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Greenhouse gas accounting, characterization and mitigation at the city scale

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Greenhouse gas accounting, characterization and mitigation at the city scale
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Hillman, Tim
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English
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xiv, 179 leaves : ; 28 cm.

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Greenhouse gas mitigation ( lcsh )
Cities and towns ( lcsh )
Cities and towns ( fast )
Greenhouse gas mitigation ( fast )
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bibliography ( marcgt )
theses ( marcgt )
non-fiction ( marcgt )

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Thesis:
Thesis (Ph. D.)--University of Colorado Denver, 2009.
Bibliography:
Includes bibliographical references (leaves 167-179).
Statement of Responsibility:
by Tim Hillman.

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University of Colorado Denver
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Auraria Library
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435428585 ( OCLC )
ocn435428585

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GREENHOUSE GAS ACCOUNTING, CHARACTERIZATION AND MITIGATION AT THE CITY SCALE By Tim Hillman Bachelor of Science, Mechanical Engineering, Oregon State University 2000 Master of Science, Civil Engineering, University of Colorado at Boulder 2004 A thesis submitted to the University of Colorado Denver in partial fulfillment of the requirements for the degree of Doctor of Philosophy (Ph.D.) College of Engineering and Applied Sciences May 2009 A

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This thesis for the Doctor of Philosophy degree by Timothy Caleb Carew Hillman has been approved by Anu Ramaswami Aprv('/ 3D I ZDD 9 Date

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Hillman, Timothy C. C. (Ph.D., College of Engineering and Applied Sciences) Greenhouse Gas Accounting, Characterization, and Mitigation at the City-Scale Thesis directed by Professors Anu Ramaswami and Bruce Janson. ABSTRACT There is wide interest in reducing greenhouse gas (GHG) emissions at the local level, however, cities currently lack standardized tools to account for GHG emissions and quantify the effectiveness of mitigation strategies. This thesis develops three tools to support city-scale climate actions: 1. Enhancing City-Scale GHG Accounting: A new demand-centered, hybrid life cycle assessment (LCA)-based GHG inventory methodology is developed. The hybrid method combines the direct GHG emissions within a city (associated with end-use of electricity, natural gas and transportation fuels) with indirect emissions associated with manufacturing key urban materials such as water, cement, food and food packaging and transportation fuel. First tested in Denver, the hybrid LCA-based inventory was found to approach a GHG footprint based on convergence of per capita GHG emissions across scale between the city and the state(25 mt-C02e/person). 2. Surface Transport Allocation: A novel surface transportation allocation model included in the inventory method, allows for trip allocation across cities relevant for future mass transit modeling, enables airline fuel allocation across cities, shows consistency in per capita VMT estimates between commutershed and state (25-28 VMT per capita per day), while being sensitive to cities' urban form. Within the Denver commutershed, daily VMT per capita estimates ranged from 8 VMT/capita/day to over 80 VMT/capita/day among the 27 communities, with a strong positive correlation with employment

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intensity (employment/capita; R2 = 0.97) and employment density (employment/ square mile; R2 = 0.59). The hybrid inventory methodology with transport allocation model was replicated in seven other U.S. cities and yielded consistent results to those observed in Denver, CO, demonstrating robustness and applicability of the method for cities based on existing datasets. The key urban materials and airline travel accounted for 31% of the cities' GHG inventory, on average. Additionally, per capita consumption metrics in each sector are identified to better inform city-scale GHG footprints, including benchmarks for household energy use, commercial energy intensity, and, per capita VMT demand, airline trips, waste generation, cement and water consumption, and money spent on food. 3. Evaluating Building Sector Climate Actions: A first-order quantitative assessment of the effectiveness, combining performance and participation rates, often common building sector climate actions has been completed through a case study of Denver, CO. Results show that the upper estimates for performance and participation rates for all ten climate actions just offset expected demand growth over ten years, while a 20% reduction in the electric utility GHG emissions factor achieved a 5% reduction in emissions relative to the baseline. These results indicate that substantial increases in participation rates along with energy supply-side programs to reduce carbon intensity will be required to achieve long-term GHG reductions goals of cities. This abstract accurately represents the content of the candidate's thesis. We recommend its ublication. Anu Ramaswami Bruce Janson

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DEDICATION "Abo mitakuye oyasin."Lakota To all those who have come before, and to those that follow, may our journey into the field of sustainability always hold social justice at its core.

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ACKNOWLEDGEMENT First I would like to acknowledge the US Department of Education GAANN program for funding this research [Grant Number P200A030089]. I wish to recognize the many contributions and insights provided by others in developing this thesis, particularly my advisors, Anu Ramaswami and Bruce Janson. Dr. Ramaswami was instrumental in incorporating the concept of key urban materials into the greenhouse gas inventory process as well as expanding this methodology to be more of a footprint computation. Dr. Janson was integral in the development of transportation greenhouse gas allocation through the use of travel demand models. I would also like to acknowledge Dr. Mark Reiner for his insights and contributions to quantifying the greenhouse gas impacts of cement consumption in urban areas. I must also thank the City of Denver, Greenprint Denver and in particular Gregg Thomas in Denver's Department of Environmental Health for fostering the partnership with the university that allowed us to develop and apply greenhouse gas accounting techniques in a living laboratory. I want to thank Pete West from Xcel Energy for the hours of assistance helping to gather necessary utility data, and Mike Posner for the countless hours he spent collecting data for Denver's greenhouse gas inventory and climate action plan.

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I must also thank the many cities and their staff who provided their time, assistance and especially their data, without which this research could not have been completed. I want to thank Amanda Eichel at the City of Seattle; Vinh Mason and Michael Armstrong at the City of Portland; Jake Stewart, Rachel Thompson, and Jennifer Clymer at the City of Austin; Kevin Afflerbaugh at the City of Boulder; Lucinda Smith at the City of Fort Collins; and Gayle Prest at the City of Minneapolis. I would also like to acknowledge the assistance of the individuals from the metropolitan planning organizations for each of these cities who devoted hours of their time generating vital data outputs to complete the spatial allocation analysis. These individuals include: Kris Overby (Puget Sound Regional Council); Steve Hansen (Metro-Portland); Kevin Lancaster (CAMPO, Texas); Mark Filipi (Metropolitan Council, Minneapolis); and Suzette Mallette (North Front Range MPO).

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TABLE OF CONTENTS Figures .......................................................................................................................... xi Tables ......................................................................................................................... xiii Chapter 1. Introduction ........................................................................................................... 1 1.1 Greenhouse Gas Mitigation in Cities ............................................................ 2 1.2 Greenhouse Gas Research Needs at the City-Scale ...................................... 3 1.3 Research Goals and Objectives ..................................................................... 5 2. A Hybrid Life Cycle Assessment-Based GHG Inventory Methodology at the City-Scale ...................................................................................................................... 7 2.1 Introduction: Greenhouse Gas InventoriesNational to the City-Scale ...... 8 2.2 Spatial Scales, Inventory Approaches and Policy Relevance ..................... 10 2.2.1 Direct Emissions within Fixed Geographic Boundaries: .................... 10 2.2.2 Life Cycle-Based Assessment oflndirect Emissions: ........................ 12 2.3 Methodology ............................................................................................... 14 2.3.1 Background ofStudy Area: ................................................................ 14 2.3.2 Main Inventory Categories: ................................................................ 15 2.3.3 Direct Energy Use in Buildings and Facilities: ................................... 17 2.3.4 Direct Tail Pipe Transportation Energy Use: ...................................... 18 2.3.5 Embodied Energy of Key Urban Materials: ....................................... 20 2.4 Results ......................................................................................................... 29 2.4.1 Direct Energy Use in Buildings & Facilities: ..................................... 29 2.4.2 Transportation Sector .......................................................................... 31 2.4.3 Key Urban Materials ........................................................................... 33 2.4.4 Transportation Fuels ........................................................................... 33 Vll

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2.4.5 Water/Wastewater ............................................................................... 34 2.4.6 Cement in Urban Concrete .................................................................. 35 2.4.7 Food and Food Packaging: .................................................................. 35 2.4.8 Recycling and Landfilling Waste ........................................................ 36 2.4.9 Community Wide Summary ............................................................... 37 2.5 Sensitivity ................................................................................................... 41 2.6 Discussion ................................................................................................... 43 2.7 Uncertainty and Variability ......................................................................... 44 3. Spatial Allocation of Transportation Greenhouse Gas Emissions at the CityScale ............................................................................................................................ 46 3.1 Introduction ................................................................................................. 47 3.2 Overview of Spatial Allocation ofUrban Transport ................................... 50 3.3 Methodology ............................................................................................... 52 3.3.1 Spatial Allocation ofVMT ................................................................. 52 3.3.2 VMT Computation .............................................................................. 54 3.3.3 Airline Travel and Fuel Allocation ..................................................... 56 3.3.4 Surface Transport Fuel Use Computation ........................................... 56 3.4 Results ......................................................................................................... 57 3 .4.1 Analysis of 6 Regional Commuter Sheds: Replicability .................... 58 3.4.2 3.4.3 3.4.4 3.4.5 3.5 Detailed Analysis Within Region Local Relevance ......................... 61 Airline Travel Allocation .................................................................... 69 Tracking Impacts ofMode Shift ......................................................... 71 Error in Fuel Consumption from VMT Estimates: Statewide Data .... 74 Policy Implications ..................................................................................... 75 3.6 Conclusions ................................................................................................. 76 4. GHG Inventory Analysis: Eight U.S. Cities ....................................................... 78 4.1 Introduction ................................................................................................. 78 4.2 Methodology ............................................................................................... 79 Vlll

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4.2.1 4.3 4.3.1 4.3.2 4.3.3 4.3.4 4.3.5 Benchmarking ..................................................................................... 85 Results ......................................................................................................... 86 CommunityWide GHG Emissions .................................................... 87 Community Wide Benchmark Summary ............................................ 92 Multi-City GHG Emissions Variability by Sector. ............................. 97 Variation of Building Sector GHG Emissions .................................... 98 Temporal Sensitivity of GHG Emissions Data Collection ............... 104 4.4 Conclusions ............................................................................................... 106 5. First-Order Quantitative Assessment ofGHG Mitigation Options in Buildings 107 5.1 5.2 5.3 5.4 5.5 co 5.5.1 5.5.2 5.5.3 5.5.4 5.5.5 Introduction ............................................................................................... 107 Energy Use in Buildings ........................................................................... 110 Overview of GHG Mitigation Options in Buildings ................................ 113 Performance and Participation: Towards Overall Effectiveness .............. 114 First-Order Quantitative Assessment of Building Sector Actions: Denver, 118 Energy Efficiency Programs in Existing Buildings .......................... 119 Energy Efficiency New Building Programs ..................................... 125 Renewable Energy ............................................................................ 128 Behavior Change ............................................................................... 131 Summary ........................................................................................... 131 5.6 ResultsOverall Effectiveness of the Building Sector GHG Mitigation Options .................................................................................................................. 135 5.7 Observed OutcomesPortland, OR and Boulder, CO ............................. 137 5.8 Conclusions ............................................................................................... 142 6. Overall Conclusions and Recommendations .................................................... 143 Appendix A. Cement Consumption in Cities ......................................................................... 148 IX

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B. Community Characteristics and Daily Vehicle Miles ofTravel: The Denver Metro Region ............................................................................................................ 154 C. Supplemental Data for Eight Cities GHG Analysis .......................................... 157 References ................................................................................................................. 167 X

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LIST OF FIGURES Figure 2-1 Location of Denver in Colorado and within the DR COG region ......................... 15 2-2 Commute patterns for Denver's workforce .......................................................... 15 2-3 Denver's GHG emissions summary by activity in 2005 ...................................... 37 2-4 Variation in the magnitude ofDenver's per capita GHG emissions .................... 42 3-1 Commute patterns for Denver's workforce .......................................................... 49 3-2 Tracking a vehicle commute trip in Denver metro region .................................... 53 3-3 Demand and polygon daily VMT per capita for 27 cities in the DR COG region. 62 3-4 Daily per capita VMT versus population density ................................................. 65 3-5 Daily per capita VMT versus employment density .............................................. 67 3-6 Spatial allocation of airline travel emissions ........................................................ 70 3-7 Percent reduction in VMT due to a 5% mode shift .............................................. 72 3-8 Comparison of impacts on VMT estimates for a 5% mode shift ......................... 73 4-1 GHG emissions by sector for eight U.S. cities ..................................................... 88 4-2 Per capita GHG emissions by WRI scopes for eight U.S. cities .......................... 89 4-3 Per capita GHG emissions for eight cities relative their state's emissions .......... 91 4-4 Average monthly electricity use per household for eight cities ........................... 93 4-5 Average monthly natural use per household for eight cities ................................ 93 4-6 Average commercial energy use per square foot of floor area for eight cities ..... 94 4-7 Average daily vehicle miles of travel per capita for eight cities .......................... 95 4-8 Annual airline enplaned passengers (one-way trips) per capita for eight cities ... 95 4-9 Annual jet fuel use per enplaned passenger at airports serving the eight cities ... 96 4-10 Annual per capita transportation fuel consumed for eight cities ........................ 96 4-11 Daily per capita municipal solid waste generated in eight cities ........................ 97 4-12 Per capita GHG emissions by sector .................................................................. 98 xi

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4-13 Building GHG emissions per capita versus electricity GHG emissions factor. 100 4-14 Building GHG emissions per capita versus the electricity emissions factor .... 101 4-15 Building thermal energy use per capita versus heating degree ........................ 102 4-16 Building electricity use per cooling degree days .............................................. 103 4-17 Residential and commercial building energy use per capita ............................ 104 5-1 GHG emissions by sector for eight U.S. cities ................................................... 108 5-2 Commercial Energy Use, Activity, Weather, and Intensity ............................... 111 5-3 Energy Use, Activity, Intensity and Other Factors in the Residential Sector .... 111 Figure 5-4 Average U.S. household energy use and energy use intensity by size ... 112 5-5 Cumulative number of home sales from 2003 to 2006 ...................................... 122 5-6 GHG emissions reductions for 10 building sector actions ................................. 136 5-7 Building sector GHG emissions projections based on different scenarios ......... 137 5-8 Electrical energy use by sector in Portland, OR ................................................. 138 5-9 Thermal energy use (natural gas, fuel oil, etc.) by sector in Portland, OR. ....... 139 5-10 Electrical energy use by sector in Boulder, CO ................................................ 139 5-11 Thermal energy use (natural gas) by sector in Boulder, CO ............................ 140 5-12 Building sector GHG emissions from 1995 to 2005 in Portland, OR .............. 141 5-13 Building sector GHG emissions from 1995 to 2005 for Boulder, CO ............. 141 A-1 Significant events affecting Colorado aggregate use, 1951 1997 ................... 153 B-1 Daily per capita VMT versus housing density ................................................... 154 B-2 Daily per capita VMT versus population plus employment density .................. 155 Xll

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LIST OF TABLES Table 2-1 Matrix of vehicle miles traveled (VMT) .............................................................. 20 2-2 Food expenditures for food at home and associated GHG emissions for Denver 26 2-3 GHG emissions factors for EPA's WARM and ICLEI's CACPS ....................... 28 2-4 GHG emissions impacts for recycling .................................................................. 28 2-5 Summary of energy use and GHG emissions from buildings in Denver ............. 30 2-6 Transport distances, fuel use and GHG emissions by modes of transport ........... 32 2-7 Denver total waste and recycling in 2005 (excludes construction debris) ........... 36 2-8 Annual community-wide material and energy flows ........................................... 38 2-9 Denver's average per capita GHG emissions compared to averages ................... 40 2-10 Electric utility C02e emissions factor sources for Colorado .............................. 43 2-11 Cities to be analyzed in this GHG inventory research ........................................ 45 3-1 Demand-approach matrices .................................................................................. 55 3-2 Vehicle fuel economy and fleet vehicle mix ........................................................ 57 3-3 Metropolitan planning organizations (MPO) for the seven cities ........................ 59 3-4 Daily per capita VMT estimates comparing two methods ................................... 60 3-5 Summary of a few studies assessing density impacts on per capita VMT ........... 63 3-6 Spatial allocation of airline GHG emissions for five U.S. cities .......................... 71 3-7 A comparison oftop-down MFA fuel consumption estimates ............................ 75 4-1 Data source for material flow by sector for the eight cities .................................. 80 4-2 The metropolitan planning organization and contact for model data ................... 81 4-3 Summary ofGHG emissions factors by sector .................................................... 82 4-4 Comparison of grid average electricity C02 emissions factors ............................ 84 4-5 Waste disposal GHG emissions factors used by eight cities ................................ 85 4-6 Useful consumption benchmarks to be included in a GHG inventory ................. 86 Xlll

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4-7 Per capita GHG emissions for five states associated with the eight cities ........... 90 4-8 Availability and considerations for data collection by sector GHG inventories.105 5-1 Consolidated list of common GHG mitigation strategies for buildings ............. 113 5-2 Participation rates for a number of energy efficiency programs in the U.S ....... 116 5-3 Building sector climate action participation rate data available by entity .......... 117 5-4 Energy use annual growth rate projections for the business as usual.. ............... 119 5-5 Participation rates for Xcel's Windsource Program in Denver, CO in 2005 ..... 130 5-6 Range ofperformance estimates used to create the Low and High scenarios .... 133 5-7 Range of participation rates used to create the Low and High scenarios ........... 134 A-1 Total value of shipments of cement and concrete manufacturing ..................... 150 A-2 Per capita cement consumption for the 8 U.S. cities ......................................... 152 B-1 Summary of DR COG region VMT spatial allocation analysis ......................... 156 C-1 Primary contact for data at each of the eight cities ............................................ 157 C-2 Electrical energy use by sector for 8 U.S. cities ................................................ 157 C-3 Thermal energy use by sector for 8 U.S. cities .................................................. 158 C-4 Building GHG emissions by use, with total and per capita GHG emissions ..... 158 C-5 Total daily VMT per capita and total P2W GHG emissions for surface travel. 159 C-6 Summary ofP2W GHG emissions from airline travel for 8 U.S. cities ............ 159 C-7 Characteristics of the U.S. airports serving the eight cities ............................... 161 C-8 Totallandfilled waste, recycling diversion rate and total GHG emissions ........ 161 C-9 Water and wastewater treated per capita ........................................................... 162 C-1 0 Total value of shipments of cement and concrete manufacturing ................... 163 C-11 Total cement GHG emissions and percent of the city's total GHG emissions 163 C-12 Food expenditures for five major metropolitan areas ...................................... 164 C-13 GHG emissions associated with food consumption in 2003 ........................... 165 C-14 Benchmark metrics to include with the GHG inventory for eight U.S. cities. 166 XIV

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1. Introduction The present approach to housing, transportation, food and goods production, and waste generation, is consuming vast amounts of resources and is releasing ever increasing amounts of greenhouse gas emissions with documented impacts on the environment (IPCC, 2007). Societal emissions ofthe three dominant greenhouse gases (GHG)-carbon dioxide (C02 ), methane (CH4 ) and nitrous oxide (N20) (U.S. EPA, 2007) arise predominantly from the burning of fossil fuels such as petroleum, natural gas and coal to support housing, transportation, commerce and industrial production activities. A greater understanding of the source of these emissions as well as steps to mitigate them is a critical need in sustainable development. As countries enter the age of human influenced climate change, the supply of abundant fossil fuels appears to be on the decline (Bartlett, 1995 and 2000). Furthermore, the U.S. is highly dependent on vulnerable foreign supplies to meet its large demand for fossil fuels, particularly petroleum. The U.S. imports 60% of its petroleum needs, which is 25% oftotal world consumption (EIA, 2007). Balancing pursuits for stable energy supplies with deliberate consideration of lifestyle and consumer behavior patterns that may ultimately reduce the demand for these energy supplies underlines the importance of integrated, life cycle thinking when dealing with the complex systems. Fortunately, taking action to limit anthropogenic emissions of GHGs offers a unique opportunity to simultaneously protect against the risk of climate change, while also limiting consumption of resources and their associated environmental impact. 1

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1. 1 Greenhouse Gas Mitigation in Cities Recognizing the global reach of GHG pollutants, 164 countries have signed the Kyoto protocol as of July 2006, which pledges GHG emissions reductions of at least 5% relative to 1990 levels (UNFCCC, 2006). However, national level policies are increasingly being supplemented with city-scale actions to mitigate climate change, particularly in the US which has not ratified the Kyoto Protocol. Local governance at the scale of the city can offer unique opportunities to pursue sustainable development, as demonstrated in the pioneering example of Curitiba, Brazil (MacLeod, 2002). City governments are uniquely positioned to understand and develop policies that are tailored to their regional geography, regional economy and local culture. Furthermore, for the first time in the history of humankind, roughly half of the world's people are living in cities (UN, 2008). The UN reports that roughly 75% of the population in industrialized nations lives in urban areas, while 80% of the U.S. population lives in urban areas (UN, 2008). Similarly, the US Census Bureau reported that in 2003, 83% of the U.S. population resided in metropolitan statistical areas, which includes both the higher density urban core (greater than 1,000 people per square mile) and the surrounding communities within the metropolitan statistical area (US Census, 2005)1 In addition, population projections indicate a doubling of the population in the Denver area over a span of only 25 years, with similar rapid growth expected in several parts of the American West (Colorado, 2003). Because of the high concentration ofhumans in cities-which will only be compounded by their expected population growth urban areas exert a great demand l The Office of Management and Budget (OMB) defines metro areas as being composed of one or more whole counties or similar entities that contain at least one Census Bureau-defined urbanized area of 50,000 or more people. 2

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for resources, goods and services, thereby impacting the direct and the indirect flow of material, water and energy from the surrounding (and often the global) ecosystem. Likewise, the direct and the indirect emissions of pollutants, including pollutants with global reach such as greenhouse gases (GHG), can also be linked to the energy resource dynamics associated with urban areas. Therefore, understanding greenhouse gas footprints at the spatial scale of the city becomes important in the context of global efforts to mitigate climate change and environmental impact of resource consumption. Several cities in the US have embarked on climate actions (Bailey, 2007; Regelson, 2005). The mayors of more than 850 US cities (as of August 2008) have committed to meet or beat the global GHG reduction goals articulated in the Kyoto protocol (reduce GHG emissions seven percent below 1990 levels by 2012), viewing this as an opportunity to address global warming at the local level (US Mayors Climate Protection Agreement). A few cities have also joined the Chicago Climate Exchange with similar goals of reducing GHG emissions (CCX, 2006). Because cities contain a large proportion of the global human population and exert substantial direct and indirect demands on our natural capital, city-scale climate actions have the opportunity to engage vast segments of human populations as well as ameliorating impacts in large spatial areas across the globe. 1. 2 Greenhouse Gas Research Needs at the City-Scale Many cities have been working to achieve GHG emissions reductions with, unfortunately, limited progress. A recent study that evaluated the achievements of numerous U.S. cities with respect to GHG mitigation have shown that all -with the 3

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exception of Portland, ORhave experienced an often substantial increase in GHG emissions since 1990 (Bailey, 2007). Some key findings ofthis report include: "The methodologies and assumptions used to create GHG inventories differ among communities, making comparisons between cities problematic ... A standard GHG estimation methodology is not yet in place ... "In all cities, community-wide emiSSions have risen since 1990, sometimes dramatically. Based on progress to date, it is unlikely that more than one or two of our ten cities and quite possibly none, will reduce their GHG emissions 7 percent below 1990 levels by 2012." "Perhaps most importantly, several cities lacked a data base sufficient to allow city officials or interested researchers to measure the community's progress and performance." To help cities and ultimately countries-achieve their GHG mitigation goals, the following three primary research needs have been identified. Currently cities lack: 1) A consistent method for GHG inventorying at the city-scale that includes "upstream" or indirect emissions that extend beyond the geographic boundary of the city, drawing a link between inventory and footprint; 2) Benchmark criteria to better characterize GHG footprints and track progress towards goals and performance of city programs; 3) Quantitative assessment of strategies, particularly in the building sector, to inform policy development of building climate actions at the city-scale. 4

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To date, cities taking proactive steps towards GHG mitigation have typically adopted best practices, and have not quantified expected GHG savings from specific measures while incorporating participation rates. 1.3 Research Goals and Objectives The broad goal of this dissertation is two-fold. First is to develop a GHG accounting procedure that expands the traditional, boundary-limited GHG inventory to include "upstream" GHG emissions, drawing a link between an inventory and a footprint. This methodology includes a novel spatial allocation procedure for surface and airline travel GHG emissions. In addition, this methodology presents important benchmark metrics that cities could report to enhance compatibility of comparison across cities as well as to track performance of programs. Secondly this research presents a quantitative assessment of the effectiveness of building sector climate actions along with recommendations to increase GHG mitigation potential in the buildings sector at the city-scale. This research is part of a larger complementary research project being conducted in the Urban Sustainable Infrastructure Engineering Project (USIEP) at the University of Colorado at Denver (UCD). USIEP is working to approach the basic city-scale infrastructure needs including buildings, transportation, water and wastewater, and the built environment (roads, and other support structures)-in the context of sustainability. This thesis, within this larger program, targets energy use and its associated GHG emissions in buildings. In the U.S., buildings consume 37 percent of all energy consumed and 68 percent of all electricity (USGBC, 2003). Buildings also generate more than one-third of the municipal solid waste streams and 36 percent of 5

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total U.S. anthropogenic carbon dioxide emissions (USGBC, 2003). In addition, energy use in buildings accounted for about 50 percent of the City and County of Denver's GHG emissions (Ramaswami et al., 2007). Therefore, addressing energy use in buildings is a critical component of city action plans for GHG mitigation. The following outline summarizes how this dissertation addresses the three primary research needs described above. The remainder of this dissertation is organized according to the following chapters. The specific objectives of each research topic are as follows; I. Specific Objective (Ch. 2): develop a more holistic life cycle based GHG inventory methodology for cities. 2. Specific Objective (Ch. 3): expand on a methodology to spatially allocate transport GHG emissions (both vehicle and airline travel) at the city-scale. 3. Specific Objective (Ch. 4): test the robustness and applicability of hybrid, life cycle based GHG inventory in 8 U.S. cities. 4. Specific Objective (Ch. 5): perform a first-order quantitative assessment of the effectiveness of building sector climate actions, particularly with respect to participation rates and associated rates of GHG mitigation through a case study in Denver, CO. 6

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2. A Hybrid Life Cycle Assessment-Based GHG Inventory Methodology at the City-Scale The overall goal of this chapter is to develop a demand-centered hybrid life cycle assessment (LCA) based GHG inventory methodology. This methodology for conducting city-scale GHG inventories incorporates: 1) Spatial allocation of surface and airline travel across co-located cities in larger metropolitan regions, and, 2) Life cycle assessment (LCA) to quantify the embodied energy of key urban materialsfood, water, fuel and concrete. The hybrid methodology enables cities to separately report the GHG impact associated with direct end-use of energy by cities (consistent with EPA and IPCC methods), as well as the impact of extra-boundary activities such as air travel and production of key urban materials (consistent with Scope 3 protocols recommended by the World Resources Institute). The method is described as hybrid because it incorporates material and energy flow LCA techniques along with economic input-output LCA techniques to determine the life cycle GHG impacts of the production and processing of key urban materials consumed in the city. Application of this hybrid methodology to Denver, Colorado, yielded a more holistic GHG inventory that approaches a GHG footprint computation, with consistency of inclusions across spatial scale as well as convergence of city-scale per capita GHG emissions mt C02e/person/year) with state and national data. The method is shown to have relevant policy implications, and also demonstrates the utility of benchmarks in understanding energy use in various city sectors. 7

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2.1 Introduction: Greenhouse Gas Inventories National to the City-Scale International protocols have been developed for the inventorying and reporting of greenhouse gas (GHG) emissions at the national scale. The United Nations Framework Convention on Climate Change has set up reporting requirements for countries based on the Intergovernmental Panel on Climate Change (IPCC) 2006 Guidelines for National Inventories (UNFCCC, 2006; IPCC, 2006). National GHG accounts have been completed for the U.S. by the Energy Information Administration (EIA) and the Environmental Protection Agency (EPA) (EPA, 2007; EPA Annex, 2007). A supply side approach is used to account for fossil fuel use e.g., coal and natural gas use is summed for all electric utilities and diesel and gasoline fuel sales are tracked. In contrast, a demand-centered approach has been used for GHG accounting for individual businesses and municipal government operations where demand for electricity, natural gas and transport is summed "bottom up." The "Local Government Operations Protocol" is a protocol that has also been recently developed for GHG emissions associated with local government operations by the California Climate Action Registry (CCAR), The Climate Registry {TCR), ICLEI and the California Air Resources Board {The Registry, 2008). This protocol does not cover communitywide GHG emissions (emissions attributed to all residents and activities in a city versus just the government related emissions) and is largely based on methods that cover organizations as defined in the World Resources Institute "Corporate Accounting and Reporting Standard" (WRI, 2004). In the midst of this dissertation, ICLEI-Local Governments for Sustainabilityhas been developing a city-scale GHG inventory method that builds on the local government operations protocol, but is expanding on them to include communitywide GHG emissions. This 8

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protocol, the "International Local Government GHG Emissions Analysis Protocol," is currently out in draft form for public comment and review (ICLEI, 2008). To date, there is no standardized, adopted protocol for inventorying GHG emissions at the city-scale. As of March 2006, only about 10 U.S. cities had compiled a GHG inventory and evaluated their subsequent actions; inventory methods and inclusions varied widely across the 10 cities (Bailey, 2007). For example, the impact of airline travel of city residents is often ignored when the airport is located outside city boundaries. Thus, only the Cities of Aspen and Seattle had incorporated air travel emissions. Upstream GHG emissions associated with the production ofkey urban materials such as food, water, fuel and concrete have also typically been ignored when their production occurs outside city boundaries; prior to this study, only one city (Seattle, 2006) had included the upstream GHG emissions associated with asphalt use. Indeed, the City of Berkeley notes that inventories differ significantly from footprints in terms of accounting for upstream GHG emissions (Berkeley, 2007). Most importantly, no standardized calibration or benchmarking method has been used to verify the accuracy of the currently available city-scale GHG inventories. There is a need to clearly articulate the demand side bottom-up approach implicit in ICLEI and WRI protocols, and address the issues of boundary, spatial scale, and benchmarking. As more than 850 US cities embark on local-scale climate actions (GHG accounting and mitigation), developing a more standardized GHG inventory method that is consistent with national data becomes critically important. The objective of this chapter is to develop a more holistic and consistent methodology for conducting GHG inventories for US cities, viewing the city as a demand center for both energy and key urban materials. The inventory method is applied to compute community-wide and per capita GHG emission for the city of Denver, Colorado. Comparisons with 9

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national (US) and State of Colorado per capita GHG footprints are used to benchmark the results. 2.2 Spatial Scales, Inventory Approaches and Policy Relevance While material and energy flows have been studied for many years at the global and national scales (NRC 2004, Matthews et. al. 2000), as well as the much smaller scale of individual households (Bin and Dowlatabadi 2005, Lenzen 1998, Pachauri 2002, Reinders 2003), resource flows at the scale of cities are not yet well-understood due to boundary allocation issues that impact the accounting of direct and indirect resource use in cities. Thus, while national-scale GHG accounts cover all the energy uses within national boundaries, most city-scale inventories to-date account only for the "direct" GHG emissions occurring within the geographic boundaries of the city, thereby undercounting the GHG footprint of the city's resident. 2.2.1 Direct Emissions within Fixed Geographic Boundaries: The approach of counting only direct energy use within fixed geographic boundaries is reflected in ICLEI's Clean Air and Climate Protection tool (CACP) that has been used by most US cities to date. For the built environment, the ICLEI method tracks electricity and natural gas consumption within the city and traces these to GHG emissions from power plants located in the region (albeit often outside city boundaries). In the transportation sector, only tail-pipe emissions associated with vehicular traffic are counted, with the assumption that the GHG emissions associated with fuel production will be accounted for in the inventory of the "producer cities" or in the "hinterland" where the petroleum is manufactured. Lastly, cities get credit for 10

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recycling materials even though the embodied energy associated with producing these urban materials is not included in these urban inventories. This approach has two disadvantages: a) since there is no requirement that all areas of the US will conduct a full GHG accounting of their industrial processes, important GHG contributions particularly from hinterland areas that produce many urban materials are neglected; and, b) this approach effectively penalizes producer cities that produce food, fuels and other critical urban materials, while giving credit to "consumer" cities. Furthermore as only direct tailpipe emissions within fixed geographical boundaries are considered, there is no consistent allocation procedure for airline and surface transport activities. Many cities are confounded by how to deal with vehicular travel in their inventories since the current methodology does not count the entire distance of commuting trips that originate outside city boundaries, while significant pass-through trips that occur on large inter-state highways are counted that do not pertain to the city of interest. Allocating airline travel poses a similar challenge many cities have ignored this activity entirely, especially when local airports are situated outside city boundaries. Thus, incorporating upstream GHG emissions associated with key urban materials, and, developing suitable spatial allocation procedures for transportation activities (surface and airline) emerge as important added features for developing more holistic city-scale GHG inventories. These features can significantly impact not only the numeric value computed from the inventory but also the scope of climate action policies explored by cities. For example, incorporating key urban materials such as water, fuel, food/packaging and concrete into city-scale GHG inventories can highlight the need for GHG policies that promote conservation, shifts to alternative materials (e.g., green concrete) and effective recycling of these materials in cities. Incorporating airline travel into GHG inventories raises awareness of this important sector that contributes 4% to the US national GHG emissions (U.S. EPA, 2007), sparking interest in air line travel offset programs. Addressing long-distance 11

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commuting trips into the city-center will result in greater emphasis on alternative mass transit options. Likewise, urban forms that reduce the demand for motorized travel will yield a 25% greater GHG savings when upstream emissions associated with transportation fuels (diesel and gasoline; ANL, 2005) are accounted for in the inventory. Thus city-scale GHG policy development and analysis is strongly impacted by the underlying GHG inventory methodology. 2.2.2 Life Cycle-Based Assessment of Indirect Emissions: One comprehensive method available to account for upstream GHG emissions of all consumer behaviors and materials use is the economic input-output life cycle assessment (EIOLCA, 2006; Cicas et al., 2006), which links direct and indirect emissions with economy-wide monetary exchanges. Specifically for GHG emissions, economic activity is linked to energy use in each of these sectors and calibrated with total national energy use reported by the US Department of Energy's Energy Information Agency (Cicas et al., 2006). A few studies have linked economic expenditure data at the household level as reported by national consumer expenditure surveys with EIO-LCA to quantify GHG emissions from household activities (Bin and Dowlatabadi, 2005). However, applying EIO-LCA for a GHG inventory at the city-scale can be impractical due to data limitations since expenditure data at the metropolitan statistical area (MSA) level is not available publicly for all economic sectors, particularly monopolies such as electric utilities (Economic Census, 2002). Also, local features such as a greater investment in renewable energy at the local utility will not become apparent in the inventory when nationally aggregated EIO LCA emissions factors are applied. Thus in this chapter, a hybrid life cycle-based approach is proposed and applied to assess the GHG footprint for the city of Denver, 12

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coupling local direct energy use and emission factor measurements with life cycle based embodied energy use associated with urban material use. The objective of this research is to develop a demand-centered, hybrid life cycle based methodology for conducting GHG inventories for US cities. The hybrid approach accounts for direct GHG emissions associated with direct energy use in a city's built environment using energy use data provided by the local utility and modeled estimates of vehicle miles traveled to and from the city from a regional transportation model. A unique spatial allocation procedure is used both to allocate surface transport within and without city boundaries, and, from city boundaries to the regional airport to allocate air travel emissions. With significant standardization of Life Cycle Assessment (LCA) methodologies in the US, the indirect GHG emission associated with the embodied energy of producing key urban materials is also included to develop this improved hybrid approach. Based on the functionality of cities, the key urban materials considered are food, water, fuel and concrete (shelter), without which urban life would not be possible. This demand-centered hybrid LCA based inventory methodology has been applied to compute the GHG inventory for Denver, CO (Ramaswami et al., 2007), as described next. It is important to note that this methodology has stemmed from the impetus for developing a more consistent and holistic GHG inventory method appropriate and easy to use by cities in the US. This methodology is consistent with the WRI Scope 3 GHG emissions inventory protocol recommended as the most holistic and stringent protocol for businesses and corporations (WRI, 2004 ). This research is presented as a first step toward development and application of such a Scope 3 protocol to the spatial scale of cities. 13

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2. 3 Methodology 2.3.1 Background of Study Area: The City and County of Denver (referred to as Denver) covers 155 square miles in the east-central part of the state of Colorado, with Denver serving as the state's capital. Denver and the local governments of surrounding counties participate in a regional planning entity, the Denver Regional Council of Governments, or DR COG, which covers a wider area of 5100 square miles and includes 9 counties in addition to Denver. In 2005, the population for Denver and the DRCOG region were 579,744 and 2,641,753, respectively (DRCOG, 2004). The location ofDenver in the wider DRCOG area in Colorado is shown in Figure 2-1. Like most metropolitan areas, Denver is a commerce hub for this much larger area; significant exchanges of material and traffic occur between Denver and the counties in the DRGOG region. As shown in Figure 2-2, studies by DRCOG have shown that about 59% of Denver's workforce commutes from other counties. Likewise, 33% of Denver's resident workforce travels outside of Denver County for work. Spatial transportation modeling in the DRCOG region is particularly important to understand and allocate GHG impacts of transport activities to Denver and its surrounding counties, as will be described in the next section. 14

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Figure 2-1 Location of Denver in Colorado and within the DR COG region. (Source: http://www.drcog.org/ and http://www.24hours7days.com/Raised Relief Maps/) Workers Commuting Denver 59% Resident Workforce 41% Figure 2-2 Commute patterns for Denver's workforce (Source: DRCOG, 2001). 2.3.2 Main Inventory Categories: Both direct GHG emissions (associated with direct energy use in cities) and indirect GHG emissions (associated with materials often produced outside city limits) contribute to global climate change. Therefore, by viewing a city not merely as a 15

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bounded plot of land, but as a demand center for energy and materials, city-scale GHG inventories can cover both direct and indirect GHG contributions. The hybrid LCA-based city-scale GHG inventory method developed in this chapter models the city as a demand center for materials and energy, incorporating GHG emissions from three main categories: 1. Direct energy consumed in buildings and facilities, including homes, commercial, industrial and government buildings and facilities; 2. Direct (tail-pipe) emissions associated with transportation, including surface and air travel; and, 3. Indirect emissions associated with the embodied energy of key urban materials (water, food, fuel processing, and concrete), as well as end-of-life of wastes (e.g. landfill). Based on the functionality of cities, the key urban materials considered are food, water, fuel, and concrete (a dominant construction material). Cement (in concrete) is used as a proxy for construction as it has been noted to be the dominant GHG-emitting residential construction material (Keoleian, et al., 2000) and the third largest single source of C02 emissions in the US (U.S. EPA, 2007). Community wide GHG emissions among each of these categories are calculated using Equation 2-1. Methods used to account for GHG emissions in each ofthese three categories are detailed next. The three dominant GHG (C02 CH4 N20) that account for more than 98% of US GHG emissions (U.S. EPA, 2007) are inventoried and reported together as carbon dioxide equivalents (C02e); there are no known production facilities in Denver for the three remaining halocarbon GHG (HFCs, PFCs, and SF6). 16

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Equation 2-1 Calculating community wide GHG emissions Community GHG Emissions= I Energy Usex EF + IMFAxEFLcA Electricity, Natural Gas, Petro-fuels Key Urban Materials where: EF is the emissions factor per unit of energy use; MFA is the material flow analysis that quantifies the amount ofkey urban material consumed; EF LCA is the emissions factor per unit of each key urban material obtained using life cycle assessment (LCA). 2.3.3 Direct Energy Use in Buildings and Facilities: Direct GHG emissions associated with the built environment (electricity and natural gas use in homes, commercial and industrial facilities) were obtained from the local utility, along with GHG emissions factors computed at the local level for electricity, natural gas and steam supply to Denver. For Denver, these data were obtained from Xcel Energy, Colorado (West, 2007). The emissions factor for electricity was 1.75 lb C02e/k.Wh [0_22 g-C02 e/k.J], for natural gas was 5.6 kg-C02 e/therm [0.06 g C02e/k.J], and for steam generation used to heat buildings was 68 kg-C02 e/MMBtu [0.07 g-C02 e/k.J]. The GHG emissions factors reported here trace energy supply up to the level of the local power plant and include line losses for electricity and natural gas, as well as the process energy use and efficiency. Truncating the upstream boundary for electricity generation at the level of the power plant captures between 75% to 97% of the GHG emissions for natural gas-fired and coal-fired power plants, respectively (Spath et al., 1999; Spath and Mann, 2000). 17

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2.3.4 Direct Tail Pipe Transportation Energy Use: Direct (pump-to-wheels) GHG emissions from the road transport sector were estimated from regional daily vehicle miles traveled (VMT) obtained from the DRCOGs regional transportation model. DRCOG uses TransCAD, a common transportation modeling package used by many metropolitan planning organizations, to model travel demand in the nine-county region surrounding and including the City of Denver (TransCAD, 2006). The DRCOG road network model is comprised of 16,450 roadway links connecting 2,664 traffic analysis zones (TAZ's). DRCOG generates estimates ofVMT from this model to be used for local air quality modeling and impact assessment of new developments or transportation system changes. The estimates of VMT for various links of the network are compared to volume counts to refine the model and calibrate other modeling parameters. The GHG inventory method developed here, because of significant inter-county traffic that occurs (See Figure 2-2) in the DRCOG region, attempts to capture not only the VMT associated with the T AZ's located within Denver boundaries, but also the VMT associated with commuting trips originating or ending outside of Denver. To capture the full VMT associated with commuting trips in Denver, daily VMT is found by multiplying the number of trips beginning and/or ending within Denver's boundaries by the distance of the shortest travel time path between associated T AZ pairs as reported at the end ofthe DRCOG model run. Alternate paths between TAZ pairs have similar travel times at the end of the model run due to the equilibration of the assignment procedure. However, the shortest travel time path at the end of the model run can have a longer or shorter distance than alternate paths between the same T AZ pair. Thus, the effects on the aggregate VMT estimates for the region tend to balance out such that using final shortest travel time paths from the model run to compute total VMT between subregions does not introduce significant differences. 18

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When using a large network model within a software package such as TransCAD, the effort to store all paths lengths throughout the assignment process is prohibitive, so this level of accuracy was considered acceptable for the estimates of greenhouse gas emissions being made. The analysis led to the following VMT matrix (see Table 2-1). Trips with both origin and destination within Denver were allocated fully to Denver's GHG inventory, while those trips with only one end (either origin or destination) in Denver were allocated 50% to Denver and 50% to the other city. Thus, commute travel impacts are divided evenly between the counties where the residence and the workplace are situated. Applying this novel approach, the VMT demanded by Denver was computed from the DRCOG model to include the commuting trip for workers coming in to Denver from surrounding counties, as well as the extra-county commuting trips of Denver residents. Pass through trips through Denver boundaries are separated out and are not considered part of Denver's footprint-these would be associated with the cities in which these trips began or ended. The daily VMT shown in Table 2-1 was distributed across vehicle types (cars, SUVs, light trucks, etc.) through road vehicle counts data from the Colorado Department of Public Health and Environment (McCrae, 2007). Direct pump to wheels emissions from the various vehicle types were computed from emissions constants and national average fuel economies by vehicle type (ICLEI, 2003). Mass transit miles and emissions were computed obtaining bus fuel economy and annual miles traveled data, and, light rail annual miles traveled and electricity use data reported from Denver's Regional Transportation District (NTD, 2004). The DRCOG model was also used to isolate trips to Denver's airport from the City and County of Denver versus the larger DR COG region served by Denver 19

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International Airport (DIA). Note DIA is not located within the traditional Denver boundaries, and has its own set ofTAZs. Airline fuel use data at DIA were provided by DIA. The total fuel use at DIA was allocated to Denver based on the ratio of the total surface vehicle trips made from Denver boundaries to the Airport versus from the entire region to the Airport. See Table 2-1. The ratio of trips from Denver to DIA versus from the entire region to DIA is about 0.22, very much in-line with the population ratio of Denver versus the entire DR COG region (also 0.22), suggesting the validity of this method for allocating air line travel amongst several cities and counties located in a metropolitan area. A more detailed analysis of this spatial allocation method for vehicle and airline GHG emissions for a number of U.S. cities is presented in Chapter 3. Table 2-1 Matrix of vehicle miles traveled (VMT), in million miles, aii between City and County of Denver (CCD), Denver International Airport (DIA), and the Rest of the World (ROW) To (i) + ROW ceo OIA Total From 0) ROW 13,893 3,248 311 17,452 ceo 3,165 1,335 67 4,568 OIA 307 74 7 389 Total 17,365 4,657 386 22,409 Total VMT Allocated to Denver1 = 0.5 x Lau +0.5x Lau 5 billion J=CCD,DIA i=CCD,DIA Total VMT Allocated to Denver1 = [0.5 x {4,568+389) + 0.5 x (4,657+386)] 5 billion 1. Note Denver mcludes CCD and DIA 2.3.5 Embodied Energy of Key Urban Materials: Based on the functionality of cities, indirect energy use and associated GHG emissions were computed for the critical urban materials: water, fuel, food and cement. For the most part, the above four materials are produced in hinterland areas 20

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with no significant overlap with industrial energy use in Denver. These materials were selected because communities require food, water, shelter and energy to meet basic needs. These materials are not meant to be exhaustive because accounting for the GHG impacts over the life cycle of all materials used in a city would be both cost and time prohibitive. Cement was selected as a surrogate of building materials because it is the largest source of C02 emissions of any single building material in the U.S. (EPA, 2007). A small number ofmaterials for inclusion as Scope 3 emissions is consistent with WRI recommendations (WRI, 2004). Other materials and consumer goods/services used by Denver residents, e.g., TVs, cell phones, furnishings, etc., are assumed to be included in the commercial-industrial exchanges within and between cities. The embodied energy and GHG emissions associated with these materials were computed by coupling a material flow analysis of these materials through the city with an environmental life cycle assessment for these materials. 2.3.5.1 Water/Wastewater The delivery and treatment of both water supply and wastewater collection results in energy use and additional GHG emissions from methane (CH4 ) and nitrous oxide (N20) emissions. Long after this project was begun, the Local Government Operations Protocol (The Registry, 2008) was developed which accounts for GHG emissions associated with energy use, and N20 emissions from wastewater treatment. However, only GHG emissions from the energy use associated with the delivery, treatment and collection of water and wastewater or this GHG inventory analysis was accounted for in this analysis. The amount of energy consumed at the water and wastewater treatment facilities is used to calculate the GHG emissions per 21

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million gallons of water treated. Since water and wastewater delivery and treatment facilities are located both within city limits and also outside city limits and in many cases may provide water or treat wastewater effluent from a number of co-located cities, the GHG emissions are either allocated based on known volumes of water and wastewater flows or population served. Research on GHG emissions associated with centralized wastewater treatment plants has shown that CH4 and N20 emissions account for roughly 10% to 15% of a wastewater treatment plant's GHG emissions from end-use of energy (Pitterle, 2009). As a result, it is expected that the GHG emissions for wastewater treatment accounted for in this analysis could increase 10% to 15% if the computation were done to the specifications in the Local Government Operations Protocol. 2.3.5.2 Transportation Fuel Processing Transportation fuel flows demanded by the community were derived from vehicle miles driven and vehicle and fuel mix computations described above. The Argonne National Laboratory's GREET model was used to model upstream wells-to-pump emissions associated with fuel production (ANL, 2005). GREET, a national public domain database maintained by the U.S. Department of Energy, provides the energy use and GHG emissions associated with wells-to-pump (W2P) production of a wide range of conventional and advanced fuels. The GREET emission ratios for wells-to pump (W2P) GHG emissions to pump-to-wheels (P2W) GHG emissions for gasoline is 0.26 W2P/P2W and diesel is 0.22 W2P/P2W. Jet fuel is not modeled in the GREET software, but the GHG emissions ratio for W2P compared to P2W is assumed to be the same as diesel fuel, or 0.22 W2P/P2W. These emissions ratios 22

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were applied to each fuel to calculate the total GHG emissions associated with fuel production. 2.3.5.3 Cement Regional material flows of cement used in concrete construction within metropolitan areas can be assessed using economic expenditure data from the U.S. Economic Census (Economic Census, 2004) and an assumption of 14% cement content in concrete (expected range %). The Economic Census compiles economic activity by the North American Industry Classification System (NAICS) codes for most metropolitan statistical areas (MSA) in the U.S. Of interest for assessing material flows of cement is NAICS code 3273 Cement and Concrete Product Manufacturing (this is further separated into: 32732-Ready-Mix Concrete Manufacturing; 32733 Concrete Pipe, Brick, and Block Manufacturing; and 32739 Other Concrete Product Manufacturing). The Economic Census is updated every five years, with the latest Census being 2002. The 2007 Census is scheduled to be published between 2009 and 2010. Economic Census data and cement material flow data were normalized at the national level to assess the amount of cement that was consumed per some amount of economic activity. According to the Portland Cement Association, the total amount of cement consumed in the U.S. in 2002 was 103,800,000 metric tones (PCA, 2005). That same year, the Economic Census reported a total of $44.681 billion in the NAICS subsector 3273-Cement and Concrete Product Manufacturing (Economic Census, 2004). This results in a nationally aggregated cement consumption intensity of 2.32 metric tones of cement per $1,000 of transactions reported in 2002 in the NAICS subsector 3273, or 0.36 metric tones of cement per U.S capita. 23

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Cement consumption at the city-scale is determined by calculating the cement consumption per capita for the corresponding metropolitan statistical area (MSA) and applying this to the population of the city. To determine cement consumption in Denver, CO, economic expenditure data2 for NAICS subsector 3273 transactions in the Denver-Aurora Metropolitan Statistical Area (MSA) were converted to an equivalent mass of cement using the national cement consumption intensity of 2.32 metric tones of cement per $1,000 of transactions (2002 data). Then per capita consumption for the MSA was calculated and this was applied to the population of Denver. Given that the Economic Census data is for 2002, it is assumed that per capita cement consumption in 2005 was equivalent to that in 2002. For cement, a GHG emissions factor of 1 metric ton of C02e per metric ton of cement was used, in line with those reported nationally by U.S. EPA and the Portland Cement Association (PCA) that ranged from 0.97 to 1.05 mt-C02e per metric ton of cement (Hanley, 2004; PCA, 2005). Energy used for transporting the cement and the aggregate was not counted as these transportation energies may already be counted in commercial truck traffic. Methods for accounting for cement material flows into cities is analyzed further in Chapter 4, in the 8 city analysis. 2.3.5.4 Food/Food Processing The embodied energy of food and food packaging were determined from household expenditure data for ''food consumed at home" as reported in nationally coordinated 2 Reported by U.S. Economic Census Data from 2002 (http://www.census.gov/econ/census02/data!us!USOOO.HTM). 24

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Consumer Expenditure Surveys (CES) [BLS, 2005]. Household level expenditure data is aggregated to the citywide level by using the total number of households. The expenditure data was converted to equivalent energy use and GHG emissions using the public domain economic input-output (EIO) LCA software (EIO-LCA, 2006). EIO-LCA was used to calculate GHG emissions per dollar spent for each of the NAICS food subsectors under "Food at home" in the Consumer Expenditure Survey. The resulting aggregate GHG emissions factor associated with residential food and food packaging was determined to be 1.5 kg-C02e per dollar of food purchase, reported in 1997-$. A summary of these results are presented below in Table 2-2. Commercial food and food packaging are not included in this calculation, nor is the household energy use (for cooking, etc.) because it is reported separately in the household surveys. In the case ofDenver, CO, there are no major farms or food processing units within Denver, thus overlap with cooking energy and farming/processing energy does not occur. However, there will be some overlap with food transportation, commercial food storage energy, and with any local bakeries and bottling facilities. A better understanding of energy use and GHG emissions associated with food production is needed for more accuracy in this sector. A review ofEIO-LCA shows the top 10 GHG contributing sectors for food production are: 1) power generation and supply (17%); 2) grain farming (15%); 3) all other food manufacturing (10%); 4) truck transportation (5%); 5) cattle ranching and farming (5%); 6) fruit farming (5%); 7) poultry and egg production (4%); 8) animal production except cattle, poultry and eggs (4%); 9) nitrogenous fertilizer manufacturing (3%); and 10) waste management and remediation services (3%). These top 10 contributing sectors account for 70% ofthe food sector's GHG emissions. Each of these sectors involve activities that primarily, if not entirely, occur outside the boundary of the city, with the exception of some truck transport. There are no farming or animal processing facilities within the city of Denver, nor are 25

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there fertilizer production facilities. Average food miles for shipping food in the U.S. is reported to be at least 1,500 miles; much larger than the spatial extents of any city (Pirog et al., 2001). Thus, a very small percentage ofthe truck transport is going to be accounted for with the surface transport spatial allocation within the metropolitan area. Such insight of the top contributing sectors to food production GHG emissions can be used to address potential for double-counting. Table 2-2 Food expenditures for food at home and associated GHG emissions for Denver, CO Consumer EIO-LCA GHG Total GHG Expenditure GHG Emissions Emissions Survey 2003 Emissions from Food from Food 2004 (1997-$) Factor (kgPurchases Purchases C02e/1997 -$)a (mt(mt-C02e) C02e/HH) Food at Home Cereals and 448 0.95 0.43 bakery products -Meats, poultry, 827 2.19 1.81 -fish, and eggs Dairy products 363 2.68 0.97 -Fruits and 563 1.0 0.56 vegetables Other food at 1,117 1.1 1.22 home -Total Food at 3,319 1.5 4.99 1,275,000 home a. EIO-LCA 1997 Purchaser Price model 2.3.5.5 End-of-Life of Wastes At the outset of this dissertation, the GHG emissions and credits associated with the end-of-life (impacts ofboth recycling and landfilling) for the City of Denver's GHG inventory were generated with ICLEI's Clean Air and Climate Protection (CACP) software (ICLEI, 2003), which is based on the U.S. Environmental Protection 26

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Agency's Waste Reduction Model [WARM] (U.S. EPA, 2008). Since that time two protocols have been developed that deal with end-of-life disposal GHG emissions; the "Local Government Operations Protocol" (The Registry, 2008) and the "International Local Government GHG Emissions Analysis Protocol," which is currently out in draft form for public comment and review (ICLEI, 2008). The GHG emissions associated with waste, under each of these tools and protocols, incorporate both the disposal method and the composition of the waste to determine methane and C02 emissions (in the case of incineration). Neither of the latter two protocols, however, have developed a process by which a city can claim "credits" for recycling or waste diversion programs. According to the "Local Government Operations Protocol," there is no widely accepted methodology for measuring fugitive methane emissions from waste disposal sites and that they expect their guidance on calculating these emissions "will change considerably in future versions of the Protocol" (The Registry, 2008). The quantification of GHG emissions due to end-of-life of materials (which includes credits for recycling and waste diversion) in this analysis is based on the U.S. Environmental Protection Agency's Waste Reduction Model (U.S. EPA, 2008). Below is a summary of greenhouse gas (GHG) emissions factors for landfilled municipal solid waste (MSW) and waste diversion through recycling, based on EPA's Waste Reduction Model (WARM), and ICLEI's Clean Air Climate Protection Software (CACPS). Table 2-3 summarizes GHG emissions from MSW landfilled with varying degrees of LFG being recovered and flared. According to both models, a high enough LFG recovery factor will result in net negative GHG emissions associated with MSW due to in essence, "sequestering" the carbon in the materials in the landfill and combusting what methane is produced through decomposition at the landfill site. Table 2-4 summarizes the GHG emissions impacts from recycling, assuming that a landfill with an LFG recovery factor of 75%. These emissions 27

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factors are based on GHG emissions at the landfill site only, i.e., they do not include transportation of the waste to the landfill. The waste streams being land filled are assumed to consist of the average MSW composition in the US. For reference, the Denver Area Disposal Site (DADS) and Lowry Landfill were reported to have an LFG recovery factor around 75%, thus the waste emissions factor would be either 0.15 mt-C02e/ton ofMSW or (-0.26) mt-C02e/ton ofMSW based on WARM's or ICLEI's modeling estimates, respectively. Table 2-3 GHG emissions factors for EPA's WARM and ICLEI's CACPS by level of 1 dfill (LFG) [; I an 1 gas recovery actor LFG Recovery of LFG Recovery of LFG Recovery of 90% (mt-C02e/ 75% (mt-C02e/ 60% (mt-C02e/ ton tonMSW) ton MSW) MSW)2 EPA WARM -0.14 0.15 0.44 ICLEI -0.4 -0.26 -0.12 .. 1. Ennss10ns are based on a managed landfill wtth LFG recovered bemg flared 2. This LFG recovery factor is roughly equal to the "National Average" for landfill characteristics Table 2-4 GHG emissions impacts for recycling based on EPA's WARM and ICLEI's CACPS. Assumes waste is diverted from a landfill with an LFG recovery factor of75%. Recycling (mtC02e I ton MSW) EPA WARM -2.6 ICLEI -2.4 28

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2.4 Results 2.4.1 Direct Energy Use in Buildings & Facilities: Energy use data from residential buildings and industrial and commercial facilities in Denver were obtained from Xcel Energy, Colorado (Ramaswami et al., 2007). Energy use and additional building sector data for Denver are summarized in Table 2-5. Note that benchmarking data such as energy use per household for the residential sector and energy use per square foot have been collected as part of Denver's GHG inventory. It is highly recommended that cities collect benchmark data such as these as part of their GHG inventory process. The average electricity use per household (hh) is found to be 568 kWh per month in 2005, and the average natural gas use per household is 63 therms per month in 2005, in line with averages of 683 kWh/hh/mo and 65 therms/hh/mo reported in 2003 for the entire state of Colorado (SWEEP, 2003), see Table 2-5. The energy intensity of the commercial-industrial sector (energy use per square foot), combining both electricity and natural gas consumption, was 250 kBtu/sf in 2005. This is slightly higher than the typical range from 40 kBtu/sfto 240 kBtu/sfreported nationally (EIA, 2005) for commercial buildings, which is probably the result of utility data for Denver being lumped as an industrial-commercial category. 29

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Table 2-5 Summary of energy use and GHG emissions from residential buildings and industrial and commercial facilities in Denver. A. Residential Energy 1990 2000 2005 Total Number of Customers 216,941 256,593 246,522 Total Grid Electricity Used (GWh) 1,103 1,398 1,679 Electricity/household/month 424 454 568d (k Whlhhlmo) Total Natural Gas Used (million therms) 184 143 139 Nat ural Gas/household/month 71 76 63d (therms/hh/mo) Total Residential GHG emissions 1.9 2.0 2.1 (million Total Residential Per Capita GHG 4.1 3.5 3.6 emissions per person) B. Commercial-Industrial Energy 1990 2000 2005 Total Number of Customers 29,807 30,578 32,710 Total Commercial-Industrial Area 147 169 181 (million sf) Total Electricity Used (GWh) 3,814 4,627 4,9801 Total Nat ural Gas (million therms) 203 267 265 Total energy use per square foot 222 247 236d (kBtu/sf) Total commercial-industrial GHG 4.3 5.3 5.6 emissions (million Total Buildings and Facilities GHG 6.2 7.3 7.7 Emissions (million mtC02e) Data Source: All energy data from Xcel Energy for all three years. Steam generatiOn and chilled water are included in natural gas and electricity consumption, respectively. Electricity use in 2005 was estimated from trends from previous years. GWh = Giga Watt-hours of electricity = 1 million kWh. Both electricity and natural gas use can be combined and represented as kBtu (1 kWh= 3.412 kBtu; 1 therm = 100 kBtu). A. Includes 47.2 GWh of electricity from WindSource, with zero GHG emissions in 2005. B. Includes 57.3 GWh of electricity from WindSource, with zero GHG emissions in 2005. C. Energy use was converted to C02 e emissions using an emission factor of 1.75 lb C02e/kWh provided by Xcel Energy for the local area (Personal Communication, Pete West) D. Benchmark data that cities should track and report as part of their GHG inventory. 30

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2.4.2 Transportation Sector Miles traveled by personal and commercial vehicles, by mass transit, and through commercial airlines, are summarized in Table 2-6 below. The total annual vehicle miles of travel (VMT) results for personal and commercial vehicles are normalized per Denver resident and compared with national transportation data summaries {BTS, 2006a). National data for 2002 yielded 27 VMT/personlday, in the range of the 23 VMT/personlday obtained for Denver in 2000 and 2005, demonstrating that the daily VMT/person in Denver are in line with travel behaviors observed nationally. [Note: These normalized metrics represent vehicle miles traveled by all Denver citizens, including children, and therefore do not correlate exactly with miles traveled per vehicle.] The proportion of mass transit person miles traveled (PMT) versus PMT in personal motor vehicles was of the order of 5% for Denver in 2005, higher than the national ratio of 1.1% (BTS, 2006a). This appears consistent with greater mass transit use by Denver's residents and workforce. Applying the vehicle distribution provided by the CDPHE and associated fuel economies for personal vehicles, buses and light rail, community wide diesel, gasoline and electricity consumption estimates were extracted, as shown in Table 2-6. The fuel use for airline travel was obtained directly from Denver International Airport (DIA) and normalized per enplaned passenger at DIA. The fuel use per enplaned passenger at DIA in 2000 and 2005 was 22 and 18 gallons of jet fuel per passenger, respectively, in line with national statistics of 23 and 21 gallons of jet fuel per passenger for 2000 and 2005, respectively {BTS, 2006b ). Likewise, the average aircraft passenger loading at DIA of76 passengers per plane was also in line with national averages (BTS, 2006c). 31

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Combining pump-to-wheels emission factors and wells to pump emissions factors for gasoline, diesel and jet fuel, the total direct GHG emissions from the transport sector in Denver for 2005 were 5.5 million mt-C02e (note that upstream "well to pump" emissions are included in the Key Urban Materials sector below). This is split between personal and commercial vehicles, mass transit, and air travel, totaling 4.4, 0.14 and 1 million mt-C02e, respectively. Table 2-6 Transport distances, fuel use and GHG emissions by modes of transport in Denver. GHG emissions include tailpipe emissions (pump-to-wheels) as well as emissions from fuel refining (wells-to-pump). A. Personal and Commercial Motor Vehicles 2005 Annual Vehicle Miles Traveled (million VMT} 5,000 VMT/person/day* 23 Annual Person Miles Traveled (million PMT} 7,987 PMT/person!day* 38 Annual Fuel Use Gasoline (million gallons) 326 Diesel (million gallons) 44 Total GHG Emissions from Personal and Commercial 4.4 Motor Vehicle Transport (million mtC02e) B. Mass Transit Vehicles (RTD Bus I Light Rail) 2005 Annual Vehicle Miles Traveled (million VMT} 55 Annual Person Miles Traveled (million PMT) 548 PMT/person/day* 2.1 Annual Fuel/Energy Use Bus-Diesel (million gallons) 7.9 Light Rail Electricity (GWh) 28 Total GHG Emissions from Mass Transit Buses and Rail 0.13 (million mtC02e) C. Airline Travel (allocated to Denver 22%) 2005 Annual Vehicle Miles Traveled (million VMT} 36 Annual Person Miles Traveled (million PMT} 3,090 32

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PMT /person/year* 5,403 Annual Fuel Use Jet Fuel (million gallons) 87 Fleet SupportGasoline (million gallons) 0.12 Fleet SupportDiesel (million gallons) 0.09 Total GHG Emissions from Airline Travel Allocated to 1.0 Denver (million mtCOze) D. Total GHG Emissions form Transportation 5.5 Sector (million mtC02e) A. Data Source: VMT for personal-commercial veh1cles obtamed from Denver Reg1onal Council of Governments (DRCOG) transportation model with Denver as a demand center. Vehicle loading and fuel economy data from CDPHE to calculate PMT and Fuel use. B. Data Source: VMT, Fuel/Energy use and vehicle loading for buses and light rail from RTD Annual reports. C. Data Source: Fuel data for Airport operations provided by DIA for 2000 and 2005, and by Stapleton International Airport for 1990. Aircraft loading and miles traveled per gallon of fuel from National Bureau of Transportation Statistics (BTS, 2006c). 22% of all airline travel at Denver's Airport was allocated to Denver as the Airport serves the whole Front Range DRCOG region and beyond. D. GHG Emission Factors: Fuel use was converted to C02e using ICLEI's CACP for tailpipe emissions and DOE's GREET model for wells-to-pump GHG emissions for fuel refining. Since ICLEI does not incorporate airline travel, EIA's tailpipe emissions factors for jet fuel were applied for airline travel (EIA, 2006). *Miles traveled are normalized to Denver's entire population, including children, and therefore do not reflect actual average travel distances per driver or air traveler. 2.4.3 Key Urban Materials This section summarizes the GHG emissions associated with the embodied energy of "key urban materials," e.g., those materials without which life in cities could not occur, as well as the end-of-life ofwastes (e.g., landfill). 2.4.4 Transportation Fuels Applying the GREET emission ratios described in Section 2.3.5 for wells-to-pump (W2P) GHG emissions to pump-to-wheels (P2W) GHG emissions for gasoline, diesel and jet fuel to Denver's fuel consumption by various modes is shown in Table 2-8. Fuel production of the dominant fuels gasoline, diesel and jet fuel used in Denver 33

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yields a total of 1.1 million mtC02e. Fuel production contributes more than 20.2% of the total emissions associated with the transport sector in Denver (see Figure 2-3). 2.4.5 Water/Wastewater Water for Denver and several surrounding counties is provided by Denver Water, with almost all of the water collection and treatment operations occurring outside Denver city limits. Wastewater services are provided by Metro Wastewater Reclamation District located within Denver, which services Denver and some surrounding counties. The total energy used for water and wastewater operations was obtained from Denver Water and the Metro Wastewater Reclamation district, respectively, and apportioned to the residents of Denver based on the volume of treated water provided to Denver residents, and, on the bills for wastewater paid by Denver residents, respectively. Water and wastewater processing contributed 0.047 million mtC02e, less than 1% of Denver's overall GHG emissions. 34

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2.4.6 Cement in Urban Concrete Economic expenditure data3 for NAICS subsector 3273 transactions in the Denver Aurora Metropolitan Statistical Area (MSA) were converted to an equivalent mass of cement to yield an average of 0.5 metric tons of cement4 used per person in Denver in 2005. Given that the Economic Census data is for 2002, it is assumed that per capita cement consumption in 2005 was equivalent to that in 2002. This per capita cement consumption is higher than the national average of 0.38 metric tons of cement per person (PCA, 2005), consistent with greater use of concrete in urban versus rural areas. Using this estimate, the annual flow of cement into Denver was determined to be 300,000 metric tons, which correspond to GHG emissions of 300,000 mt-C02e in 2005. Energy used for transporting the cement and the aggregate was not counted as these transportation energies may already be counted in commercial truck traffic. There are no cement factories within Denver's boundary, and hence this manufacturing energy is not being double counted with Denver's industrial energy and GHG emissions. 2.4.7 Food and Food Packaging: The embodied energy of food and food packaging were determined from household expenditure data for ''food consumed at home" as reported in nationally coordinated Consumer Expenditure Surveys (CES) conducted in the Denver-Boulder-Greeley area for Year 2002 (U.S. Bureau of Labor Statistics (BLS, 2005)). Using the total number of Denver households (DRCOG, 2004), the total annual household expenditures on 3 Reported by U.S. Economic Census Data from 2002 (http://www.census.gov/econ/census02/datalus/USOOO.HTM). 4 One ton of concrete contains 0.14 tons of Portland cement. 35

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food and food packaging in Denver, adjusted to 1997 -dollars, is $3,300 per household or a total of $800 million spent on household food and food packaging in the entire city. Converting this expenditure data to equivalent energy use and GHG emissions using economic input-output (EIO) LCA software (EIO-LCA, 2006) resulted in GHG emissions associated with residential food and food packaging purchases of 1.3 million mt-C02e for Denver. 2.4.8 Recycling and Landfilling Waste The GHG impacts ofthe end-of-life of materials (e.g. landfilling, recycling, etc.) in Denver were calculated using ICLEI's CACP software, which is based on the U.S. Environmental Protection Agency's Waste Reduction Model. In the City of Denver, approximately 725,000 tons of municipal solid waste (MSW) were sent to a landfill with methane capture in 2005. In addition, the city recycled 17,900 tons of materials. The recycling of materials results in estimated GHG emissions reductions due to: 1) the reduced need to manufacture the materials from a virgin state; and 2) the less materiallandfilled thus reducing methane emissions. These activities resulted in a net GHG reduction of almost 200,000 mt-C02e in 2005 as summarized in Table 2-7. Table 2-7 Denver total waste and recycling in 2005 (excludes construction debris) Total Quantity (Tons) GHG Emissions (million mtC02e) Waste 725,000 -0.142 Recycling 17,900 -0.036 Total -0.178 36

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2.4.9 Community Wide Summary Utilizing the demand-centered LCA hybrid approach, Denver's GHG emissions totaled 14.6 million metric tons C02e in 2005, distributed among the following three sectors: 1) community-wide energy use in residential buildings and industrial/commercial facilities (52%); 2) community-wide tailpipe GHG emissions from transportation (30% ); 3) Community-wide use of key materials and waste disposal (18%). The 2005 GHG contribution by activity is shown in Figure 2-3 below. Food Cement 10% 2% Fuel Processing 7% Air Travel 6% Transit 1% Commercial Trucks 4% CityGovt Bldgs 3% Figure 2-3 Denver's GHG emissions summary by activity in 2005. The hatched regions show sectors and activities typically not included in conventional city-scale boundary-limited direct GHG emissions inventories. 37

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Material and energy flows, and associated LCA-based metrics for all flows computed for Denver are shown in Table 2-8. Important metrics for comparing these flows on a per-person or per-household basis are also shown in Table 2-8, which are very useful to benchmark the data. Table 2-8 Annual community-wide material and energy flows with associated benchmarks and GHG emission factors (EF) for various sectors in the City of Denver, CO. GHG emissions are reported in metric tons C02 equivalents (mt-C02e). sector/use community-wide data GHG EF data Total annual urban source for emission source GHG material/energy MFA factor (EF) {data type} emitted flows {data =MFA [benchmarks] xEF Buildings 6,659 GWh Xcel 0.8 Xcel 5.3 [568 [Chapman, million Electricity kWh/hhlmob] Energy kg 2006] mtUse [27 kWhlsf./yrc] {L, MD} C02e/kWh {L, ME} C02e 404 million A. "Direct" Buildings therms Xcel 5.6 2.3 Emissions in [63 [ICLEI, 2003] million Conventional Natural therms/hhlmob] Energy kg-{N, ME} Gas Use {L, MD} C02e/therm mtCity-Scale [1.5 C02e GHG therms/sf./yrc] Inventory Surface 5 billion VMT DR COG Gasoline Vehicle [25 Model PTW=9.3 3.5 Miles miles/person/day] {L, ME} kg-C02e/gal [ICLEI, 2003] million Traveled, Average Fuel [McCrae, Diesel PTW= {N, ME} mt-VMT Econ. = 15 mpg 2007] 9.5 kgC02e {L, MD} B: "Indirect" Airline 86 million [Barrillaux, or Out-of-Travel 0.9 Boundary PTW gallons jet fuel 2007] Jet Fuel [EIA, 2006] million [19.5 gallons jet [BTS, PTW=9.4 Emissions to (22% fuel per 2006c] kg-C02e/gal {L, ME} mt-Supplement allotted to C02e "Direct" Denver) passenger] {L, MD} GHG Fuel Flow in Gasoline Inventory million gallons [ICLEI, WTP=2.5 GREET 1.1 Fuel Jet Fuel: 86 2003] kg-C02e/gal [ANL, 2005] million Production Diesel/JetFuel mtDiesel: 52 {N, ME} WTP=2 kg-{N, ME} C02e Gasoline: 326 C02e/gal Total Flow: Denver0.97-1.05 [Hanley,2004] 0.3 Cement 300,000 mt Aurora million Use cement Economic mt C02e per [PCA,2005] mt[0.5 mt /capita] Census tonne cement {N, ME} C02e 38

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Food $3,300/home/yr DenverEIO-LCA Aurora 2 kg-C02e/$ Purchases (1997-$) Consumer (1997-$) [12] at Home (240,000 homes) expenditure {N, ME} Denver's Community-Wide Total GHG Emissionsd 1.3 million mtC02e 14.6 million mt-C02e nata Type: L =Local, N =National, MD= Measured Data, ME= Model Estimate. PTW =Pump to Wheels (tailpipe) GHG emissions. WTP =Wells-to-Pump GHG emissions. Note: GHG emissions for water delivery into Denver are negligible as it gets water from the mountains. bResidential energy use fer household per month. ccombined commercial-industrial energy use per square feet per year. Incorporates a credit of0.2 million mt-C02e for end oflife landfilling (with residual methane capture) and recycling in Denver, computed from ICLEI methods [ICLEI, 2003]. When emissions from key urban materials and airline travel are included in Denver's greenhouse gas footprint, per capita greenhouse gas emissions for 2005 (25 .3 mtC02e/person) coincide closely with the Colorado and national average estimates (-25.0 mtC02e/person, see Table 2-9). Denver's per capita emissions also coincide with per capita emissions computed for the State of Colorado. This consideration of both key urban materials and airline travel allows for a more complete estimation of a city's greenhouse gas footprint. Urban materials and airline travel have not usually been included in other cities' inventories, making their greenhouse gas footprint appear lower than the national average. Denver's per capita GHG emissions, without the inclusion of airline travel and key urban materials, were 18.9 mtC02e per person for 2005. This is comparable with per capita emissions of other cities in the region, as shown in Table 2-9. 39

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Table 2-9 Denver's average per capita GHG emissions compared to the national average, St t fC I d d t th C I d 'f a eo o ora o average, an 00 er o ora o c1 1es. Denver's 2005 National, State & other Per Capita Cities' Per Capita GHG Emissions GHG Emissions ( mtC02e/person) ( mtC02e/person) National1 : Direct energy use plus airline travel and Denver: 24.5 key urban materials 25.3 Colorado2 : 25.2 Direct energy use (no airline travel, fuel Denver: Other Colorado cities 3 : refining or production of concrete, food 18.9 17.8-18.4 and food packaging) 1. (EPA 2007) 2. (Strait et. al. 2007) 3. (Boulder 2004) and (Fort Collins 2004) Table 2-9 suggests that inclusion of air travel and the embodied energy of key urban materials increases the GHG account by more than 25% of the inventory obtained by addressing direct emissions alone. Moreover, the more inclusive procedure appears to track well with both national and state of Colorado averages. We are currently replicating this procedure in 6 other cities in the US to verify trends seen with both methodologies. More inclusive methodologies may be particularly appropriate for cities like Denver that have established goals based on a per capita GHG emissions basis. Moreover, the inclusion of the materials sector highlights important policy strategies for GHG mitigation such as green concrete and airline offsets. 40

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2.5 Sensitivity The annual per capita GHG emissions computed for Denver are sensitive to the parameter inputs to the model. Measured parameters, such as community-wide energy and water uses obtained from local utility billing data, and, airport jet fuel use reported for DIA, are considered to be high quality data with a high degree of certainty. Modeled parameters such as the various emissions factors or the VMT computations are considered more uncertain. Sensitivity of the per capita GHG computation to a 10% variation in the key modeled parameters is shown in Figure 2-4. The magnitude of the community-wide emissions (and hence the per capita) is most sensitive to changes in the emissions factor for electricity and surface transport VMT calculations. Because of the linear additive nature of the GHG emissions from various sectors shown in Equation 2-1, a 10% variation in the modeled parameters yields a 10% overall range in our estimate ofDenver's community-wide and per capita GHG emissions. 41

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1.0 ,----------------------------------------------, j 0.8 0.6 0.4 0.2 0.0 Figure 2-4 Variation in the magnitude of Denver's per capita GHG emissions in response to a 10% change in the modeled parameters (e.g., emissions factors). The nominal average per capita GHG emission for Denver is 25.3 mt-C02e/person. GHG emissions in Denver are most sensitive to the electricity emissions factor used. Cities have a number of sources available to estimate emissions factors for electricity production at different levels: the local utility; EPA's eGRID for state level emissions; and the regional level via the North American Reliability Council (NERC). The local-scale emission factors obtained from the utility are in the range but lower than those reported by EPA (eGRID) [0.24 g-C02e/kJ] and NERC-WECC [0.27 g-C02e/kJ], because they more accurately represent the local electricity grid mix for the Colorado Front range including electricity generation from within the Front Range region and electricity imported from other sources. These emissions factors are summarized in Table 2-10. It is recommended that cities use as local an emissions factor for electricity production as possible. 42

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T bl 2 10 El a e T CO ectnc uti tty 2e emissions fi C I d actor sources or o ora o. Grid Regions and Local Mix (Xcel EPA E-GRIDL NERC-WECCj Emissions Energy) [CO] 1 [CO] [CO,NM,AZ] (lb-C02e/kWh) 1.75 1.9 2.1 [g-C02e/kJ] [0.22] [0.24] [0.27] 1. All of Xcel's Colorado serv1ce terntory. Includes all of Xcel's generation rrnx (owned generation and imported generation). 2. Total emissions within Colorado divided by total generation, as reported to EPA. 3. The North American Reliability Council (NERC), 12 state Western Electricity Coordinating Council (WECC) Rocky Mountain region. Emissions factor derived from the National Energy Modeling System (NEMS) AEO 2001 reference case (AEO 2001). 2. 6 Discussion This research has presented a demand-centered, hybrid life cycle-based methodology for conducting GHG inventories for US cities. The hybrid approach accounts for direct GHG emissions associated with direct energy use within the city as well as the indirect GHG emission associated with the embodied energy of producing key urban materials. This inventory methodology represents an attempt at developing a more consistent and holistic GHG inventory method appropriate and easy to use by cities in the US. This methodology applied to the City of Denver produced a GHG footprint that very closely matches the national per capita average. The inclusion of indirect emissions associated with airline travel (6%) and key urban materials (18%) contributed almost 25% of Denver's total emissions. This research has demonstrated that cities should strongly consider more comprehensive GHG accounting techniques that include some key indirect emission sources as they pursue their GHG emission reduction goals. 43

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As cities explore GHG inventories that encompass WRI's scope 3 (upstream indirect emissions of materials), it will be important to acknowledge and to become aware of potential double counting. Based on the functionality of cities, this methodology includes tracking of a few key urban materials for which processing facilities (e.g. oil refineries, cement plants, water treatment facilities) are easily recognized; production of these key materials within city boundaries can be readily identified to avoid double counting across direct and indirect categories. If large such production facilities are present, allocation based on local demand would be applied (as in the case oflarge regional airports). To achieve consistency, cities must agree on a common list of key urban materials. Local-scale versus national-scale LCA-based GHG emissions factors for materials may also be an important consideration, although the major materials food and fuel are typically drawn from large distances. Finally, this research has demonstrated the importance of using benchmarks to assess the quality of information captured in a city-scale GHG inventory. In addition to overall per capita GHG emission benchmarks (Table 2-9), we have found that sector specific per capita or per household metrics (See Table 2-8) are equally important in characterizing energy use and GHG emissions in cities, for comparison with regional and national data. We recommend cities report not only aggregate and per capita GHG emissions, but also sector specific benchmarks shown in Table 2-8 to aid in understanding and in outcomes assessment. 2. 7 Uncertainty and Variability This demand-centered hybrid-LCA based methodology has been applied to a total of 8 U.S cities (including Denver, CO) to understand the impacts of this methodology on cities in different climate zones, of different size and industrial make-up. An 44

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evaluation of the uncertainty and variability of the upstream per capita footprint on a city's GHG inventory; and an assessment the applicability of this method are covered in Chapter 4. The cities that participated in this analysis include: Denver, CO; Boulder, CO; Ft. Collins, CO; Arvada, CO; Portland, OR; Seattle, W A; Minneapolis, MN; and Austin, TX. A summary of the cities and the research motivation for their selection are located in Table 2-11. These cities were selected based on their previous experience and access to data necessary to complete a GHG inventory, as well as their providing a good cross section of climates and city sizes throughout the u.s. Table 2-11 Cities to be analyzed in this GHG inventory research with motivation for their selection. Region Cities Motivation Colorado Within Denver, Boulder, Same climate DR COG Arvada Same transport modeling area can compare transport and airline allocation Colorado Outside Fort Collins Same climate as above DR COG Transport model variation Mid-size city with municipal electric utility North Minneapolis, MN Cold climate West Coast Portland, OR Moderate climate South Austin, TX Hot climate Mid-size city with municipal electric utility 45

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3. Spatial Allocation of Transportation Greenhouse Gas Emissions at the City-Scale This chapter covers an expanded analysis of the spatial allocation of transportation GHG emissions presented in Chapter 2. Greenhouse gas (GHG) accounting for individual cities in large metropolitan areas is confounded by spatial scale and boundary effects that impact the allocation of transportation fuels used for both surface transport as well as airline travel. This chapter expands on a demand-based methodology to spatially allocate transportation fuel use (material flow analysis of surface and airline travel) among co-located cities in the U.S. that are part of a larger metropolitan area commutershed. This methodology, which relies on travel demand models of metropolitan planning organizations, was first applied as part of Denver's GHG inventory in 2006. This chapter reports on the application of the same method to six major metropolitan regions across the US along with a detailed analysis of all 27 communities within the Denver metro region. The analysis of six metropolitan areas in the U.S. demonstrated that: a) the demand method produces VMT estimates allocated to cities across the commutershed that are similar to the traditional, boundary limited polygon approach (within 6%); b) airline travel emissions allocated across co-located cities by vehicular trip counts to the regional airport produced results similar to regional population allocation; and c) the method is replicable with necessary data available from all cities in this study through the corresponding metropolitan planning organizations. In addition, the demand VMT allocation method was found to be more responsive to future simulated mass transit growth, and, in addition, was sensitive to local travel demand features ofthe individual communities, of which employment intensity was found to have the largest impact on per capita VMT allocated to a city. Within the Denver commutershed, daily VMT per capita estimates ranged from 8 VMT/capita/day to over 80 VMT/capita/day among 46

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the 27 communities, with a strong positive correlation with employment intensity (employment/capita) and employment density. 3. 1 Introduction Despite high interest in city-scale GHG mitigation, GHG accounting at the scale of individual cities is confounded by spatial scale and boundary effects (Ramaswami et al., 2008). National-scale accounting for GHG emissions (IPCC, 2006) primarily focuses on emissions that occur within the geospatial boundary of a country, which include: the accounting of total emissions from energy supplied at the national scale for buildings and the industrial sector (electricity and natural gas); petroleum (gasoline, diesel, jet fuel, etc.) for surface and airline transport and industrial operations; as well as waste decay and other biological processes. Scaling-down national GHG accounts to the city-scale is challenging, because city-scale GHG accounts primarily focus on the demand for energy and materials exerted by cities. Since electric power plants, major industrial operations and oil refineries are typically located outside the spatial boundaries of most US cities, city-scale accounting procedures must include methods to spatially allocate emissions that result due to demand within the city but occur outside the city's boundary. The concepts of urban metabolism (Wolman, 1965) and "bottom-up" city-scale material flow analysis (MFA; NRC, 2004) thus become important in developing city-scale GHG inventories. Urban metabolism researchers have noted that while city-scale buildings and industrial energy consumption data are readily available from electric utilities, obtaining spatially disaggregated data on transport fuel consumed by cities is challenging (Decker, 2000). Therefore, while GHG emissions from passenger travel (surface and airline) account for 24% and 4%, respectively, of the total US GHG emissions at the national scale (EPA, 2007), appropriately apportioning these 47

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transport sector GHG emissions to the smaller spatial scale of individual cities is confounded by several factors, as noted below. Background Greenhouse gas (GHG) accounting for individual cities is confounded by spatial scale and boundary effects that impact the allocation of regional material and energy flows -particularly transportation fuels used by both surface transport as well as airline travel. Commuting trips in large metropolitan areas often traverse the boundaries of several cities and counties. For surface travel, current boundary-limited methods do not count the entire distance of commute trip (only the part that occurs within the city boundary), while significant pass-through trips that occur on large inter-state highways are counted that do not pertain to the city of interest. This is the method presently used by ICLEI-Local Governments for Sustainability; the largest association of local governments in the U.S. working on GHG accounting and mitigation, with over 400 member cities in the U.S. as of August 29, 2008 (ICLEI, 2008). Yet, most major cities in metropolitan areas are a commerce hub for a much larger area than their geographic boundaries, meaning their commutershed extends beyond their city limits. The over-arching commutershed results in significant movement of vehicles across city boundaries. For example, about 59% of the workforce in Denver, CO commutes from other cities (see Figure 3-1; DRCOG, 2001). In addition, a spatial analysis of residences in the Toronto metropolitan area showed that the percentage of a home's total GHG emissions attributable to personal vehicle use increased substantially for homes located in the lower-density suburbs due to longer commute distances (Vande Weghe and Kennedy, 2007). A spatial allocation procedure for the GHG emissions impacts of commuting through the tracking of origin and destination of vehicle trips could be a significant contribution for city-scale GHG inventory methods. 48

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Denver's Resident Workers Commuting into Denver 59% Figure 3-1 Commute patterns for Denver's workforce (DRCOG, 2001) Similarly, allocating airline travel emissions across cities co-located around large metropolitan airports has been largely ignored because regional airports often lie outside the city boundary while they serve many co-located cities. Only a few citiesAspen, CO, Seattle, W A and Denver, COincorporate airline travel emissions in their inventories, despite the fact that airline travel appears in personal GHG footprint calculators and state and national accounting (BFF, 2007; Strait et al., 2007; EPA, 2007). No consistent method has previously been available to cities to allocate airline fuel use. For example, in the case of Seattle, W A, GHG emissions were allocated based on a survey of travelers in the regional airport (identifying the percent of people who were from or were going to Seattle) (Seattle, 2007). In Aspen, CO a small resort town located in the mountains-a combination of airport fuel consumption and nationally aggregated data on flight lengths was used to allocate airline emissions (Heede, 2006). Clearly, better methods are needed to spatially allocate transportation fuel use for both air and surface travel across co-located cities in large metropolitan areas. This chapter provides a more detailed analysis of these allocation procedures and addresses policy impacts of quantifying and allocating surface and airline transport GHG emissions at the city-scale. The overall objective of this chapter is to expand 49

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upon the demand transportation allocation method previously developed for Denver, CO's GHG inventory (Ramaswami et al. 2007) and test its application in six metropolitan areas across the U.S. Specifically, this chapter addresses replicability of the method in six different US MPOs; comparison of demand-based spatial VMT allocation with traditional allocation procedures; utility of demand-based VMT allocation for spatial allocation of airline fuel use; the sensitivity of the demand allocation method to local urban form and future mass transit mode shifts; and, an error analysis of fuel consumption from VMT estimates compared to state-level data. The six metropolitan regions analyzed in this chapter correspond with the following metropolitan organizations (MPO): 1) The Denver Regional Council of Governments (DRCOG-CO); 2) The North Front Range MPO in Colorado (NFRMPO-CO); Metro Regional Center (Metro-OR) in Oregon; Puget Sound Regional Council (PSRCW A) in Washington; Metropolitan Council (Metro Council-MN) in Minnesota; and Capital Area MPO (CAMPOTX) in Texas. 3.2 Overview of Spatial Allocation of Urban Transport To date cities have relied on one of two primary methodologies to estimate surface transport fuel consumption within the city: 1) polygon VMT, which accounts only for the VMT that occurs strictly within a city's boundaries (longer trips are truncated at the city boundary while pass-thru travel is counted), and vehicle fuel economy (see Table 3-2) is used to estimate fuel consumption; or 2) estimating total fuel sales within the city-this can be estimated using either a bottom-up material flow analysis (MFA) approach (actual fuel sales within the city boundary) or a top-down MFA approach (using statewide fuel sales and VMT data to estimate fuel sales at the city level using VMT to apportion the statewide data to the city level) [Armstrong, 2007]. 50

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Both of these methods have their drawbacks, however. For example, the polygon method for VMT estimates includes pass through trips in the VMT totals (these VMT are beyond the control of the community in which the pass through is tallied and thus is irrelevant to that city's total VMT). This method is also inhibited by not counting any of the vehicle trip that is outside the city's boundary. Given the large percentage ofworkforce commutes that can move into and out of a city (59% for Denver; DRCOG, 2001; see Figure 3-1), it would be beneficial for cities to have an accounting procedure in place to track the number of vehicle trips and their total distances within the entire commuter-shed over time. In addition, fuel sales as a proxy for fuel consumption-like polygon-based VMT are susceptible to impacts of cross-boundary flow not accurately being represented. For example, commute trips into the city may refuel outside the city, while pass-through trips-over which the city has no control -may just as well refuel in the city. Finally, taxes on fuel sales aren't available at the city-scale for all U.S. cities. Portland, OR is the only city out of the 32 we analyze here that had city-scale fuel sales, and these were only for gasoline -diesel sales had to be estimated from state level data (Armstrong, 2007). Based on these drawbacks, one can articulate four desirable attributes of a spatial allocation method for transportation emissions. First, it should be replicable among cities throughout the U.S; Second, it should be sensitive to local features of a city that impact the VMT that a community is responsible for; Third, it should be sensitive to vehicle mode shift, particularly towards mass transit, so that a city can track impacts of developing future transit programs; and finally, it should enable co-located cities to allocate airline travel emissions from a regional airport. 51

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The demand method for allocating surface and airline travel is evaluated against each of these four attributes using data for 27 cities in the DRCOG region and for five other cities in MPOs located in Oregon, Washington, Minnesota and Texas. Finally an error analysis of fuel consumption based on VMT estimates is compared against state-level fuel consumption in the five states corresponding to the six MPOs. 3.3 Methodology The demand method involves three steps. First, VMT is spatially allocated using the entire trip lengths in the commuter-shed through the regional travel demand models of the local MPO. This represents the travel demand exerted by cities. Second, the road transport fuel consumption is estimated using VMT and fleet vehicle fuel economy. And third, airline travel and its associated GHG emissions are spatially allocated using vehicle trip data to generate a trip ratio between the city of interest and the airport compared to the entire region and the airport. 3.3.1 Spatial Allocation of VMT Both polygon and demand-based VMT estimates are obtained from the vehicle travel demand models used by metropolitan planning organizations (MPO), which are readily available to all major cities in the U.S. These travel demand models are comprised ofthousands of individual roadway links that are mapped and organized in slightly larger travel analysis zones (TAZs). These travel demand models produce estimates of VMT by tracking: 1) the number of daily vehicles on any roadway; 2) the length of all roadway links; 3) the origin and destination of each vehicle trip; and 4) the travel distance between the origin and destination of every trip (this distance is 52

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approximated in the model by taking the shortest travel time path between the origin and destination). VMT estimates from these models are typically used for local air quality modeling and impact assessment of new developments or transportation system changes. The estimates of VMT for various links of the network are compared to survey data and volume counts to refine the model and calibrate other modeling parameters. Figure 3-2 illustrates the primary differences between the polygon method and the demand-based method showing (1) A vehicle trip between from Denver to Boulder is truncated at the boundaries in the polygon method, while the entire trip distance (including dotted line) is apportioned equally between Denver and Boulder in the demand-based method. This figure also shows how a vehicle trip is tracked between Denver and the Denver International Airport (DIA)-trip ratios to the airport are used to allocate airline fuel use to the surrounding cities in the demand method. f '' .. ,<7'-..... '. ... t<. \ ) ..... Figure 3-2 (I) Tracking a vehicle commute trip between Boulder and Denver where the solid line represents the polygon method VMT attributed to each community and 53

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the dashed line represents the additional VMT that the demand method accounts for. (2) Tracking a vehicle trip between Denver and the Denver International Airport (DIA). 3.3.2 VMT Computation The polygon VMT is derived by taking the sum of all VMT on roadway links within the city's boundaries [the product of(l) and (2) above]. The demand-based VMT, on the other hand, is found by multiplying the number of trips beginning and/or ending within a city's boundaries by the distance of the shortest travel time path between associated T AZ pairs as reported at the end of the model run [the product of (3) and (4) above]. This VMT is then allocated 50% to the origin city and 50% to the destination city. If a vehicle trip begins and ends in one city, it receives 100% of that trip's VMT. See Table 3-1 (B) illustrating the computation of demand-based VMT. It is important to note that the parts of trips beyond the external zones of the modeling area (DRCOG area) are not included in the modeled VMT estimates. Thus long distance trips, whether trucking or personal travel, are truncated at the boundary of the modeling area. In addition, the daily VMT estimates are converted to annual VMT figures with an adjustment factor of 342 (in place of 365). This factor is the standard adjustment factor used by the Denver Regional Council of Governments (DRCOG) travel demand modeling group. It adjusts for the differences between weekday and weekend travel, and is based on past comparisons of their daily VMT estimates and regional VMT totals. This same factor was applied to each metropolitan region in this study, to convert daily VMT estimates to annual VMT. 54

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Table 3-1 Demand-approach matrices of (A) daily vehicle trips, au, and (B) daily vehicle miles of travel, aij. between City and County of Denver (CCD), Denver International Airport (DIA), and the Rest of the World (ROW). Also, (C) a comparison of metropolitan-wide daily VMT lbhd ddl hd tota s >y t e em an an polygon met o s. A) Daily Vehicle Trips To (i )+ FromJll ROW ceo DIA Total ROW 5,287,551 868,476 31,281 6,187,309 ceo 859,696 1 '149,614 9,743 2,019,053 DIA 31,037 10,534 9,101 50,672 Total 6,178,284 2,028,624 50,125 8,257,034 Trip Allocation Ratio = (accD.DIA +aviA.ccDy( Laii + Laii -aDIA.DIA) 0.221 foralli;j=DIA forall j;i=DIA = (10,534 + 9,743) I (50,125 + 50,6729,101) Metro-Region Population Allocation Ratio = 579,7 44 I 2,641, 753 0.219 B) Daily Vehicle Miles of Travel (millions) To (i )+ From 0) ROW ceo DIA Total ROW 40.62 9.50 0.91 51.03 ceo 9.26 3.90 0.20 13.36 DIA 0.90 0.22 0.02 1.14 Total 50.78 13.62 1.13 65.52 Total VMT Allocated to Denver1 = (0.5 x Laii ) +(0.5x Laii ) fiJra/1 i;j=CCD.DIA forall j;i=CCD.DIA 14.6 million = (0.5 X (13.36+1.14) + 0.5 X (13.62+1.13)] C) Total Daily Commutershed Vehicle Miles of Travel by Method Based on Demand-Method Sum 65.52 million Based on Polygon-Method Sum 66.67 million ERROR 1.7% I. Note Denver mcludes CCD and DIA The total VMT generated in the entire DRCOG area by both the traditional polygon method and the demand method are similar. The difference is 1. 7% as shown in Table 3-1 (C). 55

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3.3.3 Airline Travel and Fuel Allocation The vehicle trip matrix in metropolitan areas, derived from travel demand models (see Table 3-1 (B)), can be used as an innovative and consistent method to allocate demand for air travel. The demand approach creates a trip ratio, which is the ratio of vehicle trips between the city of interest and the airport compared to vehicle trips between the airport and the entire region. For illustrative purposes, the trip matrix generated for Denver, CO is shown in Table 3-1 (A), which summarizes the number of trips to and from: 1) the city of interest, City and County of Denver (CCD); 2) the regional airport (DIA); and all other areas within the region (ROW). Also shown in Table 3-1 (A) is the metro-region population ratio for Denver, CO. Note that the trip allocation ratio and the metropolitan area population allocation ratio are very similar at 0.22 for the Denver area suggesting the validity of this approach. Airline demand GHG emissions-derived from the jet fuel consumed at the regional airport-are allocated to individual cities based on this trip ratio. 3.3.4 Surface Transport Fuel Use Computation Once VMT is computed for a city either by the polygon approach or the demand approach, the mix of vehicles on the road and the fuel economy by vehicle class needs to be assessed to derive total fuel consumption. The quotient of the VMT by vehicle class and the fuel economy by vehicle class results in total fuel consumption, as shown in Equation 1. The average fleet fuel economy is obtained by either nationally reported averages (EPA, 2002) or locally specific data usually obtained by vehicle counts (McCrae, 2007; Lancaster, 2008; Ramaswami et al., 2007). Table 3-2 summarizes the U.S. national vehicle mix and average fuel economy by vehicle class 56

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that are used in this analysis, along with a comparison of state specific vehicle mix data for the state of Colorado and city specific vehicle mix data for Austin, TX. Fue/Consumption = LTota/VMT x VehicleMix ByCtass x Fue/Economy Byctass Eqn. 1 Table 3-2 Vehicle fuel economy and fleet vehicle mix by EPA vehicle class for the national average and Colorado specific vehicle mixes of gasoline vehicles (diesel vehicles are not shown here). EPA Vehicle Class EPA Fuel EPA National Colorado Austin [Mobile 6 Economy by Average Vehicle Area Designation] Vehicle Class Vehicle Mixb Mixb Vehicle (mpg)a Mixc Passenger Car 24.1 0.4091 0.4019 0.6147 [LDGV] Small Truck/SUV < 6,000 lbs 18.5 0.3449 0.3921 0.2073 [LDGT12] Small Truck/SUV > 6,000 lbs 14.2 0.1176 0.1337 0.0577 [LDGT34] Heavy Truck 6.3 0.0356 0.0191 0.0179 [HDGV] Motorcycle [MC] 50 0.0056 0.0031 0.01 Gasoline fraction of total vehicle mix 0.91 0.95 0.91 Gasoline vehicle average fleet fuel 20.2 20.1 22.1 economy (mpg) a. (EPA, 2002) b. Large commercial trucks comprise 8% and 5% of all vehicle miles nationally and in Colorado, respectively (McCrae, 2007) c. Large commercial trucks comprise 10% of all vehicle miles in the Austin area (Lancaster, 2008) 3.4 Results This section begins by covering a detailed analysis ofVMT estimates at the regional level for 6 metropolitan regions in the U.S. After this, feedback on the replicability of 57

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the demand method is presented. Next, a detailed analysis ofVMT estimates for 27 cities within the Denver metro (DRCOG) region is presented to demonstrate the utility of these methods at producing locally relevant VMT estimates at the city scale within a complex commuter shed. This is followed by results from an analysis for airline emissions allocation for cities around five international airports. An assessment of the polygon and demand methods' ability to track mode shift over time is analyzed next. Finally, a comparative material flow analysis (MFA) assessing error of statewide fuel consumption based on VMT estimates and sales data is presented. 3.4.1 Analysis of 6 Regional Commuter Sheds: Replicability The demand method has been used to allocate VMT to cities within 6 metropolitan regions. The per capita VMT estimates from the travel demand models in six commuter sheds around the U.S. were compared with their state level per capita VMT estimates to assess the accuracy of the travel demand method for estimating regional VMT. The six commuter sheds are the MPO's that encompass the 8 U.S. cities that we analyzed (see Table 3-3). Regional VMT was calculated by: 1) summing the VMT among all links within the travel model; and 2) summing the VMT based on all vehicle trips and their corresponding trip distances. Statewide VMT estimates were obtained by the Federal Highway Administration (FHWA, 2006). As seen in Table 3-3, the analysis of six commutersheds shows that the demand method is readily usable across various software platforms. The contacts at each MPO were surveyed about the difficulty in producing the outputs necessary to complete this demand-based analysis. Responses ranged from 4 to 8 hours necessary to complete the model manipulation to produce these results. These contacts also 58

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emphasized that the travel demand models they work with are well equipped to complete these analyses. Table 3-3 Metropolitan planning organizations (MPO) for each of the seven cities who are responsible for providing the travel demand data for demand method spatial allocation for urban travel. Metropolitan Planning Organizations Transportation (MPO)a Modeling Software Used Denver, CO Denver Regional Council of Boulder, CO TransCAD Arvada, CO Governments (DRCOG) Fort Collins, CO North Front Range MPO Trans CAD Portland, OR Metro Regional Government (Metro) EMME/2 Puget Sound Regional Council EMME/2 Seattle, WA (PSRC) Minneapolis, MN Metropolitan Council (Metro Council) Cube Voyager Austin, TX Capital Area MPO (CAMPO) TransCAD a. Each MPO 1s c1ted m the references. MPO-wide daily per capita VMT by polygon and demand methods are compared with statewide daily VMT in Table 3-4, which also summarizes the percent of the statewide population that resides within the MPO region. Daily per capita VMT estimates by demand and polygon method for each commutershed studied were within 6% of one another. This validates demand-based VMT estimates at the regional scale because the total trip based VMT, which is calculated by multiplying the total number of trips between analysis zones by the shortest trip distance (see Section on VMT Computation above), is consistently within 6% ofVMT based on summing the VMT on all the links in the region (i.e. the polygon method). Also, the per capita daily VMT seen in the commuter sheds (by either method) were similar to those reported statewide, except for Metro (the region including Portland, OR) whose daily per capita VMT of Metro was 27% lower than Oregon's statewide estimate. 59

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Table 3-4 Daily per capita VMT estimates comparing two methods for the entire commuter shed and statewide Average Daily VMT per Capita MPOMPO% %State MPO Wide Wide Difference Statewide Population Polygon Demand between inMPO Method Denver Regional Council of Governments 25.2 24.8 2% 28 57% (DRCOG); Colorado North Front RangeMPO; 26.6 25.8 3% 28 10% Colorado Metro Regional Center (Metro); 20.3 19.2 5% 26 43% Oregon Puget Sound Regional Council 24.6 23.3 5% 24 55% (PSRC); Washington Metropolitan Council; 30.2 28.9 4% 30 55% Minnesota Capital Area MPO (CAMPO); 26.3 27.9 6% 27 6% Texas This commuter shed analysis has shown that the demand-based VMT methodology provides cities with a technically feasible method for spatially allocating vehicle travel and their associated GHG emissions within the entire commutershed, overcoming the limitations of the boundary-limited polygon approach. Next, we explore the differences between demand and polygon VMT by completing a detailed analysis of27 communities within the Denver commutershed. 60

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3.4.2 Detailed Analysis Within Region Local Relevance A detailed analysis of 27 cities within the entire DR COG region has been completed to understand the local relevance and the differences of the polygon and demand based methods at allocating surface vehicle transport. The daily per capita VMT varied considerably among the 27 cities in the DRCOG region, from a low of9 daily VMT per capita to a high of 86 daily VMT per capita. The community with the highest per capita VMT of 86 encompasses the Denver Tech Center, a very high density employment center. The daily per capita VMT for each community, calculated with the polygon and demand methods is shown in Figure 3-3 as well as summarized in the Appendix (see Figure B-1 and Figure B-2). In aggregate when comparing data for 27 cities, the demand VMT per capita and the polygon VMT per capita are similar as is seen by the slope and r-squared value shown in Figure 3-3. Sometimes one method yields per capita VMT estimates that are more (or less) than the other. 61

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100 80 60 "i c 40 c cu E 20 I a. c 0 0 Demand vs. Polygon Daily VMT per Capita Methodology Comparison y = o.98x R2 = 0.82 20 40 60 80 Polygon Daily VMT Figure 3-3 Demand and polygon daily VMT per capita for 27 cities in the DRCOG region. The line represents demand and polygon estimates being equal. 100 There are a number of community characteristics and design criteria that affect vehicle use (Litman and Steele, 2008; FHWA, 2004). Some of these characteristics include housing density, income, vehicle ownership, proximity to transit, proximity to employment, as well as numerous others (Golob and Brownstone, 2005; Ewing, 1995). Numerous studies have explored the impacts ofthese characteristics on VMT, yet these characteristics' interrelated nature leads to mixed results on impacts on any of these characteristics individually (Litman and Steele, 2008; Golob and Brownstone, 2005; Kuzmyak and Pratt, 2003, Kockelman, 1995). In particular, there has been an ongoing research debate as to the impact of-or at least correlation between-density (population and/or employment per unit area) and per capita VMT. Table 3-5 summarizes density impacts on per capita VMT for a number of studies. The estimated impacts of density on VMT vary considerably, but consistently are considered to be one of a number of interrelated community characteristics that affect per capita VMT. In addition, most of the studies mentioned here only measured 62

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VMT impacts on personal travel, not overall VMT on roadways. It is important to note that commercial vehicle travel comprises about 8% of overall vehicle travel in the U.S. (McCrae, 2007). Table 3-5 Summary of a few studies assessmg densitv Impacts on per capita VMT Study Summary of Density Impacts Considerations Marshall, 2008 Golob and Brownstone, 2005 Rodriguez, et al., 2006 Litman and Steele, 2008 FHWA, 2004 on Per Capita VMT A negative relationship between daily per capita VMT (Y) and population density (X) Only covers personal travel Tremendous variability in VMT between urban areas of in 47 largest US urban areas in the same density 2000: Y = 338(XY'( -0.32) R2 = 0.36 An increase in residential density in California by 1,000 homes per square mile correlates to a decreased annual VMT of 1,200 miles Containment policies are associated with higher population densities and per capita VMT Increased density (either population or employment) tends to reduce per capita VMT. Per capita VMT I Density elasticity of 0.0 to -0.3 Per capita VMT I Density elasticity typically around 0.05 63 -Based on 2001 NHTS data and housing densities throughout California Generated a model to account for effects of multiple land use characteristics -Only covers personal travel Empirical analysis with a fixed-effects model was conducted on containment policies in 25 U.S. metropolitan areas -Summary of numerous studies Most studies attributed VMT changes to more than just density -There is likely a threshold of around 6,000 to 7,000 persons per square mile for density to have a serious impact on per capita VMT

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A few characteristics, namely housing density, population plus employment density, employment density, and employment intensity (number of employees per capita), were analyzed to assess correlation with VMT estimates for each of the 27 cities in the DRCOG region. Note that the modeled VMT estimates in this study include all travel (personal and commercial) unlike numerous other studies that focus solely on the impact on personal travel. Of the characteristics mentioned above, only employment intensity and employment density showed a strong correlation with per capita VMT. Figure 3-4 plots daily VMT per capita versus employment intensity for the demand and polygon methods, in (a) and (b) respectively. There is a very strong correlation shown here between estimated VMT and employment intensity, with an rsquared of0.97 for the demand method and 0.77 for the polygon method. 64

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Demand Daily VMT per Capita versus Employment Intensity 100 y = 21.49x + 7.89 R2 = 0.97 ... G)-80 ::Ec. > >o.j::: 60 =::E n:l> 40c_ "CJ! c: -n:l c. 20 E C1)0 c 0 0 Average% of population between 18-64 years of age: 64% 1 2 3 Employment Intensity (Employment/Population) Polygon Daily VMT per Capita versus 4 Employment Intensity y = 19.95x + 8.29 100 ... G) ::E c. > 80 -Average % of population between >---::E 60 n:l> c40 c: n:l 20 oo ll. 0 0 18-64 years of age: 64% 1 R2 = 0.77 2 3 Employment Intensity (Employment/Population) Figure 3-4 Daily per capita VMT for (a) demand, and (b) polygon methods versus employment intensity (number of employees per population). Vertical hash lines represent the average percent of a city's population that is between the ages of 18 and 64. 65 4

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Daily per capita VMT has also been plotted against employment density (employment per unit area) for the demand and polygon methods (see Figure 3-5). Employment density appears to have less of a correlation with per capita VMT than employment intensity as shown by the much lower r-squared values shown in Figure 3-5. In addition, the demand method produced better correlations with employment intensity and employment density than the polygon method (see Figure 3-4 and Figure 3-5). Finally, two additional community characteristics that were analyzed, housing density and population plus employment density, both demonstrated very little correlation with per capita VMT. Results of these community characteristics' impacts on VMT estimates are summarized in the Appendix (see Figure B-1 and Figure B-2). 66

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Demand Daily VMT per Capita versus Employment Density Y = 12.798o.ooo2x R2 = 0.59 100 ... Q)C.ca 1-80 --------::i!E c. > 60 =::i!E n:J> c_ 'C n:J n:J c. E ca Q)0 c 1: .:9 30 0 -20 0 10 0 0 Employment Density (Employees/Square Mile) Polygon Daily VMT per Capita versus Employment Density ---.-----------.----- ... y = 12.178o.ooo2x R2 = 0.32 ---1,000 2,000 3,000 4,000 5,000 6,000 7,000 Employment Density (Employees/Square Mile) Figure 3-5 Daily per capita VMT for (a) demand, and (b) polygon methods versus employment density 67 I I

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These results are based on model outputs and not empirically measured data. The travel demand models used by MPOs are calibrated against vehicle counts and housing surveys. The regional accounting of overall VMT estimates was covered in Section 3.3 .1. These correlations are not meant to represent causal relationships between employment density and VMT, but clearly they are correlated. To illustrate, communities that exhibit employment intensities greater than one have more people employed in the community than live there and thus they must be commuting from other communities. This commuting within a larger commutershed leads to increased per capita VMT being attributed to that community. In addition, the per capita VMT estimates calculated include all commercial traffic, which is expected to be higher around employment centers than residential areas. Other studies report correlations between reductions in per capita VMT and an increase in density at densities many times greater than any community within the DRCOG region. For example, the Federal Highway Administration (FHW A, 2004) reports that significant reductions in per capita VMT occur at housing densities greater than 6,000 to 7,000 housing units per square mile ( > 10 housing units per acre). But, the average housing density for the DR COG region is 1.8 households (HH) per acre, ranging from less than 1 HH/acre to 4.5 HH/acre with the city of Denver having a housing density of 2.6 HH/acre. Similarly, Frank and Pivo (1995) report that employment densities greater than 50 to 75 employees per gross acre result in significant decline in automobile commuting. However the employment density in the DR COG region averages less than 3 employees per acre with a high of 10 employees per acre in the Denver Tech Center. The city of Denver has an employment density of 5 employees per acre. Thus the employment and population densities in Denver may be lower than the thresholds stated in the research beyond which reductions in per capita VMT are observed. Figure 3-4 and Figure 3-5 indicate 68

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that both demand VMT and the polygon VMT are similar to each other, and that both are capturing local features of cities within a commutershed-chiefly employment intensity and density for the case of the DRCOPG region. However, the demand method offers other benefits with respect to spatial allocation of air travel and greater sensitivity to mass transit mode shift, as is discussed next. 3.4.3 Airline Travel Allocation To allocate airline fuel use at the airport to the numerous cities within the regional commutershed, a ratio of road trips from an individual city to the regional airport, versus the whole commutershed to the airport is used. For the case of Denver, this ratio was found to correspond closely with Denver's population ratio, and this feature was explored further for the 27 cities in DRCOG and the additional 5 cities from other MPOs. Figure 3-6 shows graphically how the allocation of airline emissions compares between vehicle trip ratios and regional population ratios. The plotted trend line, with a slope near one and a 0.96 r-squared value, demonstrates that this allocation method produces results consistent and similar to the metro region population ratio. For the smaller cities surrounding Denver in the metro region (see clump of data points in the lower left comer of Figure 3-6), the vehicle trip ratio method produced consistently lower allocation estimates than the population ratio. 69

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0.60 i 0.50 0::: 0.40 Q, 0.30 '5 0.20 Q, 0.10 0.00 0.00 Spatial Allocation of Airline Travel Trip Ratio vs. Population Ratio Portland 0.10 0.20 0.30 0.40 0.50 Regional Population Ratio y = 0.99x R2 = 0.96 0.60 ----------------------0.70 Figure 3-6 Spatial allocation of airline travel emissions via vehicle trip ratio between community and the airport as well as the regional population ratio for 5 major U.S. cities and 27 cities within the Denver metro region. The hashed line represents the two methods producing equal allocation estimates. A summary of the airport trip ratio and metropolitan area population ratio for the five major cities shown in Figure 3-6, along with the corresponding airline GHG emissions per capita allocated to each community is summarized in Table 3-6. Note that nationally, U.S. airline GHG emissions per capita for domestic flights are estimated to be 0.87 mt-C02e/capita (EPA, 2007). International travel is estimated to account for 27% of the total jet fuel consumption. Incorporating this with domestic airline travel GHG emissions results in per capita emissions of 1.2 mt-C02e/capita for airline travel (EPA Annex, 2007). These airline GHG emissions are very much in line with the city-scale per capita estimates shown in Table 3-6. The methodology proposed for allocating GHG emissions from major regional airports among co located cities is now shown to provide an accurate per capita estimate that is consistent across scale from the city to the national level. 70

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Table 3-6 Spatial allocation of airline GHG emissions for five U.S. cities around five major international airports. U.S. average airline GHG emissions per capita are 0.87 and 1 2 mt-C02e/capita for domestic onlv and all travel, respectively. Airport Metropolitan Allocated Jet Fuel per Trip Area Enplaned Enplaned Ratio Population Passengers I Passenger Ratio Capitab (gallon/pass.) Denver, CO 0.22 0.22 7.7 19 Portland, OR 0.45 0.35 4.4 26 Seattle, WA 0.15 0.16 3.6 30 Minneapolis, MN 0.14 0.11 6.6 23 Austin, TX 0.51 0.58 2.8 17 Airline GHG Emissions per Capita (mt COze/capita) 1.45 1.08 1.07 1.43 0.54 a. Fort Collms hes outside Denver's MPO travel demand modeling area, so the a1rport tr1p rat1o was estimated using Boulder's ratio between airport trip ratio and population ratio as a proxy. b. (BTS, 2006 ) 3.4.4 Tracking Impacts of Mode Shift Demand-based VMT is also more sensitive to changes in commuting patterns (both mode shifts and overall travel distances within the commutershed) than polygon VMT. To demonstrate this, a 5% mode shift for commute trips between two sample cities is modeled over a range of distances between the cities, and the resulting demand-based and polygon VMT are compared. This example is based on vehicle travel between two cities in the Denver, CO metropolitan region; Denver and Boulder (see Figure 3-2Figure 3-2). Substantial vehicle commuting is exhibited between these two communities, which are approximately 30 miles apart. Based on travel demand models, there are approximately 13,100 vehicle trips between these two communities on a daily basis. The goal of this exercise is to understand how a 5% mode shift from private vehicle trips to existing bus or to proposed rail transit affects the daily VMT estimates from the polygon and the demand-based methods. As part of this analysis, it is assumed that the mode shift occurs without adding any new vehicle trips on the roadways. In essence, all that is observed is a 5% reduction in the number of vehicle 71

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trips between these two cities. In the demand VMT method, the entire trip length is eliminated while only the truncated trip length within Denver (or Boulder) boundaries is eliminated in the polygon method (again, see Figure 3-2Figure 3-2 for a visual representation of a vehicle trip between these two cities). The demand VMT is fully responsive to the simulated VMT reduction and results in a 5% VMT reduction due to the 5% mode shift. The polygon method quantifies a 2% VMT reduction because it only accounts for the portion of the vehicle trip within each of the cities' boundaries and not the commute distance between the cities (see Figure 3-7). 1-:E > c c 0 u :::::1 "C e .. c Gl Gl D. Percent Reduction in VMT due to a 5% Mode Shift by VMT Allocation Method 6% 5% 4% 3% 2% 1% 0% Simulated Demand Response Polygon Response Figure 3-7 Percent reduction in VMT due to a 5% mode shift. Simulated VMT reduction is compared against modeled demand and polygon VMT estimates. Figure 3-8 shows how daily VMT estimates by the polygon and demand method are impacted by a 5% mode shift (trip reduction) for a range of inter-city distances. These results are based on a reduction of 660 daily trips (based on a 5% reduction in the approximately 13,180 daily trips between Denver and Boulder) with a constant intra city distance, or radius of the city, of 5 miles for the polygon method. At a five mile commute distance between these two communities, the impact of a mode shift (trip reduction) affects VMT estimates equally between the polygon and demand methods 72

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because at this distance the commutes are still largely occurring within city boundaries and are therefore counted in both methods. The demand method estimates much greater VMT reductions due to a mode shift for inter-city distances greater than the average intra-city commute distance (in this case five miles). Impact of Mode Shift on Demand-Based and Polygon Daily VMT Estimates 3 0 '000 ... --------------------... ---------------25,000 g 20,000 ca -ct; 'tJ :::J 15,000 Q)'tJ Q) 0::: 10,000 .n 5,000 __ .__. 0 0 10 20 30 40 -+-Demand VMT i Polygon VMT 1 ___________ I Inter-City (Demand) Trip Length j --------------------------------Figure 3-8 Comparison of impacts on VMT estimates by polygon and demand methods for a 5% mode shift. The results presented above indicate the demand method generates VMT data similar to the polygon method, while having the added advantage ofbeing more policy relevant with respect to air travel allocation and sensitivity to mass transit mode shifts. No matter which method is used, GHG emissions require conversion ofVMT to fuel use. The last section of this chapter evaluates the error in translating VMT to fuel use using statewide VMT and fuel use data. 73

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3.4.5 Error in Fuel Consumption from VMT Estimates: Statewide Data One of the objectives of this chapter is to compare fuel consumption estimates based on modeled VMT (along with fleet vehicle fuel economy) with fuel consumption estimates based on fuel sales data (based on a top-down MFA) to ensure that VMT estimates are reasonable surrogates of fuel consumption. The top-down MFA for both state level fuel sales and fuel consumption based on VMT and vehicle mix estimates has been compared among the five states used in this analysis to determine the variability in results from these two methods. Table 3-7 summarizes results of this analysis for the five states. State level gasoline sales were obtained from the Energy Information Administration's (EIA) State Energy Data System (EIA, 2008), and state level VMT estimates were obtained from the Federal Highway Administration through state provided highway performance monitoring system data (FHW A, 2006). Fleet vehicle mixes and fuel economy by vehicle class were based on the state specific data and national averages shown in Table 3-2. Fuel consumption estimates based on VMT estimates were within 12% error of the statewide fuel consumption data, showing consistency and reasonable accuracy for VMT and vehicle mix based fuel consumption estimates. 74

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Table 3-7 A comparison of top-down MFA fuel consumption estimates based on statewide EIA reported consumption and FHW A reported VMT and other vehicle mix estimates for 2005. EIA Reported Gasoline Gasoline Consumption based Consumption Based on FHW A Statewide % Error (Sales Data = on Statewide Sales VMT Estimates and True) Tax (gallons) Vehicle Fuel Economy (gallons) Colorado 2,154,600,000 2,416,330,000a -12% Oregon 1,575,000,000 1,752,203,995 -11% Washington 2,776,087,547 2,755,095,200 1% Minnesota 2,717,400,000 2,826,010,000 -4% Texas 12,413,051,652 11,679,207,912 6% a. Based on Colorado state specific vehicle rrux; the others are based on EPA nahonal average vehicle mix 3.5 Policy Implications The spatial allocation of vehicle and airline emissions via the demand method has important policy implications for cities. Utilizing the demand-centered VMT estimates from the local metropolitan planning organization (MPO) travel demand models provides cities with readily available data that allow them to track commuting patterns among other cities and the long-term impacts of expanding mass transit infrastructure and the resulting mode shift (e.g., light rail, commuter rail, or increased bus routes between communities) while they are completing city-scale GHG inventories. As a result, the GHG inventory at the city-scale is directly linked with a more detailed outcomes assessment of the performance of mass transit options and the transportation system as a whole, which can be used to more effectively inform future policy decisions. The demand method also provides cities with an easy and reliable method for allocating airline travel GHG emissions from regional airports to co-located cities. 75

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The inclusion of airline emissions-which is 4% of the U.S.'s GHG emissions (EPA, 2007) increases awareness about the impacts of airline travel and can spur initiatives to promote carbon offsets. For example, in Denver, CO, the inclusion of airline emissions has led to an effort to site kiosks at the Denver International Airport that would sell carbon offsets to airline passengers (Greenprint, 2007). 3. 6 Conclusions Many U.S. cities have started -with many more following suit-the process of inventorying and developing climate action plans to mitigate GHG emissions. However, there is no standardized methodology for conducting city-scale GHG inventories. In addition, GHG accounting for individual cities is confounded by spatial scale and boundary effects that impact the allocation of regional material and energy flows particularly transportation fuels used by both surface transport as well as airline travel. The demand method presented here employs the regional transportation demand models ofMPO's to spatially allocate both surface and airline transportation GHG emissions among co-located cities in metropolitan areas. This chapter presented numerous results of applying the demand method in a multi city analysis. First, an analysis of six metropolitan areas throughout the U.S. demonstrated that: 1) the demand method produces VMT estimates over the entire commuter shed that are similar to the traditional, boundary limited polygon approach (within 6%); 2) airline travel emissions allocated among co-located cities produced similar results to regional population ratios, with local variation observed among 33 cities studied; and 3) the method is replicable with necessary data available to all cities in this study through the corresponding metropolitan planning organizations. Second, a detailed analysis of27 communities within the Denver metropolitan area 76

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(DRCOG) demonstrated the ability of the demand method to be sensitive to local travel demand among communities throughout a commuter-shed. Daily VMT per capita estimates ranged from 8 VMT/capita/day to over 80 VMT/capita/day among the 27 communities, with a strong positive correlation with employment intensity (employment/capita) and employment density. Finally, the demand method has been demonstrated to have much greater ability to track mode shift over time, which has significant policy and program evaluation implications particularly for regional mass transit efforts. 77

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4. GHG Inventory Analysis: Eight U.S. Cities The objective of this chapter is to assess the variability and applicability of the hybrid LCA-based GHG inventory methodology developed in Chapter 2 by applying it to eight U.S. cities. Results of particular interest presented below include WRI scope 3 emissions such as those attributed to airline travel, cement consumption, transportation fuels production, the consumption of food and fo.od packaging, and water and wastewater treatment. In addition, key benchmarking data are presented for each city, which includes a comparison of per capita GHG emissions at the city and state level. 4. 1 Introduction A new demand-centered hybrid-LCA based GHG inventory methodology has previously been applied to complete the GHG inventory for Denver, CO (Ramaswami et al., 2007). A key finding of this inventory was that Denver's GHG inventory, with these WRI scope 3 emissions, approached a GHG footprint computation based on per capita GHG emissions that converged on the state of Colorado's per capita GHG emissions. In addition, key benchmarking metrics were developed for every sector to better track changes in the GHG footprint. To evaluate the variability and impact of upstream WRI scope 3 GHG emissions on a city's GHG inventory, to test a hypothesis that city-scale per capita GHG emissions will converge on state-level emissions with these inclusions, and to determine the availability of data to track key benchmarking metrics, this hybrid-LCA method has been applied to a total of eight U.S cities. The cities that participated in this analysis include: Denver, CO; Boulder, CO; Ft. Collins, CO; Arvada, CO; Portland, OR; Seattle, WA; Minneapolis, MN; and Austin, TX. The eight cities were selected based on their previous experience and 78

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access to data necessary to complete a GHG inventory, as well as their providing a good cross section of climates and city sizes throughout the U.S. In addition, three of the four cities in Colorado (Denver, Boulder and Arvada) were selected because they are located in the same metropolitan area to ensure that certain regional travel demand data were available. 4. 2 Methodology A demand-centered hybrid-LCA based GHG inventory methodology has been developed (see Ramaswami et al., 2008) which builds on the WRI scopes 1 and 2 inventory (end use electricity, natural gas and surface transportation fuel consumption) by incorporating the spatial allocation of airline travel and the embodied energy of key urban materials, consistent with WRI scope 3 protocols (WRI, 2004). In particular, this methodology incorporates GHG emissions from the following three sectors: 1) Direct energy consumed in buildings and facilities, including homes, commercial, industrial and government buildings and facilities 2) Direct (tail-pipe) emissions associated with transportation, including surface and air travel; and, 3) Indirect emissions associated with the embodied energy of key urban materials (water/wastewater, food, fuel processing, and concrete), as well as end-of-life ofwastes (e.g. landfill waste). Community-wide GHG emissions are calculated by summing the product of all material flows (consumption of energy or materials, or production of waste) with their associated GHG emissions factor (see Equation 1 ). The sources of energy and 79

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material consumption, waste production and the emissions factors associated with these sectors are summarized next. Community GHG Emissions = L Energy Use x EF + :LMFAx EFLcA Eqn.1 Electrici1y, Na1ural Gas. Pe1ro-fuels Key Urban Materials Data needed to complete a GHG inventory with this methodology come from numerous sources. This multi-city analysis was made possible, in large part, because each of the eight cities had either previously or was in the process of collecting the data necessary to complete a WRI scopes 1 and 2 GHG inventory. Contacts at each of the eight cities were a point of contact to provide all building energy use and waste data, as well as additional WRI scope 3 data including water/wastewater and airport fuel consumption data. The ultimate data sources for all material flows evaluated in this method are summarized in Table 4-1. All primary contacts for each of the eight cities are summarized in the Appendix (see Table C-1 ). Further details on the metropolitan planning organizations (MPO) that provided transportation data are discussed next. Table 4-1 Data source for material flow by sector for the eight cities. Emissions Sector Material Flow Unit Source Building Energy Use Electricity kWh Local utility (see primary contact) Natural Gas1 therm1 Local utility (see primary contact) Other thermal fuels1 kBtu or MMBtu1 Local utility or fuel distributor (see primary contact) Transportation Vehicles VMT Metro Planning Organization (MPO) Fleet Vehicle Mix Airline Travel Trip Allocation MPO Fuel Use at Airport Regional Airport Waste Waste Ton Locallandfill(s) or Haulers Diversion rate (see primary contact) 80

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Cement Cement Metric tonnes (derived I U.S. Economic Census from economic activity) (Economic Census, 2004) Food and Food Food Food expenditures I Consumer Expenditure Survey ($-1997) (BLS, 2005) Water and Wastewater Water/WW Million gallons I Local treatment facilities & pumping Energy Use by Facility stations (see primary contact) .. 1. Other fuels, such as 011 and propane, are often consumed m relatively small quantities and can be difficult to track. Some cities, however, consume these fuels in relatively large quantities. This GHG inventory methodology incorporates a unique technique to spatially allocate transport (both surface and airline travel) GHG emissions. For details of this method, please refer to (Ramaswami et al., 2008 and Hillman et al., 2009). Every metropolitan area (an urbanized area with a population over 50,000) has a metropolitan planning organization (MPO) to assist in coordinating regional planning and assessing environmental impacts. These organizations maintain regional travel demand models that contain comprehensive data on vehicle trips, include their origins and destinations within the region. These models are generated and calibrated with vehicle counts and survey data. The spatial allocation methodology, or demand method, uses a unique query procedure to both allocate vehicles miles of travel to communities as well as airline travel emissions within the MPO region (Hillman et al., 2008). The MPO and their primary contact solicited for these data are summarized in Table 4-2. Table 4-2 The metropolitan planning organization and contact for travel demand d ld th h moe ata or e etgJ t cities. Metropolitan Planning City Studied within MPO Contact Organizations (MPO)a Denver Regional Council of Denver, CO Boulder, CO Erik Sabina Governments (DRCOG) Arvada, CO North Front Range MPO Fort Collins, CO Suzette Mallette 81

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Metro Regional Portland, OR Steve Hansen Government (Metro-OR) Puget Sound Regional Seattle, WA Kris Overby Council_(PSRC) Metropolitan Council Minneapolis, MN Mark Filipi (Metro Council-MN) Capital Area MPO Austin, TX Kevin Lancaster (CAMPO) Community-wide GHG emissions are calculated using Equation 1. The various GHG emissions factors used in this analysis are summarized in Table 4-3, Table 4-4 and Table 4-5. Some of the sector emissions factors are dependent on local conditions and are listed as variable. This includes the electricity emissions factor, which is dependent on the region's generation mix; GHG emissions associated with waste disposal, which is based on composition of the waste, the disposal method and the conditions ofthe disposal site (U.S. EPA, 2008); and the GHG emissions associated with water and wastewater treatment, which is largely due to electricity consumption. a e -T bl 4 3 S ummaryo fGHG emissions actors b 'Y sector Emissions Sector Emissions Units Emissions Source Factor Building Energy Use Electricity kg-C02e/kWh Variable1 Variable1 Natural Gas kg-C02e/therm 5.3 EIA, 2007 Transportation Fuels (P2W) Gasoline kg-C02e/gal 9.13 EIA,2007 Diesel kg-C02e/gal 10.15 EIA, 2007 Jet Fuel kg-C02e/gal 9.87 EIA,2007 Waste Waste mt-C02e/ton Variable2 Variable2 Transport Fuel Production (W2P) Gasoline kg-C02e/gal 2.36 GREET (ANL, 2005) Diesel kg-C02e/gal 2.32 GREET (ANL, 2005) Jet Fuel kg-C02e/gal 2.25 GREET (ANL, 2005) Cement Cement mt-C02e/m-ton 1 PCA, 2005 Food and Food Packaging 82

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Food3 j kg-C02e/$-1997 l 1.5 I EIO-LCA, 2006 Water and Wastewater Water/WW I mt-C02e/MG I Variable4 I Variable4 .. I. Electricity ermssions factors vary by region throughout the U.S. See Table 4-4 for a summary of electricity emissions factors used for this analysis. 2. Emissions from waste vary considerable depending on waste composition and disposal method. See EPA WARM for more information (U.S. EPA, 2008). 3. Includes purchase for "food at home" and "food away from home" 4. Water and wastewater treatment GHG emissions are predominantly the result of natural gas and electricity use, which is location dependent. Fugitive methane emissions at the treatment facility will also be locale specific. Food sector GHG emissions have been expanded in this eight city analysis to include both expenditures for ''food at home" and ''food away from home" obtained from the Consumer Expenditure Survey (BLS, 2005). The aggregate GHG emissions factor per food expenditure (combining both ''food at home" and "food away from home" expenditures) is still 1.5 kg-C02e/$-1997 (EIO-LCA, 2006). Food GHG emissions are aggregated at the household level to determine communitywide emissions. Food expenditure data and the associated GHG emissions are summarize for each city in the Appendix (see Table C-12 and Table C-13). Electricity GHG emissions factors can be obtained from a number of sources. Cities use either emissions factors from ICLEI's CACP software (ICLEI, 2003), EPA's eGRID ( eGRID, 2006), or factors reported by the local utility that generates these factors based on the grid mix used to generate the electricity they deliver to their customers. ICLEI's CACP software and EPA's eGRID report emissions factors for very similar geographical sub-regions across the U.S., which represent the generation mix used to produce electricity for customers in that region. GHG emissions factors for electricity production for the 8 U.S. cities and the U.S. average are presented in Table 4-4. There is considerable variation among cities throughout the U.S. For example, the electricity emissions factor in Denver, CO is 83

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roughly twice that in Portland, OR. Emissions factors for each city are similar via these three sources, but ICLEI emissions factors are consistently higher than EPA's, which are higher than the local utility's. The EPA eGRID electricity emissions factor was used for cities that did not have a reported emissions factor from the local utility. Table 4-4 Comparison of grid average electricity C02 emissions factors among 8 U.S. cities I h h U S a ong Wit t e average 2005 Electricity Emissions Factors (kg-C02/kWh) ICLEI EPA eGRID Local Utility Denver, CO 0.96 0.85 0.80 Boulder, CO 0.96 0.85 0.80 Fort Collins, CO 0.96 0.85 0.77 Arvada, CO 0.96 0.85 0.80 Portland, OR 0.47 0.41 N/A Seattle, WA 0.47 0.41 N/A Minneapolis, MN 0.96 0.82 N/A Austin, TX 0.66 0.60 0.50 U.S. Average 0.68 0.60 NIA Emissions factors used by cities to estimate GHG emissions from waste disposal are obtained from the ICLEI CACP software (ICLEI, 2003), EPA's WAste Reduction Model (U.S. EPA, 2008), or recently the "Local Government Operations Protocol" (The Registry, 2008) and the "International Local Government GHG Emissions Analysis Protocol," which is currently out in draft form for public comment and review (ICLEI, 2008). As discussed in Chapter 2, the GHG emissions associated with waste, under each of these tools and protocols, incorporate both the disposal method and the composition of the waste to determine methane and C02 emissions (in the case of incineration). Neither of the latter two protocols, however, have developed a process by which a city can claim "credits" for recycling or waste diversion programs, while both the CACP software and EPA's WARM do calculate credits for waste diversion or recycling. Waste emissions factors varied considerably 84

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among seven cities that had completed inventories of their waste streams. Table 4-5 summarizes the emissions factors used in these inventories. Table 4-5 Waste disposal GHG emissions factors used by eight cities in their most recent GHG t mven ory GHG Emissions Factor for Waste (mt-C02e/ton waste) Denver, CO -0.196 Boulder, CO 0.641 Fort Collins, CO 0.290 Arvada, CO 0.150 Portland, OR 0.423 Seattle, WA -0.097 Minneapolis, MN 0.056 Austin, TX N/A 4.2.1 Benchmarking The GHG inventory process requires data collection from a number of sources and departments within a city and therefore can be time intensive to coordinate initially. In addition, cities are typically conducting GHG emission inventories as part of larger sustainability initiatives. It is recommended that during the GHG inventory process, cities track and report additional information to assist in identifying areas of GHG emissions as well as track performance of mitigation programs over time. The following benchmarks have been previously proposed as important to include as part of a city's GHG inventory report (see Ramaswami et al., 2008) and have been documented for each of the eight US cities studied here (see Table 4-6). 85

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Table 4-6 Useful consumption benchmarks to be included in a city-scale GHG t mven ory Sector Benchmark kWh!HH/mo Residential Energy therms/HH/mo sq feet/HH % RE Electricity Purchased Electricity -kWh/sf Comm. Energy Thermal kBtu/sf Total kBtu/sf % RE Electricity Purchased Transportation Gal-Jet Fuel/enp. pass. Enp. Pass./Capita Waste tons/capita Waste Diversion Gal gas/capita Transport Fuels Gal diesel/capita Gal Jet Fuel/capita Cement metric tons/capita Food $-1997/HH Water/Wastewater I ,000 Gal/capita 4.3 Results Results from this eight city analysis are presented in the following order. First, community-wide per capita GHG emissions are presented with a break-out by sector along with a comparison with state-wide per capita GHG emissions. Second, details on GHG emissions from the scope 3 inclusions along with a sensitivity analysis of these emissions sources are presented. Finally, the key benchmark data are presented for each city. All energy use and material flow data required to complete this analysis are available for reference (see Table C-2 and Table C-3 in the Appendix). The following results are not meant to replace or identify deficiencies in the reported GHG inventories of cities surveyed in this analysis. The data gathered from cities 86

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and presented here are simply meant to illustrate the process and methodology that can be used by cities, there are not necessarily representative of these cities' actual GHG emissions. Data for a number of cities could not be gathered that represented the same time frame, i.e, data were obtained that represents different years-both within a city and between cities. Thus, these results should not be used to compare or report GHG emissions for these cities in a given year. 4.3.1 Community-Wide GHG Emissions Applying Equation 1 and the emissions factors in Table 4-3 and Table 4-4, the total GHG emissions were calculated for each of the eight cities. These results are summarized in Figure 4-1. The per capita GHG emissions varied from a low of 14.6 mt-C02 e per capita to a high of25 mt-C02 e per capita, with an average of21.6 mt C02e per capita. GHG emissions from buildings represented the largest source of GHG emissions and also exhibited the greatest variability among cities. This variability is due to the weather impacts on building energy use and the range of electricity emissions factors used (see Table 4-4), as well as a relatively small amount of commercial activity in the City of Arvada compared to the other cities in this study. The scope 3 inclusions (Airline, Fuel Processing, Food, Cement, Water/Wastewater and Waste Disposal) accounted for an average of6.7 mt-C02e per capita to the city-scale inventory, or approximately 31% of the total GHG emissions. Per capita GHG emissions for each of the eight cities with and without the WRI scope 3 inclusions are also plotted in Figure 4-2 against the statewide per capita emissions. 87

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s c. Ill Q) N 0 .. :::! c. Ill 0 ... Q) c. Ill 1: .2 Ill Ill e w (!) J: (!) 20 15 1 0 5 Figure 4-1 GHG emissions by sector for eight U.S. cities 88 D Cement 11 Food !i:'l Fuel Processing (W2P) 1131 Surface Transport (P2W) 1:::1 Buildings Energy Use

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I -E tn c .2 tntn c 0 E f w G) (!)0. ::I:"i (!)N cao -o a ca 0 ... G) a.. Per Capita GHG Emissions for 8 U.S. Cities by WRI Scopes and State Comparison 1 & 2 : Scopes 1, 2 & 3 jo State _m ___ Figure 4-2 Per capita GHG emissions by WRI scopes for eight U.S. cities compared to statewide per capita emissions. The per capita GHG emissions for the states associated with the eight cities are summarized in Table 4-7. A single standardized source for state-level GHG emissions is not available at this time. The data sources included state agencies, the U.S. Environmental Protection Agency (EPA) and a private consulting company. There is likely some discrepancy in these reported emissions due to the complex nature and boundary issues associated with GHG accounting. For example, the state of Washington's reported per capita emissions appear to be rather low compared to the four other states and are significantly below Oregon's despite having the same regional electricity GHG emissions factor and similar weather and economic conditions. 89

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Table 4-7 P er capita GHG fi fi emissiOns or IVe states associate Wit t e eigJ t citle d h h h .. s. Per Capita GHG Emissions Source (mt-C02e/capita) Colorado 25.2 Strait et al., 2007 Oregon 19.83 ODE, 2004 Washington 15.1 CCS, 2007 Minnesota 29.6 Strait et al., 2007 Texas 33.23 EPA, 2001 a. GHG emisstons were not available for 2005. These enusstons were reported m 2000. It ts assumed that per capita emissions for these states haven't changed considerably between 2000 and 2005 for city-scale comparisons. Based on Figure 4-2, it appears that the inclusion of these WRI scope 3 items begins to show some convergence between city-scale per capita GHG emissions and the per capita emissions at the state-level, particularly for cities that have substantial commercial activity. The per capita GHG emissions with the WRI scope 3 inclusions for each of the eight cities are compared to the state-level per capita GHG emissions in Figure 4-3. All except three of the eight cities were within 16% of the statewide per capita estimates. In the case of Arvada, per capita emissions are 42% lower than the state level emissions, which is likely due to much less commercial activity (which affects both building energy use and vehicle use) than the other Colorado cities. Seattle's per capita emissions, despite being very similar to Portland's, were 38% higher than the state of Washington's. As noted previously, Washington's per capita emissions are likely lower than expected if a regionally based electricity emissions factor were used. Thus, Seattle having per capita emissions much higher than the state is assumed to be an anomaly. Finally, Austin's per capita emissions, despite being equal to the average ofthe eight cities, are 35% below those of Texas as a whole. Texas' per capita emissions, however, are much higher than the other states analyzed in this 90

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study, which is likely due to the fact that a substantial amount of energy production (both petroleum refining and natural gas distribution) occurs in Texas (EIA, 2005). Despite attempts to analyzed Texas' reported GHG emissions in 2000, detailed data could not be obtained to substantiate the higher than average GHG emissions per capita. Further research to explain this state's reported GHG emissions is recommended. As a result of lacking consistency on GHG emissions at the state and city level, per capita GHG emissions should be accompanied by reported sector consumption and benchmarks to improve on characterization and mitigation of emissions. 0 ca 0::: City per Capita G HG Emissions (with Scope 3 Inclusions) Relative to State per Capita 1.50 II) c 0 1.25 ------------------u;-; ,s -s 1.00 EUJ ---------------" -------- .. 0.75 a e 0.50 --{-0.25 ---ca u ... CD c.. 0.00 cP cP cP cP ofl)-' 0' <;:\flj i:>flj <:)e; q;O cP -<...+ "t'..:s Figure 4-3 Per capita GHG emissions for eight cities relative their state's emissions. The hashed area represents plus or minus 10% of the state-level per capita GHG emissions. 91

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4.3.2 Community Wide Benchmark Summary A number of benchmarks have been identified to assist cities in identifying areas of GHG emissions as well as tracking performance of mitigation programs over time (see Table 4-6). The following figures present sector specific data for each city compared to state, regional or national benchmarks. These benchmark data are also summarized for each city in Table C-14 (in the Appendix). Building energy use benchmarks are shown in Figure 4-4, Figure 4-5 and Figure 4-6. To produce the per household energy use metrics for each city, population and average number of people per household were used to estimate the number of households. These estimates can vary from the number of customers utilities report (premise counts) when producing aggregated consumption data for cities. Although premise count data wasn't available for this analysis, cities are encouraged to use premise counts to produce energy use per household metrics. Statewide average energy use and housing estimates were obtained from the State Energy Data System and the U.S. Census, respectively (EIA, 2008; U.S. Census, 2008). Producing commercial energy use per floor area metrics can be confounded by aggregated utility data that does not separate commercial from industrial energy use. The cities of Denver and Minneapolis received data from their utility that combined both commercial and industrial energy use. For this analysis it was assumed that 70% of the commerciaVindustrial energy use was due to commercial customers. It is recommended that cities try to get energy use data separated between commercial and industrial customers if possible. Commercial energy use per floor area for each city is compared against that at the census region that is reported in Commercial Building Energy Consumption Survey (EIA, 2003). 92

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Average Monthly Electricity Use per Household 0 .E D City State J: J: 1200 -.s::. :!: 1000 Cll 800 1/1 ::J 600 c::; 400 ;: -u Cll 200 w >-0 ::c 1: cP 0 ::E rQ' Qflj cP cP cP -<...+ 0'' r,-0' r,' 't?"-s cP (()0 _0{:-\>" -----------------Figure 4-4 Average monthly electricity use per household for eight cities and their respective state average. 0 .E J: 70 60 .s::. Cll 40 :ll 30 (!) 20 i 10 z 0 2: .s::. 1: 0 ::E Average Monthly Natural Gas Use per Household D City State cP cP cP 0'' r,-0' r,' (()0 cP _0{:--<...+ 't?"-s Figure 4-5 Average monthly natural use per household for eight cities and their respective state average. 93

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Average Commercial Energy Use per Floor Area ... Gl Q. D City Census Region Gl Ill 160 ;::) >-c-e'..!!! 140 Gl :I 120 c .. wm 100 :!';' 80 e Glc( 60 E ... E o 40 0 0 oU::: 20 Gl 0 C) I! Gl cP > c( 0'-' cP 00 00 -<..+ "'' b'?;' b !':>' # 'r-.;s cP e:,0 0'6 q,O ----------------------------------------------Figure 4-6 Average commercial energy use per square foot of floor area for eight cities along with their respective regional commercial energy use intensity (EIA, 2003)1. Transportation sector (include vehicle and airline travel) benchmarks are shown in Figure 4-7 through Figure 4-10. Statewide daily VMT per capita estimates were obtained from the Federal Highway Administration based on state provided highway performance monitoring system data (FHW A, 2006). The total number of US enplaned passengers (one-way trips) and the total amount of jet fuel consumed in the U.S. was obtained from the Bureau of Transportation Statistics (BTS, 2006c; BTS, 2006d). I Denver and Minneapolis provided commercial and industrial energy use combined. Therefore, 70% of the commercial-industrial total is plotted as an estimate of the commercial energy use per floor area. Portland's energy use was provided as all ofMultnornah County, but the building area is only for the City of Portland. The energy use for Portland is estimated to be 80% of Multnomah County's based on the population ratio between Portland and Multnomah County. 94

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Ill c. Ill 40 0 35 ... 30 ... 25 ::EQ. > 20 -::E 15 10 Cll C) 5 Ill ... Cll 0 > c( cP r/t' cP Average Daily VMT per Capita ; D City State cP cP o-#":>' C:J'li 01}
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Jet Fuel Use per Enplaned Passenger 35 "i.-. ; 30 Cl CLr::: m 25 ._ ca 8.-e-Q) r::: 20 Ul 0 Cl 15 ... Q) .!l 10 -Q) !! Ill r::: ca r::: 0.. Oil( 0 t USAverage= --------------28 (gal/pass.) ----------------------------------------------------------------------------Colorado Cities Portland, OR Seattle, WA Minneapolis, MN Austin, TX Figure 4-9 Annual jet fuel use per enplaned passenger at the airports serving the eight cities. i E :I Ul r:::.-. oJ!I 0 a. Qi :1 r:::.2 o-ca ... Cl s.i Ul ca r:::o f ... 1-8. ii :I r::: r::: Oil( Annual Transportation Fuel Consumed per Capita 500 450 -1--==----------400 350 300 250 200 150 +-tilPJ--r-r----illilJ-100 50 0 cP e.\ cP P-1'
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The daily per capita municipal solid waste reported by each of the cities is shown in Figure 4-11 and ranged from a low of 4.2 pounds/person/day to a high of I 0.4 pounds/person/day. The U.S. average is about 4.5 pounds per person per day. .$ 1/1 12 10 en a ii 8 i -en 5:& 6 s=-'jj 4 0 I'!! 2 ... Q) &; 0 &:-(!) 'ii cP c ti-' <:.)0 Daily per Capita Municipal Solid Waste Generated ... ---------------------------------------------------cP cP cP US Average= 4.5 lb/person/day -<..+ '""' 0"' ""' .,s'fj cP 0'6 1()0 Figure 4-11 Daily per capita municipal solid waste generated in eight cities. 4.3.3 Multi-City GHG Emissions Variability by Sector Per capita GHG emissions by sector over the range of emissions exhibited by the eight cities are shown in Figure 4-12. Building energy use is the largest GHG contributing sector (an average of 10.1 mt-C02e per capita) and exhibited the greatest variation of all sectors (7 .6 mt-C02e per capita). The variation observed for each of the WRI scope 3 inclusions (airline travel, fuel processing, food, cement, water/WW and waste) was less than 1.1 mt-C02e per capita. 97

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The disposal of municipal solid waste (MSW) is unique to the other inclusions because it can result in reported net C02 e reductions, particular when claiming credits for recycling and composting programs (U.S. EPA, 2008; ICLEI, 2003). As a result, some cities report C02 e reductions associated with their waste disposal, as is shown in Figure 4-12 (see also Table 4-5). The GHG emissions associated with waste ranged from 0.28 mt-C02e per capita to ( -0.84) mt-C02 e per capita. s c. Ill Cl) a .. Ill r::: .2 Ill Ill e w l' :I: l' Q. Ill (,) ... Cl) I Q. 16 14 12 10 6 6 4 2 0 -------2 --.----------------------T ---------------.z. ---------0":10 cl; f..' ... q<..OJ 0
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energy use to determine if any consistent trends existed in this data set. The parameters that energy use was compared against included: the electricity GHG emissions factor; weather (heating and cooling degree days); and the building floor area intensity (floor area per person). Communitywide building GHG emissions per capita for each of the eight cities was plotted against the electricity GHG emissions factor, resulting in a correlation coefficient (R2 = 0.35) [see Figure 4-13]. However, one of the eight cities, Arvada, appeared to be considerably separate from a general trend that the other seven cities exhibited. As mentioned earlier, Arvada was unique out ofthe eight cities analyzed in that it had much lower commercial and industrial energy use compared to its residential energy use (see Table C-2 and Table C-3 in the Appendix). 99

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Building GHG Emissions per Capita Versus Electricity Emissions Factor R2 = 0.3538 16.0 ------------------------------------------------------------.. --........ ,_ .. __ 1 i 14.0 -----------------------Ill CL i e 12.0 -------------------j w l C) a 10.0 C) .. 8.0 .. E .S-.1!1 6.0 en c.. en .,. 4.0 -cO ____ __.__ _____ Arvada -1 \ i -------i I --------_J i ----1 = ; 2.0 0.0 0.00 0.20 0.40 0.60 0.80 1.00 1.20 l____ --Electricity Emissions Factor (kg-C02e/kWh) Figure 4-13 Building GHG emissions per capita versus the electricity GHG emissions factor in eight cities. Arvada was removed from the sample to determine the correlation between building GHG emissions and the electricity GHG emissions factor for the other seven cities. This is shown in Figure 4-14, which has a correlation coefficient (R2 = 0.91). 100

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Building GHG Emissions per Capita Versus Electricity Emissions Factor R2 = 0.91 "' 16.0 ---------------------------.. ---------------------.. ----! c I 14.0 Ill CL "E :J 12.0 WQ; (!) s 10.0 ----------(!) .. 8.0 -------------. .. E sJ!l 6.0 Cl) 'ii g' 4.0 --------= ; 2.0 ----------__ .__---i i ---l 0.00 0.20 0.40 0.60 0.80 1.00 Electricity Emissions Factor (kg-C02e/kWh) Figure 4-14 Building GHG emissions per capita versus the electricity emissions factor-Arvada has been removed from the sample. 1.20 Building energy use was also compared against weather factors for each of the eight cities. Building thermal energy use (largely natural gas) was plotted against heating degree days (base 65 F) for each of the cities, while building electricity use was plotted against cooling degree days (base 65 F). The building thermal energy use compared against heating degree days is shown in Figure 4-15, which has removed Arvada from the sample to determine any correlation among the building energy use in the other seven cities and heating degree days. The correlation coefficient without Arvada was (R2 = 0.62), and with Arvada was (R2 = 0.47). 101

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J! a "' 0 800 700 lii 600 c.-i 500 >-E 4oo 2J ... Cll Cll 300 cfi 200 "' e 1oo Cll 0 Thermal Energy Use per Capita versus HOD R2 = 0.6226 ---,.- --n ----------] --------------. .. ---------------------- 0 1000 2000 3000 4000 5000 6000 7000 8000 Heating Degree Days (Base 65) Figure 4-15 Building thermal energy use per capita versus heating degree-Arvada has been removed from the sample. The building electricity use compared against cooling degree days is shown in Figure 4-16, which has once again removed Arvada from the sample to determine any correlation among the building electricity use in the other seven cities and cooling degree days. The correlation coefficient without Arvada was (R2 = 0. 7), and with Arvada was (R2 = 0.37). 102

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... AI a. a..ca AI U "'::JJ: c::;-AI w Electricity Use per Capita versus COD R2 = 0.7043 18 16 14 12 __. ---10 8 6 -4 2 ---------------0 0 500 1000 1500 2000 2500 3000 3500 Cooling Degree Days (Base 65) Figure 4-16 Building electricity use per cooling degree days Arvada has been removed from the sample. Finally residential and commercial building energy use per capita was compared against the building floor area intensity (residential and commercial floor area per capita) in the eight cities. Figure 4-17 shows the residential and commercial building energy use per capita versus the building floor area intensity, for seven cities (Arvada has been removed. The correlation coefficient with and without Arvada included is (R2 = 0.04) and (R2 = 0.23), respectively. 103

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Residential and Commercial Building Energy Use per Capita Versus Building Area per Capita J!l 60,000 g. 50,000 -0 ... X. ji 40,000 ---------GI ->-.!:! 30,000 m.a '-m 20,000 Cl c 10,000 l ----i ------.-.... ------i -.. ----1 l I I 0 0 200 400 600 BOO 1,000 1,200 1,400 Building Area per Capita (square feet/capita) _________ j Figure 4-17 Residential and commercial building energy use per capita versus building floor area per capita-Arvada has been removed from the sample. This analysis has confirmed that some of the change in building sector GHG emissions is correlated with the change in electricity emissions factor and weather among the different cities. 4.3.5 Temporal Sensitivity of GHG Emissions Data Collection There are important considerations for cities completing GHG inventories including the temporal sensitivity of data collection. For example, the US Economic Censuswhich is used to estimate cement consumption in a city is only updated every five years. Additionally, the travel demand models for estimating VMT and allocating airline travel emissions are only updated by the MPO every 4 to 5 years. On the other hand, data from single entities like utilities, waste haulers or treatment facilities (which accounts for building energy use, airport fuel consumption, waste, and 104

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water/WW treatment) is available on an annual basis. The frequency of data availability and other important considerations for each sector in a city's GHG inventory is summarized in Table 4-8. Table 4-8 Availability and considerations for data collection by sector for city-scale GHG inventories. GHG Frequency Emissions Data Source of Data Challenges I Considerations Sector Availability Some utilities do not separate commercial and industrial sectors; Building Utility should provide "premise" count Energy Local Utility 1 year to determine energy use per customer; Use Track heating and cooling degree days to enable weather adjusted assessment of energy use Surface Travel demand Models only update every 4 to 5 years; Transport model (from 4 to 5 years State level fuel use and VMT are MPO) updated annually; 1) Jet fuel 1) 1 year -Airports know jet fuel consumption consumed at every year; Airline airport Spatial allocation can only be updated Travel 2) Spatial 2) 4 to 5 as often as the travel demand models (4-5 allocation of years years); population estimates could be airport emissions used annually Local haulers or -GHG emissions vary considerably (can Waste waste disposal 1 year result in credits) based on disposal sites conditions and assumptions Consumer Does not account for local variability Food Expenditure 1 year (nationally aggregated emissions) Consumption aggregated for entire Survey (CES) metro region Updated data only available every 5 Cement Economic 5 years years; Census Consumption aggregated for entire metro region Local treatment Treatment facilities may serve many Water/WW facilities 1 year communities within a region; can apportion by population or customers 105

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4.4 Conclusions The demand-centered hybrid-LCA based GHG inventory methodology has been applied to eight U.S cities. This analysis has shown that city per capita GHG emissions with these WRI scope 3 inclusions resulted in five of the eight cities showing convergence within 16% of statewide per capita estimates, suggesting an effective footprint computation. Cities that showed more significant variation, e.g. Arvada, were found to have distinctly lower building energy use and transport demand. Sector-specific per capita resource consumption and travel demand benchmarking is therefore critical in understanding GHG footprints. Cities are thus encouraged to include numerous benchmark metrics with their inventory to enhance the characterization of emissions and tracking of GHG mitigation efforts. This analysis has demonstrated that data is available to generate and report both the overall GHG footprint and consumption benchmarks. An analysis of the variability of GHG emissions by sector showed that buildings were by far the largest GHG contributing sector for all eight cities as well as representing the greatest variation among sectors and cities. Finally, important temporal and data availability considerations were presented for each sector in a city-scale GHG inventory. For example, data required for the spatial allocation of vehicle and airline travel, and estimating cement consumption are only updated every four to five years. While data for energy use, waste disposal, water/WW treated, and food consumption is available on an annual basis. 106

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5. First-Order Quantitative Assessment of GHG Mitigation Options in Buildings The specific objective of this chapter is to produce a first-order quantitative assessment that evaluates the effectiveness ofbuilding sector climate actions to reduce GHG emissions through a range of performance and participation rates for each action. A case study in Denver, CO is used to assess the GHG reduction potential for a number of climate actions. As mentioned earlier, this research is part of a larger research initiative on Sustainable Urban Infrastructure at the University of Colorado Denver (UCD). Through this initiative, UCD worked with the City of Denver to complete the city's GHG inventory as well as provide technical assistance in quantifying the impacts of numerous options to help develop the city's climate action plan (Ramaswami et al., 2007; Greenprint, 2007). Baseline GHG inventorying discussed in Chapters 2 -4 were used to assess impacts of actions in the following sectors: building energy use, transportation, materials, and waste/recycling programs. Transport and materials sector actions were evaluated by Mike Whitaker and Mark Reiner. This chapter documents the assessment of GHG mitigation options in buildings completed for Denver's Greenprint climate action plan. 5. 1 Introduction In the U.S., buildings are responsible for 37 percent of all energy consumed and 68 percent of all electricity (USGBC, 2003). In addition they generate more than one third of the municipal solid waste streams and 36 percent of total U.S. anthropogenic carbon dioxide emissions (USGBC 2003). Based on the GHG inventories completed for eight U.S. cities, energy use in buildings accounted for just under 50 percent of 107

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the city's GHG emissions [see Figure 5-1]. Therefore, addressing energy use in buildings is a critical component of city action plans for GHG mitigation. i a. Ill I U a; N 0 .. :J I 'Q. Ill u ... Gl a. Ill c: .2 Ill Ill e w (!) :I: (!) Figure 5-1 GHG emissions by sector for eight U.S. cities 111 Water I WW I Waste Ill Cement 111 Food li:l Fuel Processing (W2P) l2l Air1ine (P2W) llll Surface Transport (P2W) Ell Buildings Energy Use After cities complete a GHG inventory, they must evaluate and prioritize potential climate actions to implement. Some basic questions cities may ask include: what building sector policy actions can cities take to reduce GHG emissions; how effective are they; how much do they cost; and how do cities prioritize them? The effectiveness of different climate actions, as described here, is in essence the combination of: 1) performance at the scale of individual implementation, and 2) the degree to which the action item is implemented in the community, or participation rate. Given the range in performance (energy savings) ofbuilding climate actions and particularly the range in actual community wide adoption of these measures, it is important for cities to have a basic understanding of overall impact that these actions can have on a city's total GHG emissions. 108

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Currently there is not a well developed body of literature that contains a compiled quantitative assessment of climate actions. There are numerous sources of compiled "best practices" cities can take (Apollo Alliance, 2007; NCS, 2007). These summaries often include general ranges ofperformance (energy savings at the individual building level), but data on participation rates and determining overall effectiveness are lacking. In addition, modeling tools such as DOE-2, EnergyPlus, and TRNSYS (Crawley, 2005) exist that assess performance (energy impacts) of individual climate actions, but again, these have not been combined with quantitative participation rate data to assess overall effectiveness of climate actions. Only recently has ICLEI released a beta version of their Climate and Air Pollution Planning Assistant (CAPPA; ICLEI 2008), which includes surveyed performance and participation rate data from U.S. cities on over 100 potential climate actions across all sectors. However, numerous assumptions are made and quantitative data is lacking on most of their presented climate actions. To better inform cities of how effective certain building sector climate actions can be, cities should have access to consistent, compiled and accurate data on the performance of a range of building sector actions as well as typical participation rates. This research focuses on building sector options that were evaluated during the development of the City and County of Denver's climate action plan (Greenprint, 2007). The specific goal of this chapter is to quantify the GHG impact ofbuilding sector climate actions for a period of 10 years over a range of performance and participation estimates. This is completed for 10 mitigation options in Denver, CO. 109

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5. 2 Energy Use in Buildings Energy Efficiency and Use Trends in U.S. Buildings -National Scale Building energy use in the U.S. has steadily increased over the last few decades despite gains in energy efficiency (EIA, 2001; EIA, 2003; PNNL, 2006). Put another way, the energy intensity or energy use per some unit area (Btu/sf) or output (Btu/GDP) has steadily decreased over this time period, but the primary energy required to power the buildings and their activities has gone up due to the increase in building area or output outpacing the energy efficiency gains. Research at the Pacific Northwest National Laboratory has examined this trend for the commercial, residential and industrial sectors over the last two decades (PNNL, 2006). Figure 5-2 shows that commercial building energy intensity has remained roughly constant over the last 20 years. As a result, the energy use increase (about 35%) in the commercial sector is approximately equal to the proportional increase in total floor area. Similarly, but more striking in residential buildings, Figure 5-3 shows an increase in total delivered energy use of 20% despite a decrease in energy intensity of nearly 20% over the same time period. 110

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,_, _. __ I 1..4 .L ... ...,. ..... .L C! 1.2 ,.--I ... ton ..... .... -.... I 1.0 ..... ---._. .,. )( 41 ---+--Entrg9 Consum ptlo n 0.8 .s -41--Floorspc 0.6 --.1r-1ntnsit9 lnd (wuthr tdjusttd) ----V ulhr F ctor 0.4 I I 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 Year Figure 5-2 Commercial Energy Use, Activity, Weather, and IntensityDelivered Energy, 1985-2004 (National scale; Source: PNNL) 1.4 1.2 --. -or. .A. ....... -1.0 q -n 0.8 ton ...r ... ""'" ........ ..... ...... ..... -..... -..,... -... I 0.6 )( ---4-Energy Use 41 "C ---Total Households .. 0.4 ---...-Hou sflg Size (ncl. Weather) 0.2 ---.-Intensity (per square foot) 0.0 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 Year Figure 5-3 Energy Use, Activity, Intensity and Other Factors in the Residential Sector Delivered Energy, 1985-2004 (National scale; Source: PNNL) 111

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Finally, larger homes in the U.S. consume much more energy than smaller homes; despite the larger homes showing an equally dramatic reduction in EUI (EIA, 2001). Figure 5-4 shows average U.S. household energy use and EUI by home size. Looking at the difference between households across the U.S. that are 2,000 square feet compared to 4,000 square feet, one sees that the 4,000 square foot home results in an EUI reduction of about 28%, while it also corresponds to a total energy increase of 54%. .a cn:i ... :i Q)c c w 0 ::I Q. c E c ::I <( fl) c 0 0 Household Energy Use and Energy Use Intensity by Size 180 ----------------------------120 160 140 120 100 80 60 40 "-20 0 100 80 60 40 20 0 0 2000 4000 6000 HH Size (sf) -+--Use per HH (million Btu) ---Consumption per sf (1 000 Btu/sf) Figure 5-4 Average U.S. household energy use and energy use intensity by size of home (EIA, 2001 ). 112

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5.3 Overview of GHG Mitigation Options in Buildings There are numerous building sector climate actions that cities can implement to reduce GHG emissions. Several organizations have compiled typical climate actions (SWEEP, 2002; Apollo Alliance, 2007; Braun et. al., 2006), as well as the cities that have implemented them (NCS, 2007; Bailey, 2007; Regelson, 2005), however, quantitative analysis is lacking. These compilations cover a range from efficiency measures, renewable energy purchases, and promoting technologies that foster behavior change. Part of our analysis for the development of Denver's climate action plan was to consolidate the numerous options for building GHG mitigation into a shorter list of options that would be further analyzed. The consolidated list of options analyzed in detail for Denver's climate action plan is categorized below in Table 5-1. T bl 5 1 C a e -rd d r f onso 1 ate 1st o common GHG fi b "ld" mitigation strategies or Ul mgs Strateey Buildine Sector Options Description Energy efficient building Increase energy efficiency requirements codes and building design in the local building codes. Promote codes and design practices that meet some LEED equivalency or updated IECC level. Promote Utility Demand Build on the available rebates and energy Side Management (DSM) efficient design assistance provided by Programs the local utility (Xcel). Time of Sale Energy Requires basic energy and water Efficiency Efficiency Upgrades efficiency measures be in place at a Measures residence at the time of sale. Energy Audit with Low Audits on building energy use that aim to Interest Loan identify possible improvements to the energy performance of the building. Compact Fluorescent Encourage and incentivize residents to Lamps (CFL) Promotion use CFL's. Low-Income Energy Blitz Door to door blitz with a packet of energy efficiency measures and education materials on energy conservation. 113

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Purchase renewable energy Xcel offers a purchase option at a premium to support further wind farm Renewable development. Energy Install solar thermal for hot Offer rebates, incentive and/or promote water the use of solar thermal collectors for hot water. Behavior Digital displays in a home that show real Change In-Home Energy Displays time energy use. This can be voluntary or required in new homes. Price Tiered Electric Rate or Time Apply a rate structure that ratchets up Signal of Use Rate energy costs for higher consumption or during peak times of the day. In addition to city-level actions summarized in Table 5-1, states and nations can institute GHG standards at the utility level, either through mandates of reductions in absolute emissions, a mandatory reduction in utility emissions factors (kg-C02e/kWh) or cap and trade type schemes (RGGI 2005; CCAR 2007). This research focuses on the city-scale actions shown in Table 5-l as well as demonstrating impacts of a reduction in the electric utility emissions factor. The price signal strategy is not evaluated in this analysis because the City of Denver does not have jurisdiction to enact laws that change utility rates. Rate changes or adjustments are made at the state level through the Public Utilities Commission for the State of Colorado. 5.4 Performance and Participation: Towards Overall Effectiveness The effectiveness of different climate actions is the combination of the performance at the scale of individual implementation, and the degree to which the action item is implemented in the community, or participation rate. The performance of many building sector climate actions is well documented, but participation rates for these actions is lacking. 114

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Performance-namely energy savings-of a number of building efficiency measures is well documented (SWEEP, 2002 and 2005; KEMA, 2006). For decades, computer simulation programs have been used and refined to provide an assessment of energy performance of a number of building efficiency measures, including but not limited to programs such as DOE-2, EnergyPlus, and TRNSYS (Crawley, 2005). These tools can be used to inform building code development and adoption, be used by building design professionals to determine energy efficiency options that are cost effective, as well as modeling the performance of retrofit energy saving measures in existing buildings. In a recent study, Diamond (2006) has compared modeled energy use reductions in LEED certified energy efficient buildings with actual energy use and found that of 18 buildings studied in the U.S., the actual energy use was on average 99% of that modeled. In addition, field data is increasingly becoming available on actual energy use and savings following energy efficiency upgrades (Blasnik, 2006; McCrae et al., 2005). Participation rates for building sector climate actions are affected by a number of factors. These factors include, but certainly are not limited to: the level of incentive or rebate offered; whether a program is voluntary versus mandated; and public perceptions, awareness or education level on the issues instigating the need for the action item or simply the action item itself. Table 5-2 illustrates the range of participation rates typically observed for a number of energy efficiency programs in the U.S., from voluntary to mandated programs. As can be seen, free items had participation rates around, but above 50% (Tachibana and Brattesani 2003; ACEEE 1994 and 2005). Voluntary programs targeting rebates on higher cost with higher savings potential items had much lower participation rates, less than 3% (ACEEE 2005; Hirst 1984). And finally, for a mandated time of sale energy conservation ordinance in Berkeley, CA, the participation rate was close to 90% (La Pierre 2007). 115

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These results provide the basis for evaluating program effectiveness over a range of performance estimates and participation rates, which is discussed in Section 5.5. Participation rates described here are defined as the number of individuals or entities that implement an intervention out of the total target population. Additional metrics can be used to assess energy savings potential including: opportunity rates (the number of entities that have the energy saving opportunity); adoption rates (the number of entities that implement the intervention); and retention rates (the number of entities that retain the intervention over time) [Carroll, 2006; Tachiabana and Brattesani, 2003]. This first-order analysis subsumes these additional rates into the overall participation rate as it is defined here. Table 5-2 Participation rates for a number of energy efficiency programs in the U.S. over a range o f' t' I I d d t mcen 1ve eves an a man a e. Location Program Offered Particip_ation Rate Reference RESPONSE TO FREE OR ALMOST FREE ITEMS Seattle, WA Seattle City Light CFL 57% ofhomes Tachibana and Brattesani Giveaway Program responded to free CFL (2003) offer and installed Los Angeles, Energy and Water Blitz 58% ofhomes visited in ACEEE 1994 Summer Study CA [L.A. Dept. of Water and low income areas; much on Energy Efficiency in Power, 1991] lower in higher income Buildings Proceedings, pg. neighborhoods 1.145 Vermont Multifamily low-income 20% 30% of existing Efficiency Vermont incentives provided for stock; Multifamily Low-Income comprehensive energy >90% ofnew Program efficiency upgrades construction www .efficienc)'Yermont.com/ oro!!rams/reeo.htm RESPONSE TO INCENTIVES FOR MORE EXPENSIVE UPGRADES Wisconsin Home Performance with 1% (15,000 out of 1.4 ACEEE Americas Best E-Star million homes) Energy Efficiency Programs: -Targeting existing Wisconsin Energy homes through point-ofConservation Corporation sale education, rebates, www. focusonenergy.com and low interest financing United States Residential Conservation 2.8% (5 .6% of eligible Hirst, E. (1984) Service -Utility offered homes received free 116 I I

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audits with no or lowaudits, and 50% of these interested loans completed upgrades) MANDATE AT TIME OF SALE Berkeley, CA City ordinance mandating 89% -(1988 of 2225 Personal communication, certain energy efficiency homes eligible for RECO Alice La Pierre (2007) upgrades at time of sale requirements (2003 06) Program design (whether mandate or voluntary; level of rebates or incentives, etc.) has been shown to have significant impact on the participation rates of climate actions and thus on overall effectiveness of these programs. Cities, however, do not often have access to compiled quantitative data on the participation rates typically observed for different climate actions. Other entities, particular utilities and either federal or state governments, do compile some participation rate data through many of their programs. Only recently has ICLEI in coordination with the U.S. Environmental Protection Agency developed the Clean Air and Policy and Planning Assistant (CAPPA) to compile some of this information for cities. However, participation rate data is largely lacking in the current version ofthis tool (ICLEI, 2008b). Table 5-3 summarizes the building sector climate action participation rate data gathered by these different entities. Greater coordination with utilities and regional governments, while enhancing tools like ICLEI's CAPPA will be critical in assisting cities with achieving their climate stabilization goals at the local level. The GHG impacts of building sector actions over a range of estimated performance and participation rates are discussed next. Tabl 5 3 8 'ld' e -U1 mg sector c Imate actiOn participation ra e r t d ata avai a e >yen I J 'I bl b n Participation Rate Data Notes Sources Utilities Many utilities maintain various levels of quantified participation rate data to track program performance Federal and State State agencies maintain varies levels of Governments quantified participation rate data to track 117 I I

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program performance City Governments Cities often do not have participation rate data ICLEI's CAPPA tool First generation attempt to compile performance and participation rate data for numerous climate actions for cities 5.5 First-Order Quantitative Assessment of Building Sector Actions: Denver, CO The overall effectiveness ofthe 10 building sector action items listed in Table 5-1 were modeled over a range of performance and participation rates for Denver, CO over a 10-year period (there are nine action items listed in the table, but energy efficient building codes and design were evaluated individually for commercial and residential buildings). Two scenarios were modeled for each action: 1) A low level estimate (Low) of performance (energy use savings per intervention) and participation rates; 2) A high level estimate (High) ofperformance (energy use savings per intervention) and participation rates; The combined impact of all 10 actions was compared with business as usual (BAU) with and without a simulated 20% renewable portfolio standard (RPS), which resulted in a 20% reduction in the electricity emissions factor. BAU energy use was projected into the future based on previous energy use trends. Energy use data for Denver was gathered from Xcel Energy and is reported in Denver's GHG inventory (Ramaswami et al., 2007; see also Chapter 2). The annual growth rates for electricity and natural gas consumption for the residential and the commerciaVindustrial sectors are summarized in Table 5-4. 118

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Table 5-4 Energy use annual growth rate projections for the business as usual (BAU) projection. Annual Growth Rate (%) Electricity Natural Gas Residential 2% 0.7% Commercial/Industrial 0.9% 0.8% The performance and participation data used to complete the first-order quantitative assessment ofthe GHG impacts ofbuilding sector actions in Denver, CO is discussed next. The assessment includes building actions from each of the three broad categories of climate actions shown in Table 5-1: efficiency measures for existing and new buildings; renewable energy; and behavior change. The range of performance estimates and participation rates used to create the low and high scenarios for the GHG mitigation assessment are summarized in Table 5-6 and Table 5-7. 5.5.1 Energy Efficiency Programs in Existing Buildings This section summarizes the energy efficiency measures for existing buildings that are analyzed in this assessment. The performance and participation rate data used in this assessment are summarized for each measure. The measures include: Promoting utility demand-side management (DSM) programs; Energy efficiency upgrades made to homes at time of sale; Energy audits with low interest loans provided; Compact fluorescent lamp (CFL) promotion; and Low-income energy blitzes. Promoting Utility DSM Programs Demand-side management (DSM) programs have been used by electric and gas utilities since the 1970s to modify customers' energy use patterns (Eto, 1996). The 119

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modified energy use pattern may involve both: shifting energy use from peak periods of the day to reduce "demand" (reducing the instantaneous power demand on the grid); or reducing the amount of energy consumed over a period of time (through energy efficiency or energy conservation programs). Implementation ofDSM programs and the evaluation of their effectiveness has proven to be challenging, and has shown mixed results (Wilson et al., 2008; Geller et al., 2006; Gillingham et al., 2006). Xcel Energy provides electricity and natural gas to nearly all the customers located in Denver (as well as a service territory that extends far beyond Denver). Xcel offers numerous DSM programs that aim to reduce the electricity and natural use of its customers (KEMA, 2006). Every few years, Xcel presents its electricity and natural gas DSM goals (which include program costs to achieve these goals) to the Colorado Public Utilities Commission (PUC). The most recent DSM report presented to the Colorado PUC by Xcel Energy, the "2009/201 0 Biennial Demand-Side Management Plan," was used to develop the estimated energy reductions associated with DSM program promotion in Denver (Xcel Energy, 2008). This represents overall DSM program effectiveness, i.e., combining performance and participation rate estimates to determine the overall energy impacts of the DSM programs. Xcel's program goals are to achieve up to 1,000 GWh of electricity savings and 15 million therms of natural gas savings over a 10-year period (Xcel, 2008). Denver however only represents about 20% ofXcel's service territory in Colorado and therefore the low overall program effectiveness estimate for savings in Denver was set to 20% ofXcel's overall projected savings (i.e., 200 GWh and 3 million therms of natural gas). The high program effectiveness estimate is based on doubling the amount of savings, or 40% of Xcel 's goal, that is realized in Denver ( 400 GWh and 6 million therms ofnatural gas). 120

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Time of Sale Energy Efficiency Improvements Over two decades ago, the cities of Berkeley and San Francisco, CA implemented what they call residential energy conservation ordinances (RECO). These ordinances require that a home being sold must be audited to ensure that a checklist of basic energy and water efficiency improvements are installed in the home (San Francisco, 2007; Berkeley, 2006). The upgrades included in this modeled program are similar to the RECOin Berkeley, and include: water efficient fixtures (toilet, shower heads and faucet aerators); insulation around the water heater and its associated piping; furnace ductwork sealing and insulation; attic insulation; and basic weatherization (weather stripping on exterior doors, some exterior sealing and chimney dampers). Although similar programs do exist for commercial buildings as well, only the impacts of energy efficiency upgrades at the time of sale in homes were evaluated here. Energy savings estimates for these upgrades are based on a combination of estimated savings reported by the City of Berkeley (La Pierre, 2008) and an impact evaluation report for the Energy $avings Partners program, which targets low-incoming housing in Colorado (Blasnik, 2006) largely using the very same energy efficiency actions in the RECO. The impact evaluation report found that completing basic weatherization, installing attic insulation, installing a high efficiency furnace and compact fluorescent lighting produces average overall energy savings around 25% for the low-income Colorado homes (Blasnik, 2006). Although the upgrades are similar for this modeled time of sale energy efficiency measure, a new furnace is not include and therefore energy savings are going to be lower. The modeled electricity annual savings for the low and high performing scenarios for this measure are 3% and 6%, respectively. The modeled annual natural gas savings for the low and high performing scenarios are 5% and 15%. 121

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Annually, approximately 13,000 residences-or about 5 percent of the total housing stock-are sold each year in Denver, CO. For example, 9,000 single detached homes, and 4,000 condominiums were sold in 2006 alone (Metrolist Inc., 2007). A trend of Denver single detached home sales data from 2003 to 2006 is shown in Figure 5-5. If this data trend were extrapolated out, up to 36% of Denver's homes may be accessed at time-of-sale in the 2005-2015 time span, although it is recognized that some homes could be on sale more than once in a 10 year period (see Figure 5-5). Ql E 30,000 0 :::1: Ql u Ql en Ql 0'1c ca (i)C/J 0 ... Ql ,g E ::I z a 0 1-25,000 20,000 15,000 10,000 5,000 2003-2004 2004-2005 Projected access to sale-homes from 2005 to 2015 time period is 90,000 homes (> 36% of Denver homes), based on previous annual trends, though some homes may go on sale more than once in 5 years. 2005-2006 Figure 5-5 Cumulative number of home sales from 2003 to 2006 (Metrolist, Inc. 2007). In addition to the number of home sales each year, this first-order assessment incorporates the percentage of these homes that implement the measure, i.e. the participation rate. The low and high participation rate scenarios modeled represent the range of an entirely voluntary program to a mandated city ordinance. Based on the participation rate data presented in Table 5-2, the low participation rate estimate is assumed to be similar to other national programs that are voluntary and require more 122

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expensive upgrades or around 2%. The high penetration rate scenario is based on experience from Berkeley, CA where 89% of the applicable homes actually made the upgrades (La Pierre, 2007). The high penetration rate modeled here is 89% ofDenver for sale homes over a 10-year period, i.e., 32% of Denver homes(-90,000) are projected to be accessed by a mandated time of sale program. Energy Audits with Low Interest Rate Loans This program is designed to offer energy audits for homes to identify the measures that would provide the greatest energy and cost savings, while also providing a low interest rate loan to implement the measures. The energy savings potential of this program is highly variable because it is dependant on the measures that are actually installed. The measures would likely include many of the items in the time of sale energy efficiency upgrades but would also likely incorporate new appliance and furnace upgrades. The performance estimates for this program were expanded from the time of sale estimates to incorporate these additional measures. As a result the low and high performance estimates modeled for this program were 5% and 20%, respectively, for overall energy use savings (both electricity and natural gas). This program is a voluntary program with incentives for more expensive upgrades than offering free or low cost items. Based on the range of participation rates shown for similar programs in Table 5-2, the low participation rate scenario modeled for this program is 1% of homes conducting an audit and implementing the measures. The high participation rate scenario modeled assumes a rate higher than was exhibited by a national program similar to this in the 1980's where approximately 6% ofthe homes opted to receive a free audit, but then only 50% of those actually implemented the recommended measures (Hirst, 1984). As a best case scenario, the high participation rate scenario modeled is 6% of the homes get audits and implement the measures. 123

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CFL Promotion This program is modeled after a CFL give-a-way in Seattle, W A where mailers were sent to all residents offering them coupons for a few free CFLs. An evaluation of this program showed that 57% of the homes responded to get the CFLs (Tachibana and Brattesani, 2003). In addition, participant surveys reported that 90% of additional CFL purchases were influenced "a little" or "a lot" by the initial free CFLs, resulting in likely more than two CFLs being installed in many households as a result of this program (Tachibana and Brattesani, 2003). The low and high performance estimates modeled for this CFL promotion program are based on a household installing two or five CFLs (that are assumed to be used about 3 hours per day) which achieve annual household electricity use savings of99 kWh and 246 kWh, respectively. The participation rates modeled in this analysis have been set to achieve rates slightly better than the Seattle CFL give-a-way (see Table 5-2) on the high end and roughly half on the low end. The low and high participation rate scenarios for homes installing CFLs are 30% and 60%, respectively. Low-Income Energy Blitzes This program involves targeting low-income households (typically neighborhoods) and going door-to-door offering a few basic energy and water efficiency upgrades and resource conservation education materials (SWEEP, 2005). The energy and water efficiency upgrades included in this program are very similar to those in the time of sale energy efficiency upgrades, except it doesn't involve installing new toilets or attic insulation. Based on the energy savings reported for this measures in other studies (Berkeley, 2006; Blasnik, 2006; SWEEP, 2005) the low and high performance scenarios for this program are modeled as 3% and 10% of overall energy use (both electricity and natural gas), respectively. 124

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Voluntary approaches offering nearly free products, like CFL give-a-ways or low income energy blitzes can have participation rates over 50 percent; see Table 5-2 (ACEEE, 1994; SWEEP, 2005). However, low-income energy blitzes evaluated in Colorado by the Applied Public Policy Research Institute for Study and Evaluation exhibited participation rates for the installation and retention of energy efficiency measures of 10% to 30% (Carroll, 2006). Combining this information, the low and high participation rate scenarios modeled in this analysis for qualifying low income homes are 10% to 40%, respectively. The number of low income homes has been estimated as 15% of the total housing in Denver or 36,000 homes (15% of240,000 homes) [Denver Facts, 2006]. 5.5.2 Energy Efficiency New Building Programs The building energy efficiency programs for new construction modeled in this analysis include LEED energy performance standards for new commercial construction (both LEED Silver and LEED Gold-Platinum ratings) and Energy Star for new residential construction. Numerous studies have analyzed the performance, or energy savings, of LEED certified commercial buildings compared to buildings built to code (Turner and Frankel, 2008; Kinney et al., 2003; Diamond et al., 2006). The most comprehensive study on LEED building energy performance was completed by the New Buildings Institute. They tracked energy use and the modeled performance of 121 (or 22%) of the 552 LEED certified buildings up through 2006 (Turner and Frankel, 2008). The energy use intensity (EUI) of these buildings was compared to the national average commercial building EUI that is reported in the Commercial Building Energy Consumption Survey. This study found that the median EUI for LEED Silver and LEED Gold-Platinum buildings was 32% and 44% lower than the national average, respectively. Actual energy savings are a function of the 125

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current building codes, the size of the building and the building operation. Therefore, a low and high performance scenario was modeled to account for ranges in actual energy savings. The low and high scenarios for commercial buildings built to LEED Silver standards for energy performance on an energy use intensity basis (both electricity and natural gas) was 10% and 35%, respectively. The low and high scenarios for commercial buildings built to LEED Gold-Platinum standards were estimated to reduce EUI (both electricity and natural gas) by 10% to 50%. Energy Star for new residential construction requires at least 15% better energy performance than the 2004 International Residential Code (IRC), and is reported by the Environmental Protection Agency to often perform 20% to 30% better than the IRC (ENERGY STAR, 2008). Based on these reported estimates, the low and high scenarios for energy performance of Energy Star new residential construction are modeled to reduce energy use (both electricity and natural gas) by 15% and 30%, respectively. To quantify the GHG impacts of these new construction programs, the amount of new commercial and residential construction must first be assessed. According to the Denver County Assessor, approximately 2.6 million square feet (about 1% of existing commercial building space) were being built annually in 2005. In addition, that year approximately 2,600 homes (slightly greater than 1% of existing residential buildings) were being built (Thomas, 2007). Next, the amount of this new construction (or participation rate) that meets these building performance requirements must be evaluated. These energy efficient building guidelines can be implemented through codes and regulations, or through voluntary approaches. The range in participation rates modeled for the low and high scenarios represents the difference between voluntary 126

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and mandatory (codes and regulations) programs. An analysis ofthe impacts ofnew building codes in Colorado and other southwestern states was completed by the Southwest Energy Efficiency Project (SWEEP) in 2003. This analysis reported that new energy efficiency requirements through building codes would only be met in about 90% of the new buildings built in the first 1 0 years after the codes are adopted (SWEEP, 2003). There is a lag reported due to the need to educate building officials and builders to the new requirements that result in less that 100% of the buildings meeting energy efficiency codes immediately. Thus, the high participation rate scenario modeled, which represents new building codes, is 90% of new construction over the 10-year model period for both residential and commercial construction. In Denver, building commercial buildings that are LEED certified or homes that meet requirements for Energy Star is entirely voluntary. Therefore, the existing amount of new construction that is either LEED certified (for commercial buildings) or built to Energy Star standards (for residential buildings) was used to determine the participation rate for the low participation rate scenario. According to Building Colorado Coalition, there are 7 LEED certified buildings in Denver; two are LEED Certified, one is LEED Silver, and 4 are LEED Gold (BCC, 2008). The exact dates of these buildings' construction was not obtained, but assuming that this construction occurred over the last four years means that roughly two new commercial buildings in Denver are LEED certified a year. This analysis assumes that one LEED Silver rated building and one LEED Gold rated building are built a year in Denver. According to a recent analysis ofthe energy performance of 121 LEED certified commercial buildings, the average building size is around 50,000 square feet (Turner and Frankel, 2008). Given that 2.6 million square feet of commercial space is being built annually, a 50,000 square foot LEED certified building corresponds to roughly 2% of the new commercial construction. The low participation rate scenario modeled for both, LEED Silver and LEED Gold buildings was 2% of new commercial construction. 127

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Data was not available on the amount of new residential construction in Denver that meets Energy Star for homes specifications. Therefore, this analysis assumed a low scenario participation rate of 2% of new residential construction meeting Energy Star specifications. 5.5.3 Renewable Energy This section summarizes the renewable energy measures analyzed in this assessment, which includes: purchasing renewable credits; and the use of solar thermal panels for water heating. The performance of each of these measures is presented first, followed by a discussion of the participation rates modeled in the low and high scenarios. A brief discussion on why building photovoltaic electric systems were not analyzed for Denver is included. Renewable energy purchases can imply a number of things, but in this analysis, it represents Green-e certified renewable energy credits purchased by the consumer from a program such as Xcel's Windsource. The impact of these purchases is a reduction in GHG emissions that is equivalent to the grid emissions factor (g C02e/kWh) for every unit of electricity consumed (kWh), and for the case of Denver, CO is equivalent to 1.75 lb-C02 e/kWh. This emissions factor is fixed and therefore there was no low and high performance scenario modeled for renewable energy purchases. Solar thermal panels for water heating, despite experiencing a dip in the number of new installations over the last two decades, is expected to be used as a means of reducing the GHG impact of electrical or fossil fuels based water heating more in the future (Hillman, 2004). The performance of solar thermal systems is dependant on 128

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the solar resource and is affected by the amount of hot water consumed, thus great variability is observed throughout the U.S. A performance analysis was completed as part of a larger feasibility study for the specific case of Denver, CO, which found that a solar thermal system can be easily designed to meet 50% to greater than 90% of the hot water demand of a building (Hillman, 2004). Water heating comprises about 20% of the average home's energy use (EIA, 2009). Assuming the solar hot water system meets either half or nearly the entire water heating load, the expected natural gas savings for the low and high performance scenarios are modeled to be 1 0% and 20%, respectively. Cities often pursue the use of solar electric (PV) panels on buildings in their cities. The use of PV panels can have numerous other benefits, including peak demand shaving and a form of distributed generation. However, the common assumed benefit of reducing electricity consumption from the grid (unless the building is off the grid) for the purpose of reducing GHG emissions can not be included as a full GHG reduction benefit if the utility purchases the renewable energy credits (REC) from the PV system. This is the case for many utilities in the U.S. who pay rebates to building owners to incentivize the installation ofPV systems. In the case ofXcel Energy (the utility that serves Denver, CO), the REC's are purchased by Xcel and therefore electricity produced by the PV system is included in Xcel's grid mix and thus cannot also be accounted as a GHG reduction option by the building owner or city, as this results in "double counting". As a result, the use ofPV purchased with Xcel rebates is not included in this analysis because the electricity generation cannot be claimed as a GHG reduction measure for Denver. Participation rates for purchased renewable energy and solar thermal installations are typically low, yet the renewable energy purchases do vary across utilities. Purchased renewable energy through Xcel's Windsource program in Denver, CO is summarized 129

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in Table 5-5. The amount of electricity purchased through the Windsource program is 0.2% and 1.4% of the total electricity sales for the commercial/industrial and residential customers, respectively. A summary of participation rates from other utility renewable energy purchase programs show participation rates as high as 14 percent (Bird and Brown, 2006). For this analysis, the amount of electricity purchase through the program was used to estimate GHG emissions reductions. The range of participation rates in renewable energy purchases was based on a doubling of the existing participation rates to 0.4% and 3% for commercial/industrial and residential electricity purchases, respectively (see Table 5-7). T bl 5 5 P a e -fi x 1' w d articipatlon rates or ce s m source p n rogram m enver, co. 2005 In Percentage of Customers Percentage ofTotal Electricity Participating in Xcel's Consumption that is Windsource Winsource Program (Total Windsource Electricity) (#of Customers) Denver, CO C&I0.03% (112) C&I0.2% (8.2 million kWh) Res.-3.4% (8,476) Res.-1.4% (24 million kWh) Existing data on solar water heating installations is lacking so PV installations were used as a proxy for setting the participation rate scenarios for the solar hot water measure. Xcel Energy reports that roughly 132,000 kWh were produced by on-site residential PV in 2005 (West, 2007). Using rough estimates of the average size and performance ofPV systems, this corresponds to approximately 0.03% of Denver's residences installing PV systems. Assuming that solar thermal installations on homes have a similar penetration rate to PV systems, the low and high participation rate scenarios for solar hot water installations on homes have been modeled as 0.03% and 1%, respectively. 130

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5.5.4 Behavior Change Energy savings resulting from the installation of in-home energy displays has been documented in numerous case studies (Darby, 2006; Dobson and Griffin, 1992). Based on the ranges of electricity savings reported in these studies, the low and high performance scenarios for electric energy savings through the use of in-home energy displays have been modeled as 5% and 10%, respectively. There are numerous variables that affect the ultimate performance of this measure, but one key factor contributing to the success of this measure is the use of a clearly defined goal-in terms of energy reductions-that residents can aim for (Houwelingen and Raaij, 1989). Currently there is not published data on the participation rates of programs offering in-home displays. Numerous studies have documented performance, but to-date there have been no studies tracking participation rates outside of study sample populations (Darby, 2006). This analysis models the range in impact of 1% of all homes to 100% of new homes (assuming there was a mandate requiring these in new construction) using the in-home energy displays (see Table 5-7). 5.5.5 Summary The range of performance estimates and participation rates used for each of the climate actions used in this analysis are summarized in Table 5-6 and Table 5-7, respectively. The Low scenario is based on the low performance and participation rate estimate, while the High scenario uses the high estimate for each. Energy interactions among the climate actions were not considered in this analysis. This first-order analysis is meant to demonstrate scale of GHG impact across reasonable 131

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ranges of performance and participation rate estimates for these building sector measures. 132

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Table 5-6 Range of performance estimates used to create the Low and High scenarios for the GHG mitigation assessment for building sector climate actions. Climate Action Range of Savings Important Considerations Source Commercial energy efficient 10%-35% Energy savings depend on Turner and Frankel, 2008; (EE) building codes and design (from current code; on an energy current building codes, building Kinney et al., 2003; (LEED Silver energy equivalent) use intensity basis) size. Diamond et al., 2006 EE building codes and design 10%-50% Energy savings depend on Turner and Frankel, 2008; (LEED Gold and Platinum (from current code; on an energy current building codes and Kinney et al., 2003; energy equivalent) use intensity basis) building size. Diamond et al., 2006 EE building codes and design 15%-30% Energy savings depend on ENERGY STAR, 2008 Residential; EnergyStar) (from current code) current building codes and building size. Promote Utility Demand Side 200 GWh to 400 GWh in total 20% ofXcel's DSM savings Xcel Energy, 2008; Management (DSM) Programs electricity savings, and ( 1000 GWh of electricity KEMA, 2006 Efficiency Measures [Peak power and conservation] 3 million to 6 million therms of savings and 15 million therms of natural gas saved natural gas over 1 0 years) are expected to occur in Denver; this could be doubled with additional promotion. Time of Sale Energy Efficiency 3% to 6% electricity energy Includes most basic Blisnik, 2006 Upgrades savings, and 5% to 15% natural weatherization and insulation gas savings upgrades. Energy Audit with Low Interest 5% to 20% overall energy savings Dependant on measures installed Hirst, 1984 Loan Compact Fluorescent Lamps 2% to 4% electrical savings Replacement of two to five Tachibana and Brattesani, (CFL) Promotion lightbulbs per home 2003 Low-Income Energy Blitz 3% to 10% overall energy savings Dependant on measures installed SWEEP, 2005 Purchase renewable energy 1.75 lb-C02e per kWh purchased Generation must not be included WindSource and Xcel's Renewable Energy in the grid mix grid mix Solar thermal 10% to 20% natural gas savings Dependant on system size; Hillman, 2004 displaces water heating load. Behavior Change In-Home Energy Displays 5% to 10% electrical savings Setting a goal helps. Darby, 2006 133

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Table 5-7 Range of participation rates used to create the Low and High scenarios for the GHG mitigation assessment for building sector climate actions. Range of Participation Considerations Source Rates Promoting green building 2% to >90% of new Of new buildings SWEEP, 2003 to Updating building construction codes Promote Utility Demand 20% to 40% ofXcel's Based on Xcel's DSM programs Xcel Energy, 2008; Side Management (DSM) DSM goals being implemented in Denver KEMA, 2006 Programs Time of Sale Energy 2% (voluntary From voluntary program (2%) to La Pierre, 2007 Efficiency Efficiency Upgrades program)to 89% ordinance (89%), based on for sale Measures (mandatory) ofhomes for homes in Berkeley, CA (2001sale 2003) Energy Audit with Low 1% to 6% Typically low participation rates Hirst 1984 Interest Loan Compact Fluorescent 30%-60% ofhomes Based on both homes that take Tachibana and Lamps (CFL) Promotion bulbs and replace them Brattesani, 2003 Low-Income Energy 10% to 50% Great variability in participation Carroll, 2006; SWEEP, Blitz rates 2005 Purchase renewable 0.2% to 0.4% of A doubling ofRE purchases is West, 2007; Bird and energy commercial electricity; modeled. Other utilities have Brown, 2006 Renewable and 1% to 3% of reported up to 14% participation Energy residential electricity rates. Solar thermal <<1%to 1% Solar water heating sees vary low Hillman, 2004 participation rates Behavior In-Home Energy 1% of all homes up to Primarily pilot studies performed at Darby 2006 Change Displays 1 00% of new homes this point. 134

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5.6 ResultsOverall Effectiveness of the Building Sector GHG Mitigation Options GHG emission reductions associated with each of the 10 building sector actions over the range of performance and participation rates (see Table 5-6 and Table 5-7) is shown in Figure 5-6. The large variation shown in the first three actions represents the expected difference between energy efficient building practices that are either promoted through voluntary programs (low estimate) or implemented through updated building codes (high estimate). Utility DSM exhibited the greatest GHG emissions reduction potential of all the building sector actions, even with the low estimate for program effectiveness (20% ofDSM goals are achieved in Denver) being higher than all other actions except for a commercial building code at the energy performance level of LEED Gold or Platinum. Energy efficient upgrades at time-of sale and in-home energy displays also exhibited large variation in estimated GHG reductions due to the modeled difference between voluntary programs (low participation estimate) and mandatory programs (high participation estimate). 135

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G) Commercial Residential s 400,000 '-T-----T' .......................... ......... i -----....___ ___ __, ................................... 1 Col 350,000 -----------t--------I -----------------J e 3oo.ooo :----------------------250,000 .,.. : --200,000 i J -----"---------4 I -t-i -==--+-:=----0 C) :I: C) .... 0 Q) C) c::: CIS Figure 5-6 GHG emissions reductions for 10 building sector actions over a range of performance and participation rate estimates. The GHG impacts for the four scenarios (BAU, Low Effectiveness, High Effectiveness and BAU-RPS) are shown in Figure 5-7. Under the BAU scenario, GHG emissions from buildings are expected to increase 11% over 2005 emissions. The low range of performance and participation rate estimates did not keep up with projected growth from 2005 to 2015. 2015 GHG emissions under this scenario were 8.1% greater than the 2005 emissions. The highest estimates of performance and participation rates achieved a 1.3% reduction of GHG emissions in 2015 compared to 2005 emissions from the buildings sector. The final scenario, a simulated 20% RPS that combined the BAU energy use case with a 20% reduction in the electricity emissions factor, resulted in the greatest estimated GHG emissions reduction of 5% from 2005 emissions. 136

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Building Sector Greenhouse Gas Emissions Projections to 2015 in Denver, CO G) 9.0 ---N 0 u 8.5 ,.!. I E 1: 0 8.0 BAU Low Net Effectiveness Actions != -. 7.5 :::::--=:::-::::: =---=---=---=-====: Effectiveness Actions .Q BAU-RPS 1/) 7.0 1/) e 6.5 J: I C) 6.0 t---------------2005 2015 Figure 5-7 Building sector GHG emissions projections based on different scenarios for Denver, CO out to 2015. 5. 7 Observed Outcomes Portland, OR and Boulder, CO In this section, the building energy use and building sector GHG emissions trends for two U.S. cities-Portland, OR and Boulder, COare analyzed and compared against the estimates in the first order analysis. These two cities have been pursuing building sector climate actions for a number of years and thus provide a good benchmark for what has been accomplished at the city-scale in terms of building sector energy use over time. Electrical and thermal energy use for Portland, OR over a 15-year period is shown in Figure 5-8 and Figure 5-9, respectively. Electrical and thermal energy use for Boulder, CO over a 15-year period is shown in Figure 5-10 and Figure 5-11, respectively. For both cities, residential and commercial energy use (both electrical 137

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and thermal) has increased since 1990. Residential and commercial trends vary from still increasing to potentially leveling off or declining. This data has not been weather adjusted, however, which may impact these general trends. Industrial electrical energy consumption appears to have declined for both cities relative to 1990 levels. The reasons for this have not been identified here, i.e. whether these reductions are associated with efficiency gains or a decrease in industrial activity in these cities, or both. Looking at a 10-year period (the same time period covered in the first order analysis) from 1995 to 2005, Portland's building sector electrical energy use decreased 1%. Portland's building thermal energy use increased 8% over that same period (see Figure 5-8 and Figure 5-9). Boulder's building electricity use from 1995 to 2005 increased 16%, while the building's thermal energy use decreased 21%. Electricity Use in Portland, OR -.c 3: 4,500 (!) 4,000 -----==-1 Cl) 3,500 en :::J 3,000 ----------+-Residential 2,500 0 2,000 :s 0 1,500 ..9! 1,000 w ---Commercial __.__ Industrial s 500 0 I1990 1995 2000 2005 Figure 5-8 Electrical energy use by sector in Portland, OR 138

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i --------Thermal Energy (Natural Gas, Fuel Oil, etc.) Use in Portland, OR Cl.l tn ::> 120 >.._ ttn 100 E 1: Cl.l 80 60 C'CI 1: E o 40 ... -Cl.l= J: -20 1E --+--Residential -----Commercialj --+---Industrial s 0 1-1990 1995 2000 2005 Figure 5-9 Thermal energy use (natural gas, fuel oil, etc.) by sector in Portland, OR. Electrical Energy Use -Boulder, CO 1990 1995 2000 2005 --+--Residential ------Commercial --+---lnd ustri a I Figure 5-l 0 Electrical energy use by sector in Boulder, CO. 139

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Thermal Energy Use (Natural Gas) -Boulder, co 50 Q) rn :J (i) 40 :5 30 w c ns .2 20 e Q) .. 10 .c 1-0 1990 -+--Residential -Commercial ---...-Industrial 1995 2000 2005 Figure 5-11 Thermal energy use (natural gas) by sector in Boulder, CO. The building energy use for these two cities was converted to GHG emissions so that GHG emissions trends could be analyzed against the range of projections from the first order analysis. Portland and Boulder's building sector GHG emissions increased 1% and 4% over the time period from 1995 to 2005, respectively (see Figure 5-12 and Figure 5-13). These trends fall within the range ofmodeled GHG emissions changes from the first order analysis. The low net effectiveness actions modeled in the first order analysis resulted in an 8.1% increase in building GHG emissions, while the high net effectiveness actions were modeled to achieve building GHG emissions reductions of 1.3% over a ten year period. The observed energy use and GHG emissions trends of buildings in Portland, OR and Boulder, CO help validate the first order analysis as being representative of the potential GHG emissions impacts of building sector climate actions. 140

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Building Sector GHG Emissions Portland, OR rn 5.0 c .2 G) 4.8 ana rn 4.6 --------------; E = g 4.4 ::1= o= E c-4.2 Q) C) 4.0 1995 2000 2005 Figure 5-12 Building sector GHG emissions from 1995 to 2005 in Portland, OR. rn c .2 "'-.! (I) Building Sector GHG Emissions Boulder, CO 1.5 1.3 ana A n 1.1 VI :;!. E Q) c rn o ::s:.: o= E c Q) C) 0.9 0.7 0.5 1995 2000 2005 : Figure 5-13 Building sector GHG emissions from 1995 to 2005 for Boulder, CO. 141

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5. 8 Conclusions Overall energy use and GHG emissions in the building sector at the national level (which comprise 36% of the country's C02 emissions (USGBC, 2003)) have gone up in the U.S. despite considerable efficiency gains. A first-order quantitative assessment of the effectiveness, combining performance and participation rates, of 10 common building sector climate actions has been completed through a case study of Denver, CO. Results show that the upper estimates for performance and participation rates for all10 climate actions achieved a 1.3% reduction ofGHG emissions in 2015 compared to 2005 emissions. Only the simulated 20% RPS resulted in a substantial GHG reduction of 5.1% compared to the 2005 levels. These results indicate that substantial increases in participation rates along with energy supply-side programs to reduce carbon intensity will be required to achieve long-term GHG reduction goals. Building sector GHG mitigation options exhibit a range of performance and participation rates, while quantitative outcomes assessment data of both is lacking. To enhance climate action policy development and implementation at the city-scale, it is recommended that cities track and report the performance and participation rates of their climate actions. In addition, cities should pursue greater coordination with utilities and state government agencies to evaluate the effectiveness of climate actions at the city-scale. Finally, the prioritization of climate actions at the city-scale, which must incorporate many factors (including but not limited to cost, payback, and community perceptions) should be a focus of future research as many cities are now faced with developing and implementing climate action plans to reduce GHG emissions. 142

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6. Overall Conclusions and Recommendations Over 850 U.S. cities have made public pledges to reduce GHG emissions and as a result are either in the process or will begin the process of completing GHG inventories and developing climate action plans to mitigate GHG emissions. However, there is no standardized methodology for conducting city-scale GHG inventories. In addition, GHG accounting for individual cities is confounded by spatial scale and boundary effects that impact the allocation of regional material and energy flows and their associated GHG emissions. This research has developed and benchmarked a demand-centered, hybrid life cycle based methodology for conducting GHG inventories for US cities. The hybrid approach accounts for direct GHG emissions associated with direct energy use within the city as well as the indirect GHG emission associated with the embodied energy of producing key urban materials. This inventory methodology represents an attempt at developing a holistic, demand-based GHG inventory method at the city-scale that is consistent across scale with supply-side national GHG inventory inclusions, and that is appropriate and easy to use by cities in the U.S. As part of this demand-centered methodology, a new approach to the spatial allocation of surface and airline transport among co-located cities has been presented and analyzed. The demand method presented here employs the regional transportation demand models ofMPO's to spatially allocate both surface and airline transportation GHG emissions among co-located cities in metropolitan areas. First, this spatial allocation method has been applied to six metropolitan areas throughout the U.S. demonstrated that: 1) the demand method produces VMT estimates over the entire commutershed that are similar to the traditional, boundary limited polygon 143

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approach (within 6%); 2) airline travel emissions allocated among co-located cities produced similar results to regional population ratios, with local variation observed among 33 cities studied; and 3) the method is replicable with necessary data available to all cities in this study through the corresponding metropolitan planning organizations. Second, a detailed analysis of 27 communities within the Denver metropolitan area (DRCOG) demonstrated the ability of the demand method to be sensitive to local travel demand among communities throughout a commuter-shed. Daily VMT per capita estimates ranged from 8 VMT/capita!day to over 80 VMT/capita!day among the 27 communities, with a strong positive correlation with employment intensity (employment/capita) and employment density. Finally, the demand method has been demonstrated to have much greater ability to track mode shift over time, which has significant policy and program evaluation implications particularly for regional mass transit efforts. The demand-centered GHG inventory methodology first implemented in Denver, CO has now been applied to a total of eight U.S. cities. This analysis has shown that the inclusion of key urban materials and airline travel is technically feasible, significantly impacts GHG accounts, and provides a robust accounting method at the city-scale that enables consistency across scale. Data was readily available to complete the demand-centered GHG inventory, however some limitations do exist in terms of access to annual data for some of the key urban materials identified (such as cement and food consumption). It is recommended that these sectors be analyzed further to determine impacts of locally relevant data versus nationally aggregated figures. The key urban materials and airline travel inclusions are significant. These inclusions amounted to 31% of the cities' overall GHG inventory on average. Finally, this method showed consistency across scale with five of the eight cities converging within 16% of their respective statewide per capita estimates. The 144

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remaining three cities' divergence from their statewide per capita estimates can be explained by their anomalous situations. The per capita GHG emissions for Arvada, CO were below that of Colorado's because Arvada has much less commercial and industrial activity in the city than the other cities, which not only impacts building energy use but also results in lower vehicle GHG emissions because not as many individuals are commuting into the city for work. The per capita GHG emissions for Austin, TX were much lower than the state of Texas because Texas exhibits a uniquely high per capita GHG emissions figure due to the disproportionately high fuel refining and distribution facilities located in the state. Finally, despite Seattle, WA exhibiting per capita GHG emissions consistent with Portland, OR (a comparable city in the same geographic region), the emissions were above those reported by the state of Washington due to what appears to be anomalous data gaps in Washington's reported GHG emissions. As cities explore GHG inventories that encompass WRI's scope 3 (upstream indirect emissions of materials), it will be important to acknowledge and to become aware of potential double counting. Based on the functionality of cities, this methodology includes tracking of a few key urban materials for which processing facilities (e.g. oil refineries, cement plants, water treatment facilities) are easily recognized; production of these key materials within city boundaries can be readily identified to avoid double counting across direct and indirect categories. If large such production facilities are present, allocation based on local demand could be applied (as in the case of large regional airports). To achieve consistency, cities must agree on a common list of key urban materials. Local-scale versus national-scale LCA-based GHG emissions factors for materials may also be an important consideration, although the major materials food and fuel-are typically drawn from large distances. This is an area for future research. 145

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This research has also demonstrated the importance of using benchmarks to assess the quality of information captured in a city-scale GHG inventory. Sector-specific per capita resource consumption and travel demand benchmarking is critical in understanding GHG footprints. Cities are thus encouraged to include numerous benchmark metrics with their inventory to enhance the characterization of emissions and tracking of GHG mitigation efforts. For example, it is recommended that in addition to the overall communitywide per capita GHG emission benchmark, cities should track and report sector-specific per capita, per household, per building area and possibly per economic activity metrics to enable better characterization of energy use and GHG emissions in cities. This will aid cities in making comparisons with regional and national data as well as enabling better outcomes assessment of programs. This analysis has demonstrated that the data is available to both generate and report expanded GHG inventories and consumption benchmarks. Finally, a first-order quantitative assessment of the effectiveness, combining performance and participation rates, of 10 common building sector climate actions has been completed through a case study of Denver, CO. Results show that the best estimates for performance and participation rates for all 10 climate actions achieved a 1.3% reduction ofGHG emissions in 2015 compared to 2005 emissions. Only the simulated 20% RPS resulted in a substantial GHG reduction of 5.1% compared to the 2005 levels. These results indicate that substantial increases in participation rates along with energy supply-side programs to reduce carbon intensity will be required to achieve long-term GHG reduction goals. This thesis shows that building sector GHG mitigation options exhibit a range of performance and participation rates, while quantitative outcomes assessment data of both is lacking. To enhance climate action policy development and implementation at the city-scale, it is recommended that cities track baseline GHG emissions using 146

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the expanded method developed here, and, subsequently report both the performance and participation rates of their specific climate action programs. Greater reporting on participation in climate action programs is essential for societal learning about plan implementation. In addition, cities should pursue greater coordination with utilities and state government agencies to evaluate the effectiveness of climate actions at the city-scale. Finally, the prioritization of climate actions at the city-scale, which must incorporate many factors (including but not limited to cost, payback, and community perceptions) should be a focus of future research as many cities are now faced with developing and implementing climate action plans to reduce GHG emissions. 147

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Appendix A. Cement Consumption in Cities A method for using economic activity to estimate the mass of cement used by a city has been proposed by researchers at the University of Colorado Denver (Reiner, 2007) and has been applied in the City of Denver GHG inventory (Ramaswami et al., 2007). The following assumptions were used by that method (Reiner, 2007) to convert economic activity by NAICS subsector to volumes of concrete and ultimately mass of cement: $125/yard3 assumed for average per yard price for delivered ready-mix (NAICS code: 32732) $260/yard3 assumed for precast/pre-stressed elements (NAICS codes: 32733 and 32739) 0.1446 tonnes cement/tonne concrete (0.43m3 by volume) Cement ratio at 14% mix by mass. These assumptions were used to estimate the amount of cement consumed in the MSA's of the 8 U.S. cities and at a national scale. These results are also summarized in Table A-1. Cement consumption estimates vary from a low of 0.18 metric tons of cement per capita for the Portland, Beaverton-Vancouver MSA to 0.42 metric tons of cement per capita for the Austin-Round Rock MSA. The U.S. had an estimated cement consumption of0.21 metric tons per capita. Economic data was not available for all NAICS subsectors for each MSA analyzed. This was primarily a factor for the two smaller MSA's: the Boulder MSA and the Fort Collins Loveland MSA. 148

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The per capita cement consumption for 2002 estimates based on economic data are vastly different (between 50% and 117% of the national average) then estimates reported by the cement industry. This is likely due to the occurrence or absence of major infrastructure improvements within the MSA, or the presence of major concrete exporting industries, e.g. railroad tie, reinforced concrete pipe ... etc in some MSAs. 149

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Table A-1 Total value of shipments of cement and concrete manufacturing, and the estimated cement consumption from the 2002 U.S. Economic Census for 8 US MSA' d h US h 1 b NAICS b .. san t e .. as a w o e, 'Y su sector. Value of Total Mass Cement Consumption NAICS Metropolitan Statistical Area Subsectors 1 Shipments of Cement per Capita (metric ($1,000si (metric tons) tons/capita) 32732 261,955 533,400 3273 32733 Denver-Aurora MSA 107,050 104,800 0.34 32739 112,144 109,800 3273 (Total) 481,149 748,000 32732 N/A N/A 3273 32733 N/A N/A BoulderMSA NIA 32739 N/A N/A 3273 (Total) 85,614 358,700 32732 N/A NIA 3273 32733 NIA N/A Fort Collins-Loveland MSA N/A 32739 N/A N/A 3273 (Total) 51,116 358,700 32732 138,087 281,200 Portland-Beaverton, Vancouver 3273 32733 76,069 77,500 0.18 MSA 32739 N/A N/A 3273 (Total) 214,156 358,700 32732 143,045 291,300 3273 32733 188,050 184,100 Seattle-Bellvue-Everett MSA 0.2 32739 NIA NIA 3273 (Total) 331,095 475,400 32732 267,760 545,300 Minneapolis-St. Paul, 3273 32733 147,936 144,800 0.23 Bloomington MSA 32739 N/A N/A 3273 (Total) 415,696 690,100 32732 170,779 347,800 3273 32733 Austin-Round Rock MSA 147,621 144,500 0.42 32739 49,948 50,900 3273 (Total) 368,347 543,200 Total U.S. 32732 21,601,732 43,989,000 0.21 3273 32733 6,694,104 6,554,000 32739 8,978,079 8,790,000 150

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3273 (Total) 44,681,489 59,333,000 1. Subsector 3273: Cement and Concrete Product Manufacturing 32732: Ready-mix Concrete Manufacturing 32733: Concrete Pipe, Brick and Block Manufacturing 32739: Other Concrete Product Manufacturing 2. A value for this NAICS subsector was not available. This is an estimate based on the total value reported for NAICS subsector 3273. In 2002, the U.S. consumed approximately 103.8 million metric tons ofPortland cement, according to the Portland Cement Association (PCA, 2005) resulting in per capita cement consumption of 0.36 metric tons per person. However, when Economic Census data for all NAICS subsectors of 3273 is used (given the assumptions outlined above), the estimated per capita cement consumption is 0.21 metric tons of cement per capita. Thus it is proposed that a factor of (0.36 I 0.21 = I. 74) be applied to the economic data consumption estimates. Per capita cement consumption for each of the 8 cities, incorporating this correction factor is summarized in Table A-2. The amount of cement consumed ranges from 0.32 metric tons per capita to 0.74 metric tons per capita for the Portland, BeavertonVancouver MSA and the Austin-Round Rock MSA, respectively. Economic census data is not available for each of the cement and concrete manufacturing NAICS subsectors for every MSA analyzed. In addition, the "Ready-Mix Concrete Manufacturing" (NAICS subsector 32732) covers ready mix concrete which is often distributed locally and may exhibit less cost variation across the country than the other two NAICS subsectors included in the previous method (32733-Concrete Pipe, Brick, and Block Manufacturing; and 32739Other Concrete Product Manufacturing). Thus, two additional methods that use different NAICS subsectors to estimate cement consumption from the Economic Census data have been explored. The two NAICS subsectors of interest are "Cement and Concrete Product Manufacturing" (3273) and "Ready-Mix Concrete Manufacturing" (32732). The first subsector (NAICS 3273) was the only sector that had economic data for each of the MSA's studied. The total annual cement consumption for each of these 151

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methods is estimated by calculating the amount of cement consumed in the U.S. (103.8 million metric tons; PCA, 2005) per total economic data reported in the Economic Census for the NAICS subsector of interest (see Table A-1 under Total U.S.). This factor is then applied at the MSA level to determine amount of cement consumed and ultimately the per capita cement consumption. Per capita cement consumption for each of the 8 U.S. cities derived from these two NAICS subsectors (as well as the method that uses all subsectors ofNAICS 3273) are summarized in Table A-2. Table A-2 Per capita cement consumption for the 8 U.S. cities by U.S. Economic Census data used. 1 Per Capita Cement Consumption Based on All 2002 State 3273 Sectors Based on Based on Concrete (Reiner, NAICS 3273 NAICS 32732 Aggregate Use 2007) (Only) (Only) (mt/capita) Denver, CO 0.59 0.50 0.56 2.00 Boulder, CO 0.59 0.72 0.56 2.00 Fort Collins, CO 0.59 0.46 0.56 2.00 Arvada, CO 0.59 0.50 0.56 2.00 Portland, OR 0.32 0.25 0.34 1.71 Seattle, WA 0.35 0.32 0.29 1.62 Minneapolis, MN 0.40 0.32 0.42 2.64 Austin, TX 0.74 0.67 0.64 1.18 1. Italicized numbers represent mcomplete Econorruc Census data to complete the analysis, and thus the Denver per capita consumption estimate was used. Considerations Aggregate consumption (a surrogate of cement/concrete consumption) in a region fluctuates dramatically based on large construction projects and even the amount of local economic activity. The U.S. Department of the Interior issued a preliminary report on aggregate use in the Colorado Front Range [expanded Denver Metropolitan Region] (Wilburn and Langer, 2000). Figure A-1, taken from this report, graphs aggregate consumption in the Colorado Front Range (expanded Denver Metropolitan Region) over time while overlaying major 152

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construction projects. You can see that aggregate consumption has exhibited considerable fluctuation over this 46-year period. Pe"fl'4iC'HIIC'I' 4 .. .. lllhf ... .. .,..... ........... ,--, n. .... ........... _, .... rw-.. 50000 -lSOOO lh. .. uYflrltk-tb...tiS.. ... 1 4500 4000 8 40000 r )( 35000 fll 30000 1-z Q 25000 1u ::I Q 20000 0 a:: A. 15000 10000 5000 "iUI '.-pen { .; ,.........., 51 53 55 57 59 61 63 65 67 69 71 73 75 77 79 81 83 85 87 89 91 93 95 97 Crushed stone D Sand & gravel YEAR Population Figure A-1 Significant events affecting Colorado aggregate use, 1951 1997 (Wilburn and Langer, 2000) 153 3500 3000 2500 2000 1500 1000 500 0 r )( z s j

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B. Community Characteristics and Daily Vehicle Miles of Travel: The Denver Metro Region ... 100 C. I'll .... 80 :Ea. > 60 >.I-=:E I'll> 40 c_ "C I'll ------------------------Demand Daily VMT per Capita versus Housing Density ---..-------------------------. I'll c. 20 -----------_ ______.._!___-=- _. _._ -----------------E ta c 0 ... 100 C.ta .... 80 :E c. > 60 >-t-=:E I'll> 40 c_ c::: I'll 20 me. >.I'll oo 0 ll. .. . .. 0 500 1,000 1,500 2,000 2,500 Housing Density (HH/sq. mile) Polygon Daily VMT per Capita versus Housing Density -------.---------------------------------------- ___._____ ---*---- s ---------- 3,000 0 500 1,000 1,500 2,000 2,500 3,000 Housing Density (HH/sq. mile) Figure B-1 Daily per capita VMT for (a) demand, and (b) polygon methods versus housing density 154

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Demand Daily VMT per Capita versus Population + Employment Density ... G) 100 c.,:g 'Q. 80 > >. j:: 60 =::E 40 "0,5 ; 'Q. 20 E ca Q)0 0 c 0 ------------.---... ----------- ----.---.---------.-.. ------..---.--.. 2,000 4,000 --__ .!.....__L_ ___ 6,000 8,000 Population + Employment Density [(Pop.+ Employees) per Square Mile] Polygon Daily VMT per Capita versus Population + Employment Density ... G)-100 c.,:g 1--80 ::EQ. > >.j:: 60 =:E I t-------------------------"----l -----------------------------------------10,000 "'> 40 c_ c,:g 0 -20 C)Q. >oca '00 0 l -. =-. __. ;.-n.-_-!; ... __ -_-. __ -_._._-_a.. 0 2,000 4,000 6,000 8,000 Population+ Employment Density [(Pop.+ Employees) per Square Mile] 10,000 Figure B-2 Daily per capita VMT for (a) demand, and (b) polygon methods versus population plus employment density 155

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Table B-1 Summary ofDRCOG region VMT spatial allocation analysis. Muno: Federal He11JI'I1S su,...... Erio ""'"" Longmonl: L.ar..,.tte p""'"' Thorn! on Au"" Northofonn Weslmms1er C ...... His Vbge Bnghlon Caslt. Roek Cenlen!'\181 Lak-eonv-coc.ty LI!.Uelon S...oomfeld WhuiRJ:Ige Oonvo< --E-Loc.nsvle Golden Sheridan Thla .,...d-'-t 1'\81 u ,.n af Tim ttlnm.n' PhO dl...,..lon, lont wllh the guklllne. M'ld l.tanca of Dr. Bruce Janeon and Or. Anu RM"Iuwaml n a a.ummery of tM dlmand nd pofygon VMT, the OlA trip r.ttoand a nurnbw af community af the m.jordtiM within the DRCOO r.glon. a....d on II ORCOO model output., IWPOf'lM dlilfV VMT ahould bl mutdphd 342 to get uuwat VMT Htm.taa. Ncrvemtt.r, 2001 --. --. %of Av; OR COGOema"' .. ._, Houaing Domond Polygon OIA Tot .. 18-84 Oenolty Houu>g Po ..on (Dolly (Dolly PopulaiD OIA Trtp N..-ol Avg. HH Estmated t lan:l Area (HHI ... Density ..... VMT vu 10n n Ral.io Rollo 0 Tn orHH -. -HHI""" .... 107,644 102.386 12,134 0.006 00017 31.735 236 5.142 0.83 1. 2.050 45 26.0 120,383 66,727 1804 10,789 0004 00015 28.248 43 24 2 59 4.166 008 42 992 15 99 168,122 111,751 1 504 14.341 0006 0.0023 27.165 6.2 4 1 278 5,159 064 14.0 308 0.0 40 1.405..233 1,150,887 1 221 105,455 0.040 00151 358.182 39 32 258 41,193 0.82 356 1,157 10 11.4 1.152,207 &50,553 1.355 83,937 0.032 00123 27&.oeo 41 30 282 32,037 001 260 1.232 1. 12 5 348,074 288,287 1 285 24,567 0.009 00042 78.383 44 34 20 9,445 003 89 1,081 1 7 107 83e,010 825,475 1.017 42,642 001!1 00085 11&,178 53 5.2 295 14,455 082 202 710 11 02 1,66.2,219 1,483,288 1121 109,818 0042 00337 33&,242 49 44 2.09 37,999 002 32. 1,159 1 6 12 9 4,911,122 4,940,783 0.994 307.998 0117 01288 1,038,025 47 48 2.57 119,844 005 1502 790 1.2 79 610,748 839.981 0954 36,089 0014 00102 152.540 40 42 265 13,920 083 74 1,881 29 192 1,824,405 1.831,730 0.990 109,871 0042 0.0219 459.232 4.0 4.0 2.62 41,859 062 330 1.248 1.9 120 106.934 237.900 0449 6,288 0.002 00011 31.056 34 77 2 93 2.145 058 63 341 0.5 3.9 482,568 268.831 1.796 28.0B5 0.011 00116 79,114 01 34 269 9,711 002 194 501 0.6 56 688,887 042.089 0.811 39,034 0.015 0.0051 100,259 65 7.9 2.0 13,941 003 33.2 420 0.7 4.5 2,153,283 1,831,647 1 320 102,522 0039 00278 524,294 41 3.1 2.76 37,148 0.81 28.2 1,317 2 1 14 0 3,227,088 3.122.885 1.033 14e,364 0.055 0.0325 783,984 42 4.1 229 63,914 065 43 0 1.480 2.3 13.1 811il,729 1,119,552 0.622 39,584 0015 00173 179.440 51 62 3 07 12,894 002 32 9 392 06 46 980.748 913,421 074 41,881 0 016 0.0000 255.856 3.0 30 2.26 18.531 003 13 7 1,353 21 110 1,188,858 1,578,895 0 753 48,489 0 018 0.0124 241,472 4.9 05 2 75 11.625 085 336 525 0.8 50 814.502 972.129 0.838 32,454 0.012 00007 217,954 37 45 218 14,887 062 95 1.587 2.4 13 2 14,831,557 14.724,!580 0994 582,474 0220 0.2211 2.898.064 5.0 51 2 24 280,033 067 1549 1,870 2.6 14 5 2,563,068 1,591.185 1.611 101,1U8 0039 00290 577.888 44 20 2 10 48,751 0.78 255 1,033 29 15.4 857,782 n4.794 1.107 32.250 0012 00001 245.399 35 32 213 15,141 087 2.294 30 10i 566,132 441,185 1.283 19,378 0007 00046 127,080 44 35 2.59 7,482 064 79 947 1 5 95 645,871 494,321 1 307 19,007 0.007 0.0037 133,138 4.9 3.7 2 33 8,158 0.70 93 077 14 79 212,l33 240,888 0081 5,372 0.002 0.0013 59,437 3.6 4.1 24 2,238 000 23 973 1 5 90 1,254.687 1,188.538 1.057 14,587 0.000 0.0112 308,186 4.1 39 2 67 5,483 001 0.3 050 1.0 ... 156 .., .. of Demand Polwon Pop.Jiallon VMT per VMT per with Good ca,.. Capila % (Oaiy (Oaiy Empfo,... UrbaniZed VMT/ VMT! nl 3.091 2.182 34 3,516 321 0.03 037 1 3 112 1.905 277 ""' 013 0.02 138 02 11.7 70 33,367 2.1oe 13% 032 003 937 1 5 133 10Q 30,560 2,395 ""' 044 003 1,4015 22 137 101 10.837 752 ""' 0.44 003 1,218 1 9 14.1 11.0 12,367 1,000 ""' 0.211 0 02 812 1 0 14 9 14 7 28.534 1,524 4% 026 001 070 1 4 15.1 135 116,022 8.038 30% 030 002 772 1 2 159 180 11,4n 743 ""' 0.31 002 1.551 24 180 17.3 41,808 2,527 71% 25% 038 0.02 1.244 1 9 100 10 7 1,808 191 40% 55% 026 003 255 04 170 37.8 13,105 596 ..... ""' 0.47 0.02 676 1.1 17 2 90 13,315 1,095 S8'Jb 0% 034 003 401 06 17.6 21.8 61.498 4,226 OJ% 43% 080 004 2,181 34 21.0 159 93.237 5,423 70% 44% 0.64 004 2.188 34 220 21 3 25,754 909 65% 0% 0.85 002 703 1 2 23 2 20' 30,151 1,904 85% 57 .. 0 72 005 2.201 34 234 210 35,702 1,765 S8'Jb ""' 074 004 17 24 5 32. 21.732 1.480 100% ..... 0.87 005 2.208 36 251 300 499.008 23,674 ..... 64 .. 000 0.04 3.221 50 251 253 90,908 5,371 74% ..... 095 005 3,003 59 251 15.& 28.432 1,728 97% 03% 008 005 4,300 67 268 24.0 15,046 1126 09% ""' 0 70 0.04 1,905 30 292 220 .21,826 943 112% ""' 115 005 2,347 3.7 340 26.0 8,031 489 01% 21% 1.49 009 3,492 55 39 5 44.6 53,151 2,527 70% 64% 3.64 017 0.404 100 06.0 81.3

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C. Supplemental Data for Eight Cities GHG Analysis Table C-1 Primary contact for building energy, waste, water/wastewater and regional rt fi 1 d t t h f h ht aupo ue use a a a eac o t e eigJ cities. Primary Contact (Department) Denver, CO Gregg Thomas Department of Environmental Health Boulder, CO Kevin Afflerbaugh Office of Environmental Affairs Fort Collins, CO Lucinda Smith Natural Resources Department Arvada, CO Clark Johnson City Manager's Office Portland, OR Michael Armstrong Office of Sustainable Development Seattle, WA Amanda Eichel Office of Sustainability and Environment Minneapolis, MN Michael Orange Minneapolis Sustainability Austin, TX Jake Stewart Austin Climate Protection Program Electricity consumed by sector for each of the eight cities is summarized in Table C-2. Thermal energy use (this includes fuels consumed by buildings for heating or processing such as: natural gas, heating oil, propane, etc.) is summarized in Table C-3. T bl C 2 El I a e -ectnca b fi 8US .. energy use 'Y sector or .. cities Electrical Energy Use (GWh) Residential Commercial Industrial Total Denver, CO 1,679 4,980 6,659 Boulder, CO 245 793 154 1,191 Fort Collins, CO 454 483 496 1,433 Arvada, CO 339 276 7 622 Portland, OR 2,701 3,766 1,915 8,382 Seattle, WA NIA 7,680 Minneapolis, MN 988 3,265 4,253 Austin, TX 3,740 5,500 1,760 11,000 157

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T bl C 3Th a e -b fi sus .. erma energy use 'Y sector or .. cities Thermal (Natural Gas, Propane, etc.) Energy Use (Billion Btu) Residential Commercial Industrial Total Denver, CO 13,900 26,500 40,400 Boulder, CO 2,078 1,654 617 4,349 Fort Collins, CO 3,343 1,348 3,000 7,691 Arvada, CO 2,731 1,031 0 3,761 Portland, OR 10,667 8,223 6,222 25,111 Seattle, WA 9,273 11,695 1,098 22,065 Minneapolis, MN 12,312 14,846 27,158 Austin, TX 9,741 6,278 4,225 20,244 Table C-4 Building GHG emissions by energy use along with total and per capita GHG fi h .. emissions or eigJ t cities. Total Total Building Electricity Thermal Building GHG GHG EnergyGHG GHG Emissions per Emissions Emissions Emissions Capita (mt( mt -C02e/yr) ( mt -C02e/yr) (mt-C02e/yr) C02e/person) Denver, CO 5,213,807 2,185,273 7,399,080 12.8 Boulder, CO 921,852 235,267 1,157,118 11.4 Fort Collins, co 1,105,681 416,004 1,521,685 12.1 Arvada, CO 499,265 203,455 702,721 6.7 Portland, OR 3,888,920 1,193,643 5,082,563 7.4 Seattle, WA 3,630,545 1,024,036 4,654,581 8.1 Minneapolis, MN 4,060,596 1,469,020 5,529,616 14.3 Austin, TX 5,500,000 1,050,217 6,550,217 9.7 Daily VMT estimates and associated pump-to-wheels (P2W) GHG emissions for eight U.S. cities are shown in Table C-5. 158

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Table C-5 Total daily VMT per capita and total P2W GHG emissions for surface travel-based on the demand method-for 8 U.S. cities Total Daily Total P2W GHG VMT per Capita Emissions (mtC02 e) Denver, CO 25 2,632,000 Boulder, CO 25 461,000 Fort Collins, CO 27 603,000 Arvada, CO 13 253,000 Portland, OR 24 3,801,000 Seattle, WA 26 3,578,000 Minneapolis, MN 19 1,702,000 Austin, TX 28 4,361,000 The spatial allocation of airline GHG emissions based on vehicle trip ratios to the airport as well as regional population ratios, along with the total pump-to-wheels (P2W) GHG emissions due to airline travel for each of the 8 cities is summarized in Table C-6. Note that nationally, U.S. airline GHG emissions per capita for domestic flights are estimated to be 0.87 mt-C02e/capita (EPA, 2007). International travel is estimated to account for 27% ofthe total jet fuel consumption. Incorporating this with domestic airline travel GHG emissions results in per capita emissions of 1.2 mt-C02 e/capita for airline travel (EPA Annex, 2007). These airline GHG emissions are very much in line with the city scale per capita estimates shown in Table C-6. Tabl C 6 S e -ummaryo fP2WGHG emissions fr om air me trave or .. CI Ie lfi sus t s Airport Metropolitan Airline P2W Total P2W Trip Area GHG Airline Ratio Population Emissions per GHG Ratio Capita (mtEmissions C02e/capita) (mt-C02e) Denver, CO 0.22 0.22 1.45 837,800 Boulder, CO 0.03 0.04 1.09 110,400 Fort Collins, co 0.03a 0.05 1.04 130,500 Arvada, CO 0.02 0.04 0.54 57,100 Portland, OR 0.45 0.35 1.08 740,300 Seattle, WA 0.15 0.16 1.07 617,000 Minneapolis, MN 0.14 0.11 1.43 556,200 159

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Austin, TX 0.51 0.58 0.54 363,800 c. Fort Collins lies outside Denver's MPO travel demand modeling area, so the airport trip ratio was estimated using Boulder's ratio between airport trip ratio and population ratio as a proxy. The allocation of airline GHG emissions based on fuel consumed at the airport is aimed to divide GHG emissions among the origin and destination of the airline trip similar to surface transport GHG emissions allocation. Personnel at each of the airports were questioned on the frequency of airline refueling upon landing. Although no data was compiled on this matter, three of the airport contacts confirmed that refueling happened at nearly every landing (Simonson, 2008; Hartsfield, 2008; Carpenter, 2008). This demonstrates the robustness of using aggregate fueling data at an airport as a proxy to airline use. The volume of air travel and the fuel use per enplaned passenger is varied among the four airports serving these eight U.S. cities. Characteristics of each of the airports are summarized in Table C-7. In 2005, Denver International served roughly the number of passengers as SeattleTacoma International and Portland International combined. That same year the U.S. had over 690 million enplaned passengers travel by air with an average fuel use of 28 gallons per enplaned passenger (this includes international travel). SeattleTacoma (Sea-Tac) International's higher fuel use per enplaned passenger may be the result of a higher percentage of international flights landing at Sea-Tac and refueling compared to other airports like Austin-Bergstrom International and Denver International. 160

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Tabl C 7 Ch t t e -arac ens Ics o e airpo s servmg fth us rt e eig CI IeS. th ht t U.S. Enplaned Fuel Use per Ranking by Passengers Enplaned Enplaned (milliont Passenger Passengers (gal./pass.)b (2005t Denver International 6 20.26 19.4 Portland International 33 6.67 25.8 SeattleTacoma 15 13.96 31.2 International Minneapolis-St. Paul 9 17.89 22.6 International AustinBergstrom 48 3.64 20.2 International a. (BTS, 2006c) b. 2005 U.S. average fuel use per enplaned passenger was 28 gaVpass (BTS, 2006c; BTS, 2006b) The totallandfilled waste, recycling diversion rate and total GHG emissions associated with landfilled waste for the eight cities is summarized in Table C-8. Table C-8 Totallandfilled waste, recycling diversion rate and total GHG d h d' I emissions associate wit waste Isposa. Total waste Recycling Total Total disposed in a diversion landfilled landfilled landfill rate(%) waste GHG waste GHG (tons) emissions emissions per capita (mt-C02e)1 (mtC02e/capita) Denver, CO 725,000 2% 0.19 108,750 Boulder, CO 108,954 N/A 0.16 16,343 Fort Collins, co 237,747 N/A 0.28 35,662 Arvada, CO 119,720 N/A 0.17 17,958 Portland, OR 697,283 54% 0.15 104,592 Seattle, WA 440,694 41% 0.12 66,104 Minneapolis, MN 377,178 37% 0.074 28,659 Austin, TX 721,775 17% 0.16 108,266 l. An EPA WARM enuss10ns factor of 0.15 mt-C02e/ton of waste was used (U.S. EPA, 2008). Total water and wastewater treated, energy use and GHG impacts associated with this treatment are summarized for the eight cities in Table C-9. 161

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Table C-9 Water and wastewater treated per capita, and its associated energy use and GHG ti h US .. emiSSions or eigl t Cities. Total Water and Total Energy Total GHG Emissions Wastewater Use per Water/ per Treated (WW) Treated WWTreated Water/WW (gallons /capita) (kBtu/MG) (kg-COze/capita) Denver, CO 148,400 4,310 106 Boulder, CO 126,400 4,780 100 Fort Collins, CO 107,500 6,000 119 Arvada, CO 90,600 NIA 71 Portland, OR 97,500 4,710 61 Seattle, WA 96,000 7,500 70 Minneapolis, MN 103,900 7,660 183 Austin, TX 122,100 8,500 188 The U.S. Economic Census is updated every five years, with the latest available economic census being 2002. The economic census for 2007 is expected to be completed around in 2009. Economic census data for cement and concrete products for the metropolitan statistical areas (MSA) that include the eight cities studied here have been gathered. Table C-1 0 summarizes the total value of shipments ofNAICS code 3273 (cement and concrete product manufacturing) and the associated per capita consumption of cement for the MSA's that include each of the eight cities. Per capita cement consumption based on these reported data vary across the eight cities from a low of 0.25 metric tons of cement per capita to a high of 0. 72 metric tons of cement per capita. In 2002, the U.S. consumed approximately 103.8 million metric tons of Portland cement, according to the Portland Cement Association (PCA, 2005) resulting in per capita cement consumption of0.36 metric tons per person. 162

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Table C-1 0 Total value of shipments of cement and concrete manufacturing (NAICS 3273), and the estimated per capita cement consumption from the 2002 U.S. Economic Census for 7 U.S. MSA's and the U.S. as a whole. Cement Value of NAICS Consumption per Metropolitan Statistical Area Subsectors1 Shipments Capita (metric ($1,000s) tons/capita) Denver-Aurora MSA 3273 (Total) 481,149 0.50 Boulder MSA 3273 (Total) 85,614 0.72 Fort Collins-Loveland MSA 3273 (Total) 51,116 0.46 Portland-Beaverton, 3273 (Total) 214,156 0.25 Vancouver MSA Seattle-Bellvue-Everett 3273 (Total) 331,095 0.32 MSA Minneapolis-St. Paul, 3273 (Total) 415,696 0.32 Bloomington MSA Austin-Round Rock MSA 3273 (Total) 368,347 0.67 Total U.S. 3273 (Total) 44,681,489 0.36 I. Subsector 3273: Cement and Concrete Product Manufactunng The GHG emissions associated with cement consumption and its contribution to the community-wide total for each of the eight cities are summarized in Table C-11. Cement's GHG emissions relative to the community's total varied from 1% to 3% for the eight cities. Table C-11 Total cement GHG emissions and emissions as a percent of the city's total GHG emissions CementGHG Total GHG emissions emlSSlOnS relative to the (mt-C02e) city's total Denver, CO 290,000 2.0% Boulder, CO 73,000 3.1% Fort Collins, CO 48,000 2.4% Arvada, CO 63,000 2.6% Portland, OR 172,000 1.3% Seattle, WA 184,000 1.5% Minneapolis, MN 123,000 1.2% 163

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Austin, TX I 449,000 3.0% Food expenditures for major metropolitan areas are collected through the Consumer Expenditure Survey. Household food expenditures and associated GHG emissions for the year 2003-2004 for each of the eight cities are summarized in Table C-12. All cities analyzed had food related GHG emissions between 8 mt-C02e per household and 9 mt-C02 e per household. Table C-12 Food expenditures for five major metropolitan areas, from the C E d" S fi 2003 2004 onsumer xpen Iture urvey or -Consumer Expenditure Surve' 2003 2004 ( 1997 -$) Denver; Portland, Seattle, Minneapolis, Austin, Boulder; OR WA MN TX Fort Collins; and Arvada, co Food at Home Cereals and bakery 448 428 459 444 410 products Meats, poultry, fish, and eggs 827 766 836 680 782 Dairy products 363 377 399 376 330 Fruits and vegetables 563 597 596 532 508 Other food at home 1,117 1080 1179 1078 1070 Total Food at home 3,319 3,248 3,469 3,111 3,100 Total food away from 2,144 2,226 2,510 2,602 2,230 home Total GHG Emissions from Food per HH 8.22 8.22 8.99 8.54 8.02 ( mt -C02e/HH) Total GHG emissions associated with food consumption are summarized in Table C-13. 164

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Table C-13 GHG emissions by household and community-wide associated with fi d t' 2003 00 consump ton m Total GHG Total GHG FoodGHG Emissions from Emissions from emissions Food perHH Food relative to the ( mt-C02e!HH) (million rnt-C02e) ci!}''s total Denver, CO 8.22 2.1 14.6% Boulder, CO 8.22 0.38 16.1% Fort Collins, CO 8.22 0.42 14.3% Arvada, CO 8.22 0.33 21.7% Portland, OR 8.22 2.4 19% Seattle, WA 8.99 2.5 20.7% Minneapolis, MN 8.54 1.5 15.2% Austin, TX 8.02 2.2 15.4% 165

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Table C-14 Benchmark lude with the GHG for eight U.S Boulder, Fort Collins, Minneapolis, Denver, CO co co Arvada, CO Portland, OR Seattle, WA MN Austin, TX Res. Enerf!Y kWh!HH/mo 545 444 689 687 765 740 478 1108 therms!HH/mo 45 38 51 55 30 28 60 26 kBtu!HH 6,377 5,283 7,423 7,881 5629 5316 7,585 6,423 sq feet!HH 1,107 1,458 1,684 1,442 1,278 1,321 1,683 1,321 % RE Electricity Purchased 2.8% N/A N/A N/A 4.6% NIA 1.9% N/A Comm. Energy Electricity kWh/sf 19 22.6 16 12 20 16 16 18 Thermal-kBtu/sf 88 47 45 44 43 43 71 20 Total kBtulsf 154 125 100 85 110 97 124 81 % RE Electricity Purchased 1.2% N/A N/A N/A 0.8% N/A 0.01% N/A Transportation VMT/capita 25 25 27 13 24 26 19 28 Gal/enp.pass. 19 19 19 19 26 30 23 17 Enp. Pass./Capita 8 6 6 3 4 4 7 3 Waste tons/capita 1.25 1.07 1.89 1.14 1.02 0.77 0.97 1.07 Waste Diversion 2% N/A N/A N/A 54% 41% 37% 17% Transport Fuel Gal gas/capita 435 433 459 231 400 446 315 447 Gal diesel/capita 69 71 73 37 115 128 90 158 Gal Jet Fuel/capita 149 112 107 56 112 111 148 56 Cement metric tons/capita 0.50 0.72 0.46 0.50 0.25 0.32 0.32 0.67 Food $-1997/HH 5,463 5,463 5,463 5,463 5,474 5,979 5,713 5,331 1,000 Gal/capita I 148 I 129 I 108 I 91 I 97 I 96 I 104 I 122 166

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