On Modeling and Interpreting the Economics of Catastrophic Climate Change
以气候变化为例，分析结构性不确定性对低概率高影响灾难经济学的影响，发现厚尾不确定性对气候政策的影响可能超过贴现效应。
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. Copyright by the President and Fellows of Harvard College and the Massachusetts Institute of Technology.