Adaptive mitigation strategies hedge against extreme climate futures

Giacomo Marangoni (), Jonathan R. Lamontagne, Julianne D. Quinn, Patrick M. Reed and Klaus Keller
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Giacomo Marangoni: Politecnico di Milano
Jonathan R. Lamontagne: Tufts University
Julianne D. Quinn: University of Virginia
Patrick M. Reed: Cornell University
Klaus Keller: Penn State University

Climatic Change, 2021, vol. 166, issue 3, No 11, 17 pages

Abstract: Abstract The United Nations Framework Convention on Climate Change agreed to “strengthen the global response to the threat of climate change, in the context of sustainable development and efforts to eradicate poverty” (UNFCCC 2015). Designing a global mitigation strategy to support this goal poses formidable challenges. For one, there are trade-offs between the economic costs and the environmental benefits of averting climate impacts. Furthermore, the coupled human-Earth systems are subject to deep and dynamic uncertainties. Previous economic analyses typically addressed either the former, introducing multiple objectives, or the latter, making mitigation actions responsive to new information. This paper aims at bridging these two separate strands of literature. We demonstrate how information feedback from observed global temperature changes can jointly improve the economic and environmental performance of mitigation strategies. We focus on strategies that maximize discounted expected utility while also minimizing warming above 2 °C, damage costs, and mitigation costs. Expanding on the Dynamic Integrated Climate-Economy (DICE) model and previous multi-objective efforts, we implement closed-loop control strategies, map the emerging trade-offs and quantify the value of the temperature information feedback under both well-characterized and deep climate uncertainties. Adaptive strategies strongly reduce high regrets, guarding against mitigation overspending for less sensitive climate futures, and excessive warming for more sensitive ones.

Keywords: Climate risk management; Adaptive mitigation pathways; Integrated assessment modelling; Multi-objective optimization; Direct policy search (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s10584-021-03132-x

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