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Locally-weighted meta-regression and benefit transfer

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  • Moeltner, Klaus
  • Puri, Roshan
  • Johnston, Robert J.
  • Besedin, Elena
  • Balukas, Jessica A.
  • Le, Alyssa

Abstract

Meta-regression models (MRMs) are commonly used within benefit transfer to estimate willingness to pay for environmental quality improvements. In virtually all benefit transfers of this type, a single regression model is fit to all source points in the metadata, and used to produce out-of-sample predictions for all possible policy-site applications. Despite the advantages of this approach over other types of benefit transfer, the predictive accuracy of these MRMs generally leaves room for improvement. In this paper we propose a locally-weighted regression approach to MRM estimation to enhance the accuracy of benefit transfer predictions in an environmental valuation context. We introduce the concept of locally-weighted meta-regression, provide econometric underpinnings, and discuss the construction of weight functions. We illustrate the use of cross-validation to decide between weight functions, and show how this framework can be applied in an actual benefit transfer setting. For our empirical application on willingness-to-pay for water quality improvements, we find that the proposed approach brings substantial gains in predictive accuracy in a leave-one-out setting, and measurable improvements in predictive efficiency for benefit transfer.

Suggested Citation

  • Moeltner, Klaus & Puri, Roshan & Johnston, Robert J. & Besedin, Elena & Balukas, Jessica A. & Le, Alyssa, 2023. "Locally-weighted meta-regression and benefit transfer," Journal of Environmental Economics and Management, Elsevier, vol. 121(C).
  • Handle: RePEc:eee:jeeman:v:121:y:2023:i:c:s009506962300089x
    DOI: 10.1016/j.jeem.2023.102871
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    References listed on IDEAS

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    1. repec:oup:ajagec:v:99:y:2017:i:1:p:1-23. is not listed on IDEAS
    2. Steven Farber & Antonio Páez, 2007. "A systematic investigation of cross-validation in GWR model estimation: empirical analysis and Monte Carlo simulations," Journal of Geographical Systems, Springer, vol. 9(4), pages 371-396, December.
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    Cited by:

    1. Tee, Wei Hown & Gan, Chin Kim & Sardi, Junainah, 2024. "Benefits of energy storage systems and its potential applications in Malaysia: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 192(C).

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