Environmental enforcement in dire straits: There is no protection for nothing and no data for free

Notre Dame Law Review

Volumn 85, Issue 1, 2009, Pages 55-88

  Flatt, Victor B ^a   Collins Jr , Paul M ^b  

^a UNIVERSITY OF NORTH CAROLINA   (United States)

^b UNIVERSITY OF NORTH TEXAS   (United States)

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[No Author keywords available]

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EID: 76749095830     PISSN: 07453515     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Review

References (151)

1. See, e.g., Marcilynn A. Burke, Green Peace? Protecting Our National Treasures While Providing for Our National Security, 32 WM. & MARY ENVTL. L. & POL'Y REV. 803, 805-11 (2008) (discussing requested changes to a host of federal environmental laws to accommodate national security) ;
2. Charles de Saillan, The Use of Imminent Hazard Provisions of Environmental Laws to Compel Cleanup at Federal Facilities, 27 STAN. ENVTL. L. J. 43, 204-06 (2008) (advocating several amendments to federal environmental laws) ;
3. Robert V. Percival, Environmental Law in the Twenty-First Century, 25 VA. ENVTL. L. J. 1, 2-4 (2007) (describing a "remarkable, bipartisan burst" of environmental regulation over the past three decades).
4. 42 U. S. C. §§ 7401-7671 (2006 & Supp. II 2008).
5. Draft Report to Congress on the Costs and Benefits of Federal Regulations, 65 Fed. Reg. 7197, 7203 (Feb. 11, 2000).
6. Professor Hsu presents a very good description of how environmental harms and benefits, as well as the societal costs of regulation, may be routinely under-(or sometimes over-) valued.
7. See Shi-Ling Hsu, The Identifiability Bias in Environmental Law, 35 FLA. ST. U. L. REV. 433, 440-51 (2008).
8. See William L. Andreen, Beyond Words of Exhortation: The Congressional Prescription/or Vigorous Enforcement of the Clean Water Act, 55 GEO. WASH. L. REV. 202, 203-11 (1987) ;
9. Victor B. Flatt, Spare the Rod and Spoil the Law: Why the Clean Water Act Has Never Grown Up, 55 ALA. L. REV. 595, 596-99 (2004) ;
10. Robert L. Glicksman & Dietrich H. Earnhart, The Comparative Effectiveness of Government Interventions on Environmental Performance in the Chemical Industry, 26 STAN. ENVTL. L. J. 317, 319-21 (2007) ;
11. see also JOEL A. MINTZ ET AL., ENVIRONMENTAL ENFORCEMENT 5-15 (2007) (discussing various theories and objectives of environmental enforcement).
12. See Robert W. Adler, The Two Lost Books in the Water Quality Trilogy: The Elusive Objectives of Physical and Biological Integrity, 33 ENVTL. L. 29, 49 (2003) ;
13. Clifford Rechtschaffen & David L. Markell, Improving State Environmental Enforcement Performance Through Enhanced Government Accountability and Other Strategies, 33 ENVTL. L. REP. 10, 559, 10, 565 (2003).
14. Glicksman & Earnhart, supra note 5, at 320.
15. See Richard J. Pierce, Jr., Issues Raised by Friends of the Earth v. Laidlaw Environmental Services: Access to the Courts for Environmental Plaintiffs, 11 DUKE ENVTL. L. & POL'Y F. 207, 234 (2001) (arguing that the environmental federalism standard allows no or very little second guessing of state enforcement decisions).
16. See Peter A. Appel, The Diligent Prosecution Bar to Citizen Suits: The Search for Adequate Representation, 10 WIDENER L. REV. 91, 91 (2004).
17. See Michael P. Vandenbergh, Beyond Elegance: A Testable Typology of Social Norms in Corporate Environmental Compliance, 22 STAN. ENVTL. L. J. 55, 66 (2003) (citing Clifford Rechtschaffen, Deterrence vs. Cooperation and the Evolving Theory of Environmental Enforcement, 71 S. CAL. L. REV. 1181, 1219 (1998)).
18. Victor B. Flatt, A Dirty River Runs Through It (The Failure of Enforcement in the Clean Water Act), 25 B. C. ENVTL. AFF. L. REV. 1, 17-19 (1998).
19. See Rena I. Steinzor, Myths of the Reinvented State, 29 CAP. U. L. REV. 223, 231-32 (2001).
20. CLIFFORD RECHTSCHAFFEN & DAVID L. MARKELL, REINVENTING ENVIRONMENTAL ENFORCEMENT & THE STATE/FEDERAL RELATIONSHIP 2 (2003).
21. Id.
22. See, e.g., Steinzor, supra note 12, at 233.
23. See U. S. ENVTL. PROT. AGENCY, COMPLIANCE LITERATURE SEARCH RESULTS (2007), available at http://www.epa.gov/oecaerth/resources/reports/compliance/ research/lit-results-2007.pdf (citing and summarizing more than two hundred compliance-related articles and books).
24. See discussion infra Parts IV & V.
25. See Steinzor, supra note 12, at 225, 228-32, 240-41.
26. Carol M. Rose, Rethinking Environmental Controls: Management Strategies for Common Resources, 1991 DUKE L. J. 1, 12-13.
27. See CRAIG N. JOHNSTON ET AL., LEGAL PROTECTION OF THE ENVIRONMENT 12-15, 24 (2d ed. 2007).
28. See William W. Buzbee, Asymmetrical Regulation: Risk, Preemption, and the Floor/Ceiling Distinction, 82 N. Y. U. L. REV. 1547, 1551-52 (2007).
29. Flatt, supra note 11, at 7, 13-14 (explaining the failure of states to administer the Glean Water Program).
30. See ROBERT L. GLICKSMAN ET AL., ENVIRONMENTAL PROTECTION 586-87 (5th ed. 2007) ;
31. JOHNSTON ET AL., supra note 20, at 3.
32. See GUCKSMAN ET AL., supra note 23, at 65, 586-87.
33. Clean Water Act of 1948, Pub. L. No. 80-845, 62 Stat. 1155 (codified as amended at 33 U. S. C. §§ 1251-1376 (2006 & Supp. II 2008)).
34. See JOHNSTON ET AL., supra note 20, at 6-9.
35. See Jerome M. Organ, Limitations on State Agency Authority to Adopt Environmental Standards More Stringent than Federal Standards: Policy Considerations and Interpretive Problems, 54 MD. L. REV. 1373, 1373-74 (1995).
36. JOHNSTON ET AL., supra note 20, at 9-10.
37. Flatt, supra note 11, at 15.
38. Buzbee, supra note 21, at 1567, 1587.
39. See Flatt, supra note 11, at 16-19;
40. see also Union Elec. Co. v. EPA, 427 U. S. 246, 265-69 (1976) (limiting EPA's authority to review state air pollution controls).
41. See, for example, 42 U. S. C. § 7410 (a) (5) (A) (i) (2006), originally added in 1977, which prohibits the Federal Administrator from requiring states to control indirect sources of air pollution, i.e. control of land use, for State Implementation Plan program approval.
42. Flatt, supra note 11, at 16.
43. Kirsten H. Engel, State Environmental Standard Setting: Is There a "Race" and is it "To the Bottom"?, 48 HASTINGS L. J. 271, 340-41, 359 (1997).
44. Organ, supra note 27, at 1376-77.
45. RECHTSCHAFFEN & MARKELL, supra note 13, at 2, 179-81 (noting that almost one-half of states passed audit privilege shield laws despite EPA objection).
46. Id. at 1, 156.
47. See, e.g., Daniel C. Esty, Next Generation Environmental Law: A Response to Richard Stewart, 29 CAP. U. L. REV. 183, 189 (2001) (suggesting regulatory reform initiatives are actually concerned with using deregulation in an attempt to advance the interests of groups such as small businesses).
48. RECHTSCHAFFEN & MARKELL, supra note 13, at 1-2.
49. See id. at 237-51.
50. Id. at 242.
51. Id.
52. Id. at 239-40.
53. Id. at 240-01 (explaining that Illinois and Nevada studies finding positive changes in compliance did not distinguish between changes resulting from modifications to compliance approaches and those resulting from various other reforms).
54. See Federal Water Pollution Control Act, 33 U. S. C. §§ 1251-1387 (2006). Section 402 of the CWA established the NPDES.
55. Id. § 1342.
56. Glicksman & Eamhart, supra note 5, at 329-32.
57. Id. at 347-52.
58. Id. at 352.
59. Robert L. Glicksman & Dietrich H. Earnhart, Effectiveness of Government Interventions at Inducing Better Environmental Performance: Does Effectiveness Depend on Facility or Firm Features?, 35 B. C. ENVTL. AFF. L. REV. 479, 487-89 (2008).
60. Id. at 487.
61. See infra Part II. B.1.
62. See Flatt, supra note 11, at 21-26.
63. Id. at 26-27.
64. See Buzbee, supra note 21, at 1550.
65. See Flatt, supra note 11, at 20.
66. For reasons explained in Part II. B.l, the data are not sufficient to test all of these questions. See infra notes 68-77 and accompanying text.
67. See discussion supra Introduction.
68. See Adler, supra note 6, at 49.
69. RECHTSCHAFFEN & Markell, supra note 13, at 247.
70. See Flatt, supra note 11, at 24.
71. Id.
72. See Friends of the Earth, Inc. v. Laidlaw Envtl. Servs., 528 U. S. 167, 184-85 (2000) ("Here, in contrast, it is undisputed that Laidlaw's unlawful conduct-discharging pollutants in excess of permit limits-was occurring at the time the complaint was filed. Under Lyons, then, the only 'subjective' issue here is [t]he reasonableness of [the] fear' that led the affiants to respond to that concededly ongoing conduct by refraining from use of the North Tyger River and surrounding areas. Unlike the dissent... we see nothing 'improbable' about the proposition that a company's continuous and pervasive illegal discharges of pollutants into a river would cause nearby residents to curtail their recreational use of that waterway and would subject them to other economic and aesthetic harms. The proposition is entirely reasonable..."
73. (quoting Los Angeles v. Lyons, 461 U. S. 95, 107 n. 7, 108 n. 8 (1983))).
74. Flatt, supra note 11, at 24.
75. Id.
76. Id. at 22.
77. Glicksman and Earnhart attempt to measure this. See Glicksman & Earnhart, supra note 49, at 504-09.
78. See, e.g., Mark A. Cohen, Empirical Research on the Deterrent Effect of Environmental Monitoring and Enforcement, 30 ENVTL. L. REP. 10, 245, 10, 250 (2000) (suggesting that public awareness of sanctions may damage the value and reputation of sanctioned firms, helping to increase general environmental deterrence) ;
79. Surabhi Kadambe & Kathleen Segerson, On the Role of Fines as an Environmental Enforcement Tool, 41 J. ENVTL. PLAN. & MGMT. 217 (1998) (developing a model for analyzing the effect of fine amounts on regulatory compliance) ;
80. Dorothy Thornton et al., General Deterrence and Corporate Environmental Behavior, 27 LAW & POL'Y 262, 278-83 (2005) (questioning the "explicit" deterrent effect of fines but suggesting that they "implicidy" reinforce the general deterrence of other informal sanctions).
81. But see Montserrat Viladrich Grau & Theodore Groves, The Oil Spill Process: The Effect of Coast Guard Monitoring on Oil Spills, 10 ENVTL. & RESOURCE ECON. 315, 322-24 (1997) (noting that reladvely low fines had no significant effect on the frequency or size of oil spills).
82. Flatt, supra note 11, at 18-19.
83. Id.
84. See U. S. Envtl. Prot. Agency, Integrated Data for Enforcement Analysis, http://www.epa.gov/compliance/data/systems/multimedia/idea (last visited Oct. 26, 2009) ;
85. Peter J. Fontaine, EPA's Multimedia Enforcement Strategy: The Struggle to Close the Environmental Compliance Circle, 18 COLUM. J. ENVTL. L. 31, 57-58 (1993).
86. See id.
87. Vandenbergh, supra note 10, at 87.
88. See U. S. Envtl. Prot. Agency, Enforcement & Compliance History Online, http://www.epa-echo.gov/echo (last visited Oct. 26, 2009).
89. See id.
90. See U. S. Envtl. Prot. Agency, ECHO Compliance Data Search (Air Program), http://www.epa-echo.gov/echo/compliance-report-air.html (last visited Oct. 26, 2009).
91. See id.
92. See id.
93. See Memorandum from Victor B. Flatt to Member Scholars of the Ctr. for Progressive Reform on Sufficiency of IDEA Data for Proposed Analysis (May 1, 2005) [hereinafter Flatt Memorandum] (on file with the authors).
94. Id.
95. Id. This is ostensibly because of the large computer time costs the EPA would incur through such a download. Because our study has hundreds of thousands of pieces of data, this would make analysis virtually impossible.
96. See U. S. Envd. Prot. Agency, Online Tracking Information System, http://www.epa.gov/compliance/data/systems/multimedia/aboutods.html (last visited Oct. 26, 2009).
97. See id.
98. See id.
99. Because of this difficulty, in 1998, one of the authors only conducted a comparison of two states for one environmental program, the CWA. The summarized data was received from the states themselves, and even only two years worth of data required the manual entry of over 10, 000 data points.
100. See Flatt, supra note 11, at 35 app. A.
101. See Flatt Memorandum, supra note 78.
102. 42 U. S. C. §§ 6901-6992k (2006).
103. See U. S. Envtl. Prot. Agency, Integrated Data for Enforcement Analysis, http://www.epa.gov/compliance/data/systems/multimedia/idea (last visited Oct. 26, 2009).
104. See Flatt Memorandum, supra note 78.
105. Id.
106. These data fields were received from EPA in 2004, and are on file with the authors and available online as research appendices. See Victor B. Flatt, Research Appendices, http://www.law.unc.edu/faculty/directory/ flattvictorb/research.aspx (last visited Oct. 28, 2009) [hereinafter Flatt, Research Appendices]. For EPA data pertaining to the CAA [hereinafter Clean Air Data], follow the link for "Clean Air", and for EPA data pertaining to the CWA [hereinafter Clean Water Data], follow the link for "Clean Water." These data fields and all other research appendices are also available online through the Notre Dame Law Review.
107. See Notre Dame Law Review, Archive: Vol. 85, No. 1, http://www.ndlawreview.org/archive/issue.php?vol=85&num=1 (follow link for "Research Appendices") (last visited Nov. 3, 2009) [hereinafter Notre Dame Law Review, Research Appendices]
108. See Clean Air Data, supra note 90.
109. See id.
110. See id.
111. See Clean Water Data, supra note 90. The units of analyses in these databases are the facility-inspection date and the type-classification. This means that each facility appears in the data on the basis of the date of the compliance inspection and the type of compliance inspection.
112. Rather bewilderingly, the NPDES data codebook indicates that it contains a variable composed of the number of quarters a facility is in violation of CWA requirements over the two years preceding a facility's inspection date (labeled Historic Noncompliance Quarter). According to the NPDES codebook, this variable takes on values ranging from one to eight. However, in the actual data, this variable does not take on these values, but rather contains year-quarter entries (e.g., 20011, 20012, 20013, 20014) that do not correspond to the number of quarters a facility is in violation of CWA requirements in the manner described in the codebook and do not range from one to eight (even after removing the year from the variable entries).
113. See Clean Water Data, supra note 90.
114. See sources cited infra note 101.
115. See, e.g., Riley E. Dunlap et al., Politics and Environment in America: Partisan and Ideological Cleavages in Public Support for Environmentalism, 10 ENVTL. POL. 23, 28-33 (2001) ;
116. David M. Konisky, Regulator Attitudes and the Environmental Race to the Bottom Argument, 18J. PUB. ADMIN. RES. & THEORY 321, 323 (2007) ; Eugene S. Uyeki & Lani J. Holland, Diffusion of Pro-Environment Attitudes?, 43 AM. BEHAV. SCIENTIST 646, 658-60 (2000).
117. E-mail from Phillip Shotts, Research Assistant, University of Houston Law Center, to Author (Feb. 16, 2006, 2:11 CST) (on file with the author).
118. See Jere Overdyke & Emily Buckles, Enviro Budget Contacts (May 17, 2006) (unpublished spreadsheet) (on file with author).
119. See Jere Overdyke, Flatt Research JCO Additions (May 17, 2006) (unpublished spreadsheet) (on file with author).
120. For full breakdowns of the states' budgets from each year, see Flatt, Research Appendices, supra note 90 (follow links under "State Budget Data") (last visited Oct. 2, 2009) [hereinafter State Budget Data];
121. Notre Dame Law Review, Research Appendices, supra note 90.
122. E-mail from Ben Rhem, Research Assistant, University of Houston Law Center, to Author (Sept. 22, 2009, 18:01 CST) (on file with the author) [hereinafter Rhem E-mail].
123. We obtained budget information from the following sources: Arizona Department of Environmental Quality; California Environmental Protection Agency; California State Library; Connecticut-Office of Policy and Management, Budget and Financial Management Division; Florida Department of Environmental Quality; Georgia Department of Natural Resources; Indiana Department of Environmental Management, Office of External Affairs; Kansas Department of Health and Environment; Maryland Department of Environment; North Carolina Department of Natural Resources and Environment, Budget and Planning; Ohio Environmental Protection Agency, Office of Fiscal Administration; Oregon Department of Environmental Quality; Texas Commission on Environmental Quality.
124. See State Budget Data, supra note 101.
125. See State Budget Data, supra note 101.
126. For data corrections, see Flatt, Research Appendices, supra note 90;
127. Notre Dame Law Review, Research Appendices, supra note 90.
128. Glicksman & Earnhart, supra note 49, at 487.
129. Id. at 483.
130. When we include such a variable in our CWA models, the permit modification variable fails to attain statistical significance anywhere near conventional levels.
131. See, e.g., John T. Scholz, Cooperative Regulatory Enforcement and the Politics of Administrative Effectiveness, 85 AM. POL SCI. REV. 115, 123 (1991) (testing "the corollary that Democrats will be less associated with cooperative enforcement than Republicans").
132. William D. Berry et al., Measuring Citizen and Government Ideology in the American States, 1960-93, 42 AM. J. POL. SCI. 327, 330-31 (1998). These ideology scores have subsequendy been updated through 2006.
133. See Richard C. Fording, State Citizen & Government Ideology, http://www.uky.edu/-rford/stateideology.html (last visited Sept. 20, 2009) (containing updated ideology scores through 2007).
134. Berry et al., supra note 110, at 330-31.
135. Id. at 341-43.
136. As an alternative to the Berry et al. scores, we operationalized a measure of elite ideology based on the political party affiliation of a state's legislative and executive branches scored such that: 6 = unified Democrat (governor and legislature), 5 = Democratic governor, divided legislature, 4 = Democratic governor, Republican legislature, 3 = Republican governor, Democratic legislature, 2 = Republican governor, divided legislature, 1 = unified Republican (governor and legislature). This information was collected from each state's legislative website, the website of the National Governor's Association, and individual contacts with the following agencies: Colorado Legislative Council Staff; Connecticut State Library; Kansas State Library; New Jersey State Library; Maryland Department of Legislative Services; State Library of Ohio; and New Mexico State Library. This alternative measure is correlated with the variable employed here at the 0.8 level. Substituting it for the measure utilized in this Article does not alter the substance of the results.
137. The number of dummy variables accounting for the Standard Industrial Code clusters of facilities differs in the CAA and CWA models due to the fact that a smaller number of Standard Industrial Code clusters are applicable to facilities appearing in the CWA data.
138. The mean of the dependent variable in Table 1 is 903.8 (standard deviation = 15, 159.87; range = 0 to 792, 000). The data used in Table 1 include facilities that were assessed monetary penalties for violations of CWA regulatory requirements, as well as those facilities that were not assessed monetary penalties. The data contain 25, 282 unique observations of facilities, meaning that, on average, facilities appear in the data 3.93 times. To account for this nonindependence of observations, we estimate the regression model employing robust standard errors, clustered on facility.
139. See generally, M. Arellano, Computing Robust Standard Errors for Within-Groups Estimators, 49 Oxford Bull. Econ. & Stat. 431 (1987) (explaining a formula for calculating robust standard errors).
140. See generally CHRISTOPHER H. ACHEN, INTERPRETING AND USING REGRESSION 18-34 (John L. Sullivan & Richard G. Niemi eds., 1982) (providing an overview of OLS regressions) ;
141. Damodar N. Gujarati & Dawn C. Porter, Basic Econometrics 55-80 (5th ed. 2009) (explaining the methodology behind and relative simplicity of the OLS regression model).
142. The mean of the dependent variable in Table 2 is 2250.3 (standard deviation = 60, 139.4; range = 0 to 8, 000, 000). The data used in Table 2 include three facilities that were assessed monetary penalties for violations of CAA regulatory requirements, as well as those facilities that were not assessed monetary penalties. The data contain 15, 407 unique observations of facilides, meaning that, on average, facilities appear in the data 6.59 times. To control for the nonindependence of observations, we estimate the regression model udlizing robust standard errors, clustered on facility.
143. The mean of the dependent variable in Table 3 is 1.55 (standard deviation = 2.01; range = 0 to 8). The data used in Table 3 include facilities that were in violation of CAA regulatory requirements, as well as those facilities that were not in violation of CAA regulatory requirements. The data contain 25, 282 unique observations of facilities, meaning that, on average, facilities appear in the data 3.93 times. To account for this nonindependence of observations, we estimate the negative binomial regression model employing robust standard errors, clustered on facility.
144. The negative binomial regression model (NBRM) is preferable to the OLS regression model given the makeup of our dependent variable. The NBRM is distinct from the most obvious alternative, the Poisson model, in that the NBRM does not make the assumption that the variance is equal to the conditional mean of the dependent variable. Rather, the NBRM estimates a parameter, a, that accounts for the unobserved heterogeneity among observations in the data. In order to test for the appropriateness of the NBRM as compared with the Poisson model, we estimated a log likelihood test for overdispersion in the data, which indicates that the NBRM is the more appropriate modeling strategy. For a general discussion of the NBRM, see, for example, A. COLIN CAMERON & PRAVIN K. TRTVEDI, REGRESSION ANALYSIS OF COUNT DATA 70-77 (1998) (discussing generally the use of the NBRM) and J. SCOTT LONG & JEREMY FREESE, REGRESSION MODELS FOR CATEGORICAL AND LIMITED DEPENDENT VARIABLES USING STATA 372-75 (2d ed. 2005) (explaining the advantages of NBRM compared to the Poisson regression model).
145. RECHTSCHAFFEN & MARKELL, supra note 13, at 251-52.
146. Id. at 251.
147. See E-mail from Joel Mintz, Professor of Law, Nova Southeastern University, to Author (Dec. 26, 2008, 2:11 EST) (on file with author).
148. Id.
149. See U. S. Sec. & Exch. Comm'n, Interactive Data for Financial Reporting, http://www.sec.gov/info/srnallbus/secg/interactivedata-secg.htm (last visited on Sept. 20, 2009).
150. Sam Dillon, U. S. to Require States to Use a Single School Dropout Formula, N. Y. TIMES, Apr. 1, 2008, at A16.
151. See, e.g., Clean Water Data, supra note 90.