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Going green together? Brownfield remediation and environmental justice

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Abstract

Research on the location of hazardous facilities that contribute to environmental degradation indicates an unequal distribution of such sites within low socioeconomic status communities. In this paper, we contribute to research investigating whether there are similar patterns of unequal prioritization in environmental remediation, by assessing cleanup of a broader, more diverse set of contaminated sites, all brownfields that receive federal grants, with a finer level of geographic detail than previous studies. First, we set the context by assessing the characteristics of the communities in which brownfield sites are currently located. Then, we compare the likelihood and temporal prioritization of cleaning up brownfield sites based on the composition of the neighborhoods in which they are located. We find that brownfield sites are more likely to be located in both poor and predominantly minority communities, but while sites located in poor communities tend to be cleaned up relatively quickly, sites located in communities with larger minority populations tend to be cleaned up more slowly.

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Notes

  1. See http://www.epa.gov/brownfields/overview/glossary.htm. Web site viewed by author on November 2, 2010.

  2. Public Law 107-118 (H.R. 2869): "Small Business Liability Relief and Brownfields Revitalization Act".

  3. See: www.epa.gov/brownfields/policy/ej_brochure_2009.pdf.

  4. The IRCHS is a product of the Clean Manufacturing Technology Institute at Purdue University. For a detailed description of the IRCHS, please see: http://engineering.purdue.edu/CMTI/IRCHS/.

  5. In cases where a tract had more than one site, for which cleanups began both before and after 2000, 1990 data were used. All models were also assessed with an average of 1990 and 2000 demographic characteristics, with no substantive differences in the results.

  6. The US Government Accountability Office estimated in 2004 that there were between 450,000 and 1,000,000 brownfield sites nationwide, although this is probably a conservative estimate. See: http://www.gao.gov/new.items/d0594.pdf.

  7. Since we collected the full set of data on October 1, 2009, we use this date as the censoring point. The date itself is not indicative of a milestone in the brownfield policy process.

  8. Given that our data are national, we opted to use this measure of economic factors rather than a raw dollar amount like median income because eligibility for public assistance usually takes into account the variable cost of living in different areas.

  9. Several aspects of the housing stock were included in various models, such as the median home value and median home size. These variables were dropped from the final analysis over concern that they may be highly correlated with other variables that were retained.

  10. It may well be that the actual hazard is more multiplicative than additive when more than one pollutant is present, but without knowing specific quantities of contaminants at the site, additive effects seem to be the more conservative assumption.

  11. We also assessed clustering via a count of sites located in the same census tract as the focal site. We opted to use the indicator variable due to our concern that a few census tracts that contained a very large number of sites would have an undue influence on our results.

  12. There is some overlap in the first time period between George H.W. Bush’s term and Bill Clinton’s first term. We operationalized this variable in a number of different ways: as non-ordinal dummies, as year dummies, and as months counting backward from October, 2009 to January 1990. Results were not substantially different under any other consideration of the time variable, so we opted for the ordinal category operationalization corresponding to Presidential terms as the best way to capture periods of both similar technology and political control of the federal government.

  13. Several alternate specifications of this model were assessed, including other aspects of the housing stock and socioeconomic status of the population. We add a caveat to these results by noting that we did not have data regarding the specific land-use requirements or zoning regulations that were applicable to each of the tracts in the analysis. Nevertheless, these results are useful for detailing where brownfield sites tend to be located so that the results of the subsequent analysis can be interpreted in context.

  14. In fact, removal of these change variables does not change the interpretation of the key variables assessed. We retain them, however, as a control variable to help account for those neighborhoods that were undergoing demographic changes. We should also note that quadric and interaction terms were assessed for the socioeconomic status variables; inclusion of these variables did not change the substantive results.

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Authors and Affiliations

  1. Center for Public Administration and Policy, School of Public and International Affairs, Virginia Tech, 1021 Prince Street, Alexandria, VA, 22314, USA

    Adam Eckerd

  2. University of North Carolina, Chapel Hill, NC, USA

    Andrew G. Keeler

  3. East Carolina University, Greenville, NC, USA

    Andrew G. Keeler

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  1. Adam Eckerd
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  2. Andrew G. Keeler
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Correspondence to Adam Eckerd.

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Eckerd, A., Keeler, A.G. Going green together? Brownfield remediation and environmental justice. Policy Sci 45, 293–314 (2012). https://doi.org/10.1007/s11077-012-9155-9

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