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The Local Environmental Consequences of Coal
Procurement at U.S. Power Plants
Akshaya Jha and Nick Muller∗
October 3, 2016
Abstract
There are known to be significant environmental costs associated with burningcoal in order to generate electricity. However, there are also sizable local envi-ronmental health costs associated with power plants’ coal purchase and storagebehavior. We find that a 10% increase in coal stockpiles (number of deliveries) isassociated with a 0.02% (0.07%) increase in the concentration of fine particulateson average. Local populations exposed to these fine particulates (PM2.5) haveincreased risk of lung and heart conditions. Translating the increase in PM2.5 as-sociated with plants’ coal procurement behavior into mortality risk and monetizingthis mortality risk, we calculate local environmental costs between $0.004-$0.02 perKWh. Finally, we show that the average increase in mortality rates associated withan increase in PM2.5 are substantially higher when we instrument using coal stock-piles and number of deliveries relative to OLS specifications; this result providesboth a new identification strategy for the link between mortality rates and PM2.5
as well as further empirical evidence of the link between PM2.5 and plants’ coalprocurement behavior.
∗Akshaya Jha: H. John Heinz III College, Carnegie Mellon University, 4800 Forbes Avenue,Pittsburgh, PA 15213. Email: akshayaj@andrew.cmu.edu. Nicholas Muller: Department ofEconomics, Middlebury College and NBER, 303 College Street, Middlebury, VT 05753. Email:nicholas.muller74@gmail.com.
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1 Introduction
There is significant concern regarding the environmental costs associated with coal-fired
electricity generation in the United States. The vast majority of this concern focuses
on the global and local pollutants emitted when coal is burned to produce electricity.1
Our paper instead examines the environmental consequences of the input coal purchase
and storage behavior of U.S. power plants. The paper has three components. First, we
estimate the extent to which increases in coal stockpiles and coal deliveries are associ-
ated with increases in the locally-monitored concentration of fine particulates (termed
PM2.5).2 Local populations exposed to higher PM2.5 concentration levels have increased
risk of lung and heart conditions; we map increases in PM2.5 associated with increases
in coal stockpiles (coal deliveries) into mortality risk and ultimately a dollar-per-KWh
social cost of coal stored (delivered). Finally, we test whether mortality rates are caused
by exposure to PM2.5 using the level of coal stockpiles held and the number of coal
deliveries received by nearby power plants as instruments.
We consider monthly average PM2.5 concentration levels at roughly 1,000 monitored
sites across the United States from 2002-2012. These data are collected from the Air
Quality System (AQS) database maintained by the Environmental Protection Agency
(EPA). Our primary variables of interest (coal stockpiles and number of deliveries) come
from monthly, plant-level data on coal purchases and stockpiles provided by the Energy
Information Administration (EIA). We report results considering all coal-fired power
plants within 100 miles of each air quality monitor unless otherwise specified; we also
consider specifications with 25, 50, and 200 mile bandwidths between air quality monitor
and plants. We additionally merge in monthly, meteorological data collected by roughly
1,600 monitors across the United States given that wind speed and direction, as well as
temperature and precipitation, are important determinants of the extent to which coal
stockpiles and deliveries generate PM2.5. These data are maintained by the National
Climatic Data Center (NCDC). We find that a 10% increase in coal stockpiles (number of
deliveries) is associated with a 0.02% (0.07%) increase in PM2.5 concentration on average,
1See NRC (2010) for a comprehensive report on the environmental externalities associated with energyuse.
2Jaffe et al. (2015) studies the emissions of diesel particulate matter (DPM) and coal dust from trainsin the Columbia River Gorge in Washington State; they find that passage of a diesel powered open-topcoal train result in nearly twice as much respirable PM2.5 compared to passage of a diesel-poweredfreight train.
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controlling for a wide array of other potential determinants of PM2.5 concentration levels
(described in Section 3).
We use a peer-reviewed concentration-response function (Krewski et al., 2009) to map
increases in PM2.5 to mortality risk; we consider the Value of a Statistical Life (VSL)
measure to monetize this mortality risk in 2013 dollars.3 We estimate that health costs are
roughly $12.54 ($7.52) per ton of coal stockpiled (delivered). These local environmental
costs are sizable given that U.S. coal-fired power plants pay roughly $48 per ton for coal
on average. However, if we translate tons of coal to KWh of electricity, our health costs
amount to roughly $0.004-$0.02 per KWh; our environmental health cost estimates for
coal purchased and stored are an order of magnitude lower than the local environmental
health costs typically estimated for coal burned. Summarizing, our empirical estimates of
the local environmental damages associated with purchasing and storing coal are sizable,
but not unreasonably so when compared to estimates of the local environmental damages
associated with burning coal.
Finally, we estimate the effect of PM2.5 concentration levels on mortality rates using
both ordinary least squares (OLS) and instrumental variables (IV) frameworks. We
consider separate regressions for mortality rates associated with different causes of death:
deaths due to the cardiovascular system, deaths due to the respiratory system, and
deaths due to the nervous system. We also consider total number of deaths of any
type and deaths due to external causes such as accidents (as a placebo test). These
county/month specific mortality rates come from the Centers for Disease Control and
Prevention (CDC Wonder, 2016). We include a range of controls in both OLS and
IV specifications as well as county/year fixed effects. We find an economically small
(and sometimes negative) association between PM2.5 and any of these mortality rates
when examining the OLS results. However, the effect of PM2.5 on mortality rates is
positive, statistically significant, and economically significant when we instrument using
the monthly level of coal stockpiles and monthly number of coal deliveries from nearby
power plants.
Previous empirical literature has found that the damages from air pollution are sig-
nificant in magnitude and that these damages are mostly due to the increased mortality
3Viscusi and Aldy (2003) reviews more than 60 studies of mortality risk premiums from ten countriesand approximately 40 studies that present estimates of injury risk premiums.
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risk from exposure to PM2.5.4 However, recent work in economics has challenged the
causal basis for the link between exposure to PM and the adverse effects on mortality
risk reported in the epidemiological literature.5 Observational studies relating PM2.5 and
mortality rate can be confounded by increases in economic activity, changes in weather,
etc.; we demonstrate that the coal procurement behavior of U.S. power plants aids in
empirically identifying the impact of PM2.5 exposure on mortality rates. We thus provide
empirical evidence on the link between PM2.5 exposure and mortality rates using a novel
identification strategy as well as provide further empirical evidence that there’s a link
between PM2.5 concentration levels and plants’ coal procurement behavior.
The remainder of the paper proceeds as follows. In Section 2, we elaborate on the
physical process underlying how plants’ coal purchase and storage behavior can result
in higher PM2.5 concentration levels. Section 3 lists the data sources as well as the
methodology used to estimate the relationship between plants’ coal procurement behavior
and PM2.5 concentration levels, while Section 4 presents the empirical results showing
that increases in coal purchase and storage behavior correspond to increases in PM2.5
concentration levels. In Section 5, we quantify the local environmental health costs due to
the PM2.5 increases associated with plants’ coal procurement behavior. We describe our
instrumental variables approach for identifying the effect of PM2.5 concentration levels
on mortality rates in Section 6. Finally, we conclude in Section 7.
2 Economic and Environmental Background
2.1 Input Coal Procurement
Coal-fired power plants burn coal in order to heat water into steam that drives the
turbines used to generate electricity.6 These plants purchase the majority of their input
coal from long-term contracts with coal suppliers, purchasing the remainder from spot
markets. They also store large quantities of coal on-site to hedge against coal price
4This empirical literature includes NRC (2010), of Air and Radiation: Office of Policy in Washington(2010), Muller, Mendelsohn and Nordhaus (2011), and Muller (2014).
5This work includes (among others) Currie, Neidell et al. (2005), Currie, Neidell and Schmieder(2009), and Chay and Greenstone (2003).
6See http://www.duke-energy.com/about-energy/generating-electricity/coal-fired-how.asp for a shortdescription of this process.
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and supply risks; these coal inventories are very rarely resold in practice.7 Coal is not
homogeneous; coal mined from different regions of the United States differs in heat
content, sulfur content, ash content, distance from mine to plant, etc. Coal-fired power
plants primarily value the heat content of coal; burning a ton of coal with a higher
heat content generates a larger amount of output electricity. Sulfur and ash content are
important mainly for their environmental impacts. Finally, roughly 67 percent of coal is
transported via rail. The remaining coal is shipped via barge (12%), trucks (10%) and
various other modes used primarily for short distances (ex: conveyors, pipelines, etc.).8
The local environmental costs of coal delivered from the mine to the power plant can
differ significantly based on the mode of transportation utilized.
2.2 Environmental Impacts of U.S Coal-fired Generation
It is well-known that direct emissions of NOx, SO2, and PM2.5 are associated with
elevated mortality risk among exposed populations. Earlier work has shown that U.S.
coal-fired power plants emit significant levels of NOx, SO2, and PM2.5 when burning
coal; these emissions caused approximately 27,000 deaths in the U.S. in 1999. Emissions-
induced deaths resulting from power plants burning coal fell to roughly 9,500 in 2011 due
to differences in plants’ fuel choice and the increased prevalence of scrubbing technology
(Muller, 2014).
While the environmental costs associated with burning coal are well-documented
(NRC and NAS, 2010), this paper explores the local environmental costs associated
with emissions from coal stockpiles and coal deliveries. We posit two mechanisms for
emissions from coal stockpiles. The first is wind erosion. Wind blowing over uncovered
coal stockpiles entrains fine particulates; these passive, or fugitive, dust emissions become
a constituent of ambient PM2.5. Second, coal stockpiles emit volatile gases. Specifically,
coal in open stockpiles undergoes oxidation, which releases a set of pollutants including
hydrocarbons and sulphuric gases (Zhang, 2013). The gases result in the formation of
secondary organic PM2.5 that is a constituent of the ambient PM2.5 collected at monitor-
7This lack of resale is due primarily to the fact that the coal transportation infrastructure is designedto bring coal to power plants rather than away from them; therefore, coal becomes very costly to resellonce it’s in the stockpile.
8These statistics are for the year 2013 and come from EIA’s “Today In Energy”http://www.eia.gov/todayinenergy/detail.cfm?id=16651.
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ing stations. Coal deliveries typically arrive by train; the coal carried by these trains are
typically in uncovered freight cars. Thus, we expect coal deliveries to result in increased
PM2.5 levels both due to wind erosion (dust entrainment) and gaseous discharge. Ad-
ditionally, coal trains run on diesel fuel; burning diesel is also associated with increased
PM2.5 concentration levels. Thus, the burning of diesel is another factor determining the
local environmental costs from coal deliveries.
Our empirical strategy does not distinguish between the two types of passive emissions
(dust entrainment and gaseous discharges); PM2.5 emissions resulting from both of these
mechanisms increase mortality rates in local populations and thus contribute to local
environmental health costs. Similarly, we do not disentangle how much of the PM2.5
increase from coal deliveries comes from passive emissions from coal versus emissions
from burning diesel, as both of the factors contribute to the local environmental costs
associated with transporting coal. However, our empirical strategy does control for the
direct emissions which result from burning coal, as we focus in this paper on the local
environmental costs associated with purchasing and storing coal.
3 Local Environmental Impacts of Coal Procure-
ment: Data and Methodology
This section describes the data used to estimate the association between plant-level coal
procurement behavior (coal stockpiles and number of deliveries) and PM2.5 concentra-
tions at nearby air quality monitors. We also present the empirical framework used to
measure this association, which includes specifying our set of controls for alternative
sources of PM2.5 (ex: burning coal) as well as other factors that increase or decrease
PM2.5 for a given set of sources (ex: wind speed, wind direction, and precipitation).
3.1 Data Sources
We utilize monthly, plant-level data from 2002-2012 on end-of-month fuel inventories and
fuel purchases from the Energy Information Administration (EIA).9 For plants’ coal pur-
9The data regarding fuel inventories for 2002-2012 are confidential; we obtained a research contractfrom the EIA in order to use these restricted-access data.
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chases, we have order-level data on the month of purchase, quantity purchased, delivered
price, heat content, sulfur content, ash content, origin of coal, and whether the order
came from a long-term contract or the spot market. Finally, we only consider electricity
generation plants whose “primary business purpose is the sale of electricity to the pub-
lic”10; this excludes plants that also sell significant quantities of heat (“combined heat
and power plants”) as well as commercial and industrial plants that generate electricity
for their own use.
We also use the Air Quality System (AQS) data provided by the Environmental
Protection Agency (EPA). This publicly available database includes hourly readings of
ambient PM2.5 concentrations at roughly 1,000 monitored sites across the contiguous U.S.
We aggregate these data to monthly average PM2.5 levels for each air quality monitor
for the sample period 2002-2012.
Our meteorological controls come from the quality controlled local climatological
data (QCLCD) collected by the National Climatic Data Center (NCDC); these data
include hourly wind speed and direction, dry bulb temperature, wet bulb temperature,
dew-point temperature, relative humidity, station pressure, and precipitation at approx-
imately 1,600 U.S. locations. Wind speed is of primary importance to quantifying the
local environmental costs associated with coal procurement as wind blowing over coal
stockpiles and coal deliveries generates PM2.5. We aggregate these data to the meteoro-
logical monitor/month-of-sample level by taking wind speed weighted averages of wind
direction, dry bulb temperature, wet bulb temperature, dew-point temperature, relative
humidity, and station pressure; the results presented below differ very little if we instead
take un-weighted averages. We also control for the (5, 10, 20, 30, 40, 50, 60, 70, 80,
90, 95) hourly percentiles of wind speed, calculated over all hours-of-sample for each
meteorological monitor/month-of-sample.
Finally, we control for a variety of other factors that can affect the PM2.5 concen-
tration reading at an air quality monitor in each month. First, the EPA’s Continuous
Monitoring Emissions System (CEMS) collects hourly data for each plant on SO2, CO2,
and NOx emissions (in tons) resulting from coal burned; we sum these hourly data to
the monthly level and control for total SO2, CO2, and NOx emissions for each plant in
each month-of-sample. We also control for the total monthly quantity of coal purchased
10This quotation is from the EIA Form 923 data dictionary.
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by each plant as well as the total monthly electricity generation produced by each plant;
both variables come from EIA data.
The AQS database provides the latitude and longitude for each air quality monitor,
the NCDC database provides the latitude and longitude (as well as wind direction in each
month-of-sample) for each meteorological monitor, and the EPA eGrid database provides
the latitude and longitude for each coal-fired power plant. We use these variables in order
to merge air quality monitors to coal-fired power plants and meteorological monitors.
3.2 Data Merge
We merge each air quality monitor i to meteorological monitors and coal-fired power
plants as follows:
1. For each month-of-sample, we find all meteorological monitors within M miles of
the air quality monitor i. We take a weighted average of the meteorological data
(ex: wind speed, wind direction, etc.) across these meteorological monitors for each
air quality monitor i, where we weight by the inverse of the distance between the
air quality monitor and the meteorological monitor.11
2. We consider all coal-fired power plants within M miles of air quality monitor i.
Thus, our unit of observation is an air quality monitor/power plant for each month-of-
sample, emphasizing that each air quality monitor can be associated with multiple power
plants for a given month-of-sample. We examine how the effect of coal stockpiles and
number of deliveries on PM2.5 concentration levels varies by distance between air quality
monitor and power plant (i.e: considering M = (25, 50, 100, 200) miles), the relative wind
direction between air quality monitor and power plants, as well as other factors such as
wind speed, temperature, humidity, etc. For example, we hypothesize that an air quality
monitor downwind of coal-fired power plants picks up higher levels of PM2.5 relative to
an air quality monitor upwind of these coal-fired power plants. We provide further detail
on our data sources as well as data construction in Appendix Section B.
11Our empirical results do not differ substantially if we instead take an un-weighted average of themeteorological data.
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3.3 Empirical Framework
We are interested in estimating the effect of coal stockpiles held by coal-fired power
plants on local PM2.5 concentration levels. We consider two specifications: “Log-Log”
and “Levels-Levels”. Our Log-Log specification for how coal stockpiles impact PM2.5
levels is:
log(PM2.5i,t + 1) = αc,t + θi,p + log(CSp,t)γi,p + log(
Pi,t∑k=1,k 6=p
CSk,t)ψ +Xi,p,tβ + εi,p,t (1)
where a unit of observation is an air quality monitor i and coal-fired power plant p
in month-of-sample t. Pi,t is the number of coal-fired power plants merged with each
air quality monitor i in each month t. We control for the total level of coal stockpiles
across all plants k 6= p so that the log(CSp,t) term doesn’t capture positive correlations
in stockpile increases across plants; for example, if a one ton increase in coal stockpiles
at plant p is typically associated with a 0.5 ton increase in coal stockpiles at plant q,
we do not want to include the effects of the 0.5 ton increase at plant q on PM2.5 in
our estimate of γi,p. We also control for a wide variety of alternative factors that affect
local PM2.5 levels, such as annual sulfur and ash content of coal purchased by the plant,
monthly quantity of coal received by the plant, monthly number of coal deliveries to the
plant, the plant’s monthly total electricity generation, the plant’s monthly total SO2,
CO2, and NOx emissions from coal burned (in tons), wind speed, dry bulb temperature,
wet bulb temperature, dew-point temperature, relative humidity, station pressure, and
precipitation. Finally, we include fixed effects for county-of-air-quality-monitor/month-
of-sample (αc,t) and air quality monitor/power plant (θi,p). We report a specification
considering one overall coefficient (γi,p ≡ γ), as well as specifications allowing γi,p to vary
by wind direction, wind speed, temperature, precipitation, etc.12
Our goal is to estimate how the level of PM2.5 measured by a given air quality monitor
is affected by a one ton increase in coal stockpiles, controlling for all of the other factors
listed above. The Log-Log specification in Equation 1 implies that this partial effect is:
E[PM2.5i,t|αc,t, θp,i, Xi,p,t]
dCSp,t
= γi,pexp( ˆlog(PM2.5i,t))exp(0.5σ2)CS−1i,t
12Coal-fired power plants only stock-out in roughly 0-5% of the months-of-sample for which we observethem; thus, we drop observations with zero stockpiles. The empirical results are similar if we keep thesezero stockpile observations in the sample; for these specifications, we include log(CSp,t + 1) as well as adummy variable for coal stockpile levels greater than zero 1(CSp,t > 0) instead of log(CSp,t).
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if we assume that εi,p,t|(X,α, θ) is independently and identically distributed
Normal(0, σ2). Unfortunately, this functional form for the partial effect is highly sen-
sitive to small levels of coal stockpiles (CSp,t); the partial effects resulting from this
calculation are highly right-skewed.
Due to this, we also estimate a Levels-Levels specification:
PM2.5i,t = αc,t + θi,p + CSp,tγi,p + (
Pi,t∑k=1,k 6=p
CSk,t)ψ +Xi,p,tβ + εi,p,t
This specification includes the same set of controls as listed above; the partial effect
associated with coal stockpiles is simply γi,p in the Levels-Levels case.
Finally, we also estimate both the Log-Log and Levels-Levels specifications replacing
coal stockpiles (CSp,t) with the number of coal deliveries (NDp,t) arriving at plant p in
month-of-sample t.13 We control for the same factors as described above, with the obvious
exception of the number of deliveries (our dependent variable). We interpret the effect
of deliveries on PM2.5i,t (γi,p) as stemming primarily from the diesel fuel being burned
by the trains rather than from the coal itself as we control for the monthly quantity of
coal delivered to the plant. For both coal stockpiles and number of deliveries, we use
the partial effects generated from the Levels-Levels specification in order to quantify the
local environmental health costs associated with coal purchase and storage behavior.
4 Local Environmental Impacts of Coal Procure-
ment: Empirical Findings
In this section, we present our regression results regarding the link between coal pur-
chase/stockpiling behavior and PM2.5 concentration levels. We estimate both Levels-
Levels and Log-Log specifications, finding from the Log-Log specifications that a 10%
increase in coal stockpiles (number of deliveries) is associated with a 0.02% (0.07%) in-
crease in PM2.5 concentration on average. As discussed in Section 2.2, PM2.5 particulates
are generated both by wind blowing over coal piles and from the volatile gases emitted
13We consider log(NDp,t + 1) for the Log-Log specifications given that there are a non-trivial numberof plant/month-of-sample observations where there are zero deliveries (i.e.: the plant did not purchasecoal in that month-of-sample).
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by coal piles; PM2.5 particulates are also created when diesel is burned by the trains
delivering coal. We thus expect more severe increases in PM2.5 concentrations for local
populations downwind from coal piles and train deliveries (i.e.: the wind is blowing from
the coal pile to the local population). Consistent with this intuition, we estimate that
the average PM2.5 increase associated with coal purchase and storage behavior is larger
for air quality monitors that are downwind of nearby coal-fired power plants. We also
find that the average PM2.5 increase associated with coal stockpiles is lower for higher
levels of precipitation. Finally, we examine air quality monitors near coal mines, simi-
larly finding that a higher number of deliveries coming from coal mines corresponds to
an increase in average local PM2.5 concentration levels.
4.1 Overall Effect of Coal Stockpiles and Number of Deliveries
on PM2.5 Concentrations
The top panel of Table 1 displays the results of the Levels-Levels specification with the
number of coal deliveries arriving at each plant in each month-of-sample as the covariate
of interest. With the sample restricted to plants within 25 miles of each air quality
monitor, we find that an additional delivery is associated with an increase in monthly
average PM2.5 levels of 0.016 micrograms per cubic meter (ug/m3). This regression model
explains nearly 85% of the variation in ambient PM2.5 readings. When we include plants
within 50 miles of each monitor in the estimation sample, the magnitude of the effect
falls by roughly 63% to 0.006 ug/m3. Further expanding the bandwidth to 100 miles or
200 miles reduces the partial effect of the number of coal deliveries to roughly 0.004 ug/m3.
The summary statistics corresponding to all regressions discussed in this section are in
Appendix Table A.1 (25 mile bandwidth), Table A.2 (50 mile bandwidth), Table A.3
(100 mile bandwidth), and Table A.4 (200 mile bandwidth). The bottom panel of Table
1 displays the results of the Log-Log models. Across each bandwidth (25, 50, 100, and
200 miles), the estimated elasticity ranges from 0.007-0.013. The partial effect of coal
deliveries on ambient PM2.5 is significant at the 5% level for all four bandwidths.
The top panel of Table 2 reports the results from the Levels-Levels specification where
the size (in tons) of the coal stockpiles held at each power plant in each month-of-sample
is the covariate of interest. A one-ton increase in coal stockpiles increases ambient PM2.5
levels by about 1.02e-06 ug/m3 when only plants within 25 miles of a monitor are included.
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Table 1: Overall Effect of Number of Deliveries on PM2.5 Concentration
Dependent Variable: PM2.5i,t
Dist. Bandwidth (25 Miles) (50 Miles) (100 Miles) (200 Miles)NDp,t 0.0161** 0.0061 0.0043 0.0039**
(0.0073) (0.0039) (0.0027) (0.0016)
Observations 49,543 167,627 471,410 1,418,815R-squared 0.846 0.810 0.790 0.777
Dependent Variable: log(PM2.5i,t) + 1Dist. Bandwidth (25 Miles) (50 Miles) (100 Miles) (200 Miles)log(NDp,t + 1) 0.0128*** 0.0093*** 0.0080*** 0.0072***
(0.0048) (0.0027) (0.0017) (0.0011)
Observations 49,543 167,627 471,410 1,418,815R-squared 0.850 0.818 0.800 0.787
Standard errors clustered by air quality monitor in parentheses
Facility Code/AQ Monitor Fixed Effects Included
AQ County/Month-of-Sample Fixed Effects Included
Sum of Deliveries from Other Plants Included
Meteorological and EPA Emissions Controls Included
Coal Quality, Quantity Received, and Thermal Generation Controls Included
*** p<0.01, ** p<0.05, * p<0.1
Our estimated partial effects for the 50, 100, and 200 mile bandwidths are roughly 15%-
32% the size of the effect for the 25 mile bandwidth. The association between coal
stockpiles and PM2.5 is statistically significant and positive for all four distance bins. The
bottom panel of Table 2 shows the estimated elasticities from the Log-Log specification
of the coal stockpile models. The elasticities range from 0.0014 (for plants within 200
miles of a monitor) up to 0.0061 (for plants within 25 miles of a monitor). As with
the Levels-Levels specification, the effect size is declining as expected for larger distance
bandwidths; the effects are precisely estimated for all four distance bins.
4.2 Coal Stockpiles and Number of Deliveries on PM2.5 Con-
centrations: By Wind Direction
In this subsection, we interact the covariate of interest (the number of deliveries in Table
3 and coal stockpiles in Table 4) with the bearing from coal-fired power plant to air
quality monitor. For example, a monitor directly downwind from a plant would have a
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Table 2: Overall Effect of Coal Stockpiles on PM2.5 Concentration
Dependent Variable: PM2.5i,t
Dist. Bandwidth (25 Miles) (50 Miles) (100 Miles) (200 Miles)CSp,t 1.02e-06*** 3.29e-07*** 1.79e-07*** 1.51e-07***
(2.06e-07) (9.26e-08) (5.48e-08) (3.84e-08)
Observations 48,521 162,524 456,773 1,365,465R-squared 0.847 0.811 0.792 0.779
Dependent Variable: log(PM2.5i,t) + 1Dist. Bandwidth (25 Miles) (50 Miles) (100 Miles) (200 Miles)
log(CSp,t) 0.0061** 0.0031** 0.0021** 0.0014**(0.0029) (0.0016) (0.0010) (0.0007)
Observations 47,169 158,357 442,295 1,322,144R-squared 0.849 0.819 0.800 0.787
Standard errors clustered by air quality monitor in parentheses
Facility Code/AQ Monitor Fixed Effects Included
AQ County/Month-of-Sample Fixed Effects Included
Sum of Stockpiles from Other Plants Included
Meteorological and EPA Emissions Controls Included
Coal Quality, Quantity Received, and Thermal Generation Controls Included
*** p<0.01, ** p<0.05, * p<0.1
bearing of 0◦ while a monitor directly upwind from a plant would have a bearing of 180◦.
For each plant/air quality (AQ) monitor pair, we code the AQ monitor as “downwind”
from the plant if their relative bearing is less than 90◦ and code the AQ monitor as
“upwind” from the plant if their relative bearing is greater than 90◦.
The top panel of Table 3 shows that, for the sample restricted only to plants within 25
miles of each PM2.5 monitor, the partial effect of deliveries is only statistically significant
for monitors downwind from plants. This is intuitive since coal piles generate PM2.5
particulates both by wind blowing over coal piles as well as from the volatile gases emitted
by coal piles; coal deliveries (typically by train) additionally generate PM2.5 particulates
from the fuel (typically diesel) burned to transport the coal. Thus, we should expect
more severe increases in PM2.5 concentrations for local populations downwind from coal
piles and coal deliveries. In addition, the magnitude of the effect for downwind monitors
is larger than the effect for upwind monitors for all four distance bandwidths (25, 50,
100, and 200 miles). Finally, as expected, the effect for downwind monitors is larger than
the corresponding effect reported in the top panel of Table 1 (which does not specifically
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control for the orientation between plants and monitors) for all four distance bandwidths.
Table 3: Effect of Number of Deliveries on PM2.5 Concentration: Upwind vs.Downwind
Dependent Variable: PM2.5i,t
Distance Bandwidth (25 Miles) (50 Miles) (100 Miles) (200 Miles)NDp,t×
Monitor Downwind from Plant 0.0217*** 0.0063 0.0050* 0.0050***(0.0084) (0.0044) (0.0030) (0.0019)
Monitor Upwind from Plant 0.0098 0.0059 0.0033 0.0024(0.0089) (0.0047) (0.0033) (0.0019)
Observations 49,543 167,627 471,410 1,418,815R-squared 0.846 0.810 0.791 0.778
Dependent Variable: log(PM2.5i,t) + 1Dist. Bandwidth (25 Miles) (50 Miles) (100 Miles) (200 Miles)log(NDp,t + 1)×
Monitor Downwind from Plant 0.0163*** 0.0106*** 0.0089*** 0.0083***(0.0049) (0.0029) (0.0019) (0.0012)
Monitor Upwind from Plant 0.0094* 0.0078*** 0.0070*** 0.0059***(0.0051) (0.0030) (0.0019) (0.0012)
Observations 49,543 167,627 471,410 1,418,815R-squared 0.850 0.818 0.800 0.787
Standard errors clustered by air quality monitor in parentheses
Facility Code/AQ Monitor Fixed Effects Included
AQ County/Month-of-Sample Fixed Effects Included
Sum of Deliveries from Other Plants Included
Meteorological and EPA Emissions Controls Included
Coal Quality, Quantity Received, and Thermal Generation Controls Included
*** p<0.01, ** p<0.05, * p<0.1
The bottom panel of Table 3 shows the results for the directional models expressed in
terms of elasticities. Across all bandwidths, the coefficients for deliveries to plants upwind
of the air quality monitor are significant at the 0.01 level. The elasticities range between
0.008 and 0.016, with the magnitude descending as we increase the distance bandwidth.
As with the Levels-Levels specification, we see that the PM2.5 elasticities with respect to
number of deliveries are smaller for upwind monitors relative to downwind monitors for
all four distance bandwidths. As expected, the downwind effects for number of deliveries
14
reported in Table 3 are larger than the corresponding overall estimates reported in the
bottom panel of Table 1 for all four distance bandwidths.
Table 4 displays the results for the directional models in which the size (in tonnage)
of coal stockpiles is the independent variable of interest. The interaction terms between
the (level of) coal stockpiles and the dummy variable for (relative) angles between the 0◦
to 90◦ are statistically significant across all four bandwidths. We see the same pattern
as with the number of deliveries regressions: 1) the estimated coefficients for downwind
monitors are larger than the corresponding estimates for upwind monitors across all four
distance bins, and 2) the estimated coefficients for downwind monitors are larger than
the corresponding overall estimates reported in the top panel of Table 2. These same
two findings (downwind larger than upwind and downwind larger than overall) also hold
when examining the Log-Log specifications presented in the bottom panel of Table 4.
4.3 Coal Stockpiles and Number of Deliveries on PM2.5 Con-
centrations: Interacted with Precipitation
In this subsection, we interact the covariate of interest (the number of deliveries in Table
5 and coal stockpiles in Table 6) with the logarithm of monthly average precipitation
as measured by the set of meteorological monitors within M miles of the air quality
monitor. For example, if there are four meteorological monitors within 100 miles of a
given air quality monitor (for the 100 mile bandwidth), we use the wind-speed weighted
average over both the hourly data on precipitation and over all four of these monitors
for each month-of-sample.
The top panel of Table 5 shows that an increased number of coal deliveries generates
less additional PM2.5 for higher levels of precipitation. This is intuitive, given that local
pollution levels are known to decrease with rainfall due to “wet deposition” (i.e.: PM2.5
particulates are brought from the atmosphere to the ground by rain). Thus, if burning
diesel in order to transport coal via train generates a given amount of PM2.5, less PM2.5
remains in the atmosphere for higher levels of monthly average precipitation. We interact
the logarithm of number of deliveries with monthly average precipitation in the bottom
panel of Table 5; we draw exactly the same conclusions as above using the Log-Log
specification.
15
Table 4: Effect of Coal Stockpiles on PM2.5 Concentration: Upwind vs. Downwind
Dependent Variable: PM2.5i,t
Distance Bandwidth (25 Miles) (50 Miles) (100 Miles) (200 Miles)CSp,t×
Monitor Downwind from Plant 1.14e-06*** 3.58e-07*** 1.83e-07*** 1.64e-07***(2.19e-07) (1.01e-07) (5.83e-08) (4.23e-08)
Monitor Upwind from Plant 9.18e-07*** 3.05e-07*** 1.75e-07*** 1.37e-07***(2.33e-07) (1.09e-07) (6.54e-08) (4.41e-08)
Observations 48,521 162,524 456,773 1,365,465R-squared 0.847 0.811 0.792 0.779
Dependent Variable: log(PM2.5i,t) + 1Dist. Bandwidth (25 Miles) (50 Miles) (100 Miles) (200 Miles)log(CSp,t)×
Monitor Downwind from Plant 0.0067** 0.0033** 0.0022** 0.0016**(0.0029) (0.0016) (0.0010) (0.0007)
Monitor Upwind from Plant 0.0056* 0.0029* 0.0020* 0.0012*(0.0028) (0.0016) (0.0010) (0.0007)
Observations 47,169 158,357 442,295 1,322,144R-squared 0.849 0.819 0.800 0.787
Standard errors clustered by air quality monitor in parentheses
Facility Code/AQ Monitor Fixed Effects Included
AQ County/Month-of-Sample Fixed Effects Included
Sum of Stockpiles from Other Plants Included
Meteorological and EPA Emissions Controls Included
Coal Quality, Quantity Received, and Thermal Generation Controls Included
*** p<0.01, ** p<0.05, * p<0.1
Table 6 displays the results interacting monthly average precipitation with coal stock-
piles. As with the number of deliveries, if a given level of coal stockpiles translates into a
given level of PM2.5, we should expect less of this PM2.5 to remain in the atmosphere for
higher levels of precipitation due to wet deposition. We find that this intuition holds for
both the top panel (Levels-Levels specification) and bottom panel (Log-Log specification)
of Table 6. Namely, we see from Table 6 that the interaction term between coal stockpiles
and monthly precipitation is statistically significant and negative across all four distance
bandwidths.
16
Table 5: Effect of Number of Deliveries on PM2.5 Concentration: Interacted withPrecipitation
Dependent Variable: PM2.5i,t
Distance Bandwidth (25 Miles) (50 Miles) (100 Miles) (200 Miles)NDp,t×
Constant 0.0550*** 0.0417*** 0.0465*** 0.0388***(0.0141) (0.0074) (0.0066) (0.0046)
log(Precipitation+ 1) -0.0285*** -0.0261*** -0.0322*** -0.0262***(0.0086) (0.0048) (0.0043) (0.0030)
Observations 49,543 167,627 471,410 1,418,815R-squared 0.846 0.810 0.791 0.778
Dependent Variable: log(PM2.5i,t) + 1Dist. Bandwidth (25 Miles) (50 Miles) (100 Miles) (200 Miles)log(NDp,t + 1)×
Constant 0.0279*** 0.0289*** 0.0316*** 0.0300***(0.0065) (0.0040) (0.0033) (0.0027)
log(Precipitation+ 1) -0.0116*** -0.0147*** -0.0177*** -0.0168***(0.0037) (0.0021) (0.0020) (0.0017)
Observations 49,543 167,627 471,410 1,418,815R-squared 0.850 0.818 0.800 0.787
Standard errors clustered by air quality monitor in parentheses
Facility Code/AQ Monitor Fixed Effects Included
AQ County/Month-of-Sample Fixed Effects Included
Sum of Deliveries from Other Plants Included
Meteorological and EPA Emissions Controls Included
Coal Quality, Quantity Received, and Thermal Generation Controls Included
*** p<0.01, ** p<0.05, * p<0.1
4.4 Effect of the Number of Deliveries on PM2.5 Concentrations
at Coal Mines
We also know the originating coal mine county for each coal delivery in our data-set; we
re-run our analysis by counting up the number of deliveries originating from each coal
mine county in each month-of-sample, merging each air quality monitor with coal mine
counties using the latitude and longitude of the centroid of the coal mine county.14 The
14We unfortunately do not have data on the level of coal stockpiles held at coal mines.
17
Table 6: Effect of Coal Stockpiles on PM2.5 Concentration: Interacted withPrecipitation
Dependent Variable: PM2.5i,t
Distance Bandwidth (25 Miles) (50 Miles) (100 Miles) (200 Miles)CSp,t×
Constant 1.65e-06*** 6.59e-07*** 6.95e-07*** 7.02e-07***(2.59e-07) (1.27e-07) (1.04e-07) (7.74e-08)
log(Precipitation+ 1) -4.67e-07*** -2.34e-07*** -3.48e-07*** -3.61e-07***(1.29e-07) (6.75e-08) (6.18e-08) (4.62e-08)
Observations 48,521 162,524 456,773 1,365,465R-squared 0.847 0.811 0.792 0.779
Dependent Variable: log(PM2.5i,t) + 1Dist. Bandwidth (25 Miles) (50 Miles) (100 Miles) (200 Miles)log(CSp,t)×
Constant 0.0116*** 0.0100*** 0.0107*** 0.0113***(0.0030) (0.0017) (0.0012) (0.0009)
log(Precipitation+ 1) -0.0048*** -0.0056*** -0.0065*** -0.0071***(0.0009) (0.0005) (0.0004) (0.0004)
Observations 47,169 158,357 442,295 1,322,144R-squared 0.850 0.820 0.803 0.789
Standard errors clustered by air quality monitor in parentheses
Facility Code/AQ Monitor Fixed Effects Included
AQ County/Month-of-Sample Fixed Effects Included
Sum of Stockpiles from Other Plants Included
Meteorological and EPA Emissions Controls Included
Coal Quality, Quantity Received, and Thermal Generation Controls Included
*** p<0.01, ** p<0.05, * p<0.1
bottom panel of Table 7 presents the results of this exercise for the Log-Log specification.
We find a positive and statistically significant coefficient on the logarithm of number of
deliveries for the 50, 100, and 200 mile bandwidths; however, we cannot interpret the
magnitude of the coefficients across different distance bins as we use the latitude and
longitude of the centroid of the originating coal mine county rather than the location of
the mine itself. Overall, these results indicate that coal deliveries (typically by rail) are
associated with increased PM2.5 concentrations whether coming from the mine or arriving
at the coal-fired power plant, though we see from comparing Table 7 with Table 1 that
18
the overall average effect of deliveries coming from mine counties is smaller in magnitude
relative to the overall effect of deliveries arriving at coal-fired power plants. We draw the
same qualitative conclusions when examining the Levels-Levels results displayed in the
top panel of Table 7.
Table 7: Effect of Number of Deliveries on PM2.5 Concentration: At the Coal Mine
Dependent Variable: PM2.5i,t)Dist. Bandwidth (25 Miles) (50 Miles) (100 Miles) (200 Miles)
NDp,t 0.0146 0.0013 0.0065*** 0.0084***(0.0098) (0.0062) (0.0025) (0.0023)
Observations 10,468 75,858 325,129 1,323,876R-squared 0.921 0.901 0.875 0.845
Dependent Variable: log(PM2.5i,t) + 1Dist. Bandwidth (25 Miles) (50 Miles) (100 Miles) (200 Miles)log(NDp,t + 1) 0.0061 0.0073*** 0.0039*** 0.0034***
(0.0079) (0.0023) (0.0009) (0.0007)
Observations 10,468 75,858 325,129 1,323,876R-squared 0.904 0.889 0.862 0.829
Standard errors clustered by air quality monitor in parentheses
Facility Code/AQ Monitor Fixed Effects Included
AQ County/Month-of-Sample Fixed Effects Included
Quantity Sent Control Included
Meteorological Controls Included
*** p<0.01, ** p<0.05, * p<0.1
5 Local Environmental Health Costs of Coal Pro-
curement: Quantification
The previous section measures the effect of increases in coal stockpiles and number of coal
deliveries on local PM2.5 concentration levels. This section provides details on both the
methodology and empirical results mapping these coal procurement induced increases in
PM2.5 to mortality risk and ultimately social costs (in both dollars per ton and dollars
per KWh). Summarizing our findings, we calculate that a one ton (KWh) increase in
coal stockpiles or quantity delivered is associated with local environmental costs between
$7.50-$27 ($0.004-$0.02).
19
5.1 Mapping Partial Effects into Local Environmental Costs:
Methodology
In the previous section, we discussed how to measure the extent to which fugitive and
gaseous emissions from coal stockpiles, as well as the emissions from burning diesel as-
sociated with coal deliveries, have an effect on ambient PM2.5 levels. We estimate the
impact that such emissions have on human health in this section. We focus exclusively
on mortality risk because prior research has shown that the majority of damage from
PM2.5 exposure (and air pollution exposure, more generally) is due to elevated mortality
risk (EPA (1999); Muller, Mendelsohn and Nordhaus (2011)). This aspect of the anal-
ysis relies on the partial effects from the Levels-Levels specification (which we denote
∆PM2.5). We use the following concentration-response relationship from Krewski et al.
(2009) in order to map these partial effects into the total number of deaths stemming
from coal procurement induced changes in PM2.5:
Mc,t = Popc,tMRc,t(1−1
exp(ρ∆PM2.5i)) (2)
where Popc,t is the population in county of monitor i, denoted c in month-of-sample
t, MRc,t is the county/month-of-sample specific mortality rate, and ρ is a statistically
estimated parameter reported by Krewski et al. (2009); The result of these calculations
is an estimate of the county/month-of-sample specific deaths due to increased PM2.5
exposure stemming from coal stockpiles (or deliveries).
In Equation 2 above, ∆PM2.5 denotes the partial effect of either coal stockpiles or
number of deliveries on ambient PM2.5 concentrations using the Levels-Levels specifica-
tion. However, we divide the partial effect from an additional delivery by the number of
tons delivered to plant p in month-of-sample t in order to derive a comparable marginal
damage figure for coal stockpiles versus quantity of coal delivered. We then average this
marginal effect over plants and months-of-sample for each air quality monitor i.
The next step is quantifying the costs (in dollars) of these deaths. We employ the
Value of a Statistical Life (VSL) methodology (Viscusi and Aldy, 2003) for this conver-
sion. We use a VSL of roughly $9.8 million based on the Regulatory Impact Analyses
(RIAs) for air pollution conducted by the USEPA. The monetary marginal damage from
emissions resulting from an additional ton stored in a coal stockpile in county of monitor
20
i, denoted c, at time t, is given by:
MDCSc,t = MCS
c,t V SL
5.2 Mapping PM2.5 Concentrations to Local Environmental
Costs: Empirical Results
We calculate the local environmental health costs associated with the partial effects re-
lating coal stockpiles/number of deliveries to PM2.5 emissions using the methodology
discussed above. We use the Levels-Levels specifications that account for the wind direc-
tion between air quality monitor and coal-fired power plant (i.e: the top panel of Table
3 for number of deliveries or the top panel of Table 4 for coal stockpiles).15 Table 8
shows the results from these social cost calculations for the median air quality monitor;
we present the median social cost rather than the average social cost as the air quality
monitor level distribution of these social costs is right-skewed. We see from this table
that the monetary damage per ton of coal delivered due to increased mortality rates is
roughly $41 when using the partial effects from the Levels-Levels regression model re-
stricting the sample to plants within 25 miles of monitors. The impact rises to $114 per
ton of coal stored when the partial effect estimated from the stockpile model (25 mile
bandwidth) is used. As a point of comparison, the average delivered price of coal is $48
per ton, demonstrating that these per-ton local environmental damages are quite large.
However, the social cost estimates are sensitive to the bandwidth; this is expected
given that the effects of coal procurement on PM2.5 differ significantly across radii. We
see that the median local environmental damage associated with a one ton increase in coal
stockpiled (delivered) drops precipitously to $27.17 ($13.09) if we consider the 50 mile
bandwidth instead of the 25 mile bandwidth. Our damage estimates drop even further
to $12.54 ($7.52) per ton stockpiled (delivered) if we consider the 100 mile bandwidth.
The cost estimates do not change appreciably when using the 200 mile models; thus, we
report the 100 mile models as our primary specification.
We see that the local environmental damages associated with storing coal are either
15Appendix Table A.7 and Table A.8 repeat this analysis using the models that do not explicitlycontrol for the direction between plants and PM monitors. The basic message from comparing TableA.7 with Table 8 as well as Table A.8 with Table 9 is that controlling for direction does not have a verylarge effect on the social cost estimates for coal stockpiles or coal deliveries.
21
larger or roughly similar to the local environmental costs of delivering coal. This may
be because coal deliveries occur roughly once a week while coal stockpiles are sitting
at the power plant throughout the month. Moreover, our estimated partial effects for
the number of deliveries are based on specifications controlling for the quantity of coal
delivered; thus, we are primarily capturing the effects of emissions from the diesel burned
by the trains delivering the coal. The overall local environmental impact of coal deliveries
including both the diesel burned as well as the passive and fugitive emissions associated
with coal are likely larger than the numbers we report in Table 8. In fact, one can add
our costs per ton from coal stockpiles and number of deliveries in order to get a back-
of-the-envelope number for the overall per-ton local costs of delivering coal; this number
for the 100 mile bandwidth is roughly $20 per ton delivered.
Our per-ton local environmental damage estimates are large given that the average
price of coal is roughly $48. However, Table 9 demonstrates that our estimates are not
unreasonably large when compared to estimates in the literature of the local environ-
mental costs associated with burning coal. Converting tons of coal burned to KWh of
electricity generated, our estimates of the per-KWh social costs associated with storing
(delivering) coal range from roughly $0.005 to $0.055 ($0.005 to $0.021). These local
environmental health cost estimates for coal purchased and stored are roughly an order
of magnitude lower than the local environmental health costs typically estimated for coal
burned. We should expect this given that burning coal emits significantly more PM2.5
relative to storing or transporting coal.
Table 8: Per-ton Social Cost Estimates from the Models that do control for Direction
(25 Miles) (50 Miles) (100 Miles) (200 Miles)CSp,t 113.80 27.17 12.54 9.41
numdelivp,t 41.38 13.09 7.52 10.36
Table 9: Per-KWh Social Cost Estimates from the Models that do control for Direction
(25 Miles) (50 Miles) (100 Miles) (200 Miles)CSp,t 0.0546 0.0132 0.0059 0.0047
numdelivp,t 0.0213 0.0058 0.0037 0.0048
Figure 1 is a U.S. map displaying the geographic dispersion of our air quality monitor
specific estimates of the local environmental damages from increased PM2.5 exposure
22
associated with increases in coal stockpiles. This figure indicates that local environmental
damages are higher in areas with higher populations; for example, we see particularly
high local environmental health costs associated with coal stockpiles in the Northeastern
region of the U.S. Figure 2 demonstrates directly that our estimates of the per-KWh local
environmental costs from coal stockpiling increase with total population; interestingly,
this figure also suggests a roughly linear relationship between local environmental health
costs and total population.
Figure 1: Damages Per Ton (Stockpiles) across the U.S.
6 IV Regression of PM2.5 on Mortality Rates
The previous section maps PM2.5 concentration increases associated with coal procure-
ment to mortality risk based on the concentration-response relationship reported by
Krewski et al. (2009) (an epidemiological study). This section directly quantifies the
impact of PM2.5 concentration levels on various type of mortality rates (such as deaths
related to the cardiovascular system, deaths related to the respiratory system, etc.) using
23
Figure 2: Damages Per Ton (Stockpiles) versus Total Population
0.5
11.
5St
ockp
ile D
amag
e ($
/kw
h)
0 1000000 2000000 3000000 4000000 5000000County Population
coal stockpiles and number of deliveries as an instrument for PM2.5 concentration lev-
els. We first describe the data on mortality rates used for this section. We next specify
the ordinary least squares (OLS) and instrumental variables (IV) regression frameworks
measuring the impact of PM2.5 on mortality rates due to different causes. Finally, we
present our empirical results for both OLS and IV specifications.
6.1 Data Sources For Mortality Rate Regressions
We collect monthly, county-level total number of deaths by type from the Center for
Disease Control and Prevention (CDC); these categories are deaths related to the car-
diovascular, respiratory, or nervous systems, deaths due to external causes, and total
number of deaths. We present results considering only people who are 30 years old or
older, as this is the sub-population considered in epidemiological studies such as Krewski
et al. (2009). We also provide the empirical results counting deaths of people of all ages
24
in Appendix A; these all-age results are very similar to those presented below for the
over-30 subpopulation.
We also use annual, county-level population data from the Survey of Epidemiology
and End Results (SEER) collected from the National Bureau of Economic Research
(NBER) website. We consider the annual, county-level population of people at or above
30 years old for the empirical results presented below; we use the annual, county-level
population counting people of all ages for the “all-age” mortality rate regression results
provided in Appendix A.
6.2 Empirical Framework
We estimate the following ordinary least squares (OLS) specification relating mortality
rates to local PM2.5 concentrations:
log(deathsc,tpopc,y
) = αc,y + log(PM2.5i,t + 1)γ +Xi,p,tβ + εi,p,t
where c indexes the county where air quality monitor i is located, p indexes a coal-
fired power plant, t indexes the month-of-sample, and y indexes the year-of-sample. We
include the same set of controls Xi,p,t as described in Section 3. Finally, we control for
county-year fixed effects αc,y. We estimate this OLS specification separately for each
type of mortality rate (cardiovascular, respiratory, nervous, external, and total).
In our instrumental variables (IV) specifications, we instrument for log(PM2.5i,t + 1)
with both log(CSp,t) and log(NDp,t + 1); our first-stage regression is:
log(PM2.5i,t + 1) = δc,y + log(CSp,t + 1)θ1 + log(NDp,t + 1)θ2
+Xi,p,tη + εi,p,t
As with the OLS framework, we estimate this IV specification separately for each type
of mortality rate (cardiovascular, respiratory, nervous, external, and total). We cluster
standard errors by air quality monitor. We weight both OLS and IV specifications
by county-year level population; our empirical results are qualitatively similar if we do
not weight by population. We also present IV results using only coal stockpiles as an
instrument for PM2.5 concentration levels, including the number of coal deliveries as
25
a control in both the first and second stages; our empirical findings using only coal
stockpiles as an instrument are quite similar to those presented below.
6.3 Empirical Results
We relegate the summary statistics for the empirical results in this subsection to Ap-
pendix Section A. This Appendix section also contains the empirical results from the
mortality rate regressions for the full population rather than the sub-population over 30
(see Appendix Tables A.19 through A.26); the empirical results for the full population
are quite similar to the results for the over-30 subpopulation described below. We also
obtain similar results if we only use coal stockpiles as an instrument rather than both
coal stockpiles and number of coal deliveries (see Appendix Tables A.27 through A.34).
Finally, we focus for expositional ease on the results using plants within 100 miles of the
air quality monitor; we would draw similar conclusions based on the results using the 25,
50, and 200 mile distance bandwidths instead (these results are provided in Appendix
Section A).
The top panel of Table 10 lists the empirical results when regressing mortality rate
(separately for deaths associated with the cardiovascular system, the respiratory system,
the nervous system, all people, and external causes such as accidents) on PM2.5 concen-
trations within the OLS framework described in the previous subsection. Interpreting
the first column, we find that a 10% increase in PM2.5 concentration levels corresponds
to a 0.1% increase on average in the cardiovascular mortality rate (i.e: the fraction of
the population that dies due to issues related to the cardiovascular system). We find
from Column 2 that a 10% increase in PM2.5 is associated with a 0.2% average increase
in the rate of deaths related to the respiratory system. The OLS coefficient estimates
in Columns 1 and 2 are statistically significant. However, Column 3 shows a negative
(and statistically significant) association between PM2.5 exposure and the nervous system
death rate, while the total death rate coefficient estimate (Column 4) is not statistically
significant. The death rate due to external causes (such as accidents) should not be
related to PM2.5 concentration levels; however, the OLS results in Column 6 show a
negative (and statistically significant) association between the external death rate and
PM2.5.
26
Table 10: PM2.5 on Mortality Rate: 100 Mile Bandwidth for Ages 30+
OLS SpecificationVARIABLES Cardio MR Resp. MR Nervous MR Total MR External MR
log(PM2.5 + 1) 0.0102*** 0.0226*** -0.0202*** 0.0005 -0.0169***(0.0024) (0.0037) (0.0059) (0.0018) (0.0047)
Observations 381,277 295,534 221,833 403,183 226,132R2 0.901 0.758 0.823 0.922 0.734
IV SpecificationVARIABLES Cardio MR Resp. MR Nervous MR Total MR External MR
log(PM2.5 + 1) 0.202*** 0.184** 0.261*** 0.184*** -0.0373(0.0390) (0.0826) (0.0664) (0.0328) (0.0609)
Observations 366,338 283,632 212,573 386,973 217,146R2 0.880 0.746 0.798 0.890 0.736
Standard errors clustered by air quality monitor in parentheses
County of AQ Monitor/Year-of-Sample Fixed Effects Included
Meteorological and EPA Emissions Controls Included
Coal Quality, Quantity Received, and Thermal Generation Controls Included
*** p<0.01, ** p<0.05, * p<0.1
In contrast, the bottom panel of Table 10 provides the empirical results relating PM2.5
concentration levels to mortality rates for the IV specification. As with the OLS results,
we find a positive (and statistically significant) link between PM2.5 concentration levels
and both the cardiovascular and respiratory death rates (Columns 1 and 2 of the bottom
panel of Table 10). However, the estimated coefficients in Columns 1 and 2 are much
larger for the IV specification relative to the OLS specification; the IV findings indicate
that a 10% increase in PM2.5 concentration levels results in a roughly 2% (1.8%) average
increase in the cardiovascular (respiratory) mortality rate. Moreover, the IV coefficient
estimates in Columns 3 and 4 are positive and statistically significant as expected; we
find a positive link between PM2.5 exposure and both nervous system mortality rates
as well as total mortality rates. Using the IV framework, we do not see a statistically
significant relationship between the external death rate and PM2.5 exposure in Column 5;
this finding is consistent with the intuition that the probability of death due to external
causes such as accidents should not change appreciably with PM2.5 concentration levels.
27
Thus, this lack of a relationship between PM2.5 and the external death rate provides
evidence that we are not capturing some other source of variation that simultaneously
induces increases in PM2.5 concentration levels and mortality rates.
We compare our mortality estimates to those in the literature by focusing on the
overall total mortality rate for the subsample of people over 30 years old because these
estimates are directly comparable to the numbers utilized by the USEPA in its regulatory
impact analyses for PM2.5. In particular, the USEPA typically uses the association
between PM2.5 levels and overall, post-30 mortality rates reported by Lepeule et al.
(2012) and Krewski et al. (2009). However, these studies report semi-elasticities so we
perform some back-of-the-envelope calculations to compare our numbers to the numbers
found in their studies.
Lepeule et al. (2012) report that a 1 ug/m3 increase in PM2.5 is associated with a
1.4% increase in overall, post-30 mortality rates. The annual mean PM2.5 reading in
2014 was 8.4 ug/m3. At this mean PM2.5 level, a 1 ug/m3 increase in PM2.5, amounts to
a 11.9% increase in PM2.5 levels. According to the coefficient reported by Lepeule et al.
(2012) this suggests an elasticity of 0.0140.119
= 0.12. Our estimated elasticities for the total
mortality rates of people over 30 years old from the IV specifications range between 0.09
and 0.20 depending on the distance bandwidth considered. Thus, the estimate of the
all-cause, post-30 mortality elasticity with respect to PM2.5 implied by Lepeule et al.
(2012) (which is 0.12) falls into our range of estimated elasticities (which is 0.11-0.20),
though our elasticity estimates are slightly larger than 0.12 for the 50, 100 and 200 mile
bandwidth specifications.
Importantly, PM2.5 levels have fallen since the Lepeule et al. (2012) study was pub-
lished. For example, the annual average PM2.5 level was 12.47 ug/m3 in 2008. Thus, a
1 unit change in PM2.5 from the 2008 annual average level implies an elasticity of 0.18;
this lies squarely in our estimated range of elasticities. If we instead do the same calcula-
tion using the results from Krewski et al. (2009), we obtain an elasticity of 0.0060.119
= 0.05.
Summarizing, our IV estimate of the elasticity all-cause, post-30 mortality elasticity with
respect to PM2.5 is quite similar to the Lepeule et al. (2012) estimate but larger than
the Krewski et al. (2009) estimate.
28
7 Conclusion
The environmental impacts of burning coal to generate electricity are well-documented;
in this paper, we demonstrate that there are additional local environmental health costs
associated with the PM2.5 particulates generated both by wind blowing over (unburned)
coal piles, volatile gases emitted by coal piles, as well as the diesel burned when trains
deliver coal. We find that a 10% increase in coal stockpiles (number of deliveries) is
associated with a 0.02% (0.07%) increase in PM2.5 concentration on average; these PM2.5
concentration increases are most severe for local populations downwind from coal-fired
power plants. We next use a peer-reviewed concentration-response function to map
increases in PM2.5 to mortality risk and the Value of a Statistical Life (VSL) approach
to monetize these risks. Our results indicate that health costs are roughly $12.54 ($7.52)
per ton of coal stockpiled (delivered). Given that U.S. coal-fired power plants on average
pay roughly $48 per ton for coal, these local environmental costs are sizable. However,
they are not unreasonably large, given that our estimates of the per-KWh local damages
(between $0.004-$0.02 per KWh) associated with delivering and storing coal are roughly
an order of magnitude smaller than the local environmental damages of burning coal
found in the literature.
Finally, we directly estimate the effect of PM2.5 on mortality rates by type (i.e.:
cardiovascular deaths, respiratory deaths, nervous system deaths, etc.). We find that
ordinary least squares (OLS) regressions of PM2.5 exposure on mortality rates yield eco-
nomically small, sometimes negative, coefficient estimates; in contrast, if we instrument
for PM2.5 with coal stockpiles and number of coal deliveries, we see sizable positive (and
statistically significant) effects of PM2.5 exposure on mortality rates. Our estimates using
this new identification strategy are roughly in line with those found in the epidemiolog-
ical literature. The USEPA, among others, uses the mortality effects estimated in this
epidemiological literature in order to assess the environmental costs of PM2.5 exposure;
our IV estimates provide evidence that studies using this epidemiological literature are
not significantly over-estimating or under-estimating the effects of PM2.5 on mortality
rates.
Though there are economic costs associated with covering coal stockpiles or rail cars
containing coal, these costs are almost certainly small when compared to the environ-
mental costs incurred by populations local to coal-fired power plants. Moreover, a policy
29
requiring that coal stored or delivered is covered does not require significant co-ordination
across jurisdictions as these environmental costs are incurred local to the jurisdiction
where the activity is taking place (i.e.: they are incurred near coal-fired power plants).
This stands in direct contrast to proposed policy interventions for global pollutants such
as the CO2 emissions generated when coal is burned. Finally, the local environmental
health costs associated with coal piles applies more broadly than our examination of
U.S. coal-fired power plants; for example, terminals exporting U.S. coal, railroads used
to transport coal, and coal mines may also be associated with increased PM2.5 exposure.
We plan to explore in future research whether local populations near these coal terminals,
mines, etc. are similarly affected by increased PM2.5 exposure, especially those working
closely with coal (for example, coal miners).
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31
List of Tables
1 Overall Effect of Number of Deliveries on PM2.5 Concentration . . . . . . 12
2 Overall Effect of Coal Stockpiles on PM2.5 Concentration . . . . . . . . . 13
3 Effect of Number of Deliveries on PM2.5 Concentration: Upwind vs.
Downwind . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
4 Effect of Coal Stockpiles on PM2.5 Concentration: Upwind vs. Downwind 16
5 Effect of Number of Deliveries on PM2.5 Concentration: Interacted with
Precipitation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
6 Effect of Coal Stockpiles on PM2.5 Concentration: Interacted with Pre-
cipitation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
7 Effect of Number of Deliveries on PM2.5 Concentration: At the Coal Mine 19
8 Per-ton Social Cost Estimates from the Models that do control for Direction 22
9 Per-KWh Social Cost Estimates from the Models that do control for Di-
rection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
10 PM2.5 on Mortality Rate: 100 Mile Bandwidth for Ages 30+ . . . . . . . 27
List of Figures
1 Damages Per Ton (Stockpiles) across the U.S. . . . . . . . . . . . . . . . 23
2 Damages Per Ton (Stockpiles) versus Total Population . . . . . . . . . . 24
32
List of Appendix Tables
A.1 Summary Statistics: PM2.5 with 25 Mile Radius . . . . . . . . . . . . . . 35
A.2 Summary Statistics: PM2.5 with 50 Mile Radius . . . . . . . . . . . . . . 35
A.3 Summary Statistics: PM2.5 with 100 Mile Radius . . . . . . . . . . . . . 36
A.4 Summary Statistics: PM2.5 with 200 Mile Radius . . . . . . . . . . . . . 36
A.5 Effect of Number of Deliveries on PM2.5 Concentration: Interacted with
Wind Speed . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
A.6 Effect of Coal Stockpiles on PM2.5 Concentration: Interacted with Wind
Speed . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
A.7 Per-ton Social Cost Estimates from the Models that do not control for
Direction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
A.8 Per-KWh Social Cost Estimates from the Models that do not control for
Direction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
A.9 Mortality Rate Summary Statistics: 25 Mile/Over-30 . . . . . . . . . . . 41
A.10 Mortality Rate Summary Statistics: 50 Mile/Over-30 . . . . . . . . . . . 41
A.11 Mortality Rate Summary Statistics: 100 Mile/Over-30 . . . . . . . . . . 41
A.12 Mortality Rate Summary Statistics: 200 Mile/Over-30 . . . . . . . . . . 42
A.13 PM2.5 on Mortality Rate: OLS/25 mile/Ages 30+ . . . . . . . . . . . . . 42
A.14 PM2.5 on Mortality Rate: OLS/50 mile/Ages 30+ . . . . . . . . . . . . . 42
A.15 PM2.5 on Mortality Rate: OLS/200 mile/Ages 30+ . . . . . . . . . . . . 43
A.16 PM2.5 on Mortality Rate: IV/25 mile/Ages 30+ . . . . . . . . . . . . . . 43
A.17 PM2.5 on Mortality Rate: IV/50 mile/Ages 30+ . . . . . . . . . . . . . . 44
A.18 PM2.5 on Mortality Rate: IV/200 mile/Ages 30+ . . . . . . . . . . . . . 44
A.19 PM2.5 on Mortality Rate: OLS/25 mile/All Ages . . . . . . . . . . . . . 45
A.20 PM2.5 on Mortality Rate: OLS/50 mile/All Ages . . . . . . . . . . . . . 45
A.21 PM2.5 on Mortality Rate: OLS/100 mile/All Ages . . . . . . . . . . . . . 46
A.22 PM2.5 on Mortality Rate: OLS/200 mile/All Ages . . . . . . . . . . . . . 46
A.23 PM2.5 on Mortality Rate: IV/25 mile/All Ages . . . . . . . . . . . . . . 47
A.24 PM2.5 on Mortality Rate: IV/50 mile/All Ages . . . . . . . . . . . . . . 47
A.25 PM2.5 on Mortality Rate: IV/100 mile/All Ages . . . . . . . . . . . . . . 48
A.26 PM2.5 on Mortality Rate: IV/200 mile/All Ages . . . . . . . . . . . . . . 48
A.27 PM2.5 on Mortality Rate: OLS/25 mile/30+/Only CSp,t . . . . . . . . . 49
33
A.28 PM2.5 on Mortality Rate: OLS/50 mile/30+/Only CSp,t . . . . . . . . . 49
A.29 PM2.5 on Mortality Rate: OLS/100 mile/30+/Only CSp,t . . . . . . . . . 50
A.30 PM2.5 on Mortality Rate: OLS/200 mile/30+/Only CSp,t . . . . . . . . . 50
A.31 PM2.5 on Mortality Rate: IV/25 mile/30+/Only CSp,t . . . . . . . . . . 51
A.32 PM2.5 on Mortality Rate: IV/50 mile/30+/Only CSp,t . . . . . . . . . . 51
A.33 PM2.5 on Mortality Rate: IV/100 mile/30+/Only CSp,t . . . . . . . . . . 52
A.34 PM2.5 on Mortality Rate: IV/200 mile/30+/Only CSp,t . . . . . . . . . . 52
List of Appendix Figures
A.1 Wind Rose Plot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
34
A Additional Tables and Figures
A.1 Additional Tables and Figures: Coal Procurement and
PM2.5
Table A.1: Summary Statistics: PM2.5 with 25 Mile Radius
Variable Obs Mean Std. Dev.PM2.5 (in ug/m3) 47169 11.760 3.934
Stockpiles (in Tons) 47169 212784 244683Number of Deliveries 47169 2.899 3.628
Quantity Received (in Tons) 47169 106236 123240Distance (in Miles) 47169 8.678 6.953
Relative Angle 47169 87.915 55.813Dry Bulb Temp. 47169 53.334 17.491Dew Point Temp. 47169 42.612 16.773Wet Bulb Temp. 47169 48.036 15.874
Relative Humidity 47169 70.448 7.780Station Pressure 47169 29.256 1.012
Precipitation 47169 3.442 3.8385% Wind Speed 47169 0.328 0.84295% Wind Speed 47169 15.874 3.541
Number of Plants per AQ Monitor 47169 2.012 0.974
Table A.2: Summary Statistics: PM2.5 with 50 Mile Radius
Variable Obs Mean Std. Dev.PM2.5 (in ug/m3) 158357 12.185 4.082
Stockpiles (in Tons) 158357 243452 296505Number of Deliveries 158357 3.465 4.195
Quantity Received (in Tons) 158357 128574 159356Distance (in Miles) 158357 15.605 13.901
Relative Angle 158357 86.809 55.101Dry Bulb Temp. 158357 53.701 17.228Dew Point Temp. 158357 43.043 16.661Wet Bulb Temp. 158357 48.421 15.710
Relative Humidity 158357 70.569 7.273Station Pressure 158357 29.296 0.874
Precipitation 158357 3.394 4.1165% Wind Speed 158357 0.328 0.71695% Wind Speed 158357 15.135 3.427
Number of Plants per AQ Monitor 158357 3.326 1.878
35
Table A.3: Summary Statistics: PM2.5 with 100 Mile Radius
Variable Obs Mean Std. Dev.PM2.5 (in ug/m3) 442295 12.184 4.101
Stockpiles (in Tons) 442295 268708 328885Number of Deliveries 442295 3.805 4.609
Quantity Received (in Tons) 442295 141306 176159Distance (in Miles) 442295 30.520 29.232
Relative Angle 442295 86.497 54.632Dry Bulb Temp. 442295 53.821 17.021Dew Point Temp. 442295 43.237 16.588Wet Bulb Temp. 442295 48.580 15.590
Relative Humidity 442295 70.802 7.123Station Pressure 442295 29.268 0.871
Precipitation 442295 3.441 3.9005% Wind Speed 442295 0.325 0.55595% Wind Speed 442295 14.843 3.276
Number of Plants per AQ Monitor 442295 7.368 4.249
Table A.4: Summary Statistics: PM2.5 with 200 Mile Radius
Variable Obs Mean Std. Dev.PM2.5 (in ug/m3) 1322144 12.199 4.108
Stockpiles (in Tons) 1322144 277721 341068Number of Deliveries 1322144 3.988 4.810
Quantity Received (in Tons) 1322144 146150 181364Distance (in Miles) 1322144 64.590 60.842
Relative Angle 1322144 86.872 53.969Dry Bulb Temp. 1322144 53.917 16.928Dew Point Temp. 1322144 43.474 16.494Wet Bulb Temp. 1322144 48.733 15.526
Relative Humidity 1322144 71.187 6.793Station Pressure 1322144 29.248 0.869
Precipitation 1322144 3.508 3.5095% Wind Speed 1322144 0.325 0.43995% Wind Speed 1322144 14.760 3.107
Number of Plants per AQ Monitor 1322144 27.398 12.029
36
Table A.5: Effect of Number of Deliveries on PM2.5 Concentration: Interacted withWind Speed
Dependent Variable: PM2.5i,t
Distance Bandwidth (25 Miles) (50 Miles) (100 Miles) (200 Miles)NDp,t×
Constant 0.115*** 0.119*** 0.113*** 0.125***(0.0247) (0.0154) (0.0122) (0.00932)
5% Quantile of Wind Speed -0.00704*** -0.00832*** -0.00813*** -0.00880***(0.00169) (0.00108) (0.000896) (0.000684)
Observations 49,543 167,627 471,410 1,418,815R-squared 0.846 0.810 0.791 0.778
Dependent Variable: log(PM2.5i,t) + 1Dist. Bandwidth (25 Miles) (50 Miles) (100 Miles) (200 Miles)log(NDp,t + 1)×
Constant 0.0498*** 0.0489*** 0.0433*** 0.0454***(0.0118) (0.00683) (0.00545) (0.00430)
5% Quantile of Wind Speed -0.00242*** -0.00273*** -0.00249*** -0.00269***(0.000696) (0.000438) (0.000374) (0.000292)
Observations 49,543 167,627 471,410 1,418,815R-squared 0.850 0.818 0.800 0.787
Standard errors clustered by air quality monitor in parentheses
Facility Code/AQ Monitor Fixed Effects Included
AQ County/Month-of-Sample Fixed Effects Included
Sum of Deliveries from Other Plants Included
Meteorological and EPA Emissions Controls Included
Coal Quality, Quantity Received, and Thermal Generation Controls Included
*** p<0.01, ** p<0.05, * p<0.1
38
Table A.6: Effect of Coal Stockpiles on PM2.5 Concentration: Interacted with WindSpeed
Dependent Variable: PM2.5i,t
Distance Bandwidth (25 Miles) (50 Miles) (100 Miles) (200 Miles)CSp,t×
Constant -1.38e-07 -1.77e-07 -5.54e-07*** -5.31e-07***(4.64e-07) (2.75e-07) (1.62e-07) (1.17e-07)
5% Quantile of Wind Speed 7.28e-08*** 3.33e-08** 5.07e-08*** 4.66e-08***(2.73e-08) (1.69e-08) (1.11e-08) (8.43e-09)
Observations 48,521 162,524 456,773 1,365,465R-squared 0.847 0.811 0.792 0.779
Dependent Variable: log(PM2.5i,t) + 1Dist. Bandwidth (25 Miles) (50 Miles) (100 Miles) (200 Miles)log(CSp,t)×
Constant -0.0125* -0.00379 -0.0104*** -0.0125***(0.00736) (0.00538) (0.00374) (0.00264)
5% Quantile of Wind Speed 0.00115*** 0.000459 0.000847*** 0.000950***(0.000429) (0.000326) (0.000240) (0.000176)
Observations 48,521 162,524 456,773 1,365,465R-squared 0.847 0.811 0.792 0.779
Standard errors clustered by air quality monitor in parentheses
Facility Code/AQ Monitor Fixed Effects Included
AQ County/Month-of-Sample Fixed Effects Included
Sum of Stockpiles from Other Plants Included
Meteorological and EPA Emissions Controls Included
Coal Quality, Quantity Received, and Thermal Generation Controls Included
*** p<0.01, ** p<0.05, * p<0.1
39
A.2 Additional Results: Quantification of Local Environmental
Damages
Table A.7: Per-ton Social Cost Estimates from the Models that do not control forDirection
(25 Miles) (50 Miles) (100 Miles) (200 Miles)CSp,t 108.45 26.89 12.44 9.54
numdelivp,t 43.21 13.21 8.04 10.68
Table A.8: Per-KWh Social Cost Estimates from the Models that do not control forDirection
(25 Miles) (50 Miles) (100 Miles) (200 Miles)CSp,t 0.0550 0.0132 0.0059 0.0047
numdelivp,t 0.0231 0.0059 0.0038 0.0053
40
A.3 Additional Figures/Tables: Mortality Rate Regressions
Table A.9: Mortality Rate Summary Statistics: 25 Mile/Over-30
Variable Obs Mean Std. Dev.Mortality Rate: Cardio (Per 1000 People) 47853 0.411 0.104
Mortality Rate: Respiratory (Per 1000 People) 41273 0.105 0.030Mortality Rate: Nervous (Per 1000 People) 32703 0.054 0.018
Mortality Rate: Total (Per 1000 People) 48888 1.162 0.219Mortality Rate: External (Per 1000 People) 33902 0.062 0.020
Population 48888 1328125 1137439PM2.5 (in ug/m3) 48888 12.631 3.912
Stockpiles (in Tons) 48888 162615 206161Number of Deliveries 48888 2.990 3.130
Table A.10: Mortality Rate Summary Statistics: 50 Mile/Over-30
Variable Obs Mean Std. Dev.Mortality Rate: Cardio (Per 1000 People) 151791 0.423 0.106
Mortality Rate: Respiratory (Per 1000 People) 123825 0.107 0.032Mortality Rate: Nervous (Per 1000 People) 96508 0.055 0.019
Mortality Rate: Total (Per 1000 People) 157965 1.182 0.230Mortality Rate: External (Per 1000 People) 98881 0.061 0.019
Population 157965 1320936 1155788PM2.5 (in ug/m3) 157965 12.962 4.057
Stockpiles (in Tons) 157965 248347 311645Number of Deliveries 157965 3.553 3.872
Table A.11: Mortality Rate Summary Statistics: 100 Mile/Over-30
Variable Obs Mean Std. Dev.Mortality Rate: Cardio (Per 1000 People) 415770 0.424 0.109
Mortality Rate: Respiratory (Per 1000 People) 322940 0.108 0.034Mortality Rate: Nervous (Per 1000 People) 242980 0.055 0.020
Mortality Rate: Total (Per 1000 People) 438566 1.179 0.244Mortality Rate: External (Per 1000 People) 247246 0.061 0.020
Population 438566 1216684 1120314PM2.5 (in ug/m3) 438566 12.926 4.127
Stockpiles (in Tons) 438566 303442 359938Number of Deliveries 438566 3.886 4.406
41
Table A.12: Mortality Rate Summary Statistics: 200 Mile/Over-30
Variable Obs Mean Std. Dev.Mortality Rate: Cardio (Per 1000 People) 1226960 0.420 0.113
Mortality Rate: Respiratory (Per 1000 People) 920551 0.109 0.036Mortality Rate: Nervous (Per 1000 People) 675613 0.055 0.021
Mortality Rate: Total (Per 1000 People) 48888 1.162 0.219Mortality Rate: External (Per 1000 People) 682127 0.062 0.021
Population 1304602 1006374 997919PM2.5 (in ug/m3) 1304602 12.841 4.120
Stockpiles (in Tons) 1304602 287810 352789Number of Deliveries 1304602 3.955 4.639
Table A.13: PM2.5 on Mortality Rate: OLS/25 mile/Ages 30+
VARIABLES Cardio MR Resp. MR Nervous MR Total MR External MR
log(PM2.5 + 1) 0.00846** 0.0156*** -0.0177** 0.00267 -0.0235***(0.00355) (0.00551) (0.00882) (0.00273) (0.00703)
Observations 42,988 36,961 29,122 44,021 30,255R2 0.911 0.717 0.790 0.919 0.753
Standard errors clustered by air quality monitor in parentheses
County of AQ Monitor/Year-of-Sample Fixed Effects Included
Meteorological and EPA Emissions Controls Included
Coal Quality, Quantity Received, and Thermal Generation Controls Included
*** p<0.01, ** p<0.05, * p<0.1
Table A.14: PM2.5 on Mortality Rate: OLS/50 mile/Ages 30+
VARIABLES Cardio MR Resp. MR Nervous MR Total MR External MR
log(PM2.5 + 1) 0.0073*** 0.0142*** -0.0229*** -0.0011 -0.0163***(0.0025) (0.0045) (0.0062) (0.0020) (0.0050)
Observations 138,259 112,267 86,815 144,162 89,064R2 0.903 0.737 0.780 0.918 0.729
Standard errors clustered by air quality monitor in parentheses
County of AQ Monitor/Year-of-Sample Fixed Effects Included
Meteorological and EPA Emissions Controls Included
Coal Quality, Quantity Received, and Thermal Generation Controls Included
*** p<0.01, ** p<0.05, * p<0.1
42
Table A.15: PM2.5 on Mortality Rate: OLS/200 mile/Ages 30+
VARIABLES Cardio MR Resp. MR Nervous MR Total MR External MR
log(PM2.5 + 1) 0.0121*** 0.0344*** -0.0187*** 0.0042*** -0.0112***(0.0022) (0.0033) (0.0049) (0.0015) (0.0041)
Observations 1,123,404 839,531 612,645 1,196,452 619,998R2 0.898 0.767 0.830 0.921 0.736
Standard errors clustered by air quality monitor in parentheses
County of AQ Monitor/Year-of-Sample Fixed Effects Included
Meteorological and EPA Emissions Controls Included
Coal Quality, Quantity Received, and Thermal Generation Controls Included
*** p<0.01, ** p<0.05, * p<0.1
Table A.16: PM2.5 on Mortality Rate: IV/25 mile/Ages 30+
VARIABLES Cardio MR Resp. MR Nervous MR Total MR External MR
log(PM2.5 + 1) 0.134*** 0.469*** 0.239** 0.109*** -0.104(0.0386) (0.0942) (0.0969) (0.0296) (0.0856)
Observations 42,093 36,203 28,558 42,965 29,741R2 0.903 0.609 0.769 0.908 0.752
Standard errors clustered by air quality monitor in parentheses
County of AQ Monitor/Year-of-Sample Fixed Effects Included
Meteorological and EPA Emissions Controls Included
Coal Quality, Quantity Received, and Thermal Generation Controls Included
*** p<0.01, ** p<0.05, * p<0.1
43
Table A.17: PM2.5 on Mortality Rate: IV/50 mile/Ages 30+
VARIABLES Cardio MR Resp. MR Nervous MR Total MR External MR
log(PM2.5 + 1) 0.237*** 0.443*** 0.292*** 0.200*** -0.128(0.0420) (0.0874) (0.0745) (0.0357) (0.0776)
Observations 134,023 109,051 84,565 139,454 86,894R2 0.871 0.642 0.743 0.877 0.727
Standard errors clustered by air quality monitor in parentheses
County of AQ Monitor/Year-of-Sample Fixed Effects Included
Meteorological and EPA Emissions Controls Included
Coal Quality, Quantity Received, and Thermal Generation Controls Included
*** p<0.01, ** p<0.05, * p<0.1
Table A.18: PM2.5 on Mortality Rate: IV/200 mile/Ages 30+
VARIABLES Cardio MR Resp. MR Nervous MR Total MR External MR
log(PM2.5 + 1) 0.168*** 0.272*** 0.126*** 0.141*** -0.0236(0.0183) (0.0373) (0.0324) (0.0155) (0.0376)
Observations 1,074,948 803,198 586,163 1,144,422 593,549R2 0.885 0.745 0.823 0.906 0.739
Standard errors clustered by air quality monitor in parentheses
County of AQ Monitor/Year-of-Sample Fixed Effects Included
Meteorological and EPA Emissions Controls Included
Coal Quality, Quantity Received, and Thermal Generation Controls Included
*** p<0.01, ** p<0.05, * p<0.1
44
Table A.19: PM2.5 on Mortality Rate: OLS/25 mile/All Ages
VARIABLES Cardio MR Resp. MR Nervous MR Total MR External MR
log(PM2.5 + 1) 0.0088** 0.0138** -0.0163* 0.0023 -0.0257***(0.0036) (0.0055) (0.0086) (0.0028) (0.0065)
Observations 42,996 37,054 29,489 44,027 33,708R2 0.916 0.735 0.800 0.926 0.773
Standard errors clustered by air quality monitor in parentheses
County of AQ Monitor/Year-of-Sample Fixed Effects Included
Meteorological and EPA Emissions Controls Included
Coal Quality, Quantity Received, and Thermal Generation Controls Included
*** p<0.01, ** p<0.05, * p<0.1
Table A.20: PM2.5 on Mortality Rate: OLS/50 mile/All Ages
VARIABLES Cardio MR Resp. MR Nervous MR Total MR External MR
log(PM2.5 + 1) 0.0073*** 0.0136*** -0.0201*** -0.0012 -0.0213***(0.0025) (0.0045) (0.0060) (0.0020) (0.0046)
Observations 138,259 112,267 86,815 144,162 89,064R2 0.903 0.737 0.780 0.918 0.729
Standard errors clustered by air quality monitor in parentheses
County of AQ Monitor/Year-of-Sample Fixed Effects Included
Meteorological and EPA Emissions Controls Included
Coal Quality, Quantity Received, and Thermal Generation Controls Included
*** p<0.01, ** p<0.05, * p<0.1
45
Table A.21: PM2.5 on Mortality Rate: OLS/100 mile/All Ages
VARIABLES Cardio MR Resp. MR Nervous MR Total MR External MR
log(PM2.5 + 1) 0.0100*** 0.0219*** -0.0181*** 0.0003 -0.0208***(0.0024) (0.0036) (0.0057) (0.0019) (0.0043)
Observations 381,425 296,463 224,652 403,410 258,221R2 0.911 0.779 0.834 0.932 0.760
Standard errors clustered by air quality monitor in parentheses
County of AQ Monitor/Year-of-Sample Fixed Effects Included
Meteorological and EPA Emissions Controls Included
Coal Quality, Quantity Received, and Thermal Generation Controls Included
*** p<0.01, ** p<0.05, * p<0.1
Table A.22: PM2.5 on Mortality Rate: OLS/200 mile/All Ages
VARIABLES Cardio MR Resp. MR Nervous MR Total MR External MR
log(PM2.5 + 1) 0.0117*** 0.0340*** -0.0180*** 0.0040*** -0.0139***(0.0021) (0.0033) (0.0048) (0.0015) (0.0038)
Observations 1,123,923 842,528 620,522 1,197,302 716,960R2 0.910 0.788 0.839 0.933 0.760
Standard errors clustered by air quality monitor in parentheses
County of AQ Monitor/Year-of-Sample Fixed Effects Included
Meteorological and EPA Emissions Controls Included
Coal Quality, Quantity Received, and Thermal Generation Controls Included
*** p<0.01, ** p<0.05, * p<0.1
46
Table A.23: PM2.5 on Mortality Rate: IV/25 mile/All Ages
VARIABLES Cardio MR Resp. MR Nervous MR Total MR External MR
log(PM2.5 + 1) 0.134*** 0.461*** 0.196** 0.117*** -0.0739(0.0377) (0.0887) (0.0850) (0.0286) (0.0648)
Observations 42,101 36,293 28,921 42,965 33,079R2 0.908 0.636 0.787 0.914 0.773
Standard errors clustered by air quality monitor in parentheses
County of AQ Monitor/Year-of-Sample Fixed Effects Included
Meteorological and EPA Emissions Controls Included
Coal Quality, Quantity Received, and Thermal Generation Controls Included
*** p<0.01, ** p<0.05, * p<0.1
Table A.24: PM2.5 on Mortality Rate: IV/50 mile/All Ages
VARIABLES Cardio MR Resp. MR Nervous MR Total MR External MR
log(PM2.5 + 1) 0.235*** 0.417*** 0.256*** 0.197*** -0.120**(0.0409) (0.0826) (0.0672) (0.0346) (0.0518)
Observations 134,068 109,352 85,597 139,520 97,630R2 0.881 0.679 0.766 0.890 0.752
Standard errors clustered by air quality monitor in parentheses
County of AQ Monitor/Year-of-Sample Fixed Effects Included
Meteorological and EPA Emissions Controls Included
Coal Quality, Quantity Received, and Thermal Generation Controls Included
*** p<0.01, ** p<0.05, * p<0.1
47
Table A.25: PM2.5 on Mortality Rate: IV/100 mile/All Ages
VARIABLES Cardio MR Resp. MR Nervous MR Total MR External MR
log(PM2.5 + 1) 0.193*** 0.159** 0.218*** 0.170*** -0.0951**(0.0378) (0.0788) (0.0607) (0.0308) (0.0452)
Observations 366,480 284,522 215,326 387,165 247,878R2 0.893 0.771 0.816 0.908 0.758
Standard errors clustered by air quality monitor in parentheses
County of AQ Monitor/Year-of-Sample Fixed Effects Included
Meteorological and EPA Emissions Controls Included
Coal Quality, Quantity Received, and Thermal Generation Controls Included
*** p<0.01, ** p<0.05, * p<0.1
Table A.26: PM2.5 on Mortality Rate: IV/200 mile/All Ages
VARIABLES Cardio MR Resp. MR Nervous MR Total MR External MR
log(PM2.5 + 1) 0.165*** 0.260*** 0.109*** 0.134*** -0.0467(0.0182) (0.0367) (0.0332) (0.0149) (0.0293)
Observations 1,075,444 806,053 593,727 1,145,183 686,196R2 0.900 0.770 0.834 0.921 0.760
Standard errors clustered by air quality monitor in parentheses
County of AQ Monitor/Year-of-Sample Fixed Effects Included
Meteorological and EPA Emissions Controls Included
Coal Quality, Quantity Received, and Thermal Generation Controls Included
*** p<0.01, ** p<0.05, * p<0.1
48
Table A.27: PM2.5 on Mortality Rate: OLS/25 mile/30+/Only CSp,t
VARIABLES Cardio MR Resp. MR Nervous MR Total MR External MR
log(PM2.5 + 1) 0.0086** 0.0156*** -0.0179** 0.0027 -0.0234***(0.0036) (0.0055) (0.0088) (0.0027) (0.0070)
Observations 42,988 36,961 29,122 44,021 30,255R2 0.911 0.717 0.790 0.919 0.753
Standard errors clustered by air quality monitor in parentheses
County of AQ Monitor/Year-of-Sample Fixed Effects Included
Meteorological and EPA Emissions Controls Included
Coal Quality, Quantity Received, and Thermal Generation Controls Included
*** p<0.01, ** p<0.05, * p<0.1
Table A.28: PM2.5 on Mortality Rate: OLS/50 mile/30+/Only CSp,t
VARIABLES Cardio MR Resp. MR Nervous MR Total MR External MR
log(PM2.5 + 1) 0.0073*** 0.0142*** -0.0229*** -0.0011 -0.0163***(0.0025) (0.0045) (0.0062) (0.0020) (0.0050)
Observations 138,259 112,267 86,815 144,162 89,064R2 0.903 0.737 0.780 0.918 0.729
Standard errors clustered by air quality monitor in parentheses
County of AQ Monitor/Year-of-Sample Fixed Effects Included
Meteorological and EPA Emissions Controls Included
Coal Quality, Quantity Received, and Thermal Generation Controls Included
*** p<0.01, ** p<0.05, * p<0.1
49
Table A.29: PM2.5 on Mortality Rate: OLS/100 mile/30+/Only CSp,t
VARIABLES Cardio MR Resp. MR Nervous MR Total MR External MR
log(PM2.5 + 1) 0.0101*** 0.0226*** -0.0202*** 0.0005 -0.0170***(0.0024) (0.0037) (0.0059) (0.0019) (0.0047)
Observations 381,277 295,534 221,833 403,183 226,132R2 0.901 0.758 0.823 0.922 0.734
Standard errors clustered by air quality monitor in parentheses
County of AQ Monitor/Year-of-Sample Fixed Effects Included
Meteorological and EPA Emissions Controls Included
Coal Quality, Quantity Received, and Thermal Generation Controls Included
*** p<0.01, ** p<0.05, * p<0.1
Table A.30: PM2.5 on Mortality Rate: OLS/200 mile/30+/Only CSp,t
VARIABLES Cardio MR Resp. MR Nervous MR Total MR External MR
log(PM2.5 + 1) 0.0121*** 0.0344*** -0.0187*** 0.0042*** -0.0112***(0.0022) (0.0033) (0.0049) (0.0015) (0.0041)
Observations 1,123,404 839,531 612,645 1,196,452 619,998R2 0.898 0.767 0.830 0.921 0.736
Standard errors clustered by air quality monitor in parentheses
County of AQ Monitor/Year-of-Sample Fixed Effects Included
Meteorological and EPA Emissions Controls Included
Coal Quality, Quantity Received, and Thermal Generation Controls Included
*** p<0.01, ** p<0.05, * p<0.1
50
Table A.31: PM2.5 on Mortality Rate: IV/25 mile/30+/Only CSp,t
VARIABLES Cardio MR Resp. MR Nervous MR Total MR External MR
log(PM2.5 + 1) 0.230*** 0.473*** 0.0721 0.182*** 0.00875(0.0465) (0.0984) (0.0875) (0.0359) (0.0811)
Observations 42,093 36,203 28,558 42,965 29,741R2 0.885 0.607 0.788 0.886 0.754
Standard errors clustered by air quality monitor in parentheses
County of AQ Monitor/Year-of-Sample Fixed Effects Included
Meteorological and EPA Emissions Controls Included
Coal Quality, Quantity Received, and Thermal Generation Controls Included
*** p<0.01, ** p<0.05, * p<0.1
Table A.32: PM2.5 on Mortality Rate: IV/50 mile/30+/Only CSp,t
VARIABLES Cardio MR Resp. MR Nervous MR Total MR External MR
log(PM2.5 + 1) 0.251*** 0.424*** 0.282*** 0.210*** -0.114(0.0454) (0.0852) (0.0732) (0.0380) (0.0758)
Observations 134,023 109,051 84,565 139,454 86,894R2 0.867 0.650 0.745 0.873 0.728
Standard errors clustered by air quality monitor in parentheses
County of AQ Monitor/Year-of-Sample Fixed Effects Included
Meteorological and EPA Emissions Controls Included
Coal Quality, Quantity Received, and Thermal Generation Controls Included
*** p<0.01, ** p<0.05, * p<0.1
51
Table A.33: PM2.5 on Mortality Rate: IV/100 mile/30+/Only CSp,t
VARIABLES Cardio MR Resp. MR Nervous MR Total MR External MR
log(PM2.5 + 1) 0.216*** 0.228*** 0.277*** 0.190*** -0.0757(0.0404) (0.0832) (0.0716) (0.0335) (0.0741)
Observations 366,338 283,632 212,573 386,973 217,146R2 0.876 0.739 0.795 0.888 0.735
Standard errors clustered by air quality monitor in parentheses
County of AQ Monitor/Year-of-Sample Fixed Effects Included
Meteorological and EPA Emissions Controls Included
Coal Quality, Quantity Received, and Thermal Generation Controls Included
*** p<0.01, ** p<0.05, * p<0.1
Table A.34: PM2.5 on Mortality Rate: IV/200 mile/30+/Only CSp,t
VARIABLES Cardio MR Resp. MR Nervous MR Total MR External MR
log(PM2.5 + 1) 0.170*** 0.275*** 0.112*** 0.142*** -0.0200(0.0187) (0.0377) (0.0324) (0.0158) (0.0392)
Observations 1,074,948 803,198 586,163 1,144,422 593,549R2 0.885 0.744 0.824 0.906 0.739
Standard errors clustered by air quality monitor in parentheses
County of AQ Monitor/Year-of-Sample Fixed Effects Included
Meteorological and EPA Emissions Controls Included
Coal Quality, Quantity Received, and Thermal Generation Controls Included
*** p<0.01, ** p<0.05, * p<0.1
52
B Data Appendix
B.1 Data Sources: Coal Procurement
For coal inventories and consumption, we use Forms EIA-906 (for 2002-2007) and EIA-
923 (for 2008-2012). Data on coal stocks for 2002-2012 are considered proprietary; we
obtained a research contract with the Energy Information Administration (EIA) in order
to use these data for our analysis.
For coal purchases and prices, we use Forms EIA-423 (2002-2007) and EIA-923 (2008-
2012). These data are at the “order level”; an “order” as defined by these forms is based
on the following criteria:
“Data on coal received under each purchase order or contract with a supplier should be
reported separately. Aggregation of coal receipt data into a single line item is allowed if
the coal is received under the same purchase order or contract and the purchase type,
fuel, mine type, State of origin, county of origin, and supplier are identical for each
delivery.”
In some specifications, we consider the “number of deliveries” to a plant in each month,
as measured by the number of orders each plant reports on the form for each month-of-
sample. This measure for the number of deliveries may be under-estimated to the extent
that deliveries under the same purchase order are aggregated as described in the above
quotation.
The variables included in this purchase dataset include month of purchase, quantity
purchased, delivered price, heat content, sulfur content, ash content, origin of coal, and
whether the delivery came from a long-term contract or the spot market.
53
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