Bayesian Learning About Climate Change in a Stochastic Environment. David Kelly and Charles D. Kolstad, Department of Economics, University of California, Santa Barbara, CA 93106
We consider the issue of when decision-makers will decide they know enough about climate change to act. We consider a standard optimal growth framework (a la DICE) in which decision-makers are uncertain about the climate sensitivity. Forcing is subject to random shocks and the decision-makers update their prior on climate sensitivity based on realized temperature. Appropriate mitigation is taken based on what the decision-maker learns about the system. We examine the regulatory delays that are inherent in such a learning environment.