Abstract
At the global scale, the advent of a market-based, cap-and-trade approach to reduce greenhouse gas (GHG) emissions globally has been met with skepticism by some observers, who raise equity-based concerns over who will bear the costs of slowing climate change. Since California’s passing of the Global Warming Solutions Act (AB32) in 2006, the “co-benefits” of climate policy – or health benefits that will accrue with a decline in the harmful pollutants that accompany GHGs (“co-pollutants”) – and how they relate to current patterns of environmental disparity have been added to the debate. A key concern is that while GHGs may fall statewide, the decline may not be evenly distributed, and co-benefits could wind up eluding the low-income communities and communities of color who need them most. This chapter takes an empirical look at the relationship between GHG reductions, co-pollutants, and geographic inequality in California to better understand whether cap-and-trade could actually worsen the pattern of environmental disparity. We find that there is indeed a cause for concern and offer some policy suggestions to insure that environmental justice communities are better protected.
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- 1.
For a description of how the NRDC dataset was constructed, see “Appendix A: Co-Benefits Analysis Methods” at http://www.nrdc.org/globalWarming/boosting/boostinga.pdf
- 2.
The CARB emissions inventory can be accessed at http://www.arb.ca.gov/ei/emissiondata.htm. The 2008 GHG emissions data can be accessed at http://www.arb.ca.gov/cc/reporting/ghg-rep/ghg-reports.htm
- 3.
This was the EPA’s Facility Registry System (FRS), and the shapefile was downloaded from http://www.epa.gov/enviro/geo_data.html
- 4.
The only difference is that we used PM10 rather than total PM in the health impacts index calculation, which is considered more closely tied to health endpoints.
- 5.
Health endpoint factors are the estimated number of tons per year of a particular pollutant that can be associated with each case of a health endpoint (in this case premature mortality) in within a particular geographic area (in this case air basins). See www.arb.ca.gov/planning/gmerp/march21plan/docs/health_analysis_supplement.pdf for more information, including the health endpoint factors for each air basin.
- 6.
These ratios can be accessed at http://www.arb.ca.gov/app/emsinv/emssumcat.php
- 7.
We also calculated this share for and occupied housing units for use in appropriately weighting median household income for block groups by distance from a facility or multiple facilities.
- 8.
The figures we show are for persons living below 150% of the poverty line since some argue that this is a better measure of low income for a high-cost state like California.
- 9.
Means and standard deviations discussed here are based on the natural log of the summed emissions across facilities within each of the distance ranges of a block group that were tested (in this case, 6 miles). This is a common transformation to normalize measures that exhibit a “long tail” or exponential distribution.
- 10.
We would emphasize here that the approximation of “exposure” we use here is just that – an approximation. While use of the term “exposure” in the field typically implies modeling of emissions to determine concentration at the neighborhood level, taking into account distance from the facility, how emissions are released and local wind and atmospheric patterns, for the purposes of this preliminary work, we rely on the rough approximation described here based only on total emissions and distance of residents from the facility.
- 11.
This weighting scheme implicitly sets the PM10 exposure to zero for all people beyond distance d of any facility and is imposed so that disparities are figured relative to the statewide population rather than to the population within distance, d, of a facility. While this is not a realistic assumption – in reality PM10 and other emissions disperse and deconcentrate at varying rates by distance around a facility depending on a variety of factors – in lieu of a fate and transport modeling, our method is to test a variety of distances under the assumption that the PM10 concentration is constant within each buffer and zero outside of the buffer.
- 12.
Such an outcome actually occurred in Southern California, for example, in a poorly designed system that allowed NOx emissions trading between mobile and stationary sources and led refineries to purchase and decommission “clunkers” rather than clean up near fenceline communities (see Drury et al. 1999).
- 13.
See Sadd et al. (2011) for a review of one approach for California; see also http://www.epa.gov/compliance/ej/resources/policy/ej-seat.html for an environmental justice screening approach developed by the US Environmental Protection Agency.
- 14.
Some even argue about inequality within the global South of the distribution of the benefits of the Kyoto Clean Development Mechanism (Galizzi et al. 2009).
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Acknowledgments
Aspects of this analysis were presented in more popular form in Pastor et al. (2010), which also provides more specific policy options for California. This research was supported by the William and Flora Hewlett Foundation; the conclusions and opinions are those of the researchers and do not necessarily reflect the views of the funder. We thank Diane Bailey of the Natural Resources Defense Council for kindly walking us through her earlier analysis of health impacts and Robert Vos for his assistance and comments on an earlier iteration of this work.
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Pastor, M., Morello-Frosch, R., Sadd, J., Scoggins, J. (2013). Risky Business: Cap-and-Trade, Public Health, and Environmental Justice. In: Boone, C., Fragkias, M. (eds) Urbanization and Sustainability. Human-Environment Interactions, vol 3. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-5666-3_6
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