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A Model For Change: An Approach for Forecasting Well-Being From Service-Based Decisions
Citation:
Summers, Kevin, L. Harwell, AND L. Smith. A Model For Change: An Approach for Forecasting Well-Being From Service-Based Decisions. ECOLOGICAL INDICATORS. Elsevier Science Ltd, New York, NY, 69:295-309, (2016).
Impact/Purpose:
This paper describes an approach to using HWBI in decision making models to characterize likely impacts of decisions on future well being, as well as the possible intended and unintended consequences of potential decisions. The modeling approach is (a) based on adaptations of economic, household and ecological production-like functions and (b) develops predictions that could incorporate concepts of affective forecasting to address how a community might feel as a result of decision outcomes. The ability to provide some level of affective forecasting introduces the concept that the value of a decision is also tied to people’s tendency to anticipate their future to be more intense than it will be really. In other words, how a community feels today about a potential change influences how a community believes it will feel about the same change in the future. This evaluation is the first of its kind using forecasting tools to relate service-changing community decisions to potential impacts on human well-being.
Description:
Every community decision incorporates a "forecasting" strategy (whether formal or implicit) to help visualize expected results and evaluate the potential “feelings” that people living in that community may have about those results. With more communities seeking to make decisions based on sustainable alternatives, forecasting efforts that examine potential impacts of decisions on overall community well-being may prove to be valuable for not only gaging future benefits and trade-offs, but also for recognizing a community’s affective response to the outcomes of those decisions. This paper describes a forecasting approach based on concepts introduced in the development of the U.S. Environmental Protection Agency’s (US EPA) Human Well-Being Index (HWBI) (Smith, et. al. 2014; Summers et al. 2014). The approach examines the relationships among selected economic, environmental and social services that can be directly impacted by community decisions and eight domains of human well-being. Using models developed from constructed- or fixed-effect step-wise and multiple regressions and eleven years of data (2000-2010), these relationship functions may be used to characterize likely direct impacts of decisions on future well-being as well as the possible intended and unintended secondary and tertiary effects relative to any main decision effects.
