Integrated assessment models (IAMs) are economists' primary tool for analyzing the optimal carbon tax. Damage functions, which link temperature to economic impacts, have come under fire because of their assumptions that may be incorrect in significant but a priori unknowable ways. Here I develop recursive IAM frameworks to model uncertainty, learning, and concern for misspecification about damages. I decompose the carbon tax into channels capturing state uncertainty, insurance motives, and precautionary saving. Damage learning improves ex ante welfare by 750 billion USD. If damage functions are misspecified and omit the potential for catastrophic damages, robust control may be beneficial ex post.
"Optimal Climate Policy When Damages Are Unknown."
American Economic Journal: Economic Policy,
Taxation and Subsidies: Externalities; Redistributive Effects; Environmental Taxes and Subsidies
Climate; Natural Disasters and Their Management; Global Warming
Environmental Economics: Government Policy