AN ITERATIVE PROCEDURE FOR OPTIMAL POLLUTION CONTROL UNDER INCOMPLETE INFORMATION

We consider a repeated regulation model in an oligopoly under asymmetric information with pollution. An iterative procedure is proposed where the regulator designs stationary taxes, and firms are not required to be perfectly rational. They can form and update simple beliefs about their competitors' aggregate output at each period. Two versions of the mechanism are provided depending on whether firms behave adaptively or with perfect foresight. Conditions under which the procedure converges to a unique steady state are provided. It is proved that there exists a suitable stationary tax policy that enables the firms to adjust to socially optimal choices in the long run. The tax rates of both versions are typically strictly less than the ones that result from a full information, Nash implementation. Moreover, in the myopic case, the tax rate decreases as the number of firms increases. We discuss problems relating to the potential implementation of the procedure.