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The paper presents a model of environmental resource management in an uncertain environment with measurement considerations. In particular, the model recognizes uncertainty about the dynamic dispersion relationships and in the measurement of emission rates and pollution levels. Since uncertainty enlarges the environmental resource management problem, actions that reduce uncertainty must be considered by the environmental control manager. Such considerations, however, cannot be pursued independently of pollution control actions. It is thus postulated that environmental policy issues must be analyzed in the context of an adaptive control problem with measurement considerations and a new adaptive environmental control strategy, which is obtained by introducing a function of the one‐period—ahead covariance matrix of the parameter estimates in the objective function, is suggested. This new strategy is shown to exhibit the dual effect of experimentation and caution.

The interplay between prediction and estimation is crucial in adaptive prediction problems. In its general form, the adaptive prediction problem is a difficult multiperiod optimization problem and thus too complex for practical applications. By minimizing the one‐step‐ahead forecasting error, subject to a constraint on the (weighted) trace of the inverse on the one‐step‐ahead information matrix we arrive at a very simple approach, which should be interesting for more complex situations also. In the context suggesting the problem, a pollution dispersion model with time‐varying parameters is considered, and the target is to obtain a sensible and easily implementable adaptive predictor.

Discusses recursive estimation techniques which can be used to update or revise estimates of the parameters of an economic model to account for new data. Such methods admit the possibility of proceeding with the gathering of observed data until a specified accuracy of the parameters is achieved or if the economic processes are time‐varying the parameters can be tracked. Recursive methods can also be used for adaptive learning, forecasting and control. Examines both single equation ‐ static as well as dynamic ‐ economic models.

The purpose of this paper is to examine the role of information structure in the design of the structure and parameters of an optimal regulatory mechanism. In particular, the paper analyzes both firm and public agency behavior under environmental externality internalization schemes and stochastic externality measurements of emissions and concentrations. It is shown that in general, the optimal environmental control policy involves the derivation of regulatory controls, measurement controls and the sequential estimation of inaccessible emissions and concentrations by a Kalman filter.