Probabilistic Stabilization Targets

We study stabilization targets: common environmental policy recommendations that specify a maximum probability of an environmental variable exceeding a fixed target (e.g. limit climate change to at most 2°C above preindustrial). Previous work generally considers stabilization targets under certainty equivalence. Using an integrated assessment model with uncertainty about the sensitivity of the temperature to greenhouse gas (GHG) concentrations (the climate sensitivity), learning, and random weather shocks, we calculate the optimal GHG emissions policy with and without stabilization targets. We characterize the range of feasible targets and show that in general, climate change has too much uncertainty and inertia to be controlled with the precision implied by stabilization targets. We find that uncertainty exacerbates the welfare cost of stabilization targets. First, the targets are inflexible and do not adjust to new information about the climate system. Second, the target forces the emissions policy to overreact to transient shocks. These effects are present only in a model with uncertainty. Total welfare costs in the baseline model are 4.7%, which is 66% higher than the welfare cost under certainty.