There is a growing consensus that emissions of greenhouse gases due to human activity will alter the earth’s climate, most notably by causing temperatures, precipitation levels, and weather variability to increase. The design of optimal climate change mitigation policies requires estimates of the health and other benefits of reductions in greenhouse gases; current evidence on the magnitude of the direct and indirect impacts, however, is considered insufficient for reliable conclusions (A. J. McMichael et al. 2003).

With climate change as prototype example, this paper analyzes the implications of structural uncertainty for the economics of low-probability, high-impact catastrophes. Even when updated by Bayesian learning, uncertain structural parameters induce a critical “tail fattening” of posterior-predictive distributions. Such fattened tails have strong implications for situations, like climate change, where a catastrophe is theoretically possible because prior knowledge cannot place sufficiently narrow bounds on overall damages. This paper shows that the economic consequences of fat-tailed structural uncertainty (along with unsureness about high-temperature damages) can readily outweigh the effects of discounting in climate-change policy analysis.

Our goal is to evaluate variations in rice yields for likely climate changes for Southeast Asia. To do so we integrate economic modeling with soil science crop growth simulation model, weather simulation model and global climate change models. We estimate impacts of climate change under two different climate scenarios, one assuming high future global anthropogenic pollution emissions,