Choosing regulatory options when environmental costs are uncertain

Abstract

An optimal regulatory regime is explored in which regulating a non-degradable pollution stock, e.g., the accumulation of greenhouse gases (GHGs) in the atmosphere, would be based on a model of optimal statistical decisions where it is shown when it pays to ‘act and learn’ and when to ‘learn and act’. The value of information in reducing uncertainty can be shown to be sensitive to accuracy and likelihood of scientific research results. Some interesting policy results are obtained for the dynamic intertemporal decision situation when the value of new information is an outcome of stochastic optimization with learning.