Spatial dimensions of stated preference valuation in environmental and resource economics: methods, trends and challenges

  • K Glenk
  • , RJ Johnston
  • , J Meyerhoff
  • , J Sagebiel

    Research output: Contribution to journalReview articlepeer-review

    84 Citations (Scopus)
    458 Downloads (Pure)

    Abstract

    An expanding literature addresses spatial dimensions related to the elicitation, estimation, interpretation and aggregation of stated preference (SP) welfare measures. Recognizing the relevance of spatial dimensions for SP welfare analysis and the breadth of associated scholarly work, this article reviews the primary methods, findings, controversies and frontiers in this important area of contemporary research. This review is grounded in a typology that characterizes analytical methods based on theoretical foundations and the type of statistical modelling applied. The resulting interpretive appraisal seeks to (1) summarize and contrast different theoretical arguments and points of departure within the spatial SP literature, (2) synthesize findings, insights and methods from the literature to promote a more holistic perspective on the treatment of spatial dimensions within SP welfare analysis, (3) evaluate and reconcile divergent approaches in terms of theoretical grounding, ability to identify relevant empirical effects, and relevance for SP valuation, and (4) discuss outstanding questions and research frontiers.
    Original languageEnglish
    Pages (from-to)215-242
    Number of pages28
    JournalEnvironmental and Resource Economics
    Volume75
    Issue number2
    Early online date8 Jan 2019
    DOIs
    Publication statusPrint publication - 1 Feb 2020

    Bibliographical note

    © Springer Nature B.V. 2019

    Keywords

    • Contingent valuation
    • Discrete choice experiments
    • Distance decay
    • Ecosystem services
    • Non-market goods
    • Spatial autocorrelation
    • Spatial dependence
    • Spatial heterogeneity
    • Willingness to accept
    • Willingness to pay

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