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The public health benefits of insulation retrofits in existing housing in the United States

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Methodological limitations make it difficult to quantify the public health benefits of energy efficiency programs. To address this issue, we developed a risk-based model to estimate the health benefits associated with marginal energy usage reductions and applied the model to a hypothetical case study of insulation retrofits in single-family homes in the United States. We modeled energy savings with a regression model that extrapolated findings from an energy simulation program. Reductions of fine particulate matter (PM2.5) emissions and particle precursors (SO2 and NOx) were quantified using fuel-specific emission factors and marginal electricity analyses. Estimates of population exposure per unit emissions, varying by location and source type, were extrapolated from past dispersion model runs. Concentration-response functions for morbidity and mortality from PM2.5 were derived from the epidemiological literature, and economic values were assigned to health outcomes based on willingness to pay studies. In total, the insulation retrofits would save 800 TBTU (8 x 10(14) British Thermal Units) per year across 46 million homes, resulting in 3,100 fewer tons of PM2.5, 100,000 fewer tons of NOx, and 190,000 fewer tons of SO2 per year. These emission reductions are associated with outcomes including 240 fewer deaths, 6,500 fewer asthma attacks, and 110,000 fewer restricted activity days per year. At a state level, the health benefits per unit energy savings vary by an order of magnitude, illustrating that multiple factors (including population patterns and energy sources) influence health benefit estimates. The health benefits correspond to 1.3 billion dollars per year in externalities averted, compared with 5.9 billion dollars per year in economic savings. In spite of significant uncertainties related to the interpretation of PM2.5 health effects and other dimensions of the model, our analysis demonstrates that a risk-based methodology is viable for national-level energy efficiency programs.
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Environmental Health: A Global
Access Science Source
Open Access
Research
The public health benefits of insulation retrofits in existing housing
in the United States
Jonathan I Levy*, Yurika Nishioka and John D Spengler
Address: Department of Environmental Health, Harvard School of Public Health, Boston, MA, USA
Email: Jonathan I Levy* - jilevy@hsph.harvard.edu; Yurika Nishioka - ynishiok@hsph.harvard.edu;
John D Spengler - spengler@hsph.harvard.edu
* Corresponding author
Abstract
Background: Methodological limitations make it difficult to quantify the public health benefits of
energy efficiency programs. To address this issue, we developed a risk-based model to estimate the
health benefits associated with marginal energy usage reductions and applied the model to a
hypothetical case study of insulation retrofits in single-family homes in the United States.
Methods: We modeled energy savings with a regression model that extrapolated findings from an
energy simulation program. Reductions of fine particulate matter (PM
2.5
) emissions and particle
precursors (SO
2
and NOx) were quantified using fuel-specific emission factors and marginal
electricity analyses. Estimates of population exposure per unit emissions, varying by location and
source type, were extrapolated from past dispersion model runs. Concentration-response
functions for morbidity and mortality from PM
2.5
were derived from the epidemiological literature,
and economic values were assigned to health outcomes based on willingness to pay studies.
Results: In total, the insulation retrofits would save 800 TBTU (8 × 10
14
British Thermal Units)
per year across 46 million homes, resulting in 3,100 fewer tons of PM
2.5
, 100,000 fewer tons of
NOx, and 190,000 fewer tons of SO
2
per year. These emission reductions are associated with
outcomes including 240 fewer deaths, 6,500 fewer asthma attacks, and 110,000 fewer restricted
activity days per year. At a state level, the health benefits per unit energy savings vary by an order
of magnitude, illustrating that multiple factors (including population patterns and energy sources)
influence health benefit estimates. The health benefits correspond to $1.3 billion per year in
externalities averted, compared with $5.9 billion per year in economic savings.
Conclusion: In spite of significant uncertainties related to the interpretation of PM
2.5
health effects
and other dimensions of the model, our analysis demonstrates that a risk-based methodology is
viable for national-level energy efficiency programs.
Background
Generally, the benefits of energy efficiency programs are
expressed in terms of the economic payback that will ac-
crue for individuals or organizations. To introduce envi-
ronmental benefits into this framework, some
investigators have quantified the emission reductions re-
lated to the decreased use of electricity and fossil fuel
[1,2], including criteria air pollutants and greenhouse gas-
es. While these analyses are useful, they provide only a
limited set of information, as issues such as pollutant fate
Published: 11 April 2003
Environmental Health: A Global Access Science Source 2003, 2:4
Received: 2 January 2003
Accepted: 11 April 2003
This article is available from: http://www.ehjournal.net/content/2/1/4
© 2003 Levy et al; licensee BioMed Central Ltd. This is an Open Access article: verbatim copying and redistribution of this article are permitted in all
media for any purpose, provided this notice is preserved along with the article's original URL.
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and transport or pollutant toxicity are omitted, making
environmental and economic endpoints incomparable.
Multiple studies have developed models to estimate the
health impacts of emissions from specific sources, using a
risk-based approach to link emissions of key pollutants
with dispersion models and health evidence [3–6]. While
this is a valuable approach, it can be difficult to apply in
the case of broad-based energy efficiency programs, as
these programs tend to affect multiple sources simultane-
ously (e.g., numerous homes within a state, all power
plants on the grid). Quantifying the health benefits of en-
ergy efficiency programs therefore requires a simplified
approach toward risk calculations, with adequate charac-
terization of uncertainty to aid in the interpretation of the
findings.
In a recent investigation [7], we developed a model to pre-
dict the public health benefits of increased residential in-
sulation in new housing, related to the emission
reductions of fine particulate matter (PM
2.5
) and its pre-
cursors (nitrogen dioxide and sulfur dioxide). We deter-
mined that increasing insulation from current practice to
the latest International Energy Conservation Code (IECC
2000) levels for all single-family homes built from 2001
to 2010 would save 300 TBTU (3 × 10
14
British Thermal
Units) during the ten-year period (5 TBTU for the annual
housing output per year). Linking this evidence with esti-
mated emission reductions, extrapolated results from dis-
persion model outputs and epidemiological evidence, we
concluded that the resulting public health benefits would
include approximately 60 fewer deaths, 2,000 fewer asth-
ma attacks, and 30,000 fewer restricted activity days with-
in this decade (1.1 deaths, 30 asthma attacks, and 500
restricted activity days reduced per year for all new homes
built in 2001).
Although this investigation demonstrated that a risk-
based methodology for demand-side management pro-
grams is viable and provided a crucial foundation for pol-
icy analysis, interpretation was impaired by the relatively
narrow focus. For example, the new housing market rep-
resents only a small fraction of the potential public health
benefits of insulation in the residential sector. There are
approximately 1.2 million new single-family homes built
each year [8], but the existing housing stock consists of
more than 75 million single-family homes. Furthermore,
it would be anticipated that there is greater per unit poten-
tial for energy savings in existing housing, given many
homes built prior to the promulgation of energy codes
that therefore contain relatively less insulation than newer
homes.
In spite of the anticipated greater energy savings of the ex-
isting versus new housing market, the relative public
health benefits will not necessarily be proportional. Be-
cause the geographic distribution of housing starts differs
from the distribution of existing homes, geographic pat-
terns of energy savings will differ. There are regional dif-
ferences in population density and meteorological
patterns, both of which will influence the magnitude of
health benefits. The fuels used in homes also differ both
regionally and by age of home, which will strongly influ-
ence emissions. In this paper, we apply our risk model to
the existing single-family home market in the United
States to evaluate the following questions:
- What are the magnitude and distribution of public
health benefits associated with hypothetically retrofitting
single-family homes with insulation at IECC 2000 levels
when necessary?
- How does the economic value associated with these pub-
lic health benefits compare with the economic savings for
the households?
- In general, how uncertain are public health benefit esti-
mates derived from simplified models, and what are the
major contributors to that uncertainty?
Methods
Estimating the public health benefits of increased insula-
tion in existing housing requires multiple discrete steps.
First, we quantify the state-by-state energy savings availa-
ble through increased residential insulation. We use infor-
mation about regional residential fuel utilization patterns
and power plant emissions to determine the emission re-
ductions associated with these energy savings. These emis-
sion reductions are combined with summary findings
from national-scale dispersion models and epidemiologi-
cal studies to yield estimates of the public health benefits,
and economic values are placed on health outcomes to al-
low for a direct comparison with cost savings. As many el-
ements of this framework have been described elsewhere
[7], we highlight the key elements of the methodology or
those which are unique to this analysis.
Although any investigation of this sort contains signifi-
cant uncertainties and should be considered incomplete
without detailed uncertainty analysis, we provide only
central estimates with general assessments of the magni-
tudes of various uncertainties within the Discussion sec-
tion. Quantitative uncertainty analysis is impaired by the
difficulty of adequately estimating uncertainty for compo-
nents such as the aggregate national exposures per unit
emissions and the likelihood that the epidemiological ev-
idence reflects a causal relationship, and is beyond the
scope of this analysis. A more detailed discussion of un-
certainty within our analytical framework is available else-
where [4,7].
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In addition, it is important to note that our stated meth-
odology contains some implicit boundary conditions. For
example, we do not attempt to characterize the life cycle
impacts of increased insulation manufacturing and de-
creased fuel production within this article. This implies
that we are quantifying the public health benefits of re-
duced energy consumption related to increased insula-
tion, but do not know the degree to which this might be
offset by increased emissions from insulation manufac-
turing (or augmented by upstream benefits from de-
creased fuel utilization). A detailed exploration of this
question in the new housing market indicates that the up-
stream manufacturing emissions are offset by the energy-
related public health benefits with a payback period near-
ly identical to the economic payback period for home-
owners [9,10]. This analysis focused exclusively on the
emissions associated with inputs to insulation manufac-
turing and fuel production, and did not consider dimen-
sions such as occupational risks, installation risks, or
indoor air quality. Finally, it should be noted that we as-
sume for the sake of simplicity that all existing homes are
retrofitted with insulation immediately, which has impli-
cations for the interpretation of our findings.
Energy Savings
To quantify the energy savings associated with insulation
retrofits in existing single-family homes, we use a regres-
sion-based approach to simulate energy savings in a
number of prototype homes in different cities and apply
those findings to the set of homes where insulation retro-
fits were deemed plausible. The first step was to determine
the number of homes in each state that likely require in-
creased insulation. Using information from the Residen-
tial Energy Consumption Survey (RECS) [11], we
estimated the total number of homes by state in 1999.
RECS also provided the percentage of single-family homes
by census division, yielding an estimated 74 million sin-
gle-family homes in the continental US.
We used the qualitative characterization of insulation lev-
els by age of home within an earlier version of RECS [12]
to determine the proportion of homes where insulation
retrofits were viable. Homes that were well insulated were
assumed to not require additional insulation, while
homes with adequate or poor insulation were assumed to
require more insulation. On average, 63% of single-family
homes had adequate or poor insulation. Lacking geo-
graphically resolved information on this parameter, we
assumed that this percentage applied to all states, yielding
the number of target single-family homes at a state level
(and approximately 46 million homes nationally). It
should be noted that an assumption of geographic uni-
formity stratified by age of home yielded essentially iden-
tical estimates (ranging from 59% in the South-Atlantic to
65% in New England, with census divisions defined as in
Table 1), so this is unlikely to contribute to significant un-
certainty in the analysis.
To quantify energy consumption under current condi-
tions and given insulation increased to IECC 2000 levels,
we applied regression models we developed originally for
our new housing investigation [7]. We simulated proto-
type single-family homes in a number of different cities
and applied the energy model REM/Design (Architectural
Energy Corporation, Boulder, CO) to estimate energy con-
sumption given home configuration, climate, and insula-
tion levels. We gathered current practice insulation levels
(in ceiling, wall, and floor) from an earlier energy analysis
[13], which assumed existing home insulation levels to be
uniform within census divisions (Table 1). IECC 2000 in-
sulation levels [14] are based on heating degree days rath-
er than geographic region and are represented in Figure 1.
In addition, window types (metal versus wood), door
thickness, and shading factors varied by region. Selected
home characteristics as well as heating and cooling system
features were assumed to be uniform nationally. This in-
cluded unconditioned basements, exposed ducts, an
infiltration rate of 0.67, a heating set point of 20 degrees
C (68 degrees F), and a cooling set point of 26 degrees C
(78 degrees F). Remaining home characteristics, including
floor area, heating types, and number of stories by region,
Table 1: Current insulation levels in existing single-family homes by census division [13] (batt insulation assumed for all).
NE MA SA ESC WSC ENC WNC MTN PA
Ceiling R-30 R-30 R-11 R-11 R-11 R-19 R-19 R-19 R-1
Above-grade wall R-11 R-11 R-6 R-6 R-6 R-11 R-11 R-11 R-6
Floor R-13 R-13 R-3.4 R-3.4 R-3.4 R-11 R-11 R-11 R-5.5
Definitions of census divisions: NE (Northeast): MA, ME, NH, VT, RI, CT; MA (Mid-Atlantic): NY, PA, NJ; SA (South-Atlantic): MD, VA,
WV, DC, NC, SC, GA, FL; ESC (East-South-Central): AL, MS, KY, TN; WSC (West-South-Central): TX, OK, AR, LA; ENC (East-North-
Central): MI, IL, OH, IN, WI; WNC (West-North-Central): MO, IA, MN, SD, ND, NE, KS; MTN (Mountain): ID, MT, NV, AZ, NM, UT,
CO, WY; PA (Pacific): CA, OR, WA. Note: The R-value represents the resistance of the insulation to heat flow, with higher values indicating
greater resistance. R-values are given in units of °F.ft2.h/BTU.
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were gathered from RECS [12]. Additional specifications
of house or heating and cooling system characteristics are
available from the authors upon request.
To ensure that the regression models represented the
physical realities of heat transfer, the functional form of
our regressions and the covariates selected were based on
standard heat loss equations [15]. For example, space
heating energy consumption was predicted as a function
of two covariates – the product of heating degree days and
floor area, and the product of heating degree days, floor
area, and an expression reflecting the amount of insula-
tion in the ceiling, walls, and floor (R
2
= 0.99). Finally, we
calibrated the outputs from the regression models against
reported energy consumption data from RECS to better re-
flect actual consumption patterns.
Emission Reductions
Given state-level energy savings by fuel type, we estimate
emissions of primary PM
2.5
, SO
2
(as a precursor of sulfate
particles), and NOx (as a precursor of nitrate particles).
This choice was based on the findings of past studies, in
which PM contributed the vast majority of benefits
[16,17]. As might be anticipated, our procedure to model
emissions differs for direct residential fuel combustion
Figure 1
R-values required in the 2000 International Energy Conservation Code by heating degree day zone [14].
0
10
20
30
40
50
60
01234567891011121314151617
Heating zone
R-value
Ceiling Exterior wall Floor Basement Slab Crawl
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and electricity. We summarize our approach below and
refer the reader to our earlier analysis [7] for more detail.
For direct residential fuel combustion, given the fuel type
(natural gas or fuel oil), we estimate emissions from the
AP-42 database prepared by US EPA [18]. For fuel oil,
emissions of both SO
2
and PM are given as a function of
the sulfur content in the fuel. Although the sulfur content
of fuel oil can vary somewhat on a state-by-state basis,
lacking detailed information, we assumed that a 0.5% sul-
fur content by weight was applicable nationally, as has
been done previously [19].
On the other hand, for electricity, we needed to determine
which power plants would be affected by marginal de-
creases in electricity consumption, since it is likely that the
marginal plants are not identical to the average plants in
the power pool. In our earlier analysis [7], we concluded
that only non-combined cycle fossil-fuel plants with less
than 80% capacity factors were likely to be influenced by
small demand changes in the near term, as any base load
units running at high capacity or units with supply-related
constraints (e.g., nuclear, hydroelectric) could not re-
spond to these changes. We applied these screening crite-
ria to the Emissions and Generation Resource Integrated
Database (E-GRID) [20] to determine a subset of 553
power plants potentially able to respond to demand-side
management efforts. Without a detailed simulation of the
electricity sector, it is impossible to determine precisely
which plants would respond on the margin. For our cen-
tral estimate, we assumed that each plant with positive net
generation was equally likely to be affected, regardless of
capacity or relative availability. It should be noted that
this assumption resulted in lower emission rates than a
capacity- or availability-based allocation, potentially bias-
ing our estimates downward. The magnitude of this effect
is considered in the Discussion section.
To link this information with electricity savings at the state
level, we first quantified emissions of NOx and SO
2
per
unit output for each power plant using data from E-GRID.
Since primary PM emissions were not incorporated into E-
GRID, we estimated PM
2.5
emissions using AP-42 emis-
sion factors for PM
10
(given power plant characteristics)
and estimates of the PM
2.5
/PM
10
emissions ratio. We used
this information to determine emission factors by North
American Electricity Reliability Council (NERC) region,
and determined state-level emission factors based on the
proportion of electricity consumption provided by each
NERC region [18].
Exposure Reductions
As alluded to above, a critical (and difficult) step in accu-
rately quantifying the health benefits of demand-side
management efforts involves linking emissions savings to
exposure reductions, given the substantial number of
sources and limited capacity of fate and transport models.
Recently, the risk assessment and atmospheric modeling
communities have recognized the importance of develop-
ing approaches to extrapolate estimates from other studies
in situations where input data are limited or comprehen-
sive models are impractical. Multiple studies have devel-
oped and applied a concept known as an intake fraction,
generally defined as the fraction of a pollutant or its pre-
cursor emitted that is eventually inhaled by someone
[21,22]. Mathematically, an intake fraction can be calcu-
lated using the following equation:
where iF = intake fraction; BR = population-average
breathing rate (assumed to be 20 m
3
/day); C
i
= incremen-
tal concentration of pollutant at receptor i (µg/m
3
); N
i
=
number of people at receptor i; Q = emission rate of pol-
lutant or pollutant precursor (µg/day). If the health effect
in question has a linear dose-response function with no
threshold above current ambient concentrations, an in-
take fraction provides an estimate directly interpretable
from a health perspective (since the health impacts will be
proportional to the sum of the product of population and
concentration).
To apply the intake fraction concept in our investigation,
we used predictive regression models developed in a re-
cent study [23]. This study took its intake fraction esti-
mates from an analysis [22] that used CALPUFF (a
regional-scale Lagrangian puff model) to calculate intake
fractions for primary PM
2.5
, sulfate particles (from SO
2
),
and nitrate particles (from NOx), evaluating 40 power
plants and 40 mobile sources distributed across the US. In
general, the intake fractions were strongly predicted by
population variables (e.g., population within 500 or 1000
km of a source), stack height (for primary PM
2.5
from
power plants), and miscellaneous meteorological terms
(including annual average temperature, wind speed, rela-
tive humidity, or mixing height).
To apply these regressions to the power sector, we deter-
mined the values of the appropriate covariates for each
marginal power plant identified above, resulting in intake
fraction estimates for each of 553 power plants. To assign
a representative intake fraction for electricity savings in
each state, we weighted the plant-level intake fractions by
emissions within NERC region and averaged across NERC
regions in proportion to electricity transmission patterns.
For residential combustion sources, we assumed that the
mobile source intake fraction estimates were applicable
iF
BR C N
Q
ii
i
=
××
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(given emissions at or near ground level) and calculated
intake fractions for each Metropolitan Statistical Area
(MSA) across the US. State-level intake fractions were then
estimated as a weighted average of MSA-level intake frac-
tions, weighting by number of homes.
It should be noted that, in addition to the uncertainties as-
sociated with the regression model, there are overarching
uncertainties associated with the application of CALPUFF.
For example, although it is well established that changes
in SO
2
emissions can influence nitrate particle concentra-
tions by affecting available ammonia levels [24],
CALPUFF does not capture this phenomenon for an anal-
ysis of a small subset of sources. The importance of this ef-
fect and related model uncertainties are considered in the
Discussion section.
Public Health Benefits
As in our past investigation [7], we quantify mortality and
selected morbidity benefits associated with incremental
reductions in PM
2.5
exposures. Premature mortality asso-
ciated with PM
2.5
has typically dominated public health
benefits when health endpoints were placed in economic
terms [16,17], but we include some morbidity endpoints
to illustrate outcomes with varying severity. Although ex-
cluding the complete array of morbidity endpoints will re-
sult in a systematic underestimation of benefits, we adopt
this approach for the sake of brevity. As mentioned above,
given the framing of our analysis, we determine reasona-
ble central estimates for concentration-response functions
and consider the magnitude of the uncertainty in the Dis-
cussion section.
For premature mortality, we derive our central estimate
from a follow-up analysis of the American Cancer Society
cohort study [25]. This investigation followed approxi-
mately 500,000 individuals across a 16-year period, eval-
uating mortality risk from air pollution while controlling
for numerous plausible confounders (including smoking,
alcohol consumption, body mass index, diet, and occupa-
tional exposures). Using pollution data averaged across
the study period, the authors report a relative risk for all-
cause mortality of 1.06 for a 10 µg/m
3
increase in PM
2.5
concentrations (95% CI: 1.02, 1.11). Alternatively, using
pollution data from the start of the study period, the cor-
responding relative risk was 1.04 (95% CI: 1.01, 1.08).
Based on these estimates and given the desire to provide a
parallel estimate with our earlier investigation to improve
comparability [7], we apply a concentration-response
function of a 0.5% increase in premature deaths per µg/
m
3
increase in annual mean PM
2.5
concentrations. We
consider this value to be a plausible central estimate, as it
is bounded above by the concentration-response function
from the Harvard Six Cities Study [26] and below by val-
ues from time-series studies.
As previously [7], we derive our selected morbidity con-
centration-response estimates from recent US EPA bene-
fit-cost analyses [16,17]. This results in an estimated 0.2%
increase in daily asthma attacks (among asthmatics only)
and a 0.5% increase in restricted activity days per µg/m
3
increase in PM
2.5
concentrations.
The application of concentration-response functions as
described above implicitly assumes that no population
threshold exists at current ambient concentrations in the
US and that all forms of particulate matter have identical
toxicity. The former assumption is bolstered by the lack of
documented thresholds in both cohort [25,26] and time-
series mortality [27,28] investigations. Consideration of
the latter issue is beyond the scope of our analysis, other
than to state that lacking either definitive evidence exon-
erating selected types of fine particles or quantitative evi-
dence supporting relative toxicities, an assumption of
equal toxicity is a reasonable default for a central estimate.
Economic Implications
Although the economic valuation of health endpoints is
highly uncertain, we apply the central estimates used in
the US EPA benefit-cost analyses [16,17] to illustrate the
potential magnitude of externalities avoided in relation to
the direct economic savings.
Based on the findings of a number of labor market and
contingent valuation studies, the US EPA determined a
value of statistical life of $4.8 million in 1990 US dollars,
which can be translated into an approximate value of $6
million in 2000 US dollars using Consumer Price Index
inflation rates. We apply this value assuming a five-year
lag structure for deaths from cohort mortality evidence
[16], with 25% of deaths in each of the first two years and
16.7% of deaths in the subsequent three years.
In the benefit-cost analysis of the Clean Air Act [16], an
asthma attack is given a central value of $32 (in 1990 dol-
lars), based on a willingness to pay study that focused on
avoidance of a bad asthma day. To our knowledge, no
willingness to pay study has been conducted on restricted
activity days. Given values reported in the benefit-cost
analysis of the Clean Air Act [16] of $38 for a minor re-
stricted activity day (which is more mild than a restricted
activity day) and $83 for a work-loss day (which is pre-
sumably more severe than a restricted activity day), we use
$60 (in 1990 dollars) as a reasonable central estimate. In
the Discussion, we focus on the sensitivity of conclusions
to the approach for valuation of premature mortality.
To quantify the incremental cost of insulation, we deter-
mined the average R-value increase in ceiling, walls, and
foundations, and used this to quantify the volume of in-
sulation required per home, given the square footage of
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the homes and the assumption that 0.3 inches of fiber-
glass corresponds to a unit increment in R-value. We
quantified the value of insulation in dollars per ton, based
on the reported value of shipment from the mineral wool
sector [29] and the mass of fiberglass production [30],
along with an assumed density of fiberglass of 0.06 kg/in-
ft
2
. Annual economic savings associated with the energy
reductions were based on state-level unit prices for elec-
tricity, natural gas, and fuel oil [31]. The real prices of all
fuels were assumed to be constant over time, given fore-
casts of relatively small price changes in upcoming dec-
ades [32]. For all net present value calculations, we apply
a 5% real discount rate, the central estimate used in US
EPA benefit-cost analyses [16].
Results
Energy Savings
Prior to calibration, our predictive regression models
based on REM/Design outputs systematically overpredict-
ed energy consumption as summarized in RECS. We con-
sidered performance stratified by fuel type and
combinations of census divisions, where Northeast corre-
sponds to NE and MA; Midwest to ENC and WNC; South
to SA, ESC, and WESC; and West corresponds to MTN and
PA (Table 1). Natural gas and fuel oil consumption were
within 10% of reported values in all regions but the West,
where the regression model predicted energy consump-
tion 1.7 times higher than that reported in RECS. The bias
was somewhat greater for electricity consumption (a fac-
tor of 1.4–2.6 higher across regions for gas- or oil-heated
homes and a factor of 2.4–3.4 higher for electric-heated
homes). As described in our earlier analysis [7], REM/De-
sign has been shown to somewhat overestimate energy
consumption, so this performance and the need for a cal-
ibration factor were anticipated. We calibrated the model
by simply multiplicatively scaling the regression results to
reported RECS values, stratified by region and fuel type.
According to our calibrated energy model, increasing resi-
dential insulation in the 46 million existing homes where
insulation retrofits are necessary would save approximate-
ly 800 TBTU per year – 17 MMBTU (1.7 × 10
7
BTU) per
household per year. Of this total, 39% is related to source
electricity savings, with 52% related to natural gas and
10% to fuel oil. Approximately 80% of the source electric-
ity savings are associated with space heating rather than
cooling. Thirty-five percent of energy savings occurs in
electric-heated homes, with the remainder occurring in
gas- or fuel oil-heated homes (either through reduced fos-
sil fuel combustion or reduced electricity consumption for
space cooling).
Looking at regional patterns, within electric-heated
homes, nearly 70% of the energy savings are found in the
South, principally due to the number of electric-heated
homes in the South relative to other regions (Table 2). On
a per unit basis, the savings are greatest in the Midwest
and lowest in the West, related to climate and current
practice insulation levels. For homes heated by natural gas
or fuel oil, nearly half of the energy savings are found in
the Midwest, given both the number of households and
the higher per unit energy savings. Across all fuel types,
the annual per unit energy savings for retrofitting existing
homes are approximately four times greater than the per
unit energy savings associated with increasing insulation
from current practice levels in new homes to IECC 2000
Table 2: Regional energy savings for existing single-family homes increasing insulation from current practice to IECC 2000 levels.
Northeast (NE/MA) Midwest (ENC/WNC) South (SA/ESC/WSC) West (MTN/PA) Total
All-electric homes
# households (millions) 1.0 1.6 11 3.8 17
Source savings (TBTU/year, % of national
total)
15 (5%) 31 (11%) 190 (69%) 41 (15%) 280
Per-capita savings (MMBTU/household/
year)
15 20 17 11 16
Non-electric heated homes
# households (millions) 6.5 9.9 7.5 5.3 29
Source savings, fossil fuel (TBTU/year, %
of national total)
130 (26%) 220 (46%) 97 (20%) 43 (9%) 490
Source savings, electricity (TBTU/year, %
of national total)
0.43 (2%) 1.6 (6%) 25 (89%) 0.98 (3%) 28
Source savings, total (TBTU/year, % of
national total)
130 (24%) 230 (44%) 120 (24%) 44 (8%) 520
Per-capita savings(MMBTU/household/
year)
19 23 16 8.3 18
Note: Estimates are presented to two significant figures; sums may not add due to rounding. Percentages represent the fraction of benefits within
each region.
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code levels [7], a finding that agrees with other analyses
[13].
An evaluation at the state level can provide some insight
about differentials in past energy codes as well as within-
region differences that might be important from an emis-
sions and exposure perspective. Unsurprisingly, the states
with the greatest total energy savings generally correspond
to the states with the most households – Texas, New York,
Michigan, Illinois, and Ohio represent 28% of the nation-
al energy savings (and 25% of the total households). On
a per unit basis, the states with the greatest energy savings
are North Dakota, Minnesota, West Virginia, and South
Dakota, while the smallest per unit savings are seen in Ar-
izona, New Mexico, Florida, and California, a clear indica-
tion of the importance of climate.
Emission Reductions
Given these energy savings, the aggregate emission reduc-
tions from residential fuel combustion and power plants
include approximately 3,100 fewer tons per year of PM
2.5
,
100,000 fewer tons per year of NOx, and 190,000 fewer
tons per year of SO
2
(Table 3). For all three pollutants, the
majority of emissions are linked to power plants (69% for
PM
2.5
, 76% for NOx, and 89% for SO
2
), even though only
39% of energy savings is related to electricity generation.
This can be explained by the predominance of natural gas
space heating, which has lower emissions of all pollutants
(particularly SO
2
) when compared with electricity gener-
ation from marginal power plants that are often fueled by
coal.
When we consider tons of pollutants emitted per unit en-
ergy savings across regions, it is clear that there are sub-
stantial regional variations in emissions intensity of
energy consumption (Figure 2). For primary PM
2.5
, emis-
sions per unit energy savings are similar across regions for
residential fuel combustion, but are somewhat greater in
the Midwest and the South for electricity. Similarly, for
SO
2
, the electricity-related emissions are greater in other
regions than in the West, where a greater proportion of
electricity available on the margin is generated by natural
gas. For residential fuel combustion, the emissions inten-
sity for SO
2
is far lower than that for electricity, with the
exception of the Northeast, where fuel oil combustion is
more prevalent. Finally, the emissions intensity patterns
for NOx are broadly consistent with those for PM
2.5
, indi-
cating similarities in the ratios of emission rates associated
in part with combustion temperature and efficiency.
The rankings of states, both on an aggregate basis and on
a per unit basis, differ when energy savings are translated
into emissions reductions. First considering aggregate
emission reductions, for PM
2.5
, the highest-emitting states
are those larger states with substantial electric space heat-
ing and higher-emitting power plants – Texas, Virginia,
North Carolina, and Tennessee. The top four emitting
states are identical for NOx, with Maryland having the
highest emissions of SO
2
(followed by Virginia, North
Carolina, and Tennessee). On a per unit basis, West Vir-
ginia, Kentucky, Delaware, and Virginia have the highest
emissions for PM
2.5
and NOx, with Delaware, West Vir-
ginia, Maryland, and Kentucky having the highest SO
2
emissions.
Table 3: Regional emission reductions for existing single-family homes increasing insulation from current practice to IECC 2000 levels.
Northeast (NE/MA) Midwest (ENC/WNC) South (SA/ESC/WSC) West (MTN/PA) Total
PM
2.5
(tons/year, % of
national total)
Electricity 82 (4%) 260 (12%) 1,600 (74%) 210 (10%) 2,100
Residential (NG + Oil) 210 (21%) 480 (49%) 200 (20%) 94 (10%) 990
Total 290 (9%) 740 (24%) 1,800 (57%) 300 (10%) 3,100
SO
2
(tons/year, % of
national total)
Electricity 9,100 (5%) 21,000 (12%) 130,000 (78%) 8,300 (5%) 170,000
Residential (NG + Oil) 14,000 (69%) 3,000 (15%) 3,100 (15%) 260 (1%) 20,000
Total 23,000 (12%) 24,000 (12%) 140,000 (71%) 8,600 (4%) 190,000
NOx (tons/year, % of
national total)
Electricity 2,900 (4%) 9,300 (12%) 57,000 (74%) 7,500 (10%) 77,000
Residential (NG + Oil) 6,800 (28%) 11,000 (44%) 4,700 (20%) 2,000 (8%) 24,000
Total 9,700 (10%) 20,000 (20%) 62,000 (61%) 9,500 (9%) 100,000
Note: Estimates are presented to two significant figures; sums may not add due to rounding. Percentages represent the fraction of benefits within
each region.
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Figure 2
Emissions intensities of electricity and residential fuel combustion stratified by region (tons/TBTU of source energy).
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Public Health Benefits
Using our intake fraction estimates and concentration-re-
sponse functions for mortality and morbidity, the emis-
sion reductions of particulate matter and particle
precursors can be translated into health benefits. In total,
the emission reductions correspond to 240 fewer deaths,
6500 fewer asthma attacks, and 110,000 fewer restricted
activity days per year, spread across the continental US.
This represents an approximate 0.01% decrease in rates of
the selected mortality and morbidity outcomes. The distri-
bution of mortality benefits by pollutant, source, and re-
gion is presented in Table 4, with identical patterns seen
for the morbidity outcomes.
Of the 240 annual deaths averted, more than half are as-
sociated with reduced sulfate exposures due to SO
2
emis-
sion reductions. This is largely due to reduced electricity
consumption for space heating in the South. Approxi-
mately 34% of the health benefits are related to primary
PM
2.5
emission reductions, largely due to reduced resi-
dential fuel combustion in the Northeast and Midwest
and reduced electricity consumption in the South. Nitrate
particles due to NOx emissions play a relatively minor
role, contributing only 5% of total benefits.
Because of differences in regional emissions intensities,
meteorological conditions influencing pollutant fate and
transport, and population patterns, the distribution of
health benefits across regions and states differs signifi-
cantly from the distribution of energy savings. On a re-
gional basis, although the South provides only 39% of
total energy savings, it is responsible for nearly 60% of
public health benefits. In contrast, the West provides 11%
of total energy savings but only 4% of public health ben-
efits. As shown in Figure 3, the relative contributions of
states to energy savings versus public health benefits also
vary substantially. States that contribute proportionately
more to mortality reductions than to energy savings (e.g.,
Virginia, North Carolina, Tennessee) tend to have signifi-
cant electric space heating with electricity from power
plants that emit higher amounts of SO
2
.
Looking at the benefits on a per capita basis, there are sig-
nificant differences across states in both public health and
energy savings (Figure 4). The two figures are moderately
correlated with one another (r = 0.50), with a ratio that
varies by a factor of ten (with a minimum mortality reduc-
tion per unit energy savings in North Dakota and a maxi-
mum in Maryland). This provides tangible evidence that
one cannot simply calculate public health benefits given
estimated energy savings, as population density, climate,
fuel usage, and emissions intensities significantly affect es-
timated health benefits.
Economic Implications
In total, the estimated cost of the increased insulation is
approximately $37 billion US, or an average of slightly
below $800 per existing single-family home available for
Table 4: Regional mortality reductions for existing single-family homes increasing insulation from current practice to IECC 2000 levels.
Northeast(NE/MA) Midwest(ENC/WNC) South(SA/ESC/WSC) West(MTN/PA) Total
PM
2.5
(deaths/year, % of
national total)
Electricity 2.2 (7%) 3.2 (11%) 23 (76%) 1.6 (5%) 30
Residential (NG + Oil) 18 (34%) 22 (43%) 10 (19%) 2.1 (4%) 52
Total 20 (24%) 26 (31%) 33 (40%) 3.7 (5%) 82
SO
2
– Sulfate (deaths/year, %
of national total)
Electricity 6.9 (5%) 16 (13%) 100 (79%) 3.8 (3%) 130
Residential (NG + Oil) 12 (72%) 2.4 (15%) 2.1 (13%) 0.1 (0.5%) 16
Total 18 (13%) 19 (13%) 100 (72%) 3.9 (3%) 140
NOx – Nitrate (deaths/year,
% of national total)
Electricity 0.5 (5%) 1.1 (12%) 6.4 (66%) 1.7 (18%) 9.7
Residential (NG + Oil) 1.1 (32%) 1.6 (46%) 0.5 (15%) 0.2 (7%) 3.3
Total 1.5 (12%) 2.7 (20%) 6.9 (53%) 1.9 (15%) 13
Total (deaths/year, % of
national total)
Electricity 9.5 (6%) 20 (12%) 130 (78%) 7.1 (4%) 170
Residential (NG + Oil) 30 (42%) 26 (37%) 13 (18%) 2.4 (3%) 72
Total 40 (17%) 47 (20%) 140 (60%) 9.5 (4%) 240
Note: Estimates are presented to two significant figures; sums may not add due to rounding. Percentages represent the fraction of benefits within
each region.
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retrofits. The annual economic benefits associated with
the energy savings are approximately $5.9 billion per year,
indicating a payback period of slightly over 6 years (as-
suming no change in the real price of fuel). Since this in-
cludes an array of homes in different regions and with
different baseline insulation levels, the payback period
clearly varies substantially across homes. If we apply a real
discount rate of 5%, then the net present value of the
economic savings (conservatively assuming a 50-year life-
time for all homes) is approximately $110 billion, imply-
ing a net economic savings (including the cost of
insulation) on the order of $80 billion.
When economic values are assigned to the mortality and
morbidity outcomes as described above, the environmen-
tal externalities averted through increased insulation
amount to approximately $1.3 billion per year. Over 99%
of this total is related to premature mortality, indicating
that the assumed value of statistical life is a key parameter
in this calculation (although not all morbidity outcomes
were included in our analysis). Adding this quantity to the
economic savings for the households would reduce the
payback period from over 6 years to approximately 5
years, although this involves combining private and pub-
lic benefits, has a simple characterization of the time lag
of benefits, and does not include the upstream emissions
from insulation manufacturing or fuel extraction and
processing. Nevertheless, this calculation provides a
rough indication of the relative magnitudes of environ-
mental externalities and cost savings.
Discussion
Our analysis has demonstrated that it is viable to calculate
central estimates for the public health benefits associated
with reduced residential energy consumption, making use
of previous dispersion models and epidemiological stud-
ies. Our model can provide insight about the relative mer-
its of alternative energy efficiency policies, as well as
allowing for a comparison with source controls or other
public health interventions. For example, our central esti-
mate of 240 fewer premature deaths per year from retro-
fitting existing homes can be compared with 1.1 fewer
premature deaths per year from increasing insulation in a
single year of new homes [7]. The ratio between these val-
ues is greater than 200, which exceeds the energy savings
ratio of approximately 130, illustrating the importance of
Figure 3
Percent contribution of states to energy savings and mortality reductions.
0
0.01
0.02
0.03
0.04
0.05
0.06
0.07
0.08
0.09
TX
NY
MI
IL
OH
CA
PA
MN
VA
NC
TN
MD
WI
KY
GA
IN
FL
WA
MO
MA
NJ
AL
OK
IA
SC
OR
WV
AR
LA
MS
CT
KS
CO
NE
ME
NH
ND
SD
DE
NV
UT
VT
RI
ID
MT
AZ
NM
WY
% of energy savings % of mortality reduction
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geographic patterns of housing starts. The differential be-
tween these values clearly demonstrates the substantially
greater potential in the existing housing market, even giv-
en the fact that existing home retrofits represent a "one
time" opportunity, while housing codes influencing new
homes would affect construction over multiple years.
To provide further context about the magnitude of bene-
fits, retrofitting one million existing homes today would
have the same public health benefits as requiring 5.7 mil-
lion new homes to meet IECC 2000 standards. Given the
rate of new home construction (approximately 1.2 mil-
lion new homes per year), this implies that a code change
today would yield identical annual benefits as the one
million home retrofit in about 5 years, with identical cu-
mulative benefits in 9 years. In addition, a recent study es-
timated that requiring nine older ("grandfathered") coal-
fired power plants in Illinois to meet new source stand-
ards would yield approximately 300 fewer premature
deaths per year [3], similar to the benefits of retrofitting all
eligible existing homes. Cost-effectiveness comparisons
with other interventions are not very useful, as increased
residential insulation both reduces aggregate costs (calcu-
lated as net present value) and health risks, given the
boundaries of our analysis.
While these comparisons provide some insight about the
implications of our analysis, any risk-based calculation is
deficient without careful quantitative consideration of the
uncertainties inherent in the estimates. We have deemed
a formal uncertainty analysis to be beyond the scope of
our investigation, in part because uncertainties have been
discussed elsewhere [7] and in part because it is difficult
to accurately quantify many core uncertainties. However,
we can use the findings of our analysis to provide some
qualitative prioritization of future areas of study. We sum-
marize the findings of our qualitative uncertainty analysis
in Table 5, which draws on past damage function uncer-
tainty characterization [4].
First, if the environmental externalities represent the end-
point of our analysis, it is clear that resolving uncertainties
related to mortality will be more important than resolving
uncertainties related to morbidity. However, this is partly
dependent on our underlying health and valuation as-
sumptions. We have used the cohort mortality studies and
a value of statistical life (VSL) approach. An extreme low-
er-bound calculation for mortality would first focus on
the time-series rather than cohort mortality evidence. This
decision could either be based on the belief that there is
inadequate evidence of a long-term mortality effect (al-
though reliance on cohort evidence has been supported
Figure 4
Per unit energy savings and mortality reductions by state.
0
5
10
15
20
25
30
35
40
ND
MN
WV
SD
DE
WI
KY
MD
VT
ME
MI
IA
NH
VA
NE
TN
OK
AR
OH
IL
WA
NC
OR
NY
PA
MA
IN
CT
RI
KS
AL
MO
SC
GA
WY
MS
NJ
TX
MT
LA
ID
CO
NV
UT
CA
FL
NM
AZ
Per unit energy savings
(MMBTU/household/year)
0.0E+00
2.0E-06
4.0E-06
6.0E-06
8.0E-06
1.0E-05
1.2E-05
1.4E-05
1.6E-05
1.8E-05
2.0E-05
Per unit mortality reduction
(deaths/household/year)
Per unit energy savings Per unit mortality reduction
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elsewhere [33,34]) or the belief that long-term effects in-
volve significant lags and near-term benefits might there-
fore dominate externality estimates. Given this focus, an
additional lower-bound assumption would consider a
life-year measure more appropriate than a VSL measure,
with a relatively short longevity loss assumed in the time-
series studies.
Using a value from the time-series literature of an approx-
imate 1% increase in deaths per 10 µg/m
3
increase in
PM
2.5
[27,35,36], assuming that each time-series death
represents a loss of only one life-year, and using US EPA's
value of statistical life year of $360,000 [17], our annual
mortality benefit would be decreased from $1.3 billion to
$30 million. In this extreme case, mortality still represents
77% of the benefits (albeit with a limited set of morbidity
outcomes). With the inclusion of the suite of common
morbidity outcomes (including chronic respiratory dis-
ease and hospitalizations for cardiovascular or respiratory
disease), morbidity would likely dominate monetized
health benefits in this scenario.
Given the magnitude of this change, it is clear that im-
proved understanding of the cohort mortality findings
would be paramount in refining the estimates within our
analysis. Of similar importance would be issues related to
the relative toxicity of sulfate particles, nitrate particles,
and primary combustion particles from various sources.
According to our analysis, a majority of the benefits is as-
sociated with sulfate particles, indicating that alternative
sulfate toxicities would strongly influence our findings.
On the other hand, since nitrate particles contribute only
5% of benefits, changes in conclusions about nitrate tox-
icity would not influence our aggregate calculations in a
substantial way. Appropriate economic values for prema-
ture mortality would clearly be critical as well. However,
improved understanding about cohort mortality effects
and life-years lost within epidemiological studies would
greatly inform valuation, with a significant portion of the
remaining uncertainty related to a value judgment about
whether health outcomes should be valued on a life-year
lost or willingness to pay basis.
Other significant contributors to uncertainty include the
intake fraction estimates, the emissions characterizations,
and the predictive energy models. Given the relative im-
portance of SO
2
emissions in the South, the sulfate intake
fractions in this region would be most important to
resolve. A recent analysis found that sulfate intake frac-
tions for power plants in Georgia were similar when
CALPUFF and an alternative dispersion model were
applied [37], indicating that our estimates may not be sig-
Table 5: Qualitative uncertainty characterization for demand-side management health benefits model, focusing on key model
assumptions.
Model Component Model Assumption Likely Magnitude of
Uncertainty
Effect of Alternative Assumptions
Energy model Insulation retrofits viable in 63% of homes, uniformly distributed
nationally
small -
Use of regression model to estimate REM/Design outputs small -
Calibration of regression model outputs to RECS data small -
Emissions reductions All marginal power plants equally likely to be affected by change in
electricity consumption
medium Capacity- or availability-based allocation ()
Use of AP-42 emissions data for residential fuel combustion medium -
Constant emissions from power plants and residential fuel combustion
over time
medium Emissions decrease over time given regulations
()
Focus on air emissions of PM, NOx, SO
2
small Include other criteria pollutants, air toxics ()
Intake fractions Use of regression model estimates for intake fractions for power
plants
unknown -
Use of regression model estimates for primary PM intake fractions for
residential combustion
large Apply dispersion model with more refined spa-
tial resolution ()
Use of regression model estimates for secondary PM intake fractions
for residential combustion
unknown -
Health evidence Use of American Cancer Society cohort evidence to estimate mortality
risks from PM
large Use results from Six Cities Study (); use only
time-series evidence ()
Equal toxicity of all particles large -
Linear concentration-response function with no threshold unknown Assume threshold at PM
2.5
annual NAAQS ()
Inclusion of only asthma attacks, restricted activity days for morbidity medium Incorporate other morbidity outcomes ()
Valuation Use of VSL of $6 million for mortality large -
Constant real price of fuel over time small -
Model framework Focus only on public health medium Include greenhouse gases, dependence on oil
imports, etc. ()
Focus only on emissions reductions from energy savings medium Include emissions from insulation manufacturing,
occupational risks, indoor air quality, etc. ()
Note: indicates that alternative assumption would likely reduce the net benefit estimate; indicates that alternative assumption would likely
increase the net benefit estimate
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Page 14 of 16
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nificantly biased. However, for residential fuel combus-
tion, the uncertainties may be somewhat greater. Mobile
source intake fractions may not be identical to residential
intake fractions, and the values applied may be underesti-
mates, given the importance of near-source impacts and
the relatively low geographic resolution of models under-
lying our intake fraction estimates [22]. The lack of char-
acterization of the influence of SO
2
emissions on nitrate
concentrations also contributes uncertainty, although this
is likely a relatively small bias on an annual average and
nationally integrated basis.
Our methods for energy savings and emission reduction
estimation also contribute uncertainties to the analysis,
although many of these uncertainties are likely insignifi-
cant compared to the uncertainties articulated above. For
example, since our energy models were both highly pre-
dictive of modeled energy consumption and were cali-
brated to reported energy consumption stratified by fuel
type and region, significant bias is unlikely. This is
illustrated by the fact that our aggregate energy savings es-
timate is within 20% of the value reported in a similar
analysis [13], with the difference explained in part by dif-
ferences in the assumed number of homes eligible for in-
creased insulation.
On the emissions side, many uncertainties are difficult to
quantify, since AP-42 does not provide quantitative esti-
mates of variability or uncertainty. Our assumption about
the allocation of electricity reductions across power plants
could have contributed a downward bias, with an availa-
bility-based allocation increasing benefits to 510 fewer
deaths per year and a capacity-based allocation increasing
benefits to 720 fewer deaths per year. This could theoreti-
cally represent a significant uncertainty, and one that
could be resolved with more detailed power sector mod-
els. One piece of information provides an indication that
our baseline analysis may be the most reasonable
depiction of the marginal electricity sector. A marginal
emissions analysis for the New England Power Pool
(NEPOOL) indicates marginal emission rates of 2.6 lb/
MWh of NOx and 9.4 lb/MWh of SO
2
in 1997 [38], the
identical year as our E-GRID database. These emission
rates are within 20% of our baseline values for the NPCC
NERC region (which includes both NEPOOL and New
York State), and are substantially lower than the emission
rates from the availability-based or capacity-based alloca-
tion schemes.
A final uncertainty is related to the limited scope of our
analysis. We focused largely on the PM-related mortality
benefits of energy savings, but there are clearly numerous
additional benefits, including reduced greenhouse gas
emissions, health benefits related to other pollutants
(such as ozone) or morbidity endpoints, decreased de-
pendence on oil imports, and so forth. Although many of
these endpoints are difficult to quantify, they clearly rep-
resent ancillary benefits of energy savings.
In addition to the analytical uncertainties, there are
broader limitations in applying our findings for policy
purposes. As mentioned earlier, the lack of formal life cy-
cle calculations impairs drawing conclusions about the
net public health benefits of increased residential insula-
tion. However, assuming parallelism with a life cycle in-
vestigation in new housing [9,10] would imply that the
public health impacts of increased insulation manufactur-
ing would be far less than the public health benefits of re-
duced energy consumption during the lifetime of the
home, with a payback comparable to the economic pay-
back period of 6 years. Furthermore, our analysis repre-
sents static conditions, although the housing market and
emission factors from sources (among other assump-
tions) are quite dynamic. For example, if regulations were
put into place restricting emissions from power plants, the
public health benefits of energy efficiency measures
would be reduced. In addition, insulation retrofits of this
magnitude could have significant market influences. Al-
though the energy savings only represent 1% of annual US
energy consumption (given the focus on only a subset of
the residential sector), the amount of insulation required
is substantially larger than the current annual insulation
market. If all retrofits occurred in a relatively short period,
this would significantly influence the unit price of insula-
tion. Thus, although reduced energy consumption will
have long-term public health implications, the uncertain-
ties in benefit calculations likely increase as a function of
time.
In spite of the uncertainties in our analysis, our study pro-
vides findings with potential policy implications and
demonstrates some important methodological advance-
ments in quantifying the public health benefits of energy
efficiency programs. On the former point, although it is
clear that a regulation mandating energy efficiency
retrofits in existing homes would not be tenable, the mag-
nitude of the economic and public health benefits indi-
cates that creative public policies to encourage retrofits
(e.g., low-interest loans or tax deductions) may be war-
ranted. On the methodological side, the application of a
regression model to estimate energy savings appeared rea-
sonable and illustrated the potential for evaluating nu-
merous alternative scenarios without extensive
simulations. The intake fraction methodology yielded ex-
posure estimates that were both interpretable from a
health perspective and implementable for a large-scale en-
ergy efficiency program. Although further study is needed
to reduce some core uncertainties within the analysis, the
model framework is sufficiently flexible to allow for the
integration of new scientific information.
Environmental Health: A Global Access Science Source 2003, 2 http://www.ehjournal.net/content/2/1/4
Page 15 of 16
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Conclusions
We have developed and applied a model to quantify the
public health benefits of reduced residential energy con-
sumption. Retrofitting existing single-family homes in the
US would yield an estimated 800 TBTU of annual energy
savings, which corresponds to net present value economic
gains of approximately $80 billion and public health ben-
efits that include 240 fewer premature deaths per year.
While there are substantial uncertainties in many compo-
nents of our health benefits model, our analysis demon-
strates an approach for integrating environmental and
public health benefits into energy efficiency policy
analysis.
List of abbreviations
E-GRID – Emissions and Generation Resource Integrated
Database
IECC 2000 – International Energy Conservation Code,
year 2000
MMBTU – 10
6
British Thermal Units
MSA – Metropolitan Statistical Area
NEPOOL – New England Power Pool
NERC – North American Electricity Reliability Council
NOx – nitrogen oxides
PM
2.5
– fine particulate matter (particulate matter less
than 2.5 µm in aerodynamic diameter)
PM
10
– particulate matter less than 10 µm in aerodynamic
diameter
RECS – Residential Energy Consumption Survey
SO
2
– sulfur dioxide
TBTU – 10
12
British Thermal Units
VSL – Value of statistical life
Competing interests
None declared.
Authors' contributions
JIL drafted the manuscript and assisted in the develop-
ment of the analytical model; YN developed the model to
estimate health benefits from energy savings; and JDS
conceived of the original study and participated in design
and coordination. All authors read and approved of the fi-
nal manuscript.
Acknowledgements
This work was supported by the North American Insulation Manufacturers
Association. We thank Patrick Hofstetter, Laura Lind, Douglas Norland,
Gregory Norris, and Andrew Wilson for their contributions to our earlier
analysis, which provided the foundation for this work.
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In 1996, Schwartz, Dockery, and Neas reported that daily mortality was more strongly associated with concentrations of PM2.5 than with concentrations of larger particles (coarse mass [CM]) in six U.S. cities ("original paper"/"original analyses"). Because of the public policy implications of the findings and the uniqueness of the concentration data, we undertook a reanalysis of these results. This paper presents results of the reconstruction of these data and replication of the original analyses using the reconstructed data. The original investigators provided particulate air pollution data for this paper. Daily weather and daily counts of total and cause-specific deaths were reconstructed from original public records. The reconstructed particulate air pollution and weather data were consistent with the summaries presented in the original paper. Daily counts of deaths in the reconstructed data set were lower than in the original paper because of restrictions on residence and place of death. The reconstruction process identified an administrative change in county codes that led to higher numbers of deaths in St. Louis. Despite these differences in daily counts of deaths, the estimated effects of particulate air pollution from the reconstructed dataset, using analytic methods as described in the original paper, produced combined effect estimates essentially equivalent to the originally published results. For example, the estimated association of a 10 micrograms/m3 increase in 2-day mean particulate air pollution on total mortality was 1.3% (95% confidence interval [CI] 0.9-1.7%, t = 6.53) for PM2.5 based on the reconstructed dataset, compared to the originally reported association of 1.5% (95% CI 1.1-1.9%, t = 7.41). For coarse particles, the estimated association from the reconstructed dataset was 0.4% (95% CI -0.2-0.9%, t = 1.43) compared to the originally reported association of 0.4% (95% CI -0.1-1.0%, t = 1.48). These results from the reconstructed data suggest that the original results reported by Schwartz, Dockery, and Neas were essentially replicated.