关键词: 气候变化, 不确定性, 贝叶斯分析, 期望边际效用

Abstract: There are numerous uncertainties in climate change and its economic impacts. Under Bayesian framework, the parameter is a random variable, which considers the structure uncertainty of climate change and improving the prediction accuracy. In this paper, suppose that the probability distributions for temperature change are the fat-tailed pareto distribution and the thin-tailed exponential distribution respectively. Combining data with gamma prior information, we derive the posterior predictive distribution for temperature change. Moreover, I argue that unbounded marginal utility makes little sense, and once we put a bound on marginal utility, expected marginal utility will be finite even if the distribution for outcomes is fat-tailed. Therefore, we support policies that would result in gradual GHG abatement. Finally, it is found that when the sample size is large, the prior affect to the posterior expected marginal utility is unclear. But the result is opposed as the sample is decrease.

Key words: Climate Change, Uncertainty, Bayesian Analysis, Expected Marginal Utility