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Incorporating spatial complexity and variability into the design of stated choice experiments for biodiversity policy support

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  • Badura, Tomas
  • Schaafsma, Marije

Abstract

Understanding the spatial distribution of preferences for both use and non-use values of biodiversity is crucial for designing environmental policies that maximise social value. Yet, integrating spatial factors that influence these preferences into stated preference research remains challenging. This includes designing studies that control for, vary, and accurately represent multiple spatial factors simultaneously—such as spatially heterogeneous characteristics of policy sites and spatial relationships between these sites and respondents. This paper introduces a novel approach designed to enable this. It employs a functionality through which realistic locations for change are selected from a database of all possible policy locations that fit the experimental design situation allocated to each respondent. The real-time generated, respondent tailored choice situations are then presented on individualised maps. This allows the creation of numerous, respondent-specific choice scenarios that exhibit a high degree of variation in spatial factors, both among and within respondents. The approach increases the generalisability of insights into spatial effects on stated preferences, while enhancing the realism and credibility of the choice scenarios. The methodology is implemented at a national level. Analysis of respondents’ choices shows that preferences are spatially driven, but that spatial patterns differ depending on the type of intervention. By focusing on generic terms of quantity and quality across space, we aim to demonstrate that the presented approach can be applied to a wide range of valuation problems where spatial context matters.

Suggested Citation

  • Badura, Tomas & Schaafsma, Marije, 2026. "Incorporating spatial complexity and variability into the design of stated choice experiments for biodiversity policy support," Resource and Energy Economics, Elsevier, vol. 86(C).
  • Handle: RePEc:eee:resene:v:86:y:2026:i:c:s092876552600014x
    DOI: 10.1016/j.reseneeco.2026.101565
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    JEL classification:

    • Q51 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Valuation of Environmental Effects
    • Q58 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Environmental Economics: Government Policy
    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
    • Q57 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Ecological Economics

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