Predicting Local and State-Level Water Risk Perceptions in Minnesota

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Predicting Local and State-Level Water Risk Perceptions in Minnesota

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2020-01

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Abstract

Risk perception research in the field of natural resources science and management has come a long way since the publication of “Perception of Risk” by Paul Slovic in 1987, and some scientists have started attempting to predict risk perceptions. At the time of this writing, 40 percent of assessed waters in Minnesota are reported as impaired. However, resident water risk perceptions are not aligned with technical risk assessments conducted by water experts. To better understand the mechanisms that drive heightened or lowered water risk perceptions, a mail survey was distributed to Minnesota residents across the state. The questionnaire asked residents about their values, perceived community capacity to manage water problems, and perceived awareness of water quality issues in their local area to develop a model predicting perceptions of water risk at two levels: local and statewide. In the survey, an adaptation of the values constructs created by Steg et al. (2014) was employed, and several new value constructs were created for this survey. The multivariate regression model developed was moderately effective, explaining about a third of the variance in water risk perceptions for both local and statewide levels. Consistent with previous research in Minnesota, this study has found gaps between technical assessments of risks to water in Minnesota and public perceptions of this risk. The exploratory nature of this risk perception research suggests that future investigation into the predictors of water risk perceptions should examine the reliability of the measures used in this study.

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University of Minnesota M.S. thesis. January 2020. Major: Natural Resources Science and Management. Advisor: Mae Davenport. 1 computer file (PDF); 71 pages.

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Peplinski, Jaren. (2020). Predicting Local and State-Level Water Risk Perceptions in Minnesota. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/213046.

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