There are four categories of explanations for over-compliance. The project will attempt to determine the extent to which they contribute to observed emissions levels. The four explanations are: (i) the cost of reducing emissions is zero; (ii) plants are compensating for randomness in emissions; (iii) the production technology is not smooth; and (iv) there are non-regulatory benefits of over-complying. For example, if emissions are random, then plants may want to pollute below their limit, on average, to reduce the probability that they will accidentally violate. If this explanation for over-compliance is correct, then plants should choose lower mean emissions, relative to permitted levels, the higher is the emissions variance. Similar statistical tests are developed for the other over-compliance explanations.
The project explores these issues through empirical econometric analysis using the PCS data. We develop and test hypotheses based on each of the four explanations, concentrating on (ii) and (iii).
The explanations have greatly different implications for emissions regulation. For example, the way in which violators are identified and the penalties they are subject to will be an important determinant of emissions if emissions randomness is a major cause of over-compliance. On the other hand, non-regulatory influences, such as community pressure, will be largely unaffected by enforcement strategies. Because of these differences, the intended result is a better general understanding of why plants over-comply with relevant regulations. We hope to learn the specific features of the CleanWater Act that may have contributed to over-compliance. We would also like to know the extent (in a quantitative sense) to which non-regulatory influences have led to lower emissions. These non-regulatory influences could include community, market, and firm-governance pressures.