How Learning Helps Mitigate the Worst When the Downside of Climate Change is Extreme

There is growing appreciation of the importance of low-probability, high-consequence climate change outcomes given the speed and scale at which greenhouse gas (GHG) emissions are increasing. Deep uncertainty in equilibrium climate sensitivity (ECS), the eventual temperature increase in response to changes in GHG concentrations, has persistently been a critical issue in determining an effective yet feasible hedging strategy against such tail events. In this paper, we examine how learning efforts to reduce uncertainty in ECS affects the optimal policy when the downside of climate change is indeed catastrophic. Using stochastic dynamic programming, we investigate the role of learning under a “triple worst-case” scenario, in which climate damages are catastrophic at high temperatures; the true ECS, unknown to the world, exceeds the range of current scientific beliefs; and negative emissions technologies do not materialize at scale for deployment. Under these assumptions, we find that learning primarily contributes to welfare improvements through corrections in short-run emission abatement and long-run capital investment decisions. In the short-run, a more stringent carbon abatement policy accelerates the transition to a zero-carbon economy. In the long-run, increased capital investment in anticipation of higher climate damages increases economic output and smoothens consumption. Strong precautionary measures are often infeasible due to political constraints. Our results imply that modest carbon abatement policies coupled with efforts to reduce climate uncertainty can be a feasible and effective mitigation strategy