A U.S. Human Well-being Index (HWBI) for Multiple Scales: Linking
Services Provisioning to Human Well-being Endpoints (2000-2010)
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Acknowledgments
This synthesis report was prepared by the U.S. Environmental Protection Agency (EPA), Office of
Research and Development (ORD), National Health and Environmental Effects Research Laboratory
(NHEERL), Gulf Ecology Division (GED). The following task members provided written materials and
technical information in the preparation of this document.
Lisa M. Smith, Office of Research and Development
Linda C. Harwell, Office of Research and Development
J. Kevin Summers, Office of Research and Development
Heather M. Smith, Former Student Services Contractor
Christina M. Wade, Former Student Services Contractor
Kendra R. Straub, Former Student Services Contractor
Jason L. Case, Former Student Services Contractor
Special thanks to the following individuals for contributing expertise throughout the duration of this
project:
Dr. John Talberth -Senior Economist, World Resources Institute
Dr. Susan Lovelace - Human Dimensions Program Manager, NOAA Hollings Marine Laboratory
Dr. Bruce Peacock- Environmental Quality Division, Social Science Branch Chief (Economise/National
Park Service
Dr. Kirsten Leong- Program Manager, Human Dimensions of Biological Resource Management/National
Park Service
Also special thanks to Erin Hunter and Melissa Overton (SCH 1.2.2.1 Student Services Contractors) for all
their efforts in QA/QC and report preparation, editing and formatting, Dr. Matthew Harwell
(NHEERL/GED/EAB Branch Chief) for time dedicated to the insightful content reviews and suggestions
and to Dr. John Carriger (NHEERL/GED/BPRB) and Dr. Keri Chiveralls (Research Fellow, Barbara Hardy
Institute, University of South Australia, School of Natural and Built Environments) for their constructive
technical reviews.
Photo Credits
All photos courtesy of USEPA, Eric Vance
This report should be cited as:
Smith, L. M., Harwell, L. C, Summers, J. K., Smith, H. M., Wade, C. M., Straub, K. R. and J.L Case (2014).
A U.S. Human Well-being Index (HWBI) for multiple scales: linking service provisioning to human
well-being endpoints (2000-2010). EPA/600/R-14/223.
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Table of Contents
Executive Summary ES-1
Summary of Findings ES-5
Describing Weil-Being in the U.S ES-12
Linking Well-being to Service Provisioning ES-14
Limitations of Available Data ES-18
Report Objectives and Audiences ES-18
Chapter 1 Introduction 1
Chapter 2 Methodology 8
Characterizing Well-being 9
Data Source Selection 9
Data Imputation, Outliers and Standardization 9
Calculating the HWBI 10
Uncertainty and Sensitivity 10
Relative Importance Values 11
Services Provisioning 14
Evaluating Scores 15
Modeling Service-Domain Relationships 19
Introduction and General Strategy 19
Heuristic Exploration 20
Model Averaging 20
Model Refitting and Final Estimates 20
Model Diagnostics and Predictive Performance 21
County-Level Model 22
Chapter 3 Well-being at Multiple Scales 24
Human Well-being Index 26
Connection to Nature 32
Cultural Fulfillment 36
Education 40
Health 44
Leisure Time 48
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Living Standards 52
Safety and Security 56
Social Cohesion 60
Chapter 4 Services Provisioning 67
Service Types 68
Economic Services 72
Capital Investment 75
Consumption 78
Employment 81
Finance 84
Innovation 87
Production 90
Re-distribution 93
Ecosystem Services 96
Air Quality 98
Food, Fiber and Fuel 101
Green Space 104
Water Quality 107
Water Quantity 110
Social Services 113
Activism 116
Communication 119
Community and Faith-based Initiatives 122
Educational Services 125
Emergency Preparedness 128
Family Services 131
Healthcare 134
Justice 137
Labor 140
Public Works 143
Chapter 5 Relating Services Provisioning to Well-being Endpoints 148
Interpreting Functional Relationships 149
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Summary of Service-Domain Relationships 149
Utilizing Functional Relationships 150
Chapter 6 Concluding Remarks and Future Research Efforts 160
Concluding Remarks 161
Future Research Efforts 162
REFERENCES 163
APPENDIX A 169
SUMMARY OF METRIC DATA FOR HWBI DOMAINS 169
APPENDIX B 173
SUMMARY OF METRIC DATA FOR SERVICES 173
APPENDIX C 179
MODEL ESTIMATES AND STATISTICS 179
APPENDIX D 193
SERVICE INTERACTION 193
IV
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Research Highlights
Research Highlight: Review of Existing Well-being Indices 3
Research Highlight: Tampa Bay Area Project 12
Research Highlight: Transferability of the HWBI Framework to Native American Populations 29
Research Highlight: Washington D.C 64
Research Highlight: Social and Intergenerational Equity and Human Well-being 146
Research Highlight: HWBI in Context of TRIO 157
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List of Figures
Figure ES-1. A conceptualized modeling framework showing the components of the composite index of
well-being highlighting ecosystem goods and services inputs ES-3
Figure ES-2. General Social Survey (GSS) Regions of the United States ES-4
Figure ES-Publicly available data sources used to construct the HWBI and services provisioning
assessments ES-5
Figure ES-4.Evaluation of 2000-2010 domain and HWBI scores for the states, GSS regions and nation.. ES-9
Figure ES-5. Scorecard categorizing the level of services at the national, GSS and state levels for years
2000-2010 ES-11
Figure 1-1. A conceptualized modeling framework showing the components of the composite index of
well-being highlighting ecosystem goods and services inputs 6
Figure 2-1. Application of relative importance values in the calculation of the HWBI 10
Figure 2-2. Modeling procedure to select service parameters and predicted domain scores 18
Figure 2-3. Example of state-level model performance when scaled to county data 21
Figure 3-1. Baseline (2000-2010) HWBI scores for states and GSS region 26
Figure 3-2. Human Well-being Index scorecard categorizing the level of well-being at the national, GSS
regional and state levels for the years 200-2010 27
At the county level, a wider range of HWBI scores (min=42.8±2.7; max= 60.7±2.1) was observed for the
baseline period. When compared to the range of annual HWBI scores across the counties, decadal HWBI
values <50.1 were considered low at the county scale, values >56.1, high. The distribution of the county-
level HWBI scores for the years 2000-2010 are depicted in the chloropleth map below (Fig. 3-3) 28
Figure 3-3. Cloropleth map representation of HWBI scores at the county level for years 2000-2010) 28
Figure 3-5. State and Regional Connection to Nature domain scores for the baseline period (2000-
2010) 33
Figure 3-6. Domain scorecard for Connection to Nature characterized at the national, GSS regional and
state levels 34
Figure 3-7. Cloropleth map representation of variability in Connection to nature scores at the county level
for years 2000-2010 35
Figure 3-8. Indicators and metrics of the Cultural Fulfillment domain 36
Figure 3-9. State and Regional Cultural Fulfillment domain scores for the baseline period (2000-2010). ..37
Figure 3-10. Domain scorecard for Cultural Fulfillment characterized at the national, GSS regional and
state levels for the years of 2000-2010 38
Figure 3-11. Cloropleth map representation of variability in Cultural Fulfillment scores at the county level
for years 2000-2010 39
Figure 3-12. Indicators and metrics of the Education domain 40
Figure 3-13. State and Regional Education domain scores for the baseline period (2000-2010) 41
Figure 3-14. Domain scorecard for Education characterized at the national, GSS regional and state levels
for the years of 2000-2010 42
Figure 3-15. Chloropleth map representation of variability in Education scores at the county level for
years 2000-2010 43
Figure 3-16. Indicators and metrics of the Health domain 44
Figure 3-17. Regional Health domain scores for the baseline period (2000-2010) 45
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Figure 3-18. Domain scorecard for Health characterized at the national, GSS regional and state levels for
the years of 2000-2010 46
Figure 3-19. Cloropleth map representation of variability in Health scores at the county level for years
2000-2010 47
Figure 3-20. Indicators and metrics of the Leisure Time domain 48
Figure 3-21. Regional Leisure Time domain scores for the baseline period (2000-2010) 49
Figure 3-23. Cloropleth map representation of variability in Leisure Time scores at the county level for
years 2000-2010 51
Figure 3-24. Indicators and metrics of the Living Standards domain 52
Figure 3-25. Regional Living Standards domain scores for the baseline period (2000-2010) 53
Figure 3-26. Domain scorecard for Living Standards characterized at the national, GSS regional and state
levels for the years of 2000-2010 54
Figure 3-27. Cloropleth map representation of variability in Living Standards scores at the county level for
years 2000-2010 55
Figure 3-28. Indicators and metrics of the Safety and Security domain 56
Figure 3-39. Regional Safety and Security domain scores for the baseline period (2000-2010) 57
Figure 3-30. Domain scorecard for Safety and Security characterized at the national, GSS regional and
state levels for the years of 2000-2010 58
Figure 3-31. Cloropleth map representation of variability in Safety and Security scores at the county level
for years 2000-2010 59
Figure 3-32. Indicators and metrics of the Social Cohesion domain 60
Figure 3-33. Regional Social Cohesion domain scores for the baseline period (2000-2010) 61
Figure 3-34. Domain scorecard for Safety and Security characterized at the national, GSS regional and
state levels for the years of 2000-2010 63
Figure 4-1. Scorecard categorizing Economic, Ecosystem and Social Services at the national, GSS regional
and state levels for years 2000-2010 69
Figure 4-2. Scorecard categorizing the level of Economic Services provisioning at the national, GSS
regional and state levels for the years of 2000-2010 73
Figure 4-3. Indicators and metrics of Capital Investment services 74
Figure 4-4. Scorecard categorizing the level of capital Investment provisioning at the national, GSS
regional and state levels for the years of 2000-2010 76
Figure 4-5. Indicators and metrics of Consumption services 77
Figure 4-6. Scorecard categorizing the level of Consumption provisioning at the national, GSS regional and
state levels for the years of 2000-2010 79
Figure 4-7. Indicators and metrics of Employment services 80
Figure 4-8. 2000-2010 State-level Employment scores compared to the national average score. States are
ordered within regions from highest to lowest score 81
Figure 4-9. Scorecard categorizing the level of Employment provisioning at the national, GSS regional and
state levels for the years of 2000-2010 82
Figure 4-10. Indicators and metrics of Finance services 83
Figure 4-11. 2000-2010 State-level Finance scores compared to the national average score. States are
ordered within regions from highest to lowest score 84
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Figure 4-12. Scorecard categorizing the level of Finance provisioning at the national, GSS regional and
state levels for the years of 2000-2010 86
Figure 4-13. Indicators and metrics of Innovation services 87
Figure 4-14. 2000-2010 State-level Innovation scores compared to the national average score. States are
ordered within regions from highest to lowest score 88
Figure 4-15. Scorecard categorizing the level of Innovation provisioning at the national, GSS regional and
state levels for the years of 2000-2010 89
Figure 4-16. Indicators and metrics of Production services 90
Figure 4-17. State-level Production scores compared to the national average score. States are ordered
within regions from highest to lowest score 91
Figure 4-18. Scorecard categorizing the level of Production provisioning at the National, GSS regional and
state levels for the years of 2000-2010 92
Figure 4-19. Indicators and metrics of Re-distribution services 93
Figure 4-20. State-level Re-distribution scores compared to the national average score. States are ordered
within regions from highest to lowest score 94
Figure 4-21. Scorecard categorizing the level of Re-distribution provisioning at the national, GSS regional
and state levels for the years of 2000-2010 95
Figure 4-22. Scorecard categorizing the level of Ecosystem Services provisioning at the national, GSS
regional and state levels for the years of 2000-2010 97
Figure 4-23. Indicators and metrics for Air Quality Regulation services 98
Figure 4-24. State-level Air Quality scores compared to the national average score. States are ordered
within regions from highest to lowest score 99
Figure 4-25. Scorecard categorizing the level of Air Quality provisioning at the national, GSS regional and
state levels for the years of 2000-2010 100
Figure 4-26. Indicators and metrics of Food and Fiber Provisioning services 101
Figure 4-27. State-level Air Quality scores compared to the national average score. States are ordered
within regions from highest to lowest score 102
Figure 4-28. Scorecard categorizing the level of Food, Fiber and Fuel provisioning at the national, GSS
regional and state levels for the years of 2000-2010 103
Figure 4-29. Indicators and metrics of Green Space services 104
Figure 4-30. State-level Greenspace scores compared to the national average score. States are ordered
within regions from highest to lowest score 105
Figure 4-31. Scorecard categorizing the level of Food, Fiber and Fuel provisioning at the national, GSS
regional and state levels for the years of 2000-2010 106
Figure 4-32. Indictor and metrics for Water Quality Regulation services 107
Figure 4-33. State-level Water Quality scores compared to the national average score. States are ordered
within regions from highest to lowest score 108
Figure 4-34. Scorecard categorizing the level of Water Quality at the national, GSS regional and state
levels for the years of 2000-2010 109
Figure 5-35. Indicators and metrics of Water Quality Regulation services 110
Figure 4-36. State-level Water Quantity scores compared to the national average score. States are
ordered within regions from highest to lowest score Ill
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Figure 4-37. Services scorecard categorizing the level of Water Quantity at the national, GSS regional and
state levels for the years of 2000-2010 112
Figure 4-38. Scorecard categorizing the level of Social Services provisioning at the national, GSS regional
and state levels for the years of 2000-2010 115
Figure 4-39. Indicators and metrics of the Activism services 116
Figure 4-40. State-level Activism scores compared to the national average score. States are ordered
within regions from highest to lowest score 117
Figure 4-41. Scorecard categorizing the level of Activism at the national, GSS regional and state levels for
the years of 2000-2010 118
Figure 4-42. Indicators and metrics of the Communication services 119
Figure 4-43. State-level Communication scores compared to the national average score. States are
ordered within regions from highest to lowest score 120
Figure 4-44. Scorecard categorizing the level of Communication at the national, GSS regional and state
levels for the years of 2000-2010 121
Figure 4-45. Indicators and metrics of the Community and Faith-based Initiatives 122
Figure 4-46. State-level Community and Faith-based Initiatives scores compared to the national average
score. States are ordered within regions from highest to lowest score 123
Figure 4-47. Scorecard categorizing the level of Community and Faith-based Initiatives at the national,
GSS regional and state levels for the years of 2000-2010 124
Figure 4-48. Indicators and metrics of the Education Services 125
Figure 4-49. State-level Education Services scores compared to the national average score. States are
ordered within regions from highest to lowest score 126
Figure 4-50. Scorecard categorizing the level of Education Services at the national, GSS regional and state
levels for the years of 2000-2010 127
Figure 4-51. Indicators and metrics of the Emergency Preparedness services 128
Figure 4-52. State-level Emergency Preparedness scores compared to the national average score. States
are ordered within regions from highest to lowest score 129
Figure 4-53. Services scorecard categorizing the level of Emergency Preparedness at the national, GSS
regional and state levels for the years of 2000-2010 130
Figure 4-54. Indicators and metrics of the Family Services 131
Figure 4-55. State-level Family Services scores compared to the national average score. States are
ordered within regions from highest to lowest score 132
Figure 4-56. Scorecard categorizing the level of Family Services at the national, GSS regional and state
levels for the years of 2000-2010 133
Figure 4-57. Indicators and metrics of the Healthcare services 134
Figure 4-58. State-level Healthcare scores compared to the national average score. States are ordered
within regions from highest to lowest score 135
Figure 4-59. Scorecard categorizing the level of Healthcare at the national, GSS regional and state levels
for the years of 2000-2010 136
Figure 4-60. Indicators and metrics of Justice services 137
Figure 4-61. State-level Justice scores compared to the national average score. States are ordered within
regions from highest to lowest score 138
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Figure 4-62. Scorecard categorizing the level of Justice at the national, GSS regional and state levels for
the years of 2000-2010 139
Figure 4-63. Indicators and metrics of Labor services 140
Figure 4-64. State-level Labor scores compared to the national average score. States are ordered within
regions from highest to lowest score 141
Figure 4-65. Scorecard categorizing the level of Labor at the national, GSS regional and state levels for the
years of 2000-2010 142
Figure 4-66. Indicators and metrics of Public Works services 143
Figure 4-67. 2000-2010 State-level Public Works scores compared to the national average score. States
are ordered within regions from highest to lowest score 144
Figure 4-68. Scorecard categorizing the level of Public Works at the national, GSS regional and state levels
for the years of 2000-2010 145
Figure 5-1. Relationship between services provisioning and well-being domains modeled on the 2000-
2010 scores 150
Figure 5-2. Prioritized domains for the Tampa Bay Area derived from Stakeholder input 151
Figure 5-3. 2000-2010 Calculated domain scores for the Tampa Bay Area (includes Hillsborough,
Manatee, Pasco, Pinellas and Polk counties) in order from highest to lowest priority 152
Figure 5-4. Influences of the level of service provisioning on well-being domains modeled from
relationship function equation for the Tampa Bay Area 153
Figure 5-5. Changes in domain and HWBI scores for the Tampa Bay Area as a result of service level
changes for different scenarios; Comparison of service-domain influences based on changes in the
Greenspace and Communication services 155
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List of Tables
Table ES-1. Domains, indicators, and the number of metrics used to characterize human well-being..ES-13
Table ES-2. Services assessed to characterize Economic Services provisioning, indicators for each
corresponding service and the number of metrics used in the indicator calculations ES-14
Table ES-3. Services assessed to characterize Ecosystem Services provisioning, indicators for each
corresponding service and the number of metrics used in the indicator calculations ES-15
Table ES-4. Services assessed to characterize Social Services provisioning, indicators for each
corresponding service and the number of metrics used in the indicator calculations ES-16
Table 2-1. Summary statistics for estimates of uncertainty at the various spatial and temporal scales 10
Table 2-2. List of domain calculate affected by metric bias 11
Table 2-3. Threshold values for each domain at the national, GSS regional and state levels for the years
2000-2010 15
Table 2-4. Threshold values for Economic Services provisioning at the national, GSS regional and state
levels for the years 2000-2010 16
Table 2-5. Threshold values for Ecosystem Services provisioning at the national, GSS regional and state
levels for the years 2000-2010 17
Table 2-6. Threshold values for Social Services provisioning at the national, GSS regional and state levels
for the years 2000-2010 18
Table 2-7. Summary of GLMSELECT options within the Heuristic Exploration loop 20
Table 2-8. Summary of GLMSELECT options within the Model Averaging loop 20
Table 2-9. Average frequency that parameters were included in models with different procedural
decisions 21
Table 2-10. Summary R-squared statistics for models calibrated to state-level data 21
Table 2-11. Quantile regression at county level for conditional dependence of model parameter
estimates 22
Table 2-12. Summary R-squared statistics for models calibrated to county-level data 25
Table 3-1. Domains, indicators, and the number of metrics used to characterize human well-being 27
Table 4-1. Type of Services and the corresponding number of services within each type 68
Table 4-2. Threshold values for Services provisioning at the national, GSS regional and state levels for the
years 2000-2010 70
Table 4-3. Services assessed to characterize economic provisioning, indicators for each corresponding
service and the number of metrics used in the indicator calculations 71
Table 4-4. 2000-2010 Capital Investment scores for the GSS Regions with estimated error 76
Table 4-5. 2000-2010 Consumption scores for the GSS Regions with estimated error 79
Table 4-6. 2000-2010 Employment scores for the GSS Regions with estimated error 82
Table 4-7. 2000-2010 Employment scores for the GSS Regions with estimated error 85
Table 4-8. 2000-2010 Innovation scores for the GSS Regions with estimated error 88
Table 4-9. 2000-2010 Production scores for the GSS Regions with estimated error 91
Table 4-10. 2000-2010 Re-distribution scores for the GSS Regions with estimated error 94
Table 4-11. Services assessed to characterize Ecosystem Services provisioning, indicators for each
corresponding service and the number of metrics used in the indicator calculations 96
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Table 4-12. 2000-2010 Air Quality scores for the GSS Regions with estimated error 99
Table 4-13. 2000-2010 Food, Fiber and Fuel scores for the GSS Regions with estimated error 102
Table 4-14. 2000-2010 Greenspace scores for the GSS Regions with estimated error 105
Table 4-15. 2000-2010 Water Quality scores for the GSS Regions with estimated error 108
Table 4-16. 2000-2010 Water Quantity scores for the GSS Regions with estimated error Ill
Table 4-17. Services assessed to characterize Social Services provisioning, indicators for each
corresponding service and the number of metrics used in the indicator calculations 113
Table 4-18. 2000-2010 Activism scores for the GSS Regions with estimated error 117
Table 4-19. 2000-2010 Communication scores for the GSS Regions with estimated error 120
Table 4-20. Community and Faith-based Initiatives scores for the GSS Regions with estimated error 123
Table 4-21. Education Services scores for the GSS Regions with estimated error 126
Table 4- 22. Emergency Preparedness scores for the GSS Regions with estimated error 129
Table 4-23. Family Services scores for the GSS Regions with estimated error 132
Table 4-24. Healthcare scores for the GSS Regions with estimated error 135
Table 4-25. Justice scores for the GSS Regions with estimated error 138
Table 4-26. Labor scores for the GSS Regions with estimated error 141
Table 4-27. 2000-2010 Public Works scores for the GSS regions with estimated error 144
XII
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Executive Summary
Executive Summary
Environmental scientists understand the
importance of our natural world and the
benefits people derive from our natural
resources. Social and psychology scientists
recognize the drivers that influence human
behavior and affect the human condition.
Economists identify the myriad of currencies
that frame the underpinnings of a thriving
society. Characterizing human well-being (a
multidimensional phenomenon describing
the state of people's lives) is simply a matter
of synthesizing all of that knowledge to help
shape our understanding of human beings
and their needs. However, the components of
human well-being have yet to be defined and
understood.
The U.S. EPA Office of Research and
Development's Sustainable and Healthy
Communities (SHC) research program is
intended to inform and empower decision
makers to equitably weigh and integrate
human health, socio-economic,
environmental, and ecological factors to
foster sustainability in the built and natural
environments. The primary focus of the
SHCRP is on developing tools and approaches
to help local decision makers understand the
effects of alternative policies and actions on
sustainable outcomes. The SHCRP Indicators
and Indices (l&l) Project is focused on
identifying and developing metrics that signal
that current environmental, economic, and
social trends are becoming less sustainable;
identifying, to the extent possible, the
thresholds of sustainability for such
indicators; and identifying performance
metrics that signal that approaches to
Key Terms
Sustainability: Sustainability is operationally defined as
"the continued protection of human health and the
environment while fostering economic property and
societal well-being." (A Framework for Sustainability
Indicators at EPA. 2012)
Human Well-being Index (HWBI): An index of well-
being for the U.S. based on indicators and metrics
derived from existing measures of well-being.
Domain: Collections of indicators and metrics used to
describe different components of human well-being.
The domains correspond with one or more of the
three main elements of well-being: economic,
environmental, and societal well-being.
Index/Indices: An interpretable and synergistic value
or category describing the nature, condition, or trend
of a multidimensional concept, often used singularly or
as a composite of multiple indices.
Indicator: An interpretable value or category
describing trends in some measurable aspect, often
used singularly or in combination to generate an index.
Metric: A singular unit of something measurable, often
used singularly or in combination to generate an
indicator.
Service: The wants and needs of people are met
through items (i.e., goods) and delivery of assistance
(i.e., services). Economic, ecosystem, and social
services reflect the three pillars of sustainability.
Relationship Functions: Relationship functions are
equations that model the flow of services
(provisioning) to the domains of well-being.
Total Resources Impact Outcome: TRIO encompasses
any number of holistic community decision-making
approaches that address all three pillars of
sustainability.
ES-1
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Executive Summary
increasing sustainability are working as
intended (including indicators of any
unintended consequences). Additionally, the
project aims to develop indices that combine
indicators in a way that would be useful in
capturing trends in overall sustainability. The
development of a U.S. Human Well-Being Index
(HWBI) is one specific task effort within the l&l
Project.
The intended use of the HWBI is to evaluate the
influence of social, economic and ecological
service flows on human well-being as an
integrated measure based on eight aspects of
the human condition referred to as domains.
Tracked over time, the index has the potential
to serve as a measure of sustainable human
well-being when linked to alternative decisions
that change the ecological, economic, and social
states of defined populations (Fig. ES-1). The
metrics and methodologies for constructing
multiple scale HWBI measures have been
developed for the U.S., General Social Survey
(GSS) Region, state and county assessments as
well as for specific geographic and population
group applications. These well-being endpoints
have been linked to the provisioning of services
through the derivation of relationship function
equations.
The objective of this report is to characterize
well-being at multiple scales in order to
evaluate the relationship of service flows in
terms of sustainable well-being. The HWBI
results presented represent snapshot
assessments for the 2000-2010 time period.
Based on the spatial and temporal availability of
the data, results were confidently calculated at
the state, region, and national scales. Finer
spatial resolution results, such as county,
include greater uncertainty. Provisioning is
evaluated at the state, regional and national
scales for 22 different services. The functional
relationships between services and well-being
endpoints are modeled at the state level to
examine service interactions and to evaluate
the potential influence of service flows on
domains and overall well-being.
In this report, multiple scale HWBI values are
summarized. The whole of the United States is
divided into nine GSS regions. These groupings
are the same divisions of the United States
Census. The nine regions discussed in this paper
are Pacific, Montain, West North Central, West
South Central, East North Central, East South
Central, New England, Middle Atlantic, and
South Atlantic (Fig. ES-2). The HWBI values have
also been calculated for each of the 50 states
and 3,143 counties. The baseline HWBI values
referred to in the results are based on a decadal
block of metric data (2000-2010). Services
provisioning results are presented for each
state for the baseline 2000-2010 period with
references to GSS regional and national scores.
The modeled relationships between each of the
services and the well-being domains during the
2000-2010 time period, derived from functional
equations, are also presented.
ES-2
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Executive Summary
Quality and Quantity of Capital
Ecosystem
-Air Quality
- Food. Fiber and
Fuel Provisioning
- Greenspace
-Water Quality
- Water Quantity
Goods and Services
- Activism
- Communication
-Community and
Faith-based
^Initiatives
- Education
- Emergency
Preparedness
- Family Services
- Healthcare
- Justice
- Labor
-Public Works
Economic
- Capital Investment
- Consumption
- Employment
• Finance
- Innovation
- Production
- Re-distribution
L.
Freedom of Choice and
Opportunity
Domains of Well-being
Connection to Nature
Cultural Fulfillment
Education
Leisure Time
Living Standards
Safety and Security
Social Cohesion
Well-being Elements
Environmental
Societal
Economic
Human Well-being Index
o
Figure ES-1. A conceptualized modeling framework showing the components of the composite index of well-being highlighting
ecosystem goods and services inputs.
ES-3
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Executive Summary
Figure ES-2. General Social Survey (GSS) Regions of the United States.
This report describes:
• The methods used to construct the HWBI, to evaluate service provisioning and to model
functional relationships between services and well-being domains;
• HWBI results for different spatial scales and the relevance and status of domains used to
evaluate well-being;
• Services provisioning at multiple scales;
• The linkages between economic, ecosystem and social services provisioning and HWBI
endpoints based on modeled relationship functions;
• Transferability and utility of the approach.
ES-4
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Executive Summary
Summary of Findings
The HWBI is a composite assessment covering 8 domains based on 25 indicators measured using 80
different metrics. Service flow and stock assessments include 7 economic services (23 indicators, 40
metrics), 5 ecosystem services (8 indicators, 24 metrics) and 10 social services (37 indicators, 76
metrics). Data from 64 data sources were included in the HWBI and services provisioning
characterizations (Fig. ES-3). For each U.S. county, state, and GSS region, data were acquired or imputed
for the 2000-2010 time period resulting in over 1.5 million data points included in the full assessment
linking service flows to well-being endpoints. The approaches developed for calculation of the HWBI, use
of relative importance values, service stock characterization and functional modeling are transferable to
smaller scales and specific population groups. Additionally, tracked over time, the HWBI may be useful
in evaluating the sustainability of decisions in terms of EPA's Total Resources Impact Outcome (TRIO)
approaches.
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Executive Summary
Highlighted results from the 2000-2010 characterizations:
• The U.S. HWBI was characterized as moderately high for the 2000-2010 time period.
• The New England, West North Central and Middle Atlantic GSS regional HWBI scores were
significantly higher than the U.S. for the 2000-2010 time period; the South Atlantic, East South
Central and West South Central significantly lower.
• The East South Central and West South Central regions and the South Atlantic scored
significantly higher than all other regions and the U.S. for Connection to Nature.
• The West North Central and Middle Atlantic regions scored significantly higher than the national
average score for Cultural Fulfillment.
• Three GSS regions scored below the national average and lower than all other GSS regions for
the Education domain—the East South Central, West South Central and Pacific regions.
• The New England region scored significantly higher in the Health domain than the U.S. and all
other GSS regions.
• For the domain of Leisure Time, the West North Central region scored significantly lowest.
• Four of the GSS regions scored significantly higher than the national average for the Living
Standards domain—New England, Pacific, Middle Atlantic and West North Central. The East
South Central and West South Central regions scored significantly lower than all other regions.
• The South Atlantic, East South Central and West South Central regions all scored below the
national average and significantly lower than the other GSS regions in the Safety and Security
domain.
• The highest scoring GSS regions for Social Cohesion were the West North Central, Mountain and
East North Central regions.
• Economic and Ecosystem Services were characterized as moderately low for the U.S. while
Social Services were characterized as moderately high.
• The New England and West North Central regions scored high for Social Services. The South
Atlantic region scored high for Ecosystem Services.
• In locations where provisioning of all three services types (Economic, Ecosystem and Social) is
higher, the HWBI tends to be higher.
• In areas where Social Services provisioning is low, Economic and Ecosystem services provisioning
levels, although moderate or high, may not be able to sustain higher levels of well-being; thus
emphasizing a balance among the three pillars of sustainability in regards to services
provisioning.
• While quantity and quality of services may be high, well-being endpoints may only reflect
positive service influences when the services are actually being utilized.
• Interactions among services showed relationships between individual services and domains that
differed from evaluating each service independently, revealing the most influential services for
specific domains.
• Model results can be used to identify focus areas (services) for improving priority well-being
endpoints. For example, if a domain is identified as a high priority for improvement, focus areas
would include those services with low scores that have positive relationships with that domain.
ES-6
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Executive Summary
A 2000-2010 scorecard for HWBI and the domains was generated for the Nation, GSS regions and states
(Fig. ES-3). Overall well-being scores (HWBI) and domain scores for the nation, GSS regions and states
were compared to the range of scores for all years at each spatial scale to provide an assessment of
high, moderately high, moderately low and low. At each spatial scale, scores were compared to the
range of calculated scores observed for the corresponding spatial scale during the 2000-2010 period.
Scores >75th percentile were categorized as high, scores between the 50th and 75th percentile,
moderately high, scores at or above the 25th percentile but below the 50th percentile, moderately low
and scores <25th percentile were categorized as low.
The HWBI for the U.S. was categorized as moderately high for the 2000-2010 baseline period. At the GSS
regional scale, New England, the Middle Atlantic and West North Central regions scored high for the
HWBI. The East South Central and West South Central regions scored low. All but two states (KY and OK)
scored low for HWBI in these the low scoring regions. States with low HWBI scores in other regions
included FL, NC, SC, WV (South Atlantic), and AZ and NM in the Mountain region. All regions except for
the East South Central and West South Central regions included at least one state with a high HWBI
score for the 2000-2010 assessment. The South Atlantic, Pacific and Mountain regions scored
moderately low for HWBI. The East North Central regions scored moderately high.
Nationally, Connection to Nature scores were characterized as moderately high. The East South Central
and West South Central regions scored high for the Connection to Nature domain during the 2000-2010
baseline period. All of the states in these regions with the exception of OK, scored high as well. Five of
the states in the South Atlantic region scored high for this domain. All but two states (MO, ND) in the
West North Central region scored low for Connection to Nature as did the region. Other states scoring
low for this domain included NH and VT (New England), AK and HI (Pacific) MT, UT and WY (Mountain)
and Wl (East North Central).
The Cultural Fulfillment domain was the only domain scored low for the nation. Regionally, the West
North Central and Middle Atlantic regions scored high for this domain. Cultural Fulfillment scores were
characterized as high for the following states in other GSS regions for the 2000-2010 time period: MA
and Rl (New England), UT (Mountain) and Wl (East North Central). Both the South Atlantic and East
South Central regions scored low for Cultural Fulfillment. All states in the East South Central region
scored low except for TN. The New England, West South Central and East North Central regions scored
moderately high for Cultural Fulfillment.
The U.S. scored moderately high for the domain of Education for the years 2000-2010. The West North
Central region was the only region that scored high for this domain; all other regions scored moderately
low or moderately high. Six states in four other regions had high Education scores (NH, VT, VA, AK, MT
and WY). Low scoring states were in the South Atlantic, East South Central and West South Central and
Mountain regions and included NC, SC, WV, AL, MS, AR, LA, TX, AZ and NM.
The score for the domain of Health was rated moderately high for the U.S. for the years 2000-2010. New
England was the only GSS region rated high for Health. Eight states in the other regions rated high for
health: NJ (Middle Atlantic), VA (South Atlantic), CO and UT (Moutain) and IA, MN,NE and SD (West
ES-7
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Executive Summary
North Central). The East South Central region scored low for the Health domain. Low scoring states in
other regions included FL and WV (South Atlantic), AR, LA and OK (West South Central) and NM and NV
(Moutain).
The Leisure Time score for the nation was characterized as moderately low for the years 2000-2010. GSS
regional scores were categorized as either moderately low or moderately high. Only two states were
considered to have low scores for the Leisure Time domain, WY (Mountain) and SD (West North
Central).
The Living Standards score for the U.S. was characterized as moderately low for the years 2000-2010.
The East South Central and West South Central regional scores were low. New England was the only high
scoring region for this domain; however high scoring states in other regions included: NJ and NY (Middle
Atlantic), MD (South Atlantic), CA and HI (Pacific) and MN (West North Central). All states in the East and
West Central regions were characterized as having low Living Standards scores. Living Standards scores
in states in the South Atlantic region (GA, NC, SC and WV) and in NM (Mountain) were also considered
low.
The Nation scored moderately high for the Safety and Security domain. Regionally, New England and the
Middle Atlantic were scored high with the majority of the states in these regions also scoring high. In
other regions, states categorized with high scores for Safety and Security included VA (South Atlantic), HI
(Pacific), CO, UT, WY (Mountain) and MN and ND (West North Central). All states in the West South
Central region scored low for this domain and all but one state (KY) in the East South Central region
scored low. Although a mix of low, moderate, and high scoring states were in the South Atlantic region,
the region scored low. Two other states in the Mountain region (NM and NV) also scored low for Safety
and Security.
The U.S. Social Cohesion score for the U.S. was categorized as moderately low for the years 2000-2010.
Two GSS regions (Mountain and West North Central) scored high for this domain with most of the states
in these regions scoring high or moderately high (with the exception of AZ (low) and NM (moderately
low). States in other regions scoring high for Social Cohesion included NH and VT in New England and AK
and HI in the Pacific region. With the exception of the West North Central and East North Central
regions, all regions had a least one low scoring state for this domain. The West South Central region was
the only region scored low for Social Cohesion.
ES-8
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Executive Summary
*
e e
HWBI
Connection to Nat
Vit
-------
Executive Summary
Services provisioning was assessed by service type (Economic, Ecosystem and Social) and for each
individual service within each service type for the states, GSS regions and the nation. At each spatial
scale, scores were compared to the range of calculated scores observed for the corresponding spatial
scale during the 2000-2010 period. Scores >75th percentile were categorized as high, scores between the
50th and 75th percentile moderately high, scores at or above the 25th percentile but below the 50th
percentile, moderately low and scores <25th percentile were categorized as low.
Provisioning assessments for each service type were based upon the quantity and quality of the
following services:
Economic Services (7)
Capital Investment Innovation
Consumption Production
Employment Redistribution
Finance
Ecosystem Services (5)
Air Quality Water Quality
Food, Fiber and Fuel Provisioning Water Quantity
Greenspace
Social Services (10)
Activism Family Services
Communication Healthcare
Community and Faith-Based Initiatives Justice
Educational Services Labor
Emergency Preparedness Public Works
The scorecard for 2000-2010 services provisioning for the Nation, GSS regions and states is presented in
(Fig. ES-5). Economic and Ecosystem Services were characterized as moderately low for the U.S. while
Social Services were characterized as moderately high. When compared to the annual scores for across
the GSS regions, the 2000-2010 values for Economic Services in all regions was scored as moderately
low. The South Atlantic region was the only region scored high for Ecosystem Services provisioning; the
East South Central and West South Central and West North Central regions scored moderately high. The
Pacific region was the only region characterized with low provisioning of Ecosystem Services. The New
England and West North Central Regions were scored high for the provisioning of Social Services. The
East South Central and West South Central regions scored low for Social Services. The other regions with
the exception of the South Atlantic were scored moderately high for the provisioning of Social Services.
ES-10
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Executive Summary
Moderately Low Moderately High
High
:conomicServices
'
:cosystem Services
s
ocial Services
U.S.
New England
CT
MA
ME
NH
Rl
VT
Middle Atlantic
NJ
NY
PA
South Atlantic
DE
FL
GA
MD
NC
SC
VA
WV
East South Central
AL
($}
KY
MS
TN
West South Central
AR
LA
OK
TX
o
Pacific
AK
CA
HI
OR
WA
Mountain
AZ
CO
ID
MT
NM
NV
UT
WY
West North Central
IA
KS
MN
MO
ND
NE
SD
East North Central
IL
IN
Ml
OH
Wl
r$}
\
Figure ES-5. Scorecard categorizing the level of services at the national, GSS and state levels for years 2000-2010.
ES-11
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Executive Summary
Compared to the annual state-level scores for services, fourteen states scored high for Economic
Services: ME, VT (New England), DE (South Atlantic), HI, OR, WA (Pacific), AZ, MT, UT, WY (Mountain),
IA, ND, NE, SD (West North Central). The majority of states scored low for Ecosystem Services and only
four states (AK, WY, ND and SD) scored high. The only state scored high for Social Services was Wyoming
(Mountain). Social Services were characterized moderately low-moderately high for the majority of
states; however states the states of WV (South Atlantic), MS (East South Central) and AR (West South
Central) scored low. Wyoming was the only state characterized as with high levels of provisioning of all
three service types. No state scored low for all three services for the 2000-2010 period.
Describing Weil-Being in the U.S.
The U.S. HWBI is a composite measure of human well-being calculated from eight domain scores
described by 25 indicators based on 80 metrics (Table ES-1). The HWBI was developed based on a set of
eight domains selected from an extensive literature review of existing well-being measures (Smith et al.
2013a). These domains describe aspects of the human condition covering a range of measures similar to
those identified in the Canadian Index of Well-being (Guhn 2010) and the Organisation for Economic Co-
operation and Development (OECD) Better Life Index (OECD 2011). The addition of the domain of
Connection to Nature is unique to our HWBI. This domain includes subjective measures indicative of
biophilia, the innate relationship humans have with nature. Such measures are overlooked in existing
indices and have the potential to reflect the importance of ecosystems to humans in a non-monetary
manner for the valuation of ecosystem services. In the same manner, all the domains are characterized
by subjective measures that enhance our understanding of how people view quality of life. Subjective
measures give the index a more realistic flavor in the fact that routine statistics often used to examine
well-being are tempered by people's perceptions related to specific objective measures. An approach
for using relative importance values (RIVs) can be used to derive weighting factors for the domains in
the calculation of the index (Smith et al. 2013b). The RIVs are derived from a combination of public
perception and professional opinion. Application of the RIVs interjects a perspective from the public side
that is often omitted in consideration of decision consequences.
ES-12
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Executive Summary
Table ES-1. Domains, indicators, and the number of metrics used to characterize human well-being.
DOMAIN
Connection to Nature
Health
INDICATORS
Biophilia
Activity Participation
Basic Educational Knowledge and Skills of Youth
Participation and Attainment
Social, Emotional and Developmental Aspects
Healthcare
Life Expectancy and Mortality
Lifestyle and Behavior
Physical and Mental Health Conditions
Personal Well-being
Activity Participation
Time Spent
Working Age Adults
METRICS
3
4
4
2
7
4
9
3
2
1
3
Basic Necessities
Income
Wealth
Work
Actual Safety
Perceived Safety
Risk
2
3
2
2
4
1
1
Attitude Toward Others and the Community 5
Democratic Engagement 6
Family Bonding 3
Social Engagement 3
Social support 1
ES-13
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Executive Summary
Linking Well-being to Service Provisioning
Services are categories of indicators used to describe the quantity and quality of provisioning from
economic, environmental and social sectors. Services are divided into three categories (service types):
Economic, Ecosystem and Social. In the evaluation of services provisioning, seven economic services,
five ecosystem services, and 10 social services were assessed. The indicators and metrics used to
characterize services provisioning (Tables ES-2, ES-3, and ES-4) were selected to capture the quantity
and quality of service stocks. Services included in the assessment were identified from literature reviews
in conjunction with professional consultation within the respective disciplines (economics, ecology and
sociology). Service scores were calculated for each spatial scale (county, state, GSS region and nation).
Table ES-2. Services assessed to characterize Economic Services provisioning, indicators for each corresponding service and the
number of metrics used in the indicator calculations. For more detailed information on the individual metrics see Appendix B.
SERVICE
Capital Investment
INDICATORS
Capital Formation
Commercial Durables
New Housing Starts
New Infrastructure Investments
Cost of Living
Discretionary Spending
Goods and Services
Sustainable Consumption
Employment
Employment Diversity
Underemployment
Unemployment
Governance
Loans
Savings
METRICS
ES-14
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Executive Summary
SERVICE
Innovation
Production
INDICATORS
Investment
Patents and Products
METRICS
Exports
Household Services
Market Goods and Services
Sustainable Production
Inequality
Public Support
1
1
2
1
1
5
Table ES-3. Services assessed to characterize Ecosystem Services provisioning, indicators for each corresponding service and the
number of metrics used in the indicator calculations. For more detailed information on the individual metrics see Appendix B.
SERVICE
Air Quality
INDICATORS
Usable Air
Food. Fuel and Fiber
Energy
Food and Fiber
Raw Materials
Natural Areas
Recreation and Aesthetics
METRICS
4
3
5
ES-15
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Executive Summary
SERVICE
Water Quality
INDICATORS
Usable Water
METRICS
Water Quantity
Available Water
Table ES-4. Services assessed to characterize Social Services provisioning, indicators for each corresponding service and the
number of metrics used in the indicator calculations. For more detailed information on the individual metrics see Appendix B.
SERVICE
Activism
Communication
Community and Faith-based
Initiatives
Education
Emergency Preparedness
INDICATORS
Participation
Accessibility
Industry Infrastructure
Providers
Public Service
Communication
Quality
Investment
Providers
Accessibility
Confidence
Investment
Providers
Post-Disaster Response
Pre-Disaster Planning
Responders
METRICS
3
3
1
1
2
1
1
3
1
2
1
1
1
ES-16
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Executive Summary
SERVICE INDICATORS
Family Services
Ch
€l
^x^x^S*** •»
£
Healthcare
4
Justice
XA
ia£w
Labor
**;
X^N®
6s&
Public Works
^
Accessibility
Effectiveness
Investment
Providers
Accessibility
Investment
Providers
Quality
Accessibility
Confidence
Environmental
Investment
Providers
Quality
Confidence
Effectiveness
Employee Rights
Accessibility
Investment
Providers
Quality
Quantity
METRICS
2
3
1
1
5
3
1
1
2
1
4
2
1
1
1
1
2
2
4
1
5
5
Relationship function equations were derived to examine the influence of the level of service
provisioning (service scores) on the calculated domain scores. A step-wise regression with interactions
analysis was used to model the relationships between services and domains at the state-level. The
results of this modeling exercise produced a set of equations applicable to the state, GSS regional and
national scales. A quantile regression was then conducted at the county level to capture the conditional
dependence of model parameter estimates on domain scores. Output from the model suggests which
services during the years 2000-2010 positively or negatively influenced the domain scores in the HWBI.
Additionally, those services which significantly interacted with other services to influence domain scores
are identified. The relationship function equations can be used to examine how multiple services
interact and how well-being endpoints (domains) can be influenced by levels of provisioning of those
services. This information can be used to help decision makers consider how a broader range of services
(Economic, Ecosystem and Social) may influence priority areas (domains) when levels of provisioning are
changed, addressing sustainable outcomes in a more holistic manner.
ES-17
-------
Executive Summary
Limitations of Available Data
Currently, availability and treatment of the secondary data limits interpretability of results beyond a
general characterization of human well-being and services provisioning. When large sets of diverse data
are amassed and coalesced for a single purpose, the original data collection context may be lost,
contributing to ambiguousness of results. Sparseness of available metrics at the targeted spatial and
temporal scales contributes to skewed or non-normal distributions of the data. Imputation methods
used to fill significant data gaps may impact result sensitivity by artificially limiting variability across time
and space making it more difficult to detect changes over time. Data sparseness and imputations may
also result in scores which misrepresent or under represent certain populations and locations (e.g., rural
counties).
Report Objectives and Audiences
By design this report describing the development and application of the Human Well-Being Index
(HWBI) is intended to serve two purposes - technical formulation and application. It is not always a
good idea to have multiple purposes for a report - it is easy to lose both audiences but we believe the
dual purpose for this report is necessary. The report has two objectives and two related audiences.
First, the report is intended to be technical concerning the specific formulation and development of the
HWBI and the databases and approaches used in its construction. The description contains significant
technical terminology and jargon. The materials describing the index's technical construction is primarily
in Chapter 2 and the Appendices. This material is intended to directly address the concerns of a
technical audience concerned with the conceptual and mathematical development of HWBI, its
statistical validity and uncertainty. Clearly, all audiences may be interested in this topic but likely in
varying degrees.
The second objective of this report is its application at multiple scales - national, regional, state, county
and city - and for multiple demographic populations - general, city-specific and tribal. The audience for
this objective may be less interested in the construction of the index and more interested in its
performance and utility. This audience will likely be more interested in Chapters 3 through 5.
We are not suggesting that either audience should ignore portions of the report. However, we do alert
the audiences as to the highly technical nature of Chapter 2 which may be examined in great detail by
technical audiences and skimmed by audiences more interested in the results of the application of
HWBI.
ES-18
-------
Chapter 1 Introduction
Chapter 11 Introduction
(USEPA Photo by Eric Vance)
-------
Chapter 11 Introduction
Society depends upon the flows, valuation and provisioning of various capital driven services (economic,
social and ecological). These services and the indicators that represent them are the key to our
understanding of human well-being. By extension, in today's society, decision makers (from local
neighborhoods to national-level legislators and policy makers) need knowledge and an array of tools
that allow them to think holistically about the impacts of their decisions, in qualitative and quantitative
terms, on social welfare (community well-being). In addition, the decision makers need to assess the
impact of their decisions from a social equity perspective (including environmental justice) to
understand the impact on of all members of a community as well as from an inter-generational
perspective to understand the impact on future generations. It is precisely this complex interaction of
social, economic and environmental trade-offs involved in seeking sustainable solutions that pose many
of today's challenges to decision makers (Fig. 1-1).
The Millennium Ecosystem Assessment (MEA) (2003) identified a framework for categorizing ecosystem
services - provisioning services, regulating services, cultural services and supporting services. The MEA
recognized that changes in ecosystem services have a direct effect on human well-being through
impacts on security, the basic materials for a good life, health and social and cultural relations. Together
these elements are influenced by and have an influence on the freedoms and choices available to
people. The relationship between ecosystem change (interchangeable with changes in ecosystem
services) and human well-being has both current and future dimensions and short-term impacts may
not even have the same direction as longer-term impacts, much less the same magnitudes. For example,
the overexploitation of an ecosystem may temporarily increase material well-being and alienate
immediate poverty, yet prove to be unsustainable and in the end severely reduce material well-being
and increase levels of poverty (MEA 2003).
The New Economics Foundation (NEF) Project has and continues to explore the link between physical
and psychological well-being and the environment and ecological services (NEF 2005). Climate change,
resource degradation, ozone depletion, global elemental cycles, biodiversity loss, chemical
contamination of food, air and water, alien/invasive species have all been shown to have negative
effects on physical well-being at localized and global scales. Positive impact through engagement with
the natural environment and its services has been documented on psychological well-being both
individually and at the community level. Communal green spaces in urban areas have been linked to
higher levels of community cohesion and social interaction among neighbors (Kuo and Sullivan 2001).
Pretty et al. (2007) demonstrated the various influences of access to green space on both physiological
and psychological well-being.
With a surge of research focused on the relationships between humans and the natural environment,
beyond exploitation, the need to understand the role of ecosystems in context of economic and social
systems became more apparent. In an extensive review, Smith et al. (2013a) examined 20 approaches to
assessing human well-being (e.g., Gallup-Healthways Well-Being Index, Gross National Happiness Index,
Happy Planet Index) in order to determine if existing indices fully examined the three pillars of
sustainability (economic, social and environmental). While the findings of this review suggested that
several approaches were close to a complete sustainability assessment (i.e., actively including all three
-------
Research Highlight: Review of Existing Well-being Indices
Research over the past 30 years has explored
various aspects of measuring the well-being of
people. Because there is no single agreed upon
dentition of human well-being, the ability to
evaluate policy effectiveness and the
implications of alternative decisions in context
of the three pillars of sustainability (economic,
environmental and social) is a daunting task. An
extensive literature review was conducted to
develop domains descriptive of well-being
which could be linked to ecosystem, economic
and social services provisioning (Fig. 1). Twenty
approaches for assessing human well-being
were examined in depth, including the Gallup-
Healthways Well-Being Index, Gross National
Happiness Index, and Happy Planet Index. None
of the approaches examined all three pillars of
sustainability to provide a complete assessment
applicable to the U.S.
Composite score
Well-being Index
Elements
Domains
Indicators
Specific measurements \
Figure 1. A composite index approach to Well-being indices.
The literature review identified 157 domains
and 799 indicators among the multi-
dimensional well-being measurements
reviewed (Table 1). Most well-being measures
reviewed focused on describing basic human
needs, a precursor for achieving holistic well-
being. The ability to meet basic needs is most
often captured with economic measures, but
the majority of these measures for developed
countries have ignored subjective well-being.
Further, many indices fail to distinguish the
differences between drivers of well-being and
derived societal benefits. When the drivers and
endpoints of well-being are not considered
together, targeted decision making becomes
difficult and often ineffective.
The domains identified in the literature review
were assembled to help develop the structure
of the eight core domains of U.S. HWBI. Of the
799 indicators identified, 441 fell within these
core domains while most of the rest were
categorized as measures of economic, social or
ecosystem services, or capital (natural, human,
built, or social). A detailed review of well-being
indicators and the utility of the eight domains
selected for the U.S. HWBI can be found in
Smith et al. (2013). Ultimately, the U.S. HWBI
contains eight domains comprised of 25
indicators based on 80 metrics as evaluated
based on applicability across populations, data
availability and data continuity.
-------
Table 1. Categorization of indicators from existing well-being indices into a core set of well-being domains and service, capital and
sustainability (other) categories (Smith et al. 2013). Symbol X denotes the element was directly represented and addressed in the index;
(X) denotes that element was indirectly represented but not directly addressed in the description of the indicators and domains.
The Economists Intelligence
Unit's Quality of Life Index
Australian Unity Wellbeing
Index
Human Development Index
Quality of Life Index for
developed countries
The Well Being of Nations
Sustainable Society Index
Hong Kong Quality of Life
Index 2008
Well-being in EU countries
multidimensional index of
sustainability
National Well-being Index-
Life Satisfaction
Child and Youth Well Being
Index
Canadian Index of Well-
Being
Happy Planet Index
Index of Child Well-Being in
Europe
Index of Social Health
American Demographics
Index of Well-Being
Gallup Healthways Well-
Being Index
The State of the
Commonwealth Index
QOL 2007 in Twelve of New
Zealand's Cities
Human Well-being Index
(HWBI)
EIU
Cummins et
al. 2003
UNDP 1990
-2008
Diener1995
Prescott
Allen 2001
Kerk and
Manuel
2008
Chinese
University of
Hong Kong
Centre for
QOL
Distaso
2007
Vemuri and
Costanza
Land, Lamb
and Mustillo
2001
Atkinson
Charitable
Foundation
New
Economics
Foundation
2007
Bradshaw
and
Richardson
2009
Miringoff
and
Miringoff
1999
Kacapyr
1996
2008 Gallup
Watts 2004
Kath
Jamieson
2007
Smith etal.
2013
National
National
National
National
National
National
National
National
National
National
National
National
National
National
National
National
State
Local
Multiple
Economic
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
Basic
Needs
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
Environmental
X
(X)
X
X
X
X
X
X
X
X
X
X
Happiness
(Subjective
Well-being)
X
(X)
X
X
X
X
Smith, L. M., Case, J. L, Smith, H. M., Harwell, L. C. and J. K. Summers.
Foundation for a US index. Ecological Indicators 28: 79-90.
(2013). Relating ecosystem services to domains of human well-being:
-------
Chapter 11 Introduction
pillars of well-being and sustainability), most approaches focused on one or two of the sustainability
pillars rather than on all three. As an example, Gallup Healthways Well-Being Index (Sommers 2014) was
determined to have significant information pertaining to social drivers, such as health, and little or no
information concerning the economic and environmental drivers of well-being. This lack of an index that
examined all three pillars of well-being prompted EPA to develop the index described in this report the
Human Well-being Index (HWB). The HWBI is a substantive measure of human well-being (comprised of
eight domains) that addresses well-being with a clear combination of objective and subjective indicators
and metrics that can be related to each pillar. When linked to service flows, the HWBI epitomizes the full
three-pillar approach.
In alignment with the three pillar approach to sustainability, in 2011, the Sustainable and Healthy
Communities (SHC) research program in EPA's Office of Research and Development (ORD) coined the
term TRIO for Total Resource Impacts and Outcomes (USEPA 2012a). The concept of TRIO encompasses
any number of holistic community decision-making approaches that address all three pillars of
sustainability—economic, societal and environmental. While TRIO is similar to triple-bottom line
accounting (Elkington 2001, Willard 2002, Savitz and Weber 2006), SHC developers believed the term
triple-bottom line accounting conveyed too much of an economic connotation and desired a term that
would clearly demonstrate full inclusion of all three pillars of sustainability. For example, community-
based decisions are often driven by financial burden where cost is largely described in economic terms
such as monetary valuation. A TRIO approach would evaluate both tangible costs (e.g., capital
investment, tax revenue, permitting) and less tangible costs (e.g., community service disruption, loss of
natural services) that may impact quality of life. This expanded assessment process would be
accomplished across all three pillars of sustainability in parallel to help identify not only the expected
attributes of the decision but also the unintended consequences of all decision options. In the literature,
approaches that consider the three pillars of sustainability have included many different specific
methods—The Green Scorecard, Triple Bottom-Line Accounting, Happy Planet Index, and Millennium
Ecosystem Assessment to name a few (Elkington 2001, Willard 2002, WHO 2005, Marks et al. 2006,
Savitz and Weber 2006, Phillips et al. 2011). Many others (e.g., Ecological Footprint) address one specific
aspect of sustainability (Wackernagel et al. 2002). All of these approaches relate in one way or another
to the improvement of human well-being as an endpoint. As well-being is often an endpoint of concern
regarding sustainability, SHC determined the need to adopt or develop an approach or index of human
well-being that fully embraced the TRIO aspects of the developing research program (Summers 2014).
The Human Well-Being Index (HWBI) is the culmination of this research effort.
Lack of clearly developed relationships between ecosystem, social and economic services and well-being
elements represents another clear area of difference between the HWBI and the other indices reviewed.
The wants and needs of people are met through goods (i.e., items) and services (i.e., the delivery of
assistance). Economic, ecosystem, and social services affect the three pillars of sustainability. Economic
services provide a means to generate and distribute wealth within a society. Ecosystem services ensure
that the air we breathe, the water we drink, the food we eat, and the places we live are capable of
supporting and improving life. Social services are provided by a society to benefit the people within that
society. A combination of indicators of economic, ecosystem, and social services can be used to model
-------
Chapter 11 Introduction
how changes in these provisioning of services (e.g., through management decisions) influence human
well-being.
Quality and Quantity of Capital
L.
Goods and Services
Ecosystem
-Air Quality
-Food, Fiber and
Fuel Provisioning
- Greenspace
-Water Quality
- Water Quantity
- Activism
- Communication
-Community and
Faith-based
Initiatives
- Education
- Emergency
Preparedness
- Family Services
- Healthcare
- Justice
- Labor
-Public Works
Economic
- Capital Investment
- Consumption
- Employment
- Finance
- Innovation
- Production
- Re-distribution
Domains of Well-being
Connection to Nature
Cultural Fulfillment
Leisure Time
Living Standards
Education
Safety and Security
Health
Social Cohesion
Environmental
Well-being Elements
Societal
Economic
Human Well-being Index
o
o
•o
o
01
3
.
3
CD
Figure 1-1. A conceptualized modeling framework showing the components of the composite index of well-being highlighting
ecosystem goods and services inputs.
-------
Chapter 11 Introduction
The HWBI framework serves as a roadmap that shows how goods and services influence the domains of
well-being (Fig. 1-1). By taking an inventory of stocks or measuring the levels of services, functional
relationships can be derived to quantify how changes in the provisioning of services influence HWBI.
Service indicators can both positively and negatively influence well-being. Modeling these indicators as
service functions within the HWBI framework creates a linkage between those services and the domains
of well-being. Ultimately, this linkage will help communities understand how management decisions
may affect economic, ecosystem, and social sectors.
Application of the HWBI approach is not spatially limited. The index has been conceptually developed so
that it can be applied at any spatial scale (assuming the availability of data) and to any vulnerable
population distributed through these spatial scales (also assuming available information). If data does
not exist at the desired scale, they can be collected based in the desired scale/population of interest. For
example, HWBI has been applied to Tampa, FL and its surrounding areas to assess the well-being of its
constituency to assist decision makers in evaluating alternative scenarios based on potential changes in
well-being associated with decision scenarios (see Chapter 2 Research Highlight). To represent specific
communities (from nation to state to county to city to locale), the domains of HWBI (represented by
their attendant indicators and subsequent metrics) can be weighted based on the values structure
ascertained for the specific community. Another demonstration by Smith et al. (2014a) examined a
detailed application of the HWBI across spatial scales to Native American tribal populations in order to
assess equitability differences between mean county and regional HWBI scores and those for selected
tribal populations (see Chapter 3 Research Highlight. These applications demonstrate the robustness of
the index and its transferability to whatever spatial, economic, social, or demographic grouping might be
desired. Additionally, the components of the index linked to services provisioning over time may be
valuable approach to evaluating social and intergenerational equity as a dimension of sustainability.
Various applications of HWBI are included in this report as research highlights.
This report describes the creation and application of HWIB demonstrated at multiple scales and
modeled based on services provisioning. HWBI could become a predicated endpoint for a decision
support tool it is linked to bundles of relevant service flow measures from capital-based modules (e.g.,
social, economic and natural capitals). The integrated modules, as described by Summers et al. (2012)
and Smith et al. (2013b), are intended to reflect the degree to which changes in service provisioning can
influence changes in overall human well-being. Using the basic index deconstruction model that an
index can be broken down into elements, with each element represented by domains, the domains
represented by indicators and the indicators represented by specific metrics; we have re-constructed a
human well-being index based on largely, readily available data in the United States.
-------
Chapter 21 Methodology
Chapter 2 Methodology
(USEPA Photo by Eric Vance)
-------
Chapter 21 Methodology
Characterizing Well-being
Data Source Selection
For each metric described by Smith et al. (USEPA
2012b), objective and subjective data were collected
from various publically accessible sources and
organized hierarchically by spatial and temporal
resolution (e.g., national, regional, state, and county
by year) for the years 2000-2010. When multiple
spatial scales existed for a metric, the finest scale
(e.g., county vs. state) was selected for processing.
Data source determination was primarily driven by
temporal and spatial coverage of offered data. To the
extent possible, factors such as data reliability and
credibility, historic data continuity, and future data
accessibility were consider in the data source
selection process. A summary of the metrics data for
HWBI domains is included in Appendix A.
Data Imputation, Outliers and Standardization
Data gaps caused by spatial and temporal disparities
found among data sources were filled using a carry-
forward substitution imputation technique (Zhang et
al. 2008) using cross-year county or within year state
or regional data. A secondary imputation was
accomplished in an effort to calculate imputed values
for counties exhibiting similar characteristics. From
the spatially and temporally complete data set,
county groupings were created using a combination
of the Rural-Urban Continuum Code (RUCC)
classifications (USDA 2013) and the Gini Index for
Household Income Inequality (HII) quintiles (U.S.
Census 2012). The RUCC-HII permutations generated
county data groupings that generally reflected the
relative spatial relationship of a county to the nearest
large urban center and its measured income
dispersion. Within year median values were
calculated for each RUCC-HII banding. Missing values
in the original aggregate of metric data were
substituted with the resulting RUCC-HII banding
values.
Handling the Double-Counting
When dealing with many sources of existing
data, it is not uncommon to find multiple sets of
data that include the same or similar
information. If left unchecked, information
redundancies can over inflate an aspect used to
develop an indicator which may lead to a bias in
the results. This effect is known as "double-
counting". The case for eliminating data that
describe similar things is not always straight
forward. Information, such as health metrics,
may be correlated. However separate but
related metrics bring unique facets toward
building a complete picture that cannot be
easily dismissed based on correlates alone.
"Statistically unique" versus" holistically
complete" are challenging concepts to manage
when creating an index.
The HWBI development process minimized
double-counting as much as possible in three
ways. First, HWBI calculations used a single
source to fulfill a specific data need. Additional
sources for the same or similar data were used
to fill data gaps, if appropriate, when the
primary source was lacking. Second, sensitivity
analyses were conducted to identify influencing
factors (e.g. correlations, data gaps) within and
across the indicators used to calculate the
HWBI. Suspect metrics were further reviewed to
determine the merits of observed influence on
the overall final index (e.g. literature). Finally,
the importance of the information that a metric
provided in context of all other contributing
metrics was considered before a decision to
include or exclude a set of data was made.
Box-and-whisker analyses were completed for each
fully enumerated HWBI metric. Extreme lower and
upper outlier measures were set to minimum and
maximum values, respectively. All data were
standardized on a scale from 0.1 to 0.9 following the
Organisation for Economic Co-Operation and
-------
Chapter 21 Methodology
Development's (OECD) Better Life Index approach
(OECD 2011) with minor modification to account for
the difference in scale range. The resulting HWBI
metric data set included both imputed and non-
imputed standardized data for 3,143 counties of the
U.S., which represented greater than 2.7 million data
points, collectively.
Calculating the HWBI
The HWBI was derived from indicator scores
calculated as the population weighted average of the
standardized metric values. Indicator scores were
averaged for each domain score and the geometric
mean was calculated as the final inputs for the HWBI.
Both the hierarchical organization of the metric data
and the step-wise calculation process provided the
means for examining well-being and its constituents
at multiple scales from the national level down to
individual counties.
Uncertainty and Sensitivity
Uncertainty analyses evaluated the estimated errors
associated with the HWBI scores. For each spatial and
temporal scale, the standard error for each indicator
was calculated from the standardized metric values.
Additionally, estimated errors introduced by the
imputation process were propagated to the indicator
level and added to the standard error estimate. The
total indicator error was set to the maximum value of
0.5 or 100% error when either the standard error or
the imputation error could not be estimated, or
where the total error exceeded 0.5. The indicator
error estimates were then propagated through the
index calculation to estimate the uncertainty
associated with domain, element and final index
values (Table 2-1)
Table 2-1. Summary statistics for estimates of uncertainty at the various spatial and temporal scales.
Scale
Average
Error
Number of
Observations
Standard
Deviation
Minimum Maximum
National
Census Region
GSS Region
State
County
1.77
3.64
4.79
10.04
31.06
11
44
99
561
34573
0.13
1.29
1.59
5.19
3.68
1.46
1.80
2.37
4.28
19.79
1.89
6.01
8.00
29.86
42.66
o
o
National
Census Region
GSS Region
State
County
0.54
1.10
1.45
3.08
11.38
1
4
9
51
3143
N/A
0.43
0.49
1.66
3.46
0.53
0.64
0.85
1.45
8.08
0.54
1.68
2.25
9.16
27.42
Sensitivity analyses were conducted to identify index
measures susceptible to bias caused by unknown
random or systematic error. Sensitivity to random
error was tested for each metric using a one-at-a-time
Monte Carlo simulation method by introducing zero-
mean centered normally distributed noise of varying
degrees to raw metric values and observing the
influence on output HWBI values and errors.
Sensitivity to methodological bias was evaluated by
adding fixed values (e.g., ± 1) to various subsets of
standardized metrics and recalculating the HWBI after
each treatment. The analyses were run to examine
the effects of spatial, temporal, or combined spatial-
temporal missing value imputation methods. For
random error effects, seven of 83 metrics used in
calculating the HWBI showed consistently higher bias
relative to the group average (Z > 1.65, P < 0.05)
(Table 2-2).
10
-------
Table 2-2. List of domain calculate affected by metric bias.
Chapter 21 Methodology
Domain
Indicator
Metric
Connection to Nature
Cultural Fulfillment
Safety and Security
Social Cohesion
Biophilia
Activity Participation
Actual Safety
Perceived Safety
Attitude Toward Others and
the Community
Spiritual Fulfillment
Connection to Life
Performance Arts
Attendance
Rate of Congregational
Adherence
Loss from Natural Hazards
Community Safety
City Satisfaction
Relative Importance Values
The degree to which priorities differ depends on
stakeholder perspectives (Fig. 2-1). These perception
differences are often overlooked during a decision-
making process, potentially leading to unexpected
outcomes, e.g., decreasing rather than maintaining or
increasing well-being. By using the integrated RIVs
(combination of professional opinion and public
perception), priorities of communities may be better
represented. The RIV component of the HWBI is a feature
that will allow for the "customization" of the HWBI
calculation process using priorities relevant to
stakeholders while offering an opportunity to evaluate
potential decision effects on well-being. Detailed
methodologies for calculating RIVs can be found in Smith
etal. (2013b).
[ Raw Data |
Standardization
&
Imputation
Population
Weighted
Average
Indicator
Scores
Average
I
Weighted
HWBI
Score
RIV
•4 Weighted -^
Average
[ Element 1 .
[ Scores 1"*
RIV
_ Weighted
Geometric
Mean
^ ( Domain
1 Scores
Figure 2-1. Application of relative importance values in the calculation of the HWBI.
11
-------
Research Highlight: Tampa Bay Area
Project
The Tampa Bay Estuary Program, Tampa Bay Regional Planning Council
and the U.S. Environmental Protection Agency's Sustainable and Healthy
Communities Research Program have been conducting research to
engage the public and potential new partners to help provide a common
language and foundation for incorporating the value of, and risk of losing
ecosystem services into decision making. The ultimate goal of this
research partnership is to demonstrate the importance of accounting for
ecosystem goods and services during decisions related to community
sustainability and to determine locally based values of Tampa Bay final
ecosystem services in terms of human well-being
(http://www.epa.gov/ged/tbes/).
Through a series of
stakeholder workshops,
relative importance values
were derived as public
perception input for the
Tampa Bay Demonstration
Project for the eight
domains of well-being
identified intheHWBI.
These values were
combined with professional
opinion to create a set of
weighting factors applicable
to the Tampa Bay area (Fig.
1). These relative
importance values were
used to calculate the HWBI
at the county and multi-
Tampa Bay Area Stakeholder Domain Priorities
Social Cohesion
Safety and Security
Living Standards
Leisure Time
Health
Education
Cultural Fulfillment
Connection to Nature
10%
Contribution to Well-being
12%
14%
16%
Figure 1. Prioritized domains for the Tampa Bay Area derived from Stakeholder input.
county project area scales (Fig 2. and Fig. 3) for comparison to the state, GSS regional and national values. The
RIVs derived for the Tampa Bay Project area were applied to each county in the study area and the entire
project area. The state, GSS regional and national values presented are unweighted.
The application of weighting factors determined for the Tampa Bay Project is a demonstration of the use of
stake-holder input at a smaller scale to reflect priorities in the weighted index. Once fully vetted, the
approaches used to derive these values and the index calculations will be made available as a web-based tool
to allow communities to input priorities and visualize how decisions may potentially influence different
aspects of well-being in their geographic area. Currently, the results and discussion of the Tampa Bay
demonstration are available at http://www.epa.gov/ged/tbes/tampaswellbeing.html.
12
-------
When considered in the
full model linking
ecosystem, social and
economic services to
the domains of well-
being the influence of
ecosystem provisioning
and decisions affecting
services production will
eventually be used to
examine the
relationship between
changes in ecosystem
goods and services and
well-being outcomes
specific to the Tampa
Bay Project Area.
Annual Unweighted and Weighted HWBI Scores for the Tampa Bay Project Area
(2000-2010)
• Unweighted HWBI
D Weighted HWBI
Figure 2. Comparison ofun-weighted and weighted HWBI scores for the Tampa Bay Area 2000-2010.
Future research efforts should focus on the metric level data to fill data gaps and to determine if more
appropriate metrics are available to describe the indicators used to derive domain values. As with any
community or population based- application, this will be the process needed to scale the index; however, the
methodologies used to construct the index are transferable across scale as are the indicator and domain
structures.
Annual HWBI Scores for 2000-2010
rzziu.s.
l lSouth Atlantic Region
I [Florida
I ITampa Bay Project Area
+ Hillsborough
U Manatee
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
Year
Figure 3. Comparison of annual HWBI scores for the Tampa Bay Area and individual Bay Area counties to scores for
Florida South Atlantic GSS region and the nation.
13
-------
Services Provisioning
Chapter 21 Methodology
The HWBI approach generates a measure that
characterizes the general state of well-being
within the context of the economic,
environmental, and social drivers. To assess
services provisioning, metric data quantifying
social, natural and built capital provisioning
were collected and summarized to describe the
relationship of service flows to overall well-
being (Appendix B). The same criteria used for
the HWBI were used to select metric data for
services. Descriptions of each of the 22 services,
indicators and associated metadata are
included in the report entitled "Indicators and
Methods for Evaluating Economic, Ecosystem
and Social Services Provisioning" (Smith et al.
2014b).
As with the HWBI procedure, a single
imputation method using the carry-forward
technique (Zhang et al. 2008) was used to fill
services data gaps caused by temporal
disparities found across data sources at all
spatial scales. Imputed values were calculated
based on existing data for the nearest year
within a single spatial unit. A median value
imputation method was then used as a
substitute for missing county-level metric data
points. County groupings for ecosystem services
data were created based on a combination of
the Rural-Urban Continuum Code (RUCC)
classifications (USDA 2013) and the
Environmental Quality Index (EQI) averaged
Water, Land, and Air domain score quintile
bandings (Lobdell et al. 2012). Groupings for
Economic and Social service data were created
based on RUCC classifications and EQI averaged
Social Determinants and Built domain score
quintile bandings. The nine RUCC classifications
were collapsed into four groups prior to
analysis. This RUCC-EQI combination helped to
account for the relative spatial relationship of a
county to the nearest large urban center and
the environmental factors relevant to the
different service categories. A median value was
calculated by year within each RUCC-EQI band
in an effort to calculate imputed metric values
using data from counties exhibiting similar
characteristics.
All data were standardized on a scale from 0.1
to 0.9 following the OECD Better Life Index
approach (OECD 2011). Indicator scores were
calculated as the average of the standardized
metric values. Indicator scores were then
averaged to obtain service scores (state,
regional and national service scores were
derived as the population density weighted
county values).
14
-------
Chapter 21 Methodology
Evaluating Scores
At each spatial scale, baseline scores for each domain and the HWBI were compared to the range of
calculated scores observed during the 2000-2010 period. Scores >25th percentile were categorized as
low, scores >25th percentile and less than the 50th percentile were categorized as moderately low. Scores
falling between the 50th and 75th percentile were categorized as moderately high and scores above the
75th percentile were categorized as high. The threshold values for these categories for each spatial scale
for each domain and the HWBI are presented in Table 2-3.
Table 2-3. Threshold values for each domain at the national, GSS regional and state levels for the years 2000-2010.
State
1
Connection to Nature
Cultural Fulfillment
Education
Health
Leisure Time
Living Standards
Safety and Security
Social Cohesion
HWBI
<47.1
<47.7
<44.7
<57.9
<52.9
<50.6
<57.5
<43.4
<51.7
Connection to Nature
Cultural Fulfillment
Education
Health
Leisure Time
Living Standards
Safety and Security
Social Cohesion
HWBI
<49.6
<49.1
<43.7
<58.1
<53.4
<50.5
<58.3
<43.1
<51.9
47.1-<49.9
47.7- <50. 2
44.7- <49.5
57.9-<59.3
52.9-<57.3
50.6-<52.8.
57.5-<60.8
43.4-<46.2
51.7-<53.1
GSS
•Jro
49.6- < 50.9
49.1-<49.8
43.7- <47.9
58.1-<59.2
53.4- < 57.9
50.5-<52.9
58.3- < 60.4
43.1- < 45.1
51.9- < 52.8
Moderately High
49.9-54.0
50.2-53.7
49.5-53.9
59.3-60.8
57.3-59.5
52.8-55.2
60.8-63.8
46.2-50.1
53.1-54.3
Region
Moderately High
50.9-54.3
49.8-53.0
47.9-51.9
59.2-60.8
57.9-59.7
52.9-55.5
60.4-63.0
45.1-46.7
52.8-53.9
High
>54.0
>53.7
>53.9
>60.8
>59.5
>55.2
>63.8
>50.1
>54.3
High
>54.3
>53.0
>51.9
>60.8
>59.7
>55.5
>63.0
>46.7
>53.9
| Nation
Connection to Nature
Cultural Fulfillment
Education
Health
Leisure Time
Living Standards
Safety and Security
Social Cohesion
HWBI
< 51.85
< 50.55
<44.3
<58.3
<53.4
<51.7
<59.4
<43.6
<52.2
^Iw^J HiTM
51.85- < 51.86
50.55- <50.57
44.3- < 46.5
58.3- < 59.0
53.4-<58.0
51.7- < 53. 2
59.4- < 60.1
43.6- < 45.6
52.2- < 52.5
Moderately High
51.86-51.88
50.57-50.60
46.5-52.0
59.0-59.5
58.0-59.4
53.2-54.5
60.1-61.3
45.6-46.1
52.5-53.2
High
> 51.88
> 50.60
>52.0
>59.5
>59.4
>54.5
>61.3
>46.1
>53.2
15
-------
Chapter 21 Methodology
Individual service scores were averaged by service type (economic, ecosystem, social) for each spatial
scale. The scores for each service type were compared to the range of calculated scores observed for
the corresponding spatial scale. Scores >25th percentile were categorized as low, scores >25th percentile
and less than the 50th percentile were categorized as moderately low. Scores falling between the 50th
and 75th percentile were categorized as moderately high and scores above the 75th percentile were
categorized as "high." Individual services within each service type for each spatial scale were also
compared to the range of observed scores in the same manner described above for the service types.
The threshold values for these categories for each spatial scale for each service type and the individual
services are presented in Tables 2-4, 2-5, and 2-6.
Table 2-4. Threshold values for Economic Services provisioning at the national, GSS regional and state levels for the years 2000-
2010.
Capital Investment
Consumption
Employment
Finance
Innovation
Production
Re-Distribution
Economic Services
Capital Investment
Consumption
Employment
Finance
Innovation
Production
Re-Distribution
Economic Services
Capital Investment
Consumption
Employment
Finance
Innovation
Production
Re-Distribution
Economic Services
State
Moderately
High
<52.1
<45.2
<58.7
<37.9
<40.3
<46.8
<42.0
<50.3
52.1-<65.7
45.2-< 55.6
58.7-< 63.0
37.9-< 44.3
40.3-< 43.4
46.8-< 49.8
42.0-< 45.2
50.3-< 50.8
65.7-71.4
55.6- 60.7
63.0-66.6
44.3-53.6
43.4-46.5
49.8-51.3
45.2-47.3
50.8-51.3
GSS Region
Moderately
High
<52.1
<45.2
<58.5
<37.7
<39.7
<47.4
<41.0
<48.9
52.1-<65.7
45.2-< 55.7
58.5-< 62.3
37.7-< 43.8
39.7-< 44.0
47.4-< 50.3
41.0-< 44.0
48.9-< 51.7
65.7-71.4
55.7-60.7
62.3-65.7
43.8-53.0
44.0-45.8
50.3-51.0
44.0-46.5
51.7-53.5
Nation
Moderately
High
<54.5
<48.3
<59.4
<37.8
<39.9
<48.6
<40.2
<48.9
54.5-< 65.7
48.3-< 55.7
59.4- < 62.0
37.8-< 43.9
39.9-< 43.7
48.6-< 50.7
40.2-< 44.1
48.9-< 51.1
65.7-69.2
55.7-58.9
62.0-64.1
43.9-53.0
43.7-45.6
50.7-51.0
44.1-46.1
51.1-53.2
High
>71.4
>60.7
>66.6
>53.6
>46.5
>51.3
>47.3
>51.3
High
>71.4
>60.7
>65.7
>53.0
>45.8
>51.0
>46.5
>53.5
High
>69.2
>58.9
>64.1
>53.0
>45.6
>51.0
>46.1
>53.2
16
-------
Chapter 21 Methodology
Table 2-5. Threshold values for Ecosystem Services provisioning at the national, GSS regional and state levels for the years
2000-2010.
Air Quality
Food, Fiber and Fuel
Greens pace
Water Quality
Water Quantity
Ecosystem Services
Air Quality
Food, Fiber and Fuel
Greens pace
Water Quality
Water Quantity
Ecosystem Services
Air Quality
Food, Fiber and Fuel
Greens pace
Water Quality
Water Quantity
Ecosystem Services
State
Moderately
High
<37.9
<31.5
<37.4
<31.3
<40.8
<51.0
37.9- < 59.8
31.5-< 39.6
37.4- <49.5
31.3-< 44.5
40.8-<47.5
51.0-< 54.9
59.8-76.1
39.6-43.5
49.5-51.1
44.5-62.7
47.5-54.6
54.9-56.1
GSS Region
Moderate!'
Moderately
High
<43.3
<30.7
<35.5
<41.0
<41.6
43.3-< 54.1
30.7-< 39.6
35.5-< 49.9
31.K39.3
41.0-<46.6
41.6-< 44.4
54.1-60.1
39.6-43.6
49.9-50.7
39.3-54.0
46.6-52.0
44.4-49.0
<43.3
<33.2
<39.3
<35.4
<42.6
<41.8
Nation
43.3-< 46.3
33.2-< 40.0
39.3- < 50.2
35.4-< 41.6
42.6-< 46.3
41.8-< 45.7
Moderately
High
46.3-60.6
40.0-42.9
50.2-50.6
41.6-54.7
46.3-50.1
45.7-48.2
High
>76.1
>43.5
>62.7
>54.6
>56.1
High
>60.1
>43.6
>50.7
>54.0
>52.0
>49.0
High
>60.6
>42.9
>50.6
>54.7
>50.1
>48.2
17
-------
Chapter 21 Methodology
Table 2-6. Threshold values for Social Services provisioning at the national, GSS regional and state levels for the years 2000-2010.
1 State
Activism
Communication
Community and Faith Based Initiatives
Education
Emergency Preparedness
Family Services
Healthcare
Justice
Labor
Public Works
Social Services
<47.7
<47.2
<22.2
<39.2
<42.5
<46.6
<37.2
<41.5
<41.7
<46.7
<42.9
^^^S
47.7- < 58.5
47. 2- < 49. 2
22.2- < 25.5
39. 2- < 44. 3
42.5-<46.7
46.6 -< 50.4
37. 2- < 40.0
41.5- < 44.4
41.7- < 44.4
46. 7- < 48. 9
42. 9- < 45. 6
:v1oderately
High
58.5-61.4
49.2-51.4
25.5-28.0
44.3-47.4
46.7-51.6
50.4-57.0
40.0-43.3
44.4-48.3
44.4-47.0
48.9-51.9
45.6-49.7
High
>61.4
>51.5
>28.0
>47.4
>51.6
>57.0
>43.3
>48.3
>47.0
>51.9
>49.7
GSS Region
B^^^l
_•••
Activism
Communication
Community and Faith Based Initiatives
Education
Emergency Preparedness
Family Services
Healthcare
Justice
Labor
Public Works
Social Services
<47.6
<47.8
<23.0
<39.6
<41.0
<46.4
<37.5
<41.2
<44.8
<47.8
<43.6
Moderately
47.6- < 59.0
47. 8- < 49. 5
23.0-<25.2
39. 6- < 44. 2
41.0-<45.2
46.4 -< 48.8
37. 5- < 40.0
41. 2- < 43.1
44. 8- < 44. 8
47. 8- < 49.1
43. 6- < 44. 9
Moderately
High
59.0-60.6
49.5-52.0
25.2-27.5
44.2-47.2
45.2-48.2
48.8-56.1
40.0-42.8
43.1-46.2
44.8-46.6
49.1-51.2
44.9-46.0
High
>60.6
>52.0
>27.5
>47.2
>48.2
>56.1
>42.8
>46.2
>46.6
>51.2
>46.0
Nation
Activism
Communication
Community and Faith Based Initiatives
Education
Emergency Preparedness
Family Services
Healthcare
Justice
Labor
Public Works
Social Services
<53.2
<46.8
<25.4
<39.4
<40.9
<46.5
<37.3
<39.4
<42.3
<47.7
<43.5
^^^^^n
53. 2- < 60. 07
46. 8- < 49. 3
25.4- < 25.8
39.4- < 45.1
40. 9- < 41. 7
46.5 -< 47.9
37. 3- < 40.1
39.4- < 41.3
42. 3- < 45. 2
47. 7- < 48. 6
43. 5- < 44.1
Moderately
High
60.07-60.08
49.3-50.4
25.8-26.3
45.1-47.5
41.7-45.5
47.9-55.3
40.1-41.5
41.3-44.8
45.2-47.4
48.6-49.5
44.1-45.4
High
>60.08
>50.4
>26.3
>47.5
>45.5
>55.3
>41.5
>44.8
>47.4
>49.5
>45.4
18
-------
Chapter 21 Methodology
Modeling Service-Domain Relationships
Introduction and General Strategy
Modeled data consisted of state-level annual
service and domain scores (50 states, 11
years). This scale was selected because it
provided desirable statistical properties (due
to smoothing) while still providing an
adequate number of observations. Bivariate
plots suggested that the relationships were
linear; hence, a linear model was chosen.
Only main effect (22 parameters) and two-
way interactions (231 parameters) were
explored to allow for interpretable results.
The goal of the modeling procedure was to
select a set of service parameters and
corresponding estimates for each domain
that could predict domain scores. The
general strategy for finding a set of plausible
predictive parameters is illustrated in Fig. 2-
2. It consists of a series of nested loops
(heuristic exploration, model averaging, and
model refitting). Each loop is described in
detail below. Observations for years 2002-
2004 and 2006-2009 were used for
calibrating. Observations for 2005 were used
for final model validation. Data from years
2000, 2001 and 2010 were excluded from the
analysis because of the relatively high degree
of imputation.
Model Refitting
Start:
Select all parameters
Update pool to include
only selected parameters
Model Averaging
Start
Select 75% of 2002-2009 data
at random W/0 replacement
{
Heuristic Exploration
Start:
select intercept
i
Add/remove parameter
using SELECT criterion [^\
4- )
Iterate until \^s
STOP criterion reached J
1
select model
using CHOOSE criterion
1
f Iterate (N = 200)l
select parameters that
are included at
least 20% of the time
Fit selected parameters to
2002-2009 data
Figure 2-2. Modeling procedure to select service parameters and predicted
domain scores.
19
-------
Chapter 21 Methodology
Heuristic Exploration
The SAS 9.3 GLMSELECT procedure provides
several heuristic methods that can find a set of
predictive parameters. A heuristic in the current
context refers to an algorithmic search strategy
that is employed to obtain a solution to a
problem where an exhaustive search strategy is
impractical. Table 2-7 summarizes the heuristic
options used in the GLMSELECT procedure to
obtain models within the Heuristic Exploration
loop.
Table 2-7. Summary of GLMSELECT options within the
Heuristic Exploration loop.
Table 2-8. Summary of GLMSELECT options within the
Model Averaging loop.
Option
within the
MODEL
statement
SELECTION
HIERARCHY
Value
STEPWISE
SINGLE
Sub-
option
SELECT
STOP
CHOOSE
N/A
Value
RSQUARE
75
CV(see
text for
details)
N/A
Cross-validation was accomplished by splitting
the data by year and then, for each year:
holding out the year; fitting the model to the
remaining years; and then predicting the year
held out and calculating the residual sum of
squares for the omitted year. These residuals
were then summed to estimate the total
predicted residual sum of squares.
Model Averaging
Several well-known problems exist with
heuristic, sequential parameter exploration. The
GLMSELECT procedure provides a model
averaging technique that reduces the severity
of these problems. Table 2-8 summarizes the
options used in the GLMSELECT procedure to
obtain models within the Model Averaging loop.
Option within the
MODELAVERAGE
statement
SAMPLING
NSAMPLES
TABLES(ONLY)
Value
SRS
200
PARM
EST
Sub-
option
PERCENT
N/A
MINPCT
Value
75
N/A
20
Model Refitting and Final Estimates
The model refitting stage of the procedure was
added to select parameters that were more
resilient to procedural decisions (structural
sensitivity). The initial pool consisted of the 22
main effects and 231 interactions for a total of
253 possible parameters, for each domain. The
model averaging procedure was then applied.
All parameters not remaining in the model were
removed from the pool, and the model
averaging procedure was again applied. This
was iterated until the pool of parameters
remained the same for four consecutive
iterations.
To verify that the refitting procedure
successfully selected parameters that were
resilient to procedural changes, the heuristic
exploration algorithm was re-ran with 20
different procedural changes (SELECTION
=FORWARD, STEPWISE; SELECT sub-
option=RSQUARE, ADJRSQ, AIC, A ICC, BIC, CP,
CV, SBC, SL, PRESS). The number of times that
each parameter appeared was counted. These
counts were averaged across the parameters
selected in the final model and across
parameters selected in the first round but not
remaining in the final model (Table 2-9). It is
clear that the refitting procedure successfully
excluded parameters that were not resilient to
procedural changes. Final model parameters
were estimated by fitting selected parameters
20
-------
to the calibration dataset. Final parameters,
estimates and associated statistics state,
regional and national models are in Appendix C.
Table 2-9. Average frequency that parameters were
included in models with different procedural decisions.
Domain
Connection
to Nature
Cultural
Fulfillment
Education
Health
Leisure Time
Living
Standards
Safety and
Security
Social
Cohesion
Final Model
Number of
Parameters
67
22
53
51
27
38
53
71
Average
Frequency
10.4
15.8
13.2
12.4
7.9
10.7
9.3
12.8
First Round
Only
Number of
Parameters
13
19
11
1
16
9
6
5
Average
Frequency
2.8
2.4
12.3
1.0
1.4
2.4
3.5
6.2
Model Diagnostics and Predictive
Performance
In addition to traditional model diagnostic
checks, several diagnostics were performed on
the model to test the predictability of the
service-domain equations. For each equation,
the following observed and predicted domain
score datasets were constructed:
• Cross-validation: For the state-level
calibration dataset, a final cross-validation
was performed by removing each year and
predicted the removed year by fitting the
equations to the remaining data and
running the fitted equations on the
removed data. Hence, the observed vs.
predicted dataset contains observed 2002-
2004 and 2006-2009 state-level domain
Chapter 21 Methodology
scores and year-held-out predicted score
for the same time period.
• Final Validation: The final model (i.e., the
model fitted to the state-level calibration
data) was used to predict state-level
domain score for 2005.
• County validation: The final model was used
to predict county-level domain scores for
the 2002-2004 and 2006-2009 time period.
Observed vs. predicted plots were then visually
inspected for anomalies. The R-squared statistic
for each plot is listed along with the calibrated
fit (Table 2-10).
Table 2-10. Summary R-squared statistics for models to calibrated state-
level data.
Domain
Connection
To Nature
Cultural
Fulfillment
Education
Health
Leisure
Time
Living
Standards
Safety And
Security
Social
Cohesion
Calibration
0.906
0.533
0.889
0.757
0.784
0.871
0.709
0.897
Cross
validation
0.704
0.466
0.766
0.643
0.376
0.686
0.456
0.644
Final
validation
0.910
0.506
0.897
0.789
0.832
0.852
0.686
0.885
Cou nty
0.097
0.155
0.093
0.027
0.233
0.162
0.066
0.139
The R-squared fits using calibration, cross-
validation and final validation data ranged
between 0.376 and 0.910, suggesting that the
models adequately capture variations in domain
scores observed at the state level between
years 2000 and 2010. The R-squared fits using
21
-------
Chapter 21 Methodology
county-level data ranged between 0.027 and
0.233, suggesting that model estimates fitted to
state-level data do not adequately capture
county-level variations in domain scores.
However, inspection of observed vs. predicted
plots at the county level, such as depicted in Fig.
2-3, revealed a common theme.
County Validation Plots for Safety And Security
Plot: Observed and Predicted
Sorted by Observed Score
Figure 2-3. Example of state-level model performance when
scaled to county data.
The model fitted to state-level data performed
well when predicting county-level domain
scores near average, and performed poorly but
averaged in the correct direction when
predicting scores away from the average. This
suggests that model estimates may be
conditional on the domain score. Intuitively,
this makes sense. For example, we expect that
decisions needed to lift an area out of poverty
(i.e., increase a low Living Standards score)
would be different from those needed to
elevate a wealthy area's standard of living (i.e.,
increase a high Living Standards score).
County-Level Model
A quantile regression was conducted at the
county level to capture the conditional
dependence of model parameter estimates on
domain scores (Table 2-11). Parameters
selected at the state level were fit to county-
level data for the calibration years (2002-2004,
2006-2009) using the SAS QUANTREG
procedure. Three quantiles were modeled for
each domain. An interior point algorithm with a
tolerance of 0.0001 was used to optimize the
models, and a re-sampling procedure with 100
repetitions was used to compute parameter
estimate error. Domain score cut points were
then created to assign observed scores to one
of the three quantile models. Quantiles and cut
point assignments are listed in Table 2-11. Final
estimates are listed in Appendix C.
Table 2-11. Quantile regression at county level for conditional
dependence of model parameter estimates.
Domain
Connection
to Nature
Cultural
Fulfillment
Education
Health
Leisure
Time
Living
Standards
Safety and
Security
Social
Cohesion
Quantile
Modeled
0.36
0.5
0.77
0.06
0.5
0.7
0.2
0.5
0.65
0.04
0.5
0.79
0.07
0.5
0.75
0.07
0.5
0.74
0.08
0.5
0.76
0.09
0.5
0.82
Score Assignment
0 < Score < 34.8
34.8 < Score < 57.9
57.9 < Score < 100
0 < Score < 44.9
44.9 < Score < 57.7
57.7 < Score < 100
0 < Score < 39.8
39. 8 < Score < 59
59 < Score < 100
0 < Score < 54.9
54.9 < Score < 61.9
61.9 < Score < 100
0 < Score < 45
45 < Score < 57
57 < Score < 100
0 < Score < 44.6
44.6 < Score < 55
55 < Score < 100
0 < Score < 54.8
54.8 < Score < 66.9
66.9 < Score < 100
0 < Score < 39.9
39.9 < Score < 61.9
61.9 < Score < 100
22
-------
Chapter 21 Methodology
Validation was accomplished by predicted
county-level domain scores for 2005. Both
calibration and validation R-squared statistics
are listed in Table 2-12.
Table 2-12. Summary R-squared statistics for models
calibrated to county-level data.
Domain
Connection
to Nature
Cultural
Fulfillment
Education
Health
Leisure
Time
Living
Standards
Safety and
Security
Social
Cohesion
Calibration
0.626
0.714
0.649
0.679
0.699
0.654
0.691
0.673
Final
Validation
0.639
0.703
0.665
0.667
0.590
0.615
0.686
0.615
23
-------
Chapter 31 Well-being at Multiple Scales
Chapter 3 Well-being at Multiple Scales
(USEPA Photo by Eric Vance
24
-------
Chapter 31 Well-being at Multiple Scales
In the following sections, HWBI and domain results, based on the selected indictors (Table 3-1), are
evaluated and presented at national, regional, state and county scales for the 2000-2010 baseline
period.
Table 3-1. Domains, indicators, and the number of metrics used to characterize human well-being.
DOMAIN
Connection to Nature
Health
INDICATOR
Biophilia
Activity Participation
Basic Educational Knowledge and Skills of Youth
Participation and Attainment
Social, Emotional and Developmental Aspects
Healthcare
Life Expectancy and Mortality
Lifestyle and Behavior
Physical and Mental Health Conditions
Personal Well-being
Activity Participation
Time Spent
Working Age Adults
Basic Necessities
Income
Wealth
Work
Actual Safety
Perceived Safety
Risk
Attitude Toward Others and the Community
Democratic Engagement
Family Bonding
Social Engagement
Social support
METRIC
3
4
4
2
7
4
9
3
2
1
3
2
3
2
2
4
1
1
5
6
3
3
1
25
-------
0 0
Human Well-being
Index
(2000-2010)
U.S.
52.8±0.1
Chapter 31 Well-being at Multiple Scales
HWBI values for the New England, West North
Central and Middle Atlantic Regions were
significantly higher than the U.S. HWBI for the
2000-2010 time period (Fig. 3-1). The South
Atlantic, East South Central and West South
Central Regions scored lower than the U.S.
baseline HWBI. At the state level, the lowest
(49.9±0.4) and highest (55.8±1.0) HWBI values
observed were for Louisiana and New Hampshire,
respectively.
Figure 3-1. Baseline (2000-2010) HWBI scores for states and GSS region.
26
-------
Chapter 31 Well-being at Multiple Scales
The HWBI for the U.S. was categorized as moderately high for the 2000-2010 baseline period (Fig. 3-2).
At the GSS regional scale, New England, the Middle Atlantic and West North Central regions scored high
for the HWBI. The East South Central and West South Central regions scored low. All but two states (KY
and OK) scored low for HWBI in these the low scoring regions. Kentucky and Oklahoma scored
moderately low. States with low HWBI scores in other regions included FL, NC, SC, WV (South Atlantic),
and AZ and NM in the Mountain region. All regions except for the East South Central and West South
Central regions included at least one state with a high HWBI score for the 2000-2010 assessment. The
South Atlantic, Pacific and Mountain regions scored moderately low for HWBI. The East North Central
regions scored moderately high.
Human Well-being Index (2000-2010)
New England
CT
MA
ME
NH
Rl
VT
Middle Atlantic
NJ
NY
PA
South Atlantic
DE
FL
GA
MD
NC
SC
VA
WV
East South Central
AL
KY
MS
TN
West South Central
AR
LA
OK
TX
=
Pacific
AK
CA
HI
OR
WA
Mountain
AZ
CO
ID
MT
NM
NV
UT
WY
West North Central
IA
KS
MN
MO
ND
NE
SD
East North Central
IL
IN
Ml
OH
Wl
3 5
\-/
Low Moderately Low Moderately High High
Figure 3-2. Human Well-being Index scorecard categorizing the level of well-being at the national, GSS regional and state levels
for the years 200-2010.
27
-------
Chapter 31 Well-being at Multiple Scales
At the county level, a wider range of HWBI scores (min=42.8±2.7; max= 60.7±2.1) was observed for the
baseline period. When compared to the range of annual HWBI scores across the counties, decadal HWBI
values <50.1 were considered low at the county scale, values >56.1, high. The distribution of the county-
level HWBI scores for the years 2000-2010 are depicted in the chloropleth map below (Fig. 3-3).
Low
High
Figure 3-3. Cloropleth map representation of HWBI scores at the county level for years 2000-2010.
28
-------
Research Highlight: Transferability of the HWBI Framework
to Native American Populations
The applicability and integrity of the HWBI
framework was demonstrated using metrics
scaled to assess well-being for American Indian
Alaska Native (AIAN) and large tribal populations.
The HWBI approach can be used to estimate well-
being for Native Americans collectively with a
reasonable level of confidence. The degree to
which the HWBI structure can be utilized is
dependent upon the availability and quantity of
quality data. Greater than 80% of the data
available for a national AIAN assessment were
specific to the target population, while the
remaining data were derived from the general U.S.
population. The remaining data were derived from
AIAN population weighted U.S. HWBI values.
Despite using roughly 20% non-specific population
data, the AIAN well-being signature could still be
differentiated from the U.S. HWBI.
To overcome limitations, data substitution using
the described approach, is the most robust
method for scaling the index, but the limited
availability of comparable metrics at smaller
spatial scales and for specific demographics may
also be problematic. The metrics utilized in the
U.S. HWBI range in nature from individuals'
perceptions (survey questions) to rates of
occurrences of certain behaviors and outcomes in
a population. In order to maintain index integrity
and capture the most holistic and comprehensive
picture of a population, it is sometimes necessary
to identify alternative metrics. When choosing
alternative metrics it is imperative that both the
qualitative nature of the information as well as the
type of information is as closely matched as
possible. Alternative metrics for AIAN populations
were suggested for the HWBI metric Performing
Arts
Attendance. This substitution caused a dramatic
increase in the metric and the domain score (Fig.
1).
• Original Metric
Alternate Metric
Domain: Cultural Fulfillment Indicator: Activity Participation
Figure 4. Comparison of the results of using an alternative
metric for the Activity Participation indicator in the Cultural
Fulfillment domain.
Only data that could be readily identified as AIAN-
related were collected from sources. Data records
were encoded to differentiate between single
ethnic and multi-ethnic identified information,
AIAN and AlAN-mixed, respectively. For each
record, the collection method was identified as
either random (e.g., exit polls) or complete (e.g.,
vital statistics). Metric categorization was based
upon reported ethnicity, sample size and temporal
scale data availability. All 80 metrics were
categorized into one of six categories. Raw data
were organized hierarchically by population group
and temporal resolution (e.g., AIAN and Tribal
grouping by year and decade). National AIAN and
Tribal Group datasets were created by populating
metric values from the most robust data available
according to the metric categorization process and
from existing U.S. HWBI metric data (Fig. 2).
29
-------
Use annual AIAN-speciiic
population metric data.
Use decadal AIAN-spccilic
population metric data.
Use annual AIAN-mixed
population metric data.
Use decadal AIAN-mixed
population metric data.
-Yes-
-Yes—
-Yes —
-Yes —
What is the desired
population group?
National
Tribal Grouping
I
- Yes-
Usc tribal population
weighted mean of
county metric values.
No-
Use AIAN or Tribal population
percent weighted area values.
Figure 2. Process for selecting the most robust AIAN and Tribal Group data available for HWBI assessments.
Where, tribal specific data were available, a Tribal
Group identifier was included with the data
appropriate. Tribal specific metric values were
aggregated into one of 38 Tribal Groups as
represented in the tribal assignments for the U.S.
Census (2000). Seven of the 38 Tribal Groups with
the greatest percentage of tribal specific data
were selected for HWBI and domain score
comparison. The seven tribal groups with
sufficient data include the Menominee, Navajo,
Chippewa, Blackfeet, Alaskan Athabascan, Eskimo,
and Sioux. Each of the seven Tribal groups was
compared to the county HWBI scores for which
the counties had greater than 50% of the
population identified as tribal-specific.
For the 2000-2010 period, the lowest ranked
indicator scores for the Tribal groups were in the
domains of Health, Living Standards, Safety and
Security and Social Cohesion (Fig. 3).
Differentiation between tribal domain scores is
dependent upon the specificity of the data
included in the assessment. Where AIAN, AIAN-
mixed and U.S. data comprised the majority of the
metrics used to calculate tribal indicators, rarely
were differences in domain values observed.
Differences among tribal scores were attributed to
tribal specific and county population weighted
metric data which better characterize individual
Tribal groups.
30
-------
n38 Tribal Groups
•ALASKAN
ATHABASCAN
DBLACKFEET
Connection Cultural Education Health
to Nature Fulfillment
Leisure Living Safety and Social HWBI
Time Standards Security Cohesion
Figure 3. Large Tribal Group domain and HWBI scores for the 2000-2010 time period.
31
-------
Chapter 31 Well-being at Multiple Scales
Connection to
Nature
(2000-2010)
U.S.
51.9±0.6
Connection to Nature is a domain unique to the
U.S. HWBI and links ecosystem services directly
to human well-being endpoints (Smith et al.
2014b). Humans' long time interaction with the
natural environment has formed an innately
emotional affiliation to other living organisms
(Wilson 1984, 1993). Migration to
industrialized, urban centers can detach people
from the natural environment and weaken this
innate appreciation for other living organisms,
decreasing human well-being (Kellert 1997).
This domain has a single indicator, Biophilia,
which characterizes the strength of this
emotional bond. Scores in this domain indicate
the degree to which people appreciate nature,
which is thought to contribute positively to
overall human well-being and may be linked to
environmental quality (Fig. 3-4).
Connection to
Nature
Biophilia
spiritual
fulfillment
connection to
life
Figure 3-4. Indicators and metrics of the Connection to Nature domain.
32
-------
Chapter 31 Well-being at Multiple Scales
Figure 3-5. State and Regional Connection to Nature domain scores for the baseline period (2000-2010).
The West North Central region scored lowest in
the Connection to Nature domain and was
significantly lower than the U.S. and all the
other regions with the exception of the East
North Central region (Fig. 3-5). Additionally, the
Middle Atlantic and New England regions
scored below the national average Connection
to Nature score. The East South Central and
West South Central regions and the South
Atlantic scored significantly higher than all
other regions and the U.S. Mississippi had the
highest Connection to Nature domain score
(69.5±1.8) while Alaska had the lowest
(38.8±4.8)forthe 2000-2010 baseline period.
33
-------
Chapter 31 Well-being at Multiple Scales
Nationally, Connection to Nature was characterized as moderately high. The East South Central and
West South Central regions scored high for the Connection to Nature domain during the 2000-2010
period (Fig. 3-6). All of the states in these regions with the exception of Oklahoma, scored high as well.
Five of the states in the South Atlantic region scored high for this domain. All but two states (MO, ND) in
the West North Central region scored low for Connection to Nature as did the region. Other states
scoring low for this domain included NH and VT (New England), AK and HI (Pacific) MT, UT and WY
(Mountain) and Wl (East North Central).
Connection to Nature (2000-2010)
New England
CT
MA
ME
NH
Rl
VT
Middle Atlantic
NJ
NY
PA
South Atlantic
DE
FL
GA
MD
NC
SC
VA
WV
East South Central
AL
KY
MS
TN
West South Central
AR
LA
OK
TX
Pacific
AK
CA
HI
OR
WA
Mountain
AZ
CO
ID
MT
NM
NV
UT
WY
West North Central
IA
KS
MN
MO
ND
NE
SD
East North Central
IL
IN
Ml
OH
Wl
Low Moderately Low Moderately High High
Figure 3-6. Domain scorecard for Connection to Nature characterized at the national, GSS regional and state levels.
34
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Chapter 31 Well-being at Multiple Scales
Connection to Nature scores calculated at the county level were widely distributed across the U.S. and
within states (Fig. 3-7). However, higher connection to nature scores were generally more concentrated
in the south eastern portion of the U.S. County-level scores <34.7 for this domain were considered low.
Scores >57.9.0 were characterized as high. The 2000-2010 county-level Connection to Nature domain
scores ranged from 10.0-90.0.
< 10 10-20 20-30 30-40 40-50 E-0-60 60-70 70-80 SQ-90 > 90
Figure 3-7. Cloropleth map representation of variability in Connection to nature scores at the county level for years 2000-2010.
35
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Chapter 31 Well-being at Multiple Scales
Cultural
Fulfillment
(2000-2010)
U.S.
50.5±0.6
Cultural Fulfillment is a unique aspect of well-
being that can be hard to capture because of
the nation's rich diversity. While culture is
difficult to define, most people have an intuitive
sense of what it is and can detect cultural
differences between places and groups. It
encompasses a broad swath of phenomenon
ranging from tangible, well-defined and easily
identifiable things, to intangible, elusive and
vague things. Some would argue the important
role of spirituality and culture in a person's well-
being, yet measures of culture are rarely
included in most well-being indices.
Opportunities that afford people and
communities the chance to learn and practice a
culture serve not only as building blocks of
individual and group identity, but also as
mitigators of inequities typical of cultural
exclusion and harmonizing factors that allow
groups to share common experiences that
would otherwise be obfuscated by discordant
economic disparity. It is recognized that
alternate metrics may better reflect the culture
of specific populations (see Research Highlight
p. 29); however, the Cultural Fulfillment domain
scores reported here characterize the general
U.S. population and the extent to which
individuals participate in cultural activities,
Activity Participation (Fig. 3-8).
Cultural
Fulfillment
Activity
Participation
performing arts
attendance
rates of
congregational
adherence
Figure 3-8. Indicators and metrics of the Cultural Fulfillment domain.
36
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Chapter 31 Well-being at Multiple Scales
Figure 3-9. State and Regional Cultural Fulfillment domain scores for the baseline period (2000-2010).
The West North Central and Middle Atlantic
regions scored higher than the national average
score (55±0.6) for Cultural Fulfillment and
significantly higher than the other regions with
the exception of the New England and East
North Central regions (Fig. 3-9). The East South
Central and South Atlantic regions scored below
the national average. Utah ranked highest
among the states for the Cultural Fulfillment
domain (61.5±4.5), West Virginia the lowest
(41.4±1.9).
37
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Chapter 31 Well-being at Multiple Scales
The Cultural Fulfillment domain was the only domain scored low for the nation. Regionally, the West
North Central and Middle Atlantic regions scored high for this domain (Fig. 3-10). Cultural Fulfillment
was characterized as high for the following states in other GSS regions for the 2000-2010 time period:
MA and Rl (New England), UT (Mountain) and Wl (East North Central). Both the South Atlantic and East
South Central regions scored low for Cultural Fulfillment. All states in the East South Central region
scored low except for Tennessee. The New England, West South Central and East North Central regions
scored moderately high for Cultural Fulfillment.
••• 1
^H Cultural Fulfillment (2000-2010) fc-VJ
New England
CT
MA
ME
NH
Rl
VT
Middle Atlantic
NJ
NY
PA
South Atlantic
DE
FL
GA
MD
NC
SC
VA
WV
East South Central
AL
KY
MS
TN
West South Central
AR
LA
OK
TX
Pacific
AK
CA
HI
OR
WA
Mountain
AZ
CO
ID
MT
NM
NV
UT
WY
West North Central
IA
KS
MN
MO
ND
NE
SD
East North Central
IL
IN
Ml
OH
Wl
Low Moderately Low Moderately High High
Figure 3-10. Domain scorecard for Cultural Fulfillment characterized at the national, GSS regional and state levels for the years
of 2000-2010.
38
-------
Chapter 31 Well-being at Multiple Scales
The chloropleth shows higher county-level Cultural Fulfillment scores (>59.3) predominately in the West
North Central region and the upper Middle Atlantic region (Fig. 3-11). Noticeable pockets of lower
county scores (<44.9) were observed in the lower portion of the Mississippi River Valley. Pend Oreille
County, Washington ranked lowest (16.1± 10.0) and Daniels County, Montana ranked highest (85.8±9.9)
for the Cultural Fulfillment domain for the 2000-2010 baseline period.
<10 10-20 20-30 30-40 40-50 50-60 60-70 70-SO 80-90 >90
Figure 3-11. Cloropleth map representation of variability in Cultural Fulfillment scores at the county level for years 2000-2010.
39
-------
Chapter 31 Well-being at Multiple Scales
Education
(2000-2010)
U.S.
48.0±0.2
Education has been referred to as a basic
capability leading to the expansion of other
capabilities and is fundamental to well-being
(Terzi 2004). For this reason, it is to some extent
included in the majority of well-being indices.
Our domain of Education recognizes the
importance of both the formal and informal
learning that occur throughout lifespan
development (Fig. 3-12). Scores in this domain
reflect subjective and objective outcomes
derived from both of these. Three indicators
describe this domain: Basic Educational
Knowledge and Skills of Youth, Participation and
Attainment, and Social, Emotional, and
Developmental Aspects.
Education
Basic Educational
Knowledge and
Skills of Youth
mathematics skills
science skills
reading skills
Participation and
Attainment
participation
high school
completion
post-secondary
attainment
adult literacy
Social, Emotional
and Development
Aspects
child physical health
social relationships
and emotional well-
being
prepnmary
education and care
Figure 3-12. Indicators and metrics of the Education domain.
40
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Chapter 31 Well-being at Multiple Scales
Figure 3-13. State and Regional Education domain scores for the baseline period (2000-2010).
Three GSS regions scored below the national
average for the Education domain—the East
South Central and West South Central regions
and the Pacific region (Fig. 3-13). These same
three regions scored significantly lower than all
other regions. The West North Central region
scored significantly higher than all other
regions. The top three highest scoring states
for the Education domain, AK, WY and NH, were
located in the Pacific, Mountain and New
England regions, respectively. The lowest
ranked states (SC, LA and MS) were in the South
Atlantic and the East South Central and West
South Central regions. Overall, Alaska scored
highest for the Education domain (57.9±1.2),
South Carolina lowest (39.8± 0.6).
41
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Chapter 31 Well-being at Multiple Scales
The nation scored moderately high for the domain of Education for the years 2000-2101 (Fig. 3-14). The
West North Central region was the only region that scored high for this domain; all other regions scored
moderately low or moderately high. Six states in four other regions had high Education scores (NH, VT,
VA, AK, MT and WY). Low scoring states were in the South Atlantic, East South Central and West South
Central and Mountain regions and included NC, SC, WV, AL, MS, AR, LA, TX, AZ and NM.
Education (2000-2010)
New England
CT
MA
ME
NH
Rl
VT
Middle Atlantic
NJ
NY
PA
South Atlantic
DE
FL
GA
MD
NC
SC
VA
WV
East South Central
AL
KY
MS
TN
West South Central
AR
LA
OK
TX
—
Pacific
AK
CA
HI
OR
WA
Mountain
AZ
CO
ID
^
MT
NM •
NV
UT
WY
West North Central
IA
KS
MN
MO
ND
NE
SD
East North Central
IL
IN
Ml
OH
Wl
Low Moderately Low Moderately High High
Figure 3-14. Domain scorecard for Education characterized at the national, GSS regional and state levels for the years of 2000-
2010.
42
-------
Chapter 31 Well-being at Multiple Scales
The chloropleth map shows the distribution of Education domain scores at the county level for the years
2000-2010 (Fig. 3-15). The pattern of lower scores (<41.6) for the south eastern and south western
portions of the U.S. can be clearly seen. Higher county-level scores (>61.7) were predominately focused
in the middle part of the nation. County-level Education domain scores ranged from 28.3-71.5, with
McKinley County, New Mexico ranking lowest (28.3±2.9) and Johnson County, Nebraska ranking highest
(71.413.5).
•=: 10 10-20 20-30 30-40 40-50 50-60 60-70 70-SO 30-90 > 90
Figure 3-15. Chloropleth map representation of variability in Education scores at the county level for years 2000-2010.
43
-------
Chapter 31 Well-being at Multiple Scales
~-
Health
(2000-2010)
U.S.
59.2±0.1
Health, both physical and psychological, of a
population is, without a doubt, an important
component of well-being. The domain of Health
is described by five indicators (Fig. 3-16). Scores
in this domain reflect actual and perceived
health status and outcomes (Personal Well-
being, Life Expectancy and Mortality, and
Physical and Mental Health) and determinants
of status not addressed elsewhere (Lifestyle and
Behavior and Healthcare).
Life Expectancy an'
Mortality
population with
family doctor
Figure 3-16. Indicators and metrics of the Health domain.
44
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Chapter 31 Well-being at Multiple Scales
Figure 3-17. Regional Health domain scores for the baseline period (2000-2010).
The New England region scored significantly
higher than the other GSS regions (Fig. 3-17). In
addition to the New England region, the West
North Central and Middle Atlantic regions both
scored higher than the national average for the
Health domain. The East South Central and
West South Central and South Atlantic regions
scored below the national average. Among the
states, Massachusetts (62.8± 0.6) ranked
highest for health, West Virginia (55.1± 0.3)
lowest for the years 2000-2010.
45
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Chapter 31 Well-being at Multiple Scales
The domain of Health was rated moderately high for the U.S. for the years 2000-2010 (Fig. 3-18). New
England was the only GSS region rated high for Health. New England was the only GSS region rated high
for Health. Eight states in the other regions rated high for health: NJ (Middle Atlantic), VA (South
Atlantic), CO and UT (Moutain) and IA, MN, NE and SD (West North Central). The East South Central
region scored low for the Health domain. Low scoring states in other regions included FL and WV (South
Atlantic), AR, LA and OK (West South Central) and NM and NV (Moutain).
Health (2000-2010)
New England
CT
MA
ME
NH
Rl
VT
Middle Atlantic
NJ
NY
PA
South Atlantic
DE
FL
GA
MD
NC
SC
VA
WV
East South Central
AL
KY
MS
TN
West South Central
AR
LA
OK
TX
Pacific
AK
CA
HI
OR
WA
Mountain
AZ
CO
ID
MT
-
NM
NV
UT
WY
West North Central
IA
KS
MN
MO
ND
NE
SD
East North Central
IL
IN
Ml
OH
Wl
Low Moderately Low Moderately High High
Figure 3-18. Domain scorecard for Health characterized at the national, GSS regional and state levels for the years of 2000-
2010.
46
-------
Chapter 31 Well-being at Multiple Scales
Health scores at the county level for the 2000-2010 time period ranged from 45.0±3.4 to 70.4±3.2 (Fig.
3-19). Noxubee County in Missippissi ranked lowest for the domain of Helath. Audubon County, IA
ranked highest among the counties. County Health scores in the 40-50 range were mostly observed in
the states which also scored low (FL, WV, MS, AR, OK); however, other states rated moderate for the
Health domain included counties with low Health ratings. At the county-level, scores <53.7 were
categorized as low, scores > 61.4, high.
<10 10-20 20-30 30-40 40-50 50-60 60-70 70-80 SO-90 > 90
Figure 3-19. Cloropleth map representation of variability in Health scores at the county level for years 2000-2010.
47
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Leisure Time
(2000-2010)
U.S.
56.910.1
Chapter 31 Well-being at Multiple Scales
Leisure Time is commonly deemed as necessary
for basic survival, and has increasingly been
referred to as a domain of the "good life"
(Smale et al. 2010). Domain scores reflect the
time available to an individual - apart from the
obligations of work, family and society - that is
distinguished by a perceived freedom to act and
intrinsic satisfaction. Three indicators, Activity
Participation, Time Spent and Working Age
Adults describe this domain (Fig. 3-20).
Leisure Time
Activity
Participation
Time Spent
physical activity
leisure activities
average nights on
vacation
Working Age
Adults
adults working
standard hours
adults working lon[
hours
adults who provide
care to seniors
Figure 3-20. Indicators and metrics of the Leisure Time domain.
48
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Chapter 31 Well-being at Multiple Scales
•^J New England
["pacific
Mountain
South Atlantic
| Middle Atlantic
West South Central
East North Central
East South Central
West North Central
Figure 3-21. Regional Leisure Time domain scores for the baseline period (2000-2010).
The regional decadal scores for Leisure Time for
the New England and Pacific regions were
significantly higher than the national score (56.9
±0.1) while the West North Central, East South
Central and East North Central regions were
significantly lower (Fig. 3-21). The West North
Central region also scored significantly lower
than all other regions. Among the states,
Wyoming scored lowest for the Leisure Time
domain (50.9±0.7) and Connecticut highest
(59.2±1.0).
49
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Chapter 31 Well-being at Multiple Scales
The Leisure Time score for the nation was characterized as moderately low for the years 2000-2010 (Fig.
3-22). GSS regional scores were categorized as either moderately low or moderately high. Only two
states were considered to have low scores for the Leisure Time domain-Wyoming (Mountain) and South
Dakota (West North Central).
Leisure Time (2000-2010)
New England
CT
MA
ME
NH
Rl
VT
Middle Atlantic
NJ
NY
PA
South Atlantic
DE
FL
GA
MD
NC
SC
VA
WV
East South Central
AL
KY
MS
TN
West South Central
AR
LA
OK
TX
Pacific
AK
CA
HI
OR
WA
Mountain
AZ
CO
ID
MT
NM
NV
UT
WY
West North Central
IA
KS
MN
MO
ND
NE |
•
East North Central
IL
IN
Ml
OH
Wl
Low Moderately Low Moderately High High
Figure 3-22. Domain scorecard for Leisure Time characterized at the national, GSS regional and state levels for the years of
2000-2010.
50
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Chapter 31 Well-being at Multiple Scales
County-level Leisure Time domain scores <49.4 were considered low, scores >58.1 were considered
high. The map below shows the distribution of county-level Leisure Time scores for 2000-2010 (Fig. 3-
23). Lower scores (40-50) were mostly observed in the middle portion of the U.S. and Alaska, but were
observed in all states. The highest county-level Leisure Time domain score was for Anchorage, AK
(65.1±3.7). McCreary, KY had the lowest county-level score (41.6±2.9) for this domain.
< 10 10-20 20-30 30-40 40-50 50-60 60-70 70-80 50-90 > 90
Figure 3-23. Cloropleth map representation of variability in Leisure Time scores at the county level for years 2000-2010.
51
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Chapter 31 Well-being at Multiple Scales
Living Standards
(2000-2010)
U.S.
52.8±0.01
Living Standards encompasses concepts that
the general population are most familiar with
and measures that are most often used in the
political arena to evaluate progress. In the
simplest of terms, living standards may be
described as "the physical circumstances in
which people live, the goods and services they
are able to consume and the economic
resources to which they have access" (New
Zealand Economic Social Report 2010). Scores in
this domain reflect the level of material comfort
in terms of goods, services and luxuries
available to an individual, group, or nation. This
domain is described by four indicators: Basic
Necessities, Income, Wealth, and Work (Fig.3-
24).
Living Standards
Basic Necessities
housinj
affordability
Income
median household
food security
incidence of low
persistence of low
Wealth
median home
value
mortgage debt
job quality
job satisfaction
Figure 3-24. Indicators and metrics of the Living Standards domain.
52
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Chapter 31 Well-being at Multiple Scales
Figure 3-25. Regional Living Standards domain scores for the baseline period (2000-2010).
Four of the GSS regions scored significantly
higher than the national average (52.8±0.01) for
the Living Standards domain—New England,
Pacific, Middle Atlantic and West North Central
(Fig. 3-25). The East South Central and West
South Central regions scored significantly lower
than all other regions. The highest scoring state
in the Living Standards domain for the years
2000-2010 was HI (60.1+1.8), the lowest MS
(47.2±0.4).
53
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Chapter 31 Well-being at Multiple Scales
Living Standards in the U.S. was characterized as moderately low for the years 2000-2010, as were the
East South Central and West South Central regions (Fig. 3-26). New England was the only high scoring
region for this domain; however high scoring states in other regions included: NJ and NY (Middle
Atlantic), MD (South Atlantic), CA and HI (Pacific) and MN (West North Central). All states in the East and
West Central regions were characterized as having low Living Standards scores. Living Standards scores
in states in the South Atlantic region (GA, NC, SC and WV) and in NM (Mountain) were also considered
low.
Living Standards (2000-2010)
New England
CT
MA
ME
NH
Rl
VT
Middle Atlantic
NJ
NY
PA
South Atlantic
DE
FL
GA
MD
NC
SC
VA
WV
East South Central
AL
KY
MS
TN
West South Central
AR
LA
OK
TX
Pacific
AK
CA
HI
OR
WA
Mountain
AZ
CO
ID
MT
NM
NV
UT
WY
West North Central
IA
KS
MN
MO
ND
NE
SD
East North Central
IL
IN
Ml
OH
Wl
^
Low Moderately Low Moderately High High
Figure 3-26. Domain scorecard for Living Standards characterized at the national, GSS regional and state levels for the years of
2000-2010.
54
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Chapter 31 Well-being at Multiple Scales
County-level Living Standards scores ranged from 38.9±4.2 to 63.4±3.1 (Fig. 3-27). Among the counties,
LeFlore, MS ranked lowest, Fauquier County, VA, higest. Scores <48.9 were considered low, while those
>54.9 were considered high for U.S. counties for the 2000-2010 time period. The majority of lower
scoring counties were in the Southern portions of the U.S.
< 10 10-20 20-30 30-40 40-50 50-60 60-70 70-80 BO-90 > 90
Figure 3-27. Cloropleth map representation of variability in Living Standards scores at the county level for years 2000-2010.
55
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Chapter 31 Well-being at Multiple Scales
Safety and
Security
(2000-2010)
U.S.
60.3±0.3
Safety and Security domain scores reflect the
extent that the population is free from danger,
fear and anxiety and are described by measures
of actual and perceived safety. At an individual
level, a hazardous environment and feelings of
insecurity can result in physiological and
psychological harm either directly through the
injury and trauma, or indirectly through
excessive levels of arousal. Moreover,
perceptions of an unsafe environment or
feelings of insecurity can alter individual and
community behavior and limit choices and
opportunities. Several commonly recognized
types of security (e.g. economic, food) are
captured elsewhere and are not represented
here. This domain is characterized by three
indicators: Actual Safety, Perceived Safety and
Risk (Fig.3-28).
Safety and
Security
Actual Safety
Perceived
Safety
property crime
violent crime
loss of human life
accidental
morbidity and
mortality
Li
community
safety
Li
social
vulnerability to
environmental
factors
Figure 3-28. Indicators and metrics of the Safety and Security domain.
56
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Chapter 31 Well-being at Multiple Scales
I 67.9 • ..\
Figure 3-39. Regional Safety and Security domain scores for the baseline period (2000-2010).
The following GSS regions scored above the
national average (60.3±0.3) for Safety and
Security domain scores: New England, Middle
Atlantic, Pacific and West North Central (Fig. 3-
29). New England and the Middle Atlantic
scored significantly higher than all other
regions. The South Atlantic, and East South
Central and West South Central regions all
scored below the national average and
significantly lower than the other GSS regions.
New Hampshire scored highest for the Safety
and Security domain (67.9±2.1) and Louisiana
scored the lowest (51.5±1.1) among the states.
District of Columbia scored the lowest overall
(48.0±5.2).
57
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Chapter 31 Well-being at Multiple Scales
The Nation scored moderately high for the Safety and Security domain (Fig. 3-30). Regionally, New
England and the Middle Atlantic were scored high with the majority of the states in these regions also
scoring high. In other regions, states categorized with high scores for Saftey and Security included VA
(South Atlantic), HI (Pacific), CO, UT, WY (Mountain) and MN and ND (West North Central). All states in
the West South Central region scored low for this domain and all but one state (KY) in the East South
Central region scored low. Although a mix of low, moderate, and high scoring states were in the South
Atlantic region, the region scored low. Two other states in the Mountain region (NM and NV) also scored
low for Safety and Security.
Safety and Security (2000-2010)
New England
CT
MA
ME
NH
Rl
VT
Middle Atlantic
NJ
NY
PA
South Atlantic
DE
FL
GA
MD
NC
SC
VA
WV
East South Central
AL
KY
MS
TN
West South Central
AR
LA
OK
IX
1
Pacific
AK
CA
HI
OR
WA
Mountain
AZ
CO
ID
MT
NM
NV
UT
WY
West North Central
IA
KS
MN
MO
ND
NE
SD
East North Central
IL
IN
Ml
OH
Wl
A
1
Low Moderately Low Moderately High High
Figure 3-30. Domain scorecard for Safety and Security characterized at the national, GSS regional and state levels for the years
of 2000-2010.
58
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Chapter 31 Well-being at Multiple Scales
Menominee County, Wl ranked lowest among the counties during the years 2000-2010, scoring 25.5±6.7
for Safety and Security (Fig. 3-31). Douglas County, CO scored highest (80.5±4.8). County-level Safety
and Security scores <56.5 were considered low and scores >66.6.0 were condsidered high. The darker
colors indicating lower Safety and Security scores were mostly seen in the lower Mississippi River Valley
and Southern coastal areas; however scores > 60.0 are widely distributed across the nation.
<10 10-20 20-30 30-40 40-50 50-60 60-70 70-SO 80-90 > 90
Figure 3-31. Cloropleth map representation of variability in Safety and Security scores at the county level for years 2000-2010.
59
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Chapter 31 Well-being at Multiple Scales
Social Cohesion
(2000-2010)
U.S.
44.7±0.2
Social cohesion includes the strong and positive
relationships that exist in homes, schools and
the wider community that enhance our well-
being by propagating opportunities, creating
safety nets for difficulty times, and allowing
open discussion and resolution of difficult
problems in the community. A socially cohesive
community creates and promotes civic
engagement, shared societal values, a sense of
belonging by all persons, and an appreciation of
diversity. Social Cohesion domain scores reflect
both the quantity and quality of the ties that
bind us together. This domain is described by
five indicators: Attitude Towards Others and the
Community, Democratic engagement, Family
Bonding, Social Engagement, and Social Support
(Fig. 3-32).
Social Cohesion
Attitude Towards
Others and the
Community
city
satisfaction
belonging to
community
discrimination
helping others
Democratic
Engagement
satisfaction
with
democracy
government
decisions
voter turnout
Family Bonding II Social Engagement II Social Support
close friends
and family
frequency of meals at
home
exceeded screen
time guidelines
participation in
group activities
volunteering
Figure 3-32. Indicators and metrics of the Social Cohesion domain.
60
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Chapter 31 Well-being at Multiple Scales
Figure 3-33. Regional Social Cohesion domain scores for the baseline period (2000-2010).
The highest scoring regions for Social Cohesion
were the West North Central, Mountain and
East North Central regions, all which scored
significantly higher than the nation (44.7±0.2)
and all other GSS regions (Fig. 3-33). The West
South Central region scored significantly lower
than all other GSS regions. Additionally, the East
South Central and Pacific regions scored below
the national average. Louisiana scored lowest
for the states for Social Cohesion (40.8±0.6),
Wyoming the highest (57.8±0.8).
61
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Chapter 31 Well-being at Multiple Scales
Social Cohesion for the U.S. was categorized as moderately low for the years 2000-2010 (Fig. 3-34). Two
GSS regions (Mountain and West North Central) scored high for this domain with most of the states in
these regions scoring high or moderately high (with the exception of AZ (low) and NM (moderately low).
States in other regions scoring high for Social Cohesion included NH and VT in New England and AK and
HI in the Pacific region. With the exception of the West North Central and East North Central regions, all
regions had a least one low scoring state for this domain. The West South Central region was the only
region scored low for Social Cohesion.
Social Cohesion (2000-2010)
New England
CT
MA
ME
NH
Rl
VT
Middle Atlantic
NJ
NY
PA
South Atlantic
DE
F|_
GA
MD
NC
SC
VA
WV
East South Central
AL
KY
MS
TN
West South Central
AR
LA
OK
TX
^
Pacific
AK
CA
HI
OR
WA
Mountain
AZ
CO
ID
MT
NM
NV
UT
WY
West North Central
IA
KS
MN
MO
ND
NE
SD
East North Central
IL
IN
Ml
OH
Wl
\ t
•^
Low Moderately Low Moderately High High
Figure 3-34. Domain scorecard for Safety and Security characterized at the national, GSS regional and state levels for the years
of 2000-2010.
62
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Chapter 31 Well-being at Multiple Scales
During the years 2000-2010, counties in the middle portion of the nation generally scored higher for the
Social Cohesion domain (Fig. 3-35). The highest scoring county for this domain was Steele County, MN
(73.5±3.0). Greensville County, VA (33.1±3.2) ranked lowest among the couties for Social Cohesion.
Based on the distribution of county-level Social Cohesion scores observed for the 2000-2010 timeframe,
scores <42.2.0 were considered to be low, scores >57.3, high.
<10 10-20 20-30 30-40 40-50 50-60 60-70 70-80 80-90 >90
Figure 3-35. Cloropleth map representation of variability in Social Cohesion scores at the county level for years 2000-2010.
63
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Research Highlight: Washington D.C.
Washington D.C. is home to 646,449 full-time
residents. The District ranks 23rd for most populous
cities and is the 7th largest metropolitan area. It is
richly diverse with more than 60% reporting a non-
white race. The City's demographic is firmly set
between 18 and 65 years of age. Persons age 65
years and older represent less than the national
average. Children under 18 living at home is also
less than the national average by almost a third.
The educational attainment statistics outperform
the nation for high school graduation by a small
margin and higher education by a much larger
margin. Generally, median household income is
greater for D.C. than what is observed for the
nation. However, persons living below poverty
level and homeownership rate within the District
lag behind national averages (U.S. Census Bureau,
2013).
Figure 1. Aerial view of the mall in Washington D.C.
(Photo: Library of Congress)
The District's approximately sixty-one square miles
of area houses the centers of all three branches of
the federal government, more than 170 foreign
embassies, cultural and historical centers, non-
profits and supporting businesses. During the
workweek, the city's population more than
doubles due largely to workers commuting into the
city from surrounding Virginia and Maryland. D.C.
boasts the 2nd highest percentage of parkland
within an urban setting (19% of total area) along
with a variety of museums, monuments, historic
sites and other area attractions, many of which
require little or no cost to enjoy. It is a popular
vacation destination. More than 15 million people
visit D.C. and the greater metro area annually.
Approximately 10% of all visitors are international
travelers.
Tourism is one of the District's largest employment
sectors, second only to the government, with a
growing professional and business service job
market. The sprawling cityscape presents
challenges for moving the workforce and visitors in
and out of the city. Public transportation is a
popular mode for many commuters. A local bus
system connects areas within central Washington,
while the Metro systems, Metrobus and the
Washington Metro rapid transit system, serve the
District and suburbs. The Metro averages about a
million trips a week. It is the second busiest rapid
transit system in the country.
As a community, D.C. presents an interesting case
for the Human Well-Being Index (HWBI). Spatially,
D.C. represents a city, county, and arguably a state.
Since the HWBI is not bound to any specific spatial-
scale, an index for Washington D.C. can be
calculated using the methods described in
Summers J. K., et al. (2014) using data collected
during 2000-2010. The purpose for highlighting
well-being in the D.C. community is to showcase
the transferability of the HWBI's generalized model
for use within a specific community. Additionally, it
allows us to demonstrate how the HWBI and
related components may be used to begin the
conversation about human well-being as part of
community decision-making for sustainable
outcomes. Graded results are presented in Table 1.
64
-------
Table 1. Graded HWBI and Related Indicator Ratings For Washington D.C.
Table organization depicts the Index -> Domain (Indicator aggregate) ->
Indicator (metric aggregate) structure that is the core of the HWBI.
Lower Higher
Indicator J » ,ill Score
Weil-Being Index iijllll
Connection to Nature i
biophilia ill!
Cultural Fulfillment ill!
activity participation ill!
Education il|l|
basic educational knowledge and skills of youth ,,
participation and attainment illl
social, emotional and developmental aspects ,,
Health ,|||
healthcare illl
life expectancy and mortality oQQQ
lifestyle and behavior ,.,
personal well-being ill
physical and mental health conditions ||||
Leisure Time illl
leisure activity participation illl]
time spent i
working age adults il|ll
Living Standards illl
basic necessities illl
income illl
wealth illl
work illl
Safety and Security oOQll
actual safety
perceived safety
risk
Social Cohesion
attitude toward others and the community rjOUIJ
democratic engagement illl
family bonding illl
social engagement oOOO
social support
65
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Chapter 31 Well-being at Multiple Scales
The ratings show that well-being overall tended
toward the lower side of the scale during the
time period analyzed. Primary contributors to
this rating can be seen in the Connection to
Nature, Health, Safety and Security, and Social
Cohesion domains. The remaining domains all
shifted toward the higher end. When compared
to published city data aggregates (e.g., U.S.
Census Bureau, FBI, City-Data), the HWBI
ratings are not surprising.
The more interesting story lies among the
indicators. For example, the Health domain
rating is low. The indicators suggest that the
community is generally in good health even
though the life expectancy and mortality is
rated poorly. Conversely, Living Standards rates
higher even though Wealth is the only indicator
of four that rated toward the higher end of the
scale.
Often, different perspectives concerning the
human condition may be hidden in more
traditional measures. The HWBI's hierarchically
structured indicators allow stakeholders to drill-
down to a finer resolution of information. In
this highlight, we use existing data to complete
the HWBI calculation process. However,
communities choosing to use their own data to
calculate the HWBI and associated indicators
will end up with stronger, more relevant results.
The benefit of the HWBI approach is that it
provides community stakeholders with a
starting point to begin the conversation toward
diagnosing ill-defined community problems. For
instance, could the transient nature of the
District be a contributing factor to the lower
rating in the "basic necessities" indicator? Are
the services such as discount retail and grocery
stores moving further away from the city and
toward the suburbs where much of the
workforce lives? Information patterns such as
those offered through the HWBI approach may
offer a more inclusive view of the community—
particularly those vulnerable populations whose
needs may be lost in the larger picture.
The HWBI approach is flexible—it may stand-
alone as an endpoint measure related to a suite
of services (see Chapter 5 Research Highlight) or
as another piece informing decision-making for
sustainable outcomes.
66
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Chapter 41 Services Provisioning
Chapter 4 Services Provisioning
(USEPA Photo by Eric Vance)
67
-------
Chapter 41 Services Provisioning
Service Types
Services are categories of indicators used to describe the quantity and quality of provisioning from
economic, environmental and social sectors. Services are divided into three categories (service types):
Economic, Ecosystem and Social. In the evaluation of services provisioning, seven economic services,
five ecosystem services, and 10 social services were assessed (Table 4-1). The indicators and metrics
used to characterize services provisioning were selected to capture the quantity and quality of service
stocks. Services included in the assessment were identified from literature reviews in conjunction with
professional consultation within the respective disciplines (economics, ecology and sociology). A
complete list of the services for each service type, the indicators used to describe the services and
associated metrics with data sources are included in Appendix B.
Table 4-1. Type of Services and the corresponding number of services within each type.
SERVICE TYPE
Economic
NUMBER OF SERVICES
68
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Chapter 41 Services Provisioning
Provisioning assessments for each service type were based upon the quantity and quality of the
following services:
Economic Services (7)
Capital Investment Innovation
Consumption Production
Employment Redistribution
Finance
Ecosystem Services (5)
Air Quality Water Quality
Food, Fiber and Fuel Provisioning Water Quantity
Greenspace
Social Services (10)
Activism Family Services
Communication Healthcare
Community and Faith-Based Initiatives Justice
Educational Services Labor
Emergency Preparedness Public Works
National, Regional and State-level Services Scorecard
The scorecard for 2000-2010 services provisioning for the nation, GSS regions and states is presented in
Fig. 4-1 and the corresponding threshold values for each category are included in Table 4-2. Economic
and Ecosystem Services were characterized as moderately low for the U.S. while Social Services were
characterized as moderately high. When compared to the annual scores for across the GSS regions, the
2000-2010 values for Economic Services in all regions was scored as moderately low. The South Atlantic
region was the only region scored high for Ecosystem Services provisioning; the East South Central and
West South Central and West North Central regions scored moderately high. The Pacific region was the
only region characterized with low provisioning of Ecosystem Services. The New England and West
North Central Regions were scored high for the provisioning of Social Services. The East South Central
and West South Central regions scored low for Social Services. The other regions with the exception of
the South Atlantic were scored moderately high for the provisioning of Social Services.
Compared to the annual state-level scores for services, fourteen states scored high for Economic
Services: ME, VT (New England), DE (South Atlantic), HI, OR, WA (Pacific), AZ, MT, UT, WY (Mountain),
IA, NE, ND, SD (West North Central). The majority of states scored low for Ecosystem Services and only
four states (AK, WY, ND and SD) scored high. The only state scored high for Social Services was Wyoming
(Mountain). Social Services were characterized moderately low-moderately high for the majority of
states; however states the states of WV (South Atlantic), MS (East South Central) and AR (West South
Central) scored low. Wyoming was the only states characterized as with high levels of provisioning of all
three service types. No state scored low for all three services for the 2000-2010 period.
69
-------
Chapter 41 Services Provisioning
Moderately Low Moderately High
High
:conomicServices
Ecosystem Services
Social Services
U.S.
New England
CT
MA
ME
NH
Rl
VT
Middle Atlantic
NJ
NY
PA
South Atlantic
DE
FL
GA
MD
NC
SC
VA
WV
East South Central
AL
KY
MS
TN
West South Central
AR
LA
OK
TX
r$i
Pacific
AK
CA
HI
OR
WA
Mountain
AZ
CO
ID
MT
NM
NV
UT
WY
West North Central
IA
KS
MN
MO
ND
NE
SD
East North Central
IL
IN
Ml
OH
Wl
($1
Figure 4-1. Scorecard categorizing Economic, Ecosystem and Social Services at the national, GSS regional
and state levels for years 2000-2010.
70
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Chapter 41 Services Provisioning
Table 4-2. Threshold values for Services provisioning at the national, GSS regional and state levels for the years 2000-2010.
Economic Services
Ecosystem Services
Social Services
Economic Services
Ecosystem Services
Social Services
Economic Services
Ecosystem Services
Social Services
State
<50.3
<51.0
<42.9
Moderately
50.3- < 50.8 50.8-51.3
51.0-<54.9 54.9-56.1
42. 9- < 45. 6 45.6-49.7
High
>51.3
>56.1
>49.7
GSS Region
<48.9
<41.6
<43.6
Moderately
48.9- < 51.7 51.7-53.5
41.6 -< 44.4 44.4-49.0
43. 6- < 44. 9 44.9-46.0
High
>53.5
>49.0
>46.0
Nation
<48.9
<41.8
<43.5
Moderately
1 High
48. 9- < 51.1 51.1-53.2
41.8 -< 45.7 45.7-48.2
43. 5- < 44.1 44.1-45.4
High
>53.2
>48.2
>45.4
National, regional and state level assessments for the individual services within each service type are
presented in the next three sections. The sections are organized by service type. Within each section, a
brief description of the individual services is provided along with the indicators and metrics used to
assess the level of service provisioning for the 2000-2010 time period. The national service scores are
provided for spatial comparisons. Regional service scores are provided in tables showing the estimated
error associated with the regional values. State level services provisioning scores are presented as bar
graphs (with estimated error). Within each GSS region, state scores are presented in order from highest
to lowest. Scorecards for each service are provided to categorize scores for the nation, GSS regions and
states.
71
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Economic Services
Chapter 41 Services Provisioning
Economic services provide a means to generate
and distribute wealth within a society. For
example, the economic service employment
refers to labor deployed in the production of
goods and services and can be measured by
rates, types, and diversity of job sectors.
Economic Services provisioning is characterized
by 7 services, described by 23 indicators based
on data for 40 metrics (Table 4-3). These seven
services capture the wealth and resources of a
country or community, especially in terms of
the production and consumption of goods and
services. Among the services assessed Capital
Investment and Employment, Consumption and
Production and to some extent Finance are
related to investment in equipment, structures
and a diversity of employment opportunities.
The evaluation of Redistribution of wealth is
based on the range of income inequality
observed across the nation as opposed to global
comparisons.
Table 4-3. Services assessed to characterize economic provisioning, indicators for each corresponding service and the number
of metrics used in the indicator calculations. For more detailed information on the individual metrics see Appendix B.
SERVICE
INDICATORS
Capital Formation
Commercial Durables
New Housing Starts
New Infrastructure Investments
Cost of Living
Discretionary Spending
Goods and Services
Sustainable Consumption
Employment
Employment Diversity
Underemployment
Unemployment
Governance
Loans
Savings
METRICS
2
4
1
72
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Chapter 41 Services Provisioning
SERVICE
INDICATORS
METRICS
Innovation
Investment
Patents and Products
Exports
Household Services
Market Goods and Services
Sustainable Production
Inequality
Public Support
Re-distribution
The scorecard for 2000-2010 Economic Services provisioning for the nation, GSS regions and states is
presented in Fig. 4-2. Economic Services were characterized as moderately low for the U.S. when
compared to the annual scores across the GSS regions, the 2000-2010 values for Economic Services in all
regions was scored as moderately low compared to the annual state-level scores for services, 14 states
scored high for Economic Services: ME, VT (New England), DE (South Atlantic), HI, OR, WA (Pacific), AZ,
MT, UT, WY (Mountain), IA, ND, NE, SD (West North Central). Low scoring states for Economic Services
provisioning during the years 2000-2010 included: NY, PA (Middle Atlantic), MD, WV (South Atlantic),
KY, MS, TN (East South Central), AR (West South Central) and IL, IN, Ml (East North Central).
73
-------
Chapter 41 Services Provisioning
Economic Services (2000-2010)
New England
CT
MA
ME
NH
Rl
VT
Middle Atlantic
NJ
NY
PA
South Atlantic
DE
FL
GA
MD
NC
SC
VA
WV
East South Central
AL
KY
MS
TN
West South Central
AR
LA
OK
TX
Pacific
AK
CA
HI
OR
WA
Mountain
AZ
CO
ID
MT
NM
NV
UT
WY
West North Central
IA
KS
MN
MO
ND
NE
SD
East North Central
IL
IN 1
Ml 1
OH
Wl |
Low Moderately Low Moderately High High
Figure 4-2. Scorecard categorizing the level of Economic Services provisioning at the national, GSS regional and state levels for
the years of 2000-2010.
74
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Chapter 41 Services Provisioning
Capital Investment
(2000-2010)
U.S.
58.8 ± 0.8
Capital investment creates new capital or
maintains existing capital stock. It often
pertains to the acquisition of capital assets or
fixed assets that are expected to be produced
over many years. Capital formation measures
provide a picture of investment and growth of
the material economy in which goods and
services are produced using tangible capital
assets. Generally, increases in the production of
durable goods tend to indicate economic
growth and the likelihood of job growth
especially in the manufacturing sector. New
housing starts, a type of capital investment, are
also a key part of the U.S. economy, and have
an effect on related industries, such as banking,
the mortgage sector, raw materials,
employment, construction, manufacturing and
real estate. An increase in new housing starts is
an indication of a strong economy. Well-
designed infrastructure investments have long-
term economic benefits and create jobs in the
short term. Capital investment as described by
these indicators reflects economic sustainability
that may influence various aspects of well-
being, particularly living standards in terms of
home affordability, wealth and employment.
The four indicators of Capital Investment are
Capital Formation, Commercial Durables, New
Housing Starts and New Infrastructure
Investments (Fig. 4-3).
Capital Investment
New
Infrastructure
Invesments
' i
private
equipment
investment
public equipment
investment
private structure
investment
<
Figure 4-3. Indicators and metrics of Capital Investment services.
75
-------
Chapter 41 Services Provisioning
Table 4-4. 2000-2010 Capital Investment scores for the
GSS Regions with estimated error. Regions are in order
from highest to lowest score.
Capital Investment
East North Central
Middle Atlantic
New England
West North Central
Pacific
East South Central
South Atlantic
West South Central
Mountain
59
58
58
58
58
58
58
58
58
.0
.9
.9
.9_
.8
.8
.7
.7
.6
+
+
±
±
±
+
+
±
±
0.
2.
2.
1.
2.
0.
0.
0.
1.
8
3
5
3
6
7
8
9
9
The metric data associated with the indicators
of the economic service, Capital Investment,
were only available at the national scale;
therefore, no differences for the 2000-2010
period could be distinguished at the regional or
state level (Table 4-4). However, annual
variability at the national scale did allow for
analysis of the relationships between the
provisioning of Capital Investment and each of
the eight domains of well-being. These
relationships are discussed in Chapter 4.
76
-------
Chapter 41 Services Provisioning
Based on the range of observed annual scores for Captial Investment, the 2000-2010 scores for all
spatial scales were categorized as moderately low (Fig. 4-4).
Capital Investment (2000-2010)
New England
CT
MA
ME
NH
Rl
VT
Middle Atlantic
NJ
NY
PA
South Atlantic
DE
FL
GA
MD
NC
SC
VA
WV
East South Central
AL
KY
MS
TN
West South Central
AR
LA
OK
TX
Pacific
AK
CA
HI
OR
WA
Mountain
AZ
CO
ID
MT
NM
NV
UT
WY
West North Central
IA
KS
MN
MO
ND
NE
SD
East North Central
IL
IN
Ml
OH
Wl
<^ .
Low Moderately Low Moderately High High
Figure 4-4. Scorecard categorizing the level of capital Investment provisioning at the national, GSS regional and state levels for
the years of 2000-2010.
77
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Chapter 41 Services Provisioning
Consumption
(2000-2010)
U.S.
51.610.8
Consumption, i.e., people purchasing and using
goods and services, makes up a substantial part
of the economy. People meet their basic needs
as well as fulfill non-essential desires through
personal consumption of goods and services. In
the U.S., consumer spending accounts for
approximately 70 percent of gross domestic
product or the total value of the final goods and
services in the country (Fornell et al. 2010).
Businesses respond to trends in consumer
spending by lowering prices when spending
goes down and making the opposite
adjustments when spending rebounds (Fornell
et al. 2010). The relationship between human
well-being and consumption is heavily
moderated by a number of factors such as cost,
availability, durability and prices. The
association between consumption and
sustaining well-being may be best captured
through measures of sustainable consumption,
spending on goods needed for basic necessities
and discretionary spending. Consumption
includes measures of organic food sales,
personal consumption of durable and non-
durable goods, personal consumption
expenditures, discretionary spending and the
average cost of meeting basic needs (Consumer
Price Index; CPI) (Fig. 4-5).
Sustainable I I Goods and \ t Discretionary
Consumption! I Services
m
Spending
rt ^^^H
Cost of Livin
organic food I ^durable goodsL [durable goods
consumer
prices
non-durable
goods
services
spending
Figure 4-5. Indicators and metrics of Consumption services.
78
-------
Chapter 41 Services Provisioning
Table 4-5. 2000-2010 Consumption scores for the GSS
Regions with estimated error. Regions are in order from
highest to lowest score.
Consumption
East North Central
West North Central
Middle Atlantic
New England
Pacific
East South Central
South Atlantic
West South Central
Mountain
±
51.7
51.7
51.7
51.7
51.6
51.5
51.5
51.4 ±
51.3 ±
±
±
±
0.8
1.2
2.4
2.5
2.6
0.7
0.8
0.9
1.9
No significant differences were observed
between the regions or states for the service of
Consumption (Table 4-5). With the exception of
the cost of living indicator (CPI), all other
indicator data were only available at the
national scale. Therefore, very little variation
was observed for the regional and state
Consumption scores. Temporal variance
observed was accounted for in the derivation of
service-domain functional equations in the
modeling section presented later in this report
(Chapters).
79
-------
Chapter 41 Services Provisioning
Based on the range of observed annual scores for Captial Investment, the 2000-2010 scores for all
spatial scales were categorized as moderately low (Fig. 4-6).
Consumption (2000-2010)
New England
CT
MA
ME
NH
Rl
VT
Middle Atlantic
NJ
NY
PA
South Atlantic
DE
FL
GA
MD
NC
SC
VA
WV
East South Central
AL
KY
MS
TN
West South Central
AR
LA
OK
TX
Low
Moderately Low
Pacific
AK
CA
HI
OR
WA
Mountain
AZ
CO
ID
MT
NM
NV
UT
IB
WY
West North Central 1
IA
KS
MN
MO
ND
NE
SD
East North Central
IL
IN
Ml
OH
Wl
Moderately High
1
High
Figure 4-6. Scorecard categorizing the level of Consumption provisioning at the national, GSS regional and state levels for the
years of 2000-2010.
80
-------
Chapter 41 Services Provisioning
HELP WANTED
Employment
(2000-2010)
U.S.
58.7 ± 0.2
Employment refers to labor deployed in the
production of goods and services. The
employment service can be measured by rates,
types and diversity of employment. Indicators
for this service can both positively and
negatively reflect well-being. For instance, the
stresses associated with unemployment can
negatively impact health (Charles and DeCicca
2008), while employment supports well-being
by allowing individuals to provide for the basic
needs like housing, transportation and food.
Beyond simply providing financial stability,
employment can provide people with a sense of
community and connectedness with peers (Fig.
4-7).
Employment
Employment
Diversity
Underemployment • ' Unemployment
employment rate
I manufacturing rate
self employment
Figure 4-7. Indicators and metrics of Employment services.
81
-------
Chapter 41 Services Provisioning
Table 4-6. 2000-2010 Employment scores for the GSS
Regions with estimated error. Regions are in order from
highest to lowest score.
Employment
Mountain
West North Central
New England
Pacific
West South Central
outh Atlantic
East North Central
East South Central
Middle Atlantic
61.9 ± 0.5
61.0 ± 0.6
60.7 ± 0.6
60.4 ± 0.6
60.2 ± 0.2
59.9 ± 0.4
58.7 ± 0.2
56.5 ±
56.5 ±
0.2
0.5
The Mountain, West North Central, New
England, and West South Central regions scored
significantly higher than national average (58.7±
0.2) for Employment (Table 4-6). The Central
and Middle Atlantic regions scored below the
national average Employment score and
significantly lower than the other seven GSS
regions.
All states in the New England region scored higher than the national average for the service of
Employment (Fig. 4-8). The East South Central states scored at or below the national average.
Mississippi scored significantly lower than all other states for Employment. New York scored lowest in
the Middle Atlantic region. Mississippi ranked lowest for the indicators of Employment as measured by
employment rates, manufacturing rates and self-employment, and employment diversity.
lllllllllM.llll
VT
NH
CT | ME | Rl
PJs'.v Erglarc
MA
NJ
Mid
PA
die Atl
NY
ntic
DE | VA
FL
MD
South
GA | WV | NC
Atlantic
SC
AL
E
KY | TN | MS
st South Central
OK
W
TX
?5tSOU
LA
th Cent
AR
ral
———— National average
HI
WA | CA | OR
Pacific
AK
WY
UT | MT | CO
K'SL
NM
main
ID
AZ
NV
ND | 50
ME
West
MN
sjorthC
IA | K5 | MO
entral
Wl
IL
Eastf
OH | IN | Ml
lorth Central
Figure 4-8. 2000-2010 State-level Employment scores compared to the national average score. States are ordered within
regions from highest to lowest score.
82
-------
Chapter 41 Services Provisioning
Employment for the Nation was characterized as low for the years 2000-2010 (Fig. 4-9). All of the GSS
regions were scored moderately low with the exception of the Pacific and Middle Atlantic regions which
scored low. The majority of states in the Middle Atlantic, South Atlantic and East South Central regions
scored low for Employment. Employment was characterized as moderately high in the following states:
NH and VT (New England), MT, UT, WY (Mountain) and ND, NE and SD (West North Central).
Employment (2000-2010)
New England
CT
MA
ME
NH
Rl
VT
Middle Atlantic
NJ
NY
PA
South Atlantic
DE
FL
GA
MD
NC
SC
VA
WV
East South Central
AL
KY
MS
TN
West South Central
AR
LA
OK
TX
Pacific
AK
CA
HI
OR
WA
Mountain
AZ
CO
ID
MT
NM
NV
UT
WY
West North Central
IA
KS
MN
MO
ND
NE
SD
East North Central
IL
IN
Ml
OH
Wl
Low
Moderately Low
Moderately High
High
Figure 4-9. Scorecard categorizing the level of Employment provisioning at the national, GSS regional and state levels for the
years of 2000-2010.
83
-------
Chapter 41 Services Provisioning
Finance
(2000-2010)
U.S.
47.5 ± 0.6
Finance refers to the movement of financial
assets and liabilities to facilitate exchange.
Finance is essential for economic growth and
development (Sutton and Jenkins 2007). This
service can be described and measured in terms
of Loans, Savings, and Governance (government
revenue and debts) (Fig. 4-10). Savings allow
individuals to safely store their money as well as
gain interest on their assets. Individuals and
businesses can more easily make purchases and
investments by borrowing funds from financial
institutions. Governance is largely dependent
on the revenue paid by citizens in the form of
taxes. Major areas of federal spending include
Social Security, Medicare and defense.
Figure 4-10. Indicators and metrics of Finance services.
84
-------
Chapter 41 Services Provisioning
Table 4-7. 2000-2010 Employment scores for the GSS
Regions with estimated error. Regions are in order from
highest to lowest score.
Finance
Mountain
Pacific
49.3 ± 1.5
South Atlantic
West South Central
Middle Atlantic
West North Central
48.6 ± 1.9
48.0 ± 0.6
47.8 ± 0.7
47.5 ± 1.8
47.0 ± 1.0
East North Central 46.7 ± 0.6
New England
East South Central
46.5 ± 1.9
46.1 ± 0.5
The regional Finance scores for the years 2000-
2010 did not significantly differ from the
national average (47.5±.06) with the exception
of the East South Central region which was
significantly lower than the national score
(Table 4-7).
The only state scored higher than the national average for Finance was Delaware, which ranked highest
for the loans indicator (number of net commercial, industrial, farm, individual and real estate loans and
leases) (Fig. 4-11). States scoring below the national average included GA, WV, KY, MS, AR, IN and Ml.
Mississippi ranked lowest among the states for the governance indicator (low state and local
government revenues and high public debt).
— — — — National average
llllllHl
OR
WA
CA | HI | AK
Pacific
NV | CO
ID
WY
MOL
UT
ntain
MT
AZ NM
ND | SD | NE | IA
WestNortn(
MN
entral
MO
KS
Wl
OH
Ea^tr
IL | IN | Ml
orth Central
Figure 4-11. 2000-2010 State-level Finance scores compared to the national average score. States are ordered within regions
from highest to lowest score.
85
-------
Chapter 41 Services Provisioning
Based on the range of observed annual scores for Finance, the 2000-2010 scores for all spatial scales
were categorized as moderately high (Fig. 4-12).
Finance (2000-2010)
New England
CT
MA
ME
NH
Rl
VT
Middle Atlantic
NJ
NY
PA
South Atlantic
DE
FL
GA
MD
NC
SC
VA
wv
East South Central
AL
KY
MS
TN
West South Central
AR
LA
OK
TX
Pacific
AK
CA
HI
OR
WA
Mountain
AZ
CO
ID
MT
NM
NV
UT
WY
West North Central
IA
KS
MN
MO
ND
NE
SD
East North Central
IL
IN
Ml
OH
Wl
Low Moderately Low Moderately High High
Figure 4-12. Scorecard categorizing the level of Finance provisioning at the national, GSS regional and state levels for the years
of 2000-2010.
86
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Chapter 41 Services Provisioning
Innovation
(2000-2010)
U.S.
43.110.2
Innovation is the improvement of the diversity,
type or quality of goods and services. It drives
economic growth through the development of
new products, services and markets. Innovation
is supported by research and development
expenditures which provide the financing for
scientific discoveries. It often leads to increased
worker efficiency and production of goods and
services at lower prices which helps businesses
to produce more with less (Greenstone and
Looney 2011). In general, when workers
produce more, they also earn more which is
reflected by the statistics that show
productivity, wages and benefits have increased
each year between 1950 and 2000 (Bureau of
Labor Statistics). Innovation can affect well-
being by providing communities with increased
employment and higher wages. Indicators of
Finance are Investment and Patents and
Products (Fig. 4-13).
Figure 4-13. Indicators and metrics of Innovation services.
87
-------
Chapter 41 Services Provisioning
Table 4-8. 2000-2010 Innovation scores for the GSS
Regions with estimated error. Regions are in order from
highest to lowest score.
Innovation
New England
Pacific
West South Central
East North Central
West North Central
46.7 ± 1.0
46.3 ± 0.8
43.5 ± 0.4
42.8
42.6 ±
South Atlantic
42.6 ±
Middle Atlantic 42.6
Mountain 42.3
0.2
0.4
0.3
0.6
0.6
East South Central 42.2 ± 0.3
Three GSS regions, New England, Pacific and
West South Central scored higher than the
national average score for Innovation. The East
South Central region scored below the national
average and significantly lower than all other
regions (Table 4-8).
States scoring significantly lower than the national average for Innovation (43.1±0.2) included PA
(Middle Atlantic), VA, WV, MD (South Atlantic), MS (East South Central), AK (Pacific), CO (Mountain), MO
(West North Central), and OH and IN (East North Central). All states in the New England and West South
Central regions scored at or above the national average (Fig. 4-14). Differences in Innovation scores
across the states was primarily driven by the number of new patents and products produced.
llllllll.lllllii.lli.llll
kiA Rl
ME | VT | NH | CT
New England
NY | NJ | PA
MiddleAtlantic
GA
NC
EL
SC
South;
DE
tlantk
VA
WV
MD
TN
EE
AL
EtSOUt
KV | MS
hCentral
OK | TX | AR | LA
West South Central
———— National average
Figure 4-14. 2000-2010 State-level Innovation scores compared to the national average score. States are ordered within regions
from highest to lowest score.
88
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Chapter 41 Services Provisioning
The Nation scored moderately low for Innovation based on the scores for the 2000-2010 period, as did
all GSS regions with the exception of the New England and East North Central regions (Fig. 4-15). Only
one state, Alaska, scored low for Innovation. Four states in two different GSS regions scored high for
Innovation—ME and Rl (New England) and OR and WA (Pacific).
Innovation (2000-2010)
New England
CT
MA
ME
NH
Rl
VT
Middle Atlantic
NJ
NY
PA
South Atlantic
DE
FL
GA
MD
NC
SC
VA
WV
East South Central
AL
KY
MS
TN
West South Central
AR
LA
OK
TX
Pacific
AK
CA
HI
OR
WA
Mountain
AZ
CO
ID
MT
NM
NV
UT
WY
West North Central
IA
KS
MN
MO
ND
NE
SD
East North Central
IL
IN
Ml
OH
Wl
[^ayl
Low Moderately Low Moderately High High
Figure 4-15. Scorecard categorizing the level of Innovation provisioning at the national, GSS regional and state levels for the
years of 2000-2010.
89
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Chapter 41 Services Provisioning
Production
(2000-2010)
U.S.
49.8 ± 0.5
Production is the output of both market and
non-market good and services provided by
business, government and households. If
production is increasing, it is an indicator that
the economy is strong and growing and will be
able to provide employment and potential
economic security for its citizens. Gross
Domestic Product (GDP) is the most common
measure of production. "GDP is one of the most
comprehensive and closely watched economic
statistics... to prepare forecasts of economic
performance that provide that basis for
production, investment and employment
planning" (Gutierrez et al. 2007). The United
States is one of the most productive countries
in the world; 19 percent of global GDP
(McDearman et al. 2013). This service is also
described by Exports, Household Services,
Market Goods and Services and Sustainable
Production (Fig. 4-16).
Exports
Household
Services
Market GoodsM I Sustainable
and Services I I Production
Ti
H\ \ , , . , i 1 renewable energy
volunteering valueB I durable goods 1 nroduction
gross domestic
product
Figure 4-16. Indicators and metrics of Production services.
90
-------
Chapter 41 Services Provisioning
Table 4-9. 2000-2010 Production scores for the GSS
Regions with estimated error. Regions are in order from
highest to lowest score.
Productio
Middle Atlantic
West South Central
Pacific
South Atlantic
New England
East South Central
Mountain
West North Central
East North Central
50.5
50.2
50.2
49.6
49.5
49.4
49.1
49.0
±
±
1.4
0.6
1.5
0.5
1.5
0.4
1.2
0.8
48.8 ± 0.5
The regional scores for Production did not
significantly differ from the national score
(49.8±0.5) for the years 2000-2010. However,
the East North Central region scored
significantly lower than the West South Central
and Middle Atlantic regions (Table 4-9).
Five states, NH (New England) IA (West North Central), and OH, IN, Ml (East North Central) scored below
the national average for Production during the years 2000-2010 (Fig. 4-17). The value of volunteering
(household services) and lower production of market goods and services (durable goods and GDP) were
the main contributors to lower Production scores.
MA | ME | VT | Rl | CT | NH
New England
NY | NJ | PA
MiddleAtlantic
VA
MD
wv
FL
south;
GA
Atlantic
SC | NC | DE
MS | KV | TN | AL
EastSouth Central
LA | TX | AR
WestSouthCent
OK
ral
———— National average
Figure 4-17. State-level Production scores compared to the national average score. States are ordered within regions from
highest to lowest score.
91
-------
Chapter 41 Services Provisioning
The 2000-2010 national Production score and all GSS regional scores were moderately low with the
exception of the Middle Atlantic region which scored moderately high (Fig. 4-18). Only one state,
Delaware, scored high for Production. States characterized with moderately high Production scores
included: ME (New England), NY (Middle Atlantic), AR, LA, TX (West South Central), CA and HI (Pacific)
and NV (Mountain).
Production (2000-2010)
New England
CT
MA
ME
NH
Rl
VT
Middle Atlantic
NJ
NY
PA
South Atlantic
DE
FL
GA
MD
NC
SC
VA
WV
East South Central
AL
KY
MS
TN
West South Central
AR
LA
OK
TX
Pacific
AK
CA
HI
OR
WA
Mountain
AZ
CO
ID
MT
NM
NV
UT
WY
West North Central
IA
KS
MN
MO
ND
NE
SD
East North Central
IL
IN
Ml
OH
Wl
^
Low Moderately Low Moderately High High
Figure 4-18. Scorecard categorizing the level of Production provisioning at the National, GSS regional and state levels for the
years of 2000-2010.
92
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Chapter 41 Services Provisioning
Re-distribution
(2000-2010)
U.S.
43.6 ± 0.3
Re-distribution more evenly distributes wealth
in a society. This service can be described in
terms of income inequality and re-distribution
of wealth through taxation and government
assistance. Financial inequality has a negative
impact on economic growth (Alesina and Rodrik
1994) and may influence community income
levels indirectly for instance through its impact
on crime level (Alesina and Giuliano 2008).
Inequality is addressed through expenditures on
public support such as unemployment, social
security, welfare, childcare, and individual
federal aid (Fig. 4-19). Of those expenditures,
social security is the largest re-distribution
program in the United States, larger than the
combination of all other means-tested
programs (Medicaid and food stamps etc.)
(Liebman and Feldstein 2002). Two indicators
are used to describe Re-distribution, Inequality
and Public Support.
[W^fe
Re-distribution
Inequality | I Public Support
individual federal
childcare
expenditure
welfare
expediture
social security
expediture
unemployment
expenditure
Figure 4-19. Indicators and metrics of Re-distribution services.
93
-------
Table 4-10. 2000-2010 Re-distribution scores for the GSS
Regions with estimated error. Regions are in order from
highest to lowest score.
ie-distribution
West North Central
East North Central
West South Central
Mountain
46.3 ± 0.6
44.7 ± 0.3
44.6 ± 0.4
44.4 ± 0.9
South Atlantic
44.1 ± 0.5
East South Central 43.7 ± 0.4
Middle Atlantic
42.9 ± 0.6
Pacific
42.2 ± 1.1
New England
41.5 ± 1.2
Chapter 41 Services Provisioning
The West North Central region scored
significantly higher than all other regions for the
years 2000-2010. In addition to the West North
Central region, the East North Central and West
South Central regions scored above the national
average (43.6±0.3). The Middle Atlantic, Pacific
and New England regions scored below the
national average and the New England region
scored significantly lower than all other GSS
regions (Table 4-10).
All states in the West South Central, Mountain, West North Central and East North Central regions
scored above the national average for Re-distribution (Fig. 4-20). States scoring significantly lower than
the national average and all other states in their region included: MD (South Atlantic), MS (East South
Central) and CA (Pacific). Lower scores for Re-distribution indicate an inequitable distribution of wealth
and a lesser degree of public support funding.
Illh. i iillllllnlih 11 li
VT
NH | ME | CT | MA | HI
New England
NJ | PA | NY
MiddleAtlantic
DE
SC
FL
NC
South;
VA
ttlantit
WV
GA
MD
AL | TN | KY | MS
EastSouth Central
AR | TX OK
West South Cent
LA
ral
———— National average
Figure 4-20. State-level Re-distribution scores compared to the national average score. States are ordered within regions from
highest to lowest score.
94
-------
Chapter 41 Services Provisioning
Re-Distribution (2000-2010)
New England
CT
MA
ME
NH
Rl
VT
Middle Atlantic
NJ
NY
PA
South Atlantic
DE
FL
GA
MD
NC
SC
VA
WV
East South Central
AL
KY
MS
TN
West South Central
AR
LA
OK
TX
Pacific
AK
CA
HI
OR
WA
Mountain
AZ
CO
ID
MT
NM
NV
UT
WY
West North Central
IA
KS
MN
MO
ND
NE
SD
East North Central
IL
IN
Ml
OH
Wl
^
Low Moderately Low Moderately High High
Figure 4-21. Scorecard categorizing the level of Re-distribution provisioning at the national, GSS regional and state levels for the
years of 2000-2010.
The U.S. score for Re-distribution was categorized as moderately low for the years 2000-2010 (Fig. 4-21).
Moderately high scoring GSS regions were the South Atlantic, East South Central, West South Central,
Mountain and West North Central. Re-distribution scores were considered low for the states of ME and
Rl (New England), MD (South Atlantic), MS (East South Central), and CA (Pacific). Four of the seven
states in the West North Central region (IA, ND, NE, SD) scored high for Re-distribution as did the
following states in other regions: VT (New England), AK and HI (Pacific), and NM (Mountain).
95
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Chapter 41 Services Provisioning
Ecosystem Services
Ecosystem services ensure that the air we breathe, the water we drink, the food we eat, and the places
we live are cable of supporting and improving life (Table 4-11). Ecosystem services benefit human well-
being in both subtle and profound ways, affecting income, local migration and even political conflict
(World Health Organization 2005). Ecosystems services strive to regulate and protect our environment
so that, in turn, the environment can benefit its inhabitants. While humans have the power to destroy
ecosystems, they also partially control the provisioning of services that build and improve life support
systems provided by the environment (Daily 1997, Renting 1998). Communities actively manage many
ecosystems in order to benefit from the food, fiber, timber, flood control and many other services these
habitats provide in return (World Health Organization 2005).
Table 4-11. Services assessed to characterize Ecosystem Services provisioning, indicators for each corresponding service and the
number of metrics used in the indicator calculations. For more detailed information on the individual metrics see Appendix B.
SERVICE
Air Quality
Food, Fuel and Fiber
Provisioning
INDICATORS
Usable Air
Energy
Food and Fiber
Raw Materials
Natural Areas
Recreation and Aesthetics
Usable Water
Available Water
NUMBER OF METRICS
4
3
5
4
3
96
-------
Chapter 41 Services Provisioning
Ecosystem Services provisioning for the years 2000-2010 was characterized as moderately low for the
Nation (Fig. 4-22). The only GSS region assessed as having a high Ecosystem Services score, in
comparison to the other regions, was the South Atlantic. The Pacific region scored low and all other
regions scoreed moderately low or moderately high. Ecosystem Services scores for four states in three
different GSS regions were categorized as high-AK (Pacific), WY (Mountain) and ND and SD (West North
Central). The majority of states scored low for Ecosystem Services for the 2000-2010 period.
Ecosystem Services (2000-2010)
New England
CT
MA
ME
NH
Rl
VT
Middle Atlantic
NJ
NY
PA
South Atlantic
DE
FL
GA
MD
NC
SC
VA
WV
East South Central
AL
KY
MS
TN
West South Central
AR
LA
OK
TX
Pacific
AK
CA
HI
OR
WA
Mountain
AZ
CO
ID
MT
NM
NV
UT
WY
West North Central
IA
KS
MN
MO
ND
NE
SD
East North Central
IL
IN
Ml
OH
Wl
A
Low Moderately Low Moderately High High
Figure 4-22. Scorecard categorizing the level of Ecosystem Services provisioning at the national, GSS regional and state levels
for the years of 2000-2010.
97
-------
Chapter 41 Services Provisioning
Air Quality
(2000-2010)
U.S.
50.3 ± 1.0
Natural air quality regulation maintains usable
air which is a basic necessity for human health.
Air pollution is a major environmental concern
affecting quality of life in terms of health and
overall life satisfaction (Schmitt 2013, Nowak et
al. 2006). In addition to regulatory policies, the
environment can have an effect on air quality.
For instance, vegetation, particularly trees, can
remove air pollution (Nowak et al. 2006). Clean
air can lower risk of mortality and morbidity,
chronic disease, respiratory issues and minor
discomforts (Frey 2009, Lovasi et al. 2008).
These findings underline the importance of air
quality regulating services to basic well-being.
In fact, researchers generally associate usable
air with well-being (Schmitt 2013, Frey et al.
2009, Luechinger 2009, MacKerron and
Mourato 2009, Welsch 2006). Air quality
regulating services is described by the indicator,
Usable Air, demonstrated by number of clean
air days per year (Fig. 4-23).
Air Quality
Figure 4-23. Indicators and metrics for Air Quality Regulation services.
98
-------
Chapter 41 Services Provisioning
Table 4-12. 2000-2010 Air Quality scores for the GSS
Regions with estimated error. Regions are in order from
highest to lowest score.
Air Quality
West North Central 64.9 ± 2.0
East South Central
South Atlantic
Pacific
Mountain
59.8 ± 1.2
56.6 ± 1.3
51.7 ± 2.5
51.1 ± 2.4
West South Central 51.0 ± 1.7
East North Central 48.2 ± 1.4
Middle Atlantic
New England
42.8 ± 2.8
41.8 ± 3.4
The West North Central, East South Central and
South Atlantic regions scored significantly
higher than the national average (50.3±1.0) for
Air Quality. The Middle Atlantic and New
England regions scored lower than the national
score for Air Quality. The West North Central
region scored significantly higher than all other
GSS regions for the years 2000-2010. The
Middle Atlantic and New England regions
significantly lower (Table 4-12).
At least one state in each region scored below the national average for Air Quality with the exception of
the West North Central region (Fig. 4-24). In the New England region, CT scored significantly lowest.
Both NJ and PA scored significantly lower than NY in the Middle Atlantic region. Other states scoring
significantly lower than the national average and states in their region included MD and DE (South
Atlantic), TX (West South Central), CA (Pacific), AZ and UT (Mountain) and OH (East North Central). Air
Quality state-level scores ranged from 12.9±9.5 (DE) to 88.5±2.2 (ND) for the 2000-2010 time period.
FL I WV | SC | NC | GA | MD | DE MS | AL | KY | TN
South Atlantic EastSouthCentral
———— National average
AK HI OR WA CA WY MT ID NV NM CO AZ UT ND SD IA HE MN KS MO IN Wl Ml OH
Figure 4-24. State-level Air Quality scores compared to the national average score. States are ordered within regions from
highest to lowest score.
99
-------
Chapter 41 Services Provisioning
The Air Quality score for the nation during the years 2000-2010 was moderately high as were scores for
the West South Central and Pacific regions (Fig. 4-25). Low Air Quality scores were observed for the New
England and Middle Atlantic regions. The East South Central region had the only high Air Quality score
among the GSS regions. All GSS regions included at least one state with a high Air Quality score except
for the Middle Atlantic and East North Central regions. The East South Central, West South Central,
Pacific and West North Central regions did not include any states with low Air Quality scores for the
2000-2010 period.
Air Quality (2000-2010)
New England
CT
MA
ME
NH
Rl
VT
Middle Atlantic
NJ
NY
PA
South Atlantic
DE
FL
GA
MD
NC
SC
VA
WV
East South Central
AL
KY
MS
TN
West South Central
AR
LA
OK
TX
Pacific
AK
CA
HI
OR
WA
Mountain
AZ
CO
ID
MT
NM
NV
Q2
UT
WY 1
West North Central
IA
KS
MN
MO
ND
NE
SD
East North Central
IL
IN
Ml
OH
Wl
Low Moderately Low Moderately High High
Figure 4-25. Scorecard categorizing the level of Air Quality provisioning at the national, GSS regional and state levels for the
years of 2000-2010.
100
-------
Chapter 41 Services Provisioning
Food, Fiber and
Fuel
(2000-2010)
U.S.
39.3± 0.4
This service refers to the available stocks of
naturally occurring resources. The service is
described by: raw materials used in
manufacturing, agricultural and fisheries
productivity; as sources of natural fiber and raw
materials to generate energy. This service is
closely linked to economic production as the
supply of goods and services is dependent upon
access to resources including metallic minerals,
rocks, coal, oil and gas (Rankin 2011). The
components which make up this service are
vital to maintaining or improving well-being.
The four indicators of Food, Fiber and Fuel
Provisioning include Raw Materials, Food, Fiber
and Energy (Fig. 4-26).
Food, Fiber and Fuel
Provisioning
Raw Materials
copper
reserves
commercial
fisheries
timber
volume
reserves
Figure 4-26. Indicators and metrics of Food and Fiber Provisioning services.
101
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Chapter 41 Services Provisioning
Table 4-13. 2000-2010 Food, Fiber and Fuel scores for the
GSS Regions with estimated error. Regions are in order
from highest to lowest score.
Food, Fiber and Fuel
South Atlantic
East South Central
West South Central
Pacific
West North Central
Middle Atlantic
East North Central
New England
Mountain
40.0
39.5
39.4
39.4
39.3
39.1
38.6
38.6
38.2
+
±
±
+
+
±
±
+
+
0.4
0.4
0.4
1.3
0.7
1.1
0.4
1.3
1.0
None of the GSS regional scores for Food, Fiber
and Fuel significantly differed from the national
average (38.3±0.4); however, the South Atlantic
region scored significantly higher than the
Mountain region (Table 4-13).
Food, Fiber and Fuel scores for the states ranged from 36.9±1.9 (ME) to 42.5±0.5 (ND). Ten states in four
different regions scored higher than the national average score for Food, Fiber and Fuel—VA and FL
(South Atlantic), MS and AL (East South Central), OK, AR, LA (West South Central) and ND, SD and KS
(West North Central) (Fig. 4-27). Higher scores were primarily attributed to differences in the food and
fiber indicator which included commercial fisheries landings, timber stocks and agricultural productivity.
CT MA HI OT NH ME HI NY PA VA FL MD GA DE SC WV NC MS AL TN KY OK AR LA IX
T ioi.il' Central VV^tSoith Lertr:
———— National average
Figure 4-27. State-level Air Quality scores compared to the national average score. States are ordered within regions from
highest to lowest score.
102
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Chapter 41 Services Provisioning
Food, Fiber and Fuel scores for the Nation were moderately low, as were all GSS regional scores, with
the exception of the West South Central region, which scored moderately high (Fig. 4-28). All states
(excluding Texas) in the West South Central region scored moderately high for Food, Fiber and Fuel.
States in other GSS regions scoring moderately high included: DE, FL, and VA (South Atlantic), AL and MS
(East South Central), AK, HI and WA (Pacific), and KS, NE and SD (West North Central).
Food, Fiber and Fuel (2000-2010)
New England
CT
MA
ME
NH
Rl
VT
Middle Atlantic
NJ
NY
PA
South Atlantic
DE
FL
•
MD 1
NC
SC
VA
wv
East South Central
AL
KY
MS
TN
West South Central
AR
LA
OK
TX
• 1
Low Moderately Low
Pacific
AK
CA
HI
OR
WA
Mountain
AZ
CO
ID
MT
NM
NV
UT
WY
West North Central
IA
KS
MN
MO
ND
NE
SD
East North Central
IL
IN
Ml
OH
Wl
48?
| |
Moderately High High
Figure 4-28. Scorecard categorizing the level of Food, Fiber and Fuel provisioning at the national, GSS regional and state levels
for the years of 2000-2010.
103
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Chapter 41 Services Provisioning
Green Space
(2000-2010)
U.S.
45.1 ± 0.5
Green spaces are natural areas that allow for
recreation and contemplation. Research has
found that the number and area of parks and
playgrounds in a community promotes physical
activity (Li et al. 2005) which is associated with
positive health outcomes (Godbey 2009).
Outdoor activity promotes mental wellness by
encouraging stress management and
meditation (Godbey 2009). Ecosystems also
seem to possess intrinsic spiritual value whose
tranquility and beauty support human well-
being in hard to articulate, sometimes
intangible ways (Moore 2007). Natural areas
include national parks, rangelands, wildlands
and semi-natural places. Descriptions of this
service include the extent and usage of
greenspaces, Recreation and Aesthetics and
Natural Areas (Fig. 4-29).
Green
Space
Recreation and
Aesthetics
Natural Areas
nonconsumption
activity
blue space
rural parks
observing I national parks
wildlife '
park visitors
unclassified
Figure 4-29. Indicators and metrics of Green Space services.
104
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Chapter 41 Services Provisioning
Table 4-14. 2000-2010 Greenspace scores for the GSS
Regions with estimated error. Regions are in order from
highest to lowest score.
Greenspace
South Atlantic
Pacific
45.8 ± 0.5
45.7 ± 1.7
West South Central 45.4 ± 0.6
45.3 ± 1.6
New England
East North Central 45.1 ± 0.6
Middle Atlantic
East South Central
44.6 ± 1.5
44.3 ± 0.5
West North Central
Mountain
44.2 ± 0.8
44.2 ± 1.3
The GSS regions and the nation were scored
similary for Greenspace (Table 4-14). No
signficant differences between the national and
regional socres or among the Greenspace
scores for the regions were observed for the
years 2000-2010.
Six states scored below the national average for Greenspace for the years 2000-2010, three in the South
Atlantic region (GA, NC, WV), and KY, IA and IN in the East South Central, West North Central and East
North Central regions, respectively (Fig. 4-30). Scoring above the national average were the states of
ME, FL, VA, MS, LA, HI, AK, WA, ND and Wl. The lowest 2000-2010 state-level Greenspace score was
43.1±0.8 (KY) and the highest 55.1±3.9 (HI). The states of Hawaii and Washington both ranked in the top
ten for both the Greenspace indicators (natural areas, recreation and aesthetics).
———— National average
Figure 4-30. State-level Greenspace scores compared to the national average score. States are ordered within regions from
highest to lowest score.
105
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Chapter 41 Services Provisioning
The national and GSS regional scores for Greenspace were moderately low for Greenspace during the
years 2000-2010 (Fig. 4-31). Only two states, Alaska and Hawaii scored high for Greenspace. All other
state-level scores were characterized as moderately low.
Greenspace (2000-2010)
New England
CT
MA
ME
NH
Rl
VT
Middle Atlantic
NJ
NY
PA
South Atlantic
DE
FL
GA
MD
NC
SC
VA
wv
East South Central
AL
KY
MS
TN
West South Central
AR
LA
OK
TX
1
Pacific
AK
CA
HI
OR
WA
Mountain
AZ
CO
ID
MT
NM
NV
UT
WY
West North Central
IA
KS
MN
MO
ND
NE
SD
East North Central
IL
IN
Ml
OH
Wl
.
Low Moderately Low Moderately High High
Figure 4-31. Scorecard categorizing the level of Food, Fiber and Fuel provisioning at the national, GSS regional and state levels
for the years of 2000-2010.
106
-------
Chapter 41 Services Provisioning
Water Quality
(2000-2010)
U.S.
43.3 ± 0.9
Water quality regulating services help ensure
that water is safe for human use. Water quality
services remove pollutants that enter
waterways. Pollutants can refer to a vast array
of substances including industrial and
agricultural wastes. In 1972, Congress passed
the Clean Water Act which regulates and limits
the discharge of pollutants (USEPA 2012c).
Clean water bodies support drinking water
supply and recreation opportunities. Access to
clean water protects people from water-borne
illnesses and is subsequently one of the primary
health care priorities listed by the International
Conference on Primary Health Care (WHO) (Fig.
4-32). Water quality is represented by the
indicator Clean Water as measured by water
body impairment.
Water Quality
Figure 4-32. Indictor and metrics for Water Quality Regulation services.
107
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Chapter 41 Services Provisioning
Table 4-15. 2000-2010 Water Quality scores for the GSS
Regions with estimated error. Regions are in order from
highest to lowest score.
Water Quality
South Atlantic
West South Central
East South Central
West North Central
Mountain
Middle Atlantic
New England
East North Central
Pacific
58.4
55.9
50.9
44.0
38.8
36.5
35.5
30.5
25.7
±
±
+
±
±
+
±
+
+
1.0
1.0
0.8
1.6
2.2
2.5
2.6
1.0
3.8
The South Atlantic, West South Central, and
East South Central GSS regions scored
significantly higher than the national score for
Water Quality (43.3±0.9) while all other regions
with the exception of the West North Central
region scored below the national average. The
South Atlantic region scored higher than all
other regions, the Pacific, lower than all other
regions during the years 2000-2010 (Table 4-
15).
Twenty-two states scored below the national average for Water Quality for the 2000-2010 period (Fig.
4-33). All the states in the East North Central region scored below the national average, while all states
in the West South Central region scored higher than the nation. States scoring significantly higher than
the national average and the other states within their respective regions included: Rl and VT (New
England), PA (Middle Atlantic), FL, VA, and GA (South Atlantic), MS, AL and TN (East South Central), AK
(Pacific), WY, MT, NV and NM (Mountain), and ND, SD, KS, IA, MO (West North Central). Water Quality
scores ranged from 19.8±4.9 (OR) to 72.4±6.4 (AK).
Rl VT NH ME MA CT PA NJ NY FL VA GA SC MD NC WV DE MS AL TN KY OK AR LA TX
———— National average
AK I WA I | CA | OR I WY | MT | NV | NM | OO I ID I AZ I UT I ND I SD I KS I IA MO I NE I MN I Ml I IN I IL I OH I Wl
Pacific Mountain West North Centra] East NorthCentral
Figure 4-33. State-level Water Quality scores compared to the national average score. States are ordered within regions from
highest to lowest score.
108
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Chapter 41 Services Provisioning
The Nation scored moderately high for Water Quality for the 2000-2010 time period as did the East
South Central and Pacific regions (Fig. 4-34). The South Atlantic and East North Central regions scored
low for Water Quality compared to the annual GSS regional scores. The West South Central and
Mountain regional scores were high. All state-level scores in the East North Central region were low with
the exception of Michigan. Each GSS region included at least one state with a low Water Quality score
and one state with a high score with the exception of The South Atlantic, East South Central and West
South Central regions, which had no states with low Water Quality scores and the East North Central
region with no high scores.
Water Quality (2000-2010)
New England |
CT
MA 1
ME •
NH
Rl
VT
Middle Atlantic
NJ
NY
PA
South Atlantic
DE
FL
GA
MD
NC
SC
VA
WV
East South Central
AL
KY
MS
TN
West South Central
AR
LA
OK
TX
Pacific
AK
|
r
OR
WA
Mountain
AZ
CO
ID
MT
NM
NV
UT
WY
West North Central
IA
KS
MN
MO
ND
NE
SD
East North Central
IL
IN
Ml
OH
Wl
|
Low Moderately Low Moderately High High
Figure 4-34. Scorecard categorizing the level of Water Quality at the national, GSS regional and state levels for the years of
2000-2010.
109
-------
Chapter 41 Services Provisioning
Water Quantity
(2000-2010)
U.S.
47.2 ± 1.2
Water quantity regulation is a natural system's
ability to retain and renew fresh water
resources. Water is regarded as the most
important resource for sustaining ecosystems
which in turn support human health and well-
being (UNEP 2009). Fresh water is an essential
requirement for human survival and a common
component in economic development. Drought
conditions have been linked to decreases in life
satisfaction associated with expected loss of
income and resulting physiological stress
(Carroll et al. 2009). Freshwater availability can
be evaluated with drought indices which show
long-term cumulative dry and wet conditions
reflective of groundwater and reservoir levels;
and sustainable water indices that consider
fresh water supplies in conjunction with water
use and climate change over time. The indicator
for Water Quantity is Available Water (Fig. 4-
35).
Water
Quantity
Figure 5-35. Indicators and metrics of Water Quality Regulation services.
110
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Chapter 41 Services Provisioning
Table 4-16. 2000-2010 Water Quantity scores for the GSS
Regions with estimated error. Regions are in order from
highest to lowest score.
Water Quantity
New England
East North Central
East South Central
West North Central
South Atlantic
Middle Atlantic
Mountain
Pacific
West South Central
52.0
50.4
49.2
48.3
48.0
46.9
43.4
43.2
38.1
±
±
+
+
+
+
±
±
±
2.7
1.2
0.9
1.6
0.9
3.6
2.1
2.7
1.1
Three GSS regions scored signficantly lower
than the national average (47.2±1.2) for the
Water Quantity—Mountain, Pacific and West
South Central regions. The West South Central
region scored lower than all other GSS regions
during the years 2000-2010 (Table 4-16).
For the 2000-2010 period, the lowest ranked state-level score for Water Quantity was 31.9±4.3 (AZ), the
highest 63.5±3.5 (CT). Sixteen states scored above the national average for Water Quantity during the
years 2000-2010. All states in the West South Central region scored below the Water Quantity score for
the nation (Fig. 4-36). All but three regions (New England, Middle Atlantic and East North Central) had
states with scores below the national average. Florida scored signifcantly lower than all other states in
the South Atlantic region.
CT ME VT R NH MA NY PA NJ WV 5C NC VA GA DE MD FL KY AL TN MS LA AR OK TX
———— National average
Figure 4-36. State-level Water Quantity scores compared to the national average score. States are ordered within regions from
highest to lowest score.
Ill
-------
Chapter 41 Services Provisioning
The Water Quantity score for the Nation for the years 2000-2010 was moderately high (Fig. 4-37).
Among the GSS regions, all but three regions scored moderately high for Water Quanity. These regions
included the Mountain region which scored low and the West North Central and East North Central
regions which scored moderately low. Only three regions included states with high Water Quantity
scores. All states (excluding ME and NH) in the New England region scored high for Water Quanity.
Other high scoring states were DE and WV (South Atlantic) and MT (Mountain). Low scoring states for
Water Quanity included: FL (South Atlantic), OK and TX (West South Central), AZ, NV and UT (Mountain)
and KS (West North Central).
Water Quantity (2000-2010)
New England
CT
MA
ME
NH
Rl
VT
Middle Atlantic
NJ
NY
PA
South Atlantic
DE
FL
GA
MD
NC
SC
VA
WV
East South Central
AL
KY
MS
TN
West South Central
AR
LA
OK
TX
1
E
Pacific
AK
CA
HI
OR
WA
Mountain
AZ
CO
ID
MT
NM
NV
UT
WY
West North Central
IA
KS
MN
MO
ND
NE
SD
East North Central
IL
IN
Ml
OH
Wl
4
Moderately Low Moderately High High
Figure 4-37. Services scorecard categorizing the level of Water Quantity at the national, GSS regional and state levels for the
years of 2000-2010.
112
-------
Chapter 41 Services Provisioning
Social Services
Society is built around human and social capital. Social services change social measures to improve
society. Social services can establish social norms that promote cohesion, repair and strengthen family
cohesion, and provide safe, equitable working environments which foster healthy coworker relationship
development. Social services such as activism, community and faith-based initiatives, justice (e.g.,
environmental justice) and public works can affect policies that support ecosystems, or can possibly be
used as indirect measures of our connection to nature. Social services are also strongly tied to human
health. Many large organizations within the U.S. government were formed to protect and enhance the
health of the U.S. population, and several well-known private organizations such as the American Red
Cross, United Way of America, and Ronald McDonald House Charities provide health-related services to
populations in need. Social services also supplement or improve education practices and procedures.
The Social Services used in the assessment are shown in Table 4-17.
Table 4-17. Services assessed to characterize Social Services provisioning, indicators for each corresponding service and the
number of metrics used in the indicator calculations. For more detailed information on the individual metrics see Appendix B.
SERVICE
Activism
Communication
Community and Faith-based
Initiatives
Education
INDICATORS
Participation
Accessibility
Industry Infrastructure
Providers
Public Service
Communication
Quality
Investment
Providers
Accessibility
Confidence
Investment
Providers
METRICS
3
3
1
1
2
1
1
3
1
2
2
113
-------
Chapter 41 Services Provisioning
SERVICE 1 INDICATORS
Emergency Preparedness
^
Family Services
C>n
€l
^s*JX§%i r
6,
Healthcare
4
Justice
£ A
Jij&r
Labor
**;
* 9 %>
6z£>
Public Works
^^
Post-Disaster Response
Pre-Disaster Planning
Responders
Accessibility
Effectiveness
Investment
Providers
Accessibility
Investment
Providers
Quality
Accessibility
Confidence
Environmental
Investment
Providers
Quality
Confidence
Effectiveness
Employee Rights
Accessibility
Investment
Providers
Quality
Quantity
1 METRICS
1
1
1
2
3
1
1
5
3
1
1
2
1
4
2
1
1
1
1
2
2
4
1
5
5
114
-------
Chapter 41 Services Provisioning
The nation scored moderately high for Social Services during the years 2000-2010 (Fig. 4-38). Only two
GSS regions scored high—New England and West North Central. Both the East South Central and West
South Central regions scored low as did the states of WV, MS, AR. Wyoming was the only state with a
Social Services score characterized as high.
Social Services (2000-2010)
New England
CT
MA
ME
NH
Rl
VT
Middle Atlantic
NJ
NY
PA
South Atlantic
DE
FL
GA
MD
NC
SC
VA
WV
East South Central
AL
KY
MS
TN
West South Central
AR
LA
OK
TX
Pacific
AK
CA
HI
OR
WA
Mountain
AZ
CO
ID
MT
NM
NV
UT
WY
West North Central
IA
KS
MN
MO
ND
NE
SD
East North Central
IL
IN
Ml
OH
Wl
Low Moderately Low Moderately High High
Figure 4-38. Scorecard categorizing the level of Social Services provisioning at the national, GSS regional and state levels for the
years of 2000-2010.
115
-------
Chapter 41 Services Provisioning
Activism
(2000-2010)
U.S.
55.8 ± 0.8
Activism is group or individual action
undertaken to bring about local and global
social, political, economic or environmental
change (Moola 2004). Activism empowers
communities to improve various aspects of
quality of life (Glister 2012). One of the
symptoms of a healthy community capable of
growth is activism; members are able to identify
areas of improvement and take necessary
action (Ryan and Deci 2001, Ryan et al. 1996).
The social service of Activism is described by
individuals' Participation in a variety of activities
including, boycotting, protesting, signing
petitions and donating to groups advocating
social change (Fig. 4-39).
Figure 4-39. Indicators and metrics of the Activism services.
116
-------
Chapter 41 Services Provisioning
Table 4-18. 2000-2010 Activism scores for the GSS Regions
with estimated error. Regions are in order from highest to
lowest score.
Activism
Pacific
Middle Atlantic
New England
West North Central
Mountain
South Atlantic
East North Central
West South Central
East South Central
57
57
56
56
55
55
54
53
49
.8
.7
.7
.0
.3
.1
.7
.3
.4
±
±
+
+
+
+
±
±
±
2
2
2
1
1
0
0
0
0
.3
.1
.2
.2
.8
.7
.8
.9
.7
All GSS regions scored close to the national
average (55.8±0.8) for Activism with the
exception of West South Central and East
South Central regions which scored below the
national averge. The East South Central region
also scored signficantly lower than all other GSS
regions for the years 2000-2010 (Table 4-18).
All states in the East South Central and West South Central regions scored below the national average
score for Activism (Fig. 4-40). Mississippi (East South Central) scored significantly lower than all other
states. States in other regions scoring below the national average included: NC, GA, SC and WV (South
Atlantic) AZ (Mountain) and IN (East North Central). State level Activism scores ranged from 47.7±1.0
(MS)to62.2±2.1(WY).
^
VT
NH
ME
Nev;E
MA
rvgland
CT
Rl
NY
Mid
NJ
die Ail E
PA
ntic
Wl
VA
MD
FL
Southi
NC
Atlantic
GA
SC
WV
AL
East:
KY
outtlC
MS
entral
LA
W
OK
estSou
TN
thCent
AR
ral
—— — — National average
h ' 1 1 l-lT- ' " ' ' ' tTT
HI
CA
WA
Pacific
OR
AK
MT
NV
DE
CO
MOL
NM
"it a in
ID
UT
AZ
WY
IA
ND
W<
SD
stNor
NE
thCen
MN
ra
KS
MO
Ml
OH
East!*
TX
orthC
IL
entral
IN
Figure 4-40. State-level Activism scores compared to the national average score. States are ordered within regions from highest
to lowest score.
117
-------
Chapter 41 Services Provisioning
Activism for the Nation was characterized as moderately low for the years 2000-2010 (Fig. 4-41). These
moderately low ranges were also mirrored in the GSS regions and for the most part in the states. Several
exceptions included MS with a low score and VT, MT and WY as high. Falling into the moderately high
range were NH, DE and HI.
Activism (2000-2010)
New England
CT
MA
ME
NH
Rl
VT
Middle Atlantic 1
NJ
NY
PA
South Atlantic
DE
FL
GA
MD
NC
SC
VA
wv
East South Central
AL
KY 1
MS 1
IN 1
West South Central
AR
LA
OK
TX
Pacific
AK
CA
HI
OR
WA
Mountain
AZ
CO
ID
MT
NM
NV
UT
WY
West North Central
IA
KS
MN
MO
ND
NE
SD
East North Central
IL
IN
Ml
OH
Wl
ri
Low Moderately Low Moderately High High
Figure 4-41. Scorecard categorizing the level of Activism at the national, GSS regional and state levels for the years of 2000-
2010.
118
-------
Chapter 41 Services Provisioning
Communication
(2000-2010)
U.S.
46.6 ± 0.5
Communication is the dissemination of
information which promotes public awareness.
Effective communication occurs in a loop; the
sender sends a message to a receiver and the
receiver provides feedback to the sender (Van
Tiem et al., 2001). The public depends on
information being communicated in a timely
and appropriate manner. Communication
occurs in a variety of mediums that can require
complex infrastructures (Jones et al., 2005). This
service is described by how information is
accessed, transmitted, provided and shared as
well as the quality of the information itself. The
five indicators in the service of Communication
are Accessibility, Industry Infrastructure, Public
Services Communication and Quality (Fig. 4-42).
Communication
Figure 4-42. Indicators and metrics of the Communication services.
119
-------
Chapter 41 Services Provisioning
Table 4-19. 2000-2010 Communication scores for the GSS
Regions with estimated error. Regions are in order from
highest to lowest score.
Communication
Pacific
Mountain
New England
West North Central
East North Central
South Atlantic
West South Central
Middle Atlantic
East South Central
54.2
52.2
51.5
50.2
48.2
48.0
48.0
47.6
46.7
± 1.7
± 1.2
± 1.6
± 0.7
± 0.4
± 0.5
± 0.5
± 1.4
± 0.4
Seven of the nine GSS regions scored above the
national average score (46.6±0.5) for
Communication. The Middle Atlantic and East
South Central regions did not significantly differ
from the national score. The Pacific, Mountain,
New England and West North Central regions
were scored significantly higher than the other
GSS regions (Table 4-19).
All states in the New England, Middle Atlantic, Pacific, and West North Central regions scored at or
above the national average for Communication during the years 2000-2010 (Fig. 4-43). All states in the
East South Central region scored below the national average. Over half of the South Atlantic states
scored lower than the national score. Communication scores for the states ranged from 45.3±0.7 (WV)
to 55.6±2.2 (CA). Differences in Communication scores at the state-level were primarily driven by
information infrastructure and number of providers.
l|l, (
' ' ! l4"'"n-r;---;^7
NH
MA
CT
New E
VT
Tglanc
ME
Rl
NJ
Mid
PA
lleAtl
NY
ntic
DE
FL
MD
VA
South/
GA
tlantic
NC
SC
WV
KY
Ea
TN
stSout
MS
hCent
AL
al
OK
W
TK
*5tSOU
LA
thCerrt
AR
ral
———— National average
" 1 I 1 I I 1
'
I
Figure 4-43. State-level Communication scores compared to the national average score. States are ordered within regions from
highest to lowest score.
120
-------
Chapter 41 Services Provisioning
Communication for the nation was characterized as moderately low for 2000-2010 (Fig. 4-44). However
there was variation in scores at the regional and state levels. West North Central and East North Central
regions had high overall communication scores, and some states within the New England, Pacific and
Mountain regions were also characterized as high. The Middle Atlantic, South Atlantic and East South
Central and West South Central states were characterized as predominantly low to moderately low for
communication.
Communication (2000-2010)
New England
CT
MA
ME
NH
Rl
VT
Middle Atlantic
NJ
NY
PA
South Atlantic
DE
FL
GA
MD
NC
SC
^
=
VA •
wv
East South Central
AL
KY
MS
TN
West South Central
AR
LA
OK
IX |_
Pacific
AK
CA
HI
OR
WA
Mountain
AZ
CO
ID
MT
NM
NV
ITT
WY
West North Central
IA
KS
MN
MO
ND
NE
SD
East North Central
T?1'
IL
IN •
Ml 1
OH n
Wl |
Low
Moderately Low
Moderately High
High
Figure 4-44. Scorecard categorizing the level of Communication at the national, GSS regional and state levels for the years of
2000-2010.
121
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Chapter 41 Services Provisioning
Community and
Faith-based
Initiatives
(2000-2010)
U.S.
25.8 ± 0.9
Community-based and faith-based initiatives
are outreach organizations that promote the
betterment of a community. Such organizations
are united by their ideological goals rather than
the issues they address which can range from
drug addition to education to disease
prevention. Non-profit organizations and
governing bodies often work in tandem to
promote quality of life. In 2008, President Bush
created the Office of Faith-Based Initiatives that
supported faith-based and other community
organizations in their goals to meet the social
needs in America's communities (McClain,
2008). This service is measured by the number
of non-profit organizations as well as our
investment in them, as captured in the two
indicators Investments and Providers (Fig. 4-45).
Community and Faith-
based Intiatives
Figure 4-45. Indicators and metrics of the Community and Faith-based Initiatives.
122
-------
Chapter 41 Services Provisioning
Table 4-20. Community and Faith-based Initiatives scores
for the GSS Regions with estimated error. Regions are in
order from highest to lowest score.
Community and Faith-based
Initiatives
West North Central 29.7 ±
South Atlantic 28.0 ±
New England 27.0 ±
East North Central 26.5 ±
Mountain 24.8 ±
Middle Atlantic 24.6 ±
Pacific 23.6 ±
East South Central 22.1 ±
West South Central 21.6 ±
1.4
0.9
2.7
0.9
2.1
2.6
2.8
0.8
1.0
The West North Central and South Atlantic
regions scored above the national score
(25.8±0.9) for Commuity and Faith-based
Initiatives, while the East South Central and
West South Central regions, below the national
average. The West North Central region scored
signifcantly higher than the other GSS regions,
with the exception of the South Atlantic and
New England regions (Table 4-20).
All states in the New England, West North Central and East North Central regions scored at or above the
national average for Community and Faith-based Initiatives (Fig. 4-46). Half of the states in the South
Atlantic and Mountain regions and all states in the East South Central and West South Central regions
scored below the national average. Arizona scored lowest (16.7±4.2) among the states for Community
and Faith-based Initiatives, North Dakota highest (41.6±2.0). Investment in community and faith-based
efforts and the number of non-profit organizations contributed equally to the differences in scores.
I
I
1 I T IT
Illlllllilllllliillllllll
VT | ME
Rl | MA | NH
New England
CT
NY | PA
MidoleAtl
NJ
ntic
VA | MD
WV
DE | GA | NC
South Atlantic
SC
FL
KY
Ea
MS
stSout
TN | AL
hCentral
OK
W
AR | TO
25t South Cent
LA
ral
—— — — National average
11
i-nrh-HrTTT- L---4--U4--J---!-
llll 11 Illlllllllll
AK
OR
CA
Pacific
WA
HI
WY
MT
CO
MM
MOL
ID
NV
UT
AZ
ND
5D
IA
West
NE
North C
K5
entral
MN
MO
IL
Wl
EaGtT
IN
JorthC
Ml
rr.tral
OH
Figure 4-46. State-level Community and Faith-based Initiatives scores compared to the national average score. States are
ordered within regions from highest to lowest score.
123
-------
Chapter 41 Services Provisioning
Community and Faith-based Initiatives were characterized as moderately low for the Nation (Fig. 4-47).
The East and West South Central had high regional scores with little variability in range of scores
between states. In the West North Central region IA, KS, ND, NE and SD had high Community and Faith-
based Initiative scores. The majority of the states in the Pacific region scored moderately low to low
while the majority of the New England states scored moderately high to high.
Community and Faith-based Initiatives
[2000-2010)
New England
Low
Moderately Low
Moderately High
High
Figure 4-47. Scorecard categorizing the level of Community and Faith-based Initiatives at the national, GSS regional and state
levels for the years of 2000-2010.
124
-------
Chapter 41 Services Provisioning
Educational
Services
(2000-2010)
U.S.
43.8 ± 0.3
Educational services address academic services,
as well as social, emotional and ethical areas
(Cohen 2007). Quality education is a system
maintained by investments; it is built availability
of teachers, infrastructure and access.
Communities benefit from effectively educated
persons. Quality education has far reaching
impacts such as creating happier individuals,
responsible and caring participants of society
and national prosperity (Cohe 2007; Dunne and
Hogan 2004; Marples 1999; Nodding 2003).
Educational services are described by
Accessibility, Confidence in the educational
services, Investment in the services and
Providers (i.e., infrastructure and those that
instruct) (Fig. 4-48).
alternate
education
educational
financial aid
Educational Services
Investment! Providers
confidence in
education
f
education
expenditure
pupil spending
educational
employment
student to
teacher ratio
number of
schools
Figure 4-48. Indicators and metrics of the Education Services
125
-------
Chapter 41 Services Provisioning
Table 4-21. Education Services scores for the GSS Regions
with estimated error. Regions are in order from highest to
lowest score.
Educational
New England
West North Central
Middle Atlantic
South Atlantic
Pacific
Mountain
East North Central
West South Central
East South Central
Services
46
45
44
43
43
42
42
42
41
.8
.7
.7
.4
.2
JL
.2
.2
.4
±
±
±
±
±
±
±
±
±
1
0
0
0
0
0
0
0
0
.0
.4
.7
.2
.8
.6
.2
.2
.2
The New England, West North Central and
Middle Atlantic regions scored above the
national score (43.8±0.3) for Educational
Services during the years 2000-2010 (Table 4-
21). The Mountain, East North Central, West
South Central and East South South regions
scored below the national average. The East
South Central region scored signficantly lower
than all other GSS regions.
State-level Educational Services scores during the 2000-2010 period ranged from 38.6±1.0 (NV) to
49.1±1.1 (VT). Nevada scored significantly lower than all other states. All states in the East South Central
region scored below the national average, as did six of the eight states in the South Atlantic region. All
but one state (Wl) scored below the national average for Educational Services in the East North Central
region. Half of the states in the Mountain region scored below the national average as well. All states in
the New England and Middle Atlantic regions scored at or above the national average. Higher scoring
states were characterized by more access to, greater investment in, and more providers of educational
services. Lower scoring states had more confidence in educational services provided (Fig.
4-49).
111 ii .11
_T
iiiiiiiiiiii
VT
MA
ME | NH
New England
CT
R[
PA | NV
MiddleAtl
NJ
ntic
DE | MD | VA
NC
South/
FL | 5C
tl 3 nti c
GA
WV
KY | MS
East Soul
AL
bCent
TN
al
LA
W
TX
"stSou
AR
th Cent
OK
ral
———— National average
I I I I ! J
............... „..+.
llllllli
CA
OR
HI
Pacific
AK
WA
WY
MT
NM
CO
M OL-
ID
-ita i n
AZ
UT
NV
r. M
SD
MO
West
NE
North C
ND IA
entral
KS
Wl
IL
EastP
OH
orthC
Ml
g ntra 1
IN
Figure 4-49. State-level Education Services scores compared to the national average score. States are ordered within regions
from highest to lowest score.
126
-------
Chapter 41 Services Provisioning
National Educational Services scores were moderately low for 2000-2010 (Fig. 4-50). All the GSS regions,
with the exception of New England and the Middle Atlantic (moderately high), were also characterized
as moderately low as well. Three states scores (VT, DE, MN) were in the high range for Educational
Services while the score for NM was low.
Education (2000-2010)
New England
CT
MA
ME
NH
Rl
VT
Middle Atlantic
NJ
NY
PA
South Atlantic
DE
FL
GA
MD
NC
SC
VA
wv
East South Central
AL
KY
MS
TN
West South Central
AR
LA
OK
TX
Pacific
AK
CA
HI
OR
WA
Mountain
AZ
CO
ID
MT
NM
NV
UT
WY
West North Central
IA
KS
MN
MO
ND
NE
SD
East North Central
IL
IN
Ml
OH
Wl
Low Moderately Low Moderately High High
Figure 4-50. Scorecard categorizing the level of Education Services at the national, GSS regional and state levels for the years of
2000-2010.
127
-------
Chapter 41 Services Provisioning
Emergency
Preparedness
(2000-2010)
U.S.
42.7 ± 0.9
Emergency Preparedness services protect from
and reduce the impact of disasters on
populations (Perry and Lindell 2003). "Disasters
are tragedies that overwhelm our communities,
destroy our property, and harm our
populations" (Waeckerle 1991). One of the
most effective tools in emergency preparedness
is well-trained individuals who are capable of
enacting effective emergency plans (Gebbie and
Qureshi 2002). The responsibility for emergency
preparedness lies in the hands of the many
parties, "not only with governmental agencies
but also with active, engaged, and mobilized
community residents, businesses, and
nongovernmental organizations" (Nelson et al.
2007). Services can be provided by volunteers
and paid professionals (Nelson et al. 2007). The
Emergency Preparedness service is described by
the indicators Post-disaster Responses, Pre-
planning Disaster, and Responders (Fig. 4-51).
Emergency
Preparedness
Post-disaster
Response
natural disaster
expenditure
Pre-planning
Disaster
Responders
prepared individuals
emergency
employment
Figure 4-51. Indicators and metrics of the Emergency Preparedness services.
128
-------
Chapter 41 Services Provisioning
Table 4- 22. Emergency Preparedness scores for the GSS
Regions with estimated error. Regions are in order from
highest to lowest score.
Emergency Preparedness
East North Central
West North Central
Mountain
New England
East South Central
South Atlantic
West South Central
Pacific
Middle Atlantic
50.5
48.1
47.8
45.7
45.2
44.0
41.6
40.5
37.7
±
±
+
+
+
+
±
±
±
0.9
1.3
2.0
2.5
0.7
0.8
1.0
2.6
2.4
For Emergency Preparedness, the East North
Central, West North Central, Mountain, and
New England regions scored above the national
average (42.7±.09). The Middle Atlantic region
scored significantly lower than the national
score. The West South Central, Pacific and
Middle Atlantic regions scored significantly
lower than the other GSS regions for the years
2000-2010 (Table 4-22).
Five states in four different regions scored below the national average for Emergency Preparedness: NY
(Middle Atlantic), TX (West South Central), CA and HI (Pacific) and NM (Mountain). The range of
Emergency Preparedness scores observed at the state level ranged from 34.5±6.1 (HI) to 57.1±3.1 (CT).
Higher scoring states had spent more on post disaster response and employed more emergency
responders (Fig. 4-52).
I I
~"
CT
NH
Rl
NewE
VT
r.glard
ME
MA
PA
Mid
NJ
flleAtl
NY
ntic
DE
NC
FL
SC
South/
GA
Atlantic
MD
WV
VA
AL
Ea
TN
stSout
KV
hCent
MS
d
OK
W
AR
?5tSou
LA
ch Cent
TX
ral
———— National average
1' • iZ:
AK
WA
OR
Pacific
CA
HI
ID
UT
AZ
NV
MOL
CO
itain
WY
MT
NM
IA
NE
MN
West
K5
NorttiC
ND
entral
SD
MO
Ml
IN
Ea;tf
OH
JorthC
IL
2r,tral
Wl
Figure 4-52. State-level Emergency Preparedness scores compared to the national average score. States are ordered within
regions from highest to lowest score.
129
-------
Chapter 41 Services Provisioning
The national score for Emergency Preparedness was moderately high (Fig. 4-53). Regional scores were
predominantly moderately high to high. The Middle Atlantic and West South Central GSS regions
deviated from the nation with moderately low to low scores that were also represented at the state
level. New England's state scores were either high (CT, NH, Rl) or low (MA, ME, VT). This trend was also
reflected in the Pacific region.
Emergency Preparedness (2000-2010)
New England
CT
MA
ME
NH
Rl
VT
Middle Atlantic
NJ
NY
PA
South Atlantic
DE
FL
GA
MD
NC
SC
VA
wv
East South Central
AL
KY
MS
TN
West South Central
AR
LA
OK
TX
Pacific
AK
CA
HI
OR
WA
Mountain
AZ
CO
ID
MT
NM
LTT
WY
West North Central
IA
KS
MN
MO
ND
NE
SD
East North Central
IL
Ml
OH
Wl
Moderately Low
Moderately High
High
Figure 4-53. Services scorecard categorizing the level of Emergency Preparedness at the national, GSS regional and state levels
for the years of 2000-2010.
130
-------
Chapter 41 Services Provisioning
Family Services
(2000-2010)
U.S.
51.110.5
Family Services aid and enhance the family unit.
Adults and children alike are assisted in
maintaining or improve their quality of life.
These services are a legitimate function of
society (Sargent et al. 1982). They exist to
combat social ills like poverty, violence, racism
and substance abuse (McCroskey and Meezan,
1998). "A system of well-coordinated,
accessible, family-centered services must rest
on a foundation of a healthy community that
affords adequate basic services and
opportunities for education, housing and
employment" (McCroskey and Meezan 1998).
Family services are described by Accessibility,
Effectiveness, Infrastructure, Investment and
Providers (Fig. 4-54).
Family Services
Providers
child services
expedience
sheltered
homeless
adoption
expedience
child services
[recurrent child
I maltreatment
Figure 4-54. Indicators and metrics of the Family Services.
131
-------
Chapter 41 Services Provisioning
Table 4-23. Family Services scores for the GSS Regions
with estimated error. Regions are in order from highest to
lowest score.
Family Services
West North Central
East North Central
Mountain
South Atlantic
New England
East South Central
Pacific
West South Central
Middle Atlantic
53
53
52
51
51
50
50
49
49
JL
.0
.9
.9
.7
.8
.4
.8
.7
±
±
+
+
+
+
±
±
±
0
0
1
0
1
0
1
0
1
.8
.5
.2
.5
.6
.5
.6
.6
.5
The West North Central, East North Central and
Mountain regions scored higher than the
national average (51.5±0.5) for Family Services
(Table 4-23). The West South Central and
Middle Atlantic regions scored lower than the
nation.
Five states in three regions scored below the national average for Family Services—NY (Middle Atlantic)
MS (East South Central) and TX, AR and LA (West South Central). The range of scores observed for the
years 2000-2010 at the state level was 48.4±1.4 (LA) to 58.5±1.4 (WY). All indicators played similar roles
in distinguishing between high and low scoring states (Fig. 4-55).
linn4 In 11
-*-
VT
NH
cr
New E
ME
•\glahc
Rl
MA
PA
Mid
NJ
JleAtl
NY
ntic
DE
GA
NC
VA
South/
FL
tlantlc
MD
5C
wv
TN
Ea
AL
5tSout
KV
hCent
MS
al
OK
W
TX
?5tSOU
AR
th Cent
LA
ral
— — — — National average
1 1
...I...I.J
I1!]
Mill
[ I
•Ii'Mi
AK
WA
OR
Pacific
CA
HI
WY
UT
MT
ID
Mai.
NV
-tain
AZ
CO
NM
5D
ND
IA
West
MN
North C
NE
entral
K5
MO
IN
Ml
Eastf
Wl
JorthC
OH
ehtral
IL
Figure 4-55. State-level Family Services scores compared to the national average score. States are ordered within regions from
highest to lowest score.
132
-------
Chapter 41 Services Provisioning
Family Services for the nation was characterized as moderately high for the years 2000-2010 (Fig. 4-56).
All the GSS regions were also represented in the moderately high range. However three of the four
states within the West South Central region were in the moderately low range (AR, LA and TX). Other
moderately low scoring states were NY, DE, WV, MS, CA and HI. Wyoming and South Dakota had high
scores for Family Services.
Family Services (2000-2010)
New England
CT
MA
ME
NH
Rl
VT
Middle Atlantic
NJ
NY
PA
South Atlantic
DE
FL
GA
MD
NC
SC
VA
WV
East South Central
AL
KY
MS
TN
West South Central
AR
LA
OK |
|
1
Pacific
AK
CA
HI
OR
WA
Mountain
AZ
CO
ID
MT
NM
NV
UT
WY
West North Central
IA
KS
MN
MO
ND
NE
SD
East North Central
IL
IN
Ml
OH
Wl
•»
Moderately Low Moderately High High
Figure 4-56. Scorecard categorizing the level of Family Services at the national, GSS regional and state levels for the years of
2000-2010.
133
-------
Chapter 41 Services Provisioning
Healthcare
(2000-2010)
U.S.
40.7 ± 0.3
Healthcare services address both physical and
psychological care. Healthcare is multifaceted;
while it functions to serve clients in need, many
variables impact treatment (Batalden and
Davidoff 2007). For instance, quality healthcare
is dependent upon government funding;
available infrastructure; and client perceptions
of access, affordability and quality. One of the
largest obstacles between individuals and
healthcare services is a lack of availability of
care (Litaker et al. 2005). Healthcare services
are described by Accessibility, Investment,
Providers and Quality (Fig. 4-57).
Healthcare
Accessibility I I Investment I Providers
Quality
health costs
access to
medicine
medicare
I healthcare
Iworker shortages!
national health
expenditure
confidence in
medicine
federal health
expenditures
federal hospital
expenditures
health agencies
Figure 4-57. Indicators and metrics of the Healthcare services.
134
-------
Chapter 41 Services Provisioning
Table 4-24. Healthcare scores for the GSS Regions with
estimated error. Regions are in order from highest to
lowest score.
Healthcare
Pacific
East North Central
West North Central
East South Central
Mountain
South Atlantic
New England
West South Central
Middle Atlantic
44.0
43.0
41.9
41.5
41.5
40.9
39.6
39.4
39.2
±
±
±
±
±
±
±
1.0
0.3
0.5
0.3
0.7
0.3
0.9
0.4
0.9
Four GSS regions scored above the national
average score for Healthcare—Pacifc, East
North Central, West North Central and East
South Central. The Pacific and East North
Central regions scored signficantly higher than
all other GSS regions (Table 4-24).
The observed range of Healthcare scores at the state level for the years 2000-2010 was between
36.5±2.3 (HI) and 46.9±0.9 (WY). Thirteen states in seven of the nine GSS regions scored above the
national average. Three of the four states (AR, TX and LA) scored lower than the nation in the West
South Central region (Fig. 4-58). Accessibility to healthcare and the number of healthcare providers
contributed to differences between high and low scoring states; although the perceived quality of
healthcare in states scoring low was higher than in the states scoring above average for Healthcare
provisions.
I
ii in
NH
VT
CT
NewE
ME
rvglanc
Rl
MA
PA
Mid
NJ
[JleAtl
NT
ntic
NC
GA
SC
WV
South/
FL
tlantk
VA
DE
MD
AL
E:
MS
5tSoUt
TM
hCent
KY
a]
OK
W
AR
"stSou
TX
th Cent
LA
ral
1
1
———— National average
I
-Uf, -.14.4.4..,.., LLLU—JLLL
Mm I I I
CA
WA
AK
Pacific
OR
HI
WY
NV
CO
ID
MOL
MT
rtain
UT
AZ
NM
IA
ND
MN
West
SD
North C
NE
ehtral
K5
MO
Ml
OH
Easth
IN
orthC
Wl
entral
IL
Figure 4-58. State-level Healthcare scores compared to the national average score. States are ordered within regions from
highest to lowest score.
135
-------
Chapter 41 Services Provisioning
Healthcare for the nation was characterized as moderately high for the years 2000-2010 (Fig. 4-59). The
GSS region scores were variable. The South Atlantic score was high and New England, Middle Atlantic
and Mountain were moderately low, with state scores ranging from moderately low to moderately high
excluding Wyoming. The West South Central was also represented with moderately low scoring states
(AR, LA and TX). States in the West North Central, East North Central and the South Atlantic regions had
the highest scores for healthcare (DE, NC, CA, WY, IA, NE, Ml and OH).
Healthcare (2000-2010)
New England |
CT
MA
ME
NH
Rl
VT
Middle Atlantic
NJ
NY
PA
South Atlantic
DE
FL
GA
MD
NC
SC
VA
wv
East South Central
AL
KY
MS
TN
West South Central
AR
LA
OK
TX
1
Pacific
AK
CA
HI
OR
WA
Mountain
AZ
CO
ID
MT
NM
NV
UT
WY
West North Central
IA
KS
MN
MO
ND
NE
SD
East North Central
IL
IN
Ml
OH
Wl
<;
^^
Low Moderately Low Moderately High High
Figure 4-59. Scorecard categorizing the level of Healthcare at the national, GSS regional and state levels for the years of 2000-
2010.
136
-------
Chapter 41 Services Provisioning
Justice
(2000-2010)
U.S.
42.2 ± 0.5
Justice ensures fair and equal treatment. Non-
discriminatory treatment and fair distribution of
environmental benefits reduces inequality and
ensures domestic tranquility (Sherman 2002).
These services are not determined by race,
gender, age (Sherman 2002) or geographic
location. They strive to be fair and effective in
their protection of individuals and their
environment at every level across the United
States; large and small, local, regional and
national in scope (Kurtz 2005). This service is
described by Accessibility, Confidence,
Environmental, Investment, Providers and
Quality (Fig. 4-60).
Justice
Figure 4-60. Indicators and metrics of Justice services.
137
-------
Chapter 41 Services Provisioning
Table 4-25. Justice scores for the GSS Regions with
estimated error. Regions are in order from highest to
lowest score.
Justice
West North Central
East North Central
West South Central
Mountain
East South Central
New England
Pacific
Middle Atlantic
South Atlantic
45
44
44
44
43
43
43
41
40
.4
.8
.8
.4
.8
.5
.4
.1
.3
±
±
+
+
+
+
±
±
±
0
0
0
1
0
1
1
1
0
.7
.5
.5
.1
.4
.4
.4
.3
.4
The West North Central, East North Central,
West South Central, Mountain and East South
Central regions all scored higher than the
national average (42.2±0.5) for Justice (Table 4-
25). The Middle Atlantic and South Atlantic
regions scored significantly lower than all other
GSS regions for the years 2000-2010 and the
South Atlantic region scored lower than the
national average.
Justice scores for 2000-2010 at the state level ranged from 39.4±0.7 (VA) to 58.5±1.3 (WY). States falling
below the national average included NJ (Middle Atlantic) and GA, FL and VA (South Atlantic). Over half of
the states scored significantly higher than the national score for Justice (Fig. 4-61). Environmental justice
contributed the most to observed differences in Justice scores between the highest and lowest ranking
states.
ll
I
i TT
-I--
.LL
VT
NH
ME
Nev;E
nglanc
MA
Rl
PA
Mid
NY
die All
NJ
ntic
DE
SC
NC
wv
Souths
MD
tlantic
GA
FL
VA
TN
Ea
KY
stSout
AL
hCent
MS
al
AR
W
TX
'stSou
LA
th Cent
OK
ral
"
———— National average
I I
1 I • I .. I -[ T I j ! -r
5 ~l'"t~~ "I""f
OR
HI
WA
Pacific
CA
AK
WY
MT
NM
UT
Mou
ID
"tain
AZ
CO
NV
ND
SD
IA
West
KS
\JorthC
MO
entral
NE
MN
Wl
Ml
Eastf
OH
orthC
IL
entral
IN
Figure 4-61. State-level Justice scores compared to the national average score. States are ordered within regions from highest
to lowest score.
138
-------
Chapter 41 Services Provisioning
The Justice score for the Nation was characterized as moderately high for the years 2000-2010 (Fig. 4-
62). The GSS regions were represented by the entire range of scores. New England, West North Central
and East North Central regions were moderately high for Justice and had several high scoring states
(MA, NH, VT, IA, NE, Ml and Wl). While the Middle Atlantic and South Atlantic regions were dominated
by low scoring states (NJ, NY, DE, FL, GA, MD and VA). The West South Central region scored low and the
East South Central region at the state level scored moderately low.
Justice (2000-2010)
New England
CT
MA
ME
NH
Rl
VT
Middle Atlantic
NJ
NY
PA
South Atlantic
DE
FL
GA
MD
NC
SC
VA
wv
East South Central
AL
KY
MS
TN
West South Central
AR
LA
OK
TX
Pacific
AK
CA
HI
OR
WA
Mountain
AZ
CO
ID
MT
NV
UT
WY
West North Central
IA
KS
MN
MO
ND
NE
SD
East North Central
IL
IN
Ml
OH
Wl
Moderately Low
Moderately High
High
Figure 4-62. Scorecard categorizing the level of Justice at the national, GSS regional and state levels for the years of 2000-2010.
139
-------
Chapter 41 Services Provisioning
Labor
(2000-2010)
U.S.
44.9 ± 0.4
Labor is quality of employment, productivity
and cost of work (Giampietro et al. 1993). Labor
services ensure that laborers and the
organizations that employ them exist in
harmony and equity. This service is described by
quality of employers, workplace conditions and
employee rights. Labor unions "equalize the
bargaining power between employers and
employees" and exist to protect worker
interests and living standards (Botero et al.
2004). Quality and confidence in employment is
supported by the United States Department of
Labor which protects the labor rights of
American citizens including discrimination free
and healthful work places (United States
Department of Labor 2013). The indicators for
Labor include: Confidence, Effectiveness and
Employment Rights (Fig. 4-63).
Labor
Figure 4-63. Indicators and metrics of Labor services.
140
-------
Chapter 41 Services Provisioning
Table 4-26. Labor scores for the GSS Regions with
estimated error. Regions are in order from highest to
lowest score.
Labor
Middle Atlantic
Pacific
New England
East North Central
West North Central
Mountain
South Atlantic
West South Central
East South Central
46
45
45
44
44
44
44
44
43
.7
.2
.7
.5
.4
.2
.1
.1
±
±
±
±
±
±
±
1
1
1
0
0
1
0
0
0
.0
.0
.9
.4
.5
.5
.3
.4
.3
GSS regional Labor scores for 2000-2010 did not
signficantly differ from the national average
(44.9±0.4) with the exception of the East South
Central region which scored signficantly lower
than the nation and all other regions (Table 4-
26).
States scoring below the national average score for Labor were in the South Atlantic, East South Central
and West South Central and Mountain regions (Fig. 4-64). Arizona was the only state significantly lower
than the national score in the Mountain region. Only MD and DE in the South Atlantic region scored
above the national average. All states in the East South Central region scored lower than the national
Labor score for 2000-2010. In the West South Central region, OK and AR also scored lower than the
nation. State-level 2000-2010 Labor scores ranged from 42.0±0.9 (MS) to 46.2±1.3 (NY). Lower
confidence in the people running organized labor efforts and lower union membership contributed most
to the differences between low and high scores in Labor at the state level.
I
1
ME
1
MA
T
•
y
111
VT | R | CT | NH NY
N™ England Mid
. .1. I
PA
UleAtl
f I I
INI
NJ
ntic
I
MD | VA | DE FL l\
South AtlB
I I I I I I I
I
C GA | WV | SC TN KX AL MS
ntic EastSouth Central
M,
TX | LA | QK
West South Cent
'
AR
ral
———— National average
ll
1
HI
CA
OR
Pacific
WA | AK
NM
CO
WY
MT
Mou
NV | UT | ID
italn
AZ
MO
IA
K5 | ND | SD
West North Central
NE
MIM
Wl
OH | IL | Ml
EastNorth Central
IN
Figure 4-64. State-level Labor scores compared to the national average score. States are ordered within regions from highest to
lowest score.
141
-------
Chapter 41 Services Provisioning
The Labor score at the national level was moderately low for the years of 2000-2010 (Fig. 4-65). New
England, Middle Atlantic and East North Central GSS regions were the highest scoring at moderately
high. Individual states in the New England and the Middle Atlantic regions were also moderately high.
The South Atlantic, East South Central and West South Central were dominated by moderately low
scores.
Labor (2000-2010)
New England
CT
MA
ME
NH
Rl
VT
Middle Atlantic
NJ
NY
PA
South Atlantic
DE
FL
GA
MD
NC
SC
VA
wv
East South Central
AL
KY
MS
TN
West South Central
AR
LA
OK
TX
1
Low
Pacific
AK
CA
HI
OR
WA
Mountain
AZ
CO
ID
MT
NM
NV
UT
WY
West North Central
IA
KS
MN
MO
ND
NE
SD
East North Central
IL
IN
Ml
OH
Wl
5 •%
^
Moderately Low Moderately High High
Figure 4-65. Scorecard categorizing the level of Labor at the national, GSS regional and state levels for the years of 2000-2010.
142
-------
Chapter 41 Services Provisioning
Public Works
(2000-2010)
U.S.
49.2 ± 0.4
Public works is "the combination of physical
assets, management practices, policies and
personnel necessary for government to provide
and sustain structures and services essential to
the welfare and acceptable quality of life for its
citizens"(American Public Works Association
2013). Public works includes public utilities like
water, telephone services, mass transportation,
parks services and communication facilities
(American Public Works Association, 2013).
They are critical for a nation because they are
the foundations for its infrastructure and allow
areas to grow and prosper (Lee 1996). The
Public Works service is described by indicators
of: Accessibility to public transport and safe
water; public Investment in transportation,
parks and highways; as well as the Providers,
Quality and Quantity of the provisions (Fig. 4-
66).
Figure 4-66. Indicators and metrics of Public Works services.
143
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Chapter 41 Services Provisioning
Table 4-27. 2000-2010 Public Works scores for the GSS
regions with estimated error. Regions are in order from
highest to lowest score.
Public Works
New England
Mountain
Pacific
East North Central
Middle Atlantic
West South Central
West North Central
South Atlantic
East South Central
52
51
50
49
49
48
48
48
45
.3
.0
.4
.7
.6
.9
.6
.4
.7
±
±
+
+
+
+
±
±
±
0
0
1
0
1
0
0
0
0
.9
.7
.2
.3
.0
.3
.5
.3
.3
The New England and Moutain regions scored
above the 2000-2010 national average score for
Public Works (49.2±0.4) (Table 4-27). The East
South Central regions scored significantly lower
than all other GSS regions and nation.
All states in the South Atlantic region with the exception of Maryland and Delaware fell below the
national score for Public Works (Fig. 4-67). Alabama scored significantly higher than the other states in
the East South Central region, but not above the national average. Both Alaska and Arizona scored
significantly higher than all other states; Tennessee significantly lower. The range of observed scores at
the state-level for Labor for the 2000-2010 period was 43.5±0.4-62.0±2.7. Utilities employment and the
quantity of public works related goods contributed to differences between high and low Public Works
scores at the state level.
s I I
u -1-
I
MA CT NH Rl ME VT PA NJ NY MD DE FL VA SC GA WV NC AL MS KV TN TX OK LA AR
—— National average
Li^-H^T_.JtTTTT_.^
AK
HI
WA
Pacific
OR
CA
fa
UT
CO
WY
P.'bu
m
itain
NV
NM
ID
MN
ND
IA
West
SD
North C
MO
entral
NE
KS
IL
OH
EastT
IN
JorthC
Wl
sntral
Ml
Figure 4-67. 2000-2010 State-level Public Works scores compared to the national average score. States are ordered within
regions from highest to lowest score.
144
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Chapter 41 Services Provisioning
Public Works for the nation were moderately low for years 2000-2010 (Fig. 4-68). All of the GSS regions
were also scored moderately low with the exception of the Pacific region that scored low. On a state
level Texas had the one moderately high score. All other states were moderately low or low (VT, NC,
WV, KY, TN, AR, ID, NV, KS) for Public Works.
Public Works (2000-2010)
New England
CT
MA
ME
NH
Rl
VT
Middle Atlantic
NJ
NY
PA
South Atlantic
DE
FL
GA
MD
NC
SC
VA
WV
East South Central
AL
KY
MS
TN
West South Central
AR
LA
OK
TX
Pacific
AK
CA
HI
OR
WA
Mountain
AZ
CO
ID
MT
NM
NV
UT
WY
West North Central
IA
KS
MN
MO
ND
NE
SD
East North Central
IL
IN
Ml
OH
Wl
^
Low Moderately Low Moderately High High
Figure 4-68. Scorecard categorizing the level of Public Works at the national, GSS regional and state levels for the years of 2000-
2010.
145
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Research Highlight: Social and Intergenerational Equity and
Human Well-being
A sustainable world is one in which human
needs are met equitably and without sacrificing
the ability of future generations to meet their
needs. Human well-being is described by four
primary elements - basic human needs,
economic needs, environmental needs and
subjective well-being. These elements can
interact in a myriad of ways to influence overall
well-being.
Basic
Human
Needs
Weil-
Being
Happiness
Figure 1. Elements interacting with well-being (Summers
and Smith 2014).
Two major interactional concepts can push
changes in human well-being toward a
sustainable state in space and time - social
equity and inter-generational equity. The
concept of social equity distributes well-being
over space, optimizing the well-being of all
members of society promoting spatial
sustainability of a well-being decision. The
concept of inter-generational equity distributes
well-being through time, optimizing the well-
being of present and future generations of a
population or nation, promoting temporal
sustainability of a well-being decision. The roles
of social and inter-generational equity in terms
of their influence on human well-being are
examined with a focus on more sustainable
decision-making.
To understand how social and inter-
generational equity impact the components of
well-being, these terms must first be clearly
defined. Social equity implies fair access to
livelihood, education, and resources; full
participation in the political and cultural life of
the community; and self-determination in
meeting fundamental needs. Intergenerational
equity is a value concept which focuses on the
rights of future generations. Each generation
has the right to inherit the same diversity in
natural and cultural resources enjoyed by
previous generations and to equitable access to
the use and benefits of these resources. Social
and intergenerational equity, in essence,
become the two elements that must be
incorporated in evaluations of changes in well-
being to make the desired changes sustainable.
Social equity must be defined within the
context of well-being. Social fairness or equity
can be related to any of the three primary
pillars of well-being - environmental, economic
or social.
Social equity is intertwined with the
environmental pillar of well-being. All of these
environmental issues - siting of waste facilities,
natural disasters and accessibility to green
space - impact human well-being and this
impact appears to be disproportionately borne
by low-income socio-economic communities
and communities of color. To optimize
community well-being, rather than individual
well-being, a re-distribution of these
environmental vulnerabilities would need to be
apportioned throughout the community and
not focused on specific vulnerable populations.
146
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Economic equity is understood as the pursuit of
equal opportunities and the avoidance of
severe deprivation. Equity is not the same as
equality. Economic equity is the quest for a
situation in which personal effort, preferences,
and initiative account for the differences among
people's economic achievements. Multi-
discipline evidence suggests that the pursuit of
sustainable, long-term well-being is inseparable
from a broadening of economic opportunities
and political voice to most or all of society.
The concept of social equity we adopt here
draws on the contributions of these four
thinkers by focusing on opportunities. We
acknowledge the central role of individual
responsibility and effort and focus on
eliminating disadvantage from circumstance
that lie outside the control of the individual but
that powerfully shape both the outcomes and
the actions in pursuit of those outcomes. It is
through this approach that sustainable
community well-being can be recognized and,
without it, no community well-being can be
sustained (although individual well-being of
some more privileged group might be
sustainable for a time).
The social welfare of all of a community's
inhabitants' well-being encompasses the
concept of social fairness. Issues of poverty,
education, and governmental investment in the
well-being of a community's inhabitants and all
the potential spinoffs resulting from these
issues constitute the sphere of social fairness.
Often these issues become associated with
demographics; particularly race, gender and age
(Miller et al. 2010), as well as the economically
disadvantaged (Bonilla Garcia and Gruat 2003,
Hay 2006). Social fairness or equity can be
related to any of the three primary pillars of
well-being - environmental, economic or social.
If social equity represents the spatial dimension
required to make community well-being
sustainable in the present, intergenerational
equity represents the dimension required to
make community well-being truly sustainable
through time. Intergenerational equity is a
value concept which focuses on the rights of
future generations. It is a notion that is implicit
in ecological sustainability.
A common way of conceptualizing our
obligations to the next generation is the
following: We borrow the earth from our
children (part of an ancient Native American
proverb). What follows from this folk
conception is that each generation should
restitute to the next the earth in a state at least
equivalent to what it was when it received it.
The same could be conceptualized for
economies and social drivers (the next
generation should inherit conditions at least as
good as those realized by the former
generation).
In a myriad of ways, the approaches to
sustainable development and the maintenance
or enhancement of community well-being
advocated by environmental economists and
taken up by governments in many countries
either reinforce or exacerbate inequities in
those countries. Yet equity, both social and
intergenerational, is supposed to be a central
ethical principle of sustainable development in
these countries. This suggests that either:
1. Equity is merely part of the rhetoric of
sustainable development and is not
really a central concern of those
governments, or
2. Those governments have not
understood the equity consequences of
policies being promoted by those who
have other agendas and priorities.
If equity is to be taken seriously then new ways
of decision-making that incorporate social and
environmental justice and intergenerational
issues must be found that enable the
multifaceted values associated with the
environment, economics and social change to
be fully considered and heeded. Clearly, merely
extending market values to incorporate the
environment and social change into existing
economic systems will not achieve the goal of
making changes in community well-being
sustainable.
147
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Chapter 51 Relating Services Provisioning
Chapter 5 Relating Services Provisioning to Well-being Endpoints
(USEPA Photo by Eric Vance)
148
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Chapter 51 Relating Services Provisioning
Interpreting Functional Relationships
The interpretation of the functional relationships between each of the services and the individual
domains, based on the indicators assessed for the 2000-2010 period, are depicted in Fig. 5-1. These
relationships represent the national picture and are conditional upon the mean of the state-level service
scores. The symbols represent the relationship between the level of service provisioning assessed and
the scored domains, considering all important service interactions. Green circles indicate a significantly
positive relationship between the level of service provisioning and the domain score. Red diamonds
indicate a significant negative influence between the level of provisioning and the domain score. Yellow
triangles indicate one of two situations: 1) the service had no significant positive or negative relationship
to the domain; however, the service did have significant interactions with other services included in the
domain functional equations; or 2) the service score provides important predictive information
regarding the domain score, but showed no significant interactions (plain dot) and no significant main
effects (positive/negative). Where no symbol is shown, the service was determined to have no
measurable contribution in explaining the variance in the domain score. The functional equations
derived for each domain are included in Appendix C.
Summary of Service-Domain Relationships
Each service assessed was included in at least one functional equation. Activism was included in every
functional equation. All social services, excluding Educational Services, were included in five or more of
the functional equations, as were all of the economic services and all but one ecosystem service (Food,
Fiber and Fuel). Based on the services included in the functional relationship equations (those with
symbols in the above chart), 22% of the Economic services showed positive influences on the well-being
domains; 39% of the relationships included in the equations for Ecosystem Services were positive; and
34% of the Social Services were positively related to the domains.
All services except Production, Justice and Labor showed at least one positive relationship with at least
one domain of well-being. Connection to Nature, was most frequently positively influenced by Economic
Services; Leisure Time, Living Standards and Social Cohesion, by Ecosystem Services; and Education by
Social Services. Capital Investment and Finance showed the most positive relationships to the domains
among the Economic Services. Of the Ecosystem Services, Air Quality and Greenspace had the most
positive relationships with the domains; however neither of these services showed positive influences
on the domains of Connection to Nature, Cultural Fulfillment or Living Standards. Activism showed
positive relationships to six of the eight domains; Family Services five of the eight domains.
The services included in the domain functional equations are the same regardless of spatial scale
(county, state, region, nation); however to improve predicted domain scores at the county scale for the
2000-2010 time period, different service coefficients were determined according to a categorization of
the observed domain scores (high, moderate, low). As a result, three sets of coefficients were derived
for each domain functional equation (Appendix C).
149
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Chapter 5[Relating Services Provisioning
Connection to Nature
Cultural Fulfillment
Leisure Time
Living Standards
Safety and Security
Social Cohesion
o
o
Consumption
o
o
Employment
o
Production
inflection to Nature
ultural Fulfillment
Leisure Time
Living Standards
lafety and Security
Social Cohesion
Air Quality
Food, Fiber and
Fuel
Greens pace
Water Quality
O
o
Water Quantity
_
A
Re-distribution
| Connection to Nature
| Cultural Fulfillment
| Education
•fl Health
Rjjfl Leisure Time
| Living Standards
| Safety and Security
| Social Cohesion
Activism
•
•
•
•
•
•
A
Communication
<>
•
*
Community and
Faith- based
Intiatives
•
•
•
*
o
•
Educational
Services
•
Emergency
Preparedness
*
•
Family Services
*
•
•
•
•
•
•
Healthcare
•
•
•
Justice
•
o
•
•
*
*
Labor
•
•
Public Works
•
•
*
*
Figure 5-1. Relationship between services provisioning and well-being domains modeled on the 2000-2010 scores.
Utilizing Functional Relationships
In an effort to move toward sustainable communities, identifying the things that matter most to people
should be a priority. The domains of well-being used to construct the HWBI are the foundation for
prioritizing community values. The domains included in the HWBI were selected as broad measurable
categories applicable across various spatial scales and population groups. Once a community prioritizes
values in context of the domains, then the services which have significant relationships to those high
priority domains can become the focus in a decision context. Decisions that could potentially alter
service provisioning can be evaluated in terms of how those decisions could influence well-being
endpoints deemed most important to a community's well-being.
150
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Chapter 51 Relating Services Provisioning
Decisions result in actions that effect service stocks and flows. Decisions are often made based on social,
economic or environmental impacts, but all too frequently, these three sectors are not considered in a
holistic manner. To better understand the interconnectedness of the three pillars of sustainability
(economy, society and environment) and the influence of decisions that change services provisioning on
well-being, the relationship functions derived from the 2000-2010 data sets can be used to examine
these relationships from a hind cast perspective.
Tampa Stakeholder Domain Priorities
Social Cohesion
Cultural Fulfillment
Safety and Security
Leisure Time
Education
Living Standards
Health
Connection to Nature
=
Co ntributionto Well-being
Figure 5-2. Prioritized domains for the Tampa Bay Area derived from Stakeholder input.
As a hind cast demonstration, the priority domains identified for the Tampa Bay Area project (see
Research Highlight in Chapter 3) are used as starting point. Stakeholders provided feedback about what
was most important to their community's well-being by ranking the domains. Figure 5-2 shows the
relative importance or contribution of each of the domains resulting from this prioritization exercise.
The dashed line shows the contribution of the domains if no relative importance values were derived
(equal weights). Three domains, Social Cohesion, Cultural Fulfillment and Safety and Security were
determined to be the highest priorities, while the domains of Living Standards, Health and Connection to
Nature were given lower priorities. This is not to be interpreted as these lower priority domains are not
important to the community, but rather that the community may view these domains as areas in which
the community is doing well and do not warrant improvements.
151
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Chapter 51 Relating Services Provisioning
The domain scores for the Tampa Area for the 2000-2010 period are presented in Fig. 5-3. The scored
domains are presented in order of the Tampa stakeholder priorities (highest to lowest). Examining the
domain priorities in conjunction with the domain scores for the Tampa Bay Area for the 2000-2010 time
frame shows that the domain of Social Cohesion, the highest priority domain identified for Tampa, score
significantly lower than all of the other domains, with the exception of Cultural Fulfillment. The low
score for Social Cohesion for the Tampa Area reinforces the prioritization of this domain as an area that
the stakeholders see as important to their community and an area for improvement. So based on the
relationship function equation derived for Social Cohesion, the influential services that decision makers
may want to consider when evaluating alternate scenarios in future decisions can be identified.
Tampa Bay Area Domain Scores
(2000-2010)
Score
Social Cohesion Cultural Fulfillment Safety and Security Leisure Time
Education Living Standards Health
Connection to
Nature
Domain
Figure 5-3. 2000-2010 Calculated domain scores for the Tampa Bay Area (includes Hillsborough, Manatee, Pasco, Pinellasand
Polk counties) in order from highest to lowest priority.
The relationship function equation for Social Cohesion can be used to examine service relationships to
this domain, conditional upon the service scores calculated for the Tampa Bay Area (2000-2010).
Meaning, what does the interpretation of the relationships between the services in the Social Cohesion
functional equation look like when the Tampa Area service scores are used instead of the state-level
mean for the nation? (Note: The relationship function equations for the domains are the same
regardless of spatial scale. The service interactions included in the equations affect service-domain
relationships causing them to change based on the level of services provisioning of the services included
in the functional equation.)
Based on the relationships observed between services provisioning and domain scored during the 2000-
2010 time period, seven services were shown to have a positive relationship to the domain of Social
Cohesion in the Tampa Bay Area (Fig. 5-4). During the eleven year span, increases in Capital Investment,
152
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Chapter 51 Relating Services Provisioning
Employment, Water Quality, Water Quantity, Communication, Family Services and Healthcare were
associated with increases in Social Cohesion domain scores in the Tampa Bay Area. No ecosystem
services were shown to negatively influence Social Cohesion scores; however, negative relationships
were observed between Consumption, Finance, Production, Activism and Public Works.
Connection to Nature
Cultural Fulfillment
Education
Health
Leisure Time
Living Standards
Safety and Security
Social Cohesion
Captial
Investment
Consumption
_
Employment
Finance
_
o
Innovation
_
O
_
_
Production
_
Re-distribution
_
O
_
_
Connection to Nature
Cultural Fulfillment
Education
Health
Leisure Time
Living Standards
Safety and Security
Social Cohesion
Air Quality
O
o
•o
o
Food, Fiber and
Fuel
Greenspace
_
Water Quality
Water Quantity
_
1 Connection to Nature
1 Cultural Fulfillment
Education
•fl Health
Ejfl Leisure Time
| Living Standards
Safety and Security
| Social Cohesion
Activism
•
•
•
•
•
Communication
•
•
•
•
•
•
Community and
Faith-based
Intiatives
Educational
Services
•
•
•
Emergency
Preparedness
•
•
•
•
•
•
Family Services
•
•
•
•
Healthcare
•
•
±
•
Z_i
•
•
Justice
•
•
0
Labor
•
Public Works
•
•
•
*
Figure 5-4. Influences of the level of service provisioning on well-being domains modeled from relationship function equation for the
Tampa Bay Area.
According to the 2000-2010 data for the Tampa Bay Area, although Greenspace had no significant
positive or negative relationship to the domain of Social Cohesion, there were two other possible
interactions. Either the service did have significant interactions with other services included in the
153
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Chapter 51 Relating Services Provisioning
domain functional equations, or the score for Greenspace provides important predictive information
regarding the domain score, even though there were no significant interactions and no significant main
effects (positive/negative). If efforts were made during the 2000-2010 timeframe to increase
greenspace in the Tampa Area to enhance social cohesion, what other services could have also been
changed to see a positive outcome for the decision? A better understanding of the influence of the
decision to increase greenspace in the area requires consideration of the services that interact with
Greenspace in the functional equation for Social Cohesion:
SOCIAL COHESION = GREENSPACE (0.388 - 0.521* CONSUMPTION + 0.009 * PRODUCTION - 0.023 * RE-
DISTRIBUTION + 0.0989 * COMMUNICATION + 0.179 * COMMUNITY AND FAITH-BASED INITATIVES -
0.341 * EMERGENCY PREPAREDNESS -1.1053 * FAMILY SERVICES + 0.032 * JUSTICE + 0.250 * PUBLIC
WORKS)
The equation above shows the interactions between Greenspace and other services that relate to the
Social Cohesion domain. Five services positively interact with Greenspace in the Social Cohesion
relationship function equation (Production, Communication, Community and Faith-Based Initiatives,
Justice and Public Works). By increasing services that positively interact with Greenspace, the neutral
relationship between Greenspace and Social Cohesion could potentially change to a positive
relationship; however the relationships between services that positively interact with Greenspace also
need to be considered in context of their interacting services with Social Cohesion. In this case,
Communication has the strongest positive interaction with Greenspace (0.989) and also shows a positive
relationship to the domain of Social Cohesion (Fig. 5-4). The relationship between the amount of
Greenspace and Social Cohesion can only be changed by changing services that interact with
Greenspace in the functional equation.
Three scenarios were created to exemplify domain to services interactions. In scenario A, the score for
Greenspace was increased from 48.8 to 50.0. In scenario B, only the score for Communication was
increased (50.7 to 52.0). In the scenario C, both services were increased to levels in scenarios A and B.
The changes in domain scores resulting from the changes in Greenspace alone (scenario A) did improve
Social Cohesion scores whereas increasing the Communication score (scenario B) decreased the Social
Cohesion score for the Tampa Area (Fig. 5-5). The changes in Scenario C; however, greatly increased the
Social Cohesion score. Changes in all three scenarios resulted in slightly lower Cultural Fulfillment score,
a domain ranking second in importance to the Tampa stakeholders. Scenario A also negatively affected
the domains of Health and Safety and Security as well as the HWBI. The Education domain was
negatively influenced by changes in scores in scenarios B and C. Scenario C resulted in a lower Safety
and Security score. Both scenarios B and C resulted in increased HWBI scores; however, the increase in
HWBI for scenario C was much greater (Fig. 5-5).
154
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Chapter 5 1 Relating Services Provisioning
CONNECTION TO NATURE
CULTURAL FULFILLMENT
EDUCATION
HEALTH
LEISURETIME
LIVING STANDARDS
SAFETY AND SECURITY
SOCIAL COHESION
HWBI
Original
Predicted
Score
55.7
48.4
47.1
52.9
59.9
53.1
47.5
40.2
50.3
Scenario
56.0
48.2
47.1
51.9
59.9
53.8
46.2
41.8
50.2
Scenario
55.7
48.0
41.4
61.2
66.1
56.4
47.5
34.5
50.8
Scenario
56.0
47.9
41.4
60.2
66.1
67.6
46.2
61.8
55.0
CAPITAL INVESTMENT
CONSUMPTION
EMPLOYMENT
FINANCE
INNOVATION
PRODUCTION
RE-DISTRIBUTION
AIR QUALITY
FOOD, FIBER AND FUEL PROVISIONING
GREENSPACE
WATER QUALITY
WATER QUANTITY
ACTIVISM
COMMUNICATION
COMMUNITY AND FAITH -BASED INITIATIVES
EDUCATION
EMERGENCY PREPAREDNESS
FAMILYSERVICES
HEALTHCARE
JUSTICE
LABOR
PUBLIC WORKS
Social Cohesion
Original
0
•
<>
•
<>
•
•
ScenarioA
0
•
0
•
•
•
Scenario B
0
•
•
•
A
ScenarioC
•
•
•
f
A
CONNECTION CULTURAL EDUCATION
TO NATURE FULFILLMENT
HEALTH
LEISURETIME LIVING
STANDARDS
HWBI
Original Predicted Score • Scenario A • Scenarios • Scenario C
Figure 5-5. Changes in domain and HWBI scores for the Tampa Bay Area as a result of service level changes for different
scenarios; Comparison of service-domain influences based on changes in the Greenspace and Communication services.
155
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Chapter 51 Relating Services Provisioning
Recall, Social Cohesion, Cultural Fulfillment and Safety and Security were the top three priorities for the
Tampa Area stakeholders. Under scenario A, two of the top priority domains, Cultural Fulfillment and
Safety and Security are negatively influenced (as well as the medium-priority domain of education) and
Social Cohesion slightly improves. When Communication scores were increased in scenario B, the
relationship between Greenspace and Social Cohesion became positive. Although in scenario C both
Cultural Fulfillment and Safety and Security scores slightly decreased, the increase in the Social Cohesion
(top priority) and the other domain score increases were high enough to show the greatest increase in
HWBI.
Given the significant increase in the Social Cohesion score in scenario C and the high relative importance
value (priority) of this domain, HWBI for the Tampa Area increased from the original predicted value of
50.3 to 55.0, despite the slight decrease in the scores for the other two priority domains (Cultural
Fulfillment and Safety and Security). Increases in the other domain scores in scenario C, although not
high priorities, coupled with the increased Social Cohesion domain score, resulted in a predicted HWBI
value higher than the scenario A and B scores.
This example illustrates the complexities associated with evaluating decisions regarding well-being in
context of economic, environmental and social factors. The model itself is not intended to make
decisions, but rather to be used to explore the relationships among services and the domains of well-
being in consideration of multiple factors related to decisions that may otherwise be overlooked. We
can begin to understand better the influence decisions may have had on different aspects of well-being
by examining changes in multiple services. These holistic evaluations are necessary for identifying the
benefits and unintended consequences.
156
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In 2011, the Sustainable and
Healthy Communities Research
program (SHC) in EPA's Office of
Research and Development (ORD)
coined the term TRIO for Total
Resource Impacts and Outcomes
(USEPA 2012). The concept of TRIO
encompasses any number of holistic
community decision-making
approaches that address all three
pillars of sustainability—economic,
societal and environmental. While
TRIO is similar to triple-bottom line
accounting, SHC developers
believed the term triple-bottom line
accounting conveyed too much of
an economic connotation and
desired a term that would clearly
demonstrate full inclusion of all
three pillars of sustainability. As
well-being is often an endpoint of
concern regarding sustainability,
SHC determined the need to adopt
or develop an approach or index of
human well-being that fully
embraced the TRIO aspects of the
developing research program. The
Human Well-being Index (HWBI) is the
culmination of this research effort.
County Sc«te W«ll B.lna
Louisiana
(HWBI range 43-56)
New Hampshire
(HWBI range 53 - 57)
Figure 1. Hierarchical view showing the provisioning of state-level services and county
HWBI gradients for states with the highest and the lowest HWBI.
The HWBI approach generates a measure that characterizes the general state of well-being within the context
of the economic, environmental, and social drivers. To conceptualize well-being as a TRIO measure, service
indicators for the states with the highest and lowest HWBI scores were visualized along with the county-level
well-being gradient for each of the two states (Fig. 1).
157
-------
Differences across annual HWBI
values (2000-2010) for the states
reported with the highest and
lowest well-being were significant
(t = -14.96, p < 0.0001). Similarly,
the state-scale services
provisioning values for these
states were also significantly
different (t = -2.43, p < 0.0015).
Each combination of service by
year scores was compared to the
expected median value using a
median two-sample test. The
overall difference in the number
of service provisioning scores that
fell either above or below the
median value was significant
between the states (Fig. 2).
300
250
>, 200
CJ
c
01
3
s" 150
Ll_
100
50-
0-
Frequencies Above and Below the Overall Median for value
Lft NH
State
Not Above the Median Above the Median
Figure 2. Median two-sample test showed a significant difference (x2 (1, N = 253) = 20.6021,
p < 0.0001) for services provisioning between states with the lowest and highest HWBI.
Ongoing research seeks to expand upon these
observations toward developing service-to-domain
relationship functions from which alternate HWBI
outcomes may be predicted based on changes in
the provisioning of reported services and services
interactions. The HWBI offers a necessary measure
of the influence of policies and services
(environmental, economic and social) on aspects of
social welfare and overall human well-being.
These integrated concepts of the interactions of
social, economic and environmental drivers allow a
better understanding of the human condition and
its collective relationship to service flows and, thus,
will permit decision makers to examine the impact
of specific decision alternatives on the well-being of
their constituencies. Coupling this type of decision
scenario testing with specific targets of social equity
and intergenerational equity should also permit
selected decisions to create more sustainable
conditions for communities
The HWBI approach includes critical aspects of all
three pillars of well-being in a balanced manner
such that all three pillars contribute to the well-
being of the constituency being assessed.
Furthermore, the index can be fully adjusted to the
target community (nation, state, county,
community) based on information regarding the
value structure of the community using multipliers
to reflect the relative importance of elements of the
value structure. The HWBI sets itself apart from
other existing measures, in that: (1) it openly
includes metrics associated with all three pillars of
sustainability; (2) it provides clear measures of the
uncertainty associated with the index; and (3) the
approach is easily transferable to any spatial scale
for which the appropriate information is available.
158
-------
Development of the HWBI provides a significant u-s- Environmental Protection Agency (USEPA).
. . , , (2012). Sustainable and Healthy Communities:
step forward in a community s (or larger spatial _ ^ . _ ,„.... r,/ -.,-,.,.-. ™.,^ ,-r,.
K y \ & K Strategic Research Action Plan 2012-2016; EPA
entity's) ability to assess the short- and long-term 601/R-12/005; USEPA/Office of Research and
impacts of potential decision alternatives on the Development: Washington, DC, USA.
well-being of their constituencies.
Summers, J. K., Smith, L. M., Harwell, L C., Case, J.
C, Wade, K. M, Straub, K. R. and H. M. Smith.
(2014). An Index of Human Well-Being for the U.S.:
A TRIO Approach. Sustainability 6:3916-3955
159
-------
Chapter 61 Concluding Remarks and Future Research Efforts
Chapter 6 Concluding Remarks and Future Research Efforts
(USEPA Photos by Eric Vance)
160
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Chapter 61 Concluding Remarks and Future Research Efforts
Concluding Remarks
The Human Well-Being Index (HWBI) offers a real measure of the influence of policies and services
(environmental, economic and social) on aspects of social welfare and overall human well-being
(Summers et al. 2012). These integrated concepts of the interactions of social, economic and
environmental drivers allow an improved understanding of the human condition and its collective
relationship to service flows. Thus, permitting decision makers to examine the impact of specific
decision alternatives on the well-being of their constituencies. Coupling this type of decision scenario
testing with specific targets of social equity and intergenerational equity should also permit selected
decisions to create more sustainable conditions for communities (Summers and Smith 2014).
The primary reason for the development of the HWBI is to include explicit connections between human
well-being and environmental drivers and services (Smith et al. 2013a). Earlier versions of well-being
indices were determined to address two of the three pillars of well-being well but to either ignore the
third pillar or inadequately address it. The HWBI described here includes critical aspects of all three
pillars of well-being in a balanced manner such that all three pillars contribute to the well-being of the
constituency being assessed. Furthermore, the index is fully adjusted to the target community (nation,
state, county, community) based on information regarding the value structure of the community using
multipliers to reflect the relative importance of elements of the value structure.
Increasingly, communities across the U.S. are examining the management of growth through sustainable
development. The HWBI approach allows the U.S., states, counties and communities to assess the
impact of decisions on the sustained well-being of their constituencies (e.g., effects of economic
decisions, both intended and unintended, on social and environmental well-being). Additionally, the
HWBI allows these governmental entities to assess not only the direct impacts of decisions (e.g., effects
of economic decisions on jobs) but also to assess the indirect impacts (unintended consequences) of
these decisions (e.g., economic decisions on social and environmental issues). Many earlier indices
focused on the point where human well-being and environmental conditions intersected rather than
how they related. The HWBI represents a critical advancement in this area by emphasizing the symbiotic
relationships between nature, humans and economies. Similarly, rather than vilifying all human activity
as being detrimental to the natural environment, the HWBI embraces that natural ecosystems provide
goods and services that are not only directly useful to humans but are essential for their well-being.
Since people are the beneficiaries of sustainable solutions, it is essential that metrics reflect the
dependence of humans on ecosystems—services provided that contribute to economic and social well-
being in order to progress sustainably. Our development of the HWBI provides a significant step forward
in a community's (or larger spatial entity's) ability to assess the short- and long-term impacts of potential
decision alternatives on the well-being of their constituencies.
161
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Chapter 61 Concluding Remarks and Future Research Efforts
Future Research Efforts
Many obstacles exist in developing comparable measures of human well-being at multiple spatial
scales—lack of consistently available data, transparency of performance indicators and domains
and cultural differences. In the construction of the HWBI, we have developed an index that is based on
indicators and domains that can be shown to clearly impact well-being. While the data necessary for the
HWBI implementation are not always available at smaller spatial scales, they can be collected and
applied in a meaningful way at any scale in a meaningful way. Similarly, the value-based weighting
factors (RIVs) are collected at the appropriate scale to represent the community and the demographic
populations (e.g., socio-economic groups, cultural entities) to which the index is applied. Additionally, in
the construction of the HWBI, strides were made to provide information regarding the transparent
selection and performance of indicators and the uncertainty levels associated with their use. With the
exception of the connection to nature domain, many of the domains included in the HWBI and their
associated indicators and metrics are those commonly used in similar indices developed prior to the
HWBI. The HWBI described here sets itself apart from other existing measures, in that: (1) it openly
includes metrics associated with all three pillars of sustainability; (2) it provides clear measures of the
uncertainty associated with the index; and (3) the approach is easily transferable to any spatial scale for
which the appropriate information is available.
Future research efforts should include:
(1) An examination of the applicability of the HWBI to various smaller spatial scales (e.g., urban
areas, communities, neighborhoods, zip codes areas) and selected demographic features (e.g.,
vulnerable populations like children, elderly and economically disadvantaged).
(2) Full development and demonstration of a predictive modeling framework that would allow the
assessment of future changes in the HWBI resulting from decision alternatives. Decisions
primarily result in direct changes in environmental, ecosystem, economic and social services;
not well-being. It is through changes in services and their resultant impacts that changes in well-
being for individuals and communities are realized. The research presented in this report has
initially examined the predictive relationships between individual services and well-being as well
as their interactions. Further development and testing of these relationships is necessary to
demonstrate the direct applicability of the HWBI in the decision making process.
162
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168
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APPENDIX A
SUMMARY OF METRIC DATA FOR HWBI DOMAINS
169
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Connection to Nature
Indicator
Biophilia
Metric
Percentage of people who experience a connection to all
of life
Percentage of people who are spiritually touched by the
beauty of creation
Source
GSS
GSS
Scale
Region
Region
Years Available
2004
1998, 2004
Cultural Fulfillment
Activity Participation
Percentage of people who attended a musical or non-
musical performance, or visited an art museum or art
and/or craft fair
All Denominations-Rates of adherence per 1000
population
US Census-ACS
ARDA
State
County
2002
2000, 2010
Education
Basic Educational Knowledge
and Skills of Youth
Participation and Attainment
Social, Emotional and
Developmental Aspects
Percentage of children in grades 4 and 8 with mathematics
standardized test scores at or above basic skills
Percentage of children in grades 4 and 8 with reading
standardized test scores at or above basic skills
Percentage of children in grades 4 and 8 with science
standardized test scores at or above basic skills
Percentage of people aged 16 and older who lack basic
prose literacy skills
Percentage of people aged 18 and older who obtained a
high school diploma or equivalent
Percentage of people aged 18-24 enrolled in post-
secondary education
Percentage of people aged 18 and older who obtained a
bachelor's degree or higher
Percentage of children in grades 9-12 who did not go to
school because they felt unsafe at school or on their way
to or from school
Percentage of people who read to household children
between the ages of 3 and 5 years old
Percentage of children aged 0-17 years old in excellent or
very good health
Percentage of children aged 6-17 years old that exhibit
positive social behaviors
NCES
NCES
NCES
NCES
US Census-ACS
US Census-ACS
US Census-ACS
CDC
BLS
HHS-NSCH
HHS-NSCH
State
State
State
State
County
State
County
State
State
State
State
2000, 2003-2009;
biennial
2002, 2003, 2005-2009;
biennial
2009
1992, 2003
2005-2009; annual
2000-2009; annual
2005-2009; annual
1999-2009; biennial
2002-2008; annual
2003-2007; biennial
2003, 2007
Healthcare
Life Expectancy and
Mortality
Percentage of adults who have a regular or personal
doctor or health care provider
Percentage of patients who rated the hospital overall as a
9 or 10 (on a 1-10 scale)
Asthma mortality as a percentage of total deaths (age-
adjusted)
Cancer mortality as a percentage of total deaths (age-
adjusted)
Diabetes mortality as a percentage of total deaths (age-
adjusted)
Heart disease mortality as a percentage of total deaths
(age-adjusted)
Suicide mortality as a percentage of total deaths (age-
adjusted)
Infant deaths per 1,000 live births
Life Expectancy at birth
CDC-BRFSS
Gallup
HCAHPS
CDC
CDC
CDC
CDC
CDC
CDC
CDC
County
County
County
County
County
County
County
County
County
2000-2010; annual
2008, 2009
2000-2007; annual
2000-2007; annual
2000-2007; annual
2000-2007; annual
2000-2007; annual
2000-2007; annual
2000-2007; annual
170
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Lifestyle and Behavior
Personal Well-being
Physical and Mental Health
Conditions
Number of adults drinking on average more than 1 drink
per day
Healthy Behaviors Index
Percentage of live births to mothers under 20 years old
Percentage of children in grades 9-12 who smoked
cigarettes on 20 or more days in the past month
Percentage of people who are very happy or pretty happy
(experienced happiness yesterday)
Percentage of adults who are satisfied with life
Percentage of adults who reported that they are in good
general health
Percentage of adults who have been diagnosed with
asthma in lifetime
Percentage of adults who have been diagnosed with
cancer in lifetime
Percentage of adults who have one or more household
child diagnosed with asthma in lifetime
Percentage of adults who have been diagnosed with
angina or coronary heart disease in lifetime
Percentage of adults who have been diagnosed with
depression in lifetime
Percentage of adults who have been diagnosed with
diabetes in lifetime
Percentage of adults who have been diagnosed with heart
attack or myocardial infarction in lifetime
Percentage of people aged 18 years and older classified as
obese (age-adjusted)
Percentage of adults who have been diagnosed with stroke
in lifetime
CDC-BRFSS
CDC-BRFSS,
Gallup
CDC
CDC-YRBSS
GSS, Gallup
CDC-BRFSS,
Gallup
CDC-BRFSS,
Gallup
CDC-BRFSS,
Gallup
CDC-BRFSS,
Gallup
CDC-BRFSS
CDC-BRFSS
CDC-BRFSS,
Gallup
CDC-BRFSS,
Gallup
CDC-BRFSS,
Gallup
CDC-NDSS
CDC-BRFSS
County
County
County
State
County
County
County
County
County
County
County
County
County
County
County
County
2000-2010; annual
2001-2010; annual
2000-2008; annual
1999-2009; biennial
2009
2005-2010; annual
2000-2010; annual
2000-2010; annual
2009-2010; annual
2001-2010; annual
2000-2010; annual
2006-2010; annual
2000-2010; annual
2000-2010; annual
2004-2008; annual
2000-2010; annual
Leisure Time
Activity Participation
Time Spent
Working Age Adults
Average number of nights away from home on vacation or
visiting friends and/or relatives
Percentage of adults who participated in physical activities
or exercises in the past 30 days
Average time spent on socializing, relaxing, leisure, and
sports
Time spent by people caring for adults
Percentage of people who work fifty or more hours per
week
Percentage of work activity that occurs during daytime
hours (9 am - 5 pm)
BLS
CDC-BRFSS
BLS
BLS
BLS and US
Census
BLS
Living Standards
Basic Necessities
Income
Wealth
Percentage of households that had high or marginal food
security
Median selected monthly owner costs as a percentage of
household income
Percentage of the population (all ages) in poverty
Median household income
Percentage of people who are currently in poverty and
stated that their financial situation has remained the same
over the past few years
Median value of owner-occupied housing units
Percentage of owner-occupied housing units without a
second mortgage or home equity loan
US Census-CPS
US Census-ACS
US Census-ACS
US Census-ACS
GSS
US Census-ACS
US Census-ACS
State
County
State
State
State
State
State
County
County
County
Region
County
County
2004-2009; annual
2000-2010; annual
2002-2009; annual
2003-2009; annual
2002-2009; annual
2003-2009; annual
2005-2009; annual
2004-2009; annual
2000-2009; annual
2000-2009; annual
2000-2008; biennial
2004-2009; annual
2004-2009; annual
171
-------
Work
Percentage of people who responded that it is not likely
that they will lose their job or be laid off
Percentage of people who are satisfied with their job
GSS
GSS, Gallup
Region
Region
2000-2008; biennial
2002, 2006
Safety and Security
Actual Safety
Perceived Safety
Risk
Total reported number of accidental morbidity and
mortality cases excluding weather events
Injuries and fatalities from hazardous weather per 100,000
people
Property crimes per 100,000 people
Violent crimes per 100,000 people
Percentage of people who feel safe walking alone at night
where they live
Social Vulnerability Index (SoVI) for the United States
CDC-NCHS
NOAA
FBI
FBI
Gallup
USoCarolina-
HVRI
Social Cohesion
Indicator
Attitude toward Others and
the Community
Democratic Engagement
Family Bonding
Social Engagement
Social Support
Metric
Percentage of people who feel close to their town or city
Percentage of people who are satisfied with the city or
area where they live
Number of reported hate crime incidents per 100,000
people
Percentage of people who think that others try to be
helpful
Percentage of people who think that others can be trusted
Percentage of people interested in politics
Percentage of U.S. citizens aged 18 years and older who
are registered to vote
Percentage of people who are satisfied with democracy in
the United States
Percentage of people who think that most government
administrators can be trusted to do what is best for the
country
Percentage of people who feel that they have a say in the
government
Percentage of U.S. citizens aged 18 years and older who
voted
Percentage of children in grades 9-12 who, on an average
school day, watch television forthree or more hours
Percentage of time spent by children aged 15-17 years old
eating at home with parents
Time spent by people reading to household children
Percentage of people who are a member of any type of
organization
Percentage of children who participate in one or more
organized activities outside of school
Percentage of people who volunteered (volunteer rate)
Percentage of people who have six or more close friends
and/or relatives
Proportion of participants responding that the usually or
always get the emotional and social support they need
Source
GSS
Gallup
FBI
GSS
GSS
GSS
US Census-CPS
GSS
GSS
GSS
US Census-CPS
CDC-YRBSS
BLS
BLS
GSS
HHS-NSCH
BLS and Census
GSS
CDC-BRFSS
County
State
County
County
County
County
2000-2010; annual
2000-2009; annual
2004-2009; annual
2004-2009; annual
2009
2000, 2007-2008
Scale
Region
County
State
Region
Region
Region
State
Region
Region
Region
State
State
State
State
Region
State
State
Region
County
Years Available
2004
2009
2004-2009; annual
2000-2008; biennial
2004, 2008
2004, 2006
2000-2008; biennial
2000, 2004
2006
2004, 2006
2000-2008; biennial
1999-2009; biennial
2003-2009; annual
2003-2009; annual
2004
2003, 2007
2002-2009; annual
2002
2004-2010; annual
Source acronyms: GSS-General Soc al Survey, US Census ACS-American Community Survey, ARDA-middle earth Association of Religion Data Archives, NCES-National Center for
Education Statistics, CDC-Center for Disease Control, BLS-Bureau of Labor Statistics, HHS-NSCH- National Survey of Children's Health, CDC-BRFSS-Behavioral Risk Factor Surveillance
System, HCAH PS-Hospital Consumer Assessment of Healthcare Providers & Systems, CDC-YRBSS-Youth Risk Behavior System , CDC-NDSS-National Diabetes Surveillance System,
GSS-General Social Survey, US Census ACS-American Community Survey, , NCES-National Center for Educat on Statistics, CDC-Center for Disease Control, BLS-Bureau of Labor
Statistics, HHS-NSCH- National Survey of Children's Health, CDC-BRFSS-Behavioral Risk Factor Surveillance System, HCAHPS-Hospital Consumer Assessment of Healthcare Providers
& Systems, CDC-YRBSS-Youth Risk Behavior System , CDC-NDSS-National Diabetes Surveillance System, CDC-NCHS-National Center for Health Statistics, NOAA- National Oceanic
and Atmospheric Administration, FBI-Federal Bureau of Investigation, USoCarolina-HVRI-Hazards and Vulnerability Research Institute, US Census-CPS-Current Population Survey.
172
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APPENDIX B:
SUMMARY OF METRIC DATA FOR SERVICES
173
-------
Economic
Service
Capital
Consumption
Employment
Finance
Indicator
Capital Formation
Commercial Durables
New Housing Starts
New Infrastructure
Investments
Available Water
Commuting
Cost of Living
Discretionary Spending
Goods and Services
Sustainable Consumption
Employment Diversity
Underemployment
Unemployment
Savings
Investment
Patents and Products
Exports
Household Services
.
Sustainable Production
Metric
Net domestic investment, percent change from previous year
Change in private inventories, percent change from previous
year
Private net investment residential, percent change from
previous year
Private net investment equipment and software, percent
change from previous year
Private net investment structures, percent change from
previous year
Public net investment equipment and software, percent
change from previous year
Public net investment structures, percent change from
previous year
Average of monthly Palmer Hydrological Drought Index
values
Water Sustainability Index
Consumer price index
Personal consumption expenditures discretionary
Personal consumption expenditures - services
Personal consumption expenditures durable goods
Personal consumption expenditures non-durable goods
Organic food sales
Employed - % of civilian/ non-institutional population
Manufacturing employment
Total full-time and part-time proprietor employment (farm &
nonfarm)
Ogive Index
Underemployed
Unemployed - % of labor force
State and local government outstanding debt per capita
State and local government revenue (from own sources) per
capita
Commercial and industrial loans from FDIC insured
institutions, change from previous year
Farm loans from FDIC insured institutions, change from
previous year
Loans to individuals from FDIC insured institutions, change
from previous year
Real estate loans from FDIC insured institutions, change from
previous year
Personal Savings, percent change from previous year
Percentage of people who thinkthat we (as a country) are
spending the right amount of money on supporting scientific
research
Research and development expenditures as a percentage of
GDP, percent change from previous year
Utility patent grants, Percent change from previous year
Net Exports
Value of household services and volunteering
GDP growth (real)
GDP growth (real), durable goods
Renewable energy production
Source
BEANIPA
BEANIPA
BEANIPA
BEANIPA
BEANIPA
BEANIPA
BEANIPA
NOAA
NRDC
BLS
BEA
BEA
BEA
BEA
OTA
BLS LAU
BLS QCEW
BEA
BLS QCEW
BLS LAU
BLS LAU
US Census, Gov
Finances
US Census, Gov
Finances
FDICSDI
FDICSDI
FDICSDI
FDICSDI
BEANIPA
GSS
NSF NCSES
USPTO PTMT
BEANIPA
BLS ATUS, Independent
sector
BEA
BEA
EIA
174
-------
Re-Distribution
Ecosystem
Service
Air Quality
Food, Fiber and Fuel
Greenspace
Water Quantity
Social
Service
Inequality
Public Support
Indicator
Usable Air
Food and Fiber
Raw Materials
Natural Areas
Recreation and Aesthetics
Usable Water
Available Water
Indicator
GINI index of income inequality, percent change from
previous year
Current transfer receipts of individuals from government,
percent change from previous year
Percentage of people who thinkthat the government
should continue spending the same amount of money on
unemployment benefits
Percentage of people who thinkthat we (as a country)
are spending the right amount of money on assistance
for childcare
Percentage of people who thinkthat we (as a country)
are spending the right amount of money on social
security
Percentage of people who thinkthat we (as a country)
are spending the right amount of money on welfare
Metric
Percentage of days with good or moderate air quality
Crude oil proved reserves
Natural gas proved reserves after lease separation
Recoverable coal reserves at producing mines
Uranium (U3O8) Reserves
Commercial fishery landings in metric tons
Net volume of saw-log portion of sawtimber trees on
forest land
Total factor productivity
Metric tons of copper reserves
Metric tons of gold reserves
Metric tons of lead reserves
Metric tons of silver reserves
Metric tons of zinc reserves
National parks gross acreage
Number of recreational visitors to a National Park
located within a state
Percentage of land designated as a rural park or wildlife
area
Unclassified land use acres such as marshes, swamps,
bare rock, deserts, tundra plus other uses not estimated,
classified, or inventoried
Percentage of people who did at least one
nonconsumptive activity within a mile of their home
Percentage of people who took a trip or outing at least
one mile from their home, but still within their resident
state, forthe primary purpose of observing,
photographing or feeding wildlife
Square miles of water per 1000 population
Percentage of assessed water bodies in a state that
received a "Good" rating (versus impaired orthreatened)
Average of monthly Palmer Hydrological Drought Index
values
Water Sustainability Index
Metric
Percentage of people who boycotted a product over the
past 5 years
Percentage of people who gave money to a group
advocating social change over the past 5 years
Percentage of people who joined a protest rally or march
over the past 5 years
Percentage of people who signed a petition or an e-mail
letter over the past 5 years
US Census, ACS
BEAREIS
GSS
GSS
GSS
GSS
Source
EPA
EIA
EIA
EIA
EIA
NOAA
USDA, FIADB
USDA, ERS
uses
uses
uses
uses
uses
NPS
NPS
USDA, ERS
USDA, ERS
US Census, Wildlife
US Census, Wildlife
US Census, USA Counties
EPA
NOAA
NRDC
Source
GSS
GSS
GSS
GSS
175
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Communication
Community and Faith-
Based Initiatives
Education
Emergency Preparedness
Family Services
Accessibility
Industry Infrastructure
Providers
Public Service
Communication
Quality
Investment
Providers
Accessibility
Confidence
Investment
Providers
Post-Disaster Response
Responders
Accessibility
Effectiveness
Investment
Providers
Percentage of households that accessed the internet at
any location (home or other location)
Percentage of people who have a telephone (either in
their home, elsewhere, or a cellular phone)
Percentage of people who have a working cell phone
Average of R-Factor where the mean distance between
the client and the server is less than 300 miles from the
Household Quality Index
Average through put in Mbps where the mean distance
between the client and the server is less than 300 miles,
from the Household Download Index
Number of licensed cellular broadcast structures
Number of people employed in information (NAICS 51)
per 100,000 population
Extent (in dollars generated) of TV and radio station
participation in community service activities, calculated
per capita
Percentage of people who have a great deal of
confidence in television
Percentage of people who have a great deal of
confidence in the people running the press
Percentage of people who think that the government is
spending the right amount on culture and the arts
Number of Registered Non-Profit Organizations per
100,000 population
Percentage of persons attending a college or university
that receive educational assistance money
Percentage of primary and secondary schools that are
charter, magnet, vocational, or other alternative
educational institutions
Total number of schools per 100,000 people
Percentage of people who have a great deal of
confidence in the people running the institution of
education
Education spending per student
Percentage of people who thinkthat our country is
spending about the right amount on improving the
nation's education system
Number of people employed in educational services
(NAICS 61) per 100,000 population
Pupil/teacher ratio
Percentage of people who thinkthat the government is
spending enough on natural disasters
Number of people employed in emergency preparedness
occupations per 100,000 population
Percentage of the homeless population that is
unsheltered
Children who received preventive services, rate per
1,000 children
Percentage of children who did not experience recurrent
maltreatment within a six month period
Percentage of children who were adopted in less than 12
months (after termination of parental rights)
Total federal outlays for grants to state and local
governments for children and families services and
promoting safe and stable families programs, in millions
of dollars
Number of people employed in family services
occupations per 100,000 population
US Census, CPS
GSS
Gallup
OOKLA
OOKLA
FCC, WTB
BEA
NAB
GSS
GSS
GSS
NCCS
US Census, CPS
NCES
NCES
GSS
NCES
GSS
BEA
NCES
GSS
BLS
HUD
ACF
ACF
ACF, AFCARS
US Budget
BLS
176
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Healthcare
Justice
Labor
Accessibility
Investment
Providers
Quality
Accessibility
Confidence
Environmental
Investment
Providers
Quality
Confidence
Effectiveness
Employee Rights
Average percentage of people who could afford needed
prescription drugs, dental care, and medical care
Health Professional Shortage Area (HPSA) score
Number of local, state, and/or regional public health
agencies, offices, and/or departments per 100,000
population
Percentage of people who can easily get medicine in the
town or city where they live
Percentage of persons enrolled in hospital insurance
and/or supplemental medical insurance (Medicare) per
county population
Direct general expenditures for health by the local
government per capita
Direct general expenditures for hospitals by the local
government per capita
Percentage of people who feel that we are spending the
right amount on improving and protecting the nation's
health
Number of people employed in healthcare occupations
per 100,000 population (includes practitioners, technical
and support, but excludes Family and General
Practitioners)
Percentage of people who have a great deal of
confidence in the people running the institution of
medicine
Average appellate court caseload clearance rate (number
of outgoing cases as a percentage of the number of
incoming cases)
Average trial court caseload clearance rate (number of
outgoing cases as a percentage of the number of
incoming cases)
Percentage of people who have complete or a great deal
of confidence in the courts and legal system
Number of concluded EPA enforcement cases
Number of persons of American Indian or Alaskan native
alone, Asian alone, native Hawaiian or other Pacific
Islander, two or more races, and any other race per
square mile per registered TRI facility
Number of persons of black or African American race per
square mile per registered TRI facility
Number of persons of white race per square mile per
registered TRI facility
Percentage of people who feel that we are spending the
right amount of money on halting the rising crime rate
Percentage of people who feel that we are spending the
right amount of money on improving and protecting the
environment
Number of people employed in legal, police and sheriff's
patrol officers, and probation officers and corrective
treatment occupations per 100,000 population
Percentage of people who feel that the government is
(very and quite) successful at controlling crime
Percentage of people who have a great deal of
confidence in the people running the institution of
organized labor
Number of recordable work-related injuries and illnesses
per 100 full-time employees
Number of EEO charges per 1000 employed
Percentage of people who are a member of a labor union
CDC, NHIS
HHS, HRSA
ASTHO
Gallup
HHS, CMS
US Census, Gov Division
US Census, Gov Division
GSS
BLS
GSS
NCSC, CSP
NCSC, CSP
GSS
EPA
EPA and Census
EPA and Census
EPA and Census
GSS
GSS
BLS
GSS
GSS
BLS
EEOC
BLS CPS
177
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Public Works
Accessibility
Investment
Providers
Quality
Quantity
Percentage of people who say that it is easy to get clean
and safe water in the city or area where they live
Percentage of the labor force who use public
transportation to get to work
Per capita state and local government expenditure on
utilities, transportation, parks and recreation, sewerage
and solid waste treatment
Percentage of people who think that we are spending
the right amount of money on highways and bridges
Percentage of people who think that we are spending
the right amount of money on mass transportation
Percentage of people who think that we are spending
the right amount of money on parks and recreation
Number of people employed in utilities (NAICS 22) per
100,000 population
Materials recovery from municipal solid waste by
recycling and composting
Percentage of bridges that are not structurally deficient
Percentage of electric power customers who were
affected by large disturbances or unusual electric
events/outages
Percentage of National Plan of Integrated Airport
Systems (NPIAS) airports with runways rated in good or
fair condition
Percentage of road miles with a roughness index
category of acceptable or better (represented by an
International Roughness Index value less than or equal to
170 inches/mile)
Count of public-use airport facilities (publicly or privately
owned)
Percent of NERC subregions meeting Summer peak
energy reserve margin targets
Percent of NERC subregions meeting Winter peak energy
reserve margin targets
Percentage of bridges that are not functionally deficient
Percentage of road miles that are not congested
(represented by a volume/service flow ratio less than
0.71)
Gallup
US Census, USA Counties
US Census, Gov Finances
GSS
GSS
GSS
BEA
EPA
DOT, Hwy Stats
EIA
DOT, BTS Annual Rpt
DOT, Hwy Stats
FAA, NFDC
NERC
NERC
DOT, Hwy Stats
DOT, Hwy Stats
Source acronyms: GSS-General Social Survey, BLS-Bureau of Labor Statistics, BEA Bureau of Economic Analysis, BEA NIPA-National Income and
Product Accounts, NOAA-National Oceanographic and Atmospheric Administration, NRDC-National Resource Defense Council, OTA-Organic
Trade Association, BLS LAU-Local Area Unemployment, BLS QCEW-Quarterly Census of Employment and Wages, FDIC SDI-Federal Deposit
Insurance Corporation Statistics on Depository Institutions, NSF NCSES-National Science Foundation National Center for Science and
Engineering Statistics, USPTO PTMT- US Patent and Trademark Office Patent Technology Monitoring Team, BLS ATUS-American Time Use
Survey, ElA-Energy Information Administration, BEA REIS-Regional Economic Information System NOAA-National Oceanographic and
Atmospheric Administration, ElA-Energy Information Administration, USDA-US Department of Agriculture, USDA FIADB-Forest Inventory and
Analysis Database, USDA ERS-Economic Research Service, EPA-Environmental Protection Agency, NRDC-National Resource Defense Council.
178
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APPENDIX C:
MODEL ESTIMATES AND STATISTICS
179
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Overview
This appendix contains final service parameter estimates and associated model statistics for each
domain. Caution should be exercised in interpreting the fit statistics and p-values due to the use of
multiple comparisons in obtaining the final model.
The final functional equations produced by the model are cumbersome to list and use by hand.
Therefore, a parameter estimates table has been provided for each domain that can easily be used in a
spreadsheet application. Each service listed in the parameters column is replaced by the corresponding
service score of interest, and then multiplied by its corresponding estimate to obtain a model term. In
the case of an interaction parameter (e.g., "Consumption * Water Quality"), the product of the two
service scores is multiplied by its corresponding estimate to obtain a model term. The "Years from 2000"
term is calculated by subtracting 2000 from the year of interest and multiplying by the estimate. These
model terms are then summed and added to the Intercept parameter estimate to obtain the predicted
domain score. Service input and domain outputs are on a 0 to 1 scale.
Note that the main effects parameter estimates and standard errors are conditional on all services being
held at 0 and are somewhat misleading (see Chapter 4 for more details).
180
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Connection to Nature
R-Square: 0.94172
Source DF Sum of Squares Mean Square
Model 61 1.33659 0.02191
Error 338 0.08272 0.00024
Corrected Total 399 1.41931
F Value Pr
>F
89.53 <.0001
Parameter
Intercept
Capital Investment
Consumption
Employment
Finance
Re-Distribution
Air Quality
Food, Fiber and Fuel Provisioning
Greenspace
Water Quality
Water Quantity
Activism
Communication
Community and Faith-Based Initiatives
Education
Emergency Preparedness
Family Services
Justice
Public Works
Years from 2000
Consumption * Food, Fiber and Fuel Provisioning
Consumption * Water Quality
Consumption * Water Quantity
Consumption * Activism
Consumption * Community and Faith-Based Initiatives
Consumption * Emergency Preparedness
Consumption * Family Services
Consumption * Public Works
Estimate
2.561
-0.062
0.498
1.429
-0.255
0.033
0.009
-0.458
3.897
0.199
-0.311
-6.353
2.819
-2.337
0.011
-4.296
0.870
-3.714
0.124
-0.001
-1.157
-0.066
0.780
-1.188
-2.782
-1.582
1.359
2.864
Standard Error
0.540
0.028
0.591
0.280
0.245
0.501
0.006
0.668
0.841
0.136
0.166
0.634
0.775
0.707
0.043
0.582
0.639
0.408
0.504
0.004
0.749
0.150
0.212
0.531
0.575
0.448
0.570
0.505
181
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Parameter
Employment * Re-Distribution
Employment * Water Quality
Employment * Emergency Preparedness
Finance * Emergency Preparedness
Finance * Family Services
Re-Distribution * Community and Faith-Based Initiatives
Re-Distribution * Emergency Preparedness
Re-Distribution * Public Works
Food, Fiber and Fuel Provisioning * Public Works
Greenspace * Water Quality
Greenspace * Activism
Greenspace * Communication
Greenspace * Community and Faith-Based Initiatives
Greenspace * Emergency Preparedness
Greenspace * Justice
Greenspace * Public Works
Water Quality * Water Quantity
Water Quality * Activism
Water Quality * Community and Faith-Based Initiatives
Water Quality * Justice
Water Quality * Public Works
Water Quantity * Activism
Water Quantity * Community and Faith-Based Initiatives
Water Quantity * Emergency Preparedness
Activism * Communication
Activism * Community and Faith-Based Initiatives
Activism * Emergency Preparedness
Communication * Community and Faith-Based Initiatives
Communication * Emergency Preparedness
Communication * Family Services
Community and Faith-Based Initiatives * Emergency
Preparedness
Community and Faith-Based Initiatives * Public Works
Emergency Preparedness * Justice
Emergency Preparedness * Public Works
Estimate
-0.415
0.236
-2.718
1.729
-0.952
3.696
0.256
-1.966
2.192
-0.451
2.305
-7.166
0.968
-0.642
2.521
-5.227
-0.164
0.707
0.268
-0.524
-0.441
-1.054
0.105
1.338
3.324
2.708
5.696
2.075
-0.976
-2.647
-4.162
1.186
5.774
1.761
Standard Error
0.427
0.113
0.395
0.349
0.405
0.574
0.533
0.650
1.199
0.250
0.707
1.432
0.720
0.690
0.505
1.059
0.068
0.266
0.206
0.188
0.202
0.315
0.237
0.197
1.082
0.761
0.773
0.906
0.726
1.088
0.587
0.613
0.622
0.520
182
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Cultural Fulfillment
R-Square: 0.52376
Source DF Sum of Mean F Value Pr > F
Squares Square
Model 21 0.34897 0.01662 191J <.0001
Error 378 0.3173 0.00084
Corrected Total 399 0.66628
Parameter
Intercept
Consumption
Innovation
Air Quality
Water Quantity
Activism
Communication
Community and Faith-Based Initiatives
Education
Emergency Preparedness
Healthcare
Justice
Years from 2000
Consumption * Activism
Innovation * Air Quality
Air Quality * Community and Faith-Based Initiatives
Air Quality * Emergency Preparedness
Water Quantity * Activism
Water Quantity * Community and Faith-Based Initiatives
Communication * Community and Faith-Based Initiatives
Community and Faith-Based Initiatives * Emergency
Preparedness
Education * Healthcare
Estimate
0.794
-0.502
0.239
-0.181
-0.261
-0.422
1.231
1.300
-0.656
-0.582
-1.133
-0.118
-0.004
0.723
-0.320
0.590
0.260
0.535
-0.573
-4.176
2.219
2.116
Standard
Error
0.276
0.153
0.098
0.100
0.143
0.201
0.314
0.740
0.408
0.167
0.489
0.055
0.002
0.323
0.152
0.171
0.126
0.243
0.291
1.245
0.655
1.052
183
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Education
R-Square: 0.91543
Source
DF Sum of Mean
Squares Square
F Value Pr > F
Model 51 1.27293 0.02496 73.86 <.0001
Error 348 0.1176 0.00034
Corrected Total 399 1.39054
Parameter
Intercept
Capital Investment
Consumption
Finance
Production
Re-Distribution
Air Quality
Food, Fiber and Fuel Provisioning
Greenspace
Water Quality
Water Quantity
Activism
Community and Faith-Based Initiatives
Education
Emergency Preparedness
Family Services
Healthcare
Justice
Public Works
Years from 2000
Consumption * Finance
Consumption * Food, Fiber and Fuel Provisioning
Consumption * Community and Faith-Based Initiatives
Consumption * Emergency Preparedness
Consumption * Justice
Consumption * Public Works
Estimate
0.325
0.141
2.087
-0.840
-1.656
0.691
-0.740
0.938
-2.438
0.005
0.436
-2.152
5.448
-0.020
1.120
0.541
-0.066
-2.058
1.286
0.005
-0.410
0.285
1.694
-1.582
-2.018
-2.626
Standard
Error
0.580
0.033
0.557
0.336
0.599
0.331
0.143
1.142
0.722
0.010
0.113
0.658
0.684
0.155
0.459
0.351
0.064
0.647
0.531
0.004
0.210
0.801
0.642
0.458
0.613
0.586
184
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Parameter
Estimate
Standard
Error
Finance * Family Services
Production * Food, Fiber and Fuel Provisioning
Production * Justice
Re-Distribution * Family Services
Air Quality * Water Quantity
Air Quality * Activism
Air Quality * Community and Faith-Based Initiatives
Air Quality * Education
Air Quality * Justice
Air Quality * Public Works
Food, Fiber and Fuel Provisioning * Activism
Food, Fiber and Fuel Provisioning * Emergency Preparedness
Greenspace * Activism
Greenspace * Emergency Preparedness
Greenspace * Public Works
Water Quantity * Emergency Preparedness
Activism * Community and Faith-Based Initiatives
Activism * Emergency Preparedness
Activism * Justice
Community and Faith-Based Initiatives
Community and Faith-Based Initiatives
Preparedness
Community and Faith-Based Initiatives
Community and Faith-Based Initiatives
Emergency Preparedness * Justice
Emergency Preparedness * Public Works
Justice * Public Works
Education
Emergency
Family Services
Justice
1.540
3.011
0.872
-1.027
0.111
-0.282
-0.197
0.298
0.965
0.680
-3.089
-2.288
1.145
-1.056
4.778
-1.111
-2.767
4.721
4.807
-1.308
-3.643
-0.531
-3.652
2.782
-2.707
-2.580
0.514
1.550
0.694
0.606
0.056
0.099
0.171
0.143
0.205
0.167
1.167
0.943
0.628
0.883
0.958
0.203
0.987
0.761
1.034
0.537
0.661
0.509
0.726
0.600
0.604
0.776
185
-------
Health
R-Square: 0.74641
Source DF Sum of Mean F Value
Squares Square
Model 37 0.12485 0.00337 28.8
Error 362 0.04242 0.00012
Corrected Total 399 0.16727
Pr>F
<.0001
Parameter
Intercept
Consumption
Innovation
Air Quality
Water Quality
Water Quantity
Activism
Communication
Community and Faith-Based Initiatives
Education
Emergency Preparedness
Family Services
Healthcare
Justice
Labor
Years from 2000
Consumption * Activism
Consumption * Community and Faith-Based Initiatives
Consumption * Family Services
Consumption * Labor
Air Quality * Communication
Air Quality * Community and Faith-Based Initiatives
Air Quality * Education
Air Quality * Emergency Preparedness
Air Quality * Justice
Water Quality * Family Services
Water Quantity * Communication
Activism * Emergency Preparedness
Estimate
0.151
0.740
0.057
-0.282
0.035
-0.233
-0.258
-1.176
1.157
0.913
-0.132
0.679
0.917
0.442
-0.823
-0.004
0.192
-0.032
-0.136
-2.049
-0.406
0.267
0.262
0.148
0.495
-0.057
0.522
-1.161
Standard Error
0.235
0.205
0.016
0.082
0.039
0.105
0.398
0.330
0.334
0.262
0.243
0.227
0.186
0.092
0.243
0.001
0.190
0.240
0.249
0.433
0.102
0.087
0.071
0.056
0.099
0.074
0.210
0.273
186
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Parameter
Activism * Healthcare
Activism * Labor
Communication * Community and Faith-Based Initiatives
Communication * Education
Communication * Emergency Preparedness
Community and Faith-Based Initiatives * Education
Community and Faith-Based Initiatives * Family Services
Community and Faith-Based Initiatives * Healthcare
Community and Faith-Based Initiatives * Justice
Emergency Preparedness * Family Services
Estimate
-0.994
3.014
2.872
-0.923
2.137
-1.761
-0.152
-1.111
-3.042
-0.676
Standard Error
0.341
0.724
0.852
0.489
0.439
0.333
0.423
0.403
0.354
0.287
Leisure Time
R-Square: 0.81651
Source DF Sum of Mean
Squares Square
Model 26 0.6012 0.02312
Error 373 0.1351 0.00036
Corrected Total 399 0.73631
F Value Pr > F
63.84 <.0001
Parameter
Intercept
Employment
Finance
Innovation
Production
Food, Fiber and Fuel Provisioning
Water Quality
Water Quantity
Activism
Communication
Community and Faith-Based Initiatives
Education
Emergency Preparedness
Healthcare
Years from 2000
Estimate
-1.878
-0.523
0.642
-0.641
2.501
4.207
-0.183
0.137
-0.721
0.132
0.960
5.089
0.056
0.309
-0.005
Standard Error
0.346
0.087
0.212
0.122
0.644
0.651
0.065
0.123
0.192
0.046
0.256
0.565
0.021
0.141
0.002
187
-------
Parameter
Employment * Water Quantity
Finance * Innovation
Finance * Education
Production * Food, Fiber and Fuel Provisioning
Production * Education
Food, Fiber and Fuel Provisioning * Water Quantity
Food, Fiber and Fuel Provisioning * Activism
Food, Fiber and Fuel Provisioning * Education
Water Quality * Community and Faith-Based Initiatives
Water Quality * Education
Water Quantity * Community and Faith-Based Initiatives
Community and Faith-Based Initiatives * Healthcare
Estimate
0.485
1.214
-2.117
-3.713
-2.963
-0.809
2.410
-7.045
-0.446
0.654
-0.731
-1.444
Standard Error
0.176
0.258
0.371
1.116
0.857
0.308
0.503
0.824
0.133
0.144
0.197
0.506
188
-------
Living Standards
R-Square: 0.83015
Source
DF Sum of Mean
Squares Square
F Value Pr > F
Model 16 0.43688 0.02731
Error 383 0.08939 0.00023
Corrected Total 399 0.52627
116.99 <.0001
Parameter
Intercept
Consumption
Employment
Finance
Production
Re-Distribution
Water Quantity
Activism
Communication
Justice
Labor
Public Works
Years from 2000
Employment * Justice
Activism * Justice
Communication * Justice
Labor * Public Works
Estimate
0.917
-0.221
0.455
0.072
0.114
-0.090
0.060
1.007
-0.916
-0.101
-1.853
-1.298
-0.003
-1.076
-1.076
2.451
3.670
Standard Error
0.198
0.030
0.136
0.019
0.039
0.027
0.010
0.167
0.292
0.280
0.320
0.263
0.001
0.306
0.344
0.639
0.622
189
-------
Safety and Security
R-Square: 0.62907
Source
DF Sum of Mean
Squares Square
F Value Pr > F
Model 21 0.50209
Error 378 0.29606
Corrected Total 399 0.79815
0.02391 30.53
0.00078
<.0001
Parameter
Intercept
Consumption
Employment
Production
Greenspace
Water Quality
Water Quantity
Activism
Communication
Community and Faith-Based Initiatives
Family Services
Justice
Public Works
Years from 2000
Consumption * Activism
Consumption * Family Services
Employment * Family Services
Greenspace * Activism
Water Quality * Community and Faith-Based Initiatives
Water Quantity * Communication
Activism * Community and Faith-Based Initiatives
Community and Faith-Based Initiatives * Justice
Estimate
1.238
1.485
-1.759
-0.087
-1.340
-0.288
-0.465
-0.494
-0.389
0.725
-0.232
0.272
0.084
-0.009
-0.501
-2.797
3.258
2.715
0.928
1.126
0.043
-2.216
Standard Error
0.278
0.434
0.330
0.060
0.480
0.056
0.239
0.251
0.230
0.324
0.344
0.207
0.050
0.003
0.581
0.626
0.561
0.820
0.222
0.473
0.486
0.681
190
-------
Social Cohesion
R-Square: 0.90557
Source
DF Sum of Mean
Squares Square
F Value Pr > F
Model 48 0.84478 0.0176 70.12
Error 351 0.0881 0.00025
Corrected Total 399 0.93288
<.0001
Parameter
Intercept
Capital Investment
Consumption
Finance
Production
Re-Distribution
Air Quality
Food, Fiber and Fuel Provisioning
Greenspace
Water Quality
Water Quantity
Activism
Community and Faith-Based Initiatives
Emergency Preparedness
Family Services
Healthcare
Justice
Public Works
Years from 2000
Capital Investment * Re-Distribution
Consumption * Re-Distribution
Consumption * Emergency Preparedness
Consumption * Family Services
Consumption * Justice
Consumption * Public Works
Finance * Re-Distribution
Finance * Community and Faith-Based Initiatives
Finance * Emergency Preparedness
Estimate
-0.006
-0.305
3.829
-0.988
-0.104
-0.150
-0.096
0.184
-1.756
-0.489
0.432
-2.027
4.491
1.379
-0.254
0.038
0.062
-0.940
0.003
1.073
-2.651
0.453
-1.270
-1.680
-3.704
0.376
-0.973
-0.577
Standard Error
0.456
0.158
0.559
0.240
0.053
0.591
0.055
0.079
0.954
0.125
0.079
0.398
0.637
0.336
0.453
0.050
0.725
0.206
0.002
0.356
0.763
0.235
0.618
0.648
0.459
0.417
0.232
0.238
191
-------
Parameter
Finance * Family Services
Re-Distribution * Greenspace
Re-Distribution * Activism
Re-Distribution * Community and Faith-Based Initiatives
Re-Distribution * Justice
Air Quality * Community and Faith-Based Initiatives
Air Quality * Emergency Preparedness
Greenspace * Family Services
Greenspace * Justice
Greenspace * Public Works
Water Quality * Activism
Water Quality * Community and Faith-Based Initiatives
Water Quality * Justice
Water Quality * Public Works
Water Quantity * Emergency Preparedness
Activism * Community and Faith-Based Initiatives
Activism * Justice
Community and Faith-Based Initiatives * Family Services
Community and Faith-Based Initiatives * Justice
Emergency Preparedness * Justice
Family Services * Justice
Estimate
2.268
1.134
2.651
-3.143
-0.756
0.385
0.070
-1.199
-1.291
5.314
-0.442
0.308
0.810
0.546
-0.997
-0.697
3.456
-1.714
-3.181
-1.686
2.218
Standard Error
0.409
1.130
0.748
0.761
0.698
0.139
0.084
0.727
1.026
0.889
0.110
0.165
0.198
0.169
0.159
0.451
0.726
0.493
0.587
0.482
0.780
192
-------
APPENDIX D:
SERVICE INTERACTION
193
-------
Overview
This appendix provides service interaction lookup tables. Each service that interacts in the model is
listed, along with the services that interact with it, sorted highest to lowest. Parentheses denote service
interactions that are negative.
194
-------
Connection to Nature
Service
Interacting Services
Service
Interacting Services
Consumption
Employment
Finance
Re-Distribution
Food, Fiber and
Fuel Provisioning
Greenspace
Water Quality
Public Works
Family Services
Water Quantity
(Water Quality)
(Food, Fiber and Fuel
Provisioning)
(Activism)
(Emergency Preparedness)
(Community and Faith-Based
Initiatives)
Water Quality
(Re-Distribution)
(Emergency Preparedness)
Emergency Preparedness
(Family Services)
Community and Faith-Based
Initiatives
Emergency Preparedness
(Employment)
(Public Works)
Public "Works
(Consumption)
Justice
Activism
Community and Faith-Based
Initiatives
(Water Quality)
(Emergency Preparedness)
(Public Works)
(Communication)
Activism
Community and Faith-Based
Initiatives
Employment
(Consumption)
(Water Quantity)
(Public Works)
(Greenspace)
(Justice)
Water Quantity
Activism
Communication
Community and
Faith-Based
Initiatives
Emergency
Preparedness
Emergency Preparedness
Consumption
Community and Faith-Based
Initiatives
(Water Quality)
(Activism)
Emergency Preparedness
Communication
Community and Faith-Based
Initiatives
Greenspace
Water Quality
(Water Quantity)
(Consumption)
Activism
Community and Faith-Based
Initiatives
(Emergency Preparedness)
(Family Services)
(Greenspace)
Re-Distribution
Activism
Communication
Public Works
Greenspace
Water Quality
Water Quantity
(Consumption)
(Emergency Preparedness)
Justice
Activism
Public Works
Finance
Water Quantity
Re-Distribution
(Greenspace)
(Communication)
195
-------
Service
Family Services
Justice
Interacting Services
(Consumption)
(Employment)
(Community and Faith-Based
Initiatives)
Consumption
(Finance)
(Communication)
Emergency Preparedness
Greenspace
Service
Interacting Services
(Water Quality)
Public Works
Consumption
Food, Fiber and Fuel
Provisioning
Emergency Preparedness
Community and Faith-Based
Initiatives
(Water Quality)
(Re-Distribution)
(Greenspace)
Cultural Fulfillment
Education
Service
Interacting Services
Service
Consumption
Activism
Innovation (Air Quality)
Air Quality Community and Faith-Based
Initiatives
Emergency Preparedness
(Innovation)
Water Quantity Activism
(Community and Faith-Based
Initiatives)
Activism
Consumption
Water Quantity
Communication
Community and
Faith-Based
Initiatives
Education
Emergency
Preparedness
Healthcare
(Community and Faith-Based
Initiatives)
Emergency Preparedness
Air Quality
(Water Quantity)
(Communication)
Healthcare
Community and Faith-Based
Initiatives
Air Quality
Education
Consumption
Finance
Production
Interacting Services
Community and Faith-Based
Initiatives
Food, Fiber and Fuel
Provisioning
(Finance)
(Emergency Preparedness)
(Justice)
(Public Works)
Family Services
(Consumption)
Food, Fiber and Fuel
Provisioning
Justice
Re-Distribution
Air Quality
Food, Fiber and
Fuel Provisioning
(Family Services)
Justice
Public Works
Education
Water Quantity
(Community and Faith-Based
Initiatives)
(Activism)
Production
Consumption
(Emergency Preparedness)
(Activism)
Greenspace Public Works
Activism
(Emergency Preparedness)
196
-------
Service
Interacting Services
Service
Interacting Services
Water Quantity
Activism
Air Quality
(Emergency Preparedness)
Justice
Emergency Preparedness
Greenspace
(Air Quality)
(Community and Faith-Based
Initiatives)
(Food, Fiber and Fuel
Provisioning)
Justice
(Greenspace)
(Water Quantity)
(Consumption)
(Food, Fiber and Fuel
Provisioning)
(Public Works)
(Community and Faith-Based
Initiatives)
Family Services Finance
Community and
Faith-Based
Initiatives
Education
Emergency
Preparedness
Consumption
(Air Quality)
(Family Services)
(Education)
(Activism)
(Emergency Preparedness)
(Justice)
Justice
Public Works
Air Quality
(Community and Faith-Based
Initiatives)
Activism
(Community and Faith-Based
Initiatives)
(Re-Distribution)
Activism
Emergency Preparedness
Air Quality
Production
(Consumption)
(Public Works)
(Community and Faith-Based
Initiatives)
Greenspace
Air Quality
(Justice)
(Consumption)
(Emergency Preparedness)
Health
Service
Service
Interacting Services
Water Quantity
Activism
Consumption Activism
(Community and Faith-Based
Initiatives)
(Family Services)
(Labor)
Air Quality Justice
Community and Faith-Based
Initiatives
Education
Emergency Preparedness
(Communication)
Water Quality (Family Services)
Interacting Services
Communication
Labor
Consumption
(Healthcare)
(Emergency Preparedness)
Communication
Community and
Faith-Based
Initiatives
Community and Faith-Based
Initiatives
Emergency Preparedness
Water Quantity
(Air Quality)
(Education)
Communication
Air Quality
197
-------
Service
Interacting Services
(Consumption)
(Family Services)
(Healthcare)
(Education)
(Justice)
Education
Emergency
Preparedness
Air Quality
(Communication)
(Community and Faith-Based
Initiatives)
Communication
Air Quality
(Family Services)
Leisure Time
Service
Interacting Services
Employment
Finance
Water Quantity
Innovation
Production
Innovation
(Education)
Finance
Food, Fiber and
Fuel Provisioning
(Education)
(Food, Fiber and Fuel
Provisioning)
Water Quality
Water Quantity
Activism
(Water Quantity)
(Production)
(Education)
Education
(Community and Faith-Based
Initiatiyes)
Employment
(Community and Faith-Based
Initiatives)
(Food, Fiber and Fuel
Provisioning)
Activism
Community and
Faith-Based
Initiatives
Food, Fiber and Fuel
Provisioning
(Water Quality)
(Water Quantity)
Service
Interacting Services
(Activism)
Family Services
(Water Quality)
(Consumption)
(Community and Faith-Based
Initiatives)
(Emergency Preparedness)
Healthcare
Justice
Labor
Service
(Activism)
(Community and Faith-Based
Initiatives)
Air Quality
(Community and Faith-Based
Initiatives)
Activism
(Consumption)
Interacting Services
(Healthcare)
Education
Water Quality
(Finance)
(Production)
(Food, Fiber and Fuel
Provisioning)
Healthcare (Community and Faith-Based
Initiatives)
Living Standards
Service
Interacting Services
Employment
(Justice)
Activism (Justice)
Communication Justice
Justice
Communication
(Employment)
(Activism)
Labor
"Public'Works"
Public Works
Labor
Safety and Security
Service
Interacting Services
Consumption
(Activism)
198
-------
Service
Interacting Services
Service
(Family Services)
Employment
Greenspace
Water Quality
Family Services
Activism
Water Quantity
Activism
Community and Faith-Based
Initiatives
Communication
Communication
Greenspace
Community and Faith-Based
Initiatives
(Consumption)
Water Quantity
Social Cohesion
Service
Interacting Services
Capital
Investment
Consumption
Re-Distribution
Emergency Preparedness
(Family Services)
(Justice)
(Re-Distribution)
(Public Works)
Finance
Family Services
Re-Distribution
(Emergency Preparedness)
(Community and Faith-Based
Initiatives)
Re-Distribution
Activism
Greenspace
Capital Investment
Finance
(Justice)
(Consumption)
(Community and Faith-Based
Initiatives)
Air Quality
Greenspace
Community and Faith-Based
Initiatives
Emergency Preparedness
Public Works
Re-Distribution
(Family Services)
(Justice)
Interacting Services
Community and
Faith-Based
Initiatives
Water Quality
Activism
(Justice)
Family Services
Justice
Employment
(Consumption)
(Community and Faith-Based
Initiatives)
Service
Interacting Services
Water Quality
Water Quantity
Justice
Public Works
Community and Faith-Based
Initiatives
(Activism)
(Emergency Preparedness)
Activism
Community and
Faith-Based
Initiatives
Emergency
Preparedness
Justice
Re-Distribution
(Water Quality)
(Community and Faith-Based
Mtiatiyes)
Air Quality
Water Quality
(Activism)
(Finance)
(Family Services)
(Re-Distribution)
(Justice)
Consumption
Air Quality
(Finance)
(Water Quantity)
(Justice)
Family Services
Finance
Justice
(Greenspace)
(Consumption)
199
-------
Service
Interacting Services
(Community and Faith-Based
Initiatives)
Justice
Activism
Family Services
Water Quality
(Re-Distribution)
(Greenspace)
(Consumption)
(Emergency Preparedness)
(Community and Faith-Based
Initiatives)
Public Works
Greenspace
Water Quality
(Consumption)
200
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BACK COVER
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