Environmental Benefits of Brownfields Redevelopment – a Nationwide Assessment

May 2020

Environmental Benefits of Brownfields Redevelopment— A Nationwide Assessment

Prepared for: U.S. Environmental Protection Agency Office of Brownfields and Land Revitalization Prepared by: ICF and Renaissance Planning EPA 560-R-20-001 Environmental Benefits of Brownfields Redevelopment – A Nationwide Assessment

Table of Contents Acronyms ...... iv Executive Summary ...... v 1. Introduction ...... 1 1.1 Background ...... 1 1.2 Project Purpose and Overall Approach ...... 2 1.3 Report Structure ...... 4 2. Model Framework and Mechanics ...... 5 2.1 Scenario Analysis Process Step 1 – Prepare Data and Model Inputs ...... 6 2.1.1 Brownfields Redevelopment Scenario Data Inputs ...... 6 2.1.2 Trend Growth Scenario Data Inputs ...... 12 2.2 Scenario Analysis Process Step 2 – Develop Scenario Parameters ...... 13 2.2.1 Brownfields Redevelopment Scenario Parameters ...... 14 2.2.2 Trend Growth Scenario Parameters ...... 23 2.3 Scenario Analysis Process Step 3 – Allocate Growth ...... 25 2.3.1 Overview of Allocation Model Steps ...... 25 2.3.2 Phases of Allocation ...... 28 2.3.3 Specific Application to 50 Metropolitan Areas ...... 31 2.4 Scenario Analysis Process Step 4 – Estimate Environmental Impacts ...... 36 2.4.1 Stormwater Impacts ...... 36 2.4.2 Transportation Impacts ...... 37 3. Model Results ...... 40 3.1 Allocation Model Results ...... 40 3.2 Translating Development Patterns to Environmental Outcomes ...... 46 3.3 Environmental Impact Model Results ...... 49 3.3.1 Impervious Surface Growth ...... 49 3.3.2 Transportation and Vehicle-Miles Traveled ...... 52 3.4 Model Uncertainties ...... 57 4. Key Findings ...... 59 4.1 Brownfields redevelopment is more location-efficient than trend growth...... 59 4.2 Growth profiles demonstrate the importance of metropolitan area growth contexts. .... 60 4.3 Brownfields development will sometimes have additional benefits for growth not allocated to brownfields...... 61 4.4 Brownfields development often can shift metropolitan area development patterns to mitigate environmental impacts...... 62 GLOSSARY ...... 64 i

Environmental Benefits of Brownfields Redevelopment – A Nationwide Assessment

APPENDICES Appendix A: ACRES Data and Location Validation Process ...... A-1 Appendix B: Estimation of Average Annual Change in Jobs ...... B-1 Appendix C: Metropolitan Area Growth Profiles and Selection of 50 CBSAs to Include in the Study ...... C-1 Appendix D: Estimating Residential VMT Based on the Built Environment ...... D-1 Appendix E: Detailed Allocation and Environmental Benefits Results ...... E-1 Appendix F: Temporal Analysis for Determining Brownfield Redevelopment Compeltion...... F-1

List of Figures Figure 1. Scenario Analysis Approach ...... 3 Figure 2. Overall Model Framework and Process Steps with Key Questions ...... 5 Figure 3. Reducing the “Universe” of Brownfields ...... 7 Figure 4. Brownfields Redevelopment Status in Milwaukee, Wisc...... 11 Figure 5. Capacity Estimation for New Activities at Brownfield Sites ...... 18 Figure 6. Brownfield Capacity Estimation Results for the Los Angeles Metro Area...... 19 Figure 7. Brownfield Attractiveness Estimation Results for the Los Angeles Metro Area ...... 21 Figure 8. Brownfield Activity Mix Estimation Results for the Los Angeles Metro Area ...... 22 Figure 9. Trend Growth Capacity Estimation Results for the Los Angeles Metro Area ...... 24 Figure 10. Trend Growth Housing Attractiveness Estimation Results for the Los Angeles Metro Area ...... 26 Figure 11. Illustration of the Allocation Phases ...... 30 Figure 12. Map of the 50 Selected CBSAs by Growth Profile ...... 34 Figure 13. Primary Allocation in BFR and TG Scenarios for the Los Angeles Metro Area ...... 42 Figure 14. Secondary Allocation in BFR and TG Scenarios for the Los Angeles Metro Area .... 43 Figure 15. Cumulative Allocation in BFR and TG Scenarios for the Los Angeles Metro Area ... 44

Environmental Benefits of Brownfields Redevelopment – A Nationwide Assessment

List of Tables Table 1. Brownfields Redevelopment Status Model Coefficients by Census Region and Metropolitan Area Size ...... 12 Table 2. Summary of the Differences in the BFR and TG Scenarios ...... 14 Table 3. Characteristics of Growth Profiles for Metropolitan Areas ...... 32 Table 4. Categorization of 50 Selected CBSAs by Growth Profile ...... 35 Table 5. D Variables and SLD Indicators ...... 38 Table 6. “Primary” and “Secondary” Allocation Concepts ...... 41 Table 7. Activities Allocated to Brownfield Sites in the “Primary” Allocation Phase ...... 45 Table 8. Understanding Environmental Results by Allocation Phase ...... 47 Table 9. Change in Impervious Surface Acres, Primary Phase ...... 50 Table 10. Change in Impervious Surface Acres, Secondary Phase ...... 51 Table 11. Change in Impervious Surface Acres, Cumulative ...... 51 Table 12. Change in Residential-Based VMT, Primary Phase ...... 53 Table 13. Change in Residential-Based VMT, Secondary Phase ...... 53 Table 14. Change in Residential-Based VMT, Cumulative ...... 54 Table 15. Change in Employment-Based VMT, Primary Phase ...... 55 Table 16. Change in Employment-Based VMT, Secondary Phase ...... 56 Table 17. Change in Employment-Based VMT, Cumulative ...... 57 Table 18. Summary of Primary Environmental Benefits of Brownfields Redevelopment versus Trend Growth Development ...... 59 Table 19. Summary of Cumulative Environmental Benefits of Brownfields Redevelopment versus Trend Growth Development ...... 63

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Environmental Benefits of Brownfields Redevelopment – A Nationwide Assessment

ACRONYMS

ACRES Assessment, Cleanup and Redevelopment Exchange System ACS American Community Survey BFR Brownfields Redevelopment (Scenario) CBG Census Block Group CBSA Core-Based Statistical Area EPA U.S. Environmental Protection Agency FHWA Federal Highway Administration FY Fiscal Year GSA U.S. General Services Administration ISGM Impervious Surface Growth Model LEHD Longitudinal Employer–Household Dynamics NLCD National Land Cover Database OBLR Office of Brownfields and Land Revitalization OMB Office of Management and Budget PAD-US Protected Areas Database of the United States SLC Smart Location Calculator SLD Smart Location Database TG Trend Growth (Scenario) VMT Vehicle-Miles Traveled

Environmental Benefits of Brownfields Redevelopment – A Nationwide Assessment

EXECUTIVE SUMMARY Brownfields cleanup and redevelopment is an important aspect of most communities’ future planning and economic development goals. A brownfield is a property, the expansion, redevelopment, or reuse of which may be complicated by the presence or potential presence of a hazardous substance, pollutant, or contaminant. Environmental liability often is seen as a barrier to redevelopment, particularly at sites that were previously used for manufacturing or other industrial uses. A growing body of research indicates that brownfields redevelopment can offer significant economic and environmental benefits compared with the development of land outside of the urban core or on previously undeveloped properties. Environmental benefits of brownfields development include reduced stormwater runoff leading to improved water quality, and reduced greenhouse gas emissions leading to improved air quality. Environmental Benefits of Brownfields Redevelopment – A Nationwide Assessment provides insight into the role that brownfields redevelopment can play in mitigating the environmental impacts of economic growth across the country. METHODOLOGY This study evaluates the environmental impacts of development patterns under two hypothetical growth scenarios – the Brownfields Redevelopment (BFR) scenario, which assumes that future redevelopment will occur at all available brownfield sites, capturing a substantial portion of new jobs and housing units; and the Trend Growth (TG) scenario, which assumes that new jobs and housing units will be distributed across a metropolitan area in a similar pattern as recent growth trends (i.e., greenfield development and without an emphasis on brownfields redevelopment). In this study, the same level of future growth and development activities (new jobs and housing units) are assumed for each scenario; however, the increase in jobs and housing units associated with growth are allocated to properties differently, based on a land use allocation model. The model estimated the environmental impacts resulting from new growth under each scenario (the BFR and TG scenarios) for the period of time between 2013 and 2030 in 50 metro areas across the United States. Model Framework The scenario analysis required a four-step process: Step 1 – Prepare Data and Model Inputs Property data for the BFR scenario was obtained from the U.S. Environmental Protection Agency’s (EPA’s) Assessment, Cleanup, and Redevelopment Exchange System (ACRES), an online database that allows EPA’s Brownfields Program grantees to electronically submit their site-specific data directly to EPA. The study used information from the ACRES database on brownfields properties not yet redeveloped. Brownfield sites that previously were redeveloped were excluded from the study’s scenario analysis because these properties are unlikely to accommodate additional jobs and housing units beyond the level of growth associated with the previous development. After all ACRES data points were reviewed, the universe of brownfield properties available to formulate the BFR scenario totaled 5,023 unique brownfields properties across the United States. The TG scenario uses Census block groups (CBGs) as the unit of analysis, and new jobs and housing units are allocated primarily to Census block groups with strong recent growth trends

Environmental Benefits of Brownfields Redevelopment – A Nationwide Assessment based on U.S. Census data. CBG features and attribute data for the entire country were obtained from EPA’s Smart Location Database. Step 2 – Develop Scenario Parameters The BFR and TG scenarios were developed using a standardized land use allocation model. The model distributes future growth in jobs and housing units across a metro area. The distribution of this growth is guided by the following questions: • How many new jobs and housing units will be added to a given metro area by 2030? • How much new development can a given location accommodate? • What locations are most likely to be (re)developed first? • What types of activities (jobs and housing) are likely to be added as a given location is developed? Step 3 – Allocate Growth The model relies on control totals to determine how much growth to allocate to each metro area under each scenario. Control totals are the number of new jobs and housing units to allocate for a given metro area over the 2013 to 2030 time period and were obtained from Woods & Poole county-level demographic and economic forecasts. Woods & Poole includes a comprehensive database that contains economic and demographic data and future estimates for the United States and all states, regions, counties, and core-based statistical areas for every year from 1970 through 2050. The allocation model proceeded in two major phases: • The first, or “primary,” phase of the BFR scenario allocates as much growth as possible to brownfield sites, based on the development capacity and activity mix estimates for each site. During the primary phase of the TG scenario, the same increments of jobs and housing units allocated to brownfields are re-allocated to non-brownfield areas. This analysis phase provides a direct comparison of the environmental impacts between localized growth at brownfield sites and growth in non-brownfield areas (e.g., outside of the urban core or on undeveloped properties). • In the “secondary” phase of both scenarios, the remaining increment of growth is allocated according to the data and parameters guiding the TG scenario. The secondary phase of analysis occurs when capacity for new growth at brownfield sites is exhausted, and all remaining growth is allocated to trending block groups. The differences in environmental impacts between the BFR and TG scenarios for this analysis phase are often small and always smaller than the differences observed in the primary phase. Secondary environmental benefits may arise from preserving development capacity in growing location-efficient neighborhoods. • Finally, the “cumulative” assessment of each growth scenario compares the environmental impacts of total growth across a broader metro area, regardless of phase. While the primary phase focuses on localized environmental impacts from brownfields redevelopment (relative to growth in non-brownfield areas), the cumulative assessment quantifies the environmental impacts based on areawide growth patterns. This modeling structure gives priority to brownfield sites in the BFR scenario and assumes that once all known brownfield capacity is developed, the remaining growth will follow recent trends. The model was applied to 50 metro areas of varying population size, geographic location, growth dynamics, development history, and density of brownfield sites. The metro areas were grouped into six growth profile categories using population and growth rate statistics to ensure that metro areas were analyzed against other metro areas of analogous size and growth. vi

Environmental Benefits of Brownfields Redevelopment – A Nationwide Assessment

Step 4 – Estimate Environmental Impacts The environmental analysis module developed for this study estimates stormwater impacts and air quality impacts. Stormwater impacts are estimated by calculating the expected growth in impervious surface area associated with each growth scenario. Impervious surface coverage is a proxy for a range of stormwater impacts, where higher impervious surface coverage (or an increase in the acreage of impervious surfaces) is generally correlated with higher runoff volumes and increased concentrations of non-point source pollutants in runoff. Air quality impacts associated with residential and employment transportation decisions related to new development are estimated by calculating changes in vehicle-miles travelled (VMT). VMT is a measure of total vehicular travel within a metro area and is a proxy for transportation-related air emissions. MODEL RESULTS Allocation Results Brownfield sites across the 50 analyzed metro areas could potentially accommodate as many as 640,000 new housing units and 1.39 million new jobs under the aggressive development scenario. These totals represent almost 13 percent and 11 percent, respectively, of total growth expected for the analyzed metro areas between 2013 and 2030. The number of housing units and jobs potentially accommodated by brownfield sites varies substantially by growth profile. For example, metro areas designated as Brownfield sites in the 50 having an Industrial Legacy growth profile (e.g., small cities analyzed metro areas could with slow growth) often have relatively large numbers of potentially accommodate brownfields and thus can absorb a large portion of new between 200,000 to 640,000 growth (housing, in particular). In contrast, brownfield sites new housing units and in metro areas characterized as Growth Hubs (e.g., 415,000 to 1,389,000 new moderate to large cities with rapid growth) only have jobs. sufficient capacity to accommodate a small proportion of new housing units and a moderate share of new jobs. A temporal analysis performed as part of this study found that, across all growth profiles, growth associated with available brownfield site capacity was reflected as an increase in new jobs typically within 4 to 9 years of the start of the BFR growth scenario and by the addition of new housing units within 6 to 12 years of the start of the BFR scenario. Brownfield site growth or development capacity is filled in the relatively near-term (4 to 9 years) in the case of jobs-oriented redevelopment, reflecting the tendency for many brownfield sites to be located in employment- rich areas. Impervious Surface Growth Results Growth and development modify existing land covers, replacing previously pervious surfaces, such as fields and forests, with pavement and rooftops (i.e., impervious surfaces). Development patterns that limit the expansion of impervious surfaces benefit the environment by mitigating the runoff of pollutants to waterbodies. In the primary analysis phase, the total impervious surface acreage added under the BFR scenario is significantly lower than that added under the TG scenario development for all metro areas analyzed. For every brownfield acre redeveloped, approximately 1.28 to 4.60 acres of impervious surface would be expected to be saved compared to having the same development occur at TG sites. This range represents the average reduction in impervious surface by

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Environmental Benefits of Brownfields Redevelopment – A Nationwide Assessment brownfields redevelopment across all analyzed metro areas. Thus, if a given metro area had 1,000 acres of developable brownfield sites, it would be reasonable to assume that redevelopment of brownfields sites would save approximately 1,280 to 4,600 acres of impervious surface. On a percentage basis, brownfields redevelopment results in average impervious surface reductions of approximately 73 percent to 80 percent compared to trend growth. When considering the cumulative impacts of brownfields redevelopment across the broader metro area, a similar picture emerges, though the magnitude of the benefits of For every brownfield acre the BFR scenario is lower than in the primary analysis redeveloped, phase, as expected. For every brownfield acre approximately 0.65 to redeveloped, approximately 0.65 to 3.16 acres of 4.60 acres of impervious impervious surface is saved relative to the TG scenario surface would be after all regional growth (beyond what the brownfield sites expected to be saved if can accommodate) is accounted for. On a percentage the same development basis, the cumulative BFR scenario yields impervious had occurred in trend surface reductions of approximately 1.3 percent to 6.6 growth (non-brownfield) percent compared to the TG scenario. areas. While the degree of reduction varies by growth profile, impervious surface area reductions from brownfields redevelopment are seen in metro areas across all growth profiles under both the base and aggressive growth scenarios. Transportation and Vehicle Miles Traveled (VMT) Results Development in central areas (e.g., central business districts, transportation hubs) typically result in residents and workers taking shorter trips and reduced automobile usage compared with development that occurs in fringe areas or areas outside the urban center. Brownfields tend to be located in densely developed, centralized areas where development typically results in fewer VMT per capita each day than development that occurs in fringe areas. Therefore, brownfields development results in fewer transportation-related air emissions. Based on this study, residential VMT is expected to be substantially lower in the BFR scenario versus the TG scenario in all growth profiles in both the primary and cumulative analyses. Based on the primary phase results across all metro areas analyzed, new residents at brownfield sites are expected to generate, on average, 7.3 to 9.7 fewer VMT per capita per day than if they moved to TG locations. On a percentage basis, brownfields redevelopment results in residential VMT reductions of approximately 25 percent to 33 percent compared to trend growth across all analyzed metro areas. The cumulative results suggest that brownfields redevelopment can result in VMT per capita reductions of 0.5 to 1.8 miles per day, on average, for all new residents after all regional growth (beyond what the brownfield sites could hold) is accounted for. These findings suggest that each brownfield acre redeveloped can reduce a metro area’s residential VMT generation by hundreds of miles per day. Travel patterns also are affected by job location. Commuting to and from work is a substantial portion of daily VMT for many people. For all analyzed metro areas, new Brownfields redevelopment jobs at brownfield sites (primary phase) are expected to reduces per capita generate 2.1 to 2.5 fewer VMT per worker per day than new residential VMT by an jobs in trending areas. On a percentage basis, this is average of 0.5 to 9.7 miles equivalent to employment VMT reductions of approximately per day and job-related VMT 8.8 percent to 10 percent compared with trend growth by 0.2 to 2.5 miles per day. across all analyzed metro areas. The cumulative results viii

Environmental Benefits of Brownfields Redevelopment – A Nationwide Assessment indicate brownfields redevelopment could result in per job VMT reductions of 0.2 to 0.5 miles per day, on average, for all new jobs after all regional growth (beyond what the brownfield sites could hold) is accounted for. In all analysis phases, each redeveloped brownfield acre generates substantially lower workplace-related VMT (30 to 190 miles) across all analyzed metro areas. KEY FINDINGS Brownfields redevelopment is more location-efficient than trend growth across key environmental metrics. Location-efficient communities are dense and vibrant, with walkable streets, access to transit, proximity to jobs, mixed land uses, and concentrations of retail and services. Location efficiency promotes development patterns that limit the strain on existing stormwater and transportation infrastructure, and the associated environmental impacts of increased stormwater and traffic loads. The reallocation of new jobs and housing to brownfield sites within a metro area will produce environmental benefits by reducing impervious surfaces and VMT. On a per acre basis, brownfields redevelopment leads to less impervious surface area being consumed or developed than trend growth development. Brownfields redevelopment also alters travel to and from the home and the workplace, mitigating growth in VMT due to the fact that housing and jobs are more efficiently located and the potential increased use of public transportation. The location efficiency advantages of brownfields are most clearly seen in the primary phase analysis results, which provide a direct comparison of the environmental impacts between localized growth at brownfield sites and growth in non-brownfield areas. Based on the temporal analysis performed as part of this study, these primary phase environmental benefits are expected to occur typically in the near term (e.g., within the first decade of brownfields redevelopment). Growth profiles demonstrate the importance of metro area growth contexts. Although it is true that brownfields redevelopment is more location-efficient than trend growth across all metro area growth profiles, the growth profiles demonstrate how the total magnitude of environmental benefits can differ dramatically. If the metro area’s brownfield sites are less centrally located, then the environmental benefits are not as great as the benefits associated with brownfields sites in more central locations. Also, if there is a limited number of brownfields or modest brownfield acreage available for redevelopment, the impact of brownfields on development patterns – and, in turn, the environment – is less significant when considering all new growth (cumulative analysis results). Environmental benefits are maximized when brownfield properties are aggressively redeveloped and growth outside urban centers is minimized. Brownfields development will sometimes produce additional benefits for growth beyond brownfield sites. Brownfields redevelopment often results in additional environmental benefits by re-shaping longer term growth patterns. Redeveloping brownfields can maximize infill development capacity, making subsequent non-brownfield growth patterns more efficient for the metro area as well. A metro area brownfields redevelopment strategy can affect more than just the residents and employees of that development, as demonstrated by the secondary and cumulative analysis phases of this study. A brownfields redevelopment strategy can also influence the behavior of neighbors and nearby employers. Not only do the residents and employees of the new development impose lower environmental impacts, those who live or work nearby also may benefit through closer services, employment, and access to other community goods.

Environmental Benefits of Brownfields Redevelopment – A Nationwide Assessment

Brownfields redevelopment can often shift metro area development patterns to mitigate environmental impacts. The effectiveness of brownfields redevelopment depends largely on the amount of growth that can be reallocated to more efficient locations relative to trend growth patterns. The cumulative findings in this study, which focus on total growth across a broader metro area and not just the brownfields portion, suggest that having robust development capacity at brownfield sites in high growth areas that have development momentum will maximize the environmental benefits of redevelopment. Brownfields redevelopment reorganizes significant amounts of new jobs and housing into smarter locations, such that the resulting development pattern substantially limits the environmental impacts of new growth.

Environmental Benefits of Brownfields Redevelopment – A Nationwide Assessment

1. INTRODUCTION 1.1 Background In the majority of urban areas in the United States, real estate development is driving the demand for infill properties and brownfields located in the urban core. However, uncertainty related to environmental liability for potential contamination at infill properties and brownfields can pose a barrier to redevelopment, particularly at properties previously used for manufacturing or other industrial uses. In some cases, the risk of investing in a potentially contaminated property is compounded by restrictive zoning rules or the need to upgrade or replace existing infrastructure to accommodate redevelopment options. In many cases, the U.S. Environmental Protection Agency’s (EPA) Brownfields Program grants may help reduce some of the risks associated with the redevelopment of potentially contaminated properties. A growing body of research and case studies conducted by EPA indicate that brownfields redevelopment (and urban redevelopment in general) can offer significant environmental benefits compared with the development of land outside of the urban core or on previously undeveloped properties. The benefits of redeveloping brownfields and infill properties include reduced stormwater runoff and subsequently improved water quality, as well as reduced greenhouse gas emissions and impacts on air quality from reductions in vehicular travel. These benefits are described in a handful of previous studies which suggest that brownfields redevelopment may result in a reduction of 25 percent to 80 percent in impervious surfaces, and stormwater runoff reductions of 43 percent to 60 percent. In addition, vehicle-miles traveled (VMT) may be reduced 7 percent to 89 percent (with most benefits in the range of 20 percent to 55 percent).1,2,3 These expected benefits derive for two main reasons: • In general, brownfield sites are typically located in the urban core, or in older neighborhoods, and previous uses of brownfield properties included the establishment of impervious surfaces and stormwater management infrastructure. The cleanup and redevelopment of brownfield properties most likely will not result in significant increases in the amount of impervious surfaces or channeled stormwater management infrastructure. In contrast, when new development occurs in greenfield areas that are characterized by open spaces and pervious surfaces, the new development transforms these areas by adding additional impervious pavements and rooftops that require stormwater runoff management solutions. These differences in development contexts and impacts result in meaningful expected differences between the stormwater and water quality impacts of brownfields redevelopment compared with development on previously undeveloped property. • From a transportation point of view, brownfield sites tend to be located closer to transportation hubs and are more accessible to commercial and recreational destinations than greenfield sites. These two factors influence travel behaviors, particularly a reduced use of personal automobiles and an increase in pedestrian traffic, bike riding, and use of public transportation. The result is reduced emissions due to reductions in VMT in areas where brownfields and infill sites are redeveloped, compared with growth or development in greenfield locations, which tend to be located outside of urban areas. In addition, many

1 U.S. Environmental Protection Agency. “Comparing Methodologies to Assess Transportation and Air Quality Impacts of Brownfields and Infill Development.” EPA 231-R-01-001. August 2001. 2 U.S. Environmental Protection Agency. “Air and Water Quality Impacts of Brownfields Redevelopment: A Study of Five Communities.” EPA 560-F-10-232. April 2011. 3 “Ten Years of Technical Assistance: Successes, Lessons Learned and a Look Forward.” 2013. Unpublished.

Environmental Benefits of Brownfields Redevelopment – A Nationwide Assessment

brownfield sites with high levels of accessibility also will make it more viable to develop higher density mixed-use development that further reduces travel by increasing the chances that employees and residents will make more trips on site or nearby, rather than traveling significant distances between residential areas and commercial or recreational areas. 1.2 Project Purpose and Overall Approach This study provides insight into the role that brownfields redevelopment can play in mitigating the air and water environmental impacts of growth across the country. It offers a standard, consistent methodology for answering the following questions at the metropolitan and national levels: • What are the estimated environmental benefits – in terms of stormwater runoff and transportation emissions – associated with redeveloping brownfields compared with greenfield development or “trend development”? • What are the environmental benefits of reallocating jobs and housing to infill locations and brownfields in a given metropolitan area? This study evaluates the environmental impacts of development patterns under two hypothetical growth scenarios: one in which brownfield sites are redeveloped and metro growth patterns are reshaped, and another where recent metro growth trends (i.e., greenfield development) persist over time. The approach developed for this study employs scenario analysis techniques described in detail in later sections of this report. The general framework for analyzing each growth scenario and assessing the potential benefits of brownfields redevelopment is shown in Figure 1. This general approach was applied to 50 metro areas (or core-based statistical areas [CBSAs])4 across the United States containing 5,023 known brownfield sites. The results at the metro level were then aggregated according to EPA region, metro area type, and nationally to develop typical ranges of stormwater and transportation emissions benefits associated with brownfields redevelopment. For all 50 metro areas included in the study, the following two scenarios were constructed and analyzed to forecast the growth of new jobs and housing units for each respective metro area from 2013 to 2030. Then the predicted jobs and housing growth numbers were allocated to potential growth areas within the metro area boundaries: • Brownfields Redevelopment (BFR) Scenario assumes that redevelopment will occur at all available brownfield sites, capturing as many of the metro area’s allocated new jobs and/or housing units as possible within the assumed development capacity for these properties. If all brownfields are redeveloped, any remaining forecasted growth in jobs and housing will be allocated in accordance with recent growth trends (i.e., allocated to non- brownfield properties). In this way, all metro area growth will be allocated, and the use of brownfield properties will be maximized to provide a model of future development that is concentrated around the redevelopment of brownfields. • Trend Growth (TG) Scenario allocates the same level of development activities (new jobs and housing units) assumed for the BFR scenario but allocates the growth in jobs and housing to properties in accordance with recent historical development trends. This provides a model of future development in which recent trends toward greenfield and outer rim development persist over time and available brownfields are not redeveloped.

4 Core-based statistical areas (CBSAs) as defined by the Office of Management and Budget (OMB) for 2016.

Environmental Benefits of Brownfields Redevelopment – A Nationwide Assessment

Figure 1. Scenario Analysis Approach

Brownfields Trend Growth Redevelopment Scenario Scenario

Comparison of the location of housing and jobs

Comparison of environmental impacts of growth

Each of these two scenarios models alternative visions for metro area growth. The context in which growth occurs can significantly influence the magnitude of the environmental impacts posed by new development, a concept referred to as “location efficiency” in urban planning and analysis.5 Location efficient communities are dense and vibrant, with walkable streets, access to transit, proximity to jobs, mixed land uses, and concentrations of retail and services.6 Location efficiency promotes development patterns that limit the strain on existing stormwater and transportation infrastructure, and the associated environmental impacts of increased stormwater and traffic loads. Location efficiency dynamics were used to estimate two measures of the environmental impacts of new growth for each scenario: • Change in impervious surface area (urban footprint): This measure serves as a proxy for stormwater runoff and non-point source pollutants impacts. • Change in vehicle-miles traveled (VMT): This measure serves as a proxy for mobile source emissions and air quality. Based on the comparisons of the two environmental impact measures under the two growth scenarios for the 50 metro areas analyzed in this study, brownfields redevelopment generally leads to limited expansion of impervious surfaces and lower VMT than trend growth development. This holds true across a variety of metro area contexts, as demonstrated through the variety of metro areas represented in the study.

5 EPA Smart Location Mapping, https://www.epa.gov/smartgrowth/smart-location-mapping 6 Center for Neighborhood Technology, https://www.cnt.org/projects/location-efficiency-hub

Environmental Benefits of Brownfields Redevelopment – A Nationwide Assessment

1.3 Report Structure This report covers the methodology for modeling the BFR and TG scenarios and discusses the environmental modeling results for each scenario for groups of similar metro areas. It also generalizes metro model results to a national level to provide typical ranges of benefits associated with brownfields redevelopment opportunities. Finally, it communicates several key findings as high-level guides for understanding the role that brownfields redevelopment can play at localized and regional/metro scales in minimizing environmental impacts. The remainder of this report is organized into the following sections, supported by a glossary and several detailed technical appendices: • Section 2 – Model Framework and Mechanics: Explains the scenario analysis process, data sources used, growth and environmental modeling procedures, and assumptions for developing alternative growth scenarios for any given metro area. • Section 3 – Model Results: Reports and interprets the environmental modeling results, comparing the BFR scenario to the TG scenario to summarize environmental benefits across the 50 analyzed metro areas. • Section 4 – Key Findings: Offers important takeaways from the scenario analysis results, focusing on implications for future investigation and EPA’s mission of environmental protection.

Environmental Benefits of Brownfields Redevelopment – A Nationwide Assessment

2. MODEL FRAMEWORK AND MECHANICS The scenario analysis process is complex. It requires using readily available, nationally consistent data to model growth and development in any given metro area in the United States and prepare estimates of the environmental impacts of new growth for alternative development scenarios. This section covers the key details of the modeling framework and mechanics in four major steps: • Step 1 (Prepare data and model inputs) involves obtaining, vetting, and processing the brownfields, historical growth, and related datasets required to develop the two alternative scenarios (brownfields redevelopment and trend growth). • Step 2 (Develop scenario parameters) focuses on adapting the data processed in Step 1 to a generalized form expected by the land use allocation model used to develop alternative growth scenarios in Step 3. • Step 3 (Allocate growth) allocates metro area growth using a land use allocation model to generate each alternative development scenario. • Step 4 (Estimate environmental impacts) estimates the environmental impacts resulting from new growth as modeled in each alternative scenario. Figure 2 outlines the step-by-step creation of each alternative scenario, the main components of each step, and interactions among the datasets and processes utilized in each scenario. It also lists the key analytical questions answered within each step. The dotted lines in the figure highlight how portions of the TG scenario development inform portions of the brownfields redevelopment scenario. For example, development capacity at brownfield sites is estimated based, in part, on the density of development in block groups near the brownfield, which is calculated as part of the TG scenario development. Figure 2. Overall Model Framework and Process Steps with Key Questions

Environmental Benefits of Brownfields Redevelopment – A Nationwide Assessment

The following sections describe each of the scenario development process steps in detail. 2.1 Scenario Analysis Process Step 1 – Prepare Data and Model Inputs

Step 1 involved obtaining and vetting the necessary datasets and preparing the model inputs to develop the BFR and TG alternative growth scenarios.

2.1.1 Brownfields Redevelopment Scenario Data Inputs ACRES data for brownfields redevelopment scenario Data for the BFR scenario was obtained from the EPA’s Assessment, Cleanup and Redevelopment Exchange System (ACRES). ACRES is an online database for EPA’s Brownfields Program grantees to electronically submit their site-specific brownfields data directly to EPA. The data in ACRES is a subset of the universe of brownfield sites in the United States. Only sites that have received and used funds from the Brownfields Program are included. There are significantly more brownfield properties across the United States than what is represented in ACRES. The data used in this study were pulled from ACRES in early March 2017 and reflect a time period from fiscal year (FY) 1995 through FY 2016. Because the data entry requirements for grantees changed over this time period, not every brownfield property in ACRES had all the necessary information available to be included in the BFR scenario. Therefore, additional data correction efforts were needed to ensure that the data used in modeling the BFR scenario would be as accurate as possible and fairly reflect the inventory of available brownfield sites in the studied metro areas. With help from EPA staff, issues of disparities in reported geographic locations, invalid or missing latitude/longitude coordinates, and questionable property size attributes were corrected using recommended review protocols. Figure 3 illustrates a summary of the results of the ACRES review and applied protocols for developing the final set of brownfield properties for consideration in this study. Figure 3 also provides descriptive statistics for the brownfield properties. Appendix A identifies the key fields from ACRES that were used in the model analysis and provides a detailed presentation of the ACRES review and applied protocols. After all ACRES data points were reviewed and the appropriate correction protocols applied, the universe of brownfield properties available to formulate the BFR scenarios totaled 5,023 unique sites. Each site must have met all of the following criteria to be included in the study:

1. Geographic criteria: a. ACRES geographic coordinates place the site inside a metro area7 in the 50 states (sites in U.S. territories were excluded). b. ACRES geographic coordinates and address information are consistent with one another (see Appendix A for details on geographic consistency).

7 Core-based statistical area (CBSA) based on Office of Management and Budget (OMB) definitions as reflected in U.S. Census geographic data.

Environmental Benefits of Brownfields Redevelopment – A Nationwide Assessment

2. Site attribute criteria: a. ACRES geographic coordinates place the site outside of a protected area (e.g., parkland, nature preserve, and managed lands) as identified via the Protected Areas Database of the United States (PAD-US).8 b. ACRES redevelopment data do not indicate that the entire site acreage will be devoted to future green space. c. ACRES site size data is less than 1,000 acres (very large sites are atypical; allocating to these sites could skew growth and/or environmental analysis results). 3. Redevelopment status criteria: a. The site is not assumed to have already been redeveloped (based on the “Determining brownfields redevelopment status” analysis [see next section]). 4. Allocation model criteria: a. The site is located within the 50 metro areas selected for modeling. b. The site was not excluded based on a pre-allocation protocol described in further detail in Section 2.3.3, Specific Application to 50 Metropolitan Areas.

Figure 3. Reducing the “Universe” of Brownfields

8 United States Geological Survey, National Gap Analysis Project (GAP) | Protected Areas Data Portal, https://www.usgs.gov/core- science-systems/science-analytics-and-synthesis/gap/science/protected-areas

Environmental Benefits of Brownfields Redevelopment – A Nationwide Assessment

Descriptive Statistics for the Universe of Brownfields Sites included in the Study

Descriptive statistic Site Size (after Planned Greenspace Number of grants application of protocols)

Minimum 0.03 acres 0.0 acres 1 Maximum 1,200 acres 180 acres 8 Mean 5.93 acres 0.14 acres 1.13 Median 1.0 acres 0.0 acres9 1 Sites by Number of Grants: 1 grant: 3663 (72.9%) 5 grants: 56 (1.1%) 9 grants: 7 (0.1%) 2 grants: 818 (16.3%) 6 grants: 26 (0.5%) 10 grants: 5 (<0.1%) 3 grants: 294 (5.9%) 7 grants: 19 (0.4%) More than 10 grants: 2 (<0.1%) 4 grants: 124 (2.5%) 8 grants: 9 (0.1%) Number of Grants by Type: Assessment Grants: 4,452 (76.7%) Multi-purpose: 5 (<1%) Cleanup Grants: 234 (4.0%) Section 128(a) State/Tribal: 620 (10.7%) BCLRF: 241 (4.2%) TBA: 254 (4.4%)

Determining brownfields redevelopment status The BFR scenario assumes that all undeveloped brownfield sites will be redeveloped to accommodate metro area growth between 2013 and 2030. However, some of the brownfield sites identified in ACRES were redeveloped prior to the model base year of 2013. These sites were removed from the scenario allocation model because they had already been redeveloped prior to 2013. They are identified in the ACRES database based on the REDEV_START_DATE and REDEV_COMPLETION_DATE fields. Specifically, any site having a redevelopment completion date prior to January 1, 2013, was removed from the scenario allocation model. Likewise, any site having a redevelopment start date prior to January 1, 2012, and having no recorded redevelopment completion date was assumed to have been redeveloped already10; these sites were also removed from the model. On the other hand, sites that have a redevelopment completion date after January 1, 2013, have been redeveloped, but any jobs or housing added to the site would not be reflected in year 2013 datasets. As such, these sites were always included in the scenario allocation model. Future growth was allocated to these sites based on the model assumptions documented below – the specifics of each site’s actual redevelopment program are unknown for the purposes of this study. In addition, if a site has not been confirmed as “ready for reuse” in the ACRES database, it was assumed to not be redeveloped as of 2013 and was included in the scenario allocation model.11

9 The vast majority of sites in ACRES have 0 acres of planned greenspace recorded. The median value (50th percentile) of this skewed distribution is actually zero. 10 An analysis of sites with valid redevelopment start and completion dates suggested that the average redevelopment period is about a year in length, although this may be due to simplified bookkeeping and not a reflection of true typical redevelopment timelines. 11 It is theoretically possible for a site to have a redevelopment completion date before 2013 and not be confirmed as “ready for reuse” in ACRES. In these cases, the redevelopment data were deemed authoritative. These sites were removed from the scenario allocation model based on redevelopment information regardless of their “ready for reuse” status.

Environmental Benefits of Brownfields Redevelopment – A Nationwide Assessment

These criteria applied only to a selection of sites in ACRES. Most sites in ACRES have no recorded information regarding redevelopment start or completion dates. For these records, the site’s redevelopment status is unclear – it is ready for redevelopment, but the actual redevelopment status is unknown. In some cases, these sites may have been redeveloped prior to the model base year, even though no redevelopment activity is recorded in ACRES. This is common among sites that received EPA assistance for assessment and/or cleanup, but the redevelopment was subsequently funded through non-EPA sources. It would be inappropriate to include brownfield sites that have already been redeveloped in the scenario allocation model because they are unlikely to accommodate additional jobs and/or housing beyond the current level of development. To address this issue, a statistical approach was taken to model the redevelopment status for any property that was “ready for reuse,”12 but was missing redevelopment date information. A binary choice model calculating the probability of brownfields redevelopment was estimated utilizing a database of sites assumed to have already been redeveloped (based on the redevelopment start and completion date criteria described above) and a selection of ACRES records for sites known to not yet be redeveloped. Site and neighborhood characteristics are provided as independent variables in the model, which calculates the likelihood that a given ACRES site had already been redeveloped by the start of 2013. This model was used to designate sites with no redevelopment data in ACRES as being “likely already developed” (redevelopment model results ≥ 0.5) or “likely undeveloped.” Sites that were likely already developed were removed from the scenario allocation model. The factors found to reliably estimate brownfield development status are described below. The relative influence of each factor on redevelopment probability varies based on its U.S. Census region location (Northeast, Midwest, South, or West) and metro population (small or large [less than or greater than 1M population]). An illustration of redevelopment status assumptions and model outputs are shown for the Milwaukee, Wisc. area in Figure 4. • Ready for reuse status: Sites indicated as “ready for reuse” in ACRES are more likely to be redeveloped than other sites. • Site size (in acres): Larger site size values from ACRES are correlated with a higher probability of redevelopment. • Number of grants for the site: If a site received funding through multiple grants, it suggests an institutional commitment to cleanup and reuse of the site. Sites with more grants are correlated with a higher probability of redevelopment. • Proximity to fixed guideway transit (share of block group area): Sites near transit station areas have a higher probability of redevelopment, based on the statistical analysis. Transit station area proximity is determined by the proportion of the Census block group (CBG) in which the site is located that is within a half mile of a fixed guideway transit station (data available from EPA’s Smart Location Database [SLD]).13 • Regional centrality: Sites near the center of a metro area are correlated with a higher probability of redevelopment. Regional centrality is measured based on auto accessibility. First, auto accessibility scores are calculated for each CBG in a metro area, defining how many jobs are reachable from each. Then these values are normalized for the region, such that the block group with the highest auto accessibility score receives a score of 1.00 and all other block groups’ scores are expressed as their respective auto accessibility

12 In ACRES, the READY_FOR_REUSE field contains a value of “Y.” 13 EPA Smart Location Mapping, https://www.epa.gov/smartgrowth/smart-location-mapping

Environmental Benefits of Brownfields Redevelopment – A Nationwide Assessment

values with respect to the maximum score (i.e., a value between zero and 1.00). Regional centrality data are available from the SLD (see SLD User Guide for additional information on the calculation of auto accessibility and regional centrality metrics).14 The general formula for the brownfields redevelopment probability model is provided below: 1

1 + 푒푏 + 푚1∗푅푓푟+ 푚2∗푆푆+ 푚3∗푁퐺+ 푚4∗퐷4푏050+ 푚5∗퐷5푐푟푖 Where:

m1, m2, m3, m4, m5 = Coefficients related to “ready for reuse,” “site size,” “number of grants,” “proximity to fixed guideway transit,” and “regional centrality,” respectively. The coefficients vary by the Census region in which the site is located and the size of the metro area in which it is located (see Table 1 below). b = Model constant, which varies by the Census region in which the site is located and the size of the metro area in which it is located (see Table 1) Rfr = Ready for reuse dummy variable (sites that are ready for reuse have a value of 1, all others have a value of zero) SS = Estimated site size (in acres) from the ACRES database (or based on assumptions consistent with the protocols described in Appendix A) NG = Number of grants administered for site assessment or cleanup D4b050 = D4b050 value (share of block group within a half mile of a fixed guideway transit station) from the SLD for the block group in which the site is located D5cri = D5cri value (regional centrality index) from the SLD for the block group in which the site is located

Combined with other quality assurance protocols (mentioned in the previous section and described in detail in Appendix A), the exclusion of sites that were assumed to have already been redeveloped left 22,347 brownfield sites in the universe for potential BFR scenario development (5,363 sites in the 50 analyzed metro areas).

14 EPA Smart Location Database Technical Documentation and User Guide, https://www.epa.gov/smartgrowth/smart-location- database-technical-documentation-and-user-guide

Environmental Benefits of Brownfields Redevelopment – A Nationwide Assessment

Figure 4. Brownfields Redevelopment Status in Milwaukee, Wisc.

Environmental Benefits of Brownfields Redevelopment – A Nationwide Assessment

Table 1. Brownfields Redevelopment Status Model Coefficients by Census Region and Metropolitan Area Size

D4b050 Ready (fixed D5cri Census Metro for Number guideway (regional Region Size Constant Reuse Site Size of Grants transit) centrality) Small -2.8883 2.2466 0.0073 0.1736 2.2664 1.7424 NORTHEAST Large -1.7022 2.0448 0.0198 0.1902 0.7399 -0.8548 Small -2.3834 2.2371 -0.0047 0.3254 NA 0.6844 MIDWEST Large -3.1296 2.3841 0.0185 0.2662 3.3115 0.9350 Small -2.5029 2.2760 0.0044 0.4666 1.6341 -0.0914 SOUTH Large -2.1597 2.0346 0.0108 0.2506 1.0031 0.4199 Small -2.8310 2.1019 0.0067 0.3630 6.0962 0.6057 WEST Large -2.5423 2.9833 0.0128 0.2588 -0.5946 0.0696

2.1.2 Trend Growth Scenario Data Inputs Unlike the BFR scenario, where future growth is allocated to brownfield properties (at the site/property level), the TG scenario utilizes CBGs as the unit of analysis, and new jobs and housing are allocated primarily to block groups with strong recent growth trends. Typically, these are greenfield areas and outer rim locations. CBG features and attribute data were obtained for the entire country from EPA’s Smart Location Database (SLD). To account for the difference in geographic scale between the two scenarios, the BFR allocation results are summarized from the site level to the block group level, and all environmental modeling (see Section 2.4) occurs at the block group scale. To prepare the data for the TG scenario, it was essential to determine: • Which block groups experienced housing growth. • Which block groups experienced employment growth. • What areas within each block group were undeveloped. • What areas within each block group are protected from future development (e.g., parks, preserves, managed lands). Any growing CBG was considered a potential location for new development in the TG scenario. A block group was deemed to be “growing” if: • New housing units were built within the block group between 2000 and 2013, according to the American Community Survey (ACS)15, and • A positive average annual change in jobs located within the block group was estimated from 2003 (or the earliest available year of Longitudinal Employer–Household Dynamics [LEHD] data16) to 2013. The average annual change in jobs was calculated as the mean

15 ACS Table B25034 – Year Structure Built (Housing Units) 16 Most states’ LEHD jobs estimates are available from 2002, but others joined the program later, meaning that the earliest available LEHD data may be from 2005, for example. Massachusetts was the last state to participate in LEHD, starting in 2011. With so few

Environmental Benefits of Brownfields Redevelopment – A Nationwide Assessment

year-over-year change in total employment for the available years’ data. See Appendix B for details on the estimation of average annual change in jobs. Block groups with higher growth rates for a given activity were considered to be more attractive for that type of growth. A block group that was experiencing only housing growth was not considered to be eligible for growth in employment and vice versa. Block groups experiencing growth in both housing and jobs were eligible for either type of growth. Finally, existing activities were assumed to be primarily located on the “developed” portions of each block group. The National Land Cover Database (NLCD) differentiates developed land cover types from undeveloped land cover types. Meanwhile, the Protected Areas Database of the United States (PAD-US) defines acreage protected from significant future development activity. Any areas of a block group outside the PAD-US features was considered “unprotected area.” The undeveloped land cover area for each block group was overlaid on its unprotected acreage to determine its greenfield area – that is, the undeveloped portions of the block group where no development prohibitions apply. The greenfield area is used later in the scenario analysis model to estimate how much new growth can occur in a given location.

2.2 Scenario Analysis Process Step 2 – Develop Scenario Parameters

Step 2 involved developing key parameters for the BFR and TG growth scenarios, including determining how much new development a location can accommodate, which locations are likely to be redeveloped first, and how many new jobs and housing are likely to be added.

The BFR and TG scenarios are generated using a standardized land use allocation model. The model distributes future growth in jobs and housing units across a metro area, and it requires several key pieces of information (parameters) that guide the allocation process. The model parameters address the following questions: • How many new jobs and housing units will be added to a given metro area by 2030? (Control Totals) • How much new development can a given location accommodate? (Capacity) • What locations are most likely to be (re)developed first? (Attractiveness) • What types of activities (jobs and housing) are likely to be added as a given location is developed? (Activity Mix) This section outlines the logic behind each of these allocation questions and describes parameter development based on available data sources for each growth scenario (see Section 2.1 above). It first discusses the BFR scenario, detailing the assumptions and processes that provide the required allocation parameters to the land use model based on ACRES brownfield site data and neighborhood characteristics (i.e., data from nearby block groups). The TG scenario is then addressed with a focus on the assumptions and processes used to estimate values for these parameters based on recent growth trends.

years of data available there, estimates of employment growth trends may be unreliable. In addition, Washington, D.C., only began participating in 2010 and may pose similar challenges. As such, the Washington, D.C., metro area and any metro area with territory in Massachusetts are not well suited for analysis in this study.

Environmental Benefits of Brownfields Redevelopment – A Nationwide Assessment

Table 2 summarizes the key differences in the parameter development assumptions for the BFR and TG scenarios. Note that for both scenarios, control totals are developed in the same way, meaning that the same numbers of new jobs and new housing units are allocated in the BFR and TG scenarios. Control totals are derived from Woods & Poole economic forecasts based on estimated 2013 activity and forecasted 2030 activity for each metro area analyzed. For example, the control total for new jobs to be allocated in a given metro area is the 2030 jobs total forecasted in Woods & Poole for all counties in the metro area minus the 2013 jobs total estimated in Woods & Poole for all counties in the metro area. The increment of “new jobs” is the jobs control total, which is then allocated to brownfield sites and trending block groups.

Table 2. Summary of the Differences in the BFR and TG Scenarios

Brownfields Scenario Development Question Redevelopment Scenario Trend Growth Scenario

How many new jobs and housing For both scenarios, control totals of new jobs and new units will be added to a given housing units are based on Woods & Poole economic metropolitan area by 2030? (Control forecasts, aggregated to the metro level (no difference). Total) How much new development can a Based on site size from Based on undeveloped and given location accommodate? ACRES with reductions for unprotected acreage in (Capacity) greenspace each growing CBG What locations are most likely to be Based on redevelopment Based on the magnitude of (re)developed first? (Attractiveness) probability estimates recent growth trends by type (residential vs. employment) What types of activities (jobs and Likely mix of new activities Based on the magnitude of housing) are likely to be added as a based on recent growth recent growth trends by given location is developed? (Activity trends type (residential vs. Mix) employment)

2.2.1 Brownfields Redevelopment Scenario Parameters The framing questions listed above guided the development of allocation model parameters for the universe of brownfield sites to simulate brownfields redevelopment through 2030 as described below. How much development can a given location accommodate? The magnitude of environmental benefits that brownfields redevelopment might confer to a metro area depends largely on the extent to which brownfields redevelopment can reshape area-wide growth patterns. This, in turn, depends on the amount of growth that can be accommodated by each available brownfield site in the metro area. The ACRES database does not provide detailed information about redevelopment capacity, local policies, or market forces governing potential redevelopment options at any given brownfield site. For this reason, the redevelopment capacity of each brownfield site was estimated for the scenario analysis model as follows.

Environmental Benefits of Brownfields Redevelopment – A Nationwide Assessment

1. The expected density of development at the site was estimated based on the characteristics of the surrounding area (see details below). 2. The size of each brownfield site was obtained using information in ACRES or based on the assumptions outlined in Appendix A. 3. The expected development density was then multiplied by the site size to determine the total number of jobs and housing units that could be developed at the site. Estimating the expected density of development at a brownfield site The expected density of development at a given brownfield site was estimated from the characteristics of the surrounding neighborhood, based on block group data. The “prevailing” density of development in the area around a brownfield site was assumed to provide a reasonable benchmark for the potential density of development at the brownfield site. Two separate prevailing density estimates were developed to model two different BFR scenarios – a “base” configuration in which a lower expected development density was estimated for each brownfield site and an “aggressive” configuration in which a higher expected development density was estimated. These alternative configurations of the BFR scenario allowed the scenario allocation model and subsequent environmental analysis to model a range of potential brownfields redevelopment benefits. In either the base or the aggressive configuration, the expected density of development at the brownfield site was estimated based on the prevailing density of development in the area. For the base configuration, the density of development at a brownfield site is expected to match the most densely developed block group in its vicinity. The prevailing density is estimated based on the highest net activity density observed at a block group within a half-mile radius around the brownfield site17 (see the notes on exceptional cases below). Net activity density was calculated as jobs plus housing units per developed acre.18 For this report, this prevailing density estimate is methodologically identical to the “greenfield” density estimation process for block groups (see Section 2.2.2 below). It represents a reasonable limit on development intensity in greenfield areas based on development intensity in the surrounding area, and this estimate is used to cap development density at brownfield sites in the base configuration. For the aggressive configuration, the density of development at a brownfield site is expected to reflect the potential for development intensification and exceed the density at the most densely developed block group in its vicinity. Thus, the prevailing density of development in the vicinity of a brownfield site could be higher than the greenfield density estimate, especially for sites located in built-out urban settings. For each site, a separate estimate of prevailing density was developed, referred to as the infill density estimate.19 The infill density estimate reflects the possibility that new growth may be added in locations that are already built-out, through redevelopment and infill projects. It is derived based on a regression analysis that estimated the increase in net activity density at the block group level from 2000 to 2010. Net activity density in 2000 was estimated based on year 2000 Census housing data, the 2001 NLCD (see Section 2.1), and 2002 LEHD jobs data.20 Net activity density in 2010 was estimated based on year 2010 Census housing data,

17 That is, around the site’s latitude/longitude coordinates after making any location adjustments according to the protocols outlined in Appendix A. 18 Activity unit density is frequently used in urban planning forecasting applications to express the total intensity of development in a given area. 19 In some cases, the infill density estimate for a brownfield site may be null because no qualifying block groups are within a half-mile of the site. The next paragraph defines the criteria that qualify a neighboring block group’s data to support infill density estimation. 20 2002 is the earliest year for which LEHD data are available.

Environmental Benefits of Brownfields Redevelopment – A Nationwide Assessment the 2011 NLCD, and 2010 LEHD jobs data. The change in net activity density was found to be a function of: • Existing activity density within each block group • Percentage of the block group’s existing activities that are jobs • Age of housing stock within the block group • Metro area population size • Proportion of the block group’s area that is currently undeveloped • Regional centrality (SLD) • Proximity to transit (SLD) The change in net activity density was calculated as the estimated infill density for all block groups having more than half of their unprotected acreage (i.e., areas outside of parks or other protected lands) already developed. For other block groups, the infill density estimate was not applicable because a large portion of the block group remains open to greenfield development. If one or more block groups having an infill density estimate were located within a half-mile radius of a brownfield site, the prevailing density estimated for that site was based on the highest infill density estimate. Thus, the prevailing activity density estimate for a given brownfield site in the aggressive configuration was sometimes the greenfield density estimate, just like in the base configuration. For cases where the brownfield site was located in a built-out urban area, the prevailing activity density estimate for the site was the infill density estimate, reflecting the potential for more intense development to occur at the site based on its contexts. In all cases, for the aggressive configuration of the BFR scenario, the prevailing density estimate (whether it reflects the greenfield density or infill density) for a given brownfield site was doubled21 to estimate the expected density of development at the site. The combination of the potential use of the infill density estimate and the doubling of the prevailing density estimate allows brownfield sites to take on a higher percentage of metro area growth in the aggressive configuration. The decision to double prevailing density in modeling the aggressive BFR configuration was vetted through a review of available literature addressing brownfields redevelopment and urban infill development.22 The reviewed studies focused largely on comparisons with greenfield and suburban locations rather than on comparable developments in the immediate vicinity of each site. There was little in the literature that provided comparison of brownfields development densities relative to nearby/adjacent infill site densities or what might constitute an aggressive or compact development density. However, while there are many site- and location-specific variables that go into development decisions (such as zoning, entitlements, and other land use planning principals), the literature does support the notion that brownfields typically are developed at higher densities than greenfield developments (sometimes significantly so), and that redevelopment density is an important determinant of expected environmental benefits justifying a remediation effort. The literature also suggests that generally all developers seek higher development densities for their projects to drive their financial viability, including brownfields

21 In 2010, the Urban Land Institute (ULI) completed a review of three studies on land use and driving, and the role that compact development can play in reducing greenhouse gas emissions. One of the studies used a doubling of density as the basis for a compact development scenario and was used here. http://uli.org/wp-content/uploads/ULI-Documents/Land-Use-and-Driving-Low- Res.pdf 22 In addition to the ULI studies cited above, other articles and studies were reviewed, including: Hendrickson, C. et. al., 2013. Estimation of Comparative Life Cycle Costs and Greenhouse Gas Emissions of Residential Brownfield and Greenfield Developments, https://www.cmu.edu/steinbrenner/brownfields/Current%20Projects/files/bf_gf-life-cycle-comparison-paper-final-submittal.pdf; Mashayekh, Y. et. al., 2012. Role of Brownfield Developments in Reducing Household Vehicle Travel, https://www.cmu.edu/steinbrenner/brownfields/epa-lca-project/ym-role-of-brownfield-developments-in-reducing-household-vehicle- travel-asce-up-1943-5444-0000113.pdf; and Paul, E., 2008. The Environmental and Economic Impacts of Brownfields Redevelopment, http://www.nemw.org/wp-content/uploads/2015/06/2008-Environ-Econ-Impacts-Brownfield-Redev.pdf.

Environmental Benefits of Brownfields Redevelopment – A Nationwide Assessment developers who are often faced with additional costs associated with properly managing and mitigating contamination. In the allocation model, the BFR scenario assumes that each brownfield redevelopment will be at least as dense as nearby developments. The growth model implicitly accounts for local growth policies and economics by estimating brownfield development capacity based on the prevailing development densities in nearby block groups. However, the literature suggests that development might be denser still in many locations. Therefore, for the aggressive BFR configuration, a density factor of two times prevailing density is the assumed increase. Examples from the literature suggest that brownfields can develop at substantially higher densities on a site-by-site basis, but the best available example of a generalized, regional “compact development” paradigm (the 2010 ULI study cited above) uses the doubling assumption to answer similar questions to those posed by this study. In short, the approach taken for this study brackets an optimistic forecast of future development by basing brownfields development densities on current prevailing densities (the base configuration) and on an assumed doubling of that density (aggressive configuration). Figure 5 provides a conceptual illustration of the capacity calculation process for brownfield sites, showing differences between the base and aggressive configurations. The blue area in the center represents a 7-acre brownfield site. The brownfield sits in a block group (demarcated by bold lines) in which the existing density is 1.67 activities per acre, as shown in the orange area below the site. In neighboring block groups, the highest observed existing density is 4.8 activities per acre, which represents the greenfield density estimate. Therefore, in the base configuration, the brownfield site will be assumed to develop to 4.8 activities (jobs plus housing units) per acre. The highest infill density estimate among block groups near the brownfield site is 5.1 activities per acre (note that some neighboring blocks have no infill density estimate since more than half of their areas remain undeveloped). Therefore, for the aggressive configuration, this higher potential density becomes the assumed prevailing density; that figure is doubled to provide an upper limit on allowable total activity density at the brownfield site (10.2 activities per acre). Since the brownfield site is 7 acres in size, its capacity is set at 71 activities (10.2 activities per acre * 7 acres = 71.4 activities, rounded down to 71 activities). For the base configuration, the greenfield density estimate of 4.8 activities per acre would be applied to the 7-acre site, yielding a capacity of 33 activities. For both the base and aggressive BFR scenario configurations, two caveats apply to the above descriptions of how expected development density was estimated for brownfield sites: • If the brownfield site was located in a rural setting, the net activity density of the block group in which the site was located was used to establish the baseline development density rather than the highest density in the vicinity. Any block group larger than 2,500 acres was considered a “rural setting.” • If no prevailing net density information was available, or if prevailing density values were less than 2 activities per acre (i.e., the brownfield site is located in a very sparsely developed area), then a minimum development density of 2 activities per acre was assumed.

Environmental Benefits of Brownfields Redevelopment – A Nationwide Assessment

Figure 5. Capacity Estimation for New Activities at Brownfield Sites

Estimating expected density of development at a brownfield site

Base Configuration Aggressive Configuration

Greenfield density estimate (maximum 4.80 activities per acre 4.80 activities per acre existing density in vicinity)

Infill density estimate (maximum NA 5.10 activities per acre modeled infill density in vicinity)

Prevailing development density 4.80 activities per acre 5.10 activities per acre * 2 = 10.20 activities per acre

Calculating site capacity

Site size 7 acres 7 acres

Capacity (site size * prevailing 7 acres * 4.80 activities per acre 7 acres * 10.2 activities per acre = development density) = 33 activities 71 activities

Figure 6 provides an example of the capacity estimation results for available brownfield sites in the Los Angeles metro area.

Environmental Benefits of Brownfields Redevelopment – A Nationwide Assessment

Figure 6. Brownfield Capacity Estimation Results for the Los Angeles Metro Area

Environmental Benefits of Brownfields Redevelopment – A Nationwide Assessment

What locations are most likely to be redeveloped first? The land use allocation model used to generate the 2030 BFR scenario requires some mechanism to identify the probable order of redevelopment at brownfield sites over the 2013 to 2030 period. This is typically provided in the form of a development “attractiveness” score. For brownfield sites, the attractiveness score was estimated as the “redevelopment probability” value generated by applying the Brownfields Redevelopment Status Model described in the “Determining brownfields redevelopment status” section above. For attractiveness scoring purposes, the model was applied to all sites that were in the allocation set (i.e., those assumed to be available for redevelopment), yielding a redevelopment probability score that ranks some sites as more likely to be redeveloped (more attractive) than others. The most attractive sites will “fill up” with redevelopment (new jobs and/or housing units up to full capacity) first.23 Figure 7 shows the results of the brownfield attractiveness scoring process for the Los Angeles metro area. What types of activities (jobs and housing) are likely to be added as a given location is developed? The estimated mix of future (allocated) growth activities (jobs and housing) at a given brownfield site was estimated based on several factors:

1. For areas of increasing growth, the mix of activities allocated to each site is guided by the jobs-to-housing ratio corresponding to recent growth patterns in the block group in which the site is located. (ACS and LEHD – see Section 2.1.2, Trend Growth Scenario Data Inputs, above). 2. If the area has not experienced growth recently, then the mix of activities allocated is based on the existing total jobs-to-housing ratios in the block group in which the site is located (ACS and LEHD). 3. If the jobs-to-housing ratio of growth in the block group, or for existing activity in the block group, is unavailable due to a lack of activities, then the mix of allocated activities is based on the jobs-to-housing ratio for recent growth in the Census tract in which the site is located. This circumstance is very rare and applies to a very small set of sites (ACS and LEHD).

Figure 8 shows the brownfield activity mix estimates for sites in the Los Angeles metro area.

23 In the BFR allocation, ultimately all sites will fill up, making the differentiation of brownfield sites by redevelopment attractiveness scores largely unnecessary. This is because the universe of available brownfield sites is limited to a subset of sites in the ACRES database, and there are not enough sites listed to accommodate all incoming growth for any given metro area. The attractiveness scoring process described here does, however, establish a standard approach for translating the ACRES database (with appropriate screening protocols applied) into a set of usable inputs for the land use allocation model, anticipating the potential for expansion of the universe of brownfield sites through expansions of, or supplements for, the ACRES database. With a large enough number of sites and sufficient development capacity, it may be possible to generate a scenario in which brownfields capture all metro area growth, leaving some sites undeveloped over the allocation horizon. In such a case, it is necessary to provide attractiveness scores to order the allocation and focus development at the sites with the highest redevelopment probabilities.

Environmental Benefits of Brownfields Redevelopment – A Nationwide Assessment

Figure 7. Brownfield Attractiveness Estimation Results for the Los Angeles Metro Area

Environmental Benefits of Brownfields Redevelopment – A Nationwide Assessment

Figure 8. Brownfield Activity Mix Estimation Results for the Los Angeles Metro Area

Environmental Benefits of Brownfields Redevelopment – A Nationwide Assessment

2.2.2 Trend Growth Scenario Parameters Guided by the same framing questions, the land use model parameters for the TG scenario are based on recent growth trends. While the BFR parameter estimates referenced brownfield sites as the unit of analysis for the primary allocation phase and then shifts to block groups for the secondary phase, the TG parameter estimates are summarized at the CBG level for both allocation phases. How much development can a given location accommodate? As with the BFR scenario, the growth capacity for each block group was estimated for the TG scenario by estimating the developable area within the block group and applying an expected density of development to that acreage. However, the TG capacity estimate blends the greenfield and infill capacity estimates described in Section 2.2.1, above, to yield total estimated development capacity. Greenfield capacity estimate: As noted in Section 2.1.2, Trend Growth Scenario Data Inputs, above, the undeveloped, unprotected portion of a block group (i.e., undeveloped area outside of parklands and other areas protected from significant development) was taken as its greenfield area. The expected density of development in TG greenfield areas was estimated in the same way as the base configuration’s prevailing density estimate described in the BFR scenario. The net activity density (jobs plus housing per developed acre) in all block groups was estimated. For each block group, the highest prevailing net density among itself and its neighbors (any adjacent block groups) was taken as its greenfield density estimate. The product of the greenfield area and the greenfield density estimate is the estimated development capacity in the greenfield portions of each block group. Infill capacity estimate: The developed, unprotected portion of a block group (i.e., developed area outside of parklands and other areas protected from significant development) was used as its infill area. The expected increase in density within the infill area was determined based on a regression analysis as described in Section 2.2.1 above. In brief, the change in net activity density was found to be a function of: • Existing activity density within the block group • Percentage of the block group’s existing activities that are jobs • Age of housing stock within the block group • Metro population size • Proportion of the block group’s area that is currently undeveloped • Regional centrality (SLD) • Proximity to transit (SLD)

These factors were used to estimate the infill density estimate – the potential change in density in portions of a block group that are already developed. The infill density estimate accounts for the potential for built-out (portions of) block groups to continue to grow. It was only calculated for block groups that have more than half of their existing unprotected acreage already developed. For other block groups, the infill density estimate is not applicable because the majority of the block group remains open for greenfield development. The product of the infill area and the infill density estimate is the estimated development capacity in the infill portions of each block group. The greenfield and infill capacity estimates were summed to produce the total development capacity estimate for each TG block group. There is no “base” or “aggressive” configuration of the TG scenario because it is the “business as usual” baseline. The TG scenario is intended to

Environmental Benefits of Brownfields Redevelopment – A Nationwide Assessment model growth trends as a frame of reference against which the BFR scenario may be compared. To assume the TG scenario follows different growth dynamics (i.e., different allowable development densities) simply because brownfields are assumed to develop to higher densities in its aggressive configuration undermines the central premise of this study. Thus, only one capacity value is calculated and provided to the land use model for trending block groups. Figure 9 shows the capacity estimation results for the Los Angeles metro area. Figure 9. Trend Growth Capacity Estimation Results for the Los Angeles Metro Area

Environmental Benefits of Brownfields Redevelopment – A Nationwide Assessment

What locations are most likely to be developed first? The attractiveness score of each block group, which determines the order in which block groups are developed for trend growth, is estimated based on each block group’s share of total metro growth in housing or jobs. For example, if a given block group “A” added 100 housing units from 2000 to 2013 and was located in a metro area that added 20,000 housing units over the same period, that block group represents one-half of 1 percent of the total growth in housing in the metro area. Block groups that experienced the highest proportion of recent metro growth are allocated additional growth prior to allocating growth to block groups that represent smaller proportions of recent growth. Continuing the example above, if block group A added 100 housing units and block group B added 1,000 housing units, block group B is clearly growing more rapidly and, therefore, the model allocates additional growth to block group B before allocating growth to block group A, assuming that block group B has the capacity to accommodate the additional growth. The attractiveness scores were calculated independently for the housing allocation and for the employment allocation.24 Figure 10 displays trend growth attractiveness scores for the Los Angeles metro area. The scores are indexed such that the block group with the highest attractiveness value is given a value of 100 and all other block groups are scored relative to that maximum score. Keeping with the example presented above, block group B has the highest housing attractiveness and would receive an index score of 100; block group A has an attractiveness value that is one-tenth of block group B and would receive an index score of 10. What types of activities (jobs and housing) are likely to be added as a given location is developed? For the TG scenario, an activity type (i.e., a job or a household) is allocated to a particular block group based on the attractiveness score of the block group. If the growth activity is an increase in households, the housing attractiveness scores determine which block group the new unit is allocated to. Alternatively, if the activity to be allocated is a job, the employment attractiveness scores determine which block group the new job is allocated to. New jobs and housing units are effectively allocated simultaneously, and one activity type does not take precedence over the other in block groups that are growing both in residential and non-residential activity.

2.3 Scenario Analysis Process Step 3 – Allocate Growth

Step 3 involved running the model to allocate growth in jobs and housing under the BFR and TG scenarios for 50 metro areas of different population size, geographic location, growth dynamics, development history, and density of brownfield sites.

2.3.1 Overview of Allocation Model Steps Once the parameters guiding the allocation of new jobs and housing units within each metro area were prepared for brownfield sites and trend growth block groups, the allocation model was run.

24 Ideally, separate housing and employment attractiveness scores would be calculated for the brownfields scenario as well. However, limited data on the details of brownfields redevelopment projects make it difficult to achieve these independent estimates. As such, the brownfields scenario uses an estimate of generalized redevelopment attractiveness and bases the mix of activities (jobs versus housing units) on prevailing growth trends or existing activities in the vicinity.

Environmental Benefits of Brownfields Redevelopment – A Nationwide Assessment

Figure 10. Trend Growth Housing Attractiveness Estimation Results for the Los Angeles Metro Area

Environmental Benefits of Brownfields Redevelopment – A Nationwide Assessment

The model relies on metro area control totals to determine how much growth to allocate to each metro area. As explained in Section 2.2, control totals are simply the number of new jobs and housing units to allocate for a given metro area. One metro area might have forecasted growth of 10,000 new jobs, while another may be expecting more than a million. For this study, the control total quantities of new jobs and new housing units for each metro area analyzed were derived from Woods & Poole county-level demographic and economic forecasts, which cover all U.S. jurisdictions. The control total amount of each activity type is distributed across the metro area on an iterative one-by-one basis. For example, starting with a control total of 100 new jobs, the model would select a brownfield site or block group location (depending on the scenario being analyzed) within the metro area to allocate a single job to, leaving 99 jobs remaining to be allocated. This process would be repeated until there are no jobs remaining to be allocated. The selection of which location to allocate each job to varies with each iteration and is guided by the other model parameters (capacity, attractiveness, and activity mix as defined in Section 2.2). The steps of the location selection process are outlined below. Determining what activity to allocate in each iteration For most regions, there are both new jobs and new housing units to allocate. Since jobs and housing units may be co-located, and since the allocation of activity to a given location reduces its capacity for additional growth in subsequent iterations, allocating one activity type prior to the other would give undue precedence to that activity type. For example, if the allocation model distributed job growth first, the allocated jobs may consume all of the available capacity at a given location before the housing allocation begins. Even if that location is attractive for housing growth, no housing units can be allocated there because there is no remaining development capacity. The same problem could occur if housing units were allocated first. There is no compelling reason to prioritize jobs over housing (or vice versa) in this way. As such, at the start of each iteration, the allocation model randomly chooses to allocate a job or housing unit. As the model works through numerous iterations, jobs and housing units are effectively allocated simultaneously. Determining where to allocate an activity Once the activity to be allocated has been determined, the location where the activity will be allocated is selected based on the attractiveness and capacity values of all of the potential locations in the metro area. In the BFR scenario, these potential locations include the available brownfield sites; in the TG scenario, they are block groups. • The influence of attractiveness scores. The selection of a location where an activity will be allocated is made by a random choice, but the probability of picking a given location is weighted by its attractiveness score. A helpful illustration of this process is to consider a dice roll. With a normal six-sided die, the probability of rolling a five is about 17 percent, the same as for any other number on the die. However, if the die has three faces showing the number five, the probability of rolling a five is 50 percent. The attractiveness scores calculated for potential allocation locations work in the same way, increasing the probability that a highly attractive location is selected and diminishing the probability that a modestly attractive location is selected. Thus, the attractiveness scores described in Section 2.2 guide the allocation model to put more activity in the most attractive locations while distributing growth throughout the metro area. • The influence of capacity scores. When a location is selected, the activity to be allocated is added to that location, and the location’s capacity for additional growth is diminished. Over the course of numerous iterations, a single location may be selected many times, and eventually, its capacity may be exhausted. When this happens, the location is “de-

Environmental Benefits of Brownfields Redevelopment – A Nationwide Assessment

activated,” and it cannot be selected again in subsequent iterations. Additional growth will be allocated to other locations that have remaining capacity. Thus, the development capacity estimates described in Section 2.2 limit the amount of activity that can be allocated to a single location. • The influence of activity mix scores (BFR scenario only). The attractiveness scores that inform the selection of a brownfield site for allocation are activity-neutral. That is, each brownfield has a single attractiveness score that is used regardless of whether a job or a housing unit is being allocated. When a brownfield site is selected for allocation, the activity mix score for the site determines whether that activity can be allocated there. For example, if there has been no recent job growth near a brownfield site, it will have a jobs activity mix score of zero and will not be available for job growth during allocation, even if it has a high redevelopment probability (attractiveness score). Thus, the activity mix values described in Section 2.2 guide the model to allocate different activity types to brownfield sites, such that their modeled development mix (when allocation is complete) resembles the recent growth momentum observed in the area around each site. The activity mix for block groups in the trend growth location is not analyzed because each block group has a separate attractiveness score for housing and jobs growth. The details about how the allocation steps described above are implemented differ slightly between the BFR and TG scenarios. For the BFR scenario, only brownfield sites are considered as potential growth locations when the allocation process begins. That is, the model is focused on allocating metro growth to the brownfield sites exclusively until all of their development capacity has been exhausted, or until all growth in the metro area has been allocated.25 If brownfield capacity is exhausted and any portion of the metro area control totals remains to be allocated, the block group locations used by the TG scenario are introduced as potential growth locations to accommodate the remaining growth. For the TG scenario, the brownfield sites are never included as potential recipient features. All of a metro area’s forecasted growth is allocated to trending block groups. 2.3.2 Phases of Allocation The process described above yields two alternative growth scenarios, modeling all forecasted growth for a given metro area. It also provides a basis for estimating the potential environmental impacts of brownfields redevelopment by focusing on the “phases” of the allocation in the BFR scenario. As noted, the BFR scenario attempts to allocate as many activities as possible to brownfield sites until their capacity for development is exhausted. The remaining The “primary” phase of the metro area growth is then allocated following the trend BFR scenario allocates as growth process. These two distinct allocation phases can much growth as possible to be mirrored in the TG scenario through a simple brownfield sites. During the accounting system: primary phase of the TG • The BFR scenario is modeled in its entirety prior to scenario, the same the TG scenario. increments of jobs and • The first, or “primary,” phase of the BFR scenario housing units allocated to allocates as much growth as possible to brownfield brownfields are re-allocated sites, based on the development capacity and to non-brownfield areas. activity mix estimates for each site.

25 For the 50 metro areas analyzed, there are no cases in which the brownfield sites accommodate all of a CBSA’s growth in jobs and housing.

Environmental Benefits of Brownfields Redevelopment – A Nationwide Assessment

• For the TG scenario allocation, the total number of jobs and housing units allocated to brownfield sites in the first “phase” of the BFR scenario is known. Thus, a corresponding first phase of the TG scenario can be run, utilizing these amounts as control totals. This “re-allocates” the same portion of growth that went to brownfields into trending areas, earmarking the locations to which growth would be expected to occur in the TG scenario if brownfields redevelopment does not occur. The “secondary” phase of • In the BFR scenario, the “secondary” phase of analysis occurs when analysis occurs when capacity for new growth at capacity for new growth at brownfield sites is exhausted, and all remaining brownfield sites is growth is allocated to trending block groups. exhausted, and all remaining • For the TG scenario, the allocation process simply growth is allocated according continues until all forecasted metro area growth is to the data and parameters allocated, but the secondary phase results are guiding the TG scenario. stored in a separate table from the primary phase results. Figure 11 provides a simple illustration of this process and the implications for each phase of growth. The first phase of allocation is referred to as the “primary” phase and the second as the “secondary” phase. In the example, 10 housing units are allocated. In the study area, there is only one known brownfield site with a capacity for two housing units, and it sits in an attractive centrally located block group. In the primary allocation phase, two of the study area’s 10 new housing units are allocated to the brownfield site. In the secondary phase, the remaining eight housing units are allocated to trending block groups because brownfield capacity has been exhausted. Six of these eight units allocated in the second phase are located in the same block group where the brownfield site is located. Thus, a total of eight out of the 10 new housing units were allocated to the central block group in the BFR scenario. The TG scenario mirrors the BFR scenario, allocating two housing units in the first phase. These two units are allocated to the central block group due to its attractiveness for housing growth. When the second phase of the TG scenario begins, there is only capacity for four additional housing units in the central block group. Therefore, only four of the eight housing units allocated in the secondary phase go to the central block group due to capacity limitations; remaining growth is distributed among the other available block groups. In total, six units are allocated to the central block group in the TG scenario, compared with eight in the BFR scenario. This example shows how available growth capacity on brownfield properties affects the spatial distribution of growth in each allocation phase, as well as in the cumulative picture of area-wide growth. These analysis phases provide the means of comparing the “primary” and “secondary” effects of redeveloping brownfields relative to the TG scenario. For each scenario and each allocation phase, the environmental impacts of new growth are estimated. In the BFR scenario, new development at brownfield sites will result in some increases in impervious surface and VMT generation. However, when that same increment of growth is allocated according to the TG scenario, the environmental impacts of new growth typically are much greater (see Section 3, Model Results, for detailed comparisons). Comparing the portion of growth allocated to brownfields versus where growth may occur in the TG scenario reveals the location efficiency of brownfield sites relative to trending locations. The primary environmental benefits of redeveloping brownfields are accomplished through this location efficiency; by diverting growth into smarter locations (e.g., infill areas and areas where brownfields are typically located), the environmental impacts of new growth are reduced.

Environmental Benefits of Brownfields Redevelopment – A Nationwide Assessment

Figure 11. Illustration of the Allocation Phases

BFR Scenario TG Scenario

Primary phase (Control total = 10 units)

Allocate as many units as possible to brownfields based Based on the number of units allocated to brownfield sites on brownfield site capacity (two units). (two units), allocate the same number to trending areas. The brownfield site cannot take any units.

Secondary phase (Control total = 10 units minus 2 allocated in the primary phase = 8 units)

Allocate the remaining units to trending areas. Capacity Allocate the remaining units to trending areas. Capacity in trending areas has not been affected by the primary in trending areas has been affected by the primary phase phase because all growth was allocated to brownfields. allocation.

Primary phase Secondary phase

allocated unit allocated unit Brownfield area

In addition, sometimes there are secondary benefits of brownfields redevelopment. These benefits arise from the fact that brownfield sites offer additional development capacity in typically efficient locations that would not be available in the TG scenario. In Figure 11 above, six housing units are allocated to the central block group in the secondary phase of the BFR scenario, while two go to peripheral locations. In the TG scenario, four units are allocated centrally and four go to peripheral locations. If the environmental impacts of growth at the central location are lower on a per unit basis than those at peripheral locations, the BFR scenario’s secondary allocation phase will have a lower impact than the TG scenario’s secondary allocation phase. Consider a hypothetical example: A given block group near the central business district of a city experiences substantial recent job growth and has a 20-acre brownfield site within its borders. In the BFR scenario, the brownfield will redevelop to its maximum capacity in the first phase of allocation. In the second phase, the non-brownfield portions of the block group will be in play, following the logic of the TG scenario. In the TG scenario, the brownfield is assumed to have not been redeveloped. Therefore, during the first phase of allocation, a large portion of the block group’s development capacity is used up. During the second phase of the TG allocation, the remaining capacity may be developed before all jobs are allocated, forcing remaining jobs to go to other, less efficient locations around the metro area. When comparing the environmental impacts of each scenario’s second phase of application, the BFR scenario will show a large number of jobs in a central city location, while the TG scenario will show a few jobs in the central city location and others in less efficient locations. The redevelopment of brownfields can confer benefits in shaping growth patterns beyond the redevelopment of the sites themselves by preserving capacity at smart locations. When the scope of analysis is expanded from a single block group to the entire metro area, it is harder to anticipate the extent to which secondary benefits will accrue. In all cases, the secondary

Environmental Benefits of Brownfields Redevelopment – A Nationwide Assessment benefits are modest compared with the primary benefits. The presence and magnitude of secondary benefits depend on the specific brownfield locations and the nature of growth trends in each metro area. For example, if a region’s brownfields are concentrated in central locations and the block groups in which they are located have experienced substantial recent growth, the BFR secondary allocation results will likely show a strong trend toward the center of the region, while the TG secondary allocation results will be more dispersed due to capacity limitations in those block groups. On the other hand, if the region’s brownfields are more dispersed, they offer no or limited additional capacity in central locations. Similarly, if the central locations in which a region’s brownfield sites are found show limited trend growth, the capacity preserved by brownfields redevelopment may not be in demand during the secondary allocation phase. In either of these cases, the secondary benefits of brownfields redevelopment are likely to be modest. The inclusion of secondary benefits in this study provides additional insight into the potential for brownfields redevelopment to shape future growth and promote development patterns that mitigate the environmental impacts of growth. Finally, the combined allocation for each scenario (combining the first and second phases) is evaluated to The “cumulative” assessment assess the “cumulative” benefits of brownfields of each growth scenario redevelopment. This grouping provides the means to compares the environmental assess the extent to which brownfields redevelopment impacts of total growth across can shift the entire metro area’s growth patterns to reduce a broader metro area, the environmental impacts of development. Since the regardless of phase. environmental models rely, in many cases, on multivariate analyses and/or non-linear formulas, the cumulative impacts are not equivalent to the sum of the primary and secondary impacts. Maps illustrating the primary, secondary, and cumulative allocation results for both BFR and TG scenarios are presented in Section 3 of this report. 2.3.3 Specific Application to 50 Metropolitan Areas The allocation approach described above was applied to 50 CBSAs (also referred to in this report as “metropolitan areas” or “metro areas”) to cover metro areas of different population size, geographic location, growth dynamics, development history, and density of brownfield sites. The method for selecting the 50 metro areas involved the following three steps: 1. Reduce the universe of metro areas: To arrive at a robust group of metro areas from which to select the final 50 for analysis, any CBSAs that had limited or problematic data were eliminated from consideration. Of the 955 CBSAs in the United States, 308 lacked ACRES brownfield sites recorded in the ACRES database or had inadequate data. Another 367 were eliminated for having low brownfield density (less than 50 total brownfield sites in the CBSA and fewer than 20 brownfield sites per 1,000 square miles of CBSA area), which is an important indicator of the impact that brownfield development could have on growth dynamics. As a result of this first step, 280 CBSAs (or 29 percent) were left for consideration. 2. Group metro areas by growth dynamics: Six growth profile categories were created using population and growth rate statistics to ensure that metro areas were analyzed against other metro areas of analogous size and growth. Future development patterns are strongly influenced by the size (population) of a metro area, and how slowly or quickly the metro area is growing. It is likely that the environmental impacts of brownfields redevelopment for large metro areas experiencing marginal growth will look very different from the environmental benefits for small, swiftly growing metro areas. Table 3 outlines the characteristics of the six

Environmental Benefits of Brownfields Redevelopment – A Nationwide Assessment

growth profiles that were developed. A detailed explanation of the criteria ranges is available in Appendix C.

Table 3. Characteristics of Growth Profiles for Metropolitan Areas26

No. Growth Capacity of No. Metro Growth Population Brownfield Chosen Profile Redevelop- Eligible for Example Cities Area Size Rate Density Density for Name ment Activity Analysis Analysis Growth Medium Moderate Moderate to High High 19 6 Austin, TX Hub and Large to Rapid High Seattle, WA

Industrial Small and Slow Low to Low to Moderate to 171 18 Sturgis, MI Legacy Tiny Moderate Moderate High Albany, NY

Stable Huge Slow High High High 4 2 Los Angeles, CA Metropolis Philadelphia, PA Slow and Medium Slow Moderate to High Moderate to 16 6 Baltimore, MD Steady and Large High High New Orleans, LA Big and Huge Moderate High High High 4 2 Atlanta, GA Growing to Rapid Dallas, TX Up and Small and Moderate All Densities All Capacities All 66 16 Durham, NC Coming Tiny to Rapid Boulder, CO Total 280 50

3. Select metro areas: The last step involved choosing metro areas from each growth profile. To understand whether metro areas within each growth profile have anything in common beyond population size and growth rate, each group was assessed according to three key indicators: population density, brownfield density, and capacity for growth. These indicators provide deeper insight into the development and industrial history of a metro area, as well as the potential for growth in the future. Of the 280 CBSAs eligible for analysis, 50 were selected to provide broad geographic coverage across the county. The number of modeled CBSAs by profile group loosely reflects the total number in each group. For example, there are 171 CBSAs characterized as Industrial Legacy metro areas, more than any other category. The number of Industrial Legacy metro areas selected for analysis is 18, more than any other category. Likewise, there are a small number of Stable Metropolis and Big and Growing metro areas nationwide (four each), and just two are included from each profile in the analysis (at least two examples from each growth profile were included among the 50 CBSAs). The 50 CBSAs analyzed offer broad coverage of the nation, geographically, such that representatives of each growth profile are found in differing regional contexts. Moreover, the selected CBSAs cover a substantial share of brownfields sites in ACRES and of the national population. Their 5,366 brownfield sites comprise 29 percent of brownfields recorded in ACRES within the 280 eligible CBSAs. In 2010, roughly 73 million people lived in one of the 50 selected CBSAs, or 24 percent of the national population at the time. The results of the analysis undertaken in this study should therefore offer a strong representative sample of the contexts in which brownfields redevelopment can occur across the country, and the typical benefits of brownfields redevelopment described should reflect average conditions for

26 The definition and numerical ranges for each characteristic (metro area size, growth rate, population density, capacity of redevelopment activity, and brownfield density) are described in Appendix C.

Environmental Benefits of Brownfields Redevelopment – A Nationwide Assessment

different metro area types and areas within the country (see the results summarized by EPA region). Figure 12 shows the 50 CBSAs by growth category geographically and Table 4 lists them. Appendix C provides additional details regarding the characterization of CBSAs and the distribution of selected CBSAs by growth profile, EPA region, and Census Region. 4. Quality review of brownfield sites in selected metro areas: Growth allocation modeling was validated using 12 CBSAs (those with asterisks in Table 4), which were selected to span a range of geographic locations, population size, development history, and growth capacity, and to optimize validation test results. During model testing, it was discovered that several brownfield sites appeared as outliers in terms of their development capacity, even after the protocols applied to the ACRES data were in place. These outlier sites overstated brownfield capacity and skewed the results of the BFR scenario. To ensure reasonable results for all CBSAs, a final screening protocol was introduced prior to running the full allocation model. The screening focused on an analysis of the top 10 percent of all brownfield sites in each CBSA (in terms of estimated development capacity). Brownfields were screened based on the reasonableness of each site’s capacity estimate. The screening identified 115 high- capacity sites that had incorrect site size estimates or location information. It also revealed 161 duplicate sites that would have overstated the brownfields capacity for certain CBSAs. The screening required 276 sites (5.14 percent) to be dropped from the analysis. The manual screening results of the top 10 percent of sites in each CBSA suggested that site duplication/co-location may be an issue throughout the ACRES dataset. To address this, an additional set of screening protocols was developed to search the ACRES database for potential duplicate sites based on site location (proximity), property name and address similarity, and site size attributes. This screening protocol was applied only to those brownfield sites in the 50 selected CBSAs and not among the 10 percent of sites subjected to the manual screening process described above. This process flagged 893 additional sites as potential duplicates in 215 sets, where a set is a group of proximate sites with similar attribute details. Each set was then manually reviewed to assess whether the flagged sites in the set were genuine duplicates. This process found a further 67 duplicate sites, which were dropped from the analysis as well as 52 sites for which location data needed to be updated. This resulted in a final total of 5,023 sites included in the allocation process.

Environmental Benefits of Brownfields Redevelopment – A Nationwide Assessment

Figure 12. Map of the 50 Selected CBSAs by Growth Profile

Environmental Benefits of Brownfields Redevelopment – A Nationwide Assessment

Table 4. Categorization of 50 Selected CBSAs by Growth Profile

CBSA Name Census Region EPA Region

Growth Hubs Austin-Round Rock, Texas South 6 Minneapolis-St. Paul–Bloomington, Minn.-Wisc.* Midwest 5 Orlando-Kissimmee-Sanford, Fla. South 4 Portland-Vancouver-Hillsboro, Ore.-Wash. West 10 Sacramento–Roseville–Arden-Arcade, Calif. West 9 Seattle-Tacoma-Bellevue, Wash.* West 10 Industrial Legacy Akron, Ohio Midwest 5 Albany-Schenectady-Troy, N.Y. Northeast 2 Allentown-Bethlehem-Easton, Pa.-N.J. Northeast 3 Ann Arbor, Mich. Midwest 5 Bangor, Maine Northeast 1 Chattanooga, Tenn.-Ga. South 4 Dayton, Ohio Midwest 5 Frankfort, Ind. Midwest 5 Freeport, Ill. Midwest 5 Great Falls, Mont. West 8 Jackson, Miss. South 4 Montgomery, Ala. South 4 New Haven-Milford, Conn. Northeast 1 Shreveport-Bossier City, La. South 6 Stockton-Lodi, Calif. West 9 Sturgis, Mich. Midwest 5 Wichita, Kan. Midwest 7 Wilson, N.C. South 4 Stable Metropolis Los Angeles-Long Beach-Anaheim, Calif.* West 9 Philadelphia-Camden-Wilmington, Pa.-N.J.-Del.-Md. Northeast 2 Slow and Steady Baltimore-Columbia-Towson, Md.* South 3 Hartford-West Hartford-East Hartford, Conn.* Northeast 1 Milwaukee-Waukesha-West Allis, Wisc.* Midwest 5 New Orleans-Metairie, La.* South 6 Rochester, N.Y.* Northeast 2 San Francisco-Oakland-Hayward, Calif.* West 9

Environmental Benefits of Brownfields Redevelopment – A Nationwide Assessment

CBSA Name Census Region EPA Region

Big and Growing Atlanta-Sandy Springs-Roswell, Ga.* South 4 Dallas-Fort Worth-Arlington, Texas* South 6 Up and Coming Albuquerque, N.M.* West 6 Big Rapids, Mich. Midwest 5 Billings, Mont. West 8 Boise City, Idaho West 10 Boulder, Colo. West 8 Burlington-South Burlington, Vt. Northeast 1 Des Moines-West Des Moines, Iowa Midwest 7 Durham-Chapel Hill, N.C. South 4 Grand Rapids-Wyoming, Mich. Midwest 5 Iowa City, Iowa Midwest 7 Knoxville, Tenn. South 4 Lakeland-Winter Haven, Fla. South 4 Morgantown, W.Va. South 3 Ogden-Clearfield, Utah West 8 Portland-South Portland, Maine Northeast 1 Winston-Salem, N.C. South 4 * CBSAs used in validating the growth allocation model.

2.4 Scenario Analysis Process Step 4 – Estimate Environmental Impacts

Step 4 involved estimating the stormwater and air quality impacts of each development scenario within each metro area.

Once the allocation of future activities for the Brownfield Redevelopment (BFR) and Trend Growth (TG) scenarios was determined for all 50 CBSAs, the environmental impacts of each development scenario within each metro area were assessed. The environmental analysis module developed for this study estimates stormwater impacts and air quality impacts. Stormwater impacts are estimated by calculating the expected growth in impervious surface area associated with each growth scenario (urban footprint expansion). Air quality impacts associated with transportation decisions related to new development are estimated by calculating changes in VMT. 2.4.1 Stormwater Impacts Impervious surface coverage is a proxy for a range of stormwater impacts, where higher impervious surface coverage is generally correlated with higher runoff volumes and increased

Environmental Benefits of Brownfields Redevelopment – A Nationwide Assessment concentrations of non-point source pollutants in runoff. Impervious surface growth refers to the total area of hard surfaces added to a location because of new development. Changes in the total area of impervious surfaces may result from construction of new buildings or from infrastructure added to support new development, such as parking lots. Increases in impervious surface are typically higher when development occurs in areas that are characterized by greenfield land covers, such as open space or agricultural uses. When new development or growth occurs in already-developed areas, the increase or expansion of impervious surfaces is usually modest. EPA’s Impervious Surface Growth Model (ISGM)27 was used to estimate the total amount of impervious surface added in each metro area under each development scenario. The model incorporates housing density, jobs density, and metro area centrality to estimate the proportion of total land area covered by impervious materials at the CBG scale. The ISGM is applied to existing conditions based on block group data obtained from EPA’s Smart Location Database (SLD). These data are updated post-allocation, and the model is reapplied. The difference between each block group’s future and existing impervious area estimates reflects the expected change in impervious surface area under each alternative growth scenario. The model equation for the ISGM is provided below: 100 % 푖푚푝 = 1 1 + ( ) 0.008 + 0.1227 ∗ 퐷1푏 + 0.093 ∗ 퐷1푐 + 7.39퐸 − 07 ∗ 퐷5푎푟 Where • % imp = Percentage of a block group’s total area that is covered by impervious surfaces • D1b = Block group’s housing density in units per unprotected acre from the SLD (variable heading “D1b”) for existing conditions or after allocation for future conditions • D1c = Block group’s employment density in jobs per unprotected acre value from the SLD (variable heading “D1c”) for existing conditions or after allocation for future conditions • D5ar = Number of jobs reachable by driving within 45 minutes from the block group from the SLD (variable heading “D5ar”) for existing conditions or after allocation for future conditions For a given block group, as new jobs and residents are allocated, the impervious surface coverage will be modeled to increase, as all of the variables named above will increase. The increases will be more pronounced in areas that are currently sparsely developed, which receive substantial growth. The increase in total impervious surface area will be least pronounced in areas that are currently heavily developed. Typically, brownfield sites are found in currently developed areas, and growth in these areas is likely to only modestly increase impervious surface area. The trend growth areas often include greenfield areas and peripheral locations where new growth will substantially increase impervious surface area. 2.4.2 Transportation Impacts Each scenario’s impacts on transportation behaviors were modeled by estimating VMT generated by new growth. VMT is a measure of total vehicular travel within a metro area and is a proxy for transportation-related air emissions. Both the BFR and TG scenarios were evaluated using two independent VMT assessments: residential and employment. Residential VMT describes the amount of driving undertaken to and from new households, and employment VMT describes the amount of driving undertaken to and from new jobs. In either case, the expectation is that new activities located in centralized, well connected, multi-modal areas will generate fewer VMT than

27 EPA Impervious Surface Growth Model, https://www.epa.gov/smartgrowth/impervious-surface-growth-model

Environmental Benefits of Brownfields Redevelopment – A Nationwide Assessment new activities in auto-oriented, fringe development areas. Analyzing both VMT measures offers a complete understanding of the impacts of new development on travel patterns under the BFR and TG scenarios. Residential VMT Household travel behavior has been shown to be responsive to various attributes of the built environment, often referred to as “D variables.” As shown in Table 5, these include several common measures, such as density of development, diversity of land uses, design of neighborhood streets, distance to transit, and access to destinations. Built environment attribute data from the SLD were used to model CBG-to-CBG variances in average per capita VMT. The SLD variables referenced are indicators of each of the five D variables commonly referenced in the transportation and land use literature (Table 5).

Table 5. D Variables and SLD Indicators

D Variables SLD Indicator Primary Impact on Travel Behavior More destinations nearby increase Density D1a Residential Density walking and biking. A greater range of destinations nearby Diversity D2 Land Use Entropy also increased the likelihood of walking and biking. Intersection Density, weighted by More direct pedestrian pathways and Design D3 three-legged, four-legged, or more distributed vehicle traffic support more intersections better walking and biking conditions. Convenient access increases the Distance D4 Distance of Transit likelihood of using transit. Greater access to destinations Accessibility to Jobs by Auto, Destinations D5ar generates shorter average vehicle gravity weighted trips.

For each scenario and phase of analysis, residential VMT is estimated using these variables. The details of this procedure are documented in Appendix D. In general, when housing units are allocated to densely developed block groups with diverse land uses, well-connected local street networks, and nearby fixed-guideway transit, and are located in central areas, the residential VMT generation rate will be relatively low. Brownfield sites are often found in these contexts. On the other hand, when the growth pattern is more dispersed and the growth scenario results in additional housing being developed in existing greenfield areas, residential VMT generation will be relatively high. In many CBSAs, substantial portions of recent growth have gone into such areas, although it varies from one metro area to the next. Employment VMT Whereas many studies have been conducted relating the D variables to household travel behaviors, there are comparably few studies covering the attributes of workplace location that influence how workers travel to their jobs. When EPA updated the SLD in 2013, it partnered with the U.S. General Services Administration (GSA) to develop the Smart Location Calculator (SLC).28 The SLC estimates workplace-related VMT based on the D variables, as well as several socio-economic and demographic variables. It consists of two primary components, each having

28 U.S. EPA-GSA Smart Location Calculator, https://www.slc.gsa.gov/slc/

Environmental Benefits of Brownfields Redevelopment – A Nationwide Assessment three steps. The first component addresses home-to-work VMT and the second estimates work- based VMT (meetings, deliveries, lunch/errands, etc.). The three steps for each component are similar. • Step 1 uses a logistic model to estimate the probability that vehicle trips are generated; • Step 2 uses a linear model to estimate the average trip length for each trip type; and • Step 3 multiplies the results of Steps 1 and 2 to yield an estimate of vehicle miles generate per job. The results for each component can be added together for a total VMT per job estimate, which in turn can be applied to total jobs to get total employment-related VMT. The SLC is the best available resource for estimating workplace VMT generation in a consistent manner across the country and was used during this study to assess employment VMT created under each development scenario.

Environmental Benefits of Brownfields Redevelopment – A Nationwide Assessment

3. MODEL RESULTS This section presents the results of the allocation and environmental benefits models, summarized by metro area profile. These results offer rule-of-thumb values indicating the potential environmental benefits of brownfields redevelopment in varying contexts. Differences between the BFR and TG scenarios are reported on a per brownfield acre basis to relativize the impacts of brownfield redevelopment. As noted later in Section 3.4 (Model Uncertainties), this study does not attempt to assess the viability of redeveloping any particular brownfield site; rather it assumes redevelopment will occur at all brownfield sites in the BFR scenario to quantify the typical differences in environmental impacts associated with growth at brownfields sites relative to growing areas in land outside of the urban core or on previously undeveloped and greenfield properties. The per brownfield acre results reported here provide a simple mechanism for quantifying the potential benefits of redeveloping an arbitrary number of brownfield sites, both in terms of direct comparisons to where activities at the brownfield site might otherwise have gone and in the context of holistic regional growth expectations. Detailed results of the allocation and environmental benefits models for each of the 50 selected metro areas are presented in Appendix E. 3.1 Allocation Model Results As discussed in Section 2.3.2, the allocation model proceeds in two major phases: • The first, or “primary,” phase of the BFR scenario allocates as much growth as possible to brownfield sites, based on the development capacity and activity mix estimates for each brownfield. During the primary phase of the TG scenario, the same increments of jobs and housing allocated to brownfields are re-allocated to trending (i.e., non-brownfield) areas. This analysis phase provides a direct comparison of the environmental impacts between localized growth at brownfield sites and growth in non-brownfield areas. • In the “secondary” phase of both scenarios, the remaining increment of growth is allocated according to the data and parameters guiding the TG scenario. In the BFR scenario, block groups have their full capacity for development available for this secondary phase because all growth in the primary phase was allocated to brownfield sites. In the TG scenario, many block groups begin the secondary phase with diminished capacity for additional development, having been assigned new jobs and/or housing units in the primary phase. The differences in environmental impacts between the BFR and TG scenarios for this secondary analysis phase are often small and always smaller than the differences observed in the primary phase. Secondary environmental benefits may arise from preserving development capacity in growing location-efficient neighborhoods. This modeling structure gives priority to brownfield sites in the BFR scenario and assumes that once all known brownfield capacity is developed, the remaining growth will follow recent trends. Table 6 outlines the key concepts to keep in mind when viewing allocation results and interpreting the environmental impact estimates described later in this section. The combination of the primary and secondary allocation phases provides a picture of “cumulative” growth and development from 2013 to 2030 within a metro area under the BFR and TG scenarios. While the primary phase focuses on localized environmental impacts from brownfields redevelopment (relative to growth in non-brownfield areas), the cumulative assessment quantifies the environmental impacts based on areawide growth patterns. The cumulative assessment quantifies the extent to which brownfields redevelopment could reshape broader metropolitan growth patterns and accompanying areawide environmental impacts.

Environmental Benefits of Brownfields Redevelopment – A Nationwide Assessment

Table 6. “Primary” and “Secondary” Allocation Concepts

Brownfields Redevelopment Trend Growth Scenario Scenario

Primary Allocation Phase

Objective Allocate as many jobs and Re-allocate the jobs and housing housing units to brownfield sites units from brownfield sites to as possible based on estimated trending locations throughout the development capacity. metro area.

Where does growth occur? At brownfield sites In neighborhoods (block groups) that have experienced substantial growth in the past decade

What are the implications? Development patterns and travel Development patterns and travel behaviors of new workers and behaviors of new workers and residents will reflect the contexts residents will reflect those typical in which the brownfield sites are in the fastest growing parts of the located. metro area.

How is development Only capacity at brownfield sites Non-brownfield capacity at capacity utilized? is absorbed. trending locations is absorbed.

What are the implications? Non-brownfield capacity remains Non-brownfield capacity is untouched, preserving capacity in reduced at trending locations, the neighborhoods in which the according to the amount of brownfields are located. activity allocated there.

Secondary Allocation Phase

Objective Allocate the remaining increment Continue allocation of jobs and of jobs and housing units to housing units to trending trending areas. locations.

Where does growth occur? In neighborhoods (block groups) In neighborhoods (block groups) that have experienced substantial that have experienced substantial growth in the past decade growth in the past decade. If a trending area’s development capacity is fully absorbed, any remaining growth will go to other trending areas.

What are the implications? Development patterns and travel Development patterns and travel behaviors of new workers and behaviors of new workers and residents will reflect those typical residents will reflect those typical in the fastest growing parts of the in the fastest growing parts of the metro area. metro area, with sufficient development capacity to accommodate the remaining growth.

Environmental Benefits of Brownfields Redevelopment – A Nationwide Assessment

Figures 13 through 15 illustrate the primary, secondary, and cumulative allocation results, respectively, of both BFR and TG scenarios in the Los Angeles metro area.

Figure 13. Primary Allocation in BFR and TG Scenarios for the Los Angeles Metro Area

Environmental Benefits of Brownfields Redevelopment – A Nationwide Assessment

Figure 14. Secondary Allocation in BFR and TG Scenarios for the Los Angeles Metro Area

Environmental Benefits of Brownfields Redevelopment – A Nationwide Assessment

Figure 15. Cumulative Allocation in BFR and TG Scenarios for the Los Angeles Metro Area

Environmental Benefits of Brownfields Redevelopment – A Nationwide Assessment

Table 7 displays the total number of new jobs and housing units allocated to brownfield sites in the primary allocation phase by growth profile (see Table 3 in Section 2.3.3 for explanations of growth profiles). The share of growth captured at brownfields, represented as a percentage of total added jobs or housing, is also reported. Ranges are reported in the table, reflecting the “base” and “aggressive” formulations for estimating the brownfield development capacity described in Section 2.2 (Scenario Analysis Process Step 2 – Develop Scenario Parameters) above. As a reminder, the base estimate assumes that brownfields will develop at densities matching the highest observed densities in nearby block groups; the aggressive estimate assumes more intense development of brownfields (roughly twice the base density estimate, generally).

Table 7. Activities Allocated to Brownfield Sites in the “Primary” Allocation Phase

Housing Jobs Percentage of Percentage of GROWTH Newly Allocated Housing Control Newly Allocated Jobs Jobs Control Housing Units PROFILE Total Total Growth Hub 42,993 to 142,484 2.8% to 9.2% 159,979 to 542,059 5.0% to 17.0% Industrial 40,473 to 117,037 13.6% to 39.2% 28,197 to 96,239 2.9% to 10.0% Legacy Stable 35,637 to 118,883 5.5% to 18.5% 97,564 to 316,045 3.7% to 11.8% Metropolis Slow and 31,083 to 103,394 6.8% to 22.6% 62,487 to 211,536 4.0% to 13.7% Steady Big and 19,514 to 66,664 1.4% to 4.8% 29,618 to 100,346 1.1% to 3.6% Growing Up and Coming 29,888 to 91,153 4.1% to 12.6% 37,124 to 123,115 2.6% to 8.6%

All 199,588 to 639,615 3.9% to 12.6% 414,969 to 1,389,340 3.3% to 11.0%

Brownfield sites in the 50 analyzed metro areas could potentially accommodate as many as 640,000 new housing units and 1.39 million new jobs under the aggressive development scenario. These totals represent almost 13 percent and 11 percent, respectively, of total growth expected for the analyzed metro areas between 2013 and 2030. However, the shares of housing units and jobs potentially accommodated by brownfield sites vary substantially by growth profile. This reflects the variability in the total number of brownfields in each metro area, the density of development around those brownfields (brownfield capacity), the types of growth observed in the vicinity of brownfields in recent years (activity mix), and overall growth rates. Metro areas designated as having an Industrial Legacy growth profile, for example, tend to be slow-growing areas and often have relatively large numbers of brownfields (as demonstrated by the number of property-specific entries in the ACRES database). As such, the allocation model shows that brownfields can absorb a large portion of new growth in these areas. Brownfield sites in Industrial Legacy metro areas are expected to take on an especially high share of housing growth (13.6% to 39.2%), reflective of modest housing growth throughout the metro area and an overall trend of higher housing growth in the areas in which brownfield sites are located. For example, residential redevelopments of former warehouse districts are common in these metro

Environmental Benefits of Brownfields Redevelopment – A Nationwide Assessment areas and provide just one example of how surrounding redevelopment positively influences housing growth in brownfield locations. In contrast, metro areas characterized as Growth Hubs are marked by rapid growth. Brownfield sites in these metro areas only have sufficient capacity to accommodate a small proportion of new housing units (2.8% to 9.2%) and a moderate share of new jobs (5.0% to 17.0%) as the rate of growth in the metro area, overall, is high. Since brownfield locations in these metro areas can accommodate a larger share of jobs than housing units, this suggests that many brownfield sites in Growth Hub metro areas are located in areas that have recently experienced relatively strong job growth. A temporal analysis of the allocation results for the BFR scenario was performed to determine the expected year of brownfield redevelopment completion (i.e., when available brownfield site capacity was filled) (see Appendix F). Across all growth profiles, available brownfield site capacity was typically filled by new jobs within 4 to 9 years of the start of the BFR scenario and by new housing units within 6 to 12 years of the start of the BFR scenario. The brownfield redevelopment completion timeframes depend largely on the base and aggressive configurations of the BFR scenario and represent a mean condition; there are cases where the timeframe is shorter and cases where it is longer. The expected years before brownfield site capacity is filled tends to be relatively near-term (4 to 9 years) in the case of jobs-oriented redevelopment and mid- to long- term (6 to 12 years) for housing-oriented redevelopment. These results generally reflect the tendency for brownfield sites to be located in employment-rich areas. Detailed results of the allocation model for each of the 50 selected metro areas are in Appendix E. 3.2 Translating Development Patterns to Environmental Outcomes As noted above, the allocation results provide distinct pictures of growth patterns, distributing new jobs and housing units to brownfield sites and trending areas in the BFR scenario and to trending areas only in the TG scenario. These growth patterns drive the assessment of the expected environmental impacts associated with new growth. The centerpiece of the environmental models is the concept of location efficiency, which posits that the effects Location efficiency of urban development are different in different parts of a promotes development metro area. Location efficient communities are dense and patterns that limit the strain vibrant, with walkable streets, access to transit, proximity on existing stormwater and to jobs, mixed land uses, and concentrations of retail and transportation infrastructure, services.29 Location efficiency promotes development and the associated patterns that limit the strain on existing stormwater and environmental impacts of transportation infrastructure, and the associated increased stormwater and environmental impacts of increased stormwater and traffic traffic loads. loads. The location efficiency concept is applied to estimate the expansion of impervious surface due to new growth and the change in VMT resulting from new growth. Illustrations of how location efficiency affects the environmental outcomes of two hypothetical scenarios are provided below: • An out-of-town company is expanding and will bring 250 new jobs to a metro area. The company is considering two sites: one in a redeveloping industrial district adjacent to the metro area’s central business district and one in a suburban research park campus. The

29 Center for Neighborhood Technology, https://www.cnt.org/projects/location-efficiency-hub

Environmental Benefits of Brownfields Redevelopment – A Nationwide Assessment

increase in impervious surface at the first site will likely be much lower than at the second site because the industrial district is located adjacent to an existing developed area, rather than the new office contributing to sprawling development outside of the urban core area. • A family has re-located to a new town. They are considering two houses: one in an in- town neighborhood with frequent bus service and one near a suburban office park with a nearby park-and-ride lot. At the in-town location, the family will likely generate fewer and shorter vehicle trips for commuting and discretionary travel than at the suburban location because the family will be located closer to existing amenities in town. While individual circumstances vary, the illustrations above reflect typical expected outcomes associated with new activities locating in different parts of a metro area. If larger numbers of new jobs and housing units come to more “efficient” areas, the resulting metro development patterns will have less significant environmental impacts. The concept of location efficiency is important to keep in mind when examining the estimated environmental impacts of the BFR and TG scenarios presented in this study. Depending on the location efficiency of brownfield sites and trend growth locations, the development patterns modeled under the BFR scenario will have greater or lesser impacts on the environment compared with the TG scenario. Summarization by allocation phase (primary, secondary, or cumulative) provides insight into how location efficiency dynamics interact with brownfield site characteristics and metro area growth trends. The magnitudes of the differences between the BFR and TG scenarios vary by metro area and are sensitive to the number, locations, and capacity of brownfield sites, as well as the character of metro area growth trends. Table 8 provides an outline of considerations that are useful for interpreting the environmental results by each allocation phase.

Table 8. Understanding Environmental Results by Allocation Phase

Primary Phase Secondary Phase Cumulative

Key question(s) Are brownfield sites Do brownfield sites Does the growth diverted to more “location- preserve capacity in brownfield sites result in a efficient” than “location-efficient” metro area growth pattern that trending areas? What trending areas? What is utilizes “location-efficient” is the magnitude of the magnitude of the places more heavily than the the difference? difference? trend? What is the magnitude of the difference?

Comparison BFR vs. TG BFR vs. TG BFR vs. TG environmental environmental environmental impacts for impacts for the cumulative impacts for the the “secondary” phase of allocations “primary” phase of allocation allocation

Factors Where are brownfield Are brownfield sites How much growth was affecting results sites located vs. located in “location- allocated to brownfield sites? where are trending efficient” trending areas? Were the brownfield sites areas located? How much growth was more “location-efficient” than allocated to brownfield trending areas? sites?

Environmental Benefits of Brownfields Redevelopment – A Nationwide Assessment

The environmental impacts of the primary allocation phase provide insight into the location efficiency of a metro area’s brownfield sites relative to its trending areas. Consider the two illustration examples noted above, as applied to thousands of new jobs and housing units. If brownfields are situated in location-efficient areas, many incoming businesses and residents will settle in areas within or nearby downtown, resulting in relatively light impacts on impervious surface and VMT generation. However, it is possible that the brownfield sites are in inefficient locations for some metro areas. Moreover, some metro areas may be experiencing growth trends that emphasize urban infill development and promote efficient development patterns. In these instances, the brownfield location may not produce less environmental impacts than traditional growth in trending areas. Thus, the primary environmental results reported in the following sections describe the relative efficiency of brownfield sites compared with the growth trend. For the secondary phase of allocation, the principal difference between the BFR and TG scenarios is the available development capacity at trending locations (see Table 6). In the BFR scenario, only brownfield capacity is utilized, so the block group in which a brownfield is located retains its development capacity as allocated activities are accommodated by brownfield sites during the primary phase. If this neighborhood is a TG location as well, some of its capacity will likely have been utilized during the primary allocation phase for the TG scenario, leaving only a fraction of that capacity available for the secondary allocation. Again, the illustration of a new company entering the marketplace and considering locating in a redeveloping industrial district is a useful aid. In the BFR scenario, the company might fill space in a brownfields redevelopment, preserving non-brownfield portions of the old industrial district for additional development by future incoming jobs or residents. In the TG scenario, however, the brownfields redevelopment is not an option, so the company consumes non-brownfield development capacity in the old industrial district. Whereas in the BFR scenario, the old industrial district could host new jobs in the brownfield location, as well as in non-brownfield portions of the area; in the TG scenario, it can only host jobs in the non-brownfield portions. Additional new jobs or residents will have to search for space in other parts of the metro area that may be less efficient. Therefore, the BFR scenario may offer environmental benefits over the TG scenario if brownfield sites are in location-efficient areas and those areas have experienced substantial growth in recent years (i.e., they are attractive locations for trend growth). Moreover, the amount of growth allocated to brownfield sites in the primary allocation phase impacts the amount of non-brownfield capacity preserved in the secondary phase, making the potential for secondary benefits reflective of the total brownfield capacity at sites in trending, location-efficient areas. The cumulative summarization of the primary and secondary allocation phases and accompanying environmental impacts represent the overall patterns of development under the BFR and TG scenarios. If the cumulative BFR scenario has less impact than the cumulative TG scenario, it indicates that the redevelopment of brownfields is expected to divert enough growth to location-efficient areas to alter metro area development patterns and reduce the environmental impacts of new growth. If brownfield sites are in areas that are not location-efficient relative to the trend growth, the cumulative results will show little difference between the BFR and TG scenarios, indicating that brownfields redevelopment would produce minimal impacts on the environmental outcomes of new growth in the metro area. In addition, there may be cases where the brownfield sites are more location-efficient than the trend growth, but the development capacity of brownfields is insufficient to meaningfully alter growth trends, allowing the impacts from growth in the secondary allocation to dominate the benefits of brownfields redevelopment, as modeled in the primary allocation. Each metro area is different. The results for the environmental impacts of new growth for all phases are described in detail in the sections that follow, grouped by growth profile, to provide a

Environmental Benefits of Brownfields Redevelopment – A Nationwide Assessment broad understanding of trends affecting the potential benefits of brownfields redevelopment across the country. Figure 13 shows the primary allocation results of both the BFR and TG scenarios in the Los Angeles metro area. 3.3 Environmental Impact Model Results The location efficiency of a site influences the environmental impacts of new growth, as modeled by the increase in impervious surface and VMT. Increases in impervious surface from new development typically brings about increased stormwater runoff volumes and greater levels of non-point source pollutants in runoff. Transportation impacts of new development are measured in terms of additional VMT generated. VMT impacts are sensitive to the location efficiency of new housing growth, as well as that of new employment growth. As such, two separate VMT estimates are provided – one focused on residential VMT and the other focused on employment VMT. The two estimates cannot be combined into a single estimate of total VMT as each includes unknown portions of trips between home and work, and a sum of the numbers would double-count VMT resulting from commuting trips. However, both VMT estimates provide insight into how development patterns mitigate vehicular travel demand and associated emissions. The models used to estimate the environmental impacts of new growth yield estimates of total changes in impervious surface and VMT. Since the metro areas evaluated each have different past and future growth trends and brownfield locations, these figures make comparisons among metro areas difficult. For the purposes of reporting and meaningful comparison across growth profiles, the model results are translated to the same unit of measure – change per redeveloped brownfield acre. This normalized measure is calculated by dividing the absolute changes yielded from the environmental models by the acreage of brownfields to which activities were allocated in the allocation model. In addition, VMT generation on a per capita or per worker basis is reported. Environmental results are presented for the primary, secondary, and cumulative allocations. The emphasis of this report is on the primary phase because it provides a direct comparison of brownfields redevelopment and trend growth. Superior location efficiency, as reflected in the primary phase results, would suggest that the redevelopment of brownfields offers environmental benefits beyond site remediation by promoting development patterns that limit the strains on existing stormwater and transportation infrastructure, and the associated environmental impacts of increased stormwater and traffic loads. The secondary phase results reveal the potential for brownfields redevelopment to allow better utilization of non-brownfield capacity in location- efficient areas to further mitigate the environmental impacts of new development. The cumulative results describe the extent to which brownfields redevelopment can be expected to shift broader metro area growth patterns to mitigate the environmental impacts of new development. The results presented below focus on the differences between the BFR and TG scenarios. The numbers and percentages reported in the tables reflect the BFR scenario results minus the TG scenario results. Negative numbers and percentages indicate that the BFR scenario confers an environmental benefit by mitigating the impacts of new development on impervious surface expansion or VMT generation. Detailed results from the environmental modeling for each of the 50 selected metro areas are in Appendix E. 3.3.1 Impervious Surface Growth Growth and development modify existing land covers, replacing previously pervious surfaces, such as fields and forests, with pavement and rooftops. Development patterns that limit the

Environmental Benefits of Brownfields Redevelopment – A Nationwide Assessment expansion of impervious surfaces benefit the environment by mitigating the runoff of pollutants to waterbodies and benefit the jurisdictions and developers responsible for providing stormwater management infrastructure. Table 9 displays the potential benefits of brownfields redevelopment in limiting impervious surface expansion.

Table 9. Change in Impervious Surface Acres, Primary Phase

Change in Impervious Surface Percent Change in Impervious GROWTH PROFILE Acres per Redeveloped Brownfield Acre (acres) Surface Acres (%) Growth Hub -3.42 to -11.8 -85.8 to -89.7 Industrial Legacy -0.57 to -2.08 -56.3 to -66.1 Stable Metropolis -0.91 to -3.44 -65.1 to -77.2 Slow and Steady -1.09 to -4.36 -65.4 to -74.4 Big and Growing -0.84 to -3.26 -63.0 to -71.9 Up and Coming -0.76 to -2.71 -64.9 to -72.2 All30 -1.28 to -4.60 -72.3 to -79.7

The total impervious surface acreage added by brownfields redevelopment was significantly lower than that added by TG development for all metro areas analyzed. For every brownfield acre redeveloped, approximately 1.28 to 4.60 acres of impervious surface would be expected to be saved if the same development had occurred at TG sites (see “All” row in Table 9). This range represents the average reduction in impervious surface by brownfields redevelopment based on the location efficiency of these sites. Thus, if a given metro had 1,000 acres of developable brownfield sites, it would be reasonable to assume that their redevelopment would save approximately 1,280 to 4,600 acres of impervious surface. On a percentage basis, brownfields redevelopment results in impervious surface reductions of approximately 73 percent to 80 percent compared to trend growth. However, the ranges vary depending on the metro area. Brownfield sites are much more location- efficient than TG sites in Growth Hub metro areas, which show the most dramatic difference between the BFR and TG scenarios. This indicates that brownfield sites are typically in centralized areas in Growth Hubs and that recent growth trends in these metro areas have emphasized fringe expansion. In contrast, Industrial Legacy metro areas represent the smallest reductions in impervious surface acres of all the growth profiles. These results suggest that brownfield sites in Industrial Legacy metro areas are in decentralized areas, the growth trend in these areas is relatively compact, or a mix of both. As described in detail above, secondary benefits are created when primary activities are allocated to brownfields in location-efficient areas, preserving non-brownfield capacity in the area surrounding the brownfield. Table 10 shows the general potential to minimize impervious surface expansion in these areas, although the sensitivity to the amount of growth allocated to the brownfield sites is easily discernible.

30 Here and throughout this section, the “All” row is not the average of the rows above. It is calculated by combining the model results for all 50 metro areas analyzed and generating a range of nationwide average values.

Environmental Benefits of Brownfields Redevelopment – A Nationwide Assessment

Table 10. Change in Impervious Surface Acres, Secondary Phase

Change in Impervious Surface GROWTH PROFILE per Redeveloped Brownfield Acre

Growth Hub -0.17 to -1.26

Industrial Legacy 0.05 to -0.13

Stable Metropolis -0.36 to -0.91

Slow and Steady -0.35 to -1.72

Big and Growing 0.91 to -0.40

Up and Coming 0.04 to -0.18

All -0.04 to -0.71

When brownfields are redeveloped “aggressively,” all metro areas are expected to see additional impervious surface benefits in the BFR scenario versus the TG scenario. The results indicate that an aggressive redevelopment approach is required to optimize the potential secondary benefits of brownfields redevelopment. The results for all metro areas combined suggest that each acre of brownfields redevelopment, when developed aggressively, will prevent up to nearly three- quarters of an acre of impervious surface from being added. However, when brownfields are developed “conservatively” under the base configuration, the secondary impervious surface benefits are minimal relative to trend growth. The cumulative benefit measure (see Table 11) combines the primary and secondary impacts to consider the full metro area implications of brownfields redevelopment on impervious surface acres. Since unequal proportions of cumulative growth occur during the primary and secondary phases, the cumulative results are not simply the sums of the primary and secondary results.

Table 11. Change in Impervious Surface Acres, Cumulative

Change in Impervious Surface per GROWTH PROFILE Redeveloped Brownfield Acre

Growth Hub -1.56 to -6.35

Industrial Legacy -0.33 to -1.80

Stable Metropolis -0.82 to -2.95

Slow and Steady -0.92 to -4.27

Big and Growing 0.52 to -2.07

Up and Coming -0.37 to -1.75

All -0.65 to -3.16

Environmental Benefits of Brownfields Redevelopment – A Nationwide Assessment

This is clearly visible in the Growth Hub metro areas, which have dramatic differences between the BFR and TG scenarios in the primary results (Table 9). The primary allocation represents a fairly small proportion of cumulative growth (see Section 3.1, Allocation Model Results), and so the cumulative results shown in Table 11 are tempered heavily by the secondary phase results (Table 10). Industrial Legacy, Big and Growing, and Up and Coming metro areas can only gain significant cumulative environmental benefits if brownfields are redeveloped aggressively. With base assumptions applied, the cumulative impact of brownfields redevelopment on impervious growth in these metro areas is negligible when cumulative regional growth is considered. 3.3.2 Transportation and Vehicle-Miles Traveled Travel behaviors often depend on local and metro area contexts. Development in central areas (e.g., central business districts, transportation hubs) typically result in shorter trips and reduced automobile usage compared with developments in fringe areas or areas outside the urban center. Travel to and from home differs from travel to and from work. Since the locations of both homes and jobs influence overall travel behaviors, residential-end and workplace-end transportation analyses are presented below. For ease of comparison with other model results, the changes in both residential and workplace VMT reported below are presented in normalized ranges, from base to aggressive, on a per redeveloped brownfield acre and a per capita basis. The per capita rates provide the most meaningful comparison across different growth profiles. They express how the BFR scenario affects daily VMT generated on a per person or per job basis, normalizing the impact so that the results are not influenced by the varying volume of growth in brownfield sites across growth profiles. The per redeveloped brownfield acre numbers are based on the expected differences in total daily VMT generated, divided by the total brownfield acreage in each metro area. The total VMT estimate used in this calculation is related to the per capita/ per job rate estimate, but also depends on the volume and density of residential/employment growth at brownfield sites. For example, if a 1-acre site has a low estimated per capita VMT generation rate, but only 10 new households are allocated there, the per redeveloped brownfield acre VMT benefit will be modest compared with a scenario in which the same site receives 100 new households. Thus, the two measures provide different insights into how brownfields redevelopment can alter travel behavior. For both residential and workplace VMT measures, the per redeveloped brownfield acre results are reported only for the primary phase of analysis to focus on the location efficiency and quantity of growth at brownfield sites. For the secondary and cumulative phases, the spatial scope of the growth and accompanying VMT modeled shift to numerous trend growth block groups in the metro area. Normalization of the VMT estimate for these region-wide growth patterns on a per brownfield acre basis provides little insight, and the VMT per capita numbers are more helpful for understanding the secondary and cumulative impacts of brownfields redevelopment on VMT generation. Residential VMT One way that brownfields redevelopment can influence the VMT associated with new growth is to divert new housing units to location-efficient areas. Brownfield locations tend to be in densely developed, centralized areas where development typically results in fewer VMT per capita each day than development that occurs in fringe development areas. This results in lower levels of total VMT (and less transportation-related air emissions) generated from new housing development when compared with trend growth locations. As shown in Table 12, residential VMT is expected to be substantially lower in the BFR scenario versus the TG scenario in all growth profiles. The per brownfield acre results suggest that a single

Environmental Benefits of Brownfields Redevelopment – A Nationwide Assessment acre of brownfields redevelopment can reduce residential VMT generation by hundreds of miles per day. This is accomplished by bringing more residents to areas that produce fewer VMT per capita per day. Indeed, the BFR scenario reduces VMT per capita generated each day by 7.3 to 9.7 miles, on average, across all analyzed metro areas. On a percentage basis, brownfields redevelopment results in residential VMT reductions of approximately 25 percent to 33 percent compared to trend growth across all analyzed metro areas. Growth Hub metro areas could see a reduction of 13.0 to 15.2 VMT per capita from primary activity allocation at brownfield sites, the largest relative reduction of residential VMT among all growth profiles. Up and Coming metro areas could also see a large reduction in estimated VMT per capita (-10.9 to -13.2) from new residents in the BFR versus TG scenarios. Both of these growth profiles are characterized by high growth rates. In many fast-growing metro areas, a substantial portion of new development occurs along the suburban periphery due to the volume of growth and cost of land. In these decentralized low-density environments, VMT generation is generally expected to be high. Thus, when housing units are diverted to location-efficient brownfield sites, residential VMT could be reduced substantially. Table 12. Change in Residential-Based VMT, Primary Phase

Change in Residential Change in Residential Percent Change in GROWTH PROFILE VMT per Redeveloped VMT per Capita Residential VMT (%) Brownfield Acre Growth Hub -270 to -1,047 -13.0 to -15.2 -43.9 to -51.0 Industrial Legacy -66.3 to -337 -3.9 to -6.8 -13.4 to -23.2 Stable Metropolis -67.4 to -347 -4.4 to -6.8 -19.6 to -30.1 Slow and Steady -87.6 to -429 -3.9 to -5.7 -16.6 to -24.3 Big and Growing -130 to -565 -7.3 to -9.3 -17.7 to -22.5 Up and Coming -142 to -525 -10.9 to -13.2 -32.1 to -38.6 All -127 to -536 -7.3 to -9.7 -25.2 to -33.1 The secondary results for residential VMT presented in Table 13 suggest that only two growth profiles will typically see reductions in per capita VMT for growth outside of brownfield sites: Stable Metropolis and Big and Growing. In all other growth profiles, residential VMT is estimated to be marginally higher in the BFR scenario versus the TG scenario for the secondary analysis phase.

Table 13. Change in Residential-Based VMT, Secondary Phase

GROWTH PROFILE Change in Residential VMT per Capita Growth Hub 0.04 to 0.08 Industrial Legacy 0.39 to 0.16 Stable Metropolis -0.39 to -0.05 Slow and Steady 0.08 to 0.24 Big and Growing -0.04 to -0.04 Up and Coming 0.22 to 0.10 All 0.01 to 0.05

Environmental Benefits of Brownfields Redevelopment – A Nationwide Assessment

One potential explanation for the higher VMT in the BFR scenario is that although the trending areas are generally less location-efficient than brownfield areas (as seen in the primary results and in the results for other measures), they may evolve into more efficient places over time. Consider the following example: From a current suburban housing development, a family may drive many miles each day for commuting, dropping children off at school, and for shopping and personal business trips. Over time, additional housing developments may come to the area, prompting retailers to build new stores closer to the family. Moreover, a new school may be built to serve the children of the burgeoning community. As the area matures, these land use changes will reduce VMT for the family as many of their daily activities are nearer. The residential VMT model is especially sensitive to these kinds of changes, particularly in metro areas that currently have relatively low area-wide average housing density. This is because the model relies on the elasticities of VMT with respect to density and access to destinations. Increases in density across numerous suburban areas could potentially push local densities above the prevailing average for the metro area, while the introduction of new jobs in these areas also would provide a slight boost to accessibility. This explanation of the findings is supported by the fact that Stable Metropolis and Big and Growing growth profiles are characterized by relatively high area-wide densities, and these areas are the only ones for which a secondary benefit is expected from the model. The cumulative results in Table 14 suggest that the lower VMT levels from the primary allocation outweigh the modest increases modeled from the secondary allocation. Overall, brownfields redevelopment can be expected to reshape metro area growth and focus it in location-efficient areas that reduce the VMT added from new households. The Industrial Legacy growth profile has the greatest cumulative reduction in VMT per capita among all growth profiles, largely driven by the relatively high portion of total housing that can be allocated to brownfields.

Table 14. Change in Residential-Based VMT, Cumulative

GROWTH PROFILE Change in Residential VMT per Capita

Growth Hub -0.4 to -1.7

Industrial Legacy -0.7 to -4.4

Stable Metropolis -0.7 to -1.8

Slow and Steady -0.5 to -2.1

Big and Growing -0.3 to -1.1

Up and Coming -0.5 to -2.3

All -0.5 to -1.8

Employment VMT Travel patterns also are affected by job location. Commuting to and from work is a substantial portion of daily VMT for many people. Thus, job growth in efficient locations often results in shorter commutes and the use of multiple modes of travel, such as available public transportation. In

Environmental Benefits of Brownfields Redevelopment – A Nationwide Assessment addition, work-related travel for meetings or work-based personal business trips (lunch, dry cleaning, or other errands) tends to generate fewer VMT in location-efficient areas. If this study only included the residential VMT estimate, the extent to which brownfields redevelopment could divert job growth to efficient locations would not be understood. For this reason, a separate estimate of employment-related VMT is included. Table 15 displays the differences in employment-based VMT between the BFR and TG scenarios for the primary phase of analysis. As with residential VMT, the employment-based VMT is reported in terms of per brownfield acre redeveloped and on a per job basis for the primary phase; the per job figures only are used for the secondary and cumulative phases. As shown, brownfield sites are consistently found in more location-efficient areas for jobs development than trending areas, regardless of the growth profile. For all analyzed metro areas, new jobs at brownfield sites are expected to generate 2.1 to 2.5 fewer VMT per worker per day than new jobs in trending areas. This difference results in substantially fewer total workplace VMT generated, such that each acre of brownfield redeveloped can be expected to reduce workplace-based VMT by 29.2 to 116 miles per day, on average. On a percentage basis, brownfields redevelopment results in employment VMT reductions of approximately 8.8 percent to 10 percent, compared with trend growth across all analyzed metro areas. Table 15. Change in Employment-Based VMT, Primary Phase

Change in Change in Percent Change in Employment VMT per GROWTH PROFILE Employment VMT per Employment VMT Redeveloped Job (%) Brownfield Acre

Growth Hub -97.9 to -382 -3.3 to -3.8 -13.2 to -15.0

Industrial Legacy -8.9 to -33.5 -1.9 to -2.1 -8.0 to -8.7

Stable Metropolis -18.3 to -73.7 -1.1 to -1.4 -5.0 to -6.2

Slow and Steady -16.9 to -90.9 -1.0 to -1.5 -4.6 to -7.1

Big and Growing -15.2 to -51.2 -1.5 to -1.5 -5.4 to -5.4

Up and Coming -13.6 to -50.2 -2.2 to -2.4 -8.5 to -9.4

All -29.2 to -116 -2.1 to -2.5 -8.8 to -10.3

Once again, the location efficiency of brownfield sites is most evident in Growth Hub metro areas – the per worker VMT generation rates are 3.3 to 3.8 miles lower in the BFR scenario than in the TG scenario, and the volume of jobs allocated per brownfield acre means that this advantage can substantially reduce daily total workplace VMT generated. Industrial Legacy areas again provide an intriguing contrast. While the per worker VMT reduction is not as great as in Growth Hubs, it is still substantial at 1.9 to 2.1 VMT per job. However, the volume of jobs allocated per brownfield acre is relatively low (recall from Section 3.1, Allocation Model Results, that brownfields in Industrial Legacy areas were often in areas with heavily residential growth trends), meaning that the total reduction in workplace VMT per brownfield acre redeveloped is modest compared with other growth profiles.

Environmental Benefits of Brownfields Redevelopment – A Nationwide Assessment

Slow and Steady metro areas have the smallest difference between the BFR and TG scenarios in terms of per worker VMT generation, suggesting that brownfield locations are only modestly more location-efficient than trending job growth areas. However, these metro areas also allocated some of the highest numbers of jobs to brownfield sites. As a result, they have the second highest total estimated VMT reduction per redeveloped brownfield acre among all growth profiles. In general, all modeled metro areas present a potential reduction in employment VMT per job from secondary activity allocation (Table 16). Of all the growth profiles, Slow and Steady metro areas could experience the greatest secondary reduction in employment VMT. The non- brownfield capacity preserved at these locations is utilized in the secondary allocation phase, compounding the brownfields redevelopment benefits by allowing more jobs to be added in efficient areas.

Table 16. Change in Employment-Based VMT, Secondary Phase

GROWTH PROFILE Change in Employment VMT per Job

Growth Hub -0.12 to -0.29

Industrial Legacy -0.06 to -0.11

Stable Metropolis -0.18 to -0.26

Slow and Steady -0.16 to -0.37

Big and Growing -0.06 to -0.06

Up and Coming -0.06 to -0.10

All -0.11 to -0.20

In the primary results, Big and Growing metro areas showed moderate changes in VMT per job, and only modest reductions in total VMT (see Table 15 above). This suggests that Big and Growing metro areas were unable to accommodate a sufficient number of new jobs at brownfield sites to capitalize on the available VMT per job reductions. The allocation results showed that Big and Growing metro areas had a jobs-heavy allocation at brownfields. Taking all of this into consideration, it appears that the brownfields in Big and Growing metro areas may not be concentrated in the densest areas of those metro areas. Otherwise, the change per redeveloped brownfield acre would likely be higher. In addition, the secondary results for employment VMT suggest that, while the redevelopment of these brownfields preserves capacity in location-efficient areas of the metro area, the additional growth that can be allocated in those areas is modest. Since Big and Growing metro areas have very high expected growth rates, it is perhaps not surprising that only a very small proportion of total job growth (1.1 percent to 3.6 percent) was allocated to brownfields (see Section 3.1, Allocation Model Results). This also helps explain the relatively small secondary benefits for these metro areas, as well as the narrow range in the base and aggressive configurations. The cumulative results for employment VMT in Table 17 indicate that brownfields redevelopment can substantially redistribute metro area growth to reduce the VMT added by incoming jobs. The impact varies by growth profile. In Growth Hubs, 100,000 incoming jobs could result in 28,000 to 88,000 fewer daily VMT if allocated to brownfield sites rather than to trending areas. For Big and

Environmental Benefits of Brownfields Redevelopment – A Nationwide Assessment

Growing metro areas, the same number of jobs would have a more modest impact, but still mitigate workplace daily VMT generation by 7,000 to 11,000. By aggressively developing brownfield sites and identifying additional, similar development opportunities (e.g., brownfields not identified in ACRES, greyfield redevelopment opportunities), these transportation benefits could be augmented, helping to preserve transportation infrastructure capacity and maintain air quality. Table 17. Change in Employment-Based VMT, Cumulative

GROWTH PROFILE Change in Employment VMT per Job

Growth Hub -0.28 to -0.88

Industrial Legacy -0.12 to -0.31

Stable Metropolis -0.22 to -0.39

Slow and Steady -0.19 to -0.53

Big and Growing -0.07 to -0.11

Up and Coming -0.12 to -0.30

All -0.18 to -0.45

3.4 Model Uncertainties In reviewing and interpreting the allocation and environmental impact results presented above, it is important to keep in mind the uncertainties inherent in the models used to develop the growth scenarios and metrics. A brief synopsis of sources of uncertainty is provided below. • ACRES data – The ACRES brownfield site locations and attributes that drive the allocation model are sometimes imprecise, missing, or otherwise questionable. The protocols described in Appendix A and the screening steps discussed in Section 2.3.3. address many of the ACRES data quality concerns, however a comprehensive detailed review of ACRES site data was not feasible as a component of the current study. Moreover, ACRES does not record every brownfield site in the country; only sites that have received and used funds from the Brownfields Program are included in ACRES. Thus, the estimated environmental benefits of brownfields redevelopment presented in this study are based on an incomplete nationwide brownfields inventory. It is likely that most metro areas contain many more brownfields than are currently being evaluated. Therefore, it is reasonable to expect that a more complete inventory of brownfield sites would further enhance the benefits of brownfields redevelopment. • Brownfields redevelopment status – As noted in Section 2.1.1, it is difficult to determine the actual redevelopment status of each brownfield site. Redevelopment accomplishments are recorded for some sites in ACRES, but comprehensive redevelopment details are unavailable. This study developed a simple model of redevelopment status to remove some sites that may have already been redeveloped from

Environmental Benefits of Brownfields Redevelopment – A Nationwide Assessment

the universe of sites used in the BFR scenario. The number, location, and attributes of sites where redevelopment activity could occur is a key factor influencing the environmental impacts of the BFR scenario and how they compare with those of the TG scenario. • Number of brownfield sites likely to be redeveloped by 2030 – This study does not attempt to assess the viability of redeveloping any particular brownfield site based on market, policy, environmental, physical, or any other set of characteristics. Rather it assumes redevelopment will occur at all brownfield sites in the BFR scenario to quantify the typical differences in environmental impacts associated with growth at brownfields sites relative to growing areas in land outside of the urban core or on previously undeveloped and greenfield properties. It is, however, unlikely that all sites in the ACRES database would be fully redeveloped by 2030, meaning the cumulative results presented above could be diluted by slower or partial redevelopment progress. • Brownfield development capacity – There is no reliable, uniform method for determining how many new jobs and/or housing units a brownfield site could accommodate in a redevelopment scenario. Each brownfield site is situated within distinctive market, policy, and environmental contexts that are difficult to describe with detail or precision for a national dataset. The approach used in this study is to assume redevelopment at brownfields will be as dense as the densest development among surrounding block groups. It is, of course, possible that many redevelopment projects would fall short of this density estimate, while others might exceed it. Moreover, the density multiplier used to factor up potential brownfield redevelopment densities in the aggressive configuration (double the prevailing density) is a coarse attempt to account for densification opportunities at brownfields. While the literature suggests that brownfields are frequently redeveloped to densities much higher than greenfield densities (sometimes as much as 10 times greenfield densities), it is less revealing when comparing brownfields densities to other potential infill locations.

Environmental Benefits of Brownfields Redevelopment – A Nationwide Assessment

4. KEY FINDINGS The environmental benefits model results from this study are summarized by growth profiles. The results require careful consideration of metro area characteristics, brownfield location and development capacity, the different allocation phases, and in some cases, the intricacies of the environmental models themselves to understand the story of how brownfields redevelopment can promote more efficient development patterns for different metro area growth profiles. This section summarizes the following key findings from the analysis of the model results: • Brownfields redevelopment is more location-efficient than trend growth across the two key environmental metrics considered in this study. • Growth profiles demonstrate the importance of metro area growth contexts. • Brownfields development will sometimes produce additional benefits for growth not allocated to brownfield sites. • Brownfields redevelopment can often shift metro area development patterns to mitigate environmental impacts. 4.1 Brownfields redevelopment is more location-efficient than trend growth. Across key environmental metrics, brownfields redevelopment is more location-efficient than the trend growth. The location efficiency of brownfield sites is demonstrated by the primary results from all of the models (Table 18). The primary phase provides a direct comparison of environmental impacts between localized growth at brownfield sites and the same increment of growth in trending (i.e., non-brownfield) areas. In rare cases, aggressive development of brownfield sites is necessary to attain the location efficiency benefits, while in all cases, the aggressive development maximizes the benefits.

Table 18. Summary of Primary Environmental Benefits of Brownfields Redevelopment versus Trend Growth Development

Environmental Metric Range of Benefits

Change in Impervious Surface Acres (per -1.28 to -4.60 acres redeveloped brownfield acre) Change in Residential VMT (per redeveloped -127 to -536 miles brownfield acre)

Change in Residential VMT (per capita) -7.3 to -9.7 miles

Change in Employment VMT (per redeveloped -29.2 to -116 miles brownfield acre)

Change in Employment VMT (per job) -2.1 to -2.5 miles

Table 18 summarizes these findings: • Impervious surface – The aggregate results for analyzed metro areas demonstrate that on a per acre basis, brownfields redevelopment leads to less impervious surface area

Environmental Benefits of Brownfields Redevelopment – A Nationwide Assessment

being consumed or developed than trend growth development. Across all growth profiles, the benefit can be from 1.28 to 4.60 fewer acres of impervious surface per acre of redeveloped brownfield. • Residential VMT – Similar reductions hold true for residential VMT on both a per brownfield acre and per capita basis in that the brownfield scenario outperforms trend growth across different profiles. Based on this study, redevelopment of brownfield locations generates 7.3 to 9.7 fewer VMT per person per day than trend development. For the metro areas analyzed and under current assumptions about brownfield development capacity, this amounts to approximately 127 to 536 fewer daily VMT from new growth for each brownfield acre redeveloped. • Employment VMT – As with residential VMT, brownfields redevelopment alters travel to and from the workplace, mitigating growth in VMT due to the fact that jobs are more efficiently located and the potential increased use of public transportation. The ranges of benefits for workplace VMT are narrower on a per acre and per job basis than the residential trends, although they are more consistent across all growth profiles. Redevelopment of brownfield locations generate 2.1 to 2.5 fewer VMT per job per day than trend development, and approximately 25.2 to 116 fewer daily employment VMT for each brownfield acre redeveloped. These findings do not imply an overall decrease in impervious surface area or VMT as a result of brownfields redevelopment directly, but rather a reduction in the growth of these measures by bringing new jobs and housing units to more efficient locations. The primary analysis results suggest that this trend of brownfield locations performing better than trend growth locations will likely be true on a project-by-project basis (for specific redevelopment opportunities at brownfields). Moreover, the benefits of brownfields redevelopment appear to be meaningful in all growth profiles considered. The primary results indicate a positive answer to the question posed at the outset of the study: The reallocation of new jobs and housing to brownfield sites within a metro area will produce environmental benefits in terms of reductions in impervious surfaces and VMT when compared with trend growth. Based on the temporal analysis performed as part of this study, these primary phase environmental benefits are expected to occur typically in the near term (e.g., within the first decade of brownfields redevelopment). In addition, this study uses only those brownfield sites inventoried in EPA’s ACRES database. It is likely that most metro areas contain many more brownfields than are currently being evaluated. Therefore, it is reasonable to expect that a more complete inventory of brownfield sites would further enhance the benefits of brownfields redevelopment. 4.2 Growth profiles demonstrate the importance of metropolitan area growth contexts. Although it is true that brownfields redevelopment is more location-efficient than trend growth across all metro area growth profiles, the profiles demonstrate how the total magnitude of environmental benefits can differ dramatically: • Allocation results – The profiles demonstrate the variability in how much future growth can be reallocated to brownfield sites. In the case of housing activities, this can span from a low of 1.4 percent in Big and Growing metro areas diverted to brownfield locations to a high of 39.2 percent in Industrial Legacy metro areas. With jobs, the percentages are not as wide ranging, but still vary from a low of 1.1 percent in Big and Growing metro areas to 17 percent in Growth Hubs. The share of growth that can be accommodated at brownfields

Environmental Benefits of Brownfields Redevelopment – A Nationwide Assessment

depends on the growth rates of each metro area and the number and locations of brownfield sites in the ACRES database. • Impervious surface – For all metro areas, brownfields redevelopment can limit the expansion of impervious surface. The magnitude of the benefit depends on the location efficiency of brownfield sites relative to prevailing growth trends. Benefit ranges varied from a maximum impervious surface reduction of 11.8 acres per brownfield acre redeveloped in the Growth Hubs to a minimum of 0.57 acres per brownfield acre redeveloped in Industrial Legacy metro areas. • Residential VMT – Similar reductions hold true for residential VMT on both a per redeveloped brownfield acre and per capita basis. Residential VMT reductions are most sensitive to the density and centrality of development in growing areas, so the profiles with brownfield sites in the densest and most accessible parts of the metro area can significantly alter travel behaviors, especially if recent housing growth has been dispersed in low-density areas outside of the urban core. On a per acre basis, residential VMT under brownfields redevelopment can be 66 to 1,047 daily VMT less than trend development. In per capita terms, brownfields redevelopment could reduce daily VMT generated by each incoming resident by 15.2 miles in Growth Hubs or just 3.9 miles in Industrial Legacy and Slow and Steady metro areas. • Employment VMT – Employment VMT reductions are less dramatic than potential residential VMT reductions but are broadly consistent across different growth profiles on a per job basis. At the high end of the spectrum, each incoming job in Growth Hub metro areas is expected to generate 3.8 fewer VMT at a brownfield site versus a trend growth location. At the low end, that figure is 1.0 fewer VMT in Slow and Steady metro areas. Brownfield sites in Growth Hubs are dense areas and can accommodate many new jobs, such that each redeveloped brownfield acre in these metro areas could reduce total employment VMT by 382 miles each day. Industrial Legacy metro areas put fewer jobs in high-density brownfield sites, resulting in a base VMT reduction estimate of just 8.9 daily VMT per redeveloped brownfield acre. Benefits associated with redeveloping brownfields depend on the location efficiency of those brownfield sites and the percentage of metro area growth that can be accommodated by such sites. For example, if the metro area’s brownfields are less centrally located, then the environmental benefits are not as great as the benefits associated with brownfields sites in more central locations. Also, if there is a limited number of brownfields or modest brownfield acreage available for redevelopment, their impact on development patterns – and, in turn, the environment – is less significant. The growth profiles demonstrate that the strategies used to maximize the benefits of brownfields redevelopment vary greatly, depending on the metro area context. Therefore, it is essential to understand the magnitude of the location efficiency of brownfield sites relative to trending areas. As the ranges in environmental benefits for each growth profile illustrate, the benefits are maximized when these brownfield properties are aggressively redeveloped and growth outside urban centers is minimized. 4.3 Brownfields development will sometimes have additional benefits for growth not allocated to brownfields. This study finds a difference in the environmental impacts between the BFR and TG scenarios during the secondary phase of growth. Prior studies focused exclusively on the increment of

Environmental Benefits of Brownfields Redevelopment – A Nationwide Assessment growth in brownfields versus the same increment of growth following recent trends (e.g., the primary phase of analysis in this study).31, 32 While those dynamics remain the focus of this effort, it is important to recognize that brownfields redevelopment may result in additional environmental benefits by re-shaping longer term growth patterns. The secondary phase of allocation models development patterns when capacity for new growth at brownfield sites is exhausted, and all remaining growth is allocated throughout the metro area to trending (i.e., non-brownfield) areas. The secondary phase results demonstrate that redeveloping brownfields can maximize infill development capacity, making subsequent non-brownfield growth patterns more efficient for the metro area as well. Thus, secondary benefits may arise from preserving development capacity in growing location-efficient neighborhoods. Although these secondary benefits are usually modest, it is still important to acknowledge that a metro area brownfields redevelopment strategy can impact more than just the residents and employees of that development. A brownfields redevelopment strategy can also influence the behavior of neighbors and nearby employers. Not only do the residents and employees of the new development impose lower environmental impacts, those who live or work nearby also may benefit through closer services, employment, and access to other community goods. 4.4 Brownfields development often can shift metropolitan area development patterns to mitigate environmental impacts. The effectiveness of brownfields redevelopment across a broader geographic scale depends on the amount of growth that can be reallocated to more efficient locations relative to the trend. The cumulative findings in this study, which focus on total areawide growth patterns and not just the brownfields portion, depend on the entire set of factors influencing the allocations for the BFR and TG scenarios. The cumulative assessment quantifies the extent to which brownfields redevelopment could reshape broader metropolitan growth patterns and accompanying areawide environmental impacts. Having robust development capacity at brownfield sites in high growth areas that have development momentum will make the greatest use of development potential at location-efficient sites and maximize the environmental benefits of redevelopment. In some metro area growth profiles, the cumulative benefits are dampened by the small share of growth that can be accommodated at brownfields. In other cases, brownfield location efficiency is not dramatically greater than trending areas. In all cases, however, brownfields redevelopment reorganizes significant amounts of new jobs and housing into smarter locations, such that the resulting development pattern substantially mitigates the environmental impacts of new growth. Table 19 provides a snapshot of the cumulative benefits of brownfields redevelopment, summarized for all 50 analyzed CBSAs.

31 U.S. Environmental Protection Agency. “Comparing Methodologies to Assess Transportation and Air Quality Impacts of Brownfields and Infill Development.” EPA 231-R-01-001. August 2001. 32 U.S. Environmental Protection Agency. “Air and Water Quality Impacts of Brownfields Redevelopment: A Study of Five Communities.” 2011. https://www.epa.gov/sites/production/files/2015-09/documents/bfenvironimpacts042811.pdf

Environmental Benefits of Brownfields Redevelopment – A Nationwide Assessment

Table 19. Summary of Cumulative Environmental Benefits of Brownfields Redevelopment versus Trend Growth Development

Metric Range of Benefits

Change in Impervious Surface Acres (per -0.65 to -3.16 acres redeveloped brownfield acre)

Change in Residential VMT (per capita) -0.5 to -1.8 miles

Change in Employment VMT (per job) -0.18 to -0.45 miles

Environmental Benefits of Brownfields Redevelopment – A Nationwide Assessment

GLOSSARY

ACRES: The U.S. Environmental Protection Agency’s (EPA) Assessment, Cleanup and Redevelopment Exchange System (ACRES). ACRES is an online database for EPA’s Brownfields Program grantees to electronically submit their site-specific brownfields data directly to EPA. The data in ACRES is a subset of the universe of brownfield sites in the United States. Only sites that have received and used funds from the Brownfields Program are included in ACRES. Property data for the Brownfields Redevelopment (BFR) growth scenario were obtained from ACRES. Brownfield: A brownfield is a property, the expansion, redevelopment, or reuse of which may be complicated by the presence or potential presence of a hazardous substance, pollutant, or contaminant. Brownfields Redevelopment (BFR) scenario: A hypothetical growth scenario which assumes that future development will occur at all available brownfield sites across the 50 metropolitan areas considered in this study. Both “base” and “aggressive” growth configurations are modeled under the BFR scenario. For the base configuration, the density of development at a brownfield site is expected to match the most densely developed block group in its vicinity. For the aggressive configuration, the density of development at a brownfield site is expected to reflect the potential for development intensification and exceed the density at the most densely developed block group in its vicinity. Built-out: Having little or no remaining buildable land available for development. Census block group (CBG): A geographical unit used by the U.S. Census Bureau that is between the Census Tract and the Census Block. It is the smallest geographical unit for which the bureau tabulates and publishes sample data (i.e., data that is only collected from a fraction of all households). Control totals: The number of new jobs and housing units to allocate for a given metropolitan area over a given time period. In this study, job and housing unit control totals are obtained from Woods & Poole county-level demographic and economic forecasts. Core-based statistical area: A U.S. geographic area defined by the Office of Management and Budget that consists of one or more counties (or equivalents) anchored by an urban center of at least 10,000 people plus adjacent counties that are socioeconomically tied to the urban center by commuting. D variables: Various attributes of the built environment, including the following five common measures: Density of development, Diversity of land use, Design of neighborhood streets, Distance to transit, and access to Destinations. Development activities: Refers to the specific types of uses for a property or site that is being developed or redeveloped. In this study, development activities include residential housing units and commercial/industrial jobs. Development activity mix refers to a combination of housing units and jobs at a given property or site. Development attractiveness: A measure of which locations are the most likely to be redeveloped first. The land use allocation model used in this study identifies the probable order

Environmental Benefits of Brownfields Redevelopment – A Nationwide Assessment of redevelopment at properties over the 2013 to 2030 study period. The most attractive sites will “fill up” with redevelopment (new jobs and/or housing units) first. Development capacity: A measure of how much redevelopment (i.e., new jobs and/or housing) a given location can accommodate. Fixed-guideway transit: Any transportation system or facility that uses and occupies a designated right-of-way or rails, including, but not limited to, rapid rail, light rail, commuter rail, busways, automated guideway transit, trolley coaches, ferryboats, and people movers. Greenfield/Greenfield development: Vacant or undeveloped tracts of land that are available for business or industrial use. They are referred to as “greenfields” because often their former usage (or in some cases, their current usage) is agricultural production, forest land, or some other undeveloped function. Greenfield sites are most often located in the urban fringe of the path of development or in rural areas where undeveloped land is more likely to be present. Greenfield development refers to the real estate development of land not previously used for residential, commercial, or industrial purposes. Growth profile: Groupings of common metropolitan areas based on size (population) and growth rate statistics (how slowly or quickly the metropolitan area is growing). In this study, six different growth profiles were developed in order to compare the environmental benefits among peer metropolitan areas, including Big and Growing, Stable Metropolis, Growth Hubs, Slow and Steady, Up and Coming, and Industrial Legacy. See Appendix C (Metropolitan Area Growth Profiles) for a detailed discussion on how growth profiles were developed for this study and examples of each profile. Impervious surface: A land surface that is covered by impenetrable materials that repel rainwater and do not permit it to infiltrate the ground. Common impervious surfaces found in urban and suburban landscapes include pavement, roads, sidewalks, driveways, parking lots, and roofs. Adding these surfaces to a landscape can alter the flow of rainwater and streams. Infill/Infill development: An urban planning term for the rededication of land in an urban environment, usually open space, to new construction. Infill development refers to the development of vacant or under-utilized parcels within existing urban areas that are already largely developed. Many communities have significant vacant land within city limits that, for various reasons, has been passed over in the normal course of urbanization. Land use allocation model: A model that distributes (or allocates) future growth in jobs and housing units across a given area. The distribution of this growth is guided by how many new jobs and housing units will be added over a given time period, how much new development a given location can accommodate, which locations are most likely to be (re)developed, and what types of activities (jobs and housing) are likely to be added as a given location is developed. Location-efficient/Location efficiency: Location efficiency refers to polices and approaches that promote development patterns which limit the strain on existing stormwater and transportation infrastructure, and the associated environmental impacts of increased stormwater and traffic loads. Location-efficient communities are dense and vibrant, with walkable streets, access to transit, proximity to jobs, mixed land uses, and concentrations of retail and services. Non-point source pollution: Pollution resulting from many diffuse sources, in direct contrast to point source pollution, which results from a single source. Non-point source pollution generally results from land runoff, precipitation, atmospheric deposition, drainage, seepage, or hydrological

Environmental Benefits of Brownfields Redevelopment – A Nationwide Assessment modification (rainfall and snowmelt) where tracing pollution back to a single source is difficult. Non-point source pollution can include excess fertilizers, herbicides, and insecticides from agricultural lands and residential areas; oil, grease, and toxic chemicals from urban runoff and energy production; and sediment from improperly managed construction sites, crop and forest lands, and eroding streambanks. Smart locations: A planning term that refers to dwellings and workplaces that are centrally located in walkable neighborhoods with great transit service and a variety of nearby destinations, enabling people to rely less on their personal vehicles for commuting and daytime trips. This can result in lower congestion and pollution impacts, in addition to reduced cost burdens on local infrastructure. Trend Growth (TG) scenario: A hypothetical growth scenario which assumes that recent metropolitan growth trends persist over time. Vehicle-miles traveled (VMT): Used in transportation planning, VMT is a measure of the amount of travel for all vehicles in a geographic region over a given time period. VMT is used as a proxy for transportation-related air emissions. In this study, air quality impacts associated with residential and job-related transportation decisions due to new development are estimated by calculating changes in VMT. Woods & Poole: A comprehensive database that contains more than 900 variables of economic data and demographic data and future estimates for the United States and all states, regions, counties, and core-based statistical areas for every year from 1970 through 2050.

Environmental Benefits of Brownfields Redevelopment – A Nationwide Assessment

APPENDICES

Environmental Benefits of Brownfields Redevelopment – A Nationwide Assessment

APPENDIX A: ACRES DATA AND LOCATION VALIDATION PROCESS

ACRES Data Inputs Used in the Study33

Variable/Factor ACRES Abbreviation

Grant ID number GRANT_ID

Property ID number PROPERTY_ID Location information (address, city, state, zip; ADDRESS, CITY, STATE_CODE, ZIP_CODE, latitude and longitude) = site LATITUDE_MEASURE, LONGITUDE_MEASURE

Property size PROPERTY_SIZE Ready for reuse READY_FOR_REUSE Redevelopment details REDEV_START_DATE, REDEV_COMPLETION_DATE No. of grants received at unique site (count of each PROPERTY_ID grants) Planned for greenspace FUTURE_GREENSPACE_ACRES

ACRES Site Location Validation Process and Decision Protocol

Variable/ ACRES Abbreviation Inventory and Results Decision Protocol Factor

Grant ID GRANT_ID 40,748 total grants None needed; all sites retained for number analysis. Multiple grants will be connected to the individual unique property ID through a relational database.

Property ID PROPERTY_ID 29,387 unique brownfield None needed; all sites retained for number properties analysis. • Geographically consistent • If all geographic variables are Location ADDRESS, CITY, sites [27,199 sites (93%)] internally consistent (when the information STATE_CODE, • Located outside 50 states address and latitude and longitude (address, ZIP_CODE, and Washington, D.C. information correspond), then they city, state, LATITUDE_MEASURE, (i.e., sites in territories) will be used. zip; latitude LONGITUDE_MEASURE [210 sites, (< 1%)] • Excluded those sites outside 50 and states and Washington, D.C. (i.e., longitude) = • Remaining sites with sites in territories). site inconsistent geographic data (address and lat/long • Included geographically data do not appear to inconsistent sites for which location correspond) [1,978 sites data have been validated or where (7%)]34

33 Other factors in the ACRES dataset were either incomplete or it was preferred to use an alternative dataset to ensure consistency. For example, although other land uses (as planned or identified for after redevelopment) would appear to be useful, it has been excluded because land uses do not necessarily indicate the amount of jobs or housing without other contextual information. See Step 2: Develop Scenario Parameters on how this contextual information is gathered and applied to forecast potential development outcomes. 34 Invalid or missing latitude or longitude, or significant distance between the latitude and longitude information and the address

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Environmental Benefits of Brownfields Redevelopment – A Nationwide Assessment

Variable/ ACRES Abbreviation Inventory and Results Decision Protocol Factor o Data validated or lat/long or address information is corrected for 181 assumed to be accurate. sites (< 1%) • Excluded those sites with o Assume lat/long unresolved geographic correct for sites where inconsistencies. internal consistency could not be verified [1,340 sites (4.5%)] o Assume address is correct for sites where internal consistency could not be verified [414 sites (1.4%)] o Unresolved location issues [43 sites (< 1%]) • Missing property size • If a property size Property PROPERTY_SIZE [2,356 sites (8%)] (PROPERTY_SIZE) is available size • “0 acres” property size and ranges from 0.1 acres to [468 sites (1.6%)] < 1,000 acres, it will be used. • Greater than 100,000 • Those sites with missing property acres [263 sites (1%)] sizes or “0 acres” property sizes will be assumed to be 1.0 acre, which is roughly the median of all accurate sites in the current dataset.35 • Excluded all sites greater than 1,000 acres. • “Y” [6,216 sites (21%)] • Those with “Y” and not assumed to Ready for READY_FOR_REUSE • “N” [23,159 sites (79%)] be already redeveloped (based on reuse • Blank [12 sites (< 1%)] redevelopment start or completion dates) will be put through the brownfields redevelopment estimation procedure. • Those with “N” and blank sites and not assumed to be already redeveloped (based on redevelopment start or completion dates) will be assumed to be available for redevelopment.

• Redevelopment • If the site has a valid Redevelop- REDEV_START_DATE, completion date before redevelopment completion date ment details REDEV_COMPLETION_ 2013 [709 sites (2.4%)] (REDEV_COMPLETION_DATE) in DATE • Redevelopment start date ACRES prior to 2013, it will be before 2012 and considered already redeveloped completion date missing and excluded from the analysis. [1,156 sites (3.9%)] • If the site has a valid redevelopment start date (REDEV_START_DATE) in ACRES prior to 2012, it will be

35 The median PROPERTY_SIZE value was used instead of the mean to avoid the problem of outlier values skewing the result (the mean size for all accurate sites in the current dataset is about 45 acres.

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Environmental Benefits of Brownfields Redevelopment – A Nationwide Assessment

Variable/ ACRES Abbreviation Inventory and Results Decision Protocol Factor considered already redeveloped and excluded from the analysis.36 • See “Determining brownfields redevelopment status” section of report to see how sites not fitting the first two bullets will be used in the analysis.

No. of grants (count of each Multiple grants associated with None needed; all sites retained for received at PROPERTY_ID grants) a specific site [2,715 sites (9%)] analysis. unique site • If ACRES indicates that a property Planned for FUTURE_GREENSPACE Include greenspace information is to be redeveloped partially as greenspace _ACRES [1,910 sites (7%)] greenspace, the site size will be reduced by the greenspace redevelopment area.

36 An analysis of sites with valid redevelopment start and completion dates suggests that the average redevelopment period is about 1 year, although this may be due to simplified bookkeeping and not a reflection of the actual typical redevelopment timeline.

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Environmental Benefits of Brownfields Redevelopment – A Nationwide Assessment

APPENDIX B: ESTIMATION OF AVERAGE ANNUAL CHANGE IN JOBS Since Longitudinal Employer–Household Dynamics (LEHD) data are synthesized from unemployment insurance records, there are sometimes notable discrepancies in local jobs estimates from one year to the next. To smooth the data, 3-year averages were taken to represent employment at each block group in a given year. For example, a block group with employment figures of 134, 48, 75, and 60 for the years 2002, 2003, 2004, and 2005, respectively, would have a 2003 employment estimate of 87 (average of 134, 48, and 75) and a 2004 employment estimate of 61 (average of 48, 75, and 60). These estimates of employment in each year were then compared over time. The average year- over-year change in jobs in each block group was calculated to reflect the overall employment growth trend in the block group. Block groups that have higher average annual growth rates will be considered more attractive for future metro area employment growth. Block groups with negative average annual growth rates will be considered as “declining” areas, and no new metro area jobs will be allocated to these block groups. A quality assurance check on average growth rates was conducted to ensure that the temporal smoothing was effective. Comparing year-to-year volatility (i.e., the absolute change in reported employment from one year to the next) of temporally smoothed estimates and un-smoothed estimates showed a drastic reduction in volatility. Nearly 30 times fewer block groups were flagged as having high volatility (measured as total volatility / average annual change > 20) after temporal smoothing. In addition, the remaining high-volatility block groups seemed to be randomly distributed geographically, suggesting that no bias exists in the LEHD data that would require additional data modifications. LEHD data varies by state. Most states’ LEHD jobs estimates are available from 2002, but others joined the program later, meaning that the earliest available LEHD data may be from 2005, for example. Massachusetts was the last state to participate in LEHD, starting in 2011. With so few years of data available there, estimates of employment growth trends may be unreliable. In addition, Washington, D.C., only began participating in 2010 and may pose similar challenges. As such, the Washington, D.C., metro area and any metro area with territory in Massachusetts are not well suited for analysis in this study.

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Environmental Benefits of Brownfields Redevelopment – A Nationwide Assessment

APPENDIX C: METROPOLITAN AREA GROWTH PROFILES AND SELECTION OF 50 CBSAS TO INCLUDE IN THE STUDY

C.1 Growth Profile Development Growth profiles were developed using 2010 core-based statistical area (CBSA) definitions because the analysis is built with 2010 population data. The final brownfields analysis is reported using 2016 CBSA definitions. This study developed profiles to present and compare the benefits among peer metro areas. Profiles were developed based on population and growth rate to ensure that metro areas were analyzed against other metro areas of analogous size and growth. Size Metro area size was measured using 2010 U.S. Census population data. Each of the 280 CBSAs eligible for analysis were categorized and organized into the following classes:

Size Class Definition

Tiny Less than 250,000

Small 250,000 to 1,500,000

Medium 1,500,000 to 3,000,000

Large 3,000,000 to 5,000,000

Huge More than 5,000,000

Growth Rate Growth rate was measured by the percent change in population from 2015 to 2030. This was calculated using the number of households added between 2010 and 2030 (according to Woods & Poole) multiplied by the average household size in 2016 (2.3 persons). Growth rates were calculated for each of the 280 CBSAs eligible for analysis. Each of the 280 CBSAs eligible for analysis were categorized and organized into the following classes:

Growth Rate Class Definition

Slow Less than 0.15

Moderate 0.15 to 0.30

Rapid Greater than 0.30

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Environmental Benefits of Brownfields Redevelopment – A Nationwide Assessment

Size and growth rate were combined to define eight distinct growth dynamics. Each CBSA of the 280 eligible for analysis was assigned a growth profile and tabulated below.

Growth Dynamic Number of CBSAs

Huge CBSAs with moderate to rapid growth 4

Huge CBSAs with slow growth 4

Medium to Large CBSAs with moderate to rapid growth 19

Medium to Large CBSAs with slow growth 16

Small CBSAs with moderate to rapid growth 39

Small CBSAs with slow growth 39

Tiny CBSAs with moderate to rapid growth 27

Tiny CBSAs with slow growth 132

Total 280

The following characteristics were identified and combined with the growth dynamics above to develop growth profiles:

Characteristic Definition

Number of brownfield properties Derived from the 22,347 properties in ACRES selected for analysis

Population density Population per square mile

Brownfield density Brownfields per square mile

Brownfield capacity Number of housing units

Geographic commonality U.S. Census region and EPA region

Population Density (persons per square mile)

Class Definition

Low Less than 200

Medium 200 to 500

High More than 500

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Environmental Benefits of Brownfields Redevelopment – A Nationwide Assessment

Brownfield Density (brownfields per square mile)

Class Definition

High density 25th percentile Moderate density Inter-quartile range Low density 75th percentile

Brownfield Capacity (number of housing units)

Class Definition

High Less than 500 Moderate 500 to 3,000 Low More than 3,000

Based on the CBSA characteristics and growth dynamics described above, the following growth profiles were identified:

Growth Profile No. of CBSAs for Characteristics Name Consideration • Huge CBSAs with moderate to rapid growth • All in the South Big and Growing 4 • High brownfield and population density • High capacity • Huge CBSAs with slow growth • The usual suspects (Los Angeles, New York, Chicago, Stable Metropolis Philadelphia) 4 • High brownfield and population density • High capacity • Medium and large CBSAs with moderate to rapid growth • Distributed across the country Growth Hubs • Moderate to high population density 19 • High capacity • Mostly high brownfield density • Medium and large CBSAs with slow growth • Distributed across the country Slow and Steady 16 • Moderate to high population and brownfield density • High capacity • Small and tiny CBSAs experiencing moderate to rapid growth, excluding two rapidly growing CBSAs: Jacksonville, Fla., and Durham, N.C. • Southern trend – More than 50% located in the sunbelt Up and Coming • Mix of brownfield and population density 66 • Mix of capacity • Generally low and moderate brownfield density, although two CBSAs have high brownfield density: Fayetteville, N.C., and Ann Arbor, Mich. • Small and tiny CBSAs with slow growth • Majority in the Midwest Industrial Legacy • Moderate and high brownfield density 171 • Low to moderate population density • Low to moderate capacity

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Environmental Benefits of Brownfields Redevelopment – A Nationwide Assessment

C.2 Selection of 50 CBSAs to Include in the Study Of the 280 CBSAs eligible for analysis, 50 were selected to provide broad geographic coverage across the county. The number of modeled CBSAs by profile group loosely reflects the total number in each group. For example, there are 171 CBSAs characterized as Industrial Legacy metro areas, more than any other category. The number of Industrial Legacy metro areas selected for analysis is 18, more than any other category. Likewise, there are a small number of Stable Metropolis and Big and Growing metro areas nationwide (four each), and just two are included from each profile in the analysis (at least two examples from each growth profile were included among the 50 CBSAs). As shown below, the 50 CBSAs analyzed offer broad coverage of the nation, geographically, such that representatives of each growth profile are found in differing regional contexts. CBSAs were selected to ensure that the analysis included CBSAs in all EPA and Census regions.

Number of CBSAs Eligible Number of CBSAs EPA Region for Consideration Selected for Analysis

1 18 5

2 10 3

3 21 3

4 65 10

5 85 10

6 21 5

7 29 3

8 8 4

9 15 4

10 8 3

Total 280 50

Number of CBSAs Selected Number of CBSAs Census Region for Consideration Selected for Analysis

Midwest 115 13

Northeast 37 9

South 96 16

West 32 12

Total 280 50

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Environmental Benefits of Brownfields Redevelopment – A Nationwide Assessment

C.3 Summary of 50 Selected CBSAs

Overall Selection Summary

Number of Total CBSAs as Number of Number of Total CBSAs a Percentage of Development Profile Potential CBSAs in CBSAs for Selected for Potential CBSAs CBSAs Pilot Study Analysis Further Study for Analysis

Big and Growing 4 2 0 2 50%

Stable Metropolis 4 1 1 2 50%

Growth Hubs 19 2 4 6 32%

Slow and Steady 16 6 0 6 38%

Up and Coming 66 1 15 16 24%

Industrial Legacy 171 0 18 18 11%

Total 280 12 38 50 18%

Big and Growing (2)

Growth Population Brownfield Brownfield Census EPA CBSA Name Size Rate Density Density Capacity Region Region

Atlanta-Sandy Springs- Huge Moderate High Moderate High South 6 Marietta, Ga.

Dallas-Fort Worth- Huge Moderate High Moderate High South 6 Arlington, Texas

Stable Metropolis (2)

Growth Population Brownfield Brownfield Census EPA CBSA Name Size Rate Density Density Capacity Region Region

Los Angeles-Long Huge Slow High High High West 9 Beach-Anaheim, Calif.

Philadelphia-Camden- Wilmington, Pa.-N.J.- Huge Slow High High High Northeast 2 Del.-Md.

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Environmental Benefits of Brownfields Redevelopment – A Nationwide Assessment

Growth Hubs (6)

Growth Population Brownfield Brownfield Census EPA CBSA Name Size Rate Density Density Capacity Region Region

Austin-Round Rock-San Medium Rapid Moderate Moderate High South 6 Marcos, Texas

Minneapolis-St. Paul- Bloomington, Minn.- Large Moderate High High High Midwest 5 Wisc.

Orlando-Kissimmee- Medium Moderate High Low High South 4 Sanford, Fla.

Portland-Vancouver- Medium Moderate Moderate Moderate High West 10 Hillsboro, Ore.-Wash.

Sacramento–Arden- Medium Moderate Moderate High High West 9 Arcade–Roseville, Calif.

Seattle-Tacoma- Large Moderate High Moderate High West 10 Bellevue, Wash.

Slow and Steady (6)

Growth Population Brownfield Brownfield Census EPA CBSA Size Rate Density Density Capacity Region Region

Baltimore-Towson, Md. Medium Slow High High High South 3

Hartford-West Hartford- Medium Slow High High High Northeast 1 East Hartford, Conn.

Milwaukee-Waukesha- Medium Slow Moderate High High Midwest 5 West Allis, Wisc.

New Orleans-Metairie- Medium Slow Low Low High South 6 Kenner, La.

Rochester, N.Y. Medium Slow Moderate Moderate High Northeast 2

San Francisco-Oakland- Large Slow High High High West 9 Fremont, Calif.

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Environmental Benefits of Brownfields Redevelopment – A Nationwide Assessment

Up and Coming (16)

Growth Population Brownfield Brownfield Census EPA CBSA Size Rate Density Density Capacity Region Region

Albuquerque, N.M. Small Moderate Low Low Low West 3

Big Rapids, Mich. Tiny Moderate Low High Low Midwest 5

Billings, Mont. Tiny Moderate Low Low Moderate West 8

Boise City, Idaho Small Moderate Low Low Moderate West 10

Boulder, Colo. Small Rapid Moderate Moderate Low West 8

Burlington-South Tiny Moderate Low Moderate High Northeast 1 Burlington, Vt.

Des Moines-West Des Small Moderate Low Moderate High Midwest 7 Moines, Iowa

Durham-Chapel Hill, Small Rapid Moderate Moderate High South 4 N.C.

Grand Rapids- Small Moderate Moderate High High Midwest 5 Wyoming, Mich.

Iowa City, Iowa Tiny Moderate Low High Moderate Midwest 7

Knoxville, Tenn. Small Moderate Moderate Moderate High South 4

Lakeland-Winter Haven, Small Moderate Moderate Moderate Moderate South 4 Fla.

Morgantown, W.Va. Tiny Moderate Moderate Low Moderate South 3

Ogden-Clearfield, Utah Small Moderate Moderate Moderate Moderate West 8

Portland-South Portland-Biddeford, Small Moderate Low High High Northeast 1 Maine

Winston-Salem, N.C. Small Moderate Low Moderate Moderate South 4

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Environmental Benefits of Brownfields Redevelopment – A Nationwide Assessment

Industrial Legacy (18)

Growth Population Brownfield Brownfield Census EPA CBSA Size Rate Density Density Capacity Region Region

Akron, Ohio Small Slow High High High Midwest 5

Albany-Schenectady- Small Slow Moderate Moderate High Northeast 2 Troy, N.Y.

Allentown-Bethlehem- Small Slow High High High Northeast 5 Easton, Pa.-N.J.

Ann Arbor, Mich. Small Slow Moderate High High Midwest 4

Bangor, Maine Tiny Slow Low Low Moderate Northeast 1

Chattanooga, Tenn.-Ga. Small Slow Moderate Moderate Moderate South 4

Dayton, Ohio Small Slow Moderate Moderate Moderate Midwest 5

Frankfort, Ind. Tiny Slow Low Moderate Moderate Midwest 5

Freeport, Ill. Tiny Slow Low High Moderate Midwest 5

Great Falls, Mont. Tiny Slow Low Low Moderate West 8

Jackson, Miss. Small Slow Low Moderate Moderate South 4

Montgomery, Ala. Small Slow Low Moderate Moderate South 4

New Haven-Milford, Small Slow High High High Northeast 1 Conn.

Shreveport-Bossier Small Slow Low Moderate High South 6 City, La.

Stockton, Calif. Small Slow Moderate Moderate Moderate West 9

Sturgis, Mich. Tiny Slow Low High Moderate Midwest 5

Wichita, Kan. Small Slow Moderate Low High Midwest 7

Wilson, N.C. Tiny Slow Moderate Moderate Low South 4

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Environmental Benefits of Brownfields Redevelopment – A Nationwide Assessment

APPENDIX D: ESTIMATING RESIDENTIAL VMT BASED ON THE BUILT ENVIRONMENT Household travel behavior has shown to be responsive to various attributes of the built environment, often referred to as “D variables.” These include several common measures, such as the density of development, diversity of land use, design of neighborhood streets, distance to transit, and access to destinations. Built environment attribute data from the Smart Location Database (SLD) was used to model CBG-to-CBG variances in average per capita VMT. The SLD variables referenced are indicators of each of the five Ds commonly referenced in the transportation and land use literature, as shown in Table D-1.

Table D-1. D Variables and SLD Indicators Primary Impact on Travel D Variables SLD Indicator Behavior More destinations nearby increase Density D1a Residential Density walking and biking. A greater range of destinations Diversity D2 Land Use Entropy nearby also increases the likelihood of walking and biking. More direct pedestrian pathways Intersection Density, weighted by and more distributed vehicle traffic Design D3 three-legged and four-legged or more support better walking and biking intersections conditions. Convenient access increases the Distance D4 Distance of Transit likelihood of using transit. Greater access to destinations Regional Accessibility to jobs by Destinations D5ar generates shorter average vehicle auto, gravity weighted trips.

Each D variable is tabulated for every CBG in the CBSA being analyzed. The value of each D variable is tabulated for existing conditions from the SLD. For each scenario and phase of the allocation process, two of the D variables are updated to reflect the changes brought about by new development – density (D1) and destinations (D5) are both updated. There is insufficient data from the allocations to update the other D variables, so the existing values are retained after allocation. The average value of each D is calculated for the entire metro area and new block group level attributes are calculated that describe the extent to which each block group’s built environment characteristics deviate from the regional average. An example calculation for the D1 variable in block group i is shown below:

D1i - D1/D1 In numerous studies, various formulations of the D variables at household locations have been shown to influence travel behaviors, such as mode choice, trip generation, trip length, and VMT generation. A meta-analysis of these studies yielded a set of elasticities for estimating total residential VMT based on the D variables.37 These elasticities, listed by D variable in Table D-2, are the best available resource for estimating household VMT in a consistent manner across the country.

37 Ewing, Reid, and Cervero, Robert. “Travel and the Built Environment – A Meta-Analysis.” Journal of the American Planning Association, 76, May 2010.

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Environmental Benefits of Brownfields Redevelopment – A Nationwide Assessment

Table D-2. Elasticities of VMT with Respect to D Variables38

D Variables Elasticity Density -0.10 Diversity -0.09 Design -0.20 Distance 0.05 Destinations -0.35

Each elasticity value is multiplied by each block group’s corresponding D deviation value. The resulting products are summed to obtain a VMT generation rate factor. This factor represents the extent to which a block group is expected to produce more (values above 1) or less (values between zero and 1) VMT on a per capita basis than is typical for the metro area, considering its built environment characteristics. The VMT generation rate factor is then multiplied by the average daily VMT per capita rate assumed for the CBSA being analyzed. The rate is unique to each metro area, and each metro area’s rate was determined from Federal Highway Administration (FHWA) Table HM-71. However, FHWA does not generate an estimated VMT per capita for all CBSAs selected for this study. Alternate estimates of VMT per capita were identified or calculated for those CBSAs, as detailed in Table D-3.

Table D-3. Estimated VMT for Select CBSAs

VMT per CBSA Name Source capita

Albany-Schenectady-Troy, N.Y. 30.09 Brookings Institute

Equal to neighboring CBSA – Grand Rapids, Mich. – Big Rapids, Mich. 26.7 estimated VMT per capita

Equal to neighboring CBSA – Lafayette, Ind. – Frankfort, Ind. 23.18 estimated VMT per capita

Equal to neighboring CBSA – Rockford, Ill. – estimated Freeport, Ill. 24.81 VMT per capita

Average of neighboring CBSAs – Kalamazoo, Mich. Sturgis, Mich. 29.72 (30.49) and Battle Creek, Mich. (28.95) – estimated VMT per capita

Average of neighboring CBSAs – Greenville, N.C. (43), Wilson, N.C. 34.33 Rocky Mount, N.C. (28), and Goldsboro, N.C. (31) – estimated VMT per capita

Having estimated the VMT per capita for each block group in the CBSA being analyzed, the daily VMT generated by each block group’s new households is derived by multiplying the VMT per

38 Ewing, Reid, and Cervero, Robert. “Travel and the Built Environment – A Meta-Analysis.” Journal of the American Planning Association, 76, May 2010.

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Environmental Benefits of Brownfields Redevelopment – A Nationwide Assessment capita value by the estimated incoming population. Since the land use allocation model focuses on housing units rather than population, an assumed persons-per-household ratio of 2.54 (national average in 2015) was used to derive new population figures for each block group.

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Environmental Benefits of Brownfields Redevelopment – A Nationwide Assessment

APPENDIX E: DETAILED ALLOCATION AND ENVIRONMENTAL BENEFITS RESULTS

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Table E-1. Legend for Allocation and Environmental Results

Table Name Metric Unit (if applicable) HU allocated HH AllocationResults Jobs allocated Jobs Change in impervious ISGM_Primary/Secondary/Cumulative Acres surface area VMT generated by new Daily vehicle miles of ResVMT_Primary/Secondary/Cumulative (allocated) households travel Daily vehicle miles of Change in VMT per capita travel per person ResVMT_Cumulative Change in VMT generated Daily vehicle miles of by existing households travel Total VMT generated by new Daily vehicle miles of (allocated) jobs travel EmpVMT_Primary/Secondary/Cumulative Total VMT per job generated Daily vehicle miles of by new (allocated) jobs travel per employee

CHARACTERISTICS Capacity for Geographic Population Growth Profile Name Size Growth Rate Redevelopment Brownfield Density Distribution Density Activity Growth Hub Medium and Large Moderate to Rapid Well-distributed Moderate to High High High Industrial Legacy Small and Tiny Slow Majority in Midwest Low to Moderate Low to Moderate Moderate to High Stable Metropolis Huge Slow Uneven High High High Slow and Steady Medium and Large Slow Well-distributed Moderate to High High Moderate to High Big and Growing Huge Moderate to Rapid All in Southern US High High High Up and Coming Small and Tiny Moderate to Rapid Southern focused All All All

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E-2 Table E-2. Summary of Allocation and Environmental Results (by Growth Profile)

ALLOCATION Control Totals Base Aggressive Gross Gross Gross Gross HU Jobs Activity GROWTH PROFILE Gross Jobs Activity % of % of Density Density Density New HU at BF New Jobs at BF HU share of Jobs share of Gross HU Density at Density at Density at New HU at control New Jobs at control HU share of Jobs share at BF at BF at BF Total new HU Total new Jobs sites % of control total Sites % of control total allocation allocation BF Acres BF sites BF sites BF Sites BF sites total BF Sites total allocation of allocation BF Acres sites sites Sites Growth Hub 1,546,364 3,184,451 42,993 2.8% 159,979 5.0% 21% 79% 5,323 8.08 30.05 38.13 142,484 9.2% 542,059 17.0% 21% 79% 5,323 26.77 101.83 128.60 Industrial Legacy 298,519 964,345 40,473 13.6% 28,197 2.9% 59% 41% 6,094 6.64 4.63 11.27 117,037 39.2% 96,239 10.0% 55% 45% 6,094 19.21 15.79 35.00 Stable Metropolis 643,660 2,671,311 35,637 5.5% 97,564 3.7% 27% 73% 6,008 5.93 16.24 22.17 118,883 18.5% 316,045 11.8% 27% 73% 6,008 19.79 52.61 72.39 Slow and Steady 458,125 1,549,044 31,083 6.8% 62,487 4.0% 33% 67% 3,560 8.73 17.55 26.29 103,394 22.6% 211,536 13.7% 33% 67% 3,560 29.04 59.42 88.47 Big and Growing 1,403,357 2,810,559 19,514 1.4% 29,618 1.1% 40% 60% 2,837 6.88 10.44 17.32 66,664 4.8% 100,346 3.6% 40% 60% 2,837 23.50 35.37 58.87 Up and Coming 721,929 1,439,000 29,888 4.1% 37,124 2.6% 45% 55% 5,913 5.05 6.28 11.33 91,153 12.6% 123,115 8.6% 43% 57% 5,913 15.42 20.82 36.24 ALL 5,071,954 12,618,710 199,588 3.9% 414,969 3.3% 32% 68% 29,734 6.71 13.96 20.67 639,615 12.6% 1,389,340 11.0% 32% 68% 29,734 21.51 46.72 68.24

Urban Footprint Expansion ISGM Primary Phase BF TG Difference Percent Difference Difference per BF acre GROWTH PROFILE Base (ac) Aggr. (ac) Base (ac) Aggr. (ac) Base (ac) Aggr. (ac) Base Aggr. Base (ac) Aggr. (ac) Growth Hub 3,004.8 7,191.5 21,201.0 69,959.5 (18,196.2) (62,768.0) -85.83% -89.72% (3.42) (11.79) Industrial Legacy 2,696.7 6,515.8 6,173.9 19,200.2 (3,477.1) (12,684.4) -56.32% -66.06% (0.57) (2.08) Stable Metropolis 2,943.2 6,104.9 8,426.3 26,765.1 (5,483.1) (20,660.3) -65.07% -77.19% (0.91) (3.44) Slow and Steady 2,061.0 5,338.7 5,949.6 20,844.5 (3,888.6) (15,505.8) -65.36% -74.39% (1.09) (4.36) Big and Growing 1,392.3 3,617.2 3,762.6 12,865.1 (2,370.3) (9,247.9) -63.00% -71.88% (0.84) (3.26) Up and Coming 2,438.5 6,189.0 6,937.4 22,228.8 (4,498.9) (16,039.8) -64.85% -72.16% (0.76) (2.71) ALL 14,536.5 34,957.1 52,450.9 171,863.2 (37,914.3) (136,906.0) -72.29% -79.66% (1.28) (4.60)

Secondary Phase BF TG Difference Percent Difference Difference per BF acre GROWTH PROFILE Base (ac) Aggr. (ac) Base (ac) Aggr. (ac) Base (ac) Aggr. (ac) Base Aggr. Base (ac) Aggr. (ac) Growth Hub 407,657.0 364,049.7 408,549.5 370,767.9 (892.4) (6,718.3) -0.22% -1.81% (0.17) (1.26) Industrial Legacy 111,778.1 97,206.7 111,456.1 98,018.0 322.0 (811.3) 0.29% -0.83% 0.05 (0.13) Stable Metropolis 174,017.9 151,988.2 176,165.4 157,425.3 (2,147.5) (5,437.2) -1.22% -3.45% (0.36) (0.91) Slow and Steady 144,349.2 126,180.4 145,607.5 132,287.0 (1,258.3) (6,106.6) -0.86% -4.62% (0.35) (1.72) Big and Growing 328,695.5 318,942.2 326,105.9 320,079.1 2,589.7 (1,136.9) 0.79% -0.36% 0.91 (0.40) Up and Coming 217,047.9 201,581.1 216,788.9 202,622.2 259.1 (1,041.1) 0.12% -0.51% 0.04 (0.18) ALL 1,383,545.7 1,259,948.3 1,384,673.1 1,281,199.6 (1,127.4) (21,251.3) -0.08% -1.66% (0.038) (0.71)

Cumulative BF TG Difference Percent Difference Difference per BF acre GROWTH PROFILE Base (ac) Aggr. (ac) Base (ac) Aggr. (ac) Base (ac) Aggr. (ac) Base Aggr. Base (ac) Aggr. (ac) Growth Hub 415,855.8 389,569.4 424,139.6 423,370.6 (8,283.8) (33,801.1) -1.95% -7.98% (1.56) (6.35) Industrial Legacy 115,311.8 106,601.6 117,327.3 117,565.6 (2,015.5) (10,964.1) -1.72% -9.33% (0.33) (1.80) Stable Metropolis 178,287.5 163,105.9 183,241.2 180,802.1 (4,953.7) (17,696.2) -2.70% -9.79% (0.82) (2.95) Slow and Steady 147,434.8 135,223.8 150,712.1 150,427.0 (3,277.3) (15,203.1) -2.17% -10.11% (0.92) (4.27) Big and Growing 330,713.5 324,948.3 329,234.8 330,827.9 1,478.7 (5,879.5) 0.45% -1.78% 0.52 (2.07) Up and Coming 220,897.2 212,678.4 223,110.4 223,046.0 (2,213.1) (10,367.6) -0.99% -4.65% (0.37) (1.75) ALL 1,408,500.7 1,332,127.5 1,427,765.4 1,426,039.1 (19,264.7) (93,911.7) -1.35% -6.59% (0.65) (3.16)

Residential VMT RES VMT Primary Phase BF TG Difference Percent Difference Difference per BF acre GROWTH PROFILE Base (mi) Aggr. (mi) Base (mi) Aggr. (mi) Base (mi) Aggr. (mi) Base Aggr. Base (mi/ac) Aggr. (mi/ac) Growth Hub 1,834,149 5,359,628 3,271,324 10,932,003 (1,437,176) (5,572,375) -43.93% -50.97% (270.0) (1,046.9) Industrial Legacy 2,618,955 6,798,607 3,023,236 8,853,293 (404,280) (2,054,686) -13.37% -23.21% (66.3) (337.2) Stable Metropolis 1,665,429 4,854,891 2,070,498 6,940,275 (405,069) (2,085,384) -19.56% -30.05% (67.4) (347.1) Slow and Steady 1,564,873 4,765,935 1,876,601 6,294,413 (311,728) (1,528,478) -16.61% -24.28% (87.6) (429.4) Big and Growing 1,710,959 5,506,931 2,080,006 7,108,437 (369,047) (1,601,506) -17.74% -22.53% (130.1) (564.6) Up and Coming 1,776,879 4,928,156 2,615,699 8,032,068 (838,820) (3,103,912) -32.07% -38.64% (141.9) (524.9) ALL 11,171,244.2 32,214,147.9 14,937,363.2 48,160,488.2 (3,766,119.0) (15,946,340.3) -25.21% -33.11% (126.66) (536.29)

Secondary Phase BF TG Difference Percent Difference Difference per BF acre GROWTH PROFILE Base (mi) Aggr. (mi) Base (mi) Aggr. (mi) Base (mi) Aggr. (mi) Base Aggr. Base (mi/ac) Aggr. (mi/ac) Growth Hub 120,393,623 112,701,128 120,245,181 112,409,356 148,442 291,772 0.12% 0.26% 27.9 54.8 Industrial Legacy 22,542,090 16,863,813 22,282,697 16,786,558 259,393 77,255 1.16% 0.46% 42.6 12.7 Stable Metropolis 36,528,273 31,603,288 37,133,855 31,672,332 (605,582) (69,044) -1.63% -0.22% (100.8) (11.5) Slow and Steady 27,375,117 22,929,703 27,283,281 22,709,725 91,836 219,977 0.34% 0.97% 25.8 61.8 Big and Growing 135,566,922 130,511,959 135,712,325 130,637,763 (145,404) (125,804) -0.11% -0.10% (51.3) (44.3) Up and Coming 54,361,077 49,231,730 53,974,452 49,064,666 386,625 167,064 0.72% 0.34% 65.4 28.3 ALL 396,767,101.6 363,841,620.4 396,631,791.1 363,280,401.1 135,310.5 561,219.3 0.03% 0.15% 4.55 18.87

Page 1 of 3

E-3 Table E-2. Summary of Allocation and Environmental Results (by Growth Profile)

Cumulative BF TG Difference Percent Difference Difference per BF acre GROWTH PROFILE Base (mi) Aggr. (mi) Base (mi) Aggr. (mi) Base (mi) Aggr. (mi) Base Aggr. Base (mi/ac) Aggr. (mi/ac) Growth Hub 121,347,494 114,998,500 123,025,166 121,817,262 (1,677,672) (6,818,762) -1.36% -5.60% (315.18) (1,281.01) Industrial Legacy 24,639,208 21,936,948 25,183,152 25,290,141 (543,945) (3,353,194) -2.16% -13.26% (89.26) (550.24) Stable Metropolis 37,895,554 35,421,883 39,101,756 38,332,124 (1,206,202) (2,910,242) -3.08% -7.59% (200.77) (484.41) Slow and Steady 28,507,294 26,215,326 29,072,644 28,747,137 (565,350) (2,531,811) -1.94% -8.81% (158.82) (711.22) Big and Growing 136,436,982 132,943,221 137,587,812 137,075,665 (1,150,830) (4,132,445) -0.84% -3.01% (405.69) (1,456.76) Up and Coming 55,383,215 52,053,300 56,341,897 56,382,260 (958,682) (4,328,960) -1.70% -7.68% (162.13) (732.09) ALL 404,209,746.8 383,569,176.9 410,312,427.8 407,644,589.5 (6,102,681.0) (24,075,412.6) -1.49% -5.91% (205.24) (809.68)

VMT per capita RES VMT Primary Phase BF TG Difference Percent Difference Difference per BF acre GROWTH PROFILE Base (mi/capita) Aggr. (mi/capita) Base (mi/capita) Aggr. (mi/capita) Base (mi/capita) Aggr. (mi/capita) Base Aggr. Base (mi/capita/ac) Aggr. (mi/capita/ac) Growth Hub 16.54 14.58 29.49 29.74 (13.0) (15.2) -43.93% -50.97% (0.002) (0.003) Industrial Legacy 25.08 22.52 28.95 29.32 (3.9) (6.8) -13.37% -23.21% (0.001) (0.001) Stable Metropolis 18.11 15.83 22.52 22.63 (4.4) (6.8) -19.56% -30.05% (0.001) (0.001) Slow and Steady 19.51 17.87 23.40 23.60 (3.9) (5.7) -16.61% -24.28% (0.001) (0.002) Big and Growing 33.98 32.02 41.31 41.33 (7.3) (9.3) -17.74% -22.53% (0.003) (0.003) Up and Coming 23.04 20.96 33.92 34.15 (10.9) (13.2) -32.07% -38.64% (0.002) (0.002) ALL 21.69 19.52 29.01 29.18 (7.3) (9.7) -25.21% -33.11% (0.00) (0.00)

Secondary Phase BF TG Difference Percent Difference Difference per BF acre GROWTH PROFILE Base (mi/capita) Aggr. (mi/capita) Base (mi/capita) Aggr. (mi/capita) Base (mi/capita) Aggr. (mi/capita) Base Aggr. Base (mi/capita/ac) Aggr. (mi/capita/ac) Growth Hub 31.04 31.12 31.00 31.04 0.04 0.08 0.12% 0.26% 0.000 0.000 Industrial Legacy 33.86 36.02 33.47 35.85 0.39 0.16 1.16% 0.46% 0.000 0.000 Stable Metropolis 23.29 23.34 23.67 23.39 (0.39) (0.05) -1.63% -0.22% (0.000) (0.000) Slow and Steady 24.85 25.05 24.76 24.81 0.08 0.24 0.34% 0.97% 0.000 0.000 Big and Growing 37.97 37.84 38.01 37.88 (0.04) (0.04) -0.11% -0.10% (0.000) (0.000) Up and Coming 30.45 30.25 30.23 30.15 0.22 0.10 0.72% 0.34% 0.000 0.000 ALL 31.56 31.82 31.55 31.77 0.01 0.05 0.03% 0.15% 0.00 0.00

Cumulative BF TG Difference Percent Difference Difference per BF acre GROWTH PROFILE Base (mi/capita) Aggr. (mi/capita) Base (mi/capita) Aggr. (mi/capita) Base (mi/capita) Aggr. (mi/capita) Base Aggr. Base (mi/capita/ac) Aggr. (mi/capita/ac) Growth Hub 30.42 28.82 30.84 30.53 (0.4) (1.7) -1.36% -5.60% (0.000) (0.000) Industrial Legacy 31.99 28.48 32.70 32.84 (0.7) (4.4) -2.16% -13.26% (0.000) (0.001) Stable Metropolis 22.82 21.33 23.55 23.08 (0.7) (1.8) -3.08% -7.59% (0.000) (0.000) Slow and Steady 24.12 22.18 24.60 24.32 (0.5) (2.1) -1.94% -8.81% (0.000) (0.001) Big and Growing 37.68 36.72 38.00 37.86 (0.3) (1.1) -0.84% -3.01% (0.000) (0.000) Up and Coming 29.73 27.95 30.25 30.27 (0.5) (2.3) -1.70% -7.68% (0.000) (0.000) ALL 30.89 29.31 31.36 31.15 (0.5) (1.8) -1.49% -5.91% (0.00) (0.00)

Employment VMT EMP VMT Primary Phase BF TG Difference Percent Difference Difference per BF acre GROWTH PROFILE Base (mi) Aggr. (mi) Base (mi) Aggr. (mi) Base (mi) Aggr. (mi) Base Aggr. Base (mi/ac) Aggr. (mi/ac) Growth Hub 3,411,071 11,544,657 3,932,221 13,577,768 (521,150) (2,033,110) -13.25% -14.97% (97.9) (382.0) Industrial Legacy 630,100 2,146,057 684,571 2,350,039 (54,471) (203,981) -7.96% -8.68% (8.9) (33.5) Stable Metropolis 2,082,177 6,722,492 2,192,107 7,165,220 (109,930) (442,728) -5.01% -6.18% (18.3) (73.7) Slow and Steady 1,255,855 4,244,041 1,316,095 4,567,511 (60,239) (323,469) -4.58% -7.08% (16.9) (90.9) Big and Growing 751,780 2,545,709 795,023 2,691,036 (43,243) (145,327) -5.44% -5.40% (15.2) (51.2) Up and Coming 864,304 2,847,529 944,663 3,144,444 (80,359) (296,915) -8.51% -9.44% (13.6) (50.2) ALL 8,995,287.3 30,050,486.1 9,864,679.5 33,496,016.8 (869,392.2) (3,445,530.7) -8.81% -10.29% (29.24) (115.88)

Secondary Phase BF TG Difference Percent Difference Difference per BF acre GROWTH PROFILE Base (mi) Aggr. (mi) Base (mi) Aggr. (mi) Base (mi) Aggr. (mi) Base Aggr. Base (mi/ac) Aggr. (mi/ac) Growth Hub 76,633,195 66,568,897 77,009,147 67,331,437 (375,952) (762,540) -0.49% -1.13% (70.6) (143.3) Industrial Legacy 23,363,228 21,640,310 23,422,879 21,736,297 (59,651) (95,987) -0.25% -0.44% (9.8) (15.8) Stable Metropolis 60,149,694 54,779,455 60,623,938 55,387,043 (474,243) (607,588) -0.78% -1.10% (78.9) (101.1) Slow and Steady 34,761,102 31,190,219 34,992,808 31,688,971 (231,706) (498,751) -0.66% -1.57% (65.1) (140.1) Big and Growing 76,853,734 74,849,207 77,012,624 75,008,611 (158,890) (159,403) -0.21% -0.21% (56.0) (56.2) Up and Coming 36,160,630 33,880,944 36,247,245 34,014,542 (86,615) (133,599) -0.24% -0.39% (14.6) (22.6) ALL 307,921,583.1 282,909,031.6 309,308,639.4 285,166,900.6 (1,387,056.3) (2,257,869.1) -0.45% -0.79% (46.65) (75.93)

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E-4 Table E-2. Summary of Allocation and Environmental Results (by Growth Profile)

Cumulative BF TG Difference Percent Difference Difference per BF acre GROWTH PROFILE Base (mi) Aggr. (mi) Base (mi) Aggr. (mi) Base (mi) Aggr. (mi) Base Aggr. Base (mi/ac) Aggr. (mi/ac) Growth Hub 80,044,267 78,113,554 80,941,368 80,909,205 (897,101) (2,795,651) -1.11% -3.46% (168.53) (525.21) Industrial Legacy 23,993,328 23,786,367 24,107,450 24,086,336 (114,122) (299,969) -0.47% -1.25% (18.73) (49.22) Stable Metropolis 62,231,872 61,501,947 62,816,045 62,552,263 (584,173) (1,050,316) -0.93% -1.68% (97.24) (174.83) Slow and Steady 36,016,957 35,434,261 36,308,902 36,256,481 (291,945) (822,221) -0.80% -2.27% (82.01) (230.97) Big and Growing 77,605,513 77,394,916 77,807,647 77,699,646 (202,133) (304,730) -0.26% -0.39% (71.26) (107.42) Up and Coming 37,024,933 36,728,472 37,191,907 37,158,986 (166,974) (430,514) -0.45% -1.16% (28.24) (72.81) ALL 316,916,870.4 312,959,517.7 319,173,318.9 318,662,917.4 (2,256,448.5) (5,703,399.7) -0.71% -1.79% (75.89) (191.81)

VMT per job EMP VMT Primary Phase BF TG Difference Percent Difference Difference per BF acre GROWTH PROFILE Base (mi/job) Aggr. (mi/job) Base (mi/job) Aggr. (mi/job) Base (mi/job) Aggr. (mi/job) Base Aggr. Base (mi/job/ac) Aggr. (mi//job/ac) Growth Hub 21.32 21.30 24.58 25.05 (3.3) (3.8) -13.25% -14.97% (0.001) (0.001) Industrial Legacy 22.35 22.30 24.28 24.42 (1.9) (2.1) -7.96% -8.68% (0.000) (0.000) Stable Metropolis 21.34 21.27 22.47 22.67 (1.1) (1.4) -5.01% -6.18% (0.000) (0.000) Slow and Steady 20.10 20.06 21.06 21.59 (1.0) (1.5) -4.58% -7.08% (0.000) (0.000) Big and Growing 25.38 25.37 26.84 26.82 (1.46) (1.45) -5.44% -5.40% (0.001) (0.001) Up and Coming 23.28 23.13 25.45 25.54 (2.2) (2.4) -8.51% -9.44% (0.000) (0.000) ALL 21.68 21.63 23.77 24.11 (2.1) (2.5) -8.81% -10.29% (0.00) (0.00)

Secondary Phase BF TG Difference Percent Difference Difference per BF acre GROWTH PROFILE Base (mi/job) Aggr. (mi/job) Base (mi/job) Aggr. (mi/job) Base (mi/job) Aggr. (mi/job) Base Aggr. Base (mi/job/ac) Aggr. (mi//job/ac) Growth Hub 25.34 25.19 25.46 25.48 (0.12) (0.29) -0.49% -1.13% (0.000) (0.000) Industrial Legacy 24.96 24.93 25.02 25.04 (0.06) (0.11) -0.25% -0.44% (0.000) (0.000) Stable Metropolis 23.37 23.26 23.55 23.52 (0.18) (0.26) -0.78% -1.10% (0.000) (0.000) Slow and Steady 23.38 23.32 23.54 23.69 (0.16) (0.37) -0.66% -1.57% (0.000) (0.000) Big and Growing 27.64 27.62 27.69 27.68 (0.057) (0.059) -0.21% -0.21% (0.000) (0.000) Up and Coming 25.79 25.75 25.86 25.85 (0.06) (0.10) -0.24% -0.39% (0.000) (0.000) ALL 25.23 25.19 25.35 25.39 (0.11) (0.20) -0.45% -0.79% (0.00) (0.00)

Cumulative BF TG Difference Percent Difference Difference per BF acre GROWTH PROFILE Base (mi/job) Aggr. (mi/job) Base (mi/job) Aggr. (mi/job) Base (mi/job) Aggr. (mi/job) Base Aggr. Base (mi/job/ac) Aggr. (mi//job/ac) Growth Hub 25.14 24.53 25.42 25.41 (0.28) (0.88) -1.11% -3.46% (0.000) (0.000) Industrial Legacy 24.88 24.67 25.00 24.98 (0.12) (0.31) -0.47% -1.25% (0.000) (0.000) Stable Metropolis 23.30 23.02 23.52 23.42 (0.22) (0.39) -0.93% -1.68% (0.000) (0.000) Slow and Steady 23.25 22.87 23.44 23.41 (0.19) (0.53) -0.80% -2.27% (0.000) (0.000) Big and Growing 27.61 27.54 27.68 27.65 (0.07) (0.11) -0.26% -0.39% (0.000) (0.000) Up and Coming 25.73 25.52 25.85 25.82 (0.12) (0.30) -0.45% -1.16% (0.000) (0.000) ALL 25.11 24.80 25.29 25.25 (0.18) (0.45) -0.71% -1.79% (0.00) (0.00)

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E-5 Table E-3. Summary of Allocation and Environmental Results (by EPA Region)

ALLOCATION Control Totals Base Aggressive

Gross Jobs Gross Activity Gross HU Gross Jobs Gross Activity EPA REGION New HU at BF New Jobs at BF HU share of Jobs share of Gross HU Density at BF Density at Density at BF New HU at % of control New Jobs at % of control HU share of Jobs share of Density at BF Density at BF Density at BF Total new HU Total new Jobs sites % of control total Sites % of control total allocation allocation BF Acres sites BF sites Sites BF sites total BF Sites total allocation allocation BF Acres sites sites Sites 1 123,539 404,379 11,699 9.5% 20,469 5.1% 36% 64% 2,142 5.46 9.56 15.02 38,489 31.2% 67,795 16.8% 36% 64% 2,142 17.97 31.65 49.62 2 219,571 979,653 31,127 14.2% 67,042 6.8% 32% 68% 4,928 6.32 13.60 19.92 103,700 47.2% 212,379 21.7% 33% 67% 4,928 21.04 43.09 64.14 3 259,459 619,541 6,549 2.5% 18,267 2.9% 26% 74% 1,626 4.03 11.23 15.26 19,578 7.5% 60,527 9.8% 24% 76% 1,626 12.04 37.22 49.27 4 591,190 1,285,124 16,919 2.9% 22,745 1.8% 43% 57% 3,823 4.43 5.95 10.38 55,253 9.3% 75,695 5.9% 42% 58% 3,823 14.45 19.80 34.25 5 467,010 1,246,516 37,072 7.9% 59,070 4.7% 39% 61% 4,812 7.70 12.28 19.98 105,307 22.5% 201,956 16.2% 34% 66% 4,812 21.89 41.97 63.86 6 1,754,205 3,548,474 33,322 1.9% 46,674 1.3% 42% 58% 4,984 6.69 9.37 16.05 113,307 6.5% 158,093 4.5% 42% 58% 4,984 22.74 31.72 54.46 7 81,028 229,368 14,407 17.8% 2,525 1.1% 85% 15% 1,217 11.84 2.07 13.91 41,897 51.7% 8,599 3.7% 83% 17% 1,217 34.43 7.07 41.49 8 117,916 231,477 1,244 1.1% 5,640 2.4% 18% 82% 525 2.37 10.74 13.10 3,759 3.2% 19,197 8.3% 16% 84% 525 7.16 36.54 43.70 9 833,709 2,844,639 28,814 3.5% 142,460 5.0% 17% 83% 3,838 7.51 37.12 44.63 95,684 11.5% 483,645 17.0% 17% 83% 3,838 24.93 126.02 150.95 10 624,327 1,229,539 18,435 3.0% 30,077 2.4% 38% 62% 1,840 10.02 16.35 26.36 62,641 10.0% 101,454 8.3% 38% 62% 1,840 34.04 55.14 89.18 ALL 5,071,954 12,618,710 199,588 3.9% 414,969 3.3% 32% 68% 29,734 6.71 13.96 20.67 639,615 12.6% 1,389,340 11.0% 32% 68% 29,734 21.51 46.72 68.24

Urban Footprint Expansion ISGM Primary Phase BF TG Difference Percent Difference Difference per BF acre EPA REGION Base (ac) Aggr. (ac) Base (ac) Aggr. (ac) Base (ac) Aggr. (ac) Base Aggr. Base (ac) Aggr. (ac) 1 1,208 3,182 2,871 9,538 (1,663) (6,356) -57.93% -66.63% (0.78) (2.97) 2 2,799 5,920 7,853 24,632 (5,054) (18,713) -64.35% -75.97% (1.03) (3.80) 3 715 1,736 1,952 6,252 (1,237) (4,517) -63.39% -72.24% (0.76) (2.78) 4 1,440 3,743 6,964 17,689 (5,524) (13,946) -79.33% -78.84% (1.45) (3.65) 5 2,720 6,690 8,336 26,887 (5,616) (20,197) -67.37% -75.12% (1.17) (4.20) 6 2,285 5,554 6,584 22,377 (4,298) (16,822) -65.29% -75.18% (0.86) (3.38) 7 424 1,045 1,468 4,376 (1,045) (3,330) -71.15% -76.11% (0.86) (2.74) 8 347 899 891 3,008 (545) (2,109) -61.11% -70.11% (1.04) (4.01) 9 1,612 3,660 9,911 36,191 (8,299) (32,530) -83.73% -89.89% (2.16) (8.48) 10 986 2,528 5,620 20,913 (4,634) (18,385) -82.45% -87.91% (2.52) (9.99) ALL 14,537 34,957 52,451 171,863 (37,914) (136,906) -72.29% -79.66% (1.28) (4.60)

Secondary Phase BF TG Difference Percent Difference Difference per BF acre EPA REGION Base (ac) Aggr. (ac) Base (ac) Aggr. (ac) Base (ac) Aggr. (ac) Base Aggr. Base (ac) Aggr. (ac) 1 45,759 38,812 46,037 39,528 (277) (716) -0.60% -1.81% (0.13) (0.33) 2 91,133 72,782 91,180 75,190 (47) (2,408) -0.05% -3.20% (0.01) (0.49) 3 67,238 62,662 66,885 63,539 353 (877) 0.53% -1.38% 0.22 (0.54) 4 173,307 165,034 169,741 164,672 3,565 362 2.10% 0.22% 0.93 0.09 5 146,393 125,323 147,308 128,817 (916) (3,494) -0.62% -2.71% (0.19) (0.73) 6 422,631 406,054 420,447 408,055 2,184 (2,001) 0.52% -0.49% 0.44 (0.40) 7 29,424 26,422 29,579 26,670 (155) (248) -0.52% -0.93% (0.13) (0.20) 8 40,202 37,871 40,369 37,656 (166) 215 -0.41% 0.57% (0.32) 0.41 9 197,262 164,894 202,443 175,031 (5,181) (10,137) -2.56% -5.79% (1.35) (2.64) 10 170,197 160,095 170,685 162,042 (488) (1,947) -0.29% -1.20% (0.27) (1.06) ALL 1,383,546 1,259,948 1,384,673 1,281,200 (1,127) (21,251) -0.08% -1.66% (0.04) (0.71)

Cumulative BF TG Difference Percent Difference Difference per BF acre EPA REGION Base (ac) Aggr. (ac) Base (ac) Aggr. (ac) Base (ac) Aggr. (ac) Base Aggr. Base (ac) Aggr. (ac) 1 47,416 43,542 48,699 48,657 (1,284) (5,115) -2.64% -10.51% (0.60) (2.39) 2 95,126 83,157 97,981 97,420 (2,855) (14,263) -2.91% -14.64% (0.58) (2.89) 3 68,340 65,757 68,552 68,955 (213) (3,198) -0.31% -4.64% (0.13) (1.97) 4 175,352 170,896 175,154 178,923 198 (8,027) 0.11% -4.49% 0.05 (2.10) 5 150,846 137,955 154,914 154,522 (4,068) (16,567) -2.63% -10.72% (0.85) (3.44) 6 426,197 416,360 426,035 427,193 162 (10,833) 0.04% -2.54% 0.03 (2.17) 7 29,937 27,815 30,912 30,721 (974) (2,906) -3.15% -9.46% (0.80) (2.39) 8 40,972 40,232 41,207 40,520 (235) (289) -0.57% -0.71% (0.45) (0.55) 9 202,564 181,725 210,786 206,812 (8,223) (25,087) -3.90% -12.13% (2.14) (6.54) 10 171,751 164,688 173,524 172,315 (1,773) (7,627) -1.02% -4.43% (0.96) (4.14) ALL 1,408,501 1,332,127 1,427,765 1,426,039 (19,265) (93,912) -1.35% -6.59% (0.65) (3.16)

Residential VMT RES VMT Primary Phase BF TG Difference Percent Difference Difference per BF acre EPA REGION Base (mi) Aggr. (mi) Base (mi) Aggr. (mi) Base (mi) Aggr. (mi) Base Aggr. Base (mi/ac) Aggr. (mi/ac) 1 717,489 2,225,528 835,132 2,772,270 (117,643) (546,742) -14.09% -19.72% (55) (255) 2 1,528,206 4,459,188 1,866,149 6,264,039 (337,943) (1,804,851) -18.11% -28.81% (69) (366) 3 377,382 990,756 474,316 1,430,559 (96,934) (439,803) -20.44% -30.74% (60) (270) 4 1,350,097 3,924,450 1,731,620 5,663,652 (381,523) (1,739,202) -22.03% -30.71% (100) (455) 5 2,322,148 6,148,108 2,672,426 7,611,821 (350,277) (1,463,714) -13.11% -19.23% (73) (304) 6 2,510,126 7,846,458 3,232,832 11,004,155 (722,706) (3,157,697) -22.36% -28.70% (145) (634) 7 580,193 1,289,619 1,124,963 3,262,094 (544,770) (1,972,475) -48.43% -60.47% (448) (1,621) 8 69,549 188,480 77,087 228,154 (7,538) (39,673) -9.78% -17.39% (14) (76) 9 1,403,778 4,169,193 1,724,574 5,831,004 (320,797) (1,661,811) -18.60% -28.50% (84) (433) 10 312,276 972,368 1,198,264 4,092,741 (885,988) (3,120,373) -73.94% -76.24% (481) (1,696) ALL 11,171,244 32,214,148 14,937,363 48,160,488 (3,766,119) (15,946,340) -25.21% -33.11% (127) (536)

Page 1 of 4

E-6 Table E-3. Summary of Allocation and Environmental Results (by EPA Region)

Secondary Phase BF TG Difference Percent Difference Difference per BF acre EPA REGION Base (mi) Aggr. (mi) Base (mi) Aggr. (mi) Base (mi) Aggr. (mi) Base Aggr. Base (mi/ac) Aggr. (mi/ac) 1 8,181,259 6,303,220 8,142,496 6,277,228 38,762 25,991 0.48% 0.41% 18 12 2 11,839,919 7,456,368 11,777,835 7,423,131 62,083 33,237 0.53% 0.45% 13 7 3 18,455,036 17,519,551 18,319,122 17,432,646 135,914 86,905 0.74% 0.50% 84 53 4 57,658,883 53,950,639 57,060,380 53,596,486 598,504 354,153 1.05% 0.66% 157 93 5 32,119,539 27,248,357 31,999,913 27,073,203 119,626 175,154 0.37% 0.65% 25 36 6 165,201,226 157,495,673 165,306,316 157,671,527 (105,089) (175,855) -0.06% -0.11% (21) (35) 7 4,929,693 2,871,374 4,888,596 2,880,506 41,097 (9,132) 0.84% -0.32% 34 (8) 8 7,339,809 7,193,007 7,391,656 7,224,907 (51,847) (31,900) -0.70% -0.44% (99) (61) 9 51,209,424 47,046,337 51,725,674 46,858,826 (516,250) 187,511 -1.00% 0.40% (135) 49 10 39,832,315 36,757,095 40,019,802 36,841,941 (187,488) (84,846) -0.47% -0.23% (102) (46) ALL 396,767,102 363,841,620 396,631,791 363,280,401 135,311 561,219 0.03% 0.15% 5 19

Cumulative BF TG Difference Percent Difference Difference per BF acre EPA REGION Base (mi) Aggr. (mi) Base (mi) Aggr. (mi) Base (mi) Aggr. (mi) Base Aggr. Base (mi/ac) Aggr. (mi/ac) 1 8,715,437 7,924,663 8,934,219 8,930,553 (218,783) (1,005,890) -2.45% -11.26% (102) (470) 2 13,018,283 10,605,156 13,578,768 13,522,597 (560,485) (2,917,441) -4.13% -21.57% (114) (592) 3 18,723,169 18,145,183 18,744,988 18,713,931 (21,818) (568,749) -0.12% -3.04% (13) (350) 4 58,431,243 56,113,653 58,637,823 58,771,585 (206,580) (2,657,932) -0.35% -4.52% (54) (695) 5 33,924,951 31,862,138 34,522,329 34,268,889 (597,378) (2,406,751) -1.73% -7.02% (124) (500) 6 166,503,139 160,836,265 168,206,303 167,614,779 (1,703,164) (6,778,514) -1.01% -4.04% (342) (1,360) 7 5,328,236 3,762,529 5,938,253 5,940,460 (610,017) (2,177,931) -10.27% -36.66% (501) (1,790) 8 7,392,558 7,327,313 7,455,821 7,415,938 (63,263) (88,625) -0.85% -1.20% (120) (169) 9 52,144,106 49,636,861 53,201,227 51,910,545 (1,057,122) (2,273,684) -1.99% -4.38% (275) (592) 10 40,028,625 37,355,416 41,092,696 40,555,312 (1,064,071) (3,199,896) -2.59% -7.89% (578) (1,739) ALL 404,209,747 383,569,177 410,312,428 407,644,590 (6,102,681) (24,075,413) -1.49% -5.91% (205) (810)

VMT per capita RES VMT Primary Phase BF TG Difference Percent Difference Difference per BF acre EPA REGION Base (mi/capita) Aggr. (mi/capita) Base (mi/capita) Aggr. (mi/capita) Base (mi/capita) Aggr. (mi/capita) Base Aggr. Base (mi/capita/ac) Aggr. (mi/capita/ac) 1 23.77 22.41 27.67 27.92 (3.9) (5.5) -14.09% -19.72% (0.002) (0.003) 2 19.03 16.67 23.24 23.41 (4.2) (6.7) -18.11% -28.81% (0.001) (0.001) 3 22.33 19.61 28.07 28.32 (5.7) (8.7) -20.44% -30.74% (0.004) (0.005) 4 30.93 27.53 39.67 39.73 (8.7) (12.2) -22.03% -30.71% (0.002) (0.003) 5 24.28 22.63 27.94 28.02 (3.7) (5.4) -13.11% -19.23% (0.001) (0.001) 6 29.20 26.84 37.60 37.64 (8.4) (10.8) -22.36% -28.70% (0.002) (0.002) 7 15.61 11.93 30.27 30.18 (14.7) (18.2) -48.43% -60.47% (0.012) (0.015) 8 21.67 19.43 24.02 23.53 (2.3) (4.1) -9.78% -17.39% (0.004) (0.008) 9 18.88 16.89 23.20 23.62 (4.3) (6.7) -18.60% -28.50% (0.001) (0.002) 10 6.57 6.02 25.19 25.32 (18.6) (19.3) -73.94% -76.24% (0.010) (0.010) ALL 21.69 19.52 29.01 29.18 (7.3) (9.7) -25.21% -33.11% 0.000 0.000

Secondary Phase BF TG Difference Percent Difference Difference per BF acre EPA REGION Base (mi/capita) Aggr. (mi/capita) Base (mi/capita) Aggr. (mi/capita) Base (mi/capita) Aggr. (mi/capita) Base Aggr. Base (mi/capita/ac) Aggr. (mi/capita/ac) 1 28.35 28.73 28.22 28.61 0.1 0.1 0.48% 0.41% 0.000 0.000 2 24.35 24.94 24.22 24.83 0.1 0.1 0.53% 0.45% 0.000 0.000 3 28.28 28.31 28.07 28.17 0.2 0.1 0.74% 0.50% 0.000 0.000 4 38.92 39.02 38.51 38.76 0.4 0.3 1.05% 0.66% 0.000 0.000 5 28.96 29.20 28.85 29.01 0.1 0.2 0.37% 0.65% 0.000 0.000 6 37.21 37.20 37.23 37.24 (0.0) (0.0) -0.06% -0.11% (0.000) (0.000) 7 28.68 28.44 28.44 28.53 0.2 (0.1) 0.84% -0.32% 0.000 (0.000) 8 24.38 24.42 24.56 24.53 (0.2) (0.1) -0.70% -0.44% (0.000) (0.000) 9 24.66 24.71 24.91 24.61 (0.2) 0.1 -1.00% 0.40% (0.000) 0.000 10 25.48 25.36 25.60 25.42 (0.1) (0.1) -0.47% -0.23% (0.000) (0.000) ALL 31.56 31.82 31.55 31.77 0.0 0.0 0.03% 0.15% 0.000 0.000

Cumulative BF TG Difference Percent Difference Difference per BF acre EPA REGION Base (mi/capita) Aggr. (mi/capita) Base (mi/capita) Aggr. (mi/capita) Base (mi/capita) Aggr. (mi/capita) Base Aggr. Base (mi/capita/ac) Aggr. (mi/capita/ac) 1 27.34 24.86 28.03 28.02 (0.7) (3.2) -2.45% -11.26% (0.000) (0.001) 2 22.98 18.72 23.97 23.87 (1.0) (5.2) -4.13% -21.57% (0.000) (0.001) 3 27.97 27.11 28.00 27.96 (0.0) (0.8) -0.12% -3.04% (0.000) (0.001) 4 38.31 36.79 38.44 38.53 (0.1) (1.7) -0.35% -4.52% (0.000) (0.000) 5 28.16 26.44 28.65 28.44 (0.5) (2.0) -1.73% -7.02% (0.000) (0.000) 6 36.79 35.54 37.17 37.03 (0.4) (1.5) -1.01% -4.04% (0.000) (0.000) 7 25.49 18.00 28.41 28.42 (2.9) (10.4) -10.27% -36.66% (0.002) (0.009) 8 24.30 24.09 24.51 24.38 (0.2) (0.3) -0.85% -1.20% (0.000) (0.001) 9 24.24 23.08 24.73 24.13 (0.5) (1.1) -1.99% -4.38% (0.000) (0.000) 10 24.85 23.19 25.51 25.18 (0.7) (2.0) -2.59% -7.89% (0.000) (0.001) ALL 30.89 29.31 31.36 31.15 (0.5) (1.8) -1.49% -5.91% (0.000) (0.000)

Page 2 of 4

E-7 Table E-3. Summary of Allocation and Environmental Results (by EPA Region)

Employment VMT EMP VMT Primary Phase BF TG Difference Percent Difference Difference per BF acre EPA REGION Base (mi) Aggr. (mi) Base (mi) Aggr. (mi) Base (mi) Aggr. (mi) Base Aggr. Base (mi/ac) Aggr. (mi/ac) 1 456,319 1,500,831 491,910 1,641,506 (35,591) (140,674) -7.24% -8.57% (16.62) (65.68) 2 1,450,653 4,575,047 1,545,203 4,964,757 (94,550) (389,710) -6.12% -7.85% (19.19) (79.08) 3 397,697 1,307,631 439,503 1,458,812 (41,806) (151,181) -9.51% -10.36% (25.71) (92.98) 4 504,490 1,669,807 552,154 1,843,755 (47,664) (173,948) -8.63% -9.43% (12.47) (45.50) 5 1,377,765 4,707,268 1,499,361 5,172,089 (121,595) (464,821) -8.11% -8.99% (25.27) (96.61) 6 1,152,229 3,900,438 1,224,756 4,146,806 (72,527) (246,368) -5.92% -5.94% (14.55) (49.44) 7 62,050 211,088 64,001 220,623 (1,951) (9,534) -3.05% -4.32% (1.60) (7.83) 8 135,309 459,356 139,291 474,476 (3,982) (15,120) -2.86% -3.19% (7.58) (28.78) 9 2,786,900 9,458,224 3,212,866 11,210,077 (425,966) (1,751,852) -13.26% -15.63% (110.99) (456.46) 10 671,874 2,260,795 695,635 2,363,117 (23,761) (102,323) -3.42% -4.33% (12.91) (55.61) ALL 8,995,287 30,050,486 9,864,680 33,496,017 (869,392) (3,445,531) -8.81% -10.29% (29.24) (115.88)

Secondary Phase BF TG Difference Percent Difference Difference per BF acre EPA REGION Base (mi) Aggr. (mi) Base (mi) Aggr. (mi) Base (mi) Aggr. (mi) Base Aggr. Base (mi/ac) Aggr. (mi/ac) 1 9,498,617 8,307,880 9,520,776 8,371,561 (22,158) (63,681) -0.23% -0.76% (10.35) (29.73) 2 22,198,450 18,624,989 22,320,708 18,885,514 (122,258) (260,524) -0.55% -1.38% (24.81) (52.86) 3 14,717,401 13,651,232 14,746,845 13,719,623 (29,445) (68,390) -0.20% -0.50% (18.11) (42.06) 4 31,432,046 30,092,305 31,471,404 30,171,389 (39,358) (79,084) -0.13% -0.26% (10.30) (20.69) 5 30,974,884 27,093,302 31,117,175 27,416,261 (142,291) (322,959) -0.46% -1.18% (29.57) (67.12) 6 95,382,766 92,294,581 95,577,830 92,537,910 (195,064) (243,328) -0.20% -0.26% (39.14) (48.83) 7 6,043,832 5,877,746 6,059,276 5,897,601 (15,443) (19,855) -0.25% -0.34% (12.69) (16.31) 8 5,322,514 4,980,343 5,352,052 4,997,822 (29,538) (17,479) -0.55% -0.35% (56.23) (33.27) 9 62,710,844 54,177,686 63,414,249 55,124,240 (703,405) (946,554) -1.11% -1.72% (183.28) (246.63) 10 29,640,229 27,808,967 29,728,324 28,044,981 (88,095) (236,015) -0.30% -0.84% (47.88) (128.26) ALL 307,921,583 282,909,032 309,308,639 285,166,901 (1,387,056) (2,257,869) -0.45% -0.79% (46.65) (75.93)

Cumulative BF TG Difference Percent Difference Difference per BF acre EPA REGION Base (mi) Aggr. (mi) Base (mi) Aggr. (mi) Base (mi) Aggr. (mi) Base Aggr. Base (mi/ac) Aggr. (mi/ac) 1 9,954,936 9,808,711 10,012,685 10,013,067 (57,749) (204,356) -0.58% -2.04% (26.96) (95.41) 2 23,649,103 23,200,036 23,865,911 23,850,270 (216,808) (650,234) -0.91% -2.73% (43.99) (131.94) 3 15,115,098 14,958,864 15,186,349 15,178,435 (71,251) (219,571) -0.47% -1.45% (43.82) (135.04) 4 31,936,536 31,762,112 32,023,558 32,015,143 (87,022) (253,031) -0.27% -0.79% (22.76) (66.19) 5 32,352,649 31,800,570 32,616,536 32,588,350 (263,887) (787,780) -0.81% -2.42% (54.84) (163.73) 6 96,534,994 96,195,019 96,802,585 96,684,716 (267,591) (489,696) -0.28% -0.51% (53.69) (98.26) 7 6,105,882 6,088,834 6,123,276 6,118,223 (17,394) (29,389) -0.28% -0.48% (14.29) (24.15) 8 5,457,823 5,439,700 5,491,343 5,472,298 (33,520) (32,598) -0.61% -0.60% (63.81) (62.05) 9 65,497,745 63,635,910 66,627,116 66,334,317 (1,129,371) (2,698,406) -1.70% -4.07% (294.26) (703.09) 10 30,312,104 30,069,761 30,423,959 30,408,099 (111,856) (338,337) -0.37% -1.11% (60.79) (183.87) ALL 316,916,870 312,959,518 319,173,319 318,662,917 (2,256,448) (5,703,400) -0.71% -1.79% (75.89) (191.81)

VMT per job EMP VMT Primary Phase BF TG Difference Percent Difference Difference per BF acre EPA REGION Base (mi/job) Aggr. (mi/job) Base (mi/job) Aggr. (mi/job) Base (mi/job) Aggr. (mi/job) Base Aggr. Base (mi/job/ac) Aggr. (mi//job/ac) 1 22.29 22.14 24.03 24.21 (1.7) (2.1) -7.24% -8.57% (0.00) (0.00) 2 21.64 21.54 23.05 23.38 (1.4) (1.8) -6.12% -7.85% (0.00) (0.00) 3 21.77 21.60 24.06 24.10 (2.3) (2.5) -9.51% -10.36% (0.00) (0.00) 4 22.18 22.06 24.28 24.36 (2.1) (2.3) -8.63% -9.43% (0.00) (0.00) 5 23.32 23.31 25.38 25.61 (2.1) (2.3) -8.11% -8.99% (0.00) (0.00) 6 24.69 24.67 26.24 26.23 (1.6) (1.6) -5.92% -5.94% (0.00) (0.00) 7 24.57 24.55 25.35 25.66 (0.8) (1.1) -3.05% -4.32% (0.00) (0.00) 8 23.99 23.93 24.70 24.72 (0.7) (0.8) -2.86% -3.19% (0.00) (0.00) 9 19.56 19.56 22.55 23.18 (3.0) (3.6) -13.26% -15.63% (0.00) (0.00) 10 22.34 22.28 23.13 23.29 (0.8) (1.0) -3.42% -4.33% (0.00) (0.00) ALL 21.68 21.63 23.77 24.11 (2.1) (2.5) -8.81% -10.29% (0.00) (0.00)

Secondary Phase BF TG Difference Percent Difference Difference per BF acre EPA REGION Base (mi/job) Aggr. (mi/job) Base (mi/job) Aggr. (mi/job) Base (mi/job) Aggr. (mi/job) Base Aggr. Base (mi/job/ac) Aggr. (mi//job/ac) 1 24.74 24.68 24.80 24.87 (0.1) (0.2) -0.23% -0.76% (0.00) (0.00) 2 24.32 24.27 24.46 24.61 (0.1) (0.3) -0.55% -1.38% (0.00) (0.00) 3 24.48 24.42 24.53 24.54 (0.0) (0.1) -0.20% -0.50% (0.00) (0.00) 4 24.90 24.88 24.93 24.95 (0.0) (0.1) -0.13% -0.26% (0.00) (0.00) 5 26.09 25.94 26.21 26.25 (0.1) (0.3) -0.46% -1.18% (0.00) (0.00) 6 27.24 27.22 27.29 27.29 (0.1) (0.1) -0.20% -0.26% (0.00) (0.00) 7 26.64 26.62 26.71 26.71 (0.1) (0.1) -0.25% -0.34% (0.00) (0.00) 8 23.57 23.46 23.70 23.54 (0.1) (0.1) -0.55% -0.35% (0.00) (0.00) 9 23.21 22.95 23.47 23.35 (0.3) (0.4) -1.11% -1.72% (0.00) (0.00) 10 24.71 24.65 24.78 24.86 (0.1) (0.2) -0.30% -0.84% (0.00) (0.00) ALL 25.23 25.19 25.35 25.39 (0.1) (0.2) -0.45% -0.79% (0.00) (0.00)

Page 3 of 4

E-8 Table E-3. Summary of Allocation and Environmental Results (by EPA Region)

Cumulative BF TG Difference Percent Difference Difference per BF acre EPA REGION Base (mi/job) Aggr. (mi/job) Base (mi/job) Aggr. (mi/job) Base (mi/job) Aggr. (mi/job) Base Aggr. Base (mi/job/ac) Aggr. (mi//job/ac) 1 24.62 24.26 24.76 24.76 (0.1) (0.5) -0.58% -2.04% (0.00) (0.00) 2 24.14 23.68 24.36 24.35 (0.2) (0.7) -0.91% -2.73% (0.00) (0.00) 3 24.40 24.15 24.51 24.50 (0.1) (0.4) -0.47% -1.45% (0.00) (0.00) 4 24.85 24.72 24.92 24.91 (0.1) (0.2) -0.27% -0.79% (0.00) (0.00) 5 25.95 25.51 26.17 26.14 (0.2) (0.6) -0.81% -2.42% (0.00) (0.00) 6 27.20 27.11 27.28 27.25 (0.1) (0.1) -0.28% -0.51% (0.00) (0.00) 7 26.62 26.55 26.70 26.67 (0.1) (0.1) -0.28% -0.48% (0.00) (0.00) 8 23.58 23.50 23.72 23.64 (0.1) (0.1) -0.61% -0.60% (0.00) (0.00) 9 23.02 22.37 23.42 23.32 (0.4) (0.9) -1.70% -4.07% (0.00) (0.00) 10 24.65 24.46 24.74 24.73 (0.1) (0.3) -0.37% -1.11% (0.00) (0.00) ALL 25.11 24.80 25.29 25.25 (0.2) (0.5) -0.71% -1.79% (0.00) (0.00)

Page 4 of 4

E-9 Table E-4. Allocation Results (by Metro/CBSA)

Aggressive Base Aggressive Base BF Acreage HU Control Jobs Control Jobs Proportion of Housing Proportion CBSA NAME GROWTH PROFILE EPA REGION HU Allocated Pct of Control HU Allocated Pct of Control Jobs Allocated Pct of Control Jobs Allocated Pct of Control (ac) Total Total Allocation of Allocation 10420 Akron, OH Industrial Legacy 5 743.5 10,210 10,210 100.00% 6,801 66.61% 63,109 10,769 17.06% 2,695 4.27% 51.3% 48.7% 10580 Albany-Schenectady-Troy, NY Industrial Legacy 2 322.7 33,509 6,171 18.42% 1,815 5.42% 121,641 17,109 14.07% 5,071 4.17% 73.5% 26.5% 10740 Albuquerque, NM Up and Coming 3 298.3 104,554 2,443 2.34% 772 0.74% 155,921 20,938 13.43% 6,250 4.01% 89.6% 10.4% 10900 Allentown-Bethlehem-Easton, PA-NJ Industrial Legacy 5 837.8 38,552 23,923 62.05% 7,664 19.88% 100,297 10,865 10.83% 3,173 3.16% 31.2% 68.8% 11460 Ann Arbor, MI Industrial Legacy 4 229.0 21,182 4,816 22.74% 1,423 6.72% 71,372 7,543 10.57% 2,231 3.13% 61.0% 39.0% 12060 Atlanta, GA Big and Growing 6 1,582.0 593,956 47,849 8.06% 14,024 2.36% 1,087,431 44,144 4.06% 12,990 1.19% 48.0% 52.0% 12420 Austin-Round Rock, TX Growth Hub 6 908.8 282,865 26,748 9.46% 7,914 2.80% 534,172 38,843 7.27% 11,481 2.15% 59.2% 40.8% 12580 Baltimore, MD Slow and Steady 3 713.9 144,427 15,490 10.73% 5,237 3.63% 442,734 38,775 8.76% 11,678 2.64% 71.5% 28.5% 12620 Bangor, ME Industrial Legacy 1 583.1 4,258 4,167 97.86% 1,286 30.20% 23,860 2,900 12.15% 1,038 4.35% 41.0% 59.0% 13660 Big Rapids, MI Up and Coming 5 66.4 3,387 452 13.35% 140 4.13% 4,369 157 3.59% 53 1.21% 25.8% 74.2% 13740 Billings, MT Up and Coming 8 80.9 12,118 1,676 13.83% 517 4.27% 26,007 12,040 46.30% 3,512 13.50% 87.8% 12.2% 14260 Boise City, ID Up and Coming 10 142.8 66,015 1,269 1.92% 405 0.61% 122,727 8,180 6.67% 2,433 1.98% 86.6% 13.4% 14500 Boulder, CO Up and Coming 8 28.7 44,903 76 0.17% 22 0.05% 97,662 821 0.84% 240 0.25% 91.5% 8.5% 15540 Burlington-South Burlington, VT Up and Coming 1 171.0 19,283 2,137 11.08% 633 3.28% 48,884 9,171 18.76% 2,797 5.72% 81.1% 18.9% 16860 Chattanooga, TN-GA Industrial Legacy 4 228.6 23,958 3,419 14.27% 1,032 4.31% 62,125 5,761 9.27% 1,705 2.74% 62.8% 37.2% 19100 Dallas, TX Big and Growing 6 1,254.7 809,401 18,815 2.32% 5,490 0.68% 1,723,128 56,202 3.26% 16,628 0.96% 74.9% 25.1% 19380 Dayton, OH Industrial Legacy 5 123.0 3,947 3,947 100.00% 1,923 48.72% 62,451 421 0.67% 123 0.20% 9.6% 90.4% 19780 Des Moines-West Des Moines, IA Up and Coming 7 278.5 42,502 19,517 45.92% 7,625 17.94% 124,643 81 0.06% 30 0.02% 0.4% 99.6% 20500 Durham-Chapel Hill, NC Up and Coming 4 68.2 70,451 7,118 10.10% 2,078 2.95% 144,506 17,438 12.07% 5,129 3.55% 71.0% 29.0% 23140 Frankfort, IN Industrial Legacy 5 50.2 181 181 100.00% 175 96.69% 1,790 186 10.39% 62 3.46% 50.7% 49.3% HU 23300 Freeport, IL Industrial Legacy 5 51.0 1 1 100.00% 1 100.00% 2,747 2,658 96.76% 777 28.29% 100.0% 0.0% 24340 Grand Rapids-Wyoming, MI Up and Coming 5 801.6 69,571 14,918 21.44% 4,567 6.56% 156,851 16,727 10.66% 4,949 3.16% 52.9% 47.1% 24500 Great Falls, MT Industrial Legacy 8 71.4 1,796 1,080 60.13% 322 17.93% 5,409 1,929 35.66% 565 10.45% 64.1% 35.9% JOBS 25540 Hartford, CT Slow and Steady 1 419.7 42,612 7,267 17.05% 2,159 5.07% 160,973 30,113 18.71% 8,851 5.50% 80.6% 19.4% 26980 Iowa City, IA Up and Coming 7 94.4 13,145 738 5.61% 210 1.60% 33,946 7,906 23.29% 2,312 6.81% 91.5% 8.5% 27140 Jackson, MS Industrial Legacy 4 335.6 32,730 209 0.64% 79 0.24% 99,728 8,751 8.77% 2,649 2.66% 97.7% 2.3% 28940 Knoxville, TN Up and Coming 4 1,016.9 66,852 24,236 36.25% 7,584 11.34% 139,970 4,384 3.13% 1,487 1.06% 15.3% 84.7% 29460 Lakeland-Winter Haven, FL Up and Coming 4 1,146.4 42,569 2,780 6.53% 850 2.00% 76,989 5,645 7.33% 1,789 2.32% 67.0% 33.0% 31080 Los Angeles, CA Stable Metropolis 9 1,643.3 483,286 34,954 7.23% 10,324 2.14% 1,932,010 125,228 6.48% 36,914 1.91% 78.2% 21.8% 33340 Milwaukee, WI Slow and Steady 5 755.3 53,915 19,328 35.85% 5,701 10.57% 192,358 27,290 14.19% 7,982 4.15% 58.5% 41.5% 33460 Minneapolis, MN Growth Hub 5 1,133.5 286,841 31,942 11.14% 9,695 3.38% 660,284 131,305 19.89% 38,799 5.88% 80.4% 19.6% 33860 Montgomery, AL Industrial Legacy 4 340.5 21,494 3,899 18.14% 1,239 5.76% 53,108 346 0.65% 165 0.31% 8.2% 91.8% 34060 Morgantown, WV Up and Coming 3 613.8 10,478 1,645 15.70% 540 5.15% 20,886 814 3.90% 339 1.62% 33.1% 66.9% 35300 New Haven-Milford, CT Industrial Legacy 1 295.0 17,227 16,626 96.51% 4,923 28.58% 92,590 11,307 12.21% 3,331 3.60% 40.5% 59.5% 35380 New Orleans, LA Slow and Steady 6 513.5 44,462 11,293 25.40% 3,371 7.58% 147,547 5,606 3.80% 1,659 1.12% 33.2% 66.8% 36260 Ogden-Clearfield, UT Up and Coming 8 344.3 59,099 927 1.57% 383 0.65% 102,399 4,407 4.30% 1,323 1.29% 82.6% 17.4% 36740 Orlando-Kissimmee-Sanford, FL Growth Hub 4 363.0 250,338 5,711 2.28% 1,729 0.69% 523,892 25,725 4.91% 7,561 1.44% 81.8% 18.2% 37980 Philadelphia-Camden-Wilmington, PA-NJ-DE-MD Stable Metropolis 2 4,364.5 160,374 83,929 52.33% 25,313 15.78% 739,301 190,817 25.81% 60,650 8.20% 69.5% 30.5% 38860 Portland-South Portland, ME Up and Coming 1 673.1 40,159 8,292 20.65% 2,698 6.72% 78,072 14,304 18.32% 4,452 5.70% 63.3% 36.7% 38900 Portland-Vancouver-Hillsboro, OR-WA Growth Hub 10 1,037.6 235,500 7,777 3.30% 2,289 0.97% 427,473 32,435 7.59% 9,687 2.27% 80.7% 19.3% 40380 Rochester, NY Slow and Steady 2 241.1 25,688 13,600 52.94% 3,999 15.57% 118,711 4,453 3.75% 1,321 1.11% 24.7% 75.3% 40900 Sacramento--Roseville--Arden-Arcade, CA Growth Hub 9 1,220.4 168,008 16,711 9.95% 5,625 3.35% 359,291 252,912 70.39% 74,494 20.73% 93.8% 6.2% 41860 San Francisco, CA Slow and Steady 9 916.3 147,021 36,416 24.77% 10,616 7.22% 486,721 105,299 21.63% 30,996 6.37% 74.3% 25.7% 42660 Seattle, WA Growth Hub 10 659.7 322,812 53,595 16.60% 15,741 4.88% 679,339 60,839 8.96% 17,957 2.64% 53.2% 46.8% 43340 Shreveport-Bossier City, LA Industrial Legacy 6 724.6 23,521 8,602 36.57% 2,523 10.73% 56,196 13,298 23.66% 3,916 6.97% 60.7% 39.3% 44700 Stockton-Lodi, CA Industrial Legacy 9 58.0 35,394 7,603 21.48% 2,249 6.35% 66,617 206 0.31% 56 0.08% 2.6% 97.4% 44780 Sturgis, MI Industrial Legacy 5 249.3 405 405 100.00% 405 100.00% 2,260 1,578 69.82% 457 20.22% 79.6% 20.4% 48620 Wichita, KS Industrial Legacy 7 844.1 25,381 21,642 85.27% 6,572 25.89% 70,779 612 0.86% 183 0.26% 2.8% 97.2% 48980 Wilson, NC Industrial Legacy 4 6.9 4,773 136 2.85% 41 0.86% 8,266 0 0.00% 0 0.00% 0.0% 100.0% 49180 Winston-Salem, NC Up and Coming 4 87.9 56,843 2,929 5.15% 864 1.52% 105,168 102 0.10% 29 0.03% 3.4% 96.6%

Page 1 of 1

E-10 Table E-5. Impervious Surface Area Results, Primary Phase (by Metro/CBSA)

Change in impervious surface area (ac) Change in impervious surface area per brownfield acre redeveloped (ac) Brownfields Trend growth Difference Percent Difference Brownfields Trend growth Difference Percent Difference CBSA NAME GROWTH PROFILE EPA REGION BF ACREAGE (ac) Aggressive Base Aggressive Base Aggressive Base Aggressive Base Aggressive Base Aggressive Base Aggressive Base Aggressive Base 10420 Akron, OH Industrial Legacy 5 743.5 695 394 1,834 840 -1,138 -446 -62% -53% 0.94 0.53 2.47 1.13 -1.53 -0.60 -62% -53% 10580 Albany-Schenectady-Troy, NY Industrial Legacy 2 322.7 482 180 2,063 599 -1,582 -418 -77% -70% 1.49 0.56 6.39 1.86 -4.90 -1.30 -77% -70% 10740 Albuquerque, NM Up and Coming 3 298.3 403 165 2,274 703 -1,872 -538 -82% -77% 1.35 0.55 7.63 2.36 -6.28 -1.80 -82% -77% 10900 Allentown-Bethlehem-Easton, PA-NJ Industrial Legacy 5 837.8 1254 511 3,322 1,040 -2,068 -529 -62% -51% 1.50 0.61 3.96 1.24 -2.47 -0.63 -62% -51% 11460 Ann Arbor, MI Industrial Legacy 4 229.0 392 138 953 277 -562 -139 -59% -50% 1.71 0.60 4.16 1.21 -2.45 -0.61 -59% -50% 12060 Atlanta, GA Big and Growing 6 1,582.0 1738 697 7,290 2,145 -5,552 -1,448 -76% -68% 1.10 0.44 4.61 1.36 -3.51 -0.92 -76% -68% 12420 Austin-Round Rock, TX Growth Hub 6 908.4 901 469 5,508 1,642 -4,607 -1,173 -84% -71% 0.99 0.52 6.06 1.81 -5.07 -1.29 -84% -71% 12580 Baltimore, MD Slow and Steady 3 888.9 1111 466 3,774 1,177 -2,663 -711 -71% -60% 1.25 0.52 4.25 1.32 -3.00 -0.80 -71% -60% 12620 Bangor, ME Industrial Legacy 1 583.1 441 179 820 269 -378 -90 -46% -33% 0.76 0.31 1.41 0.46 -0.65 -0.15 -46% -33% 13660 Big Rapids, MI Up and Coming 5 66.1 43 15 71 22 -28 -7 -40% -33% 0.65 0.22 1.08 0.33 -0.43 -0.11 -40% -33% 13740 Billings, MT Up and Coming 8 80.9 385 156 1,812 529 -1,427 -373 -79% -71% 4.76 1.92 22.38 6.54 -17.62 -4.61 -79% -71% 14260 Boise City, ID Up and Coming 10 142.8 128 48 1,212 361 -1,084 -313 -89% -87% 0.89 0.34 8.49 2.53 -7.59 -2.19 -89% -87% 14500 Boulder, CO Up and Coming 8 28.7 29 9 59 16 -30 -7 -51% -44% 0.99 0.31 2.04 0.56 -1.05 -0.25 -51% -44% 15540 Burlington-South Burlington, VT Up and Coming 1 171.0 197 79 1,349 395 -1,152 -316 -85% -80% 1.15 0.46 7.89 2.31 -6.74 -1.85 -85% -80% 16860 Chattanooga, TN-GA Industrial Legacy 4 228.6 349 116 869 254 -520 -138 -60% -54% 1.53 0.51 3.80 1.11 -2.27 -0.61 -60% -54% 19100 Dallas, TX Big and Growing 6 1,253.9 1879 695 5,575 1,618 -3,696 -922 -66% -57% 1.50 0.55 4.45 1.29 -2.95 -0.74 -66% -57% 19380 Dayton, OH Industrial Legacy 5 123.0 97 37 343 159 -246 -122 -72% -77% 0.79 0.30 2.79 1.29 -2.00 -0.99 -72% -77% 19780 Des Moines-West Des Moines, IA Up and Coming 7 278.5 288 100 1,616 633 -1,328 -533 -82% -84% 1.03 0.36 5.80 2.27 -4.77 -1.91 -82% -84% 20500 Durham-Chapel Hill, NC Up and Coming 4 68.2 156 87 1,912 563 -1,756 -476 -92% -85% 2.29 1.27 28.03 8.25 -25.74 -6.97 -92% -85% 23140 Frankfort, IN Industrial Legacy 5 50.2 25 17 35 23 -10 -6 -28% -27% 0.50 0.34 0.70 0.47 -0.20 -0.13 -28% -27% 23300 Freeport, IL Industrial Legacy 5 51.0 43 17 250 75 -207 -58 -83% -77% 0.85 0.33 4.91 1.47 -4.06 -1.14 -83% -77% 24340 Grand Rapids-Wyoming, MI Up and Coming 5 801.5 1,025 375 3,122 949 -2,097 -574 -67% -60% 1.28 0.47 3.90 1.18 -2.62 -0.72 -67% -60% 24500 Great Falls, MT Industrial Legacy 8 71.4 179 57 383 112 -204 -55 -53% -49% 2.51 0.80 5.37 1.57 -2.86 -0.77 -53% -49% 25540 Hartford, CT Slow and Steady 1 419.7 945 329 2,767 795 -1,822 -467 -66% -59% 2.25 0.78 6.59 1.89 -4.34 -1.11 -66% -59% 26980 Iowa City, IA Up and Coming 7 95.4 221 94 823 235 -603 -141 -73% -60% 2.31 0.99 8.63 2.47 -6.32 -1.48 -73% -60% 27140 Jackson, MS Industrial Legacy 4 336.6 454 152 910 276 -456 -124 -50% -45% 1.35 0.45 2.70 0.82 -1.36 -0.37 -50% -45% 28940 Knoxville, TN Up and Coming 4 1,016.9 1,166 474 3,047 967 -1,881 -492 -62% -51% 1.15 0.47 3.00 0.95 -1.85 -0.48 -62% -51% 29460 Lakeland-Winter Haven, FL Up and Coming 4 1,164.4 612 232 939 290 -327 -58 -35% -20% 0.53 0.20 0.81 0.25 -0.28 -0.05 -35% -20% 31080 Los Angeles, CA Stable Metropolis 9 1,675.8 1,067 468 5,929 1,675 -4,862 -1,207 -82% -72% 0.64 0.28 3.54 1.00 -2.90 -0.72 -82% -72% 33340 Milwaukee, WI Slow and Steady 5 755.5 1219 438 3,474 1,026 -2,255 -588 -65% -57% 1.61 0.58 4.60 1.36 -2.98 -0.78 -65% -57% 33460 Minneapolis, MN Growth Hub 5 1,133.5 2,160 847 14,238 4,110 -12,078 -3,263 -85% -79% 1.91 0.75 12.56 3.63 -10.66 -2.88 -85% -79% 33860 Montgomery, AL Industrial Legacy 4 343.5 218 96 435 147 -217 -51 -50% -35% 0.63 0.28 1.27 0.43 -0.63 -0.15 -50% -35% 34060 Morgantown, WV Up and Coming 3 613.8 222 83 204 72 18 12 9% 16% 0.36 0.14 0.33 0.12 0.03 0.02 9% 16% 35300 New Haven-Milford, CT Industrial Legacy 1 295.0 696 260 1,854 527 -1,159 -266 -62% -51% 2.36 0.88 6.29 1.79 -3.93 -0.90 -62% -51% 35380 New Orleans, LA Slow and Steady 6 528.5 603 224 1,593 468 -990 -244 -62% -52% 1.14 0.42 3.01 0.89 -1.87 -0.46 -62% -52% 36260 Ogden-Clearfield, UT Up and Coming 8 344.3 306 125 754 234 -448 -109 -59% -47% 0.89 0.36 2.19 0.68 -1.30 -0.32 -59% -47% 36740 Orlando-Kissimmee-Sanford, FL Growth Hub 4 363.0 285 107 8,327 4,103 -8,041 -3,996 -97% -97% 0.79 0.29 22.94 11.30 -22.15 -11.01 -97% -97% 37980 Philadelphia-Camden-Wilmington, PA-NJ-DE-MD Stable Metropolis 2 4,454.0 5,037 2,475 20,836 6,751 -15,799 -4,276 -76% -63% 1.13 0.56 4.68 1.52 -3.55 -0.96 -76% -63% 38860 Portland-South Portland, ME Up and Coming 1 673.1 903 361 2,748 886 -1,845 -525 -67% -59% 1.34 0.54 4.08 1.32 -2.74 -0.78 -67% -59% 38900 Portland-Vancouver-Hillsboro, OR-WA Growth Hub 10 1,037.0 1527 575 2,693 790 -1,165 -215 -43% -27% 1.47 0.55 2.60 0.76 -1.12 -0.21 -43% -27% 40380 Rochester, NY Slow and Steady 2 241.1 401 144 1,733 503 -1,332 -359 -77% -71% 1.66 0.60 7.19 2.09 -5.53 -1.49 -77% -71% 40900 Sacramento--Roseville--Arden-Arcade, CA Growth Hub 9 1,220.4 1,445 644 22,186 6,087 -20,741 -5,443 -93% -89% 1.18 0.53 18.18 4.99 -17.00 -4.46 -93% -89% 41860 San Francisco, CA Slow and Steady 9 922.3 1060 460 7,504 1,979 -6,444 -1,519 -86% -77% 1.15 0.50 8.14 2.15 -6.99 -1.65 -86% -77% 42660 Seattle, WA Growth Hub 10 658.3 873 363 17,008 4,470 -16,135 -4,106 -95% -92% 1.33 0.55 25.84 6.79 -24.51 -6.24 -95% -92% 43340 Shreveport-Bossier City, LA Industrial Legacy 6 729.6 433 200 2,411 711 -1,978 -510 -82% -72% 0.59 0.27 3.30 0.97 -2.71 -0.70 -82% -72% 44700 Stockton-Lodi, CA Industrial Legacy 9 58.0 88 40 572 170 -484 -130 -85% -76% 1.52 0.69 9.87 2.93 -8.35 -2.24 -85% -76% 44780 Sturgis, MI Industrial Legacy 5 250.3 127 69 197 91 -70 -22 -35% -25% 0.51 0.27 0.79 0.36 -0.28 -0.09 -35% -25% 48620 Wichita, KS Industrial Legacy 7 844.1 537 229 1,936 600 -1,400 -371 -72% -62% 0.64 0.27 2.29 0.71 -1.66 -0.44 -72% -62% 48980 Wilson, NC Industrial Legacy 4 6.9 6 2 13 4 -7 -2 -54% -50% 0.87 0.28 1.89 0.56 -1.02 -0.28 -54% -50% 49180 Winston-Salem, NC Up and Coming 4 87.9 105 35 285 83 -180 -48 -63% -58% 1.19 0.40 3.24 0.95 -2.05 -0.55 -63% -58%

Page 1 of 1 E-11 Table E-6. Impervious Surface Area Results, Secondary Phase (by Metro/CBSA)

Change in impervious surface area (ac) Change in impervious surface area per brownfield acre redeveloped (ac) Brownfields Trend growth Difference Percent Difference Brownfields Trend growth Difference Percent Difference CBSA NAME GROWTH PROFILE EPA REGION BF ACREAGE (ac) Aggressive Base Aggressive Base Aggressive Base Aggressive Base Aggressive Base Aggressive Base Aggressive Base Aggressive Base 10420 Akron, OH Industrial Legacy 5 743.5 4,023 5,359 4,035 5,382 -12 -23 -0.30% -0.43% 5.41 7.21 5.43 7.24 -0.02 -0.03 -0.30% -0.43% 10580 Albany-Schenectady-Troy, NY Industrial Legacy 2 322.7 12,361 13,920 12,597 14,003 -235 -83 -1.87% -0.60% 38.31 43.14 39.04 43.40 -0.73 -0.26 -1.87% -0.60% 10740 Albuquerque, NM Up and Coming 3 298.3 22,575 24,325 22,978 24,199 -403 127 -1.76% 0.52% 75.69 81.55 77.04 81.13 -1.35 0.42 -1.76% 0.52% 10900 Allentown-Bethlehem-Easton, PA-NJ Industrial Legacy 5 837.8 9,227 11,502 9,323 11,501 -97 2 -1.04% 0.02% 11.01 13.73 11.13 13.73 -0.12 0.00 -1.04% 0.02% 11460 Ann Arbor, MI Industrial Legacy 4 229.0 6,340 7,034 6,455 7,068 -115 -34 -1.78% -0.48% 27.69 30.71 28.19 30.86 -0.50 -0.15 -1.78% -0.48% 12060 Atlanta, GA Big and Growing 6 1,582.0 126,058 131,197 126,629 130,553 -571 644 -0.45% 0.49% 79.68 82.93 80.04 82.52 -0.36 0.41 -0.45% 0.49% 12420 Austin-Round Rock, TX Growth Hub 6 908.4 63,870 67,797 64,544 67,929 -675 -132 -1.05% -0.19% 70.31 74.63 71.05 74.78 -0.74 -0.14 -1.05% -0.19% 12580 Baltimore, MD Slow and Steady 3 888.9 37,696 40,377 38,160 40,146 -464 230 -1.21% 0.57% 42.41 45.42 42.93 45.16 -0.52 0.26 -1.21% 0.57% 12620 Bangor, ME Industrial Legacy 1 583.1 2,202 2,827 2,228 2,829 -27 -2 -1.19% -0.09% 3.78 4.85 3.82 4.85 -0.05 0.00 -1.19% -0.09% 13660 Big Rapids, MI Up and Coming 5 66.1 823 872 825 867 -2 5 -0.29% 0.61% 12.45 13.20 12.49 13.12 -0.04 0.08 -0.29% 0.61% 13740 Billings, MT Up and Coming 8 80.9 2,944 4,253 2,977 4,283 -33 -30 -1.12% -0.70% 36.37 52.55 36.78 52.92 -0.41 -0.37 -1.12% -0.70% 14260 Boise City, ID Up and Coming 10 142.8 22,019 22,930 22,182 22,943 -163 -13 -0.74% -0.06% 154.20 160.59 155.35 160.68 -1.14 -0.09 -0.74% -0.06% 14500 Boulder, CO Up and Coming 8 28.7 11,121 11,202 10,568 11,251 553 -49 5.23% -0.44% 386.95 389.75 367.71 391.47 19.24 -1.72 5.23% -0.44% 15540 Burlington-South Burlington, VT Up and Coming 1 171.0 6,908 7,809 6,939 7,857 -31 -48 -0.45% -0.60% 40.39 45.66 40.58 45.94 -0.18 -0.28 -0.45% -0.60% 16860 Chattanooga, TN-GA Industrial Legacy 4 228.6 7,065 7,648 7,077 7,592 -12 56 -0.17% 0.74% 30.90 33.45 30.95 33.20 -0.05 0.25 -0.17% 0.74% 19100 Dallas, TX Big and Growing 6 1,253.9 192,884 197,498 193,450 195,552 -566 1,946 -0.29% 1.00% 153.82 157.50 154.27 155.95 -0.45 1.55 -0.29% 1.00% 19380 Dayton, OH Industrial Legacy 5 123.0 4,549 5,527 4,547 5,473 2 55 0.05% 1.00% 36.98 44.93 36.96 44.49 0.02 0.44 0.05% 1.00% 19780 Des Moines-West Des Moines, IA Up and Coming 7 278.5 14,238 15,238 14,312 15,312 -74 -75 -0.51% -0.49% 51.12 54.71 51.38 54.97 -0.26 -0.27 -0.51% -0.49% 20500 Durham-Chapel Hill, NC Up and Coming 4 68.2 14,966 16,253 15,114 16,245 -148 8 -0.98% 0.05% 219.31 238.17 221.48 238.06 -2.17 0.11 -0.98% 0.05% 23140 Frankfort, IN Industrial Legacy 5 50.2 126 140 128 148 -1 -7 -1.07% -5.06% 2.52 2.80 2.55 2.95 -0.03 -0.15 -1.07% -5.06% 23300 Freeport, IL Industrial Legacy 5 51.0 9 186 8 189 0 -4 3.66% -1.94% 0.17 3.64 0.16 3.71 0.01 -0.07 3.66% -1.94% 24340 Grand Rapids-Wyoming, MI Up and Coming 5 801.5 19,474 21,595 19,593 21,654 -119 -59 -0.61% -0.27% 24.30 26.94 24.45 27.02 -0.15 -0.07 -0.61% -0.27% 24500 Great Falls, MT Industrial Legacy 8 71.4 560 831 567 826 -7 6 -1.23% 0.67% 7.84 11.64 7.94 11.56 -0.10 0.08 -1.23% 0.67% 25540 Hartford, CT Slow and Steady 1 419.7 12,692 14,704 12,907 14,650 -215 54 -1.66% 0.37% 30.24 35.04 30.76 34.91 -0.51 0.13 -1.66% 0.37% 26980 Iowa City, IA Up and Coming 7 95.4 3,810 4,453 3,939 4,517 -130 -64 -3.29% -1.41% 39.93 46.68 41.29 47.34 -1.36 -0.67 -3.29% -1.41% 27140 Jackson, MS Industrial Legacy 4 336.6 12,609 13,237 12,544 13,001 65 236 0.52% 1.82% 37.46 39.33 37.27 38.63 0.19 0.70 0.52% 1.82% 28940 Knoxville, TN Up and Coming 4 1,016.9 17,496 19,469 17,503 19,483 -7 -14 -0.04% -0.07% 17.21 19.15 17.21 19.16 -0.01 -0.01 -0.04% -0.07% 29460 Lakeland-Winter Haven, FL Up and Coming 4 1,164.4 12,343 12,934 12,363 12,827 -19 107 -0.16% 0.84% 10.60 11.11 10.62 11.02 -0.02 0.09 -0.16% 0.84% 31080 Los Angeles, CA Stable Metropolis 9 1,675.8 103,521 109,932 106,793 112,121 -3,271 -2,188 -3.06% -1.95% 61.78 65.60 63.73 66.91 -1.95 -1.31 -3.06% -1.95% 33340 Milwaukee, WI Slow and Steady 5 755.5 15,444 18,078 15,863 18,217 -419 -139 -2.64% -0.76% 20.44 23.93 21.00 24.11 -0.55 -0.18 -2.64% -0.76% 33460 Minneapolis, MN Growth Hub 5 1,133.5 71,595 82,993 74,441 83,740 -2,846 -748 -3.82% -0.89% 63.17 73.22 65.68 73.88 -2.51 -0.66 -3.82% -0.89% 33860 Montgomery, AL Industrial Legacy 4 343.5 7,795 8,081 7,782 7,893 13 188 0.17% 2.39% 22.70 23.53 22.66 22.98 0.04 0.55 0.17% 2.39% 34060 Morgantown, WV Up and Coming 3 613.8 2,391 2,536 2,401 2,540 -10 -3 -0.40% -0.13% 3.90 4.13 3.91 4.14 -0.02 -0.01 -0.40% -0.13% 35300 New Haven-Milford, CT Industrial Legacy 1 295.0 5,165 6,742 5,414 6,806 -249 -65 -4.60% -0.95% 17.51 22.86 18.36 23.08 -0.84 -0.22 -4.60% -0.95% 35380 New Orleans, LA Slow and Steady 6 528.5 16,734 17,919 16,835 18,166 -101 -248 -0.60% -1.36% 31.66 33.90 31.85 34.37 -0.19 -0.47 -0.60% -1.36% 36260 Ogden-Clearfield, UT Up and Coming 8 344.3 23,246 23,916 23,544 24,009 -298 -92 -1.26% -0.39% 67.53 69.47 68.39 69.74 -0.86 -0.27 -1.26% -0.39% 36740 Orlando-Kissimmee-Sanford, FL Growth Hub 4 363.0 69,763 71,788 69,269 69,599 494 2,189 0.71% 3.15% 192.19 197.77 190.83 191.74 1.36 6.03 0.71% 3.15% 37980 Philadelphia-Camden-Wilmington, PA-NJ-DE-MD Stable Metropolis 2 4,454.0 48,467 64,085 50,633 64,045 -2,166 41 -4.28% 0.06% 10.88 14.39 11.37 14.38 -0.49 0.01 -4.28% 0.06% 38860 Portland-South Portland, ME Up and Coming 1 673.1 11,845 13,678 12,039 13,895 -195 -217 -1.62% -1.56% 17.60 20.32 17.89 20.64 -0.29 -0.32 -1.62% -1.56% 38900 Portland-Vancouver-Hillsboro, OR-WA Growth Hub 10 1,037.0 47,965 50,619 48,486 50,535 -521 83 -1.07% 0.17% 46.25 48.81 46.76 48.73 -0.50 0.08 -1.07% 0.17% 40380 Rochester, NY Slow and Steady 2 241.1 11,954 13,127 11,961 13,132 -7 -4 -0.06% -0.03% 49.59 54.46 49.62 54.48 -0.03 -0.02 -0.06% -0.03% 40900 Sacramento--Roseville--Arden-Arcade, CA Growth Hub 9 1,220.4 20,746 37,813 22,654 39,540 -1,908 -1,728 -8.42% -4.37% 17.00 30.98 18.56 32.40 -1.56 -1.42 -8.42% -4.37% 41860 San Francisco, CA Slow and Steady 9 922.3 31,660 40,145 36,561 41,296 -4,901 -1,151 -13.41% -2.79% 34.33 43.53 39.64 44.78 -5.31 -1.25 -13.41% -2.79% 42660 Seattle, WA Growth Hub 10 658.3 90,111 96,648 91,374 97,206 -1,263 -558 -1.38% -0.57% 136.89 146.82 138.81 147.67 -1.92 -0.85 -1.38% -0.57% 43340 Shreveport-Bossier City, LA Industrial Legacy 6 729.6 6,508 8,220 6,596 8,246 -89 -26 -1.34% -0.32% 8.92 11.27 9.04 11.30 -0.12 -0.04 -1.34% -0.32% 44700 Stockton-Lodi, CA Industrial Legacy 9 58.0 8,967 9,372 9,023 9,486 -56 -114 -0.62% -1.20% 154.74 161.73 155.71 163.69 -0.97 -1.96 -0.62% -1.20% 44780 Sturgis, MI Industrial Legacy 5 250.3 53 140 54 138 0 2 -0.19% 1.59% 0.21 0.56 0.21 0.55 0.00 0.01 -0.19% 1.59% 48620 Wichita, KS Industrial Legacy 7 844.1 8,374 9,733 8,418 9,749 -45 -17 -0.53% -0.17% 9.92 11.53 9.97 11.55 -0.05 -0.02 -0.53% -0.17% 48980 Wilson, NC Industrial Legacy 4 6.9 1,273 1,279 1,220 1,126 53 153 4.31% 13.57% 184.72 185.58 177.08 163.41 7.64 22.17 4.31% 13.57% 49180 Winston-Salem, NC Up and Coming 4 87.9 15,384 15,583 15,345 14,907 39 677 0.25% 4.54% 175.05 177.33 174.61 169.63 0.44 7.70 0.25% 4.54%

Page 1 of 1 E-12 Table E-7. Impervious Surface Area Results, Cumulative (by Metro/CBSA)

Change in impervious surface area (ac) Change in impervious surface area per brownfield acre redeveloped (ac) Brownfields Trend growth Difference Percent Difference Brownfields Trend growth Difference Percent Difference BF ACREAGE CBSA NAME GROWTH PROFILE EPA REGION (ac) Aggressive Base Aggressive Base Aggressive Base Aggressive Base Aggressive Base Aggressive Base Aggressive Base Aggressive Base 10420 Akron, OH Industrial Legacy 5 743.5 4,897 5,813 6,190 6,208 -1,293 -394 -20.89% -6.35% 6.59 7.82 8.33 8.35 -1.74 -0.53 -20.89% -6.35% 10580 Albany-Schenectady-Troy, NY Industrial Legacy 2 322.7 13,384 14,254 14,517 14,556 -1,133 -301 -7.80% -2.07% 41.48 44.18 44.99 45.11 -3.51 -0.93 -7.80% -2.07% 10740 Albuquerque, NM Up and Coming 3 298.3 23,703 24,699 24,987 24,809 -1,284 -110 -5.14% -0.44% 79.47 82.81 83.77 83.18 -4.30 -0.37 -5.14% -0.44% 10900 Allentown-Bethlehem-Easton, PA-NJ Industrial Legacy 5 837.8 10,713 12,074 12,429 12,458 -1,716 -384 -13.81% -3.09% 12.79 14.41 14.84 14.87 -2.05 -0.46 -13.81% -3.09% 11460 Ann Arbor, MI Industrial Legacy 4 229.0 6,881 7,211 7,307 7,308 -426 -97 -5.83% -1.32% 30.05 31.49 31.91 31.91 -1.86 -0.42 -5.83% -1.32% 12060 Atlanta, GA Big and Growing 6 1,582.0 128,614 132,086 132,906 132,396 -4,293 -310 -3.23% -0.23% 81.30 83.49 84.01 83.69 -2.71 -0.20 -3.23% -0.23% 12420 Austin-Round Rock, TX Growth Hub 6 908.4 66,225 68,670 69,144 69,284 -2,918 -614 -4.22% -0.89% 72.90 75.59 76.12 76.27 -3.21 -0.68 -4.22% -0.89% 12580 Baltimore, MD Slow and Steady 3 888.9 39,433 41,017 41,375 41,136 -1,942 -119 -4.69% -0.29% 44.36 46.14 46.54 46.28 -2.18 -0.13 -4.69% -0.29% 12620 Bangor, ME Industrial Legacy 1 583.1 2,709 3,031 3,081 3,083 -373 -51 -12.09% -1.67% 4.65 5.20 5.28 5.29 -0.64 -0.09 -12.09% -1.67% 13660 Big Rapids, MI Up and Coming 5 66.1 867 887 895 889 -28 -1 -3.18% -0.15% 13.11 13.42 13.54 13.44 -0.43 -0.02 -3.18% -0.15% 13740 Billings, MT Up and Coming 8 80.9 4,427 4,726 4,737 4,790 -310 -64 -6.55% -1.33% 54.69 58.39 58.53 59.18 -3.84 -0.79 -6.55% -1.33% 14260 Boise City, ID Up and Coming 10 142.8 22,584 23,108 23,268 23,264 -685 -156 -2.94% -0.67% 158.16 161.83 162.95 162.92 -4.79 -1.10 -2.94% -0.67% 14500 Boulder, CO Up and Coming 8 28.7 11,156 11,212 10,615 11,263 542 -51 5.10% -0.45% 388.18 390.13 369.34 391.90 18.84 -1.77 5.10% -0.45% 15540 Burlington-South Burlington, VT Up and Coming 1 171.0 7,324 7,954 8,191 8,219 -867 -265 -10.59% -3.22% 42.83 46.51 47.90 48.06 -5.07 -1.55 -10.59% -3.22% 16860 Chattanooga, TN-GA Industrial Legacy 4 228.6 7,542 7,799 7,882 7,825 -340 -27 -4.31% -0.34% 32.99 34.11 34.47 34.23 -1.48 -0.12 -4.31% -0.34% 19100 Dallas, TX Big and Growing 6 1,253.9 196,335 198,628 197,922 196,839 -1,587 1,789 -0.80% 0.91% 156.57 158.40 157.84 156.98 -1.27 1.43 -0.80% 0.91% 19380 Dayton, OH Industrial Legacy 5 123.0 4,661 5,573 5,708 5,671 -1,047 -97 -18.35% -1.71% 37.89 45.31 46.40 46.10 -8.51 -0.79 -18.35% -1.71% 19780 Des Moines-West Des Moines, IA Up and Coming 7 278.5 14,523 15,337 15,749 15,870 -1,226 -533 -7.78% -3.36% 52.14 55.06 56.54 56.97 -4.40 -1.91 -7.78% -3.36% 20500 Durham-Chapel Hill, NC Up and Coming 4 68.2 15,677 16,501 16,752 16,715 -1,075 -214 -6.41% -1.28% 229.73 241.81 245.48 244.95 -15.75 -3.14 -6.41% -1.28% 23140 Frankfort, IN Industrial Legacy 5 50.2 161 166 185 187 -24 -21 -12.95% -11.34% 3.20 3.30 3.68 3.72 -0.48 -0.42 -12.95% -11.34% 23300 Freeport, IL Industrial Legacy 5 51.0 129 227 259 263 -130 -36 -50.04% -13.78% 2.54 4.44 5.08 5.15 -2.54 -0.71 -50.04% -13.78% 24340 Grand Rapids-Wyoming, MI Up and Coming 5 801.5 21,236 22,180 22,450 22,514 -1,214 -334 -5.41% -1.48% 26.50 27.67 28.01 28.09 -1.51 -0.42 -5.41% -1.48% 24500 Great Falls, MT Industrial Legacy 8 71.4 859 922 944 935 -85 -12 -9.00% -1.31% 12.04 12.92 13.23 13.09 -1.19 -0.17 -9.00% -1.31% 25540 Hartford, CT Slow and Steady 1 419.7 14,394 15,245 15,455 15,372 -1,061 -128 -6.86% -0.83% 34.30 36.33 36.83 36.63 -2.53 -0.30 -6.86% -0.83% 26980 Iowa City, IA Up and Coming 7 95.4 4,354 4,634 4,703 4,732 -350 -99 -7.43% -2.08% 45.63 48.57 49.29 49.60 -3.66 -1.03 -7.43% -2.08% 27140 Jackson, MS Industrial Legacy 4 336.6 13,418 13,494 13,395 13,258 23 236 0.17% 1.78% 39.87 40.09 39.80 39.39 0.07 0.70 0.17% 1.78% 28940 Knoxville, TN Up and Coming 4 1,016.9 18,761 19,968 20,381 20,390 -1,620 -422 -7.95% -2.07% 18.45 19.64 20.04 20.05 -1.59 -0.42 -7.95% -2.07% 29460 Lakeland-Winter Haven, FL Up and Coming 4 1,164.4 13,054 13,194 13,237 13,095 -183 99 -1.38% 0.75% 11.21 11.33 11.37 11.25 -0.16 0.08 -1.38% 0.75% 31080 Los Angeles, CA Stable Metropolis 9 1,675.8 105,802 110,720 111,496 113,426 -5,694 -2,706 -5.11% -2.39% 63.14 66.07 66.53 67.69 -3.40 -1.61 -5.11% -2.39% 33340 Milwaukee, WI Slow and Steady 5 755.5 17,097 18,635 19,052 19,144 -1,954 -508 -10.26% -2.66% 22.63 24.67 25.22 25.34 -2.59 -0.67 -10.26% -2.66% 33460 Minneapolis, MN Growth Hub 5 1,133.5 77,999 85,072 87,092 87,321 -9,093 -2,249 -10.44% -2.58% 68.81 75.05 76.84 77.04 -8.02 -1.98 -10.44% -2.58% 33860 Montgomery, AL Industrial Legacy 4 343.5 8,018 8,180 8,189 8,030 -171 150 -2.09% 1.87% 23.34 23.82 23.84 23.38 -0.50 0.44 -2.09% 1.87% 34060 Morgantown, WV Up and Coming 3 613.8 2,621 2,623 2,593 2,607 28 16 1.07% 0.62% 4. 27 4.27 4.22 4.25 0.05 0.03 1.07% 0.62% 35300 New Haven-Milford, CT Industrial Legacy 1 295.0 6,000 7,040 7,275 7,294 -1,275 -253 -17.53% -3.48% 20.34 23.87 24.66 24.73 -4.32 -0.86 -17.53% -3.48% 35380 New Orleans, LA Slow and Steady 6 528.5 17,433 18,168 18,319 18,599 -887 -431 -4.84% -2.32% 32.98 34.38 34.66 35.19 -1.68 -0.82 -4.84% -2.32% 36260 Ogden-Clearfield, UT Up and Coming 8 344.3 23,789 24,111 24,224 24,219 -435 -108 -1.79% -0.45% 69.10 70.04 70.37 70.35 -1.26 -0.31 -1.79% -0.45% 36740 Orlando-Kissimmee-Sanford, FL Growth Hub 4 363.0 70,779 72,107 74,939 72,401 -4,160 -294 -5.55% -0.41% 194.99 198.65 206.45 199.46 -11.46 -0.81 -5.55% -0.41% 37980 Philadelphia-Camden-Wilmington, PA-NJ-DE-MD Stable Metropolis 2 4,454.0 57,304 67,568 69,306 69,815 -12,002 -2,247 -17.32% -3.22% 12.87 15.17 15.56 15.67 -2.69 -0.50 -17.32% -3.22% 38860 Portland-South Portland, ME Up and Coming 1 673.1 13,115 14,145 14,655 14,732 -1,539 -586 -10.50% -3.98% 19.49 21.02 21.77 21.89 -2.29 -0.87 -10.50% -3.98% 38900 Portland-Vancouver-Hillsboro, OR-WA Growth Hub 10 1,037.0 50,170 51,371 50,730 51,183 -561 188 -1.11% 0.37% 48.38 49.54 48.92 49.36 -0.54 0.18 -1.11% 0.37% 40380 Rochester, NY Slow and Steady 2 241.1 12,470 13,304 13,597 13,610 -1,128 -306 -8.29% -2.25% 51.73 55.19 56.41 56.46 -4.68 -1.27 -8.29% -2.25% 40900 Sacramento--Roseville--Arden-Arcade, CA Growth Hub 9 1,220.4 32,461 41,364 43,149 44,874 -10,687 -3,510 -24.77% -7.82% 26.60 33.89 35.36 36.77 -8.76 -2.88 -24.77% -7.82% 41860 San Francisco, CA Slow and Steady 9 922.3 34,397 41,065 42,629 42,850 -8,232 -1,785 -19.31% -4.17% 37.30 44.53 46.22 46.46 -8.93 -1.94 -19.31% -4.17% 42660 Seattle, WA Growth Hub 10 658.3 91,935 97,273 98,317 99,078 -6,382 -1,805 -6.49% -1.82% 139.66 147.77 149.35 150.51 -9.69 -2.74 -6.49% -1.82% 43340 Shreveport-Bossier City, LA Industrial Legacy 6 729.6 7,754 8,646 8,903 8,917 -1,149 -271 -12.91% -3.04% 10.63 11.85 12.20 12.22 -1.57 -0.37 -12.91% -3.04% 44700 Stockton-Lodi, CA Industrial Legacy 9 58.0 9,064 9,415 9,538 9,636 -474 -222 -4.97% -2.30% 156.41 162.46 164.59 166.28 -8.19 -3.83 -4.97% -2.30% 44780 Sturgis, MI Industrial Legacy 5 250.3 195 219 262 261 -67 -42 -25.69% -16.11% 0.78 0.87 1.05 1.04 -0.27 -0.17 -25.69% -16.11% 48620 Wichita, KS Industrial Legacy 7 844.1 8,938 9,967 10,269 10,310 -1,331 -343 -12.96% -3.32% 10.59 11.81 12.17 12.21 -1.58 -0.41 -12.96% -3.32% 48980 Wilson, NC Industrial Legacy 4 6.9 1,279 1,280 1,233 1,129 46 151 3.74% 13.37% 185.58 185.85 178.88 163.93 6.70 21.92 3.74% 13.37% 49180 Winston-Salem, NC Up and Coming 4 87.9 15,487 15,618 15,608 15,002 -121 616 -0.78% 4.10% 176.23 177.72 177. 61 170.71 -1.38 7.01 -0.78% 4.10%

Page 1 of 1 E-13 Table E-8. Residential VMT Results, Primary Phase (by Metro/CBSA)

VMT generated by new (allocated) households (mi) VMT generated by new (allocated) households per brownfield acre redeveloped (mi/ac) Brownfields Trend growth Difference Percent Difference Brownfields Trend growth Difference Percent Difference BF ACREAGE CBSA NAME GROWTH PROFILE EPA REGION (ac) Aggressive Base Aggressive Base Aggressive Base Aggressive Base Aggressive Base Aggressive Base Aggressive Base Aggressive Base 10420 Akron, OH Industrial Legacy 5 743.5 679,441 478,361 741,267 493,378 -61,826 -15,016 -8.34% -3.04% 913.90 643.43 997.06 663.63 -83.16 -20.20 -8.34% -3.04% 10580 Albany-Schenectady-Troy, NY Industrial Legacy 2 322.7 361,651 113,607 510,777 149,931 -149,126 -36,324 -29.20% -24.23% 1,120.84 352.10 1,583.02 464.67 -462.18 -112.58 -29.20% -24.23% 10740 Albuquerque, NM Up and Coming 3 298.3 72,043 38,858 177,121 56,055 -105,078 -17,196 -59.33% -30.68% 241.54 130.28 593.83 187.93 -352.29 -57.65 -59.33% -30.68% 10900 Allentown-Bethlehem-Easton, PA-NJ Industrial Legacy 5 837.8 1,497,013 497,800 1,685,181 536,833 -188,168 -39,033 -11.17% -7.27% 1,786.88 594.19 2,011.48 640.78 -224.60 -46.59 -11.17% -7.27% 11460 Ann Arbor, MI Industrial Legacy 4 229.0 345,882 105,704 395,722 116,348 -49,841 -10,643 -12.59% -9.15% 1,510.40 461.59 1,728.04 508.07 -217.64 -46.48 -12.59% -9.15% 12060 Atlanta, GA Big and Growing 6 1,582.0 4,333,863 1,346,328 5,491,266 1,609,349 -1,157,402 -263,021 -21.08% -16.34% 2,739.50 851.03 3,471.11 1,017.29 -731.61 -166.26 -21.08% -16.34% 12420 Austin-Round Rock, TX Growth Hub 6 908.4 1,344,168 480,440 2,586,574 770,152 -1,242,406 -289,712 -48.03% -37.62% 1,479.71 528.89 2,847.40 847.81 -1367.69 -318.93 -48.03% -37.62% 12580 Baltimore, MD Slow and Steady 3 888.9 818,831 304,665 1,154,431 386,469 -335,600 -81,803 -29.07% -21.17% 921.13 342.73 1,298.66 434.75 -377.53 -92.02 -29.07% -21.17% 12620 Bangor, ME Industrial Legacy 1 583.1 177,874 66,365 331,121 101,553 -153,246 -35,188 -46.28% -34.65% 305.04 113.81 567.84 174.15 -262.80 -60.34 -46.28% -34.65% 13660 Big Rapids, MI Up and Coming 5 66.1 22,159 7,332 35,380 10,539 -13,221 -3,207 -37.37% -30.43% 335.24 110.92 535.25 159.43 -200.02 -48.51 -37.37% -30.43% 13740 Billings, MT Up and Coming 8 80.9 76,549 26,744 104,408 32,324 -27,859 -5,580 -26.68% -17.26% 945.75 330.42 1,289.94 399.36 -344.20 -68.94 -26.68% -17.26% 14260 Boise City, ID Up and Coming 10 142.8 58,364 20,869 88,989 28,493 -30,626 -7,624 -34.42% -26.76% 408.74 146.16 623.22 199.55 -214.48 -53.39 -34.42% -26.76% 14500 Boulder, CO Up and Coming 8 28.7 3,101 897 3,548 1,015 -447 -117 -12.59% -11.58% 107.91 31.21 123.45 35.30 -15.54 -4.09 -12.59% -11.58% 15540 Burlington-South Burlington, VT Up and Coming 1 171.0 114,446 36,315 174,015 50,935 -59,570 -14,620 -34.23% -28.70% 669.19 212.34 1,017.52 297.83 -348.32 -85.49 -34.23% -28.70% 16860 Chattanooga, TN-GA Industrial Legacy 4 228.6 275,924 84,922 330,253 98,489 -54,329 -13,567 -16.45% -13.77% 1,206.81 371.42 1,444.42 430.76 -237.62 -59.34 -16.45% -13.77% 19100 Dallas, TX Big and Growing 6 1,253.9 1,173,068 364,631 1,617,171 470,657 -444,103 -106,026 -27.46% -22.53% 935.51 290.79 1,289.67 375.34 -354.17 -84.55 -27.46% -22.53% 19380 Dayton, OH Industrial Legacy 5 123.0 217,189 102,163 267,672 129,186 -50,483 -27,023 -18.86% -20.92% 1,765.62 830.53 2,176.02 1,050.21 -410.40 -219.68 -18.86% -20.92% 19780 Des Moines-West Des Moines, IA Up and Coming 7 278.5 462,856 213,429 1,636,080 635,967 -1,173,223 -422,538 -71.71% -66.44% 1,661.72 766.24 5,873.77 2,283.22 -4212.05 -1516.97 -71.71% -66.44% 20500 Durham-Chapel Hill, NC Up and Coming 4 68.2 389,239 170,916 691,084 201,041 -301,845 -30,125 -43.68% -14.98% 5,703.97 2,504.64 10,127.25 2,946.09 -4423.28 -441.45 -43.68% -14.98% 23140 Frankfort, IN Industrial Legacy 5 50.2 10,233 9,694 10,467 10,027 -234 -332 -2.23% -3.31% 203.97 193.23 208.63 199.85 -4.66 -6.62 -2.23% -3.31% 23300 Freeport, IL Industrial Legacy 5 51.0 74 84 77 67 -3 17 -3.50% 24.94% 1.45 1.64 1.50 1.31 -0.05 0.33 -3.50% 24.94% 24340 Grand Rapids-Wyoming, MI Up and Coming 5 801.5 978,637 313,278 1,179,561 361,621 -200,924 -48,343 -17.03% -13.37% 1,221.04 390.87 1,471.73 451.19 -250.69 -60.32 -17.03% -13.37% 24500 Great Falls, MT Industrial Legacy 8 71.4 39,306 11,996 48,594 14,236 -9,288 -2,240 -19.11% -15.74% 550.59 168.03 680.68 199.41 -130.10 -31.38 -19.11% -15.74% 25540 Hartford, CT Slow and Steady 1 419.7 451,818 138,821 548,138 161,007 -96,320 -22,186 -17.57% -13.78% 1,076.60 330.79 1,306.12 383.65 -229.51 -52.87 -17.57% -13.78% 26980 Iowa City, IA Up and Coming 7 95.4 38,842 12,152 47,388 13,756 -8,546 -1,603 -18.03% -11.65% 407.11 127.37 496.68 144.17 -89.57 -16.80 -18.03% -11.65% 27140 Jackson, MS Industrial Legacy 4 336.6 30,432 11,831 29,873 11,993 558 -162 1.87% -1.35% 90.42 35.15 88.76 35.64 1.66 -0.48 1.87% -1.35% 28940 Knoxville, TN Up and Coming 4 1,016.9 1,612,838 561,575 2,590,496 810,670 -977,659 -249,095 -37.74% -30.73% 1,586.06 552.25 2,547.49 797.21 -961.43 -244.96 -37.74% -30.73% 29460 Lakeland-Winter Haven, FL Up and Coming 4 1,164.4 263,657 83,189 244,572 75,237 19,085 7,952 7.80% 10.57% 226.43 71.44 210.04 64.61 16.39 6.83 7.80% 10.57% 31080 Los Angeles, CA Stable Metropolis 9 1,675.8 1,453,307 463,510 2,071,314 610,534 -618,006 -147,024 -29.84% -24.08% 867.25 276.60 1,236.04 364.33 -368.79 -87.74 -29.84% -24.08% 33340 Milwaukee, WI Slow and Steady 5 755.5 1,048,160 313,376 1,210,736 356,943 -162,577 -43,568 -13.43% -12.21% 1,387.35 414.79 1,602.54 472.45 -215.19 -57.67 -13.43% -12.21% 33460 Minneapolis, MN Growth Hub 5 1,133.5 1,663,885 567,988 2,447,928 740,261 -784,043 -172,272 -32.03% -23.27% 1,467.97 501.11 2,159.69 653.10 -691.73 -151.99 -32.03% -23.27% 33860 Montgomery, AL Industrial Legacy 4 343.5 299,210 113,936 487,106 149,522 -187,896 -35,586 -38.57% -23.80% 871.19 331.74 1,418.27 435.35 -547.08 -103.61 -38.57% -23.80% 34060 Morgantown, WV Up and Coming 3 613.8 99,883 33,858 99,008 31,792 875 2,066 0.88% 6.50% 162.74 55.16 161.31 51.80 1.43 3.37 0.88% 6.50% 35300 New Haven-Milford, CT Industrial Legacy 1 295.0 1,057,308 320,785 1,127,479 332,123 -70,171 -11,338 -6.22% -3.41% 3,584.70 1,087.59 3,822.61 1,126.03 -237.91 -38.44 -6.22% -3.41% 35380 New Orleans, LA Slow and Steady 6 528.5 422,050 132,952 524,195 155,325 -102,144 -22,373 -19.49% -14.40% 798.57 251.56 991.83 293.89 -193.27 -42.33 -19.49% -14.40% 36260 Ogden-Clearfield, UT Up and Coming 8 344.3 69,524 29,912 71,604 29,512 -2,080 400 -2.90% 1.36% 201.95 86.89 207.99 85.73 -6.04 1.16 -2.90% 1.36% 36740 Orlando-Kissimmee-Sanford, FL Growth Hub 4 363.0 458,631 143,432 588,745 178,645 -130,113 -35,213 -22.10% -19.71% 1,263.48 395.14 1,621.93 492.15 -358.45 -97.01 -22.10% -19.71% 37980 Philadelphia-Camden-Wilmington, PA-NJ-DE-MD Stable Metropolis 2 4,454.0 3,401,583 1,201,919 4,868,961 1,459,964 -1,467,378 -258,045 -30.14% -17.67% 763.72 269.85 1,093.17 327.79 -329.45 -57.94 -30.14% -17.67% 38860 Portland-South Portland, ME Up and Coming 1 673.1 424,082 155,204 591,517 189,515 -167,435 -34,311 -28.31% -18.10% 630.09 230.60 878.86 281.58 -248.77 -50.98 -28.31% -18.10% 38900 Portland-Vancouver-Hillsboro, OR-WA Growth Hub 10 1,037.0 486,571 147,625 458,560 133,355 28,011 14,270 6.11% 10.70% 469.20 142.35 442.19 128.59 27.01 13.76 6.11% 10.70% 40380 Rochester, NY Slow and Steady 2 241.1 695,955 212,680 884,301 256,255 -188,347 -43,574 -21.30% -17.00% 2,887.06 882.27 3,668.39 1,063.03 -781.33 -180.76 -21.30% -17.00% 40900 Sacramento--Roseville--Arden-Arcade, CA Growth Hub 9 1,220.4 978,939 350,882 1,305,005 412,496 -326,066 -61,614 -24.99% -14.94% 802.14 287.51 1,069.32 338.00 -267.18 -50.49 -24.99% -14.94% 41860 San Francisco, CA Slow and Steady 9 922.3 1,329,122 462,378 1,972,613 560,601 -643,491 -98,223 -32.62% -17.52% 1,441.14 501.35 2,138.87 607.85 -697.73 -106.50 -32.62% -17.52% 42660 Seattle, WA Growth Hub 10 658.3 427,433 143,782 3,545,191 1,036,416 -3,117,758 -892,634 -87.94% -86.13% 649.32 218.42 5,385.54 1,574.43 -4736.22 -1356.01 -87.94% -86.13% 43340 Shreveport-Bossier City, LA Industrial Legacy 6 729.6 573,308 185,775 784,949 227,349 -211,641 -41,574 -26.96% -18.29% 785.76 254.62 1,075.83 311.60 -290.07 -56.98 -26.96% -18.29% 44700 Stockton-Lodi, CA Industrial Legacy 9 58.0 407,825 127,007 482,072 140,943 -74,247 -13,935 -15.40% -9.89% 7,037.53 2,191.67 8,318.76 2,432.14 -1281.23 -240.47 -15.40% -9.89% 44780 Sturgis, MI Industrial Legacy 5 250.3 31,318 32,072 33,552 33,572 -2,235 -1,500 -6.66% -4.47% 125.10 128.12 134.03 134.11 -8.93 -5.99 -6.66% -4.47% 48620 Wichita, KS Industrial Legacy 7 844.1 787,921 354,611 1,578,626 475,241 -790,706 -120,629 -50.09% -25.38% 933.49 420.13 1,870.28 563.04 -936.79 -142.92 -50.09% -25.38% 48980 Wilson, NC Industrial Legacy 4 6.9 6,700 2,241 8,505 2,447 -1,805 -206 -21.22% -8.41% 972.48 325.24 1,234.41 355.11 -261.93 -29.86 -21.22% -8.41% 49180 Winston-Salem, NC Up and Coming 4 87.9 241,938 72,350 297,297 87,229 -55,359 -14,880 -18.62% -17.06% 2,753.05 823.28 3,382.99 992.59 -629.94 -169.32 -18.62% -17.06%

Page 1 of 1 E-14 Table E-9. Residential VMT Results, Secondary Phase (by Metro/CBSA)

VMT generated by new (allocated) households (mi) VMT generated by new (allocated) households per brownfield acre redeveloped (mi/ac) Brownfields Trend growth Difference Percent Difference Brownfields Trend growth Difference Percent Difference BF ACREAGE CBSA NAME GROWTH PROFILE EPA REGION (mi) Aggressive Base Aggressive Base Aggressive Base Aggressive Base Aggressive Base Aggressive Base Aggressive Base Aggressive Base 10420 Akron, OH Industrial Legacy 5 743.5 0 242,620 0 242,245 0 375 #DIV/0! 0.15% 0.00 326.34 0.00 325.84 0.00 0.50 #DIV/0! 0.15% 10580 Albany-Schenectady-Troy, NY Industrial Legacy 2 322.7 2,244,166 2,597,912 2,233,203 2,576,716 10,963 21,196 0.49% 0.82% 6,955.20 8,051.55 6,921.23 7,985.86 33.98 65.69 0.49% 0.82% 10740 Albuquerque, NM Up and Coming 3 298.3 7,354,639 7,462,993 7,322,116 7,396,179 32,523 66,815 0.44% 0.90% 24,657.65 25,020.93 24,548.62 24,796.93 109.04 224.01 0.44% 0.90% 10900 Allentown-Bethlehem-Easton, PA-NJ Industrial Legacy 5 837.8 1,006,578 2,123,657 1,006,614 2,106,503 -36 17,154 0.00% 0.81% 1,201.48 2,534.86 1,201.53 2,514.39 -0.04 20.48 0.00% 0.81% 11460 Ann Arbor, MI Industrial Legacy 4 229.0 1,334,259 1,609,307 1,322,115 1,599,727 12,144 9,580 0.92% 0.60% 5,826.46 7,027.54 5,773.43 6,985.71 53.03 41.83 0.92% 0.60% 12060 Atlanta, GA Big and Growing 6 1,582.0 62,051,453 65,924,043 62,143,704 65,692,733 -92,251 231,311 -0.15% 0.35% 39,223.67 41,671.59 39,281.98 41,525.38 -58.31 146.22 -0.15% 0.35% 12420 Austin-Round Rock, TX Growth Hub 6 908.4 24,102,462 25,845,261 24,163,261 25,837,273 -60,799 7,988 -0.25% 0.03% 26,532.87 28,451.41 26,599.80 28,442.62 -66.93 8.79 -0.25% 0.03% 12580 Baltimore, MD Slow and Steady 3 888.9 9,643,665 10,400,639 9,586,101 10,336,303 57,564 64,336 0.60% 0.62% 10,848.50 11,700.05 10,783.74 11,627.67 64.76 72.37 0.60% 0.62% 12620 Bangor, ME Industrial Legacy 1 583.1 6,897 228,525 7,173 229,223 -276 -698 -3.85% -0.30% 11.83 391.90 12.30 393.10 -0.47 -1.20 -3.85% -0.30% 13660 Big Rapids, MI Up and Coming 5 66.1 224,553 249,177 225,615 241,665 -1,062 7,512 -0.47% 3.11% 3,397.17 3,769.70 3,413.24 3,656.06 -16.07 113.65 -0.47% 3.11% 13740 Billings, MT Up and Coming 8 80.9 654,074 724,624 650,464 719,448 3,611 5,176 0.56% 0.72% 8,080.98 8,952.60 8,036.37 8,888.66 44.61 63.95 0.56% 0.72% 14260 Boise City, ID Up and Coming 10 142.8 4,371,605 4,413,741 4,321,151 4,343,298 50,454 70,443 1.17% 1.62% 30,615.63 30,910.71 30,262.28 30,417.38 353.34 493.33 1.17% 1.62% 14500 Boulder, CO Up and Coming 8 28.7 2,082,508 2,086,018 2,123,220 2,146,701 -40,712 -60,683 -1.92% -2.83% 72,460.27 72,582.40 73,876.84 74,693.83 -1416.57 -2111.43 -1.92% -2.83% 15540 Burlington-South Burlington, VT Up and Coming 1 171.0 1,346,600 1,454,354 1,346,734 1,440,500 -133 13,853 -0.01% 0.96% 7,873.94 8,504.00 7,874.72 8,422.99 -0.78 81.01 -0.01% 0.96% 16860 Chattanooga, TN-GA Industrial Legacy 4 228.6 1,947,391 2,166,046 1,937,708 2,141,016 9,683 25,030 0.50% 1.17% 8,517.28 9,473.61 8,474.93 9,364.14 42.35 109.47 0.50% 1.17% 19100 Dallas, TX Big and Growing 6 1,253.9 68,460,506 69,642,878 68,494,059 70,019,593 -33,553 -376,714 -0.05% -0.54% 54,596.32 55,539.24 54,623.08 55,839.67 -26.76 -300.42 -0.05% -0.54% 19380 Dayton, OH Industrial Legacy 5 123.0 0 134,321 0 133,150 0 1,170 #DIV/0! 0.88% 0.00 1,091.95 0.00 1,082.43 0.00 9.51 #DIV/0! 0.88% 19780 Des Moines-West Des Moines, IA Up and Coming 7 278.5 1,829,299 2,793,449 1,842,350 2,775,175 -13,051 18,274 -0.71% 0.66% 6,567.46 10,028.90 6,614.31 9,963.29 -46.85 65.61 -0.71% 0.66% 20500 Durham-Chapel Hill, NC Up and Coming 4 68.2 5,915,183 6,353,467 5,910,069 6,331,268 5,114 22,199 0.09% 0.35% 86,682.04 93,104.73 86,607.10 92,779.42 74.94 325.31 0.09% 0.35% 23140 Frankfort, IN Industrial Legacy 5 50.2 0 354 0 339 0 15 #DIV/0! 4.53% 0.00 7.06 0.00 6.75 0.00 0.31 #DIV/0! 4.53% 23300 Freeport, IL Industrial Legacy 5 51.0 0 0 0 0 0 0 #DIV/0! #DIV/0! 0.00 0.00 0.00 0.00 0.00 0.00 #DIV/0! #DIV/0! 24340 Grand Rapids-Wyoming, MI Up and Coming 5 801.5 4,237,242 5,022,250 4,226,808 4,981,356 10,434 40,894 0.25% 0.82% 5,286.77 6,266.22 5,273.75 6,215.20 13.02 51.02 0.25% 0.82% 24500 Great Falls, MT Industrial Legacy 8 71.4 31,783 65,510 31,982 64,548 -198 961 -0.62% 1.49% 445.21 917.63 447.99 904.16 -2.78 13.47 -0.62% 1.49% 25540 Hartford, CT Slow and Steady 1 419.7 2,650,342 3,031,338 2,623,799 3,017,771 26,544 13,567 1.01% 0.45% 6,315.30 7,223.15 6,252.05 7,190.82 63.25 32.33 1.01% 0.45% 26980 Iowa City, IA Up and Coming 7 95.4 780,979 810,501 777,662 808,010 3,317 2,491 0.43% 0.31% 8,185.50 8,494.93 8,150.74 8,468.82 34.76 26.11 0.43% 0.31% 27140 Jackson, MS Industrial Legacy 4 336.6 4,497,606 4,501,083 4,491,943 4,518,052 5,663 -16,969 0.13% -0.38% 13,363.46 13,373.79 13,346.63 13,424.21 16.83 -50.42 0.13% -0.38% 28940 Knoxville, TN Up and Coming 4 1,016.9 4,366,093 6,063,252 4,354,399 6,057,358 11,694 5,895 0.27% 0.10% 4,293.62 5,962.60 4,282.12 5,956.81 11.50 5.80 0.27% 0.10% 29460 Lakeland-Winter Haven, FL Up and Coming 4 1,164.4 3,489,080 3,649,189 3,449,306 3,600,996 39,774 48,193 1.15% 1.34% 2,996.46 3,133.97 2,962.30 3,092.58 34.16 41.39 1.15% 1.34% 31080 Los Angeles, CA Stable Metropolis 9 1,675.8 27,160,017 28,666,081 27,249,099 29,299,953 -89,082 -633,872 -0.33% -2.16% 16,207.58 17,106.32 16,260.74 17,484.58 -53.16 -378.26 -0.33% -2.16% 33340 Milwaukee, WI Slow and Steady 5 755.5 2,182,930 3,043,769 2,191,084 3,048,662 -8,155 -4,893 -0.37% -0.16% 2,889.35 4,028.76 2,900.14 4,035.24 -10.79 -6.48 -0.37% -0.16% 33460 Minneapolis, MN Growth Hub 5 1,133.5 19,597,055 21,303,391 19,423,081 21,245,993 173,974 57,398 0.90% 0.27% 17,289.59 18,795.01 17,136.10 18,744.37 153.49 50.64 0.90% 0.27% 33860 Montgomery, AL Industrial Legacy 4 343.5 2,134,537 2,468,064 2,112,109 2,365,689 22,429 102,375 1.06% 4.33% 6,214.99 7,186.09 6,149.68 6,888.02 65.30 298.08 1.06% 4.33% 34060 Morgantown, WV Up and Coming 3 613.8 521,247 591,404 524,429 586,641 -3,182 4,763 -0.61% 0.81% 849.25 963.56 854.44 955.80 -5.18 7.76 -0.61% 0.81% 35300 New Haven-Milford, CT Industrial Legacy 1 295.0 40,587 824,014 38,902 818,834 1,686 5,180 4.33% 0.63% 137.61 2,793.74 131.89 2,776.18 5.72 17.56 4.33% 0.63% 35380 New Orleans, LA Slow and Steady 6 528.5 1,535,337 1,900,843 1,531,658 1,895,852 3,679 4,991 0.24% 0.26% 2,905.03 3,596.61 2,898.07 3,587.16 6.96 9.44 0.24% 0.26% 36260 Ogden-Clearfield, UT Up and Coming 8 344.3 4,424,642 4,463,658 4,419,241 4,460,959 5,400 2,698 0.12% 0.06% 12,852.62 12,965.95 12,836.93 12,958.11 15.69 7.84 0.12% 0.06% 36740 Orlando-Kissimmee-Sanford, FL Growth Hub 4 363.0 24,603,402 24,975,205 24,423,496 24,714,498 179,906 260,707 0.74% 1.05% 67,779.83 68,804.11 67,284.21 68,085.89 495.62 718.22 0.74% 1.05% 37980 Philadelphia-Camden-Wilmington, PA-NJ-DE-MD Stable Metropolis 2 4,454.0 4,443,271 7,862,192 4,423,234 7,833,903 20,038 28,290 0.45% 0.36% 997.60 1,765.21 993.10 1,758.85 4.50 6.35 0.45% 0.36% 38860 Portland-South Portland, ME Up and Coming 1 673.1 2,258,793 2,643,028 2,260,621 2,636,168 -1,828 6,860 -0.08% 0.26% 3,356.06 3,926.94 3,358.77 3,916.75 -2.72 10.19 -0.08% 0.26% 38900 Portland-Vancouver-Hillsboro, OR-WA Growth Hub 10 1,037.0 13,796,601 14,128,851 13,845,422 14,264,621 -48,822 -135,770 -0.35% -0.95% 13,304.08 13,624.47 13,351.16 13,755.40 -47.08 -130.92 -0.35% -0.95% 40380 Rochester, NY Slow and Steady 2 241.1 768,931 1,379,814 766,694 1,367,216 2,237 12,598 0.29% 0.92% 3,189.79 5,723.95 3,180.51 5,671.69 9.28 52.26 0.29% 0.92% 40900 Sacramento--Roseville--Arden-Arcade, CA Growth Hub 9 1,220.4 12,012,719 12,851,193 11,878,728 12,770,914 133,991 80,279 1.13% 0.63% 9,843.18 10,530.23 9,733.39 10,464.45 109.79 65.78 1.13% 0.63% 41860 San Francisco, CA Slow and Steady 9 922.3 6,148,499 7,618,713 6,010,389 7,617,476 138,109 1,237 2.30% 0.02% 6,666.70 8,260.83 6,516.95 8,259.49 149.75 1.34 2.30% 0.02% 42660 Seattle, WA Growth Hub 10 658.3 18,588,889 21,289,723 18,675,367 21,411,884 -86,478 -122,160 -0.46% -0.57% 28,238.57 32,341.44 28,369.94 32,527.02 -131.37 -185.58 -0.46% -0.57% 43340 Shreveport-Bossier City, LA Industrial Legacy 6 729.6 1,345,915 1,888,200 1,338,845 1,860,865 7,069 27,335 0.53% 1.47% 1,844.68 2,587.92 1,834.99 2,550.46 9.69 37.46 0.53% 1.47% 44700 Stockton-Lodi, CA Industrial Legacy 9 58.0 1,725,102 2,073,437 1,720,610 2,037,332 4,492 36,105 0.26% 1.77% 29,768.81 35,779.76 29,691.29 35,156.71 77.52 623.04 0.26% 1.77% 44780 Sturgis, MI Industrial Legacy 5 250.3 0 0 0 0 0 0 #DIV/0! #DIV/0! 0.00 0.00 0.00 0.00 0.00 0.00 #DIV/0! #DIV/0! 48620 Wichita, KS Industrial Legacy 7 844.1 261,096 1,325,743 260,494 1,305,411 602 20,332 0.23% 1.56% 309.33 1,570.67 308.62 1,546.59 0.71 24.09 0.23% 1.56% 48980 Wilson, NC Industrial Legacy 4 6.9 287,895 293,298 284,861 283,047 3,034 10,251 1.07% 3.62% 41,784.47 42,568.62 41,344.07 41,080.80 440.40 1487.82 1.07% 3.62% 49180 Winston-Salem, NC Up and Coming 4 87.9 5,375,192 5,579,972 5,310,480 5,448,730 64,712 131,242 1.22% 2.41% 61,165.14 63,495.36 60,428.77 62,001.93 736.37 1493.43 1.22% 2.41%

Page 1 of 1 E-15 Table E-10. Residential VMT Results, Cumulative (by Metro/CBSA)

VMT generated by new (allocated) households (mi) VMT generate Brownfields Trend growth Difference Percent Difference Brownfields BF ACREAGE CBSA NAME GROWTH PROFILE EPA REGION (ac) Aggressive Base Aggressive Base Aggressive Base Aggressive Base Aggressive Base 10420 Akron, OH Industrial Legacy 5 743.5 555,329 660,247 729,885 727,923 -174,557 -67,676 -23.92% -9.30% 746.96 888.08 10580 Albany-Schenectady-Troy, NY Industrial Legacy 2 322.7 2,472,061 2,671,711 2,716,493 2,718,075 -244,432 -46,364 -9.00% -1.71% 7,661.50 8,280.27 10740 Albuquerque, NM Up and Coming 3 298.3 7,351,511 7,478,898 7,432,482 7,431,481 -80,971 47,416 -1.09% 0.64% 24,647.17 25,074.25 10900 Allentown-Bethlehem-Easton, PA-NJ Industrial Legacy 5 837.8 2,309,845 2,571,161 2,628,199 2,620,183 -318,354 -49,023 -12.11% -1.87% 2,757.10 3,069.02 11460 Ann Arbor, MI Industrial Legacy 4 229.0 1,663,563 1,708,746 1,704,245 1,710,825 -40,682 -2,079 -2.39% -0.12% 7,264.47 7,461.77 12060 Atlanta, GA Big and Growing 6 1,582.0 63,610,523 66,519,768 67,201,252 67,167,374 -3,590,729 -647,606 -5.34% -0.96% 40,209.18 42,048.16 12420 Austin-Round Rock, TX Growth Hub 6 908.4 24,414,769 26,068,120 26,411,551 26,500,884 -1,996,782 -432,764 -7.56% -1.63% 26,876.67 28,696.74 12580 Baltimore, MD Slow and Steady 3 888.9 10,163,050 10,616,047 10,666,698 10,698,358 -503,648 -82,311 -4.72% -0.77% 11,432.77 11,942.37 12620 Bangor, ME Industrial Legacy 1 583.1 156,568 286,850 326,697 326,345 -170,129 -39,495 -52.08% -12.10% 268.50 491.92 13660 Big Rapids, MI Up and Coming 5 66.1 242,640 255,208 259,186 251,587 -16,546 3,620 -6.38% 1.44% 3,670.80 3,860.93 13740 Billings, MT Up and Coming 8 80.9 701,525 742,338 735,979 745,301 -34,453 -2,963 -4.68% -0.40% 8,667.23 9,171.46 14260 Boise City, ID Up and Coming 10 142.8 4,390,419 4,422,294 4,373,276 4,360,274 17,143 62,019 0.39% 1.42% 30,747.38 30,970.61 14500 Boulder, CO Up and Coming 8 28.7 2,084,442 2,086,572 2,125,643 2,147,382 -41,201 -60,809 -1.94% -2.83% 72,527.54 72,601.68 15540 Burlington-South Burlington, VT Up and Coming 1 171.0 1,357,011 1,459,072 1,492,747 1,481,578 -135,736 -22,505 -9.09% -1.52% 7,934.81 8,531.59 16860 Chattanooga, TN-GA Industrial Legacy 4 228.6 2,105,506 2,216,822 2,251,160 2,233,797 -145,654 -16,975 -6.47% -0.76% 9,208.83 9,695.69 19100 Dallas, TX Big and Growing 6 1,253.9 69,332,698 69,917,214 69,874,413 70,420,437 -541,716 -503,224 -0.78% -0.71% 55,291.88 55,758.02 19380 Dayton, OH Industrial Legacy 5 123.0 160,053 208,563 259,888 259,344 -99,834 -50,781 -38.41% -19.58% 1,301.14 1,695.50 19780 Des Moines-West Des Moines, IA Up and Coming 7 278.5 2,157,588 2,893,708 3,357,010 3,361,757 -1,199,422 -468,049 -35.73% -13.92% 7,746.06 10,388.84 20500 Durham-Chapel Hill, NC Up and Coming 4 68.2 5,927,065 6,367,852 6,502,992 6,501,155 -575,928 -133,303 -8.86% -2.05% 86,856.16 93,315.53 23140 Frankfort, IN Industrial Legacy 5 50.2 9,119 8,526 10,372 10,269 -1,253 -1,743 -12.08% -16.97% 181.76 169.95 23300 Freeport, IL Industrial Legacy 5 51.0 63 81 77 66 -14 15 -17.69% 22.37% 1.24 1.58 24340 Grand Rapids-Wyoming, MI Up and Coming 5 801.5 5,023,936 5,274,426 5,323,103 5,313,546 -299,167 -39,121 -5.62% -0.74% 6,268.32 6,580.86 24500 Great Falls, MT Industrial Legacy 8 71.4 63,001 75,105 79,719 78,275 -16,718 -3,170 -20.97% -4.05% 882.49 1,052.04 25540 Hartford, CT Slow and Steady 1 419.7 3,020,275 3,146,498 3,145,319 3,169,372 -125,045 -22,875 -3.98% -0.72% 7,196.78 7,497.55 26980 Iowa City, IA Up and Coming 7 95.4 798,222 816,233 811,174 817,471 -12,952 -1,237 -1.60% -0.15% 8,366.23 8,555.01 27140 Jackson, MS Industrial Legacy 4 336.6 4,498,555 4,503,944 4,498,130 4,522,867 425 -18,923 0.01% -0.42% 13,366.28 13,382.29 28940 Knoxville, TN Up and Coming 4 1,016.9 5,109,446 6,357,804 6,787,940 6,814,217 -1,678,495 -456,413 -24.73% -6.70% 5,024.63 6,252.27 29460 Lakeland-Winter Haven, FL Up and Coming 4 1,164.4 3,735,989 3,727,666 3,676,585 3,670,571 59,404 57,095 1.62% 1.56% 3,208.51 3,201.36 31080 Los Angeles, CA Stable Metropolis 9 1,675.8 28,338,508 29,036,410 29,152,565 29,858,109 -814,057 -821,699 -2.79% -2.75% 16,910.84 17,327.31 33340 Milwaukee, WI Slow and Steady 5 755.5 2,824,659 3,234,476 3,364,891 3,391,935 -540,231 -157,459 -16.05% -4.64% 3,738.75 4,281.18 33460 Minneapolis, MN Growth Hub 5 1,133.5 20,707,663 21,682,732 21,659,806 21,914,084 -952,143 -231,352 -4.40% -1.06% 18,269.43 19,129.68 33860 Montgomery, AL Industrial Legacy 4 343.5 2,423,904 2,572,912 2,575,325 2,512,320 -151,421 60,592 -5.88% 2.41% 7,057.52 7,491.37 34060 Morgantown, WV Up and Coming 3 613.8 630,621 628,225 614,751 615,148 15,870 13,077 2.58% 2.13% 1,027.46 1,023.55 35300 New Haven-Milford, CT Industrial Legacy 1 295.0 815,461 1,054,884 1,142,307 1,142,164 -326,845 -87,280 -28.61% -7.64% 2,764.74 3,576.49 35380 New Orleans, LA Slow and Steady 6 528.5 1,901,098 2,018,457 2,042,153 2,046,565 -141,055 -28,108 -6.91% -1.37% 3,597.09 3,819.15 36260 Ogden-Clearfield, UT Up and Coming 8 344.3 4,478,344 4,488,543 4,474,597 4,484,864 3,748 3,679 0.08% 0.08% 13,008.61 13,038.23 36740 Orlando-Kissimmee-Sanford, FL Growth Hub 4 363.0 24,866,768 25,064,180 24,890,790 24,855,891 -24,021 208,289 -0.10% 0.84% 68,505.38 69,049.23 37980 Philadelphia-Camden-Wilmington, PA-NJ-DE-MD Stable Metropolis 2 4,454.0 7,083,375 8,859,144 9,179,559 9,243,648 -2,096,184 -384,504 -22.84% -4.16% 1,590.35 1,989.04 38860 Portland-South Portland, ME Up and Coming 1 673.1 2,575,347 2,768,132 2,823,482 2,814,760 -248,135 -46,629 -8.79% -1.66% 3,826.38 4,112.82 38900 Portland-Vancouver-Hillsboro, OR-WA Growth Hub 10 1,037.0 14,168,240 14,243,129 14,218,916 14,372,124 -50,676 -128,996 -0.36% -0.90% 13,662.46 13,734.67 40380 Rochester, NY Slow and Steady 2 241.1 1,049,721 1,487,428 1,626,546 1,617,046 -576,825 -129,618 -35.46% -8.02% 4,354.60 6,170.37 40900 Sacramento--Roseville--Arden-Arcade, CA Growth Hub 9 1,220.4 12,044,302 12,926,131 12,673,079 13,021,886 -628,777 -95,755 -4.96% -0.74% 9,869.06 10,591.63 41860 San Francisco, CA Slow and Steady 9 922.3 7,256,523 8,004,389 7,901,530 8,149,368 -645,007 -144,980 -8.16% -1.78% 7,868.11 8,679.01 42660 Seattle, WA Growth Hub 10 658.3 18,796,757 21,363,203 21,963,120 22,360,298 -3,166,363 -997,095 -14.42% -4.46% 28,554.35 32,453.06 43340 Shreveport-Bossier City, LA Industrial Legacy 6 729.6 1,577,178 1,979,580 2,085,410 2,071,043 -508,232 -91,462 -24.37% -4.42% 2,161.64 2,713.17 44700 Stockton-Lodi, CA Industrial Legacy 9 58.0 1,997,528 2,177,176 2,183,371 2,171,865 -185,843 5,312 -8.51% 0.24% 34,469.86 37,569.91 44780 Sturgis, MI Industrial Legacy 5 250.3 28,830 29,532 33,483 33,390 -4,652 -3,858 -13.89% -11.55% 115.17 117.97 48620 Wichita, KS Industrial Legacy 7 844.1 806,719 1,618,295 1,772,276 1,759,025 -965,557 -140,731 -54.48% -8.00% 955.76 1,917.27 48980 Wilson, NC Industrial Legacy 4 6.9 293,663 295,072 293,104 285,375 559 9,696 0.19% 3.40% 42,621.66 42,826.08 49180 Winston-Salem, NC Up and Coming 4 87.9 5,489,194 5,616,245 5,591,313 5,530,804 -102,118 85,441 -1.83% 1.54% 62,462.39 63,908.12

Page 1 of 4 E-16 Table E-10. Residential VMT Results, Cumulative (by Metro/CBSA)

ed by new (allocated) households per brownfield acre redeveloped (mi/ac) Change in VMT per capita (mi/capita) Trend growth Difference Percent Difference Brownfields Trend growth Difference Percent Difference CBSA NAME Aggressive Base Aggressive Base Aggressive Base Aggressive Base Aggressive Base Aggressive Base Aggressive Base 10420 Akron, OH 981.75 979.11 -234.79 -91.03 -23.92% -9.30% -0.69 -0.60 -0.59 -0.59 -0.10 -0.01 17.78% 1.71% 10580 Albany-Schenectady-Troy, NY 8,419.06 8,423.96 -757.55 -143.69 -9.00% -1.71% -1.60 -1.49 -1.46 -1.48 -0.14 -0.01 9.83% 0.54% 10740 Albuquerque, NM 24,918.64 24,915.28 -271.47 158.97 -1.09% 0.64% -2.25 -2.16 -2.19 -2.26 -0.06 0.11 2.63% -4.81% 10900 Allentown-Bethlehem-Easton, PA-NJ 3,137.10 3,127.53 -380.00 -58.51 -12.11% -1.87% -1.24 -1.18 -1.15 -1.16 -0.09 -0.01 8.12% 1.24% 11460 Ann Arbor, MI 7,442.12 7,470.85 -177.65 -9.08 -2.39% -0.12% -1.22 -1.18 -1.18 -1.18 -0.04 -0.01 3.39% 0.65% 12060 Atlanta, GA 42,478.94 42,457.52 -2269.75 -409.36 -5.34% -0.96% -3.05 -2.93 -2.89 -2.92 -0.16 -0.01 5.58% 0.24% 12420 Austin-Round Rock, TX 29,074.80 29,173.14 -2198.13 -476.40 -7.56% -1.63% -4.29 -4.14 -4.09 -4.10 -0.20 -0.04 4.89% 1.02% 12580 Baltimore, MD 11,999.35 12,034.96 -566.57 -92.59 -4.72% -0.77% -1.41 -1.31 -1.31 -1.32 -0.10 0.00 7.66% -0.26% 12620 Bangor, ME 560.26 559.65 -291.76 -67.73 -52.08% -12.10% -1.78 -1.61 -1.59 -1.59 -0.19 -0.03 12.02% 1.80% 13660 Big Rapids, MI 3,921.12 3,806.16 -250.32 54.77 -6.38% 1.44% -1.12 -1.10 -1.09 -1.08 -0.03 -0.01 3.12% 1.30% 13740 Billings, MT 9,092.89 9,208.06 -425.67 -36.61 -4.68% -0.40% -1.97 -1.60 -1.79 -1.55 -0.18 -0.05 10.09% 2.90% 14260 Boise City, ID 30,627.33 30,536.27 120.06 434.34 0.39% 1.42% -2.71 -2.66 -2.71 -2.75 0.00 0.09 -0.11% -3.31% 14500 Boulder, CO 73,961.13 74,717.53 -1433.59 -2115.84 -1.94% -2.83% -1.20 -1.20 -1.31 -1.38 0.11 0.17 -8.18% -12.65% 15540 Burlington-South Burlington, VT 8,728.50 8,663.18 -793.68 -131.59 -9.09% -1.52% -2.44 -2.23 -2.18 -2.22 -0.26 -0.01 11.99% 0.63% 16860 Chattanooga, TN-GA 9,845.87 9,769.93 -637.05 -74.24 -6.47% -0.76% -1.68 -1.63 -1.61 -1.64 -0.08 0.01 4.85% -0.91% 19100 Dallas, TX 55,723.89 56,159.34 -432.01 -401.31 -0.78% -0.71% -2.68 -2.65 -2.73 -2.78 0.04 0.13 -1.59% -4.52% 19380 Dayton, OH 2,112.74 2,108.32 -811.60 -412.82 -38.41% -19.58% -0.74 -0.65 -0.64 -0.64 -0.10 -0.01 15.54% 1.95% 19780 Des Moines-West Des Moines, IA 12,052.16 12,069.21 -4306.10 -1680.36 -35.73% -13.92% -2.29 -2.26 -2.25 -2.27 -0.04 0.01 1.78% -0.27% 20500 Durham-Chapel Hill, NC 95,295.90 95,268.98 -8439.74 -1953.45 -8.86% -2.05% -3.61 -3.29 -3.17 -3.18 -0.44 -0.11 13.87% 3.35% 23140 Frankfort, IN 206.73 204.69 -24.97 -34.74 -12.08% -16.97% -0.38 -0.36 -0.33 -0.33 -0.05 -0.03 15.62% 8.37% 23300 Freeport, IL 1.50 1.30 -0.27 0.29 -17.69% 22.37% -0.85 -0.60 -0.55 -0.54 -0.30 -0.06 55.02% 11.46% 24340 Grand Rapids-Wyoming, MI 6,641.59 6,629.67 -373.27 -48.81 -5.62% -0.74% -1.88 -1.76 -1.72 -1.73 -0.17 -0.03 9.67% 1.71% 24500 Great Falls, MT 1,116.67 1,096.44 -234.18 -44.40 -20.97% -4.05% -0.44 -0.41 -0.41 -0.41 -0.03 0.00 8.52% 0.00% 25540 Hartford, CT 7,494.74 7,552.06 -297.96 -54.51 -3.98% -0.72% -1.20 -1.10 -1.07 -1.08 -0.13 -0.02 12.40% 1.60% 26980 Iowa City, IA 8,501.98 8,567.98 -135.75 -12.97 -1.60% -0.15% -1.84 -1.74 -1.69 -1.67 -0.16 -0.06 9.44% 3.65% 27140 Jackson, MS 13,365.02 13,438.52 1.26 -56.22 0.01% -0.42% -4.10 -4.01 -4.00 -4.02 -0.10 0.01 2.44% -0.35% 28940 Knoxville, TN 6,675.26 6,701.10 -1650.63 -448.84 -24.73% -6.70% -3.20 -3.12 -3.11 -3.11 -0.10 -0.01 3.08% 0.34% 29460 Lakeland-Winter Haven, FL 3,157.49 3,152.33 51.02 49.03 1.62% 1.56% -1.74 -1.78 -1.81 -1.83 0.07 0.06 -3.72% -3.14% 31080 Los Angeles, CA 17,396.62 17,817.65 -485.78 -490.34 -2.79% -2.75% -1.30 -1.26 -1.26 -1.26 -0.04 0.00 3.03% 0.10% 33340 Milwaukee, WI 4,453.80 4,489.60 -715.05 -208.41 -16.05% -4.64% -0.96 -0.90 -0.88 -0.89 -0.08 -0.01 8.81% 1.26% 33460 Minneapolis, MN 19,109.46 19,333.80 -840.03 -204.11 -4.40% -1.06% -1.63 -1.46 -1.41 -1.43 -0.22 -0.03 15.63% 2.21% 33860 Montgomery, AL 7,498.40 7,314.95 -440.88 176.42 -5.88% 2.41% -2.57 -2.56 -2.63 -2.77 0.06 0.21 -2.42% -7.48% 34060 Morgantown, WV 1,001.60 1,002.25 25.86 21.31 2.58% 2.13% -1.66 -1.66 -1.68 -1.69 0.02 0.02 -1.45% -1.32% 35300 New Haven-Milford, CT 3,872.88 3,872.40 -1108.14 -295.91 -28.61% -7.64% -0.83 -0.77 -0.76 -0.76 -0.07 -0.01 8.57% 0.68% 35380 New Orleans, LA 3,863.98 3,872.33 -266.89 -53.18 -6.91% -1.37% -0.54 -0.52 -0.52 -0.53 -0.02 0.01 4.56% -1.87% 36260 Ogden-Clearfield, UT 12,997.73 13,027.55 10.89 10.69 0.08% 0.08% -2.15 -2.13 -2.15 -2.21 0.00 0.08 0.08% -3.50% 36740 Orlando-Kissimmee-Sanford, FL 68,571.56 68,475.42 -66.18 573.82 -0.10% 0.84% -3.36 -3.26 -3.33 -3.46 -0.03 0.21 0.83% -5.92% 37980 Philadelphia-Camden-Wilmington, PA-NJ-DE-MD 2,060.98 2,075.37 -470.63 -86.33 -22.84% -4.16% -0.87 -0.80 -0.76 -0.77 -0.11 -0.03 14.16% 3.90% 38860 Portland-South Portland, ME 4,195.06 4,182.10 -368.67 -69.28 -8.79% -1.66% -1.36 -1.24 -1.18 -1.17 -0.18 -0.06 14.94% 5.15% 38900 Portland-Vancouver-Hillsboro, OR-WA 13,711.32 13,859.06 -48.87 -124.39 -0.36% -0.90% -1.81 -1.81 -1.81 -1.83 0.00 0.02 -0.06% -1.32% 40380 Rochester, NY 6,747.47 6,708.06 -2392.87 -537.70 -35.46% -8.02% -0.91 -0.88 -0.88 -0.90 -0.03 0.01 3.23% -1.30% 40900 Sacramento--Roseville--Arden-Arcade, CA 10,384.28 10,670.09 -515.22 -78.46 -4.96% -0.74% -2.76 -2.21 -1.97 -1.97 -0.80 -0.23 40.46% 11.81% 41860 San Francisco, CA 8,567.48 8,836.21 -699.37 -157.20 -8.16% -1.78% -0.83 -0.75 -0.72 -0.71 -0.11 -0.03 15.90% 4.43% 42660 Seattle, WA 33,364.40 33,967.76 -4810.06 -1514.70 -14.42% -4.46% -1.79 -1.70 -1.68 -1.67 -0.12 -0.03 6.87% 1.95% 43340 Shreveport-Bossier City, LA 2,858.21 2,838.52 -696.57 -125.36 -24.37% -4.42% -2.04 -1.94 -1.92 -1.99 -0.12 0.05 6.31% -2.46% 44700 Stockton-Lodi, CA 37,676.81 37,478.25 -3206.95 91.66 -8.51% 0.24% -0.95 -0.94 -0.95 -0.99 0.00 0.05 0.08% -5.09% 44780 Sturgis, MI 133.75 133.39 -18.58 -15.41 -13.89% -11.55% -0.42 -0.37 -0.31 -0.31 -0.11 -0.06 34.42% 19.38% 48620 Wichita, KS 2,099.70 2,084.00 -1143.94 -166.73 -54.48% -8.00% -1.36 -1.31 -1.30 -1.33 -0.06 0.02 4.93% -1.47% 48980 Wilson, NC 42,540.49 41,418.77 81.17 1407.30 0.19% 3.40% -0.91 -0.91 -1.00 -1.13 0.10 0.22 -9.57% -19.32% 49180 Winston-Salem, NC 63,624.41 62,935.87 -1162.02 972.25 -1.83% 1.54% -2.38 -2.37 -2.37 -2.48 -0.01 0.11 0.43% -4.50%

Page 2 of 4 E-17 Table E-10. Residential VMT Results, Cumulative (by Metro/CBSA)

Change in VMT per capita per brownfield acre redeveloped (mi/capita/ac) Change in VMT generated by existing households (mi) Brownfields Trend growth Difference Percent Difference Brownfields Trend growth Difference CBSA NAME Aggressive Base Aggressive Base Aggressive Base Aggressive Base Aggressive Base Aggressive Base Aggressive Base 10420 Akron, OH 0.00 0.00 0.00 0.00 0.00 0.00 17.78% 1.71% -309,954 -434,354 -439,494 -435,466 129,540 1,112 10580 Albany-Schenectady-Troy, NY 0.00 0.00 0.00 0.00 0.00 0.00 9.83% 0.54% -1,568,851 -1,463,618 -1,370,735 -1,298,725 -198,116 -164,893 10740 Albuquerque, NM -0.01 -0.01 -0.01 -0.01 0.00 0.00 2.63% -4.81% -2,142,813 -2,056,628 -1,867,833 -1,763,641 -274,980 -292,987 10900 Allentown-Bethlehem-Easton, PA-NJ 0.00 0.00 0.00 0.00 0.00 0.00 8.12% 1.24% -1,064,134 -1,020,294 -991,840 -964,662 -72,294 -55,632 11460 Ann Arbor, MI -0.01 -0.01 -0.01 -0.01 0.00 0.00 3.39% 0.65% -461,218 -446,479 -431,418 -396,773 -29,799 -49,706 12060 Atlanta, GA 0.00 0.00 0.00 0.00 0.00 0.00 5.58% 0.24% -17,092,973 -16,427,827 -16,074,654 -15,490,639 -1,018,318 -937,189 12420 Austin-Round Rock, TX 0.00 0.00 0.00 0.00 0.00 0.00 4.89% 1.02% -7,813,831 -7,539,287 -7,398,995 -7,229,643 -414,837 -309,644 12580 Baltimore, MD 0.00 0.00 0.00 0.00 0.00 0.00 7.66% -0.26% -4,031,786 -3,777,182 -3,537,342 -3,247,648 -494,444 -529,534 12620 Bangor, ME 0.00 0.00 0.00 0.00 0.00 0.00 12.02% 1.80% -289,149 -307,499 -300,490 -299,737 11,341 -7,762 13660 Big Rapids, MI -0.02 -0.02 -0.02 -0.02 0.00 0.00 3.12% 1.30% -61,196 -59,911 -59,343 -56,889 -1,852 -3,022 13740 Billings, MT -0.02 -0.02 -0.02 -0.02 0.00 0.00 10.09% 2.90% -360,965 -292,459 -326,048 -279,382 -34,916 -13,078 14260 Boise City, ID -0.02 -0.02 -0.02 -0.02 0.00 0.00 -0.11% -3.31% -1,710,979 -1,682,959 -1,591,596 -1,508,629 -119,383 -174,330 14500 Boulder, CO -0.04 -0.04 -0.05 -0.05 0.00 0.01 -8.18% -12.65% -394,356 -393,792 -309,417 -244,888 -84,939 -148,904 15540 Burlington-South Burlington, VT -0.01 -0.01 -0.01 -0.01 0.00 0.00 11.99% 0.63% -581,891 -532,102 -514,174 -506,717 -67,717 -25,385 16860 Chattanooga, TN-GA -0.01 -0.01 -0.01 -0.01 0.00 0.00 4.85% -0.91% -1,018,781 -981,415 -952,179 -886,880 -66,601 -94,536 19100 Dallas, TX 0.00 0.00 0.00 0.00 0.00 0.00 -1.59% -4.52% -17,416,603 -17,221,106 -15,844,975 -13,731,928 -1,571,629 -3,489,178 19380 Dayton, OH -0.01 -0.01 -0.01 -0.01 0.00 0.00 15.54% 1.95% -327,377 -503,652 -504,940 -452,338 177,563 -51,313 19780 Des Moines-West Des Moines, IA -0.01 -0.01 -0.01 -0.01 0.00 0.00 1.78% -0.27% -1,407,428 -1,380,923 -1,341,626 -1,299,034 -65,803 -81,889 20500 Durham-Chapel Hill, NC -0.05 -0.05 -0.05 -0.05 -0.01 0.00 13.87% 3.35% -2,074,406 -1,891,315 -1,812,442 -1,795,977 -261,964 -95,338 23140 Frankfort, IN -0.01 -0.01 -0.01 -0.01 0.00 0.00 15.62% 8.37% -9,345 -10,125 -11,205 -11,139 1,860 1,014 23300 Freeport, IL -0.02 -0.01 -0.01 -0.01 -0.01 0.00 55.02% 11.46% -19,816 -16,467 -15,421 -14,896 -4,395 -1,571 24340 Grand Rapids-Wyoming, MI 0.00 0.00 0.00 0.00 0.00 0.00 9.67% 1.71% -1,947,552 -1,828,274 -1,747,140 -1,691,759 -200,413 -136,515 24500 Great Falls, MT -0.01 -0.01 -0.01 -0.01 0.00 0.00 8.52% 0.00% -40,499 -38,670 -37,462 -35,307 -3,037 -3,363 25540 Hartford, CT 0.00 0.00 0.00 0.00 0.00 0.00 12.40% 1.60% -1,547,991 -1,419,440 -1,337,610 -1,227,475 -210,381 -191,964 26980 Iowa City, IA -0.02 -0.02 -0.02 -0.02 0.00 0.00 9.44% 3.65% -311,704 -293,187 -277,347 -264,131 -34,358 -29,056 27140 Jackson, MS -0.01 -0.01 -0.01 -0.01 0.00 0.00 2.44% -0.35% -2,454,476 -2,407,523 -2,287,405 -2,093,442 -167,071 -314,081 28940 Knoxville, TN 0.00 0.00 0.00 0.00 0.00 0.00 3.08% 0.34% -3,155,037 -3,075,752 -3,054,682 -3,043,326 -100,355 -32,426 29460 Lakeland-Winter Haven, FL 0.00 0.00 0.00 0.00 0.00 0.00 -3.72% -3.14% -1,251,864 -1,274,598 -1,249,817 -1,170,402 -2,047 -104,196 31080 Los Angeles, CA 0.00 0.00 0.00 0.00 0.00 0.00 3.03% 0.10% -14,747,287 -14,336,525 -13,210,611 -11,323,801 -1,536,676 -3,012,725 33340 Milwaukee, WI 0.00 0.00 0.00 0.00 0.00 0.00 8.81% 1.26% -1,588,477 -1,495,733 -1,433,738 -1,321,637 -154,738 -174,096 33460 Minneapolis, MN 0.00 0.00 0.00 0.00 0.00 0.00 15.63% 2.21% -5,670,091 -5,078,372 -4,748,561 -4,506,778 -921,530 -571,594 33860 Montgomery, AL -0.01 -0.01 -0.01 -0.01 0.00 0.00 -2.42% -7.48% -1,056,362 -1,057,477 -1,013,962 -945,090 -42,400 -112,387 34060 Morgantown, WV 0.00 0.00 0.00 0.00 0.00 0.00 -1.45% -1.32% -249,866 -250,293 -253,540 -242,212 3,674 -8,081 35300 New Haven-Milford, CT 0.00 0.00 0.00 0.00 0.00 0.00 8.57% 0.68% -620,134 -699,074 -694,852 -664,668 74,718 -34,407 35380 New Orleans, LA 0.00 0.00 0.00 0.00 0.00 0.00 4.56% -1.87% -752,968 -719,825 -690,482 -598,915 -62,486 -120,910 36260 Ogden-Clearfield, UT -0.01 -0.01 -0.01 -0.01 0.00 0.00 0.08% -3.50% -1,130,397 -1,123,245 -1,019,615 -957,039 -110,782 -166,205 36740 Orlando-Kissimmee-Sanford, FL -0.01 -0.01 -0.01 -0.01 0.00 0.00 0.83% -5.92% -8,191,541 -7,940,900 -7,566,635 -7,130,990 -624,906 -809,910 37980 Philadelphia-Camden-Wilmington, PA-NJ-DE-MD 0.00 0.00 0.00 0.00 0.00 0.00 14.16% 3.90% -5,192,102 -4,826,080 -4,556,583 -4,285,394 -635,518 -540,685 38860 Portland-South Portland, ME 0.00 0.00 0.00 0.00 0.00 0.00 14.94% 5.15% -916,750 -834,402 -794,928 -777,779 -121,822 -56,623 38900 Portland-Vancouver-Hillsboro, OR-WA 0.00 0.00 0.00 0.00 0.00 0.00 -0.06% -1.32% -4,314,148 -4,299,756 -4,223,622 -3,790,854 -90,525 -508,902 40380 Rochester, NY 0.00 0.00 0.00 0.00 0.00 0.00 3.23% -1.30% -1,035,188 -1,019,282 -994,325 -922,629 -40,863 -96,653 40900 Sacramento--Roseville--Arden-Arcade, CA 0.00 0.00 0.00 0.00 0.00 0.00 40.46% 11.81% -6,157,793 -4,930,778 -4,246,992 -3,887,040 -1,910,801 -1,043,738 41860 San Francisco, CA 0.00 0.00 0.00 0.00 0.00 0.00 15.90% 4.43% -3,666,506 -3,297,628 -3,096,241 -2,863,404 -570,265 -434,224 42660 Seattle, WA 0.00 0.00 0.00 0.00 0.00 0.00 6.87% 1.95% -6,689,716 -6,359,555 -6,245,310 -6,043,539 -444,406 -316,016 43340 Shreveport-Bossier City, LA 0.00 0.00 0.00 0.00 0.00 0.00 6.31% -2.46% -1,003,676 -957,771 -927,860 -905,186 -75,816 -52,585 44700 Stockton-Lodi, CA -0.02 -0.02 -0.02 -0.02 0.00 0.00 0.08% -5.09% -561,788 -559,423 -517,032 -453,316 -44,756 -106,107 44780 Sturgis, MI 0.00 0.00 0.00 0.00 0.00 0.00 34.42% 19.38% -19,151 -16,669 -22,429 -21,981 3,278 5,312 48620 Wichita, KS 0.00 0.00 0.00 0.00 0.00 0.00 4.93% -1.47% -834,444 -857,023 -849,405 -803,245 14,961 -53,777 48980 Wilson, NC -0.13 -0.13 -0.15 -0.16 0.01 0.03 -9.57% -19.32% -82,014 -82,411 -73,084 -55,769 -8,930 -26,643 49180 Winston-Salem, NC -0.03 -0.03 -0.03 -0.03 0.00 0.00 0.43% -4.50% -1,764,278 -1,755,489 -1,692,817 -1,438,576 -71,460 -316,914

Page 3 of 4 E-18 Table E-10. Residential VMT Results, Cumulative (by Metro/CBSA)

Change in VMT generated by existing households per brownfield acre redeveloped (mi/ac) Percent Difference Brownfields Trend growth Difference Percent Difference CBSA NAME Aggressive Base Aggressive Base Aggressive Base Aggressive Base Aggressive Base 10420 Akron, OH -29.47% -0.26% -416.91 -584.24 -591.15 -585.74 174.24 1.50 -29.47% -0.26% 10580 Albany-Schenectady-Troy, NY 14.45% 12.70% -4,862.24 -4,536.10 -4,248.23 -4,025.06 -614.01 -511.04 14.45% 12.70% 10740 Albuquerque, NM 14.72% 16.61% -7,184.14 -6,895.19 -6,262.22 -5,912.90 -921.92 -982.29 14.72% 16.61% 10900 Allentown-Bethlehem-Easton, PA-NJ 7.29% 5.77% -1,270.18 -1,217.85 -1,183.89 -1,151.45 -86.29 -66.40 7.29% 5.77% 11460 Ann Arbor, MI 6.91% 12.53% -2,014.05 -1,949.69 -1,883.92 -1,732.63 -130.13 -217.06 6.91% 12.53% 12060 Atlanta, GA 6.33% 6.05% -10,804.73 -10,384.28 -10,161.03 -9,791.87 -643.69 -592.41 6.33% 6.05% 12420 Austin-Round Rock, TX 5.61% 4.28% -8,601.75 -8,299.52 -8,145.08 -7,958.66 -456.67 -340.87 5.61% 4.28% 12580 Baltimore, MD 13.98% 16.31% -4,535.50 -4,249.08 -3,979.28 -3,653.39 -556.22 -595.69 13.98% 16.31% 12620 Bangor, ME -3.77% 2.59% -495.87 -527.33 -515.31 -514.02 19.45 -13.31 -3.77% 2.59% 13660 Big Rapids, MI 3.12% 5.31% -925.81 -906.37 -897.78 -860.65 -28.02 -45.72 3.12% 5.31% 13740 Billings, MT 10.71% 4.68% -4,459.66 -3,613.29 -4,028.27 -3,451.72 -431.39 -161.57 10.71% 4.68% 14260 Boise City, ID 7.50% 11.56% -11,982.48 -11,786.25 -11,146.41 -10,565.37 -836.07 -1220.89 7.50% 11.56% 14500 Boulder, CO 27.45% 60.80% -13,721.51 -13,701.87 -10,766.09 -8,520.82 -2955.42 -5181.06 27.45% 60.80% 15540 Burlington-South Burlington, VT 13.17% 5.01% -3,402.47 -3,111.35 -3,006.51 -2,962.91 -395.96 -148.43 13.17% 5.01% 16860 Chattanooga, TN-GA 6.99% 10.66% -4,455.83 -4,292.40 -4,164.54 -3,878.93 -291.29 -413.47 6.99% 10.66% 19100 Dallas, TX 9.92% 25.41% -13,889.50 -13,733.60 -12,636.15 -10,951.02 -1253.35 -2782.57 9.92% 25.41% 19380 Dayton, OH -35.17% 11.34% -2,661.38 -4,094.40 -4,104.87 -3,677.25 1443.48 -417.15 -35.17% 11.34% 19780 Des Moines-West Des Moines, IA 4.90% 6.30% -5,052.88 -4,957.72 -4,816.64 -4,663.73 -236.24 -293.99 4.90% 6.30% 20500 Durham-Chapel Hill, NC 14.45% 5.31% -30,398.69 -27,715.64 -26,559.82 -26,318.54 -3838.87 -1397.11 14.45% 5.31% 23140 Frankfort, IN -16.60% -9.10% -186.27 -201.81 -223.35 -222.02 37.08 20.21 -16.60% -9.10% 23300 Freeport, IL 28.50% 10.55% -388.47 -322.82 -302.32 -292.03 -86.15 -30.80 28.50% 10.55% 24340 Grand Rapids-Wyoming, MI 11.47% 8.07% -2,429.95 -2,281.12 -2,179.89 -2,110.79 -250.05 -170.33 11.47% 8.07% 24500 Great Falls, MT 8.11% 9.52% -567.29 -541.68 -524.75 -494.57 -42.54 -47.11 8.11% 9.52% 25540 Hartford, CT 15.73% 15.64% -3,688.59 -3,382.28 -3,187.29 -2,924.86 -501.30 -457.42 15.73% 15.64% 26980 Iowa City, IA 12.39% 11.00% -3,267.00 -3,072.92 -2,906.89 -2,768.38 -360.11 -304.54 12.39% 11.00% 27140 Jackson, MS 7.30% 15.00% -7,292.83 -7,153.32 -6,796.43 -6,220.11 -496.41 -933.21 7.30% 15.00% 28940 Knoxville, TN 3.29% 1.07% -3,102.66 -3,024.69 -3,003.97 -2,992.81 -98.69 -31.89 3.29% 1.07% 29460 Lakeland-Winter Haven, FL 0.16% 8.90% -1,075.12 -1,094.64 -1,073.36 -1,005.15 -1.76 -89.49 0.16% 8.90% 31080 Los Angeles, CA 11.63% 26.61% -8,800.36 -8,555.24 -7,883.35 -6,757.41 -917.00 -1797.83 11.63% 26.61% 33340 Milwaukee, WI 10.79% 13.17% -2,102.52 -1,979.77 -1,897.71 -1,749.33 -204.81 -230.44 10.79% 13.17% 33460 Minneapolis, MN 19.41% 12.68% -5,002.46 -4,480.42 -4,189.44 -3,976.12 -813.02 -504.29 19.41% 12.68% 33860 Montgomery, AL 4.18% 11.89% -3,075.74 -3,078.98 -2,952.28 -2,751.76 -123.45 -327.23 4.18% 11.89% 34060 Morgantown, WV -1.45% 3.34% -407.10 -407.80 -413.09 -394.63 5.99 -13.17 -1.45% 3.34% 35300 New Haven-Milford, CT -10.75% 5.18% -2,102.50 -2,370.15 -2,355.83 -2,253.49 253.33 -116.65 -10.75% 5.18% 35380 New Orleans, LA 9.05% 20.19% -1,424.70 -1,361.99 -1,306.47 -1,133.21 -118.23 -228.78 9.05% 20.19% 36260 Ogden-Clearfield, UT 10.87% 17.37% -3,283.56 -3,262.78 -2,961.76 -2,779.99 -321.80 -482.79 10.87% 17.37% 36740 Orlando-Kissimmee-Sanford, FL 8.26% 11.36% -22,566.85 -21,876.36 -20,845.30 -19,645.14 -1721.55 -2231.22 8.26% 11.36% 37980 Philadelphia-Camden-Wilmington, PA-NJ-DE-MD 13.95% 12.62% -1,165.72 -1,083.54 -1,023.04 -962.15 -142.69 -121.39 13.95% 12.62% 38860 Portland-South Portland, ME 15.32% 7.28% -1,362.08 -1,239.73 -1,181.08 -1,155.60 -181.00 -84.13 15.32% 7.28% 38900 Portland-Vancouver-Hillsboro, OR-WA 2.14% 13.42% -4,160.14 -4,146.26 -4,072.85 -3,655.53 -87.29 -490.73 2.14% 13.42% 40380 Rochester, NY 4.11% 10.48% -4,294.32 -4,228.33 -4,124.80 -3,827.38 -169.52 -400.95 4.11% 10.48% 40900 Sacramento--Roseville--Arden-Arcade, CA 44.99% 26.85% -5,045.68 -4,040.26 -3,479.97 -3,185.03 -1565.70 -855.24 44.99% 26.85% 41860 San Francisco, CA 18.42% 15.16% -3,975.52 -3,575.56 -3,357.20 -3,104.74 -618.33 -470.82 18.42% 15.16% 42660 Seattle, WA 7.12% 5.23% -10,162.42 -9,660.87 -9,487.32 -9,180.80 -675.10 -480.06 7.12% 5.23% 43340 Shreveport-Bossier City, LA 8.17% 5.81% -1,375.62 -1,312.70 -1,271.70 -1,240.63 -103.91 -72.07 8.17% 5.81% 44700 Stockton-Lodi, CA 8.66% 23.41% -9,694.35 -9,653.54 -8,922.03 -7,822.54 -772.32 -1831.00 8.66% 23.41% 44780 Sturgis, MI -14.62% -24.17% -76.50 -66.59 -89.60 -87.81 13.10 21.22 -14.62% -24.17% 48620 Wichita, KS -1.76% 6.69% -988.61 -1,015.36 -1,006.33 -951.64 17.73 -63.71 -1.76% 6.69% 48980 Wilson, NC 12.22% 47.77% -11,903.27 -11,961.00 -10,607.24 -8,094.16 -1296.02 -3866.84 12.22% 47.77% 49180 Winston-Salem, NC 4.22% 22.03% -20,075.99 -19,975.98 -19,262.83 -16,369.77 -813.16 -3606.21 4.22% 22.03%

Page 4 of 4 E-19 Table E-11. Employment VMT Results, Primary Phase (by Metro/CBSA)

Total VMT generated by new (allocated) jobs (mi) Brownfields Trend growth Difference Percent Difference Brown

CBSA NAME GROWTH PROFILE EPA REGION BF ACREAGE (ac) Aggressive Base Aggressive Base Aggressive Base Aggressive Base Aggressive 10420 Akron, OH Industrial Legacy 5 743.5 255,041 63,401 265,106 65,876 -10,065 -2,475 -3.80% -3.76% 343.05 10580 Albany-Schenectady-Troy, NY Industrial Legacy 2 322.7 377,770 112,435 434,104 127,637 -56,334 -15,202 -12.98% -11.91% 1,170.80 10740 Albuquerque, NM Up and Coming 3 298.3 491,945 147,496 547,177 163,669 -55,232 -16,173 -10.09% -9.88% 1,649.33 10900 Allentown-Bethlehem-Easton, PA-NJ Industrial Legacy 5 837.8 223,190 65,207 255,574 74,743 -32,385 -9,535 -12.67% -12.76% 266.41 11460 Ann Arbor, MI Industrial Legacy 4 229.0 158,259 46,899 167,622 48,479 -9,363 -1,580 -5.59% -3.26% 691.09 12060 Atlanta, GA Big and Growing 6 1,582.0 1,100,228 323,750 1,174,244 345,791 -74,017 -22,041 -6.30% -6.37% 695.47 12420 Austin-Round Rock, TX Growth Hub 6 908.4 923,069 273,030 983,018 290,786 -59,949 -17,756 -6.10% -6.11% 1,016.15 12580 Baltimore, MD Slow and Steady 3 888.9 793,264 240,674 892,503 267,900 -99,239 -27,226 -11.12% -10.16% 892.37 12620 Bangor, ME Industrial Legacy 1 583.1 71,802 26,501 78,530 28,010 -6,728 -1,509 -8.57% -5.39% 123.13 13660 Big Rapids, MI Up and Coming 5 66.1 4,012 1,372 4,083 1,400 -71 -27 -1.74% -1.94% 60.69 13740 Billings, MT Up and Coming 8 80.9 296,918 86,637 308,145 89,742 -11,226 -3,105 -3.64% -3.46% 3,668.38 14260 Boise City, ID Up and Coming 10 142.8 205,175 61,534 229,361 67,968 -24,187 -6,434 -10.55% -9.47% 1,436.90 14500 Boulder, CO Up and Coming 8 28.7 15,971 4,668 18,153 5,311 -2,182 -643 -12.02% -12.11% 555.69 15540 Burlington-South Burlington, VT Up and Coming 1 171.0 216,266 66,502 238,892 72,020 -22,626 -5,518 -9.47% -7.66% 1,264.57 16860 Chattanooga, TN-GA Industrial Legacy 4 228.6 131,431 38,963 140,243 41,332 -8,812 -2,370 -6.28% -5.73% 574.84 19100 Dallas, TX Big and Growing 6 1,253.9 1,445,481 428,030 1,516,791 449,232 -71,310 -21,202 -4.70% -4.72% 1,152.75 19380 Dayton, OH Industrial Legacy 5 123.0 8,931 2,610 10,086 2,963 -1,155 -353 -11.45% -11.92% 72.60 19780 Des Moines-West Des Moines, IA Up and Coming 7 278.5 2,272 866 2,161 822 111 43 5.14% 5.28% 8.16 20500 Durham-Chapel Hill, NC Up and Coming 4 68.2 367,722 108,148 439,964 128,631 -72,242 -20,483 -16.42% -15.92% 5,388.66 23140 Frankfort, IN Industrial Legacy 5 50.2 3,999 1,389 4,385 1,515 -385 -126 -8.79% -8.34% 79.71 23300 Freeport, IL Industrial Legacy 5 51.0 53,200 15,553 68,055 20,029 -14,855 -4,476 -21.83% -22.35% 1,042.93 24340 Grand Rapids-Wyoming, MI Up and Coming 5 801.5 346,174 102,660 421,207 124,307 -75,034 -21,647 -17.81% -17.41% 431.92 24500 Great Falls, MT Industrial Legacy 8 71.4 45,733 13,405 46,935 13,724 -1,202 -318 -2.56% -2.32% 640.61 25540 Hartford, CT Slow and Steady 1 419.7 658,503 193,644 711,947 207,817 -53,445 -14,173 -7.51% -6.82% 1,569.10 26980 Iowa City, IA Up and Coming 7 95.4 194,512 56,890 202,890 58,498 -8,378 -1,608 -4.13% -2.75% 2,038.70 27140 Jackson, MS Industrial Legacy 4 336.6 216,995 66,118 220,910 67,113 -3,916 -995 -1.77% -1.48% 644.74 28940 Knoxville, TN Up and Coming 4 1,016.9 107,614 37,266 112,052 37,939 -4,437 -673 -3.96% -1.77% 105.83 29460 Lakeland-Winter Haven, FL Up and Coming 4 1,164.4 150,064 47,801 135,404 42,748 14,660 5,052 10.83% 11.82% 128.88 31080 Los Angeles, CA Stable Metropolis 9 1,675.8 2,629,061 774,831 2,746,008 807,678 -116,947 -32,848 -4.26% -4.07% 1,568.88 33340 Milwaukee, WI Slow and Steady 5 755.5 576,781 168,816 633,727 184,784 -56,946 -15,968 -8.99% -8.64% 763.43 33460 Minneapolis, MN Growth Hub 5 1,133.5 3,201,694 946,849 3,471,454 1,012,561 -269,760 -65,712 -7.77% -6.49% 2,824.71 33860 Montgomery, AL Industrial Legacy 4 343.5 5,851 2,908 9,049 4,282 -3,198 -1,374 -35.34% -32.09% 17.03 34060 Morgantown, WV Up and Coming 3 613.8 22,422 9,527 19,132 7,934 3,290 1,592 17.20% 20.07% 36.53 35300 New Haven-Milford, CT Industrial Legacy 1 295.0 230,750 67,975 250,057 71,631 -19,307 -3,656 -7.72% -5.10% 782.34 35380 New Orleans, LA Slow and Steady 6 528.5 121,584 36,104 132,413 38,923 -10,829 -2,819 -8.18% -7.24% 230.05 36260 Ogden-Clearfield, UT Up and Coming 8 344.3 100,734 30,599 101,244 30,514 -510 85 -0.50% 0.28% 292.61 36740 Orlando-Kissimmee-Sanford, FL Growth Hub 4 363.0 529,655 155,745 616,011 180,901 -86,356 -25,155 -14.02% -13.91% 1,459.14 37980 Philadelphia-Camden-Wilmington, PA-NJ-DE-MD Stable Metropolis 2 4,454.0 4,093,431 1,307,346 4,419,212 1,384,429 -325,781 -77,082 -7.37% -5.57% 919.05 38860 Portland-South Portland, ME Up and Coming 1 673.1 323,510 101,697 362,079 112,432 -38,569 -10,735 -10.65% -9.55% 480.66 38900 Portland-Vancouver-Hillsboro, OR-WA Growth Hub 10 1,037.0 798,032 238,969 745,560 223,106 52,472 15,863 7.04% 7.11% 769.54 40380 Rochester, NY Slow and Steady 2 241.1 103,846 30,872 111,441 33,137 -7,595 -2,265 -6.82% -6.84% 430.79 40900 Sacramento--Roseville--Arden-Arcade, CA Growth Hub 9 1,220.4 4,834,620 1,425,107 6,373,529 1,820,306 -1,538,909 -395,199 -24.15% -21.71% 3,961.47 41860 San Francisco, CA Slow and Steady 9 922.3 1,990,064 585,745 2,085,480 583,533 -95,416 2,212 -4.58% 0.38% 2,157.79 42660 Seattle, WA Growth Hub 10 658.3 1,257,588 371,372 1,388,196 404,562 -130,608 -33,190 -9.41% -8.20% 1,910.41 43340 Shreveport-Bossier City, LA Industrial Legacy 6 729.6 310,077 91,315 340,340 100,024 -30,263 -8,709 -8.89% -8.71% 424.98 44700 Stockton-Lodi, CA Industrial Legacy 9 58.0 4,480 1,218 5,060 1,349 -580 -131 -11.47% -9.74% 77.30 44780 Sturgis, MI Industrial Legacy 5 250.3 34,246 9,909 38,412 11,183 -4,166 -1,274 -10.84% -11.40% 136.80 48620 Wichita, KS Industrial Legacy 7 844.1 14,305 4,295 15,572 4,681 -1,268 -386 -8.14% -8.25% 16.95 48980 Wilson, NC Industrial Legacy 4 6.9 0 0 0 0 0 0 #DIV/0! #DIV/0! 0.00 49180 Winston-Salem, NC Up and Coming 4 87.9 2,217 643 2,500 729 -283 -86 -11.34% -11.79% 25.22

E-20 Page 1 of 3 Table E-11. Employment VMT Results, Primary Phase (by Metro/CBSA)

Total VMT generated by new (allocated) jobs per redeveloped brownfield acre (mi/ac) Total VMT per job generated by new (allocated) jobs (mi/job) nfields Trend growth Difference Percent Difference Brownfields Trend growth Difference Percent

CBSA NAME Base Aggressive Base Aggressive Base Aggressive Base Aggressive Base Aggressive Base Aggressive Base Aggressive 10420 Akron, OH 85.28 356.59 88.61 -13.54 -3.33 -3.80% -3.76% 23.68 23.53 24.62 24.44 -0.93 -0.92 -3.80% 10580 Albany-Schenectady-Troy, NY 348.46 1,345.39 395.58 -174.59 -47.11 -12.98% -11.91% 22.08 22.17 25.37 25.17 -3.29 -3.00 -12.98% 10740 Albuquerque, NM 494.50 1,834.50 548.73 -185.18 -54.22 -10.09% -9.88% 23.50 23.60 26.13 26.19 -2.64 -2.59 -10.09% 10900 Allentown-Bethlehem-Easton, PA-NJ 77.83 305.06 89.22 -38.66 -11.38 -12.67% -12.76% 20.54 20.55 23.52 23.56 -2.98 -3.01 -12.67% 11460 Ann Arbor, MI 204.80 731.97 211.70 -40.89 -6.90 -5.59% -3.26% 20.98 21.02 22.22 21.73 -1.24 -0.71 -5.59% 12060 Atlanta, GA 204.65 742.26 218.58 -46.79 -13.93 -6.30% -6.37% 24.92 24.92 26.60 26.62 -1.68 -1.70 -6.30% 12420 Austin-Round Rock, TX 300.56 1,082.14 320.11 -65.99 -19.55 -6.10% -6.11% 23.76 23.78 25.31 25.33 -1.54 -1.55 -6.10% 12580 Baltimore, MD 270.74 1,004.01 301.37 -111.64 -30.63 -11.12% -10.16% 20.46 20.61 23.02 22.94 -2.56 -2.33 -11.12% 12620 Bangor, ME 45.45 134.67 48.04 -11.54 -2.59 -8.57% -5.39% 24.76 25.53 27.08 26.98 -2.32 -1.45 -8.57% 13660 Big Rapids, MI 20.76 61.77 21.17 -1.07 -0.41 -1.74% -1.94% 25.55 25.90 26.00 26.41 -0.45 -0.51 -1.74% 13740 Billings, MT 1,070.38 3,807.08 1,108.75 -138.70 -38.36 -3.64% -3.46% 24.66 24.67 25.59 25.55 -0.93 -0.88 -3.64% 14260 Boise City, ID 430.94 1,606.28 476.00 -169.39 -45.06 -10.55% -9.47% 25.08 25.29 28.04 27.94 -2.96 -2.64 -10.55% 14500 Boulder, CO 162.43 631.62 184.80 -75.92 -22.37 -12.02% -12.11% 19.45 19.45 22.11 22.13 -2.66 -2.68 -12.02% 15540 Burlington-South Burlington, VT 388.86 1,396.87 421.12 -132.30 -32.26 -9.47% -7.66% 23.58 23.78 26.05 25.75 -2.47 -1.97 -9.47% 16860 Chattanooga, TN-GA 170.41 613.38 180.77 -38.54 -10.36 -6.28% -5.73% 22.81 22.85 24.34 24.24 -1.53 -1.39 -6.28% 19100 Dallas, TX 341.35 1,209.62 358.26 -56.87 -16.91 -4.70% -4.72% 25.72 25.74 26.99 27.02 -1.27 -1.28 -4.70% 19380 Dayton, OH 21.21 81.99 24.09 -9.39 -2.87 -11.45% -11.92% 21.21 21.22 23.96 24.09 -2.74 -2.87 -11.45% 19780 Des Moines-West Des Moines, IA 3.11 7.76 2.95 0.40 0.16 5.14% 5.28% 28.05 28.86 26.68 27.41 1.37 1.45 5.14% 20500 Durham-Chapel Hill, NC 1,584.81 6,447.31 1,884.97 -1058.64 -300.16 -16.42% -15.92% 21.09 21.09 25.23 25.08 -4.14 -3.99 -16.42% 23140 Frankfort, IN 27.68 87.39 30.20 -7.68 -2.52 -8.79% -8.34% 21.50 22.40 23.57 24.43 -2.07 -2.04 -8.79% 23300 Freeport, IL 304.91 1,334.16 392.66 -291.23 -87.75 -21.83% -22.35% 20.02 20.02 25.60 25.78 -5.59 -5.76 -21.83% 24340 Grand Rapids-Wyoming, MI 128.09 525.54 155.10 -93.62 -27.01 -17.81% -17.41% 20.70 20.74 25.18 25.12 -4.49 -4.37 -17.81% 24500 Great Falls, MT 187.78 657.44 192.23 -16.83 -4.46 -2.56% -2.32% 23.71 23.73 24.33 24.29 -0.62 -0.56 -2.56% 25540 Hartford, CT 461.42 1,696.45 495.19 -127.35 -33.77 -7.51% -6.82% 21.87 21.88 23.64 23.48 -1.77 -1.60 -7.51% 26980 Iowa City, IA 596.27 2,126.50 613.12 -87.81 -16.85 -4.13% -2.75% 24.60 24.61 25.66 25.30 -1.06 -0.70 -4.13% 27140 Jackson, MS 196.45 656.38 199.41 -11.63 -2.96 -1.77% -1.48% 24.80 24.96 25.24 25.34 -0.45 -0.38 -1.77% 28940 Knoxville, TN 36.65 110.19 37.31 -4.36 -0.66 -3.96% -1.77% 24.55 25.06 25.56 25.51 -1.01 -0.45 -3.96% 29460 Lakeland-Winter Haven, FL 41.05 116.29 36.71 12.59 4.34 10.83% 11.82% 26.58 26.72 24.01 23.91 2.57 2.81 10.71% 31080 Los Angeles, CA 462.38 1,638.66 481.98 -69.79 -19.60 -4.26% -4.07% 20.99 20.99 21.93 21.88 -0.94 -0.89 -4.29% 33340 Milwaukee, WI 223.45 838.81 244.58 -75.37 -21.14 -8.99% -8.64% 21.14 21.15 23.22 23.15 -2.09 -2.00 -8.99% 33460 Minneapolis, MN 835.36 3,062.71 893.34 -238.00 -57.97 -7.77% -6.49% 24.38 24.40 26.44 26.10 -2.05 -1.69 -7.77% 33860 Montgomery, AL 8.47 26.35 12.47 -9.31 -4.00 -35.34% -32.09% 29.55 29.67 26.15 25.95 3.40 3.72 12.98% 34060 Morgantown, WV 15.52 31.17 12.93 5.36 2.59 17.20% 20.07% 27.55 28.10 23.50 23.40 4.04 4.70 17.20% 35300 New Haven-Milford, CT 230.46 847.79 242.86 -65.46 -12.39 -7.72% -5.10% 20.41 20.41 22.12 21.50 -1.71 -1.10 -7.72% 35380 New Orleans, LA 68.31 250.54 73.65 -20.49 -5.33 -8.18% -7.24% 21.69 21.76 23.65 23.48 -1.96 -1.71 -8.28% 36260 Ogden-Clearfield, UT 88.88 294.09 88.64 -1.48 0.25 -0.50% 0.28% 22.86 23.13 22.97 23.06 -0.12 0.06 -0.50% 36740 Orlando-Kissimmee-Sanford, FL 429.06 1,697.05 498.36 -237.90 -69.30 -14.02% -13.91% 20.59 20.60 23.95 23.93 -3.36 -3.33 -14.02% 37980 Philadelphia-Camden-Wilmington, PA-NJ-DE-MD 293.52 992.19 310.83 -73.14 -17.31 -7.37% -5.57% 21.45 21.56 23.16 22.83 -1.71 -1.27 -7.37% 38860 Portland-South Portland, ME 151.10 537.97 167.05 -57.30 -15.95 -10.65% -9.55% 22.62 22.84 25.31 25.25 -2.70 -2.41 -10.65% 38900 Portland-Vancouver-Hillsboro, OR-WA 230.44 718.94 215.14 50.60 15.30 7.04% 7.11% 24.60 24.67 22.99 23.03 1.62 1.64 7.04% 40380 Rochester, NY 128.07 462.29 137.46 -31.51 -9.40 -6.82% -6.84% 23.32 23.37 25.07 25.10 -1.74 -1.73 -6.96% 40900 Sacramento--Roseville--Arden-Arcade, CA 1,167.73 5,222.45 1,491.55 -1260.98 -323.82 -24.15% -21.71% 19.12 19.13 25.20 24.44 -6.08 -5.31 -24.15% 41860 San Francisco, CA 635.11 2,261.25 632.71 -103.46 2.40 -4.58% 0.38% 18.90 18.90 19.81 18.83 -0.91 0.07 -4.58% 42660 Seattle, WA 564.15 2,108.82 614.57 -198.41 -50.42 -9.41% -8.20% 20.67 20.68 22.82 22.53 -2.15 -1.85 -9.41% 43340 Shreveport-Bossier City, LA 125.15 466.46 137.09 -41.48 -11.94 -8.89% -8.71% 23.32 23.32 25.59 25.54 -2.28 -2.22 -8.89% 44700 Stockton-Lodi, CA 21.02 87.32 23.28 -10.02 -2.27 -11.47% -9.74% 21.75 21.75 24.56 24.09 -2.82 -2.35 -11.47% 44780 Sturgis, MI 39.58 153.44 44.67 -16.64 -5.09 -10.84% -11.40% 21.70 21.68 24.34 24.47 -2.64 -2.79 -10.84% 48620 Wichita, KS 5.09 18.45 5.55 -1.50 -0.46 -8.14% -8.25% 23.37 23.47 25.44 25.58 -2.07 -2.11 -8.14% 48980 Wilson, NC 0.00 0.00 0.00 0.00 0.00 #DIV/0! #DIV/0! 0.00 0.00 #DIV/0! 49180 Winston-Salem, NC 7.31 28.45 8.29 -3.22 -0.98 -11.34% -11.79% 22.17 22.17 25.00 25.13 -2.83 -2.96 -11.34%

E-21 Page 2 of 3 Table E-11. Employment VMT Results, Primary Phase (by Metro/CBSA)

Total VMT per job generated by new (allocated) jobs per brownfield acre redeveloped (mi/job/ac) Difference Brownfields Trend growth Difference Percent Difference

CBSA NAME Base Aggressive Base Aggressive Base Aggressive Base Aggressive Base 10420 Akron, OH -3.76% 0.03 0.03 0.03 0.03 0.00 0.00 -3.80% -3.76% 10580 Albany-Schenectady-Troy, NY -11.91% 0.07 0.07 0.08 0.08 -0.01 -0.01 -12.98% -11.91% 10740 Albuquerque, NM -9.88% 0.08 0.08 0.09 0.09 -0.01 -0.01 -10.09% -9.88% 10900 Allentown-Bethlehem-Easton, PA-NJ -12.76% 0.02 0.02 0.03 0.03 0.00 0.00 -12.67% -12.76% 11460 Ann Arbor, MI -3.26% 0.09 0.09 0.10 0.09 -0.01 0.00 -5.59% -3.26% 12060 Atlanta, GA -6.37% 0.02 0.02 0.02 0.02 0.00 0.00 -6.30% -6.37% 12420 Austin-Round Rock, TX -6.11% 0.03 0.03 0.03 0.03 0.00 0.00 -6.10% -6.11% 12580 Baltimore, MD -10.16% 0.02 0.02 0.03 0.03 0.00 0.00 -11.12% -10.16% 12620 Bangor, ME -5.39% 0.04 0.04 0.05 0.05 0.00 0.00 -8.57% -5.39% 13660 Big Rapids, MI -1.94% 0.39 0.39 0.39 0.40 -0.01 -0.01 -1.74% -1.94% 13740 Billings, MT -3.46% 0.30 0.30 0.32 0.32 -0.01 -0.01 -3.64% -3.46% 14260 Boise City, ID -9.47% 0.18 0.18 0.20 0.20 -0.02 -0.02 -10.55% -9.47% 14500 Boulder, CO -12.11% 0.68 0.68 0.77 0.77 -0.09 -0.09 -12.02% -12.11% 15540 Burlington-South Burlington, VT -7.66% 0.14 0.14 0.15 0.15 -0.01 -0.01 -9.47% -7.66% 16860 Chattanooga, TN-GA -5.73% 0.10 0.10 0.11 0.11 -0.01 -0.01 -6.28% -5.73% 19100 Dallas, TX -4.72% 0.02 0.02 0.02 0.02 0.00 0.00 -4.70% -4.72% 19380 Dayton, OH -11.92% 0.17 0.17 0.19 0.20 -0.02 -0.02 -11.45% -11.92% 19780 Des Moines-West Des Moines, IA 5.28% 0.10 0.10 0.10 0.10 0.00 0.01 5.14% 5.28% 20500 Durham-Chapel Hill, NC -15.92% 0.31 0.31 0.37 0.37 -0.06 -0.06 -16.42% -15.92% 23140 Frankfort, IN -8.34% 0.43 0.45 0.47 0.49 -0.04 -0.04 -8.79% -8.34% 23300 Freeport, IL -22.35% 0.39 0.39 0.50 0.51 -0.11 -0.11 -21.83% -22.35% 24340 Grand Rapids-Wyoming, MI -17.41% 0.03 0.03 0.03 0.03 -0.01 -0.01 -17.81% -17.41% 24500 Great Falls, MT -2.32% 0.33 0.33 0.34 0.34 -0.01 -0.01 -2.56% -2.32% 25540 Hartford, CT -6.82% 0.05 0.05 0.06 0.06 0.00 0.00 -7.51% -6.82% 26980 Iowa City, IA -2.75% 0.26 0.26 0.27 0.27 -0.01 -0.01 -4.13% -2.75% 27140 Jackson, MS -1.48% 0.07 0.07 0.08 0.08 0.00 0.00 -1.77% -1.48% 28940 Knoxville, TN -1.77% 0.02 0.02 0.03 0.03 0.00 0.00 -3.96% -1.77% 29460 Lakeland-Winter Haven, FL 11.76% 0.02 0.02 0.02 0.02 0.00 0.00 10.71% 11.76% 31080 Los Angeles, CA -4.08% 0.01 0.01 0.01 0.01 0.00 0.00 -4.29% -4.08% 33340 Milwaukee, WI -8.64% 0.03 0.03 0.03 0.03 0.00 0.00 -8.99% -8.64% 33460 Minneapolis, MN -6.49% 0.02 0.02 0.02 0.02 0.00 0.00 -7.77% -6.49% 33860 Montgomery, AL 14.34% 0.09 0.09 0.08 0.08 0.01 0.01 12.98% 14.34% 34060 Morgantown, WV 20.07% 0.04 0.05 0.04 0.04 0.01 0.01 17.20% 20.07% 35300 New Haven-Milford, CT -5.10% 0.07 0.07 0.07 0.07 -0.01 0.00 -7.72% -5.10% 35380 New Orleans, LA -7.30% 0.04 0.04 0.04 0.04 0.00 0.00 -8.28% -7.30% 36260 Ogden-Clearfield, UT 0.28% 0.07 0.07 0.07 0.07 0.00 0.00 -0.50% 0.28% 36740 Orlando-Kissimmee-Sanford, FL -13.91% 0.06 0.06 0.07 0.07 -0.01 -0.01 -14.02% -13.91% 37980 Philadelphia-Camden-Wilmington, PA-NJ-DE-MD -5.57% 0.00 0.00 0.01 0.01 0.00 0.00 -7.37% -5.57% 38860 Portland-South Portland, ME -9.55% 0.03 0.03 0.04 0.04 0.00 0.00 -10.65% -9.55% 38900 Portland-Vancouver-Hillsboro, OR-WA 7.11% 0.02 0.02 0.02 0.02 0.00 0.00 7.04% 7.11% 40380 Rochester, NY -6.91% 0.10 0.10 0.10 0.10 -0.01 -0.01 -6.96% -6.91% 40900 Sacramento--Roseville--Arden-Arcade, CA -21.71% 0.02 0.02 0.02 0.02 0.00 0.00 -24.15% -21.71% 41860 San Francisco, CA 0.38% 0.02 0.02 0.02 0.02 0.00 0.00 -4.58% 0.38% 42660 Seattle, WA -8.20% 0.03 0.03 0.03 0.03 0.00 0.00 -9.41% -8.20% 43340 Shreveport-Bossier City, LA -8.71% 0.03 0.03 0.04 0.04 0.00 0.00 -8.89% -8.71% 44700 Stockton-Lodi, CA -9.74% 0.38 0.38 0.42 0.42 -0.05 -0.04 -11.47% -9.74% 44780 Sturgis, MI -11.40% 0.09 0.09 0.10 0.10 -0.01 -0.01 -10.84% -11.40% 48620 Wichita, KS -8.25% 0.03 0.03 0.03 0.03 0.00 0.00 -8.14% -8.25% 48980 Wilson, NC #DIV/0! 0.00 0.00 0.00 0.00 0.00 0.00 #DIV/0! #DIV/0! 49180 Winston-Salem, NC -11.79% 0.25 0.25 0.28 0.29 -0.03 -0.03 -11.34% -11.79%

E-22 Page 3 of 3 Table E-12. Employment VMT Results, Secondary Phase (by Metro/CBSA)

Total VMT generated by new (allocated) jobs (mi) Total VMT gen Brownfields Trend growth Difference Percent Difference Brownfields BF CBSA NAME GROWTH PROFILE EPA REGION ACREAGE (ac) Aggressive Base Aggressive Base Aggressive Base Aggressive Base Aggressive Base 10420 Akron, OH Industrial Legacy 5 743.5 1,291,268 1,489,460 1,292,843 1,491,686 -1,575 -2,226 -0.12% -0.15% 1,736.86 2,003.44 10580 Albany-Schenectady-Troy, NY Industrial Legacy 2 322.7 2,734,948 3,055,439 2,756,000 3,063,855 -21,052 -8,416 -0.76% -0.27% 8,476.25 9,469.53 10740 Albuquerque, NM Up and Coming 3 298.3 3,561,869 3,957,310 3,577,595 3,963,026 -15,727 -5,716 -0.44% -0.14% 11,941.76 13,267.54 10900 Allentown-Bethlehem-Easton, PA-NJ Industrial Legacy 5 837.8 2,144,816 2,331,727 2,153,395 2,335,486 -8,580 -3,758 -0.40% -0.16% 2,560.12 2,783.22 11460 Ann Arbor, MI Industrial Legacy 4 229.0 1,522,600 1,653,518 1,541,190 1,659,744 -18,591 -6,227 -1.21% -0.38% 6,648.91 7,220.60 12060 Atlanta, GA Big and Growing 6 1,582.0 28,238,145 29,090,963 28,269,230 29,106,963 -31,085 -16,000 -0.11% -0.05% 17,849.76 18,388.84 12420 Austin-Round Rock, TX Growth Hub 6 908.4 12,885,023 13,620,960 12,949,597 13,641,236 -64,574 -20,276 -0.50% -0.15% 14,184.31 14,994.45 12580 Baltimore, MD Slow and Steady 3 888.9 9,601,376 10,260,793 9,653,062 10,283,586 -51,686 -22,793 -0.54% -0.22% 10,800.93 11,542.73 12620 Bangor, ME Industrial Legacy 1 583.1 568,653 619,860 569,142 619,823 -489 37 -0.09% 0.01% 975.19 1,063.01 13660 Big Rapids, MI Up and Coming 5 66.1 109,564 112,287 109,619 112,205 -55 82 -0.05% 0.07% 1,657.55 1,698.75 13740 Billings, MT Up and Coming 8 80.9 357,371 578,249 361,830 580,037 -4,459 -1,788 -1.23% -0.31% 4,415.26 7,144.17 14260 Boise City, ID Up and Coming 10 142.8 3,240,824 3,408,518 3,251,050 3,412,563 -10,227 -4,045 -0.31% -0.12% 22,696.43 23,870.85 14500 Boulder, CO Up and Coming 8 28.7 2,228,669 2,242,801 2,235,281 2,267,478 -6,611 -24,678 -0.30% -1.09% 77,545.91 78,037.60 15540 Burlington-South Burlington, VT Up and Coming 1 171.0 1,050,445 1,222,407 1,057,160 1,222,965 -6,715 -558 -0.64% -0.05% 6,142.24 7,147.74 16860 Chattanooga, TN-GA Industrial Legacy 4 228.6 1,386,552 1,487,183 1,389,625 1,488,672 -3,072 -1,489 -0.22% -0.10% 6,064.35 6,504.47 19100 Dallas, TX Big and Growing 6 1,253.9 46,611,062 47,762,771 46,739,381 47,905,661 -128,318 -142,890 -0.27% -0.30% 37,171.68 38,090.16 19380 Dayton, OH Industrial Legacy 5 123.0 1,520,829 1,529,849 1,524,548 1,540,646 -3,719 -10,797 -0.24% -0.70% 12,363.46 12,436.79 19780 Des Moines-West Des Moines, IA Up and Coming 7 278.5 3,407,881 3,410,784 3,412,033 3,417,592 -4,152 -6,808 -0.12% -0.20% 12,234.80 12,245.22 20500 Durham-Chapel Hill, NC Up and Coming 4 68.2 3,328,022 3,656,648 3,353,690 3,665,493 -25,668 -8,845 -0.77% -0.24% 48,769.37 53,585.11 23140 Frankfort, IN Industrial Legacy 5 50.2 38,516 41,376 38,645 41,875 -129 -499 -0.33% -1.19% 767.71 824.72 23300 Freeport, IL Industrial Legacy 5 51.0 2,322 50,419 2,272 50,874 50 -455 2.19% -0.89% 45.52 988.42 24340 Grand Rapids-Wyoming, MI Up and Coming 5 801.5 3,593,206 3,903,493 3,611,759 3,910,327 -18,553 -6,834 -0.51% -0.17% 4,483.21 4,870.36 24500 Great Falls, MT Industrial Legacy 8 71.4 84,430 117,728 84,793 117,524 -363 204 -0.43% 0.17% 1,182.66 1,649.09 25540 Hartford, CT Slow and Steady 1 419.7 3,152,606 3,673,005 3,178,263 3,682,071 -25,657 -9,066 -0.81% -0.25% 7,512.11 8,752.13 26980 Iowa City, IA Up and Coming 7 95.4 683,911 835,329 696,410 839,541 -12,498 -4,212 -1.79% -0.50% 7,168.13 8,755.15 27140 Jackson, MS Industrial Legacy 4 336.6 2,322,888 2,480,805 2,326,392 2,479,314 -3,505 1,491 -0.15% 0.06% 6,901.85 7,371.06 28940 Knoxville, TN Up and Coming 4 1,016.9 3,487,280 3,562,085 3,488,796 3,563,599 -1,516 -1,514 -0.04% -0.04% 3,429.39 3,502.95 29460 Lakeland-Winter Haven, FL Up and Coming 4 1,164.4 1,733,348 1,827,289 1,736,870 1,829,853 -3,522 -2,564 -0.20% -0.14% 1,488.62 1,569.30 31080 Los Angeles, CA Stable Metropolis 9 1,675.8 41,786,942 43,982,609 42,156,494 44,351,224 -369,553 -368,616 -0.88% -0.83% 24,936.11 26,246.37 33340 Milwaukee, WI Slow and Steady 5 755.5 3,945,747 4,422,737 3,984,931 4,441,850 -39,184 -19,114 -0.98% -0.43% 5,222.63 5,853.97 33460 Minneapolis, MN Growth Hub 5 1,133.5 14,430,300 17,049,509 14,681,534 17,148,373 -251,234 -98,864 -1.71% -0.58% 12,731.19 15,042.00 33860 Montgomery, AL Industrial Legacy 4 343.5 1,388,865 1,394,307 1,389,410 1,397,638 -545 -3,331 -0.04% -0.24% 4,043.86 4,059.71 34060 Morgantown, WV Up and Coming 3 613.8 487,988 499,298 488,966 500,233 -978 -935 -0.20% -0.19% 795.07 813.49 35300 New Haven-Milford, CT Industrial Legacy 1 295.0 1,885,769 2,074,530 1,904,152 2,083,349 -18,383 -8,820 -0.97% -0.42% 6,393.52 7,033.50 35380 New Orleans, LA Slow and Steady 6 528.5 3,453,970 3,555,258 3,463,853 3,568,086 -9,883 -12,828 -0.29% -0.36% 6,535.30 6,726.95 36260 Ogden-Clearfield, UT Up and Coming 8 344.3 2,309,873 2,383,736 2,315,919 2,387,013 -6,046 -3,277 -0.26% -0.14% 6,709.68 6,924.23 36740 Orlando-Kissimmee-Sanford, FL Growth Hub 4 363.0 12,066,941 12,513,277 12,085,353 12,521,528 -18,411 -8,251 -0.15% -0.07% 33,243.18 34,472.79 37980 Philadelphia-Camden-Wilmington, PA-NJ-DE-MD Stable Metropolis 2 4,454.0 12,992,513 16,167,086 13,230,549 16,272,713 -238,035 -105,628 -1.80% -0.65% 2,917.06 3,629.81 38860 Portland-South Portland, ME Up and Coming 1 673.1 1,650,406 1,908,815 1,662,844 1,912,567 -12,438 -3,752 -0.75% -0.20% 2,452.13 2,836.07 38900 Portland-Vancouver-Hillsboro, OR-WA Growth Hub 10 1,037.0 9,529,192 10,101,783 9,601,883 10,134,583 -72,692 -32,800 -0.76% -0.32% 9,189.01 9,741.17 40380 Rochester, NY Slow and Steady 2 241.1 2,897,528 2,975,925 2,898,965 2,984,140 -1,437 -8,214 -0.05% -0.28% 12,019.95 12,345.16 40900 Sacramento--Roseville--Arden-Arcade, CA Growth Hub 9 1,220.4 2,618,489 7,217,739 2,821,022 7,382,250 -202,533 -164,511 -7.18% -2.23% 2,145.58 5,914.19 41860 San Francisco, CA Slow and Steady 9 922.3 8,138,992 9,873,384 8,509,898 10,033,075 -370,905 -159,691 -4.36% -1.59% 8,824.96 10,705.52 42660 Seattle, WA Growth Hub 10 658.3 15,038,951 16,129,927 15,192,048 16,181,177 -153,097 -51,250 -1.01% -0.32% 22,845.83 24,503.14 43340 Shreveport-Bossier City, LA Industrial Legacy 6 729.6 1,106,380 1,352,814 1,115,849 1,355,884 -9,469 -3,070 -0.85% -0.23% 1,516.38 1,854.13 44700 Stockton-Lodi, CA Industrial Legacy 9 58.0 1,633,263 1,637,113 1,636,825 1,647,700 -3,563 -10,587 -0.22% -0.64% 28,184.00 28,250.43 44780 Sturgis, MI Industrial Legacy 5 250.3 16,735 44,026 16,714 43,854 20 172 0.12% 0.39% 66.85 175.87 48620 Wichita, KS Industrial Legacy 7 844.1 1,785,954 1,797,720 1,789,158 1,802,143 -3,205 -4,423 -0.18% -0.25% 2,115.91 2,129.85 48980 Wilson, NC Industrial Legacy 4 6.9 205,523 205,354 205,342 202,812 181 2,543 0.09% 1.25% 29,829.18 29,804.68 49180 Winston-Salem, NC Up and Coming 4 87.9 2,650,286 2,651,581 2,654,720 2,662,751 -4,435 -11,170 -0.17% -0.42% 30,158.01 30,172.74

E-23 Page 1 of 3 Table E-12. Employment VMT Results, Secondary Phase (by Metro/CBSA)

nerated by new (allocated) jobs per brownfield acre redeveloped (mi/ac) Total VMT per job generated by new (allocated) jobs (mi/job) Trend growth Difference Percent Difference Brownfields Trend growth Difference Percent Difference

CBSA NAME Aggressive Base Aggressive Base Aggressive Base Aggressive Base Aggressive Base Aggressive Base Aggressive Base 10420 Akron, OH 1,738.98 2,006.44 -2.12 -2.99 -0.12% -0.15% 24.67 24.65 24.70 24.69 -0.03 -0.04 -0.12% -0.15% 10580 Albany-Schenectady-Troy, NY 8,541.50 9,495.62 -65.25 -26.08 -0.76% -0.27% 26.16 26.21 26.37 26.28 -0.20 -0.07 -0.76% -0.27% 10740 Albuquerque, NM 11,994.49 13,286.71 -52.73 -19.16 -0.44% -0.14% 26.39 26.44 26.50 26.48 -0.12 -0.04 -0.44% -0.14% 10900 Allentown-Bethlehem-Easton, PA-NJ 2,570.36 2,787.71 -10.24 -4.49 -0.40% -0.16% 23.98 24.01 24.08 24.05 -0.10 -0.04 -0.40% -0.16% 11460 Ann Arbor, MI 6,730.09 7,247.79 -81.18 -27.19 -1.21% -0.38% 23.85 23.92 24.15 24.01 -0.29 -0.09 -1.21% -0.38% 12060 Atlanta, GA 17,869.41 18,398.96 -19.65 -10.11 -0.11% -0.05% 27.07 27.08 27.10 27.09 -0.03 -0.01 -0.11% -0.05% 12420 Austin-Round Rock, TX 14,255.39 15,016.77 -71.09 -22.32 -0.50% -0.15% 26.01 26.06 26.14 26.10 -0.13 -0.04 -0.50% -0.15% 12580 Baltimore, MD 10,859.07 11,568.37 -58.14 -25.64 -0.54% -0.22% 23.77 23.80 23.90 23.86 -0.13 -0.05 -0.54% -0.22% 12620 Bangor, ME 976.03 1,062.94 -0.84 0.06 -0.09% 0.01% 27.13 27.16 27.15 27.16 -0.02 0.00 -0.09% 0.01% 13660 Big Rapids, MI 1,658.38 1,697.50 -0.83 1.24 -0.05% 0.07% 26.01 26.02 26.03 26.00 -0.01 0.02 -0.05% 0.07% 13740 Billings, MT 4,470.35 7,166.25 -55.09 -22.09 -1.23% -0.31% 25.59 25.71 25.91 25.79 -0.32 -0.08 -1.23% -0.31% 14260 Boise City, ID 22,768.05 23,899.18 -71.62 -28.33 -0.31% -0.12% 28.29 28.33 28.38 28.37 -0.09 -0.03 -0.31% -0.12% 14500 Boulder, CO 77,775.95 78,896.26 -230.04 -858.66 -0.30% -1.09% 23.01 23.02 23.08 23.27 -0.07 -0.25 -0.30% -1.09% 15540 Burlington-South Burlington, VT 6,181.50 7,151.00 -39.27 -3.26 -0.64% -0.05% 26.45 26.52 26.62 26.54 -0.17 -0.01 -0.64% -0.05% 16860 Chattanooga, TN-GA 6,077.78 6,510.99 -13.44 -6.51 -0.22% -0.10% 24.60 24.61 24.65 24.64 -0.05 -0.02 -0.22% -0.10% 19100 Dallas, TX 37,274.02 38,204.11 -102.33 -113.95 -0.27% -0.30% 27.96 27.99 28.04 28.07 -0.08 -0.08 -0.27% -0.30% 19380 Dayton, OH 12,393.69 12,524.56 -30.23 -87.77 -0.24% -0.70% 24.52 24.55 24.58 24.72 -0.06 -0.17 -0.24% -0.70% 19780 Des Moines-West Des Moines, IA 12,249.70 12,269.66 -14.90 -24.44 -0.12% -0.20% 27.36 27.37 27.39 27.43 -0.03 -0.05 -0.12% -0.20% 20500 Durham-Chapel Hill, NC 49,145.51 53,714.73 -376.14 -129.62 -0.77% -0.24% 26.19 26.24 26.39 26.30 -0.20 -0.06 -0.77% -0.24% 23140 Frankfort, IN 770.28 834.66 -2.57 -9.94 -0.33% -1.19% 24.01 23.94 24.09 24.23 -0.08 -0.29 -0.33% -1.19% 23300 Freeport, IL 44.55 997.34 0.98 -8.91 2.19% -0.89% 26.09 25.59 25.53 25.82 0.56 -0.23 2.19% -0.89% 24340 Grand Rapids-Wyoming, MI 4,506.36 4,878.88 -23.15 -8.53 -0.51% -0.17% 25.64 25.70 25.78 25.74 -0.13 -0.04 -0.51% -0.17% 24500 Great Falls, MT 1,187.74 1,646.22 -5.08 2.86 -0.43% 0.17% 24.26 24.30 24.37 24.26 -0.10 0.04 -0.43% 0.17% 25540 Hartford, CT 7,573.24 8,773.73 -61.14 -21.60 -0.81% -0.25% 24.09 24.15 24.29 24.20 -0.20 -0.06 -0.81% -0.25% 26980 Iowa City, IA 7,299.13 8,799.30 -131.00 -44.15 -1.79% -0.50% 26.26 26.41 26.74 26.54 -0.48 -0.13 -1.79% -0.50% 27140 Jackson, MS 6,912.27 7,366.63 -10.41 4.43 -0.15% 0.06% 25.53 25.55 25.57 25.54 -0.04 0.02 -0.15% 0.06% 28940 Knoxville, TN 3,430.88 3,504.44 -1.49 -1.49 -0.04% -0.04% 25.72 25.72 25.73 25.73 -0.01 -0.01 -0.04% -0.04% 29460 Lakeland-Winter Haven, FL 1,491.64 1,571.50 -3.02 -2.20 -0.20% -0.14% 24.32 24.32 24.36 24.36 -0.05 -0.03 -0.20% -0.14% 31080 Los Angeles, CA 25,156.64 26,466.33 -220.53 -219.97 -0.88% -0.83% 23.14 23.22 23.35 23.42 -0.21 -0.20 -0.89% -0.84% 33340 Milwaukee, WI 5,274.49 5,879.27 -51.86 -25.30 -0.98% -0.43% 23.90 23.99 24.14 24.09 -0.24 -0.10 -0.98% -0.43% 33460 Minneapolis, MN 12,952.85 15,129.23 -221.65 -87.22 -1.71% -0.58% 27.28 27.43 27.75 27.59 -0.47 -0.16 -1.71% -0.58% 33860 Montgomery, AL 4,045.45 4,069.41 -1.59 -9.70 -0.04% -0.24% 26.32 26.34 26.33 26.40 -0.01 -0.06 -0.04% -0.24% 34060 Morgantown, WV 796.66 815.02 -1.59 -1.52 -0.20% -0.19% 24.31 24.30 24.36 24.35 -0.05 -0.05 -0.20% -0.19% 35300 New Haven-Milford, CT 6,455.85 7,063.40 -62.33 -29.90 -0.97% -0.42% 23.20 23.24 23.43 23.34 -0.23 -0.10 -0.97% -0.42% 35380 New Orleans, LA 6,554.00 6,751.22 -18.70 -24.27 -0.29% -0.36% 24.35 24.39 24.42 24.47 -0.07 -0.09 -0.28% -0.36% 36260 Ogden-Clearfield, UT 6,727.24 6,933.75 -17.56 -9.52 -0.26% -0.14% 23.57 23.58 23.63 23.62 -0.06 -0.03 -0.26% -0.14% 36740 Orlando-Kissimmee-Sanford, FL 33,293.90 34,495.52 -50.72 -22.73 -0.15% -0.07% 24.22 24.23 24.26 24.25 -0.04 -0.02 -0.15% -0.07% 37980 Philadelphia-Camden-Wilmington, PA-NJ-DE-MD 2,970.50 3,653.52 -53.44 -23.72 -1.80% -0.65% 23.69 23.82 24.12 23.98 -0.43 -0.16 -1.80% -0.65% 38860 Portland-South Portland, ME 2,470.61 2,841.64 -18.48 -5.57 -0.75% -0.20% 25.88 25.93 26.08 25.98 -0.20 -0.05 -0.75% -0.20% 38900 Portland-Vancouver-Hillsboro, OR-WA 9,259.11 9,772.79 -70.10 -31.63 -0.76% -0.32% 24.12 24.18 24.31 24.26 -0.18 -0.08 -0.76% -0.32% 40380 Rochester, NY 12,025.91 12,379.24 -5.96 -34.08 -0.05% -0.28% 25.37 25.36 25.38 25.43 -0.01 -0.07 -0.05% -0.28% 40900 Sacramento--Roseville--Arden-Arcade, CA 2,311.54 6,048.99 -165.95 -134.80 -7.18% -2.23% 24.61 25.34 26.52 25.92 -1.90 -0.58 -7.18% -2.23% 41860 San Francisco, CA 9,227.12 10,878.67 -402.17 -173.15 -4.36% -1.59% 21.34 21.67 22.31 22.02 -0.97 -0.35 -4.36% -1.59% 42660 Seattle, WA 23,078.40 24,580.99 -232.57 -77.85 -1.01% -0.32% 24.32 24.39 24.56 24.47 -0.25 -0.08 -1.01% -0.32% 43340 Shreveport-Bossier City, LA 1,529.36 1,858.34 -12.98 -4.21 -0.85% -0.23% 25.79 25.88 26.01 25.94 -0.22 -0.06 -0.85% -0.23% 44700 Stockton-Lodi, CA 28,245.48 28,433.13 -61.48 -182.70 -0.22% -0.64% 24.59 24.60 24.65 24.75 -0.05 -0.16 -0.22% -0.64% 44780 Sturgis, MI 66.77 175.18 0.08 0.69 0.12% 0.39% 24.54 24.42 24.51 24.32 0.03 0.10 0.12% 0.39% 48620 Wichita, KS 2,119.70 2,135.09 -3.80 -5.24 -0.18% -0.25% 25.45 25.46 25.50 25.53 -0.05 -0.06 -0.18% -0.25% 48980 Wilson, NC 29,802.97 29,435.64 26.21 369.04 0.09% 1.25% 24.86 24.84 24.84 24.54 0.02 0.31 0.09% 1.25% 49180 Winston-Salem, NC 30,208.47 30,299.85 -50.46 -127.11 -0.17% -0.42% 25.22 25.22 25.27 25.33 -0.04 -0.11 -0.17% -0.42%

E-24 Page 2 of 3 Table E-12. Employment VMT Results, Secondary Phase (by Metro/CBSA)

Total VMT per job generated by new (allocated) jobs per brownfield acre redeveloped (mi/job/ac) Brownfields Trend growth Difference Percent Difference

CBSA NAME Aggressive Base Aggressive Base Aggressive Base Aggressive Base 10420 Akron, OH 0.03 0.03 0.03 0.03 0.00 0.00 -0.12% -0.15% 10580 Albany-Schenectady-Troy, NY 0.08 0.08 0.08 0.08 0.00 0.00 -0.76% -0.27% 10740 Albuquerque, NM 0.09 0.09 0.09 0.09 0.00 0.00 -0.44% -0.14% 10900 Allentown-Bethlehem-Easton, PA-NJ 0.03 0.03 0.03 0.03 0.00 0.00 -0.40% -0.16% 11460 Ann Arbor, MI 0.10 0.10 0.11 0.10 0.00 0.00 -1.21% -0.38% 12060 Atlanta, GA 0.02 0.02 0.02 0.02 0.00 0.00 -0.11% -0.05% 12420 Austin-Round Rock, TX 0.03 0.03 0.03 0.03 0.00 0.00 -0.50% -0.15% 12580 Baltimore, MD 0.03 0.03 0.03 0.03 0.00 0.00 -0.54% -0.22% 12620 Bangor, ME 0.05 0.05 0.05 0.05 0.00 0.00 -0.09% 0.01% 13660 Big Rapids, MI 0.39 0.39 0.39 0.39 0.00 0.00 -0.05% 0.07% 13740 Billings, MT 0.32 0.32 0.32 0.32 0.00 0.00 -1.23% -0.31% 14260 Boise City, ID 0.20 0.20 0.20 0.20 0.00 0.00 -0.31% -0.12% 14500 Boulder, CO 0.80 0.80 0.80 0.81 0.00 -0.01 -0.30% -1.09% 15540 Burlington-South Burlington, VT 0.15 0.16 0.16 0.16 0.00 0.00 -0.64% -0.05% 16860 Chattanooga, TN-GA 0.11 0.11 0.11 0.11 0.00 0.00 -0.22% -0.10% 19100 Dallas, TX 0.02 0.02 0.02 0.02 0.00 0.00 -0.27% -0.30% 19380 Dayton, OH 0.20 0.20 0.20 0.20 0.00 0.00 -0.24% -0.70% 19780 Des Moines-West Des Moines, IA 0.10 0.10 0.10 0.10 0.00 0.00 -0.12% -0.20% 20500 Durham-Chapel Hill, NC 0.38 0.38 0.39 0.39 0.00 0.00 -0.77% -0.24% 23140 Frankfort, IN 0.48 0.48 0.48 0.48 0.00 -0.01 -0.33% -1.19% 23300 Freeport, IL 0.51 0.50 0.50 0.51 0.01 0.00 2.19% -0.89% 24340 Grand Rapids-Wyoming, MI 0.03 0.03 0.03 0.03 0.00 0.00 -0.51% -0.17% 24500 Great Falls, MT 0.34 0.34 0.34 0.34 0.00 0.00 -0.43% 0.17% 25540 Hartford, CT 0.06 0.06 0.06 0.06 0.00 0.00 -0.81% -0.25% 26980 Iowa City, IA 0.28 0.28 0.28 0.28 -0.01 0.00 -1.79% -0.50% 27140 Jackson, MS 0.08 0.08 0.08 0.08 0.00 0.00 -0.15% 0.06% 28940 Knoxville, TN 0.03 0.03 0.03 0.03 0.00 0.00 -0.04% -0.04% 29460 Lakeland-Winter Haven, FL 0.02 0.02 0.02 0.02 0.00 0.00 -0.20% -0.14% 31080 Los Angeles, CA 0.01 0.01 0.01 0.01 0.00 0.00 -0.89% -0.84% 33340 Milwaukee, WI 0.03 0.03 0.03 0.03 0.00 0.00 -0.98% -0.43% 33460 Minneapolis, MN 0.02 0.02 0.02 0.02 0.00 0.00 -1.71% -0.58% 33860 Montgomery, AL 0.08 0.08 0.08 0.08 0.00 0.00 -0.04% -0.24% 34060 Morgantown, WV 0.04 0.04 0.04 0.04 0.00 0.00 -0.20% -0.19% 35300 New Haven-Milford, CT 0.08 0.08 0.08 0.08 0.00 0.00 -0.97% -0.42% 35380 New Orleans, LA 0.05 0.05 0.05 0.05 0.00 0.00 -0.28% -0.36% 36260 Ogden-Clearfield, UT 0.07 0.07 0.07 0.07 0.00 0.00 -0.26% -0.14% 36740 Orlando-Kissimmee-Sanford, FL 0.07 0.07 0.07 0.07 0.00 0.00 -0.15% -0.07% 37980 Philadelphia-Camden-Wilmington, PA-NJ-DE-MD 0.01 0.01 0.01 0.01 0.00 0.00 -1.80% -0.65% 38860 Portland-South Portland, ME 0.04 0.04 0.04 0.04 0.00 0.00 -0.75% -0.20% 38900 Portland-Vancouver-Hillsboro, OR-WA 0.02 0.02 0.02 0.02 0.00 0.00 -0.76% -0.32% 40380 Rochester, NY 0.11 0.11 0.11 0.11 0.00 0.00 -0.05% -0.28% 40900 Sacramento--Roseville--Arden-Arcade, CA 0.02 0.02 0.02 0.02 0.00 0.00 -7.18% -2.23% 41860 San Francisco, CA 0.02 0.02 0.02 0.02 0.00 0.00 -4.36% -1.59% 42660 Seattle, WA 0.04 0.04 0.04 0.04 0.00 0.00 -1.01% -0.32% 43340 Shreveport-Bossier City, LA 0.04 0.04 0.04 0.04 0.00 0.00 -0.85% -0.23% 44700 Stockton-Lodi, CA 0.42 0.42 0.43 0.43 0.00 0.00 -0.22% -0.64% 44780 Sturgis, MI 0.10 0.10 0.10 0.10 0.00 0.00 0.12% 0.39% 48620 Wichita, KS 0.03 0.03 0.03 0.03 0.00 0.00 -0.18% -0.25% 48980 Wilson, NC 3.61 3.61 3.61 3.56 0.00 0.04 0.09% 1.25% 49180 Winston-Salem, NC 0.29 0.29 0.29 0.29 0.00 0.00 -0.17% -0.42%

E-25 Page 3 of 3 Table E-13. Employment VMT Results, Cumulative (by Metro/CBSA) Total VMT generated by new (allocated) jobs (mi) Brownfields Trend growth Difference Percent Difference BF ACREAGE CBSA NAME GROWTH PROFILE EPA REGION (ac) Aggressive Base Aggressive Base Aggressive Base Aggressive Base 10420 Akron, OH Industrial Legacy 5 743.5 1,546,309 1,552,860 1,557,949 1,557,562 -11,640 -4,701 -0.75% -0.30% 10580 Albany-Schenectady-Troy, NY Industrial Legacy 2 322.7 3,112,718 3,167,874 3,190,104 3,191,493 -77,386 -23,618 -2.43% -0.74% 10740 Albuquerque, NM Up and Coming 3 298.3 4,053,814 4,104,806 4,124,773 4,126,695 -70,959 -21,889 -1.72% -0.53% 10900 Allentown-Bethlehem-Easton, PA-NJ Industrial Legacy 5 837.8 2,368,005 2,396,935 2,408,970 2,410,228 -40,964 -13,294 -1.70% -0.55% 11460 Ann Arbor, MI Industrial Legacy 4 229.0 1,680,859 1,700,417 1,708,812 1,708,224 -27,954 -7,807 -1.64% -0.46% 12060 Atlanta, GA Big and Growing 6 1,582.0 29,338,373 29,414,712 29,443,474 29,452,754 -105,102 -38,042 -0.36% -0.13% 12420 Austin-Round Rock, TX Growth Hub 6 908.4 13,808,092 13,893,990 13,932,615 13,932,022 -124,523 -38,032 -0.89% -0.27% 12580 Baltimore, MD Slow and Steady 3 888.9 10,394,640 10,501,467 10,545,564 10,551,486 -150,924 -50,019 -1.43% -0.47% 12620 Bangor, ME Industrial Legacy 1 583.1 640,455 646,361 647,672 647,834 -7,217 -1,472 -1.11% -0.23% 13660 Big Rapids, MI Up and Coming 5 66.1 113,576 113,660 113,702 113,605 -126 55 -0.11% 0.05% 13740 Billings, MT Up and Coming 8 80.9 654,289 664,886 669,975 669,779 -15,685 -4,893 -2.34% -0.73% 14260 Boise City, ID Up and Coming 10 142.8 3,445,999 3,470,052 3,480,412 3,480,531 -34,413 -10,479 -0.99% -0.30% 14500 Boulder, CO Up and Coming 8 28.7 2,244,640 2,247,469 2,253,433 2,272,790 -8,793 -25,321 -0.39% -1.11% 15540 Burlington-South Burlington, VT Up and Coming 1 171.0 1,266,711 1,288,909 1,296,052 1,294,985 -29,341 -6,075 -2.26% -0.47% 16860 Chattanooga, TN-GA Industrial Legacy 4 228.6 1,517,983 1,526,145 1,529,867 1,530,004 -11,884 -3,859 -0.78% -0.25% 19100 Dallas, TX Big and Growing 6 1,253.9 48,056,544 48,190,801 48,256,172 48,354,893 -199,629 -164,092 -0.41% -0.34% 19380 Dayton, OH Industrial Legacy 5 123.0 1,529,760 1,532,459 1,534,633 1,543,609 -4,874 -11,150 -0.32% -0.72% 19780 Des Moines-West Des Moines, IA Up and Coming 7 278.5 3,410,153 3,411,650 3,414,194 3,418,414 -4,041 -6,764 -0.12% -0.20% 20500 Durham-Chapel Hill, NC Up and Coming 4 68.2 3,695,744 3,764,796 3,793,654 3,794,124 -97,910 -29,328 -2.58% -0.77% 23140 Frankfort, IN Industrial Legacy 5 50.2 42,515 42,765 43,030 43,390 -514 -625 -1.20% -1.44% 23300 Freeport, IL Industrial Legacy 5 51.0 55,522 65,973 70,328 70,903 -14,806 -4,931 -21.05% -6.95% 24340 Grand Rapids-Wyoming, MI Up and Coming 5 801.5 3,939,379 4,006,153 4,032,966 4,034,634 -93,587 -28,481 -2.32% -0.71% 24500 Great Falls, MT Industrial Legacy 8 71.4 130,163 131,134 131,727 131,248 -1,565 -114 -1.19% -0.09% 25540 Hartford, CT Slow and Steady 1 419.7 3,811,109 3,866,650 3,890,210 3,889,888 -79,102 -23,238 -2.03% -0.60% 26980 Iowa City, IA Up and Coming 7 95.4 878,423 892,219 899,299 898,039 -20,876 -5,820 -2.32% -0.65% 27140 Jackson, MS Industrial Legacy 4 336.6 2,539,882 2,546,922 2,547,303 2,546,427 -7,421 495 -0.29% 0.02% 28940 Knoxville, TN Up and Coming 4 1,016.9 3,594,895 3,599,350 3,600,848 3,601,538 -5,953 -2,188 -0.17% -0.06% 29460 Lakeland-Winter Haven, FL Up and Coming 4 1,164.4 1,883,412 1,875,090 1,872,274 1,872,601 11,138 2,489 0.59% 0.13% 31080 Los Angeles, CA Stable Metropolis 9 1,675.8 44,416,003 44,757,439 44,902,502 45,158,903 -486,499 -401,463 -1.08% -0.89% 33340 Milwaukee, WI Slow and Steady 5 755.5 4,522,528 4,591,552 4,618,658 4,626,635 -96,130 -35,082 -2.08% -0.76% 33460 Minneapolis, MN Growth Hub 5 1,133.5 17,631,994 17,996,358 18,152,988 18,160,934 -520,994 -164,576 -2.87% -0.91% 33860 Montgomery, AL Industrial Legacy 4 343.5 1,394,715 1,397,215 1,398,458 1,401,920 -3,743 -4,705 -0.27% -0.34% 34060 Morgantown, WV Up and Coming 3 613.8 510,410 508,825 508,098 508,168 2,312 657 0.46% 0.13% 35300 New Haven-Milford, CT Industrial Legacy 1 295.0 2,116,519 2,142,505 2,154,209 2,154,980 -37,690 -12,475 -1.75% -0.58% 35380 New Orleans, LA Slow and Steady 6 528.5 3,575,554 3,591,362 3,596,266 3,607,009 -20,712 -15,647 -0.58% -0.43% 36260 Ogden-Clearfield, UT Up and Coming 8 344.3 2,410,607 2,414,335 2,417,163 2,417,527 -6,555 -3,192 -0.27% -0.13% 36740 Orlando-Kissimmee-Sanford, FL Growth Hub 4 363.0 12,596,596 12,669,022 12,701,364 12,702,429 -104,768 -33,407 -0.82% -0.26% 37980 Philadelphia-Camden-Wilmington, PA-NJ-DE-MD Stable Metropolis 2 4,454.0 17,085,944 17,474,432 17,649,761 17,657,142 -563,817 -182,710 -3.19% -1.03% 38860 Portland-South Portland, ME Up and Coming 1 673.1 1,973,917 2,010,511 2,024,923 2,024,999 -51,007 -14,487 -2.52% -0.72% 38900 Portland-Vancouver-Hillsboro, OR-WA Growth Hub 10 1,037.0 10,327,224 10,340,752 10,347,443 10,357,689 -20,220 -16,937 -0.20% -0.16% 40380 Rochester, NY Slow and Steady 2 241.1 3,001,374 3,006,797 3,010,405 3,017,277 -9,031 -10,480 -0.30% -0.35% 40900 Sacramento--Roseville--Arden-Arcade, CA Growth Hub 9 1,220.4 7,453,109 8,642,845 9,194,551 9,202,556 -1,741,442 -559,710 -18.94% -6.08% 41860 San Francisco, CA Slow and Steady 9 922.3 10,129,056 10,459,129 10,595,377 10,616,608 -466,321 -157,479 -4.40% -1.48% 42660 Seattle, WA Growth Hub 10 658.3 16,296,539 16,501,299 16,580,244 16,585,739 -283,705 -84,440 -1.71% -0.51% 43340 Shreveport-Bossier City, LA Industrial Legacy 6 729.6 1,416,457 1,444,129 1,456,189 1,455,908 -39,732 -11,779 -2.73% -0.81% 44700 Stockton-Lodi, CA Industrial Legacy 9 58.0 1,637,742 1,638,330 1,641,886 1,649,049 -4,143 -10,719 -0.25% -0.65% 44780 Sturgis, MI Industrial Legacy 5 250.3 50,980 53,935 55,126 55,037 -4,145 -1,102 -7.52% -2.00% 48620 Wichita, KS Industrial Legacy 7 844.1 1,800,258 1,802,014 1,804,730 1,806,824 -4,472 -4,809 -0.25% -0.27% 48980 Wilson, NC Industrial Legacy 4 6.9 205,523 205,354 205,342 202,812 181 2,543 0.09% 1.25% 49180 Winston-Salem, NC Up and Coming 4 87.9 2,652,502 2,652,223 2,657,220 2,663,480 -4,718 -11,256 -0.18% -0.42%

E-26 Page 1 of 3 Table E-13. Employment VMT Results, Cumulative (by Metro/CBSA) Total VMT generated by new (allocated) jobs per brownfield acre redeveloped (mi/job) Total VMT per job generated Brownfields Trend growth Difference Percent Difference Brownfields Trend growth

CBSA NAME Aggressive Base Aggressive Base Aggressive Base Aggressive Base Aggressive Base Aggressive Base 10420 Akron, OH 2,079.91 2,088.72 2,095.57 2,095.05 -15.66 -6.32 -0.75% -0.30% 24.50 24.61 24.69 24.68 10580 Albany-Schenectady-Troy, NY 9,647.05 9,818.00 9,886.89 9,891.19 -239.84 -73.20 -2.43% -0.74% 25.59 26.04 26.23 26.24 10740 Albuquerque, NM 13,591.09 13,762.05 13,828.99 13,835.43 -237.90 -73.39 -1.72% -0.53% 26.00 26.33 26.45 26.47 10900 Allentown-Bethlehem-Easton, PA-NJ 2,826.52 2,861.06 2,875.42 2,876.92 -48.90 -15.87 -1.70% -0.55% 23.61 23.90 24.02 24.03 11460 Ann Arbor, MI 7,339.99 7,425.40 7,462.06 7,459.49 -122.07 -34.09 -1.64% -0.46% 23.55 23.82 23.94 23.93 12060 Atlanta, GA 18,545.23 18,593.49 18,611.67 18,617.53 -66.44 -24.05 -0.36% -0.13% 26.98 27.05 27.08 27.08 12420 Austin-Round Rock, TX 15,200.45 15,295.01 15,337.53 15,336.88 -137.08 -41.87 -0.89% -0.27% 25.85 26.01 26.08 26.08 12580 Baltimore, MD 11,693.30 11,813.47 11,863.08 11,869.74 -169.78 -56.27 -1.43% -0.47% 23.48 23.72 23.82 23.83 12620 Bangor, ME 1,098.33 1,108.45 1,110.70 1,110.98 -12.38 -2.53 -1.11% -0.23% 26.84 27.09 27.14 27.15 13660 Big Rapids, MI 1,718.24 1,719.51 1,720.15 1,718.68 -1.90 0.83 -0.11% 0.05% 26.00 26.02 26.02 26.00 13740 Billings, MT 8,083.64 8,214.55 8,277.42 8,275.00 -193.79 -60.45 -2.34% -0.73% 25.16 25.57 25.76 25.75 14260 Boise City, ID 24,133.33 24,301.79 24,374.34 24,375.18 -241.01 -73.39 -0.99% -0.30% 28.08 28.27 28.36 28.36 14500 Boulder, CO 78,101.60 78,200.03 78,407.56 79,081.06 -305.96 -881.03 -0.39% -1.11% 22.98 23.01 23.07 23.27 15540 Burlington-South Burlington, VT 7,406.80 7,536.60 7,578.37 7,572.12 -171.56 -35.52 -2.26% -0.47% 25.91 26.37 26.51 26.49 16860 Chattanooga, TN-GA 6,639.18 6,674.88 6,691.16 6,691.76 -51.98 -16.88 -0.78% -0.25% 24.43 24.57 24.63 24.63 19100 Dallas, TX 38,324.44 38,431.50 38,483.64 38,562.37 -159.20 -130.86 -0.41% -0.34% 27.89 27.97 28.00 28.06 19380 Dayton, OH 12,436.06 12,458.00 12,475.68 12,548.64 -39.62 -90.64 -0.32% -0.72% 24.50 24.54 24.57 24.72 19780 Des Moines-West Des Moines, IA 12,242.96 12,248.33 12,257.46 12,272.61 -14.51 -24.28 -0.12% -0.20% 27.36 27.37 27.39 27.43 20500 Durham-Chapel Hill, NC 54,158.04 55,169.92 55,592.82 55,599.70 -1434.78 -429.78 -2.58% -0.77% 25.58 26.05 26.25 26.26 23140 Frankfort, IN 847.42 852.40 857.68 864.86 -10.25 -12.46 -1.20% -1.44% 23.75 23.89 24.04 24.24 23300 Freeport, IL 1,088.45 1,293.33 1,378.71 1,389.99 -290.25 -96.66 -21.05% -6.95% 20.21 24.02 25.60 25.81 24340 Grand Rapids-Wyoming, MI 4,915.13 4,998.44 5,031.90 5,033.98 -116.77 -35.54 -2.32% -0.71% 25.12 25.54 25.71 25.72 24500 Great Falls, MT 1,823.27 1,836.87 1,845.18 1,838.46 -21.92 -1.59 -1.19% -0.09% 24.06 24.24 24.35 24.26 25540 Hartford, CT 9,081.20 9,213.55 9,269.69 9,268.92 -188.49 -55.37 -2.03% -0.60% 23.68 24.02 24.17 24.16 26980 Iowa City, IA 9,206.82 9,351.42 9,425.63 9,412.42 -218.81 -61.00 -2.32% -0.65% 25.88 26.28 26.49 26.45 27140 Jackson, MS 7,546.60 7,567.51 7,568.64 7,566.04 -22.05 1.47 -0.29% 0.02% 25.47 25.54 25.54 25.53 28940 Knoxville, TN 3,535.22 3,539.60 3,541.07 3,541.75 -5.85 -2.15 -0.17% -0.06% 25.68 25.72 25.73 25.73 29460 Lakeland-Winter Haven, FL 1,617.50 1,610.35 1,607.93 1,608.21 9.57 2.14 0.59% 0.13% 24.48 24.38 24.34 24.35 31080 Los Angeles, CA 26,504.99 26,708.74 26,795.31 26,948.31 -290.32 -239.57 -1.08% -0.89% 23.00 23.18 23.25 23.39 33340 Milwaukee, WI 5,986.06 6,077.42 6,113.30 6,123.86 -127.24 -46.43 -2.08% -0.76% 23.51 23.87 24.01 24.05 33460 Minneapolis, MN 15,555.90 15,877.36 16,015.55 16,022.56 -459.65 -145.20 -2.87% -0.91% 26.70 27.26 27.49 27.50 33860 Montgomery, AL 4,060.90 4,068.18 4,071.80 4,081.87 -10.90 -13.70 -0.27% -0.34% 26.34 26.34 26.33 26.40 34060 Morgantown, WV 831.60 829.02 827.83 827.94 3.77 1.07 0.46% 0.13% 24.44 24.36 24.33 24.33 35300 New Haven-Milford, CT 7,175.86 7,263.96 7,303.64 7,306.26 -127.78 -42.30 -1.75% -0.58% 22.86 23.14 23.27 23.27 35380 New Orleans, LA 6,765.35 6,795.26 6,804.54 6,824.86 -39.19 -29.61 -0.58% -0.43% 24.25 24.36 24.39 24.46 36260 Ogden-Clearfield, UT 7,002.29 7,013.11 7,021.33 7,022.39 -19.04 -9.27 -0.27% -0.13% 23.54 23.58 23.61 23.61 36740 Orlando-Kissimmee-Sanford, FL 34,702.32 34,901.85 34,990.95 34,993.88 -288.62 -92.03 -0.82% -0.26% 24.04 24.18 24.24 24.25 37980 Philadelphia-Camden-Wilmington, PA-NJ-DE-MD 3,836.11 3,923.33 3,962.69 3,964.35 -126.59 -41.02 -3.19% -1.03% 23.11 23.64 23.87 23.88 38860 Portland-South Portland, ME 2,932.79 2,987.16 3,008.58 3,008.69 -75.78 -21.52 -2.52% -0.72% 25.28 25.75 25.94 25.94 38900 Portland-Vancouver-Hillsboro, OR-WA 9,958.56 9,971.60 9,978.06 9,987.94 -19.50 -16.33 -0.20% -0.16% 24.16 24.19 24.21 24.23 40380 Rochester, NY 12,450.73 12,473.23 12,488.20 12,516.70 -37.47 -43.47 -0.30% -0.35% 25.29 25.34 25.37 25.43 40900 Sacramento--Roseville--Arden-Arcade, CA 6,107.05 7,081.92 7,533.99 7,540.54 -1426.93 -458.62 -18.94% -6.08% 20.74 24.06 25.59 25.61 41860 San Francisco, CA 10,982.74 11,340.64 11,488.37 11,511.39 -505.62 -170.75 -4.40% -1.48% 20.81 21.49 21.77 21.81 42660 Seattle, WA 24,756.24 25,067.30 25,187.22 25,195.57 -430.98 -128.27 -1.71% -0.51% 23.99 24.29 24.41 24.41 43340 Shreveport-Bossier City, LA 1,941.36 1,979.29 1,995.82 1,995.43 -54.46 -16.14 -2.73% -0.81% 25.21 25.70 25.91 25.91 44700 Stockton-Lodi, CA 28,261.30 28,271.45 28,332.80 28,456.41 -71.50 -184.96 -0.25% -0.65% 24.58 24.59 24.65 24.75 44780 Sturgis, MI 203.65 215.45 220.21 219.86 -16.56 -4.40 -7.52% -2.00% 22.56 23.86 24.39 24.35 48620 Wichita, KS 2,132.86 2,134.94 2,138.15 2,140.63 -5.30 -5.70 -0.25% -0.27% 25.43 25.46 25.50 25.53 48980 Wilson, NC 29,829.18 29,804.68 29,802.97 29,435.64 26.21 369.04 0.09% 1.25% 24.86 24.84 24.84 24.54 49180 Winston-Salem, NC 30,183.23 30,180.06 30,236.92 30,308.14 -53.69 -128.09 -0.18% -0.42% 25.22 25.22 25.27 25.33

E-27 Page 2 of 3 Table E-13. Employment VMT Results, Cumulative (by Metro/CBSA) d by new (allocated) jobs (mi) Total VMT generated by new (allocated) jobs per brownfield acre redeveloped (mi/job/ac) Difference Percent Difference Brownfields Trend growth Difference Percent Difference

CBSA NAME Aggressive Base Aggressive Base Aggressive Base Aggressive Base Aggressive Base Aggressive Base 10420 Akron, OH -0.18 -0.07 -0.75% -0.30% 0.03 0.03 0.03 0.03 0.00 0.00 -0.75% -0.30% 10580 Albany-Schenectady-Troy, NY -0.64 -0.19 -2.43% -0.74% 0.08 0.08 0.08 0.08 0.00 0.00 -2.43% -0.74% 10740 Albuquerque, NM -0.46 -0.14 -1.72% -0.53% 0.09 0.09 0.09 0.09 0.00 0.00 -1.72% -0.53% 10900 Allentown-Bethlehem-Easton, PA-NJ -0.41 -0.13 -1.70% -0.55% 0.03 0.03 0.03 0.03 0.00 0.00 -1.70% -0.55% 11460 Ann Arbor, MI -0.39 -0.11 -1.64% -0.46% 0.10 0.10 0.10 0.10 0.00 0.00 -1.64% -0.46% 12060 Atlanta, GA -0.10 -0.03 -0.36% -0.13% 0.02 0.02 0.02 0.02 0.00 0.00 -0.36% -0.13% 12420 Austin-Round Rock, TX -0.23 -0.07 -0.89% -0.27% 0.03 0.03 0.03 0.03 0.00 0.00 -0.89% -0.27% 12580 Baltimore, MD -0.34 -0.11 -1.43% -0.47% 0.03 0.03 0.03 0.03 0.00 0.00 -1.43% -0.47% 12620 Bangor, ME -0.30 -0.06 -1.11% -0.23% 0.05 0.05 0.05 0.05 0.00 0.00 -1.11% -0.23% 13660 Big Rapids, MI -0.03 0.01 -0.11% 0.05% 0.39 0.39 0.39 0.39 0.00 0.00 -0.11% 0.05% 13740 Billings, MT -0.60 -0.19 -2.34% -0.73% 0.31 0.32 0.32 0.32 -0.01 0.00 -2.34% -0.73% 14260 Boise City, ID -0.28 -0.09 -0.99% -0.30% 0.20 0.20 0.20 0.20 0.00 0.00 -0.99% -0.30% 14500 Boulder, CO -0.09 -0.26 -0.39% -1.11% 0.80 0.80 0.80 0.81 0.00 -0.01 -0.39% -1.11% 15540 Burlington-South Burlington, VT -0.60 -0.12 -2.26% -0.47% 0.15 0.15 0.16 0.15 0.00 0.00 -2.26% -0.47% 16860 Chattanooga, TN-GA -0.19 -0.06 -0.78% -0.25% 0.11 0.11 0.11 0.11 0.00 0.00 -0.78% -0.25% 19100 Dallas, TX -0.12 -0.10 -0.41% -0.34% 0.02 0.02 0.02 0.02 0.00 0.00 -0.41% -0.34% 19380 Dayton, OH -0.08 -0.18 -0.32% -0.72% 0.20 0.20 0.20 0.20 0.00 0.00 -0.32% -0.72% 19780 Des Moines-West Des Moines, IA -0.03 -0.05 -0.12% -0.20% 0.10 0.10 0.10 0.10 0.00 0.00 -0.12% -0.20% 20500 Durham-Chapel Hill, NC -0.68 -0.20 -2.58% -0.77% 0.37 0.38 0.38 0.38 -0.01 0.00 -2.58% -0.77% 23140 Frankfort, IN -0.29 -0.35 -1.20% -1.44% 0.47 0.48 0.48 0.48 -0.01 -0.01 -1.20% -1.44% 23300 Freeport, IL -5.39 -1.79 -21.05% -6.95% 0.40 0.47 0.50 0.51 -0.11 -0.04 -21.05% -6.95% 24340 Grand Rapids-Wyoming, MI -0.60 -0.18 -2.32% -0.71% 0.03 0.03 0.03 0.03 0.00 0.00 -2.32% -0.71% 24500 Great Falls, MT -0.29 -0.02 -1.19% -0.09% 0.34 0.34 0.34 0.34 0.00 0.00 -1.19% -0.09% 25540 Hartford, CT -0.49 -0.14 -2.03% -0.60% 0.06 0.06 0.06 0.06 0.00 0.00 -2.03% -0.60% 26980 Iowa City, IA -0.61 -0.17 -2.32% -0.65% 0.27 0.28 0.28 0.28 -0.01 0.00 -2.32% -0.65% 27140 Jackson, MS -0.07 0.00 -0.29% 0.02% 0.08 0.08 0.08 0.08 0.00 0.00 -0.29% 0.02% 28940 Knoxville, TN -0.04 -0.02 -0.17% -0.06% 0.03 0.03 0.03 0.03 0.00 0.00 -0.17% -0.06% 29460 Lakeland-Winter Haven, FL 0.14 0.03 0.59% 0.13% 0.02 0.02 0.02 0.02 0.00 0.00 0.59% 0.13% 31080 Los Angeles, CA -0.25 -0.21 -1.10% -0.90% 0.01 0.01 0.01 0.01 0.00 0.00 -1.10% -0.90% 33340 Milwaukee, WI -0.50 -0.18 -2.08% -0.76% 0.03 0.03 0.03 0.03 0.00 0.00 -2.08% -0.76% 33460 Minneapolis, MN -0.79 -0.25 -2.87% -0.91% 0.02 0.02 0.02 0.02 0.00 0.00 -2.87% -0.91% 33860 Montgomery, AL 0.00 -0.06 0.01% -0.21% 0.08 0.08 0.08 0.08 0.00 0.00 0.01% -0.21% 34060 Morgantown, WV 0.11 0.03 0.46% 0.13% 0.04 0.04 0.04 0.04 0.00 0.00 0.46% 0.13% 35300 New Haven-Milford, CT -0.41 -0.13 -1.75% -0.58% 0.08 0.08 0.08 0.08 0.00 0.00 -1.75% -0.58% 35380 New Orleans, LA -0.14 -0.11 -0.58% -0.43% 0.05 0.05 0.05 0.05 0.00 0.00 -0.58% -0.43% 36260 Ogden-Clearfield, UT -0.06 -0.03 -0.27% -0.13% 0.07 0.07 0.07 0.07 0.00 0.00 -0.27% -0.13% 36740 Orlando-Kissimmee-Sanford, FL -0.20 -0.06 -0.82% -0.26% 0.07 0.07 0.07 0.07 0.00 0.00 -0.82% -0.26% 37980 Philadelphia-Camden-Wilmington, PA-NJ-DE-MD -0.76 -0.25 -3.19% -1.03% 0.01 0.01 0.01 0.01 0.00 0.00 -3.19% -1.03% 38860 Portland-South Portland, ME -0.65 -0.19 -2.52% -0.72% 0.04 0.04 0.04 0.04 0.00 0.00 -2.52% -0.72% 38900 Portland-Vancouver-Hillsboro, OR-WA -0.05 -0.04 -0.20% -0.16% 0.02 0.02 0.02 0.02 0.00 0.00 -0.20% -0.16% 40380 Rochester, NY -0.08 -0.09 -0.30% -0.35% 0.10 0.11 0.11 0.11 0.00 0.00 -0.30% -0.35% 40900 Sacramento--Roseville--Arden-Arcade, CA -4.85 -1.56 -18.94% -6.08% 0.02 0.02 0.02 0.02 0.00 0.00 -18.94% -6.08% 41860 San Francisco, CA -0.96 -0.32 -4.40% -1.48% 0.02 0.02 0.02 0.02 0.00 0.00 -4.40% -1.48% 42660 Seattle, WA -0.42 -0.12 -1.71% -0.51% 0.04 0.04 0.04 0.04 0.00 0.00 -1.71% -0.51% 43340 Shreveport-Bossier City, LA -0.71 -0.21 -2.73% -0.81% 0.03 0.04 0.04 0.04 0.00 0.00 -2.73% -0.81% 44700 Stockton-Lodi, CA -0.06 -0.16 -0.25% -0.65% 0.42 0.42 0.43 0.43 0.00 0.00 -0.25% -0.65% 44780 Sturgis, MI -1.83 -0.49 -7.52% -2.00% 0.09 0.10 0.10 0.10 -0.01 0.00 -7.52% -2.00% 48620 Wichita, KS -0.06 -0.07 -0.25% -0.27% 0.03 0.03 0.03 0.03 0.00 0.00 -0.25% -0.27% 48980 Wilson, NC 0.02 0.31 0.09% 1.25% 3.61 3.61 3.61 3.56 0.00 0.04 0.09% 1.25% 49180 Winston-Salem, NC -0.04 -0.11 -0.18% -0.42% 0.29 0.29 0.29 0.29 0.00 0.00 -0.18% -0.42%

E-28 Page 3 of 3 Environmental Benefits of Brownfields Redevelopment – A Nationwide Assessment

APPENDIX F: TEMPORAL ANALYSIS FOR DETERMINING BROWNFIELD REDEVELOPMENT COMPELTION A temporal analysis of the allocation results for the BFR scenario was performed to determine the expected year of brownfield redevelopment completion (i.e., when available brownfield site capacity was filled). The methodology that was followed for the analysis consisted of the following steps. 1. For each CBSA, interpolate year-over-year growth based on 2013 existing households (HH) and jobs and the 2030 HH and jobs control totals, using a compound annual growth rate formula. This gives an estimate of how much activity is added to each CBSA from one year to the next. 2. Using the trend growth attractiveness inputs for census block groups (CBGs), estimate how much growth in each year would go to each block group on a proportionate- attractiveness basis. Recall that housing and employment have different attractiveness scores in each CBG. 3. For any CBG with one or more brownfield sites located in it, assume any growth in that CBG goes to the brownfield site(s) first. 4. For each brownfield CBG, record the year in which the cumulative activity added to that CBG matches the activity allocated in our brownfield redevelopment simulation. Any later year growth in the CBG is assumed to occur at non-brownfield sites. For any given aggregation (by CBSA, by EPA Region, or Growth Profile), this methodology resulted in the mean year (or the “expected year of completion”) for when brownfield sites filled up across all CBGs. The tables below show reasonable timeframes for the brownfield redevelopment modeled in our growth simulations. The “earliest” and “latest” expected years of brownfield redevelopment completion are driven by the different results for the base and aggressive configurations.

F-1

APPENDIX F

EXPECTED YEAR OF BROWNFIELD REDEVELOPMENT COMPLETION BY CSBA (HH v JOBS) HH Jobs CBSA CBSA Name Earliest Latest Earliest Latest 10420 Akron, OH 2026 2028 2016 2017 10580 Albany-Schenectady-Troy, NY 2023 2027 2021 2023 10740 Albuquerque, NM 2018 2021 2018 2020 10900 Allentown-Bethlehem-Easton, PA-NJ 2023 2025 2018 2019 11460 Ann Arbor, MI 2021 2024 2017 2019 12060 Atlanta, GA 2021 2025 2017 2019 12420 Austin-Round Rock, TX 2017 2019 2015 2016 12580 Baltimore, MD 2023 2026 2019 2021 12620 Bangor, ME 2021 2023 2016 2017 13660 Big Rapids, MI 2016 2017 2015 2016 13740 Billings, MT 2019 2020 2021 2023 14260 Boise City, ID 2018 2021 2017 2019 14500 Boulder, CO 2016 2021 2015 2018 15540 Burlington-South Burlington, VT 2019 2020 2016 2017 16860 Chattanooga, TN-GA 2022 2026 2017 2018 19100 Dallas, TX 2018 2021 2017 2019 19380 Dayton, OH 2028 2029 2015 2015 19780 Des Moines-West Des Moines, IA 2023 2023 2014 2014 20500 Durham-Chapel Hill, NC 2026 2028 2018 2018 23140 Frankfort, IN 2026 2027 2016 2019 23300 Freeport, IL 2017 2017 2019 2022 24340 Grand Rapids-Wyoming, MI 2020 2022 2016 2018 24500 Great Falls, MT 2023 2026 2019 2022 25540 Hartford, CT 2021 2024 2018 2020 26980 Iowa City, IA 2018 2021 2018 2021 27140 Jackson, MS 2015 2017 2018 2022 28940 Knoxville, TN 2023 2024 2016 2017 29460 Lakeland-Winter Haven, FL 2021 2023 2019 2021 31080 Los Angeles, CA 2021 2024 2018 2020 33340 Milwaukee, WI 2019 2023 2019 2022 33460 Minneapolis, MN 2020 2023 2017 2018 33860 Montgomery, AL 2021 2023 2014 2014 34060 Morgantown, WV 2020 2022 2016 2017 35300 New Haven-Milford, CT 2025 2028 2017 2018 35380 New Orleans, LA 2022 2026 2015 2017 36260 Ogden-Clearfield, UT 2019 2024 2019 2023 36740 Orlando-Kissimmee-Sanford, FL 2020 2023 2016 2017 37980 Philadelphia-Camden-Wilmington, PA-NJ-DE-MD 2023 2026 2020 2023 38860 Portland-South Portland, ME 2020 2023 2017 2018 38900 Portland-Vancouver-Hillsboro, OR-WA 2016 2018 2017 2019 40380 Rochester, NY 2024 2027 2017 2018 40900 Sacramento--Roseville--Arden-Arcade, CA 2022 2024 2018 2020 41860 San Francisco, CA 2021 2025 2020 2022 42660 Seattle, WA 2018 2020 2018 2020 43340 Shreveport-Bossier City, LA 2026 2028 2015 2016 44700 Stockton-Lodi, CA 2028 2030 2016 2019 44780 Sturgis, MI 2025 2027 2016 2018 48620 Wichita, KS 2024 2024 2015 2015 48980 Wilson, NC 2019 2021 2013 2013 49180 Winston-Salem, NC 2024 2029 2014 2014

F-2 APPENDIX F

EXPECTED YEAR OF BROWNFIELD REDEVELOPMENT COMPLETION BY EPA REGION (HH v JOBS) HH Jobs HH - # Yrs to BF Redev Complete (from 2013) JOBS - # Yrs to BF Redev Complete (from 2013) EPA Region Earliest Latest Earliest Latest Earliest Latest Earliest Latest 1 2022 2024 2017 2019 9 11 4 6 2 2023 2026 2020 2022 10 13 7 9 3 2022 2024 2018 2021 9 11 5 8 4 2021 2023 2017 2018 8 10 4 5 5 2021 2024 2017 2019 8 11 4 6 6 2020 2023 2016 2018 7 10 3 5 7 2023 2024 2015 2016 10 11 2 3 8 2020 2024 2019 2022 7 11 6 9 9 2021 2025 2018 2021 8 12 5 8 10 2017 2019 2017 2019 4 6 4 6

EXPECTED YEAR OF BROWNFIELD REDEVELOPMENT COMPLETION BY GROWTH PROFILE (HH v JOBS) HH Jobs HH - # Yrs to BF Redev Complete (from 2013) JOBS - # Yrs to BF Redev Complete (from 2013) Growth Profile Earliest Latest Earliest Latest Earliest Latest Earliest Latest Big and Growing 2019 2023 2017 2019 6 10 4 6 Growth Hub 2019 2021 2017 2019 6 8 4 6 Industrial Legacy 2023 2025 2017 2018 10 12 4 5 Slow and Steady 2021 2025 2018 2020 8 12 5 7 Stable Metropolis 2022 2025 2019 2022 9 12 6 9 Up and Coming 2020 2022 2017 2018 7 9 4 5

F-3