With climate change as prototype example, this paper analyzes the implications of structural uncertainty for the economics of lowprobability, 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.

Author names: 
Weitzman, M. L.

Weitzman, M. L. (2009). On Modeling and Interpreting the Economics of Catastrophic Climate Change. Review of Economics and Statistics, 91(1), 1–19. doi:10.1162/rest.91.1.1

Geographical area: 
Policy areas considered: