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

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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.

LanguageEnglish
Pages1087-1109
JournalEnvironmental and Resource Economics
Volume68
Issue number4
DOIs
Publication statusPublished - Dec 2017

Fingerprint

Risk management
Climate change
climate change
learning
Levees
multiple use
Sea level
peak flow
river flow
floodplain
dike
Rivers
Uncertainty
risk management
analysis
Management strategy
Flood risk
Minimax regret
flood protection
effect

Keywords

  • Adaptive management
  • Climate change
  • Flexibility
  • Flood risk
  • Learning
  • Minimax regret
  • Robust optimisation

Cite this

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title = "A Minimax Regret Analysis of Flood Risk Management Strategies Under Climate Change Uncertainty and Emerging Information",
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.",
keywords = "Adaptive management, Climate change, Flexibility, Flood risk, Learning, Minimax regret, Robust optimisation",
author = "{van der Pol}, T.D. and S. Gabbert and H.P. Weikard and {van Ierland}, E.C. and E.M.T. Hendrix",
year = "2017",
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T1 - A Minimax Regret Analysis of Flood Risk Management Strategies Under Climate Change Uncertainty and Emerging Information

AU - van der Pol, T.D.

AU - Gabbert, S.

AU - Weikard, H.P.

AU - van Ierland, E.C.

AU - Hendrix, E.M.T.

PY - 2017/12

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N2 - 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.

AB - 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.

KW - Adaptive management

KW - Climate change

KW - Flexibility

KW - Flood risk

KW - Learning

KW - Minimax regret

KW - Robust optimisation

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