The Climate Change Footprint: Will We See It Before It Is Upon Us?

This paper considers the case of Bayesian learning about the relationship between the greenhouse-gas level and temperature rise. Learning takes time because of a stochastic shock to the realized global mean temperature. The paper illustrates the difficulty of quickly learning about the underlying relationship in the presence of a shock. The paper then goes on to present an integrated assessment model with endogenous learning. The model is a stochastic growth model with learning. The model is solved as a dynamic program and then simulated for particular realizations of the shock.