Regulating stock externalities

We develop a dynamic regulation game for a stock externality under asymmetric information and future market uncertainty. Within this framework, regulation is characterized as the implementation of a welfare-maximization program conditional on informational constraints. We identify the most general executable programs and find these yield simple and intuitive time-consistent policy rules that implement the stochastic first best as long as a future market exists. We apply our theory to carbon dioxide emissions trading schemes and find substantial welfare gains are possible, compared to current practices.