-8-EPA
United States
Environmental Protection
Agency
EPA600/R-17/238
October 2017
Development of a Climate Resilience
Screening Index (CRSI): An Assessment of
Resilience to Acute Meteorological Events
and Selected Natural Hazards
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EPA600/R-17/238
October 2017
Development of a Climate Resilience
Screening Index (CRSI): An Assessment of
Resilience to Acute Meteorological Events
and Selected Natural Hazards
by
J. Kevin Summers, Linda C. Harwell, Kyle D. Buck, Lisa M. Smith
Deborah N. Vivian, Justin J.Bousquin, James E. Harvey, Stephen F.
Hafner and Michelle D. McLaughlin
ORD/NHEERL/GED
1 Sabine Island Drive
Gulf Breeze, FL 32561
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Notice/Disclaimer Statement
This document has been reviewed in accordance with U.S. Environmental Protection Agency
policy and approved for publication. The information in this article has been funded wholly (or
in part) by the U.S. Environmental Protection Agency. It has been subjected to review by the
National Health and Environmental Effects Research Laboratory and approved for publication.
Approval does not signify that the contents reflect the views of the Agency, nor does mention of
trade names or commercial products constitute endorsement or recommendation for use. All
images and copyrights are the property of the U.S. Environmental Protection Agency.
2
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Table of Contents
Notice/Disclaimer Statement 2
Acronyms and Abbreviations 10
1. Introduction and Background 20
2. Approach 24
2.1. Overview of Indicator/Indices Development 24
2.2. A Review of Existing Resilience Indicators and Indices 25
2.3. Determination of Climate Event Factors to be Included in CRSI 28
2.4. The CRSI Conceptual Framework 32
2.4.1. Risk Domain 35
2.4.2. Governance Domain 37
2.4.3. Society Domain 39
2.4.4. Built Environment Domain 43
2.4.5. Natural Environment Domain 46
2.5. Metric Selection and Data Sources 47
2.6. Data Handling and Standardization 48
2.7. Calculations 50
2.7.1. Built Environment, Governance, Natural Environment and Society
Domains 50
2.7.2. Risk Domain 51
2.8. The Final Steps to CRSI 52
2.9. Uncertainty Analysis 53
3. How to Use CRSI - Its Utility and Potential Applications 56
3.1. Introduction 56
3.2. General Broad Use 56
3.3. Use by EPA Regions 57
3.4. Use by EPA Program Offices 58
3.5. Use by States, Counties, Metropolitan Areas and Communities 59
3.6. Examples 60
4. Results and Discussion - National and EPA Regions 65
4.1. Organization of Results 65
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4.2. General Broad Analyses and Results of Basic Resilience (Governance/Risk)
65
4.3. Presentation of Results 71
4.3.1. CRSI and Domain Score Bar Graphs 71
4.3.2. Six Panel Maps 72
4.3.3. Top County CRSI Values 73
4.3.4. Breakdown of the Risk Domain 74
4.3.5. Polar Plots for Nation and EPA Regions 75
4.3.6. National Results 75
4.3.7. Regional Results 81
7. Future Directions for Community Resilience to Extreme Weather Events 142
8. References 145
9. Appendices 152
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Figures
Figure E-1. Conceptual representation of the Climate Resilience Screening Index
(CRSI) Approach 12
Figure E-2. Map showing distribution of final CRSI Scores across the U.S. (2000-
2015) 13
Figure E-3. The distribution of CRSI values and domain scores (Risk, Governance,
Society, Built Environment, and Natural Environment) 14
Figure E-4. Polar plot showing the contribution of the 20 indicators associated with the
domain scores for the EPA Region 5 15
Figure 1.1 Conceptual representation of the Climate Resilience Screening Index (CRSI)
Approach 23
Figure 2.1 Number of applied resilience indices found using multi-factor composite
index measures 26
Figure 2.2 Publication elimination summary based on existing climate index
development literature (2000-2015) used to inform CRSI research efforts 27
Figure 2.3 Final CRSI conceptual framework 34
Figure 2.4 Representation of the Metric, Indicator and Domain scores for Governance,
Society, Built Environment and Natural Environment Domains of CRSI 51
Figure 2.5 Representation of the Metric, Indicator and Domain scores for Risk Domain
of CRSI 52
Figure 4.1 Linear assessment of risk versus governance based on domain scores 66
Figure 4.2 Distribution of number of counties in quartiles for risk and governance
domains based on the domain scores 67
Figure 4.3 Map of the distribution of county scores for basic resilience 68
Figure 4.4. Distribution of number of counties in quartiles for risk and governance
domains based on number of samples (redistributing the basic resilience scores). 69
Figure 4.5 Map of the re-distribution of counties to demonstrate the likelihood of
increased resilience with increased governance 70
Figure 4.6 Example summary of CRSI and domain available for the nation and each
EPA region 71
Figure 4.7 Example of six-panel maps showing the distribution of county-level CRSI and
domain scores available for the nation and for the EPA Regions 72
Figure 4.8 Example Table of highest ranking CRSI values for all U.S. counties and
counties within EPA Regions 73
Figure 4.9 Example summary of Risk domain presented for the nation and the EPA
Regions 74
Figure 4.10 Example polar plot describing the contributions of the 20 indicators to the
domain scores 75
Figure 4.11 Summary of CRSI (upper right hand value) and domain scores (light
colored bars) for the U.S, along with domain median adjusted scores showing
influence of each domain on final CRSI score (dark colored bars) 76
Figure 4.12 The distributions of CRSI values and domain scores (Risk, Governance,
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Society, Built Environment and Natural Environment) 77
Figure 4.13 Map of Risk Domain scores by county; proportion of natural exposures by
climate event type, technological exposures, losses and exposure type nationwide;
and the range of risk with the highest risk and lowest risk counties identified; as well
as, the three primary exposure types 80
Figure 4.14 Polar plot showing the contribution of the 20 indicators associated with the
domain scores for the nation 81
Figure 4.15 Summary of CRSI (upper right hand value) and domain scores (light
colored bars) for EPA Region 1 along with domain median adjusted scores showing
influence of each domain on final CRSI score (dark colored bars) 83
Figure 4.16 Summary of CRSI (upper right hand value) and domain scores (light
colored bars) for Region 1, along with domain median adjusted scores showing
influence of each domain on CRSI (dark colored bars) 84
Figure 4.17 Map of Risk Domain scores by county for Region 1; proportion of natural
exposures by climate event type, technological exposures, losses and exposure
type nationwide; and the range of risk with the highest risk and lowest risk counties
identified; as well as, the three primary exposure types in the region (If a category
was represented by <0.1 %, it was not included) 86
Figure 4.18 Polar plot showing the contribution of the 20 indicators associated with the
domain scores for the EPA Region 1 87
Figure 4.19 Summary of CRSI (upper right hand value) and domain scores (light
colored bars) for EPA Region 2, along with domain median adjusted scores
showing influence of each domain on final CRSI score (dark colored bars) 89
Figure 4.20 The distributions of EPA Region 2 CRSI values and domain scores (Risk,
Governance, Society, Built Environment and Natural Environment) 90
Figure 4.21 Map of Risk Domain scores by county for Region 2; proportion of natural
exposures by climate event type, technological exposures, losses and exposure
type nationwide; and the range of risk with the highest risk and lowest risk counties
identified; as well as, the three primary exposure types in the region 92
Figure 4.22 Polar plot showing the contribution of the 20 indicators associated with the
domain scores for the EPA Region 2 93
Figure 4.23 Summary of CRSI (upper right hand value) and domain scores (light
colored bars) for EPA Region 3, along with domain median adjusted scores
showing influence of each domain on final CRSI score (dark colored bars) 95
Figure 4.24 The distributions of EPA Region 3 CRSI values and domain scores (Risk,
Governance, Society, Built Environment and Natural Environment) 96
Figure 4.25 Map of Risk Domain scores by county for Region 3; proportion of natural
exposures by climate event type, technological exposures, losses and exposure
type nationwide; and the range of risk with the highest risk and lowest risk counties
identified; as well as, the three primary exposure types in the region 98
Figure 4.26 Polar plot showing the contribution of the 20 indicators associated with the
domain scores for the EPA Region 3 99
Figure 4.27 Summary of CRSI (upper right hand value) and domain scores (light
colored bars) for EPA Region 4, along with domain median adjusted scores
showing influence of each domain on final CRSI score (dark colored bars) 101
Figure 4.28 The distributions of EPA Region 4 CRSI values and domain scores (Risk,
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Governance, Society, Built Environment and Natural Environment) 102
Figure 4.29 Map of Risk Domain scores by county for Region 4; proportion of natural
exposures by climate event type, technological exposures, losses and exposure
type nationwide; and the range of risk with the highest risk and lowest risk counties
identified; as well as, the three primary exposure types in the region 104
Figure 4.30 Polar plot showing the contribution of the 20 indicators associated with the
domain scores for the EPA Region 4 105
Figure 4.31 Summary of CRSI (upper right hand value) and domain scores (light
colored bars) for EPA Region 5, along with domain median adjusted scores
showing influence of each domain on final CRSI score (dark colored bars) 107
Figure 4.32 The distributions of EPA Region 5 CRSI values and domain scores (Risk,
Governance, Society, Built Environment and Natural Environment) 108
Figure 4.33 Map of Risk Domain scores by county for Region 5; proportion of natural
exposures by climate event type, technological exposures, losses and exposure
type nationwide; and the range of risk with the highest risk and lowest risk counties
identified; as well as, the three primary exposure types in the region 110
Figure 4.34 Polar plot showing the contribution of the 20 indicators associated with the
domain scores for the EPA Region 5 111
Figure 4.35 Summary of CRSI (upper right hand value) and domain scores (light
colored bars) for the U.S, along with domain median adjusted scores showing
influence of each domain on final CRSI score (dark colored bars) 113
Figure 4.36 The distributions of EPA Region 6 CRSI values and domain scores (Risk,
Governance, Society, Built Environment and Natural Environment) 114
Figure 4.37 Map of Risk Domain scores by county for Region 6; proportion of natural
exposures by climate event type, technological exposures, losses and exposure
type nationwide; and the range of risk with the highest risk and lowest risk counties
identified; as well as, the three primary exposure types in the region 116
Figure 4.38 Polar plot showing the contribution of the 20 indicators associated with the
domain scores for the EPA Region 6 117
Figure 4.39 Summary of CRSI (upper right hand value) and domain scores (light
colored bars) for EPA Region 7, along with domain median adjusted scores
showing influence of each domain on final CRSI score (dark colored bars) 119
Figure 4.40 The distributions of EPA Region 7 CRSI values and domain scores (Risk,
Governance, Society, Built Environment and Natural Environment) 120
Figure 4.41 Map of Risk Domain scores by county for Region 7; proportion of natural
exposures by climate event type, technological exposures, losses and exposure
type nationwide; and the range of risk with the highest risk and lowest risk counties
identified; as well as, the three primary exposure types in the region 122
Figure 4.42 Polar plot showing the contribution of the 20 indicators associated with the
domain scores for the EPA Region 7 123
Figure 4.43 Summary of CRSI (upper right hand value) and domain scores (light
colored bars) for EPA Region 8, along with domain median adjusted scores
showing influence of each domain on final CRSI score (dark colored bars) 125
Figure 4.44 The distributions of EPA Region 8 CRSI values and domain scores (Risk,
Governance, Society, Built Environment and Natural Environment) 126
Figure 4.45 Map of Risk Domain scores by county for Region 8; proportion of natural
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exposures by climate event type, technological exposures, losses and exposure
type nationwide; and the range of risk with the highest risk and lowest risk counties
identified; as well as, the three primary exposure types in the region 128
Figure 4.46 Polar plot showing the contribution of the 20 indicators associated with the
domain scores for the EPA Region 8 129
Figure 4.47 Summary of CRSI (upper right hand value) and domain scores (light
colored bars) for EPA Region 9, along with domain median adjusted scores
showing influence of each domain on final CRSI score (dark colored bars) 131
Figure 4.48 The distributions of EPA Region 9 CRSI values and domain scores (Risk,
Governance, Society, Built Environment and Natural Environment) 132
Figure 4.49 Map of Risk Domain scores by county for Region 9; proportion of natural
exposures by climate event type, technological exposures, losses and exposure
type nationwide; and the range of risk with the highest risk and lowest risk counties
identified; as well as, the three primary exposure types in the region 134
Figure 4.50 Polar plot showing the contribution of the 20 indicators associated with the
domain scores for the EPA Region 9 135
Figure 4.51 Summary of CRSI (upper right hand value) and domain scores (light
colored bars) for EPA Region 10, along with domain median adjusted scores
showing influence of each domain on final CRSI score (dark colored bars) 137
Figure 4.52 The distributions of EPA Region 10 CRSI values and domain scores (Risk,
Governance, Society, Built Environment and Natural Environment) 138
Figure 4.53 Map of Risk Domain scores by county for Region 10; proportion of natural
exposures by climate event type, technological exposures, losses and exposure
type nationwide; and the range of risk with the highest risk and lowest risk counties
identified; as well as, the three primary exposure types in the region 140
Figure 4.54 Polar plot showing the contribution of the 20 indicators associated with the
domain scores for the EPA Region 10 141
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Tables
Table E-1. CRSI and domain scores for EPA Regions with National Average scores
(including Alaska) 18
Table 2.1 Existing measures of climate resilience included in this review, the number of
domains/indicators and metrics used in each measure 27
Table 2.2 Summarized climate impacts for regions of the U.S. from the 2014 National
Climate Assessment Report 30
Table 2.3 Summarized climate impacts and resilience issues for selected cities of the
U.S. from 100 Resilient Cities and ICLEI/RC4A (Local Governments for
Sustainability (previously the International Council for Local Environmental
lnitiatives)/Resilient Communities for America) 31
Table 2.4 Summary of literature reviewed index by topical areas of interest for
development of CRSI 33
Table 2.5 List of CRSI domains, indicators, scope and number of metrics 48
Table 3.1. CRSI and domain scores for select counties along the Texas Gulf Coast and
National Average scores (excluding Alaska) 61
Table 3.2. CRSI and domain scores for EPA Regions with National Average scores
(including Alaska) 64
Table 4.1 Top 150 counties according to CRSI values (i.e., potentially higher resilience
to climate events) 79
Table 4.2 Top 25 counties according to CRSI values in EPA Region 1 (i.e., higher
resilience to climate events) 85
Table 4.3 Highest 25 CRSI values in EPA Region 2 by county 91
Table 4.4 Counties in EPA Region 3 with the highest CRSI values 97
Table 4.5 Twenty-five counties in EPA Region 4 with the highest CRSI values 103
Table 4.6 Twenty-five counties in EPA Region 5 with the highest CRSI values 109
Table 4.7 Twenty-five counties in EPA Region 6 with the highest CRSI values 115
Table 4.8 Twenty-five highest CRSI values in the counties of EPA Region 7 121
Table 4.9 Twenty-five counties in EPA Region 8 with the highest CRSI values 127
Table 4.10 Twenty-five counties in EPA Region 9 with the highest CRSI values 133
Table 4.11 Twenty-five counties in EPA Region 10 with the highest CRSI values 139
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Acronyms and Abbreviations
BRIC
Baseline Resilience Indicators for Communities
CBNRM
Community-Based Natural Resource Management
CDRI1
Climate Disaster Resilience Index 1
CDRI2
Climate Disaster Resilience Index 2
CEQ
Council on Environmental Quality
CRS
Community Rating System
CRSI
Climate Resilience Screening Index
CWPPRA
Coastal, Wetlands Planning, Protection and Restoration Act
DOC
Department of Commerce
DOI
Department of the Interior
EPA/USEPA
U.S. Environmental Protection Agency
FEMA
Federal Emergency Management Agency
GAO
Government Accounting Office
ICLEI
Local Governments for Sustainability (previously the International Council for
Local Environmental Initiatives)
LUST
Leaking Underground Storage Tanks
M-CRD
Metrics for Community Resilience to Disaster
MERLIN-RC
Model for External Reliance of Localities In Regional Contexts
M-RD
Metrics for Resilience to Disaster
NCA
National Climate Assessment
NHEERL
National Health and Environmental Effect Research Laboratory
NFIP
National Flood Insurance Program
NGO
Non-Government Organization
NRC
National Research Council
NRCS
National Resource Conservation Service
OECD
Organization for Economic Co-Operation and Development
OMB
Office of Management and Budget
PCA
Principal Components Analysis
PRISM
Patterns of Risk using an Integrated Spatial Multi-Hazard Model
RC4A
Resilient Communities for America
RCRA
Resource Conservation and Recovery Act
SB A
Small Business Adminstration
SHC
Sustainable and Healthy Communities Research Program
SHELDUS
Spatial Hazard Events and Losses Database
SOVI
Social Vulnerability Index
TRI
Toxic Resource Inventory
USFS
United States Forest Service
USGS
United States Geological Survey
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Acknowledgments
The authors would like to thank the peer reviewers of this report:
Courtney G. Flint, Ph.D., Professor of Natural Resource Sociology, Dept. of Sociology, Social
Work and Anthropology, Associate Director, Center for Society, Economy and the Environment,
Utah State University
Peter B. Meyer, Ph.D., Professor Emeritus of Urban Policy and Economics, University of
Louisville, President and Chief Economist, The E.P. Systems Group, Inc.
Matthew Nicholson, EPA Region 3
Meaghan Bresnahan, EPA Region 6, Unable to Complete due to Hurrican Harvey
Adele Cardenas Malott, EPA Region 6
Suzanna Perea, EPA Region 6
Joyce Stubblefield, EPA Region 6
Laura J. Farris, Climate Change Coordinator and International Coordinator, EPA Region 8
Bruce Duncan, Regional Science Liaison to Office of Research and Development, EPA Region
10
Jeff Peterson, Senior Policy Advisor, EPA Office of Water
Megan Susman, EPA Office of Sustainable Communities
Dana Krishland, EPA Office of Air and Radiation
Emma Zinsmeister, EPA Office of Air and Radiation
Additionally, the authors would like to thank Ms. Virginia Houk of the National Health and
Environmental Effects Research Laboratory for organizing and conducting the peer review.
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Resilience
Executive Summary
This 2017 research report, Development of a Climate Resilience
Screening Index (CRSI): An Assessment of Resilience to Acute
Meteorological Events and Selected Natural Hazards is a
synthesis report detailing research in the development and
demonstration of the CRSI approach at both the national
and regional scales using county data. The report also
includes an extensive analysis of the conceptual framework
along with methods for metric, indicator and domain
calculation.
Natural Environment
Built Environment
Society
• Social Services
• Labor/Trade
• Safety and Security
• Economic Diversity
* Demographics
Governance
Climate & Natural Hazard Stressors
• Extent of Ecosystem Types
• Community
Preparedness
• Personal Preparedness
¦ Utility Infrastructure
* Transportation Infrastructure Characteristics
Infrastructure
• Housing
• Exposure
Risk
Bold Text Domain
CRSI Composite Index
Gradient
• Bullet Indicator
Legend
Italicized Text External Influence
Domain
Resilience
Range
Indicator
Resilience
Range
Risk
Exposure
Figure E-l. Conceptual representation of the Climate
Resilience Screening Index (CRSI) Approach.
12
Natural disasters often impose
significant and long-lasting stress on
financial, social and ecological
systems. From Atlantic hurricanes to
Midwest tornadoes to Western
wildfires, no corner of the U.S. is
immune from the threat of a
devastating climate-event. Across
the nation, there is a recognition that
the benefits of creating
environments resilient to adverse
climate events helps promote and
sustain county and community
success over time. The challenge for
communities is in finding ways to
balance the need to preserve the
socio-ecological systems on which
they depend in the face of constantly
changing natural hazard threats.
The Climate Resilience Screening
Index (CRSI) has been developed as
an endpoint for characterizing
county and community resilience
outcomes that are based on risk
profiles and responsive to changes
in governance, societal, built and
natural system characteristics. The
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Climate Resilience Screening Index (CRSI) framework (Figure E-l) serves as a conceptual
roadmap showing how acute climate events impact resilience after factoring in the county and
community characteristics. By evaluating the factors that influence vulnerability and
recoverability, an estimation of resilience can quantify how changes in these characteristics will
impact resilience given specific hazard profiles. Ultimately, this knowledge will help
communities identify potential areas to target for increasing resilience to acute climate events
(Figure E-2).
Higher
Lower
Figure E-2. Map showing distribution of final CRSI Scores across the U.S. (2000-2015). Darker
colors indicate higher resilience scores; lighter colors indicate lower resilience scores.
The index is a composite measure comprised of five domains (Risk, Governance, Society, Built
Environment, and Natural Environment), represented by 20 indicators, calculated from 117
metrics. CRSI scores have been calculated at the county level (or parish or borough) and
community resilience, and additional break out assessments are presented for individual domains
of the index as well as regional level as a composite for the years 2000-2015 (Figure E-3). In
addition, to a national assessment of resilience, EPA regional and county measured are
calculated and mapped.
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Society
Figure E-3. The distribution of CRSI values and domain scores (Risk, Governance, Society,
Built Environment, and Natural Environment).
14
Oovermnce
Built timionmcni
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Regional analyses characterize risk components, evaluate relative domain contributions to
resilience, and delineate indicator contributions within the geography. Polar plots are utilized as
a method to easily discern indicator influence (Figure E-4).
,o<^en'
Lots
Community
Preparedness
Utility
Infrastructure
Housing
Characteristics
Communication
Infrastructure
Economic
Diversity
Safety
and
Security
Social
Cohesion
Figure E-4. Polar plot showing the contribution of the 20 indicators associated with the domain
scores for the EPA Region 5. The length of the bars corresponds to the indicator score. Within a domain,
the higher indicator scores show a greater contribution to the domain score (sum of indicator scores).
CRSI was developed with input from EPA Regional Climate Coordinators and ORD Regional
Science Liaisons. The demonstration results by county and by EPA region can be used by the
Regions to engage communities in resilience discussions, be vetted with local knowledge and
potentially be used to target resources for improving resilience. CRSI results data, like EQI and
HWBI results can be made available through the Geoplatform for use in SHC tools. Overall
CRSI values, and domain scores at the county-level can inform sustainability assessments
research (4.61) and could complement climate and vulnerability assessments for developing
resilience strategies (e.g., developing water resilience strategy for Merrimack River, Lawrence,
MA (SHC 2.62)).
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'Your effort is "laudable and important." I very much
appreciate the focus on a multi-dimensional
approach to assessing climate related resilience. The
report cites important literature defining resiliency
and vulnerability. The graphics are visually appealing
and, as long as the data are accurate behind them,
likely to be helpful for various users. But I have some
concerns about the data being used in ways that
might not be appropriate given the aggregation and
operationalization issues that relate to data
availability. I appreciate that such an index endeavor
is limited to existing available data. But these
limitations need to be much more clearly
acknowledged.'
-Dr. Courtney Flint, Utah State University
Great care has been taken to ensure that the
aggregations used in CRSI are correct and the
authors have attempted to provide examples of how
to use the index. Might elements of CRSI be misused?
Of course, this is the case with any index or
aggregation of data; however, the authors have
taken great pains to ensure the accuracy and
limitations of the available data.
- Dr. Kevin Summers, U.S. EPA
Highlights of Results
In the section above, the maps and analytic results of the national application are shown. The
highlights of the national analysis show moderate to strong resilience to climate events
throughout many of the counties in the U.S. Areas with weaker overall resilience include the
Appalachians, many counties in the southeast and the western Mid-West and some counties in
southwestern Texas. Strong contributors to the final CRSI scores are natural resource
conservation, local demographics, and information pertaining to vacant structures. Weak
contributors include infrastructure associated with utilities and communications and safety and
security issues as well as the local mix of labor skills. Increases in these weak contributors could
substantially enhance resilience to acute climate events on a national scale.
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Regional analyses (Table E-l) and mapping show that EPA Region 10 (14.8) and EPA Region 1
(10.7) have the strongest overall resilience scores with EPA Region 4 (0.6) and EPA Region 6
(2.8) having weaker scores. The remaining six EPA Regions cluster together with moderate
scores (3.4-6.1). Disassembly of the CRSI scores shows that Region 10 strengths lie in its low
risk score which result in a high basic resilience score even though its governance low is less
than the national average. Although lower, its governance domain score is more than three times
the Region's risk domain score. Region 1 strengths lie in the highest governance score in the
Nation with moderate risk, and above average domain scores for social, built environment and
natural environment. On the other hand, Regions 4 and 6 have above average risk domain scores
and below average governance related to climate events scores. Driving down these lower basic
resilience scores, both regions have below average society domain scores suggesting a poorer
population, increased ethnicity (making communication for emergency response more difficult),
lower levels of social services, poorer access to health facilities, and higher level of
undocumented skilled trade laborers (making an assessment of the abundance of trade labor
difficult). Region 4 also has a below average score for its built environment suggesting less
stringent building codes, higher levels of vacant structures and weaker levels of public
infrastructure especially in Georgia and Alabama.
The utility of the index is addressed in Section 3 although the greatest level of confidence in
utility can be found in the quotes listed below by reviewers from EPA Regions in response to the
questions, "In your opinion, does the index have utility for EPA (e.g., Regions and Program
Offices)?" and "Does this utility extend to community decision makers, community planners,
and other potential stakeholders?".
"Yes - Using the data in work we do in each of our
programs relative to pollution control implications
and sustainability"
—Joyce Stubblefield, Region 6
"Absolutely! I like the discussion of ORD research
related to natural disaster and other climate event
resiliency topics..."
—Laura Farris, Region 8
"I look forward to seeing the final report and using it
in my own work."
—Matt Nicholson, Region 3
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Table E-l. CRSI and domain scores for EPA Regions with National Average scores (including Alaska); (Bold denotes
significantly below national average for CRSI and above national average for domains).
EPA Region
Risk
Governance
Built
Environment
Natural
Environment
Society
CRSI
Region 1
0.2403
0.8956
0.4916
0.4445
0.5987
10.6968
Region 2
0.3084
0.8292
0.4694
0.3860
0.5202
4.9988
Region 3
0.2715
0.6885
0.3821
0.3778
0.5117
3.3911
Region 4
0.2547
0.4976
0.3421
0.4027
0.4141
0.5849
Region 5
0.2217
0.7135
0.4070
0.4343
0.5722
6.0213
Region 6
0.2392
0.5479
0.3937
0.4229
0.4739
2.7718
Region 7
0.2087
0.5968
0.3576
0.3800
0.6092
4.1134
Region 8
0.1623
0.5572
0.3983
0.3956
0.6167
6.0857
Region 9
0.2345
0.3579
0.6204
0.4704
0.4795
6.0778
Region 10
0.1370
0.4319
0.4776
0.5315
0.4920
14.8380
National
Average
0.2288
0.5876
0.3932
0.4136
0.5156
4.2125
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"Yes, there is potential use. Regions and programs
are being asked that same question for other indices
based on similar structure (national databases;
selecting domains; comparison at a county scale)
developed by ORD (e.g., HWBI). ... Certainly, this will
have utility at the county level and for others who
can use it as is to aggregate above counties (such as
coastal states or coastal counties). ... Again, a
community of practice across index developers could
help quickly identify many issues that stakeholders
have raised."
—Bruce Duncan, Region 10
To be fair, not all reviewers were as enthusiastic. Several reviewers not associated with EPA
Regions found greater difficulty with the utility of the index. These reviewers thought it would
be very helpful to indicate how the index could be used and how it should not be used. However,
the target audience of CRSI is the EPA Regional staff working on resilience and sustainability
issues and the index and its utility appears to resonant with the Regional reviewers.
Overall, the U.S. shows good levels of resilience to acute climatic events. However, analyses
demonstrate that selected counties (hundreds of them) with higher levels of risk and low levels of
governance can improve their resilience by specifically addressing issues associated with the
governance, built environment, natural environment, and society domains. CRSI, which is meant
to be a screening tool, provides those directions investment, assistance and action by the EPA
Regions and Program Offices.
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1. Introduction and Background
Natural disasters often impose significant and long-lasting
stress on financial, social and ecological systems. From
Atlantic hurricanes to midwest tornadoes to western wildfires, SUSTAINABILITY
no corner of the U.S. is immune from the threat of a AND RESILIENCE
devastating climate-event. Statistics from the Office of
Management and Budget show the federal government has
incurred more than $357 billion in direct costs due to extreme
weather and fire events alone over the last ten years (OMB and
CEQ 2016). Starting in 2013, the U.S. Government
Accountability Office (GAO) began monitoring the high risk
fiscal exposure that the federal government faces because of
climate-related events, both acute and chronic. The GAO
recognized the sweeping impacts of these events across
multiple sectors including defense, infrastructure, health,
agriculture and local economies. In the most recent GAO
report (2017), steps to better manage this fiscal risk had only
been partially implemented. Further, the U.S. National Security
Strategy (2015) highlights efforts in strengthening county and
community resilience, suggesting that impacts from adverse
climate events represent an area of credible national security
concern.
In general terms, resilience is a characteristic in human and
natural systems exhibiting a capacity to withstand and recover
from an adverse shock or event. In towns and cities, resilience
is promoted through planning while in nature, this trait is
assumed inherent (NRC 2012; Meadows 2008). Over the last
decade, there has been a notable increase in communities
seeking sustainable economic, social and ecological solutions
for local planning concerns. However, more county and
community decision makers are recognizing that recurring and
anomalous climate events may impede achieving their
sustainability goals without appropriate and actionable
preparation. Therefore, it is not surprising that interest in the
subject of resilience related to natural disasters, both cyclic and
evolving, is growing. Across the nation, there is a recognition
that the benefits of creating environments resilient to adverse
climate events helps promote and sustain county and
community success over time. The challenge for communities
is in finding ways to balance the need to preserve the socio-
ecological systems on which they depend in the face of
constantly changing natural hazard threats.
20
"...RESILIENCE THEN BECOMES
A THEORETICAL CONSTRUCT
FOR SUSTAINABILITY THAT: A)
GUIDES AGAINST BREACHING
UNKNOWN SYSTEMS
BOUNDARIES; B) SUGGESTS
THAT CONTINUOUS CHANGES
IN CERTAIN DRIVING
VARIABLES ARE INHERENTLY
DANGEROUS (E.G.,
CONTINUOUSLY INCREASING
FISHING PRESSURE,
ESCALATING GREENHOUSE
GAS EMISSIONS, OR
CONSTANT MATERIAL
GROWTH) AND; C) WARNS
THAT SURVIVING THE BREACH
OF A MAJOR TIPPING POINT,
WHETHER HUMAN INDUCED
OR NATURAL, WILL REQUIRE
UNPRECEDENTED LEVELS OF
INVESTMENT, COOPERATION
AND OTHER FORMS OF
INSTITUTIONAL AND
SOCIETAL ADAPTATION.
HUMAN-INDUCED CLIMATE
CHANGE WILL ALMOST
CERTAINLY VALIDATE ALL
THESE ASSERTIONS."
SUSTAINABILITY VS. RESILIENCE
PUBLISHED BY RESILIENCE.ORG ON
2014-07-16
BY WILLIAM E. REES
SOURCE URL:
http://www.resilience.org/stori
ES/2014-07-16/SU STAINA BIL IT Y-VS-
RESILIENCE
-------
Resilience applies to both human and natural systems, yet the examination of resilience is often
described without appreciation of one another or in the context of opposing roles (Handmer et al.
2012)—with one system making the other more vulnerable. Previous research suggests that
positive aspects of county and community quality of life are linked to not only built
environments, but natural ones as well (Smith et al. 2012; Summers et al. 2012). Any discussion
of county and community resilience would be incomplete without considering the role of natural
ecosystems, as they have the ability to influence many of a county's and community's
vulnerability and recoverability characteristics (Summers et al. 2012, 2015).
In the context of this research, vulnerability describes the propensity or predisposition to be
adversely affected, while resilience describes the ability of a system and its component parts to
anticipate, absorb, accommodate, or recover from the effects of a hazardous event in a timely and
efficient manner (IPCC 2012). Much of the existing resilience literature focuses on either
vulnerability or recovery (e.g., Cutter et al. 2003; Frazier et al. 2014) as independent constructs
of resilience. Summers et al. (2016) suggests a more holistic relationship exists, where an
intersection of vulnerability and recoverability sits along a spectrum of resilience. The position
along this gradient where human and natural systems rest depends on their ability or capacity for
resilience. In terms of climate events, for example, both people and nature can absorb, recover
from and adapt to adverse events (Gunderson 2010; Berkes and Ross 2013). However, the degree
of resilience is reflected in the mechanisms for recovery. Natural ecosystems have innate internal
structures and functions to facilitate recovery from an adverse event (such as diversity and
redundancy) (Holling 1986; National Fish, Wildlife and Plants Climate Adaptation Partnership
2012; Melillo et al. 2014). Human systems rely on planning and preparation to mitigate against
known natural hazard exposures and reduce vulnerabilities (Tobin 1999; Magus 2010). In both
systems, the success of the recovery process is dependent on the robustness of the mechanism.
This robustness refers to the system's ability to resist or tolerate change without adapting its
initial stable configuration. In the case of nature, ecological conditions may be the determining
factor while the depth and breadth in resilience planning or governance is a pillar for resilience in
built environments. Clearly, resilience is a disputed and heavily debated subject with regard to
anthropogenic and natural systems (Patel et al. 2017). Community resilience remains an
amorphous concept that is understood and applied differently by different groups. Yet in spite of
the differences in conception and application, there are well-understood elements that are widely
proposed as important for a resilient community. All seem to agree that community resilience
(non-individual) relates to the sustained ability of a community (or other entity) to utilize
available resources to respond to, withstand, and recover (hopefully quickly) from adverse
difficulties or perturbations (FEMA 2011, 2012, 2017; RAND 2017).
Operationally, in this report, a broad definition of community has been taken.
Using a community definition of a social group of any size whose members
reside in a specific locality, share government, and often have a common
cultural and historical hertitage,"community" could be synonymous with
"county". Thus, the term "community," when used in this report means the
grouping is a county unless specified otherwise. Resilience clearly can apply
to a smaller community unit or neighborhood. That is not the case in this
report. However, in many situations smaller communities resilience can be
directly related to or driven by governance and activities at the county scale.
21
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Many counties and communities are seeking assistance from the U.S. Environmental Protection
Agency (EPA) to help fill resilience information gaps for disaster resilience planning. To better
assist counties and communities, EPA's Office of Research and Development (ORD) has
invested in research related to natural disaster and other climate event resiliency topics including:
• National Homeland Security Research Center's investigation of community resilience to
acute disaster events (USEPA 2015b)
• National Center for Environmental Assessment research on resilience to climate change
(USEPA 2016b)
• National Exposure Research Laboratory's (NERL) work with counties and communities
to assess resilience to climate events, particularly flooding (Lawrence, MA) (Zartarian
2016)
• National Risk Management Research Laboratory's (NRMRL) research focusing on
linking resilience measures to adaptive management and governance to help frame
sustainability assessments (Garmestani and Benson 2013; Garmestani and Allen 2014;
Eason et al. 2016).
Of particular interest to EPA are the development of approaches to assess county and community
resilience readiness in the face of adverse climate events. As part of EPA's Sustainable and
Healthy Communities (SHC) Research Program, a suite of indicators was developed to form the
basis of a composite index—the Climate Resilience Screening Index (CRSI). CRSI characterizes
county and community resilience based on a suite of indicators that are grouped into broad
categories or domains of county and community resiliency traits in the context of natural
disasters. CRSI is intended to be used by EPA Regions and others who work closely with
counties and communities to gauge resilience of built and natural systems to acute climate events
(e.g., hurricanes, wildfires, tornadoes, flooding). The CRSI approach focuses on characterizing
county and community resilience to these natural hazards through an understanding of the
existing conditions in socio-ecological systems - the baseline against which resilience is
quantified. The index and constituent components serve to characterize baseline conditions for
targeting resources and assessing the effectiveness of programs, policies and interventions
specifically designed to improve climate resilience. Five broad areas of common county and
community characteristics or domains are the basis for formulating the screening tool. CRSI
represents a synthesis of vulnerability and recoverability of a county's and community's built,
natural and social environments in relation to the governance of these systems and context of the
risk of natural hazard exposure (Figure 1.1).
22
-------
Acute Climate Events
Built Environment
fa-
Natural Environment
Society
Iuj—I Governance
Resilience
Figure 1.1 Conceptual representation of the Climate Resilience Screening Index (CRSI) Approach.
23
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2. Approach
2.1. Overview of Indicator/Indices Development
The methodological challenge in deriving an index of resilience to acute climate events lies in
constructing domains and indicators that are accurate representations of environmental or
societal states and trends but are easily understood by their target audiences. Methodological
challenges involve two broad sets of questions: those concerned with the design and
development of the index/indicators and those concerned with the purpose and use of the
index/indicators. Basic concerns over data availability, data quality, and the adequacy of the
algorithms used can be resolved largely through technical, scientific agreement. However, the
central issue of adjusting methods to index relevance and use has to be addressed through trade-
offs between form and function in specific societal and political settings.
WHAT IS AN INDEX?
An index is made up of many components and indicator research has a
language all its own. Here are few key definitions:
INDEX - An interpretable and synergistic value or category
describing the nature, condition or trend of a multidimensional
concept. An index can be an endpoint or final value as well as one
of several values used to create what is called a composite index.
CRSI is a composite index.
DOMAIN - Summary grouping of characteristics that is based on one
or more indicators and represents a major component of a
composite index. A domain and sub-index generally refer to the
same level of information.
INDICATOR: An interpretable value describing a trend or status a
specific feature or characteristic. An indicator may be comprised of
one or more metrics.
METRIC: A measurable or observable value - typically referred to as
"the data".
24
-------
The general technical approach is based on a familiar and common one, in use for several
decades to develop indices and compare components in away to describe the current condition
and help stakeholders identify areas to investigate for potential management actions/decisions
(Stanners et al. 2007).
The relationship among domains, indicators and metrics is shown here as a nested box using the
example of the CRSI index, the risk domain, the exposure indicator and a specific metric of
exposure.
Index
Domain
Indicator
Metric
n* |
Nan-storm
darragingwind
incidents
Exposure
2.2. A Review of Existing Resilience Indicators and Indices
A review of existing community resilience characterization methods and approaches was
conducted. The intent was to identify mainstream resilience indicators and indices and determine
the applicability of each within the scope of CRSI A Google Scholar search was analyzed
through Publish or Perish© software (7/28/15) using the following keywords: "resilience index",
ecosystems, social, economic, human resilience, and climate change. The time period of interest
was 2000-2015. The initial search produced 369 print and web publications. Material was
considered for in-depth review if described index or framework met the following criteria:
• Provided quantified or demonstration results
• Comprised of a suite of indicators or sub-indices
• Exhibited spatially scalable characteristics
• Integrated some combination of economic, ecological and social factors
• Focused on climate events or natural disasters.
25
-------
Fifty-seven candidate indicators were described in the materials reviewed. This representative
group of existing resilience indices favored integrated socio-economic and ecological
development approaches, but to varying degrees. Similarly, review results showed a notable
trend toward the use of composite indices to characterize community resilience over the 2000-
2015 time period (Figure 2.1).
14
12
I 10
T3
C
Z 8
"O
_aj
"5.
a
<
o
%
I I
2000 2001 2004 2005 2008 2009 2010 2011 2012 2013 2014 2015
Year of Publication
¦ Economic ¦ Ecological ¦ Economic+Social ¦ Ecological+Economic+Social ¦ Ecological+Economic
Figure 2.1 Number of applied resilience indices found using multi-factor composite index measures.
A pool of 27 published indices met all of the criteria. This final set of existing index
development approaches were used to further develop CRSI research efforts. Figure 2.2 briefly
decribes the literature review and culling process. Collectively, the remaining selected literature
offered 297 indicators, topical categories or domain groups with 624 related metrics (Table 2.1).
26
-------
72 indices or index
frameworks with use-
case examples
27 existing indices or
index frameworks
meeting all
acceptance literature
criteria
57 novel indices with
use-case examples
15 eliminated
conceptual models or
frameworks only
15 eliminated
duplicative use-cases
369 search results
using keyword:
"resilience index"
ecosystem, social,
economic, well-being,
climate change
87 publications
specifically related to
development of
climate or hazards
resilience indices
Figure 2.2 Publication elimination summary based on existing climate index development literature
(2000-2015) used to inform CRSI research efforts.
Table 2.1 Existing measures of climate resilience included in this review, the number of domains/indicators and metrics
used in each measure.
Index
Domains or
Indicators
Agriculture Resilience Index (Ciani
2012)
Arctic Water Resource
Vulnerability Index (Alessa et al.
2008)
11
Metrics
27
22
Index
Domains or
Indicators
Composite Measure of Ecological
Integrity (Vickerman and Kagan
2014)
Displacement Risk Index
(Esnard et al. 2011)
22
15
Metrics
22
51
Baseline Resilience Indicators for
Communities (Cutter et al. 2014)
49
49
EJ Screen Index
(U.S. EPA 2015a)
12
12
City Resilience Index (ARUP 2014)
12
12
Environmental Performance Index
(Hsu etal. 2016)
20
20
City Resilience Index to Sea Level
Rise (Baraboo and Hassan 2014)
13
Environmental Sustainability Index
(Esty etal. 2005)
21
76
Climate Disaster Resilience Index
(Joerin and Shaw 2011; Peacock et
al. 2010)
Community Resilience Index
(Kafle 2012; Renschler et al.
2010)
Community Resilience Index for
the Gulf of Mexico
(Baker 2009)
25
38
6
30
120
82
29
30
Environmental Vulnerability Index
(Pratt et al. 2004)
Flood Resilience Index
(Batica 2015)
Flood Vulnerability Index
(Balica 2012)
50
43
19
50
91
19
27
-------
Index
Domains or
Indicators
Metrics
Index
Domains or
Indicators
Metrics
Community Risk Index
(Daniell et al. 2010)
Composite Measure of Coastal
Community Resilience
(Li 2011)
Composite Measure of Community
Resilience
(Meher et a 1.2011)
Composite Measure of Regional
Resilience
(Martini 2014)
Composite Measure of Resilience
to Disasters
(Kusumastuti et al. 2014)
27
52
22
46
27
130
27
63
Household Resilience Index
(Cassidyand Barnes 2012)
Metrics for Community Resilience
to Disaster
(Burton 2015)
Resilience Factor Index
(Ainuddin and Routray 2012)
Resilience Inference Measurement
Model
(Li 2013; Lam et al. 2016)
Sustainable Society Index
(van de Kerk and Manual 2014)
16
22
16
10
21
16
75
17
33
21
A review of indicator categories and related measures presented in the literature showed that
vulnerability concerns stood out as a major recurring theme. This is not surprising since
identifying vulnerability is typically the first step toward defining resilience i.e., recognizing
hazard exposure weaknesses (e.g., Balica 2012; Batica 2015). However, vulnerability alone is
not sufficient to characterize climate resilience. In several cases, existing indices offered well-
rounded considerations for exposure vulnerability but often lacked similarly extensive measures
of recoverability from these same exposures, (e.g., Alessa et al. 2008; Joerin and Shaw 2011).
There were examples of resilience indices that included both recovery and vulnerability
indicators but these tended to compartmentalize the constructs into two distinct considerations
(e.g., Cutter et al. 2014) rather than in a synthesized fashion. While several existing indices
(ARUP 2014; Cutter et al. 1996, 2003, 2014) provided a more balanced suite of vulnerability and
recoverability resilience measures, scale or scope limited the generalizability of these indices to
fully generate suites of nationally comparable measures.
2.3. Determination of Climate Event Factors to be Included in CRSI
The National Climate Assessment summarizes the current and future impacts of climate change
in the United States (http://nca2014. globalchange.gov/reporty In this report, the likely changes in
climate events associated with geographic regions throughout the United States were assessed, as
well as the infrastructure challenges these changes would likely create (Table 2.2). Extended heat
waves (with associated drought), more frequent heavy downpours (with associated flooding), sea
level rise, enhanced insect outbreaks, increased wildfires, altered timing of streamflow, increased
and faster sea ice and glacial loss, and increased major storm events (including hurricanes,
tornadoes and superstorms) are all resultant climate changes that will likely be seen in the
coming decade. Communities (human and natural) will need to "adapt" to meet the challenges
presented by these changes. In human communities, that adaptation can take the form of
enhanced governance to increase recoverability to these events. In natural communities, the
"adaptation" likely will take the form of enhanced structural and functional redundancy to
28
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recover from stress. This combination of modified exposure and increased recoverability through
governance and natural ecosystem processes is the basis of resilience.
In initial CRSI development discussions, climate experts in each of the ten EPA regions were
interviewed to understand their views on the greatest climate challenges in their regions. These
reported challenges matched well with those identified in the National Climate Assessment and
the 100 Resilient Cities report (Rockefeller Foundation and ARUP 2014), as depicted in Figure
2.4 Rockefeller's 100 Resilient Cities helps cities around the world become more resilient to the
physical, social and econommic challenges of the 21st century. The EPA Regional interviews, the
100 Resilient Cities findings and the National Climate Assessment were combined to determine
the eleven (11) climate events that would be tracked in CRSI. These eleven climate event types
are:
Hurricanes
Tornadoes
Inland Floods
Coastal Flooding
Earthquakes
Wildfires
Drought
High Winds
Hail
Landslides
Temperature Extremes (high and low
deviations of temperature).
29
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Table 2,2 Summarized climate impacts for regions of the U.S. from the 2014 National Climate Assessment
Report. EPA regions within the regional assessment are identified in parentheses.
National Climate Assessment 2014
http://nca2014.globalchanRe.gov/report
Regional Assessments
Northeast (EPA Region 1, Region 2 and Region
3 (excluding VA)
Heat waves, heavy downpours, and sea level rise
pose growing challenges to many aspects of life in
the Northeast Infrastructure, agriculture, fisheries,
and ecosystems will be increasingly compromised.
Many states and cities are beginning to
incorporate climate change into their planning.
Southwest (EPA Region 9 and Region 8 (UT
and CO) Region 6 (NM))
Increased heat, drought, and insect outbreaks, all
linked to climate change, have increased
wildfires. Declining water supplies, reduced
agricultural yields, health impacts in cities due to
heat, and flooding and erosion in coastal areas
are additional concerns.
Southeast and Caribbean (EPA Region 3 (VA),
Region 4, Region 6 (AR and LA))
Sea level rise poses widespread and continuing
threats to the region's economy and environment.
Extreme heat will affect health, energy, agriculture,
and more. Decreased water availability will have
economic and environmental impacts.
Northwest (EPA Region 10 excluding Alaska)
Changes in the timing of streamflow reduce
water supplies for competing demands. Sea level
rise, erosion, inundation, risks to infrastructure,
and increasing ocean acidity pose major threats.
Increasing wildfire, insect outbreaks, and tree
diseases are causing widespread tree die-off.
Midwest (EPA Region 5 and Region 7 (IA and
MO))
Extreme heat, heavy downpours, and flooding will
affect infrastructure, health, agriculture, forestry,
transportation, air and water quality, and more.
Climate change will also exacerbate a range of risks
to the Great Lakes.
Alaska (EPA Region 10)
Alaska has warmed twice as fast as the rest of
the nation, bringing widespread impacts. Sea ice
is rapidly receding and glaciers are shrinking.
Thawing permafrost is leading to more wildfire,
and affecting infrastructure and wildlife habitat.
Rising ocean temperatures and acidification will
alter valuable marine fisheries.
Great Plains (EPA Region 6 (TX and OK), Region
7 (KS and NE) and Region 8 (excluding UT and
CO))
Rising temperatures are leading to increased
demand for water and energy. In parts of the
region, this will constrain development, stress
natural resources, and increase competition for
water. New agricultural practices will be needed to
cope with changing conditions.
Hawaii (EPA Region 9)
Warmer oceans are leading to increased coral
bleaching and disease outbreaks and changing
distribution of tuna fisheries. Freshwater supplies
will become more limited on many islands.
Coastal flooding and erosion will increase.
Mounting threats to food and water security,
infrastructure, health, and safety are expected to
lead to increasing human migration.
-------
Table 2.3 Summarized climate impacts and resilience issues for selected cities of the U.S. from 100 Resilient Cities and ICLEI/RC4A (Local Governments for Sustain ability
(previously the International Council for Local Environmental Initiatives)/Resilient Communities for America).
Extreme
Rainfall;
Flooding
Storms; Sea-
level rise
Erosion
Water Quality/
Quantity
nfra structure
Damage
Extreme Heat
Warming
Extensive
Wildfire
City/Climate Impacts
Severe Drought
Air Quality
Other Resi ience Issues
EPA Regior
affordable housing, social inequity
Boston. MA
Cambridge. MA
New York. NY
poor transportation system
Washington. DC
transportation and evacuation bottlenecks
Norfolk, VA
Lewes. DE
Pittsburgh. PA
environmental degradation, infra structure failure
Atlanta. GA
Broward County. FL
Miami Dade County, FL
Minneapolis, MN
Mi waukee. Wl
Grand Rapids. M
Ann Arbor. M
Chicago. IL
endemi c crime
nfrastructure failure, public health
* New Or eans. LA
nfrastructure fa i ure
Houston. TX
Dallas, TX
energy shortages, infra structure fail ure
** E Paso. TX
social inequity, epidemic drug & alcohol abuse, poor
Tuba. OK
n equity
Tucson. AZ
Dubuque. IA
crop failures
nequity, endemic crime, civil unrest
St. Louis. MO
Bou der. CO
nvasive species, disease, affordable housing
:olorado Springs. CO
Denver. CO
5aIt Lake City, UT
San Diego Bay Region. CA
Los Angeles, CA
rthquake, tsunam
Oak and. CA
social inequity, earthquake, affordable housing
ea rthquake
San Francisco. CA
* Berkeley. CA
ea rthquake
cold water species diminishing, invasive species
Eugene. OR
Beaverton. OR
King County. WA
(*) 100 Resilient Oties (**) ICLEI/RC4A & 100 Resilient Oties (x) Imports Experienced (-) Projected Imports
31
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2.4. The CRSI Conceptual Framework
No singular approach among existing composite measures of climate resilience met all of the
expected needs for developing CRSI. Collectively, however, the reviewed literature provided
many of the building blocks (e.g., suites of indicators, indicator groupings, domains). A "heat
map" table (Table 2.4) depicts the metric distribution of the final 27 existing indices across
resilience topics of interest to CRSI. To varying degrees, all of the existing indices offered
patterns of indicator groupings supporting the broad areas of interest for CRSI which formed the
basis of five sub-indices or "domains" to describe overall resilience:
• Natural Environment
• Society
• Built Environment
• Governance
• Risk.
While none of the indices reviewed provided all possible indicators of interest to CRSI, 10 of the
27 publications included information relevant for describing all five CRSI domains. The Natural
Environment, Governance and Risk domains were most frequently excluded from existing
measures. Five indices (BRIC, CDRI1, CDRI2, M-RD and M-CRD) offered fairly
comprehensive descriptions of indicators relevant for quantifying CRSI domains. The Climate
Disaster Resilience Index 2011 (CRDI1) contributed the most to the proposed CRSI structure;
addressing all domains based on a suite of 18 indicators.
Indicators and metrics from the selected literature were paired with one of the five CRSI
domains. Twenty-one domain-specific indicators were derived from 117 unique metrics. Figure
2.3 depicts the final CRSI conceptual framework. Constituents of CRSI: Domains and Indicators
of Community Resilience to Acute Climate Events. In this section, a summary description of
each CRSI domain and related indicators is provided.The summaries highlight the importance of
the domains in climate related resilience and the indicators used to characterize the five domains.
For each indicator,example measures (metrics) are listed. For more detailed information about
the individual metrics for each indicator, refer to Appendix A.
32
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T able 2.4 Summary of literature reviewed index by topical areas of interest for development of CRSI. (ARI -Agricultural Resilience Index AWRVI - Arctic Water Resource Vulnerability
Index RRIC -Baseline Resilience Indicators for Communities CRI-City Resilience Index CRISLR -City Resilience Index to Sea Level Rise CDRIl-Climate Disaster Resilience Index 2011
CDRI2-Community Disaster Resilience Index 2010 CResI-Community Resilience Index CRIG -Community Resilience Index for the Gulf of Mexico CRiskl-Community Risk Index
MCCR -Composite measure of coastal community resilience MCR-Composite measure of community resilience MRR -Composite measure of regional resilience M-RD -Composite
measure of resilience to disasters M-EI -Composite measures of ecological integrity DRI-Displacement Risk Index EJSI-EJ SCREEN Index EPI -Environmental Performance Index
ESI-Environmental Sustainability Index EVI-Environmental Vulnerability Index FRI-Flood Resilience Index FVI -Flood Vulnerability Index HRI -Household Resilience Index M-
CRD-Metrics for community resilience to disasters RFI- Resilience Factor Index RIMM -Resilience Inference Measurement model SSI-Sustainable Society Index).
CRSI Review Summary
Selected Index/Framework
Domains of
Resilience
Topic of
Interest
Candidate
Measurement
Categories
HH
Pi
%
BRIC
CRI
CRISLR
CDRI1
CDRI2
CResI
CRIG
CRiskI
MCCR
Pi
u
s
Pi
s
M-RD
M-EI
DRI
EJSI
EPI
ESI
EVI
FRI
FVI
HRI
M-CRD
RFI
RIMM
ISS
Natural
Environment
Extent of
Natural Areas
• Managed Lands
• Ecosystem Type
Integrity
• Condition
Society
Economy
• Economic
Diversity
• Employment
• Insurance
¦
Critical
Services
• Safety and
Security
• Social
• Labor/Trade
1 1
Characteristics
• Demographics
• Health
Built
Environment
Infrastructure
Integrity /
Continuity
• Communication
• Transportation
• Utilities
Structure /
Housing
Characteristics
• Non-Residential
• Residential
• Shelter
Governance
Preparedness
• Planning
• Investment
¦
Response
• Expenditure
• Time
Risk
Losses
• Property
• Human
Hazard
Exposure
• Geophysical
• Technology
Hazards
# Existing Measures related to Topic of Interest
10
20 +
33
-------
Resilience
Natural Environment
Extent of Ecosystem Types
Condition
Built Environment
Utility Infrastructure • Housing
Transportation Infrastructure Characteristics
Communication • Vacant Structures
Infrastructure
Society
Social Services
Labor/Trade
Safety and Security
Social Cohesion
Socio-Economics
Health Characteristics
Economic Diversity
Demographics
Governance
Community
Preparedness
Personal Preparedness
Natural Resource
Conservation
-~
Exposure
Climate & Natural Hazard Stressors
Legend
CRSI Composite Index
Gradient
Italicized Text External Influence
Bold Text Domain
• Bullet Indicator
Domain
Resilience
Range
! Indicator
| Resilience
Range
Figure 2.3 Final CRSI conceptual framework. Arrows projected from boxes to the left and right
represent hypothetical increases and decreases in ranges for indicators (black arrows) and domains
(colored arrows).
34
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2.4.1. Risk Domain
The risk domain of CRSI represents the characteristics of a place that
contribute to a level of exposure or loss resulting from specific hazards
A T \ (climatic events, e.g., sea level rise, hurricane, tornado, wildfire, drought,
etc.). Risk, as a construct, typically represents the likelihood that an
interaction with a hazard will result in an adverse outcome. Within the CRSI
framework, hazard exposure is dealt with wholly within the risk domain. This contrasts with
vulnerabilities, handled as both losses in the risk domain, and socioeconomic characteristics,
dealt with across multiple domains. Socioeconomic characteristics are typically the focus of
interventions taken to increase resilience. Most geologic and atmospheric hazards cannot be
controlled or predicted, and only the likelihood of an event occurring in a specific timeframe can
be calculated. In the climate resilience arena, this is the likelihood that a storm with specific
severity will occur, that sea level will rise by a certain amount, that a wildfire will occur, or
extreme total rainfall will occur. Potential for exposure results when there is more than zero
likelihood of a threat occurring in the same location as human and natural populations or the
built environment.
Risk is assessed as a product of exposure probability and vulnerabilities, or the consequences
associated with that exposure. For example, assets (e.g., a county, community or built
environment) constructed in a river's floodplain have enhanced potential exposure to flooding; or
an oil rig located near a natural ecosystem (e.g., forest), enhances the potential exposure of the
ecosystem to oil. Similarly, managed ecosystems (e.g., managed forests, agriculture) constructed
in drought prone areas, have enhanced potential exposure to drought. In each of these scenarios,
risk is the result of exposures and vulnerabilities in a system that could yield a loss. If the goal of
a county or community is to minimize negative impacts, there are two options: reduce the
exposure or reduce the vulnerability. Depending on the structure of the county or community and
the nature of the vulnerability, one option may be easier to achieve. In a flood prone county and
community, for example, risk can be reduced by either reducing exposure potential, e.g., using
residential zoning to eliminate building in flood prone areas, or by reducing vulnerability, e.g.,
by raising houses in a flood zone. In either case, identification of exposure and resulting impacts
is necessary as a means to inform the decision, and this is the intent of the risk domain. In the
CRSI model, risk is characterized by two indicators - exposure and loss. The specific natural
hazard events and technological hazard types are listed in Table 2.5. A more in-depth discussion
of the risk domain can be found in Buck et al. (2017).
Indicator: Exposure
The exposure indicator addresses the probability of hazard occurrence
across a full spectrum of geologic and atmospheric events as well as
additional technological hazards that may coincide with, or be exacerbated
by, the events. The geophysical category of metrics represents the
likelihood of occurrence of a geologic or atmospheric hazard based on
location of populations (human and non-human) and built environment. This category of metrics
is represented by metrics that characterize both historic and proximity-based likelihood of hazard
occurrence. The technological hazards category of metrics represents the probability of exposure
M
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to hazards resulting from built technologies (e.g., nuclear power plants, oil pipelines, chemical
manufacturing). The exposure indicator includes measures of:
• earthquake probability
• extreme high temperature incidents
• extreme low temperature incidents
• flood probability
• hailstorm probability
• hurricane probability
• landslide probability
• damaging wind incidents
• tornado probability
• wildfire probability.
CRSI calculates risk of exposure to acute climate events and selected natural geological hazards
(e.g., earthquakes and tektonic landslides). The index does not address long-term climate change
and its secondary effects. The one exception is sea level rise; however, CRSI uses sea level rise
as part of coastal flooding based on historic rise and not as a future measure of predicted sea rise
level from climate change. Similarly, CSRI does not directly address secondary effects of some
acute climate events (e.g., pest abundance, hydrologic shifts) but rather addresses these through
the direct acute climate events associated with them (e.g., drought, high temperatures). Similarly,
CRSI does not include standard climatic events (e.g., rainfall, snowfall).
In addition, exposure for each county, parish and borough is modified by the proximity of
technological or anthropogenic hazards including the presence of:
• Nuclear sites
• Toxic release sites
• Superfund sites
• Resource Conservation and Recovery Act (RCRA) sites.
This exposure modification is the result of the probability of exposure to a climate event in a
pixel multiplied by one plus the probability of a technological hazard being located with a 5-mile
radius for Superfund sites and a 10-mile radius of the pixel for other technological hazards; thus,
enhancing the overall exposure.
Indicator: Loss
The loss indicator addresses an aspect of a place's vulnerability represented
^ through historical loss of life and property (including crops) associated with
specific hazards. The property loss indicators describe estimated and actual
It" costs associated with property and crop losses as a direct result of a hazard.
y\A/\ I \ Many of the potential metrics for this indicator would come from the Spatial
Hazard Events and Losses Database (SHELDUS). Similarly, the human losses indicator
represents the loss of human life directly resulting from a hazard with metrics largely coming
from the SHELDUS database. The loss indicator includes human loss (i.e., fatalities and
$
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injuries), property loss (i.e., property damage) and natural area loss (i.e., increase in impervious
surface).
2.4.2. Governance Domain
" Governance" describes the collaboration of government agencies and
Non-Governmental Organization (NGOs) or private actors (e.g.,
companies, citizens, etc.) towards joint objectives within a system of rules
and regulations (e.g., hierarchies, markets, networks, counties and
communities, etc.) (Benz 2001; Liesbet and Marks 2003; Bache and
Flinders 2004a, b). Consequently, governance includes both formal and
informal coordination processes among, across and beyond different
sectors of public administration. It has been increasingly recognized that resilience problems
related to climate events can only be sufficiently handled in an integrative and ive way to include
diverse policy fields from all scales (Benz 2001) and actors from different fields (Huiteman et al.
2009; Pahl-Wostl et al. 2012; ARUP 2014). However, the administrative systems of many U.S.
federal, state, county, city and community agencies are predominantly organized by sector. This
organization makes coordination a major challenge in the wake of a severe climate event; such
as, flooding and sea level rise (Adger 2001; Adger et al. 2005b; Pahl-Wostl 2007; Unwin and
Jordan 2007; Knieling and Filho 2012), storm readiness (Wachinger et al. 2013; Adger 2001),
water/river basin management (Cosens and Williams 2012), and fire protection readiness
(Abrams et al. 2015). In light of these challenges, governance requirements for improving
collaboration between sector-administrations, governmental, and non-governmental actors and
new forms of governance must be introduced (e.g., integrated coastal zone management for
storm events, oil spills, etc.) to bolster the ability of each state, county, parish and borough to
recover from climate-related severe events (Crowder et al. 2006; Ramseur 2010; Colten et al.
2012). In CRSI, we have included three indicators in the governance domain to represent the
importance of governance in resilience to climate events. These are community preparedness,
natural resource conservation and personal preparedness.
Indicator: Community Preparedness
The community preparedness indicator addresses county and community
resilience strengthening and structure hazard mitigation. While there is
general consensus that community resilience is defined as the ability of
communities to withstand and mitigate the stress of a disaster, there is less
clarity on the precise resilience-building process (Chandra et al. 2011). In
other words, we have limited understanding regarding the specific
components that counties and communities can change or the "levers" for action that enable
counties and communities to recover more quickly (although as a screening tool a selection of
actions can be determined). Clearly, community preparedness and planning for such events helps
to foster continuity and stability, defining roles and functions, and how rebuilding of lives,
homes, livelihood, kinship and community will occur (Walsh 2007). Structural hazard mitigation
is another form of community preparedness. Structural measures are any physical construction
designed to reduce or avoid the possible impacts of hazards. Common structural preparedness
measures could include dams, flood levees, ocean wave barriers, earthquake-resistant
construction and evacuation shelters. The community preparedness indicator in CRSI includes
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measures of both county and community resilience strengthening, from Community Rating
System (CRS) information, and structural hazard mitigation, from Small Business
Administration (SBA) recovery mitigation information.
Indicator: Personal Preparedness
The personal preparedness indicator addresses individual or household
activities that help protect personal property from acute climate events.
Personal preparedness plans run the gamut, including developing a written
plan identifying risks, access to facilities and functional needs, protection of
children and the elderly, shelter plans and caring for pets. While ideal
measures, CRSI does not include measures to address all of these issues
because nationally consistent data are lacking. Instead, CRSI targets two major personal
preparedness actions that protect property; namely, availability and coverage of homeowner's
insurance and participation in the National Flood Insurance Program (NFIP). Flooding is the
most common natural disaster but many home insurance policies do not cover natural or climatic
event flooding. In 1968, Congress created the NFIP to fill this void by providing flood insurance
protection to property owners. Insurance or insurability relates to numbers of structures/property
that are insured (which can initiate recovery through an infusion of cash to start rebuilding)
(Cutter et al. 2009).
Indicator: Natural Resource Conservation
The natural resource conservation indicator addresses the protection of natural
resources from anthropogenic activities. Protected natural ecosystems are
usually better able to recover from acute climate events. Natural resource
conservation management refers to the management of natural resources (i.e.,
ecosystems) with particular focus on how management affects the quality of life
for both present and future generations as well as the sustainability of the ecosystem itself. The
Community-Based Natural Resource Management (CBNRM) approach combines conservation
objectives with the generation of economic benefits for counties and communities (Kellert et al.
2000). A limitation of using the CBNRM relates to the difficulty of reconciling and harmonizing
the objectives of socioeconomic development, diversity protection, and sustainable resource
utilization. The issue of biodiversity conservation is regarded as an important element in natural
resource management as well as in recovery potential from acute climate events. The CRSI's use
of natural resource conservation indicator related to biodiversity land protection (Land Protection
Priority Index for preserving biodiversity) targets the use of conservation protection by
states/counties/communities.
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2.4.3. Society Domain
The concept of society, as used in CRSI, includes all human aspects of a
community except the built environment. These are the constructs that
- \ represent the economic, demographic, and social interactions common to
3 urban and rural populations. Society is a group of people involved in
persistent social interaction or a large grouping of people sharing the same
geographical or social territory. These groups typically are subject to some
political authority and often similar dominant cultural expectations. More
broadly, a society may be characterized as an economic, social, industrial
or cultural infrastructure made up of, yet distinct from, a collection of individuals. Thus, society
can include the objective and subjective relationships people can have with the material world
and other people. Proposed society indicators in CRSI include demographics, economic
diversity, health characteristics, labor and trade services, safety and security, social cohesion,
social services and socio-economics.
Indicator: Demographics
The demographics indicator includes aspects of vulnerable populations.
Demographics of a county or community reflect attributes of the county's or
community's general population; namely age structure, ethnicity, and socio-
economic levels. All of these factors can influence the ability of a county or
community to recover from a disaster (Lugo 2000; Vasques-Leon et al. 2003;
Heltberg et al. 2009; Ibarraran et al. 2009; Steinbruner et al. 2013). Vulnerable
populations represent those fractions of the population that may be particularly susceptible to
impacts resulting from acute climate events. The vulnerable populations include:
• proportion of the population that is 65 years or older and living alone
• enclaves isolated by language (Non-English speaking populations)
• groups of persistent homeless persons/families
• proportion of the population under the age of 5 years
Indicator: Economic Diversity
The economic diversity indicator represents factors associated with
economic stability and recoverability. Economic diversity addresses issues
associated with a society's ability to monetarily respond and recover from a
climate event. Economic diversity relates to the array of business sectors a
county or community might have and the equitable distribution of economy.
Lack of business sector diversity can suggest a more difficult path for
economic recovery (Adger et al. 2005a; Reusch et al. 2005; Adger 2010). Employment and
employment conditions can be important for a county's or community's recoverability.
The economic diversity indicator is represented by two indices - the Gini Index (Gastwirth 1972)
and the Hachman Index (Hachman 1994). The Gini Index is a measurement of the income
distribution of a county's residents. This number, which ranges from 0 to 1 and is based on
residents' net income, helps define the gap between the rich and the poor, with 0 representing
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perfect equality and 1 representing perfect inequality. It is typically expressed as a percentage
and is often referred to as the Gini coefficient. The Hachman Index incorporates location
quotients, which measure relative industrial concentration in one area compared to that in
another area. Location quotient (LQ) is a valuable way of quantifying how the concentration of a
particular industry, cluster, occupation, or demographic group in a spatial unit (e.g., region, state,
county) compared to the nation. It can reveal what makes a particular area unique compared to
the national average. The Hachman Index is a measure of economic diversity that compares the
industry composition of a state to the industry composition of the nation by taking the total
employment of an industry in a state divided by total state employment, and comparing it to the
nation's equivalent.
Indicator: Health Characteristics
The health characteristics indicator addresses factors associated with
healthcare access, special health vulnerability populations, and specific
health problems related to or exacerbated by acute climate events. The
general health characteristics of a population emphasize conditions
associated with greater vulnerability to climate events such as respiratory or
cardiac condition changes during periods of intense heat; hospitalization
conditions requiring electronic equipment during times of loss of power
during floods, hurricanes or tornadoes; or, injuries or premature death related to extreme weather
events (Greenough et al. 2001; McMichael et al. 2003; McMichael et al. 2006; Melillo et al.
2014). Access to healthcare means the timely use of personal health services to achieve the best
health outcomes; such as, gaining entry into the health care system, accessing a health care
location where needed services are provided and finding a health care provider with whom the
patient can communicate and trust. Healthcare access is represented by a single measure of the
proportion of the county's population with health insurance. Special health-care needs
vulnerabilities represents any individual, group or community whose circumstances create a
barrier to accessing emergency services because of pre-existing health conditions or
vulnerabilities. Of particular concern are the more than 23 million U.S. residents (roughly 12%
of the total population aged 16 to 64 years) with special health-care needs due to disability (U.S.
Census Bureau 2016). This population is diverse and broadly distributed and deserves special
attention because there is an 80% chance that any person will experience a temporary or
permanent disability at some point in their lives (Kailes and Enders 2007). Specific health
problems represent the proportion of a county's population with special health issues that can be
exacerbated by acute climate events. These health conditions include:
• asthma
• cancer
• diabetes
• heart disease
• obesity
• stroke.
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Indicator: Labor and Trade Sendees
The labor and trade services indicator addresses factors related to recoverability
from an acute climate event associated with construction. In short, does a
county or community have the appropriate construction skills to provi de for
accelerated recovery and represent a resilient construction workforce? Skilled
construction labor is a segment of the workforce with a high skill level that
creates significant economic value or, in this case, recoverability through the
work performed by human capital. Labor and trade services represent the availability of skilled
labor and tradecraft that can be utilized in the aftermath of a climate event (e.g., carpenters,
bricklayers, engineers, roofers, construction workers, civil servants). This indicator includes
construction skills (represented by adjusted numbers) relating to:
~ concret e constru cti on
~ framing
~ highway construction
~ masonry
~ power construction
~ roofing
~ steel construction
~ water construction.
Indicator: Safety and Security
The safety and security indicator addresses the provisioning of
emergency and civil services. The primary definition of safety is "the
condition of being free from harm or risk", which is essentially the same
as the primary definition of security, which is "the quality or state of
being free from danger." The hi erarchy considers safety needs secondary
only to basi c physiological needs like food and water. Hie need for safety
has to do with our natural desire for a predictable, orderly world that is
somewhat within our control. In relation to the development of the CSRI, safety targets the
provisioning of the types of emergency services that would be necessaiy for a reasonable and
rapid recovery from an acute climate event. Safety and security services encompass the
availability of emergency first responders, medical personnel, civil order, and legal services.
Measurements related to these services demonstrate a county's or community5s ability to respond
and the timing of that response to the results of a climate event (e.g., flood, hurricane, tornado,
wildfire). Hie specific emergency and civil services included in the safety and security indicator
include adjusted numbers of personnel associated with emergency services, law enforcement
personnel, law enforcement support personnel and public safety7 personnel.
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Indicator: Social Cohesion
The social cohesion indicator represents the willingness of members of
a society to cooperate with each other in order to survive and prosper.
We define social cohesion as a society that works toward the resilience
of all its members, fights exclusion and marginalization, creates a sense
of belonging, promotes trust, and offers its members the opportunity of
upward mobility. Social cohesion can be an important element of
recoverability after a climate disaster. It represents community and family-centric networks and
value structures with an emphasis on the characteristics that increase the likelihood of
vulnerability (e.g., sense of place) and/or recoverability (e.g., family and social networks)
(Schwartz and Randall 2003; Adger et al. 2005b; Baussan 2015). Social cohesion plays a
significant role in the planning for resilience to acute climate events and in the execution of that
planning after an event.
The constituent elements of social cohesion (OECD 2011), include social inclusion, social
capital, and social mobility. Social capital, the resources that result from people cooperating
together toward a common end, can play an important role in event. In the CRSI framework,
social cohesion addresses access to social support. The measures of social cohesion include
volunteering and volunteer organizations, ethnic diversity and the proportion of population
native to a county or community.
Indicator: Social Sendees
The social services indicator for CRSI is represented by a range of public
services provided by government, private, and non-profit organizations.
Access to these services is critical for recovery from an acute climate event
and include the availability of services unrelated to infrastructure,
labor/trade, emergency services and civil control important for a county's
or community's response to a climate event. These services would relate to
laws, childcare, education, healthcare, and faith-based organizations. In the
CRSI framework, this indicator is represented by:
• index depicting the average medically underserved population
• number of blood and organ banks in a county relative to the county's population
• access and availability of child care facilities
• number of emergency shelter and goods providers in a county relative to the county's
population
• number of food service providers in a county relative to the county's population
• number of hospitals in a county relative to the county's population
• number of insurance claims in a county relative to the county's population
• number of educational facilities in a county relative to the county's population
and support for those facilities
• mental health services
• percent of the county population living in a health professional shortage area (HPS A)
• number of physician services in a county relative to the county's population
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• number of rehabilitative services in a county relative to the county's population
• number of religious organizations in a county relative to the county's population
• number of social advocacy facilities in a county relative to the county's population
• number of special needs transportation facilities in a county relative to the county's
population.
Indicator: Socio-Economics
• a The socio-economic indicator for the CRSI society domain relates to
employment opportunity and issues associated with personal economics,
primarily level of income. Employment opportunity is represented by overall
county-level unemployment rate. Employment and employment conditions
can be important for a county's or community's recoverability. This indicator
would include metrics like unemployment rates, underemployment rates and
the formation of human capital (Marston 1985; Cohen 2011; Peiro et al.
2015). Personal economics relate to personal finances and involves all financial decisions and
activities of an individual or household. The most basic of these activities is income, both actual
income and relative income. For the socio-economic indicator, personal economics is
represented by three measures: the proportion of a county's population that earns less than 150%
of the poverty guidelines for a specific household size, county unemployment rate and the
median income for the county.
2.4.4. Built Environment Domain
The concept of a built environment is relatively recent and it was initially
coined by social scientists (Rapoport 1976). The "built environment"
describes the man-made surroundings that provide the setting for human
activity, ranging in scale from buildings and greenspaces to
neighborhoods and cities. The scope of the built environment typically
includes supporting infrastructure such as water supply, energy networks
and transportation corridors. The built environment is a material, spatial
and cultural product of human labor that combines physical elements and energy in forms for
living, working and playing (Roof and Oleru 2008). In recent years, public health research has
expanded the definition of "built environment" to include healthy food access, community
gardens, "walkability" and "bikeability" (Lee et al. 2012). The urban fabric is a complex socio-
technical system that encompasses different scales - buildings, building stocks, neighborhoods,
cities and regions - each with different time constants, actors and institutional regimes. The term
"built environment" has also been adapted to address the relation between the built and the
"unbuilt" part of the environment This corresponds to the definition of a socio-ecological system
where the "built environment" can be considered an artifact in an overlapping zone between
culture and nature, with causation occurring in both directions. The sustainability debate and the
growing awareness of risks to the built environment due to climate change and climate events
have all helped to focus attention on the fragilities and the need to create resilience in the built
environment (Hassler and Kohler 2014). In CRSI, we have included five indicators in the built
environment domain to represent the importance of built environment in resilience to climate
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events; communications infrastructure, housing characteristics, transportation infrastructure,
utility infrastructure, and vacant structures.
Indicator: Communications Infrastructure
Continuity of communications is the ability of a county, community or
organization to execute its essential functions at its continuity facilities. This
I uVT//J J J continuity depends on the identification, availability and redundancy of critical
\> A -v communications and information technology systems to support connectivity
among key government leadership personnel, internal elements, agencies, critical
• > customers and the public during crisis and/or disaster conditions. The
communications infrastructure indicator primarily addresses a county's or community's
communications continuity in the aftermath of an acute climate event. This indicator
encompasses the number and distribution of:
• cell phone towers
• land mobile towers
• microwave towers
• paging towers
• radio broadcast towers
• TV transmission towers
• areas of no internet coverage.
Indicator: Housing Characteristics
Housing characteristics relate to the types of households distributed throughout a
county and their structural vulnerability. Structural vulnerability is a distinct
likelihood of encountering major difficulties within the county or community
atmosphere or the threat to the county or community itself because of deficient
housing or building conditions. While this concept applies to engineered
structures and the meeting of building codes and requirements in order to sustain acute climate
events, the primary issue in the indicator is physical structure (e.g., buildings), the construction
of which usually has not been through the formal building permit process. Such buildings are
obviously prevalent in the rural or non-urban areas along the periphery of municipalities. These
types of constructions also include old historic buildings. Structural vulnerability generally
pertains to the structural elements of building, e.g., load bearing walls, columns, beams, floor
and roof. The structure vulnerability indicator in CRSI addresses issues of home overcrowding,
age of home, housing unit density, major home construction and functional problems and
number of mobile homes in a county or community.
Indicator: Transportation Infrastructure
Transportation infrastructure refers to the framework that supports our transport
system. This includes roads, railways, ports and airports. National and local
governments are responsible for the development and maintenance of our
transport infrastructure. Transportation infrastructure is the fixed installations
~
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that allow vehicular traffic to operate. Transport is often a natural monopoly and a necessity for
the public and a critical element of community infrastructure in the event of an acute climate
event or the recovery from such an event. In the CRSI index, the transportation infrastructure
indicator is represented by transportation flow continuity including:
• access to highway entrances and exits
• number of and access to airports
• number of and miles of arterial roads in a county
• collector road lengths
• freight railroads
• heliports
• miles of local roads in a county
• roadway bridge access
• roadway bridge structures in a county
• seaplane bases.
One reviewer questioned the absence of public transit as a metric in this indicator. We agree with
the potential importance of public transit to resiliency from an acute climate event. However,
public transit was not included in the Transportation Infrastructure indicator for two reasons; one
technical and one practical. The technical reason is that public transit is no more part of
transportation infrastructure than automobiles. Transportation infrastructure consists of the fixed
installations supporting transportation (e.g., roads, railways, terminals). The practical reason is
that we considered the inclusion of public transit as a separate indicator but, while there was
adequate data for the topic in metropolitan areas, data was sparse or non-existent for non-
metropolitan areas which make up the bulk of the U.S.
Indicator: Utilities Infrastructure
—Public utilities are organizations that produce, deliver and maintain the
infrastructure for supporting public access to critical public health services and
power. Robust utility networks are essential for promoting quality of life during
the disaster recovery process. Utilities networks are one of the most protected
^ resources within any county or community, but areas that are sparsely populated
may lack any redundancy or rerouting options should the main utility service(s) be
compromised as a result of an adverse climate event. Within CRSI, the utilities infrastructure
indicator describes the relative availability of drinking water, sewer and power services based on
number and location.
Indicator: Vacant Structures
Vacant structures (residential and non-residential) are generally at greater risk to
an acute climate event than occupied structures. This vulnerability is often due
to a lack of maintenance, general deterioration and/or owner disinterest.
L Although not related to acute climate events, these structures are also a matter
D of increasing concern for fires. For example, Cleveland is plagued by over
12,000 vacant structures including houses, blighted buildings, schools, former manufacturing
plants and forgotten warehouses. The issue is of such concern to Detroit (with over 78,000
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vacant structures), that the city has demolished nearly 12,000 structures since 2014 resulting in a
25% reduction in vacant structure fires over the past two years (Helms 2016). By removing
dangerous vacant buildings and empty houses, safety and quality of life in Detroit is improved.
These types of buildings are particularly vulnerable to acute climate events. The CRSI vacant
structures indicator includes the number of vacant business structures in a county, the number of
vacant residences in the county and the number of other vacant buildings in the county (e.g.,
hospitals, schools, government buildings).
2.4.5. Natural Environment Domain
The natural environment is a domain that encompasses all living and non-
living things, occurring naturally in the United States. The concept of natural
environment can be distinguished by two primary components: 1) complete
ecological units that function as natural systems without extensive human
inventions (often called ecosystems) and 2) universal natural resources and
physical phenomena that lack clear-cut boundaries (e.g., air, water, climate,
radiation, magnetism) not originating from anthropogenic activities. In this
domain the natural environment is represented by two indicators - the extent
of ecosystem type and condition of natural ecosystems and managed lands. Open space and
green space are included in appropriate
Indicator: Extent of Ecosystem Types
CRSI addresses the resilience of natural ecosystsems as well as the
resilience of developed lands and dual-purpose lands. The extent
domain is necessary to gauge resilience on the proportion of land that
is undeveloped and includes the spatial extent or acreage of each
ecosystem type that occurs naturally without any significant human
intervention. Some of these measures include:
• deserts
• aquatic areas or "blue space"
• grasslands
• tundra.
wetlands,
forested areas
Indicator: Condition
CRSI addresses the resilience of natural ecosystsems as well as the
resilience of developed lands and dual-purpose lands. The condition
domain is necessary to gauge the original condition of the proportion
of land types or ecosystems that is undeveloped and includes an
assessment of the ecological condition of each ecosystem type that
occurs naturally without any significant human intervention. This
condition estimate is based on surveys completed by EPA's Office of
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Water (USEPA 2017) and Office of Air and Radiation (USEPA 2016a), USDA's Forest Service
(USFS 2017) and and Natural Resources Conservation Service (NRCS 2017a, b). The condition
indicator is related to metrics that describe the following ecological conditions in natural
communities and resources:
• biodiversity
• aquatic ecosystems condition
• forests condition
• air condition
• soils condition.
Comparison of Differing Versions of CRSI
An earlier version of the CRSI framework was published as a conceptual model (Summers et
al. 2017). The earlier conceptual model included five sub-models (risk, governance, social,
built environment and natural environment), eleven domains and 25 indicators. The authors
believed after further investigation that the domains and the indicators were largely
duplicative. In order to maintain the structural integrity of the earlier index framework, the
five sub-models were renamed domains. The original domains and indicators where
combined to create a single set of well-rounded indicators. These changes did not
significantly alter the structure of CRSI but rather introduced a different nomenclature to
simplify the CRSI structure.
2.5. Metric Selection and Data Sources
A candidate list of potential metrics was identifed based on existing literature and expert opinon.
The inventory of metrics was largely driven by the relevancy for measuring climate-events and
natural hazard impacts, ecological connections of natural systems to built and natural
environments and how well the sets of metrics fit as "proxies" for respective indicators. Metric
redundancies across the literature were encountered. Over 600 metrics were described in the
literature, many of which were duplicative. Based on the data acceptance criteria and other
approaches such as autocorrelation analysis, duplicate measures review, etc. the candidate list of
metrics were distilled through group consensus and expert counsel. Only the most robust metrics
were retained for quantification. Data acceptance criteria are described as follows:
To the extent possible, data sources were selected based on the following criteria:
• Availability and access: The data are publicly available and easy to understand, access
and extract.
• Reliability and data credibility: The data owners collected data in a manner that is vetted
by the professional community and have metadata available for review.
• Spatial preference: County-level data is preferred spatial unit for population-based
information and acres, meters, hydrologic units or similar for geospatial units.
• Coverage: Nationally consistent in scope.
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• Chronological history and the likelihood that the data will continue to be collected: Data
exhibit a consistent collection history from 2000-2015.
• Types of Data: Subjective and/or objective data specifically relevant for development of
CRSI.
Table 2.5 offers a brief overview regarding the indicators and general description for
interpretation. Detailed metric information is located in Appendix A.
2.6. Data Handling and Standardization
Acquired raw data used to populate CRSI metrics are maintained as an archive in their original
format to help ensure data transparency. Metric data are derived from raw data, are stored in
plain text format (e.g., ASCII) and are organized in hierarchical or nested structures that match
the CRSI conceptual framework. This data structure allows each level of CRSI data, from raw
data to final scores, to be examined either individually or as a whole. The plain text format
makes the data not only more available to a variety of softwares (e.g., ESRI ArcGIS®, SAS®, R,
JavaScript), but also makes the data more readable.
Table 2.5 List of CRSI domains, indicators, scope and number of metrics. Numbers in parentheses for domains show the
total number of indicators/total metrics in the domain.
Domain Indicator(s) Metric(s)
Communication Infrastructure
Communication continuity (7)
Housing Characteristics
Structure vulnerability (5)
Built
Environment
Transportation Infrastructure
Transportation flow continuity (6)
(5/24)
Utility Infrastructure
Utility Continuity (3)
Vacant Structures
Structure vulnerability (3)
Governance
(3/5)
Community Preparedness
Community resilience strengthening (2)
Natural Resource Conservation
Natural Resource Recovery (1)
Personal Preparedness
Personal property hazard protection (2)
Biodiversity, using birds as a proxy (1)
Coastal Condition (1)
Forest Condition (1)
Inland Lake Condition (1)
Natural
Environment
(2/18)
Condition
Percentage of clean air days (1)
Rivers and Streams Condition (1)
Soil Growth Suitability (1)
Soil Productivity (1)
Wetlands Condition (1)
Extent of Ecosystem Types
Agriculture Area (1)
Forested Area (1)
Grassland Area (1)
Inland Surface Water Area (1)
48
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Domain Indicator(s) Metric(s)
Marine/Estuarine Area (1)
Perennial Ice/Snow Area (1)
Protected Areas (1)
Tundra Area (1)
Wetland Area (1)
Exposure
Risk
(2/20)
Earthquake probability (1)
Extreme high temperature incidents (1)
Extreme low temperature incidents (1)
Flood probability (2)
Hailstorm probability (1)
Tornado probability (2)
Hurricane probability (2)
Landslide probability (1)
Major toxics presence (1)
Non-storm damaging wind incidents (1)
Nuclear presence (1)
RCRA sites (1)
Superfund sites (1)
Toxic release presence (1)
Wildfire probability (1)
Loss
Developed area loss (includes human and
property measures) (1)
Natural area loss (1)
Dual-benefit area loss (includes cropland and
managed area measures) (1)
Demographics
Vulnerable population (5)
Economic Diversity
Economic stability/recovery (2)
Health Characteristics
Health problems that may impact personal
Resilience (9)
Society Labor and Trade Services
Construction recovery (8)
(8/50) Safety and Security
Provisioning of emergency and civil services
(4)
Social Cohesion
Access to social support (4)
Social Services
Access provisioning to critical services (15)
Socio-Economics
Employment opportunity (1)
Personal economics (2)
A team consensus approach was used to rate every candidate metric as to whether it was or was
not a valid measure for a specific indicator. A final comprehensive review of the pool of
indicator metrics was performed to identify potential data sources. If data for a metric could be
obtained from two or more data sources, then a single source for the metric data was chosen
based on the data acceptance criteria. Metric data were averaged across all years of available
49
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data. Any remaining data gaps were not imputed for count data, as a rule. Where missing data
existed and were not expected (e.g., wetlands condition, scored indicator) then missing value was
set to null. If missing data represented a metric where a zero was meaningful, the missing value
was set to zero. For geospatial data interpolation methods were used to fill in missing data. The
interpolation method varied by metric depending on measurement—aerially-weighted, modeled,
etc. Box-and-Whisker analyses were completed for each fully enumerated CRSI metric. Extreme
lower and upper outlier measures were set to minimum and maximum values, respectively. The
maximum values were calculated to be three times the 75% percentile for each metric and the
minimum values were calculated as minus three times the 25% percentile. Any outliers of this
three times maximization technique were set to the metric value closest to the fence (Baum et al.
1970). Except for measures presented in percent or proportion, data were standardized on a scale
from 0.01 to 0.99 using a min-max normalization process as follows: (p )"=((x - xmin)/(xmax-
xmin)). The resulting CRSI metric data set included measured, modeled and filled standardized
data for the 3,135 counties of the U.S. Approximately 1.3 million metric data points were
extracted and synthesized to quantify CRSI indicators.
2.7. Calculations
2.7.1. Built Environment, Governance, Natural Environment and Society Domains
Four basic steps were used to summarize metrics to domains (Figure 2.4), except for the Risk
domain which will be discussed separately. Indicators and domains were derived using the
following approach:
• Metric data were adjusted for age, population or spatial area, as appropriate, prior to
standardization (e.g., number of hospitals in a county adjusted by the population of the
county). Count information contributing to continuity measures were not weighted.
• Average of related standardized metric values served as the basis for indicator scores
• Domain scores were obtained from the sum of appropriate standardized indicator values.
• Domains for built environment, natural environment, society and governance were
standardized in preparation for the final CRSI calculation.
50
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County
EPA Region
U.S.
Multiple Scale Metric, Indicator and Domain Score Calculations
(Governance, Society, Built Environment, Natural Environment Domains Only)
UMnof*Mtrcy
OfdlCOVU#
ndtMUBunUJ.
WU'Win
unnn
r
Mean
CttCCS
OfWCOiTCV
irjkiai»«n(K
toil
C««KCUH|
flomtniMti* nSF*«
coircv
demtr «ccf« cUi
Figure 2.4 Representation of the Metric, Indicator and Domain scores for Governance, Society,
Built Environment and Natural Environment Domains of CRSI. For this report, aggregations were
made at the EPA regional scales and national scale. Similar aggregations could be accomplished at any
appropriate scale (e.g., western regions, intermounrtain regions, coastal regions).
2.7.2. Risk Domain
The Risk domain is a probabilistic calculation based on geophysical and technological exposure
and loss described in Buck et al. 2017. The components include historical exposure, basic
likelihood of exposure factor, anthropogenic exposure, and human, property and natural
ecosystem losses. All metrics were min-max standardized. A sum of metric values representing
incidents of past natural disaster events and exposure likelihood for each county, parish and
borough was used as the basis for calculating metric scores for the Exposure Indicator. The Loss
indicator was derived from the sum of loss metric scores identified as three land type categories-
natural, developed and dual use. The domain measures were calculated as the standardized
product of total exposure divided by total loss. The approach used to calculate the Pvisk domain
scores is presented in Figure 2.5.
51
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Tornado
Wildfir
Exposu
Sub-Metric
Values
(Scores)
Summed
Drought
High Wind
Exposure
Landslide
Exposure
Hailstorm
Exposure
High Temp
Low Temp
Summed
Natural
Lands Loss
Metric
Values
(Scores)
DualUse
Lands Loss
Multiplied
Superfund
Sites
Summed
Sub-Metric
Values
(Scores)
RCRA Sites
Summed
Coastal Flood
Exposure
Probability
Region
U.S.
Indicator
Score
County
Risk Domain
Score
EPA Region
Risk Domain
Score
U.S.
Risk Domain
Score
Metric, Indicatorand Domain Score
Calculations for Risk Domain
Tornado
Exposure j
Probability y
Hurricane Sub-Metric ' Inland Flood
Exposure ) Values Exposuri
Probabiity (Scores) p,cbabl"
Figure 2.5 Representation of the Metric, Indicator and Domain scores for Risk Domain of CRSI.
2.8. The Final Steps to CRSI
All domains for each county, parish and borough (all referred to as county below) were min-max
standardized on a scale from 0.01 to 0.99. The final CRSI calculation begins as a scaled value for
recoverability/ vulnerability derived from Governance and Risk (basic CRSI) with the
Governance value being adjusted by the remaining domain scores for social, built environment
and natural environment to complete the calculation of CRSI as shown below:
CRSi(B),=% = OoyRisk
where CRSI(B)i = value of basic resilience (Recovery/Vulnerability or Ri/Vi) and Ri/Vi =
Governance in county i/Risk in county i. The overall CRSI score is calculated as:
CRSI i = (Gov i + Soc(a)iGovi + BE(a)iGovt + NE(a)Govi) I Rish
where CRSIi = the value of CRSI or adjusted resilience for county i and Soc(a)i, BE(a)i, and
NE(a)i are the adjustment multipliers for Society, Built Environment, and Natural Environment
in each county i, and Riski is the Risk score for county i. The adjust factors are calculated as:
52
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Soc(a),=^Soc,~SoCmYSoc
where Soc(a)i is the adjustment multiplier for society in county i, Soci is the social domain score
for county I and Socm is the median social domain score for all counties;
where BE(a)i is the adjustment multiplier for built environment in county i, BEi is the built
environment domain score for county I and BEm is the median built environment domain score
for all counties;
and where NE(a)i is the adjustment multiplier for natural environment in county i, NEi is the
natural environment domain score for county I and NEm is the median natural environment
domain score for all counties.
The calculation process is depicted pictorially in Figure 2.5. The domains are weighted equally
in the calculation of CRSI in this report. By no means do the domains have to be weighted
equally. If communities or counties have specific data to inform CRSI then weights can be added
to the final CRSI calculation based on local priorities regarding the domain issues. Even if, no
new data is added, domains can be weighted based on local knowledge of priorities.
2.9. Uncertainty Analysis
Uncertainty analyses is recognized as an important step in the presentation of new index
frameworks. For CRSI, this analysis will be completed as part of Next Steps for further
development of the index.
2.10 Technical Soundness of Approach
The approach used for CRSI is based on a basic method of creating an index to describe current
condition and to be used as a screening tool to determine locations in need of improvement to
increase resilience to acute climate events. The use of metrics to develop indicators and
indicators to develop domains is a standardized approach. The selection of indicators and metrics
based on scientific and social literature adds to the technical soundness of the approach.
However, there are also limitations. Literature and team technical evaluation suggested several
metrics that would be useful in representing the indicators but the decision to develop CRSI at
the county level made the use of these metrics impossible as the data representing the metrics do
not exist at the county level. Would CRSI likely be improved by their inclusion? Probably.
Limitations of data always reduce the power of indices but using the available data certainly
provides a screening level of accuracy for CRSI. Reviewers were asked specifically, "Is the
approach for the index technically and conceptually sound?" Their responses provide a better
measure of the soundness of approach than anything the authors could add:
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"Yes, the general technical approach is based on a
familiar and common one, in use for several decades
to develop indices and compare components in a
way to describe the current condition and help
stakeholders identify areas to investigate for
potential management actions/decisions (i.e.,
conceptually sound). Polar plots, scatterplots, ranked
lists, maps, and examples by county and regions
present a useful array of ways to engage
stakeholders. A key area in my opinion is Figure 3.1
(now Figure 4.1) where the scatterplot is compared
to a 45-degree line -1 think the discussion and the
figure can be made more impactful by explicitly
drawing ellipses/circles on the plot itself and
indicating potential management decisions/actions. I
do think this document is very clear about distilling
and clarifying new thinking around climate
resilience. I did not identify any flaws,..."
—Bruce Duncan Region 10
"The literature review/synthesis is a good approach
that takes advantage of existing work."
—Megan Sussman, Office of Sustainable
Communities
"Yes. Well written and succinct. I like the graphic on
pg. 3." (Figure 1-1)
—Laura Farris, Region 8
One reviewer felt the report suffers from conceptual underexplication as well as a lack of
transparency in the operationalization of concepts in the form of measures (Dr. Courtney Flint,
Utah State University). While some reviewers felt selected indicators needed further
explaination, most appreciated the level of explanation.
The reviewers were also asked: "Do the methods, results and discussion sections adequately
describe the index development approach?" and "Are tables and graphics helpful?". Again their
responses provide more information than the authors can provide through additional explanation.
In addition, the responses suggest that the level of detail and explanation is sufficient for EPA
Regional staff.
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"Yes, from my point of view."
"Resilience Graphics and Tobies clear."
—Joyce Stubblefield, Region 6
"Overall, yes, it is straightforward to follow the
developmental approach and to see how additional
information could be incorporated as new nationally
accessible knowledge is developed. I do like the
discussion here for a region - the overall comparison
and then differences within the region by location
and by domain is a good approach. However, as
much as I appreciate the evaluation by region, I think
there may be some other constructs, but maybe not
for this effort. States and Tribal lands come to mind
as being particularly useful unless you think county
governance tends to outweigh state governance,
which it probably does when it comes to land use
decisions. I think your discussion around page 35 or
so is very helpful example of how the index can be
used."
—Bruce Duncan, Region 10
"Yes. I really like the graphic on pg. 12." (now Figure
—Laura Farris, Region 8
"In general, it would help to have more of the details
of how different elements and indicators add up.
Having some counties where scores came out very
different and showing how that happened would be
useful."
—Jeff Peterson, Office of Water
"As for adequately describing the index development
approach, my biggest concern is that the
operationalization is still a bit of a black box."
—Courtney Flint, Utah State University
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3. How to Use CRSI - Its Utility and
Potential Applications
3.1. Introduction
The potential for use of the Climate Resilience Screening Index (CRSI) is very broad and with
additional localized data additions, even broader. Below, the potential uses are outlined by
spatial unit (nation, region, county, community) and a few relevant specific examples (e.g.,
specific risks, specific county comparisons with similar circumstances but different CRSI scores,
and comparisons of EPA Regions) are discussed.
Categories of purposes/uses of CRSI include:
• Describing the state of the condition of resilience at the county level and aggregated
levels above the county (i.e., the minimal intended use of the index)
• Providing a framework that might be useful for communities to expound upon the county
information using county- or community-specific data to create county- or community-
level resilience scores
• Identifying areas for management/action decisions
• Tracking changes over time at the county, state, region, and national levels (potential use
that would really need more research on which elements respond on what time scales to
management decisions)
• Improving/further developing/vetting the index (i.e., ORD furthering the research in
response to stakeholder identified uses)
The multiple application options discussed below address the utility of CRSI's extension to
community decision makers, planners and other potential stakeholders.
3.2. General Broad Use
CRSI is not intended to be "run" based on the information in this report. CRSI has been run and
its results provided for all counties in the United States (except for a few boroughs in Alaska and
no counties in U.S. Territories) in this report. Users at this point will simply apply the results that
are or can be easily provided (e.g., CRSI scores, domain scores, listings, plots of contributions to
CRSI score, maps). For any other reasonable information at the county scale and higher, readers
can contact the authors of this report and get most information. These available results can
provide broad scale comparisons of large areas across the United States. For example, at the
national level, the Appalachians, deep South and much of the West Coast states show relatively
low governance associated with climate events and higher than average levels of risk to those
climate events. The western states show higher CRSI scores than most of the U.S. (i.e., higher
resilience) even though its governance levels associated with acute climate events is lower than
much of U.S. However, the scores associated with built environment and natural environment are
higher than much of the U.S. offsetting the minimal levels of climate event governance. This
increases a low to moderate base resilience score (governance/risk) to a moderate to high CRSI
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score due to strong building codes, lower level of vacant structure, large areas of preserved and
conserved lands, and higher levels of insured homeowners.
On a broad scale, EPA regions can be compared to assess which regions (based on the mean of
county CRSI scores) have higher levels of resilience and which regions have lower levels. EPA
Region 3, 4 and 6 have lower overall levels of climate event resilience based on CRSI scores.
This does not mean that all counties in these regions have low scores. In fact, in Region 3, areas
in northern Pennsylvania and in Maryland and Virginia on the lower shores of Chesapeake Bay
have among the highest CRSI scores in the U.S. and can serve as models with valuable "lessoned
learned" for areas of West Virginia with considerably lower CRSI scores. By disassembling the
county CRSI scores, counties with low CRSI domain scores can learn from counties with higher
scores. Similarly, in Region 4, counties with low governance scores related to climate events
often show moderate to high risk to climate events scores. The Region can determine which
counties need particular assistance in becoming more resilient to climate events. In Region 6, a
region that lists enhancing resilience to climate events as a major goal, lower than average CRSI
scores are seen along the Gulf Coast with very high risk scores in Harris. Brazoria, Jefferson and
Chambers counties and low governance scores in all of these counties except Harris County. All
of these counties have been major flood victims of Hurricane Harvey. While Harris County a
reasonable level of governance associated with climate events, it's natural environment score is
very low resulting in a diminution of the governance score (i.e., Harris County has been
developing much of its natural acreage leaving small amounts of natural ecosystems to help
ameliorate flooding conditions). Aransas County, second landfall of Hurricane Harvey, has a
lower risk score with reasonable governance; however, that governance is diminished by a very
low built environment score suggesting large numbers of vacant structure, on the whole, older
buildings and poorer overall infrastructure for utilities, communications and transportation.
These low built environment domain scores suggest that, if the area experienced a major climate
event, the county would be a risk to broad scale destruction (as was evidenced in Rockport, TX).
Finally, individual counties can use CRSI scores on a broad scaled to determine nearby or similar
counties with better domain scores - finding counties which can be consulted for "lessons
learned". Counties may even be able to use CRSI scores and domain scores to pursue federal or
state funding for improvement. Most utility benefits for counties and communities are shown
below in Section 3.5.
3.3. Use by EPA Regions
This report provides CRSI and domain score information for all counties within an EPA region
specifically to allow the regions to assess climate event resilience at the spatial scales of use to
them rather than at the national level. The results by Region are shown as composites of the
national scores (i.e., average county scores from within the region but based within a national
context. This means that counties have the same CRSI and domain scores in the regional analysis
as they do in the national analysis. This permits direct comparison of EPA Regions and counties
within the regions. While direct regional comparisons may have limited value from some
regional perspectives. It does allow Program Offices (see below) to assess comparative regional
trends and allows Regions to locate other Regions with higher scores for CRSI or the domains to
be assessed as models for improvement. Regional analysis does permit comparisons of the
specific counties in their Region and allows the delineation of county CRSI and domain scores to
57
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ascertain which counties are in the most need of assistance in selected domains or overall
resilience to climate events. For example, EPA Region 4 has a low overall CRSI score due to a
number of counties in Alabama and Mississippi with relatively high risk and low governance for
climate events. Similarly, these counties also have low society and built environment domain
scores further reducing the impact of climate-related governance. In short, the counties have
minimal governance related to climate events and, if an event were to strike, these counties do
not have the composite skill mixes and demographic characteristics to ensure recoverability. To
exacerbate the situation, these counties often have large numbers of vacant structures, less
stringent building codes, and older public infrastructure. Examining these attributes of the CRSI
and domain scores permits Region 4 decision makers to determine those counties most in need of
assistance in developing their resilience to climate events.
EPA Regions can ascertain which counties in their jurisdictions are most at risk to climate events
overall as well as to individual climate event types. The Regions can also determine which high-
exposure or moderate-exposure counties have their risk levels elevated due to the proximity of
technological hazards. Harris County, Texas's low risk domain score is the product of the
combination of natural climate hazard exposures and multiple technological hazards (e.g.,
Superfund sites, RCRA sites, petro-chemical plants). Unfortunately, this exacerbation of risk has
proven true in the aftermath of Hurricane Harvey in the Harris County metropolitan and suburb
area of Houston with multiple explosions and fires at these types of technological hazards.
Similarly, Regions can ascertain the major contributors to CRSI scores at the Regional level as
well as the county level.
3.4. Use by EPA Program Offices
EPA Program Offices are most concerned with the establishment of policies and programs across
the nation and, as such, are less interested in individual county information. However, Program
Offices are interested if whole regions of the United States show relatively poor resilience to
climate events and if certain areas of the country demonstrate high exposure to climate events in
conjunction with high exposure to technological hazards addressed by EPA. EPA's Office of
Land and Emergency Management (OLEM) has a special interest in this union of climate event
exposure and technological hazards (e.g., Superfund, RCRA, active waste sites) in its
development of guidance and technical assistance to establish safe waste management practices.
Knowing the juxtaposition of counties at risk and placement of technology hazards is useful to
OLEM for both guidance and organization of clean-up activities resulting from a major waste
event.
EPA's Office of Water (OW) ensures that drinking water is safe and restores and maintains
watershed and ecosystems to protect human health, support economic and recreational activities,
and provide healthy habitats. Drinking water issues were a major problem in the aftermath of
Hurricane Katrina and is a major continuing issue in several major Texas cities as a result of
Hurricane Harvey. Wildfires can be a major source of watershed devastation, particularly in the
West as evidenced by the magnitude and spatial spread of fires during late summer 2017 in
California, Arizona, Oregon and Washington. Earthquakes, prominent in the West, can also be
source of modified drinking water as well as infrastructure destruction. Through interactions
with the ten EPA Regions, state and local governments and American Indian tribes, OW helps to
build capacity and resilience for water resources.
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EPA's Office of Air and Radiation (OAR) is concerned with air pollution prevention, radiation
protection and climate change issues among many other issues. Knowing the juxtaposition of
counties with high climate event risk exposure with radiation producing facilities (e.g., nuclear
power plants) and chemical producing facilities could be important data for the Office of
Atmospheric Programs (OAP). The interaction of climate change indicators and climate event
exposure rates as well as recovery rates for regions of the United States could also be of
importance.
EPA's Office of Sustainable Communities (OSC) supports locally led, community-driven efforts
to revitalize local economies and attain better environmental and human health outcomes
contributing to community sustainability and resilience. Knowing which counties (and
communities) display lower resilience to acute climate events could be an important factor in
evaluating which resources are placed to contribute to clean air, clean water, and other important
resilience goals of communities and counties. OSC is also interested in the development of tools,
research and case studies that promote understandings of resilience and sustainability. Finally,
the use of shared examples among counties and communities (learning from each other) in order
to provide models of behavior and action is one cornerstone for OSC.
3.5. Use by States, Counties, Metropolitan Areas and Communities
The use of CRSI results or CRSI modification is important at the state, county, metropolitan
area, and community level. While one could argue that every community is different with regard
to its likely exposure to acute climate events, governance associate with climate events and its
resilience to climate events, it is clear the counties in much of the United States can set the tone,
guidance and often specifics for emergency operations plans, and emergency response to disaster
recovery and hazard mitigation (FEMA 2011) even those developed at smaller spatial scales.
Emergency and disaster planning involves a coordinated, co-operative process of preparing to
match urgent needs with available resources (Alexander 2016). For successful responses to acute
climate events, there must be high levels of coordination and continuing cooperation among,
federal, state, county and community infrastructures (Plough et al. 2013).
Many states develop basic disaster management plans and require counties to develop
comprehensive emergency management plans and county emergency management programs that
must comply with the basic plan. Counties often engage with larger communities in the same
manner. However, in many cases smaller communities (without significant resources) simply
adopt the county plan and jointly administer the plan in their jurisdictions. For example, Florida
(a state with significant acute climate event risks) has established a Comprehensive Emergency
Management Plan (CEMP) (FDEM 2016) as the master operations document for the State of
Florida establishing a framework through which the state handles emergencies and disasters. The
CEMP consists of a basic plan which describes the process for preparedness, response, recovery
and mitigation and provides local CEMP compliance criteria (CEMP-001). In the vast majority
of counties, the County CEMP drives these activities in all communities within the county. The
exceptions are in counties with large metropolitan areas (e.g., Miami, Tampa, Orlando,
Jacksonville) which will have their own CEMPs that are required to meet the county criteria.
Thus, the county governmental unit becomes a major actor in the resilience of counties' and
communities' to acute climate events. As a result of this necessary cooperation at all levels of
government for satisfactory resilience to acute climate events, counties are often the central focus
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of specific disaster planning and preparedness for all towns, communities and jurisdictions
within the specific county. This is the case in Pensacola, FL where responsibility for this type of
preparedness and planning and the execution of emergency management falls to Escambia
County. Similarly, in Rockport, TX (the site of the second landfall of Hurricane Harvey), one of
the primary actors in emergency planning and response in Aransas County, TX. Therefore, in the
majority of cases throughout the U.S., collection of data at the county-level in appropriate and
can be augmented by specifics associated with the individual community affected by the event.
CRSI and domain scores at the county-level permit state, county and community planners to
ascertain, risks to climate events in their jurisdictions, likelihood of recovery from such an event
(resilience), the likely causes of low levels of recovery, and the identification of counties in
similar circumstances (similar risk) that have strong resilience scores.
3.6. Examples
Hurricane Harvey
In August, 2017, Hurricane Harvey had two landfalls in Texas - Rockport, TX in Aransas
County and Port Aransas, TX in Nueces County. In addition, rainfall from Hurricane Harvey
resulted in massive flooding in Houston and surrounding areas (Harris and Brazoria Counties)
and Beaumont and surrounding areas (Jefferson and Chambers Counties). Some of the worst
damage appeared to be in Rockport, a coastal city of about 10,000 that was directly in the storm's
path. Many structures were destroyed and Rockport's roads were littered with toppled power
poles. Extensive damage was also registered in Port Aransas, TX (site of the second Texas
landfall). It is estimated that it will be a longtime before the storm's catastrophic damage is
repaired. Flooding in the Houston/ Beaumont areas is the worst in history, displacing millions of
people and with flood waters expected to recede over the course of weeks to months. As an
exercise, CRSI results were examined (after the fact) to determine the magnitude and likely
locations of extensive damage and low resilience along the Texas Gulf Coast (Table 3.1).
Of these counties, CRSI scores for Aransas, Chambers, Harris, Jefferson and Refugio Counties
are significantly below the national average for CRSI suggesting significantly lesser resilience to
climate events. Of these counties, only Aransas and Refugio Counties (first Texas landfall)
display a low risk domain scores suggesting little history of major climate events (until
Hurricane Harvey) but both counties have significantly reduced built environment domain scores
suggesting that if an event were to strike these counties both would suffer significant structural
damage due to reduced public infrastructure and large proportions of vacant buildings. Both
counties also showed lower than national average levels for the society domain score suggesting
that neither county has the skills diversity to easily rebuild and neither have strong security and
security infrastructures. Hurricane Harvey also devastated Port Aransas, TX in Nueces County.
Nueces County has a significantly higher risk domain score than the national average associated
primarily with historical hurricane paths. The county is dominated by Corpus Christi, TX which
avoided much of the devastation associated with the hurricane; however, Port Aransas suffered
extensive structural damage. Port Aransas is likely much more similar to Rockport, TX in
Aransas County which demonstrates a significantly lower than average CRSI score.
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The other counties with lower CRSI scores - Chambers (1.81), Harris (1.62) andJefferson (2.82)
- all show high risk domain scores well above the national average. The Harris County risk score
is exacerbated by significant technological risks located there (e.g., chemical and oil refinery
facilities, Superfund sites). Brazoria County, located southwest of Harris county, has a lower
than average CRSI score but a significantly higher than average risk domain score. All four of
these counties are at significant risk to flooding and all four counties significantly flooded due to
the intense rainfall associated with Hurricane Harvey. Houston (in Harris County) is reported to
have had historic flooding than likely will not recede for weeks and possibly months.
Resilience from the flooding in these counties appears to be driven by differing factors based on
the CRSI and domain scores. Brazoria County has a less than average resilience score that
appears to be the result simply of a high risk but all the remaining factors tend to reduce the risk
and increase the resilience score to 3.38 (somewhat below the national average). Harris County,
on the other hand, has among the highest risk scores in Texas (0.758) again associated with
flooding and several exacerbating factors. The CRSI score for this county in significantly below
the national average at 1.62 suggesting recovery from a major event could be a very long
process. This lower resilience seems to be driven by a very low natural environment score
(0.192) suggesting that increasing development in the last decade and loss of natural lands is
significant (particularly to the north and west of Houston). Natural and open lands often provide
a buffering impact to acute climate events. They are usually damaged but tend to recover quickly
while reducing the impact of the event on surrounding populated areas. This low level of natural
ecosystems in the Houston area (often replaced by impervious surfaces) would enhance the
impact of flooding. Chambers and Jefferson Counties also have high risks levels associated with
flooding with both counties displaying significantly lower than average resilience scores
(Chambers County - 1.811 and Jefferson - 2.82). However, the remaining domain scores in both
counties suggest more rapid recovery than Harris County with Chambers County recovering at a
slower rate than Jefferson County.
Table 3.1. CRSI and domain scores for select counties along the Texas Gulf Coast and National Average scores (excluding
Alaska); (Bold denotes significantly below national average for CRSI and above national average for domains).
County
Risk
Governance
Built
Environment
Natural
Environment
Society
CRSI
Aransas
0.18045
0.51250
0.33419
0.52163
0.40423
2.657135
Brazoria
0.60166
0.58813
0.77630
0.54926
0.52368
3.384523
Chambers
0.57128
0.57957
0.51116
0.50005
0.43950
1.811030
Calhoun
0.21731
0.52463
0.43549
0.49039
0.42868
3.372704
Fort Bend
0.41124
0.59683
0.78479
0.42022
0.58024
4.526797
61
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County
Risk
Governance
Built
Environment
Natural
Environment
Society
CRSI
Harris
0.75794
0.56306
0.83741
0.19155
0.49091
1.623658
Jackson
0.12093
0.55163
0.33742
0.48061
0.53769
5.693879
Jefferson
0.52977
0.50619
0.69823
0.44851
0.52149
2.820717
Matagorda
0.25589
0.52457
0.43992
0.50342
0.43102
3.053307
Nueces
0.46478
0.55470
0.69866
0.41923
0.47736
2.980723
Refugio
0.11600
0.57228
0.26566
0.46778
0.44307
2.590426
San Patricio
0.18896
0.54902
0.48926
0.44391
0.40174
3.881974
Victoria
0.14123
0.52892
0.51231
0.51035
0.54069
8.575635
National
Average
0.23017
0.58827
0.39262
0.41210
0.51587
3.845290
County Comparisons
Direct comparisons of counties can be made with CRSI. These comparisons, might reflect
comparisons of counties with "perceived" similarities. Appendix C provides the information
necessary to compare counties (i.e., CRSI and Domain Scores). As was done in the above
examples, a reader can determine differences between or among counties from the CRSI scores
and then determine which domains drive the observed differences. These comparisons would
permit the viewer to compare counties with "perceived" similar risks and governance and, if the
CRSI scores are different, to determine what drives the differences (e.g., low domain scores in
particular areas.
62
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EPA Regional Screening Comparisons
Regional analyses (Table 3.2) and mapping show that EPA Region 10 (14.8) and EPA Region 1
(10.7) have the strongest overall resilience scores with EPA Region 4 (0.6) and EPA Region 6
(2.8) having weaker scores. The remaining six EPA Regions cluster together with moderate
scores (3.4-6.1). Disassembly of the CRSI scores shows that Region 10 strengths lie in its low
risk score which result in a high basic resilience score even though its governance low is less
than the national average. Although lower, its governance domain score is more than three times
the Region's risk domain score. Region 1 strengths lie in the highest governance score in the
Nation with moderate risk, and above average domain scores for social, built environment and
natural environment. On the other hand, Regions 4 and 6 have above average risk domain scores
and below average governance related to climate events scores. Driving down these lower basic
resilience scores, both regions have below average society domain scores suggesting a poorer
population, increased ethnicity (making communication for emergency response more difficult),
lower levels of social services, poorer access to health facilities, and higher level of
undocumented skilled trade laborers (making an assessment of the abundance of trade labor
difficult). Region 4 also has a below average score for its built environment suggesting less
stringent building codes, higher levels of vacant structures and weaker levels of public
infrastructure especially in Georgia and Alabama.
Overall, the U.S. shows good levels of resilience to acute climatic events. However, analyses
demonstrate that selected counties (hundreds of them) with higher levels of risk and low levels of
governance can improve their resilience by specifically addressing issues associated with the
governance, built environment, natural environment, and society domains. CRSI, which is meant
to be a screening tool, provides those directions investment, assistance and action by the EPA
Regions and Program Offices.
63
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Table 3.2. CRSI and domain scores for EPA Regions with National Average scores (including Alaska); (Bold denotes
significantly below national average for CRSI, significantly above national average for risk domain and simply below
national average for remaining domains which results in negative adjustment factors).
EPA Region
Risk
Governance
Built
Environment
Natural
Environment
Society
CRSI
Region 1
0.2403
0.8956
0.4916
0.4445
0.5987
10.6968
Region 2
0.3084
0.8292
0.4694
0.3860
0.5202
4.9988
Region 3
0.2715
0.6885
0.3821
0.3778
0.5117
3.3911
Region 4
0.2547
0.4976
0.3421
0.4027
0.4141
0.5849
Region 5
0.2217
0.7135
0.4070
0.4343
0.5722
6.0213
Region 6
0.2392
0.5479
0.3937
0.4229
0.4739
2.7718
Region 7
0.2087
0.5968
0.3576
0.3800
0.6092
4.1134
Region 8
0.1623
0.5572
0.3983
0.3956
0.6167
6.0857
Region 9
0.2345
0.3579
0.6204
0.4704
0.4795
6.0778
Region 10
0.1370
0.4319
0.4776
0.5315
0.4920
14.8380
National
Average
0.2288
0.5876
0.3932
0.4136
0.5156
4.2125
64
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4. Results and Discussion - National and
EPA Regions
4.1. Organization of Results
The results of the CRSI application are shown in this section as a series of maps and graphics
that delineate CRSI scores, first across the national and then across all ten EPA Regional scales.
Each series for each of the scales consists of the same six maps and two graphics: one map for
overall CRSI county results; five maps depicting county results for each of the CRSI domains
(Overall Risk, Governance, Built Environment, Natural Environment and Society); and two
graphics that break down the index to demonstrate the contributions of the five domains and the
20 indicators to the overall CRSI score. This disassembly of the index within the EPA Regions
allows each region to assess the most significant contributors to strong and/or weak resilience to
climate events.
Results from the national scale CRSI scores are further examined to explore how basic resilience
(governance/risk) relates to goverance. This is accomplished by analyzing the number of
counties, represented in a 5x5 matrix depicting the quintiles of governance and overall risk
domain scores. In essence this matrix ranges from low-low (lowest 20% risk and governance) to
high-high (highest 20% risk and governance). This analysis examines whether the distribution of
basic resilience in the U.S. is characterized by greater risk scores being matched by greater
governance scores. Similarly, the analysis assesses the number of counties with high levels of
governance but low levels of risk as well as counties with low levels of governance but high
levels of risk. Counties in either of these categories would be of interest to EPA Regions as areas
of potential investment (low governance and high risk) or areas to understand the level of
governance investment given the low level of risk (high governance and low risk).
4.2. General Broad Analyses and Results of Basic Resilience
(Governance/Risk)
An initial analysis was performed to assess whether the CRSI results associated with basic
resilience (governance and risk) varied in a predictable way. Plotting the domain values of risk
vs. governance would, from a policy standpoint, be expected to have a positive relationship -
greater risk should be accompanied by greater governance. This was tested in three ways: (1)
assessment analysis of risk domain versus governance domain scores, (2) examination of the
number of counties in the quintiles of risk versus governance (i.e., the number of counties in
each quintile combination and testing for expectation using a chi-squared test) and (3) mapping
the 25 quintile combinations to examine potential patterns.
An assessment of risk domain versus governance domain is the governance/risk ratio. The
expected result of the assessment is a 45 degree angle from low risk-low governance to high risk-
high governance. This finding would demonstrate that governance is developed in proportion to
likely risk (i.e., if you experience high risk there are governance activities/structures in place to
counter act that risk). Significant deviation from this finding could reflect an under- or over-
65
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reaction to likely risk in terms of governance activities. Placing results into quantiles allows
characterization of clusters of counties as over- or under-reacting to risk in terms of governance.
In this categorical relationship, generally any combination of risk and governance along the 45
degree angle (slope=1.0) plus or minus one category would be in the expected range. A
combination of high risk and low governance would suggest under-reacting, where as or low risk
and high governance would suggest over-reacting (new figure showing categories). Mapping
these risk-governance ratio categories by county demonstrates any clustering throughout the U.S.
to detect spatial trends.
The assessment results based on normalized risk and governance domain are shown in Figure
4.1. These results indicate that the governance score is generally higher than the risk score (206
counties, 6.6% of U.S. counties, have risk scores greater than their governance scores). This
suggests that governance activities in the majority of counties outweigh the risk of exposure to
extreme climate events.
Using All County-Level Domain Scores
Hazard Exposure Risk Potential
Figure 4.1 Linear assessment of risk versus governance based on domain scores. Ellipses represent
differing management implications with A: Low Risk-High Governance (little increased
governance necessary other than improvements for selected below-average indicators; B: High
Risk-High Governance (likely appropriate governance but any improvement in below-average
indicators a likely improvement to resilience); C: Low Risk-Low Governance (likely appropriate
governance for level of potential risk; D: High Risk-Low Governance (improvements to governance
and indicator of the CRSI domains necessary).
66
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Figure 4.2 shows the county data from the assessment analysis distributed across the categories
of risk-governance. For the majority of counties, risk is clustered in the second and third
quintile s, while governance clustered between the third and fourth quintile. While this result is
positive for the U.S., it can be misleading. The result may occur because the risk of exposure to
extreme climate events clusters largely in the second quintile demonstrating relatively low risk
while governance clusters in the fourth quintile giving the appearance of "excessive"
governance. To account for this, the distribution of basic risk among the counties was examined
using amin-max of risk-g over nance based on the distribution of the county scores to determine
the roughly 500-1000 counties with the largest risk to governance disparities (Figure 4.3). These
disparities focus on those counties with lower risk and higher governance ratios and higher risk
and lower governance allowing the identification of counties where increased governance might
be beneficial.
Basic Resilience Matrix Using True Domain Scores
High-
0I)
31
Mod-High- ( 651
0 0
gfk 00
E Moderate- i 886 396
W 00
Low-Mod -
116
© Q
34
43
0
Number of
Counties
600
400
200
LOW-
Low Low-Mod ffoderale Mod-High
Hazard Exposure Risk Potential
High
Figure 4.2 Distribution of number of counties in quartilesfor risk and governance domains based
on the domain scores.
67
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Base Resilience Based on True Governance/Risk Scores
Figure 4.3 Map of the distribution of county scores for basic resilience.
Missing
Data
Lower Governance
with Higher Risk
These county min-max scores were mapped to explore the spatial distribution of the quintiels for
any potential trends (Figure 4.3). Areas with the highest governance to risk ratio tend to be in the
northeast and scattered through midwest and Wisconsin. Areas with the lowest governance to
risk ratios appear along the west coast, and in northern and eastern North Dakota. Twenty-two
counties (<1% of total) showed very high risk scores coupled with very low governance scores.
These counties are located in EPA Region 4 (8 - Alabama, Florida, Georgia and Tennessee),
EPA Region 6 (8 - Arkansas, Louisiana, Oklahoma and Texas), EPA Region 7 (2 - Missouri and
Nebraska) and EPA Region 9 (4 - California). This clustering of the min-max scores needs to be
investigated to see if spreading out the clusters creates a better understanding of the risk versus
governance interactions.
68
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Figure 4.4 redistributes the scores in Figure 4.2 to "spread out" the variability of both the risk
and governance scores. This helps to identify the counties where the greatest return can be
expected for the governance investment dollar. This redistribution identifies 487 counties where
low governance investment will show a modest increases in resilience; the 373 counties where
moderate investment in governance should result in moderate increases in resilience; and the 355
counties (including the original 206 counties described above) where greater investment in
governance should result in the highest increases in resilience. Approximately 1204 counties
would benefit in a small way from further governance investment while 728 counties would not
really benefit from increased resilience from further investment in governance activities.
Basic Resilience Matrix Using Percentile-Ranked Domain Scores
High-
Mod-High-
Number of
Counties
¦
Low
Low-Mod
Moderate
Mod-High
High
Hazard Exposure Risk Potential
Figure 4.4. Distribution of number of counties in quartiles for risk and governance
domains based on number of samples (redistributing the basic resilience scores).
69
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The spatial distribution of these counties is shown in Figure 4.5. The areas with the highest and
lowest governance to risk ratios remain consistent with Figure 4.3 (as expected). Throughout the
eastern seaboard the ratio of scores is moderate governance to higher risk, as are the Ohio Valley
area and Great Lakes region. Lower governance to moderate risk ratios are seen through much of
the midwest and the northwest (east of the Cascades). In addition to the west coast, the lowest
ratios are seen in much of California, Indian country, Arizona, Nevada, and Utah.
Basic resilience can be modified by social activities and structures, the built environment and the
natural environment to represent overall resilience (the CRSI score). If these attributes are strong
then resilience (mainly through recoverability) can be enhanced. If these attributes are weak then
resilience for a area can be deterred. The next sections will examine basic resilience as modified
by these factors for the nation and each EPA region.
Base Resilience Percentile-Ranked Governance/Risk Scores
Figure 4.5 Map of the re-distribution of counties to demonstrate the likelihood of increased
resilience with increased governance.
Higher Governance Missing
with Lower Risk Data
Lower Governance
with Higher Risk
70
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4.3. Presentation of Results
Results in the following sections are organized by spatial sub-division (nation and EPA regions).
Figures 4.6-4.10 provide information to interpret the results in the graphics presented for the sub-
divisions. Each national and regional section includes:
• Figure 4.6: CRSI and Domain score bar graph depicting the scores and the adjustment
values for the Society, Built Environment and Natural Environment domains.
• Figure 4.7: Six-panel maps showing the distribution of the CRSI and domain scores by
county.
• Figure 4.8: Table of the highest -5% of CRSI values.
• Figure 4.9: Characterization of the Risk Domain with breakdowns of the exposure and
loss indicator scores.
• Figure 4.10: Polar bar plots describing the contributions of the indicators to the domain
scores. These plots show the scores for each indicator with the five domains. Longer bars
represent higher scores; shorter scores represent lower scores. The polar plots show the
contribution of indicator scores to the domain score.
Discussion of results follow the graphic results in each section. All CRSI and domain scores for
each region by state and each region by state and county are listed in Appendices B and C.
4.3.1. CRSI and Domain Score Bar Graphs
The spatial scale for the domain and CRSI scores is
either U.S. or EPA region. CRSI and domain scores for
the U.S. are the mean of all county scores. EPA Region
CRSI and domain scores are calculated as the mean of
all county scores in the Region.
CRSI is an index value characterizing the
resilience of the U.S or EPA region with the
range of county values. Higher scores reflect
more resilience to acute climate events.
us.
Ar
la=1 Governance
CRSI
4.21
(Range: -11.62 - 227.2)
7 Built Environment
The lighter shade bars represent the
domain scores. For risk, the higher
scores reflect greater risk. Higher scores
for the other 4 domains reflect better
governance, and greater capacity for
recoverability (less vulnerability).
Natural Environment
-as -0.4 -0.3 -0.2 -0.1 0 YV1 02 03 04 05 06 °-7 °-8 0.9
Domain Score (lighter shade ir)/Median Adjusted Score(darker shade bar)
The darker shade bars represent the domain median adjusted scores
used in the calculation of CRSI (Society, Built Environment and Natural
Environment only). For example, the value represented by the dark green
bar in the Natural Environment domain shows the degree of positive or
negative influence in the CRSI value based on the deviation above or
below the domain median score
Figure 4.6 Example summary of CRSI and domain available for the nation and each EPA region.
71
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4.3.2. Six Panel Maps
U.S. PANEL
EPA REGION PANEL
v* IpIL
Six panel chloropleth maps are
presented for CRSI and
Domain scores. The darker
colors represent higher scores
(note: darker color reflects
higher risk for Risk domain).
Higher scores with the [
exception of Risk contribute to
higher CRSI values. The maps
are intended to show the
distribution of county level
scores across the nation and
within the EPA regions.
%
d,
Figure 4.7 Example of six-panel maps showing the distribution of county-level CRSI and domain scores available for the nation and for
the EPA Regions.
72
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4.3.3. Top County CRSI Values
County
EPA
Region
.iKodlak island Borough. Alaska Region 10
2 Juneau City and Borough Alaska Region 10
J Ketchikan Gateway Borough. Alaska Region 10
4 Aleutians East Borough. Alaska Region 10
5 North Slope Borough. Alaska Region 10
6. Haines Borough. Alaska Region 10
7 : Prince of Wales • Myder Census Area. AlasKa Region 10
8. Hancock County, Maine Region l
9. Sitka City and Borough. Alaska Region 10
10. Hoonah-Angoon Census Area, Alaska Region 10
11.; Waldo County. Maine Region 1
12.; Dukes County. Massachusetts I Region l
13 Dillingham Census Area, Alaska Region 10
M Kenai Peninsula Borough. Alaska Region 10
15.: Petersburg Census Area. Alaska Region 10
16. Fairbanks North Star Borough. Alaska Region 10
17 Yakulat City and Borough. Alaska Region 10
i ? Maui County. Hawaii Region 9
19. Bonner County. Idaho Region 10
20. Aleutians West Census Area. Alaska Region 10
21. Bristol Bay Borough. Alaska Region 10
22-iMemiltan County. New York Region 2
23. f lathead County. Montana Region 8
24. Anchorage Municipality. Alaska Region 10
2b Latah County, Idaho Region 10
26. Washington County, Maine Region l
27. | Valley County. Idaho Region 10
28 Addison County, Vermont Region 1
29. Knox County. Maine Region 1
30 ;Lincoln County, Minnesota Region 3
31 Roberts County. South Dakota Region 8
Kauai County. Hawaii Region9
33. Penobscot County, Maine Region 1
34 Pierce County, Nebraska Region /
3b. Aroostook County. Maine Region 1
36. Carbon County. Wyoming Region 8
37., Itasca County, Minnesota ; Region 5
38. Lake and Peninsula Borough, Alaska Region 10
39 Hawaii County, Hawaii Region 9
40 Rutland County. Vermont Region 1
ai Somerset County, Maine Region 1
4?.: Mckinley County. New Memco Region 0
46. Daniels County. Montana Region 8
47 Grafton County. New Hampshire Region 1
48 Mono County. California Region 9
49. Coos County. New Hampshire Region 1
M Sjn Juan County. Washington Rcfiior. 10
County
| EPA Region
I. Skagway Municipality, Alaska Region 10
/. Oneida County. Wisconsin ; Region 5
J. Pipestone County. Minnetola Region 5
I Price County. Wisconsin Region 5
5. Clark County. Wisconsin Region s
». Lamoille County. Vermont Region 1
7- uintaCounty. Wyoming Regions
Day County. South Dakota ; Region 8
KoochichingCounty. Minnesota Regions
3. San Juan County. New Mexico Region 6
l. Ravalli County. Montana Region 8
/. Coconino County. Aruona Region 9
J. Lincoln County. Maine Region 1
1. Pitkin County. Colorado Regions
S . Blaine County. Idaho Region 10
5.: Beaverhead County, Montana Region 8
7 Pembina County. North Dakota : Region 8
i. Gunnison County. Colorado Region 8
>. Chaffee County. Colorado i Region 8
D. Benton County. Indiana Region 5
l Honolulu County. Hawol I Region 9
2.1 St. Lawrence County. New York Region 2
J. Essex County. Vermont Region 1
I. Shawano County. Wisconsin Regions
i Sierra County. New Mexico Region 6
».{San Miguel County, Colorado : Region 8
7 Ward County. North Dakota Regions
?. Routt County. Colorado IRegion 8
i. Chickasaw County. Iowa Region 7
3. Jefferson County. Montana Region 8
I Newton County, Indiana Region S
?. rorest County. Wisconsin Regions
». Sawyer County, Wisconsin Region 5
». Grant County. Minnesota Region 5
*. Ouray County. Colorado I Region 8
». Oceana County. Michigan Region 5
7. Sanders County, Montana Region 8
5. Piscataquis County, Maine Region 1
>¦ Vilas County, Wisconsin Region S
Eagle County, Colorado Regions
1. Fillmore County. Minnesota Regions
! Otero County. New Mexico Region 6
I. Garfield County. Colorado Region 8
». Grant County. South Dakota Region 8
Navajo County. Arizona Region 9
5. Merrimack County, New HampshRegion 1
7. Door County, Wisconsin Regions
3. Steuben County, New York Region 2
f lorence County. Wisconsin Region S
Washburn County. Wisconsin Mpgion S
County
101. St. Louis County. Minnesota
102. Washington County, Vermont
103. Pulaski County. Indiana
104 Baker County. Oregon
105 Mciean County. North Dakota
106. Grant County. NewMenco
107. Caledonia County, Vermont
108. Sweetwater County. Wyoming
109. Wrangeli City and Borough, Alaska
110. Huron County. Michigan
111- Lake County. Minnesota
112. Kalkaska County. Michigan
113- King William County. Virgmia
114. Morrison County, Minnesota
115. Umatilla County, Oregon
116. Missoula County. Montana
117. franklin County. Maine
118. Deschutes County. Oregon
119. Teton County, Montana
120 Lewis County, Newvork
121. Cass County. Minnesota
172 Jasper County, Indiana
123- Jackson County, Wisconsin
124 Polk County. Wisconsin
125. Winnesmek County. Iowa
126 Summit County. Colorado
127. Livingston County, Illinois
128- Muni mgdon County. Pennsylvania
179 Valdez-Cordova Census Area. Alaska
130 Elko County. Nevada
131. Clayton County. Iowa
132 Wasco County. Oregon
133. Deuel County. South Dakota
134. Rio Arriba County, New Mexico
1 Jb Luna County. New Me*»co
136. Ne* Perce County, Idaho
137. Nowaygo County, Michigan
138. TK»g» County. Pennsylvania
139. Sanilac County. Michigan
140. La Plata County, Colorado
141 Duchesne County. Utah
142 Missaukee County. Michigan
143 Idaho County. Idaho
144 unic-n County. Oregon
145 Lassen County, California
146. Benewah County. Idaho
147. Ashland County. Wisconsin
148. White Pine County. Nevada
149. Windham County. Vermont
150
EPA Region
Region 5
Region l
Region 5
Region 10
Region 8
Region 6
Region l
Region 8
Region 10
Region 5
Region 5
Region S
Region i
Regions
Region 10
Region 8
Region 1
Region 10
Region 8
Region 2
Regions
Region 5
Region 5
Region 5
Region 7
Region 8
Region 5
Region J
Region 10
Region 9
Region 7
Region 10
Region 8
Region 6
Region 6
Region 10
Region 5
Region 3
Region 5
Region 8
Region 8
Region 5
Region 10
Region 10
9
Region 10
Region 5
Region 9
Region 1
1—
A table including counties with the top
150 CRSI values in the U.S. follows the
~ chloropleth maps. Counties are color-
coded by EPA Region. For the EPA
Regions, the table includes counties with
the top 25 CRSI values in each Region.
Region 7
Rank
County
L
Pierce County, Nebraska
2.
Chickasaw County. Iowa
3.
Winneshiek County. Iowa
4.
Clayton County. Iowa
5.
Fayette County. Iowa
6.
Wabaunsee County, Kansas
7.
Nodaway County. Missouri
a
Marshall County, Kansas
9.
Ottawa County, Kansas
10.
Macon County, Missouri
11.
Miami County, Kansas
12.
Richardson County, Nebraska
13.
Bremer County. Iowa
14.
Nemaha County, Kansas
15.
Washington County, Kansas
16.
Shelby County, Iowa
17.
Washington County. Iowa
18.
Osage County, Missouri
19.
Kossuth County, Iowa
20.
Cherokee County. Iowa
21.
Lafayette County, Missouri
22.
Pottawatomie County, Kansas
23.
Brown County, Kansas
24.
Cedar County, Iowa
25.
Vemon County, Missouri
Figure 4.8 Example Table of highest ranking CRSI values for all U.S. counties and counties within EPA Regions. All state and county CRSI
scores can be found in Appendices B and C.
73
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4.3.4. Breakdown of the Risk Domain
Risk Domain chloropleth map
for U.S/ EPA Region
Region 9
Technological Exposures
Three Primary Exposures:
1- Drought
2- Earthquakes
3- Extreme Temps - Highs
Top three exposures in
the U.S/EPA Region
with corresponding
icons
Risk Range:
High - Orange, CA - 4.16
Low-White Pine, NV-1.74
Mean-3.02 /\
Counties with the highest
and lowest Risk Domain
scores and the average Risk
Domain Score for the
U.S./EPA Region
Natural Exposures
inland Flood
High Wind
Exposure Type
Developed
LOSS
Natural toss
46%
Duai-Benetif
U.S./EPA Region pie charts
showing: (1) the proportion of
natural exposure attributed to
individual exposure events
(upper left); (2) the proportion
of technological exposures
attributed to specific
technological hazards present
(upper right); (3) the proportion
of different types of loss
contributing to the Loss
indicator score (bottom right);
and (4) The contribution of
technological hazards and
natural exposure events to the
Exposure Indicator score
(bottom left).
Figure 4.9 Example summary of Risk domain presented for the nation and the EPA Regions.
74
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4.3.5. Polar Plots for Nation and EPA Regions
Condition
Vacant
Structures
Community
Preparedness
Natural
Resource
Conservation
Utility
infrastructure
Transportation
Infrastructure
Personal
Preparedness
Demo-
graphics
Housing
Characteristics
Communication
Infrastructure
Economic
Diversity
Soclo-
Economics
Health
Characteristics
Social Services
Safety
and
Security
Soda*
Cohesion
Polar bar charts show the scores for
each of the indicators within the five
domains. Bars are color-coded by
domain. Longer bars represent higher
scores. This graphic is intended to
show the contribution of indicator
scores to the domain score. The
indicator bars are not intended to be
compared across domains. For the
Risk Domain, higher exposure and loss
indicator scores correspond to greater
risk (more vulnerability)
Figure 4.10 Example polar plot describing the contributions of the 20 indicators to the domain
scores.
4.3.6. National Results
The U.S. is comprised of 3,143 boroughs and counties. The Climate Resilience Screening Index
(CRSI) includes 3,135 counties; excluding eight boroughs from Alaska. These eight boroughs
could not be included because they had too little data and metric values could not be imputed or
interpolated accurately. With the increase in the frequency and severity of climate events over
the last decade (e.g., Hurricanes Katrina, Ivan and Rita; Superstorm Sandy; increases in flooding,
hailstorms, and wildfires; increases in maximum temperatures; and decreases in minimum
temperatures), many U.S. Federal Agencies (e.g., FEMA, U.S. EPA, DOC and DOI) have been
assisting states prepare for these types of climate events, and Natural Environment scores. The
U.S. CRSI score is 4.21 based on the average of CRSI scores for all counties in the U.S. ranging
from -7.2 to 50.1 (including Alaska increases the max to 227.2).
The CRSI and domain scores for the nation are shown in Figure 4.11. The nation is characterized
by moderate risk, moderate to high Governance, moderate to high Society, Built Environment.
The distribution of overall CRSI values as well as the domain scores by county for the U.S. are
shown in Figure 4.12. Examples of inferences that can be made from the maps are:
• The western U.S., the Great Lakes area and the upper northeast have higher CRSI values
(higher resilience to climate events).
• The western mid-west, the southeast, western Texas and Appalachian region have lower
75
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CRSI values.
• The lower northeastern coastal area, southeast/Gulf coasts, a small area associated with
southern Lake Michigan, and southern California have the highest risk domain scores
albeit for different types of risk.
• Lower risk scores are seen in the west and upper mid-west, Alaska and Hawaii.
• Higher Governance scores are seen in the northeast, mid-Atlantic and Great Lakes areas
of the U.S.
• Lower Governance scores related to climate were observed in Appalachia, the deep south
and much of California.
• Higher Society scores are seen in the upper mid-west and mountain west.
• Lower Society scores are seen in Appalachia and the deep south. Both built and Natural
Environment domain scores were higher in the west and lower in the western Midwest
and southeast.
Many other inferences can be determined from the mapped distributions.
U.S.
Risk
CRSI
4.21
fifiT (Range:-11.52 -227.2)
Governance
•4
Society
Built Environment
Natural Environment
-0.5 -0.4 -0.3 -0.2
-0.1
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
Domain Score (lighter shade bar)/Median Adjusted Score(darker shade bar)
Figure 4.11 Summary of CRSI (upper right hand value) and domain scores (light colored bars) for
the U.S, along with domain median adjusted scores showing influence of each domain on final CRSI
score (dark colored bars).
76
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t-ovtrnanc
Society
Built Envaranmcni
Namml Environment
Figure 4.12 The distributions of CRSI values and domain scores (Risk, Governance, Society, Built
Environment and Natural Environment).
77
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In order to provide regions and counties with examples of the higher CRSI-scored counties in the
U.S., Table 4.1 shows the 150 counties in the U.S. with the highest CRSI values. Region 10 has
the most counties in the top 150 list (37 counties) followed by Region 5 (34), Region 8 (30) and
Region 1 (19). All of the remaining EPA regions (except Region 4) have three or more counties
in the top 150. This provides each EPA region with several example counties to use as role
models for counties characterized by lower CSRI scores. Counties with the lowest scores (25
counties) are predominated by Region 4(16 counties primarily in Georgia) followed by Region 8
(5) and one each in Regions 6, 7 and 10.
Risk due to climate events across the U.S. is examined in more detail in Figure 4.13. Natural
exposures due to climate events are predominated by drought (34% of counties), extreme high
temperatures (22%) and extreme low temperatures (17%). All other types of exposure due to
natural climate events are represented at <10%. Superfund sites and TRI (Toxic Release
Inventory) sites dominated the technological exposure indicator at 44% and 39%, respectively.
Technological exposure adds potential risk to counties prone to natural climate event exposures.
Nationally, losses are seen primarily on dual benefit and natural land use types (e.g., forests,
wetlands, agriculture). Most exposure comes from natural climate events although 6% of
exposure is due to exacerbated exposure resulting from proximity to technological features that
pose hazards (6%). Risk ranges from the lowest score of 0.70 in the Kodiak Island burrough of
Alaska to 7.02 in Los Alamos County, New Mexico with a national average CRSI score of 2.73.
The contributions of the 20 indicators to the national domain scores are shown in Figure 4.14.
Natural resource conservation (Governance), number of vacant structures and housing
characteristics (Built Environment) as well as demographic characteristics (Society) most
strongly influenced national domain scores. Secondary influences included levels of loss (Risk),
socio-economic characteristics and economic diversity in the Society Domain, community and
personal preparedness (Governance) and acreage of ecosystem types (Natural Environment).
Overall, CRSI values, domain scores and indicator contributions all paint a picture for the U.S.
of reasonable resilience to climate events. However, the distribution of these scores is broad.
While there are many relatively resilienct counties in the U.S., there are a number of counties in
which overall resilience to climate events is low or one or more of the domain scores are low.
Therefore, more specific results and analyses should be examined for each of the regions.
78
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Table 4.1 Top 150 counties according to CRSI values (i.e., potentially higher resilience to climate events).
Rank
County
EPA Region
Rank
County
|epa Region
Rank
County
EPA Region
1.
Kodiak Island Borough, Alaska
Region 10
SI.
Skagway Municipality, Alaska
Region 10
101.
St. Louis County, Minnesota
Region 5
2.
Juneau City and Borough, Alaska
Region 10
52.
Oneida County, Wisconsin
I Region 5
102.
Washington County, Vermont
Region 1
3.
Ketchikan Gateway Borough, Alaska
Region 10
53.
Pipestone County, Minnesota
3 Region 5
103.
Pulaski County, Indiana
Region 5
4.
Aleutians East Borough, Alaska
Region 10
54.
Price County, Wisconsin
(Region 5
104.
Baker County, Oregon
Region 10
5.
North Slope Borough, Alaska
Region 10
55.
Clark County, Wisconsin
Region 5
105.;McLean County, North Dakota
Region 8
6.
Haines Borough, Alaska
Region 10
56.
Lamoille County, Vermont
i Region 1
106.:Grant County, New Mexico
Region 6
7.
Prince of Wales-Hyder Census Area, Alaska
Region 10
57.
Uinta County, Wyoming
Region 8
107.
Caledonia County, Vermont
Region 1
8.
Hancock County, Maine
Region 1
58.
Day County, South Dakota
(Region 8
108.
SweetwaterCounty, Wyoming
Region 8
9.
Sitka City and Borough, Alaska
Region 10
59.
Koochiching County, Minnesota
: Region 5
109.
Wrangell City and Borough, Alaska
Region 10
10.
Hoonah-Angoon Census Area, Alaska
Region 10
60.
San Juan County, New Mexico
Region 6
110.
Huron County, Michigan
Region 5
11.
Waldo County, Maine
Region 1
61.
Ravalli County, Montana
|Region 8
111.
Lake County, Minnesota
Region 5
12.
Dukes County, Massachusetts
Region 1
62.
Coconino County, Arizona
(Region 9
112.
Kalkaska County, Michigan
Region 5
13.
Dillingham Census Area, Alaska
Region 10
63.
Lincoln County, Maine
(Region 1
113.
King William County, Virginia
Region 3
14.
Kenai Peninsula Borough, Alaska
Region 10
64-
Pitkin County, Colorado
j Region 8
114.
Morrison County, Minnesota
Region5
15.
Petersburg Census Area, Alaska
Region 10
65.
Blaine County, Idaho
(Region 10
115.
Umatilla County, Oregon
Region 10
16.
Fairbanks North Star Borough, Alaska
Region 10
66.
Beaverhead County, Montana
(Region 8
116.
Missoula County, Montana
Region 8
17.
Yakutat City and Borough, Alaska
Region 10
67.
Pembina County, North Dakota
(Region 8
117.
Franktin County, Maine
Region 1
18.
Maui County, Hawaii
Region 9
68.
Gunnison County, Colorado
(Region 8
118.
Deschutes County, Oregon
Region 10
19.
Bonner County, Idaho
Region 10
69.
Chaffee County, Colorado
(Region 8
119.
Teton County, Montana
Region 8
20.
Aleutians West Census Area, Alaska
Region 10
70.
Benton County, Indiana
(Regio n 5
120.
Lewis County, New York
Region 2
21.
Bristol Bay Borough, Alaska
Region 10
71.
Honolulu County, Hawaii
Region 9
121.
Cass County, Minnesota
Region 5
22.
Hamilton County, New York
Region 2
72.
St. Lawrence County, New York
(Region 2
122.
Jasper County, Indiana
Region 5
23.
Flathead County, Montana
Region 8
73.
Essex County, Vermont
(Region 1
123.
Jackson County, Wisconsin
Region 5
24.
Anchorage Municipality, Alaska
Region 10
74.
Shawano County, Wisconsin
(Region 5
124.
Polk County, Wisconsin
Region 5
25.
Latah County, Idaho
Region 10
75.
Sierra County, New Mexico
(Region 6
125.
Winneshiek County, Iowa
Region 7
26.
Washington County, Maine
Region 1
76.
San Miguel County, Colorado
j Regio n 8
126: Summit County, Colorado
Region 8
27.
Valley County, Idaho
Region 10
77.
Ward County, North Dakota
(Region 8
127.
Livingston County, Illinois
Region 5
28.
Addison County, Vermont
Region 1
78.
Routt County, Colorado
|Region 8
128.
Huntingdon County, Pennsylvania
Region 3
29.
Knox County, Maine
Region 1
79.
Chickasaw County, Iowa
(Region 7
129.
Valdez-Cordova Census Area, Alaska
Region 10
30.
Lincoln County, Minnesota
Region 5
80.
Jefferson County, Montana
Region 8
130.
Elko County, Nevada
Region 9
31.
Roberts County, South Dakota
Region S
81.
Newton County, Indiana
(Region 5
131.
Clayton County, Iowa
Region 7
32.
Kauai County, Hawaii
Region 9
82.
Forest County, Wisconsin
(Region 5
132.
Wasco County, Oregon
Region lO
33.
Penobscot County, Maine
Region 1
83.
Sawyer County, Wisconsin
(Region 5
133.
Deuel County, South Dakota
Region 8
34.
Pierce County, Nebraska
Region 7
84.
Grant County, Minnesota
; Region 5
134.
Rio Arriba County, New Mexico
Region 6
35.
Aroostook County, Maine
Region 1
85.
Ouray County, Colorado
(Region 8
135.
Luna County, New Mexico
Region 6
36.
Carbon County, Wyoming
Region 8
86.
Oceana County, Michigan
(Region 5
136.
Nez Perce County, Idaho
Region 10
37.
Itasca County, Minnesota
Region 5
Region 10
87.
Sanders County, Montana
Piscataquis County, Maine
j Region 8
(Region 1
137.
Newaygo County, Michigan
Region 5
38.
Lake and Peninsula Borough, Alaska
88.
138.
Tioga County, Pennsylvania
Region 3
39.
Hawaii County, Hawaii
Region 9
89.
Vilas County, Wisconsin
Region 5
139.
Sanilac County, Michigan
Region 5
40.
Rutland County, Vermont
Region 1
90.
Eagle County, Colorado
(Region 8
140.
La Plata County, Colorado
Region 8
41.
Somerset County, Maine
Region 1
91.
Fillmore County, Minnesota
(Region 5
141.
Duchesne County, Utah
Region 8
42-
Grand Isle County, Vermont
Region 1
92.
Otero County, New Mexico
¦ Region 6
142.
Missaukee County, Michigan
Region 5
43.
Boundary County, Idaho
Region 10
93.
Garfield County, Colorado
(Region 8
143.
Idaho County, Idaho
Region 10
44.
Lincoln County, Montana
Region 8
94.
Grant County, South Dakota
(Region 8
144. iUnion County, Oregon
Region 10
45.
McKinley County, New Mexico
Region 6
95.
Navajo County, Arizona
jRegion 9
145.
Lassen County, California
Region 9
46.
Daniels County, Montana
Region 8
96.
Merrimack County, New HampshsRegion 1
146.
Benewah County, Hdaho
Region lO
47.
Grafton County, New Hampshire
Region 1
97.
Door County, Wisconsin
¦Region 5
147.
Ashland County, Wisconsin
Region 5
48.
Mono County, California
Region 9
98.
Steuben County, New York
(Region 2
148.
White Pine County, Nevada
Region 9
49.
Coos County, New Hampshire
Region 1
99.
Florence County, Wisconsin
Region 5
149.
Windham County, Vermont
Region 1
50.
San Juan County, Washington
Region 10
100.
Washburn County, Wisconsin
Region 5
150.
Humboldt County, California
Region 9
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Natural Loss
47%
Oual-Benefit
Loss
49%
Natural
Exposure
94%
Temps
Inland Flood
7%
High Wind
Superfund
44%
RCRA
k 6%
Hurricane
1%
Natural Exposures
_ Coastal Flood
1%
.Earthquake
3%
Technological Exposures
Top Three Primary Exposures:
^ 1-Drought
I 2- Extreme Temps - Highs
Risk Range:
High - Los Alamos, NM - 7.02
Low - Kodiak Island, AK - 0.70
Mean-2.73
3- Extreme Temps - Lows
Exposure Type
Technological Exposure
Landslide
6%
Losses
Developed Loss
Hail-
4%
Figure 4.13 Map of Risk Domain scores by county; proportion of natural exposures by climate event type, technological exposures, losses and exposure type
nationwide; and the range of risk with the highest risk and lowest risk counties identified; as well as, the three primary exposure types.
80
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otnenf
Ecosystem
Type Extent
Condition
Vacant
Structures
Community
Preparedness
Natural
Resource
Conservation
Utility
Infrastructure
Transportation
Infrastructure
Personal
Preparedness
Demo-
graphics
Housing
Characteristics
Communication
Infrastructure
Economic
Diversity
Socio-
Economics
Health
Characteristics
Labor-TVade
Services
Social Services
Safety
and
Security
Social
Cohesion
Figure 4.14 Polar plot showing the contribution of the 20 indicators associated with the domain scores for
the nation. The length of the bars corresponds to the indicator score. Within a domain, the higher indicator
scores show a greater contribution to the domain.
4.3.7. Regional Results
The following sections depict the results for all ten EPA Regions.
EPA Region 1
EPA Region 1 serves Connecticut, New Hampshire, Maine, Massachusetts, Rhode Island, and Vermont.
Region 1 also serves ten federally recognized tribes within Maine, Massachusetts, Connecticut and
Rhode Island. Areas within Region 1 are prone to, and often impacted by, intense rainfall, sea level rise,
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and heatwaves. For example, Cambridge, MA has experienced extreme rain events leading to flooding
and compromising infrastructure. Nearby, Boston, MA, is projected to experience the same types of
extreme rain events, but given its proximity to the coast, flooding will be exacerbated by sea level rise
and erosion impacts are more of a concern. Since Boston is more urban, these issues have to be dealt
with in the context of affordable housing and social inequity. The 2014 EPA Region 1 Climate Change
Adaptation Plan (USEPA-R1 2014) suggests re-nourishing coasts with dredged material, performing
marsh restoration and considering "living shorelines" to combat coastal wetland erosion. Suggested
actions around severe rainfall and sea level rise focused on determining where the impacts would occur
and focusing current restoration or infrastructure improvement efforts based on that information. For
example, prioritizing restoration of tidal wetlands that have room to migrate with sea level rise.
The CRSI and domain scores for EPA Region 1 are shown in Figure 4.15. The Region is characterized
by moderate risk, moderate to high Governance, moderate to high Society, Built Environment and
Natural Environment scores. The domain scores for Society, Built Environment and Natural
Environment showed positive influences on the overall CRSI score of 10.70. The Region 1 CRSI score
ranked 2nd among the ten EPA Regions.
The overall CRSI score and 5 domain scores for EPA Region 1 are depicted in Figure 4.16. The higher
CRSI values are seen in the northern counties of Maine, a number of eastern counties in New Hampshire
and select counties in Vermont (Table 4.2). Lower CSRI scores are seen in Connecticut (3 counties),
middle Massachusetts (2), Rhode Island (2), and New Hampshire (2). The highest risk domain scores are
seen in middle Massachusetts, and most of Connecticut.
Risk due to climate events across Region 1 is examined in more detail in Figure 4.17. Natural exposures
due to climate events are dominated by drought (33% of counties), hail (22%) and extreme high
temperatures (21%). Extreme low temperatures also represent a sizeable portion of the risk potential
(13%). All other types of exposure due to natural climate events are represented at <10%. TRI (Toxic
Release Inventory) sites and Superfund sites represent a majority of the technological exposure indicator
at 64% and 28%, repectively. RCRA sites contribute only 8% of the exposure potential. In the region,
losses are represented almost exclusively (96%) in natural lands, with the other 4% of regional losses
coming from dual-benefit lands. Natural climate risk potential dominates the region, with only 21% of
risk being attributable to technological exposure potential. Risk ranges from a low score of 1.83 in
Dukes County, Massachusetts to a high score of 3.35 in Middlesex County, Massachusetts. The mean
regional risk falls below the national at 2.43.
The contributions of the 20 indicators to EPA Region 1 domain scores are shown in Figure 4.18. Higher
scores for indicators of community preparedness, natural resource conservation, demographic
characteritics and number of vacant structures contributed to higher scores in each respective domain.
These influences combined with low loss contribution from the risk domain are reflected in the Region's
higher CRSI value of 10.7 Safety and security, labor-trade services and ecosystem condition had
minimal influence on the EPA Region 1 domain scores.
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EPA Region 1
A
Risk
Governance
<¦
Society
CRSI
10.70
(Range: 1.53-50.86)
Built Environment
-m-
| Natural Environment
-0.5 -0.4 -0.3 -0.2 -0.1
0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9
Domain Score (lighter shade bar)/Median Adjusted Score(darker shade bar)
Figure 4.15 Summary of CRSI (upper right hand value) and domain scores (light colored bars) for EPA
Region 1 along with domain median adjusted scores showing influence of each domain on final CRSI score
(dark colored bars).
83
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Region 1
Society
Governance
Ruill Environment
Naiur.il Environment
Figure 4.16 Summary of CRSI (upper riglit hand value) and domain scores (light colored bars) for Region
1, along with domain median adjusted scores showing influence of each domain on CRSI (dark colored
bars).
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Table 4.2 Top 25 counties according to CRSI values in EPA Region 1 (i.e., higher resilience to climate events).
Region 1
Rank
County
1.
Hancock County, Maine
2.
Waldo County, Maine
3.
Dukes County, Massachusetts
4.
Washington County, Maine
5.
Addison County, Vermont
6.
Knox County, Maine
7.
Penobscot County, Maine
8.
Aroostook County, Maine
9.
Rutland County, Vermont
10.
Somerset County, Maine
11.
Grand Isle County, Vermont
12.
Grafton County, New Hampshire
13.
Coos County, New Hampshire
14.
Lamoille County, Vermont
15.
Lincoln County, Maine
16.
Essex County, Vermont
17.
Piscataquis County, Maine
18.
Merrimack County, New Hampshire
19.
Washington County, Vermont
20.
Caledonia County, Vermont
21.
Franklin County, Maine
22.
Windham County, Vermont
23.
Franklin County, Massachusetts
24.
Cheshire County, New Hampshire
25.
Nantucket County, Massachusetts
85
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1
Natural Exposures
Inland Flood
2%
Technological Exposures
Three Primary Exposures:
1- Drought
J 2-Hail
:i- 3- Extreme Temps - Highs
Risk Range:
High - Middlesex, MA - 3.35
Low - Dukes, MA - 1.83
Mean - 2.43
Superfund
28%
High Wind
9%
Exposure Type
Dual-Benefit
Loss
4%
/ Technological
/ Exposure
/ 21%
Figure 4.17 Map of Risk Domain scores by county for Region 1; proportion of natural exposures by climate event type, technological exposures, losses
and exposure type nationwide; and the range of risk with the highest risk and lowest risk counties identified; as well as, the three primary exposure
types in the region (If a category was represented by <0.1%, it was not included).
86
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Ecosystem
Type Extent
Exposure
Condition
Vacant
Structures
Community
Preparedness
Natural
Resource
Conservation
Utility
Infrastructure
Transportation
Infrastructure
Personal
Preparedness
Demo-
graphics
Housing
Characteristics
Communication
Infrastructure
Economic
Diversity
Socio-
Economics
Health
Characteristics
Labor-TVade
Services
Social Services
Safety
and
Security
Social
Cohesion
Figure 4.18 Polar plot showing the contribution of the 20 indicators associated with the domain scores for
the EPA Region 1. The length of the bars corresponds to the indicator score. Within a domain, the higher
indicator scores show a greater contribution to the domain score (sum of indicator scores).
EPA Region 2
Region 2 of the EPA serves New Jersey, New York, and the territories of Puerto Rico and the U.S.
Virgin Islands. Region 2 also serves eight federally recognized Indian Nations, all within New York.
Region 2 shares the same regional impacts as Region 1; intense rainfall, sea level rise, and heatwaves.
Cities such as New York, NY have experienced multiple impacts, including extreme heatwaves, sea
level rise, severe storms and erosion. The age and scale of New York's transportation infrastructure
combined with the dense population raises some unique resilience concerns. The EPA Region 2 Climate
87
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Change Adaptation Plan of 2014 (USEPA-R2 2014) suggests managing increased storm water using
green infrastructure and building more resistance to climate change impacts through investments in
infrastructure.
The CRSI and domain scores for EPA Region 2 are shown in Figure 4.19. The Region is characterized
by about average risk; high Governance; moderate to high Society and Built Environment; and, lower
Natural Environment scores. The domain scores for Society and Built Environment showed positive
influences on the overall CRSI score of 5.00 while the Natural Environment score had a negative
influence on the CRSI score. Region 2 CRSI score ranked above average in terms of overall resilience to
climate events among all EPA Regions. The higher resilience to climate events risk scores in EPA
Region 2 were seen in upper New York while the lower risk counties were in upper and western New
York (Figure 4.20 and Table 4.3). The lower resilience scores were observed in both New York and
New Jersey with five counties in each state with low CRSI values. The higher risk of climate events
counties are seen primarily in New Jersey and Long Island, New York.
Risk due to climate events across Region 2 risk is examined in more detail in Figure 4.21. Natural
exposures due to climate events are dominated by extreme high temperatures (26% of counties), drought
(23%) and hurricanes (19%). Extreme low temperatures also represent a sizeable portion of the risk
potential (12%), while all other types of exposure due to natural climate events are represented at <10%.
Superfund sites represent a majority of technological exposure indicator at 93%. TRI (Toxic Release
Inventory) and RCRA sites contribute only a combined 7% of the exposure potential. In the region,
losses are represented evenly by dual benefit lands and natural lands at 49% each, with the other 2% of
the regional losses coming from developed lands. Natural climate risk potential dominates the region,
with only 21% of the risk attributable to technological exposure potential. Risk ranges from a low score
of 1.47 in Schoharie County, New York to a high score of 5.07 in New York County, New York. The
mean regional risk falls well below the national at 2.57.
The contributions of the twenty CRSI indicators are shown in Figure 4.22. Strong positive influences on
the Region 2 domain scores come from community preparedness and natural resource conservation
(Governance), demographic characteristics (Society) and vacant structures (Built Environment). In the
Society Domain, secondary positive incluences are seen from economic diversity, socio-economic
charcateristics and higher social cohesion scores. Weak influences (and sometimes strong negative
influences) on the Region 2 score come from safety and security and labor-trade services (Society) as
well as greater exposure risk.
88
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EPA Region 2
Risk
CRSI
5.00
Range: (-0.22 -22.06)
2 =
|er=
L^l Governance
Wr
Society
"r*jpf
Built Environment
Natural Environment
-0.5 -0.4 -0.3 -0.2 -0.1 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9
Domain Score (lighter shade bar)/Median Adjusted Score(darker shade bar)
Figure 4.19 Summary of CRSI (upper right hand value) and domain scores (light colored bars) for EPA
Region 2, along with domain median adjusted scores showing influence of each domain on final CRSI score
(dark colored bars).
89
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Governance
Buill Environiiwnl
N'jiunil Knvlronnii
Region 2
Figure 4.20 The distributions of EPA Region 2 CRSI values and domain scores (Risk, Governance, Society,
Built Environment and Natural Environment).
90
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Tab le 43 Highest 25 CRSI values in EPA Region 2 by county.
Region 2
Rank
County
1.
Hamilton County, New York
2.
St. Lawrence County, New York
3.
Steuben County, New York
4.
Lewis County, New York
5.
Essex County, New York
6.
Jefferson County, New York
7.
Livingston County, New York
8.
Herkimer County, New York
9.
Franklin County, New York
10.
Clinton County, New York
11.
Warren County, New York
12.
Ontario County, New York
13.
Cayuga County, New York
14.
Ulster County, New York
15.
Schuyler County, New York
16.
Chautauqua County, New York
17.
Tompkins County, New York
18.
Madison County, New York
19.
Cattaraugus County, New York
20.
Oneida County, New York
21.
Schoharie County, New York
22.
Columbia County, New York
23.
Wyoming County, New York
24.
Sullivan County, New York
25.
Yates County, New York
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Natural Exposures
Technological Exposures
-;i-;A
Extreme Low
Extreme High Temps
26%
Coastal Flood
8%
Superund
93%
High Wind
Developed Loss.
Exposure Type
Three Primary Exposures:
1- Extreme Temps - Highs
© 2- Drought
£ 3- Hurricanes
Risk Range:
High-New York, NY-5.07
Low - Schoharie, NY - 1.47
Mean-2.57
Dual-Benefit
Loss
49%
Natural Loss
49%
Technologica
Exposure
21%
Natural
Exposure
79%
Figure 4.21 Map of Risk Domain scores by county for Region 2; proportion of natural exposures by climate event type, technological exposures,
losses and exposure type nationwide; and the range of risk with the highest risk and lowest risk counties identified; as well as, the three primary
exposure types in the region.
92
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lVvnr\enf
Ecosystem
Type Extent
Exposure
Condition
Vacant
Structures
Community
Preparedness
Natural
Resource
Conservation
Utility
Infrastructure
Transportation
Infrastructure
Personal
Preparedness
Demo-
graphics
Housing
Characteristics
Communication
Infrastructure
Economic
Diversity
Socio-
Economics
Health
Characteristics
Labor-TVade
Services
Social Services
Safety
and
Security
Social
Cohesion
Figure 4.22 Polar plot showing the contribution of the 20 indicators associated with the domain scores for
the EPA Region 2. The length of the bars corresponds to the indicator score. Within a domain, the higher
indicator scores show a greater contribution to the domain score (sum of indicator scores).
EPA Region 3
Region 3 of the EPA serves the states of Delaware, the District of Columbia, Maryland, Pennsylvania,
Virginia, and West Virginia. There is one federally recognized tribe in this region. The majority of the
Region is impacted by heatwaves, intense rainfall, and sea level rise. Washington, D.C. has been
impacted by extreme heat and rainfall events, the latter leading to flooding and infrastructure damage.
The cities' infrastructure is also a resiliency concern when it comes to evacuating during an emergency
because bottlenecks could be an issue. With the exception of extreme heat events, Norfolk, VA, has
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been burdened by the same concerns and damages as Washington D.C. Norfolk, a coastal city, is already
dealing with the impacts of erosion and sea level rise. Lewes, DE is another coastal city being impacted
by sea level rise and erosion. Pittsburgh, PA is forecasted to experience extreme rainfall, flooding and
erosion from storms, but also faces concerns about environmental degradation, infrastructure damage,
and eventual infrastructure failure. The EPA Region 3 Climate Change Adaptation Plan of May 2014
(USEPA-R3 2014) focuses on increasing tools and training materials available to help counties and
communities choose between the different adaptation strategies available to them.
A summary of the CRSI and domain scores is displayed in Figure 4.23. The CRSI score for Region 3
(3.39) is below the national average and ranked 8th among the ten EPA Regions. The regional
Governance score is moderate to high and the risk domain score is about average. The Society domain
score is average and has little influence on the CRSI score while the Built Environment and Natural
Environment domain score are below average and negatively affect the regional CRSI score. The
counties with higher resilience scores in EPA Region 3 are in upper Pennsylvania and lower Virginia
(Figure 4.24 and Table 4.4). The higher CRSI values in Region 3 occur in Pennsylvania (11 counties),
Virginia (9), Maryland (4) and West Virginia (1). The lower CRSI values were predominantly in
Virginia and West Virginia. Risk domain scores were highest in western Chesapeake Bay counties,
Delaware, and southeastern Pennsylvania.
Risk due to climate events across Region 3 is examined in more detail in Figure 4.25. Natural exposures
due to climate events are dominated by drought (27% of counties), extreme high temperatures (20%)
and extreme low temperatures (19%). Landslides also represent a sizeable portion of the risk potential
(17%), while representation of all other types of exposure due to natural climate events are <10%. TRI
(Toxic Release Inventory) sites and Superfund sites represent a majority of technological exposure
indicator at 44% and 43%, respectively. Nuclear and RCRA sites also contribute a combined 13% of the
exposure potential. In the region, losses are represented evenly by dual benefit lands and natural lands,
with less than 1% of the regional losses coming from developed lands. Natural climate risk potential
dominates the region, with only 12% of the risk attributable to technological exposure potential. Risk
ranges from a low score of 1.63 in Bradford County, Pennsylvania to a high score of 4.79 in Hopewell
City, Virginia. The mean regional risk falls above the national at 2.91.
Contributions of CRSI's twenty indicators to the overall Region 3 domain scores is displayed in Figure
4.26. The highest indicator scores contributing each domain include vacant structures (Built
Environment), demographic charcateristics (Society) and natural resource conservation and community
preparedness (Governance). Secondary contributors include housing characteristics (Built Environment),
economic diversity and socio-economic factors (Society) and higher scores for the exposure indicator
influenced higher risk to climate events in this Region. Lower contributors to the Region 3 domain
scores are communications and transportation infrastructure (Built Environment), safety and security,
and labor-trade services (Society) and loss (Risk).
94
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EPA Region 3
^ Risk
CRSI
3.39
(Range:-3.49-13.53)
m
Governance
Society
Built Environment
Natural Environment
L
-0.5 -0.4 -0.3 -0.2 -0.1 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
Domain Score (lighter shade bar)/Median Adjusted Score(darker shade bar)
Figure 4.23 Summary of CRSI (upper right hand value) and domain scores (light colored bars) for EPA
Region 3, along with domain median adjusted scores showing influence of each domain on final CRSI score
(dark colored bars).
95
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Region 3
Society
Governance
Buill Environment
Natural Environment
Figure 4*24 The distributions of EPA Region 3 CRSI values and domain scores (Risk, Governance, Society,
Built Environment and Natural Environment).
96
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Table 4.4 Counties in EPA Region 3 with the highest CRSI values.
Region 3
Rank
County
1.
King William County, Virginia
2.
Huntingdon County, Pennsylvania
3.
Tioga County, Pennsylvania
4.
Perry County, Pennsylvania
5.
Potter County, Pennsylvania
6.
Somerset County, Pennsylvania
7.
Elk County, Pennsylvania
8.
Tucker County, West Virginia
9.
Clinton County, Pennsylvania
10.
Accomack County, Virginia
11.
Charles City County, Virginia
12.
Southampton County, Virginia
13.
Warren County, Pennsylvania
14.
Bedford County, Pennsylvania
15.
King and Queen County, Virginia
16.
Worcester County, Maryland
17.
Queen Anne's County, Maryland
18.
Lycoming County, Pennsylvania
19.
Caroline County, Virginia
20.
Garrett County, Maryland
21.
Amelia County, Virginia
22.
Cambria County, Pennsylvania
23.
Powhatan County, Virginia
24.
Page County, Virginia
25.
Somerset County, Maryland
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Figure 4.25 Map of Risk Domain scores by county for Region 3; proportion of natural exposures by climate event type, technological exposures,
losses and exposure type nationwide; and the range of risk with the highest risk and lowest risk counties identified; as well as, the three primary
exposure types in the region.
Region 3
Natural Loss
50%
Dual-Benefit
Loss
50%
Drought
27%
Inland
Rood
7% ,
High Wind
Superfund
43%
RCRA
6%
Nuclear
Natural
Exposure
88%
Three Primary Exposures:
ffu 1- Drought
1 2-Extreme Temps - Highs
^ 3- Extreme Temps - Lows
Risk Range:
High - Hopewell City, VA - 4.79
Low - Bradford, PA - 1.63
Mean-2.91
Technological Exposures
7%
6% Exposure Type
Natural Exposures
Hurricane
1%
Coastal
Flood
1%
2%
Losses
98
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Ecosystem
Type Extent
Exposure
Condition
Vacant
Structures
Community
Preparedness
Natural
Resource
Conservation
Utility
Infrastructure
Transportation
Infrastructure
Personal
Preparedness
Demo-
graphics
Housing
Characteristics
Communication
Infrastructure
Economic
Diversity
Socio-
Economics
Health
Characteristics
Labor-TVade
Services
Social Services
Safety
and
Security
Social
Cohesion
Figure 4.26 Polar plot showing the contribution of the 20 indicators associated with the domain scores for
the EPA Region 3. The length of the bars corresponds to the indicator score. Within a domain, the higher
indicator scores show a greater contribution to the domain score (sum of indicator scores).
EPA Region 4
EPA Region 4 includes Alabama, Florida, Georgia, Kentucky, Mississippi, North Carolina, South
Carolina, and Tennessee. Region 4 serves six federally recognized tribes in the southeast. This region is
threatened by sea level rise and extreme heat. Inland cities, such as Atlanta, GA, have suffered from
rising temperature and extreme heat. Broward and Miami-Dade Counties in Florida have already been
impacted by sea level rise, and infrastructure damage from storms. Miami- Dade County, FL has also
suffered related issues with water quality and quantity as salt water intrusion increases with sea level
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rise. The EPA Region 4 Climate Change Adaptation Implementation Plan of 2014 (USEPA-R4 2014)
lists encouraging low-impact development and green infrastructure to abrogate increased storm events;
ensuring water conservation and efficiency are considered in water resource project permitting to protect
water quality and quantity; using dredge material to protect from sea level rise and storm surge, and
developing protocols for emergency dredging after hurricanes since they may become more frequent or
severe.
A summary of the EPA Region 4 CRSI and domain scores are shown in Figure 4.27. The overall CRSI,
0.58, is well below the national average and ranked lowest among EPA Regions. The CRSI values
reflects relatively high risk to climate events, lower Governance associated with climate events, and
lower than average Society, Built Environment and Natural Environment domain scores. Figure 4.28
shows the distribution of these scores among the counties in Region 4. The higher CRSI values are
shown in coastal North Carolina and some coastal counties in Florida. Areas of high risk to climate
events are seen in the coastal regions of the Florida peninsula and the southern Appalachians. Lower risk
scores are seen in much of Georgia and the Big Bend area of Florida. Governance scores in Region 3 are
higher in northern Kentucky and lowest in Appalachia and much of Alabama. Strong Built Environment
domain scores are seen in mid- and south peninsula Florida.
Table 4.5 lists the 25 counties in EPA Region 4 with the highest CRSI values. The higher scores are
seen in counties in North Carolina (11), South Carolina (5), Georgia (3), Florida (3) and Kentucky (1).
The counties with lower CRSI values occur almost exclusively in Georgia and in one county in
Kentucky.
Risk due to climate events across Region 4 risk is examined in more detail in Figure 4.29. Natural
exposures due to climate events are dominated by drought (35% of counties), extreme high (23%) and
low (13%) temperatures. All other types of exposure due to natural climate events are represented at
<10%. TRI (Toxic Release Inventory) sites and Superfund sites represent a majority of the technological
exposure indicator at 45% and 35%, respectively. Nuclear sites also contribute a sizeable portion of the
risk potential at 18%, while RCRA sites contribute a negligible portion at 2%. In the region, losses are
represented primarily by dual benefit lands (49%) and natural lands (48%). Only 3% of losses come
from developed lands. Natural climate risk potential dominates the region, with only 4% of the risk
being attributable to technological exposure potential. Risk ranges from a low score of 1.53 in Taylor
County, Georgia to a high score of 5.26 in Shelby County, Tennessee. The mean regional risk falls
slightly above the national at 2.83.
Contributions of CRSI's twenty indicators to the overall Region 4 domain scores is displayed in Figure
4.30. The strongest positive influences on the domain scores in Region 4 include vacant structures and
housing characteristics (Built Environment), and demographic characteristics (Society). Secondary
influences are seen in community preparedness and natural resource conservation (Governance),
economic diversity social cohesion and socio-economic characteriustics (Society), and exposure to
climate events. Lower indicator scores are seen for safety and security and labor-trade services
(Society), and utility and communications infrastructure (Built Environment).
100
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EPA Region 4
Risk
CRSI
0.58
°—V (Range:-11.52-9.19)
ef= Governance
•$*
Snr
Built Environment
Natural Environment
-0.4 -0.3 -0.2 -0.1
0.8 0.9
Domain Score (lighter shade bar)/Median Adjusted Score(darker shade bar)
Figure 4.27 Summary of CRSI (upper right hand value) and domain scores (light colored bars) for EPA
Region 4, along with domain median adjusted scores showing influence of each domain on final CRSI score
(dark colored bars).
101
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Region 4
Governance
Built Enviromncni
Natural Environment.
Figure 4.28 The distributions of EPA Region 4 CRSI values and domain scores (Risk, Governance, Society,
Built Environment and Natural Environment).
102
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Table 4.5 Twenty-five counties in EPA Region 4 with the highest CRSI values.
Region 4
Rank
County
1.
Columbus County, North Carolina
2.
Monroe County, Florida
3.
Pender County, North Carolina
4.
Thomas County, Georgia
5.
Williamsburg County, South Carolina
6.
Franklin County, Florida
7.
Bertie County, North Carolina
8.
Northampton County, North Carolina
9.
Washington County, Georgia
10.
Martin County, North Carolina
11.
Gates County, North Carolina
12.
Jefferson County, Florida
13.
Georgetown County, South Carolina
14.
Bryan County, Georgia
15.
Halifax County, North Carolina
16.
Colleton County, South Carolina
17.
Orangeburg County, South Carolina
18.
Columbia County, Florida
19.
Robeson County, North Carolina
20.
Sampson County, North Carolina
21.
Levy County, Florida
22.
Duplin County, North Carolina
23.
Currituck County, North Carolina
24.
Spencer County, Kentucky
25.
Marion County, South Carolina
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Landslide
Superfund
35%
Nuclear
18%
RCRA
2%
Natural Loss
48%
Dual-Benefit
Loss
Natural Exposures
Coastal Flood
Technological Exposures
Earthquake
3%
Technological-
Exposure
4%
Exposure Type
Three Primary Exposures:
(fly 1- Drought
I 2- Extreme Temps - Highs
^ 3- Extreme Temps - Lows
Risk Range:
High-Shelby, TN-5.26
Low - Taylor, GA - 1.53
Mean-2.83
2%
Developed
Loss
3%
Losses
Figure 4.29 Map of Risk Domain scores by county for Region 4; proportion of natural exposures by climate event type, technological exposures,
losses and exposure type nationwide; and the range of risk with the highest risk and lowest risk counties identified; as well as, the three primary
exposure types in the region.
104
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Ecosystem
Type Extent
Exposure
Condition
Vacant
Structures
Community
Preparedness
Natural
Resource
Conservation
Utility
Infrastructure
"Transportation
Infrastructure
Personal
Preparedness
Demo-
graphics
Housing
Characteristics
Communication
Infrastructure
Economic
Diversity
Socio-
Economics
Health
Characteristics
Labor-TVade
Services
Social Services
Safety
and
Security
Social
Cohesion
Figure 4.30 Polar plot showing the contribution of the 20 indicators associated with the domain scores for
the EPA Region 4. The length of the bars corresponds to the indicator score. Within a domain, the higher
indicator scores show a greater contribution to the domain score (sum of indicator scores).
EPA Region 5
Region 5 of the EPA includes Illinois, Indiana, Michigan, Minnesota, Ohio, and Wisconsin. Region 5
serves 35 federally recognized tribes in Michigan, Minnesota and Wisconsin. Region 5 is impacted by
extreme rainfall events that lead to flooding, and extreme heat. Minneapolis, MN has been affected by
warming trends, and flooding from extreme rainfall. Milwaukee, WI has suffered both cases of severe
drought and extreme rainfall that resulted in flooding and infrastructure damage. Grand Rapids, MI and
Chicago, IL have both experienced rises in temperature, and extreme rainfall resulting in flooding,
105
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erosion and infrastructure damage. Chicago has additional resilience issues of endemic crime, public
health, and infrastructure failure. Ann Arbor, MI is forecasted to suffer from rising temperatures. The
EPA Region 5 Climate Change Adaptation Implementation Plan of May 2014 (USEPA-R5 2014) states
that Region 5 is striving to use water source protection tools in order to improve the resilience of highly
vulnerable water systems. Additionally, remediation techniques for incorporating vegetation are in
review in order to become more tolerant of heat, excessive rain, and drought in the EPA's Superfund
processes.
A summary of the overall CRSI score and the domain scores for EPA Region 5 is shown in Figure 4.31.
The overall CRSI value of 6.02 is above the national average while the Risk domain score is slightly
lower than the national average (less risk). The Region 5 Governance domain score is relatively high as
is the Society domain score. The scores for the Built Environment and Natural Environment domains are
above the national average. Region 5 CRSI value ranked 5th among the ten EPA Regions.
The distribution of the overall CRSI values and the domain scores among the counties in Region 5 is
shown in Figure 4.32. Higher CRSI values, as shown in Figure 4.32 and Table 4.6, occur in the counties
of Wisconsin (10 counties), Minnesota (9), and Michigan and Indiana (3 each). The counties with the
lower CRSI values occur in Indiana and Ohio (3 counties each), Illinois (2), and one county in each of
Minnesota and Michigan. Risk domain scores are generally the lowest in northern Michigan,
northwestern and middle Wisconsin and some counties in Minnesota. The highest risk domain scores
occur along the southwestern shore of Lake Michigan. Governance and Society domain scores are
higher in many of the counties of Wisconsin and Minnesota.
Risk due to climate events across Region 5 risk is examined in more detail in Figure 4.33. Natural
exposures due to climate events are dominated by drought (33% of counties), extreme high temperatures
(24%) and extreme low temperatures (22%). All other types of exposure due to natural climate events
are represented at <10%. Superfund sites and TRI (Toxic Release Inventory) sites evenly dominated the
technological exposure indicator at 43% each. Nuclear exposure potential is also a significant
contributor to risk in this region at 11%. Regionally, losses are seen primarily in dual benefit and natural
land types (e.g., forests, wetlands, agriculture). Most exposure comes from natural climate events,
although 7% of exposure results from proximity to anthropogenic, technological infrastructure. Risk
ranges from a low score of 1.79 in Cook County, Illinois to 4.79 in Mecosta County, Michigan, with a
regional average slightly lower than the national at 2.69.
The contributions of the 20 indicators to EPA Region 5 domain scores are shown in Figure 4.34. The
strongest contributors domain scores are natural resource conservation (Governance), demographic
characteristics (Society), and vacant structures (Built Environment). Secondary contributors include
economic diversity, social cohesion, socio-economic characteristics and health characteristics (Society),
housing charcateristics (Built Environment), and personal and community preparedness (Governance).
Lower indicator scores are shown for communication and utilities infrastructure in the Built
Environment domain and saftery and security and labor and trade services in the Society domain.
106
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EPA Region 5
/ CRSI
6.02
(Range:-0.84-20.57)
Governance
Society
Built Environment
Natural Environment
-0.5 -0.4 -0.3 -0.2 -0.1 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
Domain Score (lighter shade bar)/Median Adjusted Score(darker shade bar)
Figure 4.31 Summary of CRSI (upper right hand value) and domain scores (light colored bars) for EPA
Region 5, along with domain median adjusted scores showing influence of each domain on final CRSI
score (dark colored bars).
107
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Region 5
Governance
Society:
Built Environment
Natural Environment
/
Figure 4.32 The distributions of EPA Region 5 CRSI values and domain scores (Risk, Governance, Society,
Built Environment and Natural Environment).
108
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Table 4.6 Twenty-five counties in EPA Region 5 with the highest CRSI values.
Region 5
Rank
County
1.
Lincoln County, Minnesota
2.
Itasca County, Minnesota
3.
Oneida County, Wisconsin
4.
Pipestone County, Minnesota
5.
Price County, Wisconsin
6.
Clark County, Wisconsin
7.
Koochiching County, Minnesota
8.
Benton County, Indiana
9.
Shawano County, Wisconsin
10.
Newton County, Indiana
11.
Forest County, Wisconsin
12.
Sawyer County, Wisconsin
13.
Grant County, Minnesota
14.
Oceana County, Michigan
15.
Vilas County, Wisconsin
16.
Fillmore County, Minnesota
17.
Door County, Wisconsin
18.
Florence County, Wisconsin
19.
Washburn County, Wisconsin
20.
St. Louis County, Minnesota
21.
Pulaski County, Indiana
22.
Huron County, Michigan
23.
Lake County, Minnesota
24.
Kalkaska County, Michigan
25.
Morrison County, Minnesota
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Natural Exposures
222
Three Primary Exposures:
' 1-Drought
I 2- Extreme Temps - Highs
^ 3- Extreme Temps - Lows
Risk Range:
High-Cook, IL -4.79
Low - Mecosta, Ml - 1.79
Mean-2.69
Earthquake
2%
Technological Exposures
RCRA
3%
Inland
Extreme Low
Temps
Extreme High
Temps
High Wind
6%
Landslide
Developed
Dual-
Benefit
Loss
50%
Technological Exposure Type
Exposure
7%
Natural
Exposure
93%
Figure 4.33 Map of Risk Domain scores by county for Region 5; proportion of natural exposures by climate event type, technological exposures,
losses and exposure type nationwide; and the range of risk with the highest risk and lowest risk counties identified; as well as, the three primary
exposure types in the region.
110
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Ecosystem
Type Extent
Exposure
Condition
Vacant
Structures
Community
Preparedness
Natural
Resource
.Conservation
Utility
Infrastructure
Transportation
Infrastructure
Personal
Preparedness
Demo-
graphics
Housing
Characteristics
Communication
Infrastructure
Economic
Diversity
Socio-
Economics
Health
Characteristics
Labor-TVade
Services
Social Services
Safety
and
Security
Social
Cohesion
Figure 4.34 Polar plot showing the contribution of the 20 indicators associated with the domain scores for
the EPA Region 5. The length of the bars corresponds to the indicator score. Within a domain, the higher
indicator scores show a greater contribution to the domain score (sum of indicator scores).
EPA Region 6
Region 6 of the EPA serves Arkansas, Louisiana, New Mexico, Oklahoma, and Texas. Region 6
includes 66 federally recognized tribes in Arkansas, Louisiana, New Mexico, Oklahoma, and Texas. The
entire region is threatened by extreme heat events and rising temperatures. For example, Tucson, AZ,
Houston, TX, Dallas, TX, and El Paso, TX, have all experienced different issues due to warming trends.
In cities where the heat has been, or is projected to be, accompanied by drought, such as Houston, El
Paso and Tucson, water quality and quantity sometimes becomes a concern. In New Mexico rising
in
-------
temperatures, combined with drought, and insect outbreaks, has led to increased wildfire risk. In Dallas,
TX heat waves have caused energy shortages. In Houston, TX, Dallas, TX, and El Paso, TX there has
been extreme rainfall and flooding too, resulting in erosion and damages to infrastructure in Houston;
and infrastructure damage and even failure in Dallas. The Region's coastal states, specifically Louisiana,
are threatened by sea level rise. New Orleans, LA has not only suffered infrastructure damage and
failure, but has also had issues with storm surge and erosion. Some cities in the region face other
compounding resilience issues such as social inequity in El Paso, TX and Tucson, AZ, and severe drug
and alcohol abuse in El Paso. The EPA Region 6 Climate Change Adaptation Implementation Plan of
May 2014 (USEPA-R6 2014) suggested mitigating the impact of sea level rise and coastal land loss to
erosion using restoration projects developed and implemented through three National Estuary Programs
in the region, Climate Ready Estuaries Programs, and the Coastal Wetlands Planning, Protection and
Restoration Act (CWPPRA); with a goal of protecting or restoring 9,000 acres of coastal wetlands.
A summary of the EPA Region 6 overall CRSI and the domain scores is depicted in Figure 4.35. The
overall CRSI score of 2.77 is less than the national average ranks 9th among EPA Regions.The score
appears to be the result of lower than average Governance for climate events and lower than average
scores for the Society, Built Environment and Natural Environment domains. The distribution of these
scores across the counties of Region 6 is shown in Figure 4.36. The higher CRSI values in EPA Region
6 are in New Mexico and some scattered counties in Texas and Oklahoma. The highest scores for the
risk domain occur in coastal Louisiana. Higher Governance and Society domain scores occur in northern
Oklahoma and New Mexico. Table 4.7 lists the 25 counties with the highest CRSI values in EPA Region
6. These counties are in New Mexico (12), Texas (12) and Oklahoma (1). The counties with the lowest
CRSI values are in Texas (9) and Oklahoma (1).
Risk due to climate events across Region 6 risk is examined in more detail in Figure 4.37. Natural
exposures due to climate events are predominated by drought (37% of counties), extreme high
temperatures (24%) and extreme low temperatures (15%). All other types of exposure due to natural
climate events are represented at <10%. Superfund sites represent a majority of the technological
exposure indicator at 43%, while TRI (Toxic Release Inventory) sites and nuclear facilities represent a
collective 55% of the exposure potential (29% and 26% respectively). Loses in the region are seen
primarily in dual benefit and natural land use types (e.g., forests, wetlands, agriculture). Most of
exposure comes from natural climate events, with only 2% resulting from proximity to anthropogenic,
technologic infrastructure. Region 6 risk ranges from the lowest score of 1.54 in the Winkler County,
Texas to the highest in the nation, 7.02 in Los Alamos County, New Mexico, with a regional average
slightly higher than the national at 2.80.
The contributions of the 20 indicators to the domains that comprise CRSI are shown in Figure 4.38 for
EPA Region 6. The natural resource conservation indicator score is the strongest contributor to the
Governance domain. Secondary contributions are associated with vacant structures and housing
characteristics (Built Environment), and demographic characteristics (Society). Weaker contributors are
transportation and communications infrastructure scores in the Built Environment domain and (Built
Environment), and labor-trade services scores in the Society domain.
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EPA Region 6
Risk
CRSI
2.77
SSy (Range:-9.21-18.05)
la =
er=
er= Governance
Soci<
Built Environment
Natural Environment
-0.5 -0.4 -0.3 -0.2 -0.1 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
Domain Score (lighter shade bar)/Median Adjusted Score(darker shade bar)
Figure 4.35 Summary of CRSI (upper right hand value) and domain scores (light colored bars) for the U.S,
along with domain median adjusted scores showing influence of each domain on final CRSI score (dark
colored bars).
113
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Governance
Buill Environment
Xuur.il Environn «>i:
Figure 4.36 The distributions of EPA Region 6 CRSI values and domain scores (Risk, Governance, Society,
Built Environment and Natural Environment).
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Table 4.7 Twenty-five counties in EPA Region 6 with the highest CRSI values.
Region 6
Rank
County
1.
McKinley County, New Mexico
2.
San Juan County, Mew Mexico
3.
Sierra County, New Mexico
4.
Otero County, New Mexico
5.
Grant County, New Mexico
6.
Rio Arriba County, New Mexico
7.
Luna County, New Mexico
8.
Dona Ana County, New Mexico
9.
Taos County, New Mexico
10.
Sandoval County, New Mexico
11.
Santa Fe County, New Mexico
12.
Wilson County, Texas
13.
Wharton County, Texas
14.
Webb County, Texas
15.
Cibola County, New Mexico
16.
Clay County, Texas
17.
Wise County, Texas
18.
Erath County, Texas
19.
Osage County, Oklahoma
20.
Uvalde County, Texas
21.
Victoria County, Texas
22.
Fayette County, Texas
23.
Live Oak County, Texas
24.
Kerr County, Texas
25.
Tom Green County,Texas
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Superfund
43%
Drought
Nuclear
26%
Coastal Flood
inland
Fiood
7% /
Earthquake
2%
Natural
Loss
42%
Three Primary Exposures:
1- Drought
¦ I 2- Extreme Temps - Highs
$ 3- Extreme Temps - Lows
Natural Exposures
Hurricane
3%
RCRA Technological Exposures
2%_
Risk Range:
High - Los Alamos, NM - 7.02
Low - Winkler, TX - 1.53
Mean - 2.80
Exposure Type
Exposure
2%
Landslide
2%
High
5%
Developed
Loss
6%
Losses
Figure 4.37 Map of Risk Domain scores by county for Region 6; proportion of natural exposures by climate event type, technological exposures,
losses and exposure type nationwide; and the range of risk with the highest risk and lowest risk counties identified; as well as, the three primary
exposure types in the region.
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Ecosystem
Type Extent
Exposure
Condition
Vacant
Structures
Community
Preparedness
Natural
Resource
Conservation
Utility
Infrastructure
Transportation
Infrastructure
Personal
Preparedness
Demo-
graphics
Housing
Characteristics
Communication
Infrastructure
Economic
Diversity
Socio-
Economics
Health
Characteristics
Labor-TVade
Services
Social Services
Safety
and
Security
Social
Cohesion
Figure 4.38 Polar plot showing the contribution of the 20 indicators associated with the domain scores for
the EPA Region 6. The length of the bars corresponds to the indicator score. Within a domain, the higher
indicator scores show a greater contribution to the domain score (sum of indicator scores).
EPA Region 7
Region 7 of the EPA serves Iowa, Kansas, Missouri, and Nebraska. Region 7 serves 7 federally
recognized tribes in Kansas, Nebraska, and Iowa. All of Region 7 has experienced extreme heat and
rising temperatures, in some instances creating increased demand for resources such as water and
energy. Parts of the region have also witnessed extreme rainfall events, and flooding. Dubuque, IA has
suffered crop failures due to extreme heat and severe drought, and infrastructure damages due to
extreme rainfall and flooding. St. Louis, MO is projected to experience these same impacts of extreme
117
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heat and rainfall events, with the additional concern that rainfall will cause additional erosion. St. Louis
has other resilience issues, such as social inequity, endemic crime, and civil unrest. Most of the actions
being taken by Region 7 under the regional Climate Change Adaptation Implementation Plan (USEPA-
R7 2014) are focused on the availability of water. Actions include prioritizing watershed improvements
to sources of drinking water impacted by nutrients and other contaminants, promoting precipitation
neutral technologies and practices for site remediation, and helping work within the region to
incorporate water conservation practices, energy conservation and green infrastructure.
A summary of the overall CRSI score and the domain scores for EPA Region 7 are provided in Figure
4.39. The overall CRSI score of 4.11 is close to the national average and ranks 7th among the EPA
Regions. While the Risk domain score is relatively low, the Governance and Society domain scores are
relatively high. The Built Environment and Natural Environment domain scores are lower then the
national average. Figure 4.40 shows the spatial distribution of these domain scores across the counties
comprising EPA Region 7. Table 4.8 shows the highest CRSI values are scattered through the region
with the highest county scores occuring in Iowa (10 counties), Kansas (8), Missouri (5) and Nebraska
(2). The counties with lower CRSI values are primarily in Nebraska (8 counties) and one county each in
Kansas and Missouri. Lower Governance scores are seen in southern Missouri.
Risk due to climate events across Region 7 is examined in more detail in Figure 4.41. Natural exposures
due to climate events are dominated by drought (36% of counties), extreme high temperatures (23%)
and extreme low temperatures (18%). All other types of exposure due to natural climate events are
represented at <10%. Superfund sites and TRI (Toxic Release Inventory) sites evenly influenced the
technological exposure indicator at 45% and 43%, respectively. Potential nuclear exposure is also a
major contributor to risk potential in this region at 9%. Losses in the region are seen primarily in dual
benefit and natural land use types (e.g., forests, wetlands, agriculture). Most risk exposure comes from
natural climate events, with only 3% resulting from proximity to anthropogenic, technology. Risk ranges
from a low score of 1.46 in Madison County, Nebraska to 4.27 in Sedgwick County, Kansas with a
regional average slightly under the national at 2.65.
The contributions of the twenty indicators to the overall domain scores for EPA Region 7 are shown in
Figure 4.42. The strongest contributors are natural resource conservation scores (Governance), and
vacant structures (Built Environment). Secondary contributors are the housing characteristics indicator
score in the Built Environment domain and demographic characteristics, social cohesion, socio-
economic characteristics, economic diversity and health characteristics indicator scores in the Society
domain. Communication and utility infrastructures scores (Built Environment), and safety and security
scores (Society) are weaker contributors.
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EPA Region 7
m Risk
CRSI
4.11
(Range:-5.52-19.1
ef= Governance
<¦
Society
Built Environme
Natural Environme
tJ
-0.5 -0.4 -0.3 -0.2 -0.1 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9
Domain Score (lighter shade bar)/Median Adjusted Score(darker shade bar)
Figure 4.39 Summary of CRSI (upper right hand value) and domain scores (light colored bars) for EPA
Region 7, along with domain median adjusted scores showing influence of each domain on final CRSI score
(dark colored bars).
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Region (
Figure 4.40 The distributions of EPA Region 7 CRSI values and domain scores (Risk, Governance, Society,
Built Environment and Natural Environment).
Njtuml Environment
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Table 4.8 Twenty-fivehigjiest CRSI values in the counties of EPA Region 7.
Region 7
Rank
County
1.
Pierce County, Nebraska
2.
Chickasaw County, Iowa
3.
Winneshiek County, Iowa
4.
Clayton County, Iowa
5.
Fayette County, Iowa
6.
Wabaunsee County, Kansas
7.
Nodaway County, Missouri
8.
Marshall County, Kansas
9.
Ottawa County, Kansas
10.
Macon County, Missouri
11.
Miami County, Kansas
12.
Richardson County, Nebraska
13.
Bremer County, Iowa
14.
IMemaha County, Kansas
15.
Washington County, Kansas
16.
Shelby County, Iowa
17.
Washington County, Iowa
18.
Osage County, Missouri
19.
Kossuth County, Iowa
20.
Cherokee County, Iowa
21.
Lafayette County, Missouri
22.
Pottawatomie County, Kansas
23.
Brown County, Kansas
24.
Cedar County, Iowa
25.
Vernon County, Missouri
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Natural Expsosures
Earthquake
2%
Technological Exposures
- - -
inland
Flood
7%
Extreme Low
Temps
Superfund
45%
Nuclear
9%
Landslide
High Wind
6%
Developed
Loss
Three Primary Exposures:
1- Drought
| . 2- Extreme Temps - Highs
3- Extreme Temps - Lows
Risk Range:
High - Sedgwick, KS - 4.27
Low - Madison, NE - 1.46
Mean- 2.65
Natural Loss
Dual-Benefit
Loss
49%
Technological
Exposure
3%
Exposure Type
Natural
Exposure
97%
Figure 4.41 Map of Risk Domain scores by county for Region 7; proportion of natural exposures by climate event type, technological exposures,
losses and exposure type nationwide; and the range of risk with the highest risk and lowest risk counties identified; as well as, the three primary
exposure types in the region.
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Ecosystem
Type Extent
Exposure
Condition
Vacant
Structures
Community
Preparedness
Natural
Resource
Conservation
Utility
Infrastructure
Transportation
Infrastructure
Personal
Preparedness
Demo-
graphics
Housing
Characteristics
Communication
Infrastructure
Economic
Diversity
Socio-
Economics
Health
Characteristics
Labor-TVade
Services
Social Services
Safety
and
Security
Social
Cohesion
Figure 4.42 Polar plot showing the contribution of the 20 indicators associated with the domain scores for
the EPA Region 7. The length of the bars corresponds to the indicator score. Within a domain, the higher
indicator scores show a greater contribution to the domain score (sum of indicator scores).
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EPA Region 8
Region 8 of the EPA includes Colorado, Montana, North Dakota, South Dakota, Utah, and Wyoming.
Region 8 serves 27 federally recognized tribes, located in Colorado, Montana, North Dakota, South
Dakota, Utah, and Wyoming. The Region is threatened by extreme heat events and rising temperatures
and at risk of increased demand for energy and water resources as a result. These rising temperatures in
combination with drought and insect outbreaks, has increased the risk of wildfire for some parts of the
region, specifically Utah and Colorado. Boulder and Colorado Springs, CO have both experienced
temperature rises, extensive wildfires, air quality issues, and damages to infrastructure. Boulder, CO has
experienced extreme rainfall and flooding too. In Boulder, both extreme heat and extreme rainfall have
to be considered alongside other resilience issues like invasive species, disease and affordable housing.
Denver, CO has experienced extreme heat, temperature rises and air quality issues, but not wildfires or
infrastructure damage. It is projected that Denver will eventually experience extensive wildfires as well.
Salt Lake City, UT is forecasted to face extreme heat and temperature rises potentially leading to
wildfire risks, and water quality and quantity concerns. Efforts to improve resilience in EPA Region 8
include working with states and tribal nations to integrate climate considerations into their water
programs and consider how funding mechanisms may support increased investments in water
infrastructure (USEPA-R8 2014).
A summary of the overall CRSI score and the domain scores for EPA Region 8 is provided in Figure
4.43. The CRSI value for Region 8 is 6.09, above the national average and ranking 3rd highest among the
EPA Regions. This Region also has alow Rsk score indicating a less risk to acute climate events. The
Governance and Built Environment domain scores are moderate and the Society domain score is above
the national average. The spatial distribution of these scores among the counties in Region 8 is shown in
Figure 4.44. Higher overall CRSI values are seen in western Montana, most of Wyoming and along and
below the eastern slope of the Rocky Mountains in Colorado. The highest overall CRSI values are
shown in Table 4.9 and includes counties in Colorado (8 counties), Montana (7), and North Dakota,
South Dakota and Wyoming (3 each). The counties with lower CRSI values are found in South Dakota
(6), Colorado (2) and Montana (2). Risk for climate events is relatively low throughout the region.
Risk due to climate events across Region 8 is examined in more detail in Figure 4.45. Natural exposure
due to climate events are dominated by drought (37% of counties), extreme high temperatures (18%)
and extreme low temperatures (16%). All other types of exposure due to natural climate events are
represented at <10%. Superfund sites and TRI (Toxic Release Inventory) sites influence a majority of
the technological exposure indicator at 62% and 37%, respectively. RCRA sites have little influence and
nuclear exposure potential is non-existent. In the region, losses are seen primarily in dual benefit and
natural land use types (e.g., forests, wetlands, agriculture). Most exposure comes from natural climate
events, with only 1% resulting from proximity to technological hazards. Risk ranges from a low score of
1.42 in Daniels County, Montana to 4.14 in Meade County, South Dakota with a regional average well
under the national at 2.54.
The contributions of the twenty indicators to the domain scores that comprise CRSI shown in Figure
4.46 for Region 8. The strongest contributions come from the natural resource conservation indicator
(Governance), and the vacant structures indicator (Built Environment). Secondary contributions come
from housing characteristics (Built Environment); socio-economic characteristics, demographic
characteristics and health characteristics (Society); and, exposure (risk).
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EPA Region 8
/ CRSI
6.09
(Range:-7.28-21.58)
L-= Governance
<
Society
Built Environment
Natural Environment!
-0.5 -0.4 -0.3 -0.2 -0.1 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
Domain Score (lighter shade bar)/Median Adjusted Score(darker shade bar)
Figure 4.43 Summary of CRSI (upper right hand value) and domain scores (light colored bars) for EPA
Region 8, along with domain median adjusted scores showing influence of each domain on final CRSI score
(dark colored bars).
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Region 8
Society
Governance
Hiiill Environment
Natural Enviromoenl
Figure 4.44 The distributions of EPA Region 8 CRSI values and domain scores (Risk, Governance, Society,
Built Environment and Natural Environment).
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Table 4.9 Twenty-five counties in EPA Region 8 with the highest CRSI values.
Region 8
Rank
County
1.
Flathead County, Montana
2.
Roberts County, South Dakota
3.
Carbon County, Wyoming
4.
Lincoln County, Montana
5.
Daniels County, Montana
6.
Uinta County, Wyoming
7.
Day County, South Dakota
8.
Ravalli County, Montana
9.
Pitkin County, Colorado
10.
Beaverhead County, Montana
11.
Pembina County, North Dakota
12.
Gunnison County, Colorado
13.
Chaffee County, Colorado
14.
San Miguel County, Colorado
15.
Ward County, North Dakota
16.
Routt County, Colorado
17.
Jefferson County, Montana
18.
Ouray County, Colorado
19.
Sanders County, Montana
20.
Eagle County, Colorado
21.
Garfield County, Colorado
22.
Grant County, South Dakota
23.
McLean County, North Dakota
24.
Sweetwater County, Wyoming
25.
Missoula County, Montana
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Landslide
Iniand Flood Earthquake
Wildfire
1%
High Wind
Superfund
62%
RCRA
Technological
Exposure
1%
Natural Loss
45%
Dual-Benefit
Loss
48%
inree primary bxposures:
;;C 1-Drought
| 2- Extreme Temps - Highs
6 3- Extreme Temps - Lows
Kisk Kange:
High - Meade, SD -4.14
Low - Daniels, MT- 1.42
Mean - 2.54
Natural Exposures
Technological Exposures
Developed Losses
Loss
7%
Exposure Type
Figure 4.45 Map of Risk Domain scores by county for Region 8; proportion of natural exposures by climate event type, technological exposures,
losses and exposure type nationwide; and the range of risk with the highest risk and lowest risk counties identified; as well as, the three primary
exposure types in the region.
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Ecosystem
Type Extent
Exposure
Condition
Vacant
Structures
Community
Preparedness
Natural
Resource
Conservation
Utility
Infrastructure
Transportation
Infrastructure
Personal
Preparedness
Demo-
graphics
Housing
Characteristics
Communication
Infrastructure
Economic
Diversity
Socio-
Economics
Health
Characteristics
Labor-TVade
Services
Social Services
Safety
and
Security
Social
Cohesion
Figure 4.46 Polar plot showing the contribution of the 20 indicators associated with the domain scores for
the EPA Region 8. The length of the bars corresponds to the indicator score. Within a domain, the higher
indicator scores show a greater contribution to the domain score (sum of indicator scores).
EPA Region 9
Region 9 of the EPA includes Arizona, California, Hawaii, and Nevada. Also included in this region are
the Pacific Islands (Northern Marianas, Guam, and American Samoa). Region 9 serves 148 federally
recognized tribes in Arizona, California, and Nevada. Across the region heat, drought, and insect
outbreaks have all led to increased wildfires. In Hawaii, increased ocean temperatures have heightened
risks of coral bleaching and disease. Hawaii also faces increased coastal flooding and erosion concerns.
In the San Diego Harbor region of California, extreme heat, rising temperatures, severe drought, and
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extensive wildfire are all projected. Los Angeles, CA has suffered severe drought, issues in water quality
and quantity, and infrastructure damage. Earthquakes and tsunamis are additional concerns in regards to
resilience in Los Angeles. Oakland, CA is projected to experience problems associated with sea level
rise, in addition to the resilience issues it already faces around social inequity, earthquakes, and
affordable housing. Across the Bay in San Francisco, CA earthquakes are also a concern, and
projections of rising temperatures and severe drought have will increase risk of wildfire. Berkley, CA
has experienced extreme heat and warming, extensive wildfires, and additional resilience issues around
earthquakes. The EPA Region 9 Climate Change Adaptation Implementation Plan (USEPA-R9 2014)
states that regional resilience goals include the promotion of water efficiency, conservation, and
recycling. The region also has a Coral Reef Strategy to reduce local pollution and increase coral reef
climate change resiliency.
A summary of the overall CRSI domain scores for EPA Region 9 is presented in Figure 4.47. The
overall CRSI score is above the national average and ranks 4th among the ten EPA Regions. The risk
domain score is above the national average and the Governance for climate events domain score is
below the national average. The Built Environment domain score is the highest in the nation and the
Natural Environment domain score is moderate to high. The spatial distribution of these scores among
the counties in EPA Region 9 is shown in Figure 4.48 with some of the higher CRSI values in Hawaii,
northern Nevada, northern Arizona and northern California. Table 4.10 shows the counties with the
highest CRSI values in Region 9 are in California (8 counties), Arizona (6), Nevada (5) and Hawaii (4).
The counties with lower CRSI valuesare in Calfornia (6), Nevada (3) and Hawaii (1). Low risk for
climate events is shown in Figure 4.48 for much of Arizona and Nevada and all of Hawaii. High
Governance domain scores are shown for Hawaii and much of Nevada and Arizona as well as southern
California. Higher Built Environment domain scores are seen in southern California, a swath through the
middle of Arizona and the Las Vegas region of Nevada.
Risk due to climate events across Region 9 is examined in more detail in Figure 4.49. Natural exposures
due to climate events are dominated by drought (33% of counties), earthquakes (24%) and extreme high
temperatures (15%). Extreme low temperatures account for 11%, while the remainder of the natural
exposures are represented at <10%. TRI (Toxic Release Inventory) sites and Superfund sites represent a
majority of technological exposure indicator at 37% and 33%, respectively. RCRA and nuclear sites also
contribute a sizeable portion of risk potential in this region at 16% and 14%, respectively. In the region,
losses are seen primarily in dual benefit (48%) and natural land use types (e.g., forests, wetlands,
agriculture), with 46%. Most exposure comes from natural climate events, with only 5% resulting from
proximity to technological hazards. Risk ranges from a low score of 1.74 in White Pine County, Nevada
to 4.16 in Orange County, California; with a regional average well above the national at 3.02.
The contributions of the 20 indicators to the domains that comprise CRSI for Region 9 are shown in
Figure 4.50. The strongest contributors to the Built Environment score are vacant structure and housing
characteristics., Demographic characteristics (Society), exposure to climate events (Risk), and natural
resource conservation (Governance) also show strong contributions to domain scores. Secondary
contributor indicators scores include health characteristics and economic diversity (Society), as well as
ecosystem type extent (Natural Environment). Weak contributionsare shown for the following
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indicators: community preparedness (Governance), safety and security and labor-trade services
(Society), and condition of ecosystems (Natural Environment).
EPA Region 9
Risk
CRSI
6.07
(Range:-7.46-26.98)
2f =
er=| Governance
<
Soci
Built Environment
Natural Environment
Domain Score (lighter shade bar)/Median Adjusted Score(darker shade bar)
Figure 4.47 Summary of CRSI (upper right hand value) and domain scores (light colored bars) for EPA
Region 9, along with domain median adjusted scores showing influence of each domain on final CRSI score
(dark colored bars).
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Region 9
Governance
Puill Environment
XalunU Environment
Figure 4.48 The distributions of EPA Region 9 CRSI values and domain scores (Risk, Governance, Society,
Built Environment and Natural Environment).
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Table 4.10 Twenty-five counties in EPA Region 9 with the highest CRSI values.
Region 9
Rank
County
1.
Maui County, Hawaii
2.
Kauai County, Hawaii
3.
Hawaii County, Hawaii
4.
Mono County, California
5.
Coconino County, Arizona
6.
Honolulu County, Hawaii
7.
Navajo County, Arizona
8.
Elko County, Nevada
9.
Lassen County, California
10.
White Pine County, Nevada
11.
Humboldt County, California
12.
Apache County, Arizona
13.
Yavapai County, Arizona
14.
Washoe County, Nevada
15.
Nye County, Nevada
16.
Imperial County, California
17.
Humboldt County, Nevada
18.
Churchill County, Nevada
19.
San Bernandino County, California
20.
Santa Barbara County, California
21.
Shasta County, California
22.
Cochise County, Arizona
23.
Mohave County, Arizona
24.
Pima County, Arizona
25.
Mendocino County, California
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Natural Exposures
Inland Flood
5%
Technological Exposures
RCRA
16%
Earthquake
Wildfire
High Wind
2%
Developed
Loss
Losses
Technological
Exposure
5%
Exposure Type
Three Primary Exposures:
1- Drought
2- Earthquakes
I 3- Extreme Temps - Highs
Risk Range:
High - Orange, CA - 4.16
Low-White Pine, NV-1.74
Mean - 3.02
Natural Loss
Dual-Benefit
Loss
48%
Figure 4.49 Map of Risk Domain scores by county for Region 9; proportion of natural exposures by climate event type, technological exposures,
losses and exposure type nationwide; and the range of risk with the highest risk and lowest risk counties identified; as well as, the three primary
exposure types in the region.
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Ecosystem
Type Extent
Exposure
Condition
Vacant
Structures
Community
Preparedness
Natural
Resource
Conservation
Utility
Infrastructure
Transportation
Infrastructure
Personal
Preparedness
Demo-
graphics
Housing
Characteristics
Communication
Infrastructure
Economic
Diversity
Socio-
Economics
Health
Characteristics
Labor-TVade
Services
Social Services
Safety
and
Security
Social
Cohesion
Figure 4.50 Polar plot showing the contribution of the 20 indicators associated with the domain scores for
the EPA Region 9. The length of the bars corresponds to the indicator score. Within a domain, the higher
indicator scores show a greater contribution to the domain score (sum of indicator scores).
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EPA Region 10
Region 10 of the EPA includes Alaska, Idaho, Oregon, and Washington. EPA Region 10 office serves
271 federally recognized tribes in Alaska, Idaho, Oregon, and Washington. Regional threats include
increasing ocean acidity, sea level rise, erosion, inundation, infrastructure risks. The combination of
insect outbreaks, tree disease and wildfire is resulting in widespread tree die-offs across Region 10.
Alaska has experienced significant temperature rises, increasing at double the speed of the rest of the
United States, causing glaciers to shrink and sea ice to recede. The permafrost is thawing, leading to
more wildfires. Eugene, OR has experienced severe drought and extensive wildfire. Changing ocean
temperatures have allowed for more invasive species and diminishing cold water species. Beaverton, OR
is projected to experience temperature rises, severe drought, extensive wildfires, extreme rainfall,
flooding, and issues in water quality and quantity. King County, WA has been impacted by extreme
heat, extreme rainfall, flooding, erosion, infrastructure damage and sea level rise. According to the EPA
Region 10 Climate Change Adaptation Implementation Plan (USEPA-R10), regional actions to improve
resilience include using Water Sense to encourage water efficiency, including ocean acidification
language in NEP A review comments, and incorporating green infrastructure as part of settlement
agreements.
A summary of the overall CSRI score and the domain scores for EPA Region 10 is shown in Figure
4.51. The overall CRSI score of 14.83 - is the highest in the nation. The Risk domain and Governance
domain scores are below the national averages. The Society domain score is similar to national average
and the Built Environment and Natural Environment domain scores are well above the national average.
The spatial distribution of the overall CRSI score and the domain scores among the counties of EPA
Region 10 are shown in Figure 4.52. Table 4.11 shows the higher CRSI values occur in Alaska (19
boroughs), Idaho (5 counties) and Washington (1 county). The lower CRSI values occur in Washington
(4 counties). Idaho (4), Oregon (1) and Alaska (1 borough). Overall risk for climate events appears
moderate through the region while the Governance for climate events scores are lower in southern
Oregon.
Risk due to climate events across Region 10 is examined in more detail in Figure 4.53. Natural
exposures due to climate events are dominated by drought (34% of counties), extreme high temperatures
(18%) and extreme low temperatures (16%). Earthquakes account for 15%, while the remainder of the
natural exposures are represented at <10%. Superfund sites and TRI (Toxic Release Inventory) sites
represent the majority of the technological exposure indicator at 65% and 27%, respectively. RCRA and
nuclear sites contribute a negligible portion of risk potential in this region at 1% and 7% respectively.
Losses in the region are distributed relatively evenly across dual benefit (37%), natural lands (35%) and
developed lands (28%). Most exposure comes from natural climate events, with only 2% resulting from
proximity to technological hazards. Risk ranges from the lowest score in the nation at 0.70 in Kodiak
Island Borough, Alaska to 3.99 in Curry County, Oregon, with a regional average well below the
national at 2.42.
The contributions of the twenty indicators to the domain scores that comprise CRSI are shown in Figure
4.54 for EPA Region 10. The strongest contributors to the Built Environment domain score is vacant
structures), Natural resource conservation indictor scores (Governance) and lower exposure and loss
scoresRrisk) are also strong contributors. Secondary contributions are shown for the following
indicators:housing characteristics (Built Environment); demographic characteristics, health
characteristics and economimc diversity (Society); and extent of ecosystems (Natural Environment). The
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weakest contribution scores are for safety and security (Society), utility infrastructure (Built
Environment) and ecosystem condition (Natural Environment).
EPA Region 10
^ Risk
CRSI
14.83
(Range:-4.84-227.2)
ef= Governance
Societyl
Built Environment
Natural Environment
-0.4 -0.3 -0.2 -0.1
Domain Score (lighter shade bar)/Median Adjusted Scorefdarker shade bar)
Figure 4.51 Summary of CRSI (upper right hand value) and domain scores (light colored bars) for EPA
Region 10, along with domain median adjusted scores showing influence of each domain on final CRSI
score (dark colored bars).
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Buil! Environment
'.itur.il Environment
Governance
Region 10
Figure 4.52 The distributions of EPA Region 10 CRSI values and domain scores (Risk, Governance,
Society, Built Environment and Natural Environment).
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Table 4.11 Twenty-five counties in EPA Region 10 with the highest CRSI values.
Region 10
Rank
County
1.
Kodiak Island Borough, Alaska
2.
Juneau City and Borough, Alaska
3.
Ketchikan Gateway Borough, Alaska
4.
Aleutians East Borough, Alaska
5.
North Slope Borough, Alaska
6.
Haines Borough, Alaska
7.
Prince of Wales-Hyder Census Area, Alaska
8.
Sitka City and Borough, Alaska
9.
Hoonah-Angoon Census Area, Alaska
10.
Dillingham Census Area, Alaska
11.
Kenai Peninsula Borough, Alaska
12.
Petersburg Census Area, Alaska
13.
Fairbanks North Star Borough, Alaska
14.
Yakutat City and Borough, Alaska
15.
Bonner County, Idaho
16.
Aleutians West Census Area, Alaska
17.
Bristol Bay Borough, Alaska
18.
Anchorage Municipality, Alaska
19.
Latah County, Idaho
20.
Valley County, Idaho
21.
Lake and Peninsula Borough, Alaska
22.
Boundary County, Idaho
23.
San Juan County, Washington
24.
Skagway Municipality, Alaska
25.
Blaine County, Idaho
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Natural Exposures
Technological Exposures
Three Primary Exposures:
1- Drought
Ji • 2- Extreme Temps - Highs
^ 3- Extreme Temps - Lows
Coastal Flood
1%
RCRA
inland
wldfl
Earthquake
Superfund
65%
Nuclear
7%
High Wind
Exposure Type
Technological
Exposure
Natural Loss
35%
Natural
Exposure
Risk Range:
High-Curry, OR-3.99
Low- Kodiak Island, AK-0.70
Mean - 2.42
Figure 4.53 Map of Risk Domain scores by county for Region 10; proportion of natural exposures by dimate event type, technological exposures,
losses and exposure type nationwide; and the range of risk with the highest risk and lowest risk counties identified; as well as, the three primary
exposure types in the region.
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Ecosystem
Type Extent
Exposure
Condition
Vacant
Structures
Community
Preparedness
Natural
Resource
Conservation
Utility
Infrastructure
Transportation
Infrastructure
Personal
Preparedness
Demo-
graphics
Housing
Characteristics
Communication
Infrastructure
Economic
Diversity
Socio-
Economics
Health
Characteristics
Labor-TVade
Services
Social Services
Safety
and
Security
Social
Cohesion
Figure 4.54 Polar plot showing the contribution of the 20 indicators associated with the domain scores for
the EPA Region 10. The length of the bars corresponds to the indicator score. Within a domain, the higher
indicator scores show a greater contribution to the domain score (sum of indicator scores).
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7. Future Directions for Community Resilience
to Extreme Weather Events
Every year, U.S. counties and communities face devastating losses caused by
weather-related disasters. Fires, floods, storms, other hazards and their associated
consequences have significant impacts on counties and communities, the
economy, infrastructure and the environment. The U.S. has recently experienced
a number of large scale and devastating natural disasters, including catastrophic
wildfires, far reaching floods, and damaging storms. Such events can have
personal, social, economic and environmental impacts that take many years to
dissipate. The increasing prominence of extreme weather events makes it critical for governments,
businesses and individuals to examine their anticipatory adaptation and organizational resilience to these
events (Linnenluecke et al. 2012). The private sector and all levels of government are embracing
resilience as a holistic, proactive framework to reduce risk, improve services, adapt to changing
conditions, and empower citizens (e.g., National Disaster Resilience Competition; HUD 2017;
Leadership in Community Resilience; NLC 2016, 2017).
The U.S. has and continues to cope well with natural disasters, through established and cooperative
emergency management arrangements, effective capabilities, and dedicated professional and volunteer
personnel. Americans are also renowned for their resilience to hardship, including the ability to innovate
and adapt, a strong community spirit that supports those in need and the self-reliance to withstand and
recover from disasters. A collective responsibility for resilience is needed to effectively build capacities
at multiple scales.
Our desire to have counties and communities that are minimally impacted by climate events is nearly
impossible without a strong recoverability plan and its execution following an event. These plans and
their execution maintain a community at a significant distance from ecological, economic and social
tipping points (e.g., stability, sustainability, joblessness, social inequity, ecosystem condition). Little
attention has been given to the interconnectedness of the aspects of resilience (Summers et al. 2014) as
they relate to a community's climate resilience. A community may be naturally vulnerable to climate
events or vulnerable through anthropogenic activities but its resilience to these vulnerabilities is guided
by the combination of environmental, social, economic and governance drivers.
Given the increasing regularity and severity of natural disasters, U.S. national, state and local
governments have recognized that an integrated, coordinated and cooperative effort is required to
enhance their capacities to withstand and recover from weather-related emergencies and disasters. A
disaster resilient community is one that works together to understand and manage the risks that it
confronts. Disaster resilience is the collective responsibility of all sectors of society, including all levels
of government, business, the non-government sector and individuals. If all these sectors work together
with a united focus and a shared sense of responsibility to improve disaster resilience, they will be far
more effective than the individual efforts of any one sector.
Potential role of governments
Governments, at all levels, have a significant role in strengthening the nation's resilience to disasters:
• Developing and implementing effective, risk-based land management and planning arrangements
and other mitigation activities;
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• Having effective arrangements in place to inform people about how to assess risks and reduce
their exposure and vulnerability to hazards;
• Having clear and effective education systems so people understand what options are available
and what the best course of action is in responding to a hazard as it approaches;
• Supporting individuals and counties and communities to prepare for extreme events;
• Ensuring the most effective, well-coordinated response from our emergency services and
volunteers when disaster hits; and
• Working in a swift, compassionate and pragmatic way to help counties and communities recover
from devastation and to learn, innovate and adapt in the aftermath of disastrous events.
Local, state and national governments are working collectively to incorporate the principle of disaster
resilience into aspects of natural disaster arrangements, including preventing, preparing, responding to,
and recovering from, disasters. Further future enhancements and local applications of CRSI can provide
advancements in these disaster-related resilience activities.
The Federal Emergency Management Agency (FEMA) established the Strategic Foresight Initiative
(SFI; FEMA 2012) to address this need. This initiative has brought together a wide cross-section of the
emergency management community to explore key future issues, trends and other factors, and to work
through their implications. Working collaboratively and with urgency, we are beginning to understand
the full range of changes we could encounter and the nature of our future needs; and we can begin to
execute a shared agenda for action. One of the first tasks of this initiative group should be to bring
together the representative views of all governments, business, non-government sector and the
community into a comprehensive National Disaster Resilience Strategy. This group should also be
tasked with considering further those lessons arising from the recent bushfires, floods, tornadoes and
super-storms that could benefit from national collaboration.
Role of business
Businesses can and do play a fundamental role in supporting a community's resilience to disasters. They
provide resources, expertise and many essential services on which the community depends. Businesses,
including critical infrastructure providers, make a contribution by understanding the risks that they face
and ensuring that they are able to continue providing services during or soon after a disaster.
Role of individuals
Disaster resilience is based on individuals taking their share of responsibility for preventing, preparing
for, responding to and recovering from disasters. They can do this by drawing on guidance, resources
and policies of government and other sources such as community organizations. The disaster resilience
of people and households is significantly increased by active planning and preparation for protecting life
and property, based on an awareness of the threats relevant to their locality. It is also increased by
knowing and being involved in local community disaster or emergency management arrangements, and
for many being involved as a volunteer.
Role of non-government organizations and volunteers
Non-government and community organizations are at the forefront of strengthening disaster resilience in
the United States. It is to them that Americans often turn for support or advice and the dedicated work of
these agencies and organizations is critical to helping counties and communities to cope with, and
recover from, a disaster. Building and fostering partnerships between U.S. national, state and local
governments and these agencies and organizations is essential to spreading the disaster resilience
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message and to finding practical ways to strengthen disaster resilience in the counties and communities
they serve. Strengthening the U.S.'s disaster resilience is not a stand-alone activity that can be achieved
in a set timeframe, nor can it be achieved without a joint commitment and concerted effort by all sectors
of society. But it is an effort that is worth making, because building a more disaster resilient nation is an
investment in our future.
Potential Utility of CRSI
This report has outlined the approach and application of an index to examine the resilience of U.S.
counties, EPA Regions and the nations to extreme-weather events. Further research and application
efforts to adapt CRSI for use for individual counties and communities would clearly be useful for the
development of community-specific resilience plans. The potential of using CRSI-related information by
EPA regional staff tasked with assessing resilience in their areas of the counties seems particularly
useful. Allowing EPA regions to see in one application the specifics of risk, governance, societal
attributes, built environment information and natural environment information will be important in
further development local and county-level resilience plans. Similarly, at the county level, EPA can:
(1) Assess relative risks of differing weather-related events
(2) Disassemble CRSI to determine why the resilience of certain counties are projected to be low
and others are projected to be high
(3) Provide lessons learned from one county to the next on governance and other activities that have
increased local resilience to weather-related events
(4) Provide a comparative database permitting one way to assess where investments might have the
greatest return in terms of improved resilience
(5) Provide a database that can be updated to include the most recent information on the CRSI
metrics, indicators and domains so that improvements can be tracked.
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9. Appendices
Appendix A - CRSI Database Overview
EPA's Climate Resilience Screening Index (CRSI) characterizes community resilience based on a suite
of indicators that are grouped into broad categories or domains of community resiliency traits in the
context of natural disasters. Data collected by the following institutions and organizations were used to
populate indicator metrics to quantify CRSI:
• American Lung Association
• Association of Religion Data Archives
• Centers for Disease Control and Prevention
• Instituto de Pesquisas Ecologicas Brasil
• National Telecommunication and Information Administration
• United States Census Bureau
• United States Department of Agriculture
• United States Department of Agriculture
• United States Department of Health and Human Services
• United States Department of Homeland Security
• United States Department of Housing and Urban Development
• United States Department of Justice
• United States Department of Labor
• United States Department of the Interior
• United States Department of Transportation
• United States Energy Information Administration
• United States Environmental Protection Agency
• University of Wisconsin Population Health Institute
To the extent possible, specific data sets and sources were selected for use in the development of CRSI
based on the following criteria:
• Data were publicly available and easy to obtain
• Data collection methods were credible and reliable
• Data sets were available at county-scale for population-based information and acres, meters,
hydrologic units or similar for geospatial
• Data collected was national in scope
• Data were available for all or a portion of 2000 - 2015 and were likely to collected in the future
Metrics serve as the foundation of CRSI. The following pages contain indicator heading and details
about corresponding metrics including basic information such as the data source(s) and years available,
as well as calculations performed to create the final datasets. We examined the distribution for all
metrics for pooled data (2000-2015). The distribution graphics are provided at the end of each indicator-
metric section. The y-axis scale shown in each graph reflects the true unit scale of results.
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Domain: Risk
Indicator: Exposure
The exposure indicator likelihood of hazard occurrence across a full spectrum
of geologic and atmospheric events as well as additional technological hazards
that may co-occur.
Metric List for Domain: Risk - Indicator: Exposure
Metric Variable: SprFnd Exp
Source Measurement: Score
Years Available: 2015
Smallest Geospatial Level Available: County
Missing Data Handling: Zero
Calculation Method: Score calculated based on the proportion of land that falls within a 5-mile
radius of any listed Superfund Site. Generated using ArcMap 10.4, NLCD 2011, and superfund
site locations (U.S. EPA).
Data Source: U.S. Environmental Protection Agency
https ://edg. epa. gov/metadata/catalo g/main/home .page
Metric Variable: Nuke Exp
Source Measurement: Score
Years Available: 2015
Smallest Geospatial Level Available: County
Missing Data Handling: Zero
Calculation Method: Score calculated based on the proportion of land that falls within a 10-mile
radius of any nuclear power, weapons, research, or storage facility. Generated using ArcMap
10.4, NLCD 2011, and nuclear site locations (multi source)
Data Source: Data Source: U.S. Environmental Protection Agency
https ://edg. epa. gov/metadata/catalo g/main/home .page
Metric Variable: TRI Exp
Source Measurement: Score
Years Available: 2015
Smallest Geospatial Level Available: County
Missing Data Handling: Zero
Calculation Method: Score calculated based on the proportion of land that falls within a 1/4 mile
radius of a TRI listed facility. Generated using ArcMap 10.4, NLCD 2011, and TRI site
locations.
Data Source: Data Source: U.S. Environmental Protection Agency
https ://edg. epa. gov/metadata/catalo g/main/home .page
Metric Variable: RCRA Exp
Source Measurement: Score
Years Available: 2015
Smallest Geospatial Level Available: County
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Missing Data Handling: Zero
Calculation Method: Score calculated based on the proportion of land that falls with a Va mile
radius of any RCRA site (LQGs, TSDs, and TRANSs). Generated using ArcMap 10.4, NLCD
2011 and U.S. EPA FRS geodatabase.
Data Source: Data Source: U.S. Environmental Protection Agency
https ://edg. epa. gov/metadata/catalo g/ main/home .page
Metric Variable: BasicHurr
Source Measurement: Score
Years Available: 2015
Smallest Geospatial Level Available: County
Missing Data Handling: Zero
Calculation Method: Potential tornado exposure factor based on proximity to historic hurricane
hazard source. Generated using ArcMap 10.4 and historic hurricane data (NOAA).
Data Source: Data Source: U.S. Environmental Protection Agency
https ://edg. epa. gov/metadata/catalo g/ main/home .page
Metric Variable: Basic Tndo
Source Measurement: Score
Years Available: 2015
Smallest Geospatial Level Available: County
Missing Data Handling: Zero
Calculation Method: Potential tornado exposure factor based on proximity to historic tornado
hazard source. Generated using ArcMap 10.4 and historic tornado data (NOAA).
Data Source: U.S. Environmental Protection Agency
https ://edg. epa. gov/metadata/catalo g/main/home .page
Metric Variable: Hurr Exp
Source Measurement: Score
Years Available: 2015
Smallest Geospatial Level Available: County
Missing Data Handling: Zero
Calculation Method: Score calculated based on the proportion of land impacted by past hurricane
hazards. Generated using ArcMap 10.4, NLCD 2011 and historic hurricane data (NOAA).
Data Source: U.S. Environmental Protection Agency
https ://edg. epa. gov/metadata/catalo g/main/home .page
Metric Variable: Torn Exp
Source Measurement: Score
Years Available: 2015
Smallest Geospatial Level Available: County
Missing Data Handling: Zero
Calculation Method: Score calculated based on the proportion of land impacted by past tornado
hazards. Generated using ArcMap 10.4, NLCD 2011, and historic tornado data (NOAA).
Data Source: U.S. Environmental Protection Agency
https ://edg. epa. gov/metadata/catalo g/main/home .page
Metric Variable: Inflood Exp
Source Measurement: Score
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Years Available: 2015
Smallest Geospatial Level Available: County
Missing Data Handling: Zero
Calculation Method: Score calculated based on the proportion of land potentially impacted by
inland flooding hazards. Generated using ArcMap 10.4, NLCD 2011, and rivers and streams data
(USGS).
Data Source: U.S. Environmental Protection Agency
https ://edg. epa. gov/metadata/catalo g/main/home .page
Metric Variable: CFlood Exp
Source Measurement: Score
Years Available: 2015
Smallest Geospatial Level Available: County
Missing Data Handling: Zero
Calculation Method: Score calculated based on the proportion of land potentially impacted by
coastal flooding hazards. Generated using ArcMap 10.4, NLCD 2011 coastal elevation data
(EPA).
Data Source: U.S. Environmental Protection Agency
https ://edg. epa. gov/metadata/catalo g/main/home .page
Metric Variable: EQ Exp
Source Measurement: Score
Years Available: 2015
Smallest Geospatial Level Available: County
Missing Data Handling: Zero
Calculation Method: Score calculated based on the proportion of land impacted by earthquake
hazards at a peak ground acceleration (PGA) above the chosen threshold. Generated using
ArcMap 10.4, NLCD 2011 and earthquake hazard mapping data (USGS).
Data Source: U.S. Environmental Protection Agency
https ://edg. epa. gov/metadata/catalo g/ main/home .page
Metric Variable: Fire Exp
Source Measurement: Score
Years Available: 2015
Smallest Geospatial Level Available: County
Missing Data Handling: Zero
Calculation Method: Score calculated based on the proportion of land impacted by wildfire.
Generated using ArcMap 10.4, NLCD 2011 and historic wildfire data (USGS).
Data Source: U.S. Environmental Protection Agency
https ://edg. epa. gov/metadata/catalo g/main/home .page
Metric Variable: Drght Exp
Source Measurement: Score
Years Available: 2015
Smallest Geospatial Level Available: County
Missing Data Handling: Zero
Calculation Method: Score calculated based on the proportion of land impacted by drought.
Generated using ArcMap 10.4, NLCD 2011 and historic drought data (USGS).
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Data Source: U.S. Environmental Protection Agency
https ://edg. epa. gov/metadata/catalo g/main/home .page
Metric Variable: Wind Exp
Source Measurement: Score
Years Available: 2015
Smallest Geospatial Level Available: County
Missing Data Handling: Zero
Calculation Method: Average number of annual wind events with gusts > 45 mph.
Data Source: U.S. Environmental Protection Agency
https ://edg. epa. gov/metadata/catalo g/main/home .page
Metric Variable: Hail Exp
Source Measurement: Score
Years Available: 2015
Smallest Geospatial Level Available: County
Missing Data Handling: Zero
Calculation Method: Average number of annual hail storms.
Data Source: U.S. Environmental Protection Agency
https ://edg. epa. gov/metadata/catalo g/main/home .page
Metric Variable: LndSld Exp
Source Measurement: Score
Years Available: 2015
Smallest Geospatial Level Available: County
Missing Data Handling: Zero
Calculation Method: Score calculated based on the proportion of land at moderate risk of
exposure to landslide activity. Generated using ArcMap 10.4, NLCD 2011 and landslide hazaed
data (USGS).
Data Source: U.S. Environmental Protection Agency
https ://edg. epa. gov/metadata/catalo g/main/home .page
Metric Variable: ExHTemp Exp
Source Measurement: Average deviation of annual maximum values from the 32-year average
high temps.
Years Available: 2000 - 2011
Smallest Geospatial Level Available: County
Missing Data Handling: Zero
Calculation Method: Proportion of land exposed to extreme high temperatures. Three time
periods are derived from a suite of measures from 2000-2011.
Data Source: U.S. Environmental Protection Agency
https ://edg. epa. gov/metadata/catalo g/main/home .page
Metric Variable: ExLTemp Exp
Source Measurement: Average deviation of annual minimum values from the 32-year average
high temps.
Years Available: 2000 - 2011
Smallest Geospatial Level Available: County
Missing Data Handling: Zero
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Calculation Method: The low temperature extreme values calculated for each U.S. County. Three
time periods derived from a suite of measures from 2000-2011.
Data Source: U.S. Environmental Protection Agency
https ://edg.epa. gov/metadata/catalog/main/home page
Score Distributions for Indicator: Exposure
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Indicator and Related Metrics
Indicator: Loss
$The loss indicator addresses an aspect of a place's vulnerability represented through
Q historical loss of life and property (including crops) associated with specific hazards.
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Metric List for Domain: Risk - Indicator: Loss
Metric Variable: Nat loss
Source Measurement: Score
Years Available: 2000 - 2011
Smallest Geospatial Level Available: County
Missing Data Handling: Zero
157
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Calculation Method: Score calculated based on the loss of natural land to impervious surfaces.
Calculated using ArcMap 10.4, NLCD 2011, 2006 to 2011 Percent Developed Imperviousness
Change (NLCD).
Data Source: U.S. Environmental Protection Agency
https ://edg. epa. gov/metadata/catalo g/main/home .page
Metric Variable: Dua loss
Source Measurement: Score
Years Available: 2000 - 2011
Smallest Geospatial Level Available: County
Missing Data Handling: Zero
Calculation Method: Score calculated based on the loss of natural land to impervious surfaces
and crop land. Calculated using ArcMap 10.4, NLCD 2011, 2006 to 2011 Percent Developed
Imperviousness Change (NLCD) and changes in land type such as croplands and managed areas.
Data Source: U.S. Environmental Protection Agency
https ://edg. epa. gov/metadata/catalo g/main/home .page
Metric Variable: Dev loss
Source Measurement: Score
Years Available: 2015
Smallest Geospatial Level Available: County
Missing Data Handling: Zero
Calculation Method: Score calculated based on the loss of human life and property as result of
adverse natural hazards. Summary of losses derived from Spatial Hazard Events and Losses
Database (SHELDUS) available at http://hvri.geog.sc.edu/ SHELDUS)
Data Source: U.S. Environmental Protection Agency
https ://edg. epa. gov/metadata/catalo g/main/home .page
158
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Score Distributions for Indicator: Loss
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Metric Variable: PCT_SHM
Source Measurement: Percent of Small Business Administration recovery funds spent on hazard
mitigation
Years Available: 2015
Smallest Geospatial Level Available: County
Calculation Method: N/A
Missing Data Handling: Zero fill
Data Source: Federal Emergency Management Agency https://www.fema.gov/data-feeds
Score Distributions for Indicator: Community Preparedness
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Indicator and Related Metrics
Indicator: Personal Preparedness
The personal preparedness indicator addresses individual or household activities
that help protect personal property from acute climate events.
Metric List for Domain: Governance - Indicator: Personal Preparedness
160
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Metric Variable: HOMEINS
Source Measurement: Percent of homes with mortgages (which assumes insurance coverage).
Years Available: 2014
Smallest Geospatial Level Available: County
Calculation Method: N/A
Missing Data Handling: Null fill
Data Source: American Community Survey https://www.census, gov/programs-survevs/acs/
Metric Variable: NUMNFIP
Source Measurement: Number of National Flood Insurance Program community participants
Years Available: 2015
Smallest Geospatial Level Available: County
Calculation Method: N/A
Missing Data Handling: Null fill
Data Source: Federal Emergency Management Agency https://www.fema.gov/data-feeds
Score Distributions for Indicator: Personal Preparedness
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Indicator and Related Metrics
Indicator: Natural Resource Conservation
161
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The natural resource conservation indicator addresses the protection of natural resources
from anthropogenic activities which usually aids an ecosystem's ability to recover from
acute climate events.
Metric List for Domain: Governance - Indicator: Natural Resource Conservation
Metric Variable: DIVCONS
Source Measurement: Land Protection Priority Index for preserving biodiversity*
Years Available: 2015
Smallest Geospatial Level Available: County
Calculation Method: N/A
Missing Data Handling: Null fill
Data Source: Instituto de Pesquisas Ecologicas Brasil http://www, ipe.org.br/
* Index is an inverse ordinal scale where a zero or near-zero index is best.
Score Distributions for Indicator: Natural Resource Conservation
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Indicator and Related Metrics
162
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Domain: Society
Indicator: Demographics
The demographics indicator reflects attributes of a community's population and includes
aspects of employment potential and vulnerable populations.
Metric List for Domain: Society - Indicator: Demographics
*
Metric Variable: ALONE65
Source Measurement: Percent of population age 65 or greater and living alone
Years Available: 2008-2015
Smallest Geospatial Level Available: County
Calculation Method: Counts were calculated as the sum of two variables—male and female
individuals over the age of 65 and living alone.
Missing Data Handling: Zero fill
Data Source: American Community Survey https ://www. census, gov/pro grams-survevs/acs/
Metric Variable: GRD9_25
Source Measurement: Percent of population age 25 years and over with less than 9th grade
education attainment
Years Available: 2005-2015
Smallest Geospatial Level Available: County
Calculation Method: N/A
Missing Data Handling: Zero fill
Data Source: American Community Survey https ://www. census, gov/pro grams-survevs/acs/
Metric Variable: LINGISO
Source Measurement: Percent of population exhibiting limited English language skills
Years Available: 2005-2015
Smallest Geospatial Level Available: County
Calculation Method: N/A
Missing Data Handling: Zero fill
Data Source: American Community Survey https ://www. census, gov/pro grams-survevs/acs/
Metric Variable: NODIPL25
Source Measurement: Percent of population age 25 years and over who attended high school but
did not receive a diploma
Years Available: 2006-2015
Smallest Geospatial Level Available: County
Calculation Method: N/A
Missing Data Handling: Zero fill
Data Source: American Community Survey https ://www. census, gov/pro grams-survevs/acs/
Metric Variable: POP5U
Source Measurement: Percent of population under 5 years of age
Years Available: 2005-2015
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Smallest Geospatial Level Available: County
Calculation Method: N/A
Missing Data Handling: Zero fill
Data Source: American Community Survey https://www.census, gov/programs-survevs/acs/
Score Distributions for Indicator: Demographics
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Indicator: Economic Diversity
Indicator and Related Metrics
The economic diversity indicator represents factors associated with economic
stability and recoverability within communities.
Metric List for Domain: Society - Indicator: Economic Diversity
Metric Variable: GINI
Source Measurement: Income inequality based on Gini Index
Years Available: 2006-2015
Smallest Geospatial Level Available: County
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Calculation Method: N/A
Missing Data Handling: Null fill
Data Source: American Community Survey https ://www.census.gov/programs-survevs/acs/
Metric Variable: HACHI
Source Measurement: Index of economic diversity based on Hachmann calculation method
Years Available: 2005, 2010, 2014
Smallest Geospatial Level Available: County
Calculation Method: For each county, the index is calculated as the reciprocal of the sum of
location quotients, which measures industry dependencies, weighted by the distribution of
businesses as classified by the North American Industry Classification System (NAICS).
Missing Data Handling: Null fill
Data Source: U.S. Environmental Protection Agency
https ://edg.epa. gov/metadata/catalog/main/home page
Score Distributions for Indicator: Economic Diversity
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Indicator and Related Metrics
165
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Indicator: Health Characteristics
The health characteristics indicator addresses factors associated with healthcare
access, special health vulnerability populations, and specific health problems related
to or exacerbated by acute climate events.
Metric List for Domain: Society - Indicator: Health Characteristics
Metric Variable: ASTHMA_A
Source Measurement: Percent of adult population living with asthma
Years Available: 2012, 2014, 2015
Smallest Geospatial Level Available: County
Calculation Method: Data were calculated as the average of adult individuals with asthma over
the total adult population counts for 2012, 2014, and 2015.
Missing Data Handling: Null fill
Data Source: American Lung Association, http://www.lung.org/our-
initiatives/research/monitoring-trends-in-lung-disease/
Metric Variable: ASTHMA_C
Source Measurement: Percent of population under 18 years of age living with asthma
Years Available: 2012, 2014, 2015
Smallest Geospatial Level Available: County
Calculation Method: Data were calculated as the average of individuals under 18 with asthma
over the pediatric population counts for 2012, 2014, and 2015.
Missing Data Handling: Null fill
Data Source: American Lung Association http://www.lung, org/our-
initiatives/research/monitoring-trends-in-lung-disease/
Metric Variable: CNCR
Source Measurement: Incidence of cancer per 100,000 population
Years Available: 2009-2013
Smallest Geospatial Level Available: County
Calculation Method: N/A
Missing Data Handling: Null fill
Data Source: National Cancer Institute https://www.cancer.gov/research/resources/data-catalog
Metric Variable: DBTS
Source Measurement: Percent of population living with diabetes
Years Available: 2004-2016
Smallest Geospatial Level Available: County
Calculation Method: N/A
Missing Data Handling: Null fill
Data Source: University of Wisconsin Population Health Institute
http ://www. countvhealthrankings. org/ rankings/data
Metric Variable: HLTHINS
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Source Measurement: Percent of population with at least some health insurance coverage
Years Available: 2013-2015
Smallest Geospatial Level Available: County
Calculation Method: N/A
Missing Data Handling: Zero fill
Data Source: American Community Survey https ://www.census.gov/programs-survevs/acs/
Metric Variable: HRTDS
Source Measurement: Incidence of heart disease per 1,000 population
Years Available: 2007-2013
Smallest Geospatial Level Available: County
Calculation Method: N/A
Missing Data Handling: Null fill
Data Source: United States Department of Health and Human Services,
https ://www.hhs. gov/about/agencies/omha/ about/health-data-sets/index.html
Metric Variable: OBES
Source Measurement: Percent of population diagnosed with obesity
Years Available: 2004-2013
Smallest Geospatial Level Available: County
Calculation Method: N/A
Missing Data Handling: Null fill
Data Source: University of Wisconsin Population Health Institute,
http ://www. countvhealthrankings. org/ rankings/data
Metric Variable: SPND
Source Measurement: Percent of population with cognitive and/or physical special needs
Years Available: 2008-2015
Smallest Geospatial Level Available: County
Calculation Method: N/A
Missing Data Handling: Null fill
Data Source: American Community Survey https ://www.census.gov/programs-survevs/acs/
Metric Variable: STRK
Source Measurement: Incidence of stroke per 1,000 medicare population
Years Available: 2007-2013
Smallest Geospatial Level Available: County
Calculation Method: N/A
Missing Data Handling: Null fill
Data Source: United States Department of Health and Human Services,
https ://www.hhs. gov/about/agencies/omha/about/health-data-sets/index.html
167
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Score Distributions for Indicator: Health Characteristics
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Metric Variable: FRAME
Source Measurement: Number of construction framing services per 100,000 population
Years Available: 2003-2014
Smallest Geospatial Level Available: County
Calculation Method: N/A
Missing Data Handling: Zero fill
Data Source: American Community Survey https ://www. census, gov/pro grams-survevs/acs/
Metric Variable: HWYCON
Source Measurement: Number of highway construction services per 100,000 population
Years Available: 2003-2014
Smallest Geospatial Level Available: County
Calculation Method: N/A
Missing Data Handling: Zero fill
American Community Survey https://www.census, gov/programs-survevs/acs/
Metric Variable: MASON
Source Measurement: Number of masonry services per 100,000 population
Years Available: 2003-2014
Smallest Geospatial Level Available: County
Calculation Method: N/A
Missing Data Handling: Zero fill
Data Source: American Community Survey https ://www. census, gov/pro grams-survevs/acs/
Metric Variable: PWRCON
Source Measurement: Number of power construction services per 100,000 population
Years Available: 2003-2014
Smallest Geospatial Level Available: County
Calculation Method: N/A
Missing Data Handling: Zero fill
Data Source: American Community Survey https ://www. census, gov/pro grams-survevs/acs/
Metric Variable: ROOF
Source Measurement: Number of roofing construction services per 100,000 population
Years Available: 2003-2014
Smallest Geospatial Level Available: County
Calculation Method: N/A
Missing Data Handling: Zero fill
Data Source: American Community Survey https ://www. census, gov/pro grams-survevs/acs/
Metric Variable: STEEL
Source Measurement: Number of steel construction services per 100,000 population
Years Available: 2003-2014
Smallest Geospatial Level Available: County
Calculation Method: N/A
Missing Data Handling: Zero fill
Data Source: American Community Survey https ://www. census, gov/pro grams-survevs/acs/
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Metric Variable: WTRSWCON
Source Measurement: Number of water and sewer construction services per 100,000 population
Years Available: 2003-2014
Smallest Geospatial Level Available: County
Calculation Method: N/A
Missing Data Handling: Zero fill
Data Source: American Community Survey https ://www.census.gov/programs-surveys/acs/
Score Distributions for Indicator: Labor and Trade Services
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Source Measurement: Number of emergency and civil services per 100,000 population
Years Available: 2003-2014
Smallest Geospatial Level Available: County
Calculation Method: N/A
Missing Data Handling: Zero fill
Data Source: American Community Survey https ://www.census.gov/programs-survevs/acs/
Metric Variable: LAWENFOR
Source Measurement: Number of law enforcement officers per 100,000 population
Years Available: 2004-2015
Smallest Geospatial Level Available: County
Calculation Method: N/A
Missing Data Handling: Zero fill
Data Source: Federal Bureau of Investigation https ://ucr.fbi. gov/
Metric Variable: POLPROT
Source Measurement: Number of criminal and civil services per 100,000 population
Years Available: 2000-2015
Smallest Geospatial Level Available: County
Calculation Method: Data were calculated as the aggregated sum of all State, Local, and Federal
government employees employed in the Police Protection field.
Missing Data Handling: Zero fill
Data Source: Bureau of Labor Statistics https://www.bis.gov/data/
Metric Variable: PUBSAFE
Source Measurement: Number of other public safety services per 100,000
Years Available: 2005-2015
Smallest Geospatial Level Available: County
Calculation Method: Data were calculated as the aggregated sum of all State, Local, and Federal
government employees employed in the Police Protection field.
Missing Data Handling: Zero fill
Data Source: Bureau of Labor Statistics https://www.bis.gov/data/
171
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Score Distributions for Indicator: Safety and Security
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Indicator: Social Cohesion
Indicator and Related Metrics
The social cohesion indicator represents the willingness of members of a
society to cooperate with each other in order to survive and prosper.
Metric List for Domain: Society - Indicator: Social Cohesion
Metric Variable: ETHNISO
Source Measurement: Degree of ethnic isolation based on calculated index
Years Available: 2015
Smallest Geospatial Level Available: County
Calculation Method: N/A
Missing Data Handling: Zero fill
Data Source: American Community Survey https ://www.census.gov/programs-survevs/acs/
Metric Variable: NATRES
172
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Source Measurement: Percent of population born in current state of residence
Years Available: 2005-2014
Smallest Geospatial Level Available: County
Calculation Method: N/A
Missing Data Handling: Zero fill
Data Source: American Community Survey https ://www.census.gov/programs-survevs/acs/
Metric Variable: TOTRATE
Source Measurement: Religious congregation participation per 1,000 population
Years Available: 2000, 2010
Smallest Geospatial Level Available: County
Calculation Method: N/A
Missing Data Handling: Zero fill
Data Source: Association of Religion Data Archives
http ://www.thearda. com/Archive/browse. asp
Score Distributions for Indicator: Social Cohesion
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Indicator and Related Metrics
Indicator: Social Services
173
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The social services indicator represents a range of critical services provided by
government, private, and non-profit organizations.
Metric List for Domain: Society - Indicator: Social Services
Metric Variable: AMRSURG
Source Measurement: Number of outpatient and emergency facilities per 100,000 population
Years Available: 2003-2014
Smallest Geospatial Level Available: County
Calculation Method: N/A
Missing Data Handling: Zero fill
Data Source: American Community Survey https ://www.census.gov/programs-survevs/acs/
Metric Variable: BDORGBNK
Source Measurement: Number of blood and organ banks per 100,000 population
Years Available: 2003-2014
Smallest Geospatial Level Available: County
Calculation Method: N/A
Missing Data Handling: Zero fill
Data Source: American Community Survey https ://www. census, gov/pro grams-survevs/acs/
Metric Variable: CHLDCARE
Source Measurement: Number of child care services per 100,000 population under 14
Years Available: 2003-2014
Smallest Geospatial Level Available: County
Calculation Method: N/A
Missing Data Handling: Zero fill
Data Source: American Community Survey https ://www. census, gov/pro grams-survevs/acs/
Metric Variable: EMSOCSRV
Source Measurement: Number of emergency shelter, food and goods services per 100,000
population
Years Available: 2003-2014
Smallest Geospatial Level Available: County
Calculation Method: N/A
Missing Data Handling: Zero fill
Data Source: American Community Survey https ://www. census, gov/pro grams-survevs/acs/
Metric Variable: HOSP
Source Measurement: Number of hospitals per 100,000 population
Years Available: 2003-2014
Smallest Geospatial Level Available: County
Calculation Method: N/A
Missing Data Handling: Zero fill
Data Source: American Community Survey https ://www. census, gov/pro grams-survevs/acs/
174
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Metric Variable: HPS AM
Source Measurement: Percent of population with sufficient access to mental healthcare providers
based on Healthcare Provider Service Area rating for mental health
Years Available: 2009
Smallest Geospatial Level Available: County
Calculation Method: N/A
Missing Data Handling: Zero fill
Data Source: Health Resources and Services Administration https://datawarehouse.hrsa. gov/
Metric Variable: HPSA_P
Source Measurement: Percent of population with sufficient access to primary healthcare
providers based on Healthcare Provider Service Area rating for primary care
Years Available: 2009
Smallest Geospatial Level Available: County
Calculation Method: N/A
Missing Data Handling: Zero fill
Data Source: Health Resources and Services Administration https://datawarehouse.hrsa. gov/
Metric Variable: INSADJ
Source Measurement: Number of insurance claims establishments per 100,000 population
Years Available: 2003-2014
Smallest Geospatial Level Available: County
Calculation Method: N/A
Missing Data Handling: Zero fill
Data Source: American Community Survey https ://www. census, gov/pro grams-surveys/acs/
Metric Variable: MHTHSERV
Source Measurement: Number of mental healthcare facilities per 100,000 population
Years Available: 2005-2014
Smallest Geospatial Level Available: County
Calculation Method: N/A
Missing Data Handling: Zero fill
Data Source: American Community Survey https ://www. census, gov/pro grams-survevs/acs/
Metric Variable: MUAP
Source Measurement: Score calculated based on the ability of population to access healthcare
based on average medically underserved area per population
Years Available: 2015
Smallest Geospatial Level Available: County
Calculation Method: N/A
Missing Data Handling: Null fill
Data Source: Health Resources and Services Administration https://datawarehouse.hrsa. gov/
Metric Variable: RELIGORG
Source Measurement: Number of religions organizations per 100,000 population
Years Available: 2003-2014
Smallest Geospatial Level Available: County
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Calculation Method: N/A
Missing Data Handling: Zero fill
Data Source: American Community Survey https ://www.census.gov/programs-survevs/acs/
Metric Variable: SCHOOLS
Source Measurement: Number of K-12 education and support facilities per 100,000 population
ages 5 to 18
Years Available: 2003-2014
Smallest Geospatial Level Available: County
Calculation Method: N/A
Missing Data Handling: Zero fill
Data Source: American Community Survey https ://www. census, gov/pro grams-survevs/acs/
Metric Variable: SNFAC
Source Measurement: Number of rehabilitative service facilities per 100,000 population
Years Available: 2012-2014
Smallest Geospatial Level Available: County
Calculation Method: N/A
Missing Data Handling: Zero fill
Data Source: American Community Survey https ://www. census, gov/pro grams-survevs/acs/
Metric Variable: SOCADV
Source Measurement: Number of social advocacy services per 100,000 population
Years Available: 2003-2014
Smallest Geospatial Level Available: County
Calculation Method: N/A
Missing Data Handling: Zero fill
Data Source: American Community Survey https ://www. census, gov/pro grams-survevs/acs/
Metric Variable: SPNDTRAN
Source Measurement: Number of special needs transportation services per 100,000 population
with special needs
Years Available: 2005-2014
Smallest Geospatial Level Available: County
Calculation Method: N/A
Missing Data Handling: Zero fill
Data Source: American Community Survey https ://www. census, gov/pro grams-survevs/acs/
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Score Distributions for Indicator: Social Services
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Calculation Method: N/A
Missing Data Handling: Zero fill
Data Source: American Community Survey https ://www.census.gov/programs-survevs/acs/
Metric Variable: MEDINC
Source Measurement: Median household income in inflation adjusted dollars
Years Available: 2005-2015
Smallest Geospatial Level Available: County
Calculation Method: N/A
Missing Data Handling: Zero fill
Data Source: American Community Survey https ://www. census, gov/pro grams-surveys/acs/
Metric Variable: UNEMPLOY
Source Measurement: Unemployment rate of population ages 16 years and greater
Years Available: 2006-2015
Smallest Geospatial Level Available: County
Calculation Method: N/A
Missing Data Handling: Zero fill
Data Source: American Community Survey https ://www. census, gov/pro grams-survevs/acs/
Score Distributions for Indicator: Socio-Economics
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Domain: Built Environment
Indicator: Communications Infrastructure
The communications infrastructure represents a measure of communication continuity to
support the ability of a community to perform essential functions before, during and after
a natural hazard event.
Metric List for Domain: Built Environment- Indicator: Communication Infrastructure
Metric Variable: CELLTOWER
Source Measurement: Number of cell service towers
Years Available: 2015
Smallest Geospatial Level Available: County
Calculation Method: Data were geolocated to identify the county FIPS codes based on latitude
and longitude provided in original dataset. Counts were then calculated as the sum of the
number of data records associated with each county.
Missing Data Handling: Zero fill
Data Source: Homeland Infrastructure Foundation-Level Data https://hifld-dhs-
gii.opendata.arcgis.com/
Metric Variable: INET ACC
Source Measurement: Percent of homes with access to internet service provider(s)
Years Available: 2011, 2012, 2013, 2014
Smallest Geospatial Level Available: County
Calculation Method: N/A
Missing Data Handling: Zero fill
Data Source: National Broadband Map Datasets https: /www.broadbandmap. gov/analvze
Metric Variable: LMBROAD
Source Measurement: Number of land mobile broadcast towers
Years Available: 2015
Smallest Geospatial Level Available: County
Calculation Method: Data were geolocated to identify the county FIPS codes based on latitude
and longitude provided in original dataset. Counts were then calculated as the sum of the
number of data records associated with each county.
Missing Data Handling: Zero fill
Data Source: Homeland Infrastructure Foundation-Level Data https://hifld-dhs-
gii.opendata.arcgis.com/
Metric Variable: MICROTOWR
Source Measurement: Number of microwave service towers
Years Available: 2015
Smallest Geospatial Level Available: County
Calculation Method: Data were geolocated to identify the county FIPS codes based on latitude
and longitude provided in original dataset. Counts were then calculated as the sum of the
number of data records associated with each county.
179
-------
Missing Data Handling: Zero fill
Data Source: Homeland Infrastructure Foundation-Level Data https://hifld-dhs-
gii.opendata.arcgis.com/
Metric Variable: PAGETOWR
Source Measurement: Number of paging transmission towers
Years Available: 2015
Smallest Geospatial Level Available: County
Calculation Method: Data were geolocated to identify the county FIPS codes based on latitude
and longitude provided in original dataset. Counts were then calculated as the sum of the
number of data records associated with each county.
Missing Data Handling: Zero fill
Data Source: Homeland Infrastructure Foundation-Level Data https://hifld-dhs-
gii.opendata.arcgis.com/
Metric Variable: RADTOWR
Source Measurement: Number of AM and FM radio broadcast transmission towers
Years Available: 2015
Smallest Geospatial Level Available: County
Calculation Method: Data were geolocated to identify the county FIPS codes based on latitude
and longitude provided in original dataset. Counts were then calculated as the sum of the
number of data records associated with each county.
Missing Data Handling: Zero fill
Data Source: Homeland Infrastructure Foundation-Level Data https://hifld-dhs-
gii.opendata.arcgis.com/
Metric Variable: TVTRANS
Source Measurement: Number of TV station transmitters
Years Available: 2015
Smallest Geospatial Level Available: County
Calculation Method: Data were geolocated to identify the county FIPS codes based on latitude
and longitude provided in original dataset. Counts were then calculated as the sum of the
number of data records associated with each county.
Missing Data Handling: Zero fill
Data Source: Homeland Infrastructure Foundation-Level https://hifld-dhs-
gii.opendata.arcgis.com/
180
-------
Score Distributions for Indicator: Communication Infrastructure
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Indicator: Housing Characteristics
Indicator and Related Metrics
Housing characteristics relate to the potential resilience weaknesses that the distribution
or condition of dwellings introduce to a community in context of adverse climate events.
Metric List for Domain: Built Environment - Indicator: Housing Characteristics
Metric Variable: HOME AGE
Source Measurement: Median age of residential housing
Years Available: 2005-2015
Smallest Geospatial Level Available: County
Calculation Method: N/A
Missing Data Handling: Null fill
Data Source: American Community Survey https ://www.census.gov/programs-survevs/acs/
Metric Variable: HOMECRWD
181
-------
Source Measurement: Median age of residential housing
Years Available: 2009, 2013
Smallest Geospatial Level Available: County
Calculation Method: Data from the original dataset were calculated based on the sum of renter
and owner occupancy levels.
Missing Data Handling: Zero fill
Data Source: Comprehensive Housing Affordability Strategy
https ://www.huduser. go v/portal/datasets/cp/CH AS/data querytool chas.html
Metric Variable: HOMEPROB
Source Measurement: Percent of homes with inadequate plumbing and kitchen facilities
Years Available: 2009, 2013
Smallest Geospatial Level Available: County
Calculation Method: Metric is the of sum of renter and owner occupant measures that reflect the
same condition.
Missing Data Handling: Zero fill
Data Source: Comprehensive Housing Affordability Strategy
https://www.huduser.go v/portal/datasets/cp/CH AS/data querytool chas.html
Metric Variable: HUDENSE
Source Measurement: Number of homes per square mile
Years Available: 2015
Smallest Geospatial Level Available: County
Calculation Method: Calculated as total number of housing units/total square miles (within a
county)
Missing Data Handling: Null fill
Data Source: U.S. Environmental Protection Agency
https ://edg. epa. gov/metadata/catalo g/main/home .page
Metric Variable: MOBLHOME
Source Measurement: Percent of non-permanent or mobile residential structures (excluding vans,
campers, etc.)
Years Available: 2007-2013
Smallest Geospatial Level Available: County
Calculation Method: N/A
Missing Data Handling: Zero fill
Data Source: American Community Survey https ://www.census.gov/programs-survevs/acs/
182
-------
Score Distributions for Indicator: Housing Characteristics
Indicator and Related Metrics
Indicator: Transportation Infrastructure
Transportation infrastructure refers a measure of continuity that supports flow of people,
goods and services before, during and after a climate event. This includes roads,
railways, ports and airports.
Metric List for Domain: Built Environment - Indicator: Transportation Infrastructure
Metric Variable: AIRPORT
Source Measurement: Air Transportation Facilities
Years Available: 2015
Smallest Geospatial Level Available: County
Calculation Method: Data were geolocated to identify the county FIPS codes based on latitude
and longitude provided in original dataset. Counts were then calculated as the sum of the number
of data records associated with each county.
Missing Data Handling: Zero fill
183
-------
Data Source: Homeland Infrastructure Foundation-Level Data https://hifld-dhs-
gii.opendata.arcgis.com/
Metric Variable: HELIPORT
Source Measurement: Air Transportation Facilities
Years Available: 2015
Smallest Geospatial Level Available: County
Calculation Method: Data were geolocated to identify the county FIPS codes based on latitude
and longitude provided in original dataset. Counts were then calculated as the sum of the number
of data records associated with each county.
Missing Data Handling: Zero fill
Data Source: Homeland Infrastructure Foundation-Level Data https://hifld-dhs-
gii.opendata.arcgis.com/
Metric Variable: SEAPLANE
Source Measurement: Air Transportation Facilities
Years Available: 2015
Smallest Geospatial Level Available: County
Calculation Method: Data were geolocated to identify the county FIPS codes based on latitude
and longitude provided in original dataset. Counts were then calculated as the sum of the number
of data records associated with each county.
Missing Data Handling: Zero fill
Data Source: Homeland Infrastructure Foundation-Level Data https://hifld-dhs-
gii.opendata.arcgis.com/
Metric Variable: ART ROAD
Source Measurement: Total miles of urban and rural arterial roads
Years Available: 2014
Smallest Geospatial Level Available: County
Calculation Method: Counts were calculated as the sum of the number of data records associated
with each county.
Missing Data Handling: Zero fill
Data Source: National Bridge Inventory https://www.fhwa.dot, gov/bridge/nbi/ascii.cfm
Metric Variable: BRIDGES
Source Measurement: Number of roadway bridge structures
Years Available: 2015
Smallest Geospatial Level Available: County
Calculation Method: Data were geolocated to identify the county FIPS codes based on latitude
and longitude provided in original dataset. Counts were then calculated as the sum of the number
of data records associated with each county.
Missing Data Handling: Zero fill
Data Source: National Bridge Inventory https://www.fhwa.dot, gov/bridge/nbi/ascii.cfm
Metric Variable: BRIDRATE
Source Measurement: Roadway bridge structural and functional assessment rating
Years Available: 2015
Smallest Geospatial Level Available: County
184
-------
Calculation Method: Data were geolocated to identify the county FIPS codes based on latitude
and longitude provided in original dataset. Counts were then calculated as the sum of the number
of data records associated with each county.
Missing Data Handling: Null fill
Data Source: National Bridge Inventory, United States Department of Transportation:
https ://www.fhwa. dot.gov/bridge/nbi/ascii. cfm
Metric Variable: HWYACC
Source Measurement: Percent population residing within 10-minute drive of highway
entrance/exit.
Years Available: 2014
Smallest Geospatial Level Available: County
Calculation Method: Measures derived using ArcMap 10.4, U.S. Census population estimates
and ESRI interstate access points data layer.
Missing Data Handling: Zero fill
Data Source: U.S. Environmental Protection Agency
https ://edg. epa. gov/metadata/catalo g/main/home .page
Metric Variable: RAIL
Source Measurement: Miles of operating freight rails
Years Available: 2015
Smallest Geospatial Level Available: County
Calculation Method: Counts were calculated as the sum of the miles of operating rail line
reported by major rail operators within a county.
Missing Data Handling: Zero fill
Data Source: Homeland Infrastructure Foundation-Level Data https://hifld-dhs-
gii.opendata.arcgis.com/
185
-------
Score Distributions for Indicator: Transportation Infrastructure
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Indicator and Related Metrics
Indicator: Utilities Infrastructure
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Utilities infrastructure refers to a measure of potential continuity for communities to promote
access to critical services in context of an adverse natural hazard exposure.
Metric List for Domain: Built Environment - Indicator: Transportation Infrastructure
Metric Variable: COMWATR
Source Measurement: Number of public drinking water supply facilities
Years Available: 2015
Smallest Geospatial Level Available: County
Calculation Method: N/A
Missing Data Handling: Zero fill
Data Source: Safe Drinking Water Information System https://www.epa.gov/ground-water-and-
drinking-water/safe-drinking-water-information-svstem-sdwis-federal-reporting
186
-------
Metric Variable: POWRPLNT
Source Measurement: Number of power generating facilities
Years Available: 2015
Smallest Geospatial Level Available: County
Calculation Method: N/A
Missing Data Handling: Zero fill
Data Source: United States Energy Information Administration https://www.eia.gov/
Metric Variable: WWTPLNT
Source Measurement: Number of wastewater treatment facilities
Years Available: 2015
Smallest Geospatial Level Available: County
Calculation Method: N/A
Missing Data Handling: Zero fill
Data Source: Enforcement and Compliance History Online https://echo.epa. gov
Score Distributions for Indicator: Utility Infrastructure
~
Indicator and Related Metrics
187
-------
Indicator: Vacant Structures
The vacant structures indicator includes the number of vacant business structures,
residential and public-access buildings in the county (e.g., hospitals, schools,
government buildings).
Metric List for Domain: Built Environment - Indicator: Transportation Infrastructure
Metric Variable: BUS_VAC
Source Measurement: Percent of vacant business structures
Years Available: 2008-2015
Smallest Geospatial Level Available: County
Calculation Method: Counts were calculated as the sum of the number of data records associated
with each county divided by total structures.
Missing Data Handling: Zero fill
Data Source: United States Postal Service https: /www.huduser. gov/portal/datasets/usps.html
Metric Variable: OTH_VAC
Source Measurement: Percent of vacant structures that are not identified as business or
residential
Years Available: 2008-2015
Smallest Geospatial Level Available: County
Calculation Method: Counts were calculated as the sum of the number of data records associated
with each county divided by total structures.
Missing Data Handling: Zero fill
Data Source: United States Postal Service https://www.huduser. gov/portal/datasets/usps.html
Metric Variable: RES_VAC
Source Measurement: Percent of vacant residential structures
Years Available: 2008-2015
Smallest Geospatial Level Available: County
Calculation Method: Counts were calculated as the sum of the number of data records associated
with each county divided by total structures.
Missing Data Handling: Zero fill
Data Source: United States Postal Service https://www.huduser. gov/portal/datasets/usps.html
188
-------
Score Distributions for Indicator: Vacant Structures
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Indicator and Related Metrics
189
-------
Domain: Natural Environment
Indicator: Extent of Ecosystem Types
. The extent domain includes the spatial extent or acreage of each ecosystem
type that occurs naturally without any significant human intervention.
Metric List for Domain: Natural Environment - Indicator: Extent of
Ecosystem Types
Metric Variable: AGLAND
Source Measurement: Percent
Years Available: 2011
Smallest Geospatial Level Available: County
Missing Data Handling: NULL
Calculation Method: Percent agriculture area calculated using county boundaries (U.S. Census
Bureau), ArcMap 10.4, and 2011 NLCD categories 81 (Pasture/Hay) and 82 (Cultivated Crops)
Data Source: Environmental protection Agency
Derived Data: https ://edg.epa. gov/metadata/catalog/main/home page
Raw Data: https://www.mrlc.gov/nlcd2011.php
Metric Variable: CSTLWATR
Source Measurement: Percent
Years Available: 2011
Smallest Geospatial Level Available: County
Missing Data Handling: NULL
Calculation Method: Percent Marine/Estuarine area calculated using county census tracts (U.S.
Census Bureau), ArcMap 10.4, and 2011 NLCD category 11 (Open Water)
Data Source: Environmental protection Agency
Derived Data: https ://edg. epa. gov/metadata/catalo g/main/home page
Raw Data: https://www.mrlc.gov/nlcd2011.php
Metric Variable: FOREST
Source Measurement: Percent
Years Available: 2011
Smallest Geospatial Level Available: County
Missing Data Handling: NULL
Calculation Method: Percent forested area calculated using county boundaries (U.S. Census
Bureau), ArcMap 10.4, and 2011 NLCD categories 41 (Deciduous Forest), 42 (Evergreen
Forest), and 43 (Mixed Forest)
Data Source: Environmental protection Agency
Derived Data: https ://edg. epa. gov/metadata/catalo g/main/home page
Raw Data: https://www.mrlc.gov/nlcd2011.php
Metric Variable: FRSHWATR
Source Measurement: Percent
Years Available: 2011
190
-------
Smallest Geospatial Level Available: County
Missing Data Handling: NULL
Calculation Method: Percent area of inland lakes/rivers/streams calculated using county
boundaries (U.S. Census Bureau), ArcMap 10.4, and 2011 NLCD category 11 (Open Water)
Data Source: Environmental protection Agency
Derived Data: https ://edg.epa. gov/metadata/catalog/main/home page
Raw Data: https://www.mrlc.gov/nlcd2011.php
Metric Variable: GRASSLANDS
Source Measurement: Percent
Years Available: 2011
Smallest Geospatial Level Available: County
Missing Data Handling: NULL
Calculation Method: Percent area of grasslands calculated using county boundaries (U.S. Census
Bureau), ArcMap 10.4, and 2011 NLCD category 71 (Grassland/Herbaceous)
Data Source: Environmental protection Agency
Derived Data: https ://edg. epa. gov/metadata/catalo g/main/home page
Raw Data: https://www.mrlc.gov/nlcd2011.php
Metric Variable: ICELAND
Source Measurement: Percent
Years Available: 2011
Smallest Geospatial Level Available: County
Missing Data Handling: NULL
Calculation Method: Percent area of ice/snow calculated using county boundaries (U.S. Census
Bureau), ArcMap 10.4, and 2011 NLCD category 12 (Perennial Ice/Snow)
Data Source: Environmental protection Agency
Derived Data: https ://edg. epa. gov/metadata/catalo g/main/home page
Raw Data: https://www.mrlc.gov/nlcd2011.php
Metric Variable: PROTAREA
Source Measurement: Percent
Years Available: 2016
Smallest Geospatial Level Available: County
Missing Data Handling: NULL
Calculation Method: Calculated percent of area classified as conservation lands and
preservations including marine protected areas, state recreational areas and urban greenspace
using county boundaries (U.S. Census Bureau), ArcMap 10.4, and the Protected Areas Database
of the United States (USGS)
Data Source: Environmental protection Agency
Derived Data: https ://edg. epa. gov/metadata/catalo g/main/home page
Raw Data: https://www.mrlc.gov/nlcd2011.php
Metric Variable: TUNDRA
Source Measurement: Percent
Years Available: 2011
Smallest Geospatial Level Available: County
Missing Data Handling: NULL
191
-------
Calculation Method: Percent area of tundra calculated using county boundaries (U.S. Census
Bureau), ArcMap 10.4, and 2011 NLCD categories 72 (Sedge/Herbaceous), 73 (Lichens), and
74(Moss). Alaska only
Data Source: Environmental protection Agency
Derived Data: https://edg.epa.gov/metadata/catalog/main/home.page
Raw Data: https://www.mrlc.gov/nlcd2011.php
Metric Variable: WETLANDS
Source Measurement: Percent
Years Available: 2011
Smallest Geospatial Level Available: County
Missing Data Handling: NULL
Calculation Method: Percent area of wetlands calculated using county boundaries (U.S. Census
Bureau), ArcMap 10.4, and 2011 NLCD categories 90 (Woody Wetlands) and 95 (Emergent
Herbaceous Wetlands)
Data Source: Environmental protection Agency
Derived Data : https://edg.epa.gov/metadata/catalog/main/home.page
Raw Data: https://www.mrlc.gov/nlcd2011.php
Score Distributions for Indicator: Extent of Ecosystem Types
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Indicator: Condition
The condition indicator is related to metrics that describe the condition of various
natural and managed ecosystems.
Metric List for Domain: Natural Environment - Indicator: Condition
Metric Variable: BIODIV
Source Measurement: Biodiversity based on avian taxa richness
Years Available: 2015
Smallest Geospatial Level Available: County
Calculation Method: N/A
Missing Data Handling: Null fill
Data Source: Jenkins, C.N., Van Houtan, K.S., Pimm, S.L., Sexton, J.O. 2015. U.S. protected
lands mismatch biodiversity priorities. Proceedings of the National Academy of Sciences.
112(16): 5081-5086. http://biodiversitvmapping.org/wordpress/index.php/home/
Metric Variable: CLEANAIR
Source Measurement: Percent
Years Available: 2016
Smallest Geospatial Level Available: County
Missing Data Handling: NULL
Calculation Method: Sum of days AQI rated as Good and Moderate, divided by Total number of
days with AQI data
Data Source: U.S. Environmental protection Agency
https://www.epa.gov/outdoor-air-qualitv-data/air-qualitv-index-report
Metric Variable: CSTLCOND
Source Measurement: Score
Years Available: 2011
Smallest Geospatial Level Available: County
Missing Data Handling: NA
Calculation Method: Great Lakes and near-coastal condition assessment score based on NARS
costal data. Overall condition scores were calculated for each geo-referenced location as follows
based on used for the national assessment (U.S. Environmental Protection Agency. Office of
Water and Office of Research and Development. 2015. National Coastal Condition Assessment
2010 (EPA 841-R-15-006). Washington, DC. December 2015). Final scores averaged by
summation of all sample points falling within county boundaries using census tracts (U.S.
Census Bureau) and ArcMap 10.4
Source: U.S. Environmental Protection Agency
Derived Data: https ://edg.epa. gov/metadata/catalog/main/home page
Raw Data: https://www.epa.gov/national-aquatic-resource-surveys
Metric Variable: LAKECOND
Source Measurement: Score
Years Available: 2011
Smallest Geospatial Level Available: County
193
-------
Missing Data Handling: Interpolation
Calculation Method: Inland lakes condition assessment score based on NARS Lake data.
Overall condition scores were calculated for each geo-referenced location as follows based on
used for the national assessment (U.S. Environmental Protection Agency. 2009. National Lakes
Assessment: A Collaborative Survey of the Nation's Lakes. EPA 841-R-09-001. U.S.
Environmental Protection Agency, Office of Water and Office of Research and Development,
Washington, D.C.). These values were standardized on a 0 - 1 scale, summed and re-graded
based on actual score to highest possible score ratio. Final scores created by distance weighted
average of all sample points falling within a 70-mile radius from county centroids using county
boundaries (U.S. Census Bureau) and ArcMap 10.4
Source: U.S. Environmental Protection Agency
Derived Data: https ://edg.epa. gov/metadata/catalog/main/home page
Raw Data: https://www.epa.gov/national-aquatic-resource-surveys
Metric Variable: RIVCOND
Source Measurement: Score
Years Available: 2011
Smallest Geospatial Level Available: County
Missing Data Handling: Interpolation
Calculation Method: Rivers and streams condition assessment score based on NARS Rivers and
Streams data. Overall condition scores were calculated for each geo-referenced location as
follows based on used for the national assessment (U.S. Environmental Protection Agency.
Office of Water and Office of Research and Development. National Rivers and Streams
Assessment 2008-2009: A Collaborative Survey. EPA/841/R-16/007. Washington, DC. March
2016). These values were standardized on a 0 - 1 scale, summed and re-graded based on actual
score to highest possible score ratio. Final scores created by distance weighted average of all
sample points falling within a 50-mile radius from county centroids using county boundaries
(U.S. Census Bureau) and ArcMap 10.4
Source: U.S. Environmental Protection Agency
Derived Data: https ://edg. epa. gov/metadata/catalo g/main/home page
Raw Data: https://www.epa.gov/national-aquatic-resource-surveys
Metric Variable: WLNDCOND
Source Measurement: Score
Years Available: 2011
Smallest Geospatial Level Available: County
Missing Data Handling: Interpolation
Calculation Method: Wetlands condition assessment score based on NARS wetlands data.
Overall condition scores were calculated for each geo-referenced location as follows based on
used for the national assessment (U.S. Environmental Protection Agency. 2016. National
Wetland Condition Assessment: Technical Report. EPA 843-R-15-006. U.S. EPA, Washington,
DC). These values were standardized on a 0 - 1 scale, summed and re-graded based on actual
score to highest possible score ratio. Final scores created by distance weighted average of all
sample points falling within a 100-mile radius from county centroids using county boundaries
(U.S. Census Bureau) and ArcMap 10.4
Source: U.S. Environmental Protection Agency
Derived Data: https ://edg. epa. gov/metadata/catalo g/main/home page
Raw Data: https://www.epa.gov/national-aquatic-resource-surveys
194
-------
Metric Variable: FORCOND
Source Measurement: Score
Years Available: 2000 - 2015
Smallest Geospatial Level Available: County
Missing Data Handling: NULL
Calculation Method: Forest condition assessment score is a synthesized value created from four
Forest Inventory and Analysis Database (FIAB). These are: stand age, basal area of live trees,
and disturbance observations (last observation). These three metrics were consistently measures
across all years of the assessment and more nationally complete. Disturbance codes were recoded
into 3 sub-index values where no disturbance was graded best; pest, disease and anthropogenic
disturbance graded most disturb; and remaining disturbance observations (e.g., wildfire, wildlife
damage) was considered moderate disturbance. All values were standardized on a 0 - 1 scale,
summed and re-graded based on actual score to highest possible score ratio.
Source: U.S. Environmental Protection Agency
Derived Data: https ://edg.epa. gov/metadata/catalog/main/home page
Raw Data: https://www.fs.fed.us/
Metric Variable: SOILCLASS
Source Measurement: Percent of soil classified as suitable for farming
Years Available: 2016
Smallest Geospatial Level Available: County
Missing Data Handling: NULL
Calculation Method: The USDA cropland GIS layer and the classification field from the NCCPI
dataset were used to calculate land-area weighted estimates. Census tract results were generated
using ArcMAP 10.4. Results were summed to create a final county-level measure.
Source: U.S. Environmental Protection Agency
Derived Data: https ://edg. epa. gov/metadata/catalo g/main/home page
Raw Data: https://www.nrcs.usda.gov/wps/portal/nrcs/main/soils/survey/
Metric Variable: SOILPROD
Source Measurement: Average Soil Productivity Index Score
Years Available: 2016
Smallest Geospatial Level Available: County
Missing Data Handling: NULL
Calculation Method: The USDA cropland GIS layer and the productivity index field from the
NCCPI dataset were used to calculate land-area weighted estimates. Census tract results were
generated using ArcMAP 10.4. Results were averaged to create a final county-level measure.
Source: U.S. Environmental Protection Agency
Derived Data: https ://edg. epa. gov/metadata/catalo g/main/home page
Raw Data: https ://www.nrcs.usda. gov/wps/portal/nrcs/main/soils/survev/
195
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0.75
0.50
0.25
-------
7.2 APPENDIX B
CRSI and Domain Scores Arranged by EPA Region and State
EPA
Built
Natural
REGION
State
Risk
Governance
Environment
Environment
Society
CRSI
National Average
0.229
0.588
0.393
0.414
0.516
4.213
Regional Average
0.240
0.896
0.492
0.445
0.599
10.697
1
Connecticut
0.395
0.874
0.520
0.398
0.547
3.702
1
Maine
0.115
0.923
0.499
0.484
0.565
17.971
1
Massachusetts
0.361
0.841
0.557
0.447
0.601
7.889
1
New Hampshire
0.229
0.893
0.519
0.421
0.596
9.154
1
Rhode Island
0.372
0.864
0.302
0.511
0.586
3.533
1
Vermont
0.135
0.945
0.450
0.417
0.671
12.848
EPA
Built
Natural
REGION
State
Risk
Governance
Environment
Environment
Society
CRSI
Regional Average
0.308
0.829
0.469
0.386
0.520
4.999
2
New Jersey
0.488
0.803
0.471
0.397
0.518
2.296
2
New York
0.248
0.838
0.469
0.382
0.521
5.914
EPA
Built
Natural
REGION
State
Risk
Governance
Environment
Environment
Society
CRSI
Regional Average
0.272
0.688
0.382
0.378
0.512
3.391
3
Delaware
0.474
0.725
0.586
0.547
0.472
3.495
3
District of
0.676
0.745
0.402
0.200
0.506
0.445
3
Maryland
0.366
0.741
0.494
0.463
0.518
4.506
3
Pennsylvania
0.257
0.783
0.481
0.383
0.503
5.311
3
Virginia
0.297
0.639
0.331
0.378
0.548
3.014
3
West Virginia
0.168
0.666
0.324
0.328
0.435
1.525
197
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EPA
Built
Natural
| REGION
State
Risk
Governance
Environment
Environment
Society
CRSI
Regional Average
0.255
0.498
0.342
0.403
0.414
0.585
4
Alabama
0.296
0.387
0.408
0.397
0.385
0.501
4
Florida
0.312
0.467
0.485
0.426
0.434
2.236
4
Georgia
0.224
0.498
0.282
0.395
0.420
-0.266
4
Kentucky
0.200
0.591
0.255
0.371
0.388
-0.619
4
Mississippi
0.273
0.550
0.337
0.444
0.382
1.046
4
North Carolina
0.273
0.495
0.419
0.431
0.463
2.543
4
South Carolina
0.279
0.518
0.393
0.420
0.437
1.776
4
Tennessee
0.260
0.425
0.305
0.370
0.409
-0.612
EPA
Built
Natural
REGION
State
Risk
Governance
Environment
Environment
Society
CRSI
Regional Average
0.222
0.713
0.407
0.434
0.572
6.021
5
Illinois
0.242
0.679
0.414
0.489
0.515
5.120
5
Indiana
0.219
0.679
0.360
0.452
0.570
5.757
5
Michigan
0.177
0.720
0.412
0.418
0.492
6.277
5
Minnesota
0.220
0.789
0.389
0.443
0.735
8.034
5
Ohio
0.246
0.667
0.421
0.352
0.514
3.451
5
Wisconsin
0.220
0.764
0.457
0.441
0.623
8.051
EPA
Built
Natural
REGION
State
County
Risk
Governance
Environment
Environment
Society
CRSI
Regional Average
0.239
0.548
0.394
0.423
0.474
2.772
6
Arkansas
6
0.235
0.487
0.393
0.446
0.451
2.373
6
Louisiana
6
0.338
0.529
0.430
0.457
0.479
2.535
6
New Mexico
6
0.166
0.582
0.472
0.502
0.505
7.551
6
Oklahoma
6
0.244
0.611
0.384
0.401
0.530
3.075
6
Texas
6
0.223
0.547
0.377
0.404
0.459
2.236
EPA
Built
Natural
REGION
State
Risk
Governance
Environment
Environment
Society
CRSI
Regional Average
0.209
0.597
0.358
0.380
0.609
4.113
7
Iowa
0.210
0.622
0.382
0.419
0.653
5.369
7
Kansas
0.195
0.604
0.332
0.369
0.651
4.155
198
-------
7
Missouri
0.206
0.536
0.399
0.389
0.530
3.912
7
Nebraska
0.226
0.638
0.311
0.340
0.613
2.979
EPA
Built
Natural
REGION
State
Risk
Governance
Environment
Environment
Society
CRSI
Regional Average
0.162
0.557
0.398
0.396
0.617
6.086
8
Colorado
0.203
0.551
0.453
0.396
0.555
5.565
8
Montana
0.135
0.562
0.381
0.403
0.638
7.024
8
North Dakota
0.150
0.576
0.374
0.354
0.662
5.745
8
South Dakota
0.142
0.566
0.314
0.377
0.608
4.329
8
Utah
0.211
0.537
0.495
0.463
0.617
7.772
8
Wyoming
0.142
0.520
0.464
0.441
0.658
8.950
EPA
Built
Natural
REGION
State
Risk
Governance
Environment
Environment
Society
CRSI
Regional Average
0.235
0.358
0.620
0.470
0.480
6.078
9
Arizona
0.183
0.436
0.710
0.410
0.458
8.129
9
California
0.279
0.299
0.641
0.462
0.485
4.765
9
Hawaii
0.092
0.552
0.570
0.479
0.589
14.926
9
Nevada
0.172
0.433
0.485
0.548
0.446
6.145
EPA
Built
Natural
REGION
State
Risk
Governance
Environment
Environment
Society
CRSI
Regional Average
0.137
0.432
0.478
0.531
0.492
14.838
10
Alaska
0.038
0.500
0.475
0.627
0.479
56.177
10
Idaho
0.137
0.439
0.420
0.537
0.545
8.363
10
Oregon
0.149
0.387
0.499
0.517
0.465
6.705
10
Washington
0.182
0.427
0.524
0.485
0.465
6.331
199
-------
7.3 APPENDIX C
CRSI and Domain Scores Arranged by EPA Region, State and County
200
-------
EPA
Built
Natural
REGION
State
County
Risk
Governance
Environment
Environment
Society
CRSI
National Average
0.229
0.588
0.393
0.414
0.516
4.213
Regional Average
0.240
0.896
0.492
0.445
0.599
10.697
1
Connecticut
Fairfield
0.508
0.866
0.675
0.346
0.494
2.923
1
Connecticut
Hartford
0.650
0.869
0.646
0.311
0.525
2.135
1
Connecticut
Litchfield
0.224
0.890
0.489
0.431
0.657
6.881
1
Connecticut
Middlesex
0.420
0.874
0.396
0.439
0.598
2.816
1
Connecticut
New Haven
0.491
0.863
0.659
0.403
0.499
3.243
1
Connecticut
New London
0.273
0.849
0.587
0.433
0.490
5.285
1
Connecticut
Tolland
0.306
0.892
0.341
0.403
0.551
2.855
1
Connecticut
Windham
0.289
0.887
0.370
0.420
0.561
3.483
1
Maine
Androscoggin
0.174
0.887
0.424
0.365
0.565
5.895
1
Maine
Aroostook
0.101
0.943
0.744
0.413
0.546
19.853
1
Maine
Cumberland
0.298
0.892
0.671
0.525
0.615
7.302
1
Maine
Franklin
0.094
0.934
0.490
0.421
0.502
13.434
1
Maine
Hancock
0.038
0.925
0.543
0.603
0.559
50.855
1
Maine
Kennebec
0.145
0.897
0.533
0.395
0.581
9.871
1
Maine
Knox
0.076
0.914
0.344
0.617
0.621
20.753
1
Maine
Lincoln
0.080
0.914
0.309
0.548
0.613
16.162
1
Maine
Oxford
0.116
0.936
0.505
0.388
0.505
10.568
1
Maine
Penobscot
0.104
0.923
0.786
0.390
0.565
19.975
1
Maine
Piscataquis
0.075
0.954
0.342
0.491
0.524
14.789
1
Maine
Sagadahoc
0.126
0.930
0.304
0.534
0.615
9.977
1
Maine
Somerset
0.081
0.917
0.542
0.463
0.523
18.903
1
Maine
Waldo
0.032
0.941
0.410
0.486
0.538
39.711
1
Maine
Washington
0.092
0.969
0.483
0.588
0.628
21.016
1
Maine
York
0.201
0.885
0.553
0.516
0.547
8.475
1
Massachusetts
Barnstable
0.197
0.850
0.585
0.591
0.580
10.134
1
Massachusetts
Berkshire
0.202
0.885
0.552
0.402
0.671
8.230
1
Massachusetts
Bristol
0.523
0.864
0.572
0.451
0.552
3.023
201
-------
EPA
Built
Natural
REGION
State
County
Risk
Governance
Environment
Environment
Society
CRSI
1
Massachusetts
Dukes
0.045
0.886
0.289
0.595
0.811
38.030
1
Massachusetts
Essex
0.537
0.884
0.671
0.487
0.565
3.685
1
Massachusetts
Franklin
0.147
0.901
0.542
0.419
0.707
11.983
1
Massachusetts
Hampden
0.576
0.862
0.649
0.340
0.517
2.506
1
Massachusetts
Hampshire
0.197
0.826
0.429
0.389
0.563
5.236
1
Massachusetts
Middlesex
0.591
0.872
0.819
0.259
0.585
3.115
1
Massachusetts
Nantucket
0.060
0.444
0.220
0.609
0.572
10.893
1
Massachusetts
Norfolk
0.497
0.864
0.633
0.387
0.618
3.457
1
Massachusetts
Plymouth
0.469
0.878
0.614
0.542
0.598
4.303
1
Massachusetts
Suffolk
0.465
0.875
0.417
0.426
0.465
2.037
1
Massachusetts
Worcester
0.550
0.886
0.804
0.359
0.604
3.813
1
New Hampshire
Belknap
0.151
0.883
0.350
0.385
0.621
6.505
1
New Hampshire
Carroll
0.169
0.870
0.441
0.445
0.599
7.792
1
New Hampshire
Cheshire
0.108
0.856
0.457
0.400
0.574
10.981
1
New Hampshire
Coos
0.112
0.922
0.536
0.549
0.640
17.434
1
New Hampshire
Grafton
0.129
0.905
0.785
0.468
0.571
17.559
1
New Hampshire
Hillsborough
0.461
0.900
0.631
0.334
0.574
3.327
1
New Hampshire
Merrimack
0.149
0.908
0.667
0.410
0.728
14.260
1
New Hampshire
Rockingham
0.555
0.888
0.578
0.420
0.554
2.788
1
New Hampshire
Strafford
0.338
0.877
0.380
0.419
0.505
2.697
1
New Hampshire
Sullivan
0.120
0.920
0.363
0.379
0.594
8.200
1
Rhode Island
Bristol
0.385
0.869
0.110
0.531
0.584
1.601
1
Rhode Island
Kent
0.510
0.862
0.240
0.443
0.605
1.529
1
Rhode Island
Newport
0.207
0.854
0.232
0.643
0.551
5.483
1
Rhode Island
Providence
0.510
0.863
0.535
0.326
0.551
2.298
1
Rhode Island
Washington
0.248
0.874
0.391
0.614
0.637
6.756
1
Vermont
Addison
0.089
0.989
0.473
0.490
0.712
20.832
1
Vermont
Bennington
0.154
0.933
0.435
0.469
0.613
9.414
1
Vermont
Caledonia
0.115
0.942
0.401
0.396
0.815
13.744
202
-------
EPA
REGION
State
County
Risk
Governance
Built
Environment
Natural
Environment
Society
CRSI
1
Vermont
Chittenden
0.261
0.912
0.661
0.387
0.604
6.946
1
Vermont
Franklin
0.242
0.923
0.427
0.386
0.651
5.319
1
Vermont
Grand Isle
0.063
0.985
0.334
0.365
0.714
18.709
1
Vermont
Lamoille
0.080
0.922
0.389
0.438
0.665
16.702
1
Vermont
Orange
0.140
0.961
0.400
0.351
0.689
8.895
1
Vermont
Orleans
0.185
0.957
0.436
0.439
0.793
9.509
1
Vermont
Rutland
0.090
0.929
0.520
0.441
0.662
18.980
1
Vermont
Washington
0.110
0.937
0.469
0.381
0.720
13.996
1
Vermont
Windham
0.104
0.932
0.467
0.364
0.638
12.735
1
Vermont
Windsor
0.188
0.915
0.558
0.367
0.651
8.435
203
-------
EPA
Built
Natural
REGION
State
County
Risk
Governance
Environment
Environment
Society
CRSI
National Average
0.229
0.588
0.393
0.414
0.516
4.213
Regional Average
0.308
0.829
0.469
0.386
0.520
4.999
2
New Jersey
Atlantic
0.477
0.770
0.520
0.507
0.431
2.617
2
New Jersey
Bergen
0.582
0.804
0.464
0.202
0.586
1.184
2
New Jersey
Burlington
0.538
0.812
0.567
0.558
0.547
3.290
2
New Jersey
Camden
0.532
0.795
0.444
0.296
0.506
1.315
2
New Jersey
Cape May
0.382
0.755
0.401
0.565
0.462
2.934
2
New Jersey
Cumberland
0.313
0.794
0.437
0.549
0.444
3.780
2
New Jersey
Essex
0.519
0.791
0.467
0.100
0.531
0.628
2
New Jersey
Gloucester
0.553
0.806
0.453
0.379
0.489
1.638
2
New Jersey
Hudson
0.525
0.797
0.402
0.233
0.465
0.679
2
New Jersey
Hunterdon
0.386
0.839
0.512
0.480
0.598
4.034
2
New Jersey
Mercer
0.496
0.797
0.448
0.362
0.492
1.708
2
New Jersey
Middlesex
0.522
0.785
0.601
0.253
0.561
2.125
2
New Jersey
Monmouth
0.728
0.784
0.615
0.485
0.534
2.282
2
New Jersey
Morris
0.511
0.826
0.524
0.449
0.595
2.929
2
New Jersey
Ocean
0.806
0.781
0.596
0.546
0.448
1.974
2
New Jersey
Passaic
0.494
0.816
0.384
0.432
0.522
1.877
2
New Jersey
Salem
0.209
0.816
0.338
0.503
0.459
4.096
2
New Jersey
Somerset
0.585
0.815
0.486
0.339
0.573
1.829
2
New Jersey
Sussex
0.406
0.860
0.405
0.468
0.563
2.945
2
New Jersey
Union
0.337
0.777
0.388
0.129
0.561
0.685
2
New Jersey
Warren
0.339
0.850
0.441
0.493
0.513
3.671
2
New York
Albany
0.455
0.807
0.542
0.343
0.639
2.973
2
New York
Allegany
0.118
0.871
0.482
0.343
0.385
6.393
2
New York
Bronx
0.529
0.785
0.297
0.203
0.409
-0.225
2
New York
Broome
0.381
0.822
0.505
0.335
0.497
2.543
2
New York
Cattaraugus
0.186
0.856
0.560
0.460
0.421
7.030
2
New York
Cayuga
0.139
0.878
0.464
0.368
0.571
8.215
204
-------
EPA
Built
Natural
REGION
State
County
Risk
Governance
Environment
Environment
Society
CRSI
2
New York
Chautauqua
0.152
0.834
0.567
0.399
0.414
7.490
2
New York
Chemung
0.204
0.829
0.339
0.304
0.472
2.002
2
New York
Chenango
0.160
0.867
0.393
0.362
0.474
4.597
2
New York
Clinton
0.154
0.837
0.587
0.412
0.534
9.484
2
New York
Columbia
0.155
0.829
0.412
0.387
0.603
6.865
2
New York
Cortland
0.146
0.863
0.331
0.341
0.527
4.245
2
New York
Delaware
0.210
0.836
0.519
0.375
0.485
5.207
2
New York
Dutchess
0.521
0.823
0.612
0.366
0.587
2.895
2
New York
Erie
0.463
0.810
0.713
0.296
0.550
3.278
2
New York
Essex
0.126
0.850
0.530
0.493
0.561
12.497
2
New York
Franklin
0.138
0.853
0.440
0.560
0.531
10.467
2
New York
Fulton
0.140
0.813
0.282
0.406
0.464
3.386
2
New York
Genesee
0.205
0.864
0.377
0.393
0.544
4.406
2
New York
Greene
0.188
0.830
0.465
0.356
0.495
4.869
2
New York
Hamilton
0.062
0.937
0.354
0.565
0.556
22.062
2
New York
Herkimer
0.112
0.850
0.503
0.490
0.422
10.901
2
New York
Jefferson
0.147
0.856
0.672
0.446
0.485
11.523
2
New York
Kings
0.366
0.768
0.310
0.330
0.502
1.065
2
New York
Lewis
0.095
0.901
0.463
0.456
0.516
13.405
2
New York
Livingston
0.116
0.869
0.480
0.449
0.539
11.341
2
New York
Madison
0.144
0.853
0.420
0.403
0.538
7.063
2
New York
Monroe
0.375
0.832
0.623
0.364
0.519
3.765
2
New York
Montgomery
0.143
0.856
0.260
0.322
0.492
2.146
2
New York
Nassau
0.351
0.772
0.564
0.342
0.703
4.263
2
New York
New York
0.569
0.756
0.376
0.259
0.460
0.523
2
New York
Niagara
0.279
0.822
0.432
0.425
0.481
3.453
2
New York
Oneida
0.235
0.820
0.676
0.398
0.525
6.946
2
New York
Onondaga
0.458
0.831
0.590
0.353
0.567
3.025
2
New York
Ontario
0.159
0.855
0.528
0.413
0.555
8.615
205
-------
EPA
Built
Natural
REGION
State
County
Risk
Governance
Environment
Environment
Society
CRSI
2
New York
Orange
0.520
0.828
0.704
0.362
0.591
3.379
2
New York
Orleans
0.162
0.858
0.325
0.431
0.443
4.025
2
New York
Oswego
0.166
0.852
0.565
0.330
0.389
5.619
2
New York
Otsego
0.149
0.857
0.441
0.337
0.528
6.022
2
New York
Putnam
0.411
0.825
0.403
0.463
0.633
3.106
2
New York
Queens
0.336
0.764
0.467
0.318
0.578
2.750
2
New York
Rensselaer
0.371
0.838
0.478
0.359
0.531
2.802
2
New York
Richmond
0.508
0.789
0.314
0.431
0.579
1.620
2
New York
Rockland
0.556
0.811
0.345
0.340
0.618
1.402
2
New York
Saratoga
0.473
0.836
0.535
0.355
0.566
2.631
2
New York
Schenectady
0.390
0.820
0.218
0.274
0.528
0.282
2
New York
Schoharie
0.077
0.887
0.320
0.310
0.525
6.912
2
New York
Schuyler
0.099
0.872
0.312
0.394
0.545
7.514
2
New York
Seneca
0.134
0.879
0.328
0.427
0.492
5.743
2
New York
St. Lawrence
0.117
0.857
0.691
0.508
0.453
15.680
2
New York
Steuben
0.105
0.862
0.707
0.353
0.445
14.118
2
New York
Suffolk
0.383
0.787
0.738
0.512
0.683
6.145
2
New York
Sullivan
0.183
0.815
0.553
0.361
0.512
6.476
2
New York
Tioga
0.266
0.836
0.343
0.321
0.450
1.596
2
New York
Tompkins
0.111
0.823
0.457
0.343
0.459
7.105
2
New York
Ulster
0.219
0.824
0.636
0.455
0.542
7.770
2
New York
Warren
0.139
0.847
0.435
0.439
0.599
9.113
2
New York
Washington
0.147
0.863
0.408
0.365
0.484
5.411
2
New York
Wayne
0.132
0.847
0.403
0.365
0.466
5.586
2
New York
Westchester
0.591
0.802
0.544
0.310
0.589
1.979
2
New York
Wyoming
0.122
0.882
0.406
0.353
0.504
6.711
2
New York
Yates
0.110
0.860
0.322
0.376
0.542
6.465
206
-------
EPA
Built
Natural
REGION
State
County
Risk
Governance
Environment
Environment
Society
CRSI
National Average
0.229
0.588
0.393
0.414
0.516
4.213
Regional Average
0.272
0.688
0.382
0.378
0.512
3.391
3
Delaware
Kent
0.434
0.747
0.566
0.609
0.463
3.832
3
Delaware
New Castle
0.609
0.745
0.546
0.437
0.520
2.127
3
Delaware
Sussex
0.380
0.682
0.646
0.596
0.434
4.527
District of
District of
3
Columbia
Columbia
0.676
0.745
0.402
0.200
0.506
0.445
3
Maryland
Allegany
0.277
0.716
0.421
0.429
0.441
2.728
3
Maryland
Anne Arundel
0.675
0.760
0.539
0.446
0.553
2.044
3
Maryland
Baltimore
0.594
0.706
0.393
0.156
0.381
-0.192
3
Maryland
Baltimore
0.494
0.719
0.567
0.363
0.585
2.576
3
Maryland
Calvert
0.317
0.786
0.440
0.472
0.541
3.734
3
Maryland
Caroline
0.155
0.766
0.389
0.568
0.494
7.527
3
Maryland
Carroll
0.296
0.776
0.501
0.429
0.620
4.672
3
Maryland
Cecil
0.432
0.696
0.522
0.483
0.398
2.445
3
Maryland
Charles
0.484
0.788
0.562
0.524
0.565
3.483
3
Maryland
Dorchester
0.172
0.779
0.373
0.601
0.447
6.557
3
Maryland
Frederick
0.529
0.778
0.632
0.373
0.603
2.954
3
Maryland
Garrett
0.140
0.737
0.583
0.409
0.497
9.092
3
Maryland
Harford
0.465
0.726
0.513
0.485
0.531
2.856
3
Maryland
Howard
0.565
0.768
0.399
0.355
0.627
1.652
3
Maryland
Kent
0.190
0.717
0.428
0.555
0.492
6.265
3
Maryland
Montgomery
0.590
0.757
0.530
0.344
0.569
1.920
3
Maryland
Prince George's
0.704
0.768
0.565
0.368
0.610
1.961
3
Maryland
Queen Anne's
0.173
0.769
0.531
0.529
0.581
9.384
3
Maryland
Somerset
0.124
0.715
0.407
0.609
0.390
8.549
3
Maryland
St. Mary's
0.303
0.760
0.521
0.547
0.483
4.759
3
Maryland
Talbot
0.197
0.746
0.422
0.555
0.570
6.858
3
Maryland
Washington
0.470
0.709
0.549
0.419
0.497
2.505
207
-------
EPA
Built
Natural
REGION
State
County
Risk
Governance
Environment
Environment
Society
CRSI
3
Maryland
Wicomico
0.281
0.672
0.498
0.505
0.506
4.384
3
Maryland
Worcester
0.160
0.657
0.559
0.598
0.453
9.433
3
Pennsylvania
Adams
0.262
0.768
0.530
0.382
0.508
4.269
3
Pennsylvania
Allegheny
0.705
0.721
0.706
0.151
0.529
1.435
3
Pennsylvania
Armstrong
0.188
0.789
0.487
0.295
0.480
4.018
3
Pennsylvania
Beaver
0.360
0.757
0.430
0.290
0.497
1.646
3
Pennsylvania
Bedford
0.099
0.799
0.438
0.410
0.511
9.927
3
Pennsylvania
Berks
0.367
0.759
0.666
0.365
0.513
3.919
3
Pennsylvania
Blair
0.231
0.716
0.509
0.437
0.537
5.196
3
Pennsylvania
Bradford
0.129
0.831
0.512
0.349
0.473
7.642
3
Pennsylvania
Bucks
0.521
0.752
0.724
0.318
0.634
3.280
3
Pennsylvania
Butler
0.413
0.763
0.476
0.320
0.587
2.350
3
Pennsylvania
Cambria
0.165
0.785
0.580
0.388
0.587
8.685
3
Pennsylvania
Cameron
0.107
0.882
0.214
0.584
0.467
7.528
3
Pennsylvania
Carbon
0.164
0.770
0.401
0.416
0.515
5.320
3
Pennsylvania
Centre
0.201
0.740
0.612
0.430
0.497
6.987
3
Pennsylvania
Chester
0.480
0.753
0.601
0.409
0.544
2.973
3
Pennsylvania
Clarion
0.126
0.810
0.412
0.409
0.553
7.925
3
Pennsylvania
Clearfield
0.142
0.780
0.542
0.383
0.495
8.041
3
Pennsylvania
Clinton
0.132
0.856
0.478
0.518
0.491
10.353
3
Pennsylvania
Columbia
0.193
0.808
0.398
0.375
0.491
3.917
3
Pennsylvania
Crawford
0.173
0.814
0.461
0.443
0.462
6.000
3
Pennsylvania
Cumberland
0.508
0.752
0.533
0.370
0.549
2.286
3
Pennsylvania
Dauphin
0.534
0.759
0.524
0.370
0.558
2.171
3
Pennsylvania
Delaware
0.485
0.721
0.388
0.252
0.526
0.844
3
Pennsylvania
Elk
0.110
0.745
0.354
0.572
0.562
10.912
3
Pennsylvania
Erie
0.303
0.754
0.596
0.351
0.504
3.944
3
Pennsylvania
Fayette
0.226
0.703
0.532
0.352
0.445
3.795
3
Pennsylvania
Forest
0.097
0.832
0.263
0.590
0.369
7.325
208
-------
EPA
Built
Natural
REGION
State
County
Risk
Governance
Environment
Environment
Society
CRSI
3
Pennsylvania
Franklin
0.303
0.710
0.575
0.396
0.505
3.987
3
Pennsylvania
Fulton
0.090
0.813
0.357
0.394
0.427
6.388
3
Pennsylvania
Greene
0.144
0.794
0.409
0.290
0.386
2.462
3
Pennsylvania
Huntingdon
0.102
0.847
0.478
0.486
0.525
13.197
3
Pennsylvania
Indiana
0.180
0.766
0.565
0.323
0.386
4.578
3
Pennsylvania
Jefferson
0.156
0.819
0.427
0.403
0.512
6.120
3
Pennsylvania
Juniata
0.144
0.823
0.365
0.431
0.525
6.159
3
Pennsylvania
Lackawanna
0.418
0.762
0.439
0.359
0.546
2.122
3
Pennsylvania
Lancaster
0.389
0.722
0.741
0.391
0.578
4.604
3
Pennsylvania
Lawrence
0.225
0.773
0.283
0.346
0.553
2.062
3
Pennsylvania
Lebanon
0.402
0.725
0.380
0.391
0.550
1.941
3
Pennsylvania
Lehigh
0.505
0.746
0.556
0.316
0.513
2.006
3
Pennsylvania
Luzerne
0.405
0.755
0.650
0.378
0.522
3.559
3
Pennsylvania
Lycoming
0.167
0.820
0.599
0.453
0.524
9.376
3
Pennsylvania
McKean
0.213
0.788
0.327
0.453
0.439
2.982
3
Pennsylvania
Mercer
0.238
0.786
0.448
0.389
0.521
4.025
3
Pennsylvania
Mifflin
0.161
0.747
0.332
0.466
0.512
4.842
3
Pennsylvania
Monroe
0.307
0.767
0.492
0.398
0.422
2.891
3
Pennsylvania
Montgomery
0.521
0.762
0.631
0.220
0.604
2.244
3
Pennsylvania
Montour
0.157
0.784
0.324
0.358
0.481
2.990
3
Pennsylvania
Northampton
0.500
0.754
0.522
0.320
0.509
1.865
3
Pennsylvania
Northumberland
0.189
0.758
0.418
0.348
0.475
3.492
3
Pennsylvania
Perry
0.096
0.846
0.420
0.418
0.595
12.109
3
Pennsylvania
Philadelphia
0.507
0.703
0.530
0.063
0.403
0.120
3
Pennsylvania
Pike
0.189
0.797
0.402
0.464
0.355
3.738
3
Pennsylvania
Potter
0.091
0.913
0.427
0.440
0.454
11.271
3
Pennsylvania
Schuylkill
0.244
0.766
0.617
0.365
0.508
5.370
3
Pennsylvania
Snyder
0.150
0.829
0.414
0.385
0.476
5.436
3
Pennsylvania
Somerset
0.124
0.781
0.638
0.374
0.493
11.050
209
-------
EPA
Built
Natural
REGION
State
County
Risk
Governance
Environment
Environment
Society
CRSI
3
Pennsylvania
Sullivan
0.102
0.924
0.349
0.413
0.470
7.803
3
Pennsylvania
Susquehanna
0.116
0.845
0.458
0.384
0.461
7.885
3
Pennsylvania
Tioga
0.087
0.858
0.558
0.383
0.427
12.958
3
Pennsylvania
Union
0.162
0.798
0.374
0.444
0.516
5.552
3
Pennsylvania
Venango
0.155
0.789
0.381
0.473
0.486
5.964
3
Pennsylvania
Warren
0.084
0.801
0.361
0.490
0.444
10.023
3
Pennsylvania
Washington
0.341
0.754
0.544
0.277
0.548
2.825
3
Pennsylvania
Wayne
0.152
0.809
0.486
0.393
0.588
8.086
3
Pennsylvania
Westmoreland
0.546
0.712
0.506
0.303
0.588
1.761
3
Pennsylvania
Wyoming
0.206
0.874
0.426
0.391
0.501
4.641
3
Pennsylvania
York
0.507
0.749
0.666
0.340
0.502
2.655
3
Virginia
Accomack
0.162
0.687
0.504
0.650
0.534
10.342
3
Virginia
Albemarle
0.263
0.721
0.563
0.398
0.540
4.788
3
Virginia
Alexandria
0.607
0.742
0.255
0.117
0.495
-0.522
3
Virginia
Alleghany
0.198
0.618
0.349
0.451
0.657
4.785
3
Virginia
Amelia
0.111
0.720
0.331
0.445
0.651
8.800
3
Virginia
Amherst
0.256
0.664
0.447
0.418
0.602
4.163
3
Virginia
Appomattox
0.132
0.688
0.343
0.433
0.628
6.823
3
Virginia
Arlington
0.538
0.738
0.257
0.118
0.491
-0.600
3
Virginia
Augusta
0.208
0.584
0.558
0.498
0.574
6.847
3
Virginia
Bath
0.181
0.573
0.357
0.550
0.726
7.178
3
Virginia
Bedford
0.234
0.309
0.541
0.384
0.601
3.741
3
Virginia
Bland
0.082
0.404
0.291
0.480
0.485
3.793
3
Virginia
Botetourt
0.224
0.315
0.480
0.361
0.756
4.322
3
Virginia
Bristol
0.546
0.276
0.119
0.159
0.546
-1.727
3
Virginia
Brunswick
0.115
0.640
0.361
0.427
0.532
6.082
3
Virginia
Buchanan
0.150
0.532
0.402
0.265
0.394
0.180
3
Virginia
Buckingham
0.170
0.708
0.360
0.391
0.502
3.616
3
Virginia
Buena Vista
0.395
0.617
0.095
0.250
0.424
-1.733
210
-------
EPA
Built
Natural
REGION
State
County
Risk
Governance
Environment
Environment
Society
CRSI
3
Virginia
Campbell
0.203
0.580
0.451
0.340
0.652
4.431
3
Virginia
Caroline
0.131
0.765
0.474
0.487
0.503
9.244
3
Virginia
Carroll
0.140
0.456
0.396
0.312
0.504
1.941
3
Virginia
Charles City
0.101
0.839
0.329
0.520
0.524
10.148
3
Virginia
Charlotte
0.079
0.585
0.341
0.362
0.593
6.928
3
Virginia
Charlottesville
0.515
0.699
0.164
0.026
0.539
-1.437
3
Virginia
Chesapeake
0.714
0.688
0.398
0.481
0.544
1.398
3
Virginia
Chesterfield
0.626
0.728
0.510
0.407
0.581
1.964
3
Virginia
Clarke
0.106
0.778
0.320
0.389
0.646
8.111
3
Virginia
Colonial Heights
0.655
0.703
0.158
0.169
0.593
-0.455
3
Virginia
Covington
0.309
0.597
0.010
0.207
0.590
-2.296
3
Virginia
Craig
0.159
0.542
0.258
0.502
0.555
3.492
3
Virginia
Culpeper
0.282
0.777
0.372
0.393
0.656
3.624
3
Virginia
Cumberland
0.096
0.721
0.323
0.471
0.538
8.277
3
Virginia
Danville
0.458
0.555
0.040
0.192
0.416
-2.300
3
Virginia
Dickenson
0.129
0.531
0.369
0.437
0.434
3.432
3
Virginia
Dinwiddie
0.149
0.664
0.392
0.479
0.531
6.250
3
Virginia
Emporia
0.528
0.623
0.146
0.265
0.463
-0.810
3
Virginia
Essex
0.216
0.729
0.343
0.508
0.684
5.729
3
Virginia
Fairfax
0.485
0.779
0.175
0.148
0.842
0.541
3
Virginia
Fairfax
0.569
0.755
0.474
0.308
0.557
1.529
3
Virginia
Falls Church
0.320
0.973
0.152
0.225
0.766
1.369
3
Virginia
Fauquier
0.243
0.780
0.546
0.436
0.678
6.738
3
Virginia
Floyd
0.128
0.447
0.336
0.285
0.552
0.985
3
Virginia
Fluvanna
0.158
0.749
0.397
0.359
0.511
4.382
3
Virginia
Franklin
0.331
0.709
0.146
0.330
0.399
-0.928
3
Virginia
Franklin
0.194
0.491
0.422
0.325
0.514
2.182
3
Virginia
Frederick
0.487
0.727
0.451
0.308
0.522
1.460
3
Virginia
Fredericksburg
0.476
0.764
0.144
0.176
0.578
-0.603
211
-------
EPA
Built
Natural
REGION
State
County
Risk
Governance
Environment
Environment
Society
CRSI
3
Virginia
Galax
0.272
0.518
0.114
0.193
0.391
-3.439
3
Virginia
Giles
0.113
0.455
0.342
0.485
0.576
6.128
3
Virginia
Gloucester
0.273
0.754
0.347
0.582
0.678
5.282
3
Virginia
Goochland
0.215
0.757
0.422
0.350
0.525
3.597
3
Virginia
Grayson
0.124
0.310
0.363
0.365
0.463
0.688
3
Virginia
Greene
0.155
0.764
0.340
0.378
0.536
4.227
3
Virginia
Greensville
0.122
0.603
0.333
0.436
0.251
0.445
3
Virginia
Halifax
0.154
0.586
0.402
0.361
0.504
3.468
3
Virginia
Hampton
0.576
0.722
0.285
0.570
0.483
1.446
3
Virginia
Hanover
0.399
0.743
0.474
0.376
0.677
3.164
3
Virginia
Harrisonburg
0.391
0.878
0.266
0.142
0.448
-0.476
3
Virginia
Henrico
0.656
0.727
0.374
0.319
0.588
1.009
3
Virginia
Henry
0.370
0.485
0.369
0.325
0.452
0.421
3
Virginia
Highland
0.113
0.623
0.266
0.507
0.695
8.343
3
Virginia
Hopewell
0.561
0.686
0.136
0.240
0.462
-0.813
3
Virginia
Isle of Wight
0.348
0.710
0.390
0.487
0.585
3.149
3
Virginia
James City
0.519
0.706
0.327
0.546
0.539
1.888
3
Virginia
King and Queen
0.107
0.738
0.306
0.551
0.569
9.638
3
Virginia
King George
0.252
0.774
0.418
0.478
0.464
3.861
3
Virginia
King William
0.105
0.764
0.397
0.541
0.648
13.526
3
Virginia
Lancaster
0.301
0.719
0.322
0.597
0.711
4.793
3
Virginia
Lee
0.141
0.339
0.370
0.338
0.418
-0.164
3
Virginia
Lexington
0.240
0.621
0.067
0.078
0.681
-2.798
3
Virginia
Loudoun
0.704
0.791
0.517
0.365
0.618
1.821
3
Virginia
Louisa
0.156
0.752
0.476
0.366
0.501
5.777
3
Virginia
Lunenburg
0.113
0.663
0.282
0.420
0.533
4.363
3
Virginia
Lynchburg
0.507
0.693
0.227
0.134
0.436
-1.017
3
Virginia
Madison
0.136
0.571
0.350
0.451
0.657
6.658
3
Virginia
Manassas
0.435
0.749
0.236
0.115
0.712
0.139
212
-------
EPA
Built
Natural
REGION
State
County
Risk
Governance
Environment
Environment
Society
CRSI
3
Virginia
Manassas Park
0.395
0.777
0.147
0.108
0.584
-1.061
3
Virginia
Martinsville
0.460
0.505
0.055
0.160
0.519
-2.032
3
Virginia
Mathews
0.232
0.698
0.264
0.735
0.702
6.852
3
Virginia
Mecklenburg
0.116
0.608
0.410
0.411
0.597
7.637
3
Virginia
Middlesex
0.253
0.727
0.317
0.631
0.632
5.394
3
Virginia
Montgomery
0.288
0.472
0.443
0.390
0.487
1.966
3
Virginia
Nelson
0.150
0.492
0.417
0.375
0.485
3.184
3
Virginia
New Kent
0.164
0.751
0.422
0.499
0.622
8.082
3
Virginia
Newport News
0.558
0.708
0.311
0.556
0.461
1.445
3
Virginia
Norfolk
0.523
0.725
0.294
0.386
0.433
0.587
3
Virginia
Northampton
0.209
0.693
0.335
0.673
0.561
6.402
3
Virginia
Northumberland
0.214
0.711
0.355
0.557
0.612
5.744
3
Virginia
Norton
0.551
0.334
0.026
0.240
0.526
-1.771
3
Virginia
Nottoway
0.128
0.669
0.298
0.431
0.530
4.409
3
Virginia
Orange
0.158
0.773
0.393
0.392
0.502
4.864
3
Virginia
Page
0.125
0.619
0.399
0.505
0.589
8.646
3
Virginia
Patrick
0.156
0.448
0.369
0.309
0.522
1.382
3
Virginia
Petersburg
0.517
0.668
0.177
0.296
0.417
-0.603
3
Virginia
Pittsylvania
0.198
0.569
0.540
0.421
0.530
5.472
3
Virginia
Poquoson
0.253
0.742
0.165
0.596
0.527
2.687
3
Virginia
Portsmouth
0.668
0.719
0.238
0.271
0.487
-0.036
3
Virginia
Powhatan
0.140
0.761
0.390
0.461
0.650
8.661
3
Virginia
Prince Edward
0.109
0.642
0.363
0.391
0.562
6.191
3
Virginia
Prince George
0.319
0.700
0.445
0.554
0.482
3.739
3
Virginia
Prince William
0.600
0.789
0.472
0.345
0.597
1.781
3
Virginia
Pulaski
0.189
0.451
0.369
0.401
0.518
2.308
3
Virginia
Radford
0.331
0.516
0.133
0.266
0.334
-2.477
3
Virginia
Rappahannock
0.132
0.656
0.346
0.439
0.561
5.738
3
Virginia
Richmond
0.551
0.720
0.245
0.105
0.463
-0.836
213
-------
EPA
Built
Natural
REGION
State
County
Risk
Governance
Environment
Environment
Society
CRSI
3
Virginia
Richmond
0.238
0.687
0.341
0.489
0.550
3.686
3
Virginia
Roanoke
0.460
0.508
0.210
0.104
0.536
-1.358
3
Virginia
Roanoke
0.468
0.508
0.376
0.308
0.600
0.955
3
Virginia
Rockbridge
0.158
0.415
0.446
0.444
0.572
5.193
3
Virginia
Rockingham
0.216
0.687
0.616
0.454
0.555
7.109
3
Virginia
Russell
0.124
0.271
0.398
0.368
0.438
0.780
3
Virginia
Salem
0.451
0.483
0.146
0.116
0.598
-1.485
3
Virginia
Scott
0.139
0.332
0.381
0.346
0.400
-0.134
3
Virginia
Shenandoah
0.196
0.714
0.456
0.394
0.599
5.493
3
Virginia
Smyth
0.159
0.293
0.387
0.461
0.544
3.303
3
Virginia
Southampton
0.110
0.679
0.432
0.474
0.568
10.123
3
Virginia
Spotsylvania
0.517
0.769
0.382
0.377
0.539
1.493
3
Virginia
Stafford
0.554
0.795
0.393
0.439
0.564
1.857
3
Virginia
Staunton
0.356
0.839
0.156
0.260
0.569
0.030
3
Virginia
Suffolk
0.557
0.725
0.494
0.454
0.489
2.013
3
Virginia
Surry
0.242
0.726
0.305
0.504
0.494
3.091
3
Virginia
Sussex
0.110
0.648
0.355
0.476
0.496
6.708
3
Virginia
Tazewell
0.164
0.388
0.409
0.324
0.457
1.061
3
Virginia
Virginia Beach
0.468
0.687
0.377
0.520
0.509
2.070
3
Virginia
Warren
0.363
0.771
0.333
0.354
0.577
1.818
3
Virginia
Washington
0.277
0.290
0.443
0.422
0.475
1.589
3
Virginia
Waynesboro
0.456
0.722
0.133
0.175
0.514
-1.059
3
Virginia
Westmoreland
0.251
0.714
0.297
0.463
0.651
3.676
3
Virginia
Williamsburg
0.387
0.840
0.218
0.365
0.491
0.733
3
Virginia
Winchester
0.383
0.839
0.148
0.076
0.555
-1.281
3
Virginia
Wise
0.161
0.426
0.419
0.351
0.415
1.377
3
Virginia
Wythe
0.153
0.404
0.375
0.431
0.580
3.959
3
Virginia
York
0.590
0.727
0.326
0.679
0.595
2.428
3
West Virginia
Barbour
0.090
0.708
0.327
0.293
0.445
1.920
214
-------
EPA
Built
Natural
REGION
State
County
Risk
Governance
Environment
Environment
Society
CRSI
3
West Virginia
Berkeley
0.482
0.685
0.349
0.353
0.431
0.683
3
West Virginia
Boone
0.127
0.689
0.276
0.276
0.436
-0.306
3
West Virginia
Braxton
0.075
0.691
0.337
0.435
0.442
7.018
3
West Virginia
Brooke
0.373
0.727
0.251
0.235
0.380
-0.757
3
West Virginia
Cabell
0.243
0.699
0.295
0.323
0.449
0.666
3
West Virginia
Calhoun
0.135
0.676
0.267
0.305
0.370
-0.997
3
West Virginia
Clay
0.119
0.643
0.292
0.345
0.301
-1.175
3
West Virginia
Doddridge
0.091
0.718
0.206
0.302
0.438
-1.444
3
West Virginia
Fayette
0.112
0.624
0.461
0.380
0.551
7.788
3
West Virginia
Gilmer
0.077
0.707
0.222
0.337
0.364
-2.020
3
West Virginia
Grant
0.126
0.666
0.326
0.403
0.539
4.617
3
West Virginia
Greenbrier
0.102
0.297
0.394
0.402
0.554
4.141
3
West Virginia
Hampshire
0.105
0.607
0.417
0.364
0.373
3.285
3
West Virginia
Hancock
0.354
0.700
0.168
0.259
0.359
-1.444
3
West Virginia
Hardy
0.153
0.591
0.384
0.384
0.420
2.512
3
West Virginia
Harrison
0.206
0.735
0.417
0.251
0.544
2.587
3
West Virginia
Jackson
0.150
0.710
0.365
0.272
0.439
1.434
3
West Virginia
Jefferson
0.332
0.732
0.401
0.339
0.449
1.550
3
West Virginia
Kanawha
0.224
0.710
0.475
0.260
0.519
2.846
3
West Virginia
Lewis
0.111
0.711
0.282
0.354
0.443
1.852
3
West Virginia
Lincoln
0.145
0.650
0.316
0.256
0.322
-1.700
3
West Virginia
Logan
0.120
0.679
0.373
0.236
0.363
-0.286
3
West Virginia
Marion
0.222
0.723
0.351
0.280
0.506
1.533
3
West Virginia
Marshall
0.206
0.723
0.358
0.261
0.412
0.629
3
West Virginia
Mason
0.181
0.705
0.360
0.316
0.368
0.913
3
West Virginia
McDowell
0.224
0.618
0.330
0.286
0.335
-0.630
3
West Virginia
Mercer
0.144
0.510
0.345
0.282
0.458
0.153
3
West Virginia
Mineral
0.153
0.681
0.350
0.369
0.377
1.715
3
West Virginia
Mingo
0.143
0.673
0.371
0.276
0.296
-0.547
215
-------
EPA Built Natural
REGION
State
County
Risk
Governance
Environment
Environment
Society
CRSI
3
West Virginia
Monongalia
0.399
0.725
0.385
0.289
0.404
0.646
3
West Virginia
Monroe
0.086
0.504
0.317
0.347
0.557
3.462
3
West Virginia
Morgan
0.105
0.691
0.270
0.340
0.362
-0.412
3
West Virginia
Nicholas
0.112
0.563
0.357
0.391
0.407
2.448
3
West Virginia
Ohio
0.394
0.732
0.199
0.172
0.569
-0.501
3
West Virginia
Pendleton
0.177
0.591
0.332
0.472
0.463
3.080
3
West Virginia
Pleasants
0.154
0.769
0.273
0.267
0.359
-0.908
3
West Virginia
Pocahontas
0.108
0.574
0.309
0.554
0.588
8.469
3
West Virginia
Preston
0.129
0.718
0.460
0.321
0.529
5.995
3
West Virginia
Putnam
0.274
0.718
0.413
0.313
0.573
2.600
3
West Virginia
Raleigh
0.174
0.615
0.408
0.334
0.530
3.242
3
West Virginia
Randolph
0.103
0.593
0.387
0.433
0.519
6.881
3
West Virginia
Ritchie
0.097
0.737
0.246
0.293
0.381
-1.430
3
West Virginia
Roane
0.107
0.698
0.289
0.351
0.390
0.897
3
West Virginia
Summers
0.077
0.522
0.230
0.422
0.482
1.549
3
West Virginia
Taylor
0.107
0.709
0.198
0.317
0.530
0.510
3
West Virginia
Tucker
0.066
0.711
0.320
0.476
0.486
10.510
3
West Virginia
Tyler
0.106
0.761
0.223
0.309
0.421
-0.537
3
West Virginia
Upshur
0.106
0.696
0.355
0.313
0.546
4.595
3
West Virginia
Wayne
0.169
0.683
0.395
0.350
0.365
1.849
3
West Virginia
Webster
0.137
0.565
0.249
0.418
0.222
-2.231
3
West Virginia
Wetzel
0.147
0.718
0.267
0.291
0.397
-0.514
3
West Virginia
Wirt
0.116
0.700
0.240
0.274
0.296
-3.493
3
West Virginia
Wood
0.256
0.733
0.340
0.251
0.477
0.760
3
West Virginia
Wyoming
0.217
0.638
0.317
0.309
0.369
-0.151
216
-------
EPA
Built
Natural
REGION
State
County
Risk
Governance
Environment
Environment
Society
CRSI
National Average
0.229
0.588
0.393
0.414
0.516
4.213
Regional Average
0.255
0.498
0.342
0.403
0.414
0.585
4
Alabama
Autauga
0.238
0.425
0.423
0.412
0.477
2.118
4
Alabama
Baldwin
0.418
0.376
0.772
0.490
0.446
3.624
4
Alabama
Barbour
0.093
0.461
0.372
0.449
0.337
2.351
4
Alabama
Bibb
0.246
0.330
0.355
0.433
0.403
0.533
4
Alabama
Blount
0.244
0.266
0.412
0.345
0.456
0.441
4
Alabama
Bullock
0.085
0.366
0.266
0.403
0.332
-3.296
4
Alabama
Butler
0.276
0.422
0.356
0.419
0.405
0.708
4
Alabama
Calhoun
0.669
0.315
0.456
0.396
0.399
0.430
4
Alabama
Chambers
0.140
0.422
0.308
0.369
0.303
-1.814
4
Alabama
Cherokee
0.293
0.398
0.351
0.351
0.379
-0.205
4
Alabama
Chilton
0.158
0.308
0.421
0.401
0.485
2.337
4
Alabama
Choctaw
0.274
0.458
0.262
0.421
0.243
-1.210
4
Alabama
Clarke
0.318
0.310
0.400
0.364
0.359
-0.074
4
Alabama
Clay
0.129
0.305
0.349
0.430
0.372
0.169
4
Alabama
Cleburne
0.131
0.346
0.308
0.440
0.309
-1.084
4
Alabama
Coffee
0.259
0.498
0.406
0.445
0.476
2.346
4
Alabama
Colbert
0.262
0.242
0.432
0.462
0.535
2.214
4
Alabama
Conecuh
0.275
0.330
0.318
0.440
0.172
-1.470
4
Alabama
Coosa
0.114
0.270
0.344
0.409
0.263
-2.562
4
Alabama
Covington
0.298
0.404
0.404
0.454
0.510
2.005
4
Alabama
Crenshaw
0.205
0.505
0.349
0.371
0.380
0.464
4
Alabama
Cullman
0.295
0.285
0.454
0.294
0.467
0.463
4
Alabama
Dale
0.187
0.491
0.416
0.426
0.385
2.160
4
Alabama
Dallas
0.138
0.380
0.326
0.404
0.282
-1.503
4
Alabama
DeKalb
0.287
0.485
0.441
0.322
0.356
0.537
4
Alabama
Elmore
0.360
0.367
0.480
0.440
0.487
1.899
4
Alabama
Escambia
0.347
0.355
0.434
0.475
0.304
0.797
217
-------
EPA
Built
Natural
REGION
State
County
Risk
Governance
Environment
Environment
Society
CRSI
4
Alabama
Etowah
0.420
0.291
0.400
0.348
0.470
0.317
4
Alabama
Fayette
0.182
0.444
0.336
0.400
0.385
0.428
4
Alabama
Franklin
0.242
0.407
0.376
0.400
0.336
0.216
4
Alabama
Geneva
0.128
0.427
0.348
0.442
0.390
1.620
4
Alabama
Greene
0.251
0.442
0.312
0.412
0.223
-1.097
4
Alabama
Hale
0.259
0.499
0.295
0.427
0.241
-0.739
4
Alabama
Henry
0.226
0.427
0.326
0.397
0.405
0.289
4
Alabama
Houston
0.308
0.510
0.465
0.407
0.492
2.334
4
Alabama
Jackson
0.207
0.503
0.488
0.400
0.324
2.059
4
Alabama
Jefferson
0.915
0.265
0.668
0.252
0.504
0.719
4
Alabama
Lamar
0.153
0.525
0.381
0.417
0.335
1.463
4
Alabama
Lauderdale
0.215
0.250
0.398
0.437
0.502
1.726
4
Alabama
Lawrence
0.233
0.228
0.380
0.479
0.389
0.786
4
Alabama
Lee
0.445
0.378
0.451
0.381
0.379
0.587
4
Alabama
Limestone
0.597
0.465
0.438
0.458
0.430
1.010
4
Alabama
Lowndes
0.112
0.498
0.372
0.374
0.330
0.525
4
Alabama
Macon
0.113
0.302
0.302
0.352
0.412
-1.924
4
Alabama
Madison
0.804
0.428
0.567
0.324
0.437
0.738
4
Alabama
Marengo
0.276
0.465
0.363
0.425
0.387
0.855
4
Alabama
Marion
0.331
0.525
0.482
0.384
0.353
1.350
4
Alabama
Marshall
0.361
0.479
0.391
0.330
0.356
0.091
4
Alabama
Mobile
0.536
0.338
0.647
0.449
0.457
1.986
4
Alabama
Monroe
0.311
0.273
0.377
0.382
0.362
-0.229
4
Alabama
Montgomery
0.519
0.419
0.484
0.353
0.534
1.207
4
Alabama
Morgan
0.425
0.403
0.458
0.337
0.449
0.782
4
Alabama
Perry
0.160
0.335
0.261
0.381
0.259
-3.271
4
Alabama
Pickens
0.184
0.538
0.369
0.367
0.378
0.911
4
Alabama
Pike
0.142
0.534
0.380
0.453
0.396
3.096
4
Alabama
Randolph
0.119
0.379
0.360
0.319
0.388
-0.961
218
-------
EPA
Built
Natural
REGION
State
County
Risk
Governance
Environment
Environment
Society
CRSI
4
Alabama
Russell
0.196
0.448
0.391
0.387
0.389
1.050
4
Alabama
Shelby
0.531
0.351
0.597
0.376
0.495
1.582
4
Alabama
St. Clair
0.566
0.293
0.532
0.337
0.456
0.770
4
Alabama
Sumter
0.275
0.502
0.234
0.434
0.270
-1.012
4
Alabama
Talladega
0.303
0.285
0.485
0.390
0.358
0.793
4
Alabama
Tallapoosa
0.336
0.320
0.404
0.400
0.451
0.794
4
Alabama
Tuscaloosa
0.497
0.384
0.603
0.382
0.488
1.788
4
Alabama
Walker
0.339
0.265
0.481
0.341
0.371
0.336
4
Alabama
Washington
0.352
0.396
0.448
0.415
0.320
0.674
4
Alabama
Wilcox
0.221
0.363
0.308
0.388
0.288
-1.340
4
Alabama
Winston
0.274
0.226
0.348
0.397
0.284
-1.132
4
Florida
Alachua
0.241
0.437
0.700
0.385
0.468
4.845
4
Florida
Baker
0.131
0.545
0.274
0.530
0.476
3.896
4
Florida
Bay
0.266
0.511
0.588
0.406
0.520
4.123
4
Florida
Bradford
0.096
0.465
0.320
0.421
0.524
3.999
4
Florida
Brevard
0.622
0.577
0.673
0.483
0.470
2.382
4
Florida
Broward
0.722
0.546
0.713
0.448
0.479
2.059
4
Florida
Calhoun
0.111
0.223
0.243
0.458
0.396
-2.053
4
Florida
Charlotte
0.273
0.502
0.516
0.390
0.438
2.550
4
Florida
Citrus
0.152
0.526
0.422
0.459
0.361
3.209
4
Florida
Clay
0.356
0.533
0.415
0.490
0.515
2.398
4
Florida
Collier
0.398
0.564
0.583
0.550
0.437
3.342
4
Florida
Columbia
0.156
0.415
0.459
0.488
0.506
5.343
4
Florida
DeSoto
0.242
0.541
0.404
0.301
0.292
-0.273
4
Florida
Dixie
0.122
0.494
0.283
0.456
0.361
0.624
4
Florida
Duval
0.688
0.554
0.809
0.360
0.470
2.206
4
Florida
Escambia
0.519
0.432
0.514
0.452
0.473
1.623
4
Florida
Flagler
0.260
0.513
0.461
0.349
0.449
1.858
4
Florida
Franklin
0.163
0.472
0.287
0.608
0.667
6.388
219
-------
EPA
Built
Natural
REGION
State
County
Risk
Governance
Environment
Environment
Society
CRSI
4
Florida
Gadsden
0.160
0.240
0.375
0.453
0.442
1.387
4
Florida
Gilchrist
0.061
0.450
0.256
0.389
0.417
-1.531
4
Florida
Glades
0.366
0.565
0.339
0.460
0.252
0.256
4
Florida
Gulf
0.133
0.428
0.307
0.535
0.461
3.494
4
Florida
Hamilton
0.073
0.519
0.285
0.419
0.437
2.297
4
Florida
Hardee
0.226
0.527
0.434
0.280
0.305
-0.121
4
Florida
Hendry
0.331
0.605
0.474
0.403
0.334
1.551
4
Florida
Hernando
0.253
0.523
0.348
0.405
0.374
0.722
4
Florida
Highlands
0.245
0.495
0.516
0.357
0.383
2.037
4
Florida
Hillsborough
0.604
0.564
0.867
0.298
0.445
2.455
4
Florida
Holmes
0.104
0.366
0.284
0.455
0.434
0.887
4
Florida
Indian River
0.543
0.432
0.462
0.390
0.402
0.760
4
Florida
Jackson
0.143
0.247
0.433
0.430
0.469
2.648
4
Florida
Jefferson
0.087
0.486
0.316
0.439
0.555
5.686
4
Florida
Lafayette
0.145
0.449
0.144
0.488
0.383
-1.490
4
Florida
Lake
0.488
0.348
0.676
0.412
0.430
2.068
4
Florida
Lee
0.430
0.583
0.653
0.331
0.424
2.253
4
Florida
Leon
0.375
0.436
0.518
0.494
0.541
2.915
4
Florida
Levy
0.128
0.511
0.470
0.440
0.421
5.253
4
Florida
Liberty
0.193
0.246
0.195
0.713
0.265
0.202
4
Florida
Madison
0.129
0.490
0.393
0.376
0.474
3.029
4
Florida
Manatee
0.442
0.564
0.423
0.307
0.443
0.716
4
Florida
Marion
0.329
0.354
0.642
0.445
0.411
2.929
4
Florida
Martin
0.456
0.540
0.495
0.395
0.476
1.679
4
Florida
Miami-Dade
0.623
0.392
0.831
0.536
0.456
2.926
4
Florida
Monroe
0.156
0.195
0.595
0.628
0.489
8.239
4
Florida
Nassau
0.252
0.541
0.464
0.384
0.468
2.551
4
Florida
Okaloosa
0.318
0.302
0.595
0.474
0.482
3.146
4
Florida
Okeechobee
0.153
0.565
0.390
0.379
0.263
0.355
220
-------
EPA
Built
Natural
REGION
State
County
Risk
Governance
Environment
Environment
Society
CRSI
4
Florida
Orange
0.716
0.476
0.820
0.278
0.462
1.751
4
Florida
Osceola
0.441
0.541
0.652
0.335
0.404
2.032
4
Florida
Palm Beach
0.494
0.592
0.817
0.407
0.470
3.428
4
Florida
Pasco
0.380
0.513
0.588
0.367
0.378
1.908
4
Florida
Pinellas
0.436
0.546
0.598
0.391
0.436
2.200
4
Florida
Polk
0.514
0.416
0.834
0.329
0.429
2.513
4
Florida
Putnam
0.160
0.429
0.495
0.404
0.335
2.487
4
Florida
Santa Rosa
0.470
0.381
0.557
0.469
0.440
1.875
4
Florida
Sarasota
0.428
0.563
0.476
0.285
0.519
1.286
4
Florida
Seminole
0.747
0.452
0.503
0.290
0.524
0.717
4
Florida
St. Johns
0.388
0.573
0.533
0.466
0.448
2.627
4
Florida
St. Lucie
0.532
0.503
0.449
0.311
0.398
0.464
4
Florida
Sumter
0.381
0.406
0.427
0.426
0.346
0.713
4
Florida
Suwannee
0.120
0.427
0.368
0.345
0.446
1.112
4
Florida
Taylor
0.115
0.512
0.326
0.444
0.398
2.188
4
Florida
Union
0.075
0.523
0.253
0.409
0.432
0.674
4
Florida
Volusia
0.474
0.459
0.741
0.447
0.428
2.903
4
Florida
Wakulla
0.216
0.499
0.353
0.640
0.483
4.446
4
Florida
Walton
0.172
0.271
0.538
0.496
0.430
4.461
4
Florida
Washington
0.147
0.378
0.341
0.485
0.431
2.220
4
Georgia
Appling
0.088
0.518
0.265
0.374
0.589
3.426
4
Georgia
Atkinson
0.083
0.576
0.216
0.336
0.140
-8.977
4
Georgia
Bacon
0.095
0.552
0.185
0.387
0.363
-3.063
4
Georgia
Baker
0.090
0.472
0.221
0.488
0.182
-4.248
4
Georgia
Baldwin
0.176
0.549
0.299
0.334
0.450
0.278
4
Georgia
Banks
0.230
0.627
0.246
0.316
0.411
-0.574
4
Georgia
Barrow
0.503
0.578
0.251
0.265
0.470
-0.357
4
Georgia
Bartow
0.583
0.274
0.388
0.387
0.452
0.256
4
Georgia
Ben Hill
0.109
0.536
0.217
0.360
0.373
-2.450
221
-------
EPA
Built
Natural
REGION
State
County
Risk
Governance
Environment
Environment
Society
CRSI
4
Georgia
Berrien
0.083
0.547
0.269
0.413
0.330
-0.901
4
Georgia
Bibb
0.531
0.554
0.287
0.253
0.502
-0.133
4
Georgia
Bleckley
0.100
0.518
0.267
0.456
0.449
2.342
4
Georgia
Brantley
0.113
0.517
0.278
0.389
0.352
-0.849
4
Georgia
Brooks
0.080
0.561
0.263
0.442
0.341
0.239
4
Georgia
Bryan
0.172
0.581
0.411
0.504
0.526
5.508
4
Georgia
Bulloch
0.185
0.506
0.388
0.442
0.458
2.847
4
Georgia
Burke
0.129
0.507
0.379
0.403
0.428
2.705
4
Georgia
Butts
0.179
0.583
0.236
0.359
0.483
0.243
4
Georgia
Calhoun
0.077
0.538
0.222
0.457
0.372
-0.242
4
Georgia
Camden
0.259
0.572
0.392
0.549
0.478
3.485
4
Georgia
Candler
0.069
0.468
0.175
0.429
0.434
-2.311
4
Georgia
Carroll
0.304
0.468
0.373
0.301
0.442
0.236
4
Georgia
Catoosa
0.585
0.476
0.221
0.335
0.417
-0.503
4
Georgia
Charlton
0.125
0.530
0.233
0.580
0.342
1.981
4
Georgia
Chatham
0.697
0.526
0.625
0.530
0.566
2.303
4
Georgia
Chattahoochee
0.167
0.469
0.260
0.559
0.238
0.031
4
Georgia
Chattooga
0.138
0.376
0.256
0.385
0.308
-2.811
4
Georgia
Cherokee
0.611
0.293
0.354
0.354
0.535
0.259
4
Georgia
Clarke
0.654
0.553
0.251
0.242
0.427
-0.527
4
Georgia
Clay
0.098
0.411
0.199
0.392
0.216
-6.863
4
Georgia
Clayton
0.477
0.594
0.238
0.202
0.450
-0.818
4
Georgia
Clinch
0.129
0.537
0.185
0.374
0.172
-5.511
4
Georgia
Cobb
0.558
0.396
0.380
0.210
0.514
-0.116
4
Georgia
Coffee
0.124
0.519
0.299
0.324
0.446
-0.089
4
Georgia
Colquitt
0.108
0.564
0.389
0.372
0.470
4.055
4
Georgia
Columbia
0.471
0.485
0.297
0.460
0.544
1.016
4
Georgia
Cook
0.155
0.607
0.223
0.456
0.416
0.903
4
Georgia
Coweta
0.400
0.593
0.387
0.339
0.555
1.367
222
-------
EPA
Built
Natural
REGION
State
County
Risk
Governance
Environment
Environment
Society
CRSI
4
Georgia
Crawford
0.121
0.562
0.267
0.380
0.259
-2.364
4
Georgia
Crisp
0.188
0.458
0.289
0.421
0.465
0.933
4
Georgia
Dade
0.275
0.250
0.234
0.325
0.348
-2.344
4
Georgia
Dawson
0.353
0.291
0.242
0.470
0.471
0.045
4
Georgia
Decatur
0.120
0.244
0.357
0.453
0.482
2.123
4
Georgia
DeKalb
0.483
0.545
0.441
0.170
0.493
0.224
4
Georgia
Dodge
0.088
0.574
0.308
0.380
0.391
1.087
4
Georgia
Dooly
0.090
0.516
0.242
0.458
0.349
-0.314
4
Georgia
Dougherty
0.269
0.351
0.342
0.436
0.491
1.120
4
Georgia
Douglas
0.481
0.553
0.259
0.254
0.502
-0.302
4
Georgia
Early
0.096
0.448
0.253
0.456
0.390
0.087
4
Georgia
Echols
0.084
0.533
0.197
0.403
0.094
-9.090
4
Georgia
Effingham
0.257
0.553
0.409
0.452
0.497
2.841
4
Georgia
Elbert
0.165
0.554
0.247
0.379
0.444
0.097
4
Georgia
Emanuel
0.091
0.564
0.301
0.377
0.403
0.913
4
Georgia
Evans
0.085
0.611
0.226
0.492
0.446
3.501
4
Georgia
Fannin
0.224
0.413
0.244
0.566
0.518
2.107
4
Georgia
Fayette
0.470
0.596
0.278
0.354
0.560
0.655
4
Georgia
Floyd
0.382
0.372
0.288
0.379
0.423
-0.257
4
Georgia
Forsyth
0.587
0.465
0.313
0.364
0.533
0.409
4
Georgia
Franklin
0.246
0.603
0.239
0.342
0.442
-0.200
4
Georgia
Fulton
0.551
0.530
0.709
0.210
0.490
1.632
4
Georgia
Gilmer
0.238
0.342
0.278
0.506
0.402
0.490
4
Georgia
Glascock
0.103
0.628
0.125
0.302
0.342
-6.047
4
Georgia
Glynn
0.352
0.466
0.366
0.497
0.552
2.130
4
Georgia
Gordon
0.535
0.347
0.326
0.370
0.376
-0.256
4
Georgia
Grady
0.089
0.494
0.307
0.473
0.389
2.717
4
Georgia
Greene
0.123
0.597
0.309
0.417
0.405
1.968
4
Georgia
Gwinnett
0.569
0.579
0.418
0.150
0.553
0.259
223
-------
EPA
Built
Natural
REGION
State
County
Risk
Governance
Environment
Environment
Society
CRSI
4
Georgia
Habersham
0.372
0.237
0.339
0.403
0.475
0.173
4
Georgia
Hall
0.606
0.578
0.380
0.314
0.536
0.687
4
Georgia
Hancock
0.085
0.549
0.234
0.378
0.414
-0.985
4
Georgia
Haralson
0.182
0.370
0.249
0.307
0.383
-2.531
4
Georgia
Harris
0.158
0.523
0.373
0.389
0.447
2.234
4
Georgia
Hart
0.225
0.578
0.275
0.436
0.401
0.762
4
Georgia
Heard
0.138
0.573
0.228
0.350
0.351
-1.959
4
Georgia
Henry
0.457
0.602
0.341
0.351
0.540
0.950
4
Georgia
Houston
0.504
0.565
0.329
0.387
0.470
0.627
4
Georgia
Irwin
0.086
0.533
0.207
0.370
0.437
-1.733
4
Georgia
Jackson
0.489
0.625
0.378
0.303
0.479
0.650
4
Georgia
Jasper
0.099
0.577
0.318
0.381
0.370
0.889
4
Georgia
Jeff Davis
0.084
0.522
0.185
0.352
0.360
-4.915
4
Georgia
Jefferson
0.107
0.562
0.293
0.380
0.441
1.340
4
Georgia
Jenkins
0.088
0.457
0.117
0.424
0.334
-6.054
4
Georgia
Johnson
0.133
0.592
0.269
0.392
0.308
-0.904
4
Georgia
Jones
0.113
0.575
0.340
0.382
0.481
3.233
4
Georgia
Lamar
0.215
0.579
0.228
0.397
0.422
-0.042
4
Georgia
Lanier
0.113
0.561
0.247
0.394
0.337
-1.343
4
Georgia
Laurens
0.147
0.526
0.383
0.386
0.584
4.376
4
Georgia
Lee
0.217
0.585
0.365
0.399
0.515
2.533
4
Georgia
Liberty
0.214
0.567
0.396
0.488
0.366
2.533
4
Georgia
Lincoln
0.120
0.584
0.233
0.545
0.360
2.095
4
Georgia
Long
0.109
0.264
0.268
0.473
0.302
-2.474
4
Georgia
Lowndes
0.334
0.567
0.443
0.388
0.516
2.141
4
Georgia
Lumpkin
0.251
0.250
0.273
0.558
0.393
0.487
4
Georgia
Macon
0.093
0.515
0.218
0.452
0.398
-0.124
4
Georgia
Madison
0.214
0.584
0.275
0.303
0.513
0.319
4
Georgia
Marion
0.101
0.516
0.204
0.438
0.189
-4.856
224
-------
EPA
Built
Natural
REGION
State
County
Risk
Governance
Environment
Environment
Society
CRSI
4
Georgia
McDuffie
0.148
0.575
0.236
0.439
0.548
2.430
4
Georgia
Mcintosh
0.143
0.219
0.328
0.529
0.453
1.982
4
Georgia
Meriwether
0.145
0.457
0.309
0.390
0.439
0.701
4
Georgia
Miller
0.173
0.507
0.204
0.466
0.486
0.873
4
Georgia
Mitchell
0.156
0.496
0.323
0.420
0.505
2.419
4
Georgia
Monroe
0.205
0.591
0.356
0.318
0.524
1.704
4
Georgia
Montgomery
0.072
0.591
0.237
0.371
0.283
-4.310
4
Georgia
Morgan
0.169
0.575
0.370
0.336
0.541
2.673
4
Georgia
Murray
0.248
0.242
0.295
0.432
0.231
-1.832
4
Georgia
Muscogee
0.472
0.453
0.281
0.365
0.490
0.127
4
Georgia
Newton
0.412
0.570
0.311
0.314
0.475
0.254
4
Georgia
Oconee
0.494
0.586
0.296
0.259
0.592
0.355
4
Georgia
Oglethorpe
0.145
0.585
0.264
0.408
0.311
-0.684
4
Georgia
Paulding
0.444
0.357
0.274
0.343
0.460
-0.370
4
Georgia
Peach
0.431
0.589
0.283
0.390
0.455
0.454
4
Georgia
Pickens
0.397
0.281
0.327
0.336
0.445
-0.368
4
Georgia
Pierce
0.098
0.596
0.290
0.420
0.421
2.331
4
Georgia
Pike
0.128
0.543
0.322
0.408
0.523
3.381
4
Georgia
Polk
0.378
0.290
0.291
0.342
0.416
-0.733
4
Georgia
Pulaski
0.116
0.508
0.232
0.493
0.465
2.161
4
Georgia
Putnam
0.145
0.566
0.270
0.440
0.404
1.111
4
Georgia
Quitman
0.086
0.399
0.171
0.397
0.227
-8.438
4
Georgia
Rabun
0.294
0.442
0.284
0.508
0.493
1.416
4
Georgia
Randolph
0.075
0.421
0.227
0.370
0.357
-4.825
4
Georgia
Richmond
0.591
0.481
0.341
0.355
0.556
0.599
4
Georgia
Rockdale
0.436
0.524
0.216
0.286
0.594
-0.076
4
Georgia
Schley
0.130
0.547
0.216
0.454
0.240
-2.223
4
Georgia
Screven
0.099
0.510
0.221
0.454
0.431
0.606
4
Georgia
Seminole
0.159
0.235
0.223
0.461
0.428
-1.242
225
-------
EPA
Built
Natural
REGION
State
County
Risk
Governance
Environment
Environment
Society
CRSI
4
Georgia
Spalding
0.391
0.564
0.238
0.359
0.466
-0.014
4
Georgia
Stephens
0.359
0.219
0.231
0.394
0.496
-0.617
4
Georgia
Stewart
0.094
0.443
0.227
0.403
0.292
-4.131
4
Georgia
Sumter
0.129
0.541
0.333
0.440
0.500
3.802
4
Georgia
Talbot
0.060
0.483
0.237
0.371
0.251
-8.001
4
Georgia
Taliaferro
0.106
0.659
0.057
0.383
0.112
-9.703
4
Georgia
Tattnall
0.065
0.547
0.278
0.451
0.363
1.689
4
Georgia
Taylor
0.109
0.511
0.269
0.400
0.292
-1.964
4
Georgia
Telfair
0.066
0.471
0.259
0.395
0.361
-2.377
4
Georgia
Terrell
0.060
0.560
0.187
0.398
0.332
-5.174
4
Georgia
Thomas
0.111
0.536
0.402
0.475
0.500
6.823
4
Georgia
Tift
0.257
0.534
0.315
0.390
0.515
1.338
4
Georgia
Toombs
0.089
0.554
0.289
0.407
0.420
1.679
4
Georgia
Towns
0.182
0.472
0.191
0.559
0.611
3.057
4
Georgia
Treutlen
0.080
0.522
0.165
0.334
0.459
-3.945
4
Georgia
Troup
0.290
0.501
0.366
0.412
0.448
1.273
4
Georgia
Turner
0.060
0.496
0.239
0.392
0.275
-6.063
4
Georgia
Twiggs
0.070
0.526
0.282
0.390
0.323
-1.854
4
Georgia
Union
0.168
0.204
0.231
0.586
0.543
1.951
4
Georgia
Upson
0.104
0.479
0.257
0.328
0.400
-2.328
4
Georgia
Walker
0.253
0.219
0.274
0.386
0.310
-1.942
4
Georgia
Walton
0.345
0.493
0.382
0.286
0.542
0.810
4
Georgia
Ware
0.157
0.506
0.302
0.459
0.524
2.964
4
Georgia
Warren
0.086
0.640
0.178
0.386
0.258
-4.994
4
Georgia
Washington
0.090
0.589
0.348
0.424
0.500
6.029
4
Georgia
Wayne
0.080
0.475
0.308
0.370
0.448
1.080
4
Georgia
Webster
0.100
0.517
0.130
0.441
0.189
-6.782
4
Georgia
Wheeler
0.066
0.532
0.193
0.366
0.374
-4.862
4
Georgia
White
0.265
0.493
0.219
0.426
0.527
0.606
226
-------
EPA
Built
Natural
REGION
State
County
Risk
Governance
Environment
Environment
Society
CRSI
4
Georgia
Whitfield
0.411
0.234
0.289
0.320
0.380
-1.128
4
Georgia
Wilcox
0.061
0.573
0.136
0.413
0.314
-7.121
4
Georgia
Wilkes
0.099
0.579
0.176
0.409
0.371
-2.184
4
Georgia
Wilkinson
0.114
0.590
0.263
0.395
0.509
2.308
4
Georgia
Worth
0.070
0.544
0.308
0.394
0.432
2.623
4
Kentucky
Adair
0.110
0.439
0.259
0.325
0.433
-2.023
4
Kentucky
Allen
0.132
0.318
0.265
0.354
0.259
-4.535
4
Kentucky
Anderson
0.170
0.703
0.275
0.326
0.556
1.929
4
Kentucky
Ballard
0.185
0.666
0.183
0.410
0.473
0.481
4
Kentucky
Barren
0.163
0.355
0.322
0.405
0.407
0.049
4
Kentucky
Bath
0.152
0.677
0.213
0.324
0.269
-2.831
4
Kentucky
Bell
0.199
0.454
0.277
0.451
0.224
-1.301
4
Kentucky
Boone
0.571
0.704
0.289
0.331
0.531
0.580
4
Kentucky
Bourbon
0.124
0.736
0.284
0.447
0.439
3.668
4
Kentucky
Boyd
0.304
0.665
0.236
0.396
0.399
0.166
4
Kentucky
Boyle
0.180
0.655
0.213
0.359
0.478
0.245
4
Kentucky
Bracken
0.095
0.724
0.199
0.360
0.306
-2.700
4
Kentucky
Breathitt
0.153
0.587
0.236
0.336
0.220
-3.430
4
Kentucky
Breckinridge
0.098
0.669
0.276
0.359
0.476
2.268
4
Kentucky
Bullitt
0.386
0.704
0.330
0.372
0.498
1.231
4
Kentucky
Butler
0.089
0.499
0.214
0.352
0.412
-2.896
4
Kentucky
Caldwell
0.148
0.596
0.232
0.390
0.474
0.706
4
Kentucky
Calloway
0.237
0.535
0.304
0.353
0.491
0.747
4
Kentucky
Campbell
0.389
0.719
0.174
0.329
0.473
-0.210
4
Kentucky
Carlisle
0.176
0.604
0.159
0.378
0.338
-2.156
4
Kentucky
Carroll
0.153
0.711
0.257
0.354
0.255
-1.510
4
Kentucky
Carter
0.119
0.649
0.272
0.354
0.275
-1.812
4
Kentucky
Casey
0.084
0.465
0.208
0.369
0.319
-5.279
4
Kentucky
Christian
0.288
0.534
0.363
0.449
0.383
1.230
227
-------
EPA
Built
Natural
REGION
State
County
Risk
Governance
Environment
Environment
Society
CRSI
4
Kentucky
Clark
0.172
0.631
0.308
0.350
0.424
0.833
4
Kentucky
Clay
0.163
0.545
0.224
0.424
0.218
-2.370
4
Kentucky
Clinton
0.134
0.437
0.199
0.391
0.348
-2.892
4
Kentucky
Crittenden
0.142
0.568
0.133
0.378
0.336
-3.422
4
Kentucky
Cumberland
0.128
0.437
0.135
0.350
0.353
-5.068
4
Kentucky
Daviess
0.397
0.678
0.359
0.341
0.595
1.618
4
Kentucky
Edmonson
0.099
0.437
0.199
0.443
0.266
-4.267
4
Kentucky
Elliott
0.122
0.622
0.165
0.311
0.240
-5.744
4
Kentucky
Estill
0.179
0.578
0.228
0.353
0.287
-2.137
4
Kentucky
Fayette
0.500
0.652
0.369
0.293
0.481
0.600
4
Kentucky
Fleming
0.163
0.678
0.225
0.319
0.369
-1.310
4
Kentucky
Floyd
0.170
0.621
0.282
0.243
0.385
-1.620
4
Kentucky
Franklin
0.243
0.656
0.226
0.335
0.568
0.814
4
Kentucky
Fulton
0.201
0.564
0.107
0.426
0.206
-3.479
4
Kentucky
Gallatin
0.175
0.720
0.200
0.353
0.324
-1.379
4
Kentucky
Garrard
0.115
0.632
0.253
0.359
0.428
0.242
4
Kentucky
Grant
0.103
0.677
0.299
0.370
0.481
3.164
4
Kentucky
Graves
0.200
0.531
0.288
0.373
0.447
0.473
4
Kentucky
Grayson
0.242
0.541
0.238
0.327
0.401
-0.966
4
Kentucky
Green
0.116
0.486
0.184
0.351
0.431
-2.692
4
Kentucky
Greenup
0.253
0.694
0.346
0.345
0.358
0.659
4
Kentucky
Hancock
0.204
0.731
0.222
0.363
0.475
0.726
4
Kentucky
Hardin
0.283
0.607
0.428
0.376
0.466
2.081
4
Kentucky
Harlan
0.198
0.423
0.266
0.326
0.291
-2.487
4
Kentucky
Harrison
0.135
0.698
0.236
0.394
0.479
1.744
4
Kentucky
Hart
0.138
0.461
0.215
0.332
0.264
-4.580
4
Kentucky
Henderson
0.252
0.693
0.399
0.412
0.432
2.453
4
Kentucky
Henry
0.110
0.715
0.219
0.369
0.443
0.695
4
Kentucky
Hickman
0.159
0.534
0.081
0.398
0.448
-2.468
228
-------
EPA
Built
Natural
REGION
State
County
Risk
Governance
Environment
Environment
Society
CRSI
4
Kentucky
Hopkins
0.198
0.593
0.368
0.350
0.494
2.046
4
Kentucky
Jackson
0.145
0.520
0.276
0.418
0.277
-1.200
4
Kentucky
Jefferson
0.675
0.665
0.507
0.230
0.466
0.735
4
Kentucky
Jessamine
0.344
0.632
0.283
0.364
0.436
0.398
4
Kentucky
Johnson
0.232
0.636
0.234
0.293
0.308
-1.786
4
Kentucky
Kenton
0.667
0.711
0.210
0.311
0.474
-0.051
4
Kentucky
Knott
0.123
0.590
0.287
0.346
0.215
-3.020
4
Kentucky
Knox
0.131
0.453
0.222
0.407
0.316
-2.553
4
Kentucky
Larue
0.097
0.581
0.212
0.364
0.513
0.497
4
Kentucky
Laurel
0.313
0.498
0.308
0.410
0.408
0.410
4
Kentucky
Lawrence
0.144
0.656
0.263
0.339
0.240
-2.350
4
Kentucky
Lee
0.174
0.595
0.232
0.356
0.233
-2.594
4
Kentucky
Leslie
0.160
0.552
0.243
0.412
0.164
-2.904
4
Kentucky
Letcher
0.143
0.528
0.278
0.306
0.204
-4.049
4
Kentucky
Lewis
0.138
0.631
0.166
0.328
0.217
-5.040
4
Kentucky
Lincoln
0.121
0.516
0.306
0.352
0.511
1.671
4
Kentucky
Livingston
0.237
0.610
0.189
0.352
0.440
-0.662
4
Kentucky
Logan
0.150
0.545
0.310
0.424
0.423
1.608
4
Kentucky
Lyon
0.188
0.511
0.159
0.532
0.496
1.153
4
Kentucky
Madison
0.373
0.598
0.394
0.379
0.458
1.287
4
Kentucky
Magoffin
0.300
0.596
0.178
0.294
0.214
-2.623
4
Kentucky
Marion
0.109
0.619
0.239
0.407
0.511
2.357
4
Kentucky
Marshall
0.369
0.557
0.278
0.349
0.502
0.384
4
Kentucky
Martin
0.168
0.614
0.238
0.310
0.063
-5.153
4
Kentucky
Mason
0.151
0.686
0.243
0.334
0.451
0.271
4
Kentucky
McCracken
0.615
0.605
0.290
0.373
0.517
0.507
4
Kentucky
McCreary
0.161
0.390
0.213
0.496
0.165
-3.092
4
Kentucky
McLean
0.171
0.666
0.190
0.379
0.407
-0.557
4
Kentucky
Meade
0.114
0.662
0.301
0.393
0.343
0.897
229
-------
EPA
Built
Natural
REGION
State
County
Risk
Governance
Environment
Environment
Society
CRSI
4
Kentucky
Menifee
0.204
0.612
0.261
0.412
0.386
0.383
4
Kentucky
Mercer
0.180
0.649
0.281
0.337
0.465
0.773
4
Kentucky
Metcalfe
0.067
0.403
0.143
0.375
0.266
-11.520
4
Kentucky
Monroe
0.160
0.359
0.147
0.350
0.244
-5.658
4
Kentucky
Montgomery
0.194
0.677
0.278
0.388
0.393
0.736
4
Kentucky
Morgan
0.339
0.642
0.253
0.304
0.381
-0.551
4
Kentucky
Muhlenberg
0.129
0.543
0.293
0.374
0.508
1.880
4
Kentucky
Nelson
0.162
0.687
0.346
0.404
0.566
4.397
4
Kentucky
Nicholas
0.106
0.688
0.152
0.378
0.390
-2.031
4
Kentucky
Ohio
0.137
0.668
0.299
0.376
0.486
2.522
4
Kentucky
Oldham
0.410
0.747
0.288
0.346
0.538
1.032
4
Kentucky
Owen
0.096
0.639
0.270
0.352
0.410
0.312
4
Kentucky
Owsley
0.227
0.589
0.202
0.377
0.254
-1.961
4
Kentucky
Pendleton
0.119
0.703
0.253
0.384
0.520
2.882
4
Kentucky
Perry
0.195
0.574
0.306
0.299
0.279
-1.687
4
Kentucky
Pike
0.151
0.613
0.478
0.271
0.320
1.218
4
Kentucky
Powell
0.200
0.590
0.272
0.408
0.298
-0.481
4
Kentucky
Pulaski
0.329
0.411
0.307
0.469
0.427
0.671
4
Kentucky
Robertson
0.128
0.671
0.184
0.353
0.195
-4.572
4
Kentucky
Rockcastle
0.200
0.531
0.306
0.375
0.386
0.120
4
Kentucky
Rowan
0.225
0.646
0.321
0.408
0.444
1.676
4
Kentucky
Russell
0.147
0.418
0.192
0.454
0.478
-0.114
4
Kentucky
Scott
0.252
0.708
0.338
0.382
0.453
1.736
4
Kentucky
Shelby
0.215
0.689
0.376
0.411
0.519
3.348
4
Kentucky
Simpson
0.145
0.398
0.240
0.396
0.386
-1.605
4
Kentucky
Spencer
0.087
0.730
0.278
0.349
0.568
5.104
4
Kentucky
Taylor
0.151
0.501
0.215
0.472
0.469
1.029
4
Kentucky
Todd
0.125
0.556
0.230
0.443
0.340
-0.607
4
Kentucky
Trigg
0.134
0.499
0.230
0.564
0.452
2.900
230
-------
EPA
Built
Natural
REGION
State County
Risk
Governance
Environment
Environment
Society
CRSI
4
Kentucky
Trimble
0.148
0.707
0.126
0.342
0.395
-2.303
4
Kentucky
Union
0.138
0.672
0.258
0.416
0.421
1.518
4
Kentucky
Warren
0.297
0.365
0.408
0.324
0.466
0.551
4
Kentucky
Washington
0.120
0.705
0.230
0.354
0.605
3.137
4
Kentucky
Wayne
0.121
0.334
0.188
0.352
0.332
-5.349
4
Kentucky
Webster
0.282
0.692
0.235
0.383
0.475
0.690
4
Kentucky
Whitley
0.210
0.481
0.236
0.384
0.374
-0.994
4
Kentucky
Wolfe
0.189
0.551
0.159
0.384
0.130
-4.377
4
Kentucky
Woodford
0.195
0.651
0.303
0.450
0.471
2.502
4
Mississipp
Adams
0.190
0.602
0.300
0.467
0.467
2.446
4
Mississipp
Alcorn
0.122
0.450
0.317
0.327
0.440
-0.311
4
Mississipp
Amite
0.204
0.508
0.358
0.449
0.364
1.385
4
Mississipp
Attala
0.229
0.602
0.299
0.386
0.353
0.174
4
Mississipp
Benton
0.332
0.600
0.233
0.445
0.164
-1.086
4
Mississipp
Bolivar
0.249
0.681
0.445
0.456
0.365
2.833
4
Mississipp
Calhoun
0.114
0.607
0.326
0.363
0.301
-0.375
4
Mississipp
Carroll
0.268
0.606
0.298
0.367
0.304
-0.378
4
Mississipp
Chickasaw
0.176
0.605
0.339
0.528
0.347
2.767
4
Mississipp
Choctaw
0.310
0.626
0.282
0.418
0.392
0.573
4
Mississipp
Claiborne
0.281
0.505
0.228
0.403
0.386
-0.499
4
Mississipp
Clarke
0.251
0.505
0.347
0.415
0.384
0.815
4
Mississipp
Clay
0.244
0.580
0.341
0.490
0.306
1.212
4
Mississipp
Coahoma
0.204
0.664
0.346
0.463
0.345
1.961
4
Mississipp
Copiah
0.191
0.463
0.355
0.410
0.399
1.055
4
Mississipp
Covington
0.280
0.485
0.334
0.423
0.441
1.006
4
Mississipp
DeSoto
0.638
0.677
0.433
0.423
0.449
1.182
4
Mississipp
Forrest
0.586
0.428
0.394
0.516
0.465
1.127
4
Mississipp
Franklin
0.260
0.586
0.219
0.459
0.377
0.151
4
Mississipp
George
0.178
0.358
0.328
0.533
0.473
2.649
231
-------
EPA
REGION
State
County
Risk
Governance
Built
Environment
Natural
Environment
Society
CRSI
4
Mississippi
Greene
0.208
0.426
0.324
0.481
0.317
0.448
4
Mississippi
Grenada
0.212
0.616
0.296
0.400
0.376
0.602
4
Mississippi
Hancock
0.429
0.388
0.430
0.451
0.336
0.708
4
Mississippi
Harrison
0.754
0.485
0.462
0.453
0.428
0.891
4
Mississippi
Hinds
0.392
0.521
0.554
0.400
0.519
2.550
4
Mississippi
Holmes
0.214
0.630
0.327
0.444
0.293
0.793
4
Mississippi
Humphreys
0.281
0.666
0.229
0.550
0.242
0.366
4
Mississippi
Issaquena
0.419
0.678
0.232
0.459
0.103
-0.891
4
Mississippi
Itawamba
0.141
0.507
0.262
0.400
0.370
-0.599
4
Mississippi
Jackson
0.427
0.437
0.473
0.599
0.391
2.199
4
Mississippi
Jasper
0.262
0.547
0.365
0.493
0.343
1.541
4
Mississippi
Jefferson
0.298
0.511
0.248
0.409
0.248
-1.128
4
Mississippi
Jefferson Davis
0.241
0.520
0.286
0.438
0.292
-0.279
4
Mississippi
Jones
0.260
0.496
0.415
0.455
0.435
2.216
4
Mississippi
Kemper
0.310
0.555
0.382
0.403
0.313
0.575
4
Mississippi
Lafayette
0.220
0.543
0.448
0.479
0.429
3.446
4
Mississippi
Lamar
0.513
0.501
0.424
0.480
0.442
1.324
4
Mississippi
Lauderdale
0.293
0.538
0.460
0.414
0.482
2.476
4
Mississippi
Lawrence
0.320
0.529
0.315
0.426
0.427
0.790
4
Mississippi
Leake
0.239
0.544
0.302
0.405
0.402
0.555
4
Mississippi
Lee
0.521
0.606
0.401
0.388
0.489
1.132
4
Mississippi
Leflore
0.185
0.645
0.329
0.445
0.380
1.955
4
Mississippi
Lincoln
0.197
0.519
0.383
0.378
0.511
2.387
4
Mississippi
Lowndes
0.391
0.535
0.398
0.505
0.497
2.082
4
Mississippi
Madison
0.478
0.555
0.510
0.420
0.539
2.109
4
Mississippi
Marion
0.271
0.458
0.364
0.445
0.428
1.339
4
Mississippi
Marshall
0.186
0.588
0.386
0.394
0.369
1.655
4
Mississippi
Monroe
0.157
0.546
0.415
0.483
0.429
4.337
4
Mississippi
Montgomery
0.225
0.596
0.235
0.333
0.521
0.290
232
-------
EPA
REGION
State
County
Risk
Governance
Built
Environment
Natural
Environment
Society
CRSI
4
Mississippi
Neshoba
0.283
0.559
0.363
0.419
0.366
0.977
4
Mississippi
Newton
0.263
0.588
0.306
0.463
0.475
1.805
4
Mississippi
Noxubee
0.263
0.550
0.251
0.488
0.331
0.256
4
Mississippi
Oktibbeha
0.264
0.565
0.420
0.447
0.363
1.877
4
Mississippi
Panola
0.191
0.592
0.371
0.419
0.396
2.035
4
Mississippi
Pearl River
0.384
0.366
0.427
0.472
0.405
1.202
4
Mississippi
Perry
0.211
0.412
0.318
0.655
0.307
2.234
4
Mississippi
Pike
0.237
0.510
0.346
0.439
0.471
1.841
4
Mississippi
Pontotoc
0.234
0.603
0.315
0.387
0.445
1.136
4
Mississippi
Prentiss
0.142
0.481
0.292
0.333
0.419
-0.692
4
Mississippi
Quitman
0.193
0.667
0.180
0.476
0.272
-0.760
4
Mississippi
Rankin
0.652
0.531
0.566
0.393
0.544
1.645
4
Mississippi
Scott
0.336
0.542
0.385
0.580
0.421
2.442
4
Mississippi
Sharkey
0.301
0.652
0.090
0.572
0.138
-1.440
4
Mississippi
Simpson
0.227
0.503
0.311
0.388
0.530
1.446
4
Mississippi
Smith
0.262
0.552
0.331
0.464
0.398
1.361
4
Mississippi
Stone
0.208
0.398
0.361
0.563
0.425
2.780
4
Mississippi
Sunflower
0.164
0.685
0.357
0.458
0.339
2.613
4
Mississippi
Tallahatchie
0.118
0.640
0.256
0.430
0.318
0.028
4
Mississippi
Tate
0.172
0.622
0.284
0.411
0.401
1.035
4
Mississippi
Tippah
0.144
0.591
0.327
0.330
0.348
-0.291
4
Mississippi
Tishomingo
0.125
0.332
0.278
0.471
0.342
-0.776
4
Mississippi
Tunica
0.386
0.649
0.243
0.478
0.209
-0.306
4
Mississippi
Union
0.165
0.585
0.338
0.342
0.424
0.958
4
Mississippi
Walthall
0.206
0.465
0.291
0.438
0.405
0.543
4
Mississippi
Warren
0.335
0.634
0.360
0.444
0.468
1.806
4
Mississippi
Washington
0.209
0.639
0.324
0.447
0.400
1.858
4
Mississippi
Wayne
0.196
0.426
0.312
0.445
0.304
-0.258
4
Mississippi
Webster
0.261
0.637
0.271
0.360
0.316
-0.524
233
-------
EPA
Built
Natural
REGION
State
County
Risk
Governance
Environment
Environment
Society
CRSI
4
Mississippi
Wilkinson
0.210
0.571
0.309
0.413
0.397
0.914
4
Mississippi
Winston
0.384
0.543
0.313
0.449
0.435
0.879
4
Mississippi
Yalobusha
0.095
0.610
0.260
0.460
0.360
1.472
4
Mississippi
Yazoo
0.241
0.600
0.333
0.490
0.296
1.140
4
North Carolina
Alamance
0.376
0.562
0.435
0.336
0.471
1.258
4
North Carolina
Alexander
0.200
0.517
0.335
0.345
0.466
0.856
4
North Carolina
Alleghany
0.123
0.375
0.341
0.362
0.384
-0.577
4
North Carolina
Anson
0.109
0.485
0.356
0.381
0.385
1.165
4
North Carolina
Ashe
0.120
0.257
0.378
0.354
0.480
0.654
4
North Carolina
Avery
0.174
0.259
0.393
0.434
0.481
1.810
4
North Carolina
Beaufort
0.381
0.597
0.459
0.519
0.513
2.897
4
North Carolina
Bertie
0.156
0.624
0.382
0.508
0.545
6.159
4
North Carolina
Bladen
0.132
0.471
0.482
0.481
0.367
4.991
4
North Carolina
Brunswick
0.356
0.541
0.589
0.491
0.418
3.199
4
North Carolina
Buncombe
0.506
0.251
0.502
0.332
0.551
0.964
4
North Carolina
Burke
0.286
0.253
0.444
0.441
0.383
0.953
4
North Carolina
Cabarrus
0.649
0.655
0.363
0.322
0.508
0.633
4
North Carolina
Caldwell
0.284
0.384
0.355
0.405
0.466
0.841
4
North Carolina
Camden
0.226
0.630
0.335
0.556
0.437
3.310
4
North Carolina
Carteret
0.307
0.625
0.440
0.697
0.481
4.747
4
North Carolina
Caswell
0.127
0.570
0.380
0.394
0.347
1.840
4
North Carolina
Catawba
0.506
0.458
0.519
0.370
0.509
1.484
4
North Carolina
Chatham
0.226
0.526
0.535
0.396
0.498
4.002
4
North Carolina
Cherokee
0.227
0.228
0.367
0.456
0.540
1.702
4
North Carolina
Chowan
0.143
0.594
0.287
0.582
0.463
4.907
4
North Carolina
Clay
0.169
0.212
0.325
0.522
0.556
2.681
4
North Carolina
Cleveland
0.291
0.432
0.429
0.369
0.491
1.542
4
North Carolina
Columbus
0.124
0.447
0.546
0.488
0.529
9.187
4
North Carolina
Craven
0.383
0.577
0.512
0.616
0.436
3.427
234
-------
EPA
Built
Natural
REGION
State
County
Risk
Governance
Environment
Environment
Society
CRSI
4
North Carolina
Cumberland
0.585
0.523
0.471
0.389
0.443
1.037
4
North Carolina
Currituck
0.201
0.637
0.397
0.614
0.421
5.123
4
North Carolina
Dare
0.389
0.546
0.444
0.742
0.553
4.219
4
North Carolina
Davidson
0.317
0.599
0.457
0.366
0.466
1.989
4
North Carolina
Davie
0.191
0.609
0.387
0.353
0.536
2.935
4
North Carolina
Duplin
0.105
0.550
0.520
0.383
0.339
5.170
4
North Carolina
Durham
0.585
0.540
0.394
0.328
0.451
0.483
4
North Carolina
Edgecombe
0.150
0.539
0.375
0.438
0.425
2.980
4
North Carolina
Forsyth
0.553
0.566
0.396
0.250
0.456
0.236
4
North Carolina
Franklin
0.210
0.549
0.493
0.448
0.443
3.983
4
North Carolina
Gaston
0.686
0.550
0.534
0.316
0.455
0.937
4
North Carolina
Gates
0.129
0.631
0.318
0.532
0.486
5.737
4
North Carolina
Graham
0.179
0.238
0.328
0.513
0.426
1.176
4
North Carolina
Granville
0.217
0.553
0.416
0.421
0.443
2.599
4
North Carolina
Greene
0.222
0.538
0.332
0.435
0.402
1.277
4
North Carolina
Guilford
0.570
0.572
0.514
0.360
0.470
1.320
4
North Carolina
Halifax
0.129
0.562
0.483
0.439
0.396
5.473
4
North Carolina
Harnett
0.254
0.551
0.443
0.418
0.418
2.292
4
North Carolina
Haywood
0.202
0.234
0.440
0.446
0.559
2.978
4
North Carolina
Henderson
0.474
0.250
0.403
0.301
0.520
0.179
4
North Carolina
Hertford
0.153
0.672
0.361
0.426
0.443
3.606
4
North Carolina
Hoke
0.201
0.528
0.370
0.440
0.389
1.774
4
North Carolina
Hyde
0.441
0.497
0.305
0.662
0.587
2.472
4
North Carolina
Iredell
0.582
0.609
0.471
0.325
0.500
1.109
4
North Carolina
Jackson
0.238
0.288
0.457
0.414
0.520
2.304
4
North Carolina
Johnston
0.406
0.536
0.573
0.412
0.491
2.562
4
North Carolina
Jones
0.191
0.606
0.341
0.605
0.365
3.757
4
North Carolina
Lee
0.432
0.501
0.365
0.349
0.476
0.617
4
North Carolina
Lenoir
0.184
0.530
0.363
0.427
0.465
2.497
235
-------
EPA
Built
Natural
REGION
State
County
Risk
Governance
Environment
Environment
Society
CRSI
4
North Carolina
Lincoln
0.463
0.481
0.374
0.406
0.470
0.863
4
North Carolina
Macon
0.309
0.231
0.407
0.472
0.591
2.070
4
North Carolina
Madison
0.189
0.325
0.405
0.330
0.523
1.285
4
North Carolina
Martin
0.154
0.568
0.434
0.504
0.478
5.856
4
North Carolina
McDowell
0.262
0.280
0.358
0.445
0.360
0.140
4
North Carolina
Mecklenburg
0.693
0.594
0.661
0.231
0.449
1.162
4
North Carolina
Mitchell
0.148
0.226
0.306
0.378
0.522
-0.034
4
North Carolina
Montgomery
0.134
0.522
0.420
0.379
0.355
2.021
4
North Carolina
Moore
0.199
0.503
0.501
0.318
0.483
2.856
4
North Carolina
Nash
0.222
0.551
0.501
0.433
0.474
3.977
4
North Carolina
New Hanover
0.588
0.553
0.389
0.537
0.531
1.619
4
North Carolina
Northampton
0.117
0.597
0.488
0.430
0.389
6.156
4
North Carolina
Onslow
0.390
0.632
0.459
0.555
0.364
2.395
4
North Carolina
Orange
0.184
0.560
0.365
0.442
0.442
2.646
4
North Carolina
Pamlico
0.290
0.684
0.308
0.631
0.502
3.587
4
North Carolina
Pasquotank
0.289
0.636
0.309
0.529
0.448
2.216
4
North Carolina
Pender
0.126
0.567
0.471
0.499
0.494
8.065
4
North Carolina
Perquimans
0.156
0.625
0.315
0.586
0.388
4.270
4
North Carolina
Person
0.132
0.561
0.360
0.385
0.514
3.601
4
North Carolina
Pitt
0.362
0.549
0.514
0.433
0.496
2.644
4
North Carolina
Polk
0.191
0.458
0.370
0.284
0.556
1.232
4
North Carolina
Randolph
0.187
0.564
0.539
0.391
0.435
4.376
4
North Carolina
Richmond
0.195
0.484
0.419
0.366
0.354
0.995
4
North Carolina
Robeson
0.176
0.497
0.561
0.445
0.429
5.291
4
North Carolina
Rockingham
0.224
0.515
0.448
0.383
0.438
2.268
4
North Carolina
Rowan
0.326
0.616
0.436
0.351
0.457
1.655
4
North Carolina
Rutherford
0.195
0.314
0.493
0.376
0.453
2.253
4
North Carolina
Sampson
0.126
0.555
0.489
0.401
0.417
5.259
4
North Carolina
Scotland
0.172
0.529
0.398
0.405
0.386
1.992
236
-------
EPA
Built
Natural
REGION
State
County
Risk
Governance
Environment
Environment
Society
CRSI
4
North Carolina
Stanly
0.204
0.596
0.380
0.329
0.545
2.394
4
North Carolina
Stokes
0.148
0.473
0.415
0.296
0.485
1.761
4
North Carolina
Surry
0.217
0.531
0.399
0.335
0.447
1.370
4
North Carolina
Swain
0.195
0.209
0.417
0.542
0.544
3.698
4
North Carolina
Transylvania
0.221
0.279
0.397
0.431
0.609
2.670
4
North Carolina
Tyrrell
0.565
0.574
0.247
0.701
0.417
1.374
4
North Carolina
Union
0.445
0.636
0.484
0.347
0.518
1.787
4
North Carolina
Vance
0.141
0.513
0.336
0.411
0.490
2.707
4
North Carolina
Wake
0.714
0.540
0.736
0.326
0.509
1.827
4
North Carolina
Warren
0.136
0.572
0.407
0.432
0.347
2.952
4
North Carolina
Washington
0.288
0.544
0.268
0.604
0.399
1.830
4
North Carolina
Watauga
0.165
0.256
0.385
0.353
0.450
0.199
4
North Carolina
Wayne
0.206
0.530
0.473
0.385
0.432
2.847
4
North Carolina
Wilkes
0.150
0.483
0.379
0.341
0.401
0.803
4
North Carolina
Wilson
0.308
0.540
0.380
0.451
0.484
1.978
4
North Carolina
Yadkin
0.112
0.605
0.404
0.370
0.469
4.592
4
North Carolina
Yancey
0.159
0.225
0.281
0.380
0.499
-0.685
4
South Carolina
Abbeville
0.153
0.568
0.335
0.400
0.464
2.332
4
South Carolina
Aiken
0.371
0.522
0.509
0.403
0.491
2.254
4
South Carolina
Allendale
0.192
0.469
0.182
0.390
0.268
-2.910
4
South Carolina
Anderson
0.536
0.567
0.506
0.441
0.506
1.847
4
South Carolina
Bamberg
0.115
0.516
0.275
0.370
0.333
-1.619
4
South Carolina
Barnwell
0.153
0.514
0.278
0.487
0.412
1.693
4
South Carolina
Beaufort
0.432
0.536
0.372
0.502
0.493
1.691
4
South Carolina
Berkeley
0.445
0.588
0.565
0.656
0.428
3.477
4
South Carolina
Calhoun
0.142
0.553
0.338
0.361
0.511
2.412
4
South Carolina
Charleston
0.586
0.574
0.553
0.611
0.508
2.643
4
South Carolina
Cherokee
0.295
0.534
0.376
0.260
0.355
-0.436
4
South Carolina
Chester
0.166
0.570
0.365
0.342
0.370
0.671
237
-------
EPA
Built
Natural
REGION
State
County
Risk
Governance
Environment
Environment
Society
CRSI
4
South Carolina
Chesterfield
0.148
0.459
0.373
0.383
0.343
0.449
4
South Carolina
Clarendon
0.142
0.588
0.401
0.457
0.380
3.698
4
South Carolina
Colleton
0.150
0.530
0.419
0.488
0.496
5.458
4
South Carolina
Darlington
0.172
0.428
0.402
0.400
0.409
1.665
4
South Carolina
Dillon
0.098
0.498
0.341
0.399
0.338
0.521
4
South Carolina
Dorchester
0.458
0.575
0.409
0.489
0.489
1.807
4
South Carolina
Edgefield
0.157
0.549
0.282
0.510
0.371
1.778
4
South Carolina
Fairfield
0.109
0.504
0.376
0.357
0.469
2.790
4
South Carolina
Florence
0.323
0.500
0.466
0.456
0.501
2.626
4
South Carolina
Georgetown
0.157
0.531
0.458
0.524
0.432
5.631
4
South Carolina
Greenville
0.785
0.506
0.575
0.315
0.513
1.046
4
South Carolina
Greenwood
0.226
0.496
0.325
0.370
0.504
1.163
4
South Carolina
Hampton
0.105
0.522
0.287
0.420
0.411
1.189
4
South Carolina
Horry
0.409
0.470
0.531
0.495
0.433
2.330
4
South Carolina
Jasper
0.159
0.489
0.331
0.427
0.526
2.865
4
South Carolina
Kershaw
0.207
0.454
0.434
0.341
0.526
2.331
4
South Carolina
Lancaster
0.271
0.526
0.361
0.344
0.436
0.703
4
South Carolina
Laurens
0.200
0.544
0.398
0.333
0.398
1.034
4
South Carolina
Lee
0.105
0.514
0.298
0.412
0.322
-0.455
4
South Carolina
Lexington
0.615
0.574
0.476
0.346
0.561
1.291
4
South Carolina
Marion
0.097
0.452
0.354
0.498
0.444
5.097
4
South Carolina
Marlboro
0.102
0.459
0.296
0.419
0.295
-1.373
4
South Carolina
McCormick
0.169
0.516
0.278
0.551
0.343
1.682
4
South Carolina
Newberry
0.172
0.543
0.326
0.437
0.549
3.290
4
South Carolina
Oconee
0.360
0.229
0.411
0.449
0.459
0.919
4
South Carolina
Orangeburg
0.152
0.577
0.511
0.404
0.456
5.443
4
South Carolina
Pickens
0.420
0.475
0.427
0.380
0.491
1.218
4
South Carolina
Richland
0.694
0.503
0.502
0.360
0.515
1.066
4
South Carolina
Saluda
0.107
0.500
0.286
0.439
0.329
-0.116
238
-------
EPA
Built
Natural
REGION
State
County
Risk
Governance
Environment
Environment
Society
CRSI
4
South Carolina
Spartanburg
0.747
0.588
0.521
0.302
0.483
0.893
4
South Carolina
Sumter
0.287
0.533
0.374
0.409
0.486
1.710
4
South Carolina
Union
0.140
0.571
0.363
0.358
0.353
0.788
4
South Carolina
Williamsburg
0.103
0.537
0.365
0.512
0.459
6.506
4
South Carolina
York
0.698
0.589
0.451
0.299
0.457
0.605
4
Tennessee
Anderson
0.392
0.368
0.308
0.319
0.437
-0.428
4
Tennessee
Bedford
0.228
0.345
0.358
0.283
0.422
-0.765
4
Tennessee
Benton
0.148
0.453
0.212
0.409
0.398
-1.320
4
Tennessee
Bledsoe
0.161
0.351
0.258
0.324
0.352
-2.938
4
Tennessee
Blount
0.626
0.243
0.346
0.408
0.498
0.237
4
Tennessee
Bradley
0.520
0.410
0.293
0.295
0.459
-0.350
4
Tennessee
Campbell
0.177
0.325
0.291
0.399
0.345
-1.357
4
Tennessee
Cannon
0.104
0.367
0.239
0.281
0.377
-5.421
4
Tennessee
Carroll
0.174
0.563
0.324
0.413
0.420
1.521
4
Tennessee
Carter
0.174
0.228
0.277
0.451
0.418
-0.587
4
Tennessee
Cheatham
0.141
0.568
0.306
0.406
0.485
2.370
4
Tennessee
Chester
0.137
0.536
0.244
0.419
0.413
0.202
4
Tennessee
Claiborne
0.164
0.320
0.305
0.382
0.319
-1.847
4
Tennessee
Clay
0.130
0.409
0.225
0.432
0.297
-2.675
4
Tennessee
Cocke
0.209
0.275
0.293
0.400
0.350
-1.309
4
Tennessee
Coffee
0.312
0.342
0.307
0.409
0.523
0.619
4
Tennessee
Crockett
0.139
0.592
0.312
0.359
0.409
0.783
4
Tennessee
Cumberland
0.256
0.325
0.344
0.353
0.412
-0.314
4
Tennessee
Davidson
0.595
0.524
0.524
0.255
0.447
0.716
4
Tennessee
Decatur
0.150
0.465
0.155
0.436
0.331
-2.661
4
Tennessee
DeKalb
0.155
0.296
0.282
0.360
0.375
-2.129
4
Tennessee
Dickson
0.136
0.446
0.358
0.379
0.512
2.487
4
Tennessee
Dyer
0.288
0.549
0.365
0.414
0.478
1.656
4
Tennessee
Fayette
0.234
0.713
0.424
0.358
0.457
2.648
239
-------
EPA
Built
Natural
REGION
State
County
Risk
Governance
Environment
Environment
Society
CRSI
4
Tennessee
Fentress
0.230
0.396
0.240
0.370
0.330
-1.768
4
Tennessee
Franklin
0.207
0.437
0.355
0.386
0.436
0.913
4
Tennessee
Gibson
0.359
0.575
0.356
0.363
0.485
1.020
4
Tennessee
Giles
0.124
0.449
0.337
0.337
0.416
-0.049
4
Tennessee
Grainger
0.158
0.380
0.228
0.357
0.314
-3.268
4
Tennessee
Greene
0.223
0.382
0.341
0.363
0.411
-0.045
4
Tennessee
Grundy
0.151
0.376
0.303
0.354
0.327
-2.023
4
Tennessee
Hamblen
0.471
0.455
0.244
0.336
0.406
-0.580
4
Tennessee
Hamilton
0.832
0.478
0.517
0.276
0.470
0.552
4
Tennessee
Hancock
0.133
0.263
0.259
0.317
0.318
-4.830
4
Tennessee
Hardeman
0.165
0.653
0.286
0.373
0.302
-0.442
4
Tennessee
Hardin
0.150
0.374
0.274
0.460
0.311
-1.043
4
Tennessee
Hawkins
0.169
0.385
0.317
0.319
0.362
-1.630
4
Tennessee
Haywood
0.149
0.606
0.310
0.404
0.329
0.474
4
Tennessee
Henderson
0.201
0.504
0.284
0.399
0.434
0.462
4
Tennessee
Henry
0.165
0.525
0.334
0.402
0.425
1.438
4
Tennessee
Hickman
0.150
0.305
0.234
0.410
0.460
-1.071
4
Tennessee
Houston
0.203
0.539
0.170
0.373
0.425
-1.263
4
Tennessee
Humphreys
0.157
0.393
0.283
0.441
0.513
1.515
4
Tennessee
Jackson
0.168
0.512
0.265
0.363
0.210
-2.840
4
Tennessee
Jefferson
0.252
0.465
0.291
0.360
0.442
-0.029
4
Tennessee
Johnson
0.115
0.209
0.260
0.431
0.356
-2.925
4
Tennessee
Knox
0.657
0.371
0.521
0.245
0.501
0.525
4
Tennessee
Lake
0.229
0.626
0.194
0.501
0.262
-0.490
4
Tennessee
Lauderdale
0.184
0.613
0.314
0.480
0.300
1.191
4
Tennessee
Lawrence
0.201
0.357
0.322
0.384
0.453
0.242
4
Tennessee
Lewis
0.130
0.285
0.218
0.349
0.299
-5.323
4
Tennessee
Lincoln
0.162
0.436
0.271
0.319
0.462
-0.926
4
Tennessee
Loudon
0.471
0.250
0.338
0.324
0.460
-0.317
240
-------
EPA
Built
Natural
REGION
State
County
Risk
Governance
Environment
Environment
Society
CRSI
4
Tennessee
Macon
0.150
0.344
0.243
0.331
0.390
-2.864
4
Tennessee
Madison
0.478
0.574
0.412
0.432
0.501
1.509
4
Tennessee
Marion
0.254
0.500
0.302
0.351
0.375
-0.364
4
Tennessee
Marshall
0.201
0.385
0.304
0.302
0.437
-1.004
4
Tennessee
Maury
0.264
0.323
0.366
0.347
0.500
0.511
4
Tennessee
McMinn
0.368
0.439
0.356
0.349
0.442
0.312
4
Tennessee
McNairy
0.149
0.576
0.346
0.415
0.377
1.734
4
Tennessee
Meigs
0.213
0.463
0.150
0.372
0.286
-3.107
4
Tennessee
Monroe
0.384
0.235
0.319
0.469
0.379
-0.051
4
Tennessee
Montgomery
0.429
0.546
0.359
0.425
0.418
0.856
4
Tennessee
Moore
0.112
0.409
0.237
0.333
0.518
-1.100
4
Tennessee
Morgan
0.154
0.408
0.286
0.347
0.431
-0.846
4
Tennessee
Obion
0.229
0.534
0.336
0.461
0.449
1.938
4
Tennessee
Overton
0.145
0.487
0.253
0.326
0.425
-1.401
4
Tennessee
Perry
0.112
0.389
0.203
0.345
0.381
-4.244
4
Tennessee
Pickett
0.157
0.413
0.102
0.406
0.413
-3.211
4
Tennessee
Polk
0.241
0.366
0.316
0.503
0.389
0.869
4
Tennessee
Putnam
0.215
0.398
0.375
0.309
0.470
0.380
4
Tennessee
Rhea
0.291
0.435
0.295
0.359
0.354
-0.690
4
Tennessee
Roane
0.402
0.389
0.306
0.295
0.408
-0.666
4
Tennessee
Robertson
0.254
0.562
0.355
0.392
0.492
1.731
4
Tennessee
Rutherford
0.597
0.402
0.376
0.309
0.483
0.187
4
Tennessee
Scott
0.182
0.360
0.268
0.359
0.361
-1.840
4
Tennessee
Sequatchie
0.198
0.360
0.268
0.327
0.319
-2.499
4
Tennessee
Sevier
0.509
0.238
0.385
0.401
0.424
0.161
4
Tennessee
Shelby
0.990
0.667
0.595
0.312
0.443
0.899
4
Tennessee
Smith
0.135
0.450
0.258
0.325
0.422
-1.735
4
Tennessee
Stewart
0.147
0.520
0.250
0.532
0.338
1.088
4
Tennessee
Sullivan
0.443
0.331
0.427
0.330
0.448
0.364
241
-------
EPA
REGION
State
County
Risk
Governance
Built
Environment
Natural
Environment
Society
CRSI
4
Tennessee
Sumner
0.543
0.496
0.401
0.315
0.515
0.645
4
Tennessee
Tipton
0.365
0.667
0.354
0.377
0.413
0.949
4
Tennessee
Trousdale
0.118
0.509
0.157
0.356
0.452
-2.638
4
Tennessee
Unicoi
0.159
0.204
0.258
0.458
0.403
-1.206
4
Tennessee
Union
0.162
0.367
0.204
0.364
0.366
-2.937
4
Tennessee
Van Buren
0.175
0.305
0.251
0.364
0.210
-4.107
4
Tennessee
Warren
0.116
0.270
0.303
0.296
0.392
-3.677
4
Tennessee
Washington
0.540
0.349
0.286
0.338
0.482
-0.206
4
Tennessee
Wayne
0.195
0.353
0.259
0.379
0.374
-1.494
4
Tennessee
Weakley
0.124
0.567
0.321
0.390
0.502
2.958
4
Tennessee
White
0.144
0.283
0.282
0.348
0.406
-2.170
4
Tennessee
Williamson
0.501
0.440
0.407
0.362
0.547
0.982
4
Tennessee
Wilson
0.414
0.440
0.423
0.337
0.566
1.225
242
-------
EPA
Built
Natural
REGION
State
County
Risk
Governance
Environment
Environment
Society
CRSI
National Average
0.229
0.588
0.393
0.414
0.516
4.213
Regional Average
0.222
0.713
0.407
0.434
0.572
6.021
5
Illinois
Adams
0.181
0.642
0.519
0.478
0.592
7.538
5
Illinois
Alexander
0.279
0.539
0.175
0.575
0.176
-0.842
5
Illinois
Bond
0.126
0.691
0.345
0.464
0.470
5.332
5
Illinois
Boone
0.471
0.707
0.378
0.458
0.453
1.551
5
Illinois
Brown
0.095
0.657
0.249
0.439
0.415
2.251
5
Illinois
Bureau
0.172
0.736
0.531
0.560
0.604
9.940
5
Illinois
Calhoun
0.133
0.729
0.320
0.428
0.526
5.031
5
Illinois
Carroll
0.224
0.710
0.336
0.478
0.511
3.506
5
Illinois
Cass
0.117
0.715
0.283
0.493
0.449
4.833
5
Illinois
Champaign
0.240
0.663
0.655
0.516
0.467
6.641
5
Illinois
Christian
0.182
0.695
0.433
0.569
0.610
7.966
5
Illinois
Clark
0.141
0.655
0.353
0.466
0.446
4.369
5
Illinois
Clay
0.126
0.646
0.344
0.448
0.509
5.282
5
Illinois
Clinton
0.189
0.720
0.475
0.441
0.641
7.017
5
Illinois
Coles
0.146
0.632
0.394
0.486
0.413
4.702
5
Illinois
Cook
0.687
0.667
0.828
0.199
0.498
1.953
5
Illinois
Crawford
0.186
0.651
0.282
0.487
0.450
2.589
5
Illinois
Cumberland
0.152
0.693
0.375
0.484
0.487
5.526
5
Illinois
De Witt
0.349
0.681
0.327
0.530
0.597
2.947
5
Illinois
DeKalb
0.273
0.702
0.511
0.433
0.515
4.171
5
Illinois
Douglas
0.222
0.694
0.372
0.489
0.580
4.624
5
Illinois
DuPage
0.698
0.681
0.636
0.181
0.629
1.517
5
Illinois
Edgar
0.102
0.673
0.317
0.526
0.470
7.179
5
Illinois
Edwards
0.144
0.633
0.245
0.418
0.566
2.948
5
Illinois
Effingham
0.127
0.690
0.462
0.498
0.676
11.601
5
Illinois
Fayette
0.117
0.683
0.373
0.485
0.526
7.678
5
Illinois
Ford
0.084
0.711
0.358
0.499
0.585
12.357
243
-------
EPA
Built
Natural
REGION
State
County
Risk
Governance
Environment
Environment
Society
CRSI
5
Illinois
Franklin
0.148
0.662
0.399
0.447
0.477
5.138
5
Illinois
Fulton
0.149
0.699
0.477
0.539
0.543
9.159
5
Illinois
Gallatin
0.185
0.664
0.277
0.598
0.326
2.770
5
Illinois
Greene
0.157
0.674
0.293
0.454
0.525
3.841
5
Illinois
Grundy
0.457
0.724
0.511
0.646
0.521
3.710
5
Illinois
Hamilton
0.118
0.641
0.285
0.469
0.511
4.714
5
Illinois
Hancock
0.177
0.721
0.417
0.546
0.558
7.173
5
Illinois
Hardin
0.161
0.625
0.228
0.776
0.141
2.595
5
Illinois
Henderson
0.263
0.724
0.348
0.533
0.535
3.856
5
Illinois
Henry
0.303
0.701
0.457
0.543
0.511
4.151
5
Illinois
Iroquois
0.105
0.742
0.527
0.425
0.544
12.023
5
Illinois
Jackson
0.215
0.568
0.468
0.549
0.424
4.645
5
Illinois
Jasper
0.101
0.669
0.317
0.516
0.571
8.943
5
Illinois
Jefferson
0.151
0.707
0.392
0.404
0.482
4.593
5
Illinois
Jersey
0.113
0.668
0.350
0.478
0.545
7.517
5
Illinois
Jo Daviess
0.216
0.693
0.404
0.455
0.617
5.111
5
Illinois
Johnson
0.198
0.639
0.372
0.660
0.372
4.975
5
Illinois
Kane
0.633
0.701
0.595
0.393
0.553
2.115
5
Illinois
Kankakee
0.282
0.689
0.486
0.515
0.500
4.374
5
Illinois
Kendall
0.354
0.721
0.447
0.475
0.594
3.521
5
Illinois
Knox
0.175
0.685
0.406
0.589
0.448
6.258
5
Illinois
Lake
0.621
0.691
0.613
0.458
0.540
2.429
5
Illinois
LaSalle
0.301
0.692
0.686
0.511
0.566
6.274
5
Illinois
Lawrence
0.162
0.632
0.279
0.499
0.490
3.507
5
Illinois
Lee
0.128
0.718
0.523
0.537
0.538
11.635
5
Illinois
Livingston
0.130
0.714
0.558
0.548
0.593
13.212
5
Illinois
Logan
0.213
0.733
0.369
0.500
0.531
4.640
5
Illinois
Macon
0.205
0.652
0.468
0.403
0.472
3.978
5
Illinois
Macoupin
0.132
0.698
0.511
0.460
0.619
10.658
244
-------
EPA
Built
Natural
REGION
State
County
Risk
Governance
Environment
Environment
Society
CRSI
5
Illinois
Madison
0.696
0.651
0.660
0.391
0.497
1.938
5
Illinois
Marion
0.141
0.706
0.404
0.475
0.501
6.634
5
Illinois
Marshall
0.183
0.751
0.351
0.467
0.634
5.910
5
Illinois
Mason
0.150
0.689
0.342
0.487
0.505
5.247
5
Illinois
Massac
0.216
0.596
0.296
0.494
0.436
2.102
5
Illinois
McDonough
0.153
0.675
0.434
0.508
0.430
6.046
5
Illinois
McHenry
0.612
0.714
0.574
0.432
0.604
2.438
5
Illinois
McLean
0.203
0.697
0.677
0.484
0.469
7.935
5
Illinois
Menard
0.126
0.739
0.319
0.492
0.513
6.417
5
Illinois
Mercer
0.287
0.733
0.351
0.517
0.684
4.468
5
Illinois
Monroe
0.242
0.653
0.422
0.408
0.563
3.666
5
Illinois
Montgomery
0.159
0.704
0.449
0.520
0.646
9.086
5
Illinois
Morgan
0.285
0.681
0.407
0.429
0.559
3.231
5
Illinois
Moultrie
0.098
0.693
0.334
0.551
0.513
9.586
5
Illinois
Ogle
0.274
0.718
0.580
0.523
0.531
5.814
5
Illinois
Peoria
0.437
0.671
0.424
0.508
0.522
2.463
5
Illinois
Perry
0.142
0.630
0.354
0.446
0.607
6.081
5
Illinois
Piatt
0.130
0.734
0.400
0.445
0.558
7.598
5
Illinois
Pike
0.149
0.723
0.404
0.503
0.538
7.343
5
Illinois
Pope
0.225
0.570
0.272
0.796
0.529
5.750
5
Illinois
Pulaski
0.222
0.653
0.212
0.471
0.239
-0.693
5
Illinois
Putnam
0.508
0.764
0.346
0.422
0.484
1.329
5
Illinois
Randolph
0.199
0.619
0.434
0.414
0.650
5.379
5
Illinois
Richland
0.116
0.620
0.290
0.465
0.458
3.769
5
Illinois
Rock Island
0.333
0.661
0.411
0.456
0.489
2.513
5
Illinois
Saline
0.202
0.589
0.343
0.556
0.416
3.408
5
Illinois
Sangamon
0.286
0.670
0.618
0.452
0.555
5.297
5
Illinois
Schuyler
0.110
0.653
0.286
0.479
0.536
5.881
5
Illinois
Scott
0.134
0.763
0.267
0.487
0.461
4.319
245
-------
EPA
Built
Natural
REGION
State
County
Risk
Governance
Environment
Environment
Society
CRSI
5
Illinois
Shelby
0.156
0.683
0.390
0.531
0.570
7.383
5
Illinois
St. Clair
0.728
0.646
0.506
0.364
0.464
1.100
5
Illinois
Stark
0.204
0.754
0.283
0.577
0.590
5.309
5
Illinois
Stephenson
0.159
0.681
0.355
0.460
0.557
5.346
5
Illinois
Tazewell
0.534
0.666
0.549
0.507
0.567
2.793
5
Illinois
Union
0.227
0.549
0.356
0.652
0.604
5.669
5
Illinois
Vermilion
0.222
0.659
0.464
0.571
0.451
5.324
5
Illinois
Wabash
0.151
0.616
0.290
0.459
0.395
1.942
5
Illinois
Warren
0.139
0.670
0.321
0.581
0.517
6.981
5
Illinois
Washington
0.184
0.739
0.402
0.433
0.513
4.800
5
Illinois
Wayne
0.151
0.654
0.342
0.409
0.537
4.140
5
Illinois
White
0.147
0.645
0.322
0.416
0.414
2.308
5
Illinois
Whiteside
0.153
0.697
0.420
0.545
0.471
7.049
5
Illinois
Will
0.779
0.699
0.776
0.484
0.576
2.679
5
Illinois
Williamson
0.328
0.639
0.461
0.545
0.557
3.968
5
Illinois
Winnebago
0.631
0.673
0.541
0.379
0.481
1.569
5
Illinois
Woodford
0.242
0.715
0.449
0.442
0.641
5.179
5
Indiana
Adams
0.118
0.686
0.315
0.427
0.545
5.458
5
Indiana
Allen
0.587
0.683
0.531
0.381
0.554
1.917
5
Indiana
Bartholomew
0.340
0.684
0.322
0.466
0.552
2.269
5
Indiana
Benton
0.069
0.741
0.341
0.506
0.609
15.746
5
Indiana
Blackford
0.111
0.671
0.228
0.402
0.559
3.306
5
Indiana
Boone
0.318
0.706
0.414
0.506
0.565
3.656
5
Indiana
Brown
0.095
0.664
0.348
0.572
0.585
12.036
5
Indiana
Carroll
0.146
0.690
0.353
0.507
0.592
7.117
5
Indiana
Cass
0.108
0.692
0.304
0.499
0.518
6.924
5
Indiana
Clark
0.572
0.656
0.338
0.400
0.484
0.860
5
Indiana
Clay
0.149
0.693
0.307
0.477
0.646
6.392
5
Indiana
Clinton
0.130
0.686
0.326
0.500
0.599
7.377
246
-------
EPA
Built
Natural
REGION
State
County
Risk
Governance
Environment
Environment
Society
CRSI
5
Indiana
Crawford
0.191
0.658
0.191
0.442
0.525
1.501
5
Indiana
Daviess
0.160
0.636
0.369
0.459
0.671
6.651
5
Indiana
Dearborn
0.237
0.687
0.330
0.432
0.606
3.460
5
Indiana
Decatur
0.176
0.683
0.353
0.490
0.655
6.322
5
Indiana
DeKalb
0.197
0.707
0.404
0.458
0.553
5.055
5
Indiana
Delaware
0.216
0.675
0.316
0.391
0.485
1.994
5
Indiana
Dubois
0.241
0.640
0.450
0.405
0.691
4.952
5
Indiana
Elkhart
0.572
0.695
0.430
0.408
0.485
1.396
5
Indiana
Fayette
0.106
0.669
0.240
0.451
0.501
3.798
5
Indiana
Floyd
0.539
0.647
0.286
0.309
0.538
0.414
5
Indiana
Fountain
0.080
0.696
0.294
0.497
0.500
8.536
5
Indiana
Franklin
0.126
0.659
0.311
0.478
0.693
8.136
5
Indiana
Fulton
0.089
0.670
0.329
0.501
0.653
11.916
5
Indiana
Gibson
0.228
0.657
0.384
0.421
0.582
3.760
5
Indiana
Grant
0.200
0.677
0.377
0.378
0.506
3.029
5
Indiana
Greene
0.115
0.635
0.379
0.494
0.558
8.293
5
Indiana
Hamilton
0.453
0.702
0.454
0.323
0.577
1.850
5
Indiana
Hancock
0.406
0.707
0.376
0.408
0.616
2.274
5
Indiana
Harrison
0.135
0.645
0.353
0.405
0.537
4.706
5
Indiana
Hendricks
0.477
0.707
0.475
0.430
0.583
2.465
5
Indiana
Henry
0.146
0.683
0.324
0.427
0.562
4.798
5
Indiana
Howard
0.268
0.677
0.249
0.430
0.525
1.608
5
Indiana
Huntington
0.121
0.728
0.396
0.452
0.611
9.081
5
Indiana
Jackson
0.189
0.659
0.355
0.479
0.601
5.097
5
Indiana
Jasper
0.102
0.690
0.479
0.484
0.612
13.370
5
Indiana
Jay
0.118
0.697
0.302
0.378
0.529
3.990
5
Indiana
Jefferson
0.133
0.673
0.335
0.526
0.539
6.920
5
Indiana
Jennings
0.113
0.661
0.337
0.476
0.558
7.302
5
Indiana
Johnson
0.513
0.664
0.387
0.475
0.592
1.998
247
-------
EPA
Built
Natural
REGION
State
County
Risk
Governance
Environment
Environment
Society
CRSI
5
Indiana
Knox
0.205
0.627
0.383
0.449
0.570
4.262
5
Indiana
Kosciusko
0.251
0.700
0.468
0.499
0.548
4.986
5
Indiana
LaG range
0.097
0.683
0.411
0.459
0.515
9.466
5
Indiana
Lake
0.777
0.677
0.602
0.265
0.467
1.093
5
Indiana
La Porte
0.296
0.669
0.462
0.356
0.504
2.585
5
Indiana
Lawrence
0.131
0.625
0.367
0.463
0.606
7.094
5
Indiana
Madison
0.218
0.685
0.346
0.398
0.524
2.817
5
Indiana
Marion
0.723
0.675
0.667
0.210
0.444
1.165
5
Indiana
Marshall
0.164
0.695
0.440
0.497
0.615
7.887
5
Indiana
Martin
0.129
0.662
0.300
0.571
0.576
7.689
5
Indiana
Miami
0.115
0.703
0.283
0.485
0.563
6.559
5
Indiana
Monroe
0.361
0.626
0.417
0.542
0.439
2.576
5
Indiana
Montgomery
0.109
0.716
0.392
0.483
0.630
10.901
5
Indiana
Morgan
0.213
0.701
0.465
0.493
0.566
5.913
5
Indiana
Newton
0.070
0.734
0.372
0.508
0.556
15.162
5
Indiana
Noble
0.163
0.671
0.377
0.503
0.480
5.225
5
Indiana
Ohio
0.124
0.693
0.241
0.446
0.620
5.212
5
Indiana
Orange
0.126
0.628
0.287
0.464
0.654
6.479
5
Indiana
Owen
0.181
0.681
0.331
0.489
0.554
4.712
5
Indiana
Parke
0.080
0.665
0.326
0.495
0.573
10.966
5
Indiana
Perry
0.163
0.651
0.269
0.526
0.600
5.145
5
Indiana
Pike
0.132
0.654
0.327
0.468
0.571
6.044
5
Indiana
Porter
0.615
0.685
0.544
0.377
0.564
1.900
5
Indiana
Posey
0.198
0.661
0.343
0.434
0.602
4.160
5
Indiana
Pulaski
0.074
0.685
0.342
0.470
0.647
13.871
5
Indiana
Putnam
0.116
0.700
0.406
0.514
0.618
10.881
5
Indiana
Randolph
0.093
0.695
0.311
0.465
0.575
8.540
5
Indiana
Ripley
0.157
0.705
0.348
0.527
0.780
9.322
5
Indiana
Rush
0.143
0.696
0.296
0.478
0.743
7.823
248
-------
EPA
Built
Natural
REGION
State
County
Risk
Governance
Environment
Environment
Society
CRSI
5
Indiana
Scott
0.161
0.651
0.233
0.452
0.497
2.225
5
Indiana
Shelby
0.186
0.698
0.323
0.487
0.599
5.019
5
Indiana
Spencer
0.167
0.699
0.375
0.455
0.655
6.609
5
Indiana
St. Joseph
0.513
0.667
0.480
0.403
0.473
1.690
5
Indiana
Starke
0.105
0.697
0.344
0.418
0.469
5.374
5
Indiana
Steuben
0.158
0.702
0.404
0.443
0.599
6.599
5
Indiana
Sullivan
0.112
0.665
0.391
0.460
0.574
8.657
5
Indiana
Switzerland
0.118
0.677
0.295
0.435
0.460
3.681
5
Indiana
Tippecanoe
0.344
0.672
0.469
0.457
0.501
2.989
5
Indiana
Tipton
0.079
0.669
0.318
0.454
0.696
12.605
5
Indiana
Union
0.094
0.705
0.244
0.491
0.465
5.025
5
Indiana
Vanderburgh
0.752
0.620
0.278
0.355
0.549
0.417
5
Indiana
Vermillion
0.181
0.695
0.288
0.467
0.537
3.664
5
Indiana
Vigo
0.332
0.628
0.357
0.442
0.525
2.106
5
Indiana
Wabash
0.099
0.702
0.365
0.475
0.667
11.576
5
Indiana
Warren
0.102
0.723
0.310
0.546
0.541
9.382
5
Indiana
Warrick
0.324
0.656
0.404
0.423
0.632
3.127
5
Indiana
Washington
0.119
0.658
0.313
0.433
0.571
5.739
5
Indiana
Wayne
0.184
0.687
0.319
0.422
0.534
3.376
5
Indiana
Wells
0.129
0.703
0.308
0.457
0.611
6.569
5
Indiana
White
0.120
0.697
0.447
0.502
0.587
10.591
5
Indiana
Whitley
0.183
0.713
0.324
0.455
0.573
4.462
5
Michigan
Alcona
0.086
0.762
0.347
0.457
0.474
8.627
5
Michigan
Alger
0.104
0.738
0.301
0.473
0.602
8.491
5
Michigan
Allegan
0.135
0.706
0.510
0.372
0.555
7.900
5
Michigan
Alpena
0.131
0.713
0.354
0.485
0.516
6.576
5
Michigan
Antrim
0.129
0.753
0.387
0.395
0.499
5.681
5
Michigan
Arenac
0.066
0.780
0.369
0.389
0.492
10.429
5
Michigan
Baraga
0.255
0.603
0.380
0.480
0.555
3.462
249
-------
EPA
Built
Natural
REGION
State
County
Risk
Governance
Environment
Environment
Society
CRSI
5
Michigan
Barry
0.068
0.715
0.447
0.372
0.487
11.412
5
Michigan
Bay
0.186
0.667
0.408
0.345
0.506
3.191
5
Michigan
Benzie
0.106
0.775
0.365
0.366
0.566
7.163
5
Michigan
Berrien
0.216
0.710
0.513
0.363
0.375
3.270
5
Michigan
Branch
0.133
0.741
0.373
0.417
0.414
4.302
5
Michigan
Calhoun
0.250
0.702
0.384
0.385
0.425
2.021
5
Michigan
Cass
0.187
0.710
0.363
0.426
0.358
2.283
5
Michigan
Charlevoix
0.138
0.766
0.384
0.333
0.594
5.597
5
Michigan
Cheboygan
0.095
0.746
0.447
0.446
0.561
11.897
5
Michigan
Chippewa
0.098
0.723
0.490
0.381
0.480
9.310
5
Michigan
Clare
0.082
0.702
0.368
0.401
0.427
6.166
5
Michigan
Clinton
0.165
0.708
0.431
0.390
0.600
6.042
5
Michigan
Crawford
0.133
0.720
0.344
0.660
0.442
8.478
5
Michigan
Delta
0.105
0.694
0.445
0.440
0.603
10.912
5
Michigan
Dickinson
0.203
0.679
0.418
0.546
0.691
7.359
5
Michigan
Eaton
0.234
0.724
0.431
0.395
0.533
3.805
5
Michigan
Emmet
0.141
0.738
0.410
0.523
0.649
9.880
5
Michigan
Genesee
0.511
0.669
0.438
0.331
0.415
0.908
5
Michigan
Gladwin
0.065
0.743
0.378
0.485
0.421
11.933
5
Michigan
Gogebic
0.125
0.706
0.270
0.516
0.532
5.877
5
Michigan
Grand Traverse
0.205
0.699
0.407
0.393
0.603
4.544
5
Michigan
Gratiot
0.114
0.747
0.406
0.386
0.466
6.120
5
Michigan
Hillsdale
0.084
0.745
0.311
0.370
0.441
4.204
5
Michigan
Houghton
0.164
0.647
0.335
0.423
0.533
3.817
5
Michigan
Huron
0.082
0.765
0.513
0.335
0.594
13.684
5
Michigan
Ingham
0.328
0.695
0.442
0.332
0.480
1.921
5
Michigan
Ionia
0.132
0.744
0.371
0.398
0.481
4.968
5
Michigan
Iosco
0.131
0.708
0.349
0.450
0.497
5.485
5
Michigan
Iron
0.169
0.744
0.330
0.510
0.653
6.863
250
-------
EPA
Built
Natural
REGION
State
County
Risk
Governance
Environment
Environment
Society
CRSI
5
Michigan
Isabella
0.114
0.741
0.446
0.522
0.416
9.013
5
Michigan
Jackson
0.161
0.696
0.367
0.376
0.484
3.406
5
Michigan
Kalamazoo
0.410
0.670
0.423
0.369
0.473
1.543
5
Michigan
Kalkaska
0.073
0.773
0.384
0.558
0.425
13.664
5
Michigan
Kent
0.450
0.668
0.622
0.319
0.523
2.524
5
Michigan
Keweenaw
0.222
0.770
0.311
0.451
0.279
1.155
5
Michigan
Lake
0.109
0.769
0.341
0.553
0.444
8.340
5
Michigan
Lapeer
0.136
0.720
0.383
0.407
0.513
5.527
5
Michigan
Leelanau
0.243
0.751
0.384
0.371
0.622
3.738
5
Michigan
Lenawee
0.168
0.733
0.424
0.398
0.497
4.868
5
Michigan
Livingston
0.374
0.699
0.446
0.358
0.617
2.614
5
Michigan
Luce
0.226
0.709
0.280
0.500
0.389
1.973
5
Michigan
Mackinac
0.103
0.736
0.423
0.394
0.513
8.191
5
Michigan
Macomb
0.587
0.663
0.520
0.267
0.501
1.173
5
Michigan
Manistee
0.144
0.746
0.328
0.418
0.522
4.671
5
Michigan
Marquette
0.276
0.570
0.609
0.448
0.593
5.297
5
Michigan
Mason
0.130
0.745
0.347
0.424
0.520
5.637
5
Michigan
Mecosta
0.070
0.730
0.398
0.417
0.431
9.476
5
Michigan
Menominee
0.099
0.752
0.376
0.375
0.425
5.183
5
Michigan
Midland
0.137
0.692
0.356
0.389
0.527
4.614
5
Michigan
Missaukee
0.073
0.801
0.368
0.502
0.471
12.836
5
Michigan
Monroe
0.361
0.690
0.455
0.235
0.488
1.219
5
Michigan
Montcalm
0.086
0.738
0.432
0.382
0.434
7.948
5
Michigan
Montmorency
0.092
0.735
0.365
0.524
0.414
8.744
5
Michigan
Muskegon
0.194
0.686
0.356
0.382
0.430
2.156
5
Michigan
Newaygo
0.075
0.750
0.417
0.501
0.445
12.995
5
Michigan
Oakland
0.557
0.679
0.723
0.256
0.523
2.269
5
Michigan
Oceana
0.046
0.735
0.390
0.368
0.511
14.809
5
Michigan
Ogemaw
0.150
0.735
0.363
0.493
0.452
5.363
251
-------
EPA
Built
Natural
REGION
State
County
Risk
Governance
Environment
Environment
Society
CRSI
5
Michigan
Ontonagon
0.237
0.767
0.270
0.476
0.582
3.373
5
Michigan
Osceola
0.073
0.789
0.409
0.424
0.448
11.000
5
Michigan
Oscoda
0.123
0.724
0.351
0.563
0.317
5.412
5
Michigan
Otsego
0.110
0.724
0.379
0.499
0.562
9.630
5
Michigan
Ottawa
0.247
0.684
0.584
0.314
0.588
4.728
5
Michigan
Presque Isle
0.129
0.780
0.355
0.430
0.436
4.953
5
Michigan
Roscommon
0.090
0.684
0.388
0.567
0.395
9.825
5
Michigan
Saginaw
0.180
0.675
0.510
0.396
0.439
4.838
5
Michigan
Sanilac
0.083
0.765
0.467
0.477
0.451
12.926
5
Michigan
Schoolcraft
0.121
0.746
0.350
0.535
0.496
8.019
5
Michigan
Shiawassee
0.120
0.711
0.373
0.380
0.515
5.445
5
Michigan
St. Clair
0.188
0.708
0.541
0.403
0.480
5.738
5
Michigan
St. Joseph
0.162
0.717
0.403
0.416
0.376
3.420
5
Michigan
Tuscola
0.093
0.738
0.490
0.415
0.509
11.398
5
Michigan
Van Buren
0.162
0.705
0.425
0.301
0.453
2.888
5
Michigan
Washtenaw
0.405
0.693
0.546
0.360
0.477
2.399
5
Michigan
Wayne
0.608
0.644
0.651
0.160
0.373
0.833
5
Michigan
Wexford
0.135
0.754
0.414
0.486
0.565
8.642
5
Minnesota
Aitkin
0.126
0.797
0.352
0.508
0.696
10.685
5
Minnesota
Anoka
0.655
0.706
0.358
0.299
0.673
1.092
5
Minnesota
Becker
0.154
0.817
0.455
0.567
0.769
12.528
5
Minnesota
Beltrami
0.172
0.764
0.515
0.616
0.654
11.243
5
Minnesota
Benton
0.208
0.758
0.343
0.323
0.745
4.465
5
Minnesota
Big Stone
0.096
0.825
0.187
0.472
0.649
7.903
5
Minnesota
Blue Earth
0.285
0.745
0.462
0.427
0.743
5.213
5
Minnesota
Brown
0.142
0.746
0.358
0.375
0.822
8.693
5
Minnesota
Carlton
0.190
0.797
0.444
0.473
0.673
7.715
5
Minnesota
Carver
0.480
0.741
0.429
0.403
0.688
2.551
5
Minnesota
Cass
0.134
0.827
0.468
0.599
0.631
13.375
252
-------
EPA
Built
Natural
REGION
State
County
Risk
Governance
Environment
Environment
Society
CRSI
5
Minnesota
Chippewa
0.207
0.792
0.273
0.456
0.815
6.002
5
Minnesota
Chisago
0.201
0.769
0.421
0.421
0.724
6.729
5
Minnesota
Clay
0.157
0.755
0.531
0.424
0.661
9.658
5
Minnesota
Clearwater
0.140
0.884
0.238
0.601
0.622
8.657
5
Minnesota
Cook
0.120
0.700
0.318
0.434
0.913
11.740
5
Minnesota
Cottonwood
0.273
0.791
0.339
0.354
0.799
4.159
5
Minnesota
Crow Wing
0.175
0.759
0.420
0.406
0.703
7.179
5
Minnesota
Dakota
0.672
0.693
0.552
0.397
0.632
2.057
5
Minnesota
Dodge
0.143
0.815
0.353
0.449
0.823
10.325
5
Minnesota
Douglas
0.199
0.761
0.376
0.445
0.836
7.549
5
Minnesota
Faribault
0.147
0.838
0.400
0.473
0.830
11.502
5
Minnesota
Fillmore
0.131
0.878
0.362
0.588
0.832
14.638
5
Minnesota
Freeborn
0.327
0.772
0.353
0.458
0.695
3.680
5
Minnesota
Goodhue
0.169
0.772
0.424
0.460
0.753
8.956
5
Minnesota
Grant
0.104
0.892
0.361
0.391
0.886
14.946
5
Minnesota
Flennepin
0.748
0.688
0.692
0.254
0.607
1.801
5
Minnesota
Flouston
0.169
0.783
0.384
0.599
0.723
10.059
5
Minnesota
Hubbard
0.120
0.811
0.298
0.427
0.729
9.077
5
Minnesota
Isanti
0.160
0.761
0.347
0.363
0.672
5.609
5
Minnesota
Itasca
0.106
0.762
0.534
0.630
0.692
19.703
5
Minnesota
Jackson
0.185
0.823
0.442
0.508
0.826
10.112
5
Minnesota
Kanabec
0.140
0.842
0.260
0.375
0.733
6.406
5
Minnesota
Kandiyohi
0.204
0.779
0.462
0.429
0.739
7.463
5
Minnesota
Kittson
0.136
0.837
0.386
0.402
0.724
9.368
5
Minnesota
Koochiching
0.099
0.698
0.429
0.635
0.628
16.543
5
Minnesota
Lac qui Parle
0.233
0.854
0.272
0.458
0.847
5.887
5
Minnesota
Lake
0.121
0.719
0.359
0.653
0.699
13.677
5
Minnesota
Lake of the Woods
0.162
0.699
0.249
0.628
0.848
9.685
5
Minnesota
Le Sueur
0.241
0.810
0.317
0.415
0.754
4.789
253
-------
EPA
Built
Natural
REGION
State
County
Risk
Governance
Environment
Environment
Society
CRSI
5
Minnesota
Lincoln
0.076
0.866
0.351
0.376
0.935
20.574
5
Minnesota
Lyon
0.116
0.774
0.455
0.324
0.846
12.459
5
Minnesota
Mahnomen
0.236
0.865
0.319
0.778
0.380
5.812
5
Minnesota
Marshall
0.136
0.872
0.486
0.449
0.710
12.279
5
Minnesota
Martin
0.144
0.777
0.392
0.469
0.765
10.259
5
Minnesota
McLeod
0.177
0.775
0.373
0.441
0.788
7.933
5
Minnesota
Meeker
0.229
0.816
0.334
0.409
0.776
5.390
5
Minnesota
Mille Lacs
0.144
0.826
0.351
0.404
0.776
8.861
5
Minnesota
Morrison
0.111
0.834
0.408
0.414
0.794
13.504
5
Minnesota
Mower
0.117
0.768
0.457
0.446
0.640
11.412
5
Minnesota
Murray
0.142
0.862
0.345
0.389
0.810
9.355
5
Minnesota
Nicollet
0.232
0.771
0.341
0.407
0.682
4.404
5
Minnesota
Nobles
0.231
0.792
0.460
0.410
0.681
5.896
5
Minnesota
Norman
0.220
0.863
0.340
0.413
0.744
5.657
5
Minnesota
Olmsted
0.423
0.717
0.505
0.414
0.667
3.290
5
Minnesota
Otter Tail
0.195
0.818
0.562
0.366
0.754
8.714
5
Minnesota
Pennington
0.129
0.768
0.348
0.427
0.738
9.230
5
Minnesota
Pine
0.126
0.823
0.362
0.514
0.705
11.400
5
Minnesota
Pipestone
0.078
0.761
0.391
0.396
0.786
16.883
5
Minnesota
Polk
0.153
0.803
0.599
0.414
0.704
11.773
5
Minnesota
Pope
0.176
0.842
0.273
0.414
0.791
6.490
5
Minnesota
Ramsey
0.621
0.658
0.448
0.149
0.607
0.662
5
Minnesota
Red Lake
0.152
0.861
0.313
0.407
0.597
5.723
5
Minnesota
Redwood
0.144
0.839
0.351
0.431
0.825
10.136
5
Minnesota
Renville
0.238
0.843
0.342
0.419
0.853
6.129
5
Minnesota
Rice
0.303
0.754
0.392
0.404
0.692
3.792
5
Minnesota
Rock
0.187
0.823
0.370
0.410
0.670
6.048
5
Minnesota
Roseau
0.278
0.822
0.441
0.462
0.794
6.088
5
Minnesota
Scott
0.504
0.740
0.421
0.404
0.666
2.307
254
-------
EPA
Built
Natural
REGION
State
County
Risk
Governance
Environment
Environment
Society
CRSI
5
Minnesota
Sherburne
0.472
0.740
0.411
0.366
0.770
2.643
5
Minnesota
Sibley
0.187
0.865
0.333
0.438
0.795
7.430
5
Minnesota
St. Louis
0.165
0.718
0.861
0.453
0.607
14.060
5
Minnesota
Stearns
0.314
0.743
0.587
0.412
0.746
5.688
5
Minnesota
Steele
0.389
0.748
0.376
0.408
0.708
2.943
5
Minnesota
Stevens
0.229
0.804
0.322
0.405
0.843
5.741
5
Minnesota
Swift
0.213
0.832
0.310
0.426
0.783
5.823
5
Minnesota
Todd
0.161
0.833
0.288
0.375
0.657
5.059
5
Minnesota
Traverse
0.162
0.803
0.093
0.397
0.645
1.801
5
Minnesota
Wabasha
0.154
0.792
0.320
0.584
0.792
10.644
5
Minnesota
Wadena
0.215
0.806
0.279
0.401
0.726
4.488
5
Minnesota
Waseca
0.152
0.749
0.315
0.429
0.746
7.264
5
Minnesota
Washington
0.536
0.698
0.426
0.373
0.683
2.036
5
Minnesota
Watonwan
0.231
0.798
0.341
0.401
0.690
4.545
5
Minnesota
Wilkin
0.134
0.804
0.368
0.415
0.870
11.310
5
Minnesota
Winona
0.149
0.737
0.398
0.598
0.628
10.114
5
Minnesota
Wright
0.455
0.754
0.469
0.440
0.762
3.478
5
Minnesota
Yellow Medicine
0.185
0.865
0.336
0.454
0.818
8.004
5
Ohio
Adams
0.129
0.636
0.365
0.375
0.387
2.256
5
Ohio
Allen
0.271
0.689
0.405
0.339
0.554
2.548
5
Ohio
Ashland
0.134
0.683
0.421
0.385
0.540
6.064
5
Ohio
Ashtabula
0.220
0.684
0.427
0.335
0.466
2.535
5
Ohio
Athens
0.193
0.646
0.436
0.329
0.388
1.960
5
Ohio
Auglaize
0.143
0.701
0.431
0.373
0.583
6.364
5
Ohio
Belmont
0.177
0.655
0.403
0.325
0.504
2.930
5
Ohio
Brown
0.147
0.664
0.358
0.362
0.477
3.008
5
Ohio
Butler
0.625
0.657
0.526
0.262
0.508
1.125
5
Ohio
Carroll
0.115
0.644
0.354
0.319
0.549
3.917
5
Ohio
Champaign
0.111
0.659
0.396
0.453
0.477
6.901
255
-------
EPA
Built
Natural
REGION
State
County
Risk
Governance
Environment
Environment
Society
CRSI
5
Ohio
Clark
0.313
0.656
0.321
0.389
0.482
1.325
5
Ohio
Clermont
0.346
0.650
0.352
0.317
0.524
1.140
5
Ohio
Clinton
0.223
0.679
0.365
0.333
0.463
1.705
5
Ohio
Columbiana
0.237
0.649
0.379
0.320
0.506
1.851
5
Ohio
Coshocton
0.130
0.665
0.370
0.341
0.495
3.551
5
Ohio
Crawford
0.163
0.660
0.357
0.443
0.554
4.857
5
Ohio
Cuyahoga
0.502
0.656
0.710
0.207
0.490
2.031
5
Ohio
Darke
0.171
0.682
0.413
0.432
0.591
5.899
5
Ohio
Defiance
0.120
0.687
0.368
0.374
0.623
6.775
5
Ohio
Delaware
0.418
0.663
0.457
0.461
0.522
2.490
5
Ohio
Erie
0.228
0.657
0.413
0.321
0.507
2.380
5
Ohio
Fairfield
0.330
0.668
0.457
0.413
0.517
2.785
5
Ohio
Fayette
0.194
0.685
0.386
0.396
0.510
3.547
5
Ohio
Franklin
0.662
0.629
0.671
0.256
0.463
1.447
5
Ohio
Fulton
0.170
0.717
0.424
0.320
0.669
5.551
5
Ohio
Gallia
0.170
0.613
0.372
0.347
0.351
0.878
5
Ohio
Geauga
0.262
0.694
0.481
0.428
0.612
4.708
5
Ohio
Greene
0.480
0.652
0.412
0.383
0.460
1.241
5
Ohio
Guernsey
0.158
0.684
0.433
0.325
0.513
4.095
5
Ohio
Hamilton
0.557
0.630
0.571
0.137
0.502
0.852
5
Ohio
Hancock
0.249
0.698
0.455
0.379
0.545
3.665
5
Ohio
Hardin
0.083
0.684
0.355
0.453
0.522
9.295
5
Ohio
Harrison
0.090
0.687
0.349
0.303
0.477
3.324
5
Ohio
Henry
0.116
0.721
0.393
0.376
0.641
8.179
5
Ohio
Highland
0.114
0.675
0.292
0.360
0.552
3.714
5
Ohio
Hocking
0.132
0.664
0.370
0.366
0.529
4.451
5
Ohio
Holmes
0.123
0.642
0.395
0.293
0.656
5.702
5
Ohio
Huron
0.141
0.686
0.447
0.430
0.539
7.046
5
Ohio
Jackson
0.150
0.619
0.362
0.323
0.461
1.887
256
-------
EPA
Built
Natural
REGION
State
County
Risk
Governance
Environment
Environment
Society
CRSI
5
Ohio
Jefferson
0.221
0.627
0.381
0.277
0.443
0.885
5
Ohio
Knox
0.108
0.668
0.398
0.356
0.520
5.780
5
Ohio
Lake
0.302
0.685
0.361
0.300
0.587
1.778
5
Ohio
Lawrence
0.286
0.623
0.384
0.374
0.365
0.990
5
Ohio
Licking
0.266
0.677
0.460
0.404
0.528
3.509
5
Ohio
Logan
0.117
0.658
0.429
0.366
0.548
6.631
5
Ohio
Lorain
0.519
0.683
0.498
0.300
0.492
1.376
5
Ohio
Lucas
0.521
0.682
0.483
0.190
0.476
0.713
5
Ohio
Madison
0.139
0.675
0.392
0.434
0.516
5.787
5
Ohio
Mahoning
0.368
0.643
0.460
0.312
0.525
1.813
5
Ohio
Marion
0.213
0.598
0.341
0.445
0.457
2.335
5
Ohio
Medina
0.444
0.686
0.424
0.365
0.614
2.076
5
Ohio
Meigs
0.204
0.635
0.334
0.328
0.351
0.107
5
Ohio
Mercer
0.103
0.689
0.406
0.422
0.681
11.090
5
Ohio
Miami
0.365
0.680
0.354
0.384
0.567
1.861
5
Ohio
Monroe
0.123
0.631
0.342
0.333
0.475
2.389
5
Ohio
Montgomery
0.666
0.641
0.544
0.284
0.479
1.098
5
Ohio
Morgan
0.161
0.656
0.333
0.303
0.474
1.360
5
Ohio
Morrow
0.094
0.655
0.337
0.402
0.481
5.195
5
Ohio
Muskingum
0.212
0.654
0.389
0.318
0.492
2.081
5
Ohio
Noble
0.125
0.624
0.334
0.311
0.476
1.708
5
Ohio
Ottawa
0.225
0.701
0.375
0.289
0.579
2.440
5
Ohio
Paulding
0.119
0.718
0.425
0.356
0.493
5.807
5
Ohio
Perry
0.163
0.670
0.420
0.345
0.393
2.513
5
Ohio
Pickaway
0.212
0.665
0.480
0.432
0.508
4.738
5
Ohio
Pike
0.155
0.623
0.395
0.359
0.425
2.522
5
Ohio
Portage
0.417
0.684
0.476
0.412
0.508
2.312
5
Ohio
Preble
0.131
0.697
0.374
0.423
0.448
4.666
5
Ohio
Putnam
0.159
0.730
0.423
0.318
0.723
6.684
257
-------
EPA
Built
Natural
REGION
State
County
Risk
Governance
Environment
Environment
Society
CRSI
5
Ohio
Richland
0.231
0.633
0.489
0.351
0.567
3.958
5
Ohio
Ross
0.167
0.651
0.443
0.394
0.432
3.892
5
Ohio
Sandusky
0.188
0.673
0.438
0.332
0.573
4.168
5
Ohio
Scioto
0.198
0.609
0.448
0.360
0.372
2.113
5
Ohio
Seneca
0.180
0.702
0.397
0.401
0.565
4.745
5
Ohio
Shelby
0.161
0.706
0.377
0.350
0.564
4.205
5
Ohio
Stark
0.497
0.645
0.548
0.268
0.545
1.680
5
Ohio
Summit
0.633
0.674
0.514
0.238
0.543
1.099
5
Ohio
Trumbull
0.310
0.660
0.491
0.398
0.468
2.799
5
Ohio
Tuscarawas
0.260
0.676
0.490
0.301
0.558
3.156
5
Ohio
Union
0.198
0.661
0.429
0.402
0.527
4.160
5
Ohio
Van Wert
0.270
0.689
0.336
0.388
0.531
2.147
5
Ohio
Vinton
0.162
0.663
0.333
0.385
0.363
1.303
5
Ohio
Warren
0.644
0.664
0.408
0.357
0.529
1.041
5
Ohio
Washington
0.208
0.636
0.469
0.332
0.484
3.134
5
Ohio
Wayne
0.260
0.676
0.550
0.382
0.635
5.093
5
Ohio
Williams
0.138
0.713
0.468
0.379
0.535
6.857
5
Ohio
Wood
0.354
0.705
0.594
0.367
0.529
3.467
5
Ohio
Wyandot
0.133
0.710
0.391
0.413
0.555
6.453
5
Wisconsin
Adams
0.161
0.741
0.423
0.394
0.506
5.143
5
Wisconsin
Ashland
0.107
0.766
0.364
0.484
0.737
12.788
5
Wisconsin
Barron
0.173
0.775
0.429
0.449
0.704
8.100
5
Wisconsin
Bayfield
0.113
0.819
0.309
0.497
0.640
9.905
5
Wisconsin
Brown
0.587
0.703
0.599
0.351
0.608
2.310
5
Wisconsin
Buffalo
0.119
0.851
0.365
0.393
0.599
8.096
5
Wisconsin
Burnett
0.325
0.807
0.253
0.520
0.582
2.780
5
Wisconsin
Calumet
0.350
0.727
0.443
0.431
0.623
3.405
5
Wisconsin
Chippewa
0.210
0.749
0.576
0.420
0.624
7.344
5
Wisconsin
Clark
0.102
0.841
0.491
0.508
0.668
16.759
258
-------
EPA
Built
Natural
REGION
State
County
Risk
Governance
Environment
Environment
Society
CRSI
5
Wisconsin
Columbia
0.309
0.789
0.578
0.417
0.653
5.291
5
Wisconsin
Crawford
0.138
0.794
0.376
0.384
0.641
7.272
5
Wisconsin
Dane
0.572
0.703
0.803
0.388
0.623
3.533
5
Wisconsin
Dodge
0.236
0.765
0.603
0.475
0.624
7.470
5
Wisconsin
Door
0.107
0.740
0.507
0.407
0.732
14.253
5
Wisconsin
Douglas
0.183
0.748
0.409
0.461
0.570
5.975
5
Wisconsin
Dunn
0.123
0.784
0.519
0.377
0.630
10.843
5
Wisconsin
Eau Claire
0.264
0.715
0.470
0.396
0.590
4.175
5
Wisconsin
Florence
0.081
0.774
0.347
0.617
0.472
14.071
5
Wisconsin
Fond du Lac
0.237
0.735
0.538
0.463
0.647
6.653
5
Wisconsin
Forest
0.098
0.794
0.359
0.616
0.613
14.998
5
Wisconsin
Grant
0.151
0.779
0.663
0.397
0.679
12.316
5
Wisconsin
Green
0.248
0.773
0.424
0.354
0.667
4.382
5
Wisconsin
Green Lake
0.134
0.783
0.345
0.430
0.689
8.296
5
Wisconsin
Iowa
0.115
0.841
0.475
0.353
0.699
11.706
5
Wisconsin
Iron
0.094
0.787
0.321
0.480
0.661
11.893
5
Wisconsin
Jackson
0.094
0.834
0.408
0.470
0.599
13.354
5
Wisconsin
Jefferson
0.297
0.743
0.525
0.452
0.636
5.064
5
Wisconsin
Juneau
0.158
0.794
0.464
0.460
0.570
8.118
5
Wisconsin
Kenosha
0.472
0.681
0.421
0.407
0.477
1.573
5
Wisconsin
Kewaunee
0.140
0.782
0.373
0.465
0.698
9.231
5
Wisconsin
La Crosse
0.377
0.684
0.455
0.416
0.600
2.909
5
Wisconsin
Lafayette
0.117
0.873
0.425
0.366
0.713
11.132
5
Wisconsin
Langlade
0.116
0.778
0.330
0.533
0.655
10.776
5
Wisconsin
Lincoln
0.209
0.761
0.400
0.497
0.672
6.561
5
Wisconsin
Manitowoc
0.324
0.743
0.487
0.413
0.628
3.974
5
Wisconsin
Marathon
0.243
0.744
0.703
0.432
0.615
7.787
5
Wisconsin
Marinette
0.124
0.730
0.583
0.422
0.560
11.417
5
Wisconsin
Marquette
0.115
0.820
0.422
0.393
0.655
10.432
259
-------
EPA
Built
Natural
REGION
State
County
Risk
Governance
Environment
Environment
Society
CRSI
5
Wisconsin
Menominee
0.269
0.635
0.331
0.725
0.193
2.526
5
Wisconsin
Milwaukee
0.363
0.677
0.564
0.363
0.467
2.729
5
Wisconsin
Monroe
0.156
0.777
0.511
0.411
0.616
8.732
5
Wisconsin
Oconto
0.137
0.785
0.491
0.398
0.634
9.615
5
Wisconsin
Oneida
0.096
0.730
0.498
0.509
0.691
17.314
5
Wisconsin
Outagamie
0.558
0.705
0.603
0.438
0.690
3.132
5
Wisconsin
Ozaukee
0.266
0.707
0.347
0.463
0.646
3.904
5
Wisconsin
Pepin
0.171
0.857
0.295
0.395
0.592
4.529
5
Wisconsin
Pierce
0.248
0.796
0.423
0.399
0.678
4.982
5
Wisconsin
Polk
0.114
0.806
0.416
0.473
0.729
13.264
5
Wisconsin
Portage
0.164
0.740
0.499
0.367
0.618
7.235
5
Wisconsin
Price
0.092
0.793
0.409
0.499
0.735
16.821
5
Wisconsin
Racine
0.443
0.691
0.374
0.419
0.543
1.770
5
Wisconsin
Richland
0.086
0.813
0.382
0.375
0.610
11.012
5
Wisconsin
Rock
0.320
0.696
0.566
0.419
0.516
3.898
5
Wisconsin
Rusk
0.185
0.823
0.340
0.477
0.568
5.498
5
Wisconsin
Sauk
0.174
0.763
0.566
0.375
0.659
8.547
5
Wisconsin
Sawyer
0.080
0.773
0.308
0.593
0.586
14.953
5
Wisconsin
Shawano
0.091
0.792
0.503
0.418
0.639
15.628
5
Wisconsin
Sheboygan
0.227
0.724
0.460
0.434
0.616
5.403
5
Wisconsin
St. Croix
0.312
0.765
0.520
0.431
0.673
4.911
5
Wisconsin
Taylor
0.098
0.825
0.385
0.460
0.628
12.360
5
Wisconsin
Trempealeau
0.113
0.832
0.476
0.401
0.594
11.117
5
Wisconsin
Vernon
0.119
0.812
0.408
0.371
0.643
9.013
5
Wisconsin
Vilas
0.113
0.733
0.436
0.640
0.611
14.770
5
Wisconsin
Walworth
0.370
0.714
0.510
0.474
0.519
3.406
5
Wisconsin
Washburn
0.086
0.809
0.324
0.500
0.663
14.066
5
Wisconsin
Washington
0.494
0.698
0.463
0.398
0.706
2.623
5
Wisconsin
Waukesha
0.609
0.700
0.522
0.381
0.671
2.214
260
-------
EPA
REGION
State
County
Risk
Governance
Built
Environment
Natural
Environment
Society
CRSI
5
Wisconsin
Waupaca
0.152
0.774
0.509
0.365
0.726
9.519
5
Wisconsin
Waushara
0.247
0.795
0.454
0.370
0.606
4.474
5
Wisconsin
Winnebago
0.535
0.709
0.460
0.416
0.578
2.043
5
Wisconsin
Wood
0.258
0.728
0.540
0.417
0.631
5.562
261
-------
EPA
Built
Natural
REGION
State
County
Risk
Governance
Environment
Environment
Society
CRSI
National Average
0.229
0.588
0.393
0.414
0.516
4.213
Regional Average
0.239
0.548
0.394
0.423
0.474
2.772
6
Arkansas
Arkansas
0.216
0.560
0.395
0.513
0.566
4.572
6
Arkansas
Ashley
0.139
0.506
0.346
0.456
0.527
4.214
6
Arkansas
Baxter
0.180
0.408
0.433
0.486
0.455
3.621
6
Arkansas
Benton
0.596
0.481
0.710
0.396
0.466
2.120
6
Arkansas
Boone
0.171
0.449
0.383
0.401
0.585
3.541
6
Arkansas
Bradley
0.123
0.499
0.309
0.468
0.416
2.357
6
Arkansas
Calhoun
0.150
0.508
0.310
0.483
0.379
1.766
6
Arkansas
Carroll
0.114
0.421
0.482
0.319
0.484
3.895
6
Arkansas
Chicot
0.165
0.532
0.267
0.424
0.387
0.273
6
Arkansas
Clark
0.134
0.397
0.407
0.469
0.546
5.269
6
Arkansas
Clay
0.164
0.475
0.366
0.402
0.424
1.667
6
Arkansas
Cleburne
0.352
0.453
0.421
0.395
0.490
1.441
6
Arkansas
Cleveland
0.119
0.524
0.301
0.412
0.329
-0.145
6
Arkansas
Columbia
0.229
0.506
0.384
0.457
0.494
2.728
6
Arkansas
Conway
0.259
0.567
0.357
0.401
0.516
2.002
6
Arkansas
Craighead
0.281
0.539
0.542
0.410
0.487
3.367
6
Arkansas
Crawford
0.177
0.518
0.519
0.499
0.474
5.992
6
Arkansas
Crittenden
0.469
0.559
0.427
0.449
0.419
1.334
6
Arkansas
Cross
0.182
0.551
0.375
0.435
0.558
3.927
6
Arkansas
Dallas
0.140
0.451
0.257
0.398
0.487
0.521
6
Arkansas
Desha
0.232
0.556
0.307
0.457
0.379
1.038
6
Arkansas
Drew
0.114
0.514
0.351
0.424
0.385
2.169
6
Arkansas
Faulkner
0.505
0.592
0.461
0.398
0.494
1.523
6
Arkansas
Franklin
0.214
0.526
0.392
0.551
0.411
3.413
6
Arkansas
Fulton
0.185
0.427
0.360
0.435
0.438
1.710
6
Arkansas
Garland
0.345
0.411
0.426
0.435
0.441
1.394
6
Arkansas
Grant
0.123
0.520
0.394
0.440
0.422
3.911
262
-------
EPA
Built
Natural
REGION
State
County
Risk
Governance
Environment
Environment
Society
CRSI
6
Arkansas
Greene
0.274
0.503
0.416
0.360
0.429
1.237
6
Arkansas
Hempstead
0.308
0.411
0.468
0.451
0.397
1.778
6
Arkansas
Hot Spring
0.143
0.410
0.356
0.419
0.487
2.410
6
Arkansas
Howard
0.299
0.380
0.362
0.427
0.389
0.534
6
Arkansas
Independence
0.199
0.451
0.461
0.354
0.460
2.267
6
Arkansas
Izard
0.266
0.382
0.379
0.356
0.485
0.832
6
Arkansas
Jackson
0.278
0.547
0.398
0.444
0.458
2.150
6
Arkansas
Jefferson
0.161
0.536
0.447
0.475
0.442
4.750
6
Arkansas
Johnson
0.233
0.538
0.387
0.611
0.373
3.447
6
Arkansas
Lafayette
0.251
0.548
0.238
0.458
0.406
0.422
6
Arkansas
Lawrence
0.185
0.508
0.368
0.410
0.413
1.666
6
Arkansas
Lee
0.142
0.532
0.215
0.461
0.378
-0.130
6
Arkansas
Lincoln
0.127
0.534
0.286
0.460
0.369
1.215
6
Arkansas
Little River
0.260
0.428
0.364
0.495
0.406
1.589
6
Arkansas
Logan
0.171
0.543
0.402
0.481
0.534
4.932
6
Arkansas
Lonoke
0.319
0.605
0.536
0.455
0.511
3.619
6
Arkansas
Madison
0.128
0.447
0.359
0.399
0.392
1.207
6
Arkansas
Marion
0.225
0.435
0.380
0.408
0.442
1.408
6
Arkansas
Miller
0.389
0.461
0.324
0.466
0.467
1.000
6
Arkansas
Mississippi
0.172
0.533
0.492
0.394
0.450
4.053
6
Arkansas
Monroe
0.230
0.567
0.324
0.497
0.511
2.840
6
Arkansas
Montgomery
0.145
0.358
0.324
0.555
0.412
2.736
6
Arkansas
Nevada
0.208
0.464
0.299
0.416
0.461
0.894
6
Arkansas
Newton
0.258
0.449
0.355
0.521
0.329
1.244
6
Arkansas
Ouachita
0.141
0.437
0.315
0.450
0.465
2.102
6
Arkansas
Perry
0.175
0.551
0.352
0.504
0.503
4.073
6
Arkansas
Phillips
0.131
0.540
0.279
0.471
0.307
0.325
6
Arkansas
Pike
0.144
0.444
0.367
0.469
0.471
3.462
6
Arkansas
Poinsett
0.126
0.534
0.404
0.416
0.419
3.628
263
-------
EPA
Built
Natural
REGION
State
County
Risk
Governance
Environment
Environment
Society
CRSI
6
Arkansas
Polk
0.323
0.295
0.372
0.516
0.540
1.903
6
Arkansas
Pope
0.177
0.525
0.450
0.504
0.470
5.005
6
Arkansas
Prairie
0.244
0.582
0.365
0.464
0.514
2.891
6
Arkansas
Pulaski
0.880
0.556
0.726
0.374
0.536
1.667
6
Arkansas
Randolph
0.144
0.457
0.375
0.390
0.500
2.754
6
Arkansas
Saline
0.419
0.529
0.485
0.471
0.521
2.394
6
Arkansas
Scott
0.151
0.498
0.349
0.584
0.511
5.743
6
Arkansas
Searcy
0.249
0.427
0.338
0.373
0.356
-0.234
6
Arkansas
Sebastian
0.353
0.530
0.457
0.451
0.452
2.111
6
Arkansas
Sevier
0.263
0.192
0.341
0.480
0.326
-0.304
6
Arkansas
Sharp
0.206
0.421
0.380
0.364
0.554
2.022
6
Arkansas
St. Francis
0.135
0.546
0.378
0.473
0.479
4.871
6
Arkansas
Stone
0.247
0.368
0.356
0.419
0.441
0.864
6
Arkansas
Union
0.338
0.487
0.452
0.458
0.522
2.486
6
Arkansas
Van Buren
0.331
0.458
0.400
0.387
0.458
1.133
6
Arkansas
Washington
0.322
0.465
0.668
0.359
0.458
3.195
6
Arkansas
White
0.292
0.532
0.515
0.427
0.463
2.957
6
Arkansas
Woodruff
0.219
0.564
0.310
0.492
0.263
0.529
6
Arkansas
Yell
0.179
0.535
0.414
0.556
0.423
4.672
6
Louisiana
Acadia
0.271
0.539
0.493
0.496
0.480
3.746
6
Louisiana
Allen
0.295
0.546
0.372
0.540
0.470
2.681
6
Louisiana
Ascension
0.907
0.561
0.461
0.429
0.558
1.038
6
Louisiana
Assumption
0.354
0.539
0.331
0.465
0.439
1.203
6
Louisiana
Avoyelles
0.316
0.524
0.445
0.508
0.472
2.812
6
Louisiana
Beauregard
0.261
0.530
0.442
0.482
0.448
2.955
6
Louisiana
Bienville
0.259
0.541
0.356
0.445
0.408
1.483
6
Louisiana
Bossier
0.477
0.567
0.446
0.459
0.474
1.706
6
Louisiana
Caddo
0.611
0.540
0.686
0.442
0.532
2.455
6
Louisiana
Calcasieu
0.467
0.527
0.770
0.465
0.557
3.890
264
-------
EPA
Built
Natural
REGION
State
County
Risk
Governance
Environment
Environment
Society
CRSI
6
Louisiana
Caldwell
0.129
0.528
0.350
0.464
0.395
2.896
6
Louisiana
Cameron
0.503
0.548
0.386
0.470
0.516
1.482
6
Louisiana
Catahoula
0.149
0.526
0.267
0.496
0.318
0.527
6
Louisiana
Claiborne
0.228
0.526
0.230
0.422
0.408
-0.096
6
Louisiana
Concordia
0.160
0.526
0.342
0.523
0.374
2.853
6
Louisiana
De Soto
0.183
0.560
0.468
0.459
0.475
4.720
6
Louisiana
East Baton Rouge
0.666
0.496
0.589
0.354
0.588
1.637
6
Louisiana
East Carroll
0.293
0.540
0.210
0.482
0.377
0.082
6
Louisiana
East Feliciana
0.265
0.495
0.364
0.371
0.363
0.338
6
Louisiana
Evangeline
0.257
0.522
0.359
0.480
0.421
1.878
6
Louisiana
Franklin
0.125
0.525
0.348
0.462
0.504
4.653
6
Louisiana
Grant
0.132
0.496
0.452
0.579
0.426
7.309
6
Louisiana
Iberia
0.360
0.552
0.399
0.429
0.454
1.557
6
Louisiana
Iberville
0.524
0.555
0.427
0.448
0.584
1.795
6
Louisiana
Jackson
0.244
0.525
0.318
0.416
0.521
1.714
6
Louisiana
Jefferson
0.314
0.554
0.403
0.485
0.477
2.416
6
Louisiana
Jefferson Davis
0.626
0.582
0.483
0.371
0.578
1.463
6
Louisiana
La Salle
0.156
0.523
0.392
0.472
0.487
4.394
6
Louisiana
Lafayette
0.655
0.562
0.596
0.437
0.588
2.106
6
Louisiana
Lafourche
0.442
0.574
0.521
0.397
0.484
2.012
6
Louisiana
Lincoln
0.255
0.503
0.432
0.426
0.400
1.905
6
Louisiana
Livingston
0.515
0.513
0.495
0.423
0.476
1.569
6
Louisiana
Madison
0.224
0.515
0.265
0.573
0.494
2.679
6
Louisiana
Morehouse
0.146
0.499
0.364
0.528
0.481
4.899
6
Louisiana
Natchitoches
0.171
0.546
0.485
0.543
0.418
5.802
6
Louisiana
Orleans
0.692
0.543
0.388
0.386
0.398
0.447
6
Louisiana
Ouachita
0.406
0.528
0.551
0.443
0.535
2.794
6
Louisiana
Plaquemines
0.338
0.568
0.532
0.368
0.486
2.502
6
Louisiana
Pointe Coupee
0.352
0.547
0.434
0.465
0.537
2.562
265
-------
EPA
Built
Natural
REGION
State
County
Risk
Governance
Environment
Environment
Society
CRSI
6
Louisiana
Rapides
0.313
0.515
0.717
0.507
0.559
5.652
6
Louisiana
Red River
0.202
0.532
0.353
0.462
0.448
2.419
6
Louisiana
Richland
0.129
0.541
0.315
0.483
0.574
5.411
6
Louisiana
Sabine
0.119
0.541
0.420
0.408
0.430
4.255
6
Louisiana
St. Bernard
0.321
0.598
0.450
0.330
0.519
1.954
6
Louisiana
St. Charles
0.554
0.594
0.437
0.457
0.589
1.877
6
Louisiana
St. Helena
0.206
0.419
0.290
0.430
0.443
0.583
6
Louisiana
St. James
0.473
0.562
0.362
0.464
0.550
1.584
6
Louisiana
St. John the Baptist
0.484
0.589
0.392
0.416
0.595
1.713
6
Louisiana
St. Landry
0.261
0.527
0.547
0.481
0.510
4.490
6
Louisiana
St. Martin
0.345
0.541
0.416
0.437
0.529
2.213
6
Louisiana
St. Mary
0.351
0.557
0.501
0.453
0.541
3.047
6
Louisiana
St. Tammany
0.698
0.363
0.597
0.423
0.518
1.450
6
Louisiana
Tangipahoa
0.404
0.444
0.573
0.422
0.438
2.150
6
Louisiana
Tensas
0.269
0.510
0.344
0.532
0.300
1.212
6
Louisiana
Terrebonne
0.335
0.570
0.475
0.364
0.491
2.075
6
Louisiana
Union
0.207
0.510
0.377
0.517
0.475
3.470
6
Louisiana
Vermilion
0.350
0.555
0.500
0.467
0.443
2.584
6
Louisiana
Vernon
0.215
0.578
0.498
0.538
0.412
4.829
6
Louisiana
Washington
0.254
0.258
0.348
0.441
0.371
-0.009
6
Louisiana
Webster
0.234
0.538
0.449
0.460
0.497
3.591
6
Louisiana
West Baton Rouge
0.596
0.564
0.451
0.487
0.691
2.213
6
Louisiana
West Carroll
0.113
0.565
0.328
0.495
0.367
3.342
6
Louisiana
West Feliciana
0.314
0.549
0.370
0.409
0.490
1.608
6
Louisiana
Winn
0.145
0.477
0.364
0.475
0.464
3.635
6
New Mexico
Bernalillo
0.581
0.571
0.611
0.461
0.557
2.459
6
New Mexico
Catron
0.179
0.535
0.394
0.576
0.504
5.553
6
New Mexico
Chaves
0.283
0.584
0.554
0.465
0.511
4.260
6
New Mexico
Cibola
0.100
0.578
0.429
0.534
0.454
9.271
266
-------
EPA
Built
Natural
REGION
State
County
Risk
Governance
Environment
Environment
Society
CRSI
6
New Mexico
Colfax
0.155
0.598
0.463
0.465
0.553
6.846
6
New Mexico
Curry
0.190
0.576
0.482
0.293
0.528
3.260
6
New Mexico
De Baca
0.132
0.625
0.258
0.427
0.583
3.844
6
New Mexico
Dona Ana
0.175
0.560
0.736
0.635
0.497
11.766
6
New Mexico
Eddy
0.204
0.582
0.540
0.645
0.490
7.677
6
New Mexico
Grant
0.114
0.570
0.530
0.535
0.646
13.803
6
New Mexico
Guadalupe
0.104
0.608
0.330
0.341
0.585
4.574
6
New Mexico
Harding
0.087
0.715
0.206
0.372
0.299
-2.708
6
New Mexico
Hidalgo
0.099
0.533
0.298
0.567
0.336
3.866
6
New Mexico
Lea
0.180
0.548
0.570
0.490
0.469
6.629
6
New Mexico
Lincoln
0.248
0.557
0.576
0.502
0.603
6.103
6
New Mexico
Los Alamos
0.365
0.523
0.256
0.557
0.596
2.041
6
New Mexico
Luna
0.077
0.547
0.472
0.617
0.349
13.043
6
New Mexico
McKinley
0.095
0.629
0.624
0.607
0.469
18.046
6
New Mexico
Mora
0.118
0.531
0.329
0.310
0.411
-0.183
6
New Mexico
Otero
0.110
0.517
0.601
0.659
0.438
14.514
6
New Mexico
Quay
0.116
0.570
0.412
0.392
0.608
7.141
6
New Mexico
Rio Arriba
0.134
0.600
0.619
0.574
0.553
13.058
6
New Mexico
Roosevelt
0.221
0.589
0.372
0.311
0.487
1.367
6
New Mexico
San Juan
0.122
0.621
0.693
0.620
0.519
16.431
6
New Mexico
San Miguel
0.130
0.571
0.467
0.423
0.435
5.450
6
New Mexico
Sandoval
0.147
0.643
0.642
0.544
0.510
11.526
6
New Mexico
Santa Fe
0.157
0.602
0.592
0.517
0.629
10.747
6
New Mexico
Sierra
0.087
0.566
0.360
0.762
0.484
15.566
6
New Mexico
Socorro
0.249
0.537
0.446
0.589
0.398
3.834
6
New Mexico
Taos
0.106
0.599
0.545
0.443
0.562
11.730
6
New Mexico
Torrance
0.102
0.593
0.430
0.411
0.516
7.476
6
New Mexico
Union
0.145
0.639
0.234
0.398
0.592
2.799
6
New Mexico
Valencia
0.164
0.594
0.507
0.534
0.490
7.405
267
-------
EPA
Built
Natural
REGION
State
County
Risk
Governance
Environment
Environment
Society
CRSI
6
Oklahoma
Adair
0.152
0.540
0.328
0.396
0.322
0.135
6
Oklahoma
Alfalfa
0.257
0.684
0.188
0.303
0.736
1.459
6
Oklahoma
Atoka
0.252
0.651
0.377
0.400
0.416
1.801
6
Oklahoma
Beaver
0.347
0.658
0.353
0.438
0.597
2.448
6
Oklahoma
Beckham
0.180
0.581
0.437
0.326
0.674
4.831
6
Oklahoma
Blaine
0.361
0.627
0.277
0.407
0.659
1.831
6
Oklahoma
Bryan
0.192
0.560
0.464
0.469
0.495
4.809
6
Oklahoma
Caddo
0.268
0.678
0.489
0.428
0.519
3.945
6
Oklahoma
Canadian
0.497
0.627
0.511
0.395
0.577
2.201
6
Oklahoma
Carter
0.250
0.593
0.423
0.380
0.559
3.015
6
Oklahoma
Cherokee
0.189
0.491
0.423
0.489
0.390
3.096
6
Oklahoma
Choctaw
0.135
0.591
0.417
0.516
0.439
6.151
6
Oklahoma
Cimarron
0.269
0.591
0.273
0.450
0.595
2.196
6
Oklahoma
Cleveland
0.889
0.606
0.388
0.389
0.493
0.633
6
Oklahoma
Coal
0.318
0.629
0.226
0.428
0.443
0.488
6
Oklahoma
Comanche
0.312
0.613
0.538
0.354
0.462
2.637
6
Oklahoma
Cotton
0.188
0.650
0.271
0.391
0.422
0.862
6
Oklahoma
Craig
0.150
0.624
0.369
0.529
0.619
7.523
6
Oklahoma
Creek
0.273
0.636
0.473
0.375
0.502
2.944
6
Oklahoma
Custer
0.212
0.620
0.449
0.412
0.691
5.594
6
Oklahoma
Delaware
0.257
0.530
0.459
0.381
0.407
1.900
6
Oklahoma
Dewey
0.269
0.687
0.429
0.358
0.569
3.081
6
Oklahoma
Ellis
0.288
0.688
0.346
0.409
0.634
2.992
6
Oklahoma
Garfield
0.161
0.594
0.469
0.389
0.557
5.549
6
Oklahoma
Garvin
0.168
0.655
0.408
0.342
0.490
3.253
6
Oklahoma
Grady
0.247
0.646
0.464
0.357
0.578
3.617
6
Oklahoma
Grant
0.302
0.689
0.320
0.353
0.693
2.553
6
Oklahoma
Greer
0.199
0.566
0.169
0.378
0.606
0.666
6
Oklahoma
Harmon
0.256
0.617
0.077
0.303
0.325
-3.104
268
-------
EPA
Built
Natural
REGION
State
County
Risk
Governance
Environment
Environment
Society
CRSI
6
Oklahoma
Harper
0.287
0.612
0.341
0.415
0.696
3.157
6
Oklahoma
Haskell
0.168
0.599
0.312
0.401
0.466
1.986
6
Oklahoma
Hughes
0.200
0.620
0.288
0.344
0.474
0.825
6
Oklahoma
Jackson
0.346
0.579
0.380
0.398
0.472
1.435
6
Oklahoma
Jefferson
0.209
0.705
0.216
0.426
0.486
1.345
6
Oklahoma
Johnston
0.221
0.627
0.331
0.430
0.383
1.453
6
Oklahoma
Kay
0.179
0.659
0.437
0.458
0.555
5.800
6
Oklahoma
Kingfisher
0.201
0.630
0.359
0.393
0.706
4.673
6
Oklahoma
Kiowa
0.279
0.611
0.320
0.386
0.711
2.893
6
Oklahoma
Latimer
0.191
0.549
0.319
0.420
0.470
1.861
6
Oklahoma
Le Flore
0.236
0.414
0.548
0.533
0.393
4.056
6
Oklahoma
Lincoln
0.206
0.676
0.402
0.372
0.632
4.372
6
Oklahoma
Logan
0.210
0.639
0.392
0.384
0.511
2.998
6
Oklahoma
Love
0.236
0.638
0.294
0.405
0.502
1.702
6
Oklahoma
Major
0.206
0.661
0.288
0.302
0.665
2.310
6
Oklahoma
Marshall
0.237
0.567
0.309
0.405
0.415
0.846
6
Oklahoma
Mayes
0.180
0.554
0.495
0.389
0.487
4.371
6
Oklahoma
McClain
0.240
0.648
0.433
0.363
0.639
3.965
6
Oklahoma
McCurtain
0.157
0.421
0.478
0.458
0.345
3.168
6
Oklahoma
Mcintosh
0.171
0.576
0.376
0.434
0.439
2.983
6
Oklahoma
Murray
0.153
0.568
0.331
0.373
0.598
3.525
6
Oklahoma
Muskogee
0.201
0.547
0.497
0.474
0.529
5.356
6
Oklahoma
Noble
0.161
0.661
0.366
0.432
0.580
5.174
6
Oklahoma
Nowata
0.191
0.662
0.260
0.413
0.553
2.385
6
Oklahoma
Okfuskee
0.145
0.666
0.298
0.379
0.428
1.612
6
Oklahoma
Oklahoma
0.783
0.593
0.575
0.264
0.522
1.019
6
Oklahoma
Okmulgee
0.134
0.621
0.368
0.406
0.513
4.533
6
Oklahoma
Osage
0.177
0.628
0.503
0.644
0.501
8.696
6
Oklahoma
Ottawa
0.237
0.643
0.416
0.459
0.411
2.891
269
-------
EPA
Built
Natural
REGION
State
County
Risk
Governance
Environment
Environment
Society
CRSI
6
Oklahoma
Pawnee
0.126
0.673
0.346
0.431
0.516
5.302
6
Oklahoma
Payne
0.293
0.597
0.482
0.439
0.489
3.148
6
Oklahoma
Pittsburg
0.193
0.584
0.522
0.369
0.556
5.067
6
Oklahoma
Pontotoc
0.168
0.563
0.432
0.383
0.596
4.906
6
Oklahoma
Pottawatomie
0.194
0.631
0.392
0.364
0.489
2.728
6
Oklahoma
Pushmataha
0.139
0.535
0.389
0.410
0.412
2.786
6
Oklahoma
Roger Mills
0.300
0.686
0.354
0.303
0.649
2.168
6
Oklahoma
Rogers
0.291
0.626
0.501
0.496
0.545
4.302
6
Oklahoma
Seminole
0.180
0.693
0.272
0.344
0.511
1.483
6
Oklahoma
Sequoyah
0.156
0.516
0.439
0.414
0.414
3.319
6
Oklahoma
Stephens
0.157
0.607
0.376
0.336
0.521
2.919
6
Oklahoma
Texas
0.205
0.627
0.483
0.459
0.554
5.514
6
Oklahoma
Tillman
0.171
0.578
0.257
0.412
0.476
1.234
6
Oklahoma
Tulsa
0.832
0.584
0.628
0.301
0.486
1.143
6
Oklahoma
Wagoner
0.235
0.574
0.498
0.445
0.466
3.872
6
Oklahoma
Washington
0.223
0.600
0.320
0.371
0.561
2.094
6
Oklahoma
Washita
0.239
0.657
0.331
0.375
0.699
3.485
6
Oklahoma
Woods
0.173
0.612
0.347
0.342
0.625
3.513
6
Oklahoma
Woodward
0.147
0.599
0.484
0.351
0.672
7.311
6
Texas
Anderson
0.138
0.538
0.475
0.461
0.412
5.416
6
Texas
Andrews
0.108
0.530
0.339
0.352
0.349
-0.155
6
Texas
Angelina
0.195
0.495
0.516
0.441
0.448
4.271
6
Texas
Aransas
0.180
0.513
0.334
0.522
0.404
2.657
6
Texas
Archer
0.123
0.622
0.431
0.361
0.603
6.837
6
Texas
Armstrong
0.330
0.673
0.203
0.354
0.462
-0.028
6
Texas
Atascosa
0.091
0.549
0.451
0.436
0.410
6.916
6
Texas
Austin
0.111
0.543
0.420
0.379
0.535
5.813
6
Texas
Bailey
0.236
0.535
0.241
0.367
0.405
-0.528
6
Texas
Bandera
0.085
0.408
0.373
0.387
0.529
4.629
270
-------
EPA
Built
Natural
REGION
State
County
Risk
Governance
Environment
Environment
Society
CRSI
6
Texas
Bastrop
0.291
0.549
0.524
0.293
0.465
1.977
6
Texas
Baylor
0.135
0.494
0.134
0.403
0.440
-2.176
6
Texas
Bee
0.102
0.557
0.351
0.484
0.413
4.832
6
Texas
Bell
0.496
0.181
0.680
0.463
0.447
2.038
6
Texas
Bexar
0.530
0.212
0.884
0.391
0.521
2.936
6
Texas
Blanco
0.237
0.314
0.351
0.438
0.691
2.870
6
Texas
Borden
0.139
0.672
0.133
0.396
0.386
-1.728
6
Texas
Bosque
0.114
0.600
0.423
0.424
0.554
7.526
6
Texas
Bowie
0.312
0.500
0.519
0.454
0.458
2.881
6
Texas
Brazoria
0.602
0.588
0.776
0.549
0.524
3.385
6
Texas
Brazos
0.323
0.520
0.531
0.357
0.426
2.011
6
Texas
Brewster
0.151
0.543
0.360
0.511
0.590
6.093
6
Texas
Briscoe
0.237
0.686
0.289
0.394
0.529
1.956
6
Texas
Brooks
0.270
0.513
0.228
0.408
0.165
-2.036
6
Texas
Brown
0.159
0.566
0.414
0.350
0.578
4.186
6
Texas
Burleson
0.128
0.514
0.337
0.420
0.511
3.529
6
Texas
Burnet
0.203
0.543
0.431
0.477
0.596
5.093
6
Texas
Caldwell
0.138
0.429
0.403
0.312
0.509
1.942
6
Texas
Calhoun
0.217
0.525
0.435
0.490
0.429
3.373
6
Texas
Callahan
0.159
0.583
0.384
0.389
0.537
3.904
6
Texas
Cameron
0.334
0.573
0.702
0.690
0.384
5.676
6
Texas
Camp
0.278
0.522
0.244
0.362
0.467
-0.067
6
Texas
Carson
0.468
0.647
0.380
0.426
0.635
2.038
6
Texas
Cass
0.206
0.552
0.380
0.425
0.402
1.935
6
Texas
Castro
0.203
0.556
0.233
0.533
0.324
0.604
6
Texas
Chambers
0.571
0.580
0.511
0.500
0.440
1.811
6
Texas
Cherokee
0.133
0.524
0.506
0.441
0.393
5.479
6
Texas
Childress
0.198
0.557
0.239
0.356
0.481
0.072
6
Texas
Clay
0.108
0.624
0.413
0.380
0.674
9.076
271
-------
EPA
Built
Natural
REGION
State
County
Risk
Governance
Environment
Environment
Society
CRSI
6
Texas
Cochran
0.247
0.635
0.030
0.339
0.396
-2.725
6
Texas
Coke
0.102
0.596
0.270
0.357
0.228
-3.508
6
Texas
Coleman
0.133
0.571
0.229
0.380
0.501
0.763
6
Texas
Collin
0.549
0.619
0.678
0.351
0.527
2.408
6
Texas
Collingsworth
0.250
0.607
0.168
0.288
0.644
0.112
6
Texas
Colorado
0.095
0.512
0.379
0.403
0.559
6.430
6
Texas
Comal
0.380
0.254
0.505
0.313
0.595
1.419
6
Texas
Comanche
0.115
0.548
0.338
0.417
0.433
2.848
6
Texas
Concho
0.102
0.626
0.208
0.396
0.502
1.371
6
Texas
Cooke
0.135
0.585
0.471
0.339
0.496
4.809
6
Texas
Coryell
0.143
0.562
0.445
0.497
0.446
5.930
6
Texas
Cottle
0.315
0.593
0.228
0.320
0.387
-0.799
6
Texas
Crane
0.143
0.561
0.159
0.413
0.435
-1.015
6
Texas
Crockett
0.102
0.540
0.304
0.489
0.581
6.767
6
Texas
Crosby
0.147
0.636
0.292
0.397
0.476
2.225
6
Texas
Culberson
0.214
0.574
0.187
0.344
0.456
-0.870
6
Texas
Dallam
0.299
0.613
0.258
0.338
0.509
0.435
6
Texas
Dallas
0.844
0.575
0.789
0.236
0.485
1.436
6
Texas
Dawson
0.097
0.571
0.295
0.298
0.463
-0.034
6
Texas
Deaf Smith
0.200
0.571
0.304
0.377
0.455
1.020
6
Texas
Delta
0.289
0.629
0.227
0.421
0.376
0.020
6
Texas
Denton
0.512
0.634
0.711
0.460
0.493
3.180
6
Texas
DeWitt
0.102
0.512
0.298
0.468
0.475
3.814
6
Texas
Dickens
0.212
0.630
0.091
0.401
0.403
-1.657
6
Texas
Dimmit
0.080
0.516
0.328
0.443
0.475
5.180
6
Texas
Donley
0.345
0.569
0.315
0.295
0.527
0.493
6
Texas
Duval
0.108
0.516
0.327
0.380
0.275
-1.273
6
Texas
Eastland
0.120
0.549
0.354
0.374
0.491
3.138
6
Texas
Ector
0.382
0.529
0.507
0.447
0.448
2.248
272
-------
EPA
REGION State
County
Risk
Governance
Built
Environment
Natural
Environment
Society
CRSI
6
Texas
Edwards
0.118
0.492
0.205
0.341
0.385
-3.117
6
Texas
El Paso
0.417
0.566
0.709
0.367
0.443
2.941
6
Texas
Ellis
0.446
0.604
0.716
0.371
0.538
3.319
6
Texas
Erath
0.116
0.569
0.452
0.469
0.558
8.793
6
Texas
Falls
0.155
0.536
0.326
0.426
0.405
1.615
6
Texas
Fannin
0.115
0.589
0.452
0.428
0.501
7.214
6
Texas
Fayette
0.091
0.527
0.423
0.395
0.584
8.495
6
Texas
Fisher
0.174
0.638
0.183
0.382
0.475
-0.014
6
Texas
Floyd
0.238
0.597
0.212
0.553
0.552
2.554
6
Texas
Foard
0.096
0.559
0.249
0.364
0.308
-2.910
6
Texas
Fort Bend
0.411
0.597
0.785
0.420
0.580
4.527
6
Texas
Franklin
0.231
0.521
0.271
0.334
0.393
-0.707
6
Texas
Freestone
0.198
0.550
0.406
0.370
0.432
1.969
6
Texas
Frio
0.085
0.515
0.335
0.419
0.378
2.126
6
Texas
Gaines
0.135
0.564
0.294
0.370
0.487
1.595
6
Texas
Galveston
0.753
0.568
0.608
0.408
0.472
1.486
6
Texas
Garza
0.251
0.564
0.219
0.359
0.520
0.205
6
Texas
Gillespie
0.132
0.473
0.385
0.436
0.691
7.024
6
Texas
Glasscock
0.195
0.605
0.162
0.384
0.386
-1.333
6
Texas
Goliad
0.126
0.536
0.277
0.452
0.409
1.483
6
Texas
Gonzales
0.075
0.470
0.356
0.400
0.378
1.912
6
Texas
Gray
0.262
0.539
0.210
0.202
0.478
-1.782
6
Texas
Grayson
0.180
0.547
0.685
0.320
0.512
6.463
6
Texas
Gregg
0.613
0.534
0.396
0.283
0.561
0.633
6
Texas
Grimes
0.237
0.546
0.372
0.328
0.468
1.106
6
Texas
Guadalupe
0.253
0.523
0.557
0.331
0.476
2.994
6
Texas
Hale
0.252
0.583
0.317
0.563
0.496
3.128
6
Texas
Hall
0.235
0.583
0.126
0.408
0.484
-0.542
6
Texas
Hamilton
0.131
0.548
0.311
0.439
0.484
3.087
273
-------
EPA
Built
Natural
REGION
State
County
Risk
Governance
Environment
Environment
Society
CRSI
6
Texas
Hansford
0.262
0.621
0.340
0.299
0.450
0.560
6
Texas
Hardeman
0.108
0.530
0.235
0.335
0.453
-1.197
6
Texas
Hardin
0.357
0.512
0.365
0.403
0.546
1.534
6
Texas
Harris
0.758
0.563
0.837
0.192
0.491
1.624
6
Texas
Harrison
0.356
0.516
0.523
0.422
0.432
2.233
6
Texas
Hartley
0.233
0.630
0.319
0.289
0.440
0.235
6
Texas
Haskell
0.119
0.577
0.214
0.482
0.492
2.482
6
Texas
Hays
0.454
0.180
0.587
0.310
0.557
1.326
6
Texas
Hemphill
0.393
0.575
0.251
0.320
0.489
-0.023
6
Texas
Henderson
0.141
0.565
0.536
0.441
0.439
6.667
6
Texas
Hidalgo
0.485
0.555
0.731
0.767
0.390
4.439
6
Texas
Hill
0.136
0.572
0.470
0.436
0.459
5.862
6
Texas
Hockley
0.178
0.588
0.387
0.357
0.519
2.909
6
Texas
Hood
0.280
0.566
0.469
0.399
0.535
3.033
6
Texas
Hopkins
0.104
0.583
0.425
0.367
0.559
6.857
6
Texas
Houston
0.273
0.513
0.407
0.453
0.394
1.779
6
Texas
Howard
0.146
0.573
0.322
0.502
0.465
3.939
6
Texas
Hudspeth
0.080
0.585
0.316
0.438
0.256
0.096
6
Texas
Hunt
0.243
0.600
0.576
0.331
0.384
2.899
6
Texas
Hutchinson
0.264
0.563
0.318
0.271
0.486
0.120
6
Texas
Irion
0.240
0.619
0.245
0.475
0.433
1.208
6
Texas
Jack
0.132
0.541
0.297
0.348
0.423
0.153
6
Texas
Jackson
0.121
0.552
0.337
0.481
0.538
5.694
6
Texas
Jasper
0.371
0.483
0.431
0.465
0.463
1.845
6
Texas
Jeff Davis
0.143
0.109
0.292
0.439
0.318
-2.852
6
Texas
Jefferson
0.530
0.506
0.698
0.449
0.521
2.821
6
Texas
Jim Hogg
0.262
0.545
0.219
0.381
0.470
-0.040
6
Texas
Jim Wells
0.109
0.539
0.328
0.391
0.392
1.321
6
Texas
Johnson
0.359
0.559
0.595
0.434
0.515
3.405
274
-------
EPA
REGION State
County
Risk
Governance
Built
Environment
Natural
Environment
Society
CRSI
6
Texas
Jones
0.116
0.563
0.266
0.468
0.520
3.825
6
Texas
Karnes
0.098
0.543
0.320
0.398
0.406
1.751
6
Texas
Kaufman
0.446
0.633
0.547
0.392
0.506
2.345
6
Texas
Kendall
0.144
0.420
0.432
0.423
0.678
6.559
6
Texas
Kenedy
0.117
0.610
0.304
0.447
0.273
0.496
6
Texas
Kent
0.308
0.668
0.214
0.395
0.270
-0.845
6
Texas
Kerr
0.098
0.404
0.487
0.412
0.550
8.145
6
Texas
Kimble
0.134
0.490
0.208
0.474
0.747
5.039
6
Texas
King
0.324
0.676
0.215
0.363
0.452
0.094
6
Texas
Kinney
0.097
0.574
0.315
0.416
0.225
-1.285
6
Texas
Kleberg
0.124
0.549
0.314
0.458
0.379
2.054
6
Texas
Knox
0.129
0.601
0.338
0.398
0.490
3.422
6
Texas
La Salle
0.090
0.494
0.176
0.333
0.292
-7.177
6
Texas
Lamar
0.169
0.538
0.481
0.439
0.511
5.341
6
Texas
Lamb
0.157
0.586
0.246
0.406
0.480
1.155
6
Texas
Lampasas
0.111
0.572
0.336
0.393
0.526
4.211
6
Texas
Lavaca
0.098
0.541
0.357
0.414
0.618
7.367
6
Texas
Lee
0.101
0.550
0.337
0.328
0.519
2.718
6
Texas
Leon
0.146
0.551
0.434
0.363
0.457
3.420
6
Texas
Liberty
0.316
0.539
0.508
0.428
0.467
2.731
6
Texas
Limestone
0.176
0.552
0.398
0.408
0.468
3.049
6
Texas
Lipscomb
0.334
0.749
0.332
0.371
0.375
0.850
6
Texas
Live Oak
0.094
0.558
0.384
0.458
0.547
8.289
6
Texas
Llano
0.101
0.516
0.381
0.396
0.482
4.490
6
Texas
Loving
0.092
0.805
0.223
0.411
0.308
0.140
6
Texas
Lubbock
0.492
0.576
0.599
0.437
0.495
2.477
6
Texas
Lynn
0.207
0.618
0.340
0.469
0.448
2.699
6
Texas
Madison
0.148
0.546
0.243
0.352
0.442
-0.501
6
Texas
Marion
0.249
0.523
0.256
0.447
0.341
-0.104
275
-------
EPA
Built
Natural
REGION
State
County
Risk
Governance
Environment
Environment
Society
CRSI
6
Texas
Martin
0.157
0.610
0.227
0.321
0.528
0.265
6
Texas
Mason
0.130
0.483
0.323
0.400
0.592
3.747
6
Texas
Matagorda
0.256
0.525
0.440
0.503
0.431
3.053
6
Texas
Maverick
0.143
0.572
0.446
0.419
0.269
2.215
6
Texas
McCulloch
0.098
0.504
0.275
0.456
0.414
1.728
6
Texas
McLennan
0.326
0.549
0.691
0.521
0.534
5.280
6
Texas
McMullen
0.110
0.628
0.213
0.347
0.233
-4.501
6
Texas
Medina
0.121
0.428
0.490
0.388
0.491
5.431
6
Texas
Menard
0.191
0.518
0.209
0.394
0.528
0.415
6
Texas
Midland
0.314
0.575
0.461
0.299
0.443
1.306
6
Texas
Milam
0.128
0.541
0.400
0.357
0.458
3.012
6
Texas
Mills
0.178
0.605
0.323
0.412
0.456
2.092
6
Texas
Mitchell
0.134
0.582
0.257
0.400
0.439
0.850
6
Texas
Montague
0.142
0.574
0.320
0.388
0.422
1.476
6
Texas
Montgomery
0.590
0.572
0.644
0.320
0.496
1.780
6
Texas
Moore
0.357
0.558
0.423
0.376
0.423
1.232
6
Texas
Morris
0.271
0.527
0.231
0.357
0.446
-0.386
6
Texas
Motley
0.245
0.649
0.187
0.354
0.412
-0.707
6
Texas
Nacogdoches
0.157
0.514
0.425
0.424
0.413
3.174
6
Texas
Navarro
0.324
0.589
0.498
0.364
0.434
2.050
6
Texas
Newton
0.272
0.476
0.262
0.415
0.357
-0.381
6
Texas
Nolan
0.152
0.553
0.407
0.344
0.455
2.481
6
Texas
Nueces
0.465
0.555
0.699
0.419
0.477
2.981
6
Texas
Ochiltree
0.289
0.523
0.283
0.367
0.451
0.223
6
Texas
Oldham
0.328
0.710
0.278
0.313
0.382
-0.089
6
Texas
Orange
0.624
0.505
0.446
0.394
0.458
0.898
6
Texas
Palo Pinto
0.171
0.510
0.455
0.378
0.411
2.690
6
Texas
Panola
0.186
0.527
0.395
0.443
0.378
2.201
6
Texas
Parker
0.253
0.586
0.630
0.366
0.528
4.741
276
-------
EPA
Built
Natural
REGION
State
County
Risk
Governance
Environment
Environment
Society
CRSI
6
Texas
Parmer
0.174
0.631
0.342
0.480
0.412
3.064
6
Texas
Pecos
0.157
0.539
0.502
0.372
0.512
5.064
6
Texas
Polk
0.336
0.509
0.481
0.459
0.322
1.641
6
Texas
Potter
0.477
0.566
0.482
0.424
0.607
2.274
6
Texas
Presidio
0.098
0.389
0.343
0.466
0.337
1.127
6
Texas
Rains
0.139
0.586
0.355
0.322
0.530
2.638
6
Texas
Randall
0.474
0.594
0.408
0.425
0.594
1.882
6
Texas
Reagan
0.188
0.558
0.196
0.385
0.525
0.305
6
Texas
Real
0.086
0.455
0.199
0.439
0.407
-1.566
6
Texas
Red River
0.262
0.523
0.298
0.385
0.359
-0.121
6
Texas
Reeves
0.110
0.477
0.292
0.308
0.425
-1.398
6
Texas
Refugio
0.116
0.572
0.266
0.468
0.443
2.590
6
Texas
Roberts
0.349
0.698
0.187
0.314
0.470
-0.310
6
Texas
Robertson
0.173
0.540
0.355
0.410
0.405
1.666
6
Texas
Rockwall
0.476
0.643
0.333
0.420
0.609
1.596
6
Texas
Runnels
0.231
0.554
0.220
0.500
0.546
1.910
6
Texas
Rusk
0.209
0.552
0.507
0.368
0.407
2.912
6
Texas
Sabine
0.127
0.454
0.327
0.490
0.305
1.009
6
Texas
San Augustine
0.132
0.460
0.205
0.494
0.344
-0.790
6
Texas
San Jacinto
0.263
0.513
0.275
0.493
0.347
0.531
6
Texas
San Patricio
0.189
0.549
0.489
0.444
0.402
3.882
6
Texas
San Saba
0.161
0.525
0.188
0.382
0.625
1.197
6
Texas
Schleicher
0.113
0.552
0.229
0.423
0.497
1.583
6
Texas
Scurry
0.149
0.578
0.324
0.375
0.413
1.170
6
Texas
Shackelford
0.247
0.590
0.292
0.415
0.336
0.202
6
Texas
Shelby
0.144
0.512
0.340
0.422
0.287
0.119
6
Texas
Sherman
0.244
0.663
0.239
0.317
0.444
-0.198
6
Texas
Smith
0.366
0.542
0.630
0.387
0.532
3.330
6
Texas
Somervell
0.175
0.552
0.341
0.455
0.529
3.526
277
-------
EPA
Built
Natural
REGION
State
County
Risk
Governance
Environment
Environment
Society
CRSI
6
Texas
Starr
0.174
0.523
0.473
0.745
0.193
5.698
6
Texas
Stephens
0.262
0.540
0.188
0.351
0.465
-0.702
6
Texas
Sterling
0.170
0.588
0.244
0.392
0.611
2.365
6
Texas
Stonewall
0.291
0.587
0.048
0.389
0.554
-0.825
6
Texas
Sutton
0.230
0.584
0.225
0.393
0.591
1.354
6
Texas
Swisher
0.208
0.607
0.251
0.550
0.382
1.818
6
Texas
Tarrant
0.683
0.589
0.717
0.255
0.497
1.618
6
Texas
Taylor
0.312
0.591
0.559
0.394
0.511
3.371
6
Texas
Terrell
0.169
0.595
0.245
0.376
0.207
-2.481
6
Texas
Terry
0.114
0.565
0.305
0.351
0.377
-0.159
6
Texas
Throckmorton
0.109
0.597
0.194
0.461
0.442
1.057
6
Texas
Titus
0.152
0.556
0.454
0.371
0.413
3.236
6
Texas
Tom Green
0.156
0.556
0.530
0.473
0.556
7.846
6
Texas
Travis
0.489
0.161
0.844
0.308
0.514
2.410
6
Texas
Trinity
0.201
0.499
0.310
0.436
0.381
0.728
6
Texas
Tyler
0.276
0.492
0.371
0.392
0.370
0.622
6
Texas
Upshur
0.151
0.562
0.406
0.369
0.510
3.695
6
Texas
Upton
0.107
0.631
0.292
0.492
0.448
4.648
6
Texas
Uvalde
0.067
0.488
0.378
0.460
0.484
8.659
6
Texas
Val Verde
0.082
0.561
0.485
0.375
0.424
7.420
6
Texas
Van Zandt
0.099
0.576
0.444
0.369
0.492
6.388
6
Texas
Victoria
0.141
0.529
0.512
0.510
0.541
8.576
6
Texas
Walker
0.223
0.510
0.422
0.391
0.463
2.285
6
Texas
Waller
0.311
0.554
0.417
0.371
0.508
1.846
6
Texas
Ward
0.099
0.499
0.351
0.334
0.396
0.349
6
Texas
Washington
0.225
0.509
0.356
0.484
0.541
3.160
6
Texas
Webb
0.133
0.595
0.725
0.404
0.399
9.828
6
Texas
Wharton
0.079
0.526
0.453
0.444
0.505
10.309
6
Texas
Wheeler
0.322
0.601
0.253
0.309
0.436
-0.347
278
-------
EPA
REGION
State
County
Risk
Governance
Built
Environment
Natural
Environment
Society
CRSI
6
Texas
Wichita
0.260
0.544
0.504
0.354
0.485
2.731
6
Texas
Wilbarger
0.098
0.556
0.255
0.430
0.472
2.240
6
Texas
Willacy
0.163
0.549
0.285
0.718
0.257
3.536
6
Texas
Williamson
0.368
0.183
0.725
0.451
0.558
3.587
6
Texas
Wilson
0.081
0.564
0.474
0.380
0.551
10.361
6
Texas
Winkler
0.062
0.488
0.189
0.303
0.352
-9.211
6
Texas
Wise
0.136
0.623
0.609
0.432
0.459
8.918
6
Texas
Wood
0.152
0.545
0.396
0.310
0.528
2.638
6
Texas
Yoakum
0.154
0.575
0.248
0.372
0.407
-0.315
6
Texas
Young
0.133
0.530
0.381
0.333
0.527
3.002
6
Texas
Zapata
0.154
0.524
0.384
0.422
0.265
0.692
6
Texas
Zavala
0.110
0.503
0.255
0.401
0.191
-4.160
279
-------
EPA
Built
Natural
REGION
State
County
Risk
Governance
Environment
Environment
Society
CRSI
National Average
0.229
0.588
0.393
0.414
0.516
4.213
Regional Average
0.209
0.597
0.358
0.380
0.609
4.113
7
Iowa
Adair
0.229
0.663
0.421
0.423
0.690
5.176
7
Iowa
Adams
0.303
0.670
0.206
0.327
0.647
0.971
7
Iowa
Allamakee
0.131
0.598
0.369
0.378
0.678
6.437
7
Iowa
Appanoose
0.164
0.597
0.306
0.395
0.562
2.959
7
Iowa
Audubon
0.202
0.645
0.255
0.387
0.788
4.067
7
Iowa
Benton
0.158
0.650
0.481
0.393
0.700
8.064
7
Iowa
Black Hawk
0.486
0.581
0.527
0.376
0.561
2.087
7
Iowa
Boone
0.171
0.593
0.364
0.414
0.636
4.863
7
Iowa
Bremer
0.149
0.646
0.434
0.526
0.792
11.109
7
Iowa
Buchanan
0.185
0.646
0.437
0.435
0.675
6.547
7
Iowa
Buena Vista
0.147
0.633
0.425
0.465
0.614
7.586
7
Iowa
Butler
0.202
0.664
0.398
0.488
0.736
6.785
7
Iowa
Calhoun
0.271
0.655
0.324
0.392
0.666
2.909
7
Iowa
Carroll
0.170
0.607
0.439
0.331
0.800
6.819
7
Iowa
Cass
0.191
0.627
0.384
0.445
0.843
7.308
7
Iowa
Cedar
0.146
0.647
0.441
0.511
0.677
9.657
7
Iowa
Cerro Gordo
0.217
0.588
0.423
0.424
0.618
4.480
7
Iowa
Cherokee
0.101
0.597
0.319
0.380
0.830
10.032
7
Iowa
Chickasaw
0.092
0.621
0.376
0.513
0.777
15.283
7
Iowa
Clarke
0.177
0.600
0.297
0.418
0.547
2.781
7
Iowa
Clay
0.188
0.610
0.379
0.487
0.801
7.408
7
Iowa
Clayton
0.113
0.643
0.477
0.417
0.781
13.098
7
Iowa
Clinton
0.217
0.602
0.439
0.473
0.565
4.820
7
Iowa
Crawford
0.146
0.627
0.418
0.327
0.607
5.068
7
Iowa
Dallas
0.399
0.616
0.532
0.477
0.586
3.396
7
Iowa
Davis
0.201
0.594
0.290
0.375
0.460
0.953
7
Iowa
Decatur
0.182
0.644
0.291
0.396
0.509
2.163
280
-------
EPA
Built
Natural
REGION
State
County
Risk
Governance
Environment
Environment
Society
CRSI
7
Iowa
Delaware
0.297
0.621
0.430
0.378
0.771
4.078
7
Iowa
Des Moines
0.282
0.574
0.336
0.489
0.561
2.744
7
Iowa
Dickinson
0.186
0.610
0.431
0.485
0.721
7.354
7
Iowa
Dubuque
0.322
0.584
0.494
0.372
0.684
3.600
7
Iowa
Emmet
0.151
0.596
0.302
0.483
0.688
6.204
7
Iowa
Fayette
0.096
0.613
0.362
0.479
0.730
12.385
7
Iowa
Floyd
0.132
0.605
0.385
0.405
0.635
6.609
7
Iowa
Franklin
0.198
0.616
0.312
0.415
0.641
3.668
7
Iowa
Fremont
0.258
0.685
0.253
0.456
0.728
3.526
7
Iowa
Greene
0.170
0.646
0.353
0.386
0.739
5.789
7
Iowa
Grundy
0.156
0.671
0.379
0.486
0.732
8.404
7
Iowa
Guthrie
0.189
0.647
0.390
0.397
0.625
4.720
7
Iowa
Hamilton
0.173
0.641
0.390
0.322
0.578
3.506
7
Iowa
Hancock
0.131
0.629
0.321
0.468
0.674
7.309
7
Iowa
Hardin
0.188
0.633
0.403
0.413
0.683
5.648
7
Iowa
Harrison
0.263
0.677
0.332
0.387
0.677
3.211
7
Iowa
Henry
0.156
0.621
0.395
0.504
0.599
7.000
7
Iowa
Howard
0.115
0.595
0.312
0.486
0.673
8.223
7
Iowa
Humboldt
0.153
0.650
0.328
0.343
0.542
2.822
7
Iowa
Ida
0.105
0.682
0.287
0.389
0.748
8.319
7
Iowa
Iowa
0.188
0.618
0.417
0.430
0.675
5.935
7
Iowa
Jackson
0.233
0.617
0.428
0.396
0.614
4.025
7
Iowa
Jasper
0.200
0.593
0.424
0.369
0.565
3.705
7
Iowa
Jefferson
0.189
0.563
0.271
0.413
0.630
2.842
7
Iowa
Johnson
0.332
0.599
0.550
0.493
0.548
4.076
7
Iowa
Jones
0.204
0.618
0.366
0.412
0.719
4.992
7
Iowa
Keokuk
0.167
0.667
0.340
0.446
0.600
5.081
7
Iowa
Kossuth
0.142
0.650
0.459
0.447
0.745
10.087
7
Iowa
Lee
0.256
0.574
0.401
0.481
0.624
4.100
281
-------
EPA
Built
Natural
REGION
State
County
Risk
Governance
Environment
Environment
Society
CRSI
7
Iowa
Linn
0.750
0.587
0.681
0.398
0.623
2.140
7
Iowa
Louisa
0.190
0.648
0.322
0.511
0.570
4.640
7
Iowa
Lucas
0.177
0.614
0.258
0.371
0.472
0.797
7
Iowa
Lyon
0.131
0.670
0.364
0.403
0.740
8.290
7
Iowa
Madison
0.205
0.628
0.375
0.438
0.694
5.197
7
Iowa
Mahaska
0.170
0.578
0.337
0.472
0.630
5.154
7
Iowa
Marion
0.185
0.589
0.428
0.404
0.706
5.989
7
Iowa
Marshall
0.181
0.612
0.417
0.364
0.532
3.659
7
Iowa
Mills
0.230
0.660
0.401
0.382
0.649
4.104
7
Iowa
Mitchell
0.140
0.614
0.355
0.475
0.735
8.365
7
Iowa
Monona
0.157
0.622
0.305
0.422
0.583
3.919
7
Iowa
Monroe
0.222
0.573
0.284
0.373
0.629
2.185
7
Iowa
Montgomery
0.172
0.586
0.215
0.390
0.646
2.247
7
Iowa
Muscatine
0.243
0.579
0.365
0.490
0.546
3.412
7
Iowa
O'Brien
0.123
0.620
0.380
0.463
0.712
9.481
7
Iowa
Osceola
0.236
0.623
0.390
0.462
0.716
5.102
7
Iowa
Page
0.128
0.592
0.292
0.422
0.634
5.109
7
Iowa
Palo Alto
0.158
0.638
0.375
0.406
0.697
6.353
7
Iowa
Plymouth
0.114
0.624
0.467
0.323
0.722
9.482
7
Iowa
Pocahontas
0.313
0.643
0.302
0.479
0.690
3.138
7
Iowa
Polk
0.746
0.589
0.715
0.375
0.581
2.088
7
Iowa
Pottawattamie
0.246
0.603
0.530
0.359
0.540
3.896
7
Iowa
Poweshiek
0.177
0.589
0.436
0.391
0.702
6.181
7
Iowa
Ringgold
0.360
0.664
0.228
0.390
0.664
1.487
7
Iowa
Sac
0.262
0.655
0.335
0.393
0.697
3.366
7
Iowa
Scott
0.477
0.576
0.472
0.389
0.567
1.890
7
Iowa
Shelby
0.108
0.645
0.368
0.391
0.784
10.422
7
Iowa
Sioux
0.194
0.598
0.484
0.378
0.831
7.478
7
Iowa
Story
0.311
0.599
0.587
0.376
0.530
3.622
282
-------
EPA
Built
Natural
REGION
State
County
Risk
Governance
Environment
Environment
Society
CRSI
7
Iowa
Tama
0.177
0.648
0.369
0.409
0.523
3.764
7
Iowa
Taylor
0.292
0.693
0.174
0.376
0.539
0.479
7
Iowa
Union
0.221
0.621
0.289
0.375
0.705
3.164
7
Iowa
Van Buren
0.172
0.652
0.340
0.442
0.461
3.192
7
Iowa
Wapello
0.265
0.558
0.347
0.374
0.498
1.432
7
Iowa
Warren
0.200
0.627
0.435
0.458
0.620
5.636
7
Iowa
Washington
0.131
0.629
0.420
0.464
0.740
10.273
7
Iowa
Wayne
0.261
0.666
0.256
0.386
0.522
1.234
7
Iowa
Webster
0.203
0.588
0.423
0.396
0.620
4.452
7
Iowa
Winnebago
0.145
0.616
0.331
0.442
0.767
7.524
7
Iowa
Winneshiek
0.093
0.627
0.403
0.410
0.776
13.257
7
Iowa
Woodbury
0.232
0.589
0.563
0.342
0.579
4.604
7
Iowa
Worth
0.157
0.654
0.339
0.471
0.573
5.356
7
Iowa
Wright
0.143
0.598
0.325
0.416
0.639
5.188
7
Kansas
Allen
0.126
0.552
0.355
0.425
0.614
5.935
7
Kansas
Anderson
0.125
0.575
0.298
0.425
0.802
7.884
7
Kansas
Atchison
0.106
0.572
0.328
0.412
0.584
5.740
7
Kansas
Barber
0.137
0.619
0.258
0.308
0.656
2.569
7
Kansas
Barton
0.334
0.556
0.425
0.347
0.684
2.654
7
Kansas
Bourbon
0.185
0.554
0.275
0.451
0.647
3.592
7
Kansas
Brown
0.130
0.684
0.338
0.502
0.742
9.804
7
Kansas
Butler
0.214
0.594
0.611
0.419
0.639
7.043
7
Kansas
Chase
0.226
0.623
0.273
0.406
0.506
1.513
7
Kansas
Chautauqua
0.155
0.597
0.150
0.335
0.685
1.055
7
Kansas
Cherokee
0.222
0.462
0.368
0.455
0.568
3.056
7
Kansas
Cheyenne
0.163
0.614
0.296
0.349
0.627
3.040
7
Kansas
Clark
0.200
0.661
0.306
0.319
0.555
1.761
7
Kansas
Clay
0.102
0.579
0.276
0.400
0.748
7.561
7
Kansas
Cloud
0.107
0.578
0.305
0.304
0.751
5.709
283
-------
EPA
Built
Natural
REGION
State
County
Risk
Governance
Environment
Environment
Society
CRSI
7
Kansas
Coffey
0.134
0.652
0.405
0.363
0.777
8.568
7
Kansas
Comanche
0.137
0.637
0.245
0.397
0.541
2.391
7
Kansas
Cowley
0.182
0.574
0.427
0.333
0.500
2.801
7
Kansas
Crawford
0.174
0.519
0.468
0.441
0.554
5.391
7
Kansas
Decatur
0.141
0.584
0.222
0.377
0.704
3.432
7
Kansas
Dickinson
0.132
0.579
0.397
0.406
0.721
7.970
7
Kansas
Doniphan
0.103
0.666
0.332
0.438
0.610
8.051
7
Kansas
Douglas
0.254
0.578
0.514
0.443
0.545
4.350
7
Kansas
Edwards
0.211
0.639
0.152
0.369
0.633
0.925
7
Kansas
Elk
0.213
0.649
0.275
0.340
0.421
0.210
7
Kansas
Ellis
0.181
0.569
0.422
0.288
0.734
4.650
7
Kansas
Ellsworth
0.154
0.644
0.335
0.411
0.744
6.561
7
Kansas
Finney
0.154
0.556
0.429
0.290
0.632
4.253
7
Kansas
Ford
0.186
0.566
0.429
0.440
0.569
4.886
7
Kansas
Franklin
0.129
0.609
0.393
0.402
0.634
6.877
7
Kansas
Geary
0.197
0.590
0.365
0.407
0.470
2.462
7
Kansas
Gove
0.181
0.738
0.325
0.282
0.812
4.923
7
Kansas
Graham
0.245
0.632
0.196
0.389
0.704
2.012
7
Kansas
Grant
0.254
0.535
0.242
0.316
0.597
0.510
7
Kansas
Gray
0.203
0.672
0.382
0.297
0.680
3.714
7
Kansas
Greeley
0.139
0.630
0.213
0.299
0.418
-1.776
7
Kansas
Greenwood
0.141
0.597
0.233
0.336
0.639
2.113
7
Kansas
Flamilton
0.104
0.593
0.240
0.375
0.405
-0.463
7
Kansas
Flarper
0.234
0.610
0.278
0.371
0.591
1.816
7
Kansas
Flarvey
0.157
0.586
0.413
0.422
0.666
6.584
7
Kansas
Haskell
0.201
0.645
0.260
0.400
0.612
2.587
7
Kansas
Flodgeman
0.282
0.662
0.266
0.345
0.517
0.830
7
Kansas
Jackson
0.142
0.649
0.391
0.377
0.760
7.829
7
Kansas
Jefferson
0.131
0.626
0.440
0.441
0.670
9.158
284
-------
EPA
Built
Natural
REGION
State
County
Risk
Governance
Environment
Environment
Society
CRSI
7
Kansas
Jewell
0.383
0.626
0.220
0.332
0.680
0.956
7
Kansas
Johnson
0.670
0.570
0.528
0.317
0.589
1.363
7
Kansas
Kearny
0.171
0.607
0.283
0.409
0.726
4.616
7
Kansas
Kingman
0.164
0.621
0.317
0.396
0.712
5.097
7
Kansas
Kiowa
0.293
0.705
0.330
0.320
0.748
2.875
7
Kansas
Labette
0.283
0.551
0.361
0.454
0.572
2.662
7
Kansas
Lane
0.187
0.657
0.237
0.341
0.563
1.240
7
Kansas
Leavenworth
0.269
0.579
0.437
0.387
0.575
3.063
7
Kansas
Lincoln
0.105
0.662
0.216
0.305
0.716
3.729
7
Kansas
Linn
0.194
0.604
0.425
0.504
0.660
6.542
7
Kansas
Logan
0.124
0.619
0.239
0.302
0.868
5.674
7
Kansas
Lyon
0.135
0.568
0.449
0.428
0.543
6.542
7
Kansas
Marion
0.126
0.628
0.333
0.447
0.731
8.318
7
Kansas
Marshall
0.082
0.628
0.330
0.342
0.819
11.629
7
Kansas
McPherson
0.162
0.609
0.488
0.402
0.771
8.729
7
Kansas
Meade
0.281
0.658
0.341
0.375
0.750
3.412
7
Kansas
Miami
0.120
0.590
0.493
0.406
0.733
11.203
7
Kansas
Mitchell
0.225
0.604
0.304
0.340
0.718
2.934
7
Kansas
Montgomery
0.177
0.553
0.419
0.438
0.526
4.413
7
Kansas
Morris
0.153
0.578
0.304
0.390
0.715
4.928
7
Kansas
Morton
0.172
0.604
0.249
0.398
0.434
0.579
7
Kansas
Nemaha
0.113
0.628
0.380
0.377
0.859
11.069
7
Kansas
Neosho
0.140
0.523
0.383
0.439
0.676
6.782
7
Kansas
Ness
0.188
0.626
0.230
0.420
0.558
1.947
7
Kansas
Norton
0.259
0.592
0.271
0.323
0.728
2.072
7
Kansas
Osage
0.134
0.652
0.410
0.400
0.580
6.460
7
Kansas
Osborne
0.216
0.640
0.215
0.346
0.688
1.929
7
Kansas
Ottawa
0.101
0.685
0.324
0.394
0.838
11.470
7
Kansas
Pawnee
0.161
0.571
0.244
0.354
0.690
2.764
285
-------
EPA
Built
Natural
REGION
State
County
Risk
Governance
Environment
Environment
Society
CRSI
7
Kansas
Phillips
0.226
0.655
0.302
0.337
0.787
3.700
7
Kansas
Pottawatomie
0.124
0.614
0.478
0.358
0.739
9.836
7
Kansas
Pratt
0.314
0.588
0.311
0.338
0.719
2.099
7
Kansas
Rawlins
0.129
0.636
0.211
0.273
0.672
1.460
7
Kansas
Reno
0.246
0.579
0.466
0.375
0.679
4.384
7
Kansas
Republic
0.182
0.633
0.290
0.327
0.705
3.265
7
Kansas
Rice
0.168
0.623
0.326
0.388
0.585
3.542
7
Kansas
Riley
0.225
0.572
0.437
0.417
0.508
3.381
7
Kansas
Rooks
0.232
0.646
0.283
0.346
0.705
2.742
7
Kansas
Rush
0.204
0.547
0.351
0.325
0.679
3.030
7
Kansas
Russell
0.136
0.593
0.272
0.337
0.564
1.864
7
Kansas
Saline
0.172
0.592
0.449
0.458
0.670
7.154
7
Kansas
Scott
0.144
0.521
0.269
0.332
0.761
3.819
7
Kansas
Sedgwick
0.795
0.565
0.714
0.372
0.559
1.865
7
Kansas
Seward
0.241
0.519
0.373
0.262
0.509
0.645
7
Kansas
Shawnee
0.358
0.548
0.447
0.382
0.610
2.451
7
Kansas
Sheridan
0.188
0.599
0.274
0.259
0.710
1.916
7
Kansas
Sherman
0.178
0.540
0.277
0.336
0.720
2.926
7
Kansas
Smith
0.257
0.620
0.221
0.337
0.689
1.520
7
Kansas
Stafford
0.205
0.643
0.227
0.279
0.604
0.592
7
Kansas
Stanton
0.097
0.621
0.253
0.358
0.500
1.600
7
Kansas
Stevens
0.106
0.531
0.268
0.267
0.498
-1.122
7
Kansas
Sumner
0.193
0.611
0.467
0.404
0.650
5.850
7
Kansas
Thomas
0.166
0.562
0.389
0.342
0.760
5.608
7
Kansas
Trego
0.289
0.596
0.257
0.367
0.791
2.550
7
Kansas
Wabaunsee
0.107
0.684
0.418
0.361
0.828
12.296
7
Kansas
Wallace
0.175
0.646
0.224
0.282
0.519
-0.262
7
Kansas
Washington
0.096
0.668
0.310
0.379
0.805
10.545
7
Kansas
Wichita
0.092
0.586
0.230
0.344
0.664
3.825
286
-------
EPA
Built
Natural
REGION
State
County
Risk
Governance
Environment
Environment
Society
CRSI
7
Kansas
Wilson
0.135
0.591
0.283
0.414
0.735
5.972
7
Kansas
Woodson
0.214
0.592
0.135
0.358
0.581
-0.129
7
Kansas
Wyandotte
0.653
0.543
0.294
0.262
0.454
-0.211
7
Missouri
Adair
0.113
0.561
0.404
0.466
0.488
6.553
7
Missouri
Andrew
0.116
0.619
0.379
0.435
0.551
6.779
7
Missouri
Atchison
0.089
0.679
0.326
0.401
0.585
7.685
7
Missouri
Audrain
0.108
0.555
0.456
0.422
0.623
9.528
7
Missouri
Barry
0.208
0.428
0.466
0.366
0.489
2.558
7
Missouri
Barton
0.246
0.541
0.350
0.414
0.545
2.289
7
Missouri
Bates
0.161
0.596
0.407
0.483
0.478
5.011
7
Missouri
Benton
0.147
0.505
0.440
0.494
0.517
6.171
7
Missouri
Bollinger
0.179
0.513
0.327
0.336
0.467
0.715
7
Missouri
Boone
0.360
0.554
0.596
0.387
0.501
2.994
7
Missouri
Buchanan
0.199
0.554
0.362
0.426
0.530
3.054
7
Missouri
Butler
0.248
0.400
0.441
0.401
0.511
2.278
7
Missouri
Caldwell
0.081
0.686
0.333
0.428
0.554
8.815
7
Missouri
Callaway
0.137
0.538
0.574
0.351
0.552
7.415
7
Missouri
Camden
0.206
0.503
0.533
0.282
0.584
3.707
7
Missouri
Cape Girardeau
0.338
0.419
0.540
0.358
0.664
3.087
7
Missouri
Carroll
0.104
0.624
0.333
0.365
0.579
5.240
7
Missouri
Carter
0.379
0.426
0.329
0.489
0.367
0.595
7
Missouri
Cass
0.297
0.591
0.600
0.348
0.598
4.101
7
Missouri
Cedar
0.296
0.447
0.306
0.469
0.629
2.200
7
Missouri
Chariton
0.105
0.619
0.329
0.395
0.571
5.618
7
Missouri
Christian
0.339
0.474
0.483
0.381
0.609
2.633
7
Missouri
Clark
0.178
0.619
0.287
0.432
0.341
0.651
7
Missouri
Clay
0.752
0.566
0.490
0.355
0.543
1.078
7
Missouri
Clinton
0.104
0.614
0.387
0.412
0.649
8.960
7
Missouri
Cole
0.249
0.518
0.483
0.373
0.694
4.357
287
-------
EPA
REGION
State
County
Risk
Governance
Built
Environment
Natural
Environment
Society
CRSI
7
Missouri
Cooper
0.107
0.620
0.395
0.403
0.583
7.539
7
Missouri
Crawford
0.136
0.505
0.395
0.409
0.589
5.323
7
Missouri
Dade
0.151
0.539
0.240
0.424
0.427
0.399
7
Missouri
Dallas
0.159
0.448
0.357
0.368
0.447
1.148
7
Missouri
Daviess
0.119
0.663
0.343
0.425
0.540
5.762
7
Missouri
DeKalb
0.108
0.655
0.381
0.458
0.496
7.120
7
Missouri
Dent
0.103
0.467
0.293
0.422
0.575
4.025
7
Missouri
Douglas
0.154
0.377
0.323
0.427
0.377
0.186
7
Missouri
Dunklin
0.177
0.470
0.382
0.423
0.423
2.028
7
Missouri
Franklin
0.388
0.532
0.601
0.354
0.590
2.990
7
Missouri
Gasconade
0.109
0.522
0.310
0.345
0.621
3.808
7
Missouri
Gentry
0.215
0.649
0.229
0.396
0.651
2.373
7
Missouri
Greene
0.674
0.437
0.541
0.285
0.518
0.887
7
Missouri
Grundy
0.111
0.600
0.297
0.382
0.663
5.704
7
Missouri
Harrison
0.102
0.656
0.305
0.377
0.574
5.145
7
Missouri
Henry
0.128
0.583
0.419
0.524
0.586
8.881
7
Missouri
Hickory
0.113
0.495
0.335
0.437
0.367
1.616
7
Missouri
Holt
0.100
0.700
0.292
0.416
0.688
8.507
7
Missouri
Howard
0.105
0.583
0.282
0.379
0.580
3.856
7
Missouri
Howell
0.161
0.426
0.432
0.403
0.611
4.763
7
Missouri
Iron
0.287
0.502
0.314
0.461
0.448
1.220
7
Missouri
Jackson
0.709
0.557
0.723
0.309
0.491
1.704
7
Missouri
Jasper
0.658
0.481
0.614
0.362
0.460
1.381
7
Missouri
Jefferson
0.590
0.540
0.609
0.333
0.564
1.848
7
Missouri
Johnson
0.148
0.584
0.572
0.385
0.524
7.313
7
Missouri
Knox
0.148
0.607
0.240
0.368
0.378
-0.715
7
Missouri
Laclede
0.170
0.488
0.410
0.333
0.474
1.950
7
Missouri
Lafayette
0.112
0.582
0.466
0.360
0.711
9.844
7
Missouri
Lawrence
0.259
0.470
0.406
0.380
0.591
2.497
288
-------
EPA
REGION
State
County
Risk
Governance
Built
Environment
Natural
Environment
Society
CRSI
7
Missouri
Lewis
0.187
0.610
0.323
0.398
0.548
2.813
7
Missouri
Lincoln
0.240
0.588
0.535
0.388
0.539
4.282
7
Missouri
Linn
0.106
0.614
0.370
0.408
0.534
6.158
7
Missouri
Livingston
0.104
0.548
0.319
0.357
0.636
5.016
7
Missouri
Macon
0.112
0.616
0.466
0.450
0.666
11.365
7
Missouri
Madison
0.198
0.513
0.330
0.399
0.492
1.722
7
Missouri
Maries
0.134
0.481
0.357
0.317
0.467
0.958
7
Missouri
Marion
0.152
0.548
0.395
0.396
0.578
4.665
7
Missouri
McDonald
0.163
0.474
0.387
0.264
0.447
0.205
7
Missouri
Mercer
0.087
0.629
0.177
0.367
0.498
-0.227
7
Missouri
Miller
0.121
0.508
0.480
0.297
0.545
4.858
7
Missouri
Mississippi
0.329
0.462
0.310
0.384
0.347
-0.261
7
Missouri
Moniteau
0.109
0.572
0.366
0.372
0.658
6.919
7
Missouri
Monroe
0.123
0.606
0.325
0.448
0.622
6.467
7
Missouri
Montgomery
0.105
0.557
0.380
0.396
0.573
6.351
7
Missouri
Morgan
0.125
0.507
0.455
0.354
0.485
4.327
7
Missouri
New Madrid
0.325
0.512
0.418
0.362
0.393
0.890
7
Missouri
Newton
0.318
0.480
0.526
0.326
0.542
2.350
7
Missouri
Nodaway
0.099
0.599
0.509
0.429
0.601
12.014
7
Missouri
Oregon
0.178
0.442
0.271
0.465
0.451
1.092
7
Missouri
Osage
0.094
0.529
0.407
0.322
0.790
10.220
7
Missouri
Ozark
0.187
0.419
0.332
0.399
0.570
2.167
7
Missouri
Pemiscot
0.235
0.519
0.310
0.397
0.396
0.410
7
Missouri
Perry
0.191
0.437
0.333
0.338
0.752
3.308
7
Missouri
Pettis
0.160
0.551
0.444
0.418
0.530
5.011
7
Missouri
Phelps
0.136
0.474
0.502
0.364
0.510
5.221
7
Missouri
Pike
0.138
0.566
0.402
0.433
0.538
5.517
7
Missouri
Platte
0.384
0.591
0.495
0.357
0.523
2.121
7
Missouri
Polk
0.160
0.500
0.404
0.417
0.548
4.246
289
-------
EPA
REGION
State
County
Risk
Governance
Built
Environment
Natural
Environment
Society
CRSI
7
Missouri
Pulaski
0.293
0.498
0.481
0.378
0.388
1.605
7
Missouri
Putnam
0.079
0.580
0.275
0.351
0.399
-0.509
7
Missouri
Ralls
0.127
0.591
0.388
0.418
0.608
6.668
7
Missouri
Randolph
0.112
0.564
0.414
0.375
0.640
7.567
7
Missouri
Ray
0.100
0.618
0.380
0.421
0.563
7.720
7
Missouri
Reynolds
0.150
0.422
0.323
0.453
0.574
3.497
7
Missouri
Ripley
0.141
0.402
0.263
0.467
0.385
0.059
7
Missouri
Saline
0.108
0.594
0.364
0.370
0.536
4.890
7
Missouri
Schuyler
0.071
0.652
0.282
0.353
0.391
0.583
7
Missouri
Scotland
0.164
0.606
0.211
0.400
0.357
-0.895
7
Missouri
Scott
0.274
0.505
0.431
0.347
0.557
2.192
7
Missouri
Shannon
0.152
0.424
0.339
0.486
0.317
0.958
7
Missouri
Shelby
0.080
0.635
0.348
0.412
0.581
8.971
7
Missouri
St. Charles
0.796
0.568
0.558
0.333
0.602
1.326
7
Missouri
St. Clair
0.126
0.550
0.296
0.533
0.471
4.523
7
Missouri
St. Francois
0.271
0.483
0.446
0.388
0.493
2.187
7
Missouri
St. Louis
0.597
0.509
0.254
0.010
0.374
-1.763
7
Missouri
St. Louis County
0.853
0.522
0.620
0.152
0.530
0.689
7
Missouri
Ste. Genevieve
0.120
0.472
0.397
0.350
0.632
5.275
7
Missouri
Stoddard
0.169
0.511
0.452
0.370
0.446
2.968
7
Missouri
Stone
0.179
0.424
0.477
0.431
0.587
5.081
7
Missouri
Sullivan
0.082
0.645
0.331
0.400
0.463
5.134
7
Missouri
Taney
0.298
0.390
0.569
0.468
0.413
2.909
7
Missouri
Texas
0.099
0.461
0.441
0.402
0.536
6.833
7
Missouri
Vernon
0.103
0.537
0.424
0.477
0.587
9.643
7
Missouri
Warren
0.179
0.555
0.460
0.355
0.491
3.454
7
Missouri
Washington
0.151
0.487
0.369
0.479
0.514
4.356
7
Missouri
Wayne
0.237
0.447
0.344
0.463
0.450
1.622
7
Missouri
Webster
0.151
0.504
0.498
0.283
0.574
4.340
290
-------
EPA
Built
Natural
REGION
State
County
Risk
Governance
Environment
Environment
Society
CRSI
7
Missouri
Worth
0.107
0.646
0.248
0.398
0.417
0.967
7
Missouri
Wright
0.117
0.455
0.367
0.413
0.500
3.673
7
Nebraska
Adams
0.289
0.571
0.361
0.436
0.620
2.851
7
Nebraska
Antelope
0.079
0.662
0.283
0.308
0.753
8.240
7
Nebraska
Arthur
0.186
0.727
0.119
0.234
0.535
-1.774
7
Nebraska
Banner
0.153
0.684
0.171
0.224
0.236
-5.517
7
Nebraska
Blaine
0.353
0.678
0.153
0.312
0.477
-0.595
7
Nebraska
Boone
0.077
0.646
0.306
0.299
0.681
6.937
7
Nebraska
Box Butte
0.118
0.571
0.318
0.314
0.636
3.726
7
Nebraska
Boyd
0.407
0.686
0.191
0.270
0.586
0.021
7
Nebraska
Brown
0.272
0.566
0.196
0.323
0.818
1.805
7
Nebraska
Buffalo
0.282
0.601
0.510
0.344
0.736
4.432
7
Nebraska
Burt
0.082
0.666
0.252
0.376
0.659
6.738
7
Nebraska
Butler
0.124
0.646
0.355
0.435
0.723
8.739
7
Nebraska
Cass
0.130
0.674
0.498
0.342
0.588
7.694
7
Nebraska
Cedar
0.257
0.656
0.395
0.374
0.737
4.186
7
Nebraska
Chase
0.232
0.615
0.326
0.345
0.473
1.130
7
Nebraska
Cherry
0.264
0.595
0.367
0.339
0.627
2.427
7
Nebraska
Cheyenne
0.116
0.632
0.367
0.311
0.678
6.046
7
Nebraska
Clay
0.416
0.694
0.313
0.523
0.614
2.448
7
Nebraska
Colfax
0.087
0.620
0.313
0.380
0.467
3.515
7
Nebraska
Cuming
0.096
0.608
0.332
0.387
0.719
8.894
7
Nebraska
Custer
0.234
0.658
0.471
0.234
0.724
3.895
7
Nebraska
Dakota
0.137
0.608
0.352
0.416
0.446
3.260
7
Nebraska
Dawes
0.150
0.551
0.251
0.306
0.681
2.066
7
Nebraska
Dawson
0.218
0.593
0.395
0.255
0.641
2.448
7
Nebraska
Deuel
0.130
0.691
0.278
0.347
0.449
1.273
7
Nebraska
Dixon
0.124
0.726
0.336
0.359
0.531
4.407
7
Nebraska
Dodge
0.143
0.584
0.431
0.416
0.623
6.822
291
-------
EPA
Built
Natural
REGION
State
County
Risk
Governance
Environment
Environment
Society
CRSI
7
Nebraska
Douglas
0.810
0.589
0.589
0.155
0.555
0.777
7
Nebraska
Dundy
0.121
0.607
0.275
0.307
0.502
0.672
7
Nebraska
Fillmore
0.388
0.659
0.322
0.433
0.800
2.972
7
Nebraska
Franklin
0.268
0.672
0.191
0.279
0.538
-0.287
7
Nebraska
Frontier
0.254
0.708
0.233
0.347
0.558
1.095
7
Nebraska
Furnas
0.319
0.644
0.394
0.353
0.640
2.568
7
Nebraska
Gage
0.264
0.605
0.392
0.303
0.689
2.842
7
Nebraska
Garden
0.294
0.610
0.234
0.285
0.521
-0.148
7
Nebraska
Garfield
0.196
0.586
0.234
0.266
0.531
-0.473
7
Nebraska
Gosper
0.311
0.646
0.280
0.333
0.642
1.516
7
Nebraska
Grant
0.235
0.695
0.154
0.283
0.533
-0.643
7
Nebraska
Greeley
0.329
0.676
0.257
0.281
0.601
0.701
7
Nebraska
Hall
0.295
0.576
0.439
0.325
0.691
3.054
7
Nebraska
Hamilton
0.246
0.659
0.379
0.503
0.747
5.592
7
Nebraska
Harlan
0.279
0.648
0.239
0.395
0.532
1.070
7
Nebraska
Hayes
0.196
0.652
0.121
0.279
0.284
-3.992
7
Nebraska
Hitchcock
0.185
0.643
0.241
0.282
0.400
-1.267
7
Nebraska
Holt
0.274
0.629
0.388
0.278
0.889
4.000
7
Nebraska
Hooker
0.242
0.618
0.214
0.319
0.415
-0.868
7
Nebraska
Howard
0.181
0.667
0.256
0.334
0.571
1.607
7
Nebraska
Jefferson
0.289
0.625
0.293
0.365
0.681
2.216
7
Nebraska
Johnson
0.116
0.656
0.298
0.312
0.673
4.683
7
Nebraska
Kearney
0.300
0.626
0.323
0.443
0.708
3.224
7
Nebraska
Keith
0.164
0.584
0.375
0.269
0.686
3.619
7
Nebraska
Keya Paha
0.288
0.673
0.179
0.294
0.372
-1.371
7
Nebraska
Kimball
0.219
0.595
0.136
0.304
0.509
-1.353
7
Nebraska
Knox
0.230
0.657
0.418
0.419
0.673
4.881
7
Nebraska
Lancaster
0.594
0.575
0.636
0.419
0.631
2.594
7
Nebraska
Lincoln
0.198
0.594
0.551
0.289
0.663
5.431
292
-------
EPA
Built
Natural
REGION
State
County
Risk
Governance
Environment
Environment
Society
CRSI
7
Nebraska
Logan
0.318
0.786
0.194
0.190
0.551
-0.464
7
Nebraska
Loup
0.258
0.710
0.177
0.233
0.433
-1.529
7
Nebraska
Madison
0.114
0.589
0.382
0.361
0.763
8.720
7
Nebraska
McPherson
0.279
0.660
0.149
0.233
0.483
-1.519
7
Nebraska
Merrick
0.172
0.630
0.285
0.367
0.679
3.639
7
Nebraska
Morrill
0.115
0.635
0.370
0.243
0.502
1.785
7
Nebraska
Nance
0.152
0.636
0.293
0.328
0.595
2.568
7
Nebraska
Nemaha
0.081
0.612
0.226
0.357
0.569
2.557
7
Nebraska
Nuckolls
0.213
0.607
0.199
0.370
0.630
1.330
7
Nebraska
Otoe
0.255
0.631
0.391
0.384
0.795
4.614
7
Nebraska
Pawnee
0.058
0.639
0.266
0.331
0.567
4.747
7
Nebraska
Perkins
0.128
0.640
0.330
0.340
0.763
6.685
7
Nebraska
Phelps
0.155
0.610
0.262
0.432
0.725
5.135
7
Nebraska
Pierce
0.053
0.672
0.299
0.388
0.826
19.883
7
Nebraska
Platte
0.145
0.592
0.431
0.359
0.736
7.360
7
Nebraska
Polk
0.238
0.676
0.317
0.407
0.690
3.685
7
Nebraska
Red Willow
0.104
0.546
0.313
0.305
0.677
4.416
7
Nebraska
Richardson
0.074
0.590
0.310
0.408
0.719
11.161
7
Nebraska
Rock
0.183
0.676
0.242
0.308
0.520
0.539
7
Nebraska
Saline
0.272
0.608
0.346
0.392
0.598
2.465
7
Nebraska
Sarpy
0.509
0.619
0.353
0.363
0.595
1.218
7
Nebraska
Saunders
0.140
0.658
0.453
0.423
0.708
9.277
7
Nebraska
Scotts Bluff
0.392
0.601
0.470
0.309
0.716
2.602
7
Nebraska
Seward
0.160
0.645
0.417
0.408
0.772
7.970
7
Nebraska
Sheridan
0.319
0.615
0.279
0.247
0.595
0.417
7
Nebraska
Sherman
0.283
0.650
0.240
0.297
0.611
0.777
7
Nebraska
Sioux
0.139
0.626
0.185
0.335
0.334
-2.873
7
Nebraska
Stanton
0.192
0.648
0.370
0.352
0.651
4.061
7
Nebraska
Thayer
0.411
0.690
0.338
0.370
0.750
2.371
293
-------
EPA
REGION
State
County
Risk
Governance
Built
Environment
Natural
Environment
Society
CRSI
7
Nebraska
Thomas
0.293
0.712
0.216
0.349
0.559
0.833
7
Nebraska
Thurston
0.147
0.729
0.377
0.805
0.217
7.758
7
Nebraska
Valley
0.289
0.629
0.279
0.266
0.803
2.090
7
Nebraska
Washington
0.215
0.619
0.398
0.374
0.800
5.450
7
Nebraska
Wayne
0.175
0.598
0.329
0.403
0.590
3.575
7
Nebraska
Webster
0.286
0.673
0.207
0.302
0.498
-0.184
7
Nebraska
Wheeler
0.233
0.675
0.165
0.173
0.429
-2.643
7
Nebraska
York
0.227
0.618
0.353
0.436
0.854
5.774
294
-------
EPA Built Natural
REGION State
County
Risk
Governance
Environment
Environment
Society
CRSI
National Average
0.229
0.588
0.393
0.414
0.516
4.213
Regional Average
0.162
0.557
0.398
0.396
0.617
6.086
8 Colorado
Adams
0.592
0.544
0.606
0.205
0.520
1.157
8 Colorado
Alamosa
0.121
0.536
0.389
0.347
0.601
5.036
8 Colorado
Arapahoe
0.591
0.540
0.436
0.179
0.508
0.235
8 Colorado
Archuleta
0.117
0.538
0.440
0.500
0.580
9.206
8 Colorado
Baca
0.128
0.606
0.188
0.357
0.472
-0.696
8 Colorado
Bent
0.203
0.533
0.164
0.363
0.402
-1.722
8 Colorado
Boulder
0.556
0.539
0.645
0.436
0.539
2.497
8 Colorado
Broomfield
0.702
0.550
0.227
0.246
0.524
-0.300
8 Colorado
Chaffee
0.091
0.499
0.472
0.504
0.744
15.955
8 Colorado
Cheyenne
0.106
0.639
0.294
0.265
0.497
0.482
8 Colorado
Clear Creek
0.202
0.567
0.499
0.438
0.619
5.911
8 Colorado
Conejos
0.082
0.575
0.339
0.385
0.410
2.807
8 Colorado
Costilla
0.122
0.504
0.328
0.224
0.206
-5.452
8 Colorado
Crowley
0.125
0.604
0.150
0.307
0.167
-7.285
8 Colorado
Custer
0.179
0.486
0.388
0.418
0.573
3.745
8 Colorado
Delta
0.121
0.509
0.542
0.517
0.562
10.965
8 Colorado
Denver
0.551
0.540
0.403
0.068
0.487
-0.474
8 Colorado
Dolores
0.097
0.503
0.222
0.577
0.438
3.845
8 Colorado
Douglas
0.467
0.566
0.565
0.411
0.588
2.649
8 Colorado
Eagle
0.124
0.548
0.627
0.543
0.650
14.752
8 Colorado
El Paso
0.490
0.533
0.875
0.318
0.500
3.325
8 Colorado
Elbert
0.132
0.621
0.579
0.269
0.657
8.441
8 Colorado
Fremont
0.155
0.518
0.498
0.418
0.527
5.879
8 Colorado
Garfield
0.131
0.545
0.707
0.454
0.688
14.497
8 Colorado
Gilpin
0.274
0.547
0.387
0.388
0.528
2.079
8 Colorado
Grand
0.139
0.560
0.612
0.419
0.779
12.624
8 Colorado
Gunnison
0.102
0.520
0.515
0.484
0.786
15.997
295
-------
EPA
Built
Natural
REGION
State
County
Risk
Governance
Environment
Environment
Society
CRSI
8
Colorado
Hinsdale
0.092
0.598
0.273
0.509
0.428
4.562
8
Colorado
Huerfano
0.168
0.517
0.352
0.351
0.455
1.252
8
Colorado
Jackson
0.148
0.556
0.257
0.375
0.369
-0.750
8
Colorado
Jefferson
0.553
0.535
0.647
0.379
0.625
2.568
8
Colorado
Kiowa
0.168
0.662
0.276
0.312
0.449
0.276
8
Colorado
Kit Carson
0.125
0.567
0.382
0.290
0.709
5.528
8
Colorado
La Plata
0.151
0.556
0.642
0.526
0.705
12.897
8
Colorado
Lake
0.101
0.485
0.179
0.535
0.585
4.230
8
Colorado
Larimer
0.380
0.537
0.856
0.388
0.591
5.092
8
Colorado
Las Animas
0.154
0.544
0.402
0.324
0.588
3.673
8
Colorado
Lincoln
0.133
0.616
0.416
0.391
0.485
4.811
8
Colorado
Logan
0.154
0.545
0.541
0.275
0.569
5.050
8
Colorado
Mesa
0.169
0.513
0.729
0.582
0.615
12.394
8
Colorado
Mineral
0.119
0.583
0.294
0.546
0.335
3.085
8
Colorado
Moffat
0.125
0.556
0.406
0.460
0.612
7.784
8
Colorado
Montezuma
0.134
0.499
0.488
0.506
0.550
8.366
8
Colorado
Montrose
0.163
0.508
0.605
0.524
0.636
10.204
8
Colorado
Morgan
0.234
0.536
0.474
0.230
0.541
1.824
8
Colorado
Otero
0.133
0.530
0.373
0.468
0.506
5.035
8
Colorado
Ouray
0.073
0.565
0.411
0.442
0.681
14.861
8
Colorado
Park
0.171
0.543
0.511
0.436
0.708
7.997
8
Colorado
Phillips
0.130
0.604
0.293
0.307
0.528
1.364
8
Colorado
Pitkin
0.076
0.513
0.498
0.485
0.604
16.108
8
Colorado
Prowers
0.229
0.542
0.308
0.306
0.582
1.130
8
Colorado
Pueblo
0.199
0.503
0.631
0.337
0.516
5.171
8
Colorado
Rio Blanco
0.127
0.525
0.323
0.495
0.530
5.060
8
Colorado
Rio Grande
0.313
0.549
0.404
0.353
0.542
1.787
8
Colorado
Routt
0.124
0.545
0.663
0.435
0.771
15.286
8
Colorado
Saguache
0.109
0.531
0.415
0.419
0.461
5.254
296
-------
EPA
Built
Natural
REGION
State
County
Risk
Governance
Environment
Environment
Society
CRSI
8
Colorado
San Juan
0.079
0.594
0.296
0.647
0.387
9.349
8
Colorado
San Miguel
0.093
0.583
0.475
0.517
0.668
15.411
8
Colorado
Sedgwick
0.126
0.641
0.249
0.316
0.478
0.152
8
Colorado
Summit
0.126
0.557
0.561
0.453
0.764
13.243
8
Colorado
Teller
0.168
0.546
0.505
0.452
0.650
7.637
8
Colorado
Washington
0.264
0.615
0.334
0.313
0.545
1.309
8
Colorado
Weld
0.302
0.556
0.971
0.305
0.523
6.360
8
Colorado
Yuma
0.162
0.522
0.447
0.312
0.649
4.653
8
Montana
Beaverhead
0.083
0.557
0.412
0.450
0.798
16.037
8
Montana
Big Horn
0.213
0.542
0.351
0.583
0.439
3.621
8
Montana
Blaine
0.132
0.572
0.380
0.450
0.539
5.696
8
Montana
Broadwater
0.091
0.552
0.323
0.451
0.626
8.275
8
Montana
Carbon
0.135
0.571
0.482
0.448
0.831
11.762
8
Montana
Carter
0.128
0.606
0.247
0.337
0.488
0.390
8
Montana
Cascade
0.120
0.528
0.648
0.391
0.602
11.664
8
Montana
Chouteau
0.140
0.605
0.399
0.343
0.809
7.866
8
Montana
Custer
0.132
0.525
0.383
0.311
0.820
6.985
8
Montana
Daniels
0.056
0.575
0.292
0.443
0.790
17.873
8
Montana
Dawson
0.130
0.537
0.352
0.296
0.741
5.078
8
Montana
Deer Lodge
0.080
0.505
0.233
0.541
0.516
5.822
8
Montana
Fallon
0.083
0.564
0.311
0.274
0.729
6.005
8
Montana
Fergus
0.129
0.543
0.407
0.307
0.767
6.857
8
Montana
Flathead
0.114
0.536
0.827
0.545
0.697
21.584
8
Montana
Gallatin
0.354
0.542
0.657
0.469
0.776
5.571
8
Montana
Garfield
0.375
0.621
0.271
0.327
0.540
0.556
8
Montana
Glacier
0.113
0.484
0.335
0.494
0.420
3.684
8
Montana
Golden Valley
0.084
0.669
0.216
0.281
0.249
-6.829
8
Montana
Granite
0.060
0.540
0.321
0.476
0.560
11.108
8
Montana
Hill
0.114
0.522
0.330
0.288
0.683
3.961
297
-------
EPA
Built
Natural
REGION
State
County
Risk
Governance
Environment
Environment
Society
CRSI
8
Montana
Jefferson
0.093
0.582
0.523
0.387
0.756
15.229
8
Montana
Judith Basin
0.129
0.656
0.240
0.388
0.565
2.786
8
Montana
Lake
0.116
0.550
0.481
0.515
0.590
10.841
8
Montana
Lewis and Clark
0.133
0.532
0.591
0.458
0.725
12.489
8
Montana
Liberty
0.073
0.545
0.271
0.299
0.503
-0.115
8
Montana
Lincoln
0.078
0.511
0.507
0.525
0.645
18.224
8
Montana
Madison
0.126
0.545
0.485
0.525
0.690
11.825
8
Montana
McCone
0.215
0.560
0.310
0.351
0.833
4.111
8
Montana
Meagher
0.101
0.595
0.289
0.432
0.390
1.903
8
Montana
Mineral
0.108
0.560
0.297
0.654
0.589
10.318
8
Montana
Missoula
0.114
0.536
0.616
0.442
0.642
13.437
8
Montana
Musselshell
0.136
0.531
0.224
0.299
0.586
0.119
8
Montana
Park
0.151
0.531
0.420
0.375
0.756
7.015
8
Montana
Petroleum
0.311
0.658
0.177
0.437
0.354
-0.322
8
Montana
Phillips
0.190
0.524
0.369
0.516
0.735
6.438
8
Montana
Pondera
0.098
0.610
0.319
0.409
0.576
6.063
8
Montana
Powder River
0.123
0.567
0.270
0.332
0.622
2.660
8
Montana
Powell
0.086
0.556
0.276
0.468
0.622
7.727
8
Montana
Prairie
0.206
0.541
0.206
0.363
0.510
-0.077
8
Montana
Ravalli
0.074
0.531
0.488
0.493
0.600
16.423
8
Montana
Richland
0.106
0.537
0.383
0.331
0.600
5.180
8
Montana
Roosevelt
0.153
0.565
0.455
0.595
0.514
8.194
8
Montana
Rosebud
0.163
0.562
0.500
0.308
0.558
4.588
8
Montana
Sanders
0.103
0.567
0.542
0.473
0.691
14.805
8
Montana
Sheridan
0.121
0.588
0.290
0.323
0.867
7.094
8
Montana
Silver Bow
0.079
0.493
0.348
0.401
0.682
9.441
8
Montana
Stillwater
0.154
0.606
0.419
0.337
0.634
5.162
8
Montana
Sweet Grass
0.125
0.620
0.315
0.330
0.723
5.505
8
Montana
Teton
0.106
0.629
0.386
0.432
0.865
13.429
298
-------
EPA
REGION
State
County
Risk
Governance
Built
Environment
Natural
Environment
Society
CRSI
8
Montana
Toole
0.048
0.526
0.299
0.263
0.773
10.149
8
Montana
Treasure
0.093
0.666
0.232
0.267
0.405
-2.839
8
Montana
Valley
0.266
0.552
0.450
0.407
0.912
5.799
8
Montana
Wheatland
0.109
0.528
0.268
0.308
0.574
1.148
8
Montana
Wibaux
0.168
0.567
0.197
0.296
0.573
-0.322
8
Montana
Yellowstone
0.254
0.534
0.696
0.294
0.635
5.361
8
North
Dakota
Adams
0.183
0.574
0.311
0.248
0.743
2.577
8
North
Dakota
Barnes
0.121
0.545
0.445
0.345
0.711
8.073
8
North
Dakota
Benson
0.223
0.615
0.408
0.461
0.484
3.539
8
North
Dakota
Billings
0.171
0.614
0.256
0.511
0.470
2.781
8
North
Dakota
Bottineau
0.121
0.532
0.391
0.417
0.743
8.749
8
North
Dakota
Bowman
0.107
0.506
0.327
0.291
0.990
9.677
8
North
Dakota
Burke
0.108
0.601
0.307
0.262
0.637
2.920
8
North
Dakota
Burleigh
0.361
0.506
0.530
0.300
0.727
2.998
8
North
Dakota
Cass
0.268
0.545
0.707
0.400
0.623
6.095
8
North
Dakota
Cavalier
0.122
0.551
0.363
0.442
0.867
10.702
8
North
Dakota
Dickey
0.123
0.554
0.363
0.367
0.716
6.717
8
North
Dakota
Divide
0.082
0.485
0.330
0.238
0.814
6.719
8
North
Dakota
Dunn
0.139
0.610
0.304
0.358
0.771
5.879
8
North
Dakota
Eddy
0.103
0.595
0.216
0.382
0.496
0.803
8
North
Dakota
Emmons
0.117
0.591
0.348
0.282
0.843
7.421
8
North
Dakota
Foster
0.154
0.572
0.303
0.413
0.674
4.665
8
North
Dakota
Golden Valley
0.095
0.568
0.251
0.400
0.568
3.531
8
North
Dakota
Grand Forks
0.210
0.548
0.525
0.379
0.581
4.859
8
North
Dakota
Grant
0.184
0.622
0.276
0.277
0.496
0.084
8
North
Dakota
Griggs
0.157
0.567
0.304
0.271
0.620
1.671
8
North
Dakota
Hettinger
0.122
0.612
0.279
0.261
0.719
3.344
8
North
Dakota
Kidder
0.197
0.616
0.301
0.265
0.641
1.659
8
North
Dakota
La Moure
0.116
0.624
0.405
0.333
0.655
6.948
299
-------
EPA
Built
Natural
REGION
State
County
Risk
Governance
Environment
Environment
Society
CRSI
8
North Dakota
Logan
0.163
0.610
0.308
0.250
0.695
2.522
8
North Dakota
McHenry
0.092
0.597
0.405
0.343
0.681
9.319
8
North Dakota
Mcintosh
0.113
0.600
0.318
0.300
0.736
5.578
8
North Dakota
McKenzie
0.139
0.531
0.418
0.443
0.671
7.549
8
North Dakota
McLean
0.115
0.592
0.510
0.399
0.843
13.818
8
North Dakota
Mercer
0.125
0.550
0.487
0.314
0.676
7.611
8
North Dakota
Morton
0.270
0.551
0.585
0.252
0.757
4.497
8
North Dakota
Mountrail
0.138
0.573
0.412
0.376
0.575
5.235
8
North Dakota
Nelson
0.209
0.604
0.384
0.386
0.735
4.885
8
North Dakota
Oliver
0.107
0.589
0.279
0.279
0.490
-0.194
8
North Dakota
Pembina
0.110
0.602
0.483
0.445
0.900
16.005
8
North Dakota
Pierce
0.087
0.514
0.257
0.323
0.657
3.266
8
North Dakota
Ramsey
0.174
0.537
0.344
0.408
0.873
6.727
8
North Dakota
Ransom
0.168
0.581
0.335
0.324
0.767
4.618
8
North Dakota
Renville
0.170
0.620
0.266
0.321
0.609
1.839
8
North Dakota
Richland
0.139
0.594
0.469
0.360
0.689
7.808
8
North Dakota
Rolette
0.090
0.482
0.376
0.472
0.646
10.147
8
North Dakota
Sargent
0.303
0.650
0.330
0.336
0.729
2.596
8
North Dakota
Sheridan
0.105
0.628
0.225
0.359
0.371
-1.544
8
North Dakota
Sioux
0.261
0.650
0.276
0.567
0.065
-0.355
8
North Dakota
Slope
0.172
0.652
0.183
0.415
0.378
-0.565
8
North Dakota
Stark
0.100
0.529
0.510
0.289
0.742
10.516
8
North Dakota
Steele
0.305
0.586
0.296
0.345
0.381
-0.093
8
North Dakota
Stutsman
0.100
0.537
0.496
0.345
0.723
11.253
8
North Dakota
Towner
0.093
0.610
0.339
0.377
0.618
7.045
8
North Dakota
Traill
0.135
0.625
0.387
0.383
0.744
7.865
8
North Dakota
Walsh
0.102
0.585
0.416
0.383
0.709
10.050
8
North Dakota
Ward
0.093
0.537
0.631
0.373
0.656
15.330
8
North Dakota
Wells
0.092
0.559
0.345
0.371
0.727
8.845
300
-------
EPA
Built
Natural
REGION
State
County
Risk
Governance
Environment
Environment
Society
CRSI
8
North Dakota
Williams
0.098
0.510
0.520
0.313
0.665
9.914
8
South Dakota
Aurora
0.090
0.677
0.252
0.229
0.824
5.883
8
South Dakota
Beadle
0.145
0.535
0.360
0.248
0.753
4.040
8
South Dakota
Bennett
0.151
0.455
0.212
0.555
0.330
0.212
8
South Dakota
Bon Homme
0.097
0.586
0.341
0.358
0.776
9.286
8
South Dakota
Brookings
0.103
0.547
0.463
0.403
0.634
9.881
8
South Dakota
Brown
0.143
0.548
0.462
0.278
0.819
7.493
8
South Dakota
Brule
0.144
0.555
0.267
0.358
0.767
4.541
8
South Dakota
Buffalo
0.120
0.524
0.248
0.454
0.011
-5.625
8
South Dakota
Butte
0.160
0.520
0.270
0.330
0.625
1.737
8
South Dakota
Campbell
0.078
0.581
0.204
0.297
0.306
-6.970
8
South Dakota
Charles Mix
0.129
0.600
0.383
0.466
0.574
6.952
8
South Dakota
Clark
0.089
0.615
0.246
0.377
0.640
5.126
8
South Dakota
Clay
0.067
0.534
0.325
0.333
0.508
3.212
8
South Dakota
Codington
0.086
0.539
0.380
0.387
0.739
11.127
8
South Dakota
Corson
0.162
0.628
0.287
0.590
0.135
0.656
8
South Dakota
Custer
0.198
0.499
0.395
0.497
0.713
5.945
8
South Dakota
Davison
0.155
0.525
0.365
0.275
0.772
4.448
8
South Dakota
Day
0.079
0.566
0.333
0.456
0.886
16.546
8
South Dakota
Deuel
0.055
0.636
0.335
0.382
0.636
13.073
8
South Dakota
Dewey
0.178
0.539
0.311
0.569
0.326
2.289
8
South Dakota
Douglas
0.176
0.615
0.272
0.246
0.711
1.930
8
South Dakota
Edmunds
0.088
0.580
0.332
0.319
0.849
10.376
8
South Dakota
Fall River
0.199
0.512
0.270
0.419
0.648
2.700
8
South Dakota
Faulk
0.080
0.594
0.226
0.329
0.611
2.588
8
South Dakota
Grant
0.086
0.566
0.372
0.460
0.793
14.496
8
South Dakota
Gregory
0.151
0.598
0.258
0.322
0.588
1.528
8
South Dakota
Haakon
0.275
0.557
0.254
0.290
0.721
1.322
8
South Dakota
Hamlin
0.061
0.639
0.331
0.341
0.696
11.966
301
-------
EPA Built Natural
REGION
State
County
Risk
Governance
Environment
Environment
Society
CRSI
8 South
Dakota
Hand
0.117
0.584
0.238
0.341
0.674
3.274
8 South
Dakota
Hanson
0.089
0.677
0.291
0.344
0.462
2.306
8 South
Dakota
Harding
0.212
0.616
0.234
0.345
0.519
0.498
8 South
Dakota
Hughes
0.120
0.538
0.394
0.391
0.855
10.230
8 South
Dakota
Hutchinson
0.156
0.581
0.355
0.314
0.756
5.034
8 South
Dakota
Hyde
0.076
0.578
0.223
0.273
0.511
-2.035
8 South
Dakota
Jackson
0.229
0.490
0.281
0.464
0.384
0.585
8 South
Dakota
Jerauld
0.221
0.578
0.226
0.308
0.593
0.458
8 South
Dakota
Jones
0.113
0.570
0.255
0.282
0.612
1.267
8 South
Dakota
Kingsbury
0.144
0.615
0.334
0.381
0.840
7.588
8 South
Dakota
Lake
0.089
0.539
0.315
0.427
0.701
9.002
8 South
Dakota
Lawrence
0.256
0.512
0.431
0.498
0.639
4.461
8 South
Dakota
Lincoln
0.252
0.563
0.491
0.366
0.691
4.485
8 South
Dakota
Lyman
0.153
0.581
0.338
0.369
0.488
2.261
8 South
Dakota
Marshall
0.159
0.578
0.302
0.468
0.549
3.824
8 South
Dakota
McCook
0.158
0.643
0.356
0.334
0.830
6.607
8 South
Dakota
McPherson
0.092
0.525
0.257
0.387
0.421
-0.108
8 South
Dakota
Meade
0.212
0.531
0.443
0.323
0.732
4.462
8 South
Dakota
Mellette
0.189
0.553
0.226
0.373
0.300
-1.812
8 South
Dakota
Miner
0.077
0.574
0.249
0.250
0.528
-1.463
8 South
Dakota
Minnehaha
0.324
0.554
0.597
0.289
0.647
3.474
8 South
Dakota
Moody
0.076
0.602
0.269
0.474
0.618
9.201
8 South
Dakota
Oglala Lakota
0.153
0.391
0.394
0.557
0.131
0.435
8 South
Dakota
Pennington
0.264
0.497
0.649
0.410
0.649
5.715
8 South
Dakota
Perkins
0.208
0.588
0.178
0.257
0.741
0.713
8 South
Dakota
Potter
0.081
0.588
0.069
0.282
0.617
-3.983
8 South
Dakota
Roberts
0.084
0.586
0.380
0.638
0.784
20.260
8 South
Dakota
Sanborn
0.140
0.609
0.200
0.291
0.415
-2.341
8 South
Dakota
Spink
0.086
0.601
0.339
0.320
0.754
8.991
302
-------
EPA
Built
Natural
REGION
State
County
Risk
Governance
Environment
Environment
Society
CRSI
8
South Dakota
Stanley
0.137
0.529
0.303
0.355
0.691
4.133
8
South Dakota
Sully
0.094
0.587
0.216
0.337
0.654
2.923
8
South Dakota
Todd
0.142
0.482
0.336
0.613
0.173
1.585
8
South Dakota
Tripp
0.150
0.500
0.241
0.282
0.633
0.523
8
South Dakota
Turner
0.185
0.654
0.345
0.381
0.841
6.282
8
South Dakota
Union
0.110
0.602
0.427
0.392
0.634
8.575
8
South Dakota
Walworth
0.087
0.534
0.240
0.340
0.777
6.204
8
South Dakota
Yankton
0.133
0.523
0.319
0.368
0.771
5.963
8
South Dakota
Ziebach
0.231
0.644
0.261
0.508
0.134
-0.635
8
Utah
Beaver
0.135
0.534
0.552
0.507
0.590
10.459
8
Utah
Box Elder
0.167
0.569
0.599
0.319
0.635
7.172
8
Utah
Cache
0.267
0.554
0.541
0.494
0.638
5.482
8
Utah
Carbon
0.120
0.546
0.438
0.444
0.564
7.619
8
Utah
Daggett
0.128
0.572
0.311
0.610
0.404
5.428
8
Utah
Davis
0.711
0.517
0.424
0.445
0.655
1.444
8
Utah
Duchesne
0.075
0.584
0.423
0.449
0.589
12.889
8
Utah
Emery
0.125
0.581
0.540
0.495
0.663
12.365
8
Utah
Garfield
0.146
0.573
0.493
0.454
0.615
8.272
8
Utah
Grand
0.147
0.470
0.372
0.547
0.606
6.780
8
Utah
Iron
0.136
0.500
0.610
0.457
0.592
10.365
8
Utah
Juab
0.148
0.539
0.436
0.380
0.783
7.924
8
Utah
Kane
0.107
0.523
0.412
0.479
0.688
10.703
8
Utah
Millard
0.112
0.575
0.576
0.455
0.578
12.208
8
Utah
Morgan
0.155
0.546
0.462
0.371
0.701
6.897
8
Utah
Piute
0.103
0.627
0.252
0.605
0.399
5.514
8
Utah
Rich
0.110
0.458
0.393
0.427
0.604
6.762
8
Utah
Salt Lake
0.775
0.515
0.768
0.236
0.612
1.734
8
Utah
San Juan
0.127
0.524
0.481
0.492
0.617
9.625
8
Utah
Sanpete
0.137
0.542
0.446
0.460
0.681
8.715
303
-------
EPA
Built
Natural
REGION
State
County
Risk
Governance
Environment
Environment
Society
CRSI
8
Utah
Sevier
0.134
0.553
0.463
0.496
0.683
10.084
8
Utah
Summit
0.156
0.543
0.570
0.429
0.606
8.408
8
Utah
Tooele
0.141
0.534
0.615
0.365
0.565
8.348
8
Utah
Uintah
0.117
0.518
0.518
0.546
0.601
12.161
8
Utah
Utah
0.462
0.485
0.706
0.446
0.591
3.520
8
Utah
Wasatch
0.147
0.519
0.513
0.539
0.637
9.909
8
Utah
Washington
0.266
0.486
0.612
0.585
0.619
6.658
8
Utah
Wayne
0.204
0.576
0.367
0.511
0.750
6.282
8
Utah
Weber
0.569
0.510
0.459
0.393
0.630
1.647
8
Wyoming
Albany
0.110
0.442
0.545
0.372
0.502
7.237
8
Wyoming
Big Horn
0.115
0.562
0.313
0.524
0.681
8.924
8
Wyoming
Campbell
0.227
0.530
0.683
0.397
0.634
6.938
8
Wyoming
Carbon
0.094
0.540
0.626
0.533
0.685
19.825
8
Wyoming
Converse
0.101
0.528
0.516
0.389
0.677
11.776
8
Wyoming
Crook
0.201
0.518
0.376
0.311
0.709
3.383
8
Wyoming
Fremont
0.132
0.540
0.589
0.464
0.602
10.801
8
Wyoming
Goshen
0.119
0.533
0.366
0.242
0.621
2.722
8
Wyoming
Hot Springs
0.189
0.533
0.218
0.476
0.622
2.674
8
Wyoming
Johnson
0.143
0.507
0.395
0.395
0.873
8.731
8
Wyoming
Laramie
0.171
0.512
0.566
0.268
0.599
4.999
8
Wyoming
Lincoln
0.155
0.543
0.573
0.549
0.755
12.283
8
Wyoming
Natrona
0.129
0.515
0.566
0.489
0.639
11.492
8
Wyoming
Niobrara
0.257
0.546
0.250
0.316
0.505
-0.081
8
Wyoming
Park
0.145
0.502
0.557
0.542
0.717
11.937
8
Wyoming
Platte
0.123
0.501
0.409
0.375
0.658
6.560
8
Wyoming
Sheridan
0.125
0.528
0.468
0.484
0.706
10.836
8
Wyoming
Sublette
0.153
0.510
0.499
0.590
0.705
10.926
8
Wyoming
Sweetwater
0.107
0.508
0.573
0.505
0.602
13.695
8
Wyoming
Teton
0.154
0.514
0.573
0.567
0.721
12.025
304
-------
EPA
REGION
State
County
Risk
Governance
Built
Environment
Natural
Environment
Society
CRSI
8
Wyoming
Uinta
0.063
0.516
0.472
0.458
0.586
16.655
8
Wyoming
Washakie
0.099
0.537
0.260
0.520
0.725
9.463
8
Wyoming
Weston
0.168
0.499
0.268
0.378
0.610
2.041
305
-------
EPA
Built
Natural
REGION
State
County
Risk
Governance
Environment
Environment
Society
CRSI
National Average
0.229
0.588
0.393
0.414
0.516
4.213
Regional Average
0.235
0.358
0.620
0.470
0.480
6.078
9
Arizona
Apache
0.125
0.472
0.741
0.473
0.443
11.864
9
Arizona
Cochise
0.123
0.398
0.726
0.348
0.470
9.023
9
Arizona
Coconino
0.132
0.437
0.906
0.472
0.573
16.226
9
Arizona
Gila
0.112
0.395
0.528
0.463
0.495
8.156
9
Arizona
Graham
0.108
0.454
0.427
0.457
0.540
7.169
9
Arizona
Greenlee
0.130
0.420
0.378
0.590
0.481
6.348
9
Arizona
La Paz
0.187
0.390
0.547
0.346
0.363
2.207
9
Arizona
Maricopa
0.662
0.472
0.942
0.276
0.471
2.400
9
Arizona
Mohave
0.122
0.423
0.830
0.275
0.365
8.406
9
Arizona
Navajo
0.131
0.415
0.804
0.534
0.513
14.327
9
Arizona
Pima
0.225
0.457
0.895
0.465
0.454
8.369
9
Arizona
Pinal
0.249
0.450
0.873
0.343
0.389
5.562
9
Arizona
Santa Cruz
0.123
0.477
0.420
0.456
0.399
4.062
9
Arizona
Yavapai
0.131
0.434
0.896
0.286
0.504
11.566
9
Arizona
Yuma
0.179
0.451
0.731
0.368
0.415
6.248
9
California
Alameda
0.501
0.184
0.720
0.338
0.549
2.018
9
California
Alpine
0.318
0.456
0.370
0.633
0.276
1.702
9
California
Amador
0.191
0.336
0.427
0.360
0.489
1.673
9
California
Butte
0.211
0.328
0.721
0.414
0.397
4.956
9
California
Calaveras
0.131
0.322
0.494
0.433
0.553
6.045
9
California
Colusa
0.147
0.299
0.390
0.410
0.359
0.338
9
California
Contra Costa
0.651
0.226
0.734
0.371
0.525
1.728
9
California
Del Norte
0.191
0.202
0.407
0.586
0.374
2.423
9
California
El Dorado
0.209
0.352
0.623
0.444
0.538
5.551
9
California
Fresno
0.241
0.234
0.960
0.435
0.478
7.474
9
California
Glenn
0.149
0.428
0.367
0.471
0.379
2.075
9
California
Humboldt
0.148
0.291
0.805
0.580
0.511
12.626
306
-------
EPA
Built
Natural
REGION
State
County
Risk
Governance
Environment
Environment
Society
CRSI
9
Cal
fornia
Imperial
0.155
0.482
0.894
0.438
0.379
10.853
9
Cal
fornia
Inyo
0.253
0.386
0.605
0.525
0.668
6.331
9
Cal
fornia
Kern
0.213
0.233
0.990
0.350
0.389
7.034
9
Cal
fornia
Kings
0.201
0.325
0.632
0.172
0.345
0.548
9
Cal
fornia
Lake
0.160
0.010
0.490
0.552
0.360
2.388
9
Cal
fornia
Lassen
0.117
0.462
0.630
0.535
0.529
12.797
9
Cal
fornia
Los Angeles
0.576
0.289
0.881
0.363
0.535
2.745
9
Cal
fornia
Madera
0.182
0.238
0.695
0.403
0.451
5.324
9
Cal
fornia
Marin
0.159
0.223
0.414
0.656
0.545
6.355
9
Cal
fornia
Mariposa
0.272
0.010
0.476
0.512
0.515
2.019
9
Cal
fornia
Mendocino
0.157
0.227
0.723
0.452
0.533
8.355
9
Cal
fornia
Merced
0.250
0.396
0.688
0.296
0.362
2.673
9
Cal
fornia
Modoc
0.139
0.357
0.435
0.498
0.434
4.240
9
Cal
fornia
Mono
0.089
0.363
0.586
0.553
0.644
17.455
9
Cal
fornia
Monterey
0.187
0.010
0.809
0.513
0.493
7.459
9
Cal
fornia
Napa
0.370
0.141
0.458
0.512
0.518
1.720
9
Cal
fornia
Nevada
0.198
0.305
0.596
0.525
0.536
6.247
9
Cal
fornia
Orange
0.763
0.389
0.736
0.312
0.570
1.623
9
Cal
fornia
Placer
0.424
0.393
0.711
0.418
0.575
3.410
9
Cal
fornia
Plumas
0.291
0.408
0.635
0.561
0.527
5.203
9
Cal
fornia
Riverside
0.453
0.411
0.934
0.500
0.488
4.609
9
Cal
fornia
Sacramento
0.626
0.372
0.763
0.370
0.590
2.351
9
Cal
fornia
San Benito
0.124
0.380
0.427
0.402
0.453
3.179
9
Cal
fornia
San Bernandino
0.215
0.432
0.985
0.479
0.480
10.118
9
Cal
fornia
San Diego
0.386
0.353
0.907
0.507
0.520
5.276
9
Cal
fornia
San Francisco
0.240
0.424
0.429
0.614
0.530
4.658
9
Cal
fornia
San Joaquin
0.564
0.390
0.724
0.304
0.447
1.673
9
Cal
fornia
San Luis Obispo
0.439
0.250
0.861
0.471
0.606
4.310
9
Cal
fornia
San Mateo
0.186
0.214
0.557
0.485
0.539
5.122
307
-------
EPA
Built
Natural
REGION
State
County
Risk
Governance
Environment
Environment
Society
CRSI
9
California
Santa Barbara
0.224
0.331
0.830
0.645
0.559
9.926
9
California
Santa Clara
0.363
0.212
0.682
0.420
0.552
3.167
9
California
Santa Cruz
0.173
0.231
0.382
0.625
0.532
4.802
9
California
Shasta
0.134
0.227
0.799
0.429
0.462
9.821
9
California
Sierra
0.157
0.336
0.366
0.600
0.406
3.724
9
California
Siskiyou
0.398
0.208
0.740
0.440
0.541
3.335
9
California
Solano
0.455
0.241
0.607
0.383
0.522
1.816
9
California
Sonoma
0.251
0.183
0.783
0.497
0.564
6.375
9
California
Stanislaus
0.477
0.362
0.717
0.317
0.432
1.889
9
California
Sutter
0.192
0.429
0.470
0.303
0.437
1.480
9
California
Tehama
0.299
0.376
0.550
0.495
0.406
2.862
9
California
Trinity
0.271
0.354
0.428
0.600
0.405
2.827
9
California
Tulare
0.186
0.117
0.895
0.415
0.440
7.450
9
California
Tuolumne
0.171
0.212
0.570
0.438
0.476
4.342
9
California
Ventura
0.384
0.341
0.751
0.660
0.550
5.332
9
California
Yolo
0.317
0.316
0.564
0.429
0.514
2.791
9
California
Yuba
0.233
0.320
0.372
0.374
0.362
-0.252
9
Hawaii
Hawaii
0.107
0.601
0.740
0.508
0.626
19.202
9
Hawaii
Honolulu
0.147
0.643
0.698
0.631
0.639
15.686
9
Hawaii
Kalawao
0.068
0.257
0.260
0.383
0.308
-7.455
9
Hawaii
Kauai
0.074
0.616
0.559
0.383
0.733
20.214
9
Hawaii
Maui
0.063
0.644
0.595
0.492
0.640
26.984
9
Nevada
Carson
0.331
0.404
0.224
0.554
0.594
1.600
9
Nevada
Churchill
0.116
0.474
0.544
0.505
0.519
10.238
9
Nevada
Clark
0.316
0.436
0.901
0.569
0.458
6.755
9
Nevada
Douglas
0.175
0.452
0.490
0.522
0.567
6.606
9
Nevada
Elko
0.130
0.470
0.675
0.583
0.515
13.164
9
Nevada
Esmeralda
0.101
0.454
0.282
0.586
0.010
-3.306
9
Nevada
Eureka
0.109
0.395
0.407
0.544
0.269
3.170
308
-------
EPA
REGION
State
County
Risk
Governance
Built
Environment
Natural
Environment
Society
CRSI
9
Nevada
Humboldt
0.108
0.451
0.522
0.561
0.481
10.791
9
Nevada
Lander
0.119
0.416
0.245
0.554
0.397
1.765
9
Nevada
Lincoln
0.252
0.401
0.447
0.569
0.529
4.086
9
Nevada
Lyon
0.207
0.456
0.527
0.562
0.434
5.283
9
Nevada
Mineral
0.144
0.429
0.454
0.530
0.477
6.089
9
Nevada
Nye
0.129
0.379
0.657
0.598
0.427
11.173
9
Nevada
Pershing
0.154
0.467
0.389
0.524
0.321
2.738
9
Nevada
Storey
0.266
0.441
0.309
0.347
0.498
0.364
9
Nevada
Washoe
0.183
0.433
0.841
0.552
0.517
11.206
9
Nevada
White Pine
0.082
0.407
0.333
0.658
0.575
12.737
309
-------
EPA
Built
Natural
REGION
State
County
Risk
Governance
Environment
Environment
Society
CRSI
National Average
0.229
0.588
0.393
0.414
0.516
4.213
Regional Average
0.137
0.432
0.478
0.531
0.492
14.838
10
Alaska
Aleutians East
0.012
0.411
0.342
0.990
0.305
111.828
10
Alaska
Aleutians West
0.039
0.516
0.479
0.523
0.424
23.133
10
Alaska
Anchorage
0.078
0.634
0.693
0.443
0.557
21.363
10
Alaska
Bristol Bay
0.034
0.525
0.307
0.795
0.234
22.400
10
Alaska
Denali
0.116
0.490
0.413
0.377
0.309
1.080
10
Alaska
Dillingham
0.022
0.411
0.489
0.508
0.428
37.134
10
Alaska
Fairbanks North Star
0.050
0.590
0.665
0.455
0.579
32.653
10
Alaska
Haines
0.021
0.538
0.371
0.664
0.596
62.768
10
Alaska
Hoonah-Angoon
0.010
0.378
0.416
0.502
0.357
42.005
10
Alaska
Juneau City
0.013
0.602
0.561
0.686
0.731
167.953
10
Alaska
Kenai Peninsula
0.053
0.548
0.713
0.482
0.630
35.503
10
Alaska
Ketchikan Gateway
0.010
0.591
0.464
0.614
0.617
154.851
10
Alaska
Kodiak Island
0.010
0.576
0.557
0.928
0.474
227.188
10
Alaska
Lake and Peninsula
0.045
0.337
0.488
0.718
0.247
19.594
10
Alaska
North Slope
0.038
0.427
0.747
0.959
0.365
65.884
10
Alaska
Petersburg
0.026
0.533
0.318
0.538
0.604
33.766
10
Alaska
Prince of Wales-Hyde
0.018
0.398
0.573
0.487
0.443
56.732
10
Alaska
Sitka City
0.036
0.538
0.398
0.709
0.681
46.371
10
Alaska
Skagway
0.028
0.574
0.245
0.501
0.520
17.349
10
Alaska
Valdez-Cordova
0.126
0.470
0.689
0.475
0.603
13.185
10
Alaska
Wrangell
0.011
0.430
0.269
0.520
0.373
13.694
10
Alaska
Yakutat
0.044
0.476
0.256
0.924
0.449
29.465
10
Idaho
Ada
0.345
0.449
0.562
0.586
0.580
4.434
10
Idaho
Adams
0.093
0.435
0.376
0.627
0.573
11.862
10
Idaho
Bannock
0.190
0.435
0.519
0.587
0.615
7.744
10
Idaho
Bear Lake
0.102
0.121
0.284
0.480
0.632
2.939
310
-------
EPA
REGION
State
County
Risk
Governance
Built
Environment
Natural
Environment
Society
CRSI
10
Idaho
Benewah
0.062
0.490
0.326
0.612
0.476
12.795
10
Idaho
Bingham
0.146
0.433
0.492
0.507
0.626
8.334
10
Idaho
Blaine
0.101
0.440
0.550
0.563
0.683
16.084
10
Idaho
Boise
0.106
0.448
0.500
0.637
0.313
9.040
10
Idaho
Bonner
0.064
0.435
0.636
0.531
0.582
24.582
10
Idaho
Bonneville
0.282
0.457
0.574
0.465
0.647
4.970
10
Idaho
Boundary
0.072
0.425
0.443
0.576
0.666
18.549
10
Idaho
Butte
0.141
0.493
0.256
0.485
0.403
1.121
10
Idaho
Camas
0.084
0.520
0.213
0.532
0.171
-3.155
10
Idaho
Canyon
0.431
0.455
0.507
0.366
0.518
1.678
10
Idaho
Caribou
0.142
0.399
0.332
0.473
0.568
3.961
10
Idaho
Cassia
0.101
0.454
0.452
0.542
0.644
12.409
10
Idaho
Clark
0.313
0.538
0.243
0.503
0.254
-0.247
10
Idaho
Clearwater
0.144
0.461
0.417
0.637
0.609
9.254
10
Idaho
Custer
0.173
0.431
0.419
0.636
0.592
7.360
10
Idaho
Elmore
0.116
0.439
0.549
0.624
0.508
12.384
10
Idaho
Franklin
0.130
0.393
0.411
0.493
0.529
5.662
10
Idaho
Fremont
0.131
0.445
0.414
0.531
0.612
8.067
10
Idaho
Gem
0.106
0.410
0.344
0.526
0.514
5.919
10
Idaho
Gooding
0.102
0.384
0.424
0.542
0.566
9.441
10
Idaho
Idaho
0.108
0.447
0.531
0.496
0.666
12.834
10
Idaho
Jefferson
0.095
0.470
0.412
0.524
0.673
12.339
10
Idaho
Jerome
0.133
0.378
0.514
0.504
0.485
7.042
10
Idaho
Kootenai
0.151
0.453
0.725
0.538
0.573
12.155
10
Idaho
Latah
0.046
0.456
0.488
0.473
0.538
21.298
10
Idaho
Lemhi
0.117
0.429
0.347
0.439
0.694
6.824
10
Idaho
Lewis
0.177
0.625
0.285
0.710
0.458
5.824
10
Idaho
Lincoln
0.151
0.504
0.270
0.676
0.536
6.217
311
-------
EPA
REGION
State
County
Risk
Governance
Built
Environment
Natural
Environment
Society
CRSI
10
Idaho
Madison
0.157
0.443
0.430
0.469
0.542
5.125
10
Idaho
Minidoka
0.093
0.439
0.333
0.571
0.487
7.381
10
Idaho
Nez Perce
0.105
0.449
0.480
0.582
0.612
13.007
10
Idaho
Oneida
0.146
0.485
0.246
0.510
0.546
3.197
10
Idaho
Owyhee
0.106
0.449
0.367
0.559
0.430
6.101
10
Idaho
Payette
0.135
0.348
0.359
0.465
0.440
2.329
10
Idaho
Power
0.115
0.446
0.349
0.465
0.476
3.966
10
Idaho
Shoshone
0.085
0.493
0.261
0.449
0.490
2.978
10
Idaho
Teton
0.124
0.451
0.427
0.492
0.622
8.235
10
Idaho
Twin Falls
0.125
0.407
0.592
0.501
0.599
11.176
10
Idaho
Valley
0.088
0.424
0.506
0.602
0.812
20.892
10
Idaho
Washington
0.101
0.348
0.321
0.542
0.434
3.876
10
Oregon
Baker
0.089
0.456
0.475
0.580
0.554
13.833
10
Oregon
Benton
0.176
0.324
0.468
0.511
0.432
3.842
10
Oregon
Clackamas
0.189
0.332
0.698
0.554
0.562
8.782
10
Oregon
Clatsop
0.098
0.413
0.412
0.415
0.461
4.460
10
Oregon
Columbia
0.167
0.419
0.400
0.438
0.408
2.168
10
Oregon
Coos
0.124
0.282
0.509
0.433
0.411
4.133
10
Oregon
Crook
0.130
0.433
0.416
0.509
0.517
6.232
10
Oregon
Curry
0.253
0.255
0.353
0.550
0.489
2.007
10
Oregon
Deschutes
0.123
0.425
0.661
0.553
0.561
13.431
10
Oregon
Douglas
0.163
0.313
0.837
0.461
0.429
9.342
10
Oregon
Gilliam
0.066
0.507
0.287
0.461
0.370
2.000
10
Oregon
Grant
0.108
0.485
0.372
0.616
0.521
9.434
10
Oregon
Harney
0.100
0.444
0.373
0.584
0.489
8.365
10
Oregon
Hood River
0.147
0.337
0.392
0.601
0.552
6.432
10
Oregon
Jackson
0.159
0.286
0.727
0.526
0.428
8.522
10
Oregon
Jefferson
0.125
0.450
0.485
0.487
0.391
5.667
312
-------
EPA
REGION
State
County
Risk
Governance
Built
Environment
Natural
Environment
Society
CRSI
10
Oregon
Josephine
0.142
0.171
0.477
0.518
0.399
3.504
10
Oregon
Klamath
0.121
0.292
0.703
0.435
0.435
8.984
10
Oregon
Lake
0.117
0.400
0.431
0.524
0.437
5.932
10
Oregon
Lane
0.180
0.357
0.871
0.440
0.485
9.519
10
Oregon
Lincoln
0.138
0.387
0.470
0.477
0.421
4.627
10
Oregon
Linn
0.167
0.340
0.646
0.510
0.486
7.622
10
Oregon
Malheur
0.098
0.444
0.506
0.625
0.450
12.387
10
Oregon
Marion
0.184
0.299
0.628
0.477
0.531
6.457
10
Oregon
Morrow
0.099
0.457
0.428
0.390
0.396
3.366
10
Oregon
Multnomah
0.419
0.286
0.559
0.514
0.476
2.321
10
Oregon
Polk
0.161
0.337
0.440
0.492
0.467
3.971
10
Oregon
Sherman
0.055
0.586
0.269
0.529
0.279
2.731
10
Oregon
Tillamook
0.155
0.383
0.487
0.515
0.500
5.972
10
Oregon
Umatilla
0.112
0.436
0.697
0.500
0.501
13.461
10
Oregon
Union
0.096
0.464
0.468
0.586
0.552
12.831
10
Oregon
Wallowa
0.120
0.472
0.322
0.659
0.698
11.009
10
Oregon
Wasco
0.078
0.438
0.496
0.548
0.465
13.085
10
Oregon
Washington
0.405
0.338
0.419
0.540
0.501
1.894
10
Oregon
Wheeler
0.081
0.584
0.210
0.519
0.232
-1.529
10
Oregon
Yamhill
0.207
0.295
0.561
0.522
0.445
4.586
10
Washington
Adams
0.080
0.459
0.422
0.375
0.329
1.908
10
Washington
Asotin
0.054
0.427
0.332
0.533
0.475
10.272
10
Washington
Benton
0.170
0.400
0.613
0.521
0.477
7.366
10
Washington
Chelan
0.128
0.427
0.590
0.530
0.550
10.841
10
Washington
Clallam
0.085
0.374
0.508
0.451
0.514
9.983
10
Washington
Clark
0.447
0.345
0.460
0.458
0.478
1.425
10
Washington
Columbia
0.165
0.433
0.235
0.524
0.510
2.106
10
Washington
Cowlitz
0.120
0.388
0.501
0.450
0.412
5.339
313
-------
EPA
REGION
State
County
Risk
Governance
Built
Environment
Natural
Environment
Society
CRSI
10
Washington
Douglas
0.254
0.469
0.490
0.506
0.416
3.291
10
Washington
Ferry
0.122
0.452
0.338
0.626
0.351
4.760
10
Washington
Franklin
0.158
0.431
0.461
0.396
0.486
3.713
10
Washington
Garfield
0.101
0.460
0.226
0.466
0.397
-0.132
10
Washington
Grant
0.155
0.430
0.699
0.551
0.458
9.957
10
Washington
Grays Harbor
0.096
0.408
0.597
0.436
0.400
8.933
10
Washington
Island
0.192
0.574
0.374
0.432
0.456
2.758
10
Washington
Jefferson
0.152
0.337
0.468
0.479
0.518
5.154
10
Washington
King
0.527
0.441
0.864
0.501
0.521
3.793
10
Washington
Kitsap
0.266
0.409
0.499
0.335
0.443
1.616
10
Washington
Kittitas
0.200
0.432
0.575
0.498
0.499
5.863
10
Washington
Klickitat
0.119
0.406
0.558
0.497
0.503
9.280
10
Washington
Lewis
0.144
0.394
0.613
0.460
0.451
7.246
10
Washington
Lincoln
0.114
0.503
0.524
0.385
0.624
9.406
10
Washington
Mason
0.101
0.395
0.499
0.500
0.406
7.440
10
Washington
Okanogan
0.138
0.425
0.609
0.454
0.503
8.369
10
Washington
Pacific
0.092
0.350
0.448
0.382
0.387
2.636
10
Washington
Pend Orielle
0.081
0.445
0.447
0.603
0.430
11.957
10
Washington
Pierce
0.571
0.419
0.727
0.476
0.489
2.612
10
Washington
San Juan
0.095
0.535
0.432
0.694
0.638
17.356
10
Washington
Skagit
0.141
0.437
0.569
0.570
0.507
9.542
10
Washington
Skamania
0.124
0.400
0.391
0.593
0.406
5.619
10
Washington
Snohomish
0.263
0.452
0.722
0.641
0.541
7.658
10
Washington
Spokane
0.272
0.432
0.715
0.408
0.513
4.970
10
Washington
Stevens
0.108
0.465
0.581
0.466
0.500
10.563
10
Washington
Thurston
0.531
0.417
0.564
0.464
0.522
2.042
10
Washington
Wahkiakum
0.179
0.351
0.153
0.396
0.202
-4.844
10
Washington
Walla Walla
0.103
0.419
0.543
0.369
0.432
6.018
10
Washington
Whatcom
0.188
0.429
0.694
0.557
0.533
9.015
314
-------
EPA
Built
Natural
REGION
State
County
Risk
Governance
Environment
Environment
Society
CRSI
10
Washington
Whitman
0.086
0.464
0.703
0.391
0.402
12.613
10
Washington
Yakima
0.174
0.411
0.705
0.538
0.438
8.452
315
-------
-8-EPA
United States
Environmental Protection
Agency
PRESORTED
STANDARD POSTAGE
and FEES PAID EPA
PERMIT NO. G-35
Office of Research and
Development (8101R)
Washington, DC 20460
Official Business
Penalty for Private Use
$300
316
------- |