A model for change: An approach for forecasting well-being from service-based decisions. (October 2016)

• Record Type: Journal Article
• Authors: Summers, J. Kevin; Harwell, Linda C.; Smith, Lisa M.
• Is Part Of: Ecological indicators. Volume 69(2016)
• Page Start: 295
• Page End: 309
• Publication Date: 2016-10
• Subjects: Human Well-Being Index -- Forecasting -- Relationship functions -- Affective forecasting -- Community decision-making; Environmental monitoring -- Periodicals; Environmental management -- Periodicals; Environmental impact analysis -- Periodicals; Environmental risk assessment -- Periodicals; Sustainable development -- Periodicals; 333.71405
• DOI: 10.1016/j.ecolind.2016.04.033
• Languages: English
• ISSNs: 1470-160X

Abstract

Abstract: Every community decision incorporates a "forecasting" strategy (whether formal or implicit) to help visualize expected results and evaluate the potential "feelings/responses" 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 gauging 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 11 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.