**3. Linking services to well-being**

The HWBI framework demonstrates that ecosystem, economic and social services can be linked to the domains of well-being by relationship functions (**Figure 1**). Summers et al. [66] demonstrated that relationship functions can be derived between services information and well-being domain information at the county level. Similarly, relationships exist among indicators and metrics of well-being domains that were used to develop the ecosystem, economic and social services/well-being relationships (**Table 2**). Achieving balanced decisions requires techniques to examine the potential consequences (both intended and unintended; both positive and negative) on well-being associated with changing services. Summers et al. [66] used an approach for forecasting that employs (1) models derived from ecological, social and economic production functions (e.g., [67, 68]) and (2) models examining how communities feel about decision outcomes [69, 70]. Such models require a framework for linking changes in service production to changes in well-being.

compared to actual data for model fit and construction (7 of 10 available years) with 3 years of data withheld for validation. In addition, simple Pearson product-moment correlation coefficients were determined among the eight well-being domains to address likely co-occurrences

**Table 2.** Types of capital, community good and services, and well-being domains used to construct forecasting models [66].

**Types of capital Community goods and services Domains of well-being** Social Re-distribution (Ec) Connection to nature Natural Production (Ec) Cultural fulfillment Human Innovation (Ec) Social cohesion Built Finance (Ec) Safety and security

Food, fiber and fuel provisioning (E)

Emergency preparedness (S)

Community and faith-based initiatives (S)

Greenspace (E) Water quality (E) Water quality (E) Public works (S)

Labor (S) Justice (S) Healthcare (S) Family services (S)

Education (S)

Activism (S)

Ec = Economic services, E = Ecosystem services, S = Social services.

Communication (S)

Employment (Ec) Living standards Consumption (Ec) Education Capital investment (Ec) Health Air quality (E) Leisure time

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The results of these evaluations are documented in Summers et al. [66] regarding forecast inclusion of service indicators, model fit and validation, and scenario building using the forecasting tools. Overall examples of the forecasting applications are depicted in **Figure 2** where observed and predicted are shown for the 3 years of withheld data for all 50 states (3 years of data not used in construction). Similarly, the strong inter-correlations among well-being domains are shown in **Table 3**. The use of the forecasting regressions in concert with the

of changes in multiple domains.

The functional equations for each well-being domain were determined through the use of bidirectional step-wise regression [71]. This process identified main effects and primary pairwise interactions of service indicators and identified predictive variables based on adjusted R2 and sequenced F-tests [72]. The forecasts for each year in all counties of all states were


Additionally, ecosystem services have been linked to community well-being priorities based on HWBI domains for the purpose of setting conservation targets for coastal ecosystems to

Connection to nature Describes how people feel about nature. It is measured by people's perception of nature and

Cultural fulfillment Describes people's cultural involvement. Measures include how often people participate in the

Education Covers basic skills in reading, math and science. Measures of student safety and health are also

Leisure time Describes how time is spent including: employment, care for seniors and activities that people partake in for personal enjoyment. Measures represent work-life balance. Living standards Contains information about lifestyles. It includes measures of basic necessities, wealth and

Health Characterizes people's involvement in healthy behaviors, prevalence of illness, access to

Safety and security Covers information about perceived safety, actual safety and potential for danger. Social cohesion Describes people's connection to each other and their community through measures of involvement in family, civic engagement, and the community as a whole.

The HWBI framework demonstrates that ecosystem, economic and social services can be linked to the domains of well-being by relationship functions (**Figure 1**). Summers et al. [66] demonstrated that relationship functions can be derived between services information and well-being domain information at the county level. Similarly, relationships exist among indicators and metrics of well-being domains that were used to develop the ecosystem, economic and social services/well-being relationships (**Table 2**). Achieving balanced decisions requires techniques to examine the potential consequences (both intended and unintended; both positive and negative) on well-being associated with changing services. Summers et al. [66] used an approach for forecasting that employs (1) models derived from ecological, social and economic production functions (e.g., [67, 68]) and (2) models examining how communities feel about decision outcomes [69, 70]. Such models require a framework for linking changes in

The functional equations for each well-being domain were determined through the use of bidirectional step-wise regression [71]. This process identified main effects and primary pairwise interactions of service indicators and identified predictive variables based on adjusted

and sequenced F-tests [72]. The forecasts for each year in all counties of all states were

deliver ecosystem and human benefits [65].

**Table 1.** Description of domains used in the HWBI.

**Domain Description**

150 Ecosystem Services and Global Ecology

how it affects them.

included.

income.

arts and spiritual activities.

healthcare, mortality and life expectancy.

**3. Linking services to well-being**

service production to changes in well-being.

R2

**Table 2.** Types of capital, community good and services, and well-being domains used to construct forecasting models [66].

compared to actual data for model fit and construction (7 of 10 available years) with 3 years of data withheld for validation. In addition, simple Pearson product-moment correlation coefficients were determined among the eight well-being domains to address likely co-occurrences of changes in multiple domains.

The results of these evaluations are documented in Summers et al. [66] regarding forecast inclusion of service indicators, model fit and validation, and scenario building using the forecasting tools. Overall examples of the forecasting applications are depicted in **Figure 2** where observed and predicted are shown for the 3 years of withheld data for all 50 states (3 years of data not used in construction). Similarly, the strong inter-correlations among well-being domains are shown in **Table 3**. The use of the forecasting regressions in concert with the

inter-domain correlation permits the evaluation of intended and unintended consequences of specific decisions to augment services or potentially improve well-being domains and overall

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Effective measures of human well-being can be useful to decision making at the community level. Community decision-making is based on a shared commitment to achieving realizable improvements in family, child and neighborhood conditions in order to build accountability and capacity to achieve those results. This type of decision-making achieves the best results when it:

These attributes can be accomplished through effective engagement with community stakeholders. Stakeholder engagement is a necessary process of evaluation because effective use of the HWBI as an assessment tool requires information on the relative importance of the domains of HWBI for any given community (i.e., their community value structure), as well as

Using the Relative Valuation of Multiple Ecosystem Services method (RESVI), Jordan et al. [3] queried three respondent groups to determine their overall value judgments related to various ecosystem services. The RESVI method uses an assessment where respondents are (1) briefed about policy questions to be examined with regard to the extent and nature of the ecosystem(s) and services involved, (2) asked to assign relative values to a list of ecosystem services in terms of what proportional dollar value for one service versus another, (3) application of a dollar value based on literature or research for each service type, and (4) creation of an index for all services using reference and relative values determined by the respondents. The RESVI was used with three respondent groups – programmatic regulators, research scientists, and community stakeholders. The results compared the relative values of eight ecosystem services (**Figure 3**) – habitat functions, water quality regulation, water supply, recreation, flood control, esthetics, biodiversity and climate regulation. The test groups valued

the baseline value of well-being against which we can measure change.

**4. Differences in well-being by respondent or community type**

• Assists communities in establishing and monitoring progress toward objectives

well-being.

• Uses timely, relevant and reliable data

• Authentically involves community stakeholders

• Develop a community agenda for investment

• Accurately reflect community priorities

• Reports regularly to stakeholders.

• Assesses accurately community resources and assets

• Engages multiple networks to support well-being

**Figure 2.** Comparison of observed and predicted values from forecast models for well-being based on ecosystem. Economic and social services (from [66]).


D1 = Connection to nature; D2 = Cultural fulfillment; D3 = Education; D4 = Health; D5 = Leisure time; D6 = Living standards; D7 = Safety and security; D8 = Social cohesion.

**Table 3.** Correlations (Pearson product moment) among human well-being domains (\* = p < 0.0001; N = 561) (from [66]).

inter-domain correlation permits the evaluation of intended and unintended consequences of specific decisions to augment services or potentially improve well-being domains and overall well-being.
