SEPA
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
'ICF

RENAISSANCE
PLANNING

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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

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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, Wise	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

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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
CBS A
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
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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
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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.
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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.
Brownfield sites in the 50
analyzed metro areas could
potentially accommodate
between 200,000 to 640,000
new housing units and
415,000 to 1,389,000 new
jobs.
The number of housing units and jobs potentially
accommodated by brownfield sites varies substantially by
growth profile. For example, metro areas designated as
having an Industrial Legacy growth profile (e.g., small cities
with slow growth) often have relatively large numbers of
brownfields and thus can absorb a large portion of new
growth (housing, in particular). In contrast, brownfield sites
in metro areas characterized as Growth Hubs (e.g.,
moderate to large cities with rapid growth) only have
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
the BFR scenario is lower than in the primary analysis
phase, as expected. For every brownfield acre
redeveloped, approximately 0.65 to 3.16 acres of
impervious surface is saved relative to the TG scenario
after all regional growth (beyond what the brownfield sites
can accommodate) is accounted for. On a percentage
basis, the cumulative BFR scenario yields impervious
surface reductions of approximately 1.3 percent to 6.6
percent compared to the TG scenario.
For every brownfield acre
redeveloped,
approximately 0.65 to
4.60 acres of impervious
surface would be
expected to be saved if
the same development
had occurred in trend
growth (non-brownfield)
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
jobs at brownfield sites (primary phase) are expected to
generate 2.1 to 2.5 fewer VMT per worker per day than new
jobs in trending areas. On a percentage basis, this is
equivalent to employment VMT reductions of approximately
8.8 percent to 10 percent compared with trend growth
across all analyzed metro areas. The cumulative results
Brownfields redevelopment
reduces per capita
residential VMT by an
average of 0.5 to 9.7 miles
per day and job-related VMT
by 0.2 to 2.5 miles per day.
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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.
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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.
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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).12 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.
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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
overtime. 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.
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Environmental Benefits of Brownfields Redevelopment - A Nationwide Assessment
Figure 1. Scenario Analysis Approach
Brownfields
Redevelopment Scenario
Trend Growth
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.aov/smartarowth/smart-location-mappina
6	Center for Neighborhood Technology, https://www.cnt.ora/proiects/location-efficiencv-hub

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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.
4

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Environmental Benefits of Brown fields 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
Step 1: Prepare data and
model inputs
Step 2: Develop scenario
parameters
Step 3: Allocate growth
Step 4: Estimate
environmental impacts
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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.
6
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Environmental Benefits of Brown fields 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
ACRES redevelopment data do not indicate that the entire site acreage will be
devoted to future green space.
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).
b.
c.
3.
4.
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]).
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
Geographic criteria excludes:
-	SiTesin U.S. Territories (211);
-	Geographically inconsistent
sites (43); and
-	Sites not located in CBSA
metro areas(3,255).
Redevelopment status criteria
excludes:
-	Sitesthat have already
redeveloped (1,362); and
-	Sit e s t hat a re I ike ly to h ave
redeveloped,based on a
redevelopment estimation
model (349).
Site attribute criteria excludes:
-	Sitesgreaterthanorequal
to 1,000 acres in size (64);
-	Sites in protected areas
(814); and
-	Siteswith planned
greenspace >=Site size
(942).
Allocation model criteria includes:
-	Sites in 50 selected
met ro p o lit a n a re a s (5,366);
and
-	Sit e s t h at w e re n ot exc lu d ed
from analysis in pre-
allocation screening protocol
(343).
8 United States Geological Survey, National Gap Analysis Project (GAP) | Protected Areas Data Portal, htt ps ://www. u sgs. g o v/co re -
science-svstems/science-analvtics-and-svnthesis/aap/science/protected-areas
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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
application of protocols)
Planned Greenspace
Number of grants
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%)
2	grants: 818 (16.3%)
3	grants: 294 (5.9%)
4	grants: 124 (2.5%)
5	grants: 56 (1.1%)
6	grants: 26 (0.5%)
7	grants: 19 (0.4%)
8	grants: 9 (0.1%)
9	grants: 7 (0.1%)
10	grants: 5 (<0.1 %)
More than 10 grants: 2 (<0.1 %)
Number of Grants by Type:
Assessment Grants: 4,452 (76.7%)
Cleanup Grants: 234 (4.0%)
BCLRF: 241 (4.2%)
Multi-purpose: 5 (<1%)
Section 128(a) State/Tribal: 620 (10.7%)
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.
8
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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, Wise, 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.aov/smartarowth/smart-location-mappina
9
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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
\ _|_ gb + m1*Rfr+ m2*SS+ m3*NG+ m4*D4b0S0+ m5*DScri
Where:
mi, rri2, rri3, rri4, rris = 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.aov/smartarowth/smart-location-
database-technical-documentation-and-user-guide
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Environmental Benefits of Brown fields Redevelopment - A Nationwide Assessment
Figure 4. Brownfields Redevelopment Status in Milwaukee, Wise,
MILWAUKEE, Wl
Assumed already redeveloped
X Redev complete by 1/1/2013
X Redev start by 1/1/2012
3rown
Deerv
Modeled as already redeveloped
Included for future redevelopment
~ Redev complete after 1/1/2013
enda'le
Property not ready for use
Whitefish
I—As'..
Modeled as available for future redev
Protected areas
V/////A
.Sh'or.ew.obd
Developed areas
iwauwatosa
Milwaukee
.West
Ml is.
CLjdahy.
Hales
.Corners
South
Milwaukee
j Miles N
Franklin
11
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Environmental Benefits of Brownfields Redevelopment - A Nationwide Assessment
Table 1. Brownfields Redevelopment Status Model Coefficients by Census Region and
Metropolitan Area Size
Census
Region
Metro
Size
Constant
Ready
for
Reuse
Site Size
Number
of Grants
D4b050
(fixed
guideway
transit)
D5cri
(regional
centrality)
NORTHEAST
Small
-2.8883
2.2466
0.0073
0.1736
2.2664
1.7424
Large
-1.7022
2.0448
0.0198
0.1902
0.7399
-0.8548
MIDWEST
Small
-2.3834
2.2371
-0.0047
0.3254
NA
0.6844
Large
-3.1296
2.3841
0.0185
0.2662
3.3115
0.9350
SOUTH
Small
-2.5029
2.2760
0.0044
0.4666
1.6341
-0.0914
Large
-2.1597
2.0346
0.0108
0.2506
1.0031
0.4199
WEST
Small
-2.8310
2.1019
0.0067
0.3630
6.0962
0.6057
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
12
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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.
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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
Scenario Development Question
Brownfields
Redevelopment Scenario
Trend Growth Scenario
How many new jobs and housing
units will be added to a given
metropolitan area by 2030? (Control
Total)
For both scenarios, control totals of new jobs and new
housing units are based on Woods & Poole economic
forecasts, aggregated to the metro level (no difference).
How much new development can a
given location accommodate?
(Capacity)
Based on site size from
ACRES with reductions for
greenspace
Based on undeveloped and
unprotected acreage in
each growing CBG
What locations are most likely to be
(re)developed first? (Attractiveness)
Based on redevelopment
probability estimates
Based on the magnitude of
recent growth trends by
type (residential vs.
employment)
What types of activities (jobs and
housing) are likely to be added as a
given location is developed? (Activity
Mix)
Likely mix of new activities
based on recent growth
trends
Based on the magnitude of
recent growth trends by
type (residential vs.
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.
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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.
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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-Drivina-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%20Proiects/files/bf gf-life-cvcle-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-proiect/vm-role-of-brownfield-developments-in-reducina-household-vehicle-
travel-asce-up-1943-5444-0000113.pdf: and Paul, E., 2008. The Environmental and Economic Impacts of Brownfields
Redevelopment, http://www.nemw.ora/wp-content/uploads/2015/06/2008-Environ-Econ-lmpacts-Brownfield-Redev.pdf.
16
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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.
17
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Environmental Benefits of Brownfields Redevelopment - A Nationwide Assessment
Figure 5. Capacity Estimation for New Activities at Brownfieid Sites
Existing density: 1.75
Infill density: NA
7-acre
brownfieid
Existing density: 3.48
Infill density: NA
Existing density: 2.12
Infill density: 3.98
Existing density: 1.67
Infill density: NA
Existing density: 4.80
Infill density: 5.10
Existing density: 3.09
Infill density: 4.76
Existing density: 0.89
Infill density: NA
Existing density: 1.05
Infill density: 2.02
Estimating expected density of development at a brownfieid site
Base Configuration
Aggressive Configuration
Greenfield density estimate (maximum
existing density in vicinity)
4.80 activities per acre
4.80 activities per acre
Infill density estimate (maximum
modeled infill density in vicinity)
NA
5.10 activities per acre
Prevailing development density
Calculating site capacity
Site size
4.80 activities per acre
7 acres
5.10 activities per acre * 2 :
10.20 activities per acre
7 acres
Capacity (site size * prevailing
development density)
7 acres * 4.80 activities per acre
= 33 activities
7 acres * 10.2 activities per acre :
71 activities
Figure 6 provides an example of the capacity estimation results for available brownfieid sites in
the Los Angeles metro area.
18
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Environmental Benefits of Brown fields Redevelopment - A Nationwide Assessment
Figure 6. Brownfield Capacity Estimation Results for the Los Angeles Metro Area
mi
Palmdale

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x
NTs
Glendale
_ Loi "
^ Angeles
it 4
\ %
K'w
¦I'f -r$
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CBSA
Estimated
development capacity
at brownfield sites
| 14,000
I | Base
¦ Aggressive
Land Use
Developed, Open
~
~
~
~
Space
Developed, Medium
Intensity
Developed, Low
Intensity
Developed, High
Intensity
Protected areas
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19
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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.
20
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Environmental Benefits of Brown fields Redevelopment - A Nationwide Assessment
Figure 7. Brownfield Attractiveness Estimation Results for the Los Angeles Metro Area
-"'waggles
, ' '^ "	^	¦ I'ltl
Glendale
^ LOS'1

Ligl
(L

flip"
LOS ANGELES, CA
CBSA
Estimated
attractivess
scores at brownfield
sites
¦ Least attractive
I Most attractive
Land Use
Developed, Open
~
~
~
~
Space
Developed, Medium
Intensity
Developed, Low
Intensity
Developed, High
Intensity
^ Protected areas
¦si IiA _
Sl ¦ iaB
mj.
s
K
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-¦ Long
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21
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Environmental Benefits of Brown fields Redevelopment - A Nationwide Assessment
Figure 8. Brownfield Activity Mix Estimation Results for the Los Angeles Metro Area
' 'z / ¦ ¦	—""
Glendale
LOS® *
Angeles
" m
LOS ANGELES, CA
CBSA
Relative mix of
activities at
brownfield sites
O
Housing trend
¦ Jobs trend
Land Use
I—| Developed, Open
'—' Space
Developed, Medium
Intensity
Developed, Low
Intensity
Developed, High
~
~

' Intensity
% Protected areas

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-------
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
23
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Environmental Benefits of Brown fields 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
:1 ¦ i
l
I Palmdale
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Santa — --
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LI
11 /
V
.	v					'	'	/	'
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-
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LOS ANGELES, CA
CBSA
Estimated
development capacity
at trending block
groups
190,000
¦ Capacity
Land Use
Developed, Open
~
~
~
~
Space
Developed, Medium
Intensity
Developed, Low
Intensity
Developed, High
Intensity
Protected areas
Riversi
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r If-- _Lon9
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24
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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.
25
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Environmental Benefits of Brown fields Redevelopment - A Nationwide Assessment
Figure 10. Trend Growth Housing Attractiveness Estimation Results for the Los Angeles Metro
Area
I Palmdale
r Santal
|Glarita
Glendale
r i^J_
Mes
Corona
.Garden .Grove
Irvine
LOS ANGELES, CA
CBSA
Estimated attractiveness
scores at trending block
groups
ll 50
¦	Housing index (0 -100)
¦	Jobs index (0 -100)
Land Use
I—| Developed, Open
'—' Space
I—| Developed, Medium
'—' Intensity
I—| Developed, Low
'—' Intensity
I—| Developed, High
—' Intensity
Protected areas
26
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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-
27
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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
metro area growth is then allocated following the trend
growth process. These two distinct allocation phases can
be mirrored in the TG scenario through a simple
accounting system:
•	The BFR scenario is modeled in its entirety prior to
the TG scenario.
•	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.
The "primary" phase of the
BFR scenario allocates as
much growth as possible to
brownfield sites. 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.
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.
28
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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.
In the BFR 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.
For the TG scenario, the allocation process simply
continues until all forecasted metro area growth is
allocated, but the secondary phase results are
stored in a separate table from the primary phase
results.
The "secondary" phase of
analysis occurs when
capacity for new growth at
brownfield sites is
exhausted, and all remaining
growth is allocated according
to the data and parameters
guiding the TG scenario.
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.
29
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Environmental Benefits of Brownfields Redevelopment - A Nationwide Assessment
Figure 11. Illustration of the Allocation Phases
BFR Scenario
Primary phase (Control total = 10 units)
TG Scenario
Allocate as many units as possible to brownfields based
on brownfield site capacity (two units).





Based on the number of units allocated to brownfield sites
(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
in trending areas has not been affected by the primary
phase because all growth was allocated to brownfields.
#
##

#
####
Allocate the remaining units to trending areas. Capacity
in trending areas has been affected by the primary phase
allocation.
A
Primary phase
allocated unit
#
Secondary phase
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
30
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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.
The "cumulative" assessment
of each growth scenario
compares the environmental
impacts of total growth across
a broader metro area,
regardless of phase.
Finally, the combined allocation for each scenario
(combining the first and second phases) is evaluated to
assess the "cumulative" benefits of brownfields
redevelopment. This grouping provides the means to
assess the extent to which brownfields redevelopment
can shift the entire metro area's growth patterns to reduce
the environmental impacts of development. Since the
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
31
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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
Growth
Profile
Name
Metro
Area Size
Growth
Rate
Population
Density
Capacity of
Redevelop-
ment Activity
Brownfield
Density
No.
Eligible for
Analysis
No.
Chosen
for
Analysis
Example Cities
Growth
Hub
Medium
and Large
Moderate
to Rapid
Moderate to
High
High
High
19
6
Austin, TX
Seattle, WA
Industrial
Legacy
Small and
Tiny
Slow
Low to
Moderate
Low to
Moderate
Moderate to
High
171
18
Sturgis, Ml
Albany, NY
Stable
Metropolis
Huge
Slow
High
High
High
4
2
Los Angeles, CA
Philadelphia, PA
Slow and
Steady
Medium
and Large
Slow
Moderate to
High
High
Moderate to
High
16
6
Baltimore, MD
New Orleans, LA
Big and
Growing
Huge
Moderate
to Rapid
High
High
High
4
2
Atlanta, GA
Dallas, TX
Up and
Coming
Small and
Tiny
Moderate
to Rapid
All Densities
All Capacities
All
66
16
Durham, NC
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.
32
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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.
33
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Environmental Benefits of Brown fields Redevelopment - A Nationwide Assessment
SerittleJWA
BillingsfMT
Portland, OR
Burlington, VT,.
Rochester, NY"
Milwaukee, Wl
Portland, ME
Aibany;;NY
I GrandJJ
Rapids^W1
\ Des FreeportrIL
Moines. IA
Hartford; Cjj
Akron, OH Allentown PA
;K:Ne.W-
Haven, CT
Morgontown, WV
Frankfort, IN
Boulder, CO
Sacramento, CA
^Philadelphia, PA
.Dayton, OH.
Stockton, CA
Francisco, CA
Durham, NC^
Knoxville.-TN
Wilson; NC
Winston'
Salem, NC
Qhattanooga. TN
Albuquemue, NM
I Los \
lAngeles, CA,
Atlanta, GA
Jockson, MS
Dallas;TX
Montgomeryi.AL
Shreveport, LA
Austin, TXj
Orleans, LA
Orlando, FL
LakelanaTFL
1,000
Growth Profile

Growth Hub

Industrial Legacy

Stable Metropolis

Slow and Steady

Big and Growing

Up ond Coming
34
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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.-Wise*
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, III.
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
Wchita, Kan.
Midwest
7
Wlson, 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, Wise*
Midwest
5
New Orleans-Metairie, La*
South
6
Rochester, N.Y*
Northeast
2
San Francisco-Oakland-Hayward, Calif*
West
9
35
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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-Wnter Haven, Fla.
South
4
Morgantown, W.Va.
South
3
Ogden-Clearfield, Utah
West
8
Portland-South Portland, Maine
Northeast
1
Wnston-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
36
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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
% imp = 			j		
1 + \MWTMTFTTW[bTQWTTW[cTT3W^WTmar)
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.aov/smartarowth/impervious-surface-growth-model
37
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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
Density
D1a
Residential Density
More destinations nearby increase
walking and biking.
Diversity
D2
Land Use Entropy
A greater range of destinations nearby
also increased the likelihood of walking
and biking.
Design
D3
Intersection Density, weighted by
three-legged, four-legged, or
more intersections
More direct pedestrian pathways and
more distributed vehicle traffic support
better walking and biking conditions.
Distance
D4
Distance of Transit
Convenient access increases the
likelihood of using transit.
Destinations
D5ar
Accessibility to Jobs by Auto,
gravity weighted
Greater access to destinations
generates shorter average vehicle
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/
38
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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.
39
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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.
40
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Environmental Benefits of Brownfields Redevelopment - A Nationwide Assessment
Table 6. "Primary" and "Secondary" Allocation Concepts

Brownfields Redevelopment
Scenario
Trend Growth Scenario
Primary Allocation Phase
Objective
Allocate as many jobs and
housing units to brownfield sites
as possible based on estimated
development capacity.
Re-allocate the jobs and housing
units from brownfield sites to
trending locations throughout the
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
behaviors of new workers and
residents will reflect the contexts
in which the brownfield sites are
located.
Development patterns and travel
behaviors of new workers and
residents will reflect those typical
in the fastest growing parts of the
metro area.
How is development
capacity utilized?
Only capacity at brownfield sites
is absorbed.
Non-brownfield capacity at
trending locations is absorbed.
What are the implications?
Non-brownfield capacity remains
untouched, preserving capacity in
the neighborhoods in which the
brownfields are located.
Non-brownfield capacity is
reduced at trending locations,
according to the amount of
activity allocated there.
Secondary Allocation Phase
Objective
Allocate the remaining increment
of jobs and housing units to
trending areas.
Continue allocation of jobs and
housing units to trending
locations.
Where does growth occur?
In neighborhoods (block groups)
that have experienced substantial
growth in the past decade
In neighborhoods (block groups)
that have experienced substantial
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
behaviors of new workers and
residents will reflect those typical
in the fastest growing parts of the
metro area.
Development patterns and travel
behaviors of new workers and
residents will reflect those typical
in the fastest growing parts of the
metro area, with sufficient
development capacity to
accommodate the remaining
growth.
41
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Environmental Benefits of Brown fields Redevelopment - A Nationwide Assessment
Figures 13 through 15 illustrate the primary, secondary, arid 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
|Brownfield Scenario
Anaheim
Irvine
Palmdale
Santa
Clarita
/ VTf r
Glendale
- -L
Los Angeles

; Long
Beach
Primary Allocation
|| 14,000
¦ Allocated Housing
I Allocated Jobs
v/ZTA
LOS ANGELES, CACBSA
15
30
Miles
N
Land Use
I I Developed, Open Space
I I Developed, Medium Intensity
I I Developed, Low Intensity
a Developed, High Intensity
'/// Protected areas
Trend Growth Scenario
Palmdale
Santa
Clarita
%
-.Glendale=
ji^LosAngeles, -
— -
- -"Long r Anaheim
Beach" :
.lrvine>_-
"wm
42
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Environmental Benefits of Brown fields Redevelopment - A Nationwide Assessment
Figure 14. Secondary Allocation in BFR and TG Scenarios for the Los Angeles Metro Area
Brownfield Scenario
Trend Growth Scenario r"
LOS ANGELES, CACBSA
15
30	^
_i Miles
Secondary Allocation
||| 8,300
I i Allocated Housing
I Allocated Jobs
Land Use
I I Developed, Open Space
I I Developed, Medium Intensity
I I Developed, Low Intensity
I I Developed, High Intensity
'//, Protected areas
43
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Environmental Benefits of Brown fields Redevelopment - A Nationwide Assessment
Figure 15. Cumulative Allocation in BFR and TG Scenarios for the Los Angeles Metro Area
Brownfield Scenario
Trend Growth Scenario
LOS ANGELES, CA CBSA
15
~~L_
30
_i Miles
Cumulative Allocation
14,000
Allocated Housing
Allocated Jobs
Land Use
I I Developed, Open Space
I I Developed, Medium Intensity
I I Developed, Low Intensity
I I Developed, High Intensity
'//, Protected areas
44
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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

GROWTH
PROFILE
Newly Allocated
Housing Units
Percentage of
Housing Control
Total
Newly Allocated Jobs
Percentage of
Jobs Control
Total
Growth Hub
42,993 to 142,484
2.8% to 9.2%
159,979 to 542,059
5.0% to 17.0%
Industrial
Legacy
40,473 to 117,037
13.6% to 39.2%
28,197 to 96,239
2.9% to 10.0%
Stable
Metropolis
35,637 to 118,883
5.5% to 18.5%
97,564 to 316,045
3.7% to 11.8%
Slow and
Steady
31,083 to 103,394
6.8% to 22.6%
62,487 to 211,536
4.0% to 13.7%
Big and
Growing
19,514 to 66,664
1.4% to 4.8%
29,618 to 100,346
1.1% to 3.6%
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
45
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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
of urban development are different in different parts of a
metro area. 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.29 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 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
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.
! Center for Neighborhood Technology, https://www.cnt.org/proiects/location-efficiencv-hub
46
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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
more "location-
efficient" than
trending areas? What
is the magnitude of
the difference?
Do brownfield sites
preserve capacity in
"location-efficient"
trending areas? What is
the magnitude of the
difference?
Does the growth diverted to
brownfield sites result in a
metro area growth pattern that
utilizes "location-efficient"
places more heavily than the
trend? What is the magnitude
of the difference?
Comparison
BFR vs. TG
environmental
impacts for the
"primary" phase of
allocation
BFR vs. TG
environmental impacts for
the "secondary" phase of
allocation
BFR vs. TG environmental
impacts for the cumulative
allocations
Factors
affecting results
Where are brownfield
sites located vs.
where are trending
areas located?
Are brownfield sites
located in "location-
efficient" trending areas?
How much growth was
allocated to brownfield
sites?
How much growth was
allocated to brownfield sites?
Were the brownfield sites
more "location-efficient" than
trending areas?
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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
48
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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
49
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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
GROWTH PROFILE
Change in Impervious Surface
Acres per Redeveloped
Brownfield Acre (acres)
Percent Change in Impervious
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 "AN" 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.
50
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Environmental Benefits of Brownfields Redevelopment - A Nationwide Assessment
Table 10. Change in Impervious Surface Acres, Secondary Phase
GROWTH PROFILE
Change in Impervious Surface
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
GROWTH PROFILE
Change in Impervious Surface per
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
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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
52
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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
GROWTH PROFILE
Change in Residential
VMT per Redeveloped
Brownfield Acre
Change in Residential
VMT per Capita
Percent Change in
Residential VMT (%)
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
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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
i
O
4^
0
1
Industrial Legacy
-0.7 to -4.4
Stable Metropolis
I
o
1—^
0
1
00
Slow and Steady
-0.5 to -2.1
Big and Growing
i
o
CO
1—^
0
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
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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
GROWTH PROFILE
Change in
Employment VMT per
Redeveloped
Brownfield Acre
Change in
Employment VMT per
Job
Percent Change in
Employment VMT
(%)
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.
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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
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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
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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.
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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
redeveloped brownfield acre)
-1.28 to -4.60 acres
Change in Residential VMT (per redeveloped
brownfield acre)
-127 to -536 miles
Change in Residential VMT (per capita)
-7.3 to -9.7 miles
Change in Employment VMT (per redeveloped
brownfield acre)
-29.2 to -116 miles
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
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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
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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
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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.aov/sites/production/files/2015-09/documents/bfenvironimpacts042811 .pdf
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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
redeveloped brownfield acre)
-0.65 to -3.16 acres
Change in Residential VMT (per capita)
-0.5 to -1.8 miles
Change in Employment VMT (per job)
-0.18 to -0.45 miles
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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
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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
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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.
66
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APPENDICES
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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
GRANTJD
Property ID number
PROPERTYJD
Location information (address, city, state, zip;
latitude and longitude) = site
ADDRESS, CITY, STATE CODE, ZIP CODE,
LATITUDE_MEASURE, LONGITUDE_MEASURE
Property size
PROPERTY_SIZE
Ready for reuse
R E AD Y_F O R_R E U S E
Redevelopment details
REDEV_START_DATE, REDEV_COMPLETION_DATE
No. of grants received at unique site
(count of each PROPERTYJD grants)
Planned for greenspace
FUTURE_GREENSPACE_ACRES
ACRES Site Location Validation Process and Decision Protocol
Variable/
Factor
ACRES Abbreviation
Inventory and Results
Decision Protocol
Grant ID
number
GRANTJD
40,748 total grants
None needed; all sites retained for
analysis. Multiple grants will be
connected to the individual unique
property ID through a relational
database.
Property ID
number
PROPERTYJD
29,387 unique brownfield
properties
None needed; all sites retained for
analysis.
Location
information
(address,
city, state,
zip; latitude
and
longitude) =
site
ADDRESS, CITY,
STATE CODE,
ZIP CODE,
LATITUDE MEASURE,
LONGITUDE MEASURE
•	Geographically consistent
sites [27,199 sites (93%)]
•	Located outside 50 states
and Washington, D.C.
(i.e., sites in territories)
[210 sites, (< 1%)]
•	Remaining sites with
inconsistent geographic
data (address and lat/long
data do not appear to
correspond) [1,978 sites
(7%)l34
•	If all geographic variables are
internally consistent (when the
address and latitude and longitude
information correspond), then they
will be used.
•	Excluded those sites outside 50
states and Washington, D.C. (i.e.,
sites in territories).
•	Included geographically
inconsistent sites for which location
data have been validated or where
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/
Factor
ACRES Abbreviation
Inventory and Results
Decision Protocol


o Data validated or
corrected for 181
sites (< 1%)
o Assume lat/long
correct for sites where
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%1)
lat/long or address information is
assumed to be accurate.
• Excluded those sites with
unresolved geographic
inconsistencies.
Property
size
PROPERTY_SIZE
•	Missing property size
[2,356 sites (8%)]
•	"0 acres" property size
[468 sites (1.6%)]
•	Greater than 100,000
acres [263 sites (1%)]
•	If a property size
(PROPERTY_SIZE) is available
and ranges from 0.1 acres to
< 1,000 acres, it will be used.
•	Those sites with missing property
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.
Ready for
reuse
READY_FOR_REUSE
•	"Y" [6,216 sites (21%)]
•	"N" [23,159 sites (79%)]
•	Blank [12 sites (< 1%)]
•	Those with "Y" and not assumed to
be already redeveloped (based on
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.
Redevelop-
ment details
REDEV START DATE,
REDEV COMPLETION
DATE
•	Redevelopment
completion date before
2013 [709 sites (2.4%)]
•	Redevelopment start date
before 2012 and
completion date missing
[1,156 sites (3.9%)]
•	If the site has a valid
redevelopment completion date
(REDEV_COMPLETION_DATE) in
ACRES prior to 2013, it will be
considered already redeveloped
and excluded from the analysis.
•	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/
Factor
ACRES Abbreviation
Inventory and Results
Decision Protocol



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
received at
unique site
(count of each
PROPERTYJD grants)
Multiple grants associated with
a specific site [2,715 sites (9%)]
None needed; all sites retained for
analysis.
Planned for
greenspace
FUTURE GREENSPACE
_ACRES
Include greenspace information
[1,910 sites (7%)]
• If ACRES indicates that a property
is to be redeveloped partially as
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.l 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
CBS As
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
Name
Characteristics
No. of CBSAs for
Consideration
Big and Growing
•	Huge CBSAs with moderate to rapid growth
•	All in the South
•	High brownfield and population density
•	High capacity
4
Stable Metropolis
•	Huge CBSAs with slow growth
•	The usual suspects (Los Angeles, New York, Chicago,
Philadelphia)
•	High brownfield and population density
•	High capacity
4
Growth Hubs
•	Medium and large CBSAs with moderate to rapid growth
•	Distributed across the country
•	Moderate to high population density
•	High capacity
•	Mostly high brownfield density
19
Slow and Steady
•	Medium and large CBSAs with slow growth
•	Distributed across the country
•	Moderate to high population and brownfield density
•	High capacity
16
Up and Coming
•	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
•	Mix of brownfield and population density
•	Mix of capacity
•	Generally low and moderate brownfield density, although two
CBSAs have high brownfield density: Fayetteville, N.C., and Ann
Arbor, Mich.
66
Industrial Legacy
•	Small and tiny CBSAs with slow growth
•	Majority in the Midwest
•	Moderate and high brownfield density
•	Low to moderate population density
•	Low to moderate capacity
171
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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.
EPA Region
Number of CBSAs Eligible
for Consideration
Number of CBSAs
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

Census Region
Number of CBSAs Selected
for Consideration
Number of CBSAs
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
Development Profile
Number of
Potential
CBSAs
Number of
CBSAs in
Pilot Study
Number of
CBSAs
Selected for
Further Study
Total
CBSAs for
Analysis
Total CBSAs as
a Percentage of
Potential CBSAs
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)
CBSA Name
Size
Growth
Rate
Population
Density
Brownfield
Density
Brownfield
Capacity
Census
Region
EPA
Region
Atlanta-Sandy Springs-
Marietta, Ga.
Huge
Moderate
High
Moderate
High
South
6
Dallas-Fort Worth-
Arlington, Texas
Huge
Moderate
High
Moderate
High
South
6
Stable Metropolis (2)
CBSA Name
Size
Growth
Rate
Population
Density
Brownfield
Density
Brownfield
Capacity
Census
Region
EPA
Region
Los Angeles-Long
Beach-Anaheim, Calif.
Huge
Slow
High
High
High
West
9
Philadelphia-Camden-
Wilmington, Pa.-N.J.-
Del.-Md.
Huge
Slow
High
High
High
Northeast
2
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Environmental Benefits of Brownfields Redevelopment - A Nationwide Assessment
Growth Hubs (6)
CBSA Name
Size
Growth
Rate
Population
Density
Brownfield
Density
Brownfield
Capacity
Census
Region
EPA
Region
Austin-Round Rock-San
Marcos, Texas
Medium
Rapid
Moderate
Moderate
High
South
6
Minneapolis-St. Paul-
Bloomington, Minn.-
Wisc.
Large
Moderate
High
High
High
Midwest
5
Orlando-Kissimmee-
Sanford, Fla.
Medium
Moderate
High
Low
High
South
4
Portland-Vancouver-
Hillsboro, Ore.-Wash.
Medium
Moderate
Moderate
Moderate
High
West
10
Sacramento-Arden-
Arcade-Roseville, Calif.
Medium
Moderate
Moderate
High
High
West
9
Seattle-Tacoma-
Bellevue, Wash.
Large
Moderate
High
Moderate
High
West
10
Slow and Steady (6)
CBSA
Size
Growth
Rate
Population
Density
Brownfield
Density
Brownfield
Capacity
Census
Region
EPA
Region
Baltimore-Towson, Md.
Medium
Slow
High
High
High
South
3
Hartford-West Hartford-
East Hartford, Conn.
Medium
Slow
High
High
High
Northeast
1
M i Iwa u kee-Wa u kes h a-
West Allis, Wise.
Medium
Slow
Moderate
High
High
Midwest
5
New Orleans-Metairie-
Kenner, La.
Medium
Slow
Low
Low
High
South
6
Rochester, N.Y.
Medium
Slow
Moderate
Moderate
High
Northeast
2
San Francisco-Oakland-
Fremont, Calif.
Large
Slow
High
High
High
West
9
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Environmental Benefits of Brownfields Redevelopment - A Nationwide Assessment
Up and Coming (16)
CBS A
Size
Growth
Rate
Population
Density
Brownfield
Density
Brownfield
Capacity
Census
Region
EPA
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
Burlington, Vt.
Tiny
Moderate
Low
Moderate
High
Northeast
1
Des Moines-West Des
Moines, Iowa
Small
Moderate
Low
Moderate
High
Midwest
7
Durham-Chapel Hill,
N.C.
Small
Rapid
Moderate
Moderate
High
South
4
Grand Rapids-
Wyoming, Mich.
Small
Moderate
Moderate
High
High
Midwest
5
Iowa City, Iowa
Tiny
Moderate
Low
High
Moderate
Midwest
7
Knoxville, Tenn.
Small
Moderate
Moderate
Moderate
High
South
4
Lakeland-Winter Haven,
Fla.
Small
Moderate
Moderate
Moderate
Moderate
South
4
Morgantown, W.Va.
Tiny
Moderate
Moderate
Low
Moderate
South
3
Ogden-Clearfield, Utah
Small
Moderate
Moderate
Moderate
Moderate
West
8
Portland-South
Portland-Biddeford,
Maine
Small
Moderate
Low
High
High
Northeast
1
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)
CBS A
Size
Growth
Population
Brownfield
Brownfield
Census
EPA
Rate
Density
Density
Capacity
Region
Region
Akron, Ohio
Small
Slow
High
High
High
Midwest
5
Albany-Schenectady-
Troy, N.Y.
Small
Slow
Moderate
Moderate
High
Northeast
2
Allentown-Bethlehem-
Easton, Pa.-N.J.
Small
Slow
High
High
High
Northeast
5
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, III.
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,
Conn.
Small
Slow
High
High
High
Northeast
1
Shreveport-Bossier
City, La.
Small
Slow
Low
Moderate
High
South
6
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
D Variables
SLD
Indicator
Primary Impact on Travel
Behavior
Density
D1a
Residential Density
More destinations nearby increase
walking and biking.
Diversity
D2
Land Use Entropy
A greater range of destinations
nearby also increases the
likelihood of walking and biking.
Design
D3
Intersection Density, weighted by
three-legged and four-legged or more
intersections
More direct pedestrian pathways
and more distributed vehicle traffic
support better walking and biking
conditions.
Distance
D4
Distance of Transit
Convenient access increases the
likelihood of using transit.
Destinations
D5ar
Regional Accessibility to jobs by
auto, gravity weighted
Greater access to destinations
generates shorter average vehicle
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 /' is shown below:
D1j - T5j/Dj
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
CBSA Name
VMT per
capita
Source
Albany-Schenectady-Troy, N.Y.
30.09
Brookings Institute
Big Rapids, Mich.
26.7
Equal to neighboring CBSA - Grand Rapids, Mich. -
estimated VMT per capita
Frankfort, Ind.
23.18
Equal to neighboring CBSA - Lafayette, Ind. -
estimated VMT per capita
Freeport, III.
24.81
Equal to neighboring CBSA - Rockford, III. - estimated
VMT per capita
Sturgis, Mich.
29.72
Average of neighboring CBSAs - Kalamazoo, Mich.
(30.49) and Battle Creek, Mich. (28.95) - estimated
VMT per capita
Wilson, N.C.
34.33
Average of neighboring CBSAs - Greenville, N.C. (43),
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.
D-2
-------
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.
D-3
-------
Environmental Benefits of Brownfields Redevelopment - A Nationwide Assessment
APPENDIX E: DETAILED ALLOCATION AND ENVIRONMENTAL
BENEFITS RESULTS
E-1
-------
Table E-l. Legend for Allocation and Environmental Results
Table Name
Metric
Unit (if applicable)

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


CHARACTERISTICS
Growth Profile Name
Size
Growth Rate
Geographic
Distribution
Population
Density
Capacity for
Redevelopment
Activity
Brownfield Density
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
E-2
Page 1 of 1
-------
Table E-Z.Summary of Allocation and Environmental Results (by Growth Profile)
ALLOCATION

Contro
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)
RES VMT
Residential 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-Z.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)
E-4
Page 2 of 3
-------
Table E-Z.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)
E-5
Page 3 of 3
-------
Table E-4. Allocation Results (by Metro/CBSA)


Aggressive
Base

Aggressive
Base

CBSA
NAME
GROWTH PROFILE
EPA REGION
BF Acreage
(ac)
HU Control
Total
HU Allocated
Pet of Control
HU Allocated
Pet of Control
Jobs Control
Total
Jobs Allocated
Pet of Control
Jobs Allocated
Pet of Control
Jobs Proportion of
Allocation
Housing Proportion
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, Ml
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, Ml
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%
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, Ml
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%
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, Wl
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, Ml
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, Ml
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, Ml
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, Ml
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, Wl
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, Ml
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%
E-11
Page 1 of 1
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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 |
CBSA
NAME
GROWTH PROFILE
EPA REGION
WSSU
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, Ml
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, Ml
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, Ml
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, Wl
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, Ml
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%
E-13
Page 1 of 1
-------
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 |
CBSA
NAME
GROWTH PROFILE
EPA REGION
WSSu
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, Ml
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, Ml
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, Ml
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, Wl
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, Ml
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%
E-14
Page 1 of 1
-------
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 |
CBSA
NAME
GROWTH PROFILE
EPA REGION
BF ACREAGE
(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, Ml
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, Ml
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, Ml
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, Wl
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, Ml
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%
E-15
Page 1 of 1
-------
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
CBSA
NAME
GROWTH PROFILE
EPA REGION
BF ACREAGE
(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, Ml
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, Ml
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, Ml
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, Wl
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, Ml
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
E-16
Page 1 of 4
-------
Table E-10. Residential VMT Results, Cumulative (by Metro/CBSA)

d 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, Ml
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, Ml
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, Ml
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, Wl
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, Ml
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%
E-17
Page 2 of 4
-------
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, Ml
-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, Ml
-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, Ml
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, Wl
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, Ml
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
E-18
Page 3 of 4
-------
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, Ml
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, Ml
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, Ml
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, Wl
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, Ml
-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-ll. 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, Ml
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, Ml
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, Ml
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, Wl
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, Ml
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-ll. Employment VMT Results, Primary Phase (by Metro/CBSA)
Total VMT generated by new (allocated) jobs per redeveloped brownfield acre (mi/ac)
fields	|	Trend growth	I	Difference	I Percent Difference
Brownfields
Total VMT per job generated by new (allocated) jobs (mi/job)
Trend growth I	Difference	I 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, Ml
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, Ml
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, Ml
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, Wl
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, Ml
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-ll. Employment VMT Results, Primary Phase (by Metro/CBSA)
Difference
Total VMT per job generated by new (allocated) jobs per brownfield acre redeveloped (mi/job/ac)
Brownfields	I	Trend growth	I	Difference	I 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, Ml
-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, Ml
-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, Ml
-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, Wl
-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, Ml
-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 ger

Brownfields
Trend growth
Difference
Percent Difference
Brownfields
CBSA
NAME
GROWTH PROFILE
EPA REGION
BF
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, Ml
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, Ml
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, Ml
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, Wl
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, Ml
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)

lerated 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, Ml
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, Ml
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, Ml
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, Wl
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, Ml
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
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, Ml
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, Ml
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, Ml
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, Wl
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, Ml
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
CBSA
NAME
GROWTH PROFILE
EPA REGION
WSSW
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, Ml
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, Ml
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, Ml
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, Wl
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, Ml
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 generatec

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, Ml
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, Ml
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, Ml
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, Wl
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, Ml
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)

I 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, Ml
-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, Ml
-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, Ml
-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, Wl
-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, Ml
-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, Ml
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, Ml
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, Ml
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, Wl
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, Ml
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
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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
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