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Economic analysis of adaptive strategies for flood risk management under climate change

Climate change requires reconsideration of flood risk management strategies. Cost-benefit analysis (CBA), an economic decision-support tool, has been widely applied to assess these strategies. This paper aims to describe and discuss probabilistic extensions of CBA to identify welfare-maximising floo...

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Autores principales: van der Pol, Thomas D., van Ierland, Ekko C., Gabbert, Silke
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Netherlands 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6108003/
https://www.ncbi.nlm.nih.gov/pubmed/30197565
http://dx.doi.org/10.1007/s11027-015-9637-0
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author van der Pol, Thomas D.
van Ierland, Ekko C.
Gabbert, Silke
author_facet van der Pol, Thomas D.
van Ierland, Ekko C.
Gabbert, Silke
author_sort van der Pol, Thomas D.
collection PubMed
description Climate change requires reconsideration of flood risk management strategies. Cost-benefit analysis (CBA), an economic decision-support tool, has been widely applied to assess these strategies. This paper aims to describe and discuss probabilistic extensions of CBA to identify welfare-maximising flood risk management strategies under climate change. First, uncertainty about the changes in return periods of hydro-meteorological extremes is introduced by probability-weighted climate scenarios. Second, the analysis is extended by learning about climate change impacts. Learning occurs upon the probabilistic arrival of information. We distinguish between learning from scientific progress, from statistical evidence and from flood disasters. These probabilistic extensions can be used to analyse and compare the economic efficiency and flexibility of flood risk management strategies under climate change. We offer a critical discussion of the scope of such extensions and options for increasing flexibility. We find that uncertainty reduction from scientific progress may reduce initial investments, while other types of learning may increase initial investments. This requires analysing effects of different types of learning. We also find that probabilistic information about climate change impacts and learning is imprecise. We conclude that risk-based CBA with learning improves the flexibility of flood risk management strategies under climate change. However, CBA provides subjective estimates of expected outcomes and reflects different decision-maker preferences than those captured in robustness analyses. We therefore advocate robustness analysis in addition to, or combined with, cost-benefit analysis to support local investment decisions on flood risk reduction and global strategies on allocation of adaptation funds for flood risk management.
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spelling pubmed-61080032018-09-05 Economic analysis of adaptive strategies for flood risk management under climate change van der Pol, Thomas D. van Ierland, Ekko C. Gabbert, Silke Mitig Adapt Strateg Glob Chang Original Article Climate change requires reconsideration of flood risk management strategies. Cost-benefit analysis (CBA), an economic decision-support tool, has been widely applied to assess these strategies. This paper aims to describe and discuss probabilistic extensions of CBA to identify welfare-maximising flood risk management strategies under climate change. First, uncertainty about the changes in return periods of hydro-meteorological extremes is introduced by probability-weighted climate scenarios. Second, the analysis is extended by learning about climate change impacts. Learning occurs upon the probabilistic arrival of information. We distinguish between learning from scientific progress, from statistical evidence and from flood disasters. These probabilistic extensions can be used to analyse and compare the economic efficiency and flexibility of flood risk management strategies under climate change. We offer a critical discussion of the scope of such extensions and options for increasing flexibility. We find that uncertainty reduction from scientific progress may reduce initial investments, while other types of learning may increase initial investments. This requires analysing effects of different types of learning. We also find that probabilistic information about climate change impacts and learning is imprecise. We conclude that risk-based CBA with learning improves the flexibility of flood risk management strategies under climate change. However, CBA provides subjective estimates of expected outcomes and reflects different decision-maker preferences than those captured in robustness analyses. We therefore advocate robustness analysis in addition to, or combined with, cost-benefit analysis to support local investment decisions on flood risk reduction and global strategies on allocation of adaptation funds for flood risk management. Springer Netherlands 2015-03-01 2017 /pmc/articles/PMC6108003/ /pubmed/30197565 http://dx.doi.org/10.1007/s11027-015-9637-0 Text en © The Author(s) 2015 https://creativecommons.org/licenses/by/4.0/ Open Access This article is distributed under the terms of the Creative Commons Attribution License which permits any use, distribution, and reproduction in any medium, provided the original author(s) and the source are credited.
spellingShingle Original Article
van der Pol, Thomas D.
van Ierland, Ekko C.
Gabbert, Silke
Economic analysis of adaptive strategies for flood risk management under climate change
title Economic analysis of adaptive strategies for flood risk management under climate change
title_full Economic analysis of adaptive strategies for flood risk management under climate change
title_fullStr Economic analysis of adaptive strategies for flood risk management under climate change
title_full_unstemmed Economic analysis of adaptive strategies for flood risk management under climate change
title_short Economic analysis of adaptive strategies for flood risk management under climate change
title_sort economic analysis of adaptive strategies for flood risk management under climate change
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6108003/
https://www.ncbi.nlm.nih.gov/pubmed/30197565
http://dx.doi.org/10.1007/s11027-015-9637-0
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