Optimally Climate Sensitive Policy under Uncertainty and Learning

S. Jensen and C. P. Traeger

Working paper (November 2014)

URL: http://tinyurl.com/nf6vy8a

The equilibrium response of the global temperature to greenhouse gas emissions is highly uncertain. We derive the optimal climate policy under uncertainty, acknowledging Baysian uncertainty, passive and active learning, and temperature stochasticity. Our analysis employs a stochastic dynamic programming implementation of the integrated assessment model DICE (Nordhaus, 2008). We find that the stochasticity of temperatures induces precautionary savings, while Bayesian uncertainty over the climate’s sensitivity to greenhouse gas emissions increases the optimal present day carbon tax by approximately 25

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