This project has two objectives. One is to develop a theoretical understanding of a generic environmental problem of which global warming and acid deposition are two examples. That problem is the optimal tradeoff between acting to control environmental externalities versus delaying control when faced with uncertainty coupled with learning. The second objective is to apply one or more of these theoretical models to the global warming problem, addressing the policy question of the extent to which the U.S. and other countries should control greenhouse gas emissions now versus waiting until more is known about the problem before controlling. This is one of the key issues in the public policy debate on global warming and, thus, results of this project may be very significant. The two driving features of the models developed will be learning that occurs over time reducing uncertainty and the stock feature of externalities such as global warming, whereby the stock causes damage but only the flow can be controlled. The approach of the theoretical part of the project is to develop a stochastic optimal control problem and to investigate the comparative static properties of the resulting model. The empirical portion of the project will numerically implement one of the theoretical models to the extent necessary to develop policy conclusions. This will involve adapting an empirical model developed elsewhere, perhaps the Nordhaus model, rather than developing a totally new one.
