Abstract:
Climate policy is complicated by the considerable compounded uncertainties over the costs and benefits of abatement. We don’t even know the probability distributions for future temperatures and impacts, making cost-benefit analysis based on expected values challenging to say the least. There are good reasons to think that those probability distributions are fat-tailed, which implies that if social welfare is based on the expectation of a CRRA utility function, we should be willing to sacrifice close to 100% of GDP to reduce GHG emissions. I argue that unbounded marginal utility makes little sense, and once we put a bound on marginal utility, this implication of fat tails goes away: Expected marginal utility will be finite even if the distribution for outcomes is fat-tailed. Furthermore, depending on the bound on marginal utility, the index of risk aversion, and the damage function, a thin-tailed distribution can yield a higher expected marginal utility (and thus a greater willingness to pay for abatement) than a fat-tailed one.
