Incorporating environmental equity into risk assessment: A case study of power plant air pollution control strategies

1. Incorporating environmental equity into risk assessment: A case study of power plant air pollution control strategies Jonathan Levy, Sc.D. Assistant Professor of Environmental Health and Risk Assessment, Harvard School of Public Health The David Bradford Seminars in Science, Technology and Environmental Policy, Princeton University April 17, 2006

2. Risk assessment – basic definition (NRC, 1983)

3. Environmental justice – basic definitions • A societal goal, defined as the provision of adequate protection from environmental toxicants for all people, regardless of age, ethnicity, gender, health status, social class, or race (Sexton and Anderson, 1993). • The fair treatment and meaningful involvement of all people regardless of race, color, national origin, or income with respect to the development, implementation, and enforcement of laws, regulations, and policies. (U.S. EPA, 1998).

6. PM From Chestnut et al., 2006

7. Central health estimates (primary + secondary PM, annual) Levy et al., 2002

8. Questions asked… • Are populations near the plant “disproportionately” affected by the plant emissions? • Would emission control reduce “environmental injustice”?

9. Benefits from NOx and SO2 controls at Salem and Brayton (mg/m3 of PM2.5, annual avg) Levy et al., 2002

10. Analytical challenge • Risk analysts have developed simple, meaningful indicators that can capture the magnitude of the benefits of pollution control from a source or set of sources • QALYs, deaths, hospitalizations, etc. • Is there a simple, meaningful indicator that can capture the distribution of the benefits of pollution control from a source or set of sources, in a way that informs EJ concerns?

11. “Equity” = Distribution of Health Benefits “Efficiency” = Magnitude of Health Benefits

12. Our approach • Clarify terminology • Develop inequality indicators that are meaningful in a pollution control context • Evaluate whether the premise behind our indicators is supported by environmental justice or risk assessment practitioners • Apply indicators to a case study of national power plant control strategies to determine information value • Extend model to local-scale pollution control decision where small-scale demographics may be influential

13. Key points on terminology • Important communication gaps between risk assessment and environmental justice related in part to loose terminology • EJ: Equality = equal access/participation (process) • RA: Equality = equal outcomes • Moving to equity (or justice) requires determination of those inequalities that are deemed unjust and unfair (avoidable? undeserved? remediable?), which is well beyond domain of quantitative analysis • We focus here on equality of outcomes, considering subpopulations of concern from EJ perspective Levy et al., 2006

14. Developing indicators • Numerous income inequality studies developed axiomatic approach to select indicators • We modify the standard list of axioms and propose additional axioms relevant to health benefits analysis

15. Standard axioms

16. Scale invariance • In economics, supported for the case of changing income to different currencies • For risk assessment, parallel argument for concentration measures • For real changes in income/risk, it is less clear • Argument for increased inequality: Absolute gaps have increased, new assets have not been distributed equitably • Argument for decreased inequality: Diminishing marginal utilities of income/risk • We do not require scale invariance in this context (but would not reject a scale-invariant measure)

17. Anonymity • Runs counter to basic premise of environmental justice (concern with sociodemographic factors and comparisons between groups) • Understanding geographic/demographic patterns of health risks may facilitate the development of pollution control strategies • We reject anonymity (and prefer indicators where relevant individual characteristics can be incorporated)

18. Additional axioms (1) • The analyst must not impose a value judgment about the relative importance of transfers at different percentiles of the risk distribution

19. Additional axioms (2) • The welfare measure must be as close to a measure of health risk as possible. If quantifying risk is impossible or there is no differential susceptibility, then exposure should be evaluated. If quantifying exposure is impossible or there is no differential exposure, then concentrations in relevant media should be evaluated.

20. Additional axioms (3) • The inequality indicator should not be applied without consideration of the baseline distribution of risk.

21. Additional axioms (4) • The inequality indicator should be estimated for the geographic scope and resolution that are used for the health benefits analysis, but the sensitivity of the findings to scope and resolution should be evaluated. In particular, an inequality indicator should be estimated with the finest geographic resolution possible, given available data and analytical capabilities.

22. Additional axioms (5) • When efficiency-equality tradeoffs are important for policy decisions, the inequality indicator should be derived for multiple competing policy alternatives. If this is not possible, qualitative interpretations are most appropriate.

23. Some candidate indicators • Gini coefficient • Variance of logarithms • Atkinson index • Note: We evaluated 19 indicators, but present a subset to illustrate key issues

24. The Gini coefficient

25. Modifying Gini for pollution control

26. Equation for Gini Average absolute difference between all pairs of individuals, normalized by dividing by twice the mean

27. Evaluating Gini • Widely used and satisfies many basic criteria, but… • Not subgroup decomposable unless subgroups strictly ordered by income • Most sensitive to transfers in middle of distribution • Structured on rank of incomes rather than absolute value, yielding somewhat arbitrary weights • Gini may not be interpretable in many applications, but could be considered for sensitivity analyses

28. Evaluating variance of logs • Theoretically appealing, esp. for lognormal data • However… • Violates principle of transfers • Marginal transfers from high-risk to low-risk increase variance of logs if high value greater than e times geometric mean of the distribution • Implicitly attaches more weight to transfers at the low end than at the high end of the distribution • Only subgroup decomposable if geometric means replace arithmetic means in subgroup data • Not applicable to health benefits analysis…

29. Atkinson index • Member of generalized entropy family (derived specifically to be decomposable) • Fulfills transfer principle • Societal preferences about inequality incorporated through e • Higher e = more weight on transfers at low end

30. Conclusions about indicators • Atkinson index, if applied appropriately, best addresses needs of inequality assessment in health benefits analysis • Could be supplemented by other indices for sensitivity analyses: • Gini: Alternate viewpoint about inequality (comparisons to all those better off vs. average, additive vs. weighted additive formulation) • Theil: Alternate statistical formulations from generalized entropy family

31. Power plant case study • What would happen if we used cap-and-trade programs to reduce emissions from power plants nationally, rather than mandatory controls for all plants? • Would this result in an “environmental injustice”? • What do optimal reductions given a national emissions cap look like, considering efficiency and equity?

32. EPA Announces Landmark Clean Air Interstate Rule (March 10, 2005) “CAIR will permanently cap emissions of sulfur dioxide (SO2) and nitrogen oxides (NOx) in the eastern United States… Under CAIR, states will achieve the required emissions reductions using one of two options for compliance: 1) require power plants to participate in an EPA-administered interstate cap and trade system that caps emissions in two stages, or 2) meet an individual state air emission limits through measures of the state's choosing.”

33. Criticism… • The data released by environmentalists this week show about 50 percent of the 1,041 coal-fired electric generating units expected to be in operation in 2020 would not be equipped with the best pollution equipment on the market to reduce sulfur dioxide and nitrogen oxide emissions [under CAIR] • The distribution of emission controls has become a controversial topic for the Bush administration since it linked its air pollution policies to a market-friendly, cap-and-trade system. EPA officials maintain the administration's approach is the most cost-effective for the electric utility industry while guaranteeing the decline of air pollution because of obligatory emission caps on power plants. Greenwire February 24, 2006

36. Extracted from EGRID, NEI for 425 plants S-R matrix, county resolution C-R function from ACS, county mortality PM

37. Model validation for seven power plants in GA (Levy et al., 2003)

38. Emission reductions • Developed logical (or illogical) approaches by which 75% reductions could be achieved, to span efficiency/equity space • 75% reductions from all plants • Reductions to meet target emission rates in lb/MMBTU (for those above target) • Elimination of plants with highest/lowest health benefit per unit emissions of SO2/NOx/primary PM • Elimination of plants in counties with highest/lowest background PM2.5 concentrations • Elimination of highest/lowest emitters of SO2/NOx/primary PM • Supplemented with random emission control scenarios

39. How do we capture equity? • Given policy context/nature of debate, primarily concerned about spatial equity • Multiple ways we might incorporate “baseline” (Axiom 1) • For concentrations: Total PM2.5, power plant-related PM2.5 • For health risk: Total mortality, PM2.5-related mortality, power plant PM2.5-related mortality • Multiple inequality indicators • Consideration of concentrations vs. health risks

40. Indicator: Atkinson index, e = 0.25 Outcome: Mortality Baseline: PM-related mortality

41. Sulfate/SO2 iF

42. Reductions in concentrations (High SO2 iF)

43. Baseline concentrations

44. Post-control baseline concentrations (High SO2 iF)

45. Does the choice of indicator matter?

46. Indicator: Atkinson index, e = 0.25 Outcome: Mortality Baseline: PM-related mortality

47. Indicator: Atkinson index, e = 0.75 Outcome: Mortality Baseline: PM-related mortality

48. Indicator: Atkinson index, e = 3.0 Outcome: Mortality Baseline: PM-related mortality

49. Indicator: Theil Outcome: Mortality Baseline: PM-related mortality

50. Indicator: Gini Outcome: Mortality Baseline: PM-related mortality