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Investigating the Relationship between Stakeholder Opinion about Wildfire Management and Landscape Context Using GIS

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  • Pamela Kaval

    ()
    (University of Waikato)

  • John Loomis

    (Colorado State University)

  • Dave Theobald

    (Colorado State University)

Abstract

Colorado residents living in the wildland urban interface (WUI) were asked about their perception of wildfire risk and their willingness-to-pay (WTP) for three fire management procedures: fuel reduction by thinning, fire suppression and prescribed fires. Respondent home locations were then digitized to enable the calculation of wildfire danger variables from various GIS map layers. These two processes resulted in perceived and actual wildfire risk variables which were then compared and analyzed. Perceived and actual fire danger variables were then used as explanatory variables in WTP functions. Results show that each fire management technique had different variables that would increase a persons WTP. However, overall, WTP values for each of the approaches were substantial. We believe this information shows that people living in the WUI would be willing-to-pay for an annual “wildfire management fee” to offset risks they consciously take by living in the WUI. This fee could potentially decrease the wildfire management cost burden that is currently incurred by taxpayers.

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File URL: ftp://mngt.waikato.ac.nz/RePEc/wai/econwp/0609.pdf
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Bibliographic Info

Paper provided by University of Waikato, Department of Economics in its series Working Papers in Economics with number 06/09.

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Length: 23 pages
Date of creation: 26 Sep 2006
Date of revision:
Handle: RePEc:wai:econwp:06/09

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Keywords: Contingent valuation; GIS; wildfire; wildland urban interface;

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  1. W. Michael Hanemann, 1994. "Valuing the Environment through Contingent Valuation," Journal of Economic Perspectives, American Economic Association, vol. 8(4), pages 19-43, Fall.
  2. Cameron, Trudy Ann, 1988. "A new paradigm for valuing non-market goods using referendum data: Maximum likelihood estimation by censored logistic regression," Journal of Environmental Economics and Management, Elsevier, vol. 15(3), pages 355-379, September.
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