Mapping the clean air haves and have-nots

doi:10.1126/science.abb0943

GSTDTAP > 气候变化

DOI	10.1126/science.abb0943
	Mapping the clean air haves and have-nots
	Lala Ma
	2020-07-31
发表期刊	Science
出版年	2020
英文摘要	Environmental quality can affect a host of health and economic outcomes, and the heterogeneous distribution of pollution potentially explains a substantial portion of the gaps in outcomes between groups of different socioeconomic statuses ([ 1 ][1]). Founded on the Civil Rights Movement of the 1950s and 1960s and the antitoxics movement in the late 1970s, the environmental justice movement emerged to address the fact that exposure of minorities to pollution was not necessarily random ([ 2 ][2]). Yet, despite documentation of environmental disparities between different racial and ethnic groups ([ 3 ][3], [ 4 ][4]), we have little systematic evidence about the evolution of environmental disparities over time and the factors contributing to their change. On page 575 of this issue, Colmer et al. ([ 5 ][5]) document trends in the spatial distribution of airborne particulate matter smaller than 2.5 µm in diameter (PM2.5). They find that although PM2.5 concentration has substantially decreased in North America over the past three decades, the relative ranking of PM2.5 across the United States has remained notably stable: The most and least polluted tracts in 1981 remain the most and least polluted in 2016, with disadvantaged communities more likely to have higher pollution ranks at any given time. Since 1981, U.S. federal policy-making has required the use of benefit-cost analysis (BCA) to evaluate economically important regulations. In 1994, the U.S. government officially recognized environmental justice as a necessary component in federal decision-making. Yet, although regulatory analysis requires investigation of a policy's distributional impact, the degree to which distributional effects are factored into decision-making is unclear when economic efficiency is the primary framework for policy evaluation. BCA tests whether a policy's winners gain enough to compensate the losers (the Kaldor-Hicks criterion), but it does not outline how (or require that) these compensations will be made in practice. Other policy tools (e.g., income transfers) could also arguably be used to achieve distributional goals in a more direct and efficient manner, and pragmatic reasons of political opposition, lack of technical guidance for distributional analysis, and data constraints can prevent BCA from incorporating distributional analysis ([ 6 ][6]). Furthermore, although theories about the causes of environmental injustices exist—from firm siting decisions to individual location choice and the interplay between these forces as mediated through (Coasian) bargaining and regulatory intervention—there is still considerable uncertainty as to the weight that each pathway holds in contributing to inequitable exposure ([ 7 ][7]). This creates difficulty in crafting policies that produce the intended consequences. Colmer et al. 's examination of the persistence in neighborhood pollution rankings is an initial step toward understanding broader trends in the distribution of environmental well-being. They combine newly available data on annual PM2.5 estimates at 0.01°-by-0.01° grids from ([ 8 ][8]) with census-tract boundaries. The paper's application of fine-grained pollution data to neighborhood characteristics improves the measurement of neighborhood exposure because local pollutants can vary greatly over short distances and yield disparate health implications within communities. The focus on ambient air quality, rather than on a particular type of polluting facility, broadens our perspective on air pollution exposure, because it captures emissions from both stationary and mobile sources. When comparing a tract's pollution rank with the characteristics of its residents, the authors find that changes in rank are not strongly correlated with changes in population size or aggregate demographic characteristics. Colmer et al. 's findings highlight outstanding issues relevant to implementing environmental justice in practice. It is, for instance, unclear what rank persistence for locations implies about the trend in distributional equity. Rank persistence may be of second-order importance for environmental equity if considered alongside the magnitude of air quality improvements across the entire pollution distribution. Regulation, with its focus on efficiency, is likely to target the dirtiest places first, which would drive pollution reductions in the most disadvantaged communities. Thus, air quality could be improved for all people and disproportionately for people of lower socioeconomic status even if the relative positions of groups within the pollution distribution remained the same. Conversely, the persistence of pollution rankings and fall in pollution across locations may not translate to reduced exposure for populations. Census tracts, with populations of between 1200 and 8000, vary greatly in their pollution sources, and populations are unevenly distributed within tracts. Aggregating exposure to broader communities can obscure the relationship between pollution and socioeconomic status ([ 9 ][9]). In the face of air quality changes, people may respond (by migrating) in ways that mitigate or amplify disparities in exposure without being reflected in aggregate data ([ 10 ][10]). The implications of behavioral responses become even more relevant after factoring in interactions with markets. People are heterogeneous in their willingness and ability to pay for clean air. If air quality improvements drive up demand for particular areas and are reflected in higher housing prices, then residents may find themselves paying a higher price for cleaner air, even if they would rather spend their resources on other things. Relocation is potentially a costly option; however, with the persistence in rankings, those living in the highest pollution-ranked communities may not have this option. Forces such as environmental gentrification can alter welfare (and gaps in welfare) as a result of the equilibrium market price effects that accompany improvements in environmental quality. Concern that low-income and other vulnerable populations are being “priced out” are reflected in calls to consider the effects of social displacement in the evaluation of climate resilience programs ([ 11 ][11]). Other forces such as stigma ([ 12 ][12]) or endogenous neighborhood change resulting from the location choices of initial residents ([ 13 ][13]) may prevent prices from responding to environmental improvements and reduce the risks of gentrification and displacement. Air pollution, and PM2.5 in particular, is only one facet of environmental health. The story becomes more complex when we consider cumulative exposure that combines environmental assaults from alternative pollution pathways. More broadly, increasing global climate risks have implications for inequitable exposure to a host of other threats, such as hurricanes, floods, and heat waves. These risks will also have implications for distributional equity through differential access to insurance and social safety nets that mitigate losses and facilitate adaptation ([ 14 ][14]). Further investigation to determine what caused the downward shift in the pollution distribution and the preservation of rankings over time could inform policies that avoid undesirable distributional consequences. Implementing environmental justice is therefore ultimately inseparable from assessing the mechanisms that created the inequities in the first place. 1. [↵][15]1. J. Currie , Am. Econ. Rev. 101, 1 (2011). [OpenUrl][16][CrossRef][17] 2. [↵][18]1. L. W. Cole, 2. S. R. Foster , From the Ground Up: Environmental Racism and the Rise of the Environmental Justice Movement (NYU Press, 2001). 3. [↵][19]U.S. General Accounting Office (GAO), “Siting of hazardous waste landfills and their correlation with racial and economic status of surrounding communities” (Technical Report RCED-83-168, GAO, 1983). 4. [↵][20]Commission for Racial Justice, United Church of Christ, “Toxic wastes and race in the United States: A national report on the racial and socio-economic characteristics of communities with hazardous waste sites” (Technical Report, United Church of Christ, 1987). 5. [↵][21]1. J. Colmer et al ., Science 369, 575 (2020). [OpenUrl][22][CrossRef][23] 6. [↵][24]1. L. A. Robinson, 2. J. K. Hammitt, 3. R. J. Zeckhauser , Rev. Environ. Econ. Policy 10, 308 (2016). [OpenUrl][25][CrossRef][26] 7. [↵][27]1. S. Banzhaf, 2. L. Ma, 3. C. Timmins , J. Econ. Perspect. 33, 185 (2019). [OpenUrl][28][CrossRef][29][PubMed][30] 8. [↵][31]1. J. Meng et al ., Environ. Sci. Technol. 53, 5071 (2019). [OpenUrl][32][CrossRef][33][PubMed][34] 9. [↵][35]1. P. Mohai, 2. R. Saha , Demography 43, 383 (2006). [OpenUrl][36][CrossRef][37][PubMed][38][Web of Science][39] 10. [↵][40]1. B. Depro, 2. C. Timmins, 3. M. O'Neil , J. Assoc. Environ. Resour. Econ. 2, 439 (2015). [OpenUrl][41] 11. [↵][42]1. I. 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领域	气候变化 ; 资源环境
URL	查看原文
引用统计
文献类型	期刊论文
条目标识符	http://119.78.100.173/C666/handle/2XK7JSWQ/286853
专题	气候变化资源环境科学
推荐引用方式 GB/T 7714	Lala Ma. Mapping the clean air haves and have-nots[J]. Science,2020.
APA	Lala Ma.(2020).Mapping the clean air haves and have-nots.Science.
MLA	Lala Ma."Mapping the clean air haves and have-nots".Science (2020).