Location: 368A Heady Hall
Description: Ivan Rudik
"Calibrating Informational Dynamics: Learning the Sensitivity of Climate to Emissions"
Abstract: Carbon dioxide (CO2) concentrations are projected to increase for the foreseeable future. We know emitting more CO2 will warm the earth, but by how much is uncertain despite large amounts of research devoted to this area. How sensitive the climate is to CO2, the climate sensitivity, is a major determinant of optimal policy stringency. Uncertainty and learning about the climate sensitivity have been studied by economists through dynamic stochastic climate-economy models. Although the dynamics of our beliefs about the climate sensitivity are surely important, economists have largely abstained from trying to accurately calibrate this component of the climate-economy system due to computational burdens. This has resulted in stark inconsistencies between climate-economy models suggesting relatively fast rates of learning, and the reality that we have not honed down our beliefs for decades. Here we calibrate the informational dynamics of a Bayesian climate policymaker. We nest a general Bayesian learning framework into a dynamic stochastic climate-economy model in a way that closely matches how climate scientists actually estimate the climate sensitivity distribution. We generalize previous approaches by not making distributional assumptions about climate sensitivity beliefs nor the climate data generating process that have resulted in artificially fast learning in climate-economy models. We show that our approach can correctly recover a climate sensitivity distribution consistent with climate scientists’ current best estimates, and it can accurately recover the dynamics of real world climate sensitivity beliefs. Simulations of optimal policy and beliefs suggest that future learning will be several times slower than suggested by previous work, and that future climate policy may not be as flexible to new observations of CO2 and temperature as once believed.
Contact Person: Ivan Rudik