Dynamic nonpoint-source pollution control policy: ambient transfers and uncertainty

When a regulator cannot observe or infer individual emissions, corrective policy must rely on ambient pollution data. Assuming this kind of environment, we study a class of differential games of pollution control with profit functions that are polynomial in the global pollution stock. Given an open-loop emissions strategy satisfying mild regularity conditions, an ambient transfer scheme is exhibited that induces it in Markov-perfect equilibrium (MPE). Proposed transfers are a polynomial function of the difference between actual and desired pollution levels; moreover, they are designed so that in MPE no tax or subsidy is ever levied. Their applicability under stochastic pollution dynamics is studied for a symmetric game of polluting oligopolists with linear demand. We discuss a quadratic scheme that induces agents to adopt Markovian emissions strategies that are stationary and linearly decreasing in total pollution. Total expected ambient transfers are non-positive and their magnitude is linearly increasing in physical volatility, the size of the economy, and the absolute value of the slope of the inverse demand function. However, if the regulator is interested in inducing a constant emissions strategy then, in expectation, transfers vanish. The total expected ambient transfer is compared to its point-source equivalent.