I agree with David Owen’s recent blog post that David Adelman’s article, The Collective Origins of Toxic Air Pollution: Implications for Greenhouse Gas Trading and Toxic Hotspots, makes significant contributions to our awareness of the sources of toxic pollution and our collective responsibility for reducing emissions. He focuses on the distributional implications of GHG trading for associated co-pollutants, addressing two important environmental justice issues: the extent to which its impacts on industrial emissions could lead to changes in relative levels of toxic emissions, and the extent to which a GHG trading program could exacerbate racial disparities. He focuses on the degree to which a trading program would cause industrial hotspots or racial disparities, and his analysis shows that a GHG trading program for industrial sources would, in most instances, not play a substantial role in causing either of these consequences, largely because mobile and nonpoint sources are the primary cause of most air toxics hotspots. Those observations are important to the debate about a GHG trading program’s distributional implications for toxics hotspots.
I write to add one additional consideration to the analysis: a GHG trading program’s implications for cumulative pollution levels. Even if a GHG trading program would not cause an industrial hotspot – would not substantially change relative air toxics levels -- the value of small changes in cumulative pollution is also relevant to the larger debate over a GHG trading program’s impacts on air toxics hotspots.
I start by acknowledging Adelman’s valuable insights about industry’s relative role in air toxics pollution. Because Adelman’s concern is the role of industry in creating hotspots, his definition of hotspots focuses on industry’s absolute and relative contribution to air toxics pollution. He defines a countywide industrial hotspot by industry’s absolute contribution: a county is considered an industrial hotspot if industry contributes a cancer risk greater than 10 per million. He defines a census tract industrial hotspot where industry’s absolute contribution to cancer risk exceeds 20 per million and industry’s relative contribution is at least 30% of total air toxics emissions. Using this definition, industrial hotspots are relatively rare: nationwide, only 12 counties and 240 census tracts (out of 65,000 census tracts) are industrial hotspots of air toxics. Nationwide, mobile and nonpoint sources, not industry, are primarily responsible for air toxics pollution. (As Adelman observes, the same is not true for all criteria pollutants; energy facilities significantly contribute to sulfur dioxide emissions and, to a somewhat lesser extent, to nitrogen oxide emissions. But this blog, like his article, focuses primarily on air toxics, not criteria pollutants.)
Adelman demonstrates that the paucity of industrial toxic hotspots has important implications for GHG trading programs. First, because mobile and nonpoint sources dominate air toxics pollution in most of the country, GHG trading is unlikely to cause large percentage shifts in communities’ exposure to air toxics. As Adelman notes (and Dave Owen’s recent blog post highlighted), if industry contributes 10 percent of a locality’s toxic emissions and a GHG emissions program requires a 20 percent emissions reduction, then imposing that reduction requirement directly on the local industries would garner only a 2 percent reduction in toxic emissions. Thus, a GHG trading program that lets facilities purchase allowances instead of reducing emissions would forego only a 2 percent reduction in local toxic emissions.
A second valuable insight about industry’s limited relative role is that, in most instances, GHG trading is unlikely to increase racial inequities in air toxics exposure. That stems, in part, from industry’s relatively minor contribution to cumulative air toxic risks. Because trading is unlikely to substantially affect relative levels of air toxics cancer risks, it is unlikely to substantially shift the racial distribution of exposures. And in the small number of census tracts where industrial emissions do play a substantial role in air toxics risks, he observes that the racial disparities are slight, so that a GHG trading program’s emissions patterns would not lead to increased racial inequities in risk distribution.
Adelman’s analysis addresses key environmental justice concerns about distributional equity. The analysis does not, however, address one additional issue that is also important to the debate about GHG trading program’s and toxic hotspots: the significance of co-pollutant reductions on absolute pollution levels, even if industrial emissions are not the dominant source of emissions and the trading program would not lead to large percentage shifts in air toxics levels. In other words, one additional issue to consider is the importance of any reductions – even small reductions – in severely polluted areas. In highly polluted areas, the concern is not just whether trading will cause hotspots, but whether trading will eliminate opportunities to achieve incremental reductions in serious cumulative harms.
To identify areas where GHG trading could potentially make a significant difference in toxic emissions and, therefore, affect the relative distribution of pollution, Adelman rightly focuses on industrial hotspots: the counties and census tracts where industrial emissions most affect air toxics levels. As he notes, however, the focus on industrial hotspots does not correspond to toxic hotspots more broadly, since the nation’s most toxic hotspots are primarily caused by mobile and nonpoint sources, not industrial sources. He observes that, because “large populations and high population densities” generate high air toxics emissions from mobile and nonpoint sources, they “all but foreclose the emergence of industrial hotspots in metropolitan areas where the cancer risks from air toxics are typically the highest.” (322) Industrial hotspots typically appear only in places that combine highly toxic industries (generating significant industrial emissions) with small, low density, populations that generate relatively few mobile and small point source emissions.
Therefore, although assessing a GHG trading program’s impacts on industrial hotspots tells us where a GHG trading program covering industrial sources could have the biggest percentage impact on air toxics emissions, that analysis does not address a trading program’s impacts on areas experiencing the highest levels of air toxics pollution. To assess the value of achieving co-pollutant reductions, it is important to consider not only the percentage reductions that could be achieved or the distributional equity implications of those reductions, but also the importance of achieving reductions in absolute levels of pollution.
To see this more clearly, imagine two census tracts. Tract 1 is in a rural, low-density, region. A steel mill there poses a cancer risk of 20 per million and contributes 50 percent of the census tract’s overall air toxics risk of 40 per million. Under Adelman’s analysis, Tract 1 would qualify as an industrial hotspot because industrial sources pose a risk of at least 20 per million and industrial sources contribute more than 30 percent of the tract’s overall air toxics risk. If a GHG reduction program required a 20 percent decrease in emissions, and facilities in that area chose to purchase allowances rather than reduce emissions, then that area would forego reducing cancer risks by 4 per million, forgoing a 10 percent reduction in cumulative air toxics pollution that could otherwise have been achieved. (The 10 percent reduction is calculated as follows: A 20 percent reduction of the 20 per million cancer risk would lead to a reduction in risk of 4 per million. Reducing Tract 1’s air toxics risk from 40 per million to 36 per million is a 10 percent overall reduction in risk. Note that, for purposes of explanation, this example is highly simplified and likely exaggerated; it assumes that a facility would purchase allowances rather than reduce emissions and that GHG and co-pollutant emissions are perfectly correlated.)
In contrast, assume Tract 2 is in a dense urban environment that more closely resembles Los Angeles than Tract 1’s rural character. Assume several industries pose a cancer risk of 10 per million and contribute 10 percent of the census tract’s overall air toxic risk of 100 per million; the other 90 percent of the air toxics risk stems from mobile and nonpoint sources. Tract 2 would not qualify as an “industrial hotspot” under Adelman’s definition because industry contributes less than 20 per million to the overall cancer risk and because industry contributes less than 30 percent to the tract’s overall risk level. Assuming a twenty percent GHG reduction requirement in Tract 2, and assuming that local industries purchased allowances rather than reducing emissions, Tract 2 would forego reducing cancer risks by 2 per million, foregoing a 2 percent reduction in cumulative air toxics pollution that could otherwise have been achieved. (The 2 percent reduction is calculated as follows: A 20 percent reduction of the 10 per million cancer risk could reduce air toxic risks by 2 per million. Reducing Tract 2’s air toxics risk from 100 per million to 98 per million is a 2 percent overall reduction in risk.)
Adelman’s analysis of industrial hotspots helps us see that a GHG trading program could make the most relative difference in associated air toxics emissions in Tract 1 because there it could cause Tract 1 to forego a 10 percent emissions reduction, whereas in Tract 2, where industrial emissions are a smaller component of overall emissions, the trading program would cause Tract 2 to forego only a 2 percent reduction. In Tract 1, there is a greater possibility that the foregone reductions could change that tract’s degree of air toxics pollution relative to other areas, and cause racial inequities to the degree Tract 1 is located in a disproportionately minority area.
These insights address key equity questions, as measured by relative pollution levels. But a trading program’s role in alleviating absolute pollution levels, not just its impact on relative pollution levels, is also relevant to the discussion of GHG trading and hotspots. The cumulative toxics risk in Tract 2 is 100 per million, substantially more than the 40 per million in Tract 1. Of course, reductions in both tracts are important, but the 2 percent reduction in Tract 2 could be as or more important than the 10 percent reduction in Tract 1. Though the smaller percentage reduction in Tract 2 will not strongly affect relative pollution levels and is unlikely to change distributional equity, the reduction could be more important to the impacted communities in Tract 2 than in Tract 1. The difference in Tract 2 (reducing the cancer risk from 100 to 98 per million) might be less dramatic than in Tract 1 (reducing the cancer risk from 40 to 36 per million), but arguably the need for reductions is greater in Tract 2 than in Tract 1.
Wherever harm is caused by cumulative sources, incremental reductions from any one source, particularly a smaller source, will not lead to substantial changes in overall emissions. But one factor in considering the importance of the reduction is the extent of the need for the reduction, not only the relative change in emissions or its impact on distributional equity. Residents subject to multiple sources of pollution hope for reductions from all sources, even if the reductions achieved by any one source cannot, by definition, solve the entire problem. The presence of high emissions from mobile and nonpoint sources should not minimize the value of industrial emission reductions in the most seriously polluted areas.
A focus on the impacts of trading on industrial hotspots provides important insights that contribute substantially to the debate about GHG trading and hotspots, but its limits in fully resolving that debate are revealed by the article’s suggested regulatory fix. In order to mitigate the risk that a GHG trading program could lead to significant shifts in relative pollution in the limited numbers of industrial hotspots he has identified (most of which are rural, low-density communities where a single industry causes most of the air toxics pollution), he identifies modifications to a cap-and-trade program to reduce the risk of adverse shifts in emissions. His suggested solution addresses the role of a GHG trading program in causing industrial hotspots. But it does not address the impact of trading on toxic hotspots that are not primarily caused by industry. If we are going to modify a GHG emissions trading program to address co-pollutant consequences, should that effort be focused on these areas, rather than the areas experiencing more intense air toxics pollution? True, protections in industrial hotspots are likely to have a greater relative impact on pollution than they would in areas suffering from many sources of pollution. But it’s not clear that this approach would target improvement where it’s most needed.
To be clear: I am not suggesting that a GHG trading program or carbon tax should not be adopted because of its co-pollutant drawbacks relative to more direct regulatory measures. This dialogue concerns only the distributional impact of GHG trading on co-pollutants. As I have written elsewhere, a full analysis of a trading program’s co-pollutant implications must also consider other dimensions to the choice between market-based and traditional regulation, including relative stringency, relative flexibility, enforceability, participatory opportunities, and other factors, some of which might (or might not) provide countervailing co-pollutant benefits. In addition, other benefits of a GHG trading program or carbon tax could potentially outweigh the co-pollutant considerations articulated here. And, if politically viable (a big “if”), more aggressive efforts to reduce mobile and nonpoint air toxics could be more effective than attempting to achieve co-pollutant reductions through a GHG trading program, particularly if that effort were to adversely impact the viability of GHG control efforts. I write, instead, simply to add another dimension to the discrete debate about GHG trading and air toxic hotspots: a trading program’s impact on cumulative pollution in the nation’s most polluted areas.