Preliminary Report on Wood Smoke
Valuation
RTF Technical Report. 1
September 24, 2014
1. Introduction
The Regional Technical Forum (RTF) is a technical advisory committee of the
Northwest Power and Conservation Council (Council). The Council periodically
develops a power plan for the Pacific Northwest as directed by the Northwest Power
Act of 1980. Under the Northwest Power Act, the Council may consider
environmental impacts in analyzing the cost of electric generation and conservation
resources. Thus the net effect of electric conservation measures on emissions, and
on health, could be considered as an environmental benefit, or cost, in the Council’s
analysis, if the Council determines impacts are quantifiable and directly
attributable to the efficiency measure2.
This report is part of a RTF investigation into the attribution of health benefits due
to the reduction of wood smoke caused by the adoption of energy efficiency
measures. The objective of this attribution investigation is to understand if the
health benefits from reduced wood smoke can be quantified and monetized given
the current state of science in this field. This report investigates the technical
underpinnings of quantification methods, identifies the data requirements needed
for analysis, and describes the uncertainties around the required data, analysis and
the results.
This report employs the DHP program as an example to understand if health
benefits from wood smoke reduction are monetizable and quantifiable. It should be
noted that other electric energy efficiency measures, such as home weatherization,
may also be associated with reductions in wood smoke and potential health benefits.
Direct correspondence to Mohit Singh-Chhabra (Mohit@PtarmiganResearch.com) and Josh Rushton
(Josh@RushtonAnalytics.com).
2 See the Council’s draft issue paper on Methodology for Determining Quantifiable Environmental Costs and
Benefits for a discussion of the Council’s responsibilities under the Northwest Power Act.
http://www.nwcouncil.org/media/7137910/NWPCC-Env-Methodology-Issue-paper-Sept-10-FINAL.pdf
1
1.1.
Identification of Wood Heat Savings from Electric
Conservation
The RTF developed energy savings estimates for conversion of residential zonal
electric heating systems to ductless heat pumps in 20133. This study analyzed
billing data on approximately 3,400 recent DHP installations to estimate electric
energy savings.
This DHP billing analysis found that in cold climate zones, homes with
supplemental wood heating saved considerably less electricity than homes with no
supplemental wood heating. Statistical analysis was performed to estimate the
impact of the installation of DHP on the use of wood as supplemental fuel in all
three climate zones in the Pacific Northwest.
The results of the analysis identified both electric and wood heat savings from DHP
installation. Wood heat savings provide the benefit to the customer in the form of
(1) reduced cost to the customer because of less wood use, and (2) possible health
benefits from reduced wood smoke. The RTF accounts for cost savings from reduced
wood use in its cost effectiveness estimate, but it does not account for any health
benefits due to reduced wood smoke.
1.2.
Wood Smoke Reduction and Links to Health Impacts
Burning wood leads to the formation of a special class of small particulate
pollutants that cause respiratory, cardiovascular, and other health hazards if
ingested. These particles, called PM2.5 particles, are less than 2.5 microns in
diameter.
Once this health impact of reduced wood smoke was discovered, a back of the
envelope calculation was performed for the RTF to understand the magnitude of
this impact. This back of the envelope calculation indicated that health benefits
from reduced PM2.5 particles in the atmosphere could be as high as 1$ per kWh of
wood heat avoided. This approximately calculated benefit was large enough (on the
order of ten times the retail cost of electricity) to warrant further investigation4.
Reference DHP analysis here.
A simple calculation presented to the RTF by Ecotope Consulting indicated health benefits of reduced wood
smoke could exceed the value of the electricity saved by the DHP measure.
3
4
2. Phase 1: Screening Level Study
The RTF commissioned a screening level assessment of the health impacts of
reducing wood smoke in the Pacific Northwest to better understand the order of
magnitude of the effect5. This assessment used EPA recommended methodology to
estimate the economic value of reduced health risk due to decreased residential
wood heat use.
The health benefits realized from decreased wood smoke are attributable to
reduction in concentration of PM2.5 particles present in wood smoke. Apart from
PM2.5 particles, wood smoke emissions also contain SO2, NOx, NH3, and volatile
organic compounds (VOCs) that lead to downstream formation of PM2.5 through
chemical reactions. The RTF commissioned screening study was conducted with the
Environmental Protection Agency’s Co-Benefit Risk Assessment (COBRA)6
screening model. The study was performed by Abt Associates (Abt).
The screening study investigated the effects of reducing wood heat in residential
single family homes in the Pacific Northwest by 25%, 50 %, 75%, and 100%. The
results showed a decrease in ambient pollution levels due to reduced wood smoke
emissions and thus a positive effect on human health.
Table 1 and Table 2 present estimates of health benefits and their economic values,
respectively, for each health effect and emissions reduction scenario in the Pacific
Northwest study area. For example, a scenario with 50% reduction in emissions
from wood combustion in 2017 resulted in a range of total health benefit estimates
of $0.8-$1.9 billion (2010$, 7% discount rate) for 2017. For a 100% reduction in
emissions from wood combustion in 2017, Abt estimated the total health benefits in
the range of $1.7-$3.8 billion (2010$, 7% discount rate) for 2017.
The main driver of the monetized health benefits is the avoided premature
mortality among adults, which constitutes over 98% of the total monetized health
benefits. Also note that the impacts appear to be approximately linear in the
magnitude of the emissions reduction. That is, the numbers of cases of avoided
health effects from the 100% emissions reduction scenario are approximately double
the number of cases avoided from the 50% emissions reduction scenario.
“Final Summary of the Methodology and Results of Estimating the Health Impacts of Displacing Wood Heat
with Electricity in the Pacific Northwest” (April, 2014). Abt Associates Memorandum to the RTF.
6 COBRA is a free screening level tool available on the EPA website:
http://epa.gov/statelocalclimate/resources/cobra.html#what
According to the EPA “COBRA does not replace regulatory quality analyses. COBRA serves as a preliminary
screening tool to identify those scenarios that might benefit from further evaluation with the more sophisticated
air quality modeling approaches that are currently available”
5
Table 1. Study-area health effects due to reductions in 2017 wood smoke emissions
Health Incident Avoided
Number of Cases Avoided
25% Reduction
50% Reduction
75% Reduction
100% Reduction
Adult Mortality (low)
55
111
166
222
Adult Mortality (high)
126
251
376
501
>0
>0
>0
>0
Non-fatal Heart Attacks (low)
6
12
17
23
Non-fatal Heart Attacks (high)
54
108
161
214
Resp. Hosp. Adm.
11
23
34
46
CVD Hosp. Adm.
14
27
41
55
Acute Bronchitis
91
182
273
364
Upper Res. Symptoms
1,664
3,328
4,992
6,655
Lower Res. Symptoms
1,165
2,326
3,485
4,640
24
48
72
95
48,683
97,316
145,898
194,430
Work Loss Days
8,220
16,435
24,645
32,849
Asthma Exacerbations
1,745
3,489
5,232
6,975
Infant Mortality
Asthma ER Visits
MRAD
Table 2. Monetized study-area health effects due to reductions in 2017 wood smoke
emissions
Health Incident
Avoided
Total Health Effects
(low)
Total Health Effects
(high)
Adult Mortality (low)
Adult Mortality (high)
Infant Mortality
Non-fatal Heart
Attacks (low)
Non-fatal Heart
Attacks (high)
Resp. Hosp. Adm.
CVD Hosp. Adm.
Acute Bronchitis
Upper Res. Symptoms
Lower Res. Symptoms
Asthma ER Visits
MRAD
Work Loss Days
Asthma Exacerbations
Economic Value (Millions 2010$, 7% discount rate)
25%
Reduction
$425.8
50%
Reduction
$851.3
75%
Reduction
$1,276.5
100%
Reduction
$1,701.3
$960.9
$1,920.0
$2,877.5
$3,833.3
$418.1
$947.4
$1.1
$0.7
$835.9
$1,893.2
$2.1
$1.4
$1,253.3
$2,837.3
$3.2
$2.1
$1,670.4
$3,779.7
$4.3
$2.8
$6.4
$12.8
$19.1
$25.4
$0.3
$0.5
>$0.0
$0.1
>$0.0
>$0.0
$3.3
$1.6
$0.1
$0.6
$1.1
$0.1
$0.1
>$0.0
>$0.0
$6.6
$3.2
$0.2
$0.9
$1.6
$0.1
$0.2
$0.1
>$0.0
$9.9
$4.8
$0.3
$1.3
$2.1
$0.2
$0.2
$0.1
>$0.0
$13.2
$6.4
$0.4
The initial study produced additional findings that inform this report. In particular:




There is a nearly linear relationship between reduction in concentration of
PM2.5 and health impacts realized. Moreover, the screening level study
found that no minimum PM2.5 reduction level threshold is necessary to realize
health benefits from PM2.5 reduction.
Reductions of PM2.5 emissions in a county has spillover effects in the
concentration of PM2.5 levels in adjacent counties. Geographic location of the
reductions need to be modeled.
Population density has a large effect on health impacts. Small changes in
atmospheric PM2.5 concentration levels in densely populated areas produce
significant health impacts.
The valuation of health impacts is predominantly driven by changes in adult
mortality and the associated monetary value assigned to increased mortality


The study suggested the health benefit- of electricity used by a DHP to
displace wood heat was on the order of 10 times higher than the retail cost of
the electricity on a per kWh basis
The wood smoke reduction levels tested in the initial study were many times
higher than expected levels of wood smoke reduction associated with electric
energy efficiency programs that reduce supplemental wood heat.
Findings from the screening level study suggested that further analysis was
warranted to better understand the expected scale and range of health benefits
from electric efficiency programs. As wood heat displacement by efficiency programs
varies by climate zone, the analysis was conducted to reflect differential countylevel emissions reductions. COBRA was used to conduct this analysis as well.
A four-step process common to most methods of estimating the monetary value of a
public health benefit associated with an initiative to reduce harmful emissions
(including the Abt screening study) was followed for the subsequent phase of the
analysis. The four steps in this process are:
1. Pacific Northwest Emission Quantification: Estimate total change in
expected emissions the initiative is expected to cause.
2. Dispersion Modeling: Use dispersion modelling to estimate expected change
in ground level pollution
3. Quantification of Health Effects: Estimate the public health effect based on
epidemiological research.
4. Monetization of Health Effects: Determine the monetary value of the
estimated public health effect.
Each step brings up its own set of questions, and the analysis methodology draws
on multiple data sets and studies to answer these questions. The remainder of this
report describes the Phase II of this investigation, which is the RTF research into
expected health benefits from energy efficiency programs in the Pacific Northwest.
This research description is organized around these four basic steps.
3. Phase II – RTF Research
3.1.
Pacific Northwest Emission Quantification: Programmatic
Wood Heat Reduction
This section quantifies the link between program interventions and emission
changes. It first describes how the RTF estimates per-unit wood heat reductions
associated with weatherization and equipment measures. Next, it shows how the
RBSA is used to estimate program technical potential (the number of units that
could be installed in the region). Finally, it describes how changes in wood heat are
related to changes in emissions based on county-level mixes of wood heating devices
and wood fuel types.
Our opinion is that technical potential and the link between wood heat and
emissions have relatively minor sources of uncertainty. Different data sources
might yield better precision for isolated territories, but the current estimates are
well supported by existing data as described below. The per-measure wood heat
estimates are more subtle so the next section emphasizes issues related to
uncertainty.
The RTF currently has two separate estimates of wood heat effects due to
weatherization and equipment measures. One estimate applies specifically to
ductless heat pumps (DHPs) and is derived from billing data gathered in a DHP
pilot program. The other applies to weatherization and (ducted) heat pump
measures and is derived from the RBSA. In terms of uncertainty, the connection
between DHPs and wood heat is more clearly supported by empirical data than the
connection between wood heat and other measures. Both estimates are discussed in
this section, but the COBRA scenarios and health effect estimates elsewhere in this
report focus on effects related to DHPs.
Weatherization and Non-DHP Equipment Upgrades
Since some of the participants in electric efficiency programs use supplemental
heating fuels, the current policy separates measure savings into electric savings,
wood-fuel savings, and gas savings. The RTF expresses the wood savings in terms
of the electric heating energy that would have been saved if no one used wood heat
(see Appendix A).
For cost effectiveness inputs, wood savings is currently valued at the retail price of
the displaced electric savings. This policy does not require any estimate of the
actual cords of wood that are saved, and the RTF does not maintain direct estimates
of wood fuel savings per se. This section provides a rough estimate of the range of
wood heat savings due to weatherization and non-DHP equipment measures.
Appendix A describes how the RTF uses RBSA data to estimate the electric energy
displaced by wood fuels. Since the method is central to the RTF’s current wood heat
valuation, this report would seem incomplete without the discussion in the
appendix. That analysis, however, does not directly address our present concern.
The basic result is that wood fuels meet about 10% of the heating load, on average,
in electrically heated7 single-family homes in the Northwest. (Table 14 shows this
for heating zone 1; results are similar in zones 2 and 3.) This does not tell us how
many cords of wood are being burned, or how many cords are saved by program
weatherization measures.
We can use the RBSA to estimate the average number of cords of wood that are
burned in electrically heated homes, but a fundamental question remains: How
does wood fuel consumption change when we install weatherization measures in
homes that use wood heat. Different occupants will respond differently, so we can
only hope to answer this question in terms of averages. For discussion, imagine a
weatherization measure that reduced a home’s heat load by 15% and consider three
possible scenarios:
1. Occupants like the aesthetic qualities of wood heat and do not change their
wood consumption at all when their heating load changes.
2. Occupants use wood as a primary heating fuel, and their consumption
changes in proportion to heating load.
3. Occupants use wood heat during the coldest part of the year when their other
equipment cannot meet load. Post weatherization, the other equipment can
meet the load at almost all times, so wood fuel consumption decreases
dramatically.
Most program populations include representatives of each of these scenarios. Wood
heat reductions due to weatherization must depend on the mix of participant types.
This issue would need additional study before the RTF could reliably quantify wood
heat savings do to weatherization and non-DHP heating equipment upgrades.
The estimates in the following table assume wood consumption changes, on
average, in proportion to heating load (as in scenario 2 above). These estimates are
Here, “electrically heated” refers to homes that have some form of permanently-installed electric heat and do
not have a gas furnace or boiler. The presence of a fireplace or heat stove (any fuel) does not exclude a home
from this definition.
7
very rough. The intention is only to provide an order-of-magnitude reference point,
not a rigorous estimate.
Table 3 Wood savings based on 6th Plan estimates of achievable measure potential
by 2029.
Measure
Total estimated energy savings
(2029 achievable potential)
(MWa)
(MWh)
Derived wood
savings
(Cords)*
Weatherization (all
290
2,540,400
84,700
measures)
Furnace to HP
100
8,760,000
28,860
conversion
* Assumes 10% of load is met by wood heat in pre- and post-cases, 6070 kWh per
cord, and 50% efficiency of wood burning device. Formula is Cords =
(kWh)*(0.10)*(1/5700)*(1/0.50).
As noted above, these estimates are order-of-magnitude approximations. The
COBRA scenarios described in this document are based on wood savings due to
ductless heat pumps; they do not use the figures in Table 3. By comparison to Error!
Reference source not found., we see that in terms of regional potential, wood savings
due to weatherization is likely to be of the same order of magnitude as wood savings
due to DHPs.
Ductless Heat Pumps
The first part of this section describes the current empirical basis of the RTF’s
understanding of wood heat savings due to ductless heat pumps (DHPs). The next
part briefly shows how per-DHP savings values are used to derive the potential
savings figures that inform the COBRA scenarios used throughout this report.
Empirical basis for DHP wood savings estimates
In this section, we first present a view of the basic evidence that supports the RTF’s
estimates of wood heat savings due to the installation of ductless heat pumps. We
then present estimates of the average amount of wood saved per DHP (detailed
calculations are presented in Appendix B).
The central data source behind these estimates is from the impact evaluation for
NEEA’s DHP pilot program (Baylon, et al, 2013). The pilot program installed
almost 3,400 ductless heat pumps in the Pacific Northwest, and collected pre- and
post-DHP billing data for each. The impact evaluation was based on pre- and postDHP billing data for nearly 3,400 homes and sub-metered end-use data collected for
about 100 sites.
The wood savings estimates in this section rely on basic evidence identified in the
billing analysis; the following table gives a view of this evidence in terms of simple
averages.
Table 4. Pre-DHP and post-DHP heating energy averages based on billing data
Homes without wood heat1
Heating
zone
Pre-DHP,
kWh
Post-DHP,
kWh
1
7,603
4,810
2
10,973
9,025
3
11,326
9,441
Pre minus
post,
kWh2
(SE)3
2,793
(93)
1,948
(384)
1,885
(487)
Homes with wood heat
Pre-DHP,
kWh
6,112
7,263
8,553
Post-DHP,
kWh
5,159
7,334
9,175
Pre minus
post,
kWh2
(SE)3
952
(140)
-71
(435)
-622
(460)
1
This wood heat designation is based on a telephone survey. In subsequent site visits, researchers
discovered that the survey failed to capture a significant amount of wood heat.
2 Since some homes designated as having no wood heat actually do have some wood heat (see
previous note), savings among homes that truly have no wood heat should be greater than what is
indicated in the table. As a result, the true savings gap between homes with and without wood heat
should be even greater.
3 The table shows approximate standard error (SE) for each of the kWh savings values. At the 95%
confidence level, an estimate’s error band is plus/minus about two standard errors .
The table shows that the electric savings due to ductless heat pumps is much lower
in homes with wood heat than in homes without wood heat. This is true in all
climate zones, and cannot be accounted for by statistical error.8
Appendix B demonstrates a method for deriving wood savings estimates from the
data collected in the pilot study. The method presented there is very similar to
methods used in the RTF’s DHP savings estimates, but they are not identical. The
differences are due to the fact that this paper seeks to connect estimates to the data
in the simplest and clearest way possible, whereas RTF savings estimates need to
satisfy additional requirements (consistency with other measures, for example).
8 The
negative savings values in zone-2 and zone-3 homes with wood heat are not statistically significant (the
negative signs may be artifacts of statistical error). However, the differences between these savings values and
those of homes without wood heat are much too large to be explained by statistical error.
The following table summarizes the wood savings estimates developed in the
appendix.
Table 5 Estimated wood savings due to ductless heat pumps
Average wood savings per DHP
(energy delivered to space,
kWh)*
Homes with
Average across
Heating zone
Pre-DHP
Post-DHP
wood heat
all homes
1
27%
3%
2135 - 2176
598 - 609
2
48%
36%
722 - 1318
144 - 264
3
36%
18%
2213 - 2615
1549 - 1831
* The range of saving values reflects uncertainty about the magnitude of comfort
take-back. Low-end savings values correspond to greater comfort take-back, and
high-end savings values correspond to no comfort take-back.
Percent of load met with wood
(homes with wood heat)
In Table 5, the range of values in the wood savings estimates reflects the likely
range of comfort take-back effects (see Appendix B). Uncertainty due to other
factors, such as statistical error in estimates of the actual percent of load met with
wood or in the RTF’s model calibration, is not reflected in the ranges.
The main logical weakness in these estimates is that the DHP study did not use a
control group, so it is possible that some external phenomenon may have driven a
region-wide reduction in wood burning that happened to coincide with the DHP
pilot program. (For example, maybe there happened to be more burn bans in the
period after the program than in the period before the program.) We mention this
issue for completeness, not to suggest that it represents a significant threat to the
study’s main findings. Few studies are able to eliminate all possible logical gaps.
Regional Potential for DHP Wood Savings
To run COBRA, one must define emission-reduction scenarios. In this section, we
show how RBSA data is used to estimate the total wood savings that would be
expected if every eligible home in the region installed a DHP.9
The RBSA provides estimates of the number of homes in each heating zone that
have zonal electric heat. Combined with the estimates of the previous section, these
Currently, most efficiency programs that offer ductless heat pumps target zonal-electric homes for DHP
installation. Because of this, we take zonal-electric heat as an assumed eligibility requirement in this report.
9
yield estimates of the total potential wood savings in terms of thermal energy
delivered to conditioned space. These estimates are provided in the following table.
Table 6. Candidates for DHP Installation and Total Potential Wood Savings
(Energy Content)
Heating Zone
1
2
3
Number of
zonal electric
homes
407,986
111,150
34,559
Wood savings,
energy delivered to space
Each home (kWh)
604
204
1690
Region total (MWh)
246,400
22,700
58,400
The RBSA also include data on types of heating appliances used throughout the
region. With this information, together with estimates of average appliance
efficiency and heating fuel energy content, we can obtain estimates of wood savings
in terms of cords of wood and tons of pellets.
The calculations that follow assume the average cord-wood appliance is 50%
efficient, and the average pellet stove efficiency is 85%. They also assume average
energy content of 6070 kWh (20.7 MBtu) per cord of wood10 and 3770 kWh (13.0
MBtu) per ton of pellets11.
Table 7 Weighted Average Cords and Pellets Burnt per SF Home to meet Annual
Heating Load
Heating
Zone
1
2
3
10
11
Share of total woodfuel energy
Cord
Pellets
wood
85.5%
14.5%
87.8%
12.2%
99.6%
0.4%
Energy content
(MWh)
Cord
Pellets
wood
210,709
35,691
19,931
2,769
58,147
253
Quantity saved
Cords of
wood
69,426
6,567
19,159
Values for Douglass fir, from http://forestry.usu.edu/htm/forest-products/wood-heating.
See Appendix A of http://cta.ornl.gov/bedb/pdf/BEDB4_Full_Doc.pdf.
Tons of
pellets
11,138
864
79
Scenario Definition
The COBRA model requires inputs in terms of percentage or absolute decrease in
pollutants annually. This percentage decrease is calculated using potential tons of
wood combustion reduction (Table 6) and data on wood burning appliance in the
Pacific Northwest.
11 different indoor wood combustion appliances are considered in this analysis, each
appliance has a different emission rate and efficiency. Data on heating appliance
types and wood types burned are available at the county level, and data on wood
heating appliance efficiencies are available from EPA. While there is some
uncertainty in these appliance efficiencies, the science behind them is well
understood and tested in the laboratory.
To calculate a reduction in total pollutants, it was assumed that wood combustion
displaced due to DHP is in the same proportion as the existing wood appliance
distribution. The EPA RWC (Residential Wood Combustion) provides data on the
distribution of different wood heating appliances in the Pacific Northwest.
The RWC also provides emission rates from each wood burning appliance in terms
of tons of pollutant released per ton of wood burnt. The total reduction in emission
due to avoided wood heat is thus calculated as a product of (1) tons of cords and
pellets avoided, (2) weighted average mix of emission factors from existing
appliances in the Pacific Northwest.
Table 8. Scenario Inputs: Wood Smoke Pollutant Reduced due to Complete Zonal to
DHP Conversion12
Tons reduced
HZ1
HZ2
HZ3
Ammonia
52
5
12
Volatile Organic Compounds 1,294
119
308
Primary PM2.5
997
92
238
Sulfur Dioxide
17
2
4
Nitrogen Oxides
107
10
25
Pollutant
HZ1
3.64%
4.48%
4.07%
4.16%
4.03%
% Reduction
HZ2
0.34%
0.41%
0.38%
0.38%
0.37%
HZ3
2.7%
2.8%
2.8%
3.0%
3.0%
Although, the method to calculate baseline emissions (calculated by Abt Associates) and expected decrease in
emissions due to DHP (calculated by RTF Contract Staff) program differ, the magnitude in expected reduction
in pollutants are sufficiently accurate for the purpose of this report. Which is to explore if health benefits from
decrease in wood smoke of the order expected from energy efficiency program and quantifiable and monetizable
to be considered in cost effectiveness calculation.
12
The percentage reduction in pollutants presented in Table 8 are used as inputs to
the COBRA model. Wood smoke contains PM2.5 particles, and other pollutants
(presented in Table 8). These other pollutants undergo chemical reactions that
create more PM2.5 particles downstream. These chemical reactions are well
understood, and modeled as a part of dispersion modeling (next step of the
analysis).
Generation Re-dispatch
As electric heat displaces wood heat, more load is added to the grid. The electric
load added to the grid can be calculated as annual energy consumption of an energy
savings measure (e.g. DHP) multiplied by the fraction of wood heat displaced. This
added load will cause some generation re-dispatch that would not have existed in
the absence of the program. This paper does not quantify this trade off and its
effects.
3.2.
Dispersion Modeling
As per EPA methodology, the geographic dispersion and creation of post emission
PM2.5 particles is estimated through computational dispersion modeling. These
dispersion models consider local weather patterns, geography, and other physical
attributes to model the creation, spread and dissipation of pollutants (PM2.5).
Dispersion modeling uses mathematical formulations to characterize the
atmospheric processes that disperse a pollutant emitted by a known source.
Dispersion models use air pollutant emissions and meteorological inputs to predict
atmospheric pollutant concentrations at selected receptor locations. Dispersion
modeling in COBRA relies on the S-R (source – receptor) matrix. The S-R Matrix
defines the relationship between annual pollutant concentration at a single receptor
in each county (located at the center of the county) and an emission source13.
As per the COBRA manual, the S-R matrix method is a screening tool and provides
crude estimate of pollutant concentrations. Limited S-R matrix validation literature
exists. The S-R matrix dispersion modeling in COBRA was calibrated using EPA
FRM (Federal Reference Method) pollutant monitoring sites. The S-R matrix is
based on the Climatological Regional Dispersion Model (CRDM). Relative to more
sophisticated and resource-intensive three-dimensional modeling approaches, the
CRDM does not fully account for all the complex chemical interactions that take
place in the atmosphere in the secondary formation of PM2.514.
COBRA user manual, Appendix A. http://www.epa.gov/statelocalclimate/documents/pdf/COBRA-manual.pdf
Instead it relies on more simplistic species dispersion-transport mechanisms supplemented with chemical
conversion at the receptor location
13
14
State-of-the-art dispersion models (e.g. CALPUFF, AERMOD, and CMAQ) use a
more detailed approach to model atmospheric chemistry. This detailed approach
gives results with improved precision at the county level, both in terms of
concentration of PM2.5 and the mass of PM2.5 that remains in the county versus that
dispersed across county lines. As the study area of this report is the Pacific
Northwest as opposed to individual counties, precision obtained through COBRA is
comparable to precision obtained through more sophisticated tools. Still, due to the
limited validation studies of the S-R Matrix models, EPA recommends COBRA be
treated as a screening tool only15.
DHP Scenario Modeling Results
The emission reduction values presented in Table 8 are the inputs for the COBRA
model. The predicted health effects and valuation results modeled by COBRA are
presented in Table 9Error! Reference source not found.. The epidemiological
connection between pollution changes and health effects is explained in Section 4.3,
and the monetary valuation in Section 4.4. For now, we mainly use the table to
observe that the bulk of the monetary valuation is due to changes in adult
mortality.
15
Correspondence with Abt Associates.
Table 9 DHP Program Scenario COBRA Modeling Results
Health Incident Avoided
Cases
Adult Mortality (low)
Adult Mortality (high)
Infant Mortality
Non-fatal Heart Attacks (low)
Non-fatal Heart Attacks (high)
Resp. Hosp. Adm.
CVD Hosp. Adm.
Acute Bronchitis
Upper Res. Symptoms
Lower Res. Symptoms
Asthma ER Visits
MRAD
Work Loss Days
Asthma Exacerbations
12.46
28.23
0.03
1.30
12.03
2.63
3.09
21.80
396.41
277.74
5.59
11,257.00
1,901.14
415.50
Total Health Benefits (low)
Total Health Benefits (high)
Economic Value (2010$, 7%
discount rate)
$93,610,148
$238,084,165
$248,204
$152,579
$1,414,524
$68,622
$74,400
$10,399.55
$10,706
$7,498
$2,382.19
$474,802
$287,072.59
$23,683.43
$94,970,497
$240,706,459
The total health benefits of running a reducing wood smoke due to running a DHP
program range between $14 and $35.7 million dollars. Almost 99% of these benefits
are due to reductions in adult mortality. The majority of the health benefits are
realized from a reduction in PM 2.5 levels in the atmosphere. PM 2.5 particles are
particles smaller than 2.5 micrometers or less. These particles are small enough to
penetrate the human respiratory and cardiovascular system and cause adverse
health effects Figure 1, Figure 2, and Figure 3 show the geographic distribution of
base-level PM 2.5 concentration, estimated reductions in PM 2.5 due to the DHP
program, and the density of the resulting health benefits. The intention behind
these maps is to show the geographic relationship between location of PM 2.5
reduction and the health benefit. These maps are colored to show this geographic
relationship, the darker the color, the higher the density value of the property
presented.
Figure 1 Base levels of PM 2.5 concentration (micro grams/ cu. meter)
Figure 2 Post-dispersion reduction (delta) in PM 2.5 concentration (micro grams/ cu.
meter)
Figure 3 Density of health effects ($, lower limit) due to wood smoke reduction
The reduction in PM 2.5 concentration is dispersed throughout the Pacific
Northwest, and a function of PM 2.5 production and atmospheric dispersion. The
health effects are realized in areas with higher population density.
3.3.
Quantification of Health Effects
Existing Literature
To estimate the health effect associated with a change in air quality, COBRA uses
health impact relationships that have been used by the EPA’s Office of Air Quality
Planning and Standards for Regulatory Impact Assessments. In evaluating health
impacts of air pollution, the EPA relies on the synthesis of the clinical, toxicological,
and epidemiological evidence regarding exposure to PM2.5 and other pollutants.
In previous sections, we’ve seen that PM2.5 exposure is associated with premature
mortality risk, and that reductions in adult premature mortality risk account for
the largest share of the economic value of the health benefits of wood smoke
reduction. The current understanding of the mortality risk associated with PM2.5 is
supported by two large epidemiological studies:


Harvard Six Cities Cohort (Lepeule et al, 2012). This study tracks ~8,000
participants living in 6 Eastern/Midwestern US cities starting from 1974;
ambient PM2.5 observations ranged from 11 to 24 μg/m3.
American Cancer Society Cohort (Krewski et al, 2009). This study tracks
~500,000 participants in 116 US cities starting from 1982; Ambient PM2.5
ranged from 5.8 to 22.2 μg/m3.
Based on these studies, together with clinical and toxicological evidence, the EPA’s
position is that “collectively, the evidence is sufficient to conclude that a causal
relationship exists between long-term exposures to PM2.5 and mortality.” 16
However, researchers usually cannot directly observe mortality effects, so the link
between PM2.5 and mortality has been controversial at times.The review
committee’s commentary to the Krewski study offers perspective on the current
state of the science and politics (Krewski et al, 2009, pp. 118-9):
In 1997, the [Harvard Six Cities Study and the American Cancer Society
Study] came under intense scrutiny when the U.S. EPA used the results
to support new NAAQS standards for PM2.5 and to maintain the
standards for particles of 10 μm or smaller in aerodynamic diameter
(PM10) that were already in effect. Members of Congress and industry,
the scientific community, and others interested in the regulation of air
quality scrutinized the studies’ methods and their results…
To address the public controversy, Harvard University, the ACS,
Congress, the U.S. EPA, and representatives of the motor vehicle
industry requested that the Health Effects Institute organize an
independent reanalysis of the data from these studies. The investigators
agreed to provide access to their data to a team of analysts to be selected
by HEI through a competitive process. HEI’s Board of Directors approved
the request. HEI then assembled an Expert Panel to provide scientific
oversight of the Reanalysis Project on HEI’s behalf and to ensure that the
Reanalysis would be conducted by independent and impartial
investigators. The Panel recommended that Dr. Daniel Krewski of the
University of Ottawa and his team conduct the Reanalysis. The HEI
Board of Directors approved the Panel’s recommendation of Dr. Krewski
in November 1997.
Reference: Integrated Science Assessment (ISA) for Particulate Matter released in 2009 [FRL-9090-9; Docket
ID No. EPA-HQ-ORD-2007-0517]
16
We do not attempt to evaluate epidemiological studies in this report. Appendices B
and C of the COBRA manual provide a succinct overview of the studies that support
the estimated health effects, as well as references to the studies themselves and
secondary literature.
In the remainder of this section, we focus on the nature of the link estimated in the
studies and the way the link is implemented in COBRA’s calculations.
The main valuation component: A concrete example
In this section, we take a detailed look at some critical steps in COBRA’s mortality
calculations for the following hypothetical policy objective:
Promote ductless heat pumps in sufficient quantity to decrease
residential wood smoke emissions in Clackamas County, Oregon, by 10%
beginning in the year 2017.
Our intention is to make the calculation steps as concrete as possible so we can
understand (1) exactly what the COBRA outputs mean, and (2) the kind of evidence
behind the main results. This development emphasizes clarity over precision—we
do not claim that our calculations are particularly precise, nor does COBRA claim
great precision for isolated examples.
To keep the discussion self-contained, we first give an overview of the monetized
health benefits. Based on this overview, we define a narrow question that we will
use to keep our discussion concrete.
Narrowing the Question
In COBRA, we enter our policy objective (above) by first limiting our intervention
scenario to Clackamas County, Oregon, then highlighting “RESIDENTIAL WOOD”
in the scenario definition and specifying a 10% reduction in each of the five
pollutants (PM2.5, SO2, NOX, NH3, and VOC). For this scenario, the COBRA Health
Effects table shows a range of values, $1.22M to $2.75M, for total health-effects.
This valuation is for multiple years’ health impacts that are due to a single year
(2017) of emissions change; since the change is persistent, this value recurs, with
adjustments, annually once the change has occurred. (We will verify this
interpretation in the calculations below.)
A closer look at the COBRA Health Effects Table reveals that a single health
outcome—adult mortality—accounts for over 98% of the total health-effects
valuation. The adult mortality effect ranges in value from $1.20M to $2.72M.
Furthermore, about 74% of the Pacific Northwestal adult mortality effect is from
Clackamas County and neighboring Multnomah County. This is because these are
populous counties in locations that experience significant air quality improvements
under the defined scenario. These observations are summarized in Table 10.
Table 10. Main contributors to health effect valuation
Valuation range
(all health effects)
Valuation range
(adult mortality)
Percent of
valuation
total (all health
effects)*
32.6%
Clackamas
$0.40M - $0.91M
$0.40M - $0.90M
County
Multnomah
$0.50M - $1.14M
$0.49M - $1.12M
40.7%
County
Other
$0.31M - $0.70M
$0.31M - 0.70M
25.3%
Total
$1.22M - $2.75M
$1.20M - $2.72M
98.6%
* Values in the second column, divided by the total values in the last row of the
first column.
The last column in the table shows that 40.7% of the total valuation of all health
effects is due to adult mortality in Multnomah County. In the sections that follow,
we trace the origins of the adult mortality value for Multnomah County. The bottom
row of the last column in Table 10 shows that a similar treatment of all affected
counties would account for 98.6% of the total valuation of all health effects.
In Table 10 we see that COBRA provides a range of values for adult mortality
effects (for Multnomah County, the range is $0.49M - $1.12M). The lower limit of
this range is based on the mortality-PM2.5 link, as estimated by Krewski (2009); the
upper limit is based on the estimate of Lepeule (2009). The only difference in the
calculations behind the upper and lower limits is the different estimates of
epidemiological links. The two calculations use identical values for the change in
ground-level pollutants and the value of a statistical life. The next sections focus on
the lower limit for Multnomah County; the upper limit can be traced through
similar steps.
In the next section, we discuss the second component, the epidemiological link and
related COBRA calculations; our goal is to describe the nature of the predicted
health benefit, and to draw a line from the underlying epidemiological research to
the predictions.
The Epidemiological Link
In this section, we will examine the calculations behind COBRA’s estimate of
$0.49M for the low end of the mortality effect associated with the specified scenario
in Multnomah County. This value is based on an estimated long-term average
decrease in ambient PM2.5 of 0.002 μg/m3 for Multnomah County. For this section,
we take the 0.002 μg/m3 figure as given and focus on the epidemiological
consequences (as estimated by Krewski et al).
The estimated change in adult mortality is based on Krewski (2009), and this study
quantifies the link between mortality and PM2.5 in a specific form. Once we
understand the exact epidemiological link, we will know precisely what kind of
change in adult mortality is being evaluated. This will help us evaluate the central
valuation question17 in Section 3.4:
To what extent do the VSL studies provide insight into how much
people would pay for the estimated risk reduction?
Form of the Epidemiological Link
In this section, we examine the type of estimate Krewski et al provide for the
PM2.5/adult-mortality link that is used in the calculations that lead to our example’s
$0.49M estimate for adult mortality in Multnomah County. In the study, the
researchers fit a model that estimates the hazard functions change in response to
changes in long-term PM2.5 exposure levels. Roughly, the hazard function gives an
individual’s probability of dying between age 𝑡 and age 𝑡 + 1, given that he or she
has survived to age t.18 Different individuals have different hazard functions
depending on sex, occupation, family history, exposure to pollutants, and other
variables.
In the study, researchers estimate that, on average, hazard functions decrease by
about 0.6% (i.e., they decrease by a factor of 0.994) with every 1 μg/m3 decrease in
PM2.5 concentrations.19 (In Krewski (2009), see equation 3 on page 16 and the
surrounding discussion; the parameter value, 0.006, is from Table 4 on page 126 of
the same document.)
The monetary value of any proposition is only meaningful if people are willing to pay for the proposition.
Furthermore, we can only estimate the value if we have insight into how much they would pay for it. In placing
a value on a change in mortality risk, we must take special care to ensure that we have insight into how much
people would pay for the type of change being offered.
18 For clarity and simplicity, we describe the model in terms of discrete time units, though continuous-time
models are more common in research applications. The distinction is not important to our current objective.
19 We express the results in approximate terms to avoid the more cumbersome notation used by researchers.
17
The expression does not tell us anything about the hazard function itself; it only
tells us how adult mortality hazard rates change, on average, when we change our
assumption about long-term PM2.5 exposure.
Example. Imagine two populations that are demographically similar but
reside in different areas, Pacific Northwest A and Pacific Northwest B.
The only systematic difference between the populations is that the
average PM2.5 concentration in Pacific Northwest B is 20 μg/m3 higher
than the concentration in Pacific Northwest A. The model tells us that
the adult-mortality hazard rate for population B will be about 20 × 0.006
= 1.2% higher than the adult mortality rate for population A. Thus, if
1.00% of 40-year-olds in group A die in a typical year, then about 1.012%
of the 40-year-olds in group B die in a typical year.
In every age group, population B adults die at a faster rate than their
counterparts in population A. It follows that population B will eventually
have a lower proportion of older people than population A, so the average
life expectancy in population B must be lower than that of population A.
However, the model does not say whether the lower life expectancy is due
to lots people dying a little sooner or a few people dying a much sooner.
Perhaps there are genetic or lifestyle factors that predisposed some
people but not others to the diseases hastened by higher PM2.5 levels.
These questions are difficult to resolve, but they can have important
consequences for monetary valuation.
The ∆PM2.5 amount in our Multnomah County scenario is much smaller than in the
example just given (0.002 μg/m3 versus 20 μg/m3), but the basic issues are the same.
We will see in the next section that Multnomah County’s $0.49M valuation figure
corresponds to a very small change in the expected number of deaths (less than onetenth of a single death).
We emphasize that the actual health benefit takes the form of a small change in the
mortality rates of a large number of individuals, so a fractional number of expected
deaths does not cause any great conceptual difficulty. The example highlights the
fact that the epidemiological research does not provide a detailed description of the
health benefits. In principle, detailed physiological knowledge may someday allow
researchers to refine the health benefit proposition by identifying subpopulations
that are more (or less) affected by changes in PM2.5. This would not alter the
expected number of averted deaths. But it would alter the way risk changes are
distributed in the population, which may change the proposition’s total value.
Mortality Estimates
To apply the Krewski result in a given scenario, we need to know the average
baseline mortality rate in the population being studied.
The Centers for Disease Control (CDC) publishes death rates, by age, for each
county in the United States. The death rate for any age group is simply the number
of deaths in the group, divided by the total number of people in the group. For
example, if there are 10,000 40-year-olds in a county and 16 die in a typical year,
then the rate for that group is 0.16%. A group’s observed death rate is a good proxy
for the group’s average hazard function value (the average probability of dying in
the current year, given survival up to the current year). Likewise, the number of
deaths is a good proxy for the sum of the current-year hazard function values across
all group members. This observation is at the heart of COBRA’s mortality
calculations.
Table 11 shows COBRA’s estimated adult mortality changes for Clackamas and
Multnomah counties under the scenario defined at the beginning of this section. The
table shows that Multnomah County’s $0.49M low-end valuation figure is
associated with an expected mortality change of 0.059 units (deaths). As described
above, this refers to the mortality change due to a single year’s change in the PM2.5
concentration.
Table 11. Mortality Estimates and Valuation.
Clackamas County
Multnomah County
Other
Total
Range of expected change
(adult mortality)
0.047 – 0.106
0.059 – 0.133
0.035 – 0.080
0.140 – 0.320
Range of values for change
(adult mortality)
$0.40M - $0.90M
$0.49M - $1.12M
$0.31M - 0.70M
$1.20M - $2.72M
The low-end mortality figures in the table are based on expression (1) and the
baseline mortality rate of the affected population. In 2017, our scenario is expected
to permanently decrease Multnomah County’s average PM2.5 concentration by 0.002
μg/m3 (relative to baseline). Health effects due to a long-term exposure will not
materialize in the first year of an air quality change. But eventually, an individual
in Multnomah County should expect his mortality hazard rate to decrease by
approximately 0.006*0.002 = 1.2*10-5.
Above, we noted that a population’s total number of deaths in a given year is a
proxy for the sum of all population members’ hazard rates for that year. COBRA
uses population and mortality projections that are based on data compiled by the
CDC and the U.S. Census. For Multnomah County, 455,205 residents over age 30
are projected for 2017, and 5,105 of these are projected to die in that year.20
Combining this with equation (2), we estimate an expected mortality change of
5,105 × [−1.2 × 10−5 ] = 0.059
(This is the lower-bound figure given in Table 11 for Multnomah County.) Ignoring
population growth and other demographic changes, we expect approximately 0.059
fewer deaths in Multnomah County each year after the long-term health effects
have had time to materialize (the exact figure would change from one year to the
next because of evolving baseline assumptions).
COBRA handles the mortality lag for long-term effects by distributing the 0.059
figure across a 20-year span that begins in 2017. This feature applies different timelags to different portions of the effect and discount’s each portion’s VSL-based
valuation back to 2017 (users can select either 3% or 7% discount rates). This is
described further in the COBRA User Manual (see page F-5) and the references
therein.
In this section, we have described precisely what COBRA estimates refer to: total
health effects due to a one-year change in emissions. We also described the form of
the actual public health benefit. Proper valuation requires that subjects in
valuation research must be presented with propositions that are similar to the
benefit we are trying to value. The next section describes valuation research.
3.4.
Monetization of Health Effects
EPA is currently using a suite of economic values for avoiding various health risks.
These values (Table 12) are embedded in the COBRA modeling software and are
applied to the quantified health impacts calculated within COBRA to obtain
economic value of health impact realized due to decreased wood smoke.
In the Krewski study, the adult mortality variable was defined in terms of deaths of individuals age 30 and
above.
20
Table 12 EPA Approved Mortality and Morbidity Benefit
Health Incident Avoided
Time-varying costsa
Adult Mortalityb (3% discount rate)
Adult Mortalityb (7% discount rate)
Non-Fatal Heart Attacks (3%
discount rate)
Non-Fatal Heart Attacks (7%
discount rate)
Costs incurred in the year of exposure
Infant Mortalityb
Hospital Admissions (Respiratory,
Cardiovascular-related)
Asthma Emergency Room Visits
Acute Bronchitis
Respiratory Symptoms (Upper,
Lower)
Asthma Exacerbations (attacks,
shortness of breath, and wheezing)
Minor Restricted Activity Days
Work Loss Days
a.
b.
Incidence
Classification
Economic Value
(2010$)
Mortality
Mortality
Morbidity
$8,434,924
$7,512,853
$33,259 - $263,795
Morbidity
$31,446 - $253,247
Mortality
Morbidity
$9,401,680
$15,430 - $41,002
Morbidity
Morbidity
Morbidity
$388 - $464
$477
$21 - $33
Morbidity
$57
Morbidity
Morbidity
$68
$151
In COBRA, most health effects and their economic values are expected to occur in the year of analysis.
However, since all avoided cases of adult mortality are not expected to occur in the year of analysis,
COBRA uses a discount rate to calculate the value of all avoided cases of adult mortality in present
terms. In addition, while avoided cases of non-fatal heart attacks are expected to occur in the year of
analysis, the costs associated with this health effect would occur over multiple years. Thus, while a
COBRA emissions scenario may result in a certain number of cases of non-fatal heart attacks in 2017,
all economic benefits associated with these emissions changes would not accrue in that same year. The
values in each year are discounted to present terms.
Following EPA (2012),21 COBRA assumes that some of the incidences of premature adult mortality
related to PM2.5 exposures occur in a distributed fashion over the 20 years following exposure. This lag
adjustment does not apply to infant mortality, because Woodruff et al. (1997) estimate the number of
infant deaths occurring in the same year as the emissions change.22
U.S. EPA. (2012). Regulatory Impact Analysis for the Final Revisions to the National Ambient Air Quality
Standards for Particulate Matter. EPA-452/R-12-005. December 2012. Research Triangle Park, NC: Office of Air
and Radiation, Office of Air Quality Planning and Standards.
22 Woodruff, T. J., Grillo, J., & Schoendorf, K. C. (1997). The relationship between selected causes of
postneonatal infant mortality and particulate air pollution in the United States. Environmental Health
Perspectives, 105(6), 608-612.
21
All health impacts can be classified as either morbidity or mortality impacts. Most
of EPA’s recommended morbidity benefits23 are based on published estimates of the
costs of treating the illness (can include both direct medical costs and costs of lost
productivity). Asthma exacerbations and MRADs (minor restricted activity days)
are estimated through WTP (willingness to pay) methods. The overwhelming
majority of the monetized health benefit come from avoided premature mortality,
which is primarily measured through WTP methods. The willingness to pay method
attempts to understand the amount society is willing to pay to reduce micro-risks
due to environmental pollutants. This is explained later in Section 4.4.1.
The EPA has conducted extensive research on this subject, defines mortality in
terms of VSL (Value per Statistical Life). This report investigates the mortality
valuation in detail as mortality contributes approximately 99% of all health benefits
from reduced wood smoke.
Value of Statistical Life
EPA recommends a VSL of $ 7.4 million (2008 $), the magnitude of this value
makes the VSL a key parameter driving the cost-benefit of environmental
regulatory analysis. As per the EPA24, the VSL is a summary measure for the dollar
value of small changes in mortality risk experienced by a large number of people.
VSL estimates are derived from aggregated estimates of individual values for small
changes in mortality risks. For example, if 10,000 individuals are each willing to
pay $500 for a reduction in risk of 1/10,000, then the value of saving one statistical
life equals $500 times 10,000 — or $5 million. Note, this does not mean that any
single identifiable life is valued at this amount. Rather, the aggregate value of
reducing a collection of small individual risks is, in this case, worth $5 million.
The term, VSL can often be misleading. It may be erroneously interpreted as the
value of a lost life. However, VSL represents the amount society is willing to pay to
reduce an incremental risk to life. Researchers have started to define VSL in terms
of $ per micro risk. This metric can be cumbersome, harder to read than total
dollars, but the two are algebraically equivalent.
EPA lists the following, Table 13, as the base for its recommended central estimate.
EPA fit a Weibull curve to these estimates to obtain a central estimate of $7.4
COBRA manual, Appendix F. http://epa.gov/statelocalclimate/documents/pdf/cobra-2.61-user-manual-july2013.pdf
24 “Guidelines for Preparing Economic Analysis” National Center of Environmental Economics, US EPA. (Dec.
2010, Updated May, 2014)
23
million, with a standard deviation of $4.7 million. EPA recommends that the central
estimate of VSL be used for all its analysis25.
Table 13 EPA Guidelines Summary VSL
Study
Method
Kniesner and Leeth (1991 - US)
Smith and Gilbert (1984)
Dillingham (1985)
Butler (1983)
Miller and Guria (1991)
Moore and Viscusi (1988)
Viscusi, Magat, and Huber
(1991)
Marin and Psacharopolous
(1982)
Gegax et al. (1985)
Kniesner and Leeth (1991 Australia)
Gerking, de Haan and Schulze
(1988)
Cousineau, Lecriox, and Girard
(1988)
Jones - Lee (1989)
Dillingham (1985)
Viscusi (1978)
R.K. Smith (1974)
V.K. Smith (1983)
Olson (1981)
Viscusi (1981)
R.S. Smith (1974)
Moore and Viscusi (1988)
Kniesner and Leeth (1991 Japan)
Herzog and Schlottman (1987)
Leigh and Folsom (1984)
Leigh (1987)
Garen (1988)
Revealed Preference
Revealed Preference
Revealed Preference
Revealed Preference
Stated Preference
Revealed Preference
Stated Preference
Value of
Statistical Life
$0.85
$0.97
$1.34
$1.58
$1.82
$3.64
$4.01
Revealed Preference
$4.13
Stated Preference
Revealed Preference
$4.86
$4.86
Stated Preference
$4.98
Revealed Preference
$5.34
Stated Preference
Revealed Preference
Revealed Preference
Revealed Preference
Revealed Preference
Revealed Preference
Revealed Preference
Revealed Preference
Revealed Preference
Revealed Preference
$5.59
$5.71
$6.07
$6.80
$6.92
$7.65
$9.60
$10.57
$10.69
$11.18
Revealed Preference
Revealed Preference
Revealed Preference
Revealed Preference
$13.36
$14.21
$15.31
$19.80
“Guidelines for Preparing Economic Analysis – Appendix B” National Center of Environmental Economics,
US EPA. (Dec. 2010, Updated May, 2014
25
There are two methods used to estimate VSL, revealed preference method (focusing
on the labor market studies of wages and on-the-job risks) and stated preference
method (in which the values are elicited directly using choice experiments). Both
methods calculate VSL by estimating society’s WTP for reduction in micro risk.
The revealed preference method analyses the amount of incremental payment a
worker receives for incremental increase in fatality risk on the job. After
statistically controlling for factors such as education, and occupation, this method
attempts to answer the question: how much compensation do workers receive for
bearing extra risk? The stated preference method establishes a VSL by asking
people hypothetical questions on how much they are willing to pay to reduce a micro
risk to fatality.
Non-EPA VSL Estimates
The EPA estimates of VSL are not used by other government agencies. The reason
for different values used by other government agencies comes down to agency
specific risk valuation and available literature at the time of valuation. The U.S.
OMB (Office of Management and Budget) suggests using a VSL between $1 and $
10 million for all government agencies26.
The FDA (Food and Drug Administration) uses a VSL of $ 5 million (not adjusted
for inflation and the real income year). This estimate of $5 million is roughly in the
middle of the OMB suggested range of $1 to $10 million. The CMS (Center for
Medicare and Medicaid Services) uses a VSL of $ 5 million as well, citing that $ 5
million is in the middle of the OMB provided range27.
The DOT (Department of Transportation) relies on 9 wage risk studies for its VSL
estimates (published in 2004 or later). In a recent update, the DOT updated its VSL
estimate to a value very similar to the EPA. Adjusting for income, and year, the
DOT VSL is $9.1 million, compared to EPA’s value of $9.2 million (2012 $)28.
Age Adjustments to VSL
Although studies and methods have been proposed to vary VSL with age, all federal
agencies employ a uniform VSL number across all age groups. A study by Aldy and
Viscusi argues that VSL does vary by age29. The study states that VSL from
Office of Management and Budget, circular A-4. http://www.whitehouse.gov/omb/circulars_a004_a-4#17
“How US Government Agencies Value Mortality Risk Reductions” Lisa A. Robinson (2003)
https://www.law.upenn.edu/institutes/regulation/papers/RobinsonValues.pdf
28 Robinson, Lisa A., and James K. Hammitt. 2014. “Research Synthesis and the Value per Statistical Life.”
Regulatory Policy Program Working Paper RPP-2014-14. Cambridge.
http://www.hks.harvard.edu/var/ezp_site/storage/fckeditor/file/RPP_2014_14_Robinson_Hammitt.pdf
29 “Age Differences in the Value of Statistical Life – Revealed Preference Evidence” Aldy and Viscusy (April
2007). Discussion Paper, Resources for the Future.
26
27
revealed preference studies increases with age, peaks in mid-life, and subsequently
declines. According to the study, the assumption that a 60 year old has a lower VSL
than a 20 year old is false because a 60 year old is likely to have lower tolerance for
mortality risk and higher savings. The SAB (Science Advisory Board) in a 2007
memo to the EPA recommended that: “Although the literature on the relationship
between age and the VSL is growing, the Committee does not believe that it is
sufficiently robust to allow the Agency to use a VSL that varies with age.”30
Attempts to reduce the VSL based on age has led to controversy. In 2003, EPA
attempted to use a lower VSL for people 65 years and older, but was met with
opposition and decided to keep the same VSL for all age groups. In 2008 EPA met
serious opposition when it lowered the VSL estimate by a million dollars. These
policy controversies led to a legislative proposal that would ban practices which
reduced the value of life based on demographic factors, including age31.
While studies of individual WTP (willingness-to-pay) indicate that the VSL varies
with age and income, varying VSL estimates based on age leads to questions on the
fairness of policy. Hence, federal agencies generally apply the same mean VSL
estimates across all individuals potentially affected by their regulations—regardless
of age, income, or other characteristics27.
VSL Review Conclusions and Recommendation
Determining a reliable point estimate for VSL is problematic. Using a range of
estimates may better inform policy choices. Although different studies on VSL have
been conducted at various points of time, with differing methodology, and dataset,
they all produce VSL within an order of magnitude ($1 - $ 12 Million, Table 17).
That such differing studies produce VSL within one order of magnitude is a
significant finding32. The best way to conceptualize VSL is to understand it as an
order of magnitude estimate of value placed by a society on the mortality risk
reduction. Although it is theoretically possible to arrive at a point estimate, the
authors of this report don’t recommend using that point estimate alone for policy
analysis. A scenario level analysis using high, medium and low estimate of VSL
should be conducted. In the situation where the high and low end of VSL produce
different results in cost benefit analysis, a Delphi process (such as an RTF
SAB Advisory on EPA's Issues in Valuing Mortality Risk Reduction. (October 12, 2007)
http://nepis.epa.gov/Adobe/PDF/P10007U3.PDF
31 “The Devaluation of Life: W. Kip. Viscusi (2009).
32 It should be noted that there may be some confirmation bias that influences studies to estimate VSL within
an order of magnitude. As this report did not attempt to review each VSL calculation in detail, this statement is
only intended as a caution.
30
discussion) should determine how the results of the cost benefit analysis should be
interpreted.
The EPA meta-analysis of VSL is the most thorough study of VSL estimates from
recognized wage risk and WTP studies. This meta-analysis recommends a median
VSL of $7.4 million (in 2008, $) with a standard deviation of $4.7 million.
Appendix A. Wood heat and non-DHP measures.
The RTF uses the RBSA to estimate how wood heat affects electric energy savings
due to non-DHP measures. The RBSA includes data from site audits, utility bills,
and occupant interviews. The interview responses describe “wood” fuel
consumption in terms of cords of wood, tons of pellets, and gallons of propane. The
RTF uses the billing- and wood-fuel data to estimate the differences in electric
heating energy that are associated with different levels of wood fuel consumption.
This analysis only looks at electrically heated single-family homes (homes with a
gas furnace, for example, are excluded). Table 14 summarizes the results of this
analysis for heating zone 1 (results are similar in zones 2 and 3).
Table 14 Wood Heat and Electric Heating Energy in Heating Zone 1
Wood (kWh) 6K to 12K
Wood (kWh) Over 12K
Total
Electric heating
energy
difference
(affected homes)
-20%
Percent of Zone
1 homes
affected
Net electric
heating energy
difference
30%
-6.2%
-39%
11%
-4.3%
-10.5%
To understand what the table says, consider the first row: On average, a home that
consumes between 6,000 and 12,000 kWh-equivalent in wood fuels will use about
20% less electric heating energy than a similar home that uses no wood fuel;33
according to the RBSA, about 30% of the homes in heating zone 1 have wood heat in
this range; the net effect is that is that zone-1 homes use about 0.20*0.30 = 6.2%
less, on average, because of the presence of wood heat.34
The kWh difference for affected homes was estimated in a regression that included variables to control for
climate, building envelope, and square footage.
34 The equation 0.20*0.30 = 0.062 includes rounding: To one more digit, the figures in the product are 0.204 and
0.304. 35 Using the RBSA, we can estimate the average efficiency of wood burning devices in the region. We
33
In terms of statistical error bounds, the net differences in the final column of Table
14 have approximately 20% precision at the 90% confidence level. For example, the
net kWh difference associated with moderate wood heat usage is within about 1.2
percentage points of -6.2%. Also, the effect sizes in the first column are physically
reasonable: The average electric heating energy for electrically heated homes in
Zone 1 is around 7,500 kWh, so it is not surprising to see a 20% reduction in homes
whose wood fuel consumption close to the average heat load, nor is it surprising to
see a 39% reduction among homes whose wood fuel consumption is above the
average heat load. Furthermore, the results are similar in zones 2 and 3, and
among manufactured homes. For these reasons, we do not consider the numerical
values in Table 14 to be a major source of uncertainty.
However, this analysis does not directly address the topic of the present report. The
analysis summarized in Table 14 describes changes in electric heating energy that
are associated high levels of wood heat. This is not the same as our immediate
concern, changes in wood heat associated with different levels of weatherization and
equipment efficiency?
discuss this in greater detail below but do not consider the issue to be a major obstacle to the quantification of
wood heat.
Appendix B. Wood heat and ductless heat pumps
Table 4 in the main body of this report showed that the electric savings due to
ductless heat pumps is much lower in homes with wood heat than in homes without
wood heat. This appendix presents the RTF’s evidence for DHP-related wood heat
savings in greater detail. It also develops wood savings estimates from this
evidence in order to convey the extent to which these savings may be realistically
quantified.
The calculations that follow are complicated by the need to normalize estimates to
RBSA building averages and long-term weather; some of these complications are
commonplace among current RTF energy estimates. In any event, we emphasize
that the estimates themselves ultimately rely on the same basic evidence identified
in the billing analysis and illustrated in Table 4.
As a reminder, the central data source that provided this evidence is the impact
evaluation for NEEA’s DHP pilot program (Baylon, et al, 2013). The pilot program
installed almost 3,400 ductless heat pumps in the Pacific Northwest, and collected
pre- and post-DHP billing data for all participants. The impact evaluation was
based on billing data and sub-metered end-use data collected for about 100 sites.
The analysis described in Appendix A shows that homes with high levels of wood
heat tend to use 20% to 40% less electric heating energy, so it is natural to expect
lower savings in these homes. However, the savings figures in Table 4 differ by
much more than 20-40%. The impact evaluation concluded that occupants must
burn less wood after installing a DHP. This assertion, which is central to our
present concern, is well supported by the data. See (Baylon, et al, 2013) for the full
analysis that leads to this conclusion.
As with weatherization, the DHP analysis is based on observed differences in
electric energy, so changes in wood heat must derive from the initial results. But
unlike the weatherization analysis, the DHP analysis gives clear insight into actual
changes in heating electric heating energy associated with ductless heat pumps,
and this provides a much better basis for estimating changes in wood heat.
To obtain results that reflect the residential building stock in the Northwest, the
RTF uses a calibrated engineering model to estimate residential energy
consumption and savings. For DHPs, electric and wood savings are calculated
separately for each heating zone. The procedure is based on these elements:

Estimates of differences in electric heating energy intensities (kWh/sq. ft.)
between homes with and without wood heat. Heating energy intensities are
based on billing analysis results (Table 4) and home sizes (about 2300
participants with wood heat and 1100 without).

RBSA-based estimates of the fraction of homes in the region that have wood
heat (about 1400 homes in the RBSA).

Estimates of total heat load that reflect the building characteristics in the
RBSA. These are based on an engineering model that is calibrated using a
subset of the pilot study for which researchers collected detailed audit and submeter. The sub-meter study included 78 sites with negligible wood heat.
The values used in our wood heat calculations are summarized in Table 15.
Table 15 Inputs to Wood Savings Estimates
Heating
zone
1
2
3
Electric heating energy intensity
(kWh/ft2)
Pre-DHP
Pre-DHP
Post-DHP
Post-DHP
homes
homes
homes
homes
without
with
without
with
wood heat
wood heat
wood heat
wood heat
5.02
3.66
3.18
3.08
6.84
3.55
5.62
3.58
5.87
3.73
4.89
4.00
Percent of
DHPeligible
homes that
have wood
heat
28%
20%
70%
The values in the table can be used to estimate the fraction of heating load that is
met with wood heat in the pre- and post-DHP cases.
As an example, consider pre-DHP homes in heating zone 1. Within this group, the
average electric heating energy intensities of homes without wood heat is 5.02
kWh/ft2, and the average for homes with wood heat is 3.66 kWh/ft2. Based on this,
we estimate that on average, zone 1 homes that have wood heat meet about
3.66/6.02 = 73% of their heat load with electricity, and they meet the remaining 27%
with wood. Similarly, we estimate that these homes meet 97% of their heating
loads with electricity and 3% with wood after installing DHPs.
The next step is to use these heating shares to estimate the amount of heating
energy that is contributed by wood-burning devices.
Before continuing, we must clarify the distinction between the amount of thermal
energy used to heat a house (the heat load) and the amount of electric energy used
to meet the heat load. These are not always the same, even in homes that are
exclusively heated by electric appliances. One example is ducted heating systems,
which require fan energy and often lose heat through leaky ducts. For another
example, the amount of thermal energy that a heat pump delivers into the house is
greater than the amount of electric energy used to power the heat pump.
The total amount of electric energy needed to heat home without any
supplementary heating fuels. This is not always the same as the amount of thermal
energy used to heat the house (the heat load). Ducted heating systems require fan
energy and lose heat through ducts, and the amount of thermal energy that a heat
pump delivers into the house is greater than the amount of electric energy used to
power the heat pump.
The RTF’s current DHP measures apply to homes whose primary electric heat
source is zonal-electric (baseboard heaters). Zonal electric heat is 100% efficient –
the amount of thermal energy delivered to the house equals the amount of electric
energy consumed by the baseboard heaters – so in the pre-DHP case, the heat load
is the same as the electric heating energy.
In the post-DHP case, the heat load and the electric energy needed to meet the load
are not necessarily the same because occupants sometimes increase their
thermostat settings when they obtain an efficient heat source such as a DHP (this
phenomenon is called “comfort take-back”). The RTF’s energy model calibration
focuses on the energy consumed by heating appliances, not the thermal energy the
appliances produce. Because of this, the model only produces calibrated heat load
estimates in the case of homes with zonal electric heat (see previous paragraph).
For other heat sources, the calibration adjustments may reflect comfort take-back,
or uncertain model inputs (such as a heat pump’s effective coefficient of
performance), or both.
Table 16 shows three types of estimates. The first is the pre-DHP heat load, which
is the same as the electric energy used to heat a zonal-electric home with no
supplemental heat; if there is no comfort take-back, this is the same as heat load in
the post-DHP case. The second is a rough estimate of the post-DHP heat load that
assumes the relevant RTF calibration adjustments are entirely due to comfort takeback. The third is the estimated electricity used to heat the home in the post-DHP
case; this is only provided to underscore the distinction between heat load and
energy used to meet load.
Table 16 Energy estimates from based on calibrated engineering model
Heat load,
Heat load (high)1
Heating energy2
Heating zone
zonal-electric
DHP homes
DHP homes
(kWh)
(kWh)
(kWh)
1
9,068
10,434
5,757
2
10,987
12,644
7,680
3
14,528
16,760
11,307
1 As explained above, the RTF’s current methods do not yield a calibrated estimate
of the average post-DHP heat load. The first two columns in the table bracket the
range likely values. The true value depends on the role of comfort take-back in the
RTF’s model calibration.
2 Heating energy refers to the amount of electric energy needed to heat a home that
has no supplemental fuels. This is not the same as the heat load (see above).
The calculations following Table 17 estimated that zone 1 homes with wood heat
meet about 27% of their heating loads with wood prior to installing DHPs. Based
on Table 16 this corresponds to 0.27*9,086 = 2,448 kWh delivered into the
conditioned space. Assuming the average wood-burning appliance efficiency is
around 50%, this says that the average energy content of the wood burned by these
homes is about 4,900 kWh.35
The same calculation estimated that 3% of the load is met with wood in the postDHP case. Depending on comfort take-back, this corresponds to between 272 and
313 kWh delivered into the conditioned space. We therefore estimate that, on
average, when a DHP is installed in a zone 1 home that has wood heat, the wood
saved is an amount that would deliver between 2135 and 2176 kWh of thermal
energy into the conditioned space. Assuming 50% wood-burning efficiency and 6000
kWh per cord, that’s little more than 2/3 of a cord.
Since 28% of DHP-eligible homes in zone 1 have wood heat, we estimate the overall
average wood savings as 598 - 609 kWh (delivered to the conditioned space.
Using the RBSA, we can estimate the average efficiency of wood burning devices in the region. We discuss
this in greater detail below but do not consider the issue to be a major obstacle to the quantification of wood
heat.
35
The following table summarizes the wood savings calculations for all three zones.
Table 17 Estimated wood savings due to ductless heat pumps
Average wood savings per DHP
(energy delivered to space,
kWh)*
Homes with
Average across
Heating zone
Pre-DHP
Post-DHP
wood heat
all homes
1
27%
3%
2135 - 2176
598 - 609
2
48%
36%
722 - 1318
144 - 264
3
36%
18%
2213 - 2615
1549 - 1831
* The range of saving values reflects uncertainty about the magnitude of comfort
take-back. Low-end savings values correspond to greater comfort take-back, and
high-end savings values correspond to no comfort take-back.
Percent of load met with wood
(homes with wood heat)
In Table 17, the range of values in the wood savings estimates reflects the likely
range of comfort take-back effects. Uncertainty due to other factors, such as
statistical error in estimates of heating energy intensity or in the RTF’s model
calibration, is not reflected in the ranges.
The main logical weakness in these estimates is that the DHP study did not use a
control group, so it is possible that some external phenomenon may have driven a
region-wide reduction in wood burning that happened to coincide with the DHP
pilot program. (For example, maybe there happened to be more burn bans in the
period after the program than in the period before the program.) We mention this
issue for completeness, not to suggest that it represents a significant threat to the
study’s main findings. Few studies are able to eliminate all possible logical gaps.