OPTIMAL POLICY UNDER UNCERTAINTY AND LEARNING ABOUT CLIMATE CHANGE: A STOCHASTIC DOMINANCE APPROACH

Global climate change presents a classic problem of decision making under uncertainty with learning. We provide stochastic dominance theorems that provide new insights into when abatement and investment into low carbon technology should increase in risk. We show that R&D into low-carbon technologies and near-tern abatement are in some sense opposites in terms of risk. Abatement provides insurance against the possibility of major catastrophes; R&D provides insurance against the possibility that climate change is marginally worse than average. We extend our results to the comparative statics of learning.