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 situation where firms' actions contribute to a stock externality. The regulator and firms have asymmetric information about serially correlated (abatement) costs. With price-based policies such as taxes, the regulator learns about the evolution of both stock and costs. This ability...

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...