Managing Catastrophic Climate Risks Under Model Uncertainty Aversion
提出一种稳健的风险管理方法应对灾难性气候变化，通过分析减排模型，发现模型不确定性（尤其是模型分歧随减排的变化）是驱动最优减排的关键因素，且模型不确定性要求更高水平的减排。
We propose a robust risk management approach to deal with the problem of catastrophic climate change that incorporates both risk and model uncertainty. Using an analytical model of abatement, we show how aversion to model uncertainty influences the optimal level of mitigation. We disentangle the role of preferences from the structure of model uncertainty, which we define by means of a simple measure of disagreement across models. With data from an expert elicitation about climate change catastrophes, we quantify the relative importance of these two effects and calibrate a numerical integrated assessment model of climate change. The results indicate that the structure of model uncertainty, and specifically how model disagreement varies in abatement, is the key driver of optimal abatement and that model uncertainty warrants a higher level of climate change mitigation. This paper was accepted by Manel Baucells, decision analysis.