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Real options analysis of climate-change adaptation: investment flexibility and extreme weather events

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  • Graeme Guthrie

    (Victoria University of Wellington)

Abstract

Investments in climate-change adaptation will have to be made while the extent of climate change is uncertain. However, some important sources of uncertainty will fall over time as more climate data become available. This paper investigates the effect on optimal investment decision-making of learning that reduces uncertainty. It develops a simple real options method to value options that are found in many climate-change adaptation contexts. This method modifies a binomial tree model frequently applied to climate-change adaptation problems, incorporating gradual learning using a Bayesian updating process driven by new observations of extreme events. It is used to investigate the timing, scale, or upgradable design of an adaptation project. Recognition that we might have more or different information in the future makes flexibility valuable. The amount of value added by flexibility and the ways in which flexibility should be exploited depend on how fast we learn about climate change. When learning will occur quickly, the value of the option to delay investment is high. When learning will occur slowly, the value of the option to build a small low-risk project instead of a large high-risk one is high. For intermediate cases, the option to build a small project that can be expanded in the future is high. The approach in this paper can support efficient decision-making on adaptation projects by anticipating that we gradually learn about climate change by the recurrence of extreme events.

Suggested Citation

  • Graeme Guthrie, 2019. "Real options analysis of climate-change adaptation: investment flexibility and extreme weather events," Climatic Change, Springer, vol. 156(1), pages 231-253, September.
    • Handle: RePEc:spr:climat:v:156:y:2019:i:1:d:10.1007_s10584-019-02529-z
    DOI: 10.1007/s10584-019-02529-z
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    1. Guthrie, Graeme, 2023. "Optimal adaptation to uncertain climate change," Journal of Economic Dynamics and Control, Elsevier, vol. 151(C).
    2. Justus Ochieng & Lilian Kirimi & Dennis O. Ochieng & Timothy Njagi & Mary Mathenge & Raphael Gitau & Miltone Ayieko, 2020. "Managing climate risk through crop diversification in rural Kenya," Climatic Change, Springer, vol. 162(3), pages 1107-1125, October.
    3. Graeme Guthrie, 2021. "Discounting, Disagreement, and the Option to Delay," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 80(1), pages 95-133, September.
    4. G. Guthrie, 2021. "Adapting to Rising Sea Levels: How Short-Term Responses Complement Long-Term Investment," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 78(4), pages 635-668, April.
    5. Veruska Muccione & Thomas Lontzek & Christian Huggel & Philipp Ott & Nadine Salzmann, 2023. "An application of dynamic programming to local adaptation decision-making," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 119(1), pages 523-544, October.

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