A sequential model to link contextual risk, perception and public support for flood adaptation policy

Wanyun Shao, Siyuan Xian, Ning Lin, Mitchell J. Small

Research output: Contribution to journalArticlepeer-review

41 Scopus citations

Abstract

The economic damage from coastal flooding has dramatically increased over the past several decades, owing to rapid development in shoreline areas and possible effects of climate change. To respond to these trends, it is imperative for policy makers to understand individuals' support for flood adaptation policy. Using original survey data for all coastal counties of the United States Gulf Coast merged with contextual data on flood risk, this study investigates coastal residents' support for two adaptation policy measures: incentives for relocation and funding for educational programs on emergency planning and evacuation. Specifically, this study explores the interactive relationships among contextual flood risks, perceived flood risks and policy support for flood adaptation, with the effects of social-demographic variables being controlled. Age, gender, race and partisanship are found to significantly affect individuals' policy support for both adaptation measures. The contextual flooding risks, indicated by distance from the coast, maximum wind speed and peak height of storm surge associated with the last hurricane landfall, and percentage of high-risk flood zone per county, are shown to impact one's perceptions of risk, which in turn influence one's support for both policy measures. The key finding –risk perception mediates the impact of contextual risk conditions on public support for flood management policies – highlights the need to ensure that the public is well informed by the latest scientific, engineering and economic knowledge. To achieve this, more information on current and future flood risks and options available for mitigation as well as risk communication tools are needed.

Original languageEnglish (US)
Pages (from-to)216-225
Number of pages10
JournalWater Research
Volume122
DOIs
StatePublished - 2017

All Science Journal Classification (ASJC) codes

  • Water Science and Technology
  • Ecological Modeling
  • Pollution
  • Waste Management and Disposal
  • Environmental Engineering
  • Civil and Structural Engineering

Keywords

  • Contextual flood risk factors
  • Flood adaptation
  • Policy support
  • Risk perception

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