Examining state environmental regulatory policy design

Abstract

This paper develops a scheme for characterizing variation in the structure of state environmental regulatory policy design. The rules, policy tools and incentives built into regulatory policies affect the manner in which policies are implemented, and, ultimately, the actions of target populations toward the ambient environment. Variation in state concentrated animal feeding operation (CAFO) regulation is examined as a specific case of subnational regulatory policy design. These operations can have substantial environmental impacts on local communities and large cumulative impacts on waterways. Using a content analysis of CAFO policy across states, the paper shows state policy designs to vary on three dimensions: scope, stringency and prescription. This research calls attention to the use of more detailed measures of environmental policy design rather than unitary measures typically used in environmental policy research.

Notes

1 Animal units are a relative measure used to determine the impact of different varieties of livestock. For example, 700 mature dairy cows or 10 000 hogs less than 55 pounds are each classified as 1000 AU.

2 The statutes of two states, North Carolina and Washington, are coded. Each of these state's administrative code references state statute almost exclusively for specific provisions, thus statute provisions are included as part of the coding scheme.

3 A subsequent court decision regarding the federal rule change during July 2005 somewhat altered the playing field for states; however, the data used in this paper are specific to the period before the court decision.

4 Upon review of the values in Table 2, it should be noted that the top end of the variable ranges for virtually all of the dimensional measures is much larger than the mean. This is simply reflective of the policy environment in which CAFOs operate. There is a great deal of variation in policy design approaches offered by each state, as is noted by the categories presented in Table 2 and the graphical evidence presented in Figure 1. This should be taken as evidence of pioneering state efforts (e.g. Illinois) or learning from past states' mistakes (e.g. South Carolina) versus a large portion of states with little or no CAFO program (e.g. Montana or Rhode Island).

5 The hierarchical cluster analysis routine used in the SPSS 12.0 for Windows statistical package is employed. The cluster separations are based upon the squared Euclidian distance between the standardized measures of design requirements index, setback requirements index, number of definitions, length of purpose section, general provisions index and total amount of numbered provisions. The results return one outlier (Hawaii).