Social and Behavioral Sciences
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AbstractWe analyze the impact of damage uncertainty on optimal mitigation policies in the integrated assessment of climate change. Usually, these models analyzeuncertainty by averaging deterministic paths. In contrast, we build a consistentmodel deriving optimal policy rules under persistent uncertainty. For this purpose,we construct a close relative of the DICE model in a recursive dynamic programming framework. Our recursive approach allows us to disentangle effects of risk, risk aversion, and aversion to intertemporal substitution. We analyze different ways how damage uncertainty can affect the DICE equations. We compare the optimal policies to those resulting from the wide-spread ex-ante uncertainty approach averaging deterministic paths.