Adaptation to Catastrophic Events with Two Layers of Uncertainty: Central Planner Perspective

We study optimal adaptation to extreme climate events in a setup where events are dynamically uncertain and the decision maker does not know the true probabilities of events. We analyze different policy decision rules minimizing expected welfare losses for sites with different expected damages from the catastrophic event. We show under which conditions it is optimal to wait before implementing prevention measures in order to obtain more information about the underlying probabilistic process. This waiting time crucially depends on the information set of the planner and the implemented learning procedure. We study different learning procedures of the planner, ranging from simple perfect learning to two-layers Bayesian updating in the form of Dirichlet mixture processes. This latter, to the best of our knowledge, is a novel tool in the climate adaptation economics literature.