000			01164nam a22001457a 4500
999			_c516225 _d516225
008			210219b ||||| |||| 00| 0 eng d
100			_aRudi, Ivan _924257
245			_aOptimal climate policy when damages are unknown
260			_a American Economic Journal: Economic Policy
300			_a12(2), May, 2020: p.340-373
520			_aIntegrated 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. – Reproduced
773			_a American Economic Journal: Economic Policy
906			_aCLIMATE CHANGE
942			_cAR