, "... meet the needs of
the present without compromising the ability of future generations to meet their own needs"
(Brundtland, 1987), is certainly not new. For example, Rachel Carson's (1963) popularly
written book, Silent Spring, focused on the harmful effects of human-introduced persistent
pollutants on wildlife. Yet, in the light of the recent findings of the MEA (2005), the
awakening of the public's consciousness appears to have generated only incremental social
change in the past fifty years. In fact, the MEA (2005) states that, due to human activity,
approximately 60 percent of the world's ecological services have been degraded or are being
used unsustainably (p. 1).
Yet, not all societies, nor communities within societies, appear to negatively impact
ecosystem services equally or for the same reasons. Research compiled by the World
Resources Institute posits that issues of empowerment and social justice lie at the heart of
environmental degradation (MEA, 2005). Other research (e.g. Bar-Yam, 1997b; Tainter,
7
1988), posits that societal complexity to meet external demands and forces, such as those
imposed by the environment or economy, drives environmental degradation.
Some of this research regarding human system complexity is based on
thermodynamic laws governing open systems, and specifically, the theory of dissipative
structures (Adams, 1982; 1988; Allen, 1997, Dyke, 1988; Prigogine et al., 1977,
Straussfogel, 1998). Unlike closed systems, open systems are open to matter and energy
resources; only open systems can build and maintain internal complexity, such as humans,
cities, or cells. Informed by the Laws of Thermodynamics, the theory of dissipative structures
maintains that there is a reciprocal relationship between the complexity of a system (i.e. its
internal 'structure', which is comprised of interacting 'parts' and 'processes') and entropy
(i.e. the dissipation of energy gradients), which is generated by the complexity of the system
and in the system's external environment. The entropy that is generated by the system can be
called the 'entropy debt' of system complexity (Dyke, 1988; Straussfogel & Becker, 1996).
Moreover, external constraints influence internal system complexity (Bar-Yam, 1997b;
Prigogine et al., 1977) and reciprocally, internal conditions influence the system's
interactions with its external environment (Allen, 1994; Macy, 1991; Prigogine et al., 1977).
This research begins with the premise that municipalities are operational 'real-world'
examples of complex open systems and dissipative structures (Adams, 1988). Conceptually,
municipalities can be understood as complex open systems that behave according to open
system thermodynamics, and more specifically, to the theory of dissipative structures.
Through such a conceptual lens, municipalities garner energy from their surroundings to
build internal complexity or 'structure' such as, social order, infrastructure, and
communication networks. The entropy debt of the complexity of municipalities (in this
8
research called, 'community complexity') is the impact municipalities have on their natural
environment. Such environmental impact is problematic when it compromises the ecological
integrity of the natural resources upon which municipalities rely (in this research called,
'community entropy debt'). Given the necessary relationship between the energy derived
from natural resources such as food, fiber, and fuel (in this research called, 'energy
throughput') to maintain municipal complexity, the sustainable use of the natural resources
and ecological services providing these energy throughputs is considered of paramount
importance.
Furthermore, external conditions, such as climate, culture, or the political
superstructure, necessarily influence the complexity of, and therefore, the entropy debt of
municipalities. Yet, internal conditions, such as infrastructure (e.g. amount and type) and
forward-thinking humans, also influence their entropy debt. This reciprocal relationship
between external constraints and internal 'structural' conditions gives rise to municipalities
that are structurally different. Such municipalities could also exemplify different entropy
debts. Moreover, this could be the case even for municipalities that are constrained by similar
external environments (i.e. climate, culture, political superstructure).
9
Research Questions
This thesis asks:
• How can an open systems conceptual framework, based on the theory of dissipative
structures, highlight the energetic-entropic relationship between 'municipal
complexity' and the natural environment?
• How can such an open systems conceptual framework effectively be operationalized
and applied to municipalities as open systems and as dissipative structures?
• What can an analysis of the conceptual framework parameters reveal about systemic
drivers of anthropogenic environmental degradation?
Conducted in the systems inquiry paradigm, and informed by the literature that has
developed conceptual frameworks of understanding human systems (at a variety of scales) as
complex open systems and as dissipative structures, this thesis research views five British
Columbia municipalities through the lens of a conceptual framework based on the
thermodynamic laws governing complex, open systems, specifically, the theory of dissipative
structures. It operationalizes the conceptual framework by abstracting from the theory of
dissipative structures these inextricably linked parameters: 'energy throughput', 'system
complexity', and 'entropy debt'. After mapping the real-world dimensions to their respective
parameters, this research applies surrogate measures to the parameters of this analogical
model. Finally, it conducts a comparative analysis of the surrogate measures for the selected
municipalities to investigate, a) the extent to which the analogical model can reveal (internal)
systemic drivers of anthropogenic environmental degradation, b) the efficacy of the
analogical model, and c) the utility of the conceptual framework.
10
Objectives
To answer these research questions, the objectives of this thesis are to:
1. Explicate a conceptual framework based on the theory of dissipative structures that
can highlight the entropic cost of system complexity in the environment external to
the system
2. Operationalize the conceptual framework by mapping the appropriate real-world
dimensions to the framework parameters: for energy throughput, complexity, and
entropy debt
3. Identify and apply municipal-specific surrogate measures to these parameters and
compare the data
4. Identify the links between the surrogate data of the framework parameters
Overview
The thesis begins with Chapter One, which informs the reader of the literature in
which this thesis research is nested. Chapter Two explicates municipalities as open systems
and as dissipative structures. Chapter Three presents the methodology undertaken to
operationalize the conceptual framework of understanding municipalities as complex, open
systems and as dissipative structures. Chapter Four presents the results of the methodology.
The thesis concludes with Chapter Five, which discusses the results and answers the three
thesis research questions.
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CHAPTER ONE: CONTEXT
Introduction
This chapter contains these sections:
1. Meaning and measure in the emerging science of sustainability
2. Open systems conceptual frameworks approaches
3. Opportunities for research
The first section identifies the issue identified in the sustainability literature toward
which this research directed. The second section identifies some open systems conceptual
frameworks, relevant to this thesis research, which have been developed by researchers to
provide clarity to the meaning, measure, or progress toward sustainability. The third section
identifies, from this open systems conceptual frameworks literature, the opportunity for the
research undertaken in this thesis. Some of the concepts central to understanding the open
systems conceptual frameworks literature, namely, open systems versus closed systems,
exergy, entropy, and the theory of dissipative structures, is found in Appendix A.
Meaning & Measure in the Emerging Science of Sustainability
Researchers admit that sustainability, the greater context within which human
induced (i.e. anthropogenic) natural environmental degradation is framed (Brown et al.,
1987), remains ill-defined today and its progress difficult to assess (Levin, 1993; Haberl et
al, 2003; Mayer et al., 2004; Brown et al., 1987). Despite the "vague and elusive" (Levin,
1993) nature of sustainability, more recently, "A new field of sustainability science is
emerging that seeks to understand the fundamental character of interactions between nature
12
and society" (Kates et al., 2001, p. 641). Kates et al. (2001) further acknowledge that
continued sustainability research should necessarily focus on nature and society relationships
requiring an interdisciplinary approach.
... [The] research itself must be focused on the character of nature-society interactions, on our ability to guide those interactions along sustainable trajectories, and on ways of promoting the social learning that will be necessary to navigate the transition to sustainability" (Kates et al., 2001, p. 641).
While some sustainability researchers distinguish social, and economic, from
environmental sustainability (Goodland & Daly, 1996), others do not. They develop
integrative conceptual frameworks through which to understand or analyse society-
environment relationships fundamental to sustainability (Mayer et al., 2004). This thesis
research is nested in this literature. This literature is summarized in the following sections.
Open Systems Conceptual Framework Approaches
Open systems conceptual frameworks are pervasive in research pertaining to living
systems, ecosystems, and social systems. In 1979, Lovelock boldly argued that the Earth is a
living system. Twenty years later and within the larger context of complexity science, Levin
(1998) described the biosphere as a complex adaptive system. In the social sciences, systems
approaches in the social sciences date to the early years of general systems theory emerging
in the 1950s with Talcott Parson's The Social System (1951) with further influences from
ecology (Odum, 1983), and emerging in anthropological studies of energy requirements of
societies (Rapapport, 1971; Kemp, 1971), growing and collapsing civilizations (Tainter,
1988), and societal processes (Adams, 1982; 1988). Moreover, a rich array of conceptual
frameworks has been developed in recent years to better understand the relationship between
humans and the natural environment. Mayer et al. state, "Some sustainability concepts view
13
human societies as part of the system and blend both economic and ecological theory ...
Others have turned to physics and the principles of thermodynamics for a common currency
with which to quantify sustainability across disciplines" (2004, p. 419).
Generally speaking, the open system conceptual frameworks reviewed here
incorporate ecological or thermodynamic concepts or both in the study of sustainability.
Moreover, most recently, some are blended with conceptual contributions from the emerging
science of complexity. Each subsection summarizes: the conceptual framework, some
operational examples, and contributions to sustainability science. The conceptual frameworks
reviewed are 1) resilience, 2) exergy, 3) entropy law and ecology in economics, 4) socio-
economic metabolism, 5) emergy, and 6) dissipative structures.
Resilience frameworks
Resilience theory is among the influences stemming from and continuing to further
the theory and management of ecosystems (Jorgensen & Miiller, 2000). Ecologist, C.S.
Holling (1973) distinguished the concept of system 'stability' from its 'resilience',
maintaining that the measure of ecosystem health is not its ability to return to initial
conditions when faced with some external shock, but to be able to absorb the shock without
losing its systemic integrity. Thereby, resilience is defined as the capacity of a system to
maintain its function and integrity, even if it changes internally, by absorbing shocks or
disturbances (Holling, 1973). The study of resilience has since expanded from ecological to
social and ecological systems (Gunderson & Holling, 2001; Adger, 2000). For example,
operationally, resilience research helps understand and manage integrated domains of social
systems and ecological systems in social-ecological systems (SES) (Folke et al., 1998).
14
A conceptual framework fundamental to resilience theory is the adaptive cycle,
whereby, "social-ecological systems ... are interlinked in never-ending adaptive cycles of
growth, accumulation, restructuring, and renewal" (Holling, 2001, p. 392). The conceptual
framework of the adaptive cycle has been studied in many contexts including the responses
of ecosystems to resource management institutions and personal or corporate crises (p. 394).
Adaptive cycles have also been explored in terms of system hierarchy, called 'panarchy',
whereby adaptive cycles interact across scales (Gunderson & Holling, 2001). Resilience
research maintains that sustainability "is the capacity to create, test, and maintain adaptive
capability" (Holling, 2001).
Exergy frameworks
Exergy has become a powerful conceptual and analytical tool in the study of
engineered systems (e.g. Bejan, 2002; Lozano & Valero, 1993) and self-organized systems
(e.g. Kay, 2000; Wall & Gong, 2001, Gong & Wall, 2001). For example, to further an
understanding of natural ecosystems, Kay (2000) argues that the exergy degradation principle
holds that ecosystems, given the constant flux of energy available in the environment, resist
being moved farther from thermodynamic equilibrium by 'complexifying' over time.
Specific to natural resources, Wall (1977) argues that natural resources provide a
gradient of energy to be consumed (i.e. as work) by open systems with internal structures or
organization capable of doing so. Wall (1977) explains that while solar energy, specifically,
the energy gradient difference with the earth, drives all the chemical, physical, and biological
processes of the Earth system, humans are unable to consume this energy directly. They rely
on natural resources, which have already converted solar energy to some degree and on the
technological mechanisms designed to capture their energy. Wall (1977) defines natural
15
resources as deposits (e.g. oils, minerals), funds (e.g. plants, animals), and natural flows (e.g.
running water, sunlight, ocean currents). This exergetic framework (Wall, 1977) conceives
known and profitable natural resources as reserves, the use of deposits as unsustainable, and
the use of funds and natural flows as potentially sustainable (Wall & Gong, 2001). Wall and
Gong (2001) define sustainability exergetically as the stable balance of the use of funds and
deposits with their generation via solar inputs.
Emergy theory & analysis
'Emergy', coined by H. Odum in 1983 (Odum & Odum, 2001), has emerged as a
theory that holds that self-organizing systems survive in the natural environment by
maximizing energy consumption most efficiently, i.e. by 'empower' (rooted in biologist
Alfred Lotka's maximum power principle (1922)). More specifically, H.T Odum's (1996)
maximum empower principle is central to the conceptual framework that holds that, "In the
self-organizational process, systems develop those parts, processes, and relationships that
maximize useful empower" (Odum & Odum, 2001, p. 71). Emergy refers to the, "available
energy of one kind that has to be used up directly and indirectly to make a product or
service" (Odum & Odum, 2001, p. 67). It provides a method of normalizing the units of
energy from natural sources, such as solar, wind, fossil fuels or food to an expression of
'solar emCalories' (i.e. solar equivalents) and 'emdollars' (Odum & Odum, 2001) or more
typically, 'solar emergy joules' (sej) (Odum, 1996; Brown & Ulgiati, 1997). Using
conversion factors in their calculation, solar emCalories represent measures of solar energy
equivalents that determine the value of any product or service (Odum & Odum, 2001);
emergy can, for example, replace market driven values determined by supply and demand
(Odum, 1994).
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Although emergy is not a thermodynamic concept in and of itself, it emerges from an
ecosystems approach to understanding natural capital as a product of the flow of solar energy
in the context of the biosphere considered as a self-organizing system (Brown & Ulgiati,
1999; Odum & Odum, 2001). Brown and Ulgiati (1999) define natural capital as the stores
(i.e. nonrenewable and renewable) "of material and energy from which environmental
services are drawn" (p. 3). Therefore, emergy links societies and their economies to the flows
of solar energy by considering the solar energy embodied in natural resources (and their
services) that humans derive from Earth's natural capital and equilibrating their use into the
making of products and services (p. 3). Moreover, to fuel its economies, sustainability
indices have emerged, such as the Emergy Sustainability Index (ESI) (Brown & Ulgiati,
1997).
The entropy and ecology in economics
The field of economics has incorporated ecological and thermodynamic concepts
since the mid-twentieth century. Exemplifying thermodynamic concepts in economics,
Georgescu-Roegen (1971) posited that thermodynamic laws, specifically, entropy, governs
economic processes. Moreover, Mulder and Van den Bergh (2001) argue that the Second
Law provides a useful construct to understand environmental degradation with respect to the
economic process. For example, as an open system, the economy relies on inputs (and
internal cycling) of material and energy resources. Martinez-Alier (1999) explains,
"However, if the scale of the economy is too large and its speed is too rapid, then the natural
cycles cannot produce the resources or absorb or assimilate the residues such as, for instance,
heavy metals or carbon dioxide". In this sense, dissipation of energy (i.e. entropy production)
impacts the natural environment in the anthropocentrically defined and 'negative' context of
17
environmental degradation encompassed in the discussion of (un)sustainability.
As a broad example, inspired by the environmental movement of the 1960s,
ecological economics is so named to incorporate the theoretical concepts of systems ecology
(Ropke, 2004). Ecological economics conceives that the, "human economy is embedded in
nature, and economic processes are also always natural processes in the sense that they can
be seen as biological, physical, and chemical processes and transformations" (Rjopke, 2004,
p. 296). On a conceptual level, ecological economics takes an anthropocentric view toward
sustainability, such that humankind is seen to be responsible for the care of the
environmental resources upon which it relies, as well as for its own well-being (Costanza,
1996, p. 980). An early contribution to biophysical thinking in economics came with
Boulding (1966), who spoke of Earth, its resources, and its economies as a spaceship - and
the economy as a "spaceman economy". Boulding (1966) used the analogy of a spaceship to
emphasize Earth's finite resources.
Socio-economic metabolism
The conceptual frameworks of socio-economic metabolism provide a more recent
example of biophysical concepts in economics. Socio-economic metabolism represents an
open system conceptual framework used to understand societal processes. Its conceptual
framework highlights, "the material input, processing and releases of societies, and the
corresponding energy turnover" (Fischer-Kowalski & Haberl, 1998, p. 573). As such, the
metabolism of society is analogous to the metabolism of organisms, whereby societies
require a throughput of energy and material resources; and societies can be profiled by this
throughput (Fischer-Kowalski & Haberl, 1998). Similarly, 'industrial metabolism' refers to
industrial processes (Ayers & Simonis, 1994, xii). Fischer-Kowalski et al. (1994) state, "The
18
notion of industrial metabolism draws attention to a materialistic view of the economy as a
physical system, driven by energy flows" (p. 337). By operationalizing socio-economic
metabolism frameworks, researchers have developed a variety of accounting methods of
energy and material flows.
Regarding their contribution to sustainability science, Haberl (2001) argues, "Most
metabolism research is being carried out with the aim of contributing to sustainable
development" (p. 13). Contributions include analytical tools such as Life cycle assessment
(LCA), Material flow analysis (MFA), Energy flow analysis (EFA), and Material and energy
flow analysis (MEFA). Life Cycle Assessment analyses the 'cradle to grave' impact that a
product has on the natural environment, from its production to its generation as waste. In
addition, exergy has been incorporated in this assessment method as a consistent measure of
environmental degradation (Gong & Wall, 2001). Material flow analysis is the study of
masses of physical flows and masses of substances (Haberl, 2001, p. 11). Used as an
accounting tool, MFA measures the input of materials (e.g. tonnes of imports, domestic
extraction, flows of soil, and water resources, etc.) required by and output of waste (e.g.
tonnes of exports, air emissions and solid waste) of socio-economic systems (Matthews et al.,
2000). Finally, Haberl et al. (2003) propose a conceptual framework that combines energy
and material accounting (MEFA), which considers human consumption of net primary
products (NPP). Briefly, net primary products refers to the solar energy captured in the
biomass of vegetation, which, when lost to human activities, is unavailable for
photosynthesis and, therefore, ecosystem function (Vitousek et al., 1986).
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Dissipative structure frameworks
The theory of dissipative structures presented by Prigogine and his colleagues (e.g.
Prigogine et al., 1977; Prigogine & Stengers, 1984; Nicolis & Prigogine, 1989) has emerged
in conceptual frameworks applied in the social and natural sciences. In the natural sciences,
the theory of dissipative structures has been applied to the study of living systems. For
example, Kay (2000) states, "Life should be viewed as the sophisticated end in the
continuum of development of natural dissipative structures from physical, to chemical
autocatalytic, to living systems" (p. 10).
In the social sciences, the theory of dissipative structures has been applied to the rate
and patterns of growth of cities (Allen, 1997; Dyke, 1988; Straussfogel, 1991). Similarly,
Adams (1982; 1988) sets out a detailed conceptualization of society and its evolutionary
processes as conduits of energy - specifically, as dissipative structures. In detail, Adams
argues that societal development, evolution, internal complexity, and external relations
decidedly adhere to the thermodynamic laws governing open systems, specifically, the theory
of dissipative structures. As dissipative structures, societies can maximize energy
throughputs or maintain steady states and, reciprocally, must generate entropy outside the
societal system (1988; 1982). Adams (1988) states, "human social structures are not merely
sets of human beings but are, rather, assemblages of interdependent dissipative and
equilibrium structures. Moreover, it is assumed that the combinations vary from one society
to another ... some use a great deal more energy than others ..." (p. 24).
Conceptual frameworks based upon the theory of dissipative structures have
contributed to the discussion of sustainability through their application to a variety of
systems and at different scales. For example, Ho and Ulanowicz (2005) derive sustainability
20
indicators by analyzing large scale human systems (i.e. agriculture, economics) by way of the
characteristics of organisms (as dissipative structures), which the authors argue represent, "an
ideal sustainable system" (p. 39). Alberti (1996) views cities as dissipative structures and
then develops a conceptual framework of interrelationships based on dimensions of
sustainability (i.e. social and ecological) to assess the qualities of 'urban ecological space'.
On a smaller scale yet, Hermanowicz (2004) examines entropy change and energy flux in
simple systems called unit operation and processes (UOP). His paper seeks to clarify the
definition of sustainability by applying thermodynamic fundamentals to relatively simple
dissipative systems.
Research Opportunity
The conceptual frameworks summarized above exemplify those whose development
to better understand society-environment relationships contribute to the measure and
meaning of, and practice toward, sustainability. Most generally, those reviewed take an
ecological or thermodynamic conceptual approach to their frameworks to understand the
interactions between society and the natural environment. Of course, as ecological systems
are subject to the thermodynamic laws, a great deal of overlap exists. Each framework
presents advantages in its conceptual approach.
Conceptual similarities
The research highlighted in this literature review shares several aspects in common.
First, they utilize open systems conceptual frameworks to study society-environment
relationships. Second, they explore some or all of the following research steps. They 1)
explore theoretical principles, in this case, ecological and/or thermodynamic, 2) develop a
21
conceptual framework based upon these principles (i.e. they construct a lens), 3) map the
conceptual framework to a specific system (i.e. they look at a system through the lens) to
elucidate, "phenomena, to order material, revealing patterns" (Rapoport, 1985). This step
requires 'mapping' the theoretical principles to the system under investigation. In some
cases, researchers 4) further develop analytical tools, such as indices and measures, having
repeatedly conduced inductive research.
Conceptual differences
Yet, fundamental differences between each type of framework (i.e. ecological or
thermodynamic) provide unique conceptual and operational advantages. For example, the
ecological approach conceives human systems as nested hierarchies of interdependent
systems, which exist within the larger ecological systems. The social-ecological system is, in
effect, a coupled human and natural environment system, thereby eliminating the need to
explicate the boundaries 'between' social and natural environment systems for some analysis.
This conceptual approach effectively highlights the capacity of the entire system to adapt to
change over time, for example, due to ecological disturbances caused by humans. This type
of framework lends itself operationally to the analysis of practices toward sustainability. For
example, Berkes and Folke (1998) focus on the link between social and ecological systems
by examining case studies of adaptive natural resources management practices. Similarly, the
research conducted by Gunderson et al. (1995) is premised on the idea that, "Finally,
sustainable development is neither an ecological problem, a social problem, nor an economic
problem. It is an integrated combination of all three. Effective investments in sustainable
development therefore simultaneously retain and encourage the adaptive capabilities of
people, business enterprises, and nature" (p. 32).
22
Alternatively, the thermodynamic approach offers sustainability research a rich
conceptualization of the energy, matter, and information resources derived from the natural
environment required to sustain human systems in various states of complexity. Another
conceptual advantage of thermodynamic frameworks is that they lend themselves to analysis
of environmental impacts from human activity. This opportunity is possible because of their
conceptual capacity to differentiate the system from the system's external environment. This
is evidenced in entropy law in economics, elucidating the 'ecological debt' (Rees &
Wackernagel, 1996) that economic systems incur in nature. Moreover, conceptual
frameworks based on dissipative structures engender further opportunity to highlight energy
throughput, waste outputs (i.e. entropy production), and internal complexity in the context of
external environmental constraints and internal systemic conditions.
Opportunity for research
Clearly, open systems conceptual frameworks - whether conceptually ecological or
thermodynamic - have contributed insights to sustainability science, by offering definition,
measure, or management practices toward sustainability. Yet, conceptual frameworks based
on the theory of dissipative structures to study anthropocentric environmental degradation
could be more robust. While the thermodynamic concept of dissipative structures has been
operationalized to model patterns of urban growth (Allen, 1997; Straussfogel, 1991) and
explain the evolution of cities (Dyke, 1988), societal (Adams, 1982; 1988), and world-system
processes (Straussfogel, 1997), it has not been specifically operationalized in the manner
proposed in this research project.
For example, Alberti (1996), viewing cities as dissipative structures, develops a
framework based on dimensions of sustainability (i.e. social and ecological) to assess the
23
qualities of 'urban ecological space' rather than dimensions of dissipative structures. Yet, the
theory of dissipative structures offers the conceptual opportunity to understand the
interrelationship between a system's complexity, its energy throughput requirement, and the
reciprocal production of entropy. In another example, Adams (1988) views society as a
dissipative structure. However, although Abel (1998) admits that Adams (1988), "produced a
groundbreaking and extensive synthesis of much of complexity theory with anthropology ...
by incorporating] many of the issues raised by complexity theory, which include[s] ...
dissipative structures . . ." this work does not extend analysis to human impact on the natural
environment. By Adams' own admission,
... my topic is society. Since human societies have sought and already felt the impact of ecological deterioration, it seems likely that the species may ultimately suffer similarly. My concern is that the proximate problem, however, lies in human society, and so a focus there is not out of place for understanding what we are doing to the ecology (1988, p. 12).
Opportunities exist to explore the issue of sustainability using open systems
conceptual frameworks, specifically, the theory of dissipative structures to explore, "... the
character of nature-society interactions ..." (Kates et al., p. 641). The opportunity lies in its
principles, which provide opportunities to highlight a variety of system characteristics. Such
a conceptual framework provides this research with the opportunity to distinguish a complex
system from its external environment. More specifically and pertaining to human systems, it
highlights the energetic and entropic relationship between a human system's internal
complexity and its impact on the natural environment upon which it relies (Straussfogel &
Becker, 1996). Yet, the interrelationship of complexity and its entropy debt is not,
necessarily, specific to the theory of dissipative structures; it is also general to the principles
of open systems thermodynamics. However, the theory of dissipative structures is deemed to
be a general theory of open systems thermodynamics pertaining to living and social systems
(Prigogine et al., 1977; Prigogine & Stengers, 1984). As a result, a literature has evolved that
has mapped this theory to a variety of systems, including human systems. This literature
provides this research with much of the guidance it requires to operationalize the theory of
dissipative structures in regards to municipalities (explained in detail in Chapter Three:
Methodology). Yet, is it possible for such a conceptual framework to reveal 'phenomena' and
'patterns' of systemic drivers of environmental degradation (at the scale of municipalities)?
A link to sustainability?
Research, for example by Tainter (1988) and Bar-Yam (1997b), posits that societal
complexity, that is, the internal complexity needed to meet external conditions, such as
demands and forces imposed on the society by the environment or the economy, drives
environmental degradation. Yet, neither all societies, nor communities within those societies,
appear to negatively impact ecosystem services equally or for the same reasons. For example,
Allen (1994) explains,
So much of human attention is focused on playing a role in groups where values are generated internally, and the physical world outside is largely irrelevant. It is therefore naive to believe that underneath the rich tapestry of life there is a rational scheme within which the complexities of the world would appear as being necessary and unavoidable ... Evolution is creative beyond reason, and in that lies its resilience, since it is not framed to respond to any particular limited scheme (p. 94).
Moreover, Holling (2001) argues that human systems are unique in that they are forward-
thinking and intentional. And Adams (1988) admits that some societies "use a great deal
more energy than others" (p. 24). Clearly, given the same external constraints, it is possible
for the 'internality' (Macy, 1991), meaning internal system structure and associated
processes, of different human systems to differ. Therefore, it should be just as possible for
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the internal complexity of human systems to impact environmental degradation, and to
varying degrees, as the assertion that external conditions drive human system complexity.
This research explores the conceptual implications that human systems, specifically,
municipalities as complex open systems and as dissipative structures (in this research,
simplified as 'community systems'), can be different from one another given the same
external constraints. More specifically, this research explores the implications that such
municipalities can generate different 'entropy debts' resulting from different structural
characteristics. In this way, this research intends to contribute to the literature that explores
dissipative structures conceptual frameworks in the context of the issue of sustainability.
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CHAPTER TWO: MUNICIPALITIES AS DISSIPATIVE STRUCTURES
This chapter describes municipalities through the conceptual lens of complex open
systems and dissipative structures. The main sections of this chapter are:
1. Municipalities as open systems, and
2. Municipalities as dissipative structures
Municipalities as Complex, Open Systems
As complex open systems, municipalities are open to material and energy resources.
More specifically, they are self-organizing and hierarchical (see Appendix A for details). For
example, they are comprised of subsystems such as firms, agencies, and families, which are
also complex and open (Adams, 1988). They are also embedded in other complex systems
such as, political superstructures and the natural environment. In this way municipalities can
be called 'nearly-decomposable' (Simon, 1973), meaning they are behaviorally differentiated
from the subsystems of which they are comprised and the 'suprasystems' (Laszlo, 1972) of
which they are a part. In addition, they are 'imperfectly centralized' (Adams, 1988),
meaning, they rely on fluxes of resources from their relative and larger suprasystems (used
synonymously in this thesis with 'supersystem'). Moreover, municipalities are self-
organizing, meaning they internalize resources such that they self-reproduce in a steady-state,
or, in the face of internal or external fluctuations, some new self-reproducing state (Adam,
1988; Allen, 1997; Straussfogel, 1997; Tainter, 1988). Thus, as open systems, municipalities
are self-reproducing and exhibit system complexity (explained in detail in the following
sections), which distinguishes them from their external environments.
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Municipalities as Dissipative Structures
The purpose of this section is to provide an overview of what municipalities 'look
like' through the lens of dissipative structures. This section defines municipalities as
dissipative structures and then describes them in the context of the concepts relevant to the
thesis research, a) complexity, energy throughput, entropy debt, b) external constraints and
internal conditions, c) developmental vs. evolutionary change.
Definition
The research of Adams (1982; 1988) informs the "window of attention" (Odum &
Odum, 2001) this research, which understands municipalities as complex open systems and
as dissipative structures. Adams (1988) argues that societies are dissipative structures.
Moreover, he argues that the major regulatory levels comprising whole societies are
dissipative structures because they are 'axial' to (Adams, 1988), meaning, integral to, the
function of the whole society. Examples include settlements (e.g. cities or municipalities),
sub-national jurisdictions (e.g. provinces or states), and supranational bodies (e.g. United
Nations). Adams (1988) explains, "Boundary definitions of a society are important because
they explicitly interpose a model of the structure ... [that] implies that the work of the society
should be organized to yield benefits for the "whole" society" (p. 147). For example, while
culture and self-identity generate self-organized human systems, such as firms or clubs, it is
the political regulatory sector that controls the flux of resources from one hierarchical level to
the other in a larger society (Adams, 1988).
This thesis argues that municipalities are part of the political regulatory system that
controls the flux of resources within and are, therefore, integral to the whole nation within
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which they are nested. This is evident in the political boundaries to which municipalities
adhere. For example, the local municipal governments control for the purpose of acquiring
some of the flow from market processes. They capture the flow of resources to maintain their
infrastructure, upon which the citizenry and firms rely. Reciprocally, the citizenry and firms
perform work, which furthers the flow of resources, in the form of taxes paid, to the local
governments. Therefore, the political boundary of a municipality provides this research with
its operational "window of attention" (Odum & Odum, 2001) to study municipalities as
complex open systems and as dissipative structures.
For the purposes of this research, to ensure that the politically bounded entity, in this
case, a municipality, is understood to be a self-organized, self-reproducing open system in a
hierarchy of other human and natural environmental systems and not to be confused with its
political definition, the municipalities under investigation may also be called 'societal
systems' or 'community systems'.
Complexity, energy throughput & entropy debt
Municipalities are comprised of 'parts' such as roads, buildings, and pipes, and other
complex systems such as, humans, families, and neighbourhoods. Associated with these parts
are 'processes' by which the parts interact. Examples include economies, communication
networks, and inter-personal relationships. These 'parts' and 'processes' of municipalities
comprise their 'structure'. For practical purposes, system structure and system complexity
may be used interchangeably. Maintaining or building this structure requires a throughput of
energy. The natural environment provides municipalities with material and energy resources,
such as food, fiber, and fuel. This is the energy throughput to the municipality. Reciprocally,
municipal 'structure' or system complexity necessitates an entropy debt in the natural
29
environment from which it draws its energy and material resources. In a regenerating
geobiosphere, this is considered problematic when the draw on natural resources exceeds its
regenerative capacity and the accumulation of wastes exceeds their ability to be absorbed.
External constraints & internal conditions
The external environment of a dissipative structure constrains the realm of
possibilities in which its 'structure' can exist (Prigogine & Stengers, 1984). As dissipative
structures, municipalities are also 'constrained' by their external environment. Practically,
the types and quantities of natural resources and the larger socio-political superstructure
define the realm of possibilities within which the structure of municipalities can be found.
"... [T]he environment of a dissipative structure is not only a source of constraint, but also a
source of input for maintenance; the destination of degraded forms of the resources and
energy utilized as inputs; and a source of potential transformation" (Straussfogel, 1997, p.
123). While the external environment constrains and influences the possibilities within which
municipalities can be found to exist, it does not necessarily define the state in which they do
exist at any given time.
Municipal structure is generated from within, as it is constrained from without. For
example, its internal structure, i.e. microstructure, changes over time. Moreover, while
system dynamics are not value-laden in the physical sense, human system dynamics are.
Humans are capable of acting within and upon their microstructure in a forward-thinking
manner imbued with vision and values (Holling, 2001). As such, municipal structure may
conform to external constraints, but how it does so involves conditions given by the
microstructure itself (e.g. the infrastructure and other complex systems), which includes
value-laden and decision-making humans. A shopping mall is built here, a highway there,
30
and a new subdivision is added. While one neighbourhood is gentrified, yet another
deteriorates.
'Developmental' change
Over time, municipalities, even those subject to similar external constraints, can
become qualitatively and quantitatively different.
Open systems, subject to exchanges of energy and matter with their environments, and with non-linear interactions between the micro-elements can give rise to macrostates of organization and behavior that undergo bifurcation that is, for identical external conditions, various possible structures can exist, each of which is perfectly compatible with the microscopic interactions (Allen, 1997, p. 18).
Developmental change occurs over time in a municipality (as a 'community system') because
of the irreversible and non-linear processes involved in system dynamics. For this reason, the
direction or path of development that a municipality takes over time will always be sensitive
to initial conditions. Therefore, different municipalities will follow different developmental
paths; and, their paths necessarily diverge.
Allen (1997) likens differences between systems constrained by the same external
factors to origami. Imagine two pieces of paper that are identical to each other. A fold in the
paper is like a bifurcation (i.e. an amplification of naturally occurring fluctuations to a critical
point at which the system's structure must change). Each paper is folded, first, the same. But
at the second and third folds, the papers are folded in different locations. Yet, each of these
locations are compatible with the condition of that paper's history of folds at that time;
otherwise the paper would tear (Allen, 1997). After several more folds, one paper becomes
an origami swan and the other an origami fish. In the same way, local perturbations give rise,
over time, to structural differences in systems. For example, the availability of provincial tax
31
dollars, in conjunction with the processes internal to the municipalities, e.g. communication
networks and social networks, is capable of bringing any number of structural changes into
existence in any number of different ways.
'Evolutionary' change
Additionally, external constraints may also change. Examples include climate change,
resource scarcity, or technological innovation. When these constraints (or even internal
conditions) evoke fundamental changes to municipal processes, a municipality is said to
undergo an 'evolutionary' change. In the context of system dynamics, evolutionary change
requires that the parts and processes of the system microstructure to break to form entirely
different patterns (Straussfogel, 1998). This is called 'symmetry breaking'; a discontinuity
exists between the previous state and the new state. "It is only when the cyclic phenomena or
the microdynamics creating the observable trends begin to oscillate beyond their normal
range due to some innovative force, that such discontinuity can occur" (Straussfogel, 1998, p.
21). Using an extreme example, an earthquake generates a massive force that the
microstructure of a municipality may not absorb. The municipal microstructure necessarily
conforms to the constraint; buildings collapse and lives perish.
The following chapter, Chapter Three: Methodology, explains the details of the
methodology of this research.
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CHAPTER THREE: METHODOLOGY
Introduction
This thesis undertakes its research within the systems inquiry paradigm. It begins
with the premise that municipalities can be understood as complex open systems and
dissipative structures. This premise requires this research to take a conceptual framework
approach. This chapter introduces the reader to systems inquiry and provides a rationale for
the conceptual framework approach to the research. Then, the chapter explains the
methodology of this research project. More specifically, it presents each methodological step
by topic, specifying its rationale, assumptions, and the specific methods (i.e. procedures)
undertaken in the research. This chapter presents the:
1. Systems inquiry approach
2. Rationale for conceptual framework approach
3. Methodology
a. Specify the analogical model
b. Select the systems as case studies
c. Map the system dimensions
d. Apply surrogate measures
e. Undertake the analysis
Systems Inquiry
Research focusing on the relationship between humans and the natural environment
could be conducted with a wide range of research approaches, from post-positivism to
33
constructivism, or systems inquiry. For example, the post-positivist approach applies a
question deduced from theory to a deconstructed 'thing' or issue - in the lab or in situ - in
order to draw data for the broad purpose of generalizing the findings to predict similar
'things' or issues. Such an approach assumes that a real world exists that can be known
objectively (Lincoln & Guba, 1985). Using another example, a constructivist approach
applies a question to which the answer is drawn, inductively, from the real world for the
broad purpose of exacting meaning from that contextual situation or experience. This
approach assumes a real world that cannot be separated from any human experience,
including the subjective knowing of the researcher (Lincoln & Guba, 1985).
Alternatively, a systems inquiry approach applies a question about "... problems
concerned with the systemic/relational aspects of complex systems" using a, "... set of
coherent and related methods and tools that [are] applicable to ... the analysis of systems"
(Banathy, 1996, p. 40). The approach is based upon the theory that systems share common
isomorphies (structural similarities) and correspondences (relationships with the system's
environment) (von Bertalanffy, 1968; Boulding, 1956; Laszlo, 1972). This approach assumes
that knowing is immanent, meaning, it is inherent and referenced in the mind of the knower
(Banathy, 1996).
Systems scholars argue that complex systems, such as living or human systems,
cannot be understood by deconstructing them into their component parts and then by
summing the findings of those parts (von Bertalanffy, 1968; Laszlo, 1972). Gallopin (2003)
argues that the relationship between human societies and the natural environment upon which
they rely defies deconstruction or reduction because processes and relationships exist in
context of the whole. They are characteristically not detectable in the component parts when
34
they are deconstructed (Bar-Yam, 1997a; Laszlo, 1972). Therefore, a post-positivist approach
to this research question would not be appropriate. A strictly contextual inquiry is likewise
not appropriate for this research question. Rather than seeking to disclose, directly, the
meaning of individual human experiences in the context of their communities, this thesis
research seeks to compare municipalities according to their systemic characteristics. As such,
systems inquiry offers an appropriate inquiry paradigm.
Ervin and Alexander Laszlo (1997) explain the benefits of systems inquiry. They
state,
While the sciences move toward theoretical syntheses through the construction of grand unified theories in physics and similar embracing theoretical frameworks in other realms of inquiry, the humanities manifest a countervailing trend towards relativistic positions on issues of societal evolution with a corresponding reticence for generally applicable normative viewpoints on designs for the human future. The advantage of the systems sciences is their potential to provide a trans-disciplinary framework for a simultaneously critical and normative exploration of the relationships between and among human beings and their social, cultural, and natural environments ... Systems sciences do much to render the complex dynamics of human socio-cultural and politico-economic change comprehensible. Observed phenomena in the natural and man-made universe do not come in neat disciplinary packages labeled scientific, humanistic, and transcendental: they invariably involve complex combinations of fields, and the multifaceted situations to which they give rise require an holistic approach for their solution. The systems sciences provide such an approach and can consequently be considered a field of inquiry rather than a collection of specific disciplines (p. 6).
About systems methodologies
As Laszlo and Laszlo (1997) argue, while systems inquiry does not involve reduction
into 'parts', systems inquiry does involve some process of simplification to 'render complex
dynamics comprehensible' (p. 6). Laszlo and Laszlo (1997) explain,
The principle heuristic innovation of the systems approach is what may be called 'reduction to dynamics' as contrasted with 'reduction to components', as practiced in the methodologies of classical science. Phenomena in the
35
observed world are usually too complex to be understood by modeling all their parts and interactions; some form of simplification is necessary (p. 9).
Yet a wide variety of processual 'simplifications' general to systems, i.e., methodologies,
have been developed by and for the systems sciences. This breadth of variety has occurred
because analyses of system types (e.g. mechanical, biological, social) and scales (e.g. watch,
cell, nation) can involve a variety of starting assumptions about their isomorphics and
correspondences. Additionally, at this time, a single general systems theory has not been
developed such that only one methodology appropriately applies to all systems. Rather,
tenets for systems 'simplification' to their 'dynamics' have been derived from such sciences
as biology and physics and include conceptual contributions from ecology to neurology.
Moreover, in recent years, complexity science has contributed a variety of methodologies
based on concepts including networks and system dynamics (Bar-Yam, 1997a). In all, across
the systems and complexity sciences, methodologies range from mathematical and modeling,
to metaphor and contextual/self-defining (Banathy, 1996; Bar-Yam, 1997a). Their purposes
range from predictive, to system management, or to understanding human experiences.
Rationale for Conceptual Frameworks
Conceptual frameworks bring theories and their principles and concepts together to 1)
conceptualize or "see" the system under investigation, 2) highlight structure or
correspondence, and 3) provide the basis for an analytical tool.
As we seek sustainable futures, we grapple with understanding complex systems of people and nature. Both the social and ecological components of these systems have long histories of discipline-based scientific inquires -replete with theories, methods, and findings. One way of understanding how these components interact is to link them in a common framework. This is a "systems" approach, in which a universal or common framework can be used to unite different components in the same system (Westley et al., 2001, p. 103).
36
Conceptual frameworks allow researchers to avoid computational and design
limitations inherent in computer-based models and simulations (Bar-Yam, 1997a). They are
particularly useful for "... expressing some previously unnoticed aspect, hoping for future
development of a suitable algorithm", which is arguably better than, "... starting] with
premature mathematical models ... and possibly restricting the field of vision" (p. 182). A
conceptual framework advantageously provided this research project with both a conceptual
understanding of the open systems under investigation (i.e. municipalities) and the basis for
developing a tool with which to analyze them.
Specifying the system
This thesis research looked at municipalities through the conceptual 'lens' of open
systems, and more specifically, dissipative structures (see Chapter Two). It did so to a)
highlight the relationship between the structure (i.e. 'system complexity') and energy
requirements (i.e. 'energy throughput') of, and anthropogenic environmental degradation (i.e.
'entropy debt') generated by a municipality, and b) provide guidance from the literature to
operationalize these parameters (by mapping them to municipalities and by applying
surrogate measures to them). The latter is explained in the following sections.
Methodology
With no one systems methodology to choose from, Laszlo and Laszlo (1997) guided
the methodology of this research. Traditional analysis, they argue, requires steps to 1)
deconstruct that which is to be explained from its context, 2) explain its given properties, and
3) sum the explanations to the contextual whole. By contrast, systems analysis requires steps
to 1) identify the phenomena under investigation, thereby also defining the context, 2)
37
identify the type and scale of system, 3) focus on its processes or structures, and 4) integrate
the perspective with the overall phenomenon (Laszlo & Laszlo, 1997, p. 11, 12).
The analysis of this thesis research adhered to systems analysis because it, 1)
identified the phenomena under investigation to be the energetic-entropic relationship
between a 'community system' and the natural environment upon which it relies for
energetic resources, 2) identified that the type and scale of system to be a municipality as a
complex open system and as a dissipative structure, 3) identified its processes to be those
linked according to thermodynamic principles (i.e. the framework parameters, explained in
more detail in the following sections), and 4) integrated the perspective with the overall
phenomenon by investigating the links between the structural characteristics of 'community
systems' and the natural environment.
A conceptual framework based on the theory of dissipative structures, provided this
research with the means to launch Laszlo and Laszlo's (1997) four-step approach to systems
analysis/synthesis (p. 11). It simultaneously conceptualized and provided the basis for the
analytical tool to analyze the "window of attention" (Odum & Odum, 2001), i.e.,
municipalities as open systems and as dissipative structures. More specifically, the
methodology of this research a) specified an analogical model abstracted from the theory of
dissipative structures; b) to which dimensions of selected municipalities and the natural
environment were mapped, c) to which surrogate measures are applied, and d) from which an
analysis was conducted.
Specifying the analogical model
The methodology of this research, first, required specifying an analogical model
abstracted from the theory of dissipative structures to which key dimensions of the selected
38
municipalities (and the natural environment) could be mapped and to which surrogate
measures could be applied.
Rationale for an abstracted analogical model
Once a conceptual framework of understanding has been developed to 'see' the real-
world in a certain way, an analytical tool that remains consistent with that framework can be
used to investigate phenomena of the real-world. Models can be understood as non-linguistic
entities that mediate theory and the observed world (Morgan & Morrison, 1999). According
to Morgan and Morrison, "[Models] function as tools or instruments and are independent of,
but mediate between things; and like tools, can often be used for many different tasks" (1999,
p. 11). This interpretation holds that models are somewhat autonomous from the theories or
data from which they are derived, which is what allows them to, "function as instruments of
investigation" (p. 10). The analytical tool of this research, abstracted from the theory of
dissipative structures, offers a means to investigate the energetic-entropic relationship
between municipalities (as complex open, and societal systems) and the natural environment.
It does so by analogy.
The literature reveals several categorical perspectives of models as they are used in
science. For example, Morgan and Morrison (1999) distinguish the 'semantic' view of
theoretical models (e.g. Bohr's model of the atom) from Hesse's (1963) 'analogical' view of
models. Analogical models relate processes or dimensions of one entity to the study of
another based on their similarities (Hesse, 1963). Although the 'parts' of a dissipative
structure and the 'parts' of a community systems are different, the relationships between the
'parts' of each entity correspond proportionally. Shelley (2003) refers to this type of analogy
as a 'shared structure' type, "... because they emphasize the presence of mappings or
39
alignments of hierarchically structured, causal relationships shared between source and target
analogs" (p. 7). Hesse (1963) explains that analogies can correspond proportionally
'vertically' when they result from the same cause, and 'horizontally' when the 'parts' are
mapped to their equivalents. For correct conclusions to be inferred about the municipalities
under investigation via an analogical model (and argued by analogical reasoning), a
proportional correspondence must exist between the relationships of the model parameters
and the relationships of the target dimensions, in this case, dimensions of municipalities (and
the natural environment) (Juthe, 2005).
Specifying the model parameters
This thesis research constructed an analogical model by abstracting the theory of
dissipative structures to the parameters, a) energy throughput, b) system complexity, and c)
entropy debt. Briefly, an abstraction-type model is one in which all details that are not
relevant to the investigation at hand are omitted (Frigg & Hartmann, 2006). Yet, the
parameters of the abstracted analogical model retain the larger theoretical principles of key
relationships held in the theory of dissipative structures. It retains the principle that open
system 'structure' of a municipality (requiring an energy throughput) 'dissipates' the
resources of its natural environment upon which it relies, as the natural resources of that
environment constrains the 'structure' of the municipality as 'community system'. Moreover,
'vertical' correspondence (i.e. the relationships corresponding between the parameters and
the dimensions) is satisfied on the basis that dissipative structures and municipalities as
dissipative structures are driven by the same 'cause', namely, the Second Law. The
'horizontal' correspondences (i.e. the parameters are mapped to the correct dimensions) are
supported by the literature. This is explained in more detail in the following sections.
40
Selecting municipalities as case studies
The methodology of this research required, once the model parameters were
specified, that empirical data be collected. This required selecting municipalities.
Rationale for case study
The analytical tool used in this thesis research requires an application to some aspect
of the real-world. Hesse (1963) explains, "Whether the hypotheses thus suggested turn out to
be true, is, as always, a matter for empirical investigation. The logic of analogy, like the logic
of induction, may be descriptive without being justificatory" (p. 62). Therefore, a case study
of selected municipalities provides an appropriate approach. Moreover, several
municipalities were selected in order to be able to compare results.
Community system selection criteria
The municipalities must be subject to similar external constraints, since these
constrain the realm of possibilities in which their 'system complexity' can be found. This
requires holding constant external constraints such as, a) climate, b) the socio-political
superstructure, and c) social stability, and d) key internal variables, i.e. population and e)
system dynamics. 2) Internal system complexity variables should not be held constant.
Table 1. Summary of community system selection criteria
Community system type Factors held constant Complexity variables
Two or more for comparison
Subject to developmental dynamics
Similar population size
Climate
Socio-political superstructure
Socio-political stability
Population characteristics
Infrastructure amount and type
Activities associated with infrastructure
41
These criteria are elucidated using the following examples. A municipality (i.e.
'community system') located in a cold climate may necessitate a higher entropy debt than a
similarly-sized municipality located in a warm climate. A municipality located within a
socio-political superstructure that imposes high energy efficiency standards may necessitate a
lower entropy debt than a similarly sized municipality existing within a superstructure that
imposes low energy efficiency standards. Poor internal mechanisms for coping with external
stressors introduces the possibility of unregulated behavior regarding environmental
resource-use, which contributes to environmental degradation (Adger, 2000). Therefore,
social stability should also be a consideration. Lastly, it is important for municipal
populations to be similar because larger (or smaller) populations can collectively achieve
profoundly different system complexity based on the amount of energy resources their
numbers can garner. The municipalities should also be dynamically similar, meaning they
should be subject to developmental versus evolutionary dynamics, especially pertaining to
external forces. For example, a municipality subject to an extreme weather event could
develop entirely different building construction regulations necessitating a higher (or lower)
entropy debt compared to a municipality with different building construction regulations in
the absence of that extreme weather event.
Finally, to provide effective empirical evidence of internal system 'structural'
dimensions that are linked to environmental degradation, internal complexity variables (e.g.
infrastructure type and population activities) should not be held constant. It is precisely the
similarities and differences of these variables that can elucidate links to the entropy debts
among different municipalities.
42
Procedures for selecting the municipalities
Table 2 summarizes the process to select the municipalities. Only those municipalities
that met the selection criteria and the data selection criteria (explained in the following
sections) were selected (see Appendix C, Table CI).
Table 2. Summary of the systems considered for study
Municipalities considered
Developmental dynamics
Political stability
Similar climate
Similar
superstructure
Similar population
Consistent data
Okotoks, Canmore, Cochrane
V V V V V X
Kitimat, Terrace X V V V V X
Greater Auckland, NZ, Greater Victoria, CA
V V V X V X
C-Saanich, Colwood, Esq ui ma It, Langford, Oak Bay
V V V V V V
Mapping the system dimensions to the corresponding parameters
The methodology of this research required, once the abstracted analogical model
parameters were identified and the municipalities were selected, that dimensions of the
selected municipalities (and the natural environment) be mapped to the parameters. For
simplicity, once the dimensions were mapped to the analogical model parameters, they were
simply called 'framework parameters'.
Defining the 'energy throughput' dimension
In theory, all energy inputs to a human system should be considered as the system's
'energy throughput'. In the context of societies and their subsystems, Adams explains,
43
While most people would have no problem in conceiving of coal or petroleum as energy forms, the present argument requires that we also regard human beings, human behavior, social groups, and assemblages of social interactions as energy forms. Similarly, mental processes located in the brain, writing on paper, and sound waves in the air are also energy forms (1988, p. 16).
Therefore, the corresponding municipal dimension of the 'energy throughput' parameter is
all energy inputs to a municipality.
Defining the 'community entropy debt' dimension
Recall that entropy production of a dissipative structure is the sum of the influx of
energy and the irreversible processes internal to the system (Prigogine & Stengers, 1984).
Dyke (1988) calls the dissipation of energy necessitated by system complexity 'entropy
debt', thereby stressing the cost to the external environment (i.e. disorder) of the system's
internal structure (i.e. order). "Internally, their [dissipative structures] order is maintained (or
even increased), which occasions an "entropy debt" that is paid by the increased disorder of
their environment" (p. 114). The entropy debt of municipalities is defined as the sum of:
waste outputs from irreversible internal processes and the dissipation of natural resources.
This definition is guided by Prigogine and Stengers' (1984) representation of entropy (see
Equation 1, Appendix A).
Natural resources, meaning fuel, fiber and food, a component of the total ecological
services the MEA (2005) deems necessary for human well-being, represent some of the
energy resources external to and upon which human society relies. While their dissipation to
maintain or build human systems, including the waste generated via irreversible processes,
indeed may contribute to entropy in the greater universe, natural resources are constantly
being renewed by the abiotic and biotic processes, which are driven by solar energy.
Moreover, some system wastes are used as fuel for others. For the purpose of this research,
44
the concept of entropy debt is further defined as 'community entropy debt' to focus on
anthropogenic environmental degradation. Therefore, the corresponding dimension of the
'entropy debt' parameter is anthropogenic environmental degradation (as per waste outputs
from irreversible internal processes and the dissipation of natural resources), referred to as
'community entropy debt'.
Defining the 'community complexity' dimension
Recall that complex systems are characteristically self-stabilizing (Laszlo, 1972),
hierarchical (Simon, 1973), and self-organizing (Laszlo, 1972; Prigogine & Stengers, 1984).
Without these fundamental characteristics, complex open systems could not self-reproduce or
exhibit emergent properties (von Bertalanffy, 1968). Yet, actual measures of complexity,
such as the amount of information required to describe a system (Bar-Yam, 1997a), are less
informative to this research than surrogate measures of the emergent behavior of
municipalities as complex open systems. Nicolis and Prigogine (1989) explain, "It is more
natural, or at least less ambiguous, to speak of complex behavior rather than complex
systems ..." (p. 8). Therefore, the corresponding municipal dimension of the 'system
complexity' parameter is the emergent behavior of municipalities, referred to as 'community
complexity'. It is comprised of and generated by interacting 'parts' (e.g. infrastructure and
humans) and 'processes' (e.g. communication networks) in the context of the external
environment (Chapter Two).
Applying surrogate measures
The methodology of this research required, once the 'real-world' dimensions were
mapped to the model parameters, that surrogate measures be applied to the parameters.
45
Rationale for using surrogate measures
Different types of measures could be applied to the analogical model, including actual
measures, indicators, surrogate measures. Actual measures are extensive, especially for the
model parameters of energy throughput and entropy debt, as demonstrated in the literature
review. However, actual measures remain imperfect and cumbersome. For example,
Hermanowicz (2004) notes, "While the material balance of the energetic approaches to
quantification of human activities have been promoted as indices of "sustainability", neither
of them is theoretically sound nor easy to use" (p. 3). Gong and Wall (2001) admit that
exergy measures have been criticized for their inability to adequately quantify large-scale
environmental impacts on biological entities. Moreover, for complexity, actual measures are
not only cumbersome but in some cases impractical (see Appendix A, Complex Systems).
Moreover, computational tools can be used; however, definitive or accurate extrapolations
are not always guaranteed (Bar-Yam, 1996a; Shiner et al., 1999).
A measureable indicator could provide a reasonable alternative. The Organization of
Economic Co-operation and Development (OECD) defines an indicator (here, with respect to
the environment) as,
... a parameter, or a value derived from parameters, which points to, provides information about, or describes the state of a phenomenon/environment/area, with a significance extending beyond that directly associated with a parameter value ... They reduce the number of measurements and parameters that normally would be required to give an "exact" presentation ... (INECE, p. 5).
Yet, indicators for complexity, energy throughput, and entropy debt concepts of and based on
the theory of dissipative structures are not expressly given in the literature. Rather, the
literature supports data choices, which can be called, more generally, 'surrogate measures'.
46
Surrogate measures, unlike exact or actual measures or indicators of "an 'exact'
presentation of a situation" (INECE, p. 5), offer some opportunity for analysis even without
the certainties associated with actual measures or indicators. A surrogate measure is the
quantification of an associated aspect of the factor that the researcher seeks to measure
(Pfleeger, 1994) and are most useful when they are strongly associated with the factors under
investigation (Belovsky et al., 2004). The surrogate measures of this research are detailed in
the following section.
The preferred surrogate data
Table 3 summarizes the parameters, the corresponding dimensions, and preferred
surrogate measures for each parameter. The rationale for the preferred surrogate measures are
provided in the following sections.
Table 3. Summary of analogue model, corresponding dimensions, and preferred surrogates Framework parameters Corresponding Preferred surrogate
dimensions measures
Energy throughput (matter, information, energy)
Entropy debt
System complexity
Energy throughput (matter, information, energy utilized by municipalities)
Community entropy debt (anthropogenic environmental degradation generated by municipalities)
Community complexity (emergent characteristics of municipal 'structure')
Fossil fuel, wood fiber, hydroelectric energy consumption
* Environmental statistics of air, water, land emissions from point, area, and mobile sources; consumer waste data; loss of ecosystems -using societal activity data
Population (social, economic) and infrastructure char., population activities
Note. Shown above are the proportional correspondences between the model parameters and the community system and nat. env't dimensions. Not explicitly shown above is that the relationships between these parameters/dimensions share a causal relationship, namely, the Second Law. •Surrogate measures of community entropy debt are guided by Holmberg eta/.'s (1996) principles of sustainability and Azar et al.'s (1996) sustainability indicators. Specific substances identified by Azar eta/. (1996) to be accumulating unsustainably in the ecosphere and technosphere include: C02, CH4, N 20 (i.e. GHGs) and NOx, NH3, SOx, and VOC (i.e. CACs), metals such as cadmium, lead, copper, and mercury.
47
'Energy throughput' surrogate measures & assumptions.
While it is impractical to quantify all energy sources that support the self-reproduction
of community systems, consumer energy sources of fiber, fossil fuel, and hydro-electricity
provided the preferred surrogate measure for the energy requirements of community systems
in industrialized countries. This choice was supported by Wall and Gong (2001) who state,
"Industrial society only uses a very small part of the direct exergy flow from the sun, e.g.
within agriculture and forestry" (p. 140). In fact, they estimate that the majority of the exergy
used within industrialized society originates from fossils fuels (62%), the rest of which, "is
composed of mainly wood for construction and paper, firewood, food, hydro power and
nuclear deposits" (p. 14). On the basis that the majority of exergy of industrialized societies
is derived from fossil fuel, fiber, and hydro electricity (Wall & Gong, 2001), the research
used energy consumption data as the surrogate measure of the energy throughput requirement
for municipalities.
To remain consistent with the conceptual framework, this research made these
assumptions. 1) The preferred surrogate data, i.e. energy consumption of fossil fuel, wood
fiber, and hydroelectricity, adequately represented 'energy throughput'. 2) One hundred
percent of residential (versus commercial and industrial) sector activities were assumed to be
associated with municipal self-reproduction. 3) Commercial and industrial activities that
served the municipalities could be estimated using percentages of the non-basic (i.e. for local
demand) versus the basic (i.e. for export) sectors of the economy. 5) Omitting energy
consumption associated with travel on provincial highways reasonably estimated mobile
activities associated with municipal self-reproduction. 6) Given that municipalities are only
semi-autonomous with regard to their larger supersystems (Simon, 1973), this research
48
disregarded, when possible, energy throughput data associated with, for example, the larger
political super-system, e.g. space-heating provincial buildings. For practical reasons it also
ignored activities associated with municipal self-reproduction but which occurred outside the
municipalities, e.g. energy consumption associated with business trips outside the selected
municipalities.
'Community entropy debt' surrogate measures & assumptions.
While researchers admit that the issue of sustainability is fundamentally
anthropocentric (Haberl et al., 2003), the sustainability literature nonetheless defines and
provides indicators for the unsustainable use (i.e. dissipation) of natural resources. The
preferred surrogate measures for community entropy debt in this research was guided by
Azar et al.'s (1996) sustainability indicators, which are based on Holmberg et al.'s (1996)
four principles of sustainability. Briefly, Holmberg et al.'s (1996) four principles of
sustainability are that 1) lithospheric substances and 2) human-produced substances should
not accumulate in the ecosphere, 3) the productivity of the ecosphere should not be degraded,
and 4) resources should be used effectively (e.g. not wasted) (p. 17). Azar et al. (1996)
developed societal activity indicators, which advantageously describe the activities of
selected municipal populations. By contrast, state of the environment indicators or measures
of ambient pollution cannot be associated with the activities of municipal-specific
populations.
For Holmberg et al.'s (1996) sustainability principles, Azar et al. (1996) demonstrate
that human use of some heavy metals (copper, lead, cadmium, and mercury), non-metals
(carbon, sulphur, and phosphorus), and other substances (CO2, CH4, N2O, NOx, SOx, and
some VOCs) are deemed to be accumulating world-wide in the ecosphere unsustainably.
49
Moreover, Azar et al. (1996) argue that loss of ecosystems indicate ecospheric degradation.
Additionally, this research considered consumer waste and material recycling data as
preferred surrogates measures of natural resources consumption.
This research assumed the following. 1) Those substances being used unsustainably at
the global level approximated those being used unsustainably at the 'local' level. 2) The
preferred data of point, area, and mobile emissions of the substances listed above, flowing
into the air, onto the land, and into the water, consumer waste, and loss of ecosystems data
associated with municipal population activities adequately represented 'community entropy
debt'. 3) One hundred percent of emissions generated from residential sector activities were
assumed to be associated with community system self-reproduction. 4) Commercial and
industrial activities that serve the community system can be estimated using percentages of
the non-basic (i.e. for local demand) versus the basic (i.e. for export) sectors of the economy.
4) Given that municipalities are only semi-autonomous with regard to their larger
supersystems (Simon, 1973), this research disregarded, when possible, community entropy
debt data associated with, for example, the larger political super-system. For practical
reasons it also ignored data associated with activities supporting community system self-
reproduction that occurred outside the municipalities. 5) It was assumed that hydro-electricity
generated from dams built in the past did not generate entropy debt in the present. 6) The use
of Smart cars and hybrids was not considered to be significant in 1995 and 2004. 7) One
hundred percent of loss of sensitive ecosystems data were assumed to be associated with
community system serving activities.
'Community complexity' surrogate measures & assumptions.
System complexity defined according to behavioral characteristics as opposed to
50
'amount' of complexity, extended advantageously to any number of data sources that
described the emergent properties of community systems. For municipalities, statistics
describing populations, population activities, and infrastructure provided preferred surrogate
data. More specifically, published secondary data sources provided municipal-specific
economic, social, and demographic information, including population density, dwelling
types, and income distributions. While not exact measures of complexity, or representative of
indicators of complexity, rather, as surrogate descriptions of emergence, this research project
assumed that descriptive statistics of population and infrastructure characteristics and
population activities adequately represented 'community complexity'.
Criteria to select available data
While the preferred data informed the data choices, the available data did not always
match the preferred data. Therefore, selection criteria were developed and used to select
among the available data. The criteria stipulated that the data must be 1) municipal-specific
(so that the data were comparable), 2) indicative of 'societal activities' (e.g. as opposed to
indicative of the state of the environment), 3) consistently collected for all selected
municipalities, and 4) consistent with the theoretical principles associated with the
framework parameters.
Time.
The influence of 'community complexity' on the relationship between 'energy
throughput' and 'community entropy debt' was best shown over time. By contrast, data
showing one snapshot in time would have demonstrate the relationship of the framework
parameters; however, especially the surrogate measures of 'community complexity' would
51
have provided little explanation for the relationship between surrogate measures of 'energy
throughput' and 'community entropy debt'. Selecting data over a period of time, and if
unavailable, then data in two snapshots in time, provided an opportunity for the surrogate
'community complexity' data to explain the surrogate 'energy throughput' data. Therefore,
the time periods of the data sets selected were aligned, where possible.
Procedures undertaken to select and prepare the final data set for analysis
Table 4 summarizes the data that were selected among the available data, which met
the data selection criteria. See Appendix B for the detailed data preparation procedures.
Table 4. Summary of available data corresponding to the preferred data Framework parameters Preferred surrogate Available data
(i.e. dimensions) measure
Energy throughput Fossil fuel, wood fiber, Fossil fuel, wood fiber, hydroelectric
hydroelectric energy energy consumption
consumption
Community entropy Air emissions (point, *Air emissions (point, area, mobile)
debt area, mobile)
Water emissions (point, *Water emissions (point)
area, mobile)
Land emissions (point, *Land emissions (point)
area, mobile)
Consumer waste; loss of Loss of sensitive ecosystems
ecosystems
Community complexity Population characteristics Population growth, population
(population, social, density, median age, average
economic) income, % labour force participation
Population activities Modes of travel (i.e. drivers vs.
walkers/transiters/passengers/cyclists
Infrastructure Dwelling type; length of roadways;
characteristics arterial fraction; commuting distances
Economic structure Labour force composition
Note. Shaded cells indicate preferred surrogate measures for which limited data were available. *0f
the available emissions data, only data for CACs and GHGs were abundant. Data for emissions of
metals and human-made substances (especially area source) were limited or non-existent.
52
Compiling the final data sets involved (see Appendix C for the detailed data tables):
a) Estimating non-basic economic sector activities of the municipalities (Table CI, C2),
b) Matching available data to the preferred energy consumption data (i.e. fossil fuel,
wood fiber, and hydro-electricity) for two periods in time,
a. Accepting natural gas and hydroelectricity energy consumption data, omitting
wood burning and LPG data, and improving municipal per capita light fuel
oil consumption for residential space heating; applying non-basic multiplier
to the totals; normalizing the data per capita using municipal population data
for 1995 and 2004 (Table C3, C4, C5),
c) Matching available data to the preferred community entropy debt data (i.e.
consistently collected data of C02 , CH4, N20, NOx, NH3, SOx, and VOC, metals,
human-made substances, and loss of sensitive ecosystems) for two periods of time,
a. Accepting all emissions data (Table C6, C7) except omitting metals and
human-made contaminant data that overlapped the data provided by British
Columbia Ministry of Environment and compiled from the National Pollution
Release Inventory of Environment Canada and for which the reporting
standards changed from 1995 to 2004 (Table C8, C9), omitting area source
emissions normalized per capita for which no other method of municipal
allocation reasonably could be estimated, reallocating area source emissions
with a reasonably estimated surrogate, omitting air emissions from marine,
air, and non-road machines, reallocating 1995 mobile emissions (Table CIO,
CI 1); applying non-basic multiplier, normalizing data per capita (CI2, CI3),
b. Accepting sensitive ecosystem data, 1992-2002, (Table CI4).
53
Table 5. Summary of available data - sources investigated, selection criteria, and actions required Framework Surrogate Data Municipal- Societal Consistent Notes Actions parameter data type source specific activities methods required
Energy Energy SENES Most V V Direct and Improve light throughput consumption estimated fuel oil
(fuel, fiber, measures consumption hydro) data; omit
data
Community Air BCE V V V Reported Check for entropy emissions: measures overlap with debt point NPRI
NPRI V V V Reported See above; measures check
reporting standards
Air SENES Some V V Estimated Omit emissions: measures municipal area data allocated
by population; improve
Air SENES Some V V Estimated Omit emissions: measures municipal mobile data allocated
by population; improve
Water BCE V V V Reported Check for emissions: measures overlap with point NPRI
Water BCE X X V Does not N/A emissions: CRD meet area criteria
Water N/A N/A N/A N/A Data N/A emissions: unavailable mobile
Land BCE V V V Reported Check for emissions: measures overlap with point NPRI
Land N/A N/A N/A N/A Data N/A emissions: unavailable area
Land N/A N/A N/A N/A Data N/A emissions: unavailable mobile
Ecosystem SEI V V V Direct None degradation measure
Community Socio-econ. Canada V V V N/A None complexity Data Census
Note. SENES (SENES Environmental Consulting Ltd.); NPRI (National Pollutant Release Program); BCE (British Columbia Environment); CRD (Capital Regional District); SEI (Sensitive Ecosystem Inventory).
54
Analysis
The methodology of this research required that the surrogate data be analysed to
elucidate links, if any, existing between the parameters - as specified by the analogical model
informed by the theory of dissipative structures.
Rationale for analysis
One of the objectives of this thesis research was to identify the links between the
surrogate measures of the framework parameters: energy throughput, community complexity,
and community entropy debt. Yet, no specific method in the literature could serve as a guide
on exactly how this should be done. This research conducted a method of analysis derived
from the premise that energy throughput and internal complexity are directly related (Adams,
1988; Kay, 2000; Prigogine & Stengers, 1984) and that entropy debt and energy throughput
are directly related (Prigogine & Stengers, 1984). Therefore, if surrogates of community
entropy debt depend on the amount and type of energy consumed; and, if the amount and
type of energy consumed depends on surrogates of complexity (i.e. complexity variables)
then the results of the following methods will elucidate the complexity variables that are
linked to energy consumption. The results will also elucidate the amount and type of energy
consumption that is linked to the surrogates of community entropy debt.
Procedures for conducting the analysis
To conduct the analysis of the data this research, a) tabled the energy consumption
data and identified the similarities and differences among the municipalities, and b) tabled
the emissions and loss of sensitive ecosystems data and identified the similarities and
differences among the municipalities. From the main trends revealed in the energy
55
consumption, emissions and loss of sensitive ecosystems data, this research further, c)
graphed emissions (i.e. dependent variable) to the energy consumption data (independent
variable), d) graphed the energy consumption data (i.e. dependent variable) to the complexity
variables (i.e. independent variables). Furthermore, where the pattern (i.e. the municipal
rankings) in the energy consumption surrogate data most consistently matched the pattern
(i.e. the municipal ranking) of the complexity variable (e.g. median age, length of roadway,
commuting distance), a link in the data was assumed to exist.
Comparisons were conducted of municipal rankings (ordinal scale) as opposed to
between the surrogate measures themselves as these measures were often estimates and
comprised a portion of an unknown total. Yet, the data selection criteria ensured that the
surrogate data were comparable (per capita per municipality) despite the lack of certainty in
and robustness of their 'actual' (surrogate) measure.
56
CHAPTER FOUR: RESULTS
Introduction
This chapter provides the results of 1) selecting the municipalities, 2) preparing the
final data sets, 3) identifying the differences and similarities among the municipalities by
comparing their energy throughput surrogates, 4) identifying the differences and similarities
among the municipalities by comparing their community entropy debt surrogates, 5)
identifying the differences and similarities among the municipalities by comparing their
energy throughput surrogates versus their community entropy debt surrogates (i.e. their
energy-entropy ratio), and 6) identifying which complexity variables correspond to the
similarities and differences among the municipal energy consumption and loss of sensitive
ecosystem surrogate data.
Selecting the Municipalities
Central Saanich (pop. 16,347), Colwood (pop. 14,768), Esquimalt (pop. 17,229),
Langford (pop. 21,585), and Oak Bay (pop. 18,853), British Columbia (BC Stats, 2004),
adhered to the community selection criteria and offered available data that met the data
selection criteria. These municipalities were located in a similar island geography, i.e. on
Vancouver Island, and were subject to the same local regional government, i.e., the Capital
Regional District (see Appendix C, Table CI), provincial government, i.e., the Government
of British Columbia, and climate, i.e. moderate, wet; additionally, their population sizes were
similar. Regarding their system dynamics, in recent history, none of these municipalities had,
for example, experienced extreme weather events. All were political stable as demonstrated
by regular election processes and civil peace and order. Yet, evolutionary dynamics took
57
place in the municipality of Langford when it was incorporated in 1992. All the
municipalities had access to new sources energy at roughly the same time. For example,
natural gas was introduced to these municipalities starting in 1990, whereby the resource was
made available to all the selected municipalities (but not necessarily to all households) at
approximately the same time1. Yet, like the analogy of the origami swan and fish, the
selected municipalities exemplified different structural histories, meaning, each municipality
presented different 'original' structures, leading to differences in their structures over time.
This was evident in the differences in their population descriptions, infrastructure, and
municipal histories.
Figure 1. Map of the Capital Regional District, its municipalities and electoral areas, and the five selected municipalities (bolded) as 'community systems'.
1 Personal communication with Hans Mertins, Terasen Gas representative, February 21, 2008
58
Final Data Sets
The final data sets are summarized in the following sections of surrogate data for the
parameters: 'energy throughput', 'community entropy debt', and 'community complexity'.
All the data are shown per capita to facilitate comparisons between municipalities.
Final energy throughput surrogate data tables
The final energy consumption data for the years 1995 and 2004 are shown in Table 6
and 7. The data represent surrogates of energy throughput to the selected municipalities. The
detailed data tables are found in Appendix C, Tables C3, C4, and C5.
Table 6. Summary of1995 energy consumption data per municipality
*Energy consumption per type (GJ)
Central Saanich
Colwood Esquimalt Langford Oak Bay
Fossil fuel 728,417 518,011 590,328 853,732 619,171
Hydro-electricity 432,475 323,355 349,792 409,394 406,725
Total GJ energy consumption per municipality, 1995
1,160,892 841,366 940,120 1,263,126 1,025,896
Total GJ energy consumption per capita per municipality, 1995
78 59 55 70 56
Note. * Energy consumption per type represents fossil fuel (gasoline, diesel, natural gas, light fuel oil) and hydro-electric types of residential, commercial, and industrial sectors of building space-heating, transportation, and street lighting activities. Energy consumption data adapted from "Greenhouse gas and energy use inventory for the Capital Region 2004," by B. McEwen & D. Hrebenyk, 2006, SENES Consultants Ltd.
59
Table 7. Summary of2004 energy consumption data per municipality
*Energy consumption per type (GJ)
Central Saanich
Colwood Esquimalt Langford Oak Bay
Fossil fuel
Hydro-electricity
723,271
526,057
516,934
358,744
590,602
390,935
830,563
578,441
692,361
434,165
Total GJ energy consumption per municipality, 2004
1,249,328 875,679 981,537 1,409,003 1,126,526
Total GJ energy consumption per capita per municipality, 2004
76 59 57 65 60
% Change of GJ energy consumed per municipality, 1995-2004
-1% 0% 3% -8% 7%
Note. * Energy consumption per type represents fossil fuel (gasoline, diesel, natural gas, light fuel oil) and hydro-electric types of residential, commercial, and industrial sectors of building space-heating, transportation, and street lighting activities. % change of energy consumption calculated as: 2004 per capita consumption -1995 per capita energy consumption / 2004 per capita consumption. Energy consumption data adapted from "Greenhouse gas and energy use inventory for the Capital Region 2004," by B. McEwen & D. Hrebenyk, 2006, SENES Consultants Ltd.
Final community entropy debt surrogate data tables
The final surrogate data of the community entropy parameter for the years 1995 and 2004
are shown in Table 8, 9, and 10. The detailed community entropy debt data are tabled in
Appendix C in Tables C6, C7, C8, C9, C12, C13, and C14.
See Table C14: Loss of Sensitive Ecosystems, 1992-2002 for a summary of the loss of
sensitive ecosystem data.
60
Table 8. Summary of1995 emissions data per municipality Emissions per type Central Colwood Esquimalt Langford Oak Bay
Saanich
Point source emissions - 0.01 0.19 0.05
Area source emissions 18,376.04 14,690.88 24,215.00 20,105.10 18,825.39
Mobile source 32,801.98 22,180.26 26,986.08 37,820.68 22,055.48 emissions Total tonnes emissions 51,178.02 36,871.15 51,201.27 57,925.84 40,880.87 per municipality, 1995
Total tonnes per 3.4 2.6 3.0 3.2 2.2 capita per municipality, 1995
Note. Summary of Tables C8 - C13. Data adapted from "Greenhouse gas and energy use inventory for the Capital Region 2004," by B. McEwen & D. Hrebenyk, 2006, SENES Consultants Ltd.
Table 9. Summary of2004 emissions data per municipality Emissions per type Central
Saanich Colwood Esquimalt Langford Oak Bay
Point source emissions - - 0.19 - -
Area source emissions 22,071.11 17,878.35 27,611.33 23,586.95 26,468.32
Mobile source 28,405.90 19,047.60 20,431.60 32,479.50 18,940.60
emissions
Total tonnes air 50,477.01 36,925.95 48,043.11 56,066.45 45,408.92
emissions per municipality, 2004
Total tonnes per 3.1 2.5 2.8 2.6 2.4
capita per municipality, 2004
Note. Summary of Tables C8 - C13. Data adapted from "Greenhouse gas and energy use inventory for the Capital Region 2004," by B. McEwen & D. Hrebenyk, 2006, SENES Consultants Ltd.
61
Table 10. Summary of change of community entropy debt, 1995 - 2004 and 1992-2002 Community entropy Central Colwood Esquimalt Langford Oak Bay debt per type Saanich
% Change of per -11 -3 -8 -24 8 capita tonnes emissions per municipality, a1995-
2004
% Loss of sensitive Z8 JL5 4^3 JjO ecosystems per municipality, b1992-2002 Note. Summary of Tables C8 - C14. % change emissions calculated as: (2004 per capita emissions - 1995 per capita emissions / 2004 per capita emissions. aData adapted from "Greenhouse gas and energy use inventory for the Capital Region 2004," by B. McEwen & D. Hrebenyk, 2006, SENES Consultants Ltd. b Data adapted from "Summary Report: Redigitizing of Sensitive Ecosystem Inventory Polygons to Exclude Disturbed Areas" by AXYS Environmental Consulting Ltd., Sidney B.C.
Final community complexity surrogate data
Tables of descriptive statistics were compiled of population (i.e. density, growth,
median age, average income, labour force participation), population activities (i.e.
commuting patterns), infrastructure (i.e. road length, arterial fraction, and dwelling type), and
economic structure (i.e. labour force composition) of the selected municipalities. These data
are tabled in Appendix C, Tables CI5 to C22.
Summary of time periods of the data sets
Figure 2 shows the timeframe within which the data were compiled for this research.
The data roughly corresponded to a ten-year period from 1992-94 to 2002-04. More
specifically, energy consumption data were collated by SENES (McEwen & Hrebenyk,
2006a) represented municipal data in 1995 and 2004. Likewise, emissions data were collated
by SENES (McEwen & Hrebenyk, 2006b) and gathered from the National Pollutant Release
62
Inventory (NPRI) and the British Columbia Ministry of Environment (BCE) for emissions in
1995 and 2004. Loss of sensitive ecosystems from the Sensitive Ecosystems Inventory (SEI)
(i.e. community entropy debt parameter) completed by AXYS Environmental Consulting
Ltd. (2005) represented municipal data from approximately 1992 to 2002. The period of
Canada Census data chosen (i.e. for the community complexity parameter) was from 1996
and 2001. These data fell 'within' the energy throughput and community entropy debt data.
1992 *Sensitive ecosystems (AXYS) 1993
1994
1995 Air emissions (SENES, NPRI) Other emissions (BCE)
1996 Socio-economic data (Statistics Canada, BC Stats) 1997
1998
1999
2000
2001 Socio-economic data (Statistics Canada, BC Stats) 2002 *Sensitive ecosystems (AXYS) 2003
2004 Air emissions (SENES, NPRI) Other emissions (BCE)
Note. *Sensitive ecosystem (i.e. AXYS) data describe loss of sensitive ecosystems from 1992 - 2002. All other sources (i.e. SENES, NPRI, BCE, Statistics Canada, BC Stats) describe discrete data per year specified.
Figure 2. Periods of time represented by the selected data.
Comparing Energy Throughput Surrogates
Municipal similarities and differences regarding energy throughput data are
summarized in a) total energy consumption per fuel type (Figure 3 and 4), b) total energy
consumption per activity (Figure 5 and 6), and c) fuel type per activity (Figure 7 and 8), in
1995 and 2004 for each municipality. Energy consumption is shown in GJ per capita per
63
municipality per year. Selected municipal energy consumption patterns are highlighted in a
table at the end of the section.
Total energy consumption (GJ) 1995, 2004
Figure 3 and Figure 4 provide the total energy consumption in GJ per fuel type (per
capita per municipality) in 1995 and 2004 respectively. The fuel types are: hydro-electricity
and fossil fuels (gasoline, diesel, light fuel oil, and natural gas).
1995 GJ I Fuel Type
I 1
_ C-Saanmh
0 Colwood
• Esquimalt
'. Langfcrd
•_ Oak Ba>
Figure 3. Total 1995 fossil fuel and hydro-electricity consumption as surrogate measures of municipal energy throughput.
In 1995, overall, fossil fuel consumption was greater for all municipalities than
hydro-electric energy consumption. For all fuel types consumed, Esquimalt and Oak Bay,
followed by Colwood, utilized comparatively less energy (GJ/yr) than Langford and Central
Saanich.
64
2004 G J I Fuel Type SHU
I !
GJ energy consumed per capita per municipality
Figure 4. Total 2004 fossil fuel and hydro-electricity consumption as surrogate measures of municipal energy throughput.
In 2004, overall, fossil fuel consumption remained greater for all municipalities than
hydro-electricity consumption. For all fuel types consumed, Esquimalt and Colwood utilized
comparatively less energy (per capita GJ/yr) than Langford and Central Saanich. Again, on
both extremes, Central Saanich utilized the most energy (per capita GJ/yr) and Esquimalt the
least.
Over time, Oak Bay and Esquimalt's per capita energy consumption rate of all fuel
types in 2004 was higher than in 1995. The rate of energy consumption in Central Saanich,
Colwood, and Langford in 2004 remained the same or decreased from 1995.
Energy consumption (GJ) per activity 1995, 2004
Figure 5 and Figure 6 provide the energy consumption in GJ per activity type, (per
capita per municipality) in 1995 and 2004 respectively. The activity types are: residential
space-heating, commercial/industrial space-heating, mobile (i.e. transportation2), and street
lighting.
Mobile energy consumption and mobile emissions includes only automotive transportation (marine, non-road, and air transport did not meet the data selection criteria)
65
1995 GJ / Act ivi ty
&
J?
Space-heating - Ftes.
Space-heating-Garm/lnd.
Transportation
Streedighting
5 10 15 20 25 30 35 40 45
GJ energy consumed per capita per municipality
50
Figure 5. Total fossil fuel and hydro-electricity consumption in 1995 by activity type (per capita per municipality).
In 1995, residential building space-heating comprised the greatest proportion of the
total energy consumed of the activity types shown. This activity was followed by energy
consumption for transportation activities. Commercial and industrial building space-heating
comprised comparatively less of the proportion of energy consumption of all the activity
types. Municipal street-lighting comprised a comparatively negligible proportion of the
energy consumed of all activity types.
66
2004 GJ / Act iv i ty
• G-Saanich
® CDlwood
• Esquimau
• Langford
B Oak Bay
Figure 6. Total fossil fuel and hydro-electricity consumption in 2004 by activity type (per capita per municipality).
In 2004, the pattern of energy consumption per activity type remained similar among
the municipalities as in 1995.
Over time, residential space-heating patterns remained approximately the same from
1995 to 2004. Yet, while Central Saanich, Colwood, and Esquimalt expressed a slight
increase in residential building space-heating over time, Oak Bay expressed a modestly
higher increase and Langford a decrease in residential space-heating energy consumption per
capita over time (also shown in detail in Table 11). Regarding commercial and industrial
space-heating, all municipalities showed a relative increase in the proportion of energy
consumption for this activity type over time. Regarding energy consumption associated with
transportation activities, improved energy efficiency standards from 1995 to 2004 (McEwen
& Hrebenyk, 2006a) were reflected in the data. For example, all per capita energy
consumption data associated with transportation decreased over time. Again, energy
consumed for municipal street-lighting was proportionally negligible.
67
Space-heating - Ftes.
jjj_ Space-heating-[i* Cbrrm/lnd.
Transportation
Streaiighting
10 15 20 25 30 35 40
GJ energy consumed per capita per municipality
Energy consumption (GJ) per fuel type per activity 1995, 2004
Figure 7 and Figure 8 provide the energy consumption (GJ) of fuel type per activity
(per capita per municipality) in 1995 and 2004 respectively. The fuel type per activities are:
fossil fuel consumption for residential space-heating, fossil fuel consumption for
commercial/industrial space-heating, fossil fuel consumption for transportation activities,
hydro-electric consumption for residential space-heating, hydro-electric consumption for
commercial/industrial space-heating, and hydro-electric consumption for municipal street
lighting activities.
Fossil fuel - F^s. space-
Bectririty - Qyrm/lnd space-heating
Bectricrty - IVlricipei
• C-Saarich
• Colwood
lEsqurrelt
• Larcford
I CBkBay
0 5 10 15 20 30 35 40 45 50
Figure 7. Detailed breakdown of fuel type consumed per activity type in 1995 (per capita per municipality).
In 1995, Figure 7 shows that transportation contributed to the highest consumption of
fossil fuel, residential building space-heating the next highest, followed by
commercial/industrial space-heating. Hydro-electricity consumption for residential building
68
space-heating exceeded fossil fuel consumption for residential space-heating for all
municipalities. Residential space-heating contributed to the highest consumption of hydro-
electricity, followed by commercial/industrial space-heating. More specifically, Central
Saanich and Langford had the highest per capita consumption of fossil fuel and hydro-
electricity in all activity types, except for commercial/industrial space-heating. Their
proportional consumption of fossil fuel for transportation was higher than their proportional
consumption of fossil fuel for residential space-heating, compared with the other
municipalities. Esquimalt's per capita consumption of hydro-electricity and fossil fuel was
lowest for residential space-heating. It was also the highest consumer of fossil fuels for
commercial/industrial space-heating. Oak Bay's per capita consumption of fossil fuel for
transportation activities was the lowest of the municipalities.
2004 GJ / Fuel type per activity
Figure 8. Detailed breakdown of fuel type consumed per activity type in 2004
In 2004, Figure 8 shows that the overall pattern of energy consumption type per
activity remained similar to 1995.
69
Table 11. Municipal highlights of energy consumption for building space-heating Per capita energy consumption
C-Saanich Colwood Esquimalt Langford Oak Bay
% change, 1995 to 2004 of total energy consumed
-1 0 3 -8 7
Transportation GJ/1995
27.9 19.6 17.6 26.6 15.1
Transportation GJ/2004
24.4 18.1 16.6 21.1 14.1
% change, 1995-2004
-14% -8% -6% -26% -7%
Building space-heating GJ/1995 Building space-heating GJ/2004
49.6
52.0
39.3
41.1
37.5
40.2
43.8
44.1
40.4
45.4
% change, 1995 to 2004 in all building space-heating
4% 5% 7% 1% 11%
Res. space-heating GJ/1995 Res. space-heating GJ/2004
38.8
40.3
32.2
33.1
24.5
26.4
34.7
32.7
33.0
38.0
% change, 1995 to 2004 in res. space-heating
4% 3% 7% -6% 13%
Res. fossil fuel space-heating GJ/1995 Res. fossil fuel space-heating GJ/2004
15.9
15.2
14.3
14.5
10.3
10.6
16.2
13.2
14.9
19.0
% change, 1995 to 2004 in res. fossil fuel space-heating
-5% 1% 3% -23% 21%
Comm/ind. space-heating GJ/1995
7.3 4.7 7.6 6.2 5.1
Comm/ind. space-heating GJ/2004
11.7 8.0 13.8 11.4 7.5
% change, 1995 to 2004 comm/ind. space-heating
38% 41% 45% 45% 32%
Note. % change overtime calculated 2004-1995/2004. Per capita building space-heating and transportation were calculated using "Municipal population estimates," BC Stats, 1995 and 2004. Retrieved on March 24, 2008 from http://www.bcstats.gov.be.ca/data/pop/pop/estspop.asp#totpop
70
Table 11 highlights some of the municipal per capita rates of energy consumption
and changes in their rates over time (expressed in % change). Table 11 shows the greatest
decrease its per capita energy consumption over time occurred in Langford (-8%);
conversely, the greatest increase in per capita energy consumption over time occurred in Oak
Bay (7%).
In 1995 and 2004, Central Saanich was the highest per capita consumer of fossil fuel
for transportation; by contrast Oak Bay was the lowest per capita consumer of fossil fuel for
transportation. Over time, Langford showed the greatest decrease in the rate of per capita
fossil fuel consumption for transportation (i.e. -26% from 1995 to 2004). Additionally, over
time, fossil fuel consumption for transportation decreased in all municipalities.
In 1995 and 2004, Central Saanich was the highest per capita consumer of energy for
all building space-heating and, specifically, residential space-heating in 1995 and 2004. By
contrast, Esquimalt was the lowest per capita consumer of energy for building space-heating
and, specifically, residential space-heating in 1995 and 2004. Over time, Oak Bay's per
capita % increase in residential space-heating was greater than the other municipalities (i.e.
13%). By contrast, Langford showed a decrease in residential space-heating (i.e. -6%).
Moreover, Oak Bay showed the greatest % increase in fossil fuel consumption for
residential space-heating from 1995 to 2004 (21%). By contrast, Langford showed the
greatest decrease in fossil fuel consumption for residential space-heating (-23%).
All energy consumption for commercial/industrial space-heating increased over time
in all the municipalities. Esquimalt's per capita fossil fuel consumption for
commercial/industrial space-heating was higher in 1995 and 2004 compared with the other
municipalities.
71
Comparing Community Entropy Debt Surrogates
Municipal similarities and differences regarding the surrogate measures of
community entropy debt is divided into two sets of data, a) air, water, land emissions of
criteria air contaminants (CAC), greenhouses gases (GHG), metals, and other of point, area,
and mobile sources (Figure 9 and 10) and b) loss of sensitive ecosystems (Figure 11).
Emissions (tonnes) per capita per municipality 1995, 2004
Figure 9 and Figure 10 provide emissions (tonnes) of point, area, and mobile sources.
Of note, point source emissions (i.e. those attributable to the self-reproduction of the
community system) were negligible compared to area and mobile sources of emissions. Area
source emissions represented an unknown fraction of actual area source emissions; mobile
source emissions were estimated3. Therefore, the proportional representation of area versus
mobile source emission types per municipality is unknown.
3 There is a small variation to the mobile emissions pattern in 1995 such that it does not quite match the transportation energy consumption pattern for that year, as would be expected. This is because of the allocation ratio this thesis research used to estimate municipal-specific differences in lieu of a method that was otherwise not available from the original reports (McEwen & Hrebenyk, 2006a, 2006b). Consequently, Esquimalt's per capita mobile source emissions are slightly over-estimated and Colwood's slightly under-estimated.
72
1995 Emiss ions ( tonnes) per capi ta per munic ipa l i ty
• C-Saanichf • Colwood • Esquimalt B Langford
Oak Bay
0.50 1.00 1.50 2.00 2.50 3.00 Per capita emissions (tonnes)
3 50
Figure 9. All available 1995 criteria air contaminants (CAC), greenhouse gases (GHG), metals, and other emissions of point, area, and mobile sources, and including residential, commercial, and industrial sectors (as per Table C6) (per capita per municipality).
In 1995, the municipality generating the lowest per capita total emissions (based on
available and municipal-specific data) was Oak Bay, followed by Colwood. The municipality
generating the highest per capita total emissions was Central Saanich, followed by Langford.
More specifically, Oak Bay ranked comparatively lower in its area and mobile source
emissions than the other municipalities.
73
Although Central Saanich generated the highest per capita emissions in total, it did
not rank highest in all categories of source types among the municipalities. For example,
Esquimalt emitted the highest per capita area source emissions. In the mobile source
category, the highest emitter of contaminants was Central Saanich, followed by Langford.
2004 Emiss ions ( tonnes) per capi ta per munic ipa l i t y
• C-Saanich • Colwood • Esquimalt II Langford
Oak Bay
0.50 1.00 1.50 2.00 2.50 Per capita emissions (tonnes)
3.00 3.50
Figure 10. All available 2004 air, water, land emissions of greenhouse gases (GHG), criteria air contaminants (CAC), metals, and other of point, area, and mobile sources, and including residential, commercial, and industrial sectors (as per Table C7) (per capita per municipality).
74
In 2004, the municipality generating the lowest per capita total emissions was Oak
Bay, followed by Colwood. Yet, Oak Bay did not emit the lowest per capita tonnage of
contaminants in all categories. While Oak Bay remained the lowest per capita emitter of
contaminants associated with fossil fuel consumption in the mobile source category, it was
the second highest emitter (i.e. second to Esquimalt) in the area source category.
The municipality generating the highest per capita total emissions was Central
Saanich. Again, Central Saanich was not the highest per capita emitter in all categories. For
example, Esquimalt was the highest emitter in the area source category. Yet, Central Saanich
remained overall the highest per capita emitter in 2004, evidently due to its mobile emissions.
Over time, overall per capita emissions decreased in 2004 from 1995. According to
McEwen and Hrebenyk (2006b, pg. vii), this was due to the conversion of residential light
fuel oil space-heating to natural gas, and to improved fuel efficiency standards over time.
Table 12. Municipal highlights of Langford and Oak Bay's per capita emissions, 1995-2004 % change, 1995-2004, per capita per municipality
C-Saanich
Colwood Esquimalt Langford Oak Bay
*Total emissions -11 -3 -8 -24 8
Emissions from fossil fuel - all space-heating (res./comm./ind.)
10 21 25 -13 36
Emissions from fossil fuel - res. space-heating4
13 18 20 0 32
Emissions from fossil fuel - all mobile
-26 -21 -33 -40 -19
**Emissions from total fossil fuel - all space-heating & mobile
-11 -3 -8 -24 8
Note. Tota l emissions included residential, commercial/industrial space-heating, commercial activities, and mobile emissions. **Emissions from commercial area source activities and point sources were negligible. Percentages were calculated as 2004-1995/2004 per capita per municipality and were not otherwise proportional to the other municipalities. Per capita emissions were calculated using "Municipal population estimates," BC Stats, 1995 and 2004. Retrieved on March 24, 2008 from http://www.bcstats.qov.be.ca/data/pop/pop/estspop.asp#totpop. Adapted from Tables C6, C7, C l l , C12, C13.
4 For example, while Langford's per capita rate of fossil fuel consumption decreased from 1995 to 2004 by 23%, and conversions from light fuel oil to natural gas were estimated to have increased from 1995 to 2004 (McEwen & Hrebenyk, 2006a; 2006b), rates of emissions for residential space-heating did not appear to have decreased accordingly. This is likely an error resulting from different estimation methods having been applied to the energy consumption and emissions data sets.
75
Table 12 highlights Langford's per capita decrease in total tonnes of emissions (point,
area, mobile) (-24%) from 1995 to 2004 (with the non-basic multipliers applied). Regarding
emissions, the table shows Langford's per capita decrease in overall emissions (tonnes) was
largely attributable to a decrease in mobile emissions (- 40%) and a decrease in space-heating
(- 13%). Emissions (tonnes) from point sources and area source commercial activities (e.g.
bakeries, welding shops) were negligible (given the available data).
By contrast, Oak Bay's increased its per capita total tonnes of emissions (point, area,
mobile) (8%) from 1995 to 2004 (with the non-basic multipliers applied). The table shows
Oak Bay's per capita increase in its overall emissions (tonnes) was largely attributable to its
% change in fossil fuel related emissions for residential, commercial, and industrial space-
heating (36%), and especially, due to its percentage change in fossil fuel related emissions
for residential space-heating (32%).
Loss of sensitive ecosystems (ha) per capita per municipality 1992-2002
Loss of sensitive ecosystems represented the second available surrogate measure of
community entropy debt.
Sensitive ecosystems lost (ha) per capita per municipality 1992-2002
C-Saanich
| Colwood
S. Esquimalt
c
= Langford
Oak Bay 0.00000 0.00100 0.00200 0.00300 0.00400 0.00500 0.00600
Sensitive ecosystems lost (ha)
mm
i
1 * ' • '
Figure 11. Sensitive ecosystems lost between 1992 and 2002 per capita per municipality
76
Figure 11 shows that, from approximately 1992 to 2002, per capita loss of sensitive
ecosystems was highest in Langford and lowest for Oak Bay and Esquimalt.
Comparing the Energy-entropy Ratios
The following figures {Figure 12, 13, 14) graph the surrogate measures of the energy
throughput parameter to the surrogate data of the community entropy debt parameter. The
two sets of surrogate data for the community entropy debt parameter are, a) emissions in
tonnes per capita per municipality in 1995 and 2004, and b) loss of sensitive ecosystems in
hectares per capita per municipality over ten years from 1992-2002. However, while energy
throughput data from 1995 and 2004 was related graphically to emissions in 1995 and 2004,
loss of sensitive ecosystems could not be similarly represented. This occurred because the
units of measure of lost ecosystems over ten years (i.e. 1992-2002) could not be equilibrated
to units of emissions produced and energy consumed in the year 1995 and 2004.
Therefore, data were graphed for a) energy consumption versus emissions data in
1995 for all municipalities (Figure 12), b) energy consumption versus emissions data in 2004
for all municipalities (.Figure 13), c) energy consumption versus emissions, showing the
change in this relationship over time (i.e. from 1995 to 2004) {Figure 14).
77
Energy consumption (GJ) versus emissions (tonnes) 1995, 2004
1995 Energy consumption (GJ) vs. emissions (tonnes) por capita per municipality
>c 10
Energy consumption (GJ) vs.
: o OSaarlch o Colwood • Esqumatt ' Langfond
•8 Oak Bay
JO
2.0
• C-Saaiich Colwood
• E iqumalt Larijfoid Ccik Bay
70
(GJ)
Figure 12. Foreground: Small-scale view of 1995 energy consumption (GJ) versus 1995 emissions (tonnes) per capita per municipality shows their relative differences. Background: Large-scale view of the community systems shows how closely they are clustered.
Figure 12 shows a small-scale view (in the foreground) of the relationship between
energy consumption and emissions in 1995 per capita per municipality. According to the
surrogate data collected, it was evident that Central Saanich and Langford expressed a
relatively high energy consumption and a relatively high measure of emissions. By contrast,
Oak Bay and Colwood expressed relatively low per capita energy consumption and a
relatively low measure of emissions.
Esquimalt did not fit the pattern of high energy consumption and high emissions, and
low energy consumption and low emissions. Figure 12 shows that Esquimalt generated a
higher per capita rate of emissions than the other municipal populations for the amount of
energy consumed.
78
2004 Energy consumption (GJ) vs. emissions (tonnes) per capita per municipality
3r.
Energy consumption (GJ) vs. emissions (tonnes) per capita per municipality
I 50 60 70
Energy consumed (GJ)
80
Enurgy consumncl (GJ)
" «. -Sv>"l Jl Colwood
• Esqdmdt Longford
s Oak Bay
Figure 13. Foreground: Small scale view of 2004 energy consumption (GJ) versus 2004 emissions (tonnes) per capita per municipality shows the relative differences in their energy consumption and emissions patterns.
In 2004, some patterns of fuel consumption to emissions among the selected
communities were similar to 1995. Central Saanich remained in the extreme of high energy
consumption and high emissions, while Oak Bay remained in the relative extreme of low
energy consumption and low emissions. Given the pattern of the other municipalities, which
exhibited a trend of low energy consumption with low emissions and high energy
consumption with high emissions, Esquimalt's per capita emissions were relatively high for
its energy consumption.
79
1995 - 2004 Energy consumption (GJ) vs. emissions (tonnes) per capita per municipality
Energy consumption (G J)
Figure 14. Small scale view of 1995 and 2004 energy consumption (GJ) versus 1995 and 2004 emissions (tonnes) per capita per municipality. The arrow direction denotes change in energy consumption vs. emissions from 1995 to 2004.
Figure 14 shows the change in time in the relationship between per capita energy
consumption (GJ) and per capita emissions (tonnes) for each municipality. In general, the
municipalities maintained their relative positions to each other, except for Langford.
Langford decreased its per capita energy consumption and emissions over time due to a
decrease in fossil fuel consumption for transportation and residential space-heating (Table 11
and Table 12). By contrast, Oak Bay increased its per capita energy consumption and
emissions over time due to an increase in per capita fossil fuel consumption for residential
space-heating over time (Table 11 and Table 12). Esquimalt maintained its position as a
relatively low per capita energy consumer and moderately high per capita emitter (tonnes).
Esquimalt's moderately high per capita emissions (tonnes) were attributable to its
commercial space-heating activities using fossil fuel (Table 13).
80
Yet, Esquimau's pattern is anomalous to the other municipalities in yet another way.
While Langford, Central Saanich, and Oak Bay's per capita increase or decrease in energy
consumption corresponded to a respective increase or decrease in emissions, Esquimalt's per
capita emissions decreased over time while its energy consumption increased over time
(Table 14). Colwood's per capita energy consumption to emissions (tonnes) ratio remained
similar in 2004 and 1995.
Table 13. Esquimau's proportionally high commercial sector area source emissions/energy consumption, 1995
Proportional representation of Central Colwood Esquimalt Langford Oak Bay selected data, 1995 Saanich
Municipal mobile source 14% 9% 11% 16% 9% emissions Municipal area source emissions 8% 6% 10% 8% 8%
Municipal area source, 5% 5% 4% 7% 6%
residential Municipal area source, 2% 2% 6% 2% 2%
commercial
Area source commercial, agriculture
Area source commercial, space heating using fossil fuel
*Area source commercial, commercial activity
Note. * Area source data of commercial activities are not robust. Proportionally represented, emissions data are shown for each municipality. Percentages are calculated as municipal mobile emissions (GJ/yr): all emissions (GJ/yr); municipal area emissions (GJ/yr): all emissions (GJ/yr); municipal area source, commercial (GJ/yr): all area source (GJ/yr); municipal area source, residential (GJ/yr): all area source emissions (GJ/yr); etc. Adapted from Table C6 and C12.
Table 13 represents the emissions data proportionally to show that while Esquimalt's
share of mobile emissions were moderate, its share of area source emissions were
proportionally high (10% of the total emissions), compared to the other municipalities. This
was due to its proportion of emissions associated with commercial enterprises (6% of the
total area source emissions). Of its commercial sector emissions, emissions associated with
81
1%
16%
0%
0%
11%
0%
0%
46%
0%
0%
14%
0%
0%
12%
0%
commercial/industrial space-heating using fossil fuel comprised 46% (of the total
commercial area source emissions). By contrast, other commercial activities (e.g. bakeries,
welding, agriculture) were negligible5 (0%). This trend of Esquimalt's proportionally high
percentage of emissions due to commercial space-heating was even more pronounced in
2004 (Figure 8).
Table 14. Esquimalt's decreased emissions and increased energy consumption, 1995, 2004
% Change 1995-2004 per Central Colwood Esquimalt Langford Oak Bay capita per municipality Saanich
•Total emissions (tonnes) -11% : 3 % : 8 % -24% 8%
Emissions from fossil fuel -11% -3% -8% -24% 8% (tonnes)
Total energy consumption -1% 0% 3% -8% 7%
(GJ)
Fossil fuel consumption -10% -4% -1% -24% 9%
(GJ)
Hydroelectricity 10% 7% 10% 15% 4%
consumption (GJ)
Note. *Emissions from commercial area source activities and point sources were negligible. Percentages were calculated as 2004-1995/2004 per capita per municipality. Per capita emissions were calculated using "Municipal population estimates," BC Stats, 1995 and 2004. Retrieved on March 24, 2008 from http://www.bcstats.gov.be.ca/data/pop/pop/estspop.asp#totpop. Adapted from Tables C6, C7, C l l , C12, and C13.
Table 14 highlights Esquimalt's per capita increase in energy consumption and
decrease in emissions over time. This pattern was different from the pattern of the other
municipalities. The pattern of the other municipalities showed that a decrease in fossil fuel
consumption mirrored a decrease in emissions (due to fossil fuel consumption); and,
conversely, and an increase in fossil fuel consumption mirrored an increase in emissions (due
to fossil fuel consumption). Esquimalt by contrast, increased its energy consumption over
time (3%) while decreasing its emissions over time (-8%). A break-down of the percentage
5 Note, the data for these commercial activities were not robust.
82
change over time of per capita hydro-electricity and fossil fuel consumption shows that per
capita hydro-electricity consumption increased over time in Esquimalt (10%).
Estimating 'community entropy debt' of loss of ecosystems 1992-2002
This section estimates the community entropy debt of the municipalities by using an
ordinal scale to integrate the loss of hectares of ecosystems, occurring over a decade, to
tonnes of emissions per annum. Integrating the community entropy debt data using an ordinal
scale and comparing it to energy throughput data, normalized in the same fashion estimated
the 'direction' of change - increased or decreased - of community entropy debt and energy
throughput.
Table 15. Estimating energy-entropy ratio using ordinal ranking Ordinal ranking of per capita rates, surrogate measures
C-Saanich
Colwood Esquimalt Langford Oak Bay
Total energy consumption GJ, 1995
1 3 5 2 4
Total energy consumption GJ, 2004
1 4 5 2 3
Sum of ranking 2 7 10 4 7
Total ranking surrogate measures/energy throughput
1 3 4 2 3
Total tonnes emissions 1995 1 4 3 2 5
Total tonnes emissions 2004 1 4 2 3 5
Sensitive ecosystems lost (ha) per capita 1992-2002
3 2 4 1 5
Sum of ranking 5 10 9 5 15
Total ranking surrogate measures/comm unity entropy debt
1 3 2 1 4
Energy t h r o u g h p u t : Commun i t y e n t r o p y deb t as per ava i lab le su r roga te da ta
1 :1 3 :3 4 :2 2 : 1 3 :4
Note. Ordinal ranking of energy consumption (GJ) in 1995, 2004 versus ordinal ranking of community entropy debt (i.e. emissions in tonnes and loss of ecosystems in hectares) from 1992-2002. 1= highest energy consumed, emissions, ha of ecosystems lost (per capita); 5 = lowest. This method assumed that the relative difference of energy consumption in 1995 and in 2004 were similar to energy consumption over a span of a decade (i.e. 1995 - 2004).
83
Table 15 highlights that Central Saanich (1:1) and Langford (2:1) had comparatively
high per capita energy-entropy ratios. By contrast, Oak Bay (3:4) had a comparatively low
energy-entropy ratio. Esquimalt had comparatively low per capita 'energy throughput' and
moderately high 'community entropy debt' (4:2) as given by the surrogate data.
Linking the Data
This section shows the results of comparing the surrogate complexity variables to the
main trends revealed in the municipal per capita energy consumption data. The trends are
categorically, 1) general trends o f fue l type associated with activities, and 2) patterns of rate
of energy consumption associated with population activities and other characteristics. The
patterns refer to the ranking of the municipal per capita energy consumption and complexity
variables. More specifically, the complexity variables were compared to a) transportation
energy consumption of fossil fuel, b) residential space-heating total energy consumption, and
c) loss of sensitive ecosystems. Table 16 summarizes the main trends in the energy
consumption data, their respective municipal ranking, and the complexity variables that were
used in the analysis.
84
Table 16. Summary of key trends, municipal ranking & complexity surrogate variable Energy consumption
trend (per capita) Municipal ranking
General trends C-Saanich
Colwood Esquimalt Langford Oak Bay
Complexity variable
Energy type (1995, 2004): 1. fossil fuel 2. hydroelectricity
V V V V V N/A
Activity type, all energy (1995, 2004): 1. space-heating 2. transportation
V V V V V N/A
Activity type, fossil fuel (1995, 2004): 1. transportation 2. res. space-heating 3.
V V V V X* N/A
comm. space-heating Activity type, hydroelectricity (1995, 2004): 1. res. space-heating 2.
V V V V V N/A
comm. space-heating Fossil fuel, decrease V V V V aEnergy efficiencies (1995, 2004) Municipal patterns Fossil fuel, transportation (1995, 2004)
1 3 4 2 5 Population char's, infrastructure char's, commuting patterns
% decrease in fossil fuel, transportation (1995, 2004)
1 3 5 2 4 % change pop. growth, population char's, commuting patterns
Total energy, res. space-heating 1995
1 4 5 2 3 Population char's, dwelling type
Total energy, res. 1 3 5 4 2 Same as above space-heating 2004
% decrease energy consumption for res. space-heating (1995, 2004)
3 2 4 5 % change pop. growth, population char's, new dwelling construction (1991-2001)
Fossil fuel, comm.. 2 5 1 3 4 % labour force space-heating (1995, 2004)
distribution per economic sector
Loss of ecosystems (1992-2002)
3 2 4 1 5 % change pop. growth, population char's
Note. The ordinal ranking scale indicates 1 - highest and 5 -lowest energy consumer, unless otherwise indicated. *Oak Bay's per capita consumption of fossil fuel for res. space-heating exceeds consumption for transportation in 2004. **Oak Bay's per capita consumption of fossil fuel increases from 1995 to 2004 despite efficiencies. ***Langford's per capita energy consumption for residential space-heating decreased over time, whereas, the other municipal per capita consumption rates increased over time. "Energy efficiencies according to McEwen & Hrebenyk (2006a). bPopulation characteristics refer to: population density, median age, average income, % labour force participation.
85
General trends
Municipal differences were not typically evident in the 'general trends' of the energy
throughput parameter (Table 16). For the most part, in the category of 'general trends', all
municipalities adhered to the main trends listed. The surrogate complexity variables did not
provide meaning beyond the details already evident in the energy consumption data (i.e. fuel
type, activity type, etc.).
Linking complexity variables to transportation energy consumption
Two main trends were evident in the per capita municipal transportation energy
consumption data: 1) the ranking of rate of municipal per capita fossil fuel consumption
(from highest to lowest) in 1995 and 2004, i.e., Central Saanich, Langford, Colwood,
Esquimalt, and Oak Bay, and 2) the ranking of the rate of decrease of municipal per capita
fossil fuel consumption from 1995 to 2004 (from highest to lowest), i.e. Langford, Central
Saanich, Colwood, Oak Bay, and Esquimalt. The complexity variables that were graphed
relative to the transportation energy consumption data were: population characteristics,
transportation infrastructure, and commuting patterns (Appendix D1 - D13).
Population characteristics vs. rate of fossil fuel consumption, transportation
Of the population characteristics, i.e. age structure, population density, average
income, and labour force participation, the pattern of municipal population densities (1996,
2001) appeared most consistent with the municipal pattern of per capita fossil fuel
consumption for transportation (1995, 2004).
86
1995 Transportation energy consumption vs. 1996 Population density, per mtnkaparity
30 -as -20 «
•„ C-Saanch oCotvuxxJ • Esqiirrelt ' Langfbid ® Oak Bay
sou ioon 2nci0 2500 Population density (rtW)
2004 Transport-toon enercjy consumption vs. 2001
I
o OSaandi | o Cok/vood ft Esqiimalt v Langford
!®CBkBay 1000 1500 2000
mrrf)
Figure 15. 1995 transportation energy consumption (GJ/capita) versus 1996 population density (# per square km), per municipality; 2004 transportation energy consumption (GJ/capita) versus 2001 population density (# per square km), per municipality
Figure 15 demonstrates that the 1996 and 2001 ranking of municipal population
densities (#/km2) were similar to the 1995 and 2004 ranking of municipal per capita rates of
transportation energy consumption. For example, the municipalities with the lowest
population density, i.e. Central Saanich and Langford, also consumed the highest rates of
fossil fuel energy (GJ/year). By contrast, Oak Bay and Esquimalt had the highest population
densities of the selected municipalities; they also consumed the lowest per capita rates of
fossil fuel (GJ/year). Despite the general correlation, Esquimalt's population density was the
highest but its transportation energy consumption was not the lowest among the
municipalities.
Infrastructure characteristics vs. rate of fossil fuel consumption, transportation
Of the transportation infrastructure data, i.e. length of roadways (km) and arterial
fraction of total roadways (%), the pattern of relative lengths of roadway infrastructure (km)
among the municipalities (2001) appeared most consistent with the municipal pattern of per
capita fossil fuel consumption for transportation (2004).
87
1995 Transportation energy consumption vs. 2001 Length of roadways, per municipality
!
25
20 15
10 •>
o OSaanch
©Colwood
• Esqumalt
Langford
SCBkBay
100 1S0 Length of roadways (km}
.2001 Length of
1iX)
Length of p
1W
oOSaarich
O Colwood
• Esqumalt
^ Langford
« CSk Bay
Figure 16. 1995 and 2004 transportation energy consumption vs. 2001 roadway length (km). Both data sets excluded travel on provincial highways. Both data sets excluded travel on provincial highways.
Figure 16 demonstrates that the municipalities with the most roadway infrastructure
(measured in km of roadways), i.e. Central Saanich, Langford, and Colwood, also consumed
the highest per capita rates of fossil fuel for transportation (GJ/year). By contrast, Oak Bay
and Esquimalt had the least roadway infrastructure of the selected municipalities; they also
consumed the lowest per capita rates of fossil fuel (GJ/year). Despite the general correlation,
Oak Bay's per capita transportation energy consumption was lower than Esquimalt's; yet,
Esquimalt had the least amount of roadway infrastructure (km).
Activity characteristics vs. rate of fossil fuel consumption, transportation
Of the activities associated with infrastructure data, i.e. commuting distances (km), %
composition of drivers of total commuters, and % composition of walkers, bus transiters, and
cyclists of total commuters, the municipal patterns of all three variables appeared most
consistent with the municipal pattern of per capita fossil fuel consumption for transportation
(in 1995 and 2004).
88
2004 Transportation energy consumption vs. 2001 Commuting distance, per municipality
I
30
25
20
15
10
5
0
OC-Saaridij
u
O Colwood 1
• o
• Esqiimalt : • • Esqiimalt :
• Langfbrcl
• Oak Bay j
8 10 12
! (to work) (km)
14
Figure 17. 2004 transportation energy consumption vs. 2001 commuting distances to work (km). 1996 commuting distances to work were unavailable from Statistics Canada.
Figure 17 demonstrates that the municipalities with the longest commuting distances
to work, i.e. Central Saanich (13 km), Langford (10 km), and Colwood (9 km), also
consumed the highest per capita rate of fossil fuel for transportation. By contrast, commuting
distances to work were shortest in the municipalities with the lowest fossil fuel consumption
for transportation, i.e. Oak Bay (4 km) and Esquimalt (3 km). Despite the general correlation,
Esquimalt's commuting distance to work was lowest; yet, Oak Bay's per capita rate of fossil
fuel consumption for transportation was lower than Esquimalt's.
1995 Transportation Driving
I
x is
20 15
10
5
0
g, 1 0 C-Saarich
| o Colwood
• # Esqun ult # Esqun ult
1 si* Langford
1 • CakBay
SO s(tow**10
Driving vs. 2001
9
o C-Saarichl
o Cokvood
• Esqurreft
® Langford
f$ C&kBay
n 2n .10 eu ho % Driving cuniiufieis of total comrnjtcfs (to work)
Figure 18. 1995 transportation energy consumption (GJ/capita) versus 1996 % driving commuters (of total commuters to work), per municipality; 2004 transportation energy consumption (GJ/capita) versus 2001 % driving commuters (of total commuters to work), per municipality
89
| 30-
o 25
f I f fi 9 1fi •
municipality & cyclists, pur
2004 Transportation enengy consurrption vs. 2001 % Passengers, transfers, vualker & cyclists, per
municipality
i •-< * 0
OSvinii,
oCofoood
• Esqumalt
Langlbrd
s eek Bay
% Pa
!
TGSj/lKii, OColWXld
• Esquimalt
^ Langford
eCakH.iy
s (to work)
Figure 19. 1995 transportation energy consumption (GJ/capita) versus 1996 % walkers, public transiters, and cyclist commuters (of total commuters to work) per municipality; 2004 transportation energy consumption (GJ/capita) versus 2001 % walkers, public transiters, and cyclist commuters (of total commuters to work) per municipality.
Figure 18 and Figure 19 demonstrate that the municipalities with the greatest
percentage composition of drivers and the lowest percentage composition of public transit
commuters/walkers/cyclists, i.e. Central Saanich, Langford and Colwood, also had the
highest per capita rates of fossil fuel consumption for transportation. Reciprocally, the lowest
percentage composition of drivers and the highest percentage composition of public transit
commuters/walkers/cyclists were found in Oak Bay and Esquimalt. Despite the general
correlation, Esquimalt had the greatest percentage composition of walkers, transiters, and
cyclists and the lowest percentage composition of drivers; yet, Oak Bay's per capita rate of
fossil fuel consumption for transportation was lower than Esquimalt's.
Population characteristics vs. decrease fossil fuel consumption, transportation
Of the percentage change in population characteristics (from 1996 to 2001), i.e. %
change in age structure, % change in population density, % change in population growth, %
change in average income, and % change in labour force participation, the relative pattern of
the rate of change in municipal population growth and population density appeared most
90
consistent with the municipal pattern of the rate of decrease in per capita fossil fuel
consumption for transportation (from 1995 to 2004).
% Change transportation energy consumption (1995-2004) vs.
% Change papulation (1996-2001)
L-
II 2 2 4 6 OOSaarich
OColwood !
• Esqurreft
Langford
© CbkBay
L-
II OOSaarich
OColwood !
• Esqurreft
Langford
© CbkBay
L-
II O
OOSaarich
OColwood !
• Esqurreft
Langford
© CbkBay
L-
II OOSaarich
OColwood !
• Esqurreft
Langford
© CbkBay
L-
II OOSaarich
OColwood !
• Esqurreft
Langford
© CbkBay
L-
II si
OOSaarich
OColwood !
• Esqurreft
Langford
© CbkBay
III i P P i l t * * % Cliaiina in popiJalion (1996-2U01)
% Change transportation energy consumption (1995-2004) vs.
% Change papulation density (1996-2001)
3 in population dunsity (1996-2001)
Figure 20. Percentage change in transportation energy consumption (1995 to 2004) versus percentage change population (1996 to 2001); Percentage change in transportation energy consumption (1995 to 2004) versus percentage change population density (1996 to 2001).
Figure 20 demonstrate that the municipalities with the greatest rate of increase in
population and population density over time, i.e. Langford and Central Saanich, also showed
the greatest rate of decrease in per capita fossil fuel consumption for transportation.
Reciprocally, the municipalities that showed a decrease in population and a decrease in
population density over time, i.e. Esquimalt, Colwood, and Oak Bay, also showed a slower
rate of decrease in per capita fossil fuel consumption. Despite the general correlation in the
clusters of municipalities at each end of the spectrum, the exact ranking order of the
complexity variables for each municipality did not match the pattern of rate of decrease of
transportation energy consumption.
Activities characteristics vs. decrease fossil fuel consumption, transportation
Of the activities associated with infrastructure data, i.e. commuting distances (km), %
composition of drivers of total commuters, and % composition of walkers, bus transiters, and
cyclists of total commuters, none of the municipal patterns of these variables appeared most
91
consistent with the rate of decrease in per capita fossil fuel consumption for transportation (in
1995 and 2004). The rates of change in composition of drivers versus walkers, etc. of the
total commuters (to work) did not appear linked to, for example, Langford's rapid decrease in
its per capita rate of fossil fuel consumption for transportation from 1995 to 2005.
Linking complexity variables to space-heating energy consumption
In 1995 and 2004, commercial/industrial and residential space-heating, taken together,
comprised the greatest proportion of total energy consumption (fossil fuel and hydro-
electricity) of the activity types in all municipalities. Fossil fuel consumption for residential
space-heating was second to fossil fuel consumption for transportation. Two trends were
evident in the municipal per capita rates of energy consumption for building space-heating.
First, two main trends were evident in the per capita municipal residential space-
heating data, 1) Central Saanich had the highest per capita rate of energy consumption
(GJ/year) for residential space-heating and Esquimalt had the lowest rate, while Langford,
Oak Bay, Colwood fell in the middle, and 2) over time, from 1995 to 2004, Oak Bay
increased its per capita residential space-heating energy consumption; by contrast, Langford
decreased its per capita residential space-heating energy consumption. The complexity
variables that were graphed relative to the residential space-heating energy consumption data
were: population characteristics, transportation infrastructure, and commuting patterns (D14
- D24).
Second, the main trend evident in the per capital municipal commercial and industrial
space-heating data was, 3) over time, from 1995 to 2004, Esquimalt increased its per capita
fossil fuel energy consumption for commercial and industrial space-heating at a greater rate
than the other municipalities (see Table 11). Percentage labour force distribution, as a
92
surrogate estimate, of municipal economic structure was compared to the municipal per
capita commercial and industrial space-heating usage patterns.
Population characteristics vs. residential space-heating
In 1995 and 2004, Central Saanich's per capita rate of energy consumption for
residential space-heating was consistently the highest; by contrast, Esquimalt's was
consistently the lowest. The ranking order of the other municipal residential space-heating
consumption rates fluctuated between 1995 and 2004. Of the descriptive statistics, i.e. age
structure, population density, population growth, income, and labour force participation,
population density appeared most consistent with pattern of municipal per capita energy
consumption for residential space-heating.
>15
-W -35
I f 25
! :
O C-Saanich
© Cofwood • Esqiimalt ' Langfbrl
® Oak Bay
sco iaoo 1500 axw
i p I
vs. 20011 i density, per municipality
o C-Saanich
ocotaood
• Esqiimalt
' Langford
$C&kBay
1500
mni)
Figure 21. 1995 Residential per capita space-heating energy consumption versus 1996 population density (#/km2); 2004 residential per capita space-heating energy consumption versus 2001 population density (#/km2).
Figure 21 demonstrates that the municipality with the highest population density (i.e.
Esquimalt) also consumed the lowest rate of energy for residential space-heating.
Conversely, the municipality with the lowest population density (i.e. Central Saanich) also
consumed the highest rate of energy for residential space-heating. However, the pattern of
93
municipal population densities did not correlate, in all cases, to the ranking order of
municipal per capita residential space-heating rates.
Dwelling infrastructure vs. residential space-heating
Of the dwelling infrastructure characteristics, i.e. per capita high-density dwellings, per
capita low-density dwellings (1996, 2001), the municipal patterns of both dwelling type
variables appeared most consistent with the municipal patterns of per capita energy
consumption (i.e. hydro-electricity and fossil fuel) for residential space-heating (in 1995 and
2004).
srgy consumption per municipality
I -4(1
15
30
| f 20 !? ™
5 0
, C-S.k* K1
GColMood
• Esqumalt
Langford
» GskBay
I II
Residential space-heating energy consumption vs. 2001 Hgh density dwellings. |
• 35
30
I I » ; is:
; 10 s B
o OSaaricH
oCcfaood
• EsqJmaft |
# Ldi ylijui
»QekBav
10 SO 30 40 50 l» 70
Figure 22. 1995 Residential per capita space-heating energy consumption versus 1996 % composition of high-density dwellings (of total dwellings); 2004 residential per capita space-heating energy consumption versus 2001 % composition of high-density dwellings (of total dwellings).
Figure 22 demonstrates that the municipality with the high percentage composition of
high-density dwellings, i.e. Esquimalt, also showed the lowest per capita rate of energy
consumption for residential space-heating. By contrast, Central Saanich was among the
municipalities with a lower percentage composition of high-density housing and also showed
the highest per capita rate of energy consumption for residential space-heating. Despite the
general correlation, Colwood's percentage composition of high-density housing was the
94
lowest; yet, its residential per capita rate of energy consumption for residential space-heating
was not the highest. Moreover, the pattern was more consistent in 1995 than 2004.
vs. 1996 Low density duellings, per municipality vs. 2001 Low density dwellings, per municipality
Figure 23. 1995 Residential per capita space-heating energy consumption versus 1996 % composition of low-density dwellings (of total dwellings); 2004 residential per capita space-heating energy consumption versus 2001 % composition of low-density dwellings (of total dwellings).
Figure 23 demonstrates that the municipality with the lowest percentage composition
of low-density dwellings, i.e. Esquimalt, also showed the lowest per capita rate of energy
consumption for residential space-heating. By contrast, Central Saanich was among the
municipalities with the highest percentage composition of low-density housing and also
showed the highest per capita energy consumption for residential space-heating. Despite the
general correlation, Colwood's percentage composition of low-density housing was the
highest; yet, its residential per capita rate of energy consumption for residential space-heating
was not the highest. Moreover, the pattern was more consistent in 1995 than 2004.
Population characteristics vs. change in residential space-heating
Of the percentage change in population characteristics (from 1996 to 2001), i.e. %
change in age structure, % change in population density, % change in population growth, %
change in average income, and % change in labour force participation, the relative pattern of
95
the rate of change in municipal population growth and population density appeared
somewhat consistent with the municipal pattern of the rate of decrease (and increase) in per
capita energy consumption for residential space-heating (from 1995 to 2004). Recall that,
from 1995 to 2004, Oak Bay increased its per capita residential space-heating energy
consumption; by contrast, Langford decreased its per capita residential space-heating energy
consumption.
(1995-2004) vs. % Change population (1996-2001)
0 oC-Saarich
OCdvwod
• Esqumalt
Langfbrd
CnkRav
%Cha
% Change residential space-heating consumption (1995-2004) vs.
% Change population density (1996-2001) 0 IS
•
c °
O -6 I 5 10 1
M-
OC-Saarich
OCofoood
• Esqumalt
Langford
«CBkBay
Figure 24. % Change in residential space-heating consumption (1995-2004) vs. % change in population (1996-2001); % Change in residential space-heating consumption (1995-2004) vs. % change in population density (1996-2001).
Figure 24. demonstrates that the municipality with the greatest rate of increase in
population and population density over time (1996-2001), i.e. Langford, also showed the
greatest rate of decrease in residential space-heating from 1996 to 2004. Yet, at the other
extreme, the pattern does not apply for Oak Bay. For example, although Oak Bay increased
its per capita residential space-heating energy consumption from 1995 to 2004, its rate of
change in population and population density was not the lowest.
Dwelling infrastructure vs. change in residential space-heating
Of the percentage change in dwelling infrastructure, i.e. % change in dwelling
infrastructure type (e.g. low and high density) and % change in new house construction, the
96
municipal patterns of % change in composition of newly constructed houses appeared most
consistent with the municipal patterns of the rate of change in per capita energy consumption
for residential space-heating (1995 to 2004).
I < £ !
(1995-2004) vs. "/•Composition of new dwellings (1991-2001)
0.15 •
0.1 -
0.05-
llfPl -0.05
s p i t , . ^ „ .. % Composition of newly constructed dwellings, per capita/ municipality
10 15 20 25
OCSaaiichl
oGolwood
• Esqurralt
; Langford
• Oak Bay
Figure 25. % Change in per capita residential space-heating consumption (1996-2004) vs. % change in percentage composition of newly constructed dwellings: total dwellings (1991-2001).
Figure 25 demonstrates that the municipality with the greatest increase percentage
composition of newly constructed homes (1991-2001), i.e. Langford, also showed a decrease
in residential space-heating energy consumption (1995-2004). By contrast, Oak Bay showed
low percentage composition of newly constructed homes (1991-2001) and, reciprocally,
showed an increase in its rate of per capita energy consumption for residential space-heating
over time (1995-2004).
Labour force distribution vs. increased commercial/industrial space-heating
Table C21, which estimates economic structure of the selected communities using
labour force distribution, does little to explain Esquimalt's moderately high area source
emissions due to commercial activities (compared to the other municipalities). The difference
97
in distribution of the labour force among each industry division as a surrogate estimate of
economic structure, per municipality, is unremarkable.
Esquimalt's moderately high emissions are better explained with the emissions data
of this research. Table 13 demonstrates that Esquimalt's moderately high emissions is related
to its relatively high proportion of commercial space-heating with fossil fuels.
Linking complexity variables to loss of ecosystems
From 1992 to 2002, the municipalities with the greatest to the least loss of hectares of
sensitive ecosystems per capita were: Langford, Colwood, Central Saanich, Esquimalt and
Oak Bay (Figure 11). Of the municipalities, Langford (8%) experienced the greatest change
in loss of per capita hectares over time compared with Colwood (5%), Central Saanich (4%),
Esquimalt (3%) and Oak Bay (1%). The complexity variables that were graphed relative to
the loss of sensitive ecosystems data were: % change in population characteristics and
percentage composition of sensitive ecosystems of total municipal land area (Appendix D25
- D27).
Population characteristics vs. loss of ecosystems
Of the rates of percentage change of the population characteristics (1996-2001), i.e. %
change age structure, % change population density, % change population, % change income,
and % change labour force participation, the relative pattern of the rate of change in
municipal population growth and population density appeared most consistent with the
municipal pattern of loss of sensitive ecosystems from 1992 to 2002.
98
f •6
I
Loss of sensitive ecosystems (1992-2002) vs. % Change population (1996-2001)
—I
oOSaarich'
o Cotaood
• Esqiimalt
& Langfbid
& CBkBay
> -
Loss of sensitive ecosystems (1992-2002) vs. % Change population density (1996-2001)
-es-
-aa-
% Change in
Figure 26. Loss of sensitive ecosystems vs. % change in population; loss of sensitive ecosystems (1992-2002) vs. % change in population density (1996-2001).
Figure 26 demonstrates that the municipality with the highest rate of population
growth and population density, i.e. Langford, was also the municipality with the highest rate
of per capita loss of sensitive ecosystems. By contrast, Oak Bay and Esquimalt's populations
and population densities remained relatively stable (1996-2001) and, reciprocally, showed
relatively small losses in area of sensitive ecosystems. Despite the general correlation, the
municipal patterns of percentage change in population and population density did not match
the loss of sensitive ecosystems data for all the municipalities. For example, Central Saanich
experienced a growth in population and an increase in population density; yet, its loss of
sensitive ecosystems was, compared to Langford, only modest. In another example, Colwood
lost 3.6% of its sensitive ecosystems from 1992-2002; yet, Colwood's population remained
stable (and decreased somewhat) from 1996-2001.
99
Percentage composition of sensitive ecosystems
l! 1
0.6
0.5
0.4
0.3
02 0-1
Lass of sensitive ecosystems (1992-2002) vs. % Composition of sensitive ecosystems of total land
area (1996-2001)
o ) 10 20 30 40 ga
-0.1
% Composition of sensitive
OC-Saarich
O Colwood
• Esqumalt
» Langford
• Cek Bay
of total Kind area, per municipality
Figure 27. Loss of sensitive ecosystems (1992-2002) per capita vs. percentage composition of area of sensitive ecosystems to total municipal land area.
Figure 27 shows that the municipality with the greatest percentage composition of land
area in sensitive ecosystems, i.e. Langford, also demonstrated the greatest rate of loss of
sensitive ecosystems over time (1992-2002). By contrast, Esquimalt and Oak Bay had the
smallest percentage composition of land area in sensitive ecosystems; reciprocally, these
municipalities also showed the smallest rate of loss in sensitive ecosystems over time.
Summary of Findings
Table 17 and 18 summarize the main findings of this research, which applied surrogate
data to the parameters: energy throughput, community entropy debt, and complexity, and
related complexity variables to municipal patterns in energy consumption and loss of
sensitive ecosystems. Table 17 summarizes the complexity variables most consistently
matching the municipal patterns of transportation, space-heating energy consumption and
100
loss of sensitive ecosystems. Table 17 summarizes the general trends revealed in the energy
data.
Table 17. Summary of community complexity surrogates linked to municipal patterns of energy consumption
Low "energy throughput: High community bentropy debt
• Esquimalt
higher % composition of commercial/industrial space-heating activities using fossil fuel see category (low energy: low entropy)
High aenergy throughput: high community bentropy debt
• Central Saanich • Langford
lower population density more roadway infrastructure (km) longer commuting distances to work greater % comp. of commuting drivers lower % composition of commuting walkers, transiters, cyclists greater % comp. of low density housing lower % comp. of high density housing
Low aenergy throughput: low community bentropy debt
• Oak Bay • Colwood
higher population density less roadway infrastructure (km) shorter commuting distances to work lower % comp. of commuting drivers greater % composition of commuting walkers, transiters, cyclists lower % comp. of low density housing greater % comp. of high density housing
High aenergy throughput: low community bentropy debt
• No municipalities represented
none revealed
^Decrease in energy consumption over time Loss of sensitive ecosystems over time
increase in population over time (re: transportation, loss s.e.s) increase in population density over time (re: transportation, loss s.e.s) greater % composition of newer homes (re: residential space-heating) **energy efficiencies (e.g. automotive and space-heating using fossil fuel ) higher % composition of sensitive ecosystems (re: sensitive ecosystems loss)
/Vote. aEnergy throughput parameter represented by surrogates: fossil fuel, hydro-electricity consumption for transportation, space-heating (residential, commercial, industrial), proportional to activities that were 'community system' serving. b Community entropy debt parameter represented by surrogates: point, area, and mobile source emissions (CAC, GHG, metals) and loss of sensitive ecosystems proportional to activities that were 'community system' serving. *Refers to a decrease in transportation and residential space-heating per capita rates for the municipalities in which decreases occurred over time; Note, results are not necessarily causal. **Improved energy efficiencies was demonstrated in the data as per McEwen & Hrebenyk, 2006a rather than with the complexity data; however, new home construction was a surrogate for energy efficiency.
101
Table 18. Summary of the general trends revealed in the energy consumption data
Fuel type consumed (1995, 2004): 1. fossil fuel 2. hydroelectricity
Activity type, all energy (1995, 2004): 1. space-heating 2. transportation
Activity type, fossil fuel (1995, 2004): 1. transportation 2. res. space-heating 3. comm. space-heating
Activity type, hydroelectricity (1995, 2004): 1. res. space-heating 2. comm. space-heating
Fossil fuel, decrease (1995, 2004)
Note. These general trends largely were evident among all the per capita municipal rates of energy consumption.
102
CHAPTER FIVE: DISCUSSION
Introduction
The discussion is divided into these sections:
1. Discussing the findings
2. Discussing the analogical model
3. Discussing the conceptual framework
4. How the objectives were met
5. Opportunities for further research
The first section, Discussing the findings, discusses the results of the analysis. This
section also discusses the limitations of the data. The second section, Discussing the
analogical model, discusses the lessons learned from operationalizing the theory of
dissipative structures using an abstracted analogical model specifying three parameters. The
third section, Discussing the conceptual framework, discusses the conceptual framework
based on the theory of dissipative structures, including the concept of entropy debt. It
discusses its conceptual advantages and criticisms of the theory of dissipative structures.
These sections conclude with answers to the three thesis questions posed at the outset of this
research. The fourth section reviews how the objectives were met. The final section discusses
the opportunities for further research.
Discussing the Findings
The purpose of conducting an analysis of the framework parameters was to reveal, if
any, internal systemic (i.e. community complexity) drivers of anthropocentric environmental
103
degradation of the selected municipalities. The following sections explain in detail the
findings of the analysis, which involved, first, comparing surrogate measures of 'community
entropy debt' and 'energy throughput', specifically, per capita rates of energy consumption,
emissions, and loss of sensitive ecosystems associated with municipal populations. When
emissions were compared to rates of energy consumption (and incorporating loss of sensitive
ecosystems data), which the research called the 'energy-entropy ratio', municipal differences
were revealed. In this way, the research demonstrated that emissions were linked to the type
and rate of energy consumed. Second, to investigate if and which structural characteristics of
the selected municipalities were linked to the energy-entropy ratio the municipalities were
'unpackaged', so to speak. This analysis involved comparing surrogate measures of'energy
throughput' and 'community complexity'. Specifically, municipal structural characteristics
were compared to municipal per capita rates of energy consumption. Where patterns (i.e.
municipal rankings) matched consistencies in the data sets became evident. In this way the
research demonstrated that some structural characteristics were linked to rates of energy
consumption. This section answers the thesis question, What can an analysis of the
conceptual framework parameters reveal about systemic drivers of anthropogenic
environmental degradation?
Community entropy debt surrogates vs. energy consumption surrogates
In summary, the community entropy debt parameter was represented and measured
by emissions (i.e. comprised primarily of tonnes/yr of CACs and GHGs in two snapshots of
time, 1995 and 2004, from space-heating, transportation, and some commercial activities)
and loss of sensitive ecosystems data (ha lost from approximately 1992-2002). The energy
throughput parameter was represented and measured by energy consumption data of fossil
104
fuels (except LPG) and hydroelectricity (i.e. GJ/yr in two snapshots of time, 1995 and 2004,
primarily from space-heating and transportation activities). In the context of the limitations in
the data sets (explained in detail in the following sections), a comparison of the municipal
ranking of the community entropy debt surrogates and the municipal ranking of the energy
throughput surrogates (i.e. the 'energy-entropy ratio') demonstrated that, for the selected
municipalities:
• Emissions (type and rate) depended on energy consumption (type and rate), specified,
in part, by activity type
The data provided evidence that municipal per capita rates of emissions (type and
rate) depended on the fuel (type and rate) consumed. This was evidenced in three ways. First,
Central Saanich and Langford's comparatively high emissions mirrored relatively high per
capita rates of energy consumption, contrasted to Oak Bay and Colwood's comparatively low
rates of energy consumption, which mirrored relatively low emission rates (Figure 14).
These examples infer that the rate of emissions depend on the rate of fossil fuel consumed.
Second, Esquimalt did not match the relative pattern of energy consumption to emissions.
Esquimalt's per capita rate of emissions was moderately high given its relatively low energy
consumption rate. As demonstrated in Table 13, fossil fuel consumption for commercial and
industrial space-heating contributed to Esquimalt's energy to emissions ratio. This example
infers that rate of emissions is specified, in part, by activity type. Third, over time, Oak Bay
increased its per capita energy consumption and emissions due to an increase in fossil fuel
consumption for residential space heating. Conversely, Langford decreased its per capita
energy consumption and emissions over time due to a decease in fossil fuel consumption for
transportation and residential space-heating; reciprocally, it increased its hydro-electricity
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consumption (Table 14). These examples infer that the rate of emissions depends on the type
of fuel consumed (i.e. light fuel oil, gasoline, diesel, natural gas vs. hydro-electricity).
Moreover, the overall reduction in energy consumption and associated emissions over time
reflected mobile and space-heating energy efficiencies (McEwen & Hrebenyk, 2006a).
Therefore, for the selected municipalities, type and rate of emissions depended on the type
and rate of energy consumed.
Yet, activity type did not necessarily specify the type of energy consumed (e.g. fossil
fuel versus hydro-electricity). For example, that an activity, such as residential space-heating,
occurred did not necessitate the type of fuel that was used. Furthermore, activity type did not
necessarily specify the rate of energy type consumed. For example, the data did not
demonstrate why Oak Bay increased its fossil fuel consumption for residential space-heating
or why Esquimalt's commercial space-heating was proportionally higher than the other
municipalities (Table 11) and resulted in a greater proportion of emissions due to fossil fuel
consumption than the other municipalities (Table 13). Other factors, not evidenced in the
data, could have accounted for these examples such as, internal systemic factors (explored in
the research and discussed in the next section), external systemic factors (not explored in the
research), and individual choices (not explored in the research).
Energy consumption surrogates vs. complexity surrogates
In summary, the energy throughput parameter was represented and measured by
energy consumption data of fossil fuels (except LPG) and hydroelectricity (i.e. GJ/yr, taken
in two snapshots in time, 1995, 2004, and primarily from space-heating and transportation
activities). The community complexity parameter was represented and measured by
descriptive statistics (i.e. complexity variables) of population characteristics, infrastructure
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characteristics, activities associated with infrastructure, and economic structure (i.e. typically
in 1996 and 2001). In the context of the limitations in the data sets (discussed in the next
section), a comparison of the municipal ranking of the energy throughput surrogates and the
municipal ranking of the complexity variables demonstrated that, for the selected
municipalities:
• Selected complexity variables were not relevant to the general trends evidenced in the
energy consumption data (Table 16, 18)
• Some complexity variables, but not others, corresponded and, therefore, appeared
linked to municipal patterns of energy consumption (Table 17)
The general trends evident in the energy consumption data (Table 18), which did not
result in comparative differences in per capita rates, could not be linked to the selected
complexity variables. The complexity variables demonstrated differences among the
characteristics of the municipal populations, their activities, and the municipal infrastructure
(Table 16); therefore, these data could not be compared to the general trends that were
applicable to all the municipalities.
The data provided evidence that municipal per capita rates of energy consumption
depended on some of the complexity variables explored by the research, i.e., type of
infrastructure, commuting distances, commuter types, and population density (Figure 15-19,
22-24). More specifically, the presence/absence of amount and type of infrastructure
appeared linked to patterns of per capita energy consumption. For example, length of
roadway corresponded to municipal per capita rates of fossil fuel consumption for
transportation. That is, more roadway infrastructure corresponded to a higher municipal per
capita rate of fossil fuel consumption for transportation; conversely, less roadway
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infrastructure corresponded to a lower municipal per capita rate of fossil fuel consumption
for transportation. Additionally, a higher percentage of low density housing corresponded to
a greater rate of per capita energy consumption for residential space-heating; conversely, a
higher percentage of high density housing corresponded to a lower municipal per capita rate
of energy consumption for residential space-heating. In addition to infrastructure
characteristics, some activities associated with that infrastructure appeared linked to
municipal per capita rates of energy consumption. For example, longer commuting distances
(to work), a higher percentage composition of drivers, and a lower percentage composition of
walkers, passengers, cyclists, and transiters corresponded to higher municipal per capita rates
of energy consumption for transportation. The inverse corresponded to a lower per capita rate
of energy consumption for transportation. Of the population characteristics, median age,
average income, and percentage labour force participation consistently did not appear linked
to municipal energy consumption patterns; yet, population density did. A low population
density corresponded to higher municipal per capita rates of energy consumption for
transportation and residential space-heating. The inverse appeared linked to lower municipal
per capita rates of energy consumption for transportation and residential space-heating.
Over time, changes in municipal per capita rates of energy consumption for
transportation and residential space-heating appeared linked, perhaps counter-intuitively, to
population growth and population density (Figures 20, 21, 25, 26). For example, rates of
population growth and population density roughly corresponded to decreases in energy
consumption for transportation and residential space-heating. The inverse corresponded to an
increase in energy consumption for transportation and residential space-heating. Yet, it
should be noted, population growth and population density data provide little detail about
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how a population might be distributed across a landscape and how energy efficiencies are
gained or lost with economies of scale. Yet, as a surrogate measure of energy efficiencies, a
higher percentage composition of newer homes corresponded with higher rates of decrease in
energy consumption for residential space-heating over time. While loss of sensitive
ecosystems appeared somewhat linked to the change in population growth and population
density over time (Figures 27, 28), the rate of loss of sensitive ecosystems corresponded
more consistently with the percentage composition of sensitive ecosystems of the total
municipal area. For example, the greater the percentage composition of the municipal area
comprised of sensitive ecosystems, the greater was the per capita rate of their loss.
Limitations of the data
This section focuses on the limitations associated with the data used to conduct the
analysis and the repercussions of these limitations. The most significant issue with the data
was 1) the robustness of the emissions, loss of sensitive ecosystems, energy consumption
data. Furthermore, 2) estimations and assumptions associated with preparing the final data
sets presented further limitations to the robustness of the data. The lack of robustness in the
data sets affected the extent to which the data could be analysed and the extent to which the
data could represent each framework parameter. Regarding the data analysis, 3) the utility of
the complexity variables affected the extent to which meaningful conclusions could be drawn
about the internal system 'structure' characteristics that corresponded to rates of municipal
per capita energy consumption.
First, of the preferred data, the available data were clearly not robust for area source
emissions, including commercial activities, non-road mobile emissions, and some residential
sector activities, such as paint and lawn care substances. Of less significance, point source
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emissions data were also not robust. Yet, that the point source data set was not robust was of
little significance to the integrity of the final data sets because industrial activities did not
predominate in the selected municipalities. However, as a result of the lack of robustness in
the area source emissions data, CACs and GHGs were well-represented in the data; yet,
metals and human-made substances were not. Moreover, some data were not available to this
research at all such as, consumer waste and substances leaking into the ecosphere from wear,
for example, tire wear. These data were either not municipal-specific or available at all. By
comparison, the energy consumption data was the most comprehensive set as it often
represented direct measures compiled from the energy providers. Yet, while the measures of
emissions, loss of sensitive ecosystems, and energy consumption varied from estimates to
exact measures, the integrity of their comparative municipal per capita differences were
maintained to an extent appropriate for their analysis in this research. For this reason, that no
community was identified as having had a high energy throughput versus a low community
entropy debt could indicate a lack of robustness in the data as much as it could represent the
absence of a municipality meeting these characteristics.
Second, further limitations of the final data sets resulted from estimations, assumptions,
and generalizations. Estimation methods, such as emission factors and surrogate allocation
ratios affected the accuracy of the final data set, even though the comparability of the data
were maintained. Assumptions also introduced limitations. For example, hydro-electricity
was not associated with emissions after dam construction; yet the repercussion of hydro-
electric dams on wildlife habitat would have continued beyond the date the dam was
constructed. Generalizations were involved in including/excluding data. For example, this
research did not include data associated with the residential or commercial sectors that
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supported community system self-reproduction, but which occurred outside the community
systems. For example, emissions generated by a Central Saanich resident conducting
business in Nelson were omitted, even though these activities support the self-reproduction
of Central Saanich. In another example, although mobile emissions associated with traffic to
and from a multi-national fast-food restaurant were captured, the impact incurred from
clearing tropical rainforests in South America to raise cattle on behalf of that multi-national
were not. As such, only the loss of ecosystems within municipal boundaries were given as
surrogate measures of community entropy debt, not all possible losses of ecosystem
productivity. These examples further demonstrate the limitations inherent in the data used for
analysis in this research.
Third, the complexity variables were chosen to represent some social, economic, and
infrastructural characteristics of a municipality and its population. To reiterate, they were
chosen for the purpose of testing the relationships among the framework parameters, and
therefore, also, to test the utility of the analogical model in the way it was made operational.
As the set of complexity variables were not intended to be comprehensive or exhaustive,
some significant variables may not have been analysed while other, less significant variables,
may have been analysed. For this reason, it could not be concluded that the complexity
variables that did appear linked to the selected energy consumption data were the most
significant indicators of municipal system structure related to rates of energy consumption.
Implications of the data limitations
Finally, these limitations presented two fundamental challenges to interpreting the
data. First, none of the complexity variables exactly matched the patterns of municipal per
capita energy consumption rates nor necessarily in both timeframes. Yet, the limitations
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inherent in the data generated some uncertainty about the significance of these anomalies.
For example, it was unclear if the anomalies in the patterns demonstrated the influence of
some other structural characteristic(s) or one or several limitations in the data sets. For this
reason, only the most obvious trends and patterns among the data sets were analysed for
links. This included links in the energy consumption versus the emissions data; and, links in
the complexity variables versus the energy consumption data. Therefore, any anomalies in
the municipal patterns were not pursued for further meaning.
Second, the difference between the available compared to the preferred data sets and
the lack of robustness in the available data both contributed to uncertainty about the extent to
which the surrogate data represented the framework parameters. Moreover, some data sets
could not be adequately combined. For example, the surrogates of 'community entropy debt',
i.e., emissions and loss of sensitive ecosystems data, were combined, but only crudely using
an ordinal scale to do so (Table 15). As a result, the data analysis (community entropy debt
vs. energy throughput; energy throughput vs. system complexity) resulted in conclusions
inferred from the trends and linkages between surrogates (and only those that could be
analysed) rather than from analysis of the framework parameters themselves. Additionally,
because of the limited empirical data provided by this research, none of the findings should
be and were not generalized to any other municipality than the ones selected for this study.
Thesis Question # 3
• What can an analysis of the conceptual framework parameters reveal about systemic
drivers of anthropogenic environmental degradation?
Given the limitations in the data discussed, and in answer to Thesis Question #3, it
was not possible to report on an analysis of the correspondences between the framework
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parameters. Additionally, it was not possible to conclude, with any certainty, that structural
characteristics of municipalities were causal of (i.e. were 'drivers' of) environmental
degradation by way of energy consumption. Nor was it possible to generalize any of the
findings in the context of other municipalities for which data were not collected. Yet, in the
context of the selected municipalities, the analysis of the surrogates of the framework
parameters inferred the following findings.
More generally, the findings of this thesis research demonstrated that the selected
municipalities (as complex open systems and as dissipative structures) with similar
population sizes and similarly constrained, were quantitatively different. For example, the
complexity variables and the surrogate 'community entropy debt' an 'energy throughput'
data demonstrated that the selected municipalities were structurally different, consumed
energy, emitted contaminants, and displaced sensitive ecosystems at a variety of different per
capita rates. This finding is consistent with the literature that conceives municipalities
(Adams, 1988) and, more generally, human systems (Allen, 1997; Prigogine et al., 1977) as
dissipative structures whereby external environments constrain only the realms of
possibilities in which they can be found. Accordingly, 'community system' structure is
manifested in a myriad of different ways and at any given time according to its 'parts', e.g.
forward-thinking/value-laden humans and infrastructure, and 'processes', e.g.
communication networks and local/global economy, in interaction with the external
environment, e.g. culture, climate, socio-political superstructure.
More specifically, some complexity variables appeared linked to patterns of energy
consumption (Table 17) of the selected municipalities. Moreover, in the cases studied,
emissions depended on the type and rate of energy consumed for space-heating and
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transportation (.Figure 14). As such, emissions also depended on the extent to which the
municipal populations engaged in activities, such as residential and commercial space-
heating and transportation activities. However, the research did not demonstrate why some
activities were undertaken and why some fuel types were consumed in some municipalities
more than others. While exploring consumer fuel choices and external systemic influences
fell outside the scope of this research, some internal 'structural' system characteristics did
appear linked to higher rates of municipal per capita energy consumption patterns for
transportation and space-heating. These higher rates of energy consumption further generated
higher rates of emissions, which were considered un-sustainable at the global scale (Azar et
al., 1996), when they were associated with the consumption of certain fuel types, i.e. fossil
fuels. The structural characteristics of the high energy-entropy ratio municipalities appeared
to be: low population densities, more roadway infrastructure, a higher percentage
composition of low density dwellings, a higher composition of drivers, longer commuting
distances (to work), and a higher percentage composition of older homes. However, it was
not clear if and how per capita rates of energy consumption were linked to loss of ecosystem
productivity.
Discussing the Analytical Tool & Lessons Learned
The purpose of conducting an analysis of empirical data of the cases (i.e. the selected
municipalities) was to test the efficacy of the analytical tool, i.e. the analogical model
comprised of the framework parameters of 'energy throughput', 'community complexity',
and 'community entropy debt'. The literature reviewed for this research provides examples
of research based on thermodynamic conceptual frameworks that define and measure
progress toward sustainability by seeking measures of material (e.g. Ayres & Simonis, 1994)
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or energy flows (e.g. Brown & Ulgiati, 1997; Hermanowicz, 2004; Odum, 1996). However,
this research project did not seek actual measures of its framework parameters. Rather, it
applied surrogate measures to framework parameters (to which system dimensions were
mapped) using existing data. It did so to investigate empirical evidence of the relationship
between community system complexity, its energy requirements, and its entropy debt. What
follows is a brief discussion of the advantages and disadvantages of the analytical tool
employed in this thesis research. This section answers the thesis question, How can an open
systems conceptual framework (highlighting the energetic-entropic relationship between
community complexity and the natural environment) be operationalized effectively and
applied to municipalities as open systems and as dissipative structures?
Advantages
Contrasted to examples of issues associated with actual measures, this section
explores the advantages in this research of having used surrogate measures, including, 1)
requiring no reference state, 2) requiring no 'energy' accounting estimates, and 3)
incorporating loss of ecosystem productivity. Additionally, this section discusses the
strengths of the model itself, specifically, 4) the relationships between the framework
parameters, and 5) the applicability of the analogical model to a variety of different cases and
using existing data.
Exergy analysis focuses on gradients of energy available to perform work, whereby
natural resources represent free energy (Wall & Gong, 2001). By calculating the loss of
natural resources using exergy measures, Wall and Gong (2001) capture the repercussions of
the rates of natural resource consumption against its regeneration in the geobiosphere. Their
research also captures the repercussions of releasing concentrations of toxins into the
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environment, which, as a concentration, introduces unwanted energy gradients. Yet, exergy
analysis has difficulty conceptualizing the deleterious effect of dilutions on the natural
environment such as, releasing into the atmosphere carbon dioxide as a gas (i.e. a dilution)
from carbon trapped in fossil fuel (i.e. a concentration) (Hermanowicz, 2004). Moreover,
reference states are critical to exergy measures. Hermanowicz (2004) argues that entropy,
"... does not require that the current "standard" environment (or any arbitrary standard) be
assumed as a reference level. From a theoretical point of view, dispensing with an assumed
reference state has an advantage because it does not predicate that the present state of the
environment is somehow preferred or desirable" ( p. 5). The dissipative structures conceptual
framework made operational with an abstracted analogical model rather than by way of
exergy measures advantageously accounts for concentrations, such as toxins, dilutions such
as carbon dioxide gas (contributing to the greenhouse effect), and requires no measure of a
reference state.
Emergy analysis advantageously incorporates human services as emergetic inputs just
as it considers the emergetic inputs of natural resources. Yet, emergy analysis has been
criticized for the challenges involved in estimating emergetic inputs associated with
geological processes over long periods of time (Hau & Bakshi, 2004). The analogical model
of this research advantageously applies surrogate measures guided by sustainability
indicators to community entropy debt parameters, thus circumventing errors associated with
the accounting estimates used in emergy analysis.
Proponents of the material and energy flow analysis (MEFA) toward measures of
sustainability argue that its analysis, by contrast to material flow analysis, which omits the
impact of land use, appropriately accounts for losses in ecosystem productivity (Haberl et al.,
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2003). The analogical model of this research advantageously includes loss of sensitive
ecosystem data as a surrogate measure of community entropy debt. Regarding applying
surrogate measures, Haberl et al. (2003), the authors of the material and energy flow analysis
(MEFA) framework, advocate the benefits of being able to incorporate existing statistical and
indicator data associated with socio-economic processes. Similarly, surrogate measures
applied to the parameters of the analogical model of this research can come from statistical
and indicator data sets provided that the community systems under investigation are subject
to similar (e.g. developmental) dynamics. By contrast to emergy and exergy analysis,
existing data are readily available and require little normalization. Moreover, existing data
are often adequately methodologically consistent over time.
Strengths of the model
The analogical model offered this research with a means to study the relationship
between the structural characteristics of a municipality (as an open system and as a
dissipative structure) and the natural environment upon which it relies for resources. This
was accomplished by mapping corresponding dimensions of municipalities and the natural
environment to the framework parameters, which are related to each other according to the
theory of dissipative structures. Once the mapping was completed and surrogate measures
were applied to the parameters, the relationships between the parameters of the analogical
model provided this research with the means to study the corresponding relationships among
the dimensions of the selected municipalities (and the natural environment). That
relationships in the surrogate data were demonstrated in this thesis research validates the
integrity of the analogical model.
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Additionally, pertaining to the parameters, the analogical model allows researchers to
ask any number of types of questions of a variety of cases, and, possibly, at different scales
(e.g. families, nations, industries). For example, the analogical model could be used to ask a
question about the comparative differences in community entropy debt of municipalities
subject to different system dynamics. For example, the model could have been employed to
compare the community entropy debt of a municipality subject to evolutionary dynamics
such as, the planned community of Kitimat, British Columbia, and a municipality subject to
developmental dynamics (and with a similar sized population) such as, Terrace, British
Columbia. The model could also be used to compare the community entropy debts of
municipalities located in different climates or in the context of different cultures. Its efficacy
could also be tested to explore human systems at different scales such as, organizations,
villages, families, industries, or nations. That this research provided evidence of links in the
surrogate data encourages further investigation of similarly constrained municipal cases, and
encourages further research into the model's capacity to investigate differently constrained
municipal cases or other human systems at different scales.
Disadvantages
This section discusses the disadvantage of using an analogical model to which
surrogate measures were applied. Additionally, this section discusses the weaknesses of the
model itself. Using an analogical model to which surrogate measures were applied to study
anthropogenic environmental degradation introduced approximation, and therefore,
imprecision. Approximation was evident in 1) the type of contaminants considered, 2) the
measure of emissions as a surrogate for environmental degradation, 3) the breadth of data
sets available, and 4) differentiating one system from another to which data were attributed.
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The weaknesses of the model itself included 5) the limitations associated with using existing
data.
For example, Hau and Bakshi (2004) point out that, for example, sustainability
measures of industrial processes do not distinguish the effects of solid wastes and air
emissions on the local versus the global natural environments. Similarly, the preferred data
choices for the 'community entropy debt' parameter of this research can be criticized for
being guided by global scale accumulations in the ecosphere of natural and human-made
substances (Azar et al., 1996). The community entropy debt surrogate measures of this
research are not sensitive to the affect of local ecospheric accumulations of contaminants.
Rather, this research assumes that global accumulations of metals and criteria air
contaminants are relevant to local contexts. Moreover, the community entropy debt surrogate
data, like some other methods of measurement, are not sensitive to ecosystem resilience
(Haberl et al., 2003).
Material flow analysis has been criticized for measuring sustainability by units of
mass, such that processes utilizing less materials are deemed to be more sustainable
(Hermanowicz, 2004). Yet, mass alone is a poor estimate of environmental degradation. For
example, a highly toxic substance may have a lower mass than a less toxic substance.
Similarly, this research can be criticized for measuring accumulations of criteria air
contaminants and metals by mass (i.e. in tonnes). Measuring ecospheric accumulations in
tonnes does not distinguish the impact of substances that are highly toxic in relatively small
quantities from the substances that accumulate in the environment in relatively high
quantities (i.e. carbon dioxide gas).
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Finally, although this research includes the dissipation of natural resources in its
definition of community entropy debt, the preferred data sets only indirectly addresses rates
of consumption beyond the regenerative capacity of the geobiosphere. This is because of the
criteria required to select municipal-specific data, which focuses on societal activities rather
than state of the environment indicators. For example, this research includes in the preferred
data set tonnes of consumer waste, which is only an indirect surrogate for rates of natural
resource consumption. Yet, Ayers and Simonis (1994) consider "a good measure of
unsustainability is dissipative usage" (p. 12), distinguishing between dissipation in the
performance of work from "uses where the material could be recycled or re-used in principle,
but is not" (p. 12). However, measures of materials, which included recycling material and
consumer waste, were not comprehensively captured in the available data.
In the effort to operationalize key parameters informed by the theory of dissipative
structures by way of an analogical model met with the challenge of discerning the activities
between municipalities (i.e. horizontal) as well as between the municipalities and their larger
political superstructure (i.e. vertical). Discerning one municipal-specific data set from
another was relatively straight-forward. Data associated with municipal populations were
readily available. However, discerning municipal-specific data from regional district or
provincial data was more challenging. This occurred because data were collected at different
scales for regulatory needs (and 'housed') at different levels of government, such as the
Province of British Columbia (e.g. environmental statistics) and the Capital Regional District
(e.g. solid waste data). Moreover, some data were practically inaccessible in the private
sector (e.g. bottle recycling). Although solid waste and environmental data were relevant to
this research, some municipal-specific data sets could not be readily extracted from systems
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of data management operating at other scales. This issues arising from discerning data
horizontally and vertically, which requires assumptions and generalizations, result from the
characteristics of complex and open systems. Laszlo (1972) explains,
The organic boundaries themselves are interfaces, permeable to some -though not to all - informations, energies and substances. As a consequence we also get ordered wholes in the sciences dealing with multi-organic units: swpraorganic systems, which can be analysed to individual organisms and component subsystems. And even the supraorganic wholes from a continuum, broken only by relative boundaries, with still larger systems, as evidenced by such systems concepts as continental ecologies and planetary economic and social systems (Laszlo, 1972, p. 24).
Weaknesses of the model
The weaknesses of the model itself center around the limitations associated with
using existing data and the operational challenges of applying surrogate measures to the 'real
world' dimensions, which were mapped to the framework parameters. Some of these issues
have been discussed in the previous section, i.e., issues of estimation, generalization, the
breadth of the available data, and differentiating data sets associated with one system from
another. Yet, additionally, existing data of environmental contaminants were typically
collected and compiled for a variety of different purposes, which may or may not have been
compatible with the theoretical framework of this research. For example, data on criteria air
contaminants are collected to indicate human health (Bunce, 1994) as much to indicate their
impact on the natural environment; and, human health was not expressly a criterion for its
selection. This type of discrepancy in the underlying purpose behind the existing data
involved some 'translation' to make existing data theoretically compatible with the
framework parameters. For all the disadvantages listed in the previous section associated
with the conceptual frameworks that seek actual measures, these frameworks (e.g. emergy,
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exergy) advantageously can develop measures that are theoretically compatible with the
framework at the outset of the analysis.
While mapping dimensions of the 'real world' to the framework parameters was a
relatively straight-forward exercise, applying surrogate measures that were supported by the
literature to these dimensions was not. This was certainly the case for selecting surrogate
measures of the framework parameter 'community entropy debt'. The operational challenge
centered around the data selection criteria, which required that data associated with un-
sustainability should be attributable to a given human system population. Yet, community
entropy debt refers to the dissipation of energy resources in the environment external to a
system, specifically, defined in this research as anthropocentric environmental degradation.
As such, the theoretical basis of the community entropy debt parameter should have lent
itself more aptly to state of environment indicators. However, state of the environment
indicators are not attributable to given populations. While this operational challenge did not
compromise the integrity of the findings of the research, it did affect its operational facility.
Thesis Question # 2
• How can such an open systems conceptual framework effectively be operationalized
and applied to municipalities as open systems and as dissipative structures?
In summary, the analogical model abstracted from the theory of dissipative structures,
as it was operationalized, effectively avoided issues associated with attaining actual measures
of energy and material flows, such as establishing reference states or estimating the emergy
of lithospheric processes. Additionally, it advantageously included surrogates of ecosystem
productivity losses and was methodologically consistent with existing and historical data.
Yet, it was not sensitive to environmental impacts at the local scale and ecosystem resilience.
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Additionally, societal indicators (a data selection criteria of the surrogate measures) only
indirectly captured rates of natural resource-use that exceed their regeneration.
The weaknesses of model itself arose from the challenges of making it operational.
For example, existing data was not necessarily entirely theoretically compatible with the
framework parameters. This required some 'translating' existing data to meet the theoretical
needs of the framework parameters. Yet, despite the weaknesses involved in operationalizing
the model parameters, the model presented some opportunities apparent from its strengths.
Links between the surrogate measures of the framework parameters were, in fact, revealed in
the analysis. This finding alone provides encouragement for further research of other cases of
human systems. Building a model with considerably more empirical data could lead to a
model capable of discerning 'structural' characteristics associated with higher or lower
community entropy debts of selected human systems.
Discussing the Conceptual Framework
The conceptual framework of this thesis research is based on thermodynamic
principles of open systems, specifically the theory of dissipative structures, and includes the
concept of entropy debt. The purpose of articulating a conceptual framework based on
thermodynamic principles, as discussed in the literature review, was to provide this research,
more generally, with a) a "common currency" (Mayer et al., 2004, p. 41) of understanding
issues emerging from interacting systems, such as anthropogenic environmental degradation
in the larger context of sustainability, b) an advantageous conceptual capacity to differentiate
a complex system from its external environment (which is particularly relevant to the Second
Law), and c) a theoretical basis for highlighting the energetic and entropic relationship
between open system complexity and the external environment upon which it relies for
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resources. The purpose of further articulating a conceptual framework based on the theory of
dissipative structures was to provide this research with a general thermodynamic theory of
open systems deemed to apply to human systems, such as cities and societies. As a result, a
body of literature has emerged over time that explicates human systems through the
conceptual 'lens' of the theory of dissipative structures (Adams, 1982; 1988; Allen, 1997,
Dyke, 1988; Prigogine et al, 1977, Straussfogel, 1997; Straussfogel, 1998; Straussfogel &
Becker, 1996). It is this literature that d) supports the operational aspects of the research. For
example, together with the theory of dissipative structures (Nicolis & Prigogine, 1989;
Prigogine et al, 1977, Prigogine & Stengers, 1984) this literature informed the definitions of
the parameters of the analogical model, the system dimensions that were mapped to those
parameters, and guided the criteria involved in selecting the municipalities. Finally, the
purpose of including the concept of 'entropy debt' in the conceptual framework was largely
practical. The thermodynamic principle of 'entropy production' (see Equation 1; Appendix
A), has not been comprehensively mapped to the matter and energy resources of the natural
environment (as free energy) and their degradation (as entropy); whereas, its more general
conception, 'entropy debt' has been mapped so, conceptually (Straussfogel & Becker, 1996).
Yet, despite the operational advantages of understanding municipalities as dissipative
structures, having attempted to operationalize the theory of dissipative structures invited
other issues. But before discussing the issues inherent in the theory of dissipative structures,
the following section reviews some of the conceptual advantages of the theory of dissipative
structures (the operational advantages having already been discussed).
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Advantages of the theory of dissipative structures
Prigogine and his colleagues (Prigogine et al., 1977; Prigogine & Stengers, 1984;
Nicolis & Prigogine, 1989) developed a comprehensive theoretical understanding of the
thermodynamic principles governing open systems, which was considered applicable to
living and to social systems (Prigogine, 1986; Prigogine et al., 1977; Prigogine & Stengers,
1984) and which focused advantageously on the Second Law. First, the theory of dissipative
structures emerges from the study of chemical systems but was thought to apply generally to
other types of open systems. For example, citing both societal and living systems, Prigogine
(1986) states, "In thermodynamics, we are dealing with 'embedded' systems: interaction with
the outside works through entropy flow plays an essential role. This immediately brings us
closer to objects like towns or living systems, which can only survive because of their
embedding in their environment" (p. 497). Second, the theory of dissipative structures
focuses on the Second Law. This focus on entropy advantageously differentiates the system
from its environment, from which it draws resources critical to its existence. Prigogine et al.
(1977) state that, "... the basis for any natural law describing the evolution of social systems
must be the physical laws governing open systems, i.e., systems embedded in their
environment with which they exchange matter and energy" (p. 2). This is a centric
conceptualization, that is, centric to the existence of the system. This is conceptually
advantageous for a research project conducted in the context of a biosphere, itself a system
of systems, which abides by the First Law, whereby the wastes of one system provide fodder
for another. Therefore, the theory of dissipative structures is a general theory and provides a
comprehensive entropic conceptualization of open systems, including human systems, which
125
avails this theory appropriately to the requirements of this research. Yet, the theory of
dissipative structures is not without criticism.
Limitations of the theory of dissipative structures
Researchers (e.g. Kay, 2002; Corning, 2002) have explored the limitations of the
theory of dissipative structures in arriving at their own thermodynamic conceptual
frameworks. In one example, Kay (2002) challenges of the theory of dissipative structures as
a general principle of all open systems, especially of living systems, based on the limitations
of its scope and the imprecision of its terms. More specifically, Kay (2002) demonstrates that
some of the assumptions inherent in parts of the theory of dissipative structures (i.e. the
minimum entropy production rule) do not necessarily hold for all complex systems, such as
living systems. He states, "This set of constraints [assumptions] means that the minimum
entropy production rule and most of Prigogine's results apply to a very restricted set of
systems. A general far from equilibrium thermodynamics and theory of self-organization
does not exist. Any attempt to apply Prigogine's theory must be viewed with skepticism
because of its lack of generality" (Kay, 2002, p. 2).
Moreover, Kay (2002) argues that the imprecision of the term and use of 'dissipation'
as 'entropy production' leads to yet more confusion among researchers. For example, he
explains that entropy production, as a result of irreversible processes, is termed 'dissipation',
according to the theory of dissipative structures. He argues, "However, dissipation is often
used as a synonym for waste energy that is dumped out the back end of a systems or heat
released by a system. This second law notion (amount of irreversibility) gets confused with a
first law notion, energy flux of some sort" (p. 3). Recall that energy, entropy, and exergy are
different, albeit inter-related, terms; they must be explicitly understood and used so as not to
126
confuse their relationship with the First and Second Laws. For this reason, Kay (2000)
develops a general thermodynamic framework for self-organized and hierarchical (i.e.
holoarchic) systems based on an understanding of dissipative structures. However, he prefers
'exergy' to explain and quantify the resources external to the system as energy gradients and
the embodiment of those gradients within the system with the performance of work.
In another example, Corning (2002) offers an alternative framework to understanding
biological evolution than those based on the Second Law. Before arriving at
'thermoeconomic', Corning (2002) critiques various general thermodynamic theories of
complex open systems. In his critique, Corning cites issues arising from thermodynamic
frameworks, including Prigogine's theory of dissipative structures. Corning reveals some
imprecision in its terms and, therefore, in the theory itself. Similar to Kay (2002), Corning
(2002) argues that this imprecision leads theorists to elevate the theory of dissipative
structures to the status of a general theory for all self-organized open systems, to which, he
claims, it is not adequate. For example, Corning (2002) notes that Prigogine does not
succinctly define internal complexity, that is, 'structure', which Prigogine refers to as 'order'.
Moreover, he explains that Prigogine does not address structure versus behavior. He states,
Another problem is that Prigogine makes no distinction between "order" and functional "organization". In fact, he uses the terms interchangeably. Thus, he sees no difficulty in applying the same explanatory principle both to the formation of convection cells (Benard cells) ... complex control mechanisms associated with glycolysis ... This is a theory that seriously overreaches (p. 61). ... As a consequence, subsequent generations of physicists and laymen alike have often uncritically accepted the claim that the entropy law applies to everything in the universe (p. 62).
Yet, while exergy appears to provide a more explicit understanding of
thermodynamic interactions of hierarchical systems than entropy, Kay (2002) does not
disagree that system complexity engenders a cost to the free energy in the external
127
environment of any one system. Moreover, Corning recognizes that entropy is the necessary
result of irreversible processes involved in self-organization. To this he argues, "Entropy
represents a constraint on thermodynamic processes, not a cause of them; it measures the
energetic "wastes" associated with any real-world dynamic process. It's a cost of doing
business in the biosphere" (p. 65). Although Corning argues that entropy presents conceptual
difficulties to explain the flow of energy through the hierarchies of systems, such as the
biosphere, recall entropy's advantages when conceptualizing, "...systems embedded in their
environment with which they exchange matter and energy" (Prigogine et al., 1977, p. 2).
Moreover, this research assumes that, as open systems, human systems must abide by the
Laws of Thermodynamics. As such, this research understands system complexity (i.e.
structure from the performance of work, or stated otherwise, from irreversible processes) to
result in some permanent loss of consumable energy in the environment external to the
system (i.e. dissipation). Therefore, while the theory of dissipative structures lacks some
explicitness of its terms, it remains theoretically intact for its use as the underpinnings of the
conceptual framework proposed in this thesis research.
Thesis Question # 1
• How can an open systems conceptual framework based on the theory of dissipative
structures, including the concept of entropy debt, highlight the energetic-entropic
relationship between municipal 'complexity' and the natural environment?
Finally, despite the evidence that the theory of dissipative structures lacks
generalization to living systems (Kay, 2000) and perhaps also to societal systems, researchers
have continued to explore its concepts in the context of human and human-environment
systems. From social system dynamics (Allen, 1997; Dyke, 1988; Straussfogel, 1991) to their
128
sustainability (Straussfogel & Becker, 1996) this research of human system 'structure' (i.e.
complexity) and its entropy debt (i.e. dissipation) begins with the premise that these systems
are dissipative structures. Adams (1988) explains, "This approach has also been criticized as
an extension of a metaphor. This is hardly a crime since, on the one hand, metaphors have an
important role in science and, on the other, it is not clear that metaphors are involved at all"
(Adams, 1988, p. 6). Yet, given the criticisms outlined in the previous section, this thesis
begins with the underlying assumption that 1) the theory of dissipative structures is a general
theory applying to all complex systems, including human systems and 2) municipalities are
complex open systems and dissipative structures.
As developing a general conceptual framework for all open systems fell outside the
bounds of this research, the literature that explored human systems through the lens of
dissipative structures was critical to informing the operational aspects of this research.
Moreover, the conceptual advantages of doing so included advantageously conceptualizing
municipalities as open systems embedded in the natural environment upon which they rely
for energy and matter resources and by which they are simultaneously constrained.
Moreover, the conceptual framework highlights the energy throughput requirements of
system complexity and the 'entropy debt' (Dyke, 1988) of this complexity, which occurs in
the environment external to the system. Moreover, the literature that focused on conceiving
human systems as dissipative structures (Adams, 1982; Adams, 1988; Allen, 1997; Dyke,
1988; Straussfogel, 1997; Straussfogel, 1998; Straussfogel & Becker, 1996) informed the
definitions required to map municipalities to the analogical model parameters. Second, this
thesis research abstracted from the theory of dissipative structures and its related literature
these inextricably linked parameters: energy throughput, system complexity, and entropy
129
debt. By mapping real-world system dimensions to these parameters and by applying
surrogate measures to these parameters, by analogy, this thesis research highlighted the
energy requirements of building and maintaining the complexity of selected municipalities
and the entropic cost to the natural environment of doing so.
How the Objectives Were Met
The objectives of this thesis were to:
1. Explicate a conceptual framework based on the theory of dissipative structures that
could highlight the entropic cost of system complexity in the environment external to
the system
This objective was met in Chapter Two: Municipalities as Dissipative Structures. This
chapter described municipalities as open systems and as dissipative structures. It further
highlighted the relationship between the energy requirements to maintain and build the
structural complexity of municipalities and the entropic cost to the natural environment of
doing so (i.e. anthropogenic environmental degradation).
2. Operationalize the conceptual framework by mapping the appropriate real-world
dimensions to the framework parameters: for energy throughput, complexity, and
entropy debt
This objective was met in Chapter Three: Methodology, which defined the framework
parameters and further defined the corresponding municipal and natural environment
dimensions.
3. Identify and apply municipal-specific surrogate measures to these parameters and
compare the data
130
This objective was met in Chapter Three: Methodology, where preferred surrogate data were
stipulated, as supported by the literature, and where the criteria to select among the available
data were established.
4. Identify the links between the surrogate data of the framework parameters
This objective was met in Chapter Four: Results, where the municipalities were selected and
the data sets were analysed revealing some links in the data (Table 17 and Table 18).
Opportunities for Further Research
In the absence of a robust data set of the preferred data for each model parameter (e.g.
area source data were weakly represented), the extent to which the surrogate data represent
the energy throughput and community entropy debt remains uncertain. However, certainty
could be improved with indicators. Indicators of each parameter would alleviate the necessity
to collect an ideally robust data set of surrogate measures. Yet, revealing indicators requires
further testing of the abstracted model used in this thesis research. With indicators generated
from repeated testing, the analogical model of this research presents an opportunity to
provide sustainability science with an analytical tool with which to relate key structural
attributes of community systems to anthropocentric environmental degradation. Given that,
Up to half of Canada's greenhouse gas (GHG) emissions (350 million tonnes) are under the direct or indirect control or influence of municipal governments. By 2012, communities could cut GHG emissions by 20 to 50 Mt from municipal operations and community-wide initiatives with investments in environmental infrastructure and sustainable transportation infrastructure (FCM, 2008).
The analogical model of this research has the potential to be developed into a analytical to
provide policy makers and urban planners with a scenario-planning tool or, simply, a tool to
measure progress toward or away from sustainability.
131
CONCLUSION
In conclusion, the findings of this thesis research demonstrate some links appear to
exist in the surrogate measures of community system complexity (i.e. 'structure), its energy
requirements, and resulting anthropocentric environmental degradation. While it appears that
some structural characteristics are linked to a higher or to a lower energy-entropy ratio, more
research should be conducted to test the extent to which the results of this research are
general to all municipalities similarly constrained. This should involve research directed
toward attaining indicators for each parameter. The findings provide encouragement for
continued research to develop the analogical model of this thesis research into an analytical
tool for municipal planners and other decision-makers to use to gauge progress toward or
away from sustainability. In this way, this research provides further encouragement that
operationalizing the concept of 'entropy debt' can contribute to the emerging science of
sustainability, which, "seeks to understand the fundamental character of interactions between
nature and society" (Kates et al., 2001, p. 641).
132
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APPENDIX A-Concepts
Appendix A explains in detail the following theories and concepts:
• Systems theory
• Principles of complex, open systems
• Thermodynamic laws governing complex, open systems
Brief Introduction to Systems Theory
Systems theory represents a myriad of theories derived from and applied to the study
of chemical (e.g. Prigogine & Stengers, 1984), biological (e,g, Varela & Maturana, 1974; von
Bertalanffy, 1968), ecological (e.g. Kay, 2000; Holling & Gunderson, 2002; Odum, 1983),
social (e.g. Abel, 1998; Ayers & Simonis, 1994; Odum & Odum, 2001), and economic
systems (e.g. Georgescu-Roegen, 1971). Systems theory is founded upon the premise that
systems share in common characteristics regardless of their type (e.g. biological, chemical,
and ecological) or scale (e.g. microscopic or macroscopic). Systems theory co-evolved in the
middle of the twentieth century with other similar intellectual streams including, cybernetics
- the study of artificial and goal-oriented systems (Weiner, 1948), chaos theory - the study of
systems with strange attractors (see Gleick, 1987), and information theory - the study of
entropy and information (Shannon & Weaver, 1949). A system was defined as being
comprised of two or more inter-related 'parts' resulting in its having emergent properties,
meaning, unique characteristics and behaviours, which are not found in its individual parts
(von Bertalanffy, 1968). Researchers including biologist Ludwig von Bertalanffy, economist
Kenneth Boulding, physiologist Ralph Gerald, and mathematician Anatol Rapoport
attempted to unify the sciences with a general systems theory creating opportunities to allow
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principles of one system to apply to, and inform, another (von Bertalanffy, 1968). Since these
early years of systems theory development, researchers have applied the principles to
manage, construct, and understand systems (Banathy, 1996).
Principles of Complex, Open Systems
Recall that a system is comprised of at least two or more interconnected parts,
whereby the parts are integral to the whole, and the emergent properties of the whole are not
found in the parts (von Bertalanffy, 1968). This definition remains relevant today. For
example, a human is a system comprised of interconnected parts having the emergent
property of consciousness. Yet, the human system is comprised of cells and organs, which do
not exhibit the emergent property of human consciousness. However, differences exist
among systems in the universe; they are fundamentally 'open', 'closed', or 'isolated'. Open
systems, such as those prevalent in the biosphere, are 'complex' (von Bertalanffy, 1968).
Complex, open systems are characteristically 'self-organizing' (Laszlo, 1972; Prigogine &
Stengers, 1984), 'self-stabilizing' (Laszlo, 1972), and 'hierarchical' (Simon, 1973). These
concepts are explicated in the following sections.
Open versus closed systems
The Second Law of Thermodynamics (Second Law), which states that energy, while
it may not be created nor destroyed (i.e. First Law of Thermodynamics), will dissipate over
time; in other words, entropy increases towards its maximum in an isolated system. The
Second Law was first conceived in the nineteenth century by Sadi Carnot, who theorized that
the potential energy of the universe, itself an isolated system, i.e. theoretically closed to the
in and outflow of energy and matter, will eventually dissipate to a maximum state, called
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'thermodynamic equilibrium'. In another example, a machine, itself a closed system - closed
to matter, but open to energy - also abides by the Second Law. Even with inputs of energy
(i.e. fuel), without material inputs (i.e. parts), the machine eventually breaks down, and then
corrodes over time; again, its potential energy will dissipate over time. Yet, open systems,
such as living organisms and societies, concentrate energy and create structure, which
appears to contradict the Second Law. They are said to maintain a state 'far from
thermodynamic equilibrium' (Prigogine & Stengers, 1984) because they are open - open to
energy and matter, which they metabolize with an internal structure capable of doing do
(Schroedinger, 1944; von Bertalanffy, 1968).
Complex systems
Some researchers claim that the system characteristics of self-stabilization, hierarchy,
and especially, self-organization (Kay, 2000) define system complexity, which is a function
of the system's interacting components or 'parts' (Allen, 1997; Laszlo, 1972; Nicolis &
Prigogine, 1989). System complexity can also be measured by the information required to
describe the system (Bar-Yam, 1997a) and characterized by both the amount of energy used
by (Brooks & Wiley, 1986; Adams, 1988) and stored in the system (Kay, 2000), and the
complexity profile of coherent behaviors at various scales (Bar-Yam, 1997b; 2004). Despite
these various approaches to system complexity, by definition, a complex system must be an
open system. Bertalanffy states, "Self-differentiating systems that evolve toward higher
complexity (decreasing entropy) are, for thermodynamic reasons, possible only as open
systems ..."(1968, p. 97).
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Self-stabilization & hierarchy
Complex, open systems can maintain steady states despite internal fluctuations and
perturbations imposed on them by their external environment. This is what is meant by
adaptive self-stabilization (Laszlo, 1972). Self-stabilization is necessary for complex systems
to maintain consistent relationships with changing external environments in the context of in
and outflows of information, energy, and matter. They do so by constantly adjusting and
readjusting to dynamic external conditions (Weiner, 1948). "Thus, ordered wholes, by virtue
of their characteristics, are self-stabilizing, in or around, steady states. Given unrestrained
forces introducing perturbations which do not exceed their threshold of self-stabilization,
they will tend to return to their enduring states prescribed by their constant constraints"
(Laszlo, 1972, p. 40).
Hierarchy theory subscribes to the concept that complex systems are comprised of
semi-autonomous subsystems whereby only some information, energy, and matter is
exchanged between subsystems and between subsystems and their larger supersystems
(Simon, 1973; Holling, 2001). "As long as the transfer from one level to another is
maintained, the interactions within the levels themselves can be transformed ... without the
whole system losing its integrity" (Holling, 2001, p. 392). Hierarchic levels may also be
defined as 'holons' (Laszlo, 1972; Koestler, 1978) to reflect the mutual interactions and
interdependence between systems and the 'supersystem' in which they are nested, and the
'subsystems' of which they are comprised. Hierarchical (used interchangeably here with
holon) systems are particularly capable of re-organizing internally to maintain system
integrity, despite perturbations generated internally or in the environment (Simon, 1973).
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Self-organization
Complex systems are adaptive and self-organizing (Laszlo, 1972; Prigogine &
Stengers, 1984), whereby their internal structures may reorganize to meet changes in their
external environment. Self-organization involves external perturbations (Laszlo, 1972;
Prigogine & Stengers, 1984), inherent system instability (Prigogine & Stengers, 1984; Casti,
2002), and internal conditions (Ashby, 1962; Laszlo, 1972). The external environment to the
system generates a constant flux of energy and matter, which the system absorbs and/or to
which it reacts. However, at times, the external environment may impose forces or
incremental, yet persistent, perturbations. These external forces or perturbations, together
with naturally occurring internal fluctuations (i.e. irregularities or instabilities) (Prigogine &
Stengers, 1984), can build to a certain threshold, above which, the system spontaneously re-
organizes. Therefore, not only is the new, self-organized system defined by the new
constraints of the external environment, but it is also subject to its 'original' internal
conditions (Laszlo, 1972; Prigogine & Stengers, 1984).
Thermodynamics of Open Systems
Complex, open systems are characteristically hierarchical, self-stabilizing, and self-
organizing, whereby the system's emergent properties are not found in its parts (von
Bertalanffy, 1968; Laszlo, 1972; Casti, 2002; Macy, 1991). Moreover, the thermodynamic
processes, i.e. those involving heat, work, and energy in the context of the First and Second
Laws, of open systems are unique. Unlike closed systems, which typically tend toward
thermodynamic equilibrium over time, e.g. a tin can will rust, open systems process
throughput energy to maintain states far from equilibrium with their external environment.
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Researchers have studied the thermodynamic processes governing complex, open systems in
both non-living and living systems (e.g. Nicolis & Prigogine, 1989; Kay, 2000; Kauffman,
2000).
The following section discusses in more detail the following thermodynamic
concepts, 1) dissipative structures, 2) entropy, and 3) exergy.
Dissipative Structures
Internal structure or organization is essential for open systems to remain in steady-
states far from thermodynamic equilibrium despite the constant influx of energy and matter
from the system's external environment. For internal structure to emerge in observance of the
Second Law of Thermodynamics, organization within the system must be counterbalanced
by energy dissipation, which necessarily must occur in its external environment. Chemist Ilya
Prigogine, whose work with the thermodynamics of open chemical systems earned him the
Nobel Prize in 1977, refers to these open systems as 'dissipative structures' (Prigogine et al.,
1977; Prigogine & Stengers, 1984; Nicolis & Prigogine, 1989). As dissipative structures,
open systems build internal 'structure' by 'dissipating' energy into the external environment.
Dissipative structures are open systems, which necessarily exist far from
thermodynamic equilibrium, and, therefore, can become unstable from external or internal
perturbations. The system undergoes qualitative structural change or 'symmetry breaking'
when the naturally occurring fluctuations within the system amplify to a critical point, called
the 'bifurcation point'. Under a threshold defined by the bifurcation point, the integrity of the
system is maintained as the perturbations are absorbed by the internal structure of the system.
However, over this threshold, the perturbations accumulate; internal fluxes amplify through
positive feedback, driving the system farther from equilibrium. When this occurs, the internal
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structure of the open system spontaneously restructures. The state to which it restructures is
constrained by the system's internal conditions, its past structures, and its interaction with the
specific set of perturbations (Prigogine & Stengers, 1984). System dynamics are driven by
nonlinear processes that are characteristically irreversible and sensitive to initial conditions.
Take, for example, the affect of an influx of energy on a fluid. As explained by
Nicolis and Prigogine (1989), heat enters a fluid sitting on a hot plate; it first moves from
molecule to molecule (conductively). Then, with more energy added to the system, that
structure destabilizes. Yet, given the right gradient conditions, beyond the bifurcation
threshold, spontaneous coherent behavior occurs. Called Benard cells, these convection cells
are structured in coherent patterns - one turning clockwise, the next one counterclockwise,
and so on (Nicolis & Prigogine, 1989). The new pattern results directly from the influx of
heat energy, but it is also an inherent outcome of its original internal conditions (Nicolis &
Prigogine, 1989). With yet more energy to the system, at another bifurcation point, the
convection cells break down in preference of 'turbulence', a chaotic state (Nicolis &
Prigogine, 1989). Therefore, resulting from increased energy to the system, each state change
becomes more dissipative - from random, to structured (i.e. complex), to chaotic. Dissipative
structures, which may be living, such as a cell, or non-living, such as a heated fluid (Nicolis
& Prigogine, 1989), or highly complex, such as a society (Adams, 1988) or an ecosystem
(Kay, 2000), are simultaneously self-organizing and energy-dissipating.
Entropy
Dissipative structures, which may be living or non-living, dissipate energy into the
external environment while at the same time generating structure or organization internally.
Energy dissipation is defined here as increasing entropy production (Prigogine & Stengers,
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1984); it is described by Equation 1. The 'entropy' produced to maintain or increase a
system's organization can be called the 'entropy debt' of system complexity (Dyke, 1988;
Straussfogel & Becker, 1996). Entropy debt emphasizes the dissipative cost to energy
gradients and material structure of the external environment incurred at the expense of
maintaining or increasing system complexity.
Entropy is degraded energy, which is, ultimately, 'non-consumable' (Salthe, 2007).
The Second Law dictates that entropy is increasing in the universe, which is assumed to be
an isolated system, meaning, it is closed to an influx of matter and energy. This means that,
due to natural processes, the energy of the universe is degrading, i.e. entropy is increasing,
and that the universe is tending toward thermodynamic equilibrium. While entropy is
increasing in the universe, open systems appear to defy the Second Law by increasing their
internal organization, thereby decreasing their entropy. However, the low entropy embodied
in an open, self-organized system is 'borrowed' from its environment. In a self-organized
state, an open system performs work in the process of 'building' the internal organization.
Self-organization occurs because work can be and is performed by the system. Work occurs
when consumable energy is available to a system capable of utilizing it. When energy is
utilized, work is performed, and when work is performed, entropy results (Salthe, 2007).
While the open system eventually returns to equilibrium with its external environment, the
consumable energy used in and for its self-organization is permanently lost.
Laszlo (1972) states, "Every system produces entropy relative to time" (p. 44). One
equation for describing the entropy of an open system is:
dS = dSe + dS; [1]
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This equation (Prigogine & Stengers, 1984, p. 118) denotes that, for an open system, the
overall rate of entropy change (dS) is equated to the sum of entropy flux (dSe) (i.e. from the
flow of energy to and from the system) and the energy dissipated due to the irreversible
processes of the system itself (dSj). An irreversible process is one in which an energy
gradient, once 'consumed' to perform work, can be recreated but cannot be regained.
When the overall value of entropy of the system (dS) is positive, the system is in a
state of progressive disorganization e.g. a vacating inner city neighbourhood or a decadent
forest. When the overall value of entropy of the system (dS) is negative, the system is in a
state of progressive organization (Laszlo, 1972), e.g. a gentrifing neighbourhood or a
maturing ecosystem. Again, the overall entropy (dS) of a system is the sum of entropy
generated by the system internally (dSj), and the entropy generated by the system because of
the inflow of energy from outside the system (dSe).
To explain dSe, a system may experience a positive or negative flux of entropy. For
example, when a system experiences a negative entropy influx, it is building internal
organization. In other words, the entropy of the open system is decreasing (as energy is
garnered by the system). Schroedinger (1944) calls this importing 'negentropy'. As explained
earlier, "[this] does not violate the Second Law of thermodynamics since the decrease in
entropy within an open system is always offset by the increase of entropy in its
surroundings" (Laszlo, 1972, p. 44). Alternatively, when the system experiences a positive
entropy influx, it is losing internal organization. In other words, the entropy of the open
system is increasing (as energy is flowing back to the environment). Therefore, dSe can be a
positive or a negative number.
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To explain dS,, an open system must, by definition, produce entropy via the
irreversible processes of its internal organization. Therefore, the value of dSj must always be
positive. This is because the irreversible processes of heat loss, friction, and heat conduction
are necessary processes of the system. "In other words, d,S [dSJ can only be positive or
vanish in the absence of irreversible processes" (Prigogine & Stengers, 1984, p. 118). The
differences in type and amount of dSi entropy production of a system depend upon its
internal structure and the processes by which it is bound to its surroundings.
Exergy
Exergy, from the Greek 'ex' meaning out of and 'ergy' meaning work (Scott, 2003),
is the energy that is consumed to perform work. In complex open systems, exergy is
degraded in the performance of work to generate, maintain, and increase internal structure.
Exergy is the energy that is effectively used by the system. Alternatively, one can say,
"Exergy is a measure of the quality of energy" (p. 6). Yet, exergy is not synonymous with
energy. Most fundamentally, while energy is conserved (First Law), exergy is not. Thus,
while energy does not explain the quality of energy used to perform work, exergy does.
Therefore, energy at equilibrium does not have exergy and stated reciprocally, exergy cannot
exist without differences in equilibrium (i.e. gradients relative to the environment) (Scott,
2003).
In the performance of work, high quality energy is degraded to lower quality energy;
systems are not one hundred percent efficient. Scott states, "It was Sadi Carnot who
discovered Nature's law that there is an upper limit to the fraction of heat that could be
converted to work even by a perfect machine ... [and] all energy conversion processes -
from nuclear power plants to photosynthesis and metabolism" (Scott, 2003. p. 370).
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Moreover, exergy degradation is directly proportional to entropy production (Bejan, 2002).
Exergy degradation is fundamental to the internal structure or organization of open systems
and to life itself (Bejan, 2002; Kay, 2000). Reciprocally, open system structure or
organization must have the capacity to extract exergy (Scott, 2003).
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APPENDIX B - Data preparation
Appendix B explains in detail the available data considered for this research and the
actions required to generate the final surrogate data sets for the framework parameters:
energy throughput, community entropy debt, and community complexity.
Summary of Available Data
Identifying the available data involved, a) considering all data sources, b) matching
available data to the preferred data, and c) selecting data from two points in time. The
sources for the available data included governments and private consultants. As these sources
collected their data with different purposes than those stipulated for this research, this section
explains how their data matched the surrogate measures preferred for this thesis research.
Available energy throughput data
Overview of data sources
The surrogate measures of energy throughput for this research were provided by an
energy consumption study conducted by SENES Consulting Ltd. (SENES) and authored by
McEwen and Hrebenyk (2006a), on behalf of the Capital Regional District (CRD) (i.e. the
regional government), within which the five selected municipalities resided. The SENES
study compiled energy consumption data from a variety of sources, from estimates based on
Canada Census data to energy consumption rates retrieved directly from energy providers for
1995, 2004 and 2012. The SENES energy consumption study (McEwen & Hrebenyk,,
2006a) was conducted in conjunction with the SENES air emissions study of municipalities
in the CRD (McEwen & Hrebenyk, 2006b).
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Matching available to preferred energy throughput data
More specifically, the study (McEwen & Hrebenyk, 2006a) listed energy
consumption data from point (i.e. from single and specific), area (i.e. from non-specific), and
mobile (i.e. from transportation/moving) sources. These data were further attributed to the
following economic sectors: industrial, commercial, and residential. Although the study did
not define 'industry' per se, it associated large industrial fixed facilities with point source
emissions (McEwen & Hrebenyk, 2006b, p. 1). This thesis research assumed that roughly the
same industrial facilities were represented in the energy consumption data as were
represented in the air emissions data. This was a reasonable assumption, as relatively few
industries existed in the CRD in 1995 and 2004 (McEwen & Hrebenyk, 2006a). Likewise,
although the SENES study did not formally define 'commercial', it specified that any source
of emission or energy consumption that could not be attributed to a fixed point was
considered an area source emission (2006b, p. 2). Forms of energy inventoried were: hydro-
electricity, fossil fuels, and wood fibre (McEwen & Hrebenyk, 2006a). Both emissions and
energy consumption data focused on transportation, building space heating, and landfill
activities (McEwen & Hrebenyk, 2006a; 2006b). The landfill data were omitted at the outset
since solid wastes were collected from a circumscribed region with no means to apportion the
data to the municipalities.
The energy consumption inventories (McEwen & Hrebenyk, 2006a; 2006b) were
compiled for the year 2004, and, typically, from consumption rates supplied by the energy
providers. In the absence of actual energy consumption data, the authors estimated
consumption rates. For example, energy consumption rates for the year 1995 were often
estimated. Starting with regional totals (i.e. actual or estimated) the authors often applied
155
'allocation ratios', which was based on a surrogate means of apportioning the municipal data.
The apportioning surrogates used for the inventory included numbers of individuals (i.e.
municipal population size), dwelling types, and number of kilometers travelled. For the
purposes of the inventory report, data were also forecasted to the year 2012; however, the
forecasted data were not considered in this research, as they were the least reliable of the
inventory data. The backcast 1995 data was used in this research to provide a second data
point in time, which was necessary in order to reveal changes in energy consumption (and
community entropy debt) over time. While this research necessarily pursued an
understanding of the methods employed by SENES to construct the energy and emissions
data used, the accuracy of SENES's data (McEwen & Hrebenyk, 2006a; 2006b) were not
audited.
In summary, the SENES inventory (McEwen & Hreenyk, 2006a) provided data for
hydro-electric, fossil fuel, and wood fiber energy consumption, which matched the preferred
data set (i.e. hydro-electricity, fossil fuel and wood fiber) for the framework parameter,
energy throughput. Moreover, data was selected for the year 1995 and 2004.
Available community entropy debt data
Overview of data types considered
Environmental data were available from different levels of government (i.e. regional,
provincial, federal) and their departments (e.g. BC Ministry of Environment, Environment
Canada) as per regulatory requirements under British Columbia's Environmental
Management Act, 2004 and Canada's Canadian Environmental Protection Act, 1999. These
data were either estimated or direct measures. Estimated measures included sample (e.g.
156
collected in the natural environment, such as air emissions), estimated (e.g. using emissions
factors), or reported (e.g. permitted) data. Direct measures included such measures as loss of
hectares of ecosystems over time.
More specifically, sample-data estimating quantities of ambient pollutant types, could
not be attributed to a specific municipality and, therefore, could not be used in this research.
Emissions factors, another method used to estimate air emissions, are estimated average
masses of air emissions associated with a given unit of activity (e.g. litre of fuel burned,
number of cattle raised) (USEPA, 2008). Emission factors have been developed to facilitate
estimations of emissions over large areas, such as regions, provinces, and countries (USEPA,
1995; GVRD, 2002). Providing that environmental data derived from emissions factors were
estimated consistently for each activity per municipality, they were considered for this thesis
research. Reported data, such as those required by provincial or federal legislation to be
reported or require a permit were also considered. Notably, permitted emissions data were
somewhat problematic to use as surrogate measures of community entropy debt since
industrial emitters may have emitted more or less of the amount for which they had been
permitted.
Regarding direct measures of anthropogenic environmental degradation, these were
less common; however, they did exist. For example, data regarding the number of hectares of
sensitive ecosystems lost over a period of time or the tonnes of waste generated per year were
considered.
Overview of data sources
Surrogate measures of energy throughput for this research were provided by, a) the
National Pollutant Release Inventory (NPRI), an emissions reporting program administered
157
(and required) by Environment Canada, a jurisdictional body of the Government of Canada,
b) the BC Ministry of Environment (BCE), a jurisdictional body of the Province of British
Columbia, c) SENES Consulting Ltd. (SENES), and d) AXYS Environmental Consulting
Ltd. (AXYS). Other data were available, but were not suitable for the purpose of this
research. The extent to which the data from these sources matched the preferred data is
discussed in more detail in the following section.
Matching available to preferred community entropy debt data
More specifically, the NPRI required certain point source industries, that is, those
defined according to annual working-hours and use of substance-type, to report their
emissions within set limits (EC, 2007a). The NPRI was not a voluntary program as legislated
according to the Canadian Environmental Protection Act, 1999 (CEPA 1999), subsection
46(1). This source (i.e. NPRI) provided this research with reported emission amounts from
point source facilities within the selected municipalities that were required to report
emissions. These data were largely heavy metals and human-made substances. The heavy
metals for which there was available data that overlapped with Azar et al.'s (1996) indicators
of substances that were accumulating unsustainably in the ecosphere included, cadmium,
lead, copper, and mercury. However, other metals and human-made substances reported to
the NPRI were also assumed to be accumulating in the ecosphere unsustainably.
The BC Ministry of Environment (BCE) required emitters to apply for emissions
permits, within the prohibitions and standards of the Waste Discharge Regulation (WDR)
and the Hazardous Waste Regulation (HWR) given by the Environmental Management Act,
2004 (BCEMA, 2004). Permits were required for air, effluent, and refuse waste (BC
Environment, 2007) of substance types defined in Schedule 1 of the WDR (July 8, 2004).
158
This research requested all permit application data from the BCE's Permit Administration
System (WASTE database) of the Environmental Management Branch (Business Services)
for the five communities under investigation. It did so for the years 1995 and 2004. This
research assumed that all permit holders were point source emitters. This source (i.e. BCE)
provided this research with air, water, and land emissions data from point sources. These data
largely represented criteria air contaminants and heavy metals.
SENES completed an air emissions inventory (McEwen & Hrebenyk, 2006b), together
with the previously discussed energy consumption inventory (McEwen & Hrebenyk, 2006a),
for the Capital Regional District. This study (McEwen & Hrebenyk, 2006b) collated point
(i.e. from single and specific sources), area (i.e. from non-specific sources), and mobile (i.e.
from transportation/moving sources) criteria air contaminants and greenhouse gas emissions
data as per the guidelines of the Partners for Climate Protection Program (PCPP) of the
Federation of Canadian Municipalities (FCM, 2008). The emissions inventory (McEwen &
Hrebenyk, 2006b) provided data for substances (i.e. criteria air contaminants - CACs and
greenhouse gases - GHGs) that overlapped with those substances Azar et al. (1996)
calculated were accumulating unsustainably world-wide. The contaminants that overlapped
were: C02 , CH4, N 2 0 (i.e. GHGs) and NOx, NH3, SOx, and VOC (i.e. CACs). Yet, some
assumptions were made. Although NH3 was said to be accumulating in the technosphere
(meaning, within society), but not yet in the ecosphere (Azar et al., 1996, p. 97), this research
included this substance in the list of surrogate measures of community entropy debt. It did so
because it assumed that this substance would eventually leak into and accumulate in the
ecosphere. Additionally, VOCs refer to a rather large category of volatile organic
compounds, many of which are naturally occurring in the environment and some of which
159
are human-made substances. Therefore, this research assumed that all anthropogenic VOCs
reported as criteria air contaminants also represented those contaminants that contribute to
anthropogenic environmental degradation. The SENES air emissions inventory provided this
research with comparable criteria air contaminant and greenhouses gas emissions data from
point, area, and mobile sources for each of the selected municipalities.
Finally, AXYS generated an inventory of hectares of sensitive ecosystems on eastern
Vancouver Island from 1992 to 2002. It further calculated the area of these ecosystems that
were lost due to population growth and economic development. It provided data for the
Capital Regional District and its municipalities. This source (i.e. AXYS) provided this
research with loss of sensitive ecosystem data.
Other data were available, but were not suitable for this research. Suitable data could
not be retrieved for water emissions (area or mobile) through the Capital Regional District's
(CRD) storm sewer data collection program. The sample collection methods were neither
consistently collected nor municipal-specific. All other available emissions data (i.e. BCE's
Environmental Monitoring System (EMS)) represented ambient sample data that could not
be attributed to any specific municipality. Additionally, consumer waste data could not
reliably be allocated to each municipality; therefore, they were not used (CRD, 2004).
Finally, the CRD required permits for contaminants listed in the Environmental Management
Act that were emitted at a volume lower than the standard set under the provincial HWR
(CRD, 2006). However, the CRD did not permit specifically for each substance type, nor
were the data publically available; therefore, these data could not be used for this research.
In summary, the available community entropy debt data did not match all the data of
its preferred set. The preferred data set consisted of loss of ecosystem productivity and air,
160
water, and land emissions from point, area, and mobile sources as guided by Azar et al.
(1996) indicators of sustainability. Yet, the available data consisted of air (point, area, mobile
sources), water (point source), and land (point source emissions). Furthermore the
sustainability indicators (Azar et al., 1996) included natural and human-made substances
accumulating in the ecosphere at rates greater than their natural flows. The available data that
overlapped the preferred data were criteria air contaminants, heavy metals, and human-made
substances. Of these substances, data were relatively abundant for criteria air contaminants
and greenhouse gases. By contrast, the available data were limited for metals and human-
made substances. Loss of ecosystem productivity was represented with SEI data.
Available community complexity data
Community complexity data were provided by Statistics Canada and British
Columbia Statistics. Municipal websites provided historical information about the selected
community systems.
Statistics Canada provided this research with consistently collected socio-economic
and community profile data for each of the municipalities comprising the community systems
under investigation in this research. Moreover, these data were collected for 1996 and 2001.
While Census data were collected using consistent methods from one census period to
another, some changes occurred in the categories within which the data are collected from
one Census to the next. For example, industrial classification sectors changed from the 1996
Canada Census to the 2001 Canada Census.
Other data sources included BC Stats and municipal websites. BC Stats data were
used in this research; however, their use was limited as some of their municipal data were
161
agglomerated. Capital Regional District and the individual municipal websites provided this
research with historical information, such as incorporation dates.
In summary, the socio-economic and community profile statistics provided by
Statistics Canada and BC Stats matched the preferred community complexity data. It did so
by providing, a) social, economic, and demographic descriptive statistics to describe
municipal populations, b) commuting trends to describe municipal population activities, c)
types of dwellings and length of roadways to describe infrastructure, and d) labour force
composition to estimate municipal descriptions, such as economic structure. Additionally,
these data were available for the years 1996 and 2001.
Summary of Final Data
Compiling the final data sets involved, a) improving the available data to meet the
data selection criteria, if and where possible, and b) considering assumptions regarding the
available data, which included estimating the non-basic economic activities per sector and
applying it, as a multiplier, to the final data.
Establishing the final data from the available data involved considering the data
selection criteria, as given, to be 1) municipal-specific (so that the data were comparable), 2)
indicative of 'societal activities', 3) consistently collected for all selected communities, and
4) consistent with the theoretical principles associated with the model parameters. Data were
accepted, omitted, or improved. Furthermore, establishing the final data from the available
data involved considering the assumptions regarding the data. At the outset, it was assumed
that a) all residential activities were community system serving, and b) commercial and
industrial activities serving the community systems could be estimated using percentages of
the non-basic (i.e. for local demand) versus the basic (i.e. for export) sectors of the municipal
162
economies (Table C2).
Energy throughput data - actions required
McEwen and Hrebenyk (2006a) collated hydro-electricity, natural gas, light fuel oil,
and LPG (liquid petroleum gas) usage data for the 13 municipalities in the CRD for
industrial, commercial, and residential building space-heating. Additionally, the authors
collated fossil fuel usage data for vehicular transportation. They collated these data from a
variety of sources discussed further in the following subsections, a) building space-heating,
b) transportation and, c) other. As the authors collated energy consumption data with the
intention of providing municipal estimates, and, further, in the absence of auditing the data
collection methods, it was assumed that all the data were consistently collected. Detailed
energy throughput data are found in the Appendix C in Table C3 and Table C4.
Building space-heating
For hydro-electricity consumption, the authors reported that BC Hydro supplied
municipal-specific direct electricity consumption data for building space heating for the years
1995 and 2004 (McEwen & Hrebenyk, 2006a, p. 5). Therefore, no action was required for
the hydro-electricity building space-heating data. Both 1995 and 2004 data were direct
measures. Moreover, they were municipal-specific and were indicative of human activities.
For natural gas consumption, Terasen Gas supplied municipal-specific natural gas
consumption data for building space heating for 2004 only. However, the authors estimated
natural gas consumption for the CRD for the year 1995 based on year 2000 consumption
rates and anecdotal information from Terasen Gas, as records for 1995 were not available
(2006a, p. 5). The authors estimated 1995 natural gas consumption rates, per municipality, by
163
using the same ratios of natural gas rates consumed, per municipality, in 2004. This required
the assumption that, although access to natural gas was limited in the CRD in 1995, natural
gas was equally available to all municipalities. In a telephone conversation with H. Mertins,
Business Development Manager, Terasen Gas stated that it was likely that, by 1995, all
municipalities in the CRD had access to natural gas since main pipelines were being installed
to large institutional customers from 1990 to 1993 (personal communication, February 21,
2008). Therefore, no action was required for the natural gas building space-heating data as
2004 data were direct measures, 1995 data were reasonably estimated, and all data were
municipal-specific and indicative of human activities.
For light fuel oil consumption, the authors reported that actual light fuel oil
consumption rates for building space heating of CRD municipalities were not available from
suppliers (McEwen and Hrebenyk, 2006a). As such, the authors, first, estimated 2004
consumption rates for the CRD as a whole from, a household survey conducted by Natural
Resources Canada (NRCan) in 2003, dwelling types likely to be using fuel oil in the CRD
from Statistics Canada (2001), and an average consumption rate provided by Terasen Gas
(2006b, p. 45). Light fuel oil consumption was further estimated for 1995 from discussions
with energy providers, who estimated that 75 percent of households using natural gas in 2004
were using light fuel oil in 1995 (p. 7). Second, the authors allocated the total light fuel oil
consumption for the CRD for 2004 and 1995 to the municipalities by population. It was
assumed that apartment dwellings were not heated with light fuel oil (p. 6) and that there
were no significant commercial and industrial light fuel oil consumers for building space-
heating (p. 5). While this thesis research considered light fuel oil consumption rates for the
CRD in 2004 and 1995 to have been reasonably estimated, the rates were not adequately
164
municipal-specific because municipal rates were allocated based on housing types general to
the CRD. Therefore, the action required for the building space-heating light fuel oil
consumption data was to determine the municipal-specific breakdown of light fuel oil
consumption using housing-type data per municipality in lieu of housing-type data per CRD
allocated by municipal populations. Table C5: Estimated residential fuel oil consumed (GJ)
per municipality: 1995, 2004, calculated fuel oil consumption rates per municipality by a)
removing the apartments from dwelling totals per municipality, b) multiplying these dwelling
totals by the estimated percentage likely to be heated by fuel oil (i.e. 47%, from McEwen and
Hrebenyk, 2006a, p. 6), which generated a new allocation ratio specific to dwellings likely to
be heated by light fuel oil per municipality and, d) applying the revised allocation ratio to the
five municipalities.
For LPG, the authors reported that actual LPG consumption rates for building space-
heating in the CRD were not available from energy providers (McEwen & Hrebenyk, 2006a).
As such, the authors estimated LPG consumption data for residential space heating using data
from the 2000 British Columbia emissions inventory (Wakelin & Rensing, 2004). Again, it
was assumed that LPG use for commercial and industrial space heating was negligible
(2006a). However, the LPG consumption totals for the CRD were allocated to each
municipality in 1995 and 2004 by population (p. 5). Therefore, the action required for these
data was to omit them in the absence of a municipal-specific apportioning surrogate.
For wood fiber consumption, the authors reported that consumption rates in the CRD
were estimated using a study conducted by BC Environment (BCMWLP, 2005). The data for
the CRD were then allocated to each municipality/electoral district using a ratio based on
165
population (p. 7). Therefore, the action required for these data was to omit them in the
absence of a municipal-specific apportioning surrogate.
Finally, the authors converted all energy consumption data to gigajoule base units
using conversion factors published in the U.S. Environmental Protection Agency (USEPA)
AP-42, Fifth Edition: Compilation of Air Pollutant Emission Factors (USEPA, 1995). The
conversion factors (2006a, p. 4) allowed data with different based units to be reported with a
common base unit.
Transportation
For vehicular fossil fuel consumption, McEwen and Hrebenyk (2006b) reported that
consumption rates in the CRD were estimated with vehicle distances traveled in each
municipality provided by the Capital Regional District in 2001, fuel tax receipts provided by
the Province of British Columbia (p. 50) for 1996 and 2004, and the USEPA MOBILE
model. McEwen and Hrebenyk (2006a) estimated vehicle kilometers traveled from fuel
consumption from fuel tax receipts and compared these data with the results of the MOBILE
model and the CRD's traffic simulation model called EMME/2 with 2001 traffic volumes,
including the results of a household survey of vehicle use on local roadways, provided by the
Capital Regional District. Of note, the CRD's traffic simulation model (EMME/2) accounted
for municipal differences in distance and types of routes: arterial and collector routes and
minor streets (2006b, p. 55). Moreover, fuel tax receipts accounted for differences in fuel
efficiency (2006a, p. 10). However, this research assumed that vehicles with high fuel
efficiency (i.e. hybrids, Smart cars) were not widely used in the CRD in 1995 or 2004 as did
the authors (McEwen & Hrebenyk, 2006a). Also of note, travel along provincial highways
was omitted from the municipal vehicle transportation data; it was assumed that local routes
166
more accurately represented travel of municipal populations (McEwen & Hrebenyk, 2006a).
Therefore, no action was required for the transportation fossil fuel consumption for 2004 and
1995. The data were reasonably estimated, municipal-specific, and indicative of human
activities.
No public data were available for fossil fuel consumption for air and marine traffic in
the CRD. Moreover, communications with P. Connolly, Community Relations Coordinator,
Victoria Airport Authority (personal communication, February 21, 2008) confirmed a lack of
air travel data attributable to individuals according to their origin of residence. Similarly, no
public data were available for attributing marine fossil fuel use to any particular municipal
populations. Therefore, this research omitted aircraft and marine fossil fuel consumption
data.
Other
For hydro-electricity consumption of street lighting per municipality, McEwen and
Hrebenyk (2006a) reported that consumption rates were retrieved from BC Hydro for 1996
and 2004 per municipality. Therefore, no action was required for the street lighting data.
Estimating community system portion of energy throughput
Only those energy consumption data that could be attributed to the self-reproduction
of the selected community systems (i.e. they were community system serving) were to be
included in the total energy throughput data sets. This included activities occurring inside the
municipal boundaries associated with the larger socio-political superstructure. Yet, CRD,
provincial, and federal building space-heating data were not discernable in the available
space-heating data. Therefore, these data remained in the energy consumption totals.
167
Community system serving energy consumption associated with transportation was estimated
by omitting energy use from traffic travelling on provincial highways. The community
system serving potion of activities associated with commercial and industrial enterprises
were estimated using percentages of the non-basic (i.e. for local demand) versus the basic
(i.e. for export) sectors of the economy.
Estimates of the non-basic sector of the economy was derived from anecdotal
information provided by the Greater Victoria Development Agency (GVDA)6. The GVDA
provided estimates of percentage contribution to the local economy for the main economic
sectors of the economy in the CRD. These included mining, forestry, agriculture,
manufacturing, utilities, transport and storage, services and advanced technology. This
research further grouped these sectors under the larger economic sectors of commercial and
industrial and averaged their estimated non-basic sector contribution to the local economy.
The average estimated non-basic sector activities were: industrial sector (10%) and
commercial sector (70%). The percentage of activities of the residential sector with
community system serving activities was assumed to be 100% (see Table C2). These
percentages (i.e. non-basic multiplier) were applied to the energy consumption data (see
Table C3 and C4) to establish the final energy consumption data used in this research.
No data were available to this research regarding community system serving activities
occurring outside the municipalities, e.g. hydro-electricity used by an entrepreneur on
business in another part of Vancouver Is.
6 Personal communication with S. Angus, Economic Development Officer, March 8, 2008.
168
Community entropy debt data - actions required
Of the available data that met the theoretical requirements of the framework
parameters, point source data were collated from the NPRI (i.e. reported) and the BCE (i.e.
permitted). Area and mobile source emissions were collated in an air emissions study
conducted by McEwen and Hrebenyk (2006b) (SENES). As each data source involved
different issues requiring different actions to meet the data selection criteria of this research,
the data were addressed by source, a) point source: NPRI and BCE, b) area source: SENES,
and c) mobile source: SENES.
Point source: NPRI & BC Environment (Land, water, air)
As the National Pollutant Release Inventory (NPRI) and British Columbia's Ministry
of Environment (BCE) Permit Administration System (WASTE database) were driven by
different legislations, the Canadian Environmental Protection Act, 1999 and British
Columbia's Environmental Management Act, 2004 respectively, it was possible that facilities
reporting pollutants to the NPRI were also requesting permits from the BCE. Therefore, the
action required here involved checking data from both sources to ensure no data overlapped.
This was completed by comparing the names and addresses of facilities reporting to the
NPRI and with those housed within the WASTE database (i.e. BCE).
The NPRI's list of reportable substances and, therefore, the number of facilities that
were required to report, grew substantially since 2002 (EC, 2006). Therefore, the action
required here involved searching the NPRI for changes to the reporting standards required of
substances in 1995 as in 2004 (EC, 2007b). This involved omitting reported data of
substances whose reporting standards changed between 1994 and 2004. This action is
detailed in Table C8.
169
Permitted point source emissions data were retrieved from BCE's Permit
Administration System (WASTE database). The action required here involved sorting the
permit data per municipality per year (i.e. 1995 and 2004) and further sorting the data by
substance type. This action is detailed in Table C9.
Criteria air contaminants, greenhouse gas, metals, and other substances were grouped
and then summed, per municipality. The non-basic multiplier was applied to the total
emissions as shown in Table C6 and C7 (i.e. 0.1 for industrial sector point source emissions).
The extent to which these permitted and reported data accurately measured actual point
source emissions within each of the municipalities was clearly limited; however, the methods
with which they were generated and assigned to each municipality were consistent and
municipal-specific. Moreover, the data indicated human activities (i.e. 'societal activities'),
and were, therefore, attributable to each municipality, as opposed to indicating the state of
the environment, which could not.
Area source: SENES (air)
The study conducted in the Capital Regional District of air emissions (CACs and
GHGs) by SENES Environmental Consultants Ltd. (McEwen & Hrebenyk, 2006b) was the
source of area and mobile data for this research. Area source emissions represented all those
emissions generated by societal activities, which were not mobile, and which could or were
not measured from any one point source.
McEwen and Hrebenyk (2006b) used emission factors to estimate municipal area
source air emissions of the CRD for these activities: agricultural, outdoor burning, space
heating, landfill, and commercial activities (2006b, p. 10). McEwen and Hrebenyk (2006b)
determined area source emissions in the CRD in 2004 using emission factors presumed to be
170
correct. The authors estimated the 1995 area source emissions by reducing the 2004
emissions by the same fraction as the change in population in the CRD from 2004 to 1995.
McEwen and Hrebenyk (2006b) further estimated regional differences in air emissions by
allocating the estimated air emissions for the CRD as a whole to each municipality using
differences in population, area in farmland (McEwen & Hrebenyk, 2006b, p. 8), or
number/type of dwellings. However, of these methods, the authors often relied on municipal
populations allocate regional emissions to each municipality. This was evident in Table C10,
which shows the municipal allocation method used to distribute the estimated regional air
emission. Therefore, the action required here was to, where possible, reallocate any area
source emissions using a method other than population.
For agricultural activities and the corresponding air emissions estimates, the authors
(McEwen & Hrebenyk, 2006b) allocated regional emissions totals to the CRD municipalities
by applying a ratio of hectares of land within the Agriculture Land Reserve (ALR) per
municipality to all ALR lands within the CRD. This ratio was applied to the estimated
regional total. Therefore, no action was required here.
For incineration and backyard burning, propane and wood consumption (for building
space-heating), and commercial activities, including bulk gasoline storage, asphalt
production, and solvent evaporation, the authors allocated regional total to each municipality
in the CRD using populations. No other method was reasonably available to this research
project to allow for the basis of some other allocation ratio. Therefore, the action required
here was to omit these data.
For light fuel oil consumption and commercial activities including, bakeries, dry
cleaning, metal degreasing, surface coating, and printing inks, the authors allocated regional
171
totals of corresponding emissions to each municipality in the CRD using municipal
populations. Therefore, the required action here was to reallocate the air emissions based on
these activities using another method than population. The revised method of allocating air
emissions from light fuel oil consumption (for building space-heating) to each municipality
based on dwelling-type is detailed in Table C5. The revised method of allocating air
emissions from commercial activities was derived from the number and location of business
enterprises likely to be engaged in the activity. For bakeries, enterprises engaged in baking
per municipality per CRD generated a ratio (# municipal bakeries: all CRD bakeries), which
was then applied to the CRD air emissions corresponding with bakeries. This exercise was
repeated so as to reallocate dry cleaning emissions using a ratio based on dry cleaning
businesses (# dry cleaners per municipality: all CRD dry cleaners), metal degreasing
emissions based on car wash businesses, surface coating emissions based on auto body
businesses, and printing ink emissions based on printer businesses. The results are
summarized in Table CI 1.
Estimates of criteria air contaminants and greenhouse gases emitted by area sources
in the municipalities of this thesis research are summarized in Table C6 and C7. The
estimated non-basic multiplier was applied to each emissions category (i.e. CAC, GHG) per
sector type relevant to area source emissions (i.e. commercial, residential). As such, an
estimated non-basic multiplier of 0.7 for commercial, and 1.0 for residential activities would
apply to their emissions. The area source calculations are expanded in Table C12 and Table
CI3. Again, the extent to which the estimated area source emissions accurately measured all
actual point source emissions within each of the municipalities was limited; however, the
172
methods with which they were generated and assigned to each municipality were consistent,
municipal-specific, and the data indicated societal activities.
Mobile source: SENES (air)
The study conducted in the Capital Regional District of air emissions (CACs and
GHGs) by SENES Environmental Consultants Ltd. (McEwen & Hrebenyk, 2006b) provided
the source mobile data for this research. The authors generated air emissions data for aircraft,
marine, non-road (i.e. small engines), and automobiles using a modeling tool called MOBILE
6.2C. This modeling tool estimated mobile emissions using emissions factors associated with
mobile type. Rail emissions were not available to this research.
For aircraft emissions, the authors (McEwen & Hrebenyk, 2006b) generated
reasonable regional estimate of air emissions from aircraft activities for the Victoria
International Airport and the Victoria Harbour. However, this research found no means to
allocate these emissions to each municipality. Therefore, these data were omitted.
For marine traffic, the authors (McEwen & Hrebenyk, 2006b) compiled estimated air
emissions data of passenger ferries and ocean-going, commercial vessels (e.g. fishing), and
recreational vessels (p. 23). Although it was possible for this thesis research to extract marine
activities more likely to be associated with municipal populations, for example, to separate
recreational and passenger marine activities from ocean-going vessel activities, no adequate
method of allocating these data to the populations of the municipalities was readily available
to this thesis research. Therefore, these data were also omitted.
For non-road activity emissions, the authors (McEwen & Hrebenyk, 2006b) generated
municipal-specific air emissions estimates of agricultural vehicles and airport, commercial,
lawn-care, recreational and industrial equipment. Regional CRD estimates were calculated
173
using another emissions model, similar to the MOBILE 6.2C, called USEPA NONROAD (p.
25). The authors used a variety of methods to allocate regional CRD estimates to each
municipality. For example, air emissions from agricultural vehicles were allocated to the
municipalities using a ratio based on the fraction of hectares of land in the ALR.
Additionally, housing starts in the CRD were used to estimate commercial construction
equipment use. However, for all other equipment types, population was used to allocate these
emissions. A breakdown of these data and their allocation ratios were not available.
Therefore, this thesis research omitted these non road air emissions.
For motor vehicle activity emissions in 2004, the authors (McEwen & Hrebenyk,
2006b) generated reasonable estimates of municipal-specific data (explained in detail in
subsection Transportation of section Energy throughput data — actions required). Of note,
the authors omitted from their estimates vehicle traffic on provincial highways to isolate
'local' municipal traffic from 'through' traffic. This required the assumption that local traffic
did not regularly frequent the provincial highway for local travel. Details of estimated mobile
air emissions associated with municipal populations are found in Table C6 and CI. It should
be noted that the authors (McEwen & Hrebenyk, 2006b) reasonably estimated 1995 motor
vehicle data; however, it was unclear from the methods the authors used how these data were
allocated to the municipalities (p. 14). Therefore, the action required for these data involved
calculating a ratio of allocation based on 2001 traffic volumes (municipal traffic volume:
CRD traffic volume; see Table Cl l ) . The ratio was then applied to 1995 air emissions
estimates.
174
Other
From the study, Redigitizing of Sensitive Ecosystems Inventory Polygons to Exclude
Disturbed Areas (AXYS, 2005), the authors compared air photos taken in 2002 of eastern
Vancouver Island to those taken from 1990 to 1992. The purpose of comparing air photos
was to establish, in hectares, loss of sensitive ecosystems. The ecosystems deemed to be
sensitive was part of an initial study that was conducted in the CRD and funded by provincial
and federal governments, over a five-year period from 1993 to 1997. This initial inventory
established seven ecologically sensitive ecosystems: wetland, riparian, older forest,
woodland, terrestrial herbaceous, sparsely vegetated, and coastal bluff ecosystems (p. i). By
digitizing 1992 sensitive ecosystems, meaning, by establishing their initial area using
computer-based software, it was possible for the authors to calculate their loss by redigitizing
them a decade later. This involved calculating the difference in area per sensitive ecosystem
type per municipality. As these data were municipal-specific, no action was required. These
data are summarized in Table CI 4: Loss of Sensitive Ecosystems, 1992-2002.
Estimating community portion of community entropy debt
Only those emissions and loss of ecosystems data that could be attributed to the self-
reproduction of the selected community systems (i.e. they were community system serving)
were to be included in the total community entropy debt data sets. Yet, emissions associated
with building space-heating of CRD, provincial, and federal buildings could not be removed
from the data. Moreover, emissions and losses to ecosystem productivity associated with
community serving activities, but which occurred outside the municipalities, were not
included in the community entropy debt surrogate data. Emissions from fossil fuel
consumption on provincial highways were omitted to better estimate community system
175
serving transportation activities. This requires the assumption that traffic on local routes
represented patterns of municipal population activities. However, the emissions estimated to
contribute to community system self-reproduction were estimated using a non-basic
multiplier calculated (see Table C2) for the industrial (point source; multiplier 0.1),
commercial (area source; multiplier, 0.7), and residential (residential space-heating, mobile
source; multiplier 1.0) economic sectors. The multipliers were applied to the emissions data
and are reflected in the final community entropy debt data tables.
Community complexity data -actions required
Tables of descriptive statistics were compiled of population (i.e. density,
demographic, income, housing structure), population activities (i.e. commuting patterns),
infrastructure (i.e. roads), and economic structure (i.e. labour force composition) of the
selected municipalities. These data were consistently collected and municipal specific,
therefore, no actions were required. In addition, percentage change over time from 1996 to
2001, are shown. These are tabled in Appendix C as Tables CI5 to C22.
176
APPENDIX C - Data tables
Table CI
Capital Regional District municipalities and electoral areas
Municipality (DM) Electoral Area (EA)
Cen t ra l Saan i ch ( C - S a a n ) Salt Spring Is. (Capital F)
C o l w o o d South Gulf Is. (Capital G)
E s q u i m a l t Juan de Fuca (Capital H, Part 1 and 2)
Highlands
L a n g f o r d
Metchosin
North Saanich
Oak Bay
Saanich
Sidney
Sooke (Capital Subdivision C
prior to 1999)
Victoria
View Royal
Note. Brackets contain names designated by Statistics Canada. From "Capital
Regional District Air Contaminant Emissions Inventory for 2004," by B. McEwen
and D. Hrebenyk, 2006, Prepared for the Capital Regional District by SENES
Consulting Ltd. (Available from the Capital Regional District, 625 Fisgard St.,
Victoria BC, V9W 2S5).
177
Table CIO
Estimate of non-basic economic activities per sector
Sectors and associated
activities
aEst. Non-basic
(%)
activities aEst. Basic activities (%)
bIndustrial sector 10 90
Mining negligible negligible
Forestry negligible negligible
Manufacturing 10 90
bCommercial sector 70 30
Agriculture 85 15
Fishing 30 70
Utilities 100 0
Transport and Storage 100 0
Services 100 0
Adv. Technology 2 98
bResidential sector 100 O
Note. Estimates of non-basic activities as per personal communication with S. Angus, Greater
Victoria Development Agency, March 8, 2008. These were further estimated to non-basic activities
per sector. The breakdown of economic sectors into 'industrial' and 'commercial' are also
estimates. Commercial enterprises do not comprise a 'residential' sector; however, this sector is
listed here to provide this thesis research with an estimate that 100% of residential activities (i.e.
household) are community system-serving (i.e. driving to work, mowing lawns, etc.)
178
Tabl
e CI 1
Det
aile
d 19
95 e
nerg
y co
nsum
ptio
n da
ta: A
ctio
ns ta
ken
of a
vaila
ble
data
& re
sults
per
mun
icip
ality
Munic
ipal
Energ
y C
onsu
mpti
on -
Build
ings,
Tra
nsp
ort
ati
on &
Str
eet
lighti
ng:
1995
Ener
gy
type
per
sec
tor
(GJ)
Act
ion
aN
on-b
asic
Cen
tral
Col
woo
d
Esq
uim
alt
Langfo
rd
Oak
Bay
multip
lier
Saa
nic
h
Build
ing
Ener
gy
Con
sum
ption
(G
J) (
per
ye
ar)
Natu
ral
gas
- re
sidenti
al
None
1.0
21,0
11
18,4
77
15,4
24
20,0
38
46,1
60
Natu
ral
gas
- co
mm
erc
ial
None
0.7
67,7
46
29,1
45
93,2
94
83,8
05
62,5
84
Natu
ral
gas
- in
dust
rial
None
0.1
4,6
90
4,7
08
21,2
46
1,6
40
2,5
73
Light
fuel
oil
- re
sidenti
al
reallo
cate
d b
y
dw
elli
ng-t
ype
1.0
217,6
97
185,8
74
160,2
28
271,1
35
229,6
20
LPG
-
com
merc
ial
om
itte
d
LPG
-
resi
denti
al
om
itte
d
Wood b
urn
ing
om
itte
d
Ele
ctri
city
- r
esi
denti
al
None
1.0
342,1
09
255,4
73
243,0
65
330,9
72
334,3
99
Ele
ctri
city
- c
om
merc
ial
None
0.7
86,5
41
66,0
45
87,2
46
74,6
02
71,3
86
Ele
ctri
city
- i
ndust
rial
None
0.1
3,0
07
687
19,2
99
2,9
91
109
Tra
nsp
orta
tion
Ener
gy
Con
sum
ption
(G
J) (
per
km
tr
avel
ed)
Gaso
line
& d
iese
l N
one
1.0
417,2
73
279,8
08
300,1
37
477,1
15
278,2
34
Str
eet
lighting
(GJ)
(per
m
unic
ipal
ity)
Ele
ctri
city
- m
unic
ipal
None
1.0
818
1,1
50
182
829
831
Tot
al G
J Ener
gy
Con
sum
ption
per
munic
ipal
ity,
1995
1,1
60,8
92
841,3
66
940,1
20
1,2
63,1
26
1,0
25,8
96
Tot
al G
J Ener
gy
Con
sum
ption
bper
cap
ita
per
m
unic
ipal
ity,
78
59
55
70
56
1995
Not
e.
bNon
-bas
ic m
ultipl
ier,
est
imat
ed a
s pe
r pe
rson
al c
omm
unic
atio
n w
ith S
. Ang
us,
Gre
ater
Vic
toria
Dev
elop
men
t Age
ncy,
Mar
ch 8
, 20
08.
Per
capi
ta d
ata
from
"M
unic
ipal
pop
ulat
ion e
stim
ates
," B
C S
tats
, 19
95.
Ener
gy c
onsu
mpt
ion d
ata
adap
ted f
rom
"G
reen
hou
se g
as
and e
nerg
y us
e in
vent
ory
for
the
Cap
ital
Reg
ion 2
004,"
by
B.
McE
wen
& D
. H
rebe
nyk,
200
6, S
ENES
Con
sultan
ts L
td.
179
Tabl
e CI 1
Det
aile
d 20
04 e
nerg
y co
nsum
ptio
n da
ta: A
ctio
ns t
aken
of a
vaila
ble
data
& re
sults
per
mun
icip
ality
Munic
ipal
Energ
y C
onsu
mpti
on -
Build
ings,
Tra
nsp
ort
ati
on &
Str
eet
lighti
ng:
2004
Ener
gy
type
per
sec
tor
(GJ)
Act
ion
aN
on-b
asic
Cen
tral
Col
woo
d
Esq
uim
alt
Langfo
rd
Oak
Bay
multip
lier
Saa
nic
h
Build
ing
Ener
gy
Con
sum
ption
(G
J) (
per
ye
ar)
Natu
ral
gas
- re
sidenti
al
None
1.0
84,0
43
73,9
07
61,6
98
80,1
53
184,6
38
Natu
ral
gas
- co
mm
erc
ial
None
0.7
72,0
01
30,9
75
99,1
53
89,0
68
66,5
15
Natu
ral
gas
- in
dust
rial
None
0.1
4,9
84
5,0
03
22,5
98
1,7
43
2,7
35
Light
fuel
oil
- re
sidenti
al
reallo
cate
d b
y
dw
elli
ng-t
ype
1.0
163,8
64
139,9
10
120,6
06
204,0
87
172,8
38
Pro
pane
- co
mm
erc
ial
om
itte
d
Pro
pane
- re
sidenti
al
om
itte
d
Wood b
urn
ing
om
itte
d
Ele
ctri
city
- r
esi
denti
al
None
1.0
410,1
92
275,2
81
271,8
56
422,2
08
358,4
19
Ele
ctri
city
- c
om
merc
ial
None
0.7
108,6
51
79,4
60
91,0
11
151,9
01
71,1
80
Ele
ctri
city
- i
ndust
rial
None
0.1
5,6
44
3,0
48
25,5
68
2,5
96
312
Tra
nsp
orta
tion
Ener
gy
Con
sum
ption
(G
J) (
per
km
tr
avel
ed)
Gaso
line
& d
iese
l N
one
1.0
398,3
79
267,1
39
286,5
47
455,5
12
265,6
36
Str
eet
lighting
(GJ)
(per
m
unic
ipal
ity)
Ele
ctri
city
- m
unic
ipal
None
1.0
1,5
70
956
2,5
01
1,7
36
4,2
54
Tot
al G
J Ener
gy
Con
sum
ption
per
munic
ipal
ity,
2004
1,2
49,3
28
875,6
79
981,5
37
1,4
09,0
03
1,1
26,5
26
Tot
al G
J Ener
gy
Con
sum
ption
bper
cap
ita
per
m
unic
ipal
ity,
2004
76
59
57
65
60
Not
e. aN
on-b
asic
mul
tipl
ier,
est
imat
ed a
s pe
r pe
rson
al c
omm
unic
atio
n w
ith S
asha
Ang
us,
Gre
ater
Vic
toria
Dev
elop
men
t Age
ncy,
Mar
ch 8
, 20
08.
Per
capi
ta d
ata
from
"M
unic
ipal
pop
ulat
ion e
stim
ates
," B
C S
tats
, 20
04.
Ener
gy c
onsu
mpt
ion d
ata
adap
ted fro
m "
Gre
enhou
se g
as
and e
nerg
y us
e in
vent
ory
for
the
Cap
ital
Reg
ion 2
004,"
by
B.
McE
wen
& D
. H
rebe
nyk,
200
6, S
ENES
Con
sultan
ts L
td.
180
Table CIO
Estimated residential fuel oil consumed (GJ) per municipality: 1995, 2004
Municipality aApartm't aTotal bEst. Allocation Est. Est.
in (%) of dwellings dwellings ratio of est. residential residential
total without using # dwellings fuel oil fuel oil
dwellings apt's (#) fuel oil using fuel oil consumed consumed
(#) per (GJ), 1995 (GJ), 2004
municipality
C - Saanich 9 5,400 2,538 0.05469945 217,697 163,864
Colwood 5 4,610 2,167 0.04670359 185,874 139,910
Esquimalt 47 3,975 1,868 0.04025949 160,228 120,606
Langford 5 6,725 3,161 0.06812647 271,135 204,087
Oak Bay 26 5,695 2,677 0.05769521 229,620 172,838
CRD Total 30 98,720 46,399 1 c3,979,873 c2,995,710
(13 DM & 3
EA's)
Note. Total dwellings per municipality minus apartments are multiplied by 0.47 (estimated total of
dwellings in the CRD using light fuel oil), provided by SENES, to calculate a ratio of estimated
dwellings using fuel oil, per municipality in the CRD. The allocation ratio is applied to ctotal fuel oil
consumption data provided by SENES Consulting Ltd. (p. 9), to generate light fuel oil consumption
in 2004 and 1995, per municipality. aFrom "Profile of Marital Status, Common-law Status, Families,
Dwellings and Households, 2001 Census," Statistics Canada, 2002, Released October 22, 2002.
Statistics Canada Catalogue no. 95F0487XCB2001001. Retrieved on February 14, 2008 from
http://wwwl2.statcan.ca/english/Profil01/CP01/Index.cfm?Lang=. b ' c From "Greenhouse gas and
energy use inventory for the Capital Region 2004," by B. McEwen & D. Hrebenyk, 2006, Prepared
for the Capital Regional District by SENES Consultants Ltd.
181
Tabl
e CI 1
Det
aile
d 19
95 c
omm
unity
ent
ropy
deb
t dat
a: A
ctio
ns ta
ken
of a
vaila
ble
data
& re
sults
per
mun
icip
ality
Com
munit
y Entr
opy
Debt:
1995
Em
issi
ons
type
per
Act
ion
*N
on-b
asic
Cen
tral
Col
woo
d
Esq
uim
alt
Langfo
rd
Oak
Bay
sect
or
(GJ)
m
ultip
lier
Saa
nic
h
aPoi
nt
sourc
e em
issi
ons
(rep
orte
d,
per
mitte
d
- in
dust
. se
ctor
) in
ton
nes
per
annum
per
m
unic
ipal
ity
CA
C
Om
it r
egio
nal em
issi
ons,
0.1
-
-0.1
9
0.0
2
-
GH
G
subs
tanc
es inc
onsi
sten
tly
o.l
M
eta
ls
repo
rted
, an
d o
verlap
q j
Q^
er
from
mul
tipl
e so
urce
s q
IM/A
N
/A
0.0
1
N/A
N
/A
0.0
3
N/A
bAre
a so
urc
e em
issi
ons
(est
imat
ed
- re
s. a
nd c
omm
. se
ctor
) in
ton
nes
per
annum
per
m
unic
ipal
ity
CA
C
Om
it d
ata
allo
cate
d b
y 1.0
; 0.7
187.8
3
60.8
0
37.8
7
38.4
4
30.1
6
GH
G
popu
lation
; im
prov
e i.
O;
0.7
18,1
88.2
1
14,6
30.0
8
24,1
77.1
3
20,0
66.6
7
18,7
95.2
3
Meta
ls
whe
re p
ossi
ble
1.0
; 0.7
N
/A
N/A
N
/A
N/A
N
/A
Oth
er
1.0
; 0.7
N
/A
N/A
N
/A
N/A
N
/A
cMob
ile so
urc
e em
issi
ons
in t
onnes
per
annum
per
m
unic
ipal
ity
CA
C
Om
it d
ata
allo
cate
d b
y 1.0
4,4
10.1
8
3,1
41.7
5
6,5
64.3
3
5,3
57.1
5
3,1
24.0
7
GH
G
popu
lation
; im
prov
e iq
M
eta
ls
wh
ere
P°
ssib
le
l.o
28,3
91.8
0
N/A
19,0
38.5
1
N/A
20,4
21.7
5
N/A
32,4
63.5
3
N/A
18,9
31.4
1
N/A
Oth
er
1.0
N
/A
N/A
N
/A
N/A
N
/A
Tot
al t
onnes
air e
mis
sion
s per
munic
ipal
ity,
1995
51
,17
8.0
2
36
,87
1.1
5
51
,20
1.2
7
57
,92
5.8
4
40
,88
0.8
7
Tot
al t
onnes
dper
cap
ita
per
munic
ipal
ity,
1995
3.7
2
.6
3.0
3
.2
2.2
Not
e. *
Non
-bas
ic m
ultipl
ier,
est
imat
ed a
s pe
r pe
rson
al c
omm
unic
atio
n w
ith S
. Ang
us,
Gre
ater
Vic
toria
Dev
elop
men
t Age
ncy,
Mar
ch 8
, 20
08,
deta
iled in T
able
C2.
aPo
int
sour
ce e
mis
sion
s as
sum
ed e
quiv
alen
t to
ind
ustr
ial se
ctor
and
''Are
a so
urce
em
issi
ons
to c
omm
erci
al a
nd r
esid
ential
se
ctor
s; s
ee T
able
CIO
for
typ
es.
Mul
tipl
ier
calc
ulat
ions
exp
ande
d in T
able
C12
and
C13
; c M
obile
sou
rce
emis
sion
s ex
clud
e pr
ovin
cial
hig
hway
s.
Crite
ria
air
cont
amin
ants
(CAC)
incl
ude
CO
, SO
x, N
Ox,
NH
3, V
OC.
Gre
enho
use
gase
s (G
HG
) in
clud
e C
02,
CH
4, N
20. d
Per
capi
ta d
ata
from
"M
unic
ipal
pop
ulat
ion e
stim
ates
," B
C S
tats
, 19
95. Air e
mis
sion
s da
ta a
dapt
ed fro
m "
Gre
enho
use
gas
and e
nerg
y us
e in
vent
ory
for
the
Cap
ital
Reg
ion 2
004,"
by
B.
McE
wen
& D
. H
rebe
nyk,
200
6, S
ENES
Con
sultan
ts L
td.
182
Tabl
e CI 1
Det
aile
d 20
04 c
omm
unity
ent
ropy
deb
t dat
a: A
ctio
ns ta
ken
of a
vaila
ble
data
& re
sults
per
mun
icip
ality
Em
issi
ons
type
per
sect
or
(GJ)
Act
ion
Com
munit
y Entr
opy
Debt:
2004
aNon
-bas
ic
Cen
tral
Col
woo
d
multip
lier
Saa
nic
h
Esq
uim
alt
Langfo
rd
Oak
Bay
Poin
t so
urc
e em
issi
ons
(rep
orte
d,
per
mitte
d)
in t
onnes
per
annum
per
m
unic
ipal
ity
CA
C
GH
G
Meta
ls
Oth
er
Om
it r
egio
nal em
issi
ons,
0.1
su
bsta
nces
inc
onsi
sten
tly
o.l
re
port
ed,
and o
verlap
q
^
from
NPR
I an
d B
CE
Q
j so
urce
s
N/A
N
/A
0.1
9
N/A
N
/A
N/A
Are
a so
urc
e em
issi
ons
CA
C
GH
G
Meta
ls
Oth
er
Om
it d
ata
allo
cate
d b
y po
pula
tion
; im
prov
e w
here
pos
sibl
e
1.0
; 0.7
1.0
; 0.7
1.0
; 0.7
1.0
; 0.7
192.1
4
21,8
78.9
8
N/A
N/A
64.7
2
17,8
13.6
3
N/A
N/A
42.3
3
27,5
66.2
7
N/A
N/A
42.7
2
23,5
44.2
3
N/A
N/A
39.4
5
26,4
28.8
7
N/A
N/A
Mob
ile s
ourc
e em
issi
ons
CA
C
GH
G
Meta
ls
Oth
er
Om
it d
ata
allo
cate
d b
y po
pula
tion
; im
prov
e w
here
pos
sibl
e
1.0
1.0
1.0
1.0
2,2
51.0
0
1,5
09.0
0
1,6
19.3
0
26,1
54.9
0
17,5
38.6
0
18,8
12.3
0
N/A
N
/A
N/A
N/A
N
/A
N/A
2,5
73.8
0
1,5
01.0
0
29,9
05.7
0
17,4
39.6
0
N/A
N
/A
N/A
N
/A
Tot
al t
onnes
per
munic
ipal
ity,
2004
Tot
al t
onnes
bper
cap
ita
per
munic
ipal
ity,
2004
50
,47
7.0
1
36
,92
5.9
5
48
,04
0.3
8
3.1
2
.5
2.8
56
,06
6.4
5
45
,40
8.9
2
2.6
2
.4
Not
e. *
Non
-bas
ic m
ultipl
ier,
est
imat
ed a
s pe
r pe
rson
al c
omm
unic
atio
n w
ith S
. Ang
us,
Gre
ater
Vic
toria
Dev
elop
men
t Age
ncy,
Mar
ch 8
, 20
08,
deta
iled in T
able
C2.
aPo
int
sour
ce e
mis
sion
s as
sum
ed e
quiv
alen
t to
ind
ustr
ial se
ctor
and
fiA
rea
sour
ce e
mis
sion
s to
com
mer
cial
and
re
side
ntia
l se
ctor
s; s
ee T
able
C10
for
typ
es.
Mul
tipl
ier
calc
ulat
ions
exp
ande
d in T
able
C12
and
C13
; cM
obile
sou
rce
emis
sion
s ex
clud
e pr
ovin
cial
hig
hway
s. C
rite
ria
air
cont
amin
ants
(CAC)
incl
ude
CO
, SO
x, N
Ox,
NH
3, V
OC.
Gre
enho
use
gase
s (G
HG
) in
clud
e C
02,
CH
4, N
20. d
Per
capi
ta d
ata
from
"M
unic
ipal
pop
ulat
ion e
stim
ates
," B
C S
tats
, 20
04.
Air e
mis
sion
s da
ta a
dapt
ed fro
m "
Gre
enho
use
gas
and e
nerg
y us
e in
vent
ory
for
the
Cap
ital
Reg
ion 2
004,"
by
B.
McE
wen
& D
. H
rebe
nyk,
200
6, S
ENES
Con
sultan
ts L
td.
183
Tabl
e CI 1
Poin
t sou
rce
emiss
ions
dat
a re
porte
d to
the N
atio
nal
Pollu
tant
Rel
ease
Inv
ento
ry (
NPR
I) p
er m
unic
ipal
ity
Munic
ipalit
ies
Subst
ance
(to
nnes
per
annum
)
Em
issi
on
of
faci
litie
s fo
r
1995,
2004
Rep
ort
NH
3
VO
C
1,2
,4-
Trim
ethyl
ben
zene
Ben
zene
Eth
ylben
zene
n-H
exan
e Tol
uen
e Xyl
ene
Chlo
rine
3 Y
ear
1995
2002
2003
2003
2003
1999
2003
2003
N/A
1995
C-S
aanic
h
Colw
ood
Esq
uim
alt
Langfo
rd
Ch
art
er'
s
Cre
ek
Oak
Bay
None
Non
e
N/A
*
-
Yes
0.2
None
0.2
2004
C-S
aanic
h
Colw
ood
Esq
uim
alt
Imperi
al
Oil
Langfo
rd
Oak
Bay
None
N/A
*
Yes N/A
*
None
52
0.02
2 0.
108
0.0
25
0.4
9
0.2
95
0.08
8
Not
e. F
acili
ties
rep
orting
sub
stan
ces
but
whi
ch fal
l ou
tsid
e cr
iter
ia o
f th
is r
esea
rch a
re ind
icat
ed b
y N
/A* (
for
exam
ple,
em
issi
ons
from
a
Gov
ernm
ent
of C
anad
a fa
cilit
y);
no s
ubst
ance
s re
port
ed a
re ind
icat
ed b
y 'n
one'
; 3ye
ar s
ubst
ance
was
add
ed t
o th
e N
PRI
list
of r
equi
red
repo
rtab
le s
ubst
ance
s. S
hade
d c
ell in
dica
tes
subs
tanc
e re
port
ed c
onsi
sten
tly
in 1
995 a
s in
200
4. A
ll ot
her
subs
tanc
es w
ere
adde
d t
o th
e N
atio
nal
Pol
luta
nt R
elea
se I
nven
tory
lis
t as
late
as
2003
as
indi
cate
d; t
hese
are
om
itte
d in the
fin
al d
ata
of p
oint
sou
rce
emis
sion
s.
184
Tabl
e CI 1
Poin
t sou
rce
emiss
ions
per
mits
iss
ued
by B
ritish
Col
umbi
a En
viro
nmen
t, pe
r m
unic
ipal
ity
Munic
ipalit
ies
Subst
ance
(to
nnes
per
annum
)
Perm
itte
d
emis
sion
s:
Rep
ort
Crite
ria
Air C
onta
min
ants
(C
AC)
Chlo
rine
Surf
acta
nts
O
il an
d
1995,
2004
CO
N
Ox
S0
X
VO
C
gre
ase
1995
C-S
aanic
h
N/A
*
--
--
Colw
ood
Petr
o-C
an
Yes
0.1
259
Esq
uim
alt
Imperi
al
Oil
Yes
0.0
549
0.2
196
1.5
764
om
itte
d
Langfo
rd
Pay
Less
Gas
Yes
0.0
105
0.0
918
Phelp
s D
evelo
p.
Yes
0.0
373
Oak
Bay
None
--
--
2004
C-S
aanic
h
None
--
--
Colw
ood
N/A
*
--
-
Esq
uim
alt
Imperi
al
Oil
Yes
0.0
549
0.2
196
1.5
764
om
itte
d
Langfo
rd
N/A
*
--
--
Oak
Bay
N
one
--
--
Not
e. F
acili
ties
rep
orting
sub
stan
ces
but
whi
ch fal
l ou
tsid
e cr
iter
ia o
f th
is r
esea
rch a
re ind
icat
ed b
y N
/A* (
for
exam
ple,
em
issi
ons
from
a C
apital
Reg
iona
l D
istr
ict
faci
lity)
; 'o
mitte
d' s
ubst
ance
s ar
e th
ose
whi
ch o
verlap
with s
ubst
ance
s re
port
ed t
o N
PRI
(Tab
le C
6);
perm
itting
sta
ndar
ds a
re a
ssum
ed t
o be
the
sam
e in
199
5 a
s in
200
4; n
ote
that
, w
here
fee
s do
not
app
ly,
load
ing d
ata
is n
ot r
ecor
ded (
i.e.
phos
phat
e);
cont
amin
ants
are
ass
umed
to
exce
ed p
erm
itte
d lim
its
as o
ften
as
they
do
not
mee
t th
em.
185
Table CIO
Activity type, municipal allocation method for associated air emissions, and action required
Year: 1995/2004 Municipal allocation
Selected CRD activities: Area source aMethod Required action
Agricultural treatments - comm. ALR None
Tilling soil - comm. ALR None Land clearing - comm. ALR None
Agricultural burning - comm. ALR None Animal husbandry - comm. ALR None Wind erosion (agricult'l) - comm. ALR None
Inciner. & backyard burning - res. Population / Omit
Natural Gas - res. Energy provider None Natural Gas - comm. Energy provider None
Light fuel oil - res. Population Reallocate as per Table C3
Propane - comm. Population , : Omit
Propane - res. Population Omit
Fireplace; adv. tech. - res. Population Omit
Fireplace; conventional - res. Population Omit Central furnace/boiler- res. Population Omit Fireplace insert, catalytic - res. Population Omit
Woodstoves - res. Population Omit
Bulk gasoline- comm. Population Omit Asphalt - comm. Population Omit
Bakeries - comm. Population Reallocate as per Table C l l
Solvent evaporation - comm. Population Omit Dry cleaning - comm. Population Reallocate as per Table C l l
Glues, adhesives, sealants - comm. Population Omit Metal degreasing - comm. Population Reallocate as per Table C l l
Surface coatings - comm. Population Reallocate as per Table C l l
Printing inks - comm. Population Reallocate as per Table C l l
Note. a Allocation method provided from personal communication with B. McEwen, January 24, 2008. Shaded cells highlight the area source activities allocated by population. Adapted from "Capital Regional District Air Contaminant Emission Inventory for 2004" by B. McEwen and B. Hrebenyk, 2006, p. 10. (Available from the CRD, 625 Fisgard St., Victoria BC, V9W 2S6).
186
Tabl
e C
I 1
Sum
mar
y of
allo
catio
n ra
tios
refle
ctin
g m
unic
ipal
diff
eren
ces
of20
04 a
rea
& 1
995
mob
ile s
ourc
e em
issio
ns
Alloca
tion r
ati
os
eA
ctiv
ity
type
Surr
ogate
use
d
C-
Colw
ood
Esq
uim
alt
Langfo
rd
Oak
Bay
for
allo
cati
on
Saanic
h
Agri
cult
ure
A
LR
0.1
523
0.0
047
0.0
037
0.0
094
0.0
053
aS
pace
heati
ng;
nat.
gas
- re
s.
Consu
mpt'
n d
ata
0.0
591
0.0
519
0.0
434
0.0
563
0.1
297
aS
pace
heati
ng;
nat.
gas
- co
m.
Consu
mpt'
n d
ata
0.0
383
0.0
264
0.1
075
0.0
319
0.0
288
bS
pace
heati
ng;
fuel
oil
- re
s.
Dw
elli
ng t
ype
0.0
547
0.0
467
0.0
403
0.0
681
0.0
577
Com
merc
ial
bakin
g
cBakeri
es
0.0
392
0.0
196
0.0
784
0.0
392
0.0
392
Dry
cle
anin
g
cDry
cle
aners
0.0
256
0.0
513
0.0
769
0.0
256
0.0
769
Meta
l degre
asi
ng
cCar
wash
es
0.2
000
0.0
000
0.0
000
0.1
000
0.0
500
Pri
nti
ng -
inks
cPri
nt
shops
0.0
465
0.0
000
0.0
000
0.0
233
0.0
000
Surf
ace
coati
ng
cAuto
pain
t sh
ops
0.0
714
0.0
714
0.0
000
0.0
000
0.0
000
Inci
ner.
& b
ack
yard
burn
ing
None
availa
ble
Space
heati
ng;
pro
pane
None
availa
ble
Space
heati
ng;
wood
None
availa
ble
Solid
wast
e la
ndfill
None
availa
ble
Bulk
gaso
line
None
availa
ble
Asp
halt
N
one
availa
ble
Solv
ent
evapora
tion
None
availa
ble
Glu
es,
adhesi
ves,
seala
nts
N
one
availa
ble
"Moto
r vehic
le t
raff
ic,
1995
Tra
ffic
volu
mes
0.0
345
0.0
2314
0.0
2483
0.0
3946
0.0
2301
Not
e. 3 A
lloca
tion
s pr
ovid
ed b
y B.
McE
wen
, pe
rson
al c
omm
unic
atio
n, J
anua
ry 2
4, 2
008.
bSee
Tab
le C
3 for
cal
cula
tion
. Allo
cations
(# b
usin
ess
ente
rprise
s pe
r ty
pe p
er m
unic
ipal
ity:
# C
RD
ent
erpr
ises
per
typ
e) fro
m Y
ello
w P
ages
- V
icto
ria,
20
06. d
1995 m
otor
veh
icle
allo
cation
rat
io (
mun
icip
al v
olum
e: C
RD
vol
ume)
bas
ed o
n 2
001 t
raffic
vol
um
es (
McE
wen
&
Hre
beny
k, 2
006b
, p.
55)
. eAre
a so
urce
act
ivity
type
hea
ding
s fr
om "
Cap
ital
Reg
iona
l D
istr
ict
Air C
onta
min
ant
Emis
sion
In
vent
ory
for
2004
" by
B.
McE
wen
and
B.
Hre
beny
k, 2
006,
p.
10.
187
Tabl
e C
I2. E
xpan
ded
deta
ils o
f199
5 ar
ea s
ourc
e co
mm
unity
ent
ropy
deb
t dat
a (n
ot n
orm
alize
d to
per
capi
ta)
Are
a so
urc
e em
issi
ons
Non-b
asic
multip
lier
C-S
aanic
h
Col
woo
d
Esq
uim
alt
Langfo
rd
Oak
Bay
CAC -
res
iden
tial
1.0
13.9
6
11.7
2
10.0
8
16.7
0
15.8
7
GH
G -
res
iden
tial
1.0
12,8
61.6
1
11,0
10.1
8
9,46
4.19
15
,692
.81
14,8
45.9
7
Met
als
- re
side
ntia
l 1.
0
--
--
-
Oth
er -
res
iden
tial
1.0
--
--
-
CAC -
com
mer
cial
0.
7
173.
87
49.0
8
27.7
9
21.7
4
14.2
9
GHG
- c
omm
erci
al
0.7
5,32
6.60
3,
619.
90
14,7
12.9
4
4,37
3.86
3,
949.
26
Met
als
- co
mm
erci
al
0.7
--
--
-
Oth
er -
com
mer
cial
0.
7
--
--
-
Tota
l ar
ea C
AC
187.
83
60.8
0
37.8
7
38.4
4
30.1
6
Tota
l are
a G
HG
18
,188
.21
14,6
30.0
8
24,1
77.1
3
20,0
66.6
7
18,7
95.2
3
Tota
l are
a m
etal
s -
--
--
Tota
l are
a ot
her
--
--
-
Table
CI
3. E
xpan
ded
deta
ils o
f200
4 ar
ea s
ourc
e co
mm
unity
ent
ropy
deb
t dat
a (n
ot n
orm
alize
d to
per
cap
ita)
Are
a so
urc
e em
issi
ons
Non-b
asic
multip
lier
C-S
aanic
h
Col
woo
d
Esq
uim
alt
Langfo
rd
Oak
Bay
CAC -
res
iden
tial
1.0
17.7
4
15.2
9
13.0
5
20.5
5
24.7
6
GHG
- res
iden
tial
1.0
16,2
23.4
8
13,9
66.5
6
11,9
32.2
0
18,8
96.0
4
22,2
31.7
9
Met
als
- re
side
ntia
l 1.
0
--
--
-
Oth
er -
res
iden
tial
1.0
--
--
-
CAC -
com
mer
cial
0.
7
174.
40
49.4
3
29.2
8
22.1
7
14.6
9
GHG
- c
omm
erci
al
0.7
5,65
5.50
3,
847.
07
15,6
36.8
0
4,64
8.19
4,
197.
08
Met
als
- co
mm
erci
al
0.7
--
--
-
Oth
er -
com
mer
cial
0.
7
--
--
-
Tota
l ar
ea C
AC
192.
14
64.7
2
42.3
3
42.7
2
39.4
5
Tota
l ar
ea G
HG
21
,878
.98
17,8
13.6
3
27,5
66.2
7
23,5
44.2
3
26,4
28.8
7
Tota
l ar
ea m
etal
s -
--
--
Tota
l ar
ea o
ther
-
--
--
188
Tabl
e CI 1
Loss
of S
ensit
ive
Ecos
yste
ms f
rom
199
2 to
200
2 pe
r m
unic
ipal
ity
C-S
aanic
h
Colw
ood
Esq
uim
alt
Langfo
rd
Oak
Bay
Ha
SE 1
992
369.3
286.0
30.5
1,6
63.1
68.6
aH
a la
nd a
rea
1993
5007
2069
1005
3736
1637
% S
E o
f to
tal
land a
rea,
(ap
pro
x.)
1993
7
14
3
45
4
Ha
SE 2
002
359.0
276.1
29.2
1,5
42.7
67.9
Ha
land a
rea
2002
5007
2069
1005
3726
1875
% S
E o
f to
tal
land a
rea,
2002
7
13
3
41
4
% L
oss
ha
SE 1992-2
002
2.9
3.6
4.5
7.8
1.0
% L
oss
ha
SE 1
992-2
002 p
er c
apit
a 0.0
630
0.0
670
0.0
075
0.5
578
0.0
037
Not
e. S
E (
Sen
sitive
Eco
syst
ems)
. aAs
Lang
ford
was
onl
y in
corp
orat
ed in 1
992,
mun
icip
al s
ourc
e da
ta is
not
avai
labl
e un
til
1993
. %
co
mpo
sition
of to
tal m
unic
ipal
are
a ca
lcula
ted u
sing
are
as fro
m B
C S
tatist
ics.
200
3. 2
002 M
unic
ipal
Sta
tist
ics.
Rel
ease
d S
epte
mbe
r 20
03.
Sch
edul
e 20
1 -
Gen
eral
Sta
tist
ics
2002
; BC S
tatist
ics.
199
4. 1
993 M
unic
ipal
Sta
tist
ics.
Rel
ease
d S
ept.
199
4. S
ched
ule
201 -
Gen
eral
Sta
tist
ics
1993
. Ret
riev
ed fro
m h
ttp:
//w
ww
.cse
rv.g
ov.b
c.ca
/LG
D/inf
ra/s
tatist
ics_
inde
x.ht
m (a
cces
sed S
epte
mbe
r, 1
4, 2
007)
. Sen
sitive
eco
syst
em d
ata
adap
ted fro
m "
Sum
mar
y Rep
ort:
Red
igitiz
ing o
f Sen
sitive
Eco
syst
em I
nven
tory
Pol
ygon
s to
Exc
lude
Dis
turb
ed
Are
as"
by A
XYS
Env
iron
men
tal Con
sultin
g L
td., S
idne
y British
Col
umbi
a.
189
Tabl
e C
I 1
Mun
icip
al p
opul
atio
n, p
opul
atio
n de
nsity
, and
cha
nge
in p
opul
atio
n,
1996
-200
1
a1996
b2001
Perc
ent
Change
1996-2
001
Munic
ipal
ity
Popula
tion
La
nd ar
ea
Den
sity
per
Po
pula
tion
La
nd ar
ea
Den
sity
per
%
Chan
ge
in
%
Chan
ge
(sq.
km)
sq.
km
(sq.
km)
sq.
km
pop
ula
tion
D
ensi
ty
C-S
aanic
h
14,6
11
42.5
9
343.0
6
15,3
48
41.3
9
370.8
4.8
7.5
Colw
ood
13,8
48
17.8
7
774.9
3
13,7
45
17.7
6
773.8
-0
.7
-0.1
Esq
uim
alt
16,1
51
6.6
8
2,4
17.8
1
16,1
27
7.0
4
2,2
89.6
-0
.1
-5.6
Langfo
rd
17,4
84
40.5
4
431.2
8
18,8
40
39.3
6
478.6
7.2
9.9
Oak
Bay
17,8
65
10.5
8
1,6
88.5
6
17,7
98
10.3
8
1,7
15.3
-0
.4
1.6
Not
e. a F
rom
Sta
tist
ics
Can
ada,
199
6 C
ensu
s, E
lect
roni
c Are
a Pr
ofile
s. C
atal
ogue
94F
0048
XW
E U
pdat
ed M
arch
17,
199
9. D
ate
mod
ified
200
5-04-2
5.
Ret
riev
ed o
n F
ebru
ary
18,
2008
fro
m h
ttp:
//w
ww
l2.s
tatc
an.c
a/en
glis
h/c
ensu
s96/d
ata/
prof
iles/
. bF
rom
Sta
tist
ics
Can
ada.
200
2. 2
001
Com
mun
ity
Prof
iles.
Rel
ease
d J
une
27,
2002
. La
st m
odifi
ed:
2005
-11-
30.
Sta
tist
ics
Can
ada
Cat
alog
ue n
o. 9
3F0
053XIE
. Ret
riev
ed o
n
Sep
tem
ber
13,
2007
fro
m htt
p://
ww
wl2
.sta
tcan
.ca/
engl
ish/P
rofil
01/C
P01/I
nde
x.cf
m?L
ang=
E.
190
Tabl
e CI 1
Mun
icip
al p
opul
atio
n ag
e str
uctu
re a
nd c
hang
e in
age
stru
ctur
e, 1
996-
2001
a1996
b2001
% C
hange
1996-2
001
Munic
ip.
Tot
al
0-1
4
15-6
4
65+
M
edia
n
Tot
al
0-1
4
15-6
4
65+
M
edia
n
Tot
. 0-1
4
15-6
4
65+
M
edia
n
age
age
age
C -
14,6
11
2705
9295
2615
39.7
15,3
45
2700
10025
2620
43
4.8
-0
.2
7.3
0.2
7.7
Saanic
h
Colw
ood
13,8
48
3485
9170
1205
32.9
13,7
45
3250
9165
1330
36.5
-0
.7
-7.2
-0
.1
9.4
9.9
Esq
uim
alt
16,1
51
2765
10770
2605
37.9
16,1
25
2500
11120
2505
38.9
-0
.1
-10.6
3.1
-4
.0
2.6
Langfo
rd
17,4
84
4015
11875
1590
33.8
18,8
40
4150
12830
1860
36.3
7.2
3.3
7.4
14.5
6.9
Oak
Bay
17,8
65
2740
10140
4990
45.2
17,8
00
2520
10610
4670
47.8
-0
.4
-8.7
4.4
-6
.9
5.4
Not
e. aFr
om S
tatist
ics
Can
ada,
199
6 C
ensu
s, E
lect
roni
c Are
a Pr
ofile
s. C
atal
ogue
94F
0048
XW
E U
pdat
ed M
arch
17,
199
9. D
ate
mod
ified
200
5-04
-25.
Ret
riev
ed o
n F
ebru
ary
18,
2008
fro
m h
ttp:
//w
ww
l2.s
tatc
an.c
a/en
glis
h/c
ensu
s96/d
ata/
prof
iles/
Inde
x.cf
m.
bFro
m S
tatist
ics
Can
ada.
200
2. 2
001
Com
mun
ity
Prof
iles.
Rel
ease
d J
une
27,
2002
. La
st m
odifi
ed:
2005
-11-
30.
Sta
tist
ics
Can
ada
Cat
alog
ue n
o. 9
3F0
053XIE
. Ret
riev
ed o
n S
epte
mbe
r 13
, 20
07 fro
m h
ttp:
//w
ww
l2.s
tatc
an.c
a/en
glis
h/P
rofil
01/C
P01/I
nde
x.cf
m?L
ang =
E.
191
Tabl
e C
I 1
Mun
icip
al p
opul
atio
n in
com
e an
d ch
ange
in in
com
e, 1
996-
2001
1996
2001
Munic
ip.
Ave
. %
%
%
%
%
Ave
. %
%
%
%
%
M
unic
ip.
inco
me
Pers
ons
Labou
r W
ork'
g
Wor
k'g
Dw
ellin
g
inco
me
Pers
ons
Labou
r W
ork'
g
Wor
k'g
Dw
ellin
g
with low
fo
rce
in C
SD
in
CD
ow
ned
w
ith low
fo
rce
in C
SD
in
CD
ow
ned
in
com
e par
tici
p.
of r
es.
of r
es.
inco
me
par
tici
p.
of r
es.
of r
es.
C -
30,1
95
6.8
67.1
15.8
62.9
81.7
33,8
05
5.9
67.8
18.7
58.0
81.9
S
aanic
h
Colw
ood
27,4
00
8.2
72.2
6.3
75.3
74.5
30,4
93
9.2
69.5
6.8
73.3
72.7
Esq
uim
alt
24,0
18
22.3
64.7
23.9
56.5
46.5
27,3
79
20.1
66.4
20.3
61.5
47.9
Langfo
rd
25,3
72
15.1
71.9
12.9
65.8
78.5
28,2
38
13.2
72.1
12.0
64.3
76.5
Oak
Bay
36,6
85
11.2
56.5
8.3
72.2
72.4
40,4
06
10.6
55.4
7.0
69.2
73.7
% C
hange
1996-2
001
% A
ve.
%
%
%
%
%
inco
me
Pers
ons
Labou
r W
ork'
g
Wor
k'g
Dw
ellin
g
with low
fo
rce
in C
SD
in
CD
ow
ned
in
com
e par
tici
p.
of r
es.
of r
es.
C -
11
-15
1
16
-8
0
Saanic
h
Colw
ood
10
11
-4
7
-3
-2
Esq
uim
alt
12
-11
3
-18
8
3
Langfo
rd
10
-14
0
-8
-2
-3
Oak
Bay
9
-6
-2
-19
-4
2
Not
e. A
vera
ge inc
ome
of p
opul
atio
n a
ged 1
5 a
nd o
lder
(em
ploy
men
t an
d o
ther
); %
per
sons
with lo
w inc
ome
befo
re t
ax;
labo
ur f
orce
par
tici
pate
ra
te in a
ge 1
5 a
nd o
lder
; pe
rcen
t pe
rson
s w
orki
ng w
ithin
the
cen
sus
subd
ivis
ion (
i.e.
mun
icip
ality)
of
resi
denc
e; p
erce
nt o
f to
tal pe
rson
s w
orki
ng
within
the
cen
sus
divi
sion
(i.e
. CRD
), y
et,
not
within
thei
r ce
nsus
sub
divi
sion
; pe
rcen
tage
of to
tal pr
ivat
e oc
cupi
ed d
wel
lings
tha
t ar
e ow
ned (
i.e.
vers
us
rent
ed).
Fro
m S
tatist
ics
Can
ada,
199
6 a
nd 2
001.
Ele
ctro
nic
Com
mun
ity P
rofil
es.
Ret
riev
ed o
n S
epte
mbe
r 15
, 20
07 fro
m
htt
p://
ww
wl2
.sta
tcan
.ca/
engl
ish/p
rofil
01/C
P01/I
nde
x.cf
m?L
ang=
E.
192
Tabl
e C
I 1
Mun
icip
al o
ccup
ied,
priv
ate
dwel
ling
type
and
cha
nge
in d
welli
ng t
ype,
199
6-20
01
aO
ccupie
d,
pri
vate
dw
elli
ng t
ype
1996
Munic
ip.
Tot
. #
dw
ellin
g
dw
ellin
gs/
To
t. lo
w d
en
sity
%
com
pos
itio
n o
f lo
w
To
t. h
igh
%
com
pos
itio
n o
f hig
h
capita
densi
ty t
ype:
tot
al
den
sity
den
sity
typ
e: t
otal
C-S
aanic
h
5,4
40
0.3
7
4,02
0 74
1,
415
26
Colw
ood
4,6
85
0.3
4
4,00
5 85
67
5 14
Esq
uim
alt
7,3
60
0.4
6
2,93
0 40
4,
415
60
Langfo
rd
6,3
95
0.3
7
4,70
5 74
1,
690
26
Oak
Bay
7,7
05
0.4
3
5,29
0 69
2,
410
31
bO
ccupie
d,
pri
vate
dw
elli
ng t
ype
2001
Tot
. #
dw
ellin
g
dw
ellin
gs/
To
t. lo
w d
en
sity
%
com
pos
itio
n o
f lo
w
To
t. h
igh
%
com
pos
itio
n o
f hi
gh
capita
den
sity
typ
e: t
otal
d
en
sity
den
sity
typ
e: t
otal
C-S
aanic
h
5,9
15
0.3
9
4,36
0 74
1,
555
26
Colw
ood
4,8
65
0.3
5
3,82
0 79
1,
050
22
Esq
uim
alt
7,5
60
0.4
7
3,03
0 40
4,
530
60
Langfo
rd
7,0
50
0.3
7
5,06
0 72
1,
990
28
Oak
Bay
7,7
40
0.4
3
5,35
5 69
2,
390
31
% C
hange
of o
ccupie
d,
pri
vate
dw
elli
ng t
ype
1996-2
006
Tot
. #
dw
ellin
g
To
t. #
lo
w
% cha
nge
in com
positio
n o
f To
t. #
hig
h
% c
hang
e in
com
positio
n
den
sity
lo
w d
ensity
d
en
sity
of
low
den
sity
C-S
aanic
h
8
8 0
9 1
Colw
ood
4
-5
-9
36
33
Esq
uim
alt
3
3 1
3 0
Langfo
rd
9
7 -3
15
6
Oak
Bay
0
1 1
-1
-1
Not
e. aFr
om S
tatis
tics
Can
ada,
199
6 C
ensu
s, E
lect
roni
c Are
a Pr
ofile
s. C
atal
ogue
94F
0048
XW
E U
pdat
ed M
arch
17,
199
9. D
ate
mod
ified
200
5-04
-25.
Ret
riev
ed o
n F
ebru
ary
18,
2008
fro
m h
ttp:
//w
ww
l2.s
tatc
an.c
a/en
glis
h/ce
nsus
96/d
ata/
prof
iles/
Inde
x.cf
m.bF
rom
"P
rofil
e of
Mar
ital Sta
tus,
Com
mon
-la
w S
tatu
s, F
amili
es,
Dw
ellin
gs a
nd H
ouse
hold
s, 2
001 C
ensu
s,"
Sta
tistic
s Can
ada,
200
2, R
elea
sed O
ctob
er 2
2, 2
002.
Sta
tistic
s Can
ada
Cat
alog
ue
no.
95F0
487X
CB20
0100
1.
193
Tabl
e C
I 1
Mun
icip
al c
omm
utin
g pa
ttern
s an
d ch
ange
in co
mm
utin
g, 1
996-
2001
aC
om
mute
rs 1
996
"Com
mute
rs 2001
Munic
ip.
Med
. #
*%
# **0/ 0
O
ther
M
ed.
# *%
#
**
%
Oth
er
com
mt'g
Drive
rs
Drive
rs
Pass
'ger
Pa
ss'g
rs,
com
mt'g
Drive
rs
Drive
rs
Pass
'ger
s Pa
ss'g
rs,
dis
t.
tran
sit,
tr
ansi
t,
dis
t.
tran
sit,
tr
ansi
t,
(km
) cy
cle,
w
alke
rs
cycl
e,
wal
kers
(k
m)
cycl
e,
wal
kers
cy
cle,
w
alke
rs
C-S
aanic
h
unav
ail.
5370
80
415
19
75
13.1
5760
82
350
17
60
Colw
ood
unav
ail.
4840
76
610
23
95
8.6
4850
76
500
22
115
Esq
uim
alt
unav
ail.
4205
57
1080
40
190
3.1
4445
58
575
41
150
Langfo
rd
unav
ail.
6540
81
495
18
75
10.1
7310
81
525
19
75
Oak
Bay
unav
ail.
4495
67
655
32
110
4.2
4290
65
380
34
95
% C
hange
in c
om
mute
rs,
1996-2
001
Munic
ip.
Med
. %
%
co
mm
t'g
Drive
rs
Pass
'ger
stra
dis
t. (
km)
nsi
t, c
ycle
, w
alke
rs
C-S
aanic
h
N/A
2
-2
Colw
ood
N/A
0
-1
Esq
uim
alt
N/A
1
1
Langfo
rd
N/A
0
1
Oak
Bay
N/A
-2
2
Not
e.
Munic
ipal
mean c
om
muti
ng d
ista
nce
s (t
o pla
ce o
f w
ork
) not
available
for
1996.
*Perc
enta
ge
com
posi
tion o
f dri
vin
g c
om
mute
rs:
tota
l co
mm
ute
rs.
••Perc
enta
ge
com
posi
tion o
f pass
engers
etc
: to
tal
com
mute
rs.
aA
dapte
d f
rom
Sta
tist
ics
Canada,
1996 C
ensu
s, E
lect
ronic
Are
a Pro
file
s. C
ata
logue
94F0048X
WE U
pdate
d M
arc
h 1
7,
1999.
Date
modif
ied 2
005-0
4-2
5.
Retr
ieved o
n F
ebru
ary
18,
2008 f
rom
htt
p:/
/ww
wl2
.sta
tcan.c
a/e
nglish
/censu
s96/d
ata
/pro
file
s/.
bFro
m S
tati
stic
s C
anada.
2002.
2001 C
om
munit
y Pro
file
s. R
ele
ase
d J
une
27,
2002.
Last
modif
ied:
2005-1
1-3
0.
Sta
tist
ics
Canada
Cata
logue
no.
93F0053X
IE.
Retr
ieved o
n S
epte
mber
13,
2007 f
rom
htt
p:/
/ww
wl2
.sta
tcan.c
a/e
nglish
/Pro
fil0
1/C
P01/I
ndex.c
fm?L
ang =
E.
194
Tabl
e C
20
Mun
icip
al r
oad
infr
astru
ctur
e by
type
, 20
01
Road T
ype
per
Munic
ipalit
y,
2001
Com
munity
Art
eria
l Col
lect
or
Loca
l Tot
al
"Tot
al (k
m)
Art
eria
l (k
m)
(km
) (k
m)
(km
) per
ca
pita
Frac
tion
(%)
C-S
aanic
h
39.3
75.6
111.0
225.9
0.0
15
17.4
Colw
ood
18.0
51.1
80.0
149.1
0.0
11
12.1
Esq
uim
alt
18.5
21.6
51.6
91.7
0.0
06
20.2
Langfo
rd
22.3
59.3
101.5
183.1
0.0
10
12.2
Oak
Bay
37.6
32.2
65.8
135.6
0.0
08
27.7
Not
e. A
rter
ial ca
tego
ry d
oes
not
incl
ude
prov
inci
al h
ighw
ays.
aPe
r ca
pita
cal
cula
ted u
sing
200
1 p
opul
atio
n
stat
istics
fro
m,
Sta
tist
ics
Can
ada.
200
2. 2
001 C
omm
unity
Pro
files
. Rel
ease
d J
une
27,
2002
. La
st m
odifi
ed:
2005-1
1-3
0.
Sta
tist
ics
Can
ada
Cat
alog
ue n
o. 9
3F0
053XIE
. Ret
riev
ed o
n S
epte
mbe
r 13
, 20
07 fro
m
htt
p:/
/ww
wl2
.sta
tcan
.ca/
englis
h/P
rofil
0l/
CP0
1/I
ndex
.cfm
?Lan
g=
E.
Ada
pted
fro
m "
Cap
ital
Reg
iona
l D
istr
ict
Air C
onta
min
ant
Emis
sion
Inv
ento
ry f
or 2
004"
by
B.
McE
wen
and
B.
Hre
beny
k, 2
006,
Tab
le C
.5,
p. 5
5.
Tabl
e C
I 1
Mun
icip
al e
cono
mic
stru
ctur
e by
% to
tal l
abou
r for
ce c
ompo
sitio
n by
indu
stry
divi
sion,
200
1 on
ly
Munic
ipalit
ies
Indust
ry
Div
isio
ns
C-S
aanic
h
%
Col
woo
d
%
Esq
uim
alt
%
Langfo
rd
%
Oak
Bay
%
Agri
cult
ure
, fish
ing,
210
3
45
1
15
0
80
1
65
1
trappin
g,
fore
stry
M
inin
g
10
0
15
0
0
0
10
0
0
0
Manufa
cturi
ng
425
7
295
5
335
5
540
6
265
4
Const
ruct
ion
450
7
435
7
400
6
805
10
230
4
Tra
nsp
ort
ati
on/s
tora
ge
400
6
325
5
310
4
465
6
160
3
Uti
litie
s 55
1
65
1
30
0
65
1
35
1
Whole
sale
tra
de
240
4
180
3
165
2
360
4
175
3
Reta
il tr
ade
1060
16
970
16
1035
15
1465
18
760
12
Finance
/insu
rance
350
5
315
5
225
3
350
4
290
5
Real
est
ate
/insu
rance
175
3
110
2
190
3
150
2
360
6
agents
G
overn
ment
serv
ice
1055
16
1450
24
2255
32
1610
19
1075
17
Educa
tion s
erv
ices
600
9
385
6
350
5
545
7
980
16
Healt
h a
nd s
oci
al
1010
16
1005
17
930
13
1305
16
1265
20
serv
ices
Acc
om
modati
on,
food
390
6
395
7
875
12
605
7
530
9
& bevera
ge
Tota
l la
bour
forc
e and
6430
100
5990
100
7115
100
8355
100
6190
100
% co
mposi
tion
Not
e. S
hade
d c
ells
rep
rese
nt t
he h
ighe
st p
erce
ntag
e co
mpo
sition
of th
e la
bour
for
ce b
y in
dust
ry d
ivis
ion.
1996
%
com
posi
tion
of
labo
ur f
orce
by
indu
stry
div
isio
n w
as n
ot r
emar
kabl
y di
ffer
ent,
exc
ept
Esqu
imal
t ha
d a
low
er o
vera
ll 'g
over
nm
ent
serv
ice'
com
posi
tion
, pr
esum
ably
bec
ause
of a
mor
e re
cent
tre
nd o
f Es
quim
alt
bein
g s
een a
s a
bedr
oom
co
mm
unity
of V
icto
ria.
Som
e se
rvic
e (i.e
. bu
sine
ss a
nd o
ther
) ca
tego
ries
hav
e be
en o
mitte
d for
ade
quat
e co
mpa
riso
n w
ith
1996
cen
sus
cate
gories
, w
hich
cha
nged
som
ewha
t in
200
1. F
rom
Sta
tist
ics
Can
ada,
200
1 C
ensu
s. "
Prof
ile o
f la
bour
for
ce
activi
ty,
clas
s of
wor
ker,
occ
upat
ion,
ind
ustr
y, p
lace
of w
ork,
mod
e of
tra
nsp
orta
tion
, la
ngua
ge o
f w
ork
and u
npai
d w
ork"
. Fe
brua
ry 1
1, 2
003 r
elea
se.
Cat
alog
ue 9
5049
0XCB20
0100
1. R
etriev
ed o
n F
ebru
ary
18,
2008
fro
m
htt
p://
ww
wl2
.sta
tcan
.ca/
engl
ish/c
ensu
s01/p
rodu
cts/
stan
dard
/pro
files
/Ret
riev
ePro
file.
cfm
196
Tabl
e CI 1
Perc
enta
ge o
f new
er h
omes
per
mun
icip
ality
Munic
ipalit
ies
# d
wellin
gs
const
ruct
ed
% o
f new
ly c
onst
ruct
ed
1991-2
001
dw
elli
ngs
from
1991-2
001:
all
dw
elli
ngs
C-
Saanic
h
945
16%
Colw
ood
480
10%
Esq
uim
alt
760
10%
Langfo
rd
1,6
75
24%
Oak
Bay
205
3%
Not
e. %
cal
cula
ted a
s #
dw
ellin
gs c
onst
ruct
ed:
tota
l #
dw
ellin
gs,
per
mun
icip
ality.
aFr
om "
Prof
ile
of M
arital
Sta
tus,
Com
mon
-law
Sta
tus,
Fam
ilies
, D
wel
lings
and
Hou
seho
lds,
200
1 C
ensu
s,"
Sta
tist
ics
Can
ada,
200
2, R
elea
sed O
ctob
er 2
2, 2
002.
Sta
tist
ics
Can
ada
Cat
alog
ue n
o.
95F0
487XCB2001001.
197
APPENDIX D - Complexity variables
Transportation energy consumption vs. complexity variables
1995 Transportation energy consumption vs. 19 Population density, per municipality
30 e | 2S I 20 C
If «
U tt s s >»
O C-Saanich
O Colwood
• Esquimalt
' Langford
© Oak Bay
2004 Transportation energy consumption vs. 2001 ulation density, per r Populc
m
\ ^ >
{• 20
I f i'. > 3. : n [ S 10 i £ I - i>
O C-Saanich |
O Colwood
• Esquimalt
Langford
# Oak Bay
1500 2000
< « W )
Figure Dl. 1995 Transportation energy consumption (GJ/yr) versus 1996 population density; and, 2004 Transportation energy consumption (GJ/yr) versus 2001 population density
1995 Transportation energy consumption vs. 1996 Median age, per municipality
I 8. »
i f i s
O C-Saanich|
© Colwood
• Esquimalt
# Langford
e Oak Bay
Transportation energy consumption vs. 2001 age, per municipality
I ? TO
I f " I I =
s
O C-Saanich
Q Colwood
• Esquimalt
# Langford
» Oak Bay
Figure D2. 1995 Transportation energy consumption (GJ/yr) versus 1996 median age; and 2004 Transportation energy consumption (GJ/yr) versus 2001 median age
1995 Transportation c Average income, per municipality
! a. I 20 Z I 4 ; :=
a • • 5
ill
i i
2004 Transportation energy consumption vs. 2001
E 30
1 25 a.
!* SO
S a. (» (0 t o (0
? s !>
r 0 U)
O C-Saanich
G Colwood
• Esquimalt
# Langford
» Oak Bay
Figure D3. 1995 Transportation energy consumption (GJ/yr) versus 1996 average income; and 2004 Transportation energy consumption (GJ/yr) versus 2001 average income
198
1995 Transportation energy consumption vs. 1996 % Labour force participation, per municipality
I
2004 Transportation energy consumption vs. 2001 % Labour force participation, per municipality
£ JO f • §
I 20
•o jS I I 0 §
O C-Saanich
Q CoKvood
• Esquimalt
a Langford
s Oak Bay
20 40 60
Labour torco participation (°-'j )
Figure D4. 1995 Transportation energy consumption (GJ/yr) versus 1996 labour force participation (%); and 2004 Transportation energy consumption (GJ/yr) versus 2001 labour force participation (%)
%Ch<
% Change population (1996-2001)
©
O CSaarich
C Crtwrfxl
• Esqurreft
Langford
® CfekBay
%ChanooinpopJ.iBcin(1996-2001)
% Change transportation energy consumption (1995-2004) vs.
% Change population density (1996-2001)
1S OOSaarichj
OCdvtood
• Esqumaft
Lidl IjlLjl d
$Q*Bay
Figure D5. % Change transportation energy consumption (GJ/yr) (1995-2004) versus % Change population (1996-2001); % Change transportation energy consumption (GJ/yr) (1995-2004) versus % change population density (1996-2001)
% Change transportation energy consumption (1995-2004) vs.
% Change median age (1996-2001)
O C-Saarich
o Colwood
• Esqumalt
Langford
% Change transportation c (1995-2004) vs.
% Change average income (1996-2001)
i$l
•u.i
O Cofoood
• Esqumalt
Langford
@ Clik Br/
Figure D6. % Change transportation energy consumption (GJ/yr) (1995-2004) versus % change median age (1996-2001); % Change transportation energy consumption (GJ/yr) (1995-2004) versus % change average income (1996-2001)
199
% Change transportation energy consumption (1995-2004) vs.
% Change laboir force participation (1996-2001)
.b
I
6-- 6 - 4 - 2 ) 2
® * •
O
aa-f
o C-Saarich
oCdvwxxl
• Esqiimalt
• s Langford
• CBkBay
% Change in labour force participation (1996-2001)
Figure D7. % Change transportation energy consumption (GJ/yr) (1995-2004) versus % change labour force participation (1996-2001)
1995 Transportation energy consumption vs. 2001 Length of roadways, per municipality
I OC-Stii'J-1
QCotoood
• Esqumaft
Langford
©Qik&iy
2004 Transportation« Length of roadways. per municipality
100
Length of n
O OSaaridi
O Cotvwod
Langford
(.1*
100
Langlhrf
Figure D8. 1996 Transportation energy consumption (GJ/yr) versus 2001 length of roadways (km); 2004 Transportation energy consumption (GJ/yr) versus 2001 length of roadways (km), per municipality
Transportation energy consumption vs. 2001 % Arterial fraction, per municipality
I 0 OC-Saarich
O Colnood •
• Esqurrott • Esqurrott
•v; ijn yfuiU
$ Oak Bay
2004 Transportation energy consun^ption \ % Arterial fraction, per r
Figure D9. 1996 Transportation energy consumption (GJ/yr) versus 1995 % arterial fraction; 2004 Transportation energy consumption (GJ/yr) versus 2001 % arterial fraction, per municipality
200
2004 Transportation energy consumption vs. 2001 Commuting distance, per municipality
30
25
20
15
10
5
0
OC-Saarich
u
• O CotwDod
® • Esqumalt
* • Esqumalt
» Langford
• Oak Bay
2 4 6
Convikiting 8 10 12 (to worfc) (km)
14
Figure D10. 2004 Transportation energy consumption (GJ/yr) versus 2001 commuting distance (km to work), per municipality. 1996 commuting distances unavailable from Statistics Canada
2004 Transportation energy consumption vs. 2001 Driving commuters, per municipality
e I
I " I ! 1 S
i"" 5 0
o C-Saanich
© Colwood
• Esquimalt
o Langford
• Oak Bay
1995 Transportation energy consumption vs. 1996 Driving commuters, per municipality
20 % Driving c
Figure Dll. 2004 Transportation energy consumption (GJ/yr) versus 2001 % composition of driving commuters, per municipality; and 1995 Transportation energy consumption (GJ/yr) versus 1996 % composition of driving commuters, per municipality
19£ 5 Transportation energy consumption vs. 1991 Passengers, transiters, walker & cyclists, per
municipality
5 %
! «P O C-Saanich
1 O Colwood
* ! « • • Esquimalt
i f
• Esquimalt
r s Langford
s • Oak Bay
£ 10 20 30 40 50
Passengers, transiters, cyclist, walkers (to work)
30
2!
2004 Transportation energy consumption vs. 2001 % Passengers, transiters, walker & cyclists, per
municipality _
I1: o u-Saamcn
o Colwood
• Esquimalt
Langford
$ Oak Bay
0 10 20
% Passengers, tran cyclist, v
40- SO
»(to work)
Figure D12. 1995 Transportation energy consumption (GJ/yr) v ersus 1996 % composition of passengers, transiters, walker & cyclists, per municipality; and 2004 Transportation energy consumption versus 2001 % Passengers, transiters, walker & cyclists, per municipality
201
% Change transportation energy consumption (1995-2004) vs.
% Change driving conrnuters (1996-2001)
E |
I * c f1
£
J -2 -1 1 1 2 OC-Saarich
OCokAood
• Esqiimalt
' Uincford
• CBk Bay
E |
I * c f1
£
• < •
>
OC-Saarich
OCokAood
• Esqiimalt
' Uincford
• CBk Bay
E |
I * c f1
£
O
OC-Saarich
OCokAood
• Esqiimalt
' Uincford
• CBk Bay
E |
I * c f1
£
OC-Saarich
OCokAood
• Esqiimalt
' Uincford
• CBk Bay
E |
I * c f1
£
OC-Saarich
OCokAood
• Esqiimalt
' Uincford
• CBk Bay
E |
I * c f1
£
OC-Saarich
OCokAood
• Esqiimalt
' Uincford
• CBk Bay
commuters (lo vuorti) (199&2001)
% Change transportation energy consumption (1985-2004) vs.
% Change pass'grs, cyclists & walkers <1996-2001)
Figure D13. % Change in transportation energy consumption (GJ/yr) (1995-2004) versus % change in % composition of driving commuters (to work) 1996-2001; % Change in transportation energy consumption (GJ/yr) (1995-2004) versus % change in % composition of passengers, walkers, transiters, and cyclist commuters (to work) 1996-2001
Residential space heating vs. complexity variables
1*W5 Residential space-heating energy consumption vs. 1996 Pofxjation density, per municipality
2004 Residential space-heating energy consumption vs. 2001 Population density, per municipality
SI
us -40 • :v> • JO - -23--20
lli -TO- — 5 -0-
• Cdwul
• Esqdmait
Langford
g 1
u !
4'j 40 -'35— -:k> -25 20 r -
l| g. IS-§ 10
iCBkBay - iS
• Fbqjnti't
»CBkBay
Figure D14. 1996 Residential space-heating energy consumption (per capita, GJ/yr) versus 1996 population density (#/sq.km); 2004 Residential space-heating energy consumption (per capita, GJ/yr) versus 2001 population density (#/sq.km)
2004 Residential spaoe-heating energy c
: O C-Saaidi
OCotwxxl
• EsqUrrelt
?? Langford
© Oak Bay
O C-Saaich
OCofaood
© Esqiirr t
% Langford
tt CfekBay
Figure D15. 1995 1996 Residential space-heating energy consumption (per capita, GJ/yr) versus 1996 median age; 2004 Residential space-heating energy consumption (per capita, GJ/yr) versus 2001 median age
202
vs. 1996Average income, per municipality
V o C-Sih«vii
OColvtood
• Esq unit
& Oak Bay
2004 Residential space-heating energy cuvuifjlion vs. 2001 Average income, per mniapality
f- iich
OCohAood
• Esqiimeit
o Langford
®C6kBay
Figure D16. 1996 Residential space-heating energy consumption (per capita, GJ/yr) versus 1996 average income ($/yr); 2004 Residential space-heating energy consumption (per capita, GJ/yr) versus 2001 average income ($/yr)
1995 Residential space-heating energy consimpti on vs. 1996% Labour force participation, per
municipality •15
40
an 2f) 1-5 in s n
o C-S.vmcJi
o Colwood
• Esqumalt
Langlbrd
® C&kBay
H)
2004 Residential space-heating consultation Bon, per
10 35
30
i f » ? 120
I ~ 10 I :
OC-Saarich
OCdtAOOd
• Esqumalt
Langfofd
® CBkBcy
(J) (%)
Figure D17. 1996 Residential space-heating energy consumption (per capita, GJ/yr) versus 1996 % labour force participation ; 2004 Residential space-heating energy consumption (per capita, GJ/yr) versus 2001 % labour force participation
% Change residential space-heating (1995-2004) vs.
% Change population (1996-2001)
% Change in (1SBM001)
OOSaarich
oColwood
• Esqumalt
Langford
« CBk Bay t
(1995-2004) vs. % Change population density (1996-2001)
•
•
°
0 -5 ) 5 10 1
&4_
«
o GSaarich
oColv\«>d
• Esqumalt
4 Largfbrd
©Oak Bay
Figure D18. % Change in residential space-heating energy consumption (per capita, GJ/yr) (1995-2004) versus 1996 % change in population (1996-2001); % Change in residential space-heating energy consumption (per capita, GJ/yr) (1995-2004) versus % change in population density (1996-2001)
203
I) IS
0.1
% Change residential spaco-heating conaaifjiion (1995-2004) vs.
"/.Change mccfan ago (1996-2001)
% Change residential
P. OIK
i o C-Saarich 8
© Colwood
• EsqJmalt
Langford
• QakBay
% Change in median age (199&-2001)
t n s
01 •
oor.
% Change average income (1996-2001)
o
-1105
01
8 10 12
i OC-Saarich|
oCotoood
I Esqumalt
s Langford
l CBk Bay
% Changs II
Figure D19. % Change in residential space-heating energy consumption (1995-2004) versus % change in median age (1996-2001); % Change in residential space-heating energy consumption, GJ/yr) (1995-2004) versus % change in average income (1996-2001)
% Change residential space-heating consumption (1995-2004) vs.
% Change labour force participation (1996-2001)
!a C
hange
in r
es. s
pace
-heating
enen
consu
rnption
(199
5-20
04)
• O C-Saarich
O Colwood I
• EsqJmalt i
• Langford |
• CekBay I
!a C
hange
in r
es. s
pace
-heating
enen
consu
rnption
(199
5-20
04)
•
O C-Saarich
O Colwood I
• EsqJmalt i
• Langford |
• CekBay I
!a C
hange
in r
es. s
pace
-heating
enen
consu
rnption
(199
5-20
04)
G o
O C-Saarich
O Colwood I
• EsqJmalt i
• Langford |
• CekBay I
!a C
hange
in r
es. s
pace
-heating
enen
consu
rnption
(199
5-20
04)
3 -4 -2 I ) 2 1
O C-Saarich
O Colwood I
• EsqJmalt i
• Langford |
• CekBay I
!a C
hange
in r
es. s
pace
-heating
enen
consu
rnption
(199
5-20
04)
O C-Saarich
O Colwood I
• EsqJmalt i
• Langford |
• CekBay I
% Change in labour force participation (1996-2001)
Figure D20. % Change in residential space-heating energy consumption (per capita, GJ/yr) (1995-2001) versus % change percentage labour force participation (1996-2001)
Figure D21. 1996 Residential space-heating energy consumption (per capita, GJ/yr) versus 1996 % composition of low density housing; 2004 Residential space-heating energy consumption (per capita, GJ/yr) versus 2001 % composition of low density housing
204
Residential space-heating energy consimpti or 1996 Hgh density ctaellings, per municipality
tfi 40
'J5 • 3 0 - -» -
20 —
IS J— 10
O C-Saarich
QCofaood
• EsqUrralt
Langford
® CBkBay
vs. 2001 Hgh density dwellings, per municipality
?o 30
4 ' j
40
30
75
20
15 10
5
0
OG-Saarichs
s Colwood
• Esqumalt i
^ Langford
# CBkBay
Figure D22. 1996 Residential space-heating energy consumption (per capita, GJ/yr) versus 1996 % composition of high density housing; 2004 Residential space-heating energy consumption (per capita, GJ/yr) versus 2001 % composition of high density housing
C h a " 9 e (1Q9S2004) vs. % Change low density type dwellings (1996-2001)
j 946-•
•
0
|o -8 -6 -4 -2 )
m 005
6H-
oC-Saarich
o Colwood
• Esqumalt
Langlond
©CBkBay
% Change in % conpasition of low typE (1996-2001)
% Change residential space-heating oonsimption (1995-2004) vs.
% Change high density type dwellings (1996-2001)
-9r»-
o C-Saarich
oCotwood
• Esqumalt
s Langford
» Oak Bay
% Change in % corrposiSon of high density type (19962001)
Figure D23. % Change in residential space-heating energy consumption (1995-2004) versus % change in low density housing; 2004 Residential space-heating energy consumption (1995-2004) versus 2001 % composition of low density housing
% Change residential space-heating consumption (1995-2004) vs.
% Composition of new dwellings (1991-2001)
I .? | 0.1
» » 0.05 a r
i f ° c g ^ S -0.05-
8 S? -0.1
%Ca
9
3
o C-Saarich
o Colwood |
• Esqumalt j
Larglbrd
Oak Bay
I .? | 0.1
» » 0.05 a r
i f ° c g ^ S -0.05-
8 S? -0.1
%Ca
•
3
o C-Saarich
o Colwood |
• Esqumalt j
Larglbrd
Oak Bay
I .? | 0.1
» » 0.05 a r
i f ° c g ^ S -0.05-
8 S? -0.1
%Ca
© °
3
o C-Saarich
o Colwood |
• Esqumalt j
Larglbrd
Oak Bay
I .? | 0.1
» » 0.05 a r
i f ° c g ^ S -0.05-
8 S? -0.1
%Ca
> 5 10 15 20 25 : 3
o C-Saarich
o Colwood |
• Esqumalt j
Larglbrd
Oak Bay
I .? | 0.1
» » 0.05 a r
i f ° c g ^ S -0.05-
8 S? -0.1
%Ca
3
o C-Saarich
o Colwood |
• Esqumalt j
Larglbrd
Oak Bay
I .? | 0.1
» » 0.05 a r
i f ° c g ^ S -0.05-
8 S? -0.1
%Ca Tposition of nevriy constructed dwellings, per cafHal rrunicip
Figure D24. % Change in residential space-heating energy consumption (1995-2004) versus % composition of new dwellings of total dwellings (1991-2001)
205
Loss of sensitive ecosystems vs complexity variables
Loss of sensitive ecosystems (1992-2002) vs. % Change population (1996-2001)
I I •6
o O S a a r i t f i i
oCcfaood
• EsqJmalt
o O S a a r i t f i i
oCcfaood
• EsqJmalt
o O S a a r i t f i i
oCcfaood
• EsqJmalt
o O S a a r i t f i i
oCcfaood
• EsqJmalt
® Langford
VCekBay ;
® o ® Langford
VCekBay ; > fM—
1 2 4 6
® Langford
VCekBay ;
% Change in population, per n
Loss of sensitive ecosystems (1992-2002) vs. % Change population density (1996-2001)
(MS-
£ 0
0 -5 1 5 10 1
l O O S a a r i c h
jQCcfaood
l EsqJmalt
Langford
^ CbkBay
% ; Figure D25. Loss of sensitive ecosystems (1992-2002) versus % change in population (1996-2001); Loss of sensitive ecosystems (1992-2002) versus % change in population density (1996-2001)
Lossofs asyster ! (1996-2001)
o i
02i
0 1 ;
o 01
O OSaaich
o Cctuvood
• Esqjrrait
Lcngbd
# CbkBay
5(1992-2002) vs. i income (1996-2001)
m 04 [13
o 01 0
-01
o o
O CSagrich
O CcMood
• Esqurr^t
Urgfbtd
® Cbk Bay
Figure D26. Loss of sensitive ecosystems (1992-2002) versus % change in median age (1996-2001); Loss of sensitive ecosystems (1992-2002) versus % change in average income
Loss of sensitive ecosystems (1992-2002) vs. % Change labour force participation (1996-2001)
3
0 C-Saarich
o Cotaood
• EsqJmalt
§ LaTjfbrd
& C&kBay 3
0 C-Saarich
o Cotaood
• EsqJmalt
§ LaTjfbrd
& C&kBay 3
0 C-Saarich
o Cotaood
• EsqJmalt
§ LaTjfbrd
& C&kBay 3
0 C-Saarich
o Cotaood
• EsqJmalt
§ LaTjfbrd
& C&kBay 3
0 C-Saarich
o Cotaood
• EsqJmalt
§ LaTjfbrd
& C&kBay 3 O
0 C-Saarich
o Cotaood
• EsqJmalt
§ LaTjfbrd
& C&kBay 3
> -4 -2 ) 2
0 C-Saarich
o Cotaood
• EsqJmalt
§ LaTjfbrd
& C&kBay
I ! i "
02
01
0 !
01
Loss of sensitive ecosystems (1992-2002) vs.
(1996-2001)
L
o C-Saarich
oCcfoood
• Esqiimatt
Langford
$ Oak Bay
Figure D27. Loss of sensitive ecosystems (1992-2002) versus % change in population (1996-2001); Loss of sensitive ecosystems (1992-2002) versus % change in population density (1996-2001)
206