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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

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slide1

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

environmental justice basic definitions
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).
slide6

PM

From Chestnut et al., 2006

questions asked
Questions asked…
  • Are populations near the plant “disproportionately” affected by the plant emissions?
  • Would emission control reduce “environmental injustice”?
benefits from nox and so 2 controls at salem and brayton m g m 3 of pm 2 5 annual avg
Benefits from NOx and SO2 controls at Salem and Brayton (mg/m3 of PM2.5, annual avg)

Levy et al., 2002

analytical challenge
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?
slide11

“Equity” = Distribution of Health Benefits

“Efficiency” =

Magnitude of Health Benefits

our approach
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
key points on terminology
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

developing indicators
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
scale invariance
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)
anonymity
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)
additional axioms 1
Additional axioms (1)
  • The analyst must not impose a value judgment about the relative importance of transfers at different percentiles of the risk distribution
additional axioms 2
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.
additional axioms 3
Additional axioms (3)
  • The inequality indicator should not be applied without consideration of the baseline distribution of risk.
additional axioms 4
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.
additional axioms 5
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.
some candidate indicators
Some candidate indicators
  • Gini coefficient
  • Variance of logarithms
  • Atkinson index
    • Note: We evaluated 19 indicators, but present a subset to illustrate key issues
equation for gini
Equation for Gini

Average absolute difference between all pairs of individuals, normalized by dividing by twice the mean

evaluating gini
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
evaluating variance of logs
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…
atkinson index
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
conclusions about indicators
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
power plant case study
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?
epa announces landmark clean air interstate rule march 10 2005
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.”

criticism
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

slide36

Extracted from

EGRID, NEI

for 425 plants

S-R matrix,

county

resolution

C-R function

from ACS,

county mortality

PM

emission reductions
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
how do we capture equity
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
slide40

Indicator: Atkinson index, e = 0.25

Outcome: Mortality

Baseline: PM-related mortality

slide46

Indicator: Atkinson index, e = 0.25

Outcome: Mortality

Baseline: PM-related mortality

slide47

Indicator: Atkinson index, e = 0.75

Outcome: Mortality

Baseline: PM-related mortality

slide48

Indicator: Atkinson index, e = 3.0

Outcome: Mortality

Baseline: PM-related mortality

slide49

Indicator: Theil

Outcome: Mortality

Baseline: PM-related mortality

slide50

Indicator: Gini

Outcome: Mortality

Baseline: PM-related mortality

slide51

Indicator: MLD

Outcome: Mortality

Baseline: PM-related mortality

slide53

Indicator: Atkinson index, e = 0.75

Outcome: Mortality

Baseline: PM-related mortality

slide54

Indicator: Atkinson index, e = 0.75

Outcome: Mortality

Baseline: All-cause mortality

slide55

Indicator: Atkinson index, e = 0.75

Outcome: Mortality

Baseline: Power plant

PM-related mortality

slide56

Indicator: Atkinson index, e = 0.75

Outcome: Mortality

Baseline: None

slide59

Indicator: Atkinson index, e = 0.75

Outcome: Mortality

Baseline: PM-related mortality

slide60

Indicator: Atkinson index, e = 0.75

Outcome: Concentrations

Baseline: Background PM2.5

slide62

Indicator: Atkinson index, e = 0.75

Outcome: Mortality

Baseline: PM-related mortality

Pollutant: PM2.5, NOx, SO2

slide63

Indicator: Atkinson index, e = 0.75

Outcome: Mortality

Baseline: PM-related mortality

Pollutant: SO2 only

slide64

Indicator: Atkinson index, e = 0.75

Outcome: Mortality

Baseline: PM-related mortality

Pollutant: NOx only

slide65

Indicator: Atkinson index, e = 0.75

Outcome: Mortality

Baseline: PM-related mortality

Pollutant: PM only

what can we conclude i
What can we conclude? (I)
  • The emissions reductions are substantial enough that the efficiency differences among scenarios are not huge in relative terms (but may be important in absolute terms)
  • For power plants and PM, strong concordance between the more efficient and more equitable strategies, implying limited tradeoffs
what can we conclude ii
What can we conclude? (II)
  • Our conclusions are robust across numerous indicators and formulations, but clearly show the importance of properly accounting for baseline/background conditions
what s missing
What’s missing?
  • Linkage with economics of power plant control
    • Plausibility of control options, economic efficiency/equity considerations
  • Equity other than spatial equity
    • No subgroup decomposability
  • Important effect modifiers (no variability in C-R), differential susceptibility
  • Consideration of local (vs. national) perspective
concluding thoughts
Concluding thoughts
  • Axiomatic approach allowed us to develop inequality indicators that are meaningful and can be used in parallel with standard indicators of efficiency
  • Power plant case demonstrates the viability of the approach and the potential for formally optimizing on efficiency and equity
  • Ongoing analyses with smaller spatial scales, socioeconomic equity concerns, effect modification/differential baseline disease rates will allow for further refinement
acknowledgments
Acknowledgments
  • Susan Chemerynski
  • Jessica Tuchmann
  • Julia Forgie
  • Sue Greco
  • Andrew Wilson
  • Len Zwack
  • National Science Foundation (SES-0324746)
references
References
  • Levy JI, Spengler JD. Modeling the benefits of power plant emission controls in Massachusetts. J Air Waste Manage Assoc 52: 5-18 (2002).
  • Levy JI, Wilson AM, Evans JS, Spengler JD. Estimation of primary and secondary particulate matter intake fractions for power plants in Georgia. Environ Sci Technol 37: 5528-5536 (2003).
  • Levy JI, Chemerynski SM, Tuchmann JT. Incorporating concepts of inequality and inequity into health benefits analysis. International Journal for Equity in Health 5:2 (2006).