How Realistic Are Estimates of Health Benefits from Air Pollution Control?

Abstract

Chapter 13 established that income is an important confounder of some air pollution-associated health effects: low income increases health risks and is also associated with living in areas having higher air pollution levels. This raises the public health question: would reducing air pollution levels without addressing the other correlates of low income that might contribute to increased health risks, be effective in reducing health risks? Decades of regulatory science and political opinion have answered this question loudly and repeatedly in the affirmative. As discussed in subsequent chapters, practical experience has been much less encouraging, with substantial reductions in air pollution often making no clear contribution to causing improved public health. However, notwithstanding this experience, media, regulatory, and advocacy reports and recommendations continue to overwhelmingly assert that fine particulate matter (PM2.5, i.e., particulate matter smaller than 2.5 μm in diameter) in outdoor air kills people and causes serious health problems. Regulatory proposals to further decrease currently permitted levels of pollutants are supported by reference to large estimated or predicted health benefits from doing so. For example, in early 2011, the EPA released the results of its cost-benefit analysis of the 1990 Clean Air Act Amendments (CAAA). The assessment made two striking claims (EPA 2011a): (1) As of 2020, the CAAA would produce estimated health benefits valued at approximately two trillion (i.e., two thousand billion) dollars per year, compared to estimated compliance costs of only about $65 billion/year; and (2) The uncertainties in the cost-benefit analysis are small enough so that, “The extent to which estimated benefits exceed estimated costs and an in-depth analysis of uncertainties indicate that it is extremely unlikely the costs of 1990 Clean Air Act Amendment programs would exceed their benefits under any reasonable combination of alternative assumptions or methods identified during this study” (emphasis in original).