Optimal Climate Policy When Damages are Unknown
- American Economic Journal: Economic Policy (Forthcoming)
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. If damage functions are misspecified and omit the potential for catastrophic damages, robust
control may be beneficial ex post.
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