Abstract
Over two decades have passed since the federal policy on environmental justice (EO 12898) was issued. However, empirical evidence indicates that injustice persists and that US states vary in their adoption of the terms of the environmental justice (EJ) policy. Moreover, studies of the explanations for the variation in states’ adoption of EJ policy are rare and have yielded puzzling findings—e.g., environmental interest groups are not associated with states’ EJ policy adoption, or the severity of problems is associated inversely with their adoption. We examined the progress and variation in states’ EJ policy adoption as of 2005 using fuzzy-set qualitative comparative analysis. Our analysis showed first that a strong environmental interest group presence, combined with high racial diversity and low problem severity, is sufficient for a high level of EJ policy adoption, especially in Western states. Second, when environmental interest group presence is weak, if it is combined, again, with high racial diversity and the presence of a more liberal state government, a high level of EJ policy adoption also occurs. This is observed in the East coast, Midwestern, and Southern regions of the USA. Environmental politics and policy research can benefit from a configurational approach, especially when there is no guiding theory on the conjunctional effects of key factors.
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Notes
Ringquist and Clark (2002) considered state’s innovativeness in environmental policy as one of the internal political factors that captures state political and economic characteristics. They tested this theoretical idea using the Green Index, which is the aggregated index of cross-sectional programmatic indicators in the latter half of the 1980s. The Green Index ranks states in terms of their “green conditions” and “green policies” and has been used widely in environmental politics and policy studies (Konisky and Woods 2012). Considering Konisky and Woods (2012), we used the “index of environmental policy portion” of the Green Index to represent state’s innovativeness in environmental policy in the past.
Alabama, Arkansas, Arizona, Colorado, Kansas, Mississippi, New Hampshire, Virginia, and Wyoming.
California, Connecticut, Hawaii, Massachusetts, Maryland, Michigan, Pennsylvania, Rhode Island, Vermont, and Wisconsin.
Per capita Sierra Club membership (S) = Sierra Club membership in 2000/US Census 2000 population estimates. As the number was too small, it was multiplied by 1000.
Racial Diversity (R) = 1 – ((% African American)2 + (% White)2 + (% Asian)2 + (% American Indian and Alaska Native)2 + (% Native Hawaiian and Other Pacific Islander)2 + (% Some Other)2 + (% Two or More Races)2).
Given the difficulty of deciding the crossover point of problem severity based on TRI emission levels, we ran a separate, more data-driven analysis with a different crossover point based on the median value (17.70) of the dataset. With this decision, a configuration that was covered by Iowa (Iowa used to be identified with row 8 in Table 3) was identified with a consistency score of 0.84 and a PRI value of 0.59. When this configuration was included for the minimization, two separate intermediate solutions were generated: D*s*R + S*R*t + D*g*s*t ⇒ EJP (solution 1), and D*s*R + D*R*t + D*g*s*t + g*R*t ⇒ EJP (solution 2). The first solution includes two paths reported in Table 4, and the additional D*g*s*t. The new path suggested that high EJ policy adoption also can be produced with the combination of strong Democratic control, lack of historical environmental innovativeness, weak interest group presence, and low emission problems. The second solution included four paths that are somewhat different from the first, but still included the D*s*R path identified in Table 4. Note that this data-driven calibration strategy, especially using the mean or median values, is not encouraged in QCA because these values are highly susceptible to changes in sample size (Schneider and Wagemann 2012).
The QCA software is italicized in this paper. We used fsQCA (non-italicized) to refer to fuzzy-set QCA as an analytical approach.
We performed analyses based on different cutoffs for consistency (e.g., 0.90, 0.87, and 0.85) to check for the sensitivity of the results, as well as the substantive insights that they provided. This process led us to report the results based on the consistency cutoff of 0.835.
In our analysis, the conservative solution was D*R*t + D*s*R + g*S*R*t ⇒ EJP and the parsimonious solution was D*R + S*R ⇒ EJP. The intermediate solution is given in Table 4.
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Acknowledgments
We thank Eva Thomas, Heather E. Campbell and Joanna Lucio, who provided valuable comments and helped improve the article. In addition, we thank David M. Konisky and Lily Hsueh who helped us identify data used in this article. We are also benefited from the presentation at the 2nd International Conference for Public Policy in 2015.
Funding
The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The National Research Foundation of Korea (NRF-2013-S1A3A205395).
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Kim, Y., Verweij, S. Two effective causal paths that explain the adoption of US state environmental justice policy. Policy Sci 49, 505–523 (2016). https://doi.org/10.1007/s11077-016-9249-x
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DOI: https://doi.org/10.1007/s11077-016-9249-x