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Spatial heterogeneity of willingness to pay for forest management

Mikolaj Czajkowski (), Wiktor Budzinski (), Danny Campbell (), Marek Giergiczny and Nick Hanley ()

No 2015-07, Discussion Papers in Environment and Development Economics from University of St. Andrews, School of Geography and Sustainable Development

Abstract: The paper investigates spatial heterogeneity of the public’s preferences for the implementation of a new country-wide forest management and protection program in Poland. Spatial econometric methods and high resolution geographical information system (GIS) data related to forest characteristics are used to explain individual-specific willingness to pay (WTP) values, derived from a discrete choice experiment (DCE) study. We find that respondents’ WTP is higher the closer they live to their nearest forest, and the scarcer forests are in the area where they live. Interestingly, the more highly ecologically valuable forests in respondents’ area, the more they prefer extending areas of national forest protection. In addition, we investigate spatial patterns in individual-specific WTP scores and in latent class membership probabilities, finding that preferences are indeed spatially clustered. We argue that this clustering should be taken into account in both benefits analysis and policy-making.

Keywords: discrete choice experiment; contingent valuation; willingness to pay; spatial heterogeneity of preferences; forest management; passive protection; litter; tourist infrastructure; mixed logit; Kriging; spatial-lag (search for similar items in EconPapers)
JEL-codes: Q23 Q28 I38 Q51 Q57 Q58 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-agr, nep-dcm and nep-env
Date: 2015-02
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Citations: View citations in EconPapers (5) Track citations by RSS feed

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Related works:
Journal Article: Spatial Heterogeneity of Willingness to Pay for Forest Management (2017) Downloads
Working Paper: Spatial heterogeneity of willingness to pay for forest management (2015) Downloads
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