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The influence of dwelling type and residential density on the appropriated carrying capacity of Canadian… Walker, Lyle Andrew 1995

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THE INFLUENCE OF DWELLING TYPE A N D RESIDENTIAL DENSITY ON THE APPROPRIATED C A R R Y I N G CAPACITY OF C A N A D I A N HOUSEHOLDS by Lyle Andrew Walker B.E.S., The University of Waterloo, 1992 A THESIS SUBMITTED IN PARTIAL F U L F I L L M E N T OF THE REQUIREMENTS FOR THE DEGREE OF M A S T E R OF SCIENCE in THE F A C U L T Y OF G R A D U A T E STUDIES School of Community and Regional Planning We accept this thesis as conforming to the requireiLstandard THE UNIVERSITY OF BRITISH C O L U M B I A September 1995 © Lyle Andrew Walker, 1995 In presenting this thesis in partial fulfilment of the requirements for an advanced degree at the University of British Columbia, I agree that the Library shall make it freely available for reference and study. I further agree that permission for extensive copying of this thesis for scholarly purposes may be granted by the head of my department or by his or her representatives. It is understood that copying or publication of this thesis for financial gain shall not be allowed without my written permission. The University of British Columbia Vancouver, Canada Department of DE-6 (2788) ABSTRACT This thesis compares the "ecological footprints" of Canadian households living in various dwelling types and densities. A household's "ecological footprint" is the land area required to produce the biophysical resources it uses and to assimilate the wastes it generates sustainably. Urban density, measured as the number of persons or dwelling units per unit land area, directly and indirectly influences resource consumption for housing, transportation, and infrastructure. This thesis tests the hypothesis that as density increases the ecological footprint per occupant will decrease. Ecological footprint calculations are conducted for households living in a typical detached house, townhouse, walk-up and high-rise apartment in Canada. Three variations on the detached house are included: a standard efficiency house, an R2000 house, and a house on a small lot. The method links housing characteristics with the consumption of directly occupied land, forest products, and fossil energy. The consumption of forest products and fossil energy are translated into land area equivalents using forest productivity and the C 0 2 assimilation capacity of forests. An occupant of a detached house was estimated to have the largest ecological footprint, at about 1.5 ha. The smallest per occupant ecological footprints were for high-rise and walk-up apartments at about 60 and 64% of the detached house value, followed by the townhouse at 78%. Therefore as density increases, the per occupant ecological footprint declines. Occupants in R2000 and small lot houses have ecological footprints approximately 84% and 92% the size of the standard house respectively. The largest components of the ecological footprints are operating energy for housing and transportation. The dwelling type associated with the largest per occupant ecological footprint, detached houses, form the majority of the existing and newly built housing stock. The main policy implication is that higher densities should be promoted in order to reduce the ecological footprint of housing. ii TABLE OF CONTENTS Abstract ii Table of Contents iii List of Tables v List of Figures vi Acknowledgments vii 1.0 SUSTAINABILITY, DWELLING TYPE, AND DENSITY 1 1.1 Sustainability and Making Choices 1 1.2 Purpose and Objectives 3 1.3 Resource Consumption Related to Dwelling Type 4 1.4 Comparing the Dwelling-Related Resource Consumption of Households 6 1.5 .Research Method 8 1.6 Significance of Thesis 9 1.7 Organization of Thesis 10 2.0 THE APPROPRIATED CARRYING CAPACITY CONCEPT 11 2.1 Sustainability and the Maintenance of Natural Capital 11 2.2 The Appropriation of Carrying Capacity and Ecological Footprints 13 2.3 The Ecological Footprint Consumption by Land Use Matrix 15 2.4 Assessment of the Ecological Footprint / Appropriated Carrying Capacity Concept 17 2.5 Related Ecological Footprint / Appropriated Carrying Capacity Applications 20 3.0 THE INFLUENCE OF DWELLING TYPE AND DENSITY ON CONSUMPTION PATTERNS 23 3.1 Dwelling Type, Density, and Consumption Patterns 23 3.2 Measures of Density 23 3.3 Dwelling Type and Resource Consumption for Housing 24 3.4 Density and Resource Consumption for Transportation 29 3.5 Density and Resource Consumption for Infrastructure 35 3.6 Assessment of Literature 38 4.0 THE ECOLOGICAL FOOTPRINT CALCULATION PROCEDURE 40 4.1 Applying the Appropriated Carrying Capacity Concept 40 4.2 Modifications to the Consumption by Land Use Matrix 42 4.3 Household Characteristics Used in the Calculation Procedure 45 4.4 The Direct Linkages Between Housing Characteristics and Consumption 46 4.5 The Lot Size, Mirrored Density, and Transportation Energy Consumption Linkage 47 4.6 Energy, Material, and Land Calculations 48 4.7 Conversion of Fossil Energy and Forest Products Consumption to Land Area Equivalents.. 49 4.8 Adjusting Energy Consumption by the Fossil Energy Factor 50 4.9 Results from the Calculation Procedure and Interpretation 51 4.10 Assumptions, Simplifications and Limitations 52 iii TABLE OF CONTENTS (CONTINUED) 5.0 CALCULATION OF CONSUMPTION COEFFICIENTS AND BASE CONSUMPTION 55 5.1 .Consumption Coefficients and Base Consumption 55 5.2 .Calculations for Housing 55 5.2.1 Embodied Energy for Housing (a21.1) 56 5.2.2 Operating Energy for Housing (a21.2) 57 5.2.3 Wood Consumption for Housing Construction (f21.1) 60 5.2.4 Wood Consumption for Housing Operation (f21.2) 61 5.3 Calculations for Passenger Transportation 62 5.3.1 Embodied Energy for Private Transportation Vehicles (a31.1) 62 5.3.2 Embodied Energy for Public Transportation Vehicles (a32.1) 63 5.3.3 Operating Energy for Intracity Passenger Transportation (a31.2 and a32.2) 63 5.3.4 Operating Energy for Intercity Passenger Transportation (a31.2 and a32.2) 64 5.4 Calculations for Infrastructure 65 5.4.1 Directly Occupied Land for Roads and Right-of-ways (b36.1) 66 5.4.2 Directly Occupied Land for Buried and Off-site Infrastructure (b37.1) 68 5.4.3 Embodied Energy for Roads and Right-of-ways (a36.1) 69 5.4.4 Embodied Energy for Buried Infrastructure and Off-site Services (a37.1) 70 5.4.5 Operating Energy for Roads and Right-of-ways (a37.1) 71 5.4.6 Operating Energy for Buried Infrastructure and Off-site Services (a37.2) 72 5.5 Summary of Consumption Coefficients and Base Consumption 73 6.0 RESULTS AND DISCUSSION 75 6.1 Description of Housing Archetypes 75 6.2 Analysis of Ecological Footprint Results 77 6.2.1 Comparison of Ecological Footprints 77 6.2.2 Components of Ecological Footprints 81 6.2.3 Comparison of Energy Consumption Calculations with Burby's Study 84 6.2.4 Comparison of Ecological Footprint Calculations with WackernageFs Thesis 86 6.2.5 Reducing a Household's Ecological Footprint 88 6.2.6 Comparison with Trends in Housing and Efficiency 89 6.2.7 Assessment of Method and Results 90 6.3 Policy Implications for Planning 92 6.4 Directions for Further Research 96 Bibliography 100 Appendix A: Measurement Units and Conversion Factors I l l Appendix B: Sources and Notes for Tables and Figures 112 Appendix C: Ecological Footprint Calculations by Dwelling Type 120 iv LIST OF TABLES Tables Table 2-1: The Consumption by Land Use Matrix for an Average Canadian (1991) 15 Table 3-1: Favourability of District Energy Systems by Land Use Type 26 Table 3-2: Household Expenditures on Accommodation by Dwelling Type in Canada, 1992 28 Table 3-3: Density and the Level and Frequency of Public Transit Service 31 Table 3-4: Commuting Patterns by Dwelling Type in Major Metropolitan Areas, 1991 32 Table 3-5: Household Expenditures on Transportation by Dwelling Type in Canada, 1992 34 Table 3-6: The Cost of Infrastructure Service per Dwelling Unit by Density, 1987 Dollars 38 Table 4-1: The Revised Consumption by Land Use Matrix 44 Table 4-2: Fossil Energy Factor Calculations 51 Table 5-1: Embodied Energy Consumption Coefficients for Housing (a21.1) 57 Table 5-2: Operating Energy Consumption Coefficients for Housing (a21.2) 59 Table 5-3: Wood Requirements for the Construction of a Standard House (f21.1) 60 Table 5-4: Wood Consumption Coefficients for Housing Construction (f21.1) 61 Table 5-5: Wood Consumption Coefficients for Housing Operation (f21.2) 62 Table 5-6: Embodied Energy Consumption Coefficients for Private Vehicles (a31.1) 63 Table 5-7: Operating Energy Consumption Coefficients for Intercity Passenger Transport (a31.2anda32.2) 64 Table 5-8: Base Consumption of Operating Energy for Intercity Passenger Transport (a31.2anda32.2) 65 Table 5-9: Directly Occupied Land Consumption Coefficients for Roads and Sidewalks (b36.1).. 66 Table 5-10: Base Consumption of Directly Occupied Land for Roads and Sidewalks (b36.1) 67 Table 5-11: Base Consumption of Directly Occupied Land for Right-of-ways (c36.1) 68 Table 5-12: Embodied Energy Consumption Coefficients for Roads and Sidewalks (a36.1) 69 Table 5-13: Base Consumption of Embodied Energy for Roads and Sidewalks (a36.1) 69 Table 5-14: Embodied Energy Consumption Coefficients for Buried Infrastructure (a37.1) 70 Table 5-15: Base Consumption of Embodied Energy for Buried Infrastructure (a37.1) 71 Table 5-16: Base Consumption of Operating Energy for Street and Traffic Lights (a36.2) 72 Table 5-17: Base Consumption of Operating Energy for Off-site Services (a37.2) 73 Table 5-18: Summary of Consumption Coefficients 74 Table 5-19: Summary of Base Consumption Calculations 74 Table 6-1: Profile of Housing Archetypes 76 Table 6-2: Results of Per Household Ecological Footprint Calculations by Dwelling Type 78 Table 6-3: Results of Per Occupant Ecological Footprint Calculations by Dwelling Type 78 Table 6-4: Ratios of Ecological Footprint to Lot Size by Dwelling Type 84 Table 6-5: Comparison of Energy Consumption Calculations with Burby's Study 85 Table 6-6: Calculation of Average Ecological Footprint Weighted by Dwelling Type 86 Table 6-7: Comparison of Ecological Footprint Calculations with Wackernagel's Calculations.... 87 v LIST OF FIGURES Figures: Fig. 3-1: Influence of Dwelling Type on Resource Consumption for Housing 25 Fig. 3-2: Influence of Dwelling Type and Density on Resource Consumption for Transportation 30 Fig. 3-3: Urban Density Versus Gasoline Use Per Capita Adjusted for Vehicle Efficiency 33 Fig. 3-4: Influence of Dwelling Type and Density on Resource Consumption for Infrastructure 36 Fig. 4-1: Components in the Ecological Footprint Calculation Procedure 41 Fig. 6-1: Comparison of Per Occupant EcologicalFootprints by Detached House Subtype (ha/capita) 80 Fig. 6-2: Comparison of Per Occupant Ecological Footprints by Dwelling Type (ha/capita) 80 Fig. 6-3: Comparison of Consumption Components of Per Occupant Ecological Footprints by Dwelling Type (ha/capita) 81 Fig. 6-4: Comparison of Consumption Components of Ecological Footprints by Dwelling Type(%) 83 Fig. 6-5: Comparison of Land Use Components of Ecological Footprints by Dwelling Type(%) 83 vi ACKNOWLEDGEMENTS This thesis arose out of a report I prepared for the University of British Columbia (UBC) Task Force on Planning Healthy and Sustainable Communities during the summer of 1994 entitled "Estimating the Influence of Housing Choice and Density on a Household's Appropriated Carrying Capacity: A Calculation Procedure." I would like to thank Mathis Wackernagel, former Ph.D. student, School of Community and Regional Planning (SCARP), UBC, and Janette Mcintosh, Task Force coordinator, for their feedback and support on the earlier report. For the preparation of this thesis, I would particularly like to thank Dr. William Rees, professor from SCARP, for his thoughtful feedback on the draft chapters of this thesis. I would also like to express my thanks to Mark Roseland, Professor from the Department of Geography, Simon Fraser University, and Tom Hutton, professor from SCARP, for their useful comments and interest in this thesis. I also appreciate the interesting discussions with Jim Sussex, a SCARP Master's student; Hijran Shawkat, a Master's student in the School of Architecture, UBC; and Yoshi Wada, a SCARP Ph.D. student, over the concept of the ecological footprint and the ideas developed in this thesis. I am also indebted to Jim Sussex and Julia Berardinucci, a close friend, for reviewing the first few chapters of this thesis. In addition, I wish to thank my friends and family for their support throughout my studies at UBC. There were numerous people who provided me with data and directed me to sources of information that are used in this thesis. I would like to thank the following people in particular: Andre Bourbeau, Natural Resources Canada; Raymond Cole, School of Architecture, UBC; Duncan Hill, Canada Mortgage and Housing Corporation; Karoly Krajczar, Greater Vancouver Regional District; Celeste Piress, BC Hydro; and Steve Taylor, School of Architecture, UBC. I gratefully acknowledge the financial support of the Natural Science and Engineering Research Council. I also would like to acknowledge the permission of Gower Publishing Limited to reproduce a graph from Cities and Automobile Dependence: An International Sourcebook by Peter Newman and Jeffrey Kenworthy for use in this thesis. vii L_ SUSTAINABILITY. DWELLING TYPE. AND DENSITY 1.1 Sustainability and Making Choices One of the greatest challenges of our time is for the human population to learn to live sustainably on the Earth. Ehrlich (1994, 42) summarizes the sustainability problem confronting human society: Humanity is currently supporting 5.5 billion people, but only by the rapid destruction and dispersal of vital natural capital, especially rich agricultural soils (Brown and Wolfe 1984; WRI 1992), ice-age groundwater (WRI 1992), and biodiversity (Ehrlich and Wilson 1991). Further we are simultaneously consuming, co-opting, or eliminating some 40% of the basic energy supply for all terrestrial animals (Vitousek et al. 1986). The scale of the physical economy is now clearly too large for the capacity of life-support systems to maintain it over the long term. The next doubling period of the world's human population is expected to be reached by the year 2050, adding another 5.5 billion people to a world that already may be effectively "full" (Goodland 1991; Daly 1991; Brown and Kane 1994). The scale of our economic activities is also growing exponentially placing ever greater stress on the natural environment and on the creatures that share our planet. The existing situation of alarming environmental degradation combined with trends that indicate more of the same at an even greater scale suggests that we cannot proceed with business as usual. In response to this predicament, the World Commission on Environment and Development (WCED) released Our Common Future in 1987 which called for a change in course towards "sustainable development." The WCED, also known as the Brundtland Commission, defined sustainable development as "development that meets the needs of the present without compromising the ability of future generations to meet their own needs" (WCED 1987, 43). The Brundtland Commission's definition of sustainable development has been both hailed for bold new approaches and criticized for being too vague and open to interpretation, but it does provide some useful defining characteristics. It 1 recognizes that certain basic human needs should be satisfied, that there should be equity between generations, and that the integrity of the ecosystem should not be compromised. Many alternative definitions and interpretations of sustainable development and sustainability have been offered (Daly and Cobb 1989; Jacobs 1991; Rees 1989). For present purposes, a definition of a sustainable society by Meadows et al. will suffice (1992, 209); it is "one that can persist over generations, one that is far-seeing enough, flexible enough, and wise enough not to undermine either its physical or its social systems of support." A fuller discussion of sustainability will be presented in a later chapter. We will now turn to the fundamental question: how to achieve sustainability. Achieving sustainability is about making choices regarding our level of population and consumption. The general groups in an economy that make decisions are individuals, households, firms, and government. These groups decide upon or have influence over the three main factors that represent the overall impact of human activities on the environment as shown in the following equation (Statistics Canada 1991): Effect on environment = population x socio-economic activity per capita x environmental effect per unit of socio-economic activity The first term, population, is affected by the reproductive choices made by individuals and families. In addition, government can influence the number of children a family has through tax credits. The second and third terms in the equation comprise the environmental impact of consumption per capita. Individuals, households, and firms that produce economic goods or consume them influence the environmental impact of consumption. Decisions by government, such as taxing gasoline, can affect consumption by individuals, households, and firms. Each decision made leads us closer to or further 2 away from sustainability. No single decision is sufficient ~ achieving sustainability requires wise choices by many groups to move us towards sustainability. This thesis is concerned with a consumption choice that all households face ~ that of what dwelling type to live in. The hypothesis of this thesis is that dwelling type is one of the most important factors that affects the resource consumption patterns of households. For the purpose of this thesis, dwelling type encompasses the type of dwelling, size of dwelling, and size of lot a household uses for its principal accommodation. A new approach called "ecological footprint" analysis, or appropriated carrying capacity, allows the ecological demand of consumption to be estimated using land area as its measurement unit (Rees 1992; Rees and Wackernagel 1994; Wackernagel 1994a; Wackernagel et al. 1993). In this thesis, the tool will be used to measure the ecological resource demands of average Canadian households in typical dwelling types. This analysis is conducted to provide insight into which dwelling types are most desirable for housing people from the perspective of sustainability. 1.2 Purpose and Objectives The overall purpose of this thesis is to estimate the influence of dwelling type and residential density on household consumption patterns and the resulting ecological resource demands on a household and per occupant basis. The ecological footprint approach is used to translate the estimated ecological demands of different dwelling types into a corresponding land area. The hypothesis of this thesis is that as density increases, the dwelling-related ecological footprint per occupant will decrease all other factors being equal. The specific objectives of this study are to: 1) Develop a procedure for calculating the ecological footprint of households in a given dwelling type; 3 2) Compare the ecological footprints of average Canadian households in typical dwelling types on a household and per occupant basis; 3) Within the detached house dwelling type, compare the ecological footprints of a standard house, an energy efficient R2000 house, and a house on a small lot while holding floor space and household size constant; 4) Compare the results of the ecological footprint calculations with housing trends in Canada; and 5) Explore the policy implications of these comparisons for planning sustainable communities 1.3 Resource Consumption Related to Dwelling Type Dwelling type affects sustainability directly through the differential consumption of energy, materials, and land for housing. Dwelling type also suggests a range of dwelling and population densities that affect consumption related to transportation and urban infrastructure. The combined resource consumption for housing, transportation, and infrastructure for all households represents a significant portion of total resource consumption. In 1989, housing and transportation accounted for 21% and 28% of energy end-use demand in Canada (Environment Canada 1991, 12-11). In 1989, the average Canadian household used 96 GJ of energy to heat and cool its home, 21 GJ to heat its water, and 24 GJ to run appliances. In addition, the average household consumed 90 GJ of automobile fuel for a total final demand of 231 GJ of energy (Marbek 1989 cited in Environment Canada 1991, 12-10). In 1989, there were approximately 9 million households in Canada and the national energy end-use demand was about 7,000 PJ (Environment Canada 1991, 12-9). Therefore, households consume approximately 30% of the energy in Canada just for space heating and cooling, hot water heating, appliances, and operation of cars. 4 The production, transportation, and consumption of energy result in significant environmental impacts. The combustion of fossil fuels results in the release of carbon dioxide, a greenhouse gas. In addition, energy derived from fossil fuels is non-renewable and therefore unsustainable. The reliance on fossil fuels is particularly prominent in the transportation sector where nearly all the energy used is derived from fossil fuels. Reducing energy consumption, particularly in the housing and transportation sectors, is therefore a crucial issue that should be addressed in any strategy for achieving sustainability. Housing and transportation also consume significant quantities of another important resource ~ land. Residential land and roads, including parking lots, typically consume approximately 51% and 19% of land use respectively in large urban areas (Hodge 1991, 148). These uses of land can displace agricultural productivity and alter the functioning of ecological systems. Housing and transportation are the two largest expenditure items in most households. In Canada, expenditures on housing and transportation constitute about 25% and 17% of an average household's after-tax expenditures respectively (Statistics Canada 1993b, 35). Since housing and transportation require such a large portion of household income, they determine how much income remains for other consumption activities, such as vacations, the purchase of consumer goods, and recreational activities. Housing and its related transportation implications also affect everyone's quality of life. Housing is an important determinant of quality of life in itself. Transportation can also affect quality of life through opportunities foregone due to time lost in transit, particularly commuting. The more time someone spends commuting to work each day, the less time that person has to spend with family and friends and to engage in other activities. Transportation can also create stress and frustration through traffic congestion that detracts from our quality of life. 5 The housing decisions of some households can also indirectly affect the quality of life of other households. For example, transportation activity generates emissions of carbon monoxide, carbon dioxide, nitrogen oxides, and volatile organic compounds. The latter two emissions can degrade local air quality when they come into contact with sunlight by forming ground-level ozone. Those households that live downwind from cities that generate significant amounts of air pollution may suffer from poor air quality, not because of their own decisions, but from decisions made by upwind households and firms. In summary, housing influences other dimensions of household consumption, including transportation and infrastructure. In combination, housing and transportation consume significant household finances; require substantial amounts of energy, materials, and land; and directly and indirectly affect our quality of life. Any effective strategy designed to achieve sustainability must therefore understand how consumption can be reduced through more sustainable housing patterns. 1.4 Comparing the Dwelling-Related Resource Consumption of Households Dwelling type and residential density affect a household's consumption of land, energy, and materials associated with housing, transportation, and infrastructure services. Let us first take a closer look at each of the resource consumption items from the perspective of an individual household and then consider how to compare the effects of dwelling type from the perspective of sustainability. Land is an ecological resource that is used both directly and indirectly by housing, and different dwelling types have different levels of land consumption. A household living in a detached house directly co-opts the ecological production of the land occupied by the dwelling and its driveway. Other land directly consumed includes the road required to service the lot and the household's share of municipal, provincial, and federal roads. Land that is indirectly consumed by the household includes land for buildings and infrastructure used by firms to produce the inputs for the household's direct consumption. 6 Housing, transportation, and infrastructure consume both operating and embodied energy. Operating energy is the energy consumed in day-to-day activities, such as for space heating or for running an automobile. Embodied energy is the energy that was used to produce capital goods, such as houses or automobiles, as well as all the energy that went into the manufacture of the material inputs. Both of these types of energy are either generated from fossil fuels or from renewable sources, such as hydroelectric power. In a truly sustainable society, all the energy consumed would need to be generated from renewable sources, such as solar energy. Consumer goods are composed of renewable and non-renewable materials. Wood, for example, is a renewable resource and concrete is a non-renewable resource. Many types of building materials are used in the construction of a house, which make comparison of dwelling types difficult. All the materials, however, share the property of containing embodied energy. The renewable materials are also linked through the process of photosynthesis, which converts solar energy, water, carbon dioxide and nutrients into plant matter. Since space is required to capture solar energy, renewable resources are in turn connected to the land. Comparing the full resource consumption of different dwelling types poses several problems for conventional analytical techniques. If one dwelling uses more land, energy, and materials than another dwelling with the same number of occupants, it is safe to say that the first dwelling is more demanding on ecological resources than the other. Consider, however, the situation where one household consumes more energy and materials than another, but less land. It therefore becomes difficult to assess which household exerts a greater demand on ecological resources, since we are comparing unlike parameters. Another factor to consider when comparing dwelling types is the number of household occupants. Instead of comparing dwelling types on a household basis, it is more appropriate to conduct a per 7 occupant comparison. Household size falls from an average of 3.0 in single-family detached houses, to 2.3 in low-rise multi-unit dwellings, to 1.8 in apartments (Statistics Canada 1992c, 41). Until data are standardized per occupant, it is unclear whether resource consumption is greater for apartment dwellers, who probably consume fewer total resources per household but have less occupants, or for a household living in a detached house. A number of other factors complicate the comparison of dwelling types. For example, buildings greater than four storeys require reinforced concrete or steel, which result in different embodied energy values relative to wood, as well as different energy requirements for construction. Ecological footprint analysis is a method of aggregating different resource consumption items into a common measure, and therefore provides a way to compare the impacts of different dwelling types. 1.5 Research Method This thesis is based on a literature review followed by a comparative ecological footprint analysis of alternative dwelling types. The literature review examines: a) the concepts of sustainability and appropriated carrying capacity, and b) research and studies related to resource consumption for housing, transportation, and urban infrastructure. The first part of the calculations involves determining the quantities of land, energy, and materials consumed for each of the dwelling types under consideration. The data necessary for conducting the calculations are collected from secondary sources or are obtained from other related ecological footprint applications. The ecological footprint accounting framework is modified specifically to a housing context. The amount of energy and materials consumed is then translated into a corresponding land area using the ecological footprint concept. Finally, the ecological footprints of each of the dwelling types are compared and analyzed on a household and per occupant basis. 8 1.6 Significance of Thesis A comparative analysis of the ecological footprint of dwelling types is worth pursuing to help advance sustainability to an operational stage and to demonstrate a unique accounting approach to sustainability. These comparisons can provide insight into which dwelling types, in general, are more desirable from the perspective of sustainability. The thesis provides a quantitative comparison of the sustainability of different dwelling types, and thus provides households and planners with a sustainability indicator to inform decision-making. It should be emphasized that the calculations in this thesis are only representative of typical households and dwellings. There are variations within a dwelling type, such as household size and floor space, that may affect the ecological footprint of a specific household. This thesis integrates information from subject areas that are usually treated in isolation. Different consumption items, such as wood and energy, and several consumption sectors, such as housing and transportation, are combined in the common context of housing. Existing studies of resource consumption are limited in scope and do not assess the full range of resource demands. In studies that do consider multiple resources, such as the PLACE S model (PLAnning for Community Energy, Environmental, and Economic Sustainability) developed by Criterion (1993), the different resources analyzed are not integrated. Another problem with existing studies is that some ignore embodied energy although this is a component of total energy consumption. The ecological footprint concept provides an accounting framework for integrating the main biophysical resources that are consumed. The approach taken in this thesis is unique for several reasons. First, it considers resource consumption from the perspective of a household. Second, the variation in the ecological footprint resulting from different dwelling types is based upon those factors over which a household has choices, such as lot size and dwelling type. Finally, this is the first attempt in the literature to apply ecological footprint analysis to the comparison of dwelling types. 9 1.7 Organization This thesis is organized into six chapters. The first chapter has introduced the purpose and significance of this thesis. The next chapter, The Appropriated Carrying Capacity Concept, examines the concept of sustainability as well as the ecological footprint and appropriated carrying capacity concepts. Chapter 3, The Influence of Dwelling Type and Density on Consumption Patterns, reviews and assesses the literature on the relationship between dwelling type, density, and resource consumption. The adaptation of the ecological footprint concept to dwelling type comparisons is described in Chapter 4, The Ecological Footprint Calculation Procedure. Chapter 5, Calculation of Consumption Coefficients and Base Consumption, contains calculations that are later used in the ecological footprint calculations. Chapter 6, Results and Discussion, describes the dwelling types that will be assessed using the ecological footprint calculation procedure, compares and analyzes the results, discusses the policy implications, and presents directions for further research. The thesis contains three Appendices. Appendix A presents the notations and conversion factors used in this thesis. Since there are many tables in this work, a detailed documentation of sources and notes for each table is included in Appendix B. Appendix C presents the ecological footprint calculations for each of the dwelling types. 10 Z_ THE APPROPRIATED CARRYING CAPACITY CONCEPT 2.1 Sustainability and the Maintenance of Natural Capital The concept of natural capital from capital theory provides a useful way to conceptualize sustainability. A general definition of capital, including human-made and natural capital, is "a stock that yields a flow of valuable goods or services into the future" (Costanza and Daly 1992, 38). Daly and Cobb (1989, 72) define natural capital more specifically as "the non-produced means of producing a flow of natural resources and services." Natural income is used to describe the stream of interest from natural capital. According to the concept of Hicksian income, the depletion of capital, should not be counted as true income since the amount of productive wealth has been reduced (Daly and Cobb 1989). To be sustainable, the human economy must only depend on the stream of true natural income. Natural capital not only includes non-renewable and renewable physical resources, but also natural processes such as waste assimilation and photosynthesis (Rees and Wackernagel 1994). Rees (1994a, 3) states that "since adequate stocks of self-producing and replenishable natural capital are essential for life support (and are non-substitutable), these forms are generally more important to sustainability than are non-renewable forms." Thus according to Rees, sustainability must emphasize the maintenance of adequate stocks of renewable resources, particularly biophysical natural capital. Daly and Cobb (1989) make a distinction between "weak sustainability" and "strong sustainability" with reference to natural capital. The "weak sustainability" version is based on the assumption that human and natural capital are freely substitutable. In the "strong sustainability" interpretation, human and natural capital are considered complements rather than substitutes, requiring human-made and natural capital to remain intact separately. The second interpretation better reflects the fact that some ecosystem 11 functions, such as the ozone layer, are irreplaceable by human capital. Costanza and Daly (1992) identify four principles needed to operationalize strong sustainability from an ecological perspective: 1) Human scale should be limited to a level which, if not optimal, is at least within the carrying capacity of the remaining natural capital; 2) Technological progress should be efficiency-increasing rather than throughput-increasing; 3) Harvesting rates of renewable resources should not exceed regeneration rates and waste emissions should not exceed the renewable assimilative capacity of the environment; and 4) Non-renewabje capital should be exploited, but at a rate equal to the creation of renewable substitutes Although these principles shed light on to the nature of sustainability, Costanza and Daly (1992, 45) admit that they "fall far short of an operational blueprint complete with measurements." One stream of natural income is annual net primary production, which is the amount of energy transformed by primary producers into chemical energy by photosynthesis, less the energy used for plant metabolism. Net primary production is the basic food source for virtually all non-plant life on Earth. Vitousek et al. (1986) estimate that humans appropriate 40% of the planet's terrestrial net primary production, indicating the magnitude of the human enterprise. Clearly, it is not biophysically possible for humans to appropriate 100% of net primary production. Indeed, given global change, sustainability may well require that humans learn to live on a reduced share of net primary production. 12 Throughout the rest of this thesis, the term sustainability will be used to mean strong sustainability according to the above discussion and my practical emphasis will concern the maintenance of adequate stocks of self-producing renewable natural capital. 2.2 The Appropriation of Carrying Capacity and Ecological Footprints While net primary production is a useful measure of one stream of natural income, it does not provide an accounting framework to connect human consumption with ecological production. Rees and Wackernagel developed the concept of appropriated carrying capacity and the ecological footprint as a framework for connecting human consumption with the demand placed on natural capital (Rees 1992; Rees and Wackernagel 1994; Wackernagel 1994a). Appropriated carrying capacity (ACC) is "the biophysical resource flows and waste assimilation capacity appropriated per unit time from global totals by a defined economy or population" (Rees 1994a, 10). The ecological footprint (EF) is the corresponding "area of productive land/water required to support the defined economy or population (i.e. to produce its resource needs and assimilate its wastes) at a specified material standard of living, wherever on Earth that land may be located" (Rees 1994a, 10). Henceforth, the abbreviation of EF/ACC will be used to refer to the ecological footprint and appropriated carrying capacity concepts. Let us consider some of the basic requirements for biological productivity and how this productivity is appropriated by humans. Biological productivity is a time- and space-dependent process. Time is required for growth and accumulation of biomass and space is required to capture the solar energy, nutrients, water, and carbon dioxide, necessary for photosynthesis. Most organisms are largely confined to living on the immediate carrying capacity of their habitat, in both a spatial and temporal sense. Humans in modern industrial society, however, are able to appropriate carrying capacity not only from their own place and time but from other places and times as well. 13 Through trade, a region can appropriate ecological output from other regions around the globe. However, while individual regions are "open" in a trade sense, it should be remembered that the Earth as a whole is a materially closed system. Through technology, humans can also appropriate carrying capacity from different times. Some ecological production in the past has been stored and is available for present and future use. For example, some organic matter produced in the past has stored solar energy and is now available as fossil fuel. Future ecological production can also be diminished by actions in the present. A more robust definition of sustainability emerges when one considers the appropriation of carrying capacity by the human economy. Using carrying capacity from the past and reducing its potential for future production are unsustainable actions since the human population and economy are really living in "illusionary time." The human population and economy must eventually learn to mostly live off the "real time" renewable carrying capacity of the planet in order to be sustainable. However, use of some non-renewable resources from the past is unavoidable. At the very least, we should acknowledge that part of economic growth is based upon the appropriation of carrying capacity from "illusionary time." One way to measure some of the renewable natural capital requirements of the human economy is through the ecological footprint accounting framework. The ecological footprint concept is based on the premise that "for most types of material or energy consumption, a measurable area of land and water in various ecosystems is required to provide the consumption-related resource flows and waste sinks" (Rees and Wackernagel 1994, 372). The resulting ecological footprint provides a measure of strong sustainability since it estimates the "real time" appropriation of carrying capacity needed to support particular consumption patterns and enables comparisons with available ecosystem resources. 14 2.3 The Ecological Footprint Consumption by Land Use Matrix An accounting system that can be used in ecological footprint calculations is the consumption by land use matrix as shown in table 2-1 (Wackernagel 1994a). The rows of the matrix represent five main consumption categories: 10) food, 20) housing, 30) transportation, 40) consumer goods, and 50) resources in services. While the first four categories are self-explanatory, the last category requires a description. Resources in services are the resources that are embodied in services received but not directly consumed, such as the paper and energy embodied in a personal cheque. For specific purposes, the consumption categories can be divided into more detailed subcategories such as '31' motorized public transportation, which would be a subcategory of'30' transportation. The columns of the matrix represent specific land use categories. There are three general land use groups. The first land use group is fossil energy land. The second land use category is labeled "degraded land", such as a road surface or human-made desert, since biological productivity is no longer possible on this land. The third land use group is termed "consumed land" and consists of "garden" land, cultivated land, and modified land, such as a forest plantation. The following six land use categories are the columns in the matrix: a) fossil energy, b) degraded, c) garden, d) crop, e) pasture, and f) forest land. Table 2-1: The Consumption by Land Use Matrix for An Average Canadian, 1991 (ha/capita) Fossil Degraded Garden Crop Pasture Forest Energy Land Land Land Land Land a b c d e f TOTAL 10 FOOD 0.33 0.02 0.60 0.33 0.02 1.30 20 HOUSING 0.41 0.08 0.40 0.89 30 TRANSPORTATION 0.79 0.11 0.90 40 CONSUMER GOODS 0.52 0.01 0.06 0.13 0.17 0.89 50 RESOURCES IN SERVICES 0.29 0.01 0.30 60 TOTAL 2.34 0.21 0.02 0.66 0.46 0.59 4.28 Source: (Wackernagel 1994a, 125) 15 Directly occupied land, which includes degraded and garden land, is the only consumption item already measured in land units. For the consumption of food, forest products, and fossil energy, conversion rates are used to translate these consumption items into land equivalents. The conversion of food and forest products consumption into land equivalents involves the use of annual average production per unit area of farm and forest land. For fossil energy consumption, one conversion method is to estimate the amount of land that would be required for a renewable energy substitute, such as ethanol, to provide the same amount of hydrocarbon energy that is currently used. An alternative approach is to estimate the carbon sink forest land that would be required to absorb the carbon dioxide released by the combustion of fossil fuels to prevent the accumulation of greenhouse gases in the atmosphere. The carbon sink approach is more politically viable and immediately compelling than the renewable energy approach. Combining the five consumption categories with the six land use classes yields a 30 cell (5 rows by 6 columns) consumption by land use matrix. Each cell represents the land required to satisfy the material and energy requirements of a particular consumption item on a sustainable basis. The completed consumption by land use matrix for an average Canadian is included in table 2-1 (Wackernagel 1994a). The table shows that the ecological footprint of an average Canadian is at least 4.28 ha per capita with existing technology, based on conservative assumptions. This land is equivalent in area to a square 205 metres to a side. It is useful to compare this calculation with the actual per capita productive land available on the planet. The total land area on the Earth is 13 billion hectares, of which 8.8 billion hectares is productive crop land, pasture, or forest. In 1995, there were more than 5.7 billion human inhabitants on the planet. If everyone on the planet consumed like an average Canadian, over 24 billion hectares of land would be required to support their consumption patterns sustainably, and this assumes the land is being used sustainability. In short, approximately two additional Earth's would be required to support the consumption demands of the world population at present Canadian material standards. These 16 findings indicate that it may not be biophysically possible to bring the world's population up to North American material standards on a sustainable basis (Rees and Wackernagel 1994). 2.4 Assessment of the Ecological Footprint/Appropriated Carrying Capacity Concept The EF/ACC concept has advantages and disadvantages as a measure of natural capital. EF/ACC is compared here with monetary and energy analyses, which are more traditional analytical methods. One of the fundamental benefits of the EF/ACC concept is that it is compatible with strong sustainability and it emphasizes the need to maintain adequate stocks of self-producing natural capital. According to Wackernagel (1994a, 79), "land is used as it represents ecosystems and their photosynthetic productivity, and thereby the essence of [renewable] natural capital." Land is a more appropriate unit to measure sustainability than money and prices. Money and prices do not represent material wealth itself, only what can bought in a market (Daly and Cobb 1989). Money is abstracted from biophysical reality to such an extent that it is unsuitable as a surrogate for natural capital. For example, money can grow exponentially while natural capital is constrained by the laws of thermodynamics. Another problem inherent in monetary analyses is the use of discounting. Discounting provides a bias against future values to the extent that benefits and costs that accrue more than one generation into the future are virtually unaccounted in present value calculations. Using land as a measure avoids the problem of discounting altogether. Monetary analyses also encounter problems when attempting to value intangible environmental goods and ecological functions. Contingent valuation is used to obtain prices for environmental goods and services by asking individuals their willingness to pay for a gain or willingness to accept a loss in an environmental good or service (Pearce and Turner 1990). However, contingent valuation and social benefit-cost analysis have been severely criticized for ethical reasons, systematic bias, and theoretical 17 flaws (Kelman 1981; Vatn and Bromley 1994). In any case, according to Rees (1994a, 5), neither market nor shadow prices "reflect the size of the corresponding natural capital stocks, whether there are critical minimum levels below which stocks can no longer replenish themselves (the real measure of biophysical scarcity), the functional roles of such stocks in relevant ecosystems, or their ultimate value in sustaining life." Energy is a better measure of biophysical reality than money, but it too has its shortcomings. Energy analyses ignore the multifunctionality of ecosystems and their life support services. The ecological footprint concept addresses these ecosystem functions since "land also represents life and can be seen as a proxy for certain life-support functions such as rain collection, exchanges of gases, waste absorption, biogeochemical cycling, self-production and renewal, or link between and nutritional basis for organisms" (Wackernagel 1994a, 83). The selection of land as an indicator also addresses the maximum scale of the economy. Land is assuredly finite and represents a limit to economic growth. Low entropy energy is also finite, but does not truly reflect the fact that other factors may limit our growth according to the ecological principle of Liebig's Law of the Minimum. Finally, energy or material units only provide a relative measure of sustainability. A major strength of using land as an indicator is that its demand can be readily compared with its supply. This allows sustainability to be measured in absolute terms instead of relatively. Another merit of the EF/ACC concept is the relative ease of computation. A draft EF/ACC handbook for conducting ecological footprint calculations is available that includes data and calculations for an average Canadian (Wackernagel et al. 1993). In one application of the ecological footprint concept, Davidson and Robb (1994, 20) concluded that the ecological footprint concept was "easy to use and 18 without extensive data requirements . . . all the required technical assumptions that relate consumption items to an appropriate land area were available in the ACC handbook." Land is a particularly powerful measurement of sustainability because it is a familiar item and is therefore easy to grasp. In contrast, it is difficult to fathom the significance of a gigajoule of energy, a tonne of materials, or a trillion dollars. People are aware that there is a finite amount of land on the planet, and therefore comprehend that land is a limiting factor to economic growth. Land is also one of the three factors of production in economics, which are land, labour, and capital. The understandability of the concept and the graphic image associated with the ecological footprint give it a great heuristic value. It is necessary to be able to readily communicate the concept of sustainability to the general public. The ecological footprint succeeds as a communication tool for sustainability in both its simplicity and the profound message it conveys. A possible criticism of the EF/ACC concept involves the accuracy of the ecological footprint calculations. Rees and Wackernagel address this concern by basing their assumptions on conservative figures which result in the ecological footprint being underestimated. Therefore, no matter how "startling [the] results may appear, they are actually conservative estimates of the resource flows and productive land appropriated to sustain the human economy" (Rees and Wackernagel 1994, 373). Therefore, their calculations serve well the primary purpose of EF/ACC analysis as an heuristic tool. Another possible criticism is that the full range of waste assimilation functions or the bioproductivity that occurs in water are not included. In addition, the EF/ACC calculations do not include limits to the use of non-renewable materials; only the energy requirements of extracting and utilizing these materials are considered. These concerns, while perfectly valid, only further support Rees and WackernagePs 19 contention that the results underestimate the ecological footprint. These concerns are acknowledged by the authors as areas of further research. Another potential concern over the EF/ACC concept involves the conversion rates from energy and material units into land equivalents. A higher annual yield of timber, for example, from a forest stand means less land is required to support a population's consumption of forest products and hence gives a smaller ecological footprint. Ecological footprint calculations could possibly be misused to support cultivated monoculture forests or crops that produce higher yields. The conversion rate between forest land and wood consumption does not capture the fact that while "cultivated natural capital does substitute for natural capital proper in certain functions - those for which it is cultivated, such as timber production, [it does not substitute for] wildlife habitat or biodiversity in the case of a plantation forest" (Daly 1994, 30). One limitation of the ecological footprint concept then is that it is prone to leaving out some important qualitative aspects of sustainability, such as biodiversity. In conclusion, the EF/ACC concept is a better measure of natural capital than money or energy. Its advantages include its compatibility with strong sustainability, ease of computation, understandability, and heuristic value. The ecological footprint concept, however, is not designed to explore the qualitative aspects of sustainability. It is also a new concept and still remains to be fully tested and validated. Even with further research and refinements, it will be prone to criticism like other aggregate indicators, such as the gross domestic product. Overall, however, I believe the ecological footprint concept possesses considerable merit as a measure of sustainability and that its usefulness outweighs its disadvantages. 2.5 Related Ecological Footprint/Appropriated Carrying Capacity Applications There are several applications of the appropriated carrying capacity concept related to this thesis. Each of these applications will be described briefly to provide the research context for the present work. The 20 original EF/ACC application was the estimation of the ecological footprint of an average Canadian, described in the draft ACC Handbook (Rees 1992; Wackernagel et al. 1993). The'ACC Handbook documents the detailed calculations for each component of the ecological footprint of an average Canadian. It also describes the rationale and rates used for converting energy and material units into land area equivalents. The assumptions and calculations in the ACC Handbook can serve as the basis for other applications of the ecological footprint concept and are used here. An EF/ACC application related to housing is Shawkat's thesis on the ecological footprint of new single-family detached houses (in progress). His thesis examines how the ecological footprint of new single-family detached houses can be reduced. This thesis is somewhat similar to Shawkat's since it involves calculations for the housing portion of a household's ecological footprint. Shawkat's thesis, however, focuses exclusively on the ecological footprint of a dwelling while this study includes resource consumption for transportation. In addition, Shawkat's thesis only performs calculations for detached houses while townhouses and apartments are also considered here. There are three research papers that investigate the transportation category of the ecological footprint. Parker (1993b) qualitatively investigates the effect of urban form on the ecological footprint of a community and includes a set of criteria for reducing the ecological footprint in urban areas. In a research paper by Beck (1993), a qualitative examination is given of the policy options and strategies for reducing the transportation component of the ecological footprint. Another transportation application of the EF/ACC concept is Davidson and Robb's (1994) quantitative assessment of two options for the Lion's Gate Bridge, a bridge in Vancouver, B.C., that is in need of replacement. The ecological footprint of the existing three-lane bridge and a proposed five-lane bridge are calculated based on the influence of the bridge on settlement and travel patterns. They conduct calculations for both housing and 21 transportation consumption, and include a simplified method for linking together density with transportation energy consumption, which is adapted for use in this thesis. This thesis directly builds upon a previous research report entitled, "Estimating the Influence of Housing Choice on a Household's Appropriated Carrying Capacity: A Calculation Procedure" (Walker 1994). The purpose of the report was to develop a calculation procedure for estimating the ecological footprint of households in various dwelling types. The procedure is illustrated by conducting calculations for several housing choices of a particular household in the Lower Mainland of B.C. for three dwelling types: a single-family detached house, townhouse, and apartment. Within the detached house type, the effect of various energy efficiency levels was considered. This thesis expands on the previous work by applying the calculations to average households in typical existing dwelling types. Thus an attempt is made to obtain results more representative of the existing housing stock. Finally, this thesis includes separate calculations for infrastructure which were not examined in detail by the other applications. In summary, this thesis builds upon the above EF/ACC applications and fills in some of missing information gaps. This thesis also uniquely applies the EF/ACC concept from the perspective of households. 22 1_ THE INFLUENCE OF DWELLING TYPE AND DENSITY ON CONSUMPTION PATTERNS 3.1 Dwelling Type. Density, and Consumption Patterns This chapter reviews the literature on the influence of dwelling type and density on household resource consumption patterns for housing, transportation, and infrastructure. The resource items of interest in this review are land, energy, and forest products which are the relevant resources used in the ecological footprint accounting framework. In addition, the influence of dwelling type and density on another resource is considered - household expenditures associated with housing and transportation, and development costs related to infrastructure. The chapter begins with definitions of various measures of density. The influence of dwelling type and density on resource consumption and monetary costs are then examined for housing, transportation, and infrastructure. Finally, an overall assessment of the literature is provided from the perspective of this thesis. 3.2 Measures of Density There are several ways to measure density; the appropriate measure to use depends on the purpose of the measurement. Density in the urban development context is usually calculated for the concentration of people, jobs, or dwellings per unit area. Density is generally categorized into either a net or gross measure. Net density for residential development refers to the population or dwelling unit density per unit area of residential land, excluding streets, parks, and public areas. Gross density measures the same population or dwellings, but is calculated over the entire residential area including the land for streets, parks, and other common spaces. Gross densities are generally about 20% lower than net densities. For this thesis, a gross measure of density is preferred since it reflects the true density of both public and private land for development purposes. Density is also distinguished by geographic scale. Density calculations can be conducted for a small residential development or for an entire urban area. The scale used in the calculation of density should be specified when stating a measure of density. 23 3.3 Dwelling Type and Resource Consumption for Housing Figure 3-1 is an influence diagram that shows the affect of dwelling type on land, energy, and materials consumption for housing. Dwelling type encompasses the type of dwelling, amount of floor space, and lot size. The diagram is simplified to show only the key variables affecting resource consumption. Lot size, which is influenced by dwelling type, determines the amount of land that is directly occupied by the household. The amount of land that lies underneath the dwelling and any impervious surfaces, such as driveways, is land that is degraded since it has been rendered biologically unproductive. The lot area less the degraded land is the amount of "garden" land appropriated by the household. Dwelling type affects the operating energy of the dwelling, primarily energy for space heating and cooling. Space conditioning, which includes both heating and cooling, accounted for 64% of dwelling energy consumption in B.C. in 1992 (B.C. Energy Council 1994a, 98). Water heating is the other main energy consumer in residential buildings, but is not directly related to dwelling type. The basic contention is that "increased density reduces per capita requirements for space heating and cooling for buildings" (Lang 1985, 18). Dwelling type determines the proportion of walls and floors that are shared with other dwellings. Shared walls and floors result in less exposed surface area for heat transfer. Dwelling type also influences floor space, since apartments tend to have less floor space than townhouses which in turn are usually smaller in size than detached houses. The energy consumed for space conditioning is a function of floor space, proportion of shared walls and floors, climate, building orientation, design, and thermal efficiency of the structure. Detached houses, which have no shared walls or floors, consume the most operating energy per unit floor space when other factors are held constant. When embodied energy is added to operating energy, detached houses are still the largest consumers of energy per unit. According to Burby (1982, 13) "generally, single-family homes take almost four and a half times more energy to build and operate per household than do multi-family units." 24 Fig. 3-1: Influence of Dwelling Type and Density on Resource Consumption for Housing Floor space Dwelling type (^building lifespan construction practices Energy, material and land consumption for housing 25 Energy efficiency can reduce energy consumption in a dwelling. An R2000 house reduces energy consumption by about 48% compared to existing detached houses. A super energy efficient Advanced house can reduce energy to approximately 21-27% of existing houses. The Advanced house contains high performance windows, an integrated mechanical system, energy efficient appliances and lighting, high levels of insulation, and air-tight construction (B.C. Energy Council 1994b; Ontario MOEE 1991) Higher densities facilitate the use of more efficient energy technologies, such as district energy (DE) systems which are used extensively in Scandinavia and northern Europe. A DE system pumps hot water, steam, or chilled water generated at locations along the system through pipes to buildings on a pipeline network for their space heating, domestic water, or industrial process needs. Users connected to the system extract energy from the pipeline network instead of generating their own energy on-site (MacRae 1992). Combined heat and power (CHP) generation is typically used in DE systems to produce electricity and useful heat, which would otherwise be an efficiency loss. Modern gas-fired CHP plants have efficiencies in excess of 85%, which is greater than the combined efficiency of separate heating plants and electrical generating stations (Rogner 1992, v). CHP systems can reduce fuel consumption by 25 to 30% compared with individual heating systems using natural gas (Rogner 1993, 116). The cost of providing a pipe network is inversely related to density and a DE system also requires a large heat load which is facilitated at high densities. Table 3-1 shows the favourability of DE systems by land use type. Table 3-1: Favourability of District Energy Systems by Land Use Type Type of Land Use: Desirability for District Energy: Downtown; high rises Very favourable Downtown; multi-storied Favourable City core; commercial buildings and multi-family apartments Possible Two-family residential Questionable Single-family residential Not possible Source: Based on (Bloomquist et al. 198: ?, 13 cited in MacRae 1992) 26 According to this table, the favourability of DE systems increases with density and with a greater mix of land uses. While the table shows that DE systems for single- and two-family residential developments are unlikely, Rogner (1992) found that a DE system for a new suburb near Toronto was economically feasible. The use of DE systems in low density suburban areas seems unrealistic based on other estimates which suggest that medium densities are necessary. In addition, the most efficient form for a DE network is a grid system (Owens 1986), while suburban streets are usually curvilinear. In Britain, a threshold of 44 units per hectare was considered to be the minimum density to introduce a DE system (cited in Owens 1986). The threshold density will vary however with other variables and with changes in technology. Many studies indicate that the introduction of DE requires a site specific evaluation. After reviewing the energy consumption literature for housing, Lang (1985, 23) concludes that "there appears to be a consensus that increased residential density is associated with lower levels of energy consumption per dwelling unit and per unit floor area." Lang provides some qualifications to his general statement. Some studies have indicated that there is a threshold beyond which increases in density increase energy consumption. This threshold was estimated to be between 10 to 50 stories, although this threshold remains undetermined. At these densities, additional energy for common services, elevators, and construction can increase energy use. Other qualifying factors include the effect of incomes, size of unit and household, and contrary findings when energy is measured per capita instead of per dwelling. There is less information on the variation in the consumption of forest products by dwelling type. An average Canadian home requires approximately 24 m 3 of wood for its frame and floors (Environment Canada 1991, 10-11). The consumption of forest products depends on the dwelling type, floor space, construction materials and building height. Above four stories, building frames require steel or reinforced concrete. The reduced amount of wood used in the frames of high-rise apartments, however, must be weighed against the increased embodied energy that is used by other building materials. Wood 27 not only has less embodied energy than steel or concrete but also "has the double advantage as a construction material that its production results in much lower C 0 2 emissions than alternative materials, and it locks up carbon for the life of the building" (Buchanan and Honey 1994, 211). The effect of dwelling type on a household's budget is related to the consumption of land, energy, and materials. Table 3-2 contains household expenditures on principal accommodation in Canada by dwelling type for 1992. The table shows that household after-tax income is highest for single-family detached households, which also have the most occupants on average. It also shows that expenditures on oil, natural gas, and electricity are all lower for apartments than detached households. It was unclear if energy consumption for elevator operation and space heating of common areas were included for apartments in this survey. These expenditure patterns reflect the literature on energy consumption. Table 3-2: Household Expenditures on Accommodation by Dwelling Type in Canada, 1992 Homeowners Tenants In single detached dwellings: In other types of dwellings: In apartments: In other types of dwellings: Household Size 3.00 2.54 1.78 2.68 Household before tax income ($) $55,780 $49,681 $28,463 $35,579 RENTED LIVING QUARTERS ($) $32 - $5,987 $6,241 -Rent $32 - $5,854 $6,061 OWNED LIVING QUARTERS ($) $6,007 $6,243 - --Maintenance, repairs, and replacements $664 $496 - --Property taxes $1,652 $1,475 - --Homeowners' insurance premiums $387 $325 --Mortgage interest $2,949 $2,826 - -WATER, FUEL, AND ELECTRICITY ($) $1,971 $1,544 $405 $1,138 -Water $211 $130 $10 $95 -Fuel oil and other liquid fuel $202 $146 $18 $120 -Piped gas $371 $278 $40 $221 -Electricity $1,116 $956 $234 $669 TOTAL EXPENDITURES ($) $8,010 $7,809 $6,394 $7,695 % of Before-Tax Income 14.4% 15.7% 22.5% 21.6% Source: (Statistics Canada 1993b, 118-123) 28 3.4 Density and Resource Consumption for Transportation Energy is the principal resource of concern for transportation. Figure 3-2 is a simplified influence diagram that illustrates the relationships between dwelling type, density, and transportation energy consumption. The diagram includes some variables that affect transportation energy consumption but are unrelated to dwelling type. The linkage between dwelling type and density occurs indirectly through a household's lot size. Residential density depends on the housing configurations of all dwellings in an area, but similar dwellings are usually grouped together. The following discussion is thus related to the influence of density in general on travel patterns and energy consumption. According to Handy (1992, 2), "transportation demand patterns can be characterized by the number of trips made, the length of trips in terms of time or distance, the split between different modes, and the speed of trip, as well as the time of day and the vehicle occupancy." In combination, these variables determine total travel and transportation energy consumption. Lang (1985, 25) states that "the general argument is that higher-density development is associated with reduced transportation energy consumption." Higher density affects travel patterns in direct and indirect ways. Higher densities shorten trip lengths and the number of trips taken, improve the viability of public transit service, and influence the modal split away from the automobile towards walking, cycling, and public transit. Owens (1986, 32) states that "the single most important factor in the relationship between urban form and transport energy requirements seems to be the physical separation of activities, determined in part by density and in part by the interspersion of different land uses." There is an inverse relationship between urban density and trip length. In addition, there tends to be a reduction in the number of trips taken. Higher densities and closer destinations allow for multi-purpose trips to be taken, called "trip chaining." Travel surveys have shown that households in new suburban areas typically take 11 trips per day compared to an average of 9 trips in older traditional neighbourhoods (Calthorpe 1993, 48). 29 Fig. 3-2: Influence of Dwelling Type and Density on Resource Consumption for Transportation Floor space Dwelling type Lot size potential ridership for public transit (urbanfom) feasibility of ublic transit 3 requency of public transit service ^ r e l i a b i l i t y embodied energy in vehicle Energy consumption for transportation 30 Density and distances between destinations influence the modes of transportation that are available and feasible for people to use. Walking, cycling, and other non-motorized modes of transport become viable when trip distances are shortened. Density affects the feasibility of public transit service through potential ridership loads. A density of at least 15 units per gross residential hectare is suggested as the threshold for effective bus service (Snohomish Transportation Authority 1994, 21). Table 3-3 presents the minimum densities for certain frequencies of public transit service. Rapid transit is only feasible at densities above 30 units per gross residential hectare, with densities higher near transit stations. Table 3-3: Density and the Level and Frequency of Public Transit Service Minimum Residential Density: (units/ha) Public Transit Service: Frequency of Service: 10 Bus 1 hour 17 Bus 1/2 hour 37 Bus Frequent service 30 (average with higher density around transit stations) Rapid transit 5 minutes in peak periods Source: (Pushkarev and Zupan 1977 cited in Ontario MOT et a . 1992, 18) The mode of transportation selected depends on convenience, cost, reliability, safety, travel time, nature of trip, and modes of transport available. Automobiles are one mode that may be unavailable to some people. Children and some elderly people, incapable or not licensed to drive, must rely on either another driver or another transportation mode. Some households may simply not be able to afford a vehicle. Automobile ownership is highest for single-family households at 94% compared to 56% for households in apartments (Statistics Canada 1992b). Table 3-4 shows the modal split for commuting by dwelling type in Canada. About 77% of single-family detached households have one or more members who commute by car compared to 57% for apartments. Most of the modal shift is from the car to transit. 31 Table 3-4: Commuting Patterns by Dwelling Type in Major Metropolitan Areas, 1991 Households Where at Least One Member: Dwelling Type: Drives Private Vehicle: (%) Uses Public Transit: (%) Walks or Cycles: (%) Single-family, detached 77 17 6 Single-family, attached 74 24 2 Apartment or flat 57 34 9 Based on: (Statistics Canada 1993a, 55) The transportation mode chosen influences energy consumption since different modes have varying efficiencies. Walking, cycling, and other non-motorized modes are the most efficient, only requiring caloric intake from food. Urban transit is more energy efficient per passenger-kilometre traveled than automobiles at typical occupancy rates. However, there is potential for significant improvements in vehicle energy efficiency. Lovins and Lovins (1995) estimate that completely redesigned ultralight hybrid cars, or "hypercars", can potentially increase efficiencies ten-fold over today's new cars. Newman and Kenworthy (1989) call the phenomena of shorter trip lengths, reduced number of trips, increased use of public transit and non-motorized modes, reduced automobile ownership, and overall reduced automobile usage "reduced automobile dependence." A study of 32 international cities showed that density was a key variable that affected the amount of automobile dependence. The researchers were able to correlate density with annual per capita gasoline consumption as shown in figure 3-3. The graph shows an exponential decrease in gasoline consumption as density increases. They discovered that above a density of 30-40 persons per gross urban hectare reduced automobile dependence was fostered. European and modern Asian cities, which have the highest densities, have the lowest levels of gasoline consumption. U.S. and Australian cities, with the lowest densities, have the highest gasoline consumption. Toronto, and five other Canadian cities that were studies later, fell somewhere in between this range (Newman, Lyons, and Kenworthy 1990). Canada has a similar modal split as the U.S. for walking and cycling, but has a greater use of public transit at 15% for all trips (Calthorpe 1993, 47). 32 Fig. 3-3: Urban Density Versus Gasoline Use Per Capita Adjusted for Vehicle Efficiency 80.000 60.000 o 0 0 o - J 2 > u c «: a a o 3 C C < Phoenix Detroit Denver 0 Los Angeles j©San Francisco Boston Washington DC 9 Chicago • New York © Toronto Copenhagen Hamburg Paris » ^Franklurt Stockholm Zurich • ^ s . _ . ? ? , _ • Brussels 50 275 Urban density (person per ha) Source: (Newman and Kenworthy 1989, 49) Reproduced with permission of publisher 33 Most of the studies on density and energy consumption are empirical. The data are usually aggregated so it is difficult to separate out the factors that affect travel and energy consumption patterns. For example, Newman and Kenworthy have been criticized for underestimating the influence of gasoline prices and income on energy consumption patterns in their study (Gomez-Ibafiez 1991). A common fault in all the research has been the inability to separate out the effect of density from other factors that also reduce travel and energy consumption, such as the socio-economic characteristics of households. Household expenditure patterns for transportation reflect the density-energy consumption relationships discussed above. Table 3-5 shows private transportation expenditures per household and per occupant are highest for homeowners living in single-family detached houses and lowest for tenants in apartments. Similarly, expenditures on fuels is highest for households in single-family detached households. Expenditures on local and commuter transportation are lowest for single-family detached households. Table 3-5: Household Expenditures on Transportation by Dwelling Type in Canada, 1992 Homeowners Tenants In single detached dwellings: In other types of dwellings: In apartments: In other types of dwellings: Household Size 3.00 2.54 1.78 2.68 Household before tax income ($) $55,780 $49,681 $28,463 $35,579 PRIVATE TRANSPORTATION ($) $6,349 $5,706 $2,869 $4,109 Purchase of automobile or truck $2,605 $2,467 $1,085 $1,625 Operation of automobile or truck $3,570 $3,084 $1,673 $2,328 -Automotive fuels $1,549 $1,280 $631 $983 -Maintenance and repair jobs $515 $469 $281 $316 -Priv. and public insurance premiums $981 $878 $483 $660 PUBLIC TRANSPORTATION ($) $419 $584 $465 $357 Local and commuter transportation $129 $248 $282 $191 Inter-city transportation $289 $336 $183 $166 TOTAL EXPENDITURES ($) $6,768 $6,290 $3,334 $4,467 % of Before-Tax Income 12.1% 12.7% 11.7% 12.6% Source: (Statistics Canada 1993b, 118-123 ) 34 According to this table, transportation accounts for over 12% of a household's before-tax income. Duany and Plater-Zyberk (1994, xiv) suggest that by reducing the number of cars a household owns by one, a $5,000 U.S. annual savings can be achieved. One item not shown is transportation subsidies. A study by Peat Marwick Stevenson & Kellogg (1993) estimated the "full" costs of various transportation modes in the Lower Mainland of B.C. They estimated that the subsidy on private vehicles was about $0.15 per passenger-kilometre in 1991, or over $3,300 per vehicle annually with an average occupancy of 1.4 and 15,500 km traveled. The subsidies include road construction and maintenance, land value for roads, pollution, parking, and unaccounted accident costs. By comparison, public transit was subsidized by $0.24 per passenger-kilometre, but fewer passenger-kilometres are traveled on public transit. The authors concluded that if the full costs of private vehicles were internalized and that users became aware of these costs, automobile use would decline accordingly. 3.5 Density and Resource Consumption for Infrastructure Figure 3-4 is an influence diagram showing the affect of density on resource consumption for infrastructure. Buildings require infrastructure services such as roads, sidewalks, street lights, and water and sanitary sewers. These services consume land, energy, and forest products. Some infrastructure, such as roads, directly occupies land. Operating and embodied energy are required for fabricating, installing, and maintaining infrastructure. Some forest products are also used, such as utility poles. Frank (1989) identifies density, lot size or width, municipal standards, characteristics of occupancy, contiguity of development, distance to central facilities, and settlement size as the main variables affecting infrastructure costs, an indicator of resource consumption. Dwelling type influences lot size and width, as these are usually specified in the local zoning by-law according to dwelling type. Gagnon (cited in D'Amour 1993) estimated the street length per unit to be 17.5 m for single-family bungalows, 4.2 m for triplexes, 1.7 m for 4 storey apartments and 1.0 m for 8 storey apartments. 35 Fig. 3-4: Influence of Dwelling Type and Density on Resource Consumption for Infrastructure Floor space /^l iffespan o f ^infrastructure Energy, material and land consumption for infrastructure 36 Lang states that embodied energy is more important than operating energy for infrastructure (Lang 1985, 33). A U.S. study estimated that embodied energy in neighbourhood infrastructure is six times higher than operating energy. The study found that single-family houses consume over three times as much embodied energy compared to multi-family houses. The main factor affecting embodied energy consumption for single-family houses was the amount of paved street while for multi-family dwellings, the main factor was the area for parking (Burby et al. 1982, 20). Even when both operating and embodied energy are taken into account, the reduction in energy consumption for infrastructure due to density is believed to be less significant relative to buildings and transportation (Lang 1985, 31). Several studies have been conducted on the effect of density on the cost of providing infrastructure. In The Costs of Sprawl, the Real Estate Research Corporation (1974) provided one of the first comprehensive quantitative analyses of the costs of suburban sprawl. The researchers found that high-density development could reduce utility costs by 15% and operating and maintenance costs by 5-6%. The study found that point infrastructure costs, such as sewage treatment, and soft infrastructure, such as schools, are largely based on population size rather than development pattern. The cost for linear utilities, however, is a function of density. Frank (1989) updated the results of the study to 1987 dollars and combined it with other related studies as summarized in table 3-6. The table shows that the costs of neighbourhood streets and on-site utilities decline from over $18,000 per unit for suburban densities to approximately $4,000 U.S. (1987 dollars) for high-rise apartments. When off-site services and soft infrastructure costs, such as schools, are included, low-density sprawl requires about $35,000 in capital costs per unit. In contrast, that figure is reduced to $18,000 for a combination of 30% detached houses and townhouses and 70% apartments (Frank 1989, 39). 37 Table 3-6: The Cost of Infrastructure Services per Dwelling Unit by Density, 1987 Dollars Density and Dwelling Type: (DU/ha) Neighbourhood Streets: (1987 U.S. $) Neighbourhood and On-site Utilities: (1987 U.S. $) TOTAL Neighbourhood: Costs (1987 U.S. $) 7.4 (3 DU/acre) (single-family) $7,083 $11,388 $18,471 24.7 (10 DU/acre) (townhouses) $4,855 $4,920 $9,775 37.1 (15 DU/acre) (garden apartments) $3,367 $3,285 $6,652 74.2 (30 DU/acre) (high-rise apartments) $1,843 $1,997 $3,840 Based on: (Frank 1989, A 10) 3.6 Assessment of Literature Resource consumption depends on many variables and indirect influences that make it difficult to isolate the impact of individual variables on total consumption. The accuracy of the quantitative estimates depend on the complexity of relationships and number of variables involved. My confidence in data quality is greatest for the studies on operating energy for housing since it is easily measured in residential buildings. A problem with apartments, however, is that the additional operating energy for elevators, heating, and lighting of common areas is sometimes not included. Embodied energy consumption for buildings is less accurate since they are based on input-output models and require life-cycle analysis, which allow for more errors to occur. The least studied resource for housing is the consumption of forest products, particularly for apartments. There is a large amount of literature on energy consumption for transportation, but it is of varying levels of quality and usefulness. Density is one of the most studied variables related to urban form. Most of the studies of the affect of spatial structure on travel patterns have been empirical rather than experimental. This has presented a problem in isolating the effect of variables on transportation patterns. 38 The affect of density on resource consumption for infrastructure has received the least amount of research attention. The research that has been conducted has focused on the costs of development, which is only an indicator of resource consumption. No data was available on the impact of density on the consumption of forest products for infrastructure, such as utility poles. One of the most glaring gaps in the literature on resource consumption related to density is the lack of integration of consumption sectors for a particular resource. Most studies concern only an individual sector, such as housing or transportation, and usually only one resource, typically energy. Even for those studies on energy, there are few studies that consider both operating and embodied energy consumption. The most integrated approach to date is the PLACE 3S computer model, which estimates the impact of different urban design scenarios on energy consumption, pollution emissions, and costs (Criterion 1993; California Energy Commission et al. 1994). PLACE 3S indicates that comprehensive planning and design can reduce per capita energy use by 50% compared to conventional development. The savings are from shifts to public transit, walking, and cycling; reduced energy use in buildings; and use of more efficient energy supply technologies. PLACE S also includes other resource items, such as water. Most of the studies reviewed did not address sustainability or only interpreted it in a relative sense. The scope of energy studies in the 1980s and 90s has expanded to include air emissions, particularly carbon dioxide. Although there are some limitations regarding the quantification of resource consumption, the studies serve several purposes for this thesis. First, the studies identify the most important variables that influence resource consumption related to dwelling type and density. Second, they show the change in direction and magnitude of resource consumption as density varies. Finally, these studies provide the best data currently available. Since this thesis relies on data from secondary sources, these studies serve as the basic sources of data used for the ecological footprint calculations. 39 4j_ THE ECOLOGICAL FOOTPRINT CALCULATION PROCEDURE 4.1 Applying the Ecological Footprint/Appropriated Carrying Capacity Concept In order to compare the dwelling-related ecological demand of households, I used the appropriated carrying capacity concept and applied it at the household level. According to Statistics Canada (1992f, 139) a household is defined as the following: A person or group of persons (other than foreign residents), who occupy the same dwelling and do not have a usual place of residence elsewhere in Canada. It may consist of a family group (census family) with or without other non-family persons, two or more families sharing a dwelling, of a group of unrelated persons, or "one person living there . . . The household is one of the most basic organizational units in society. Household members share physical resources, such as the dwelling by definition, common interior spaces, yard space, parking spaces, and the infrastructure that services the lot. If the members of the household are from the same family, income and other resources, such as transportation vehicles, can also be shared. Finally, housing decisions are generally made at the household level rather than by individuals. All of the calculations are therefore performed on a per household basis. I also divide the household results by the number of occupants to obtain the ecological footprint per occupant. The measure of interest for comparison purposes in this thesis is the ecological footprint per occupant. The ecological footprint calculation procedure consists of five components: 1) housing characteristics, 2) consumption linkages, 3) calculations in energy, material, and land units, 4) conversion of energy and material units to land area equivalents, and 5) results. Figure 4-1 graphically represents the relationships among the components. Each dwelling type has its own set of housing characteristics that represent the important factors affecting consumption. The consumption linkages provide the method for connecting the housing characteristics with the consumption of energy, materials, and land. The ecological footprint consumption by land use matrix, described in Chapter 2, is employed for organizing the calculations. 40 E co —^» CO CO w S o § Id • 2 CO P H tofj CO H u CO bTj t o .a 'c Forest Products f Garden Land c Degraded Land b Fossil Energy CONSUMPTION ITEM 20 HOUSING 30 TRANSPORTATION j 35 INFRASTRUCTURE 60 TOTAL CO « < 1J cG O Oil -£ 3 0) co 5 o 6 U 2 o H Forest Land f I Garden Land c | Degraded Land b Energy j Land j a j CONSUMPTION ITEM 20 HOUSING 30 TRANSPORTATION 35 INFRASTRUCTURE 1 H o The conversion component involves the translation of energy and material consumption into land area equivalents for comparison purposes. Totaling each of the consumption categories in land units yields the ecological footprint results per household and per occupant. Each of the components in the calculation procedure will be described in greater detail in the subsequent sections, following a description of the modifications that were made to the original consumption by land use matrix. Finally, the assumptions, simplifications, and limitations associated with the method will be stated and discussed. 4.2 Modifications to the Consumption by Land Use Matrix The calculation framework that is used for this thesis is the consumption by land use matrix. Recall that there were five consumption categories in the matrix: food, housing, transportation, consumer goods, and resources in services. To simplify the calculations, it was assumed that dwelling type and residential density do not influence the consumption of food, consumer goods, or resources in services. This assumption is reasonable for food since food consumption is unrelated to dwelling type. Dwelling type does have some influence over the consumption of consumer goods, such as furniture, and resources in services so this assumption was made to simplify the calculations. Another consumption item that is assumed to be unrelated to density is the transportation of goods. The transportation focus of this thesis only concerns the transportation of people, not goods. A separate consumption category for infrastructure was added to the consumption by land use matrix. The infrastructure category includes roads, sewers, and transmission lines. The rationale for this addition is that infrastructure is a large consumer of land. For example, streets and utilities comprise approximately 25% of land use in urban areas (Hodge 1991, 148). Infrastructure requirements are also largely a municipal responsibility and thus should be treated as a separate accounting entry. Third, 42 infrastructure requirements are a function of density, which is the subject of this thesis and therefore should be included in the calculations. Therefore three consumption categories are investigated in this thesis: housing, passenger transportation, and infrastructure. The same notation from the original consumption by land use matrix is used: category '20' represents housing and category '30' represents transportation. Transportation is subdivided into two categories: 31) motorized private and 32) motorized public transportation. Infrastructure, which was not part of the original matrix, is assigned category '35'. Infrastructure consumption is subdivided into two categories: 36) roads and right-of-ways and 37) buried and off-site infrastructure. In the original matrix, cell 'b30' contained the calculations for land degraded for roads. In this thesis, the calculations for land degraded for roads are contained in cell 'b36'. In other words, the transportation category only refers to transportation vehicles, not transportation infrastructure. Another modification to the matrix is the addition of a third digit in the consumption category number. The third digit is either . 1 to indicate embodied energy or materials or .2 to indicate operating energy or materials. For example, cell 'a31.1' is the embodied energy used in the manufacture of private automobiles. One of the subcategory numbers from the original matrix changes with this revised classification scheme. In the original matrix, housing is subdivided into category '21' for construction/maintenance and category '22' for operation. With the revised matrix, housing is grouped into category '21.1' for housing construction/maintenance and category '21.2' for housing operation. Since the consumption of food is not considered in this thesis, only four of the six land use categories are required: fossil energy, degraded land, garden land, and forest products. These land use categories retain their original notations: 'a' for fossil energy, 'b' for degraded land, 'c' for garden land, and ' f for forest products. 43 The final modification to the consumption by land use matrix involves the column totals. In the original matrix, a total for each column was contained in row 60. Only one total was given since the calculations were for an average individual Canadian. Since the calculations in this study are conducted for households, the total is calculated per household and per occupant. The total for the household is presented in row 60a, the number of occupants in the household is included in row 60b, and the ecological footprint per occupant is contained in row 60c. The revised consumption by land use matrix is presented in table 4-1. The cells not related to this study are shaded in the table. This thesis requires calculations for each of the unshaded cells in the matrix to estimate the ecological footprints for each of the dwelling types. Table 4-1 The Revised Consumption by Land Use Matrix Fossil Degraded Garden Crop Pasture Forest Energy Land Land Land Land Products Total CONSUMPTION ITEM a b c d e f 10 F O O D 20 HOUSING 21.1 Construct/Maintenance 21.2 Operation 30 T R A N S P O R T A T I O N 31.1 Private veh. - Construction 31.2 Private veh. - Operation 32.1 Public veh. - Construction 32.2 Public veh. - Operation 33.1 Transp. of goods - Const. 33.2 Transp. of goods - Operation ; 35 I N F R A S T R U C T U R E 36.1 Roads and R O W s - Const. 36.2 Roads and R O W s - Op. 37.1 Buried and Off-site - Const. 37.2 Buried and Off-site - Op. 40 C O N S U M E R G O O D S 50 R E S O U R C E S IN S E R V I C E S 60a TOTAL Per HH 60b Number of Occupants / HH 60c A V E R A G E Per Occupant 44 4.3 Housing Characteristics Used in the Calculation Procedure The first component to consider in the calculations is the housing characteristics. The relevant characteristics for this thesis are dwelling type, floor space, lot size, dwelling energy efficiency, and number of occupants. Each characteristic is defined below. Where necessary, definitions are adapted from the New Illustrated Book of Development Definitions (Moskowitz and Lindbloom 1993). Dwelling Type. There are generally four categories of dwellings. A single-family detached dwelling is a single free standing structure surrounded by yards or open space on all sides. Ground-oriented multi-unit dwellings include townhouses and have three or more attached units where each unit has access to a front or rear yard. The third category of dwelling is three to four storey apartments, such as garden and walk-up apartments. The last category is apartments greater than or equal to five storeys, which generally require elevators. Floor Space. Floor space is the interior area of the dwelling, generally measured in square feet or metres. Net floor space measures the livable floor space, excluding building stairwells, elevator shafts and other common areas in multi-unit dwellings and excluding basements in single-family dwellings. Gross floor space for apartments is the net floor space plus the common floor spaces. Lot Size. Lot size is the area of the property containing the dwelling, expressed in square feet or metres, acres, or hectares. For rectangular lots with a single building, it is simply the length of the lot multiplied by its width. In multi-unit buildings, however, lot size is divided by the number of units on the lot to estimate a per unit lot size. For example, an apartment building with 40 units on a 20,000 ft lot would have an equivalent lot size of 500 ft per unit. 45 Dwelling Energy Efficiency. Dwelling energy efficiency is the efficiency of the dwelling structure itself. There are two levels of efficiency considered in this thesis: standard and R2000. A standard efficiency dwelling is one that attains typical levels of energy efficiency for the existing housing stock. The more energy efficient R2000 buildings, mostly single-family detached houses, satisfy the higher efficiency levels of the Natural Resources Canada R2000 Program. In this thesis, calculations for the R2000 efficiency levels are only conducted for single-family detached houses. Number of Occupants in Household. The number of occupants is the number of permanent residents of the household. Each dwelling type consists of a unique set of these characteristics. Values for the characteristics are obtained for typical existing dwelling types from secondary data sources. In some cases, residential zoning is used to bound the range of values for these characteristics, such as minimum lot size and lot width. Residential zoning varies by municipality. Zoning is relatively similar, however, so a zone in one municipality is comparable to other municipalities. In order to determine values for some of the characteristics, residential zoning from the City of New Westminster, B.C., is used. 4.4 The Direct Linkages Between Housing Characteristics and Consumption There are several direct linkages between the housing characteristics and the consumption categories. Dwelling type and floor space are directly related to energy, material, and land consumption for housing. Similarly, lot size and maximum lot coverage directly determine the amount of land occupied by the building and yard. Another direct linkage is between the efficiency level and the energy consumption of the dwelling. Here it is assumed that the efficiency level only affects operating energy, and not embodied energy. 46 A slightly less direct linkage exists between lot size and energy, material, and land consumption for infrastructure. Lot size in conjunction with zoning requirements for the minimum lot width determine the frontage of the lot. The amount of linear infrastructure required to service the lot is directly proportional to the lot frontage for such items as the length of street adjacent to the property and the length of piped infrastructure. The density and form of the larger residential area determine the amount of street lighting and other infrastructure services that are required. 4.5 The Lot Size. Mirrored Density, and Transportation Energy Consumption Linkage An indirect linkage between the housing characteristics and transportation energy consumption occurs through the household's lot size. Lot size is a measure of residential density. Instead of using the actual residential density for a neighbourhood where the size of lots and the number of dwelling units vary, another measure of density is used called "mirrored density." Mirrored density is the density that would result if all households were identical in every respect to the household under consideration. The method operates from the bottom up; that is, it starts from a single household rather than from an existing residential neighbourhood. Transportation energy consumption is a function of density, and mirrored density provides a way to link dwelling type with transportation energy consumption. There are two reasons why mirrored density is used instead of actual density. First, actual densities are situation specific, while the interest of this thesis is with the general implications of dwelling type. Second, if actual density were used, those households living on larger lots in a mixed residential area are effectively being subsidized for transportation services by those households living on smaller lots. It is those people who live at higher densities that provide the necessary ridership to make public transit feasible. Using mirrored density instead of actual density better reflects the true transportation implications of housing. 47 As discussed in Chapter 3, Newman and Kenworthy (1989, 49) have compiled and graphed urban density and gasoline consumption data for 32 international cities. To use this graph for energy consumption calculations, lot size needs to be translated into urban density. First, the net mirrored residential density is calculated from the lot size. Second, the gross urban population density needs to be determined which includes other land uses such as streets, parks, industrial, commercial and institutional land. The conversion from net residential density to gross urban density is calculated using a factor of 51.2%, which represents the percentage of residential land in large urban areas (Hodge 1991, 148). The gasoline consumption that corresponds with the gross urban population density is then read from the graph. The gasoline consumption is separated into public and private transportation components. 4.6 Energy. Material, and Land Calculations There are three types of calculations performed in each cell of the consumption by land use matrix: base consumption, calculations using consumption coefficients, and multiple step calculations. Base consumption calculations are assumed to be independent of density but are computed since they form part of a household's housing, transportation, or infrastructure consumption. For the calculations involving consumption coefficients, a certain activity level or consumption quantity is multiplied by a coefficient to obtain the energy and material consumption for a cell in the matrix. An example of a consumption coefficient is the amount of embodied forest products consumed per square metre of floor space for a specific dwelling type. The annual embodied forest products consumed in the dwelling is simply the consumption coefficient multiplied by the amount of floor space. For all calculations involving consumption coefficients, a linear relationship is assumed to simplify the calculations. Chapter 5 presents the calculations for the consumption coefficients and base consumption. 48 Multiple step calculations are the third type of calculation, which as the name implies involve more than one step. An example of a multiple step calculation is the translation of lot size into mirrored density and then interpolating the transportation energy consumption from the density versus gasoline consumption graph. For the presentation of the calculations, three sheets are used: a summary calculation sheet, a multiple steps calculation sheet, and a consumption by land use matrix sheet. The summary calculation sheet displays the housing characteristics for that household, tabulates the calculations for each of the consumption categories in energy, material, and land units, and indicates the cell in the consumption by land use matrix where the result is to be placed. The multiple steps calculation sheet documents each of the steps involved in a series of calculations. The consumption by land use matrix sheet presents the results of the calculations in energy, material, and land units as well as in land area equivalents. All pertinent information necessary to perform the calculations is contained on these sheets. 4.7 Conversion of Fossil Energy and Forest Products Consumption to Land Area Equivalents Conversion rates are used to translate energy and forest products into land area equivalents. For the conversion of fossil energy consumption into land area, the area of "carbon-sink" forest required to absorb C 0 2 emissions generated from combustion of hydrocarbons is used. The net assimilation rate of C 0 2 by forests is estimated to be up to 6 tonnes of carbon/ha/year, with the rate generally falling from the tropics to the boreal forests (Rees and Wackernagel 1994, 372). An estimate of the global average C 0 2 absorption rate is 1.8 tonnes of carbon/ha/year (Wackernagel 1994a, app. 1). A GJ of fossil fuel emits approximately 18 kg of carbon into the atmosphere. Using the above estimates suggest that one hectare of average forest could annually sequester the C 0 2 of 100 GJ of fossil fuel. This fossil energy to land area ratio of 100 GJ/ha/yr is used in this thesis and is consistent with other EF/ACC applications. 49 Forest productivity estimates are used for the conversion of the consumption of forest products into land equivalents. The volume of timber produced per unit area of land varies across Canada, from an average 3 3 of 86 m /ha in the boreal forest to 300 m /ha in the coastal forests of B.C. (Environment Canada 1991, 10-11). Using an average rotation period of 70 years and an average growth of 163 m3/ha for a mature forest results in a conversion rate of 2.3 m /ha/year (Wackernagel et al. 1993, 71). Finally, all fossil energy and forest product calculations are left in energy and material units and are only converted to land equivalents in the last step of the calculations. The purpose of leaving the conversion into land equivalents until the last step is to facilitate any future changes if better conversion rates are found. 4.8 Adjusting Energy Consumption by the Fossil Energy Factor Since energy is consumed from both fossil and non-fossil fuel sources, it is necessary to adjust the total energy consumption amount by a fossil energy factor. It is only fossil energy consumption that is translated into a land equivalent. Table 4-2 shows the calculation of the fossil energy factor by consumption category. The table shows the breakdown of final energy consumption in Canada by energy source in 1991. Operating energy for transportation is almost entirely reliant on petroleum based products. Operating energy for housing is comprised of a mix of 14% oil, 47% natural gas and other fossil fuels, and 39% electricity. Since only operating energy consumption for the roads and right-of-ways category is for street lights, 100% of the energy consumption is assumed to be from electricity. Due to lack of data, all other consumption categories are assumed to have the same mix of secondary energy consumption as the Canadian total, which is 39% oil, 36% natural gas or other fossil fuel, and 25% electricity (Statistic Canada 1994a). 50 Table 4-2: Fossil Energy Factor Calculations Fossil Fossil Natural Gas energy Energy or Other adjusted Factor Oil Fossil Fuel Electricity electricity (5)=(l)+(2) (1) (2) (3) (4) +(4) 2d IKHS[\( . 21.1 Const./Maintenance 38.7% 35.6% 25.2% 92% 21.2 Operation 13.9% 46.6% 39.5% 27.6% 88% in ]|<\\\|T)KI \IIMN 31.1 Private veh - const. 38.7% 35.6% 25.2% 1 7.6% 92% 31.2 Private veh - op. 89.3% 10.0% 0.6% 0.4% 100% 32.1 Public veh - const. 38.7% 35.6% 25.2% 17.6% 92% 32.2 Public veh - op. 89.3% 10.0% 0.6% 0.4% 100% ^ I M R A M K l I" II HI" 36.1 Roads and ROWs-Const. 38.7% 35.6% 25.2% 17.6% 92% 36.2 Roads and ROWs-Op. 0% 0% 100% 69.9% 70% 37.1 Buried and Off-site-Const. 38.7% 35.6% 25.2% 17.6% 92% 37.2 Buried and Off-site-Const. 38.7% 35.6% 25.2% 17.6% 92% The fossil energy factor is equal to the energy derived from fossil fuels, mainly natural gas and oil, plus all the electricity that is generated from fossil fuels. In 1993, approximately 21% of all electricity in Canada was generated from coal, oil, and natural gas (NRCan 1994a). In addition, the generation of electricity from fossil fuels is extremely inefficient with only about 30% of the original energy reaching the final user. Therefore 3.33 units of fossil fuel used for electricity generation are required to produce the energy equivalent of 1 unit of electricity. The adjusted fossil energy factor is estimated to be 88% for operating energy for housing, 100% for operating energy for transportation, 70% for operating energy for roads and right-of-ways, and 92% for all other consumption categories. 4.9 Results from the Calculation Procedure and Interpretation The results from the calculation procedure consist of the dwelling-related ecological footprint per household and per occupant. The results do not measure the full ecological footprint, since the calculations do not include food, consumer goods, or resources in services consumption, which were 51 assumed to be independent of density. The ecological footprint results are the land areas that would be required to support that household's or occupant's dwelling-related consumption level sustainably. The purpose of this thesis is to compare the ecological footprints of households in different dwelling types. The ecological footprint per occupant for each dwelling type is thus compared in Chapter 6. The between dwelling-type difference in ecological footprints is measured in both hectares of land and also as a percentage reduction relative to a household in a single-family detached house on a typical lot. The interpretation of the final results should be treated with caution. This thesis makes several assumptions and simplifications and these should be understood when interpreting the results. Although the results from the model are quantitative, they are only estimates. Many of the assumptions, however, have been conservative and the calculations have been displayed so that if there are disagreements over the assumptions or data, the ecological footprints can be recalculated. The contribution of this thesis is that it develops a method for addressing a difficult and complex problem, but one which is important and worthy of study. 4.10 Assumptions. Simplifications and Limitations All the assumptions, simplifications, and limitations associated with the ecological footprint concept that were discussed in Chapter 2 also apply for the method used in this thesis. In addition, there are several assumptions and simplifications that were made due to time constraints, limited resources, and lack of data. In addition, there are limitations that restrict the applicability of the method to certain situations. Despite these assumptions, simplifications, and limitations, it is felt that the method still addresses the key relationships involved in the ecological implications of housing without oversimplifying the complexities involved. The following is a list of the assumptions and simplifications made in the method as well as the limitations of the study and its findings: 52 1) The application of the full method is only applicable to large urban areas. In the method, the translation of lot size to transportation energy uses a density versus gasoline consumption graph that is based on data from large international cities. The full method therefore is only appropriate to large cities and is definitely not applicable to households living in rural areas. 2) Certain consumption items are assumed to be independent of dwelling type and residential density. The main categories assumed to be unrelated to dwelling type and density are food, consumer goods, and resources in services. In addition, the length of national and provincial highways, arterial roads, and non-residential streets, distance of travel for transportation of goods, and amount of intercity passenger travel are all assumed to be unrelated to dwelling type and residential density. 3) The method does not take into account socio-economic characteristics, particularly income. If a household chooses to spend less of its income on housing, then the household has additional income available for other expenditures or for savings. For example, a household may choose to live in a smaller dwelling on a small lot because the dwelling is in a desirable community with attractive amenities. This household may not need to spend as much of its income on housing and therefore retains income that can be used for other expenditures. The full ecological footprint of the household would vary since the ecological footprint per dollar spent varies on what is purchased. 4) It is assumed that the housing characteristics used in the method are the only ones that have a significant influence over the ecological footprint of households. The method does not include characteristics that relate to behaviours within households, such as energy conservation habits or variations in climate, which influence the ecological footprint of the household. These factors vary by household and by location and were omitted in order to make the results comparable. Urban form and urban design, which both can influence transportation energy consumption, were also omitted. 53 5) Other factors that were not taken into consideration in the method were alternative building materials and construction techniques, and the recycling of building materials. The method assumes the use of typical building materials and construction practices. 6) The measure of net floor space does not capture the entire dimensions of a dwelling. Net floor space is measured in floor area above ground. Basements, however, still require energy and materials for construction and energy for heating, and this is indirectly factored into the calculation. A better measure, however, would be gross floor space, including floor space in basements. 7) The linkage between mirrored density and transportation energy consumption assumes that everyone lives exactly like that household and on the same size of lot. The calculations are not for actual households. Another assumption is that the calculation of gross urban density from mirrored residential density involves a constant conversion factor. In reality, residential density itself influences the percentage of land required for residential housing, and hence the conversion factor. 8) The consumption coefficients are based on the assumption that a linear relationship exists between activity levels and consumption amounts, and the consumption of energy and material resources. 9) There was a lack of data for some of the calculations, particularly for infrastructure, embodied energy, and consumption of forest products. In addition, my confidence is greater for data involving operating energy rather than for embodied energy. Embodied energy is calculated using input-output models that involve many interrelationships which are complex. In addition, these models are generally conducted at the national level. The transferability of embodied energy from different countries is therefore problematic. In addition, international trade in products containing embodied energy generates even further complications. 54 5_ CALCULATION OF CONSUMPTION COEFFICIENTS AND BASE CONSUMPTION 5.1 Consumption Coefficients and Base Consumption This chapter presents the calculations of consumption coefficients and base consumption that are used in the ecological footprint calculations. Consumption coefficients are the consumption amounts of a particular resource per unit of human activity or per unit of consumption. The total amount of resource consumption is then the amount of human activity multiplied by the coefficient. For example, the amount of energy embodied in a lane-metre of road is a consumption coefficient. Multiplying the length of road consumed per household by the embodied energy consumption coefficient yields the amount of embodied energy consumed for road infrastructure for that household. Base consumption is consumption that is assumed to be unrelated to dwelling type or density, but still comprises the housing, transportation, or infrastructure portion of the household's ecological footprint. Chapter 3 discussed those consumption patterns influenced by dwelling type and density. In this chapter, the base consumption calculations involve those items assumed to be independent of density. The following sections concern the calculation of consumption coefficients and base consumption for housing, transportation, and infrastructure. The cell name from the consumption by land use matrix is given for each calculation. The sources and notes for each calculation are documented in Appendix B. The chapter concludes with a summary of the consumption coefficients and base consumption results. 5.2 Calculations for Housing This section concerns the consumption coefficients for the consumption of embodied energy, operating energy, and forest products in housing. It is unnecessary to calculate consumption coefficients for directly occupied land for housing since this is easily determined. The land occupied by the buildings 55 that produce the construction materials and the landfill space required to dispose of them after demolition was excluded in order to simplify the calculations. Calculations in this section are conducted for four basic dwelling types: single-family detached houses, townhouses, walk-up and high-rise apartments. 5.2.1 Embodied Energy for Housing (a21.1) Embodied energy in dwellings includes the energy consumed in the extraction of raw or recycled materials, the processing of materials, their transportation, secondary fabrication, and assembly on-site. Lifecycle embodied energy is the initial energy consumed when the dwelling was erected; the energy for maintenance, repair, and replacement; and demolition and disposal of the structure at the end of its lifespan. A computer program called OPTIMIZE estimates the total embodied energy of a wood-frame single-family detached house to be 1,428 GJ (Sheltair 1991, 25). Dividing by the 192.2 m of floor space and an expected 40 year life yields an embodied energy coefficient of 0.186 GJ/m /year. The estimate is based on information from a Canadian input-output model. Approximately 60% of the embodied energy is required for resource extraction, primary and secondary manufacturing, and installation; 34% for maintenance and replacement activities; and 6% for the transportation and disposal of materials. Energy Use for Building Construction (Hannon et al. 1976; Stein et al. 1980) provides estimates of embodied energy coefficients per unit floor space for townhouses, garden and high-rise apartments, as well as comparisons with single-family detached houses. The study was based on 1967 data from a U.S. input-output model. Although the data is older, the results for the single-family house of 0.224 GJ/m2/year, assuming a lifespan of 40 years, are only about 20% higher than the OPTIMIZE results of 0.186 GJ/m2/year. The differences are likely attributed to improvements in energy efficiency for the more recent OPTIMIZE value as well as the fact that OPTIMIZE was based on a Canadian input-output model while Hannon's study was based on a U.S. model. 56 In order to obtain embodied energy coefficients for the multi-unit dwelling types that are comparable to the detached house from the OPTIMIZE program, the percentage embodied energy values relative to the detached house from the Hannon study are used. The percentages are then multiplied by the embodied energy coefficient for the detached house from the OPTIMIZE program to obtain the embodied energy coefficient for that dwelling type. Table 5-1 presents the embodied energy coefficients per unit floor space per year for each dwelling type. High-rise apartments require the most embodied energy per unit floor space since steel or reinforced concrete is used for the framing material, which require more embodied energy relative to wood (Buchanan and Honey 1994). In addition, the direct energy used for constructing the dwelling per unit floor space is greatest for high-rises (Hannon et al. 1976, 84-85). The total embodied energy for housing is the gross floor space multiplied by the embodied energy consumption coefficient. This value is recorded in cell a21.1 of the consumption by land use matrix. Table 5-1: Embodied Energy Consumption Coefficients for Housing (a21.1) Row No. Detached House Townhouse Walk-up Apartment High-rise Apartment Lifecycle embodied energy (GJ) (1) 1,428 N/A N/A N/A Expected life (years) (2) 40 40 40 40 Floor space (mz) (3) 192.2 N/A N/A N/A Basic embodied energy consumption coeff. for housing (GJ/m2/year) (4)=(1)/ [(2)*(3)] 0.186 N/A N/A N/A Estimated percentage relative to detached house (%) (5) 100% 92.3% 96.8% 112.4% Embodied energy consumption coeff. for housing (GJ/m2/year) (6)=(4a)* (5) 0.186 0.172 0.180 0.209 5.2.2 Operating Energy for Housing (a.21.2) Operating energy is used in space heating and cooling, domestic hot water heating, lighting and household appliances, and common building services in apartments, such as elevators. A typical existing 57 2 159 m Canadian house annually consumes approximately 100.9 GJ of energy for space heating (71%), 21.4 GJ for domestic hot water heating (14%), and 23.9 GJ for miscellaneous uses (15%), for a total of 156 GJ per year (Scanada 1992, 48). Miscellaneous uses include lighting and the operation of basic appliances. Dividing the annual energy consumption by the floor space yields an operating energy coefficient of 0.982 GJ/m /year. It should be noted that energy consumption for space heating and cooling will vary geographically by the number of heating and cooling degree-days for that area. The above data is from the STAR-database (STAtistically Representative Housing Database). Another method of estimating operating energy for a house is to use the HOT-2000 computer program. The HOT-2000 program was not used here since it is primarily intended for houses and not for other dwelling types. There was less reliable data for the multi-unit dwelling types and the data that was available was not comparable due to differences in geographic location or the data did not include the full energy consumption, usually only space heating. It was therefore necessary to use percentages relative to a detached house for the other dwelling types to estimate operating energy consumed. The percentages are taken from "Energy Efficient Large-Scale Building: Design Guidelines" (City of Toronto 1983, 21) and represent proposed U.S. building energy performance standards for new buildings applied to Toronto. Table 5-2 shows the calculation of operating energy coefficients for each dwelling type. Single-family detached houses have the highest operating energy consumption since no common walls or floors are shared with other dwellings. Townhouses are slightly better since they share walls, but not floors, with other dwellings. Walk-up apartments are the most efficient dwelling type for operating energy since both walls and floors are shared with neighbouring units. High-rise apartments are less efficient than low-rise apartments due to the higher surface area to volume ratio which results in greater heat loss, as well as additional energy used for elevators. The value that appears in cell a21.2 is the gross floor space of the dwelling multiplied by the consumption coefficient for the appropriate dwelling type. 58 Table 5-2: Operating Energy Consumption Coefficients for Housing (a21.2) Row No. Conventional Detached House Town-house Low-rise Apartment High-rise Apartment Operating energy consumption for housing (GJ/m2/year) (1) 0.982 N/D N/D N/D Percentage relative to single-family house (%) (2) 100% 83.6% 69.9% 84.9% a21.2 Operating energy consumption coeff. for housing (GJ/m2/year) (3)=0a)* (2) 0.982 0.821 0.687 0.834 For an R2000 detached house, the average operating energy is 48% of the estimated consumption for houses built to the 1975 building code standards and 77% for houses built to the 1983 Building Measures for Energy Conservation Standard (EMR 1990, 20). Most savings come from reduced energy use for space heating. To simplify the calculations, it is assumed that the average existing stock of houses is built to the 1975 building code standards. Therefore, the 48% reduction in operating energy relative to a standard existing house is used in the calculations for the R2000 house. For comparison purposes, a typical house in Canada consumes approximately 156 GJ of energy per year (Scanada 1992, 48), while the average consumption for an electrically heated R2000 home was 75 GJ (EMR 1990, 15), approximately 48% of the typical house value. An R2000 detached house is assumed to consume similar quantities of embodied energy and forest products as a conventional detached house. More accurate data for dwelling operating energy by dwelling type will become available after this thesis is completed. Natural Resources Canada (1994b) recently prepared a Survey of Household Energy Use as part of the development of a National Energy Use Database. The report describes the characteristics of the housing stock, thermal envelope, heating, air conditioning, appliances, hot water heating and lighting of a sample of households by dwelling type. In late 1995, another report using this survey as well as a survey of fuel consumption will estimate the operating energy for the sample of dwellings. 59 5.2.3 Wood Consumption for Housing Construction (f21.1) The main forest products contained in a dwelling are lumber, timber, veneer and plywood, woodwork, and building paper. The wood required to construct a standard wood-frame house are shown in table 5-3. Since the original values are in units of mass, it is necessary to convert them into volumes of wood. A 0.6 t/m conversion factor is assumed for lumber and timber, veneer and plywood, and woodwork. For building paper, a 0.546 t/m conversion factor is used (Wackernagel et al. 1993, 68). A wood loss factor is also included in the calculations since timber is lost in the steps of the production process. Table 5-3: Wood Requirements for the Construction of a Standard House (f21.1) Row No. Lumber and Timber Veneer and Plywood Woodwork Building Paper Total Mass (t) (1) 15.035 7.443 2.87 2.524 n/a Conversion rate (t wood/m3) or (t paper/m3) (2) 0.6 0.6 0.6 0.546 n/a Wood loss factor (3) 1.5 1-5 1.5 1 n/a Volume of wood (roundwood equivalent) (m3) (4)=(3)* 0)/(2) 37.6 1.8.6 7.2 4.6 68.0 Approximately 68 m of wood is required to construct a standard wood-frame detached house. In order to estimate a wood consumption coefficient, the value is divided by the floor space of a standard house and by an expected 40 year lifespan as shown in table 5-4. To simplify the calculations, it is assumed that at the end of the building's lifespan, the wood is not reused or recycled. There was no information available on the variation in wood used by dwelling type. It was therefore necessary to make assumptions regarding the variation in wood consumption by dwelling type relative to a detached house. The Handbook of Energy Use for Building Construction (Hannon et al. 1976; Stein et al. 1980) shows that embodied energy per unit floor space for wood materials decreases for townhouses, walk-up apartments, and high-rise apartments. Since the data are for embodied energy and not mass of materials, the actual percentages may not be representative of the actual wood used in buildings. I therefore made a 60 rough assumption that townhouses require about 80% of the wood used by a standard house per unit floor space, and that walk-up and high-rise apartments have wood requirements 60% and 10% relative to a house. The low value for apartments was assumed since their building frame is steel or reinforced concrete. The wood consumption coefficients for each dwelling type are presented in table 5-4. Table 5-4: Wood Consumption Coefficients for Housing Construction (f21.1) Row No. Single-family Detached House Town-house Walk-up Apartment High-rise Apartment Total lumber (m3/lifespan) (1) 68.0 N/D N/D N/D Lifespan of building (years) (2) 40 40 40 40 Average floor space (m2) (3) 192.2 N/A N/A N/A Basic wood consumption coeff. (m3 wood/ m2 floor/year) (4)=(1)/ [(2)*(3)] 0.00885 N/D N/D N/D Percentage relative to single-family detached house(%) (5) 100% 80% 60% 10% Gl.l Wood consumption coeff. for housing construction (m3 wood/m2 floor/ year) (6)=(4a)* (5) 0.00885 0.00708 0.00531 0.00089 5.2.4 Wood Consumption for Housing Operation (f21.2) In the calculation of the ecological footprint of an average Canadian, Wackernagel et al. (1993) included wood consumption for housing operation in the housing category. Table 5-5 shows the wood consumption calculation for an average Canadian. Since no data were available for the variation in wood consumption for housing operation by dwelling type, the value for cell £21.2 is assumed to be base consumption per capita. The value that appears in cell £21.2 is the average number of household occupants multiplied by the per capita wood consumption coefficient for housing operation. 61 Table 5-5: Wood Consumption Coefficients for Housing Operation (f21.2) Row No. Average Person Average Canadian paper consumption (kg/year/capita) (1) 244 Percent consumption for household operation (%) (2) 7 Household paper consumption for household operation (t/year/capita) (3)=(1)*(2)/ 1,000 0.01708 Conversion rate (t paper / mJ of wood) (4) 0.546 f21.2 Wood consumption coeff. for housing operation (m3/year/capita) (5)=(3)/(4) 0.0313 5.3 Calculations for Passenger Transportation Consumption coefficients and base consumption are calculated for energy consumption for passenger transportation in this section. The first two subsections concern the embodied energy required for transportation vehicles. The next two subsections break from the ecological footprint accounting categories and group operating energy for passenger transportation into intracity and intercity transportation, instead of private and public transportation. Intracity transportation is travel within cities and intercity travel is travel that extends outside a city, including destinations to other cities. 5.3.1 Embodied Energy for Private Transportation Vehicles (a31.1) The embodied energy consumed in the manufacture of a typical private vehicle is presented in table 5-6. The embodied energy consumption coefficient for a typical car is calculated to be approximately 12 GJ/vehicle/year. The consumption coefficient is a function of the mass of the vehicle, its lifespan, the energy embodied in the raw materials, the efficiency of the manufacturing process, and the transportation energy required to ship the car to market. The value for cell a31.1 is calculated by multiplying the average number of vehicles a household owns by the consumption coefficient. 62 Table 5-6: Embodied Energy Consumption Coefficients for Private Vehicles (a31.1) Row No. Automobile Embodied energy in vehicle construction (GJ/kg) (1) 0.100 Average vehicle mass (kg) (2) 1,050 Vehicle lifespan (years) (3) 8.6 a31.1 Private vehicle embodied energy consumption coeff. (GJ/vehicle/year) (4)=(1)*(2)/ (3) 12.2 5.3.2 Embodied Energy for Public Transportation Vehicles (a32.1) Embodied energy for public transportation vehicles was assumed to be insignificant as they tend to be used intensively over the course of their lifespan and are generally well maintained to maximize their service life. 5.3.3 Operating Energy for Intracity Passenger Transportation (a3l.2 and a32.2) For transportation within cities, the two principal modes of motorized travel are private vehicle and urban transit. Operating energy for walking, cycling, and other self-propelled modes of transport is mostly accounted for in food consumption and any additional energy required, such as oil for a bicycle chain, was assumed to be insignificant. Consumption coefficients and base consumption are not calculated for intracity transportation. Instead, the energy consumed for intracity passenger transportation is calculated using the density versus gasoline consumption graph from the Newman and Kenworthy study (1989, 49). An alternative method could be to use the annual per capita passenger-kilometres travelled by transportation mode and multiply them by the energy consumption rate per passenger-kilometre travelled for that mode. For comparison purposes, a diesel urban transit bus is eight times as energy efficient as an automobile when measured per passenger-kilometre travelled (B.C. Transit 1990, 22). As part of the National Energy Use Database, a National Private Vehicle Use Survey is currently being conducted, which would provide a sample of 63 transportation energy consumption by dwelling type (Conversation with Andre Bourbeau, NRCan, May 1995) Unfortunately, the results of this study will not be available until after this thesis is completed. 5.3.4 Operating Energy for Intercity Passenger Transportation (a31.2 and a32.2) The Newman and Kenworthy density versus gasoline consumption graph is only for intracity transportation and does not include intercity travel. It is therefore necessary to determine base consumption for intercity transportation, which is assumed to be independent of density. The principal modes of public intercity transportation are air, rail, bus, and ferry. Table 5-7 shows the consumption coefficients for the operating energy of intercity passenger vehicles. The table shows that the automobile, ferry, and airplane are the least energy efficient carriers of passengers. Table 5-7: Operating Energy Consumption Coefficients for Intercity Passenger Transport (a31.2 and a32.2) Row No. Air Intercity Rail Intercity Bus Ferry Automobile Estimated vehicle occupancy (%) (1) 60% 60% 60% 60% 34% Operating energy consumption coeff. (GJ/passenger-km) (2) 0.00388 0.00165 0.00067 0.00739 0.00247 Each year, the average Canadian travels approximately 8,800 kilometres for intercity transportation. Table 5-8 shows the breakdown of intercity travel by transportation mode. The predominant mode of travel for long distances and for travel overseas is the airplane. The car is the preferred mode of transportation for short to medium length trips, accounting for 87% of the modal share of intercity transportation. The airplane and automobile, which have the largest modal share for intercity transportation, are also the least energy efficient. The base energy consumption per capita for intercity public transportation is calculated to be approximately 4 GJ per year. The base energy consumption per capita for intercity transportation using automobiles was calculated to be approximately 19 GJ per year. 64 Table 5-8: Base Consumption of Operating Energy for Intercity Passenger Transport (a31.2 and a32.2) Public Intercity Transportation Private Intercity Transportation TOTAL Intercity Transport Row No. Air Intercity Rail Intercity Bus Ferry Automobile TOTAL Distance travelled (passenger-km/capita/ year) (1) 916 51 121 31 7,693 8,812 Modal split (%) (2) 10.4 0.6 1.4 0.4 87.3 100 Energy consumption coefficient (GJ/passenger-km) (3) 0.00388 0.00165 0.00067 0.00739 0.00247 N/A Base consumption for Intercity Passenger Transport (GJ/capita/year) (4)=(1)* (3) 3.6 0.1 0.1 0.3 19.0 23.1 Subtotal (GJ/capita/year) (5) 4.1 19.0 23.1 Consumption by land use matrix cell (6) a32.2 a31.2 N/A The values that appear in cells a31.2, operating energy for private vehicles, and a32.2, operating energy for public vehicles, are calculated according to the formula below: a31.2 = persons in household x (a31.2 per capita energy consumption for intracity travel on private modes + a31.2 per capita energy consumption for intercity travel on private modes) a32.2 = persons in household x (a32.2 per capita energy consumption for intracity travel on public modes + a32.2 per capita energy consumption for intercity travel on public modes) 5.4 Calculations for Infrastructure Infrastructure comprises the hard physical facilities and services that benefit households and society. Infrastructure is categorized into two categories for the purpose of this thesis: 36) roads and right-of-ways and 37) buried infrastructure and off-site services. The roads and right-of-ways category includes roads, sidewalks, street and traffic lights, and transmission line and pipeline right-of-ways. Buried infrastructure refers to water mains and sanitary sewers. Phone and natural gas lines were excluded from 65 the analysis due to lack of data. Off-site infrastructure includes the services of water treatment, wastewater treatment, and garbage collection and disposal. The following infrastructure calculations are for land and energy consumption. It was assumed that wood consumption for utility poles was insignificant to simplify the calculations. Consumption coefficients for infrastructure are obtained from secondary sources. The amount of infrastructure for base consumption is based on secondary sources and from per household calculations for B.C. 5.4.1 Directly Occupied Land for Roads and Right-of-ways (b36.1) Roads are classified as degraded land in cell b36.1 since no biophysical production can occur on an impervious surface. Assuming an average width of 4 metres per lane of highway yields a consumption coefficient of 0.0004 ha per lane-metre of road as shown in table 5-9. Similarly, a sidewalk is assumed to be approximately 1 m wide on average, yielding a consumption coefficient of 0.0001 ha of land consumed per linear metre of sidewalk. Table 5-9: Directly Occupied Land Consumption Coefficients for Roads and Sidewalks (b36.1) Row No. Roads Sidewalks Lane width (m) (1) 4 1 Length (m) (2) 1 1 Land area (ha) (3)=(1)*(2)/10,000 0.0004 0.0001 Land area per linear metre of infrastructure (ha/m) (4)=(3)/(2) 0.0004 0.0001 The base consumption of roads is comprised of roads for national and provincial highways, freeways, arterial and collector roads. Local roads were considered to be a function of residential density and therefore not part of base consumption. Table 5-10 shows that the base consumption of roads calculated from B.C. and Lower Mainland data was estimated to be 0.031 ha per household. 66 Table 5-10: Base Consumption of Directly Occupied Land for Roads and Sidewalks (b36.1) Row No. Federal and Provincial Highways in Province Freeways/ Expressways/ Bridges Urban Major/ Arterials/ Secondary Arterials Collectors/ Rural Roads Local Streets Geographic area (1) B.C. Lower Mainland Lower Mainland Lower Mainland Lower Mainland Lane-km in geo-graphic area (km) (2) 83,370 2,850 2,800 1,000 10,000 Lane width (m) (3) 4 4 4 4 4 Road area in geographic area (ha) (4)=(2)* 1000*(3) /l 0,000 33,348 1,140 1,120 400 4,000 Dwellings in geographic area (5) 1,251,000 675,000 675,000 675,000 675,000 Lane-metres of road per dwelling (m/DU) (6)=(2)* 1000/ (5) 66.6 4.2 4.1 1.5 14.8 Road area per dwelling (ha/DU) (7)=(4)/ (5) 0.027 0.002 0.002 0.001 0.006 Breakdown by local and non-local roads (ha/DU) (8) 0.031 0.006 Consumption by land use matrix cell (9) b36.1 (Base Consumption) b36.1 (Density Dependent) Additional land is consumed by the roads and sidewalks that directly service the household's lot. For detached houses and townhouses, the land directly occupied by roads and sidewalks is a function of lot width, assuming the lot is not located at a street corner. For apartment buildings, larger lots are required and sometimes multiple buildings occupy the same lot. The perimeter around the lot is therefore used to calculate the length of infrastructure for apartments, which assumes that these lots are surrounded by streets on all sides. The lot width per unit is calculated as follows for apartment buildings that occupy an entire block: lot width per unit (apartment buildings) = perimeter of lot / (number of buildings on lot x average number of units per building) 67 The total value for cell b36.1 is calculated as follows: b36.1 =Base consumption + lot width per dwelling unit x (road + sidewalk directly occupied land consumption coefficient) Transmission line and pipeline right-of-ways are classified as garden land in cell c36.1. Although the land under a transmission tower or under an elevated pipe is incapable of biophysical production, this is a minor part of the corridor. In fact, some utilities such as Ontario Hydro, have designed special narrow-based lattice towers to minimize the loss of land due to transmission line right-of-ways (Environment Canada 1991, 12-26). All the land required for transmission lines and pipelines was assumed to be independent of density and therefore the value for cell c36.1 is base consumption. The base consumption for transmission line and pipeline infrastructure was calculated to be approximately 0.039 ha of land per household as shown in table 5-11. Table 5-11: Base Consumption of Directly Occupied Land for Right-of-ways (c36.1) Row No. Transmission Lines Pipelines Total Land area (ha) (1) 39,430 9,217 48,647 Geographic area (2) B.C. B.C. B.C. Dwellings in geographic area (DU) (3) 1,251,000 1,251,000 1,251,000 c36.1: Land area per dwelling unit (ha/DU) (4)=(1V (3) 0.032 0.007 0.039 5.4.2 Directly Occupied Land for Buried and Off-site Infrastructure (b37.1) Since buried infrastructure is located beneath the surface, it does not directly occupy land. Although off-site infrastructure does occupy land, such as a wastewater treatment plant, the land occupied is assumed to be insignificant to simplify the calculations. 68 5.4.3 Embodied Energy for Roads and Right-of-ways (a36.1) Data were only available for the embodied energy for roads and sidewalks. The embodied energy consumption coefficients were calculated to be 0.0396 GJ per lane-metre of road per year and 0.0148 GJ per linear metre of gutter, curb, and sidewalk per year as shown in table 5-12. Table 5-12: Embodied Energy Consumption Coefficients for Roads and Sidewalks (a36.1) Row Roads Gutters Curbs Sidewalks No. (lane- (linear- (linear- (linear-metre) metre) metre) metre) Lifecycle embodied energy (GJ/m) (1) 0.9912 0.0826 0.0970 0.2652 Expected lifespan of roads (years) (2) 25 30 30 30 Annual embodied energy (3)=(l)/(2) 0.0396 0.0028 0.0032 0.0088 (GJ/m/year) Subtotal (GJ/m/year) (4) 0.0396 0.0148 Base consumption of embodied energy for roads and sidewalks is based on the length of roads and sidewalks from table 5-10. The per household length of road was calculated to be 76.4 lane-metres for non-local roads. The per household base consumption of gutters, curbs, and sidewalks was calculated to be 4.1 lane-metres assuming only urban major roads, arterials, and secondary arterials contain gutters, curbs, and sidewalks. It was also assumed that the above roads contain two lane-metres per direction of traffic which would therefore require approximately 2.05 linear metres of gutters, curbs, and sidewalks per household. The base consumption of embodied energy for roads and sidewalks was calculated to be 3.06 GJ per household per year as presented in table 5-13. Table 5-13: Base Consumption of Embodied Energy for Roads and Sidewalks (a36.1) Row No. Roads (lane-metres) Gutter, Curb, and Sidewalks (linear metres) TOTAL Based consumption of road per dwelling (1) 76.4 2.05 N/A Embodied energy consumption coefficient (GJ/m/year) (2) 0.0396 0.0148 N/A a36.1: Base consumption of embodied energy for road and sidewalk infrastructure per dwelling unit (GJ/DU/year) (3)=(0*(2) 3.03 0.03 3.06 69 The amount of on-site infrastructure consumed per household is based on the lot width per dwelling unit. The total value for cell a36.1 is calculated as follows, assuming that the road in front of the residence is two lanes and that there are sidewalks on each side of the street: a36.1 =Base consumption + lot width per dwelling unit x (road + gutter/curb/sidewalk embodied energy consumption coefficient) 5.4.4 Embodied Energy for Buried Infrastructure and Off-site Services (aS7.1) Embodied energy data were only available for buried infrastructure. Embodied energy for off-site facilities, such as wastewater treatment plants, was assumed to be insignificant. Table 5-14 shows the embodied energy consumption coefficients per linear metre for water mains and sewers. The consumption coefficient for buried infrastructure is approximately 0.0088 GJ per linear metre per year. Table 5-14: Embodied Energy Consumption Coefficients for Buried Infrastructure (a37.1) Row No. Water Main Sanitary Sewers Total Lifecycle embodied energy (GJ/m) (1) 0.228 0.479 0.707 Expected lifespan (years) (2) 80 80 80 Embodied energy consumption coefficient for buried infrastructure (GJ/m/year) (3)=(iy (2) 0.00285 0.00598 0.00884 The base consumption of buried infrastructure was assumed to be equal to the length of arterial and collector roads, which is 4.1 lane-metres per household. Assuming that on average there are four lanes of traffic and that only one pipe is located under each road yields 1.03 linear metres of buried 70 infrastructure. Table 5-15 shows that the base consumption of embodied energy for buried infrastructure was estimated to be 0.009 GJ per dwelling unit per year. Table 5-15: Base Consumption of Embodied Energy for Buried Infrastructure (a37.1) Row No. Base Consumption of Embodied Energy for Buried Infrastructure Length of road for base consumption per dwelling (m/DU) 1.03 Embodied energy consumption coefficient for buried infrastructure (GJ/m/year) (2) 0.00884 a37.1 Base consumption of embodied energy for buried infrastructure (GJ/DU/year) (3)=(1)*(2) 0.009 The on-site consumption of buried infrastructure is a function of a dwelling's lot width. The total consumption of embodied energy for buried infrastructure is based on the following formula: a37.1 = base consumption of embodied energy for buried infrastructure + lot width per dwelling unit x embodied energy consumption coefficient for buried infrastructure 5.4.5 Operating Energy for Roads and Right-of-ways (a37.1) Street and traffic lights and signals are the main consumers of operating energy for the roads and right-of-ways category. Street and traffic lighting and signaling account for up to 20% of energy consumption by municipal governments (Puget Sound Council of Governments et al. 1982, 46). Street lights operate during the night while traffic lights and signals operate on a continuous basis. In B.C., the average street light is a high-pressure sodium light rated at 170 watts, including the watts for the ballast, and consumes 680 kWh of electricity annually (2.45 GJ/year) assuming 4,000 hours of operation per year (conversation with Celeste Pires, B.C. Hydro, May 1995). 71 Table 5-16 presents the base consumption of operating energy for street and traffic lights and signals, which appears in cell a36.2 of the consumption by land use matrix. The total base consumption of energy for street and traffic lights for B.C. was divided by the number of households to estimate consumption per household. There are approximately 4 to 5 households per street light in B.C. There was insufficient information to determine how street lighting varies by dwelling type. For lower density areas, more street length per dwelling unit would result in a greater number of streets lights and hence a higher consumption of operating energy. Table 5-16: Base Consumption of Operating Energy for Street and Traffic Lights (a36.2) Row No. Street Lights Traffic Lights and Signals Infrastructure Lighting Total Geographic area (1) B.C. B.C. B.C. Number of lights (lights) (2) 239,000 N/D N/A Total electricity consumption in B.C. (MWh/year) (3) 164,167 27,091 191258 Conversion factor (MJ/kWh) (4) 3.6 3.6 3.6 Total energy consumption in B.C. (GJ/year) (5)=(3)* (4) 591,001 97,528 688,529 Number of dwellings in B.C. (DU) (6) 1,087,000 1,087,000 1,087,000 Number of lights per dwelling (lights/DU) (7)=(2)/ (6) 0.22 N/D N/A a36.2 Base consumption of operating energy for street lighting (GJ/DU/year) (8)=(5)/ (6) 0.544 0.089 0.633 5.4.6 Operating Energy for Buried Infrastructure and Off-site Services (aS7.2) The base consumption of operating energy for buried infrastructure and off-site services consists of energy used for sanitary sewerage, water treatment, and solid waste collection and disposal. Table 5-17 presents both the consumption coefficients and the calculations for the base consumption of operating energy for off-site services by dwelling type. The total amount of operating energy for off-site services 72 was calculated to be approximately 2.0 and 1.5 GJ per household per year for single-family and multi-family households respectively. Base consumption is the only value that appears in cell a37.2. Table 5-17: Base Consumption of Operating Energy for Off-site Services (a37.2) Row No. Single-family Multi-family Sewage generation (litres/DU/year) (1) 450,000 320,000 Energy rate for sanitary sewage service (MJ/litre) (2) 0.0016 0.0016 Base consumption of energy for sewage service (MJ/DU/year) (3)=0)*(2) 720 512 Water treatment (litres/DU/year) (4) 570,000 410,000 Energy rate for water treatment service (MJ/litre) (5) 0.0016 0.0016 Base consumption of energy for water treatment service (MJ/DU/year) (6)=(4)*(5) 912 656 Solid waste collection and disposal (kg/DU/year) (7) 1,100 1,100 Energy rate for solid waste collection and disposal (MJ/kg) (8) 0.306 0.306 Base consumption of energy for solid waste service (MJ/DU/year) (9)=(7)*(8) 337 337 a37.2 Base consumption of energy for off-site services (GJ/DU/year) (10)=[(3)+(6)+ (9)]/1,000 2.0 1.5 5.5 Summary of Consumption Coefficients and Base Consumption Table 5-18 presents a summary of the consumption coefficients that were calculated in this chapter for housing, infrastructure, and transportation. Table 5-19 shows a summary of the base consumption results for housing, transportation and infrastructure which were calculated in this chapter. 73 Table 5-18: Summary of Consumption Coefficients Conventional Single-family Detached House Townhouse Walk-up Apartment High-rise Apartment HOUSING a21.1 embodied energy for housing (GJ/m2/year) 0.186 0.172 0.180 0.209 a21.2 operating energy for housing (GJ/m2/year) 0.982 0.821 0.687 0.834 f21.1 wood for housing construction (m3 wood /m2/ year) 0.00885 0.00443 0.00310 0.00221 IKWSh H<l \|]iiN a31.1 embodied energy for private vehicles (GJ/vehicle/year) 12.2 l \ l KASIRI i 11 Kl-a36.1 embodied energy for roads (GJ/m/year) 0.0396 a36.1 embodied energy for gutters, curbs, and sidewalks (GJ/m/year) 0.0148 a37.1 embodied energy for buried infrastructure (GJ/m/year) 0.0088 a36.2 operating energy for off-site services (GJ/DU/year) 2.0 1.5 1.5 1.5 b36.1 directly occupied land for roads and sidewalks (ha/m) 0.0005 Table 5-19: Summary of Base Consumption Calculations All Dwelling types HOUSING f21.2 wood for housing operation (mJ wood/ capita/year) 0.0313 TRANSPORI \IIO\ a31.2 operating energy for private vehicle intercity transportation (GJ/capita/year) 19.0 a32.2 operating energy for public vehicle intercity transportation (GJ/capita/year) 4.1 IM K\S1KI i l l Kl-a36.1 embodied energy for roads and sidewalks (GJ/HH/year) 3.06 a36.2 operating energy for roads and sidewalks (street lights and traffic lights) (GJ/year/HH) 0.633 a37.1 embodied energy for buried infrastructure (GJ/HH/year) 0.009 b36.1 directly occupied land for roads and sidewalks (ha/HH) 0.031 c36.1 directly occupied land for right-of-ways (ha/HH) 0.039 6_ RESULTS AND DISCUSSION 6.1 Description of Housing Archetypes Ecological footprint calculations are conducted for four basic types of dwellings in this thesis: single-family detached, townhouse, walk-up and high-rise apartment. For each dwelling type, characteristics typical of the existing housing stock are used. The characteristics were obtained from secondary sources, including Statistics Canada and CMHC, and these are documented in Appendix B. The set of characteristics for each dwelling is called the dwelling archetype, meaning the average within the type. The dwelling archetypes and their characteristics are presented in table 6-1. Three subtypes within the detached house type are also included: a standard efficiency house, an R2000 house, and a small-lot house. The characteristics of the houses are identical except for operating energy and lot size. The R2000 house consumes 48% of the operating energy relative to the standard house. 2 2 2 2 The small-lot house has a lot size of 557 m (6,000 ft) compared to 780 m (8,400 ft ) for the standard house. The floor space, occupants, and automobiles owned are the same for all the houses. Similarly, the only relevant characteristic distinguishing walk-up and high-rise apartments is site density. All other characteristics are controlled to isolate the impact of dwelling type on the ecological footprint. In reality, there would be some variation between walk-up and high-rise apartments in terms of floor space, number of occupants, and vehicle ownership. Table 6-1 shows that the number of occupants and net floor space decrease as one moves from the single-family to higher density dwelling types. An average standard detached house has 3.0 occupants 2 2 and a net floor space of 159.8 m (1,700 ft); an average townhouse has 2.3 occupants and a floor space 2 2 2 of 120.8 m (1,300 ft); and an average apartment has 1.8 occupants and a floor space of 74.3 m (800 ft2)-75 Table 6-1: Profile of Dwelling Archetypes Row Standard R2000 Small-lot Town- Walk-up High-rise No. Detached House Detached House Detached House house Apartment Apartment 1 louschold charactciistics: -no. of occupants (Il 3.0 3.C 30 2.3 1.8 1.8 Dwelling characteristics: -no. of bedrooms 12J 3.2 3.2 3.2 2.1 1.4 1.4 -net floor space (mz) (3) 159.8 159.8 159.8 120.8 74.3 74.3 -gross floor space (m*) (4) 159.8 159.8 159.8 120.8 81.7 81.7 Buildin ..ii II iiln> li> -units per building (units) (5) 1 1 1 9 18 47 -no. of storeys (6) 2 2 2 2 3 12 -framing material (7) wood wood wood wood wood reinforced concrete -expected lifespan (years) (8) 40 40 40 40 40 40 |.ot characteristics: -lot size of property (mz) 780.4 780.4 557.4 -.'100 5,000 5,000 -lot width (m) (10) 18.3 18.3 13.7 9.0 283 283 -no. of buildings on lot (ID 1 1 1 2 2 2 -net dwelling unit density (12)=[(11) 12.8 12.8 17.3 36.0 72.0 188.7 (units/ha) •(5)]/(9) -lot size/dwelling unit (13)=(9)/ 780.4 780.4 557.4 277.7 138.9 53.2 (m2/unit) [(11)*(5)] -lot width/dwelling unit (i4)=(ioy 18.3 18.3 15 9.0 7.9 3.0 (m/unit) [(11)*(5)] -max. site coverage (%) (15) 35% 35% 35% 40% 40% 40% I'lansport characterises -no. of vehicles owned (16) 1.42 1.42 1.42 1.3 1.3 1.3 2 2 The standard detached house has a lot size of 780 nV (8,400 fr). Included in table 6-1 are the number of buildings per lot and dwellings per building for townhouses and apartments, which are used to calculate the equivalent lot size per unit. For the townhouse, walk-up, and high-rise apartment calculations, two buildings on a 0.5 ha lot are used. The townhouse, walk-up, and high-rise apartment buildings contain 9, 2 2 18, and 47 units per building respectively. The respective lot sizes per unit are 278 m (3,000 ft ) for the 2 2 2 2 townhouse, 139 m (1,500 ft) for the walk-up and 53.2 m (570 ft) for the high-rise apartment. The equivalent lot width per unit, which indicates the length of on-site infrastructure, is calculated in two ways depending on dwelling type. For detached houses and townhouses, it is simply the width of the lot. 76 For walk-up and high-rise apartments, it is assumed that the 0.5 ha lot is surrounded on four sides by street and sidewalk. The length of infrastructure per unit is therefore the perimeter of the lot divided by the number of units. The length of on-site infrastructure per unit was estimated to be 18.3 m for the detached house, 9.0 m for the townhouse, 7.9 m for the walk-up, and 3.0 m for the high-rise apartment. The type of framing material for detached houses, townhouses, and walk-up apartments was assumed to be wood, which is one of the most common framing materials in Canada. Using wood as the framing material allows compatibility with the OPTIMIZE program, which is used to calculate embodied energy for a wood-frame detached house (Sheltair 1991). For high-rise apartment buildings, reinforced concrete is more common than steel for the framing material of these buildings. There were little data on the variation in automobile ownership by dwelling type. The data only allowed an estimate of the number of automobiles for single-family and multi-unit dwellings. It was estimated that on average single-family households own 1.42 vehicles and that households in townhouse and apartments own 1.3 vehicles. The automobile ownership rate may be lower for walk-up and high-rise apartments due to fewer occupants, less parking spaces per dwelling, and generally lower incomes. 6.2 Analysis of Ecological Footprint Results 6.2.1 Comparison of Ecological Footprints The detailed calculations of the dwelling-related ecological footprints for each dwelling type are included in Appendix C. The per household and per occupant ecological footprint calculations are summarized by dwelling type in tables 6-2 and 6-3 respectively. The ecological footprints do not include land for food, transportation of goods, consumer goods, or resources in services. Therefore the term ecological footprint refers only to consumption for housing, personal transportation, and infrastructure. 77 Table 6-2: Results of Per Household Ecological Footprint Calculations by Dwelling Type Standard H20U0 Small Lot Detached Detached Detached Town- Walk-up High-rise House House House house Apartment Apartment Number of Occupants 3,0 3.0 3.0 2.3 1.8 1.8 HOUSING (ha/HH) 2.38 1.66 2.36 1.49 0.85 0.85 Emb. energy for housing 0.27 0.27 0.27 0.19 0.14 0.16 Op. energy for housing 1.38 0.66 1.38 0.87 0.49 0 .60 Occupied land for housing 0.08 0.08 0.06 0.03 0.01 0.01 Wood for housing 0.65 0.65 0.65 0.40 0.21 0.09 TRANSPORTATION (ha/HH) 1.99 1.99 1.66 1.09 0.76 0.67 Emb. energy for priv. vehicles 0.16 0.16 0.16 0.15 0.15 0.15 Op. energy for priv. vehicles 1.68 1.68 1.35 0.82 0 .53 0.45 Op. energy for public vehicles 0.16 0.16 0.15 0.12 0.09 0.08 INFRASTRUCTURE (ha/HH) 0.14 0.14 0.14 0.13 0.12 0.11 Emb. energy for infrastructure 0.04 0.04 0.03 0 .03 0 .03 0 .03 Op. energy for infrastructure 0.02 0.02 0.02 0.02 0.01 0.01 Land for infrastructure 0.08 0.08 0.08 0.07 0.07 0.07 TOTAL (ha/HH) 4.51 3.79 4 .16 2.71 1.73 1.62 Percent relative to std. house 1 0 0 % 8 4 % 9 2 % 6 0 % 3 8 % 3 6 % Table 6-3: Results of Per Occupant Ecological Footprint Calculations by Dwel l ing Type Standard H2000 Small Lot Detached Detached Detached Town- Walk-up High-rise House House House house Apartment Apartment HOUSING (ha/cap.) 0.79 0.55 0.79 0.65 0.47 0.47 Emb. energy for housing 0.09 0.09 0.09 0.08 0.08 0.09 Op. energy for housing 0.46 0.22 0.46 0.38 0.27 0 .33 Occupied land for housing 0.03 0.03 0.02 0.01 0.01 0 .00 Wood for housing 0.22 0.22 0.22 0.17 0.12 0.05 TRANSPORTATION (ha/cap.) 0.66 0.66 0.55 0.47 0.42 0.37 Emb. energy for priv. vehicles 0.05 0.05 0.05 0.06 0.08 0.08 Op. energy for priv. vehicles 0.56 0.56 0.45 0.36 0.29 0.25 Op. energy for public vehicles 0.05 0.05 0.05 0.05 0.05 0.04 INFRASTRUCTURE (ha/cap.) 0.05 0.05 0.05 0.06 0.07 0.06 Emb. energy for infrastructure 0.01 0.01 0.01 0.01 0.02 0.02 Op. energy for infrastructure 0.01 0.01 0.01 0.01 0.01 0.00 Land for infrastructure 0.03 0.03 0 .03 0.03 0.04 0.04 TOTAL (ha/cap.) 1.50 1.26 1.39 1.18 0.96 0 .90 Percent relative to std. house 1 0 0 % 8 4 % 9 2 % 7 8 % 6 4 % 6 0 % The per household ecological footprint was calculated to be approximately 4.5 ha for the detached house household. The same household and house built to R2000 standards would have an ecological footprint 84% of the standard house. An equivalent household and house on a small lot would have a footprint 78 approximately 92% of the standard house. Households in townhouses have a footprint approximately 60% the size of the standard detached house. Households living in walk-up and high-rise apartments have footprints approximately 38 to 36% that of the standard detached house. The per occupant ecological footprint for households in detached houses was estimated to be 1.5 ha ~ 65% larger than the per occupant footprints of people living in apartments. Figure 6-1 compares the per occupant ecological footprints of the detached house subtypes by consumption category. For standard houses, approximately 53% of the footprint is for housing, 44% for transportation, and 3% for infrastructure. For the R2000 house, its better energy efficiency reduces the ecological footprint to 84% of the standard detached house. For the small-lot house, the ecological footprint is reduced to 92% of the detached house value, mostly due to reduced energy consumption for transportation. The reduction in land directly occupied by the smaller lot is minimal at less than 0.01 ha per occupant. According to these calculations, an R2000 house has a lower ecological footprint than an equivalent house on a small lot. Combining both an R2000 house and a small lot reduces the ecological footprint to approximately 76% of the standard detached house value. Figure 6-2 illustrates the per occupant ecological footprints by dwelling type and consumption category. People in townhouses have ecological footprints about 78% of the value of occupants of standard detached houses. The smallest per occupant ecological footprints are for residents of apartments. The per occupant ecological footprints for high-rise and walk-up apartments are not significantly different at approximately 60 to 64% relative to the standard detached house. 79 1.60 1.40 S" 1.20 'EL ns o 2 1.00 a o.8o o o u. Eh o 0.40 UJ 0.20 0.00 Fig. 6-1: Comparison of Per Occupant Ecological Footprints by Detached House Subtype (ha/capita) 0.05 0.66 0.79 Standard Detached House WMmmmmm 0.05 0.66 0.55 R2000 Detached House 0.05 0.55 0.79 Small Lot Detached House 13 Infrastructure • Transportation • Housing Fig. 6-2: Comparison of Per Occupant Ecological Footprints by Dwelling Type (ha/capita) 1.60 1.40 I" 1 2 0 EL 0 3 0 1 1.00 Sr 0.80 o o o u .ou Eh o 2 0.40 0.20 0.00 0.05 0.66 0.79 0.06 0.47 0.07 "0742" 0.65 0.47 Standard Detached House Town-house Walk-up Apartment 0.06 0.37 0.47 d Infrastructure • Transportation • Housing High-rise Apartment 80 6.2.2 Components of Ecological Footprints Figure 6-3 compares the per occupant ecological footprints by dwelling type and consumption component. The largest differences occur in operating energy for housing and private transportation, followed by wood consumption. The reduction in wood consumed is particularly notable for occupants in high-rise apartments since little wood is used in the construction of these buildings. 0.60 _ 0.50 3 a re "re .c 0.40 o. •*-« o o 0.30 .0 0.20 a o o w 0.10 0.00 Fig. 6-3: Comparison of Consumption Components of Per Occupant Ecological Footprints by Dwelling Type (ha/capita) i | i i • 1 m ^  • B 11 ( U — X ! E L U o O) "o ^ > E g> c «fc E U J 2> c 0 ) CD c g m O Q . o o > O ) l s i i f p i S o c f l 0 § I d. O T 3 ra o cn "5 <" 8 3 • Standard Detached House • Townhouse • Walk-up Apartment • High-rise Apartment It is interesting to compare the ecological footprints of the walk-up and high-rise apartment. Recall that the floor spaces of these apartments are identical so the only differences in ecological footprint are due to dwelling type. Embodied energy for housing is greater for the high-rise apartment since the frame is concrete which contains more embodied energy than wood. The high-rise apartment also requires more operating energy for housing. However, the high-rise household consumes less transportation energy since travel distances are less and alternative modes of transportation are more feasible. The high-rise 81 apartment also has lower wood consumption since little wood is used in construction. The land directly occupied by high-rise apartments is also slightly lower than walk-up apartments. These differences between apartment types tend to offset each other resulting in similar total ecological footprints. Figure 6-4 shows each consumption component as a percentage of the total ecological footprint by dwelling type. For all dwellings, operating energy for housing and transportation account for over 60% of the ecological footprint. Wood for housing is the next most important component for all dwelling types except high-rise apartments. Embodied energy for housing and vehicles comprise 10 to 20% of a household's footprint depending on dwelling type. Operating energy for public transportation only comprises about 5% of the footprint. Embodied and operating energy for infrastructure and land directly occupied account for less than 10% of the ecological footprint for households in all dwelling types. Figure 6-5 shows the ecological footprints for each dwelling type disaggregated by land use component. The most significant land use component is carbon sink land (82-90%), which is the land required to absorb the carbon dioxide generated from the combustion of fossil fuels. Forest land is a distant second at 5 to 15% depending on dwelling type. The smallest portion of the ecological footprint is the land directly occupied by housing and infrastructure at 4 to 5%. Interestingly, directly occupied land which is the most tangible portion of a household's ecological footprint is the least significant. The magnitude of the ecological footprint is illustrated by comparing it with the land directly occupied by a household's lot. Table 6-4 shows the ratio of the ecological footprint per household to lot size. The footprint of the standard detached household is over 50 times the size of its lot. The ratio increases for townhouses and apartments since lot size per unit decreases faster than the ecological footprint. The ratio of ecological footprint to lot size is over 90 times the size of the lot for the townhouse; and over 100 and 300 times the per unit lot size for the walk-up and high-rise apartments respectively. 82 Fig. 6-4: Comparison of Consumption Components of Ecological Footprints by Dwelling Type (%) £ 40% 3- 35% o o LU •= 30% •S 25% o o LU Cfi '•V o a> in o 0 ) TO JS c 0. ; io%> 20% 15% B urn— i l for for £ AIJ i — qn for ising icles >, c n u. nergy energ ising nerg icles nerg m nerg usin nerg ort-ierg ort-nergy nfra. energ o CD eh a) g CD he CD sp CD sp CD _d - d > d . Q . d . Q -Eml Em Em O O ro i _ O CO 1 o • a c ro ro i _ ^ ~ g i — a X) ro • Standard Detached House • Townhouse • Walk-up Apartment • High-rise Apartment Fig. 6-5: Comparison of Land Use Components of Ecological Footprints by Dwelling Type {%) 90% • Carbon Sink Land • Forest Land • Occupied Land 83 Table 6-4: Ratios of Ecological Footprint to Lot Size by Dwelling Type Standard Detached Walk-up High-rise House Townhouse Apartment Apartment Ecological Footprint (ha/HH) 4.51 2.71 1.73 1.62 Lot Size (ha/HH) 0 .079 0.028 0 .014 0 .005 Ratio of EF to Lot Size 57 97 124 325 Ecological footprint analysis reveals that the land directly occupied by residential lots accounts for only a small part of the total land that is appropriated by households. The land appropriated is at least one to two orders of magnitude larger than a household's lot just for satisfying the consumption of fossil energy, forest products, and land for housing, transportation and infrastructure. When all consumption categories are considered, including food and consumer goods, the ratio of ecological footprint to lot size increases to between two to three orders of magnitude depending on dwelling type. 6.2.3 Comparison of Energy Consumption Calculations with Burby's Study Since carbon sink land comprises the largest portion of the dwelling-related ecological footprint, it is useful to compare the energy consumption calculations from this thesis with similar studies. Burby et al. (1982) provide one of the few studies on energy consumption that include calculations for both embodied energy and infrastructure. A study by Marshall Macklin Monaghan (1982) also conducts calculations for embodied energy and infrastructure but there were several errors observed, so the study is not discussed. Table 6-5 compares the energy consumption calculations from this thesis with Burby's study for a standard detached house and a multi-unit dwelling. Burby's multi-family households represent a mix of townhouses and low-rise apartments and is compared with a household in a walk-up apartment from this thesis. The embodied energy calculations for housing in this thesis are lower than Burby's for the detached house and about the same for the multi-family unit. One reason for the difference is that Burby's embodied energy coefficients are from a study by Hannon et al. (1976) which was in turn based 84 on 1967 input-output data from the U.S. This thesis used embodied energy coefficients from a more recent Canadian input-output model (Sheltair 1991). The embodied energy calculations for infrastructure from this thesis are similar to the calculations from Burby's study. Table 6-5: Comparison of Energy Consumption Calculations with the Burby (1982" ) Study Walker (1995) Standard Detached House (Canada) (GJ/HH) Burby et al. (1982) Standard Detached House (Atlanta) (GJ/HH) Walker (1995) Walk-up Apartment (Canada) (GJ/HH) Burby et al. (1982) Multi-family Dwelling (Atlanta) (GJ/HH) Emb. energy for housing 29.7 52.3 14.7 13.3 Emb. energy for infra. 4 .0 6.5 3.4 2.2 Op. energy for housing 156.9 128.2 56.1 26.7 Op. energy for transport - auto only 163.6 162.8 48.6 101.1 Op. energy for infra. 12.3 1.2 7.4 0.3 T O T A L 366.5 351.0 130.3 143.5 There is a large difference between Burby's study and this thesis for operating energy for housing. Burby's study is for Atlanta which has a much lower number of heating degree-days that result in energy savings. For the detached house, this thesis and Burby's study have similar automobile energy consumption results. For the multi-family dwelling, this thesis has lower results for automobile energy consumption than Burby's study, possibly due to different methods. Burby used an audit of household travel while this thesis used a mirrored density approach to correlate density with gasoline consumption. Operating energy for infrastructure is higher in this thesis than in Burby's study for both single- and multi-unit dwellings. This thesis defines infrastructure as hard infrastructure which includes street lighting, water and wastewater treatment, and garbage collection and disposal. The Burby study includes soft infrastructure, such as police patrol, in its category for infrastructure. Overall, the results from this thesis compare favourably with Burby's study considering the differences in method and study location. 85 6.2.4 Comparison of Ecological Footprint Calculations with Wackernagel's Dissertation It is also useful to compare the ecological footprint calculations in this thesis with similar studies. The only comparable study is Wackernagel's (1994a) calculation of the ecological footprint of an average Canadian. The thesis by Shawkat (1995) on the ecological footprint of a single-family detached house is not yet complete and does not include calculations for transportation and infrastructure. In order to make the results of this thesis comparable to Wackernagel's, the ecological footprint results of the four dwelling types calculated in this thesis are weighted by the current mix of dwelling types in Canada to obtain an average figure. The weighted average calculations are presented in table 6-6. Table 6-6: Calculation of Average Ecological Footprint Weighted by Dwelling Type Detached Walk-up High-rise Weighted House Townhouse Apartment Apartment Average Percentage of Housing Stock (%) 56.9 15.3 18.7 9.1 100.0 Number of Occupants 3 2.3 1.8 1.8 2.56 HOUSING (ha/cap.) 0.79 0.65 0.47 0.47 0.71 Emb. energy for housing 0.09 0.08 0.08 0.09 0.09 Op. energy for housing 0.46 0.38 0.27 0.33 0.42 Land for housing 0.03 0.01 0.01 0.00 0.02 Wood for housing construction 0.21 0.16 0.11 0.04 0.18 Wood for housing operation 0.01 0.01 0.01 0.01 0.01 TRANSPORTATION (ha/cap.) 0.66 0.47 0.42 0.37 0.59 Op. + emb. energy for priv. vehicle 0.61 0.42 0.37 0.33 0.54 Op. energy for public vehicles 0.05 0.05 0.05 0.04 0.05 INFRASTRUCTURE (ha/cap.) 0.05 0.06 0.07 0.06 0.05 Emb. energy for infrastructure 0.01 0.01 0.02 0.02 0.01 Op. energy for infrastructure 0.01 0.01 0.01 0.00 0.01 Land for infrastructure 0.03 0.03 0.04 0.04 0.03 TOTAL (ha/cap.) 1.50 1.18 0.96 0.90 1.35 Table 6-7 compares the dwelling-related ecological footprint weighted by dwelling type with Wackernagel's calculations for an average Canadian. The overall per occupant ecological footprint estimated in this thesis is 1.4 ha compared to Wackernagel's 1.7 ha. One difference is that this thesis calculates wood consumption for housing to be about half of Wackernagel's value. Wackernagel estimated wood consumption based on the building materials used for a standard wood-frame house from the OPTIMIZE program. This simplification overestimates average wood consumption by Canadians. 86 First, a new house is used in the OPTIMIZE program with a floor space of 192 m2, which is larger than that of the existing stock of 160 m . In order to calculate the per occupant ecological footprint, Wackernagel divided the wood consumption by the 1991 average household occupancy of 2.72 persons. In fact, the average occupancy of detached houses is closer to 3.0 so Wackernagel would have overestimated the per occupant consumption of wood. Wackernagel also does not consider that there is less wood consumption in multi-unit dwellings, particularly high-rise apartments. Table 6-7: Comparison of Ecological Footprint Calculations with Wackernagel's Calculations Walker (1995) Weighted Wackernagel Average (1994a) (ha/cap.) (ha/cap.) HOUSING 0.71 0.89 Emb. energy for housing 0.09 0.06 Op. energy for housing 0.42 0.35 Land for housing 0.02 0.08 Wood for housing construction 0.18 0.35 Wood for housing operation 0.01 0.05 P A S S E N G E R TRANSPORTATION 0.59 0.67 Op. + emb. energy for priv. vehicle 0.54 0 .60 Op. energy for public vehicles 0.05 0.07 INFRASTRUCTURE 0.05 0.11 Emb. energy for infrastructure 0.01 Op. energy for infrastructure 0.01 Land for infrastructure 0.03 0.11 TOTAL 1.35 1.67 Wackernagel also calculated land used for housing to be about four times higher compared to this thesis. In this thesis, only residential land is included in the total. Wackernagel included other land uses, such as commercial and industrial areas. Similarly, Wackernagel calculated more land for roads relative to this thesis. Wackernagel included the full right-of-way in his calculations while this thesis only used the width of pavement. Wackernagel did not include operating or embodied energy calculations for infrastructure. The embodied and operating energy calculations for housing and transportation are 87 reasonably close. Overall, the calculations from this thesis are mostly comparable with Wackernagel's results considering the differences in method and assumptions. 6.2.5 Reducing a Household's Ecological Footprint There are several ways that a household can reduce its per occupant ecological footprint. One way is to increase the occupancy of a dwelling to the point where there is a good match between the design and actual occupancy. A greater number of occupants per household would allow the sharing of the dwelling, automobiles, and infrastructure services. Operating energy consumption for housing is one of the largest components of a household's ecological footprint. Operating energy can be reduced by living in a multi-unit dwelling, through increased energy efficiency, or by reducing floor space per occupant. A standard townhouse or apartment is more energy efficient per unit floor space than a standard detached house. Alternatively, living in an R2000 dwelling can significantly reduce the operating energy for housing by approximately 50% for a detached house. Transportation energy consumption also offers a large potential for reducing a household's ecological footprint. Living in dwelling types that permit higher densities, such as apartments, result in less automobile dependence and better use of public transit and alternative transportation modes. For those households who still prefer detached houses, living in a small-lot neighbourhood increases density and reduces transportation energy consumption moderately. But living on a small lot would not bring these households above the threshold density of 30-40 persons per gross urban hectare correlated with reduced automobile dependence (Newman and Kenworthy 1989). Other ways to reduce a household's ecological footprint are more related to architecture and construction practices than to planning. Some ways to reduce the ecological footprint of a household are to reuse and 88 recycle building materials and to select building materials that have low amounts of embodied energy. Shawkat (thesis in progress) discusses various ways in which the ecological footprint can be reduced for single-family detached housing in greater detail. 6.2.6 Comparison with Trends in Housing and Efficiency There are several trends in housing and efficiency that affect the per occupant ecological footprint of households. The current stock of dwelling types in Canada is 57% single-family detached; 15% semi-detached, duplex, or townhouse; 19% low-rise apartment; and 9% high-rise apartment (Statistics Canada 1992d). There continues to be a "cultural" preference for detached houses, representing about 56% of new housing completions in 1994 (CMHC 1995). Therefore, the dwelling type with the largest per occupant ecological footprint is the form of housing that is most preferred. There is a trend towards declining household size in the last three decades, which reflects social trends of smaller families, divorce, lone-parent families, and individuals choosing to live alone. Between 1961 and 1991, the average household size decreased from 3.9 to 2.7 occupants (Statistics Canada 1961; 1994a). The age structure of the population has also changed as the population ages. The trend towards smaller households suggests a demand for smaller units. The strong demand for detached houses, however, appears to be moving in the opposite direction of household size and age structure trends. In fact, there appears to be a trend to larger units than the existing stock. In the U.S., which has a similar housing market to Canada, the median floor space of new detached houses increased from 1,762 ft in 1987 to 2,095 ft2 in 1992 (Wentling 1995, 8). At the same time, however, detached house lot sizes decreased. Significant declines in per dwelling energy consumption in Canada have also occurred. Between 1973 and 1987, dwelling energy consumption declined by about 32% (Marbek 1989 cited in Environment 89 Canada 1991, 12-29). At the same time, the number of dwellings increased in Canada by 49% between 1971 and 1986 (Statistics Canada 1971; 1992a). It is difficult to ascertain the affect of all these trends in combination on the ecological footprint of Canadians. Even if the per occupant ecological footprint of Canadians is declining, the increase in the total population may result in a greater total ecological footprint for the entire country. 6.2.7 Assessment of Method and Results The EF/ACC concept in general was assessed in Chapter 2. This section assesses the strengths and weaknesses of the method for this specific ecological footprint application. One of the main strengths of the method is that it approaches the ecological demand of a household from an integrated perspective. Instead of only considering resource consumption for housing, it also includes consumption for transportation and infrastructure. The holistic nature of the method is illustrated in the comparison of the ecological footprint of a household in a standard detached house to an identical household and house on a small lot. If only the housing portion of the ecological footprint was considered, there would be a reduction of less than 0.01 ha per occupant for the household on the small lot due to its smaller lot. However, if the relationship between density and travel patterns is also considered, a larger reduction in ecological footprint results from the decline in transportation energy consumption. By only considering housing rather than using an integrated approach, this finding would elude conventional studies. The method attempts to be complete by considering embodied energy for buildings, vehicles, and infrastructure as well as land and operating energy for infrastructure. Only recently have energy studies begun to include embodied energy. Another strength of the method is that it is able to integrate the consumption of different resources as shown by two examples. Consider the framing material of a low-rise building. If the frame is wood, less embodied energy is required relative to alternative building 90 materials. Therefore wood-frame buildings would have a lower carbon-sink footprint. However, one must also consider the land required to grow the trees. For the second example, consider the fact that buried infrastructure, including sewers, does not directly occupy land. However, infrastructure requires embodied energy for its manufacture and installation. By converting fossil energy used into carbon sink land it becomes apparent that buried infrastructure does in fact consume land. These examples illustrate the tradeoff between resources which would not be apparent when only a single resource is considered. Another strength of the method is that the calculations are well organized. Each calculation step is documented and all information necessary to perform the calculations is included on the calculation sheets. In addition, the separation of energy into an operating and embodied component arranges the data into more meaningful categories. The organization of the calculations allow the interested reader to recalculate the ecological footprints if better data are found at a later time. There was limited information on the variation in consumption patterns by dwelling type. In particular, there was a lack of data on wood used in buildings and there were few studies on resource consumption for infrastructure. In addition, the values for embodied energy consumption should be interpreted as being less accurate than operating energy. Embodied energy coefficients contain errors that may have occurred in the original input-output model, may not reflect changes in technological processes, and there is significant variation in the type and amount of building materials used in construction Another limitation of the method is that there are variations in dwelling characteristics for households relative to the values used for the dwelling archetypes. Some of the variables that will change markedly are household occupancy, dwelling size, lot size, and energy efficiency of the housing stock. In addition, there are several important characteristics that were not included in the method that impact consumption 91 patterns of households. Two of the most important latter characteristics are household income and lifestyle. These variables were excluded to simplify the method and calculations. A simplification in the method is that it uses national averages. Two regional differences are the number of heating degree-days and the mix of electricity derived from fossil fuels. The same accounting framework, however, can be used to recalculate the ecological footprints according to local conditions. The results of the ecological footprint calculations should also be interpreted with caution. The results are intended to be rough estimates of the per occupant ecological footprints of households in various dwelling types. In general, the relative differences between dwelling types are probably more accurate than the absolute values for the ecological footprints. 6.3 Policy Implications for Planning There are several policy implications for planning based on the analysis undertaken during this research. 1) Higher density living should be encouraged. The ecological footprint calculations show that, in general, as density increases the ecological footprint per occupant decreases. Occupants in single-family detached houses have the largest ecological footprint and they are also the most popular dwelling type in Canada. If we wish to reduce the ecological footprint of housing, then policies should promote higher density living. To be truly effective, a policy of increasing densities would need to be combined with an integrated set of policies including land use, transportation, and urban form. There is an ongoing debate over the merits and problems of higher densities compared to suburban sprawl. While it is beyond the scope of this thesis to enter into a full discussion of this debate, highlights 92 of both sides of the argument can be described. Arguments for higher densities include the needs of a changing demographic structure are better met, housing is more affordable, infrastructure costs are reduced, and farmland and environmental assets can better be preserved. Arguments against higher densities include the market demands low density housing, low densities provide a higher quality of life, and that the environmental benefits of higher density are exaggerated (Isin and Tomalty 1993). The finding that, in general, single-family detached households have the largest per occupant ecological footprints provides an additional argument for higher density living. According to Land and Armour (1980, 63), it is ". . . worth noting throughout the argument for higher densities is that they imply, though not necessarily involve, changes in building type from detached to multi-unit structures . . ." In addition, there are exceptions to the relationship between detached houses, low densities, and large per occupant ecological footprints. For example, new Asian families immigrating to Canada's largest metropolitan areas sometimes purchase large detached houses for housing their extended families. Therefore, these areas may contain much higher population densities than their housing form implies. These and other exceptions would need to be taken into account when formulating density policies. 2) Neighbourhoods composed of only single-family detached houses should be discouraged. In general, exclusively single-family detached neighbourhoods are likely to be automobile dependent. Therefore, to achieve densities that reduce automobile dependence a mix of dwelling types is required. For areas that insist on detached dwelling types, innovative zoning such as zero lot lines and zipper lots can be used to increase density or detached houses can be supplemented with accessory units, such as "granny flats." 93 3) Given a choice between promoting a low-rise or high-rise apartment development, the development that is most amenable to consumer and public acceptability should be supported. Low-rise and high-rise apartments were estimated to have about the same ecological footprints. High-rise apartments have greater consumption of embodied and operating energy for housing than walk-ups but less transportation energy consumption and wood for housing. The difference in the ecological footprint components of these two dwelling types offset each other giving comparable total ecological footprints per occupant. This finding provides a qualification to the general relationship between density and per occupant ecological footprints. It appears that as density increases, the ecological footprint per occupant falls at a decreasing rate and may eventually level off or even increase at very high densities. More evidence, however, would be required to verify this contention. Given a choice between supporting a low- or high-rise development, planners can support either in pursuing a policy of reducing per occupant ecological footprints. One misconception of higher density is that it means high-rise apartments. The above finding indicates that living in a low-rise apartment achieves a high density and has an equally small ecological footprint as high-rise apartments. Planners thus have the flexibility to support either medium or high density development, whichever is more publicly acceptable. In general, high-rise apartments are less accepted by the public than low-rise apartments due to their intrusive nature. In general, these findings coincide with Lang's (1985, 47) conclusion that "moderate or medium densities are preferable in terms of both energy savings and public acceptability." 4) Programs and policies that reduce operating energy consumption for housing and private vehicle transportation should be promoted as the highest priority areas for reducing the ecological footprint of households. 94 Operating energy for housing and private vehicle transportation comprise over 60% of a household's ecological footprint for all dwelling types. These two areas should therefore be targeted as high priority areas for reducing the ecological footprint of households. Interestingly, these two areas have traditionally been the focus of energy conservation programs and they are areas over which planners have significant influence. These findings coincide with the conclusion of Marshall Macklin Monaghan (1982, 5-2) that "transportation and space heating have been identified as the two aspects of new development which offer the greatest potential for energy conservation and are capable of being directly influenced by municipal planners. They are typically the two largest users of energy in urban areas." The only other area that planners have significant influence over is infrastructure, but this is only a small part of a household's ecological footprint. Reducing street widths, however, may be significant for increasing residential density, which may reduce transportation energy consumption. Planners can affect development and transportation patterns through the provision of public infrastructure and public housing. For example, the provision of transportation infrastructure, such as rapid transit lines, can encourage density being increased along preferred transportation corridors. Similarly, publicly supported housing should satisfy certain criteria compatible with small ecological footprints. 5) People need to be education about the magnitude of the ecological footprint of households and how it can be reduced. Education is important for increasing the awareness of consumers about sustainability and the impact of their consumption patterns on the environment. Households, particularly in urban areas, are disconnected from the natural environment. They may not realize the amount of resources and wastes they use and generate to satisfy their consumption patterns and therefore may be unaware of the magnitude of their ecological footprint. This thesis has shown that the land used for residential lots and 95 roads, which are the most tangible portions of a household's ecological footprint, comprise only a small part of the ecological footprint. Education about the rest of a household's ecological footprint is needed. The ecological footprint concept provides a powerful heuristic tool indicating the impact of humans on the planet. The concept and its measurement unit of land area are easy to understand. Furthermore, this thesis has applied ecological footprint analysis at a household scale which literally brings the message of sustainability closer to home. Comparing appropriated land to a household's lot size conveys the magnitude of the ecological footprint at a scale that everyone can relate. Developers and planners also need to be informed of the sustainability implications of housing since they are involved with planning and designing new residential areas. Finally, it is crucial that politicians and community organizations be informed of the desirability of higher density housing as they are the relevant decision-makers and community leaders. 6.4 Directions for Further Research The main directions for further research involve improvements in data quality, refinements to the method, and supplemental applications. 1) Improve data quality. There were several data limitations identified, particularly for wood and embodied energy used in housing construction for multi-unit dwellings. There was also a lack of data on embodied and operating energy for infrastructure. In addition, there was a lack of total operating energy data for multi-unit dwellings. Natural Resources Canada is conducting a survey of household energy use for housing as part of its National Energy Use Database (conversation with Andre Bourbeau, NRCan, May 1995). The data from the household energy use survey could be used to compare the data used in this thesis. Similarly, the data regarding automobile ownership and transportation energy use are not of sufficient detail. The 96 data for the average number of vehicles owned per household was not detailed enough to determine the variation by dwelling type. As part of the National Energy Use Database program, a National Passenger Vehicle Use Survey is being conducted, which should contain this information. 2) Conduct calculations using location specific data. Instead of using national data to calculate the ecological footprint as was done in this thesis, the method can also be adapted to specific locations. Location specific data would have an impact on space heating in buildings which requires significant amounts of energy. All other factors being equal, a household living in an area with more heating degree-days would have a larger ecological footprint. Location specific data would also change the fossil energy adjustment factor since some provinces generate more of their electricity from fossil fuels than others. The provinces with less than 5% of their electricity generated from fossil fuel are B.C., Manitoba, Quebec, and Newfoundland. The provinces with the highest portion of electricity generated from fossil fuels are Alberta (82%), Saskatchewan (64%>), and Nova Scotia (60%) (NRCan 1994a, 40). Households in provinces largely reliant on fossil fuels for electricity generation would have larger ecological footprints, all other factors being equal. 3) Determine the range in a household's per occupant ecological footprint. It would be useful to estimate the range in ecological footprints within a dwelling type. It would also be interesting to calculate the ecological footprint of the new stock of housing and compare it with the calculations for the existing housing stock to determine trends in ecological footprints. The new housing stock has different characteristics relative to the existing housing stock including different technologies, building standards, and size of dwelling units. In addition, it would also be useful to determine the potential for reduction in per occupant ecological footprints by improvements in efficiency. 97 It is interesting to speculate on the smallest and largest per occupant ecological footprints that would result using both the worst and best combination of characteristics. The largest per occupant ecological footprints would likely be for a small household of 1 or 2 occupants living in a large thermally inefficient detached house on a large lot. If site specific geographic characteristics are also included, the dwelling would be located in an area with both a high number of heating degree-days and a high percentage of electricity generated from fossil fuels, such as Edmonton, Alberta. More specifically, this household would likely be situated in a suburban area away from employment, shopping, and community facilities. The smallest per occupant ecological footprints would probably be for a relatively large 3 or 4 person household living in a low- or high-rise apartment with a floor space that matches the number of occupants. More specifically, the building would be thermally efficient with energy possibly provided by a district energy system. Geographically, this dwelling would be located in an area with a low number of heating degree-days and with electricity mostly generated from non-fossil fuel sources, such as Vancouver, B.C. This household would likely be located near downtown or close to employment, shopping, and community facilities. 4) Investigate the impact of income on the ecological footprint of households. Another important area which warrants further investigation is the impact of household income on dwelling choice and its impact on other consumption patterns. 5) Estimate the ecological footprint of new residential development. The most prescriptive use of the method developed in this thesis is to estimate the ecological footprint of new residential development before it is built. The mix of dwelling types, actual density, and infrastructure requirements can be used to assess the ecological footprint of a particular development. The ecological footprint of development alternatives can be compared and the most ecologically 98 desirable option can be determined. This thesis has compiled the basic data and consumption coefficients for conducting such an analysis. This thesis has only included residential and infrastructure data, so the set of data would need to be extended for commercial and industrial development. The development of a handbook for estimating the ecological footprint of new development would make the concept far more accessible and easier to compute. The current ecological footprint handbook (Wackernagel et al. 1993), while a useful reference, does not provide sufficient detail to assess the ecological footprint of particular households and is not organized to facilitate the calculations. A handbook for estimating the ecological footprint of new development would fill this gap. There will be a tradeoff between making a handbook simple enough to use without extensive data requirements and the loss of information due to simplifying assumptions. However, planners and developers would become familiar with the ecological implications of development and where the greatest potential lies for reducing a development's ecological footprint. This extension of the thesis would move the ecological footprint concept forward and translate it into a practical planning tool that municipal planners and developers can use to assess the sustainability of various development alternatives. It is hoped that the results and ideas presented in this thesis will stimulate further research into this interesting policy area. 99 BIBLIOGRAPHY Alberta Municipal Affairs. 1985. Energy Efficient Residential Subdivision Development in Alberta. The Planning Forum Series 3. B.C. Energy Council. 1994a. Planning Today for Tomorrow's Energy - An Energy Strategy for British Columbia. Vancouver, B.C.: B.C. Energy Council. B.C. Energy Council. 1994b. "Energy Sustainability and the Planning of Cities and Towns." Video. Vancouver, B.C.: B.C. Energy Council. B.C. Ministry of Crown Lands. 1989. British Columbia Land Statistics. Victoria, B.C. B.C. Ministry of Municipal Affairs, Recreation, and Culture. 1993. Statistics Relating to Regional and Municipal Governments in B.C. Victoria, B.C. B.C. Transit. 1990. B.C. Transit Annual Report 1989-90. Baird, George and Chan Seong Aun. 1983. Energy Cost of Houses and Light Construction Buildings. Report No. 76. Auckland: New Zealand Energy Research and Development Committee. Beck, Graham. 1993. "The Freedom to Stay: Transportation and Appropriated Carrying Capacity in the Context of Richmond's City Centre." Unpublished research paper, University of British Columbia Task Force on Planning Healthy and Sustainable Communities. Bevington, Rick and Arthur Rosenfeld. 1990. "Energy for Buildings and Homes." Scientific American 263:77-86. Bloomquist, Gordon, John Nimmons, and Kevin Rafferty. 1988. District Heating Development Guide, Legal Institutional, and Marketing Issues. Volume 1. Olympia: Washington State Energy Office. Bookout, Lloyd W. Jr et al. 1990. Residential Development Handbook. Washington, D.C.: Urban Land Institute. Boothroyd, Peter, Lawrence Green, Clyde Hertzman, Judy Lynam, Sharon Mason-Singer, Janette Mcintosh, William Rees, Mathis Wackernagel, and Robert Woollard (UBC Task Force on Planning Sustainable Communities). 1994. "Tools for Sustainability: Iteration and Implementation." In Ecological Public Health: From Vision to Practice, eds. C. Chu and R. Simpson, Centre for Health Promotion, University of Toronto. Brown, Lester and E. Wolf. 1984. Soil Erosion: Quiet Crisis in the World Economy. Worldwatch Paper 60. Washington, D.C.: Worldwatch Institute. Brown, Lester and Hal Kane. 1994. Full House: Reassessing the Earth's Population Carrying Capacity. Worldwatch Environmental Alert Series. New York: W. W. Norton and Company. Buchanan, Andrew and Brian Honey. 1994. "Energy and Carbon Dioxide Implications of Building Construction." Energy and Buildings 20:205-217. 100 Burby, Raymond, Janice Higgins, Edward Kaiser, Cynthia Matthews, and Maria Stanco. 1982. Saving Energy in Residential Development: A Report for Local Government. Chapel Hill: Center of Urban and Regional Studies, University of North Carolina. California Energy Commission, Oregon Department of Energy, and the Washington State Energy Office. 1994. PLACE 3S: A Methodology for Measuring and Designing Sustainable Urban Energy Efficiency. Prepared with Criterion Inc. and McKeever/Morris Inc. California Energy Commission. 1993. Energy-Aware Planning Guide. Sacramento: California Energy Commission. Calthorpe, Peter. 1993. The Next American Metropolis. Princeton, New Jersey: Princeton Architectural Press. Canada Mortgage and Housing Corporation (CMHC). 1977. The Conservation of Energy in Housing. NHA5149. Ottawa. Canada Mortgage and Housing Corporation (CMHC). 1995. Canadian Housing Statistics 1994. Ottawa. Canadian Consumer's Association. 1993. "Consumer Energy Efficiency - How to Save Money and Energy." Prepared with Ontario Hydro and Natural Resources Canada. City of New Westminster. 1995. City of New Westminster. Zoning By-law. 1995 version of zoning by-law. City of New Westminster, B.C. City of Toronto. 1983. Energy Efficient Large-Scale Building Design Guidelines. Toronto. Costanza, Robert and Herman Daly. 1992. "Natural Capital and Sustainable Development." Conservation Biology 6:37-46. Costanza, Robert, ed. 1991. Ecological Economics: The Science and Management of Sustainability. New York: Columbia University Press. Costanza, Robert. 1994. "Three General Principles to Achieve Sustainability." In Investing in Natural Capital: The Ecological Economics Approach to Sustainability, eds. A-M. Jasson et al. Washington, D.C.: Island Press. Criterion Inc. and McKeenen/Morris Inc. 1993. PLACE S: Planning for Community Energy, Environmental and Economic Sustainability. Overheads from presentation to B.C. Hydro, November 9, 1993. Cubukgil, A. and A. Waterhouse. 1980. The Energy Implications of Suburban Intensification. Report to the Ontario Ministry of Education. D'Amour, David. 1991. The Origins of Sustainable Development and Its Relationship to Housing and Community Planning. Sustainable Development and Housing Research Paper No. 1. Prepared for Canada Mortgage and Housing Corporation. 101 D'Amour, David. 1993. Towards an Investigation of Sustainable Housing. Sustainable Development and Housing Research Paper No. 2. Prepared for Canada Mortgage and Housing Corporation. Daly, Herman and John Cobb, Jr. 1989. For the Common Good: Redirecting the Economy Toward Community, the Environment, and a Sustainable Future. Boston: Beacon Press. Daly, Herman. 1991. "From Empty-World to Full-World Economics: Recognizing an Historic Turning Point in Economic Development." In Environmentally Sustainable Economic Development: Building on Brundtland, eds. R. Goodland et al. Paris. Daly, Herman. 1994. "Operationalizing Sustainable Development by Investing in Natural Capital." In Investing in Natural Capital: The Ecological Economics Approach to Sustainability, eds. A-M. Jassonetal. Washington, D.C.: Island Press. Davidson, Gavin and Christina Robb. 1994. "The Ecological Footprint of the Lions Gate Bridge." Unpublished term paper, School of Resource and Environmental Management, Simon Fraser University. Burnaby, B.C. De Chiara, Joseph (ed.). 1984. Time-Saver Standards for Residential Development. New York: John Wiley & Sons. De Chiara, Joseph and John Callender (eds.). 1990. Time-Saver Standards for Building Types. 3rd Edition. New York: McGraw-Hill. Duany, Andres and Elizabeth Plater-Zyberk. 1992. "The Second Coming of the American Small Town." Plan Canada May:6-13. Duany, Andres and Elizabeth Plater-Zyberk. 1994. "The Neighbourhood, the District and the Corridor." In The New Urbanism. ed. Peter Katz. Ehrlich, Paul. 1994. "Ecological Economics and the Carrying Capacity of the Earth." In Investing in Natural Capital: The Ecological Economics Approach to Sustainability, eds. A-M. Jasson et al. Washington, D.C.: Island Press. Energy, Mines and Resources Canada (EMR). 1981. Municipal Energy Audit: A Practical Guide to the Identification of Energy Expenditures. Building Series, Publication No. 3. Ottawa: Ministry of Supply and Services. . Energy, Mines and Resources Canada (EMR). 1982a. Municipalities and Energy Conservation - An Introduction. Volume 1. Ottawa: Ministry of Supply and Services. Energy, Mines and Resources Canada (EMR). 1982b. Energy and Urban Planning. EnerSave for Municipalities Report No. 3. Ottawa: Ministry of Supply and Services. Energy, Mines and Resources Canada (EMR). 1990. 1987 Energy Performance of R-2000 Homes Update: An Analysis of Measured Energy Consumption. Ottawa: Ministry of Supply and Services. Environment Canada. 1991. The State of Canada's Environment - 1991. Ottawa: Ministry of Supply and Services. 102 Essiambre-Phillips-Desjardins et al. 1995. Infrastructure Costs Associated with Conventional and Alternative Development Patterns. Draft Summary Report. Prepared for the Canada Mortgage and Housing Corporation. Fowler, Kenneth. 1992. Building Cities that Work. Montreal, Quebec: McGill-Queen's University Press. Frank, James E. 1989. The Costs of Alternative Development Patterns. Washington, D.C.: Urban Land Institute. Friedman, Avi, Vince Cammalleri, Jim Nicell, Francois Dufaux and Joanne Green. 1993. Sustainable Residential Developments: Planning, Design and Construction Principles ("Greening" the Grow Home). Prepared for Canada Mortgage and Housing Corporation. Ottawa: Ministry of Supply and Services. Gagnon, L. Energy and the 'Car-Bungalow-Suburb' Trilogy. Cited in Towards an Investigation of Sustainable Housing, D'Amour, 1993. Gibbons, John, Peter Blair and Holly Gwin. 1989. "Strategies for Energy Use." Scientific American 261:136-143. Gomez-Ibafiez, J.A. 1991. "A Global View of Automobile Dependence." Journal of the American Planning Association 57:376-379. Goodland, Robert, Herman Daly, Salah El Serafy and Bernd von Droste (eds.). 1991. Environmentally Sustainable Development: Building on Brundtland. Paris: UNESCO. Goodland, Robert. 1991. "The Case that the World has Reached Limits: More Precisely that Current Throughput Growth in the Global Economy Cannot be Sustained." In Environmentally Sustainable Economic Development: Building on Brundtland, eds. R. Goodland et al. Paris. Gordon, Deborah. 1991. Steering a New Course: Transportation. Energy and the Environment. Washington, D.C.: Island Press. Gordon, Peter and Harry Richardson. 1989. Gasoline Consumption and Cities: A Reply. Journal of the American Planning Association 55:342-346. Handy, Susan. 1992. How Land Use Patterns Affect Travel Patterns: A Bibliography. Council of Planning Librarians Bibliography 279. Chicago: Council of Planning Librarians. Hannon, B.M., R.G. Stein, B. Segal, D. Serber and C. Stein. 1976. Energy Use for Building Construction. Prepared for the Research and Development Administration. Haseltine, B.A. 1975. "Comparison of Energy Requirements for Building Materials and Structures." Structural Engineer 53:357-365. Hittman Associates Inc. 1977. Residential Energy Consumption: Detailed Geographic Analysis. Summary Report. Prepared for the U.S. Department of Housing and Urban Development. Washington, D.C.: U.S. Government Printing Office. 103 Hodge, Gerald. 1991. Planning Canadian Communities: An Introduction to the Principles. Practices, and Participants. Scarborough, Ontario: Nelson Canada. Hofstetter, Patrick. 1992. Peronsliche Energie- und C02-Bilanz. Aktion Klimaxhutz. Switzerland: Greenpeace. Honey, Brian and Andrew Buchanan. 1992. Environmental Impacts of the New Zealand Building Industry. Research Report 92/2. Christchurch, New Zealand: Department of Civil Engineering, University of Canterbury. IBI Group, The. 1993. Urban Travel and Sustainable Development. Prepared for Canada Mortgage and Housing Corporation. International Energy Agency. 1991. Energy Efficiency and the Environment. Energy and the Environment Series. Paris: OECD. Isin, Engin and Ray Tomalty. 1993. Resettling Cities: Canadian Residential Intensification Initiatives: Main Report. Prepared for Canada Mortgage and Housing Corporation. Ottawa. Jacobs, Michael. 1991. The Green Economy. Vancouver, B.C.: UBC Press. Jasson, AnnMari, Moica Hammer, Carl Folke, and Robert Costanza. 1994. Investing in Natural Capital: The Ecological Economics Approach to Sustainability. Washington, D.C.: Island Press. Katz, Peter. 1994. The New Urbanism: Toward An Architecture of Community. New York: McGraw-Hill. Kelman, Steven. 1981. "Cost-Benefit Analysis: An Ethical Critique." Regulation 5:33-40. Kenworthy, Jeffrey and Peter Newman. 1990b. "Cities and Transport Energy: Lessons from a Global Survey." Ekistics 57:258-268. Keyes, Dale and George Peterson. 1977. Metropolitan Development and Energy Consumption. Draft Land Use Center Working Paper 5049-15. Washington, D.C.: The Urban Institute. Keyes, Dale L. 1976. "Energy and Land Use: An Instrument of U.S. Conservation Policy?" Energy Policy 4:225-236. Krosea, Renate. 1990. The Greenpeace Guide to Paper. Amsterdam: Greenpeace International. Lang, Reg and Audrey Armour. 1980. Energy Conservation and the Municipal Planner. Building Series. Prepared for Energy, Mines & Resources Canada. Ottawa. Lang, Reg. 1985. Energy and Density. Prepared for Canada Mortgage and Housing Corporation. Ottawa. Leung, Hok-Lin. 1989. Land Use Planning Made Plain. Kingston, Ontario: Ronald P. Frye & Company. 104 Leung, Hok-Lin. 1993. Residential Density and Quality of Life. Prepared for the Canada Mortgage and Housing Corporation. Lindberg, James. 1982. Energy and Housing. Technical Report 147, Institute of Urban and Regional Research. Iowa City: University of Iowa. Lovins, Amory B. and L. Hunter Lovins. 1995. "Reinventing the Wheels." The Atlantic Monthly 276:75-86. Lowe, Marcia. 1990. Alternatives to the Automobile: Transport for Livable Cities. Worldwatch Paper 98. Washington, D.C.: Worldwatch Institute. Lowe, Marcia. 1991. Shaping Cities: the Environmental and Human Dimensions. Worldwatch Paper 105. Washington, D.C.: Worldwatch Institute. MacLaren, Virginia. 1992. Sustainable Urban Development in Canada: From Concept to Practice. Volume 1: Summary Report. Intergovernmental Committee on Urban and Regional Research. Toronto: ICURR Press. MacRae, Morgan. 1992. Realizing the Benefits of Community Integrated Energy Systems. Study No. 45. Calgary, Alberta: Canadian Energy Research Institute. Marbek Resource Consultants Ltd. 1989. Energy Demand in Canada 1973-1987. A Retrospective Analysis. Prepared for Energy, Mines and Resources Canada. Ottawa. Marbek Resource Consultants Ltd. 1991. Sustainable Housing: A Background Paper for the City of Montreal's Proposed Housing Design Competition. Prepared for Canada Mortgage and Housing Corporation. Ottawa. Marbek Resource Consultants Ltd. 1993a. Electricity Conservation Potential Review, 1988-2010: The Residential Sector. Phase 1: Unconstrained Potential. Collaborative Committee for the 1991-1994 Conservation Potential Review. Vancouver, B.C. Marbek Resource Consultants Ltd. 1993b. Electricity Conservation Potential Review, 1988-2010: The Commercial Sector. Phase 1: Unconstrained Potential. Collaborative Committee for the 1991-1994 Conservation Potential Review. Vancouver, B.C. Marshall Macklin Monaghan Ltd. 1982. Estimating Energy Consumption for New Development. Prepared for the Ontario Ministry of Energy. Toronto. Marshall Macklin Monaghan Ltd., Berridge Lewinberg Greenberg Ltd. and REIC Ltd. 1994. Making Choices: Alternative Development Standards Guideline. Draft. Prepared for the Ontario Ministry of Housing and Ministry of Municipal Affairs. Toronto. Meadows, Donella, Dennis Meadows and Jorgen Randers. 1992. Beyond the Limits: Confronting Global Collapse. Envisioning a Sustainable Society. Toronto: McClelland & Steward Inc. Middleton, Peter and Associates. 1980. Material and Energy Conservation Benefits of Resource Recovery Through Source Separation. Prepared for the Ontario Ministry of Environment. Toronto. 105 Moskowitz, Harvey and Carl Linbloom. 1993. The New Illustrated Book of Development Definitions. New Brunswick, New Jersey: Center for Urban Policy Research, Rutgers University. Natural Resources Canada (NRCan). 1994a. Electric Power in Canada 1993. Ottawa: Ministry of Supply and Services. Natural Resources Canada (NRCan). 1994b. 1993 Survey of Household Energy Use: National Results. Ottawa: Ministry of Supply and Services. Newman, Peter and Jeffrey Kenworthy. 1989. Cities and Automobile Dependence - An International Sourcebook. Brookfield, U.S.A.: Gower Technical. Newman, Peter and Jeffrey Kenworthy. 1991. "Transport and Urban Form in Thirty-Two of the World's Principal Cities." Transport Reviews 11:249-272. Newman, Peter and Jeffrey Kenworthy. 1992. "Is There a Role for Physical Planners?" Journal of the American Planning Association 58:353-362. Newman, Peter, Jeffrey Kenworthy, and Tom Lyons. 1990. Transport Energy Conservation Policies for Australian Cities - Strategies for Reducing Automobile Dependence. Western Australia: Murdoch University. Northeastern Illinois Planning Commission. 1981. Guidelines for Energy-Efficient Community Development (Site Development and Subdivision Design). Chicago: Illinois Institute of Natural Resources. Ontario Ministry of Environment and Energy. 1991. The Advanced House - Environmental Responsibility through Energy Efficiency. Toronto. Ontario Ministry of Municipal Affairs and Housing and Energy. 1982. Handbook for Energy Efficient Residential Subdivision Planning. Toronto: Queen's Printer. Ontario Ministry of Transportation (MOT) and Ministry of Municipal Affairs (MMA). 1992. Transit-Supportive Land Use Planning Guidelines. Toronto: Queen's Park Printer. Owens, Susan. 1986. Energy. Planning and Urban Form. London, England: Pion Publishing. Parker, Anthony. 1993a. Land Use and Automobile Dependence: Planning for Sustainability in Urban Regions. M.A. thesis, School of Community and Regional Planning, University of British Columbia. Parker, Anthony. 1993b. "Urban Form and Appropriated Carrying Capacity: An Examination of the City of Richmond 'City Centre'." Unpublished research paper, University of British Columbia Task Force on Planning Healthy and Sustainable Communities. Pearce, David and Kerry Turner. 1990. Economics of Natural Resources and the Environment. Baltimore, Maryland: John Hopkins University Press. Peat Marwick Stevenson & Kellogg (KPMG). 1993. The Cost of Transporting People in the British Columbia Lower Mainland. Transport 2021 Technical Report 11. Prepared for the Greater Vancouver Regional District. Vancouver, B.C. 106 Puget Sound Council of Governments and the Bonneville Power Administration. 1982. Energy Management Workbook for Local Governments. Puget Sound Council of Governments. Pushkarev, Boris and Jeffrey Zupan. 1977. Public Transportation and Land Use Policy. Bloomington: Indiana University Press. Real Estate Research Corporation. 1974. Costs of Sprawl - Environmental and Economic Costs of Alternative Development Patterns at the Urban Fringe. Washington, D.C. Rees, William and Mathis Wackernagel. 1994. "Ecological Footprints and Appropriated Carrying Capacity: Measuring the Natural Capital Requirements of the Human Economy." In Investing in Natural Capital: The Ecological Economics Approach to Sustainability, eds. A-M. Jasson et al. Washington, D.C: Island Press. Rees, William. 1988. "Sustainable Development and How to Achieve It." Address to the Forum for Planning Action Conference on Prospects for a Sustainable Economy, April 15-16, 1988. Rees, William. 1989. "Defining'Sustainable Development'." Center for Human Settlements Research Bulletin, Center for Human Settlements. Vancouver, B.C.: University of British Columbia. Rees, William. 1991. "Economics, Ecology, and the Limits of Conventional Analysis." Journal of Air and Waste Management Association 41:1323-27. Rees, William. 1992. "Ecological Footprints and Appropriated Carrying Capacity: What Urban Economics Leaves Out." Environment and Urbanization 4:121-130. Rees, William. 1994a. "Revisiting Carrying Capacity: Area-Based Indicators of Sustainability." Presented to the International Workshop on Evaluation Criteria for a Sustainable Economy, Gratz, Austria, April 6-7, 1994. Rees, William. 1994b. "Pressing Global Limits: Trade as the Appropriation of Carrying Capacity." Center for Human Settlements Planning Issues and Planning Responses Paper #4. Vancouver, B.C.: University of British Columbia. Regional Municipality of Ottawa-Carleton. 1981. Opportunities for Energy Conservation in Ottawa-Carleton: The Municipal Role. Draft report. Ottawa. Rogner, Hans-Holger. 1992. Techno-Economic Analysis of District Energy. Victoria, B.C.: Institute for Integrated Energy Systems, University of Victoria. Rogner, Hans-Holger. 1993. "Clean Energy Services Without Pain: District Energy Systems." Energy Studies Review 5:114-120. Roseland, Mark. 1992. Toward Sustainable Communities: A Resource Book for Municipal and Local Governments. Ottawa: National Round Table on the Environment and Economy. Royal Commission on the Future of Passenger Transportation. 1992a. Directions - The Final Report of the Royal Commission on the Future of Passenger Transportation. Volume 1. 107 Royal Commission on the Future of Passenger Transportation. 1992b. Directions - The Final Report of the Royal Commission on the Future of Passenger Transportation. Volume 2. Scanada Consultants Limited. 1992. Environmental Impact Study: Phase 1 - Development of a Database on Housing Characteristics Representative of the Canadian Housing Stock. (STAR-HOUSING Database). Prepared for Canada Mortgage and Housing Corporation. Ottawa. Science Council of Canada. 1982. Transportation in a Resource-Conscious Future: Intercity Passenger Travel in Canada. Science Council of Canada. Shawkat, Hijran. 1995. As a Step Toward Sustainability in the Canadian Housing Sector: Minimizing the Ecological Footprint of New Single Family Detached Houses. M.A. thesis (in progress), School of Architecture, University of British Columbia. Sheltair Scientific Ltd. 1991. Optimize: A Method for Estimating the Lifecycle Energy and Environmental Impact of a House. Prepared for the Canada Mortgage and Housing Corporation. Snohomish County Transportation Authority. 1989. A Guide to Land Use and Public Transportation Volume I. Washington, D.C.: U.S. Department of Transportation. Snohomish County Transportation Authority. 1993. A Guide to Land Use and Public Transportation Volume II: Applying the Concepts. Washington, D.C.: U.S. Department of Transportation. Snohomish County Transportation Authority. 1994. Creating Transportation Choices Through Zoning: A Guide for Snohomish County Communities. Lynwood, Washington: Snohomish County Transportation Authority. Statistics Canada. 1961. 1961 Census of Canada: Households and Families. Catalogue 93-510. Ottawa: Ministry of Supply and Services. Statistics Canada. 1987a. Canada Census 1986 - Profiles B.C. Part 1. Population and Dwelling Characteristics. Catalogue 94-119. Ottawa: Ministry of Supply and Services. Statistics Canada. 1987b. Canada Census 1986 - Census Tracts, Vancouver - Part 1. Catalogue 95-167. Ottawa: Ministry of Supply and Services. Statistics Canada. 1991. Human Activity and the Environment 1991. Catalogue 11-509E. Ottawa: Ministry of Supply and Services. Statistics Canada. 1992a. Households and the Environment 1991. Catalogue 11-526. Ottawa: Ministry of Supply and Services. Statistics Canada. 1992b. Passenger Bus and Urban Transit Statistics. Catalogue 53-215. Ottawa: Ministry of Supply and Services. Statistics Canada. 1992c. Dwellings and Households. 1991 Census of Canada. Catalogue 93-311. Ottawa: Ministry of Supply and Services. Statistics Canada. 1992d. Profile of CMAs and Census Agglomerations Part A. 1991 Census of Canada. Catalogue 93-337. Ottawa: Ministry of Supply and Services. 108 Statistics Canada. 1992e. Canada Census 1991 - Profile of Census Divisions and Subdivisions in B.C. Part A. Catalogue 95-384. Ottawa: Ministry of Supply and Services. Statistics Canada. 1993a. Environmental Perspectives, 1993 Studies and Statistics. Catalogue 11-528E. Ottawa: Ministry of Supply and Services. Statistics Canada. 1993b. Family Expenditure in Canada 1992. Catalogue 62-555. Ottawa: Ministry of Supply and Services. Statistics Canada. 1993c. Canada Census 1991- Census Tracts in Matsqui and Vancouver. Catalogue 95-388. Ottawa: Ministry of Supply and Services Statistics Canada. 1994a. Canada Year Book 1994. Ottawa: Ministry of Supply and Services. Statistics Canada. 1994b. Home Repair and Renovation Expenditure in Canada, 1992. Catalogue 62-201. Ottawa: Ministry of Supply and Services Statistics Canada. 1994c. Family Expenditure in Canada, Selected Metropolitan Areas, 1990. Catalogue 62-555. Ottawa: Ministry of Supply and Services Statistics Canada. 1994d. Human Activity and the Environment 1994. Catalogue 11-509E. Ottawa: Ministry of Supply and Services. Statistics Canada. 1995a. Household Facilities by Incomes and Other Characteristics 1994. Catalogue 13-218. Ottawa: Ministry of Supply and Services. Stein, R.G., C. Stein, M. Buckly and M. Green. 1980. Handbook of Energy Use for Building Construction. Washington, D.C.: U.S. Department of Energy. Steiner, Ruth L. 1994. Residential Density and Travel Patterns: A Review of the Literature and Methodological Approach. Draft Report. Washington, D.C.: Transportation Research Board. Underwood McLellan Ltd. 1983. An Introduction to Energy Conservation in Residential Development. Prepared for the Canada Mortgage and Housing Corporation. Ottawa. Urban Consultants Ltd. 1983. Municipal Energy Conservation in Alberta. Prepared for Alberta Energy and Natural Resources. Urban Development Institute Pacific Region. 1993. Back to the Future: Re-Designing Our Landscapes with Form, Place, and Density. Vancouver, B.C. U.S. Department of Transportation. 1994. Transportation Statistics Annual Report 1994. Washington, D.C. Vatn, Arild and Daniel Bromley. 1994. "Choices Without Prices Without Apologies." Journal of Environmental Economics and Management 26:129-148. Vitousek, Peter, Paul Ehrlich, Anne Ehrlich, and Pamela Matson. 1986. "Human Appropriation of the Products of Photosynthesis." Bioscience 36:368-73. 109 Wackernagel, Mathis, William Rees, Janette Mcintosh, Bob Woollard. 1993. "How Big Is Our Ecological Footprint? A Handbook for Estimating a Community's Appropriated Carrying Capacity." Discussion Draft, University of British Columbia Task Force on Planning Healthy and Sustainable Communities. Wackernagel, Mathis. 1994a. Ecological Footprint and Appropriated Carrying Capacity: A Tool for Planning Toward Sustainability. Ph.D. dissertation, School of Community and Regional Planning, University of British Columbia. Wackernagel, Mathis. 1994b. "How Big is Our Ecological Footprint? Using the Concept of Appropriated Carrying Capacity for Measuring Sustainability." Prepared with the University of British Columbia Task Force on Planning Healthy and Sustainable Communities. Wada, Yoshihiko. 1993. The Appropriated Carrying Capacity of Tomato Production: Comparing the Ecological Footprints of Hydroponic Greenhouse and Mechanized Field Operations. M.A. thesis, School of Community and Regional Planning, University of British Columbia. Wada, Yoshihiko. 1994. "Estimating the Present Carrying Capacity of the Lower Fraser Basin." Unpublished research paper, University of British Columbia Task Force on Planning Healthy and Sustainable Communities. Walker, Lyle. 1994. "Estimating the Influence of Housing Choice and Density on a Household's Appropriated Carrying Capacity: A Calculation Procedure." Unpublished research papaer, University of British Columbia Task Force on Planning Healthy and Sustainable Communities. Wentling, James and Lloyd Bookout (eds.). 1988. Density by Design. Washington, D.C.: Urban Land Institute. Wentling, James W. 1995. Housing by Lifestyle: The Component Method of Residential Design. New York: McGraw-Hill. Windsor, Duane. 1979. "A Critique of the Costs of Sprawl." American Planning Association 45:279-92. Woods Gordon Management Consultants, STARR Group, Enerplan Consultants Ltds., Allen-Drerup-White Ltd., and McCormick, Rankin & Associates Ltd. 1982. Community Development Patterns and Energy Conservation Study: Technical Appendix. Prepared for the Ontario Ministry of Municipal Affairs and Housing. World Commission on Environment and Development (WCED). 1987. Our Common Future. Oxford, U.K.: Oxford University Press. World Resources Institute (WRI). 1992. World Resources 1992-93. Oxford, U.K.: Oxford University Press. 110 APPENDIX A: MEASUREMENT UNITS AND CONVERSION FACTORS Unit Prefixes h hecto 1E2 k kilo 1E3 M mega 1E6 G giga 1E9 T tera 1E12 P peta 1E15 E exa 1E18 Length 1 ft = 0.3048 m Area 1ft2 = 0.0929 m 2 1 ha = 0.01 km2 1 ha = 10,000 m 2 1 ha = 2.47 acres 1 mi = 2.590 km2 Volume 1 ft2 = 0.02832 m 3 Mass 1 t = 1,000 kg 1 lb = 0.4536 kg Energy 1 MJ = 1 J 1 kcal = 1 btu = 1 kWh= 1E6 J 1 W*s 4187 J 0.001055 MJ 3.6 MJ Abbreviations btu ha J kWh = t British thermal unit hectare Joule kilowatt hour metric tonne Miscellaneous notations capita = HH DU one person household dwelling unit APPENDIX B: SOURCES AND NOTES FOR TABLES AND FIGURES This appendix contains the sources and notes for the tables and figures presented in this thesis. A numbering system based on the rows of the original table is used to relate the sources and notes information to the original table. The numbers in parentheses below correspond to the numbered rows in the original table. When the word "all" appears, this denotes that the entire table has only one source and that the notes apply to the whole table. Tables: Table 2-1: The Consumption by Land Use Matrix for An Average Canadian in ha/capita, 1991 (all) Sources: (Wackernagel 1994a, 125) Table 3-1: Favourability of District Energy Systems by Land Use Type (all) Sources: (Bloomquist et al. 1988, 13 cited in MacRae 1992) = Notes: The threshold density for district energy is likely to fall over time as technology improves. Table 3-2: Household Expenditures on Accommodation by Dwelling Type in Canada, 1992 Tail) Sources: (Statistics Canada 1993b, 118-123) Notes: It is assumed that the category "Homeowners in Other Types of Dwellings" includes single-family attached, rowhouse, townhouse, and condominium apartments. The term "Tenants in Other Types of Dwellings" is assumed to include single-family detached, single-family attached, rowhouse and townhouse dwellings. The total expenditure data did not add up correctly in the source material, possible due to rounding errors or missing data Table 3-3: Density and the Level and Frequency of Public Transit Service (all) Sources: (Pushkarev and Zupan 1977) cited in (Ontario MOT and MMA 1992, 18) Table 3-4: Commuting Patterns by Dwelling Type in Major Metropolitan Areas, 1991 (all) Sources: (Statistics Canada 1993a, 55) Notes: Major metropolitan areas are areas with populations greater than 100,000. The original source only contained the modal split for commuting by private vehicle and public transit. The remaining proportion not accounted for by private vehicle or public transit was attributed to either walking or cycling to work. It is possible, however, that these people may work at home and not commute to work. Table 3-5: Household Expenditures on Transportation by Dwelling Type in Canada, 1992 "Tan) Sources: (Statistics Canada 1993b, 118-123) Notes: See notes for Table 3-2 Table 3-6: The Cost of Infrastructure Services Per Dwelling Unit by Density, 1987 U.S. Dollars "(all) Sources: Based on (Frank 1989, 40) Notes: Frank's table is based on a composite of studies, including the Real Estate Research Corporation's (1974) Costs of Sprawl study, and is updated to 1987 U.S. dollars. A more recent study using Canadian data is currently in draft form and is being prepared by Essiambre Phillips Desjardins (1995) for CMHC 112 Table 4-1: The Revised Consumption by Land Use Matrix (all) Sources: Notes: Based on (Wackernagel et al. 1993, 23) and modified by author The shaded cells are not under investigation in this thesis Table 4-2: Fossil Energy Factor Calculations (1) Sources: (2) Sources: (3) Sources: (4) Sources: Notes: (Statistics Canada 1994a, 502) for final energy use demand (Statistics Canada 1994a, 502) for final energy use demand (Statistics Canada 1994a, 502) for final energy use demand (NRCan 1994a; Statistics Canada 1994a, 497) for electricity derived from fossil fuels. In 1993, 21% of all electricity in Canada was derived from fossil fuels. Since the conversion from fossil fuels to electricity is about 30% efficient, 3.33 units of fossil fuel are required to produce one unit of final electricity (cited in Wada 1994; Buchanan and Honey 1994). Therefore the electricity percentage in column (3) is multiplied by 21% electricity derived from fossil fuels and by 3.33 representing the efficiency of electricity conversion from fossil fuels (5) Derived: Table 5-1: Embodied Energy Consumption Coefficients for Housing (a21.1) (1) (2) (4) (5) Sources: Notes: (3) Notes: Derived Sources: Notes: (6) Derived (Sheltair 1991, 33, 35) for single-family detached house. The estimated lifespan of 40 years that was used in the OPTIMIZE program for single-family detached houses (Sheltair 1991, 25) is used for all dwelling types In the Sheltair (1991, 32) report, a floor space of 350 m 2 house was used for a standard new Canadian house, including the basement and garage. The floor space excluding the garage and basement is 192.2 m 2 (conversation with Hijran Shawkat, Master's student, School of Arch., UBC, May 1995, who has a copy of the actual floor plan for the house). (Hannon et al. 1976, 84-85; Stein et al. 1980, 37). Alternative data sources include Baird and Chan (1983) and Buchanan and Honey (1994). Original values from Hannon et al. (1976) and Stein et al. (1980) for embodied energy per unit floor space were 715,611 btu/ft2 for a single-family detached house (8.963 GJ/m2), 728,512 btu/ft2 for a 2-4 dwelling (8.273 GJ/m2), 764,102 btu/ft2 for garden apartments (8.677 GJ/m2), and 887,439 btu/ft2 for high-rise apartments (10.078 GJ/m2). These data are based on 1967 data from a U.S. input-output model. The 2-4 dwelling type was assumed to be similar to the embodied energy consumption in townhouses, which was the same assumption that Burby et al. (1982, 49) made. Table 5-2: Operating Energy Consumption Coefficients for Housing (a21.2) (Scanada 1992, 48) for operating energy consumption for a 159 mz detached house. (1) Sources: Notes: (2) Sources: Notes: The total 156.2 GJ of energy consumed per year was divided by the floor space to yield 0.982 GJ/m2/ year for operating energy consumption for housing. This value will vary by location due to differences in space conditioning. (City of Toronto 1983, 21) presents energy budget design levels from a proposed Building Energy Performance Standards for new buildings applied to the Toronto area. Alternative sources: (Hittman 1977; Marbek 1993a and 1993b; Woods Gordon 1982, A-8). The original values from (City of Toronto 1983, 21) used to estimate the percentages were: 0.670 GJ/m2 (100%) for detached houses, 0.560 GJ/m2 (83.6%) for semi-detached houses, 0.468 GJ/m2 (69.9%) for low-rises and 0.569 GJ/m2 (84.9%) for 113 high rises. The original table contains an error for the semi-detached house. The original value was 0.832 GJ/year for the semi-detached house but the data in million Btus/ft2 is 51 for a semi-detached house and 61 for a detached house, or 83.6% of the value. Recalculating with this percentage yields the 0.560 GJ/m value for the semi-detached house. (3) Derived Table 5-3: Wood Requirements for the Construction of a Standard House (f21.1) (1) Sources: (Sheltair 1991,28) (2) Sources: (Wackernagel et al. 1993, 68-69) Notes: Building paper conversion rate =1/1.8 (m3 wood/1 of paper) = 0.546 (t paper / m 3 wood) (3) Sources: (Brown 1985, 62) cited in (Wackernagel et al. 1993, 72) (4) Derived Table 5-4: Wood Consumption Coefficients for Housing Construction (£21.1) (1) Sources: (Wackernagel et al. 1993, 69) and row (4) of Table 5-3 Notes: The volume of wood corresponds to m3 of roundwood equivalent (2) Sources: (Sheltair 1991, 25) for 40 year lifespan assumed for all dwelling types (3) Notes: See note (3) from Table 5-1 (4) Derived (5) Sources: Assumed by author. Alternative sources: (Stein etal. 1980, 19-22) for embodied energy for wood products. Notes: Original percent embodied energy for wood products from (Stein et al. 1980): 16.1%) for detached houses, 9.4% for 2-4 dwellings, 6.3% for garden apartments and 3.5%o for high-rise apartments. The total energy embodied in the materials, excluding direct energy for construction, are: 616,804 btu/ft2 for detached houses, 522,813 for 2-4 dwellings, 534,576 for garden apartments, and 586,901 for high rise apartments. Weighting the %> embodied energy for wood products by embodied energy yields the following percentages relative to the detached house: 50% for 2-4 dwellings [ie. 9.4%*522,813 / (16.1% * 616,804)], 35% for garden, and 25 % for high-rise apartments. (6) Derived Table 5-5: Wood Consumption Coefficients for Housing Operation (£21.2) 0) Sources: (Krosea 1990, 41) and (Wackernagel et al. 1993, 42, 68-72) (2) Sources: (Environment Canada 1991, 25-26 cited in Wackernagel et al. 1993, 42, 68-72) (3) Derived (4) Sources: (Wackernagel et al. 1993, 68) (5) Derived Table 5-6: Embodied Energy Consumption Coefficients for Private Vehicles (a31.1) (1) Sources: (Hofstetter 1992) cited in (Wackernagel et al. 1993, 50) (2) Notes: average vehicle mass used by Wackernagel (1993 et al.) in the calculation of the ecological footprint of the typical Canadian (3) Sources: (Peat Marwick Stevenson & Kellogg 1993, A5) for an average 8.6 year vehicle lifespan Notes: lifespan of 7 years was used by Wackernagel in the calculation of the ecological footprint of the typical Canadian (4) Derived 114 Table 5-7: Operating Energy Consumption Coefficients for Intercity Passenger Transportation (a31.2 and a32.2) (all) Sources: (Science Council of Canada 1982, 13). Alternative source (U.S. Department of Transp 1994) (1) Notes: Science Council of Canada used a 40% vehicle occupancy rate, which corresponds to 2 of 5 seats being occupied in a car. Other sources indicate that 1.7 occupants per vehicle are more typical for intercity driving, which yields an occupancy of 34% in a five passenger vehicle. (2) Notes: Values for air, rail, bus, and ferry are from Science Council of Canada Automobile energy consumption was calculated assuming 12L/100 km is the average fuel economy for automobiles, 1.7 occupants, and 35 MJ/L of gasoline. Result: 12 L/lOOkm * 35 MJ/L * GJ/1,000 MJ /1.7 passengers = 0.00247 GJ/passenger-km. This corresponds with the energy consumption cited by Statistics Canada (1994, 103) of 420 MJ/100 vehicle-km for passenger cars in 1988. At an occupancy of 1.7 passengers, this also yields a value of 0.00247 GJ/passenger-km. Table 5-8: Base Consumption of Operating Energy for Intercity Passenger Transport (a31.2 and a32.2) (1) Sources: (Royal Commission on National Passenger Transportation 1992a, 38) for intercity domestic travel passenger-kilometres Notes: Total annual passenger-kilometres were divided by the 1991 Canadian population (27.297 million persons) to yield per capita distances (2) Derived (3) Sources: From Table 5-7 (4) Derived (5) Subtotal (6) Cell: Consumption by land use matrix cell Table 5-9: Directly Occupied Land for Roads and Sidewalks (b36.1) (1) Notes: Average road lane and sidewalk widths were assumed to be 4 metres and 1 metre respectively. Road and sidewalk widths will vary by type of road (2) Notes: Length is 1 metre to represent 1 linear metre of infrastructure (3) Derived (4) Derived Table 5-10: Base Consumption of Directly Occupied Land for Roads and Sidewalks (b36.1) (1) Notes: Geographic area that corresponds with data in rows (2), (4), and (5) (2) Sources: (B.C. Ministry of Crown Lands 1989, 17) for federal and provincial highways (conversation with Karoly Krajczar, GVRD, July 1994) for road lengths in the Lower Mainland (3) Notes: Average width of road assumed by author (4) Derived (5) Sources: (Statistics Canada 1992c) - 1991 data (6) Derived (7) Derived (8) Subtotal (9) Cell: Consumption by land use matrix cell 115 Table 5-11: Base Consumption of Directly Occupied Land for Right-of-ways (c36.1) (1) Sources: (B.C. Ministry of Crown Lands 1989, 17) (2) Notes: Geographic area that corresponds with data in row (1) and (3) (3) Sources: (Statistics Canada 1992c) - 1991 data (4) Derived Table 5-12: Embodied Energy Consumption Coefficients for Roads and Sidewalks (a36.1) (1) Sources: (Hannon et al. 1976; Stein et al. 1980). Alternative sources: (Marshall Macklin Monaghan 1982, F-3) Notes: Hannon et al. (1976) cited in (Burby et al. 1982, 50) estimated the following embodied energy consumption: 21,818 Btus per square foot for streets/ driveways (1.5" asphalt base), 28,019 Btus per linear foot for curbs (6.5" x 6.5"), 23,867 Btus per square foot for gutters (12" wide), and 23,349 Btus per square foot for sidewalks (3" concrete base). Converting to metric and normalizing by linear metre of infrastructure yields 0.9912 GJ/m for roads (4 metres wide), 0.0826 GJ/m for gutters (0.3048 metres wide), 0.0970 GJ/m for curbs, and 0.2652 GJ/m for sidewalks (1 metre wide). (2) Notes: The expected lifespan of roads was assumed to be 25 years which corresponds with Marshall Macklin Monaghan's (1982) estimate. The expected lifespan of sidewalks, curbs, and gutters was assumed to be 30 years which corresponds with the lifespans used in Burby et al.'s (1982) method. (3) (4) Derived Subtotal Table 5-13: Base Consumption of Embodied Energy for Roads and Sidewalks (a36.1) (1) (2) (3) Sources: Notes: Sources: Derived From row (6) in Table 5-10 Base consumption of roads was considered to include federal and provincial highways in the province of B.C. (divided by the number of households in B.C.) and the freeways/expressways/bridges, urban major/arterials/secondary arterials, and collectors/rural roads in the Lower Mainland (divided by the number of households in the Lower Mainland). There was no data available on the length or area occupied by sidewalks in urban areas. Sidewalks were therefore assumed to only exist on urban major/arterials/secondary arterials. Two lane-metres of road were considered to be equivalent to one linear-metre of sidewalk, curbs, and gutters in order to simplify the calculations. This means that all urban major/arterials/secondary arterials are all assumed to have on average four lanes of traffic and sidewalks on both sides of the street. From row (3) in Table 5-12 Table 5-14: Embodied Energy Consumption Coefficients for Buried Infrastructure (a37.1) (1) Sources: (Hannon et al. 1976; Stein et al. 1980). Alternative source: (Marshall Macklin Monaghan 1982, F-3) Notes: Original embodied energy values from the Hannon (1976) study (cited in Burby et al. 1982, 50) were 65,934 Btus per linear foot for an 8" concrete water main (0.228 GJ/m) and 138,288 for a 6" clay pipe (0.479 GJ/m). (2) Notes: Marshall Macklin Monaghan (1982) estimated that piped infrastructure has an expected lifespan of 100 years. This seemed high so I revised it to 80 years. (3) Derived 116 Table 5-15: Base Consumption of Embodied Energy for Buried Infrastructure (a37.1) (1) Sources: From row (6) in Table 5-10 for 4.1 lane-metres of road. Notes: Base consumption of buried infrastructure was assumed to equal the linear length of urban major/arterials/secondary arterials, which is equal to 4.1 lane-metres per household. Since we assume that there are four lanes of traffic, only 1.0 linear metres of infrastructure would be required assuming a single pipe under each road. (2) Sources: From row (3) in Table 5-17 (3) Derived Table 5-16: Base Consumption of Operating Energy for Street and Traffic Lights (a36.2) (1) Notes: Geographic area for data in rows (1), (3), (5), and (6) (2) Sources: (Conversation with Celeste Pires, B.C. Hydro, May 1995) Notes: There was no data for the number of traffic lights and signals (3) Sources: (Conversation with Celeste Pires, B.C Hydro, May 1995) (4) Notes: Standard conversion factor (5) Derived (6) Sources: (Statistics Canada 1992) - 1991 data (7) Derived (8) Derived Table 5-17: Base Consumption of Operating Energy for Off-site Services (a37.2) (all) Sources: (Middleton, Peter and Associates 1980 and Ontario MOE 1980 cited in Marshall Macklin Monaghan 1982, G-l to G-6) Table 5-18: Summary of Consumption Coefficients (all) Sources: Based on Table 5-1 through 5-17 Table 5-19: Summary of Base Consumption Calculations (all) Sources: Based on Tables 5-1 through 5-17 Table 6-1: Profile of Housing Archetypes (1) Sources: (Statistics Canada 1992c, 41) for number of occupants by dwelling type Notes: No data was available for the difference in occupancy rates between low and high-rise apartments (2) Sources: (Statistics Canada 1992c, 41) Notes: No data was available for the difference in number of bedrooms for apartments (3) Sources: (Scanada 1992, 29) for the average Canadian single-family detached house from the STAtistically Representative (STAR) Housing Database. For the typical net floor space for a townhouse, a value of 1,288 ft2 (119.7 m2) was used which corresponds with the interior unit dimension for a typical two-storey row house at a density of 18 units/acres (De Chiara 1990, 122). For both houses and townhouses, the floor space corresponds with the above ground livable floor space. For the floor space for the * 2 2 apartment units, 800 ft (74.3 m ) was considered to be a typical value for high rise apartment units (Marbek 1993b, app. 2). Notes: There will be a wide variation in actual floor spaces, particularly for single-family detached houses, but also for the other dwelling types depending on the number of bedrooms, washrooms, and design of the dwelling unit. (4) Derived For apartment units, common spaces for corridors, lobby, stairwells, and elevators were considered to represent an additional 10% of floor space. The net floor space was therefore multiplied by 1.1 to obtain the gross floor space. 17 (5) Sources Typical number of units per building for townhouse, low-rise, and high-rise apartments were based on examples used in the Time Saver Standards for Building Types Handbook (De Chiara 1990, 122-125). (6) Sources: Estimated by author. For the high-rise apartment, a 12 storey point tower with 4 units per storey was used based on a Time Saver Standards for Building Types Handbook example (De Chiara 1990, 125). (7) Notes: The framing material was estimated by the author as typical, although the typical framing material will vary geographically. Wood was chosen to make the results comparable to the wood-frame single-family detached house used in the OPTIMIZE report (Sheltair 1991). For the high-rise apartment, the two types of framing material are steel and reinforced concrete. Reinforced concrete was chosen as it is more typical than steel for residential buildings. (8) Sources: Lifespan of 40 years for a house (Sheltair 1991, 25) was assumed for dwellings (9) Sources: A lot size of 780.4 m 2 was used for the standard and R2000 houses. No data was available for typical lot sizes in Canada. Suburban homes likely have larger lot sizes and houses in older neighbourhoods probably have smaller lot sizes. For the small lot detached house, a 557 m2 minimum lot size from the City of New Westminster zoning by-law was used. For the townhouse, walk-up, and high-rise apartment, an arbitrary 5,000 m 2 lot was used as a typical large multi-building lot. Lot size is less important than the resulting density. (10) Notes: A lot 18.3 m in width was estimated for the standard and R2000 detached houses. A 13.7m lot width was estimated for the small lot house. A 9.0 m lot width was estimated for the townhouse. For the walk-up and high-rise apartments, a perimeter around the lot was calculated assuming that the lot is surrounded by roads on all four sides. The perimeter was calculated by taking the square root of the lot size and multiplying by four sides. (11) Notes: This is needed to calculate the total number of units on the lot. The number of buildings was estimated to correspond with the size of the lot. (12) Derived: Notes: The net densities correspond with the typical density ranges given by Hodge (1991, 150). Density range for single-family detached houses is 12-17 dwellings per net ha; 24-48 dwellings per net ha for row houses; and 48-96 dwellings per net ha for walkup apartments. Hodge provides values for high density dwellings based on building height: 96-192 dwellings per net ha for 5-10 storey buildings; 192-240 dwellings for 10-16 storey buildings; and 240-96 dwellings for buildings over 16 storeys. (13) Derived (14) Derived (15) Sources: City of New Westminster, B.C. zoning by-law (16) Sources: Based on (Statistics Canada 1995a, 150) - 1994 data Notes: The average number of automobiles was not provided in the source table. Only the percentage of households with a particular number of owned vehicles was given. The average number of vehicles was estimated as follows. Out of 7,380 single-family households, 658 owned no vehicles; 2,938 owned 1 vehicle, and 3,784 owned 2 or more vehicles. The breakdown of number of owned vehicles greater than 2 was not included so it is assumed that the 2 or more vehicles category means 2 vehicles per household. The average number of vehicles for single-family households is therefore (658/7380*0 + 2938/7380*1 + 3784/7380*2) = 1.42 vehicles. For multi-family households, the number of vehicles was not broken down by dwelling type. From a sample of 472 multi-family households, 85 had no car, 161 owned 1 car, and 226 owned 2 or more cars. The average number of vehicles for multifamily households is 118 therefore (85/472*0 + 161/472*1 + 226/472*2) = 1.3. The number of vehicles owned will likely fall as density increases due to the effect of smaller size households, lower incomes, and greater use of alternative modes of transportation. Table 6-2: Results of Per Household Ecological Footprint Calculations by Dwelling Type (all) Sources: From Appendix C Table 6-3: Results of Per Occupant Ecological Footprint Calculations by Dwelling Type (all) Sources: From Appendix C Table 6-4: Ratios of Ecological Footprint to Lot Size by Dwelling Type (all) Derived: Table 6-5: Comparison of Energy Consumption Calculations with Burby's Study (all) Sources: (Burby et al. 1982). Alternative source: (Marshall Macklin Monaghan 1982) Table 6-6: Calculation of Average Ecological Footprint Weighted by Dwelling Type (1) Sources: (Statistics Canada 1992d, 10) for mix of dwelling types (2) Sources: (Statistics Canada 1992c, 41) for number of occupants by dwelling type Table 6-7: Comparison of Ecological Footprint Calculations with Wackernagel's Calculations (all) Sources: (Wackernagel 1994a, 125) and (Wackernagel et al. 1993) Figures: Fig. 3-1: Influence of Dwelling Type on Resource Consumption for Housing (all) Sources: Prepared by author Notes: Each arrow pointing in a given direction indicates that the factor has "an influence' over the variable that the arrow is pointing towards Fig. 3-2: Influence of Dwelling Type and Density on Resource Consumption for Transportation (all) Sources: Prepared by author Notes: See notes for Figure 3-1. The figure primarily concerns the influence of density on transportation energy consumption. The influence of dwelling type and lot size on density depends on the characteristics of the surrounding neighbourhood Fig. 3-3: Urban Density Versus Gasoline Use Per Capita Adjusted for Vehicle Efficiency (all) Sources: (Newman and Kenworthy 1989, 49) Notes: The figure is a photocopy of the original diagram, used with permission of the publisher (Gower Technical). This graph shows gasoline consumption for travel within cities only. Intercity travel is not included in this graph Fig. 3-4: Influence of Dwelling Type and Density on Resource Consumption for Infrastructure (all) Sources: Prepared by author Notes: See notes for Figure 3-1 Fig. 4-1: Components in the Ecological Footprint Calculation Procedure (all) Sources: Prepared by author 119 APPENDIX C: ECOLOGICAL FOOTPRINT CALCULATIONS BY DWELLING TYPE Contents: Standard single-family detached house calculations R2000 single-family detached house calculations Small-lot single-family detached house calculations Standard townhouse calculations Standard walk-up apartment calculations Standard high-rise apartment calculations EXISTING STANDARD SINGLE-FAMILY DETACHED HOUSE CALCULATIONS Summary Calculation Sheet Dwelling Characteristics: Lot Characteristics: Dwelling type: Single-family detached Lot Area in m 2 : 780 .4 (m2) Stock type: Existing; standard efficiency Lot Area in ha [A]: 0 .078 (ha) A v g . no. of Persons [P]: 3.0 Lot Width [WJ: 18.3 (m) Net floor space [F]: 159.8 (m2) Site Coverage [C]: 35 (%) Gross floor space [f]: 159.8 (m2) = F No. of buildings [B] 1 Building Characteristics: DU lot area [a]: 0.078 (ha)=A/(B*U) No. of storeys in building: 2 DU lot width [w]: 18.3 (m)=W/(B*U) Framing material: wood Transportation Characteristics: No. of units per building [U]: 1 No. of Vehicles [V] 1.42 |Expected Building lifespan: 40 (years) Vehicle l ifespan: 8.6 (years) Consumption HOUSING Coefficient: Formula: Results: Units: a21.1 Embodied energy for dwelling 0 .186 * f 29.7 (GJ/year/HH) a21.2 Op. energy for dwelling 0.982 * f 156.9 (GJ/year/HH) b21.1 Directly occupied land for bldg N/A * * * 0 .023 (ha/HH) c21.1 Directly occupied land for yard N/A * * # 0 .056 (ha/HH) f21.1 Wood for building construction 0 .00885 * f 1.41 (m 3/year/HH) f21 .2 Wood for household operation 0 .0313 * P 0.09 (m 3/year/HH) TRANSPORTATION a31.1 Embodied energy private autos 12.2 * V 17.3 (GJ/year/HH) a31.2 + Op. energy private intercity 19.0 * P 57.0 (GJ/year/HH) a31.2 + Op. energy private intracity N/A * • * 110.7 (GJ/year/HH) a32.2 + Op. energy public intercity 4.1 * P 12.3 (GJ/year/HH) a32.2 + Op. energy public intracity N/A * * * 3.3 (GJ/year/HH) INFRASTRUCTURE a36.1 + Embodied energy for roads 3.06 Base 3.06 (GJ/year/HH) a36.1 + Embodied energy for roads 0 .0396 * w 0 .725 (GJ/year/HH) a36.2 Op. energy for road lights 0 .633 Base 0 .633 (GJ/year/HH) a37.1 + Embodied energy for buried infr. 0 .009 Base 0 .009 (GJ/year/HH) a37.1 + Embodied energy for buried infr. 0 .0088 * w 0.161 (GJ/year/HH) a37.2 Op. energy for off-site infr. 2.0 Base 2.0 (GJ/year/HH) b36.1 + Dir. occupied land for roads 0.031 Base 0.031 (ha/HH) b36.1 + Dir. occupied land for roads 0 .0005 * w 0 .009 (ha/HH) C36.1 Dir. occupied land for ROWs 0.039 Base 0 .039 (ha/HH) * * • [cell] + denotes calculations denotes more than ( performed t Dne value for an multiple st cell eps calculation shee 121 EXISTING STANDARD SINGLE-FAMILY DETACHED HOUSE CALCULATIONS (Con't) Multiple Step Calculation Sheet A 3 1 . 2 and A 3 2 . 2 : OPERATIONAL ENERGY FOR INTRACITY TRANSPORTATION i) Calculation of Mirrored Density DU Lot area [a] (1) 0 .0780 (ha) Number of dwelling units (2) 1 (DU) Number of occupants per dwelling (3) 3.0 (persons/DU) Net residential mirrored dwelling density (4) =(2)/(1) 12.8 (DU/net res. ha) Net residential mirrored population density (5) = (3)*(2)/(1) 38.5 (pop/net res. ha) Conversion factor from net to gross residential Ian (6) 1.2 (gross ha/net ha) Gross residential mirrored dwelling density (7) = (4)/(6) 10.7 (DU/gross res. ha) Gross residential mirrored population density (8) = (5)/(6) 32.1 (pop/gross res. ha) Conversion factor from gross res. to gross urban (9) 1.95 (urban ha/net res. ha) Gross urban mirrored dwelling density (10) = (4)/(9) 6.6 (DU/gross urban ha) Gross urban mirrored population density (11) = (5)/(9) 19.7 (pop/gross urban ha) ii) Estimation of energy consumption from density-gasoline consumption graph >This density corresponds to an annual per capita gasoline consumption of 38 ,000 M J / c a p . (Newman and Kenworthy 1989, 49) > Multiply by 3.0 household members > =38 GJ/year/cap * 3.0 persons/HH = 114 GJ/year /HH iii) Separation of energy consumption into public and private transportation >the closest city on the density-gasoline consumption graph is Adelaide, Australia >approximately 2 . 9 % of total energy use was for public transportation and 97.1 % for private transportation (Newman and Kenworthy 1989, 289) > using the same split of energy use results in: > 2 . 9 % * 114 GJ/HH/year = 3.3 GJ/HH/year for public transit (a32.2) > 9 7 . 1 % * 114 GJ/HH/year = 110.7 GJ/HH/year for private vehicles (a31.2) B21.1 and C 2 1 . 1 : DEGRADED A N D GARDEN LAND i) Calculation of "footprint" of house and garage >Max imum lot coverage is 3 5 % for an R1 zone (City of New Westminster zoning by-law) >this corresponds to a maximum footprint of: 780.4 m 2 * 3 5 % = 273.1 m 2 >this is a two storey dwell ing; assume the first floor is 115 m 2 measured by the external walls > assume the garage is 50 m 2 measured by the external walls >the total "footprint" of the house and garage is therefore approximately 165 m 2 . approximately 6 0 % of the maximum allowable lot coverage ii) Calculation of area of driveway >a driveway is typically 36 m 2 and the driveway apron is 24 m 2 (Marshall Mackl in Monaghan 1994, 12) > total area of driveway is therefore approximately 60 m 2 iii) Total land area occupied by house, garage, and driveway > = 115 m 2 + 60 m 2 + 50 m 2 = 225 m 2 = 0 .023 ha/HH (b21.1) iv) Garden land is simply the remaining land on the lot, assuming that it is left as a lawn > = 780 .4 m 2 - 225 m 2 = 555.4 m 2 = 0 .056 ha/HH (c21.1) 122 EXISTING STANDARD SINGLE-FAMILY DETACHED HOUSE CALCULATIONS (Con't) Consumption by Land Use Matrix Calculation Sheet The Consumption by Land Use Matrix in Energy, Material, and Land Units Degraded Garden Forest Energy Land h Land Products f 3 (GJ/HH) u (ha/HH) c (ha/HH) (m 3/HH) 20 HOUSING 186.6 0 .023 0 .056 1.508 21.1 Const . /Maint . 29.7 0 .023 0.056 1.414 21.2 Operation 156.9 0 .094 30 TRANSPORTATION 200.6 31.1 Private veh. - Construct. 17.3 31 .2 Private veh. - Operation 167.7 32.1 Public veh. - Construct. 32.2 Public veh. -Operation 15.6 35 INFRASTRUCTURE 6.59 0 .040 0 .039 36.1 Roads and ROWs-Const . 3.78 0 .040 0 .039 36.2 Roads and ROWs-Op. 0 .63 37.1 Buried and Off-site-Const. 0.17 37 .2 Buried and Off-site-Op. 2 .00 T O T A L Per HH 393 .86 0 .063 0.095 1.508 Conversion Factor (divide by) 100 N/A N/A 2.33 Conversion Factor Units (GJ/ha) N/A N/A (m 3/ha) The Consumption by Land Use Matrix in Land Area Equivalents (ha) Fossil Average Fossil Energy Degraded Garden Forest Per Energy Land 1 Land Land Land Total Occu-Factor a b c f Land pant (%) (ha/HH) (ha/HH) (ha/HH) (ha/HH) (ha/HH) (ha/cap) 20 HOUSING 1.66 0 .023 0 .056 0.65 2.38 0.79 21.1 Const . /Maint . 9 2 % 0.27 0 .023 0.056 0.61 0.96 0.32 21.2 Operation 8 8 % 1.38 0.04 1.42 0.47 30 TRANSPORTATION 1.99 1.99 0.66 31.1 Private veh. - Construct. 9 2 % 0.16 0.16 0.05 31.2 Private veh. - Operation 1 0 0 % 1.68 1.68 0.56 32.1 Public veh. - Construct. 32 .2 Public veh. -Operation 1 0 0 % 0.16 0.16 0.05 35 INFRASTRUCTURE 0.059 0 .040 0 .039 0 .138 0 .046 36.1 Roads and ROWs-Const . 9 2 % 0.035 0 .040 0 .039 0 .114 0 .038 36.2 Roads and ROWs-Op. 7 0 % 0.004 0 .004 0.001 37.1 Buried and Off-site-Const. 9 2 % 0.002 0 .002 0.001 37.2 Buried and Off-site-Op. 9 2 % 0.018 0 .018 0 .006 Total Per HH (ha/HH) 3.71 0.06 0 .10 0.65 4.51 1.50 Number of Occupants 3.0 3.0 3.0 3.0 3.0 Average Per Occupant (ha/cap) 1.24 0.02 0 .03 0.22 1.50 1 Adjusted by fossil energy factor 123 EXISTING R2000 SINGLE-FAMILY DETACHED HOUSE CALCULATIONS Summary Calculation Sheet Dwelling Characteristics: Lot Characteristics: Dwelling type: Single-family detached Lot Area in m 2 : 780 .4 (m2) Stock type: Existing; R2000 efficiency Lot Area in ha [A]: 0 .078 (ha) A v g . no. of Persons [P]: 3.0 Lot Width [WJ: 18.3 (m) Net floor space [F]: 159.8 (m2) Site Coverage [C]: 35 (%) Gross floor space [f]: 159.8 (m2) = F No. of buildings [B] 1 Building Characteristics: DU lot area [a]: 0 .078 (ha)=A/(B*U) No. of storeys in building: 2 DU lot width [w]: 18.3 (m)=W/(B*U) Framing material: wood Transportation Characteristics: No. of units per building [U]: 1 No. of Vehicles [V] 1.42 Expected Building lifespan: 40 (years) Vehicle l i fespan: 8.6 (years) Consumption HOUSING Coefficient: Formula: Results: Units: a21.1 Embodied energy for dwelling 0 .186 * f 29.7 (GJ/year/HH) a21.2 Op. energy for dwelling 0.982 * 4 8 % * f 75 .3 (GJ/year/HH) b21.1 Directly occupied land for bldg N/A # * * 0.023 (ha/HH) c21.1 Directly occupied land for yard N/A * * * 0.056 (ha/HH) f21.1 Wood for building construction 0 .00885 * f 1.41 (m 3/year/HH) f21 .2 Wood for household operation 0 .0313 * P 0 .09 (m 3/year/HH) TRANSPORTATION a31.1 Embodied energy private autos 12.2 * V 17.3 (GJ/year/HH) a31.2 + Op. energy private intercity 19.0 * P 57.0 (GJ/year/HH) a31.2 + Op. energy private intracity N/A * * » 110.7 (GJ/year/HH) a32.2 + Op. energy public intercity 4.1 * P 12.3 (GJ/year/HH) a32.2 + Op. energy public intracity N/A * * * 3.3 (GJ/year/HH) INFRASTRUCTURE a36.1 + Embodied energy for roads 3.06 Base 3.06 (GJ/year/HH) a36.1 + Embodied energy for roads 0 .0396 * w 0 .725 (GJ/year/HH) a36.2 Op. energy for road lights 0 .633 Base 0 .633 (GJ/year/HH) a37.1 + Embodied energy for buried infr. 0 .009 Base 0 .009 (GJ/year/HH) a37.1 + Embodied energy for buried infr. 0 .0088 * w 0.161 (GJ/year/HH) a37.2 Op. energy for off-site infr. 2.0 Base 2.0 (GJ/year/HH) b36.1 + Dir. occupied land for roads 0.031 Base 0.031 (ha/HH) b36.1 + Dir. occupied land for roads 0 .0005 * w 0 .009 (ha/HH) C36.1 Dir. occupied land for ROWs 0.039 Base 0 .039 (ha/HH) • • • |cel l | + denotes calculations denotes more than < » performed < jne value for in multiple st cell eps calculation shee i l ^ R i l 124 EXISTING R2000 SINGLE-FAMILY DETACHED HOUSE CALCULATIONS (Con't) Multiple Step Calculation Sheet A 3 1 . 2 and A 3 2 . 2 : OPERATIONAL ENERGY FOR INTRACITY TRANSPORTATION i) Calculation of Mirrored Density DU Lot area [a] (1) 0 .0780 (ha) Number of dwelling units (2) 1 (DU) Number of occupants per dwelling (3) 3.0 (persons/DU) Net residential mirrored dwelling density (4) =(2)/(1) 12.8 (DU/net res. ha) Net residential mirrored population density (5) = (3)*(2)/(1) 38.5 (pop/net res. ha) Conversion factor from net to gross residential Ian (6) 1.2 (gross ha/net ha) Gross residential mirrored dwelling density (7) = (4)/(6) 10.7 (DU/gross res. ha) Gross residential mirrored population density (8) = (5)/(6) 32.1 (pop/gross res. ha) Conversion factor from gross res. to gross urban (9) 1.95 (urban ha/net res. ha) Gross urban mirrored dwelling density (10) = (4)/(9) 6.6 (DU/gross urban ha) Gross urban mirrored population density (11) = (5)/(9) 19.7 (pop/gross urban ha) ii) Estimation of energy consumption from density-gasoline consumption graph >This density corresponds to an annual per capita gasoline consumption of 38 ,000 M J / c a p . (Newman and Kenworthy 1989, 49) > Multiply by 3.0 household members > =38 GJ/year/cap * 3.0 persons/HH = 114 GJ/year /HH iii) Separation of energy consumption into public and private transportation >the closest city on the density-gasoline consumption graph is Adelaide, Australia >approximately 2 . 9 % of total energy use was for public transportation and 97.1 % for private transportation (Newman and Kenworthy 1989, 289) >using the same split of energy use results in: > 2 . 9 % * 114 GJ/HH/year = 3.3 GJ/HH/year for public transit (a32.2) > 9 7 . 1 % * 114 GJ/HH/year = 110.7 GJ/HH/year for private vehicles (a31.2) B21.1 and C 2 1 . 1 : DEGRADED A N D GARDEN LAND i) Calculation of "footprint" of house and garage > Maximum lot coverage is 3 5 % for an R1 zone (City of New Westminster zoning by-law) >this corresponds to a maximum footprint of: 780 .4 m 2 * 3 5 % = 273.1 m 2 >this is a two storey dwell ing; assume the first floor is 11 5 m 2 measured by the external walls > assume the garage is 5 0 m 2 measured by the external walls >the total "footprint" of the house and garage is therefore approximately 165 m 2 , approximately 6 0 % of the maximum allowable lot coverage ii) Calculation of area of driveway >a driveway is typically 36 m 2 and the driveway apron is 24 m 2 (Marshall Mackl in Monaghan 1994, 12) >total area of driveway is therefore approximately 60 m 2 iii) Total land area occupied by house, garage, and driveway > = 115 m 2 + 60 m 2 + 50 m 2 = 225 m 2 = 0 .023 ha/HH (b21.1) iv) Garden land is simply the remaining land on the lot, assuming that it is left as a lawn > = 780 .4 m 2 - 225 m 2 = 555.4 m 2 = 0 .056 ha/HH (c21.1) 125 EXISTING R2000 SINGLE-FAMILY DETACHED HOUSE CALCULATIONS (Cont) Consumption by Land Use Matrix Calculation Sheet The Consumption by Land Use Matrix in Energy, Material, and Land Units Degraded Garden Forest Energy Land h Land Products f a (GJ/HH) u . (ha/HH) c (ha/HH) (m3/HH) 20 HOUSING 105.0 0 .023 0 .056 1.508 21.1 Const . /Maint . 29.7 0 .023 0.056 1.414 21.2 Operation 75.3 0 .094 30 TRANSPORTATION 200.6 31.1 Private veh. - Construct. 17.3 31.2 Private veh. - Operation 167.7 32.1 Public veh. - Construct. 32.2 Public veh. -Operation 15.6 35 INFRASTRUCTURE 6.59 0 .040 0 .039 36.1 Roads and ROWs-Const . 3.78 0 .040 0 .039 36.2 Roads and ROWs-Op. 0 .63 37.1 Buried and Off-site-Const. 0.17 37.2 Buried and Off-site-Op. 2 .00 T O T A L Per HH 312.3 0 .063 0.095 1.508 Conversion Factor (divide by) 100 N/A N/A 2.33 Conversion Factor Units (GJ/ha) N/A N/A (m 3/ha) The Consumption by Land Use Matrix in Land Area Equivalents (ha) Fossil Average Fossil Energy Degraded Garden Forest Per Energy Land 1 Land Land Land Total Occu-Factor a b c f Land pant (%) (ha/HH) (ha/HH) (ha/HH) (ha/HH) (ha/HH) (ha/cap) 20 HOUSING 0.94 0 .023 0 .056 0.65 1.66 0.55 21.1 Const . /Maint . 9 2 % 0.27 0 .023 0.056 0.61 0.96 0.32 21.2 Operation 8 8 % 0.66 0.04 0 .70 0 .23 30 TRANSPORTATION 1.99 1.99 0.66 31.1 Private veh. - Construct. 9 2 % 0.16 0.16 0.05 31.2 Private veh. - Operation 1 0 0 % 1.68 1.68 0.56 32.1 Public veh. - Construction 32.2 Public veh. -Operation 1 0 0 % 0.16 0.16 0.05 35 INFRASTRUCTURE 0.059 0 .040 0 .039 0 .138 0 .046 36.1 Roads and ROWs-Const . 9 2 % 0.035 0 .040 0 .039 0 .114 0 .038 36.2 Roads and ROWs-Op. 7 0 % 0.004 0 .004 0.001 37.1 Buried and Off-site-Const. 9 2 % 0.002 0 .002 0.001 37 .2 Buried and Off-site-Op. 9 2 % 0.018 0 .018 0 .006 Total Per HH (ha/HH) 2.99 0.06 0.10 0.65 3.79 1.26 Number of Occupants 3.0 3.0 3.0 3.0 3.0 Average Per Occupant (ha/cap) 1.00 0.02 0 .03 0.22 1.26 1 Adjusted by fossil energy factor 126 EXISTING SMALL LOT SINGLE-FAMILY DETACHED HOUSE CALCULATIONS Summary Calculation Sheet Dwelling Characteristics: Lot Characteristics: Dwelling type: Single-family detached Lot Area in m 2 : 557 .4 (m2) Stock type: Existing; standard efficiency Lot Area in ha [A]: 0 .056 (ha) A v g . no. of Persons [P]: 3.0 Lot Width [W]: 15.0 (m) Net floor space [FJ: 159.8 (m2) Site Coverage [C]: 35 (%) Gross floor space [f]: 159.8 (m2) = F No. of buildings [B] 1 Building Characteristics: DU lot area [a]: 0 .056 (ha)=A/(B*U) No. of storeys in building: 2 DU lot width [w]: 15.0 (m)=W/(B*U) Framing material: wood Transportation Characteristics: No. of units per building [U]: 1 No. of Vehicles [V] 1.42 Expected Building lifespan: 40 (years) Vehicle l ifespan: 8.6 (years) Consumption HOUSING Coefficient: Formula: Results: Units: a21.1 Embodied energy for dwelling 0 .186 * f 29.7 (GJ/year/HH) a21 .2 Op. energy for dwelling 0.982 * f 156.9 (GJ/year/HH) b21.1 Directly occupied land for bldg N/A * * # 0.023 (ha/HH) C21.1 Directly occupied land for yard N/A * # # 0.033 (ha/HH) f21.1 Wood for building construction 0 .00885 * f 1.41 (m 3/year/HH) f21 .2 Wood for household operation 0 .0313 * P 0.09 (m 3/year/HH) TRANSPORTATION a31.1 Embodied energy private autos 12.2 * V 17.3 (GJ/year/HH) a31.2 + Op. energy private intercity 19.0 * P 57 .0 (GJ/year/HH) a31.2 + Op. energy private intracity N/A » * * 78.3 (GJ/year/HH) a32.2 + Op. energy public intercity 4.1 * P 12.3 (GJ/year/HH) a32.2 + Op. energy public intracity N/A * * * 2.7 (GJ/year/HH) INFRASTRUCTURE a36.1 + Embodied energy for roads 3.06 Base 3.06 (GJ/year/HH) a36.1 + Embodied energy for roads 0 .0396 * w 0 .594 (GJ/year/HH) a36.2 Op. energy for road lights 0 .633 Base 0 .633 (GJ/year/HH) a37.1 + Embodied energy for buried infr. 0 .009 Base 0 .009 (GJ/year/HH) a37.1 + Embodied energy for buried infr. 0 .0088 * w 0 .132 (GJ/year/HH) a37.2 Op. energy for off-site infr. 2.0 Base 2.0 (GJ/year/HH) b36.1 + Dir. occupied land for roads 0.031 Base 0.031 (ha/HH) b36.1 + Dir. occupied land for roads 0 .0005 * w 0 .008 (ha/HH) C36.1 Dir. occupied land for ROWs 0.039 Base 0 .039 (ha/HH) * * * Icelll • denotes calculations denotes more than ( performed c )he~ value for jn multiple st cell eps calculation shee 127 EXISTING SMALL LOT SINGLE-FAMILY DETACHED HOUSE CALCULATIONS (Con't) Multiple Step Calculation Sheet A 3 1 . 2 and A 3 2 . 2 : OPERATIONAL ENERGY FOR INTRACITY TRANSPORTATION i) Calculation of Mirrored Density DU Lot area [a] (1) 0 .058 (ha) Number of dwelling units (2) 1 (DU) Number of occupants per dwelling (3) 3.0 (persons/DU) Net residential mirrored dwelling density (4) =(2)/(1) 17.3 (DU/net res. ha) Net residential mirrored population density (5) = (3)*(2)/(1) 52 .0 (pop/net res. ha) Conversion factor from net to gross residential Ian (6) 1.2 (gross ha/net ha) Gross residential mirrored dwelling density (7) = (4)/(6) 14.4 (DU/gross res. ha) Gross residential mirrored population density (8) = (5)/(6) 43 .3 (pop/gross res. ha) Conversion factor from gross res. to gross urban (9) 1.95 (urban ha/net res. ha) Gross urban mirrored dwelling density (10) = (4)/(9) 8.9 (DU/gross urban ha) Gross urban mirrored population density (11) = (5)/(9) 26.7 (pop/gross urban ha) ii) Estimation of energy consumption from density-gasoline consumption graph >This density corresponds to an annual per capita gasoline consumption of 27 ,000 M J / c a p . (Newman and Kenworthy 1989, 49) > Multiply by 3.0 household members > =38 GJ/year/cap * 3.0 persons/HH = 81 GJ/year /HH iii) Separation of energy consumption into public and private transportation >the closest city on the density-gasoline consumption graph is Toronto >approximately 3 . 3 % of total energy use was for public transportation and 96.7 % for private transportation (Newman and Kenworthy 1989, 343) >using the same split of energy use results in: > 3 . 3 % * 81 GJ/HH/year = 2.7 GJ/HH/year for public transit (a32.2) > 9 6 . 7 % * 81 GJ/HH/year = 78.3 GJ/HH/year for private vehicles (a31.2) B21.1 and C 2 1 . 1 : DEGRADED AND GARDEN LAND i) Calculation of "footprint" of house and garage >Max imum lot coverage is 3 5 % for an R1 zone (City of New Westminster zoning by-law) >this corresponds to a maximum footprint of: 557.4 m 2 * 3 5 % = 195.1 m 2 > this is a two storey dwell ing; assume the first floor is 115 m 2 measured by the external walls > assume the garage is 5 0 m 2 measured by the external walls >the total "footprint" of the house and garage is therefore approximately 165 m 2 . approximately 8 5 % of the maximum allowable lot coverage ii) Calculation of area of driveway >a driveway is typically 36 m 2 and the driveway apron is 24 m 2 (Marshall Mackl in Monaghan 1994, 12) > total area of driveway is therefore approximately 60 m 2 iii) Total land area occupied by house, garage, and driveway > = 115 m 2 + 60 m 2 + 50 m 2 = 225 m 2 = 0 .023 ha/HH (b21.1) iv) Garden land is simply the remaining land on the lot, assuming that it is left as a lawn > = 577 .4 m 2 - 225 m 2 = 332 .4 m 2 = 0 .033 ha/HH (c21.1) 128 EXISTING SMALL LOT SINGLE-FAMILY DETACHED HOUSE CALCULATIONS (Cont) Consumption by Land Use Matrix Calculation Sheet The Consumption by Land Use Matrix in Energy, Material, and Land Units Energy a (GJ/HH) Degraded Land h Garden Land Forest Products f u (ha/HH) c (ha/HH) (m 3/HH) 20 HOUSING 186.6 0 .023 0 .033 1.508 21.1 Const . /Maint . 29.7 0 .023 0 .033 1.414 21 .2 Operation 156.9 0 .094 30 TRANSPORTATION 167.6 31.1 Private veh. - Construct. 17.3 31.2 Private veh. - Operation 135.3 32.1 Public veh. - Construct. 32.2 Public veh. -Operation 15.0 35 INFRASTRUCTURE 6.43 0 .039 0 .039 36.1 Roads and ROWs-Const . 3.65 0 .039 0 .039 36.2 Roads and ROWs-Op. 0 .63 37.1 Buried and Off-site-Const. 0.14 37.2 Buried and Off-site-Op. 2.00 T O T A L Per HH 360.7 0.062 0 .072 1.508 Conversion Factor (divide by) 100 N/A N/A 2.33 Conversion Factor Units (GJ/ha) N/A N/A (m 3/ha) The Consumption by Land Use Matrix in Land Area Equivalents (ha) Fossil Energy Factor (%) Fossil Energy Land 1 a (ha/HH) Degraded Land b (ha/HH) Garden Land c (ha/HH) Forest Land f (ha/HH) Total Land (ha/HH) Average Per Occu-pant (ha/cap) 20 HOUSING 1.66 0 .023 0 .033 0.65 2.36 0.79 21.1 Const . /Maint . 9 2 % 0.27 0 .023 0 .033 0.61 0.94 0.31 21.2 Operation 8 8 % 1.38 0.04 1.42 0.47 30 TRANSPORTATION 1.66 1.66 0.55 31.1 Private veh. - Construct. 9 2 % 0.16 0.16 0.05 31 .2 Private veh. - Operation 100% 1.35 1.35 0.45 32.1 Public veh. - Construct. 32 .2 Public veh. -Operation 1 0 0 % 0.15 0.15 0.05 35 INFRASTRUCTURE 0.058 0 .039 0 .039 0 .135 0 .045 36.1 Roads and ROWs-Const . 9 2 % 0.034 0.039 0 .039 0.111 0 .037 36.2 Roads and ROWs-Op. 7 0 % 0.004 0 .004 0.001 37.1 Buried and Off-site-Const. 9 2 % 0.001 0.001 0 .000 37.2 Buried and Off-site-Op. 9 2 % 0.018 0 .018 0 .006 Total Per HH (ha/HH) 3.38 0.06 0.07 0.65 4 .16 1.39 Number of Occupants 3.0 3.0 3.0 3.0 3.0 Average Per Occupant (ha/cap) 1.13 0.02 0.02 0.22 1.39 1 Adjusted by fossil energy factor 129 EXISTING S T A N D A R D T O W N H O U S E CALCULATIONS Summary Calculation Sheet Dwelling Characteristics: Lot Characteristics: Dwelling type: Townhouse Lot Area in m 2 : 5 0 0 0 (m2) Stock type: Existing; standard efficiency Lot Area in ha [A]: 0 .5000 (ha) A v g . no. of Persons [P]: 2.3 Lot Width [W]: 9.0 (m) Net floor space [F]: 120.8 (m2) Site Coverage [C]: 4 0 (%) Gross floor space [f]: 120.8 (m2) = F No. of buildings [B] 2 Building Characteristics: DU lot area [a]: 0 .0278 (ha)=A/(B*U) No. of storeys in building: 2 DU lot width [w]: 9.0 (m)=W Framing material: wood Transportation Characteristics: No. of units per building [U]: . 9 No. of Vehicles [V] 1.3 Expected Building lifespan: 40 (years) Vehicle l ifespan: 8.6 (years) Consumption HOUSING Coefficient: Formula: Results: Units: a21.1 Embodied energy for dwelling 0.172 * f 20 .8 (GJ/year/HH) a21.2 Op. energy for dwelling 0.821 * f 99 .2 (GJ/year/HH) b21.1 Directly occupied land for bldg N/A * * * 0.007 (ha/HH) C21.1 Directly occupied land for yard N/A * * # 0.021 (ha/HH) f21.1 Wood for building construction 0 .00708 * f 0 .86 (m 3/year/HH) f21.2 Wood for household operation 0 .0313 * P 0.07 (m 3/year/HH) TRANSPORTATION a31.1 Embodied energy private autos 12.2 * V 15.9 (GJ/year/HH) a31.2 + Op. energy private intercity 19.0 * P 43 .7 (GJ/year/HH) a31.2 + Op. energy private intracity N/A * * * 38.7 (GJ/year/HH) a32.2 + Op. energy public intercity 4.1 * P 9.43 (GJ/year/HH) a32.2 + Op. energy public intracity N/A * * * 2.7 (GJ/year/HH) INFRASTRUCTURE a36.1 + Embodied energy for roads 3.06 Base 3.06 (GJ/year/HH) a36.1 + Embodied energy for roads 0 .0396 * w 0 .356 (GJ/year/HH) a36.2 Op. energy for road lights 0 .633 Base 0 .633 (GJ/year/HH) a37.1 + Embodied energy for buried infr. 0 .009 Base 0 .009 (GJ/year/HH) a37.1 + Embodied energy for buried infr. 0 .0088 * w 0 .079 (GJ/year/HH) a37.2 Op. energy for off-site infr. 1.5 Base 1.5 (GJ/year/HH) b36.1 + Dir. occupied land for roads 0.031 Base 0.031 (ha/HH) b36.1 + Dir. occupied land for roads 0 .0005 * w 0 .005 (ha/HH) C36.1 Dir. occupied land for ROWs 0.039 Base 0 .039 (ha/HH) * * * denotes calculations iu.-rlorm.ed on multiple steps calculation sheet [cell] + denotes more than one valueVfofjcell 130 EXISTING STANDARD TOWNHOUSE CALCULATIONS (Con't) Multiple Step Calculation Sheet A 3 1 . 2 and A 3 2 . 2 : OPERATIONAL ENERGY FOR INTRACITY TRANSPORTATION i) Calculation of Mirrored Density DU Lot area [a] (1) 0 .0278 (ha) Number of dwelling units (2) 1 (DU) Number of occupants per dwelling (3) 2.3 (persons/DU) Net residential mirrored dwelling density (4) = (2)7(1) 36 .0 (DU/net res. ha) Net residential mirrored population density (5) = (3)*(2)/(1) 82.7 (pop/net res. ha) Conversion factor from net to gross residential Ian (6) 1.2 (gross ha/net ha) Gross residential mirrored dwelling density (7) = (4)/(6) 30 .0 (DU/gross res. ha) Gross residential mirrored population density (8) = (5)/(6) 68.9 (pop/gross res. ha) Conversion factor from gross res. to gross urban (9) 1.95 (urban ha/net res. ha) Gross urban mirrored dwelling density (10) = (4)/(9) 18.4 (DU/gross urban ha) Gross urban mirrored population density (11) = (5)/(9) 42 .4 (pop/gross urban ha) ii) Estimation of energy consumption from density-gasoline consumption graph >This density corresponds to an annual per capita gasoline consumption of 18 ,000 M J / c a p . (Newman and Kenworthy 1989, 49) > Multiply by 3.0 household members > =18 GJ/year/cap * 2.3 persons/HH = 41 .4 GJ/year /HH iii) Separation of energy consumption into public and private transportation >the closest city on the density-gasoline consumption graph is Copenhagen, Denmark >approximately 6 . 5 % of total energy use was for public transportation and 93.5 % for private transportation (Newman and Kenworthy 1989, 301) >using the same split of energy use results in: > 6 . 5 % * 41 .4 GJ/HH/year = 2.7 GJ/HH/year for public transit (a32.2) > 9 3 . 5 % * 114 GJ/HH/year = 38.7 GJ/HH/year for private vehicles (a31.2 B21.1 and C 2 1 . 1 : DEGRADED A N D GARDEN LAND i) Calculation of "footprint" of townhouse > Maximum lot coverage is 4 0 % for an RT-2 townhouse zone (City of New Westminster zoning) >this corresponds to a maximum footprint of: 278 m 2 * 4 0 % = 111.2 m 2 > this is a two storey dwell ing; assume the first floor is 67 m 2 measured by the external walls (De Chiara 1990 , 122) >the total "footprint" of the townhouse is therefore approximately 2 4 % ii) Calculation of area for parking > assume that all parking is on-street. For 2 storey row houses with a density of 18 units/acre 22 on-street parking spaces can be provided (De Chiara 1990, 122). >total area for parking is therefore already counted in the infrastructure category iii) Total land area occupied by townhouse > = 67 m 2 = 0 .007 ha/HH (b21.1) iv) Garden land is simply the remaining land on the lot, assuming that it is left as a lawn > = 278 m 2 - 6 7 m 2 = 211 m 2 = 0.021 ha/HH (c21.1) 131 EXISTING STANDARD TOWNHOUSE CALCULATIONS (Con't) Consumption by Land Use Matrix Calculation Sheet The Consumption by Land Use Matrix in Energy, Material, and Land Units Degraded Garden Forest Energy Land h Land Products f a (GJ/HH) D (ha/HH) c (ha/HH) (m 3 /HH) 20 HOUSING 120.0 0 .007 0.021 0.927 21.1 Const . /Maint . 20.8 0 .007 0.021 0 .855 21.2 Operation 99.2 0 .072 30 TRANSPORTATION 110.4 31.1 Private veh. - Construct. 15.9 31 .2 Private veh. - Operation 82.4 32.1 Public veh. - Construct. 32 .2 Public veh. -Operation 12.1 35 INFRASTRUCTURE 5.64 0.036 0 .039 36.1 Roads and ROWs-Const . 3.42 0.036 0 .039 36 .2 Roads and ROWs-Op. 0 .63 37.1 Buried and Off-site-Const. 0.09 37.2 Buried and Off-site-Op. 1.50 T O T A L Per HH 236.0 0 .043 0 .060 0 .927 Conversion Factor (divide by) 100 N/A N/A 2.33 Conversion Factor Units (GJ/ha) N/A N/A (m 3/ha) The Consumption by Land Use Matrix in Land Area Equivalents (ha) Fossil Average Fossil Energy Degraded Garden Forest Per Energy Land 1 Land Land Land Total Occu-Factor a b c f Land pant (%) (ha/HH) (ha/HH) (ha/HH) (ha/HH) (ha/HH) (ha/cap) 20 HOUSING 1.06 0 .007 0.021 0 .40 1.49 0.65 21.1 Const . /Maint . 9 2 % 0.19 0.007 0.021 0.37 0.59 0.25 21.2 Operation 8 8 % 0.87 0.03 0 .90 0.39 30 TRANSPORTATION 1.09 1.09 0.47 31.1 Private veh. - Construct. 9 2 % 0.15 0.15 0.06 31 .2 Private veh. - Operation 100% 0.82 0.82 0.36 32.1 Public veh. - Construct. 32.2 Public veh. -Operation 1 0 0 % 0.121 0.12 0.05 35 INFRASTRUCTURE 0 .050 0.036 0 .039 0 .125 0.05 36.1 Roads and ROWs-Const . 9 2 % 0.031 0.036 0 .039 0 .106 0.05 36.2 Roads and ROWs-Op. 7 0 % 0.004 0 .004 0 .00 37.1 Buried and Off-site-Const. 9 2 % 0.001 0.001 0 .00 37.2 Buried and Off-site-Op. 9 2 % 0.014 0 .014 0.01 Total Per HH (ha/HH) 2.21 0.04 0.06 0 .40 2.71 1.18 Number of Occupants 2.3 2.3 2.3 2.3 2.3 Average Per Occupant (ha/cap) 0.96 0.02 0.03 0.17 1.18 1 Adjusted by fossil energy factor 132 EXISTING STANDARD WALK-UP APARTMENT CALCULATIONS Summary Calculation Sheet Dwelling Characteristics: Lot Characteristics: Housing type: Walk-up apartment Lot Area in m 2 : 5000 (m2) Stock type: Existing; standard efficiency Lot Area in ha [A] 0 .500 (ha) A v g . no. of Persons [P]: 1.8 Lot Perimeter [W] 254 .5 (m) Net floor space [F]: 74.3 (m2) Site Coverage [C] 4 0 (%) Gross floor space [f]: 81.7 (m2) = F * 1 . No. of buildings [B] 2 Building Characteristics: DU lot area [a]: 0 .0139 (ha)=A/(B*U) No. of storeys in building: 3 DU lot width [w]: 7.1 (m) = W/(B*U) Framing material: wood Transportation Characteristics: No. of units per building [U]: 18 No. of Vehicles [V] 1.3 Expected Building lifespan: 40 (years) Vehicle l ifespan: 8.6 (years) Consumption HOUSING Coefficient: Formula: Results: Units: a21.1 Embodied energy for dwelling 0 .180 * f 14.7 (GJ/year/HH) a21.2 Op. energy for dwelling 0.687 * f 56.1 (GJ/year/HH) b21.1 Directly occupied land for bldg N/A # * * 0.006 (ha/HH) c21.1 Directly occupied land for yard N/A * * • 0.008 (ha/HH) f21.1 Wood for building construction 0.00531 * f 0 .43 (m 3/year/HH) f21 .2 Wood for household operation 0 .0313 * P 0.06 (m 3/year/HH) TRANSPORTATION a31.1 Embodied energy private autos 12.2 * V 15.9 (GJ/year/HH) a31.2 + Op. energy private intercity 19.0 * P 34.2 (GJ/year/HH) a31.2 + Op. energy private intracity N/A * * * 18.5 (GJ/year/HH) a32.2 + Op. energy public intercity 4.1 * P 7.38 (GJ/year/HH) a32.2 + Op. energy public intracity N/A * * * 1.3 (GJ/year/HH) INFRASTRUCTURE a36.1 + Embodied energy for roads 3.06 Base 3.06 (GJ/year/HH) a36.1 + Embodied energy for roads 0 .0396 * w 0 .280 (GJ/year/HH) a36.2 Op. energy for road lights 0 .633 Base 0 .633 (GJ/year/HH) a37.1 + Embodied energy for buried infr. 0 .009 Base 0 .009 (GJ/year/HH) a37.1 + Embodied energy for buried infr. 0 .0088 * w 0 .062 (GJ/year/HH) a37.2 Op. energy for off-site infr. 1.5 Base 1.5 (GJ/year/HH) b36.1 + Dir. occupied land for roads 0.031 Base 0.031 (ha/HH) b36.1 + Dir. occupied land for roads 0 .0005 * w 0 .004 (ha/HH) c36.1 Dir. occupied land for ROWs 0.039 Base 0 .039 (ha/HH) * * • [celll + denotes calculations denotes more than < . performed < jne value for jn multiple st cell eps calculation shee 133 EXISTING STANDARD WALK-UP APARTMENT CALCULATIONS (Con't) Multiple Step Calculation Sheet A 3 1 . 2 and A 3 2 . 2 : OPERATIONAL ENERGY FOR INTRACITY TRANSPORTATION i) Calculation of Mirrored Density DU Lot area [a] (1) 0 .0140 (ha) Number of dwelling units (2) 1 (DU) Number of occupants per dwelling (3) 1.8 (persons/DU) Net residential mirrored dwelling density (4) =(2)/(1) 71.4 (DU/net res. ha) Net residential mirrored population density (5) = (3)*(2)/(1) 128.6 (pop/net res. ha) Conversion factor from net to gross residential Ian (6) 1.2 (gross ha/net ha) Gross residential mirrored dwelling density (7) = (4)/(6) 59.5 (DU/gross res. ha) Gross residential mirrored population density (8) = (5)/(6) 107.1 (pop/gross res. ha) Conversion factor from gross res. to gross urban (9) 1.95 (urban ha/net res. ha) Gross urban mirrored dwelling density (10) = (4)/(9) 36.6 (DU/gross urban ha) Gross urban mirrored population density (11) = (5)/(9) 65 .9 (pop/gross urban ha) ii) Estimation of energy consumption from density-gasoline consumption graph >This density corresponds to an annual per capita gasoline consumption of 11 ,000 M J / c a p . (Newman and Kenworthy 1989, 49) > Multiply by 3.0 household members > = 1 1 GJ/year/cap * 1.8 persons/HH = 19.8 GJ/year/HH iii) Separation of energy consumption into public and private transportation >the closest city on the density-gasoline consumption graph is West Berlin, Germany > approximately 6 .6% of total energy use was for public transportation and 93.4 % for private transportation (Newman and Kenworthy 1989, 349) >using the same split of energy use results in: > 6 . 6 % * 19.8 GJ/HH/year = 1.3 GJ/HH/year for public transit (a32.2) > 9 3 . 4 % * 19.8 GJ/HH/year = 18.5 GJ/HH/year for private vehicles (a31.2) B21.1 and C 2 1 . 1 : DEGRADED A N D GARDEN LAND i) Calculation of "footprint" of house and garage > Maximum lot coverage is 4 0 % for an RM-1 multiple dwelling district (garden apartments) (City of New Westminster zoning by-law) > this corresponds to a maximum footprint of: 139 m 2 * 4 0 % = 56 m 2 >this is a 3 storey dwell ing; assume the building footprint is 121 m 2 representing 3 units, or 40 .3 m 2 per unit (De Chiara 1990, 123). >assume the common entry shared by six units is 36.2 m 2 , or 6.0 m 2 per unit >the actual site coverage is therefore 46 m 2 or 33%of the lot size ii) Calculation of area for parking >e.g. from Time Saver Standards indicates 20 on-street and 16 on-site parking spaces > typical double space parking is 9 x 60 ft (parking space + aisle + space on opposite side) >8 double space parking x 50.2 m 2 = 401 m 2 for 36 units = 11.1 m 2 per unit iii) Total land area occupied by townhouse and parking > = 46 m 2 + 11 m 2 = 57 m 2 = 0 .006 ha/HH (b21.1) iv) Garden land is simply the remaining land on the lot, assuming that it is left as a lawn > = 139 m 2 - 57 m 2 = 82 m 2 = 0 .008 ha/HH (c21.1) 134 EXISTING STANDARD WALK-UP APARTMENT CALCULATIONS (Con't) Consumption by Land Use Matrix Calculation Sheet The Consumption by Land Use Matrix in Energy, Material, and Land Units Energy Degraded Land h Garden Land Forest Products f a (GJ/HH) u (ha/HH) c (ha/HH) (m 3/HH) 20 HOUSING 70.9 0 .006 0 .008 0 .490 21.1 Const . /Maint . 14.7 0 .006 0.008 0 .434 21 .2 Operation 56.1 0 .056 30 TRANSPORTATION 77.2 31.1 Private veh. - Construct. 15.9 31 .2 Private veh. - Operation 52.7 32.1 Public veh. - Construct. 32.2 Public veh. -Operation 8.7 35 INFRASTRUCTURE 5.54 0.035 0 .039 36.1 Roads and ROWs-Const . 3.34 0.035 0 .039 36.2 Roads and ROWs-Op. 0 .63 37.1 Buried and Off-site-Const. 0.07 37.2 Buried and Off-site-Op. 1.50 TOTAL Per HH 153.6 0.041 0.047 0 .490 Conversion Factor (divide by) 100 N/A N/A 2.33 Conversion Factor Units (GJ/ha) N/A N/A (m 3/ha) The Consumption by Land Use Matrix in Land Area Equivalents (ha) Fossil Fossil Energy Degraded Garden Forest Average Per Energy Land 1 Land Land Land Total Occu-Factor a b c f Land pant (%) (ha/HH) (ha/HH) (ha/HH) (ha/HH) (ha/HH) (ha/cap) 20 HOUSING 0.63 0.006 0.008 0.21 0.85 0.47 21.1 Const . /Maint . 9 2 % 0.14 0.006 0.008 0.19 0.34 0.19 21.2 Operation 8 8 % 0.49 0.02 0.52 0.29 30 TRANSPORTATION 0.76 0.76 0.42 31.1 Private veh. - Construct. 9 2 % 0.15 0.15 0.08 31.2 Private veh. - Operation 1 0 0 % 0.53 0 .53 0.29 32.1 Public veh. - Construct. 32 .2 Public veh. -Operation 1 0 0 % 0.09 0.09 0.05 35 INFRASTRUCTURE 0.050 0.035 0 .039 0 .123 0.07 36.1 Roads and ROWs-Const . 9 2 % 0.031 0.035 0 .039 0 .104 0.06 36.2 Roads and ROWs-Op. 7 0 % 0.004 0 .004 0 .00 37.1 Buried and Off-site-Const. 9 2 % 0.001 0.001 0 .00 37 .2 Buried and Off-site-Op. 9 2 % 0.014 0 .014 0.01 Total Per HH (ha/HH) 1.44 0.04 0.05 0.21 1.74 0.97 Number of Occupants 1.8 1.8 1.8 1.8 1.8 Average Per Occupant (ha/cap) 0 .80 0.02 0.03 0.12 0.97 1 Adjusted by fossil energy factor 135 EXISTING STANDARD HIGH-RISE APARTMENT CALCULATIONS Summary Calculation Sheet Dwelling Characteristics: Lot Characteristics: Dwelling type: High-rise apartment Lot Area in m 2 : 5000 (m2) Stock type: Existing; standard efficiency Lot Area in ha [A]: 0 .500 (ha) A v g . no. of Persons [PJ: 1.8 Lot Perimeter [W]: 254 .5 (m) Net floor space [F]: 74.3 (m2) Site Coverage [C]: 4 0 (%) Gross floor space [f]: 81.7 (m2) = F * 1 . No. of buildings [B] 2 Building Characteristics: DU lot area [a]: 0 .0053 (ha)=A/(B*U) No. of storeys in building: 12 DU lot width [w]: 2.7 (m) = W/(B*U) Framing material: concrete Transportation Characteristics: No. of units per building [U]: 47 No. of Vehicles [V] 1.3 Expected Building lifespan: 40 (years) Vehicle l ifespan: 8.6 (years) Consumption HOUSING Coefficient: Formula: Results: Units: a21.1 Embodied energy for dwelling 0 .209 * F 17.1 (GJ/year/HH) a21 .2 Op. energy for dwelling 0 .834 * F 68.2 (GJ/year/HH) b21.1 Directly occupied land for bldg N/A * * * 0.002 (ha/HH) C21.1 Directly occupied land for yard N/A * * * 0.003 (ha/HH) f21.1 Wood for building construction 0 .00177 * F 0 .14 (m 3/year/HH) f21 .2 Wood for household operation 0 .0313 * P 0.06 (m 3/year/HH) TRANSPORTATION a31.1 Embodied energy private autos 12.2 * V 15.9 (GJ/year/HH) a31.2 + Op. energy private intercity 19.0 * P 34.2 (GJ/year/HH) a31.2 + Op. energy private intracity N/A * * * 10.6 (GJ/year/HH) a32.2 + Op. energy public intercity 4.1 * P 7.38 (GJ/year/HH) a32.2 + Op. energy public intracity N/A * * * 0.2 (GJ/year/HH) INFRASTRUCTURE a36.1 + Embodied energy for roads 3.06 Base 3.06 (GJ/year/HH) a36.1 + Embodied energy for roads 0 .0396 * w 0.107 (GJ/year/HH) a36.2 Op. energy for road lights 0 .633 Base 0 .633 (GJ/year/HH) a37.1 + Embodied energy for buried infr. 0 .009 Base 0 .009 (GJ/year/HH) a37.1 + Embodied energy for buried infr. 0 .0088 * w 0 .024 (GJ/year/HH) a37.2 Op. energy for off-site infr. 1.5 Base 1.5 (GJ/year/HH) b36.1 + Dir. occupied land for roads 0.031 Base 0.031 (ha/HH) b36.1 + Dir. occupied land for roads 0 .0005 * w 0.001 (ha/HH) C36.1 Dir. occupied land for ROWs 0.039 Base 0 .039 (ha/HH) * * * Icelll-i-denotes calculations denotes more than < . performed c Dne value for Dn multiple st cell eps calculation shee 136 EXISTING STANDARD HIGH-RISE APARTMENT CALCULATIONS (Con't) Multiple Step Calculation Sheet A 3 1 . 2 and A 3 2 . 2 : OPERATIONAL ENERGY FOR INTRACITY TRANSPORTATION i) Calculation of Mirrored Density DU Lot area [a] (1) 0 .005 (ha) Number of dwelling units (2) 1 (DU) Number of occupants per dwelling (3) 1.8 (persons/DU) Net residential mirrored dwelling density (4) =(2)/(1) 188.7 (DU/net res. ha) Net residential mirrored population density (5) = (3)*(2)/(1) 339 .6 (pop/net res. ha) Conversion factor from net to gross residential Ian (6) 1.2 (gross ha/net ha) Gross residential mirrored dwelling density (7) = (4)/(6) 157.2 (DU/gross res. ha) Gross residential mirrored population density (8) = (5)7(6) 283 .0 (pop/gross res. ha) Conversion factor from gross res. to gross urban (9) 1.95 (urban ha/net res. ha) Gross urban mirrored dwelling density (10) = (4)/(9) 96 .8 (DU/gross urban ha) Gross urban mirrored population density (11) = (5)/(9) 174.2 (pop/gross urban ha) ii) Estimation of energy consumption from density-gasoline consumption graph >This density corresponds to an annual per capita gasoline consumption of 6 ,000 M J / c a p . (Newman and Kenworthy 1989, 49) > Multiply by 1.8 household members > = 6 GJ/year/cap * 1.8 persons/HH = 10.8 GJ/year /HH iii) Separation of energy consumption into public and private transportation >the closest city on the density-gasoline consumption graph is Tokyo, Japan >approximately 5 .7% of total energy use was for public transportation and 94 .3 % for private transportation (Newman and Kenworthy 1989, 341) >using the same split of energy use results in: > 5 . 7 % * 10.8 GJ/HH/year = 0.6 GJ/HH/year for public transit (a32.2) > 9 4 . 3 % * 10.8 GJ/HH/year = 10.2 GJ/HH/year for private vehicles (a31.2) B21.1 and C 2 1 . 1 : DEGRADED A N D GARDEN LAND i) Calculation of "footprint" of house and garage > Maximum lot coverage is 4 0 % for an RM-4 high rise multiple dwelling district (City of New Westminster zoning by-law) >this corresponds to a maximum footprint of: 5000 m 2 * 4 0 % = 2000 m 2 for the lot >an e.g. from the Time Saver Standards handbook for 2 point towers with 4 units/floor and 12 storeys on a 1 acre lot has a footprint of 916 m 2 for 94 units, 10 m 2 per unit (De Chiara 1990, 125) >the lot coverage is therefore approximately 1 8 % ii) Calculation of area for parking >from the Time Saver Standards e.g., 40 surface parking spaces are required on site for the 94 units (De Chiara 1990, 125) > parking area from example is 1,060 m 2 for the 94 units or 11 m 2 per unit iii) Total land area occupied per dwelling by building and parking > = 10 m 2 + 11 m 2 = 21 m 2 = 0 .002 ha/HH (b21.1) iv) Garden land is simply the remaining land on the lot, assuming that it is left as a lawn > = 53 m 2 - 21 m 2 = 32 m 2 = 0 .003 ha/HH (c21.1) 137 EXISTING STANDARD HIGH-RISE APARTMENT CALCULATIONS (Con't) Consumption by Land Use Matrix Calculation Sheet The Consumption by Land Use Matrix in Energy, Material, and Land Units Degraded Garden Forest Energy Land h Land Products f a (GJ/HH) u (ha/HH) c (ha/HH) (m 3/HH) 20 HOUSING 85.2 0.002 0 .003 0.201 21.1 Const . /Maint . 17.1 0.002 0 .003 0 .145 21.2 Operation 68.2 0 .056 30 TRANSPORTATION 68.2 31.1 Private veh. - Construct. 15.9 31 .2 Private veh. - Operation 44 .8 32.1 Public veh. - Construct. 32.2 Public veh. -Operation 7.6 35 INFRASTRUCTURE 5.33 0.032 0 .039 36.1 Roads and ROWs-Const . 3.17 0.032 0 .039 36.2 Roads and ROWs-Op. 0 .63 37.1 Buried and Off-site-Const. 0 .03 37.2 Buried and Off-site-Op. 1.50 T O T A L Per HH 158.8 0 .034 0 .042 0.201 Conversion Factor (divide by) 100 N/A N/A 2.33 Conversion Factor Units (GJ/ha) N/A N/A (m 3/ha) The Consumption by Land Use Matrix in Land Area Equivalents (ha) Fossil Average Fossil Energy Degraded Garden Forest Per Energy Land 1 Land Land Land Total Occu-Factor a b c f Land pant (%) (ha/HH) (ha/HH) (ha/HH) (ha/HH) (ha/HH) (ha/cap) 20 HOUSING 0.76 0.002 0 .003 0.09 0.85 0.47 21.1 Const . /Maint . 9 2 % 0.16 0.002 0 .003 0.06 0.22 0.12 21 .2 Operation 8 8 % 0.60 0.02 0.62 0.35 30 TRANSPORTATION 0.67 0.67 0.37 31.1 Private veh. - Construct. 9 2 % 0.15 0.15 0.08 31.2 Private veh. - Operation 1 0 0 % 0.45 0.45 0.25 32.1 Public veh. - Construct. 32.2 Public veh. -Operation 1 0 0 % 0.08 0 .08 0.04 35 INFRASTRUCTURE 0.048 0.032 0 .039 0 .119 0.07 36.1 Roads and ROWs-Const . 9 2 % 0.029 0.032 0 .039 0 .100 0.06 36.2 Roads and ROWs-Op. 7 0 % 0.004 0 .004 0 .00 37.1 Buried and Off-site-Const. 9 2 % 0 .000 0 .000 0 .00 37.2 Buried and Off-site-Op. 9 2 % 0.014 0 .014 0.01 Total Per HH (ha/HH) 1.48 0.03 0.04 0.09 1.64 0.91 Number of Occupants 1.8 1.8 1.8 1.8 1.8 Average Per Occupant (ha/cap) 0.82 0.02 0.02 0.05 0.91 1 Adjusted by fossil energy factor 138 

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