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A Minimax Regret Analysis of Flood Risk Management Strategies Under Climate Change Uncertainty and Emerging Information

Author

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  • T. D. Pol

    (Netherlands Bureau for Economic Policy Analysis)

  • S. Gabbert

    (Wageningen University)

  • H.-P. Weikard

    (Wageningen University)

  • E. C. Ierland

    (Wageningen University)

  • E. M. T. Hendrix

    (E.T.S.I. Informática, Campus de Teatinos)

Abstract

This paper studies the dynamic application of the minimax regret (MR) decision criterion to identify robust flood risk management strategies under climate change uncertainty and emerging information. An MR method is developed that uses multiple learning scenarios, for example about sea level rise or river peak flow development, to analyse effects of changes in information on optimal investment in flood protection. To illustrate the method, optimal dike height and floodplain development are studied in a conceptual model, and conventional and adaptive MR solutions are compared. A dynamic application of the MR decision criterion allows investments to be changed after new information on climate change impacts, which has an effect on today’s optimal investments. The results suggest that adaptive MR solutions are more robust than the solutions obtained from a conventional MR analysis of investments in flood protection. Moreover, adaptive MR analysis with multiple learning scenarios is more general and contains conventional MR analysis as a special case.

Suggested Citation

  • T. D. Pol & S. Gabbert & H.-P. Weikard & E. C. Ierland & E. M. T. Hendrix, 2017. "A Minimax Regret Analysis of Flood Risk Management Strategies Under Climate Change Uncertainty and Emerging Information," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 68(4), pages 1087-1109, December.
    • Handle: RePEc:kap:enreec:v:68:y:2017:i:4:d:10.1007_s10640-016-0062-y
    DOI: 10.1007/s10640-016-0062-y
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    5. Georgios P. Trachanas & Aikaterini Forouli & Nikolaos Gkonis & Haris Doukas, 2020. "Hedging uncertainty in energy efficiency strategies: a minimax regret analysis," Operational Research, Springer, vol. 20(4), pages 2229-2244, December.

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