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
Risk assessment –
basic definition (NRC, 1983)
Risk
Assessment
Hazard
Identification
Exposure
Assessment
Dose-Response
Assessment
Risk
Characterization
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).
Environmental justice
Egalitarian
Risk assessment/
benefit-cost analysis
Utilitarian
Process-oriented
Outcome-oriented
Focused on high-risk
subpopulations
Concerned with
proximity
Community-driven
Focused on total
population
Concerned with
exposure/risk
Analyst-driven
PM
From Chestnut et al., 2006
Central health estimates
(primary + secondary PM, annual)
Mortality
Hospital
admissions
Emergency
room visits
Asthma attacks
Brayton Point
Current Benefits
impacts
79
55
76
53
Salem Harbor
Current Benefits
impacts
33
23
32
22
1,000
700
420
300
5,300
3,700
2,200
1,600
Levy et al., 2002
Questions asked…
• Are populations near the plant
“disproportionately” affected by the plant
emissions?
• Would emission control reduce
“environmental injustice”?
Benefits from NOx and SO2 controls at Salem and
Brayton (mg/m3 of PM2.5, annual avg)
0.16
45
0.15
0.14
44
0.13
0.12
0.11
43
0.1
0.09
0.08
42
0.07
0.06
0.05
41
0.04
0.03
40
-75
0.02
-74
-73
-72
-71
-70
-69
-68
-67
0.01
0
Levy et al., 2002
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?
“Equity” = Distribution of Health Benefits
“Efficiency” =
Magnitude of Health Benefits
Our approach
1. Clarify terminology
2. Develop inequality indicators that are
meaningful in a pollution control context
3. Evaluate whether the premise behind our
indicators is supported by environmental
justice or risk assessment practitioners
4. Apply indicators to a case study of national
power plant control strategies to determine
information value
5. Extend model to local-scale pollution control
decision where small-scale demographics
may be influential
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
• 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
Standard axioms
Analytic tractability
Computable in standard applications
Appropriateness
Reflects values of decision makers
Anonymity/impartiality
Not dependent on characteristics of
affected individuals
Increase when income transferred from
poor to rich (decrease for transfer from
rich to poor)
No change for uniform proportional
increases
Pigou-Dalton transfer
principle
Scale invariance
Normalization
Follows defined range
Subgroup
decomposability
Principle of population
Segmented into constituent parts
(additive separable)
Invariance to replication of population
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
• 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)
• The analyst must not impose a value
judgment about the relative importance
of transfers at different percentiles of
the risk distribution
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)
• The inequality indicator should not be
applied without consideration of the
baseline distribution of risk.
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)
• 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
• Gini coefficient
• Variance of logarithms
• Atkinson index
– Note: We evaluated 19 indicators, but
present a subset to illustrate key issues
The Gini coefficient
Cumulative risk
1
0.8
0.6
0.4
0.2
0
0
0.2
0.4
0.6
Cumulative population
0.8
1
Modifying Gini for pollution control
Cumulative risk
1
0.8
0.6
0.4
0.2
0
0
0.2
0.4
0.6
0.8
Cumulative population
Original Lorenz curve
Post-control Lorenz curve
1
Equation for Gini
G
1
2
n
 x  x
i
i
j
j
2m
Average absolute difference between all
pairs of individuals, normalized by dividing
by twice the mean
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
• 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
 1 n  xi 
1   i 1  
x
 n
1e



1
1e
• 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
• 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
• What would happen if we used cap-andtrade 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)
“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…
• 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
Extracted from
EGRID, NEI
for 425 plants
PM
S-R matrix,
county
resolution
C-R function
from ACS,
county mortality
Model validation for seven power
plants in GA (Levy et al., 2003)
S-R/
CALPUFF,
primary PM
S-R/
CALPUFF,
SO2/sulfate
S-R/
CALPUFF,
NOx/nitrate
Bowen
0.8
1.1
0.4
Hammond
0.9
1.1
0.4
Harllee Branch
0.9
1.1
0.4
Jack McDonough
0.6
1.0
0.4
Scherer
0.9
1.0
0.4
Wansley
0.9
1.2
0.4
Yates
0.9
1.1
0.4
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?
• 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 plantrelated 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
0.002
Equity benefits (decrease in inequality indicator)
0.0018
0.0016
Indicator: Atkinson index, e = 0.25
Outcome: Mortality
Baseline: PM-related mortality
High background PM
High SO2 iF
0.0014
0.0012
High PM iF
Reduction
to target/MMBTU
High SO2
emissions
High NOx emissions
High PM emissions
0.001
75% reduction for all
Low PM iF
Low NOx iF
0.0008
Low NOx emissions
0.0006
High NOx iF
Low SO2 iF
0.0004
0.0002
0
10000
Low SO2 emissions
Low PM emissions
Low background PM
11000
12000
13000
Health benefits (Deaths/year)
14000
15000
Sulfate/SO2 iF
Reductions in concentrations
(High SO2 iF)
Baseline concentrations
Post-control baseline
concentrations (High SO2 iF)
Does the choice of indicator
matter?
0.002
Equity benefits (decrease in inequality indicator)
0.0018
0.0016
Indicator: Atkinson index, e = 0.25
Outcome: Mortality
Baseline: PM-related mortality
High background PM
High SO2 iF
0.0014
0.0012
High PM iF
Reduction
to target/MMBTU
High SO2
emissions
High NOx emissions
High PM emissions
0.001
75% reduction for all
Low PM iF
Low NOx iF
0.0008
Low NOx emissions
0.0006
High NOx iF
Low SO2 iF
0.0004
0.0002
0
10000
Low SO2 emissions
Low PM emissions
Low background PM
11000
12000
13000
Health benefits (Deaths/year)
14000
15000
0.007
Equity benefits (decrease in inequality indicator)
0.006
Indicator: Atkinson index, e = 0.75
Outcome: Mortality
Baseline: PM-related mortality
High background PM
0.005
High SO2 iF
High PM iF
0.004
High SO2
emissions
Reduction
to target/MMBTU
High PM emissions
High NOx emissions
75% reduction for all
Low NOx iF
Low PM iF
Low NOx emissions
0.003
0.002
High NOx iF
Low SO2 iF
Low SO2 emissions
Low PM emissions
0.001
Low background PM
0
10000
11000
12000
13000
Health benefits (Deaths/year)
14000
15000
0.045
Equity benefits (decrease in inequality indicator)
0.04
0.035
Indicator: Atkinson index, e = 3.0
Outcome: Mortality
Baseline: PM-related mortality
High background PM
High PM iF
0.03
High SO2 iF
0.025
Low NOx iF
High SO2 emissions
Reduction to target/MMBTU
High PM emissions
0.02
75% reduction for all
High NOx emissions
0.015
Low SO2 iF
Low NOx emissions
0.01
Low PM iF
0.005
Low SO2 emissionsHigh NOx iF
Low PM emissions
Low background PM
0
10000
11000
12000
13000
Health benefits (Deaths/year)
14000
15000
0.007
High background PM
Equity benefits (decrease in inequality indicator)
0.006
Indicator: Theil
Outcome: Mortality
Baseline: PM-related mortality
High SO2 iF
0.005
High PM iF
Reduction
to target/MMBTU
High SO2
emissions
High NOx emissions
High PM emissions
0.004
75% reduction for all
Low PM iF
0.003
Low NOx iF
Low NOx emissions
High NOx iF
0.002
Low SO2 iF
Low SO2 emissions
Low PM emissions
0.001
Low background PM
0
10000
11000
12000
13000
Health benefits (Deaths/year)
14000
15000
0.014
Equity benefits (decrease in inequality indicator)
0.012
Indicator: Gini
Outcome: Mortality
Baseline: PM-related mortality
High background PM
High SO2 iF
0.01
0.008
Reduction
to target/MMBTU
High SO2
emissions
High NOx emissions
High PM emissions
High PM iF
75% reduction for all
Low PM iF
0.006
Low NOx iF
Low NOx emissions
High NOx iF
0.004
Low SO2 iF
Low SO2 emissions
Low PM emissions
0.002
Low background PM
0
10000
11000
12000
13000
Health benefits (Deaths/year)
14000
15000
0.01
Equity benefits (decrease in inequality indicator)
0.009
0.008
Indicator: MLD
Outcome: Mortality
Baseline: PM-related mortality
High background PM
High SO2 iF
0.007
High PM iF
0.006
High SO2
emissions
Reduction
to target/MMBTU
High PM emissions
High NOx emissions
0.005
75% reduction for all
Low NOx iF
0.004
Low PM iF
0.003
Low SO2 iF
High NOx iF
Low SO2 emissions
Low PM emissions
0.002
Low background PM
0.001
0
10000
Low NOx emissions
11000
12000
13000
Health benefits (Deaths/year)
14000
15000
Does the choice of baseline
matter (and do we even need to
consider the baseline)?
0.007
Equity benefits (decrease in inequality indicator)
0.006
Indicator: Atkinson index, e = 0.75
Outcome: Mortality
Baseline: PM-related mortality
High background PM
0.005
High SO2 iF
High PM iF
0.004
High SO2
emissions
Reduction
to target/MMBTU
High PM emissions
High NOx emissions
75% reduction for all
Low NOx iF
Low PM iF
Low NOx emissions
0.003
0.002
High NOx iF
Low SO2 iF
Low SO2 emissions
Low PM emissions
0.001
Low background PM
0
10000
11000
12000
13000
Health benefits (Deaths/year)
14000
15000
0.00024
Equity benefits (decrease in inequality indicator)
0.00023
0.00022
Indicator: Atkinson index, e = 0.75
Outcome: Mortality
Baseline: All-cause mortality
High background PM
0.00021
High SO2 iF
High PM iF
High SO2 emissions
Reduction to target/MMBTU
0.0002
High PM emissions
High NOx iF
NOx emissions
LowHigh
PM iF
75% reduction
all emissions
Low for
NOx
Low NOx iF
0.00019
0.00018
Low SO2 iF
Low PM emissions
Low SO2 emissions
Low background PM
0.00017
0.00016
0.00015
0.00014
10000
11000
12000
13000
Health benefits (Deaths/year)
14000
15000
0.2
0.1
Indicator: Atkinson index, e = 0.75
Outcome: Mortality
Baseline: Power plant
PM-related mortality
High background PM
High SO2 iF
Reduction
to target/MMBTU
High SO2
emissions
Equity benefits (decrease in inequality indicator)
High PM iF
High PM emissions
75% reduction for all
High NOx emissions
Low NOx iF
Low NOx emissions
0
Low SO2 iF
-0.1
10000
-0.2
-0.3
-0.4
Low PM iF
11000
12000
13000
Low SO2 emissions
Low PM emissions
Low background PM
High NOx iF
-0.5
Health benefits (Deaths/year)
14000
15000
Health benefits (Deaths/year)
10000
0.1
0.15
Inequality indicator
0.2
0.25
11000
12000
13000
14000
Indicator: Atkinson index, e = 0.75
Outcome: Mortality
Baseline: None
Low background
PM
Low
Low PM
SO2emissions
emissions
Low PM iF
Low SO2 iF
High
LowNOx
NOxiFemissions
75% reduction for all
High NOx emissions
0.3
0.35
High PM emissions
Reduction to target/MMBTU
High SO2 emissions
Low NOx iF
High SO2 iF
High PM iF
0.4
0.45
High background PM
0.5
15000
Reductions in concentrations
Does it matter whether we use
mortality or concentrations in the
equity calculation?
0.007
Equity benefits (decrease in inequality indicator)
0.006
Indicator: Atkinson index, e = 0.75
Outcome: Mortality
Baseline: PM-related mortality
High background PM
0.005
High SO2 iF
High PM iF
0.004
High SO2
emissions
Reduction
to target/MMBTU
High PM emissions
High NOx emissions
75% reduction for all
Low NOx iF
Low PM iF
Low NOx emissions
0.003
0.002
High NOx iF
Low SO2 iF
Low SO2 emissions
Low PM emissions
0.001
Low background PM
0
10000
11000
12000
13000
Health benefits (Deaths/year)
14000
15000
0
Equity benefits (decrease in inequality indicator)
-0.0005
-0.001
High background PM
Indicator: Atkinson index, e = 0.75
Outcome: Concentrations
Baseline: Background PM2.5
High SO2 iF
-0.0015
Reduction to target/MMBTU
High NOx emissions
High SO2 emissions
High PM iF
High PM emissions
75% reduction for all
-0.002
Low NOx iF
Low PM iF
-0.0025
Low NOx emissions
Low SO2 iF
Low SO2 emissions
Low PM emissions
High NOx iF
-0.003
-0.0035
Low background PM
-0.004
-0.0045
10000
11000
12000
13000
Health benefits (Deaths/year)
14000
15000
What happens if we optimize on
subsets of pollutants (or single
pollutants)?
0.007
Equity benefits (decrease in inequality indicator)
0.006
0.005
Indicator: Atkinson index, e = 0.75
Outcome: Mortality
Baseline: PM-related mortality
Pollutant: PM2.5, NOx, SO2
High background PM
High SO2 iF
High PM iF
0.004
High SO2
emissions
Reduction
to target/MMBTU
High PM emissions
High NOx emissions
75% reduction for all
Low NOx iF
Low PM iF
Low NOx emissions
0.003
0.002
High NOx iF
Low SO2 iF
Low SO2 emissions
Low PM emissions
0.001
Low background PM
0
10000
11000
12000
13000
Health benefits (Deaths/year)
14000
15000
0.006
Equity benefits (decrease in inequality indicator)
0.005
Indicator: Atkinson index, e = 0.75
Outcome: Mortality
Baseline: PM-related mortality
Pollutant: SO2 only
High background PM
High SO2 iF
High PM iF
0.004
High SO2 emissions
Reduction to target/MMBTU
High PM emissions
High NOx emissions
Low NOx iF 75% reduction for all
0.003
Low PM iF
0.002
Low SO2 iF
Low NOx emissions
High NOx iF
Low
LowSO2
PM emissions
Low background PM
0.001
0
8000
9000
10000
Health benefits (deaths/year)
11000
12000
Equity benefits (decrease in inequality indicator)
0.0008
0.0006
Indicator: Atkinson index, e = 0.75
Outcome: Mortality
Baseline: PM-related mortality
Pollutant: NOx only
High background PM
High PM iF
High SO2 iF
0.0004
Low NOx iF
High SO2 emissions
Reduction to target/MMBTU
High PM emissions
for all
High75%
NOxreduction
emissions
0.0002
Low NOx emissions
Low SO2 iF
Low
PM emissions
Low PM
iF SO2
Low
emissions
High NOx iF
Low background PM
0
500
600
700
800
Health benefits (deaths/year)
900
1000
0.0016
Equity benefits (decrease in inequality indicator)
0.0014
0.0012
Indicator: Atkinson index, e = 0.75
Outcome: Mortality
Baseline: PM-related mortality
Pollutant: PM only
High background PM
High SO2 iF
0.001
High PM iF
High SO2 emissions
0.0008
Low NOx iF
Reduction to target/MMBTU
High PM emissions
High NOx emissions
75% reduction for all
0.0006
Low NOx emissions
Low PM iF
High NOx
iF SO2 iF
Low
0.0004
Low PM emissions
Low SO2 emissions
0.0002
0
1400
Low background PM
1500
1600
1700
1800
1900
Health benefits (deaths/year)
2000
2100
2200
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)
• Our conclusions are robust across
numerous indicators and formulations,
but clearly show the importance of
properly accounting for
baseline/background conditions
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 CR), differential susceptibility
• Consideration of local (vs. national)
perspective
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
•
•
•
•
•
•
•
Susan Chemerynski
Jessica Tuchmann
Julia Forgie
Sue Greco
Andrew Wilson
Len Zwack
National Science Foundation (SES-0324746)
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).