Role of Uncertainty in the Economics of Catastrophic Climate Change

Abstract

Using climate change as a prototype motivating example, this paper analyzes the implications of structural uncertainty for the economics of catastrophes. The paper shows that having an uncertain amplifying or scaling parameter (which is updated by Bayesian learning) induces a critical "tail thickening" of posterior-predictive distributions. Such thickened tails have strong implications for applications of expected utility theory to situations (like climate change) where a catastrophe is theoretically possible because a priori knowledge cannot place sufficiently narrow bounds on overall damages. In principle, the impact of an uncertain scale of damages on cost-benefit analysis outweighs the impact of discounting.

Keywords: climate change, catastrophes

JEL Classification: H00