We study the importance of anticipated learning - about both environmental damages and abatement costs - in determining the level and the method of controlling greenhouse gas emissions. We also compare active learning, passive learning, and parameter uncertainty without learning. Current beliefs...

A regulator anticipates learning about the relation between environmental stocks and economic damages. For a model with linear-quadratic costs and a general learning process, we show analytically that anticipated learning decreases the optimal level of abatement "at a given information set." If...

We study the importance of anticipated learning - about both environmental damages and abatement costs - in determining the level and the method of controlling greenhouse gas emissions. We also compare active learning, passive learning, and parameter uncertainty without learning. Current beliefs...

I offer a selective review of discounting and climate policy. Analytic and numerical models show that different assumptions greatly change the degree to which decisions about climate policy depend on the discount rate. I discuss a claim that standard models exaggerate the current generation's...