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Consequential life cycle air emissions externalities for plug-in electric vehicles in the PJM interconnection

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We perform a consequential life cycle analysis of plug-in electric vehicles (PEVs), hybrid electric vehicles (HEVs), and conventional gasoline vehicles in the PJM interconnection using a detailed, normative optimization model of the PJM electricity grid that captures the change in power plant operations and related emissions due to vehicle charging. We estimate and monetize the resulting human health and environmental damages from life cycle air emissions for each vehicle technology. We model PJM using the most recent data available (2010) as well as projections of the PJM grid in 2018 and a hypothetical scenario with increased wind penetration. We assess a range of sensitivity cases to verify the robustness of our results. We find that PEVs have higher life cycle air emissions damages than gasoline HEVs in the recent grid scenario, which has a high percentage of coal generation on the margin. In particular, battery electric vehicles with large battery capacity can produce two to three times as much air emissions damage as gasoline HEVs, depending on charge timing. In our future 2018 grid scenarios that account for predicted coal plant retirements, PEVs would produce air emissions damages comparable to or slightly lower than HEVs.
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Environ. Res. Lett. 11 (2016)024009 doi:10.1088/1748-9326/11/2/024009
LETTER
Consequential life cycle air emissions externalities for plug-in electric
vehicles in the PJM interconnection
Allison Weis
1
, Paulina Jaramillo
1
and Jeremy Michalek
1,2
1
Engineering and Public Policy, Carnegie Mellon University, 5000 Forbes Ave, PA 15213, USA
2
Mechanical Engineering, Carnegie Mellon University, 5000 Forbes Ave, PA 15213, USA
E-mail: paulina@cmu.edu
Keywords: electric vehicles, LCA, social costs
Supplementary material for this article is available online
Abstract
We perform a consequential life cycle analysis of plug-in electric vehicles (PEVs), hybrid electric
vehicles (HEVs), and conventional gasoline vehicles in the PJM interconnection using a detailed,
normative optimization model of the PJM electricity grid that captures the change in power plant
operations and related emissions due to vehicle charging. We estimate and monetize the resulting
human health and environmental damages from life cycle air emissions for each vehicle technology.
We model PJM using the most recent data available (2010)as well as projections of the PJM grid in
2018 and a hypothetical scenario with increased wind penetration. We assess a range of sensitivity
cases to verify the robustness of our results. We nd that PEVs have higher life cycle air emissions
damages than gasoline HEVs in the recent grid scenario, which has a high percentage of coal
generation on the margin. In particular, battery electric vehicles with large battery capacity can
produce two to three times as much air emissions damage as gasoline HEVs, depending on charge
timing. In our future 2018 grid scenarios that account for predicted coal plant retirements, PEVs
would produce air emissions damages comparable to or slightly lower than HEVs.
1. Introduction
Plug-in electric vehicle (PEV)technologies, including
plug-in hybrid electric vehicles (PHEVs)and battery
electric vehicles (BEVs), have the potential to reduce
environmental impacts from the transportation sys-
tem by reducing or eliminating tailpipe emissions.
However, the emissions associated with producing
PEVs and generating the electricity to charge PEVs
affect whether these vehicles have higher or lower life
cycle environmental and health impacts compared to
efcient gasoline vehicles (Michalek et al [1], Tessum
et al [2]). Evaluating the sustainability of different
transportation choices thus requires both a considera-
tion of the full life cycle of the technology as well as an
analysis of the impacts of the technology choice [3].
While there has been signicant research to under-
stand the life cycle environmental impacts of PEVs,
most of this prior work has followed an attributional
life cycle assessment (LCA)approach to answer the
question what air emissions are PEV charging respon-
sible for?Such approaches have assumed that PEV
charging produces emissions proportional to the
average emissions rate for electricity generation in the
political boundary (country, state, etc)or grid region
(NERC region, eGRID sub-region, interconnect, bal-
ancing area, etc)where the vehicle is charged. Alter-
natively, some studies have used hypothetical
emissions factors to evaluate a broader set of scenarios
of the effects of PEV charging [1,2,4,5]. The results
from previous work thus vary depending on the
researchers value judgment related to the emissions
that a PEV should be responsible for when charging in
a particular location.
In contrast, a consequential approach answers
what are the air emissions implications of PEV
adoption in a regionby assessing how grid operations
change in response to new charging demand. These
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consequential effects of PEV charging on grid emis-
sions have been examined using empirical top-down
methods and normative bottom-upmethods [6].
Top-down empirical models of the power system, like
those developed by Siler-Evans et al [7]and Graff Zivin
et al [8], use regressions on historical data to estimate
marginal emissions rates. Such analysis is grounded in
the actual operations of the power system. However,
the approach is limited to historical scenarios and is
only appropriate for the analysis of small changes in
generation or load. It also suffers from error in coun-
terfactual analysis because correlations in past data do
not necessarily imply causality. The gap between cor-
relation and causality is particularly evident for the
dispatch of hydroelectric plants, which may change
generation timing in response to new load but typi-
cally will not change total energy generated in response
to new load. Alternatively, bottom-up normative
models of the power system, such as those used by
Sioshansi et al [9], Peterson et al [10], Choi et al [11]
and Weis et al [12], use optimization models to esti-
mate how a power system should operate to minimize
costs subject to a variety of constraints. These models
can assess changes of grid operation in response to
new PEV load. Such analysis can model future power
plant scenarios and large load changes. While this
approach has limited scalability for modeling large
systems, and it is typically not possible to model all
possible considerations that affect grid operations in
practice, there is growing interest in using bottom-up
models of the power system in consequential LCA.
Table 1summarizes prior consequential LCA stu-
dies of PEV air emissions. Tamayao et al [6]apply two
different top-down regression models [4,5]to assess
consequential greenhouse gas (GHG)emissions in the
US, while Ma et al [13]perform their own regression
on the operation of the UK grid. Sandy [14]and Onat
et al [15]average results from ORNLs analysis of mar-
ginal emissions for electric vehicles using a dispatch
model and different vehicle charging patterns [16].
Finally, Choi et al [11]construct a bottom-up capacity
expansion and unit commitment model to assess con-
sequential GHG emissions. In this paper, we adopt the
bottom-up normative approach to model the power
system under different scenarios in the PJM system in
order to inform a consequential LCA of PEVs. Unlike
previous work, our analysis includes the valuation of
social damages associated with emissions of criteria air
pollutants as well as GHG emissions. PJM (an inde-
pendent system operator in Pennsylvania, New Jersey,
Maryland, Ohio, and several other states)is an inter-
esting power system to examine, as it is the largest
independent system operator in the United States by
population and has a large installed coal capacity. The
supplemental information (SI)includes an expanded
comparison of this study with previous life cycle stu-
dies of PEVs in the United States.
2. Methods
2.1. Life cycle boundary
We estimate the life cycle emissions of CO
2
, CO, SO
2
,
PM
2.5
,NO
x
, and VOCs for conventional, hybrid, and
PEVs, including the emissions from vehicle manufac-
turing, fuel production, and use. The analysis focuses
on the region covered by the PJM interconnection,
which has the largest electricity market in the US and
serves 13 states in the Mid-Atlantic region [17]. For
CVs and HEVs, the emissions from fuel production
and use include upstream emissions from petroleum
drilling and rening as well as the tailpipe emissions
during vehicle operations. For BEVs, fuel production
emissions include power plant emissions and
upstream emissions from coal and natural gas produc-
tion, but these vehicles do not have tailpipe emissions.
Finally, PHEVs have emissions associated with both
gasoline and electricity. Figure 1shows the scope of the
life cycle inventory. We do not consider end of life
emissions. Further, we assume a total vehicle life of
160 000 miles for all vehicle types, following Michalek
et al [1].
2.2. Vehicle and power grid scenarios
For this analysis we rely on a unit commitment and
economic dispatch (UCED)model previously devel-
oped and used to evaluate the costs and benets of
controlled charging of electric vehicles in the PJM
system [12,18]. This model uses a mixed integer linear
optimization program to minimize the costs of operat-
ing the power system to meet load given constraints,
including power plant operating constraints (mini-
mum and maximum load, ramping rates, and mini-
mum up and down time), as well as transmission and
operating reserves constraints. Furthermore, the
model incorporates vehicle charging by including
charging requirements and battery constraints.
In order to account for changes in the composition
of the PJM power plant eet that may result from
environmental regulations, renewable energy man-
dates, and changes in energy prices, we develop three
scenarios of the power system. For the rst scenario,
we use the Environmental Protection Agency (EPA)s
NEEDS database in order to represent the recent PJM
system [19]. Our second scenario, meant to represent a
future grid, includes retirement of power plants pre-
dicted by the EPA [20]and a 3% wind penetration, as
described in Weis et al [12]. Finally, in the third sce-
nario we include the power plant retirements EPA pre-
dicts, and we add 20% wind. The additional wind
generation for the future grid comes from NRELs
Eastern Wind Integration and Transmission Study
dataset [21]. For each scenario, we add wind sites in
order of capacity factor to reach the required wind
penetration level (3% for the second scenario and 20%
for the third scenario).
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Environ. Res. Lett. 11 (2016)024009
Table 1. Summary of literature estimating consequential life cycle air emissions from PEVs.
Study Location Air emissions Model type Model detail
Tamayao et al [6]US GHG Empirical Applies regression results from [4,5]
Choi et al [11]Eastern Interconnect GHG Normative Capacity expansion and unit commitment
Ma et al [13]CA, UK GHG Empirical Regression on plant operation to nd average marginal emission factor
Sandy [14]US GHG Normative Averages resulting emissions from power plant dispatch in [16]
Onat et al [15]US GHG Normative Averages resulting emissions from power plant dispatch in [16]
This study US PJM GHG, SO
2
,NO
x
,PM
2.5
, CO, VOC Normative Optimal dispatch
3
Environ. Res. Lett. 11 (2016)024009
We also account for differences in vehicle technol-
ogy using Argonne National Laboratorys 2013
GREET 1 and 2 models [22,23]. The base case PEV for
the recent grid scenario is a 2010 PHEV-35, sized to
represent the Chevy Volt. We also include a smaller
battery size based on the Toyota Plug-in Prius and a
larger battery size based on the Tesla Model S. The ef-
ciency of the long-range BEV is that of the 2012 Tesla
Model S, as measured by the EPA [24](we examine
more efcient BEVs in sensitivity analysis). For the
future grid scenarios we use GREETs 2015 PHEV-35
vehicle specications. Table 2summarizes the differ-
ences across scenarios.
2.3. Life cycle inventory
We determine the life cycle emissions for each stage
shown in gure 1for each vehicle type based on a
160 000 mile vehicle life. Table 3provides a summary
of the data used for each stage of the life cycle.
2.3.1. Emissions from power plant operations
In order to estimate the change in emissions from the
power system resulting from vehicle charging, we solve
the UCED model both with and without electric
vehicles and compare the difference in the operating
schedules of the power plants. We add the charging
load to the existing non-vehicle electricity load by
assuming electric vehicles make up 10% of the vehicle
eet in PJM. Furthermore, we assume that electric
vehicles are distributed throughout the PJM system
proportionally to population (we test alternative
assumptions in sensitivity analysis).
Using data from the National Household Travel
Survey (NHTS)[25]we estimate the vehicle charging
load, following Weis et al [12,26]. The NHTS data
provide the distance driven during each trip through-
out the day surveyed as well as the time of each trip for
approximately 100 000 passenger cars across the US.
We use the distance driven in a day, the vehicle ef-
ciency, and the electric range of the vehicle to calculate
both the distance driven in charge-depleting versus
charge-sustaining mode and the total charging load
per day. We assume that all PHEVs drive as far as pos-
sible in charge-depleting mode before switching to
charge-sustaining mode. We also assume that vehicle
charging occurs at home after the last trip of the day
and that each vehicle is fully charged by the rst trip of
the next day. For each eet/vehicle type scenario, we
calculate the hourly charging load for a scenario where
charging begins at full power following the last trip of
the day (uncontrolled charging)and a scenario where
the utility dynamically controls the rate at which each
vehicle is charged in order to minimize generation cost
(controlled charging). Controlled charging of electric
vehicles may provide operational benets to the grid,
but it also changes the emissions associated with elec-
tric vehicle charging [12], so it represents an additional
scenario for analysis. For the controlled charging sce-
narios, we use 20 representative vehicle proles to
model the load from the electric vehicles as described
in Weis et al [12]and assume that the vehicle is con-
strained to be fully charged by the rst trip of the fol-
lowing day while the charging rate is limited to Level 2
charging (7.2 kW).
2.3.2. Tailpipe emissions
The distance driven using the gasoline engine (all times
for the CV and HEV and during charge-sustaining
mode for PHEVs)and the vehicles emission rate
determine its tailpipe emissions. We assume that each
vehicle travels 160 000 miles over its lifetime. Table 4
shows tailpipe emission rates for GHG, CO, SO
2
,
PM
2.5
,NO
x
, and VOCs from the GREET 1 model [22].
While the two PHEVs burn some gasoline in charge-
depleting mode, the BEV-265 operates without any
tailpipe emissions.
2.3.3. Upstream emissions
Our life cycle emissions include the emissions from
the production, processing, and delivery of fossil fuels
(either to fuel the vehicles or to generate electricity),as
well as the emissions from vehicle and battery
Figure 1. Life cycle inventory for plug-in electric, hybrid electric, and conventional vehicles.
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Environ. Res. Lett. 11 (2016)024009
Table 2. Summary of scenarios.
Scenario Power system data Conventional vehicle Hybrid vehicle Plug-in electric vehicles
2010 GREET PHEV-10 (Plug-in Prius-sized)
Recent (2010)2010 PJM 2010 GREET ICEV 2010 GREET HEV 2010 GREET PHEV-35 (Volt-sized)
2012 BEV-265 (Tesla-sized)
Future (~2018)EPA forecasted 2018 PJM with 3% wind penetration 2015 GREET ICEV 2015 GREET HEV 2015 GREET PHEV-35 (Volt-sized)
Hypothetical high wind future EPA forecasted 2018 PJM with 20% wind penetration 2015 GREET ICEV 2015 GREET HEV 2015 GREET PHEV-35 (Volt-sized)
5
Environ. Res. Lett. 11 (2016)024009
manufacturing. Argonne National Laboratorys
GREET 1 model [22]provides the emission rates for
oil drilling and rening, which are the basis of the
upstream emissions estimate for gasoline. Upstream
emissions for vehicle and battery manufacturing come
from the GREET 2 model [23].
To account for the emissions from the production,
processing, and delivery of coal and natural gas for
power plants used to charge the electric vehicles, we
rely on the GREET 1 model, which provides these
emissions on a per MWh basis (as shown in the sup-
porting information). We then apply these emissions
factors to the amount of electricity generated from
coal and natural gas plants in the UCED model results.
We include only the upstream emissions from coal-
and natural gas-based electricity as these fuels account
for the majority of generation response to additional
vehicle charging load [12]. In the high wind scenario,
wind power also contributes to vehicle charging, but
wind requires no fuel and thus has no upstream emis-
sions (our model does not include the emissions from
building physical infrastructure).
2.4. Life cycle damages
Air emissions cause environmental degradation and
affect human health. For CO
2
, we use estimates of the
social cost of carbon that the US EPA uses for
regulatory impact assessment [27]. EPA reports this
social cost of carbon for three different discount rates:
2.5%, 3%, and 5%. We use the 3% discount rate
average value for 2010 as our base value for all
scenarios.
In order to estimate the damages from SO
2
,PM
2.5
,
NO
x
, and VOCs we use the values from the AP2 model
[28], which estimates the marginal health and envir-
onmental damages for emissions of each criteria air
pollutant in each county in the United States. This
model has many uncertain parameters, including the
value of statistical life, which is used to monetize mor-
bidity and mortality from air pollution. The AP2
model includes results from a Monte Carlo analysis
of the damages in each county for the baseline
year (2005). As a base case, we assume that these
2005 marginal damages per unit of emission in each
location apply also to the recent grid and future grid
scenarios. We use the distribution of the results from
the Monte Carlo analysis to characterize the uncer-
tainty within the AP2 model. Damages from vehicle
charging are based on the change in the annual genera-
tion and emissions from each power plant in the
UCED model that results from increased charging
load. Since the AP2 values are specic to individual
counties where emissions take place, we also need to
incorporate the location of the vehicle tailpipe emis-
sions. To do so, we allocate vehicles to counties
within PJM proportionally to population. We further
assume that each vehicle is driven within its respective
county.
Table 3. Data for the life cycle emissions for each stage.
Stage Emission rate Source Other assumptions Source
Power plant operation Short Ton/year Unit commitment
model
Driving patterns and
vehicle efciency
NHTS GREET 1, fuelec-
onomy.gov
Tailpipe lb/mile GREET 1 Driving patterns NHTS
Vehicle manufacturing Short Ton/lifetime GREET 2
Battery manufacturing Short Ton/lifetime GREET 2
Oil production Short Ton/mile GREET 1
Gasoline rening Short Ton/mile GREET 1
Coal production Short Ton/MWh GREET 1 MWh produced using coal Unit commitment model
Natural gas production Short Ton/MWh GREET 1 MWhproduced using nat-
ural gas
Unit commitment model
Table 4. Tailpipe emissions in pounds per 1000 miles from GREET 1. The recent grid scenario relies on the
data for the 2010 vehicles, while the future scenarios are based on the characteristics of the 2015 vehicles. CS:
charge-sustaining mode. CD: charge-depleting mode.
Vehicle CO
2
-eq VOC CO NO
x
PM
2.5
SO
2
2010 CV 772 0.375 6.393 0.265 0.026 0.011
2010 HEV 551 0.265 6.393 0.220 0.026 0.008
2010 PHEV-10 CS (Plug-in Prius)529 0.265 6.393 0.220 0.026 0.008
2010 PHEV-10 CD (Plug-in Prius)265 0.088 2.205 0.079 0.006 0.004
2010 PHEV-35 CS (Volt)683 0.265 6.393 0.220 0.026 0.010
2010 PHEV-35 CD (Volt)44.1 0.022 0.375 0.013 0.001 0.001
2010 BEV-265 (Tesla Model S)0.00 0.00 0.00 0.00 0.00 0.00
2015 CV 705 0.375 6.393 0.265 0.026 0.011
2015 HEV 507 0.265 6.393 0.220 0.026 0.007
2015 PHEV CS (Volt)573 0.265 6.393 0.220 0.026 0.009
2015 PHEV CD (Volt)41.9 0.016 0.397 0.014 0.001 0.001
6
Environ. Res. Lett. 11 (2016)024009
We allocate the emissions from vehicle and battery
manufacturing to US counties identied by the US
census as having automobile and parts manufacturing
activity, weighted by the number of automotive manu-
facturing workers, following Michalek et al [1].We
also allocate coal, oil, and natural gas upstream emis-
sions to US counties where mines, oil and gas elds,
and reneries are located, weighted by the production
in each county. Figure 2shows the resulting cumula-
tive probability distribution of damages from manu-
facturing, coal, oil and gas production, and oil
rening. The damage calculations assume that all
emissions and damages occur in the United States,
though in practice some of these processes occur
outside of US borders. Marginal emissions in other
countries could incur higher or lower damages than in
the US, depending on the existing pollutant con-
centrations and populations in those areas. At this
time, however, we are unable to include these dama-
ges. We allocate the damages for all life cycle stages
except for vehicle and battery manufacturing across
the years of the vehicle lifetime, ignoring changes in
the electricity grid over the vehicle life. We then used a
3% discount rate to nd the present value of these
damages, consistent with the calculation of CO
2
damages.
3. Results
3.1. Life cycle emissions
Figure 3shows the breakdown of estimated emissions
by life cycle stage for each scenario. In the recent grid
scenario, the PEVs have higher GHG, SO
2
,NO
x
, and
PM
2.5
emissions and lower CO and VOC emissions
than the HEV. Compared to the CV, PEVs have higher
SO
2
emissions and lower CO and VOC emissions,
while GHG, NO
x
and PM
2.5
emissions may be higher
or lower, depending on the PEV characteristics and
the charging scenario. Controlled charging of PEVs
increases emissions of GHGs, SO
2
,NO
x
, and PM
2.5
while reducing emissions of VOCs compared to
uncontrolled charging, due in part to the increased use
of coal-red power plants available at night. Use of
coal generation increases with controlled charging in
all scenarios because these plants have lower marginal
cost than gas plants and have more excess capacity
available in off-peak hours than during uncontrolled
charging ours (the hours immediately following the
vehiclesrst home arrival at the end of the day). For a
detailed description of the analysis and assumptions
regarding the response of power plant dispatch to
additional electric vehicle charging load see [12].
In the future grid scenarios, compared to the HEV,
the PHEV-35 has higher SO
2
emissions and lower
PM
2.5
, VOC, and CO emissions, while GHG and NO
x
emissions may be higher or lower, depending on the
charging scenario and wind power scenario. Com-
pared to the CV, the PHEV-35 has higher SO
2
and
lower GHG, NO
x
,PM
2.5
, VOC, and CO emissions.
Controlled charging of the PHEV-35 increases SO
2
and NO
x
while decreasing VOC emissions, but the
effect on GHG and PM
2.5
emissions depends on the
presence of wind, and the effect on CO emissions is
negligible.
Figure 2. Cumulative probability distribution of damages per ton of pollutant released for assumed locations of upstream production
emissions by pollutant type (using AP2 data for 2005).
7
Environ. Res. Lett. 11 (2016)024009
3.2. Life cycle damages
3.2.1. Expected values
PEVs have higher expected life cycle damages than
hybrid vehicles in the recent PJM scenario in all cases
examined, as shown in gure 4. Their expected
damages are also higher than those of conventional
vehicles, except for the case of the PHEV-10 with
uncontrolled charging. Long-range BEVs cause two to
three times as much air emissions damage as HEVs.
The electricity generation damages come largely as a
result of the SO
2
emissions from the coal plants used to
charge the vehicles in off-peak hours. Controlled
charging increases life cycle damages relative to
uncontrolled charging because of the increases in
emissions associated with higher levels of coal genera-
tion. Uncertainty is not presented here because
common sources of uncertainty create correlated
uncertainty across scenarios, so error bars could be
misleading in comparing across cases. Instead,
section 3.2.2. includes an analysis of uncertainty and
robustness in these results.
In the future scenarios, shown in gure 5, the
PHEV-35 is able to reduce life cycle damages by a few
hundred dollars over its lifetime compared to the CV
and the HEV. Again, PEVs tend to produce larger
damages under controlled charging than under
uncontrolled charging, but in the future scenarios the
PHEV-35 provides benets compared to the CV and
the HEV regardless of the charging scheme. The high-
wind future scenarios do not necessarily imply lower
consequential damages than the low-wind future sce-
narios for PEVs because added wind displaces fossil
fuel plants, which can increase the availability of coal
on the margin when PEVs charge.
Figure 3. Life cycle emissions by pollutant and life cycle stage for each vehicle type in the recent (a)and future (b)PJM grid. UC stands
for uncontrolled charging and CC stands for controlled charging.
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Environ. Res. Lett. 11 (2016)024009
3.2.2. Uncertainty and robustness
Qualitatively, our key ndings are that (1)the PEVs
cause more damage than the CV and HEV in the recent
PJM grid, (2)the PHEV-35 causes less damage than
the CV in the future PJM grid scenarios, but these
damages are not much lower than those for the HEV,
and (3)controlled charging tends to increase damages
compared to uncontrolled charging, though the PEVs
provide benets compared to the CV and HEV in the
future grid scenarios regardless of charging scheme.
To characterize uncertainty and robustness of these
ndings, we use the Monte Carlo analysis results from
the AP2 model and assess the probability that each
vehicle technology has higher life cycle air emissions
damages than the HEV. Table 5reveals that the
conclusions above are robust, especially in the recent
grid. The uncertainty in the AP2 models does not
signicantly affect the comparison across vehicle types
because most of this uncertainty is a result of
uncertainty in the value of a statistical life, which
affects damage estimates across all cases. As a result,
this uncertainty typically changes only the magnitude
of the difference between hybrids and other vehicles,
not the sign.
The results in table 5incorporate only the
uncertainty quantied in the AP2 model due to uncer-
tainty in input parameters, such as the value of a statis-
tical life. We examine the effect of some of the
other important input parameters and assumptions
through sensitivity cases summarized in table 6
Figure 4. Expected value of life cycle air emission damages for each vehicle type in the recent PJM grid. UC stands for uncontrolled
charging and CC stands for controlled charging for the electric vehicles.
Figure 5. Expected value of life cycle damages in the future PJM grid. The high wind scenario has 20% of demand met by wind. UC
stands for uncontrolled charging and CC stands for controlled charging.
9
Environ. Res. Lett. 11 (2016)024009
and detailed in the supporting information. These
include alternative assumptions for (1)emissions
damage models, (2)BEV efciency, (3)power grid
characteristics that inuence dispatch decisions, (4)
PEV adoption patterns, and (5)policy effects on con-
sequential life cycle implications. Our key ndings
are generally robust, though future fuel prices could
affect the consequential emissions benets of PEVs
versus HEVs; binding SO
2
caps would likely make
PEVs more competitive; and assessing a larger con-
sequential life cycle scope that accounts for the effect
of PEV adoption on each manufacturers vehicle
eet emissions due to US corporate average fuel
economy and GHG emission policy results in the
conclusion that PEV adoption increases damages
in all scenarios modeled (the policy extends through
2025)[29].
4. Discussion and conclusions
Using a consequential LCA approach for the vehicles
and scenarios modeled, we nd that (1)PEVs cause
more damage than HEVs in the recent PJM grid, (2)
PEVs cause comparable or slightly lower emissions
than HEVs in the future PJM grid scenarios, and (3)
utility-controlled (mostly nighttime)PEV charging
tends to increase life cycle emissions compared to
uncontrolled charging. However, (1)changes in future
fuel prices could affect whether PEVs have higher or
Table 5. Robustness of results for the damage difference between hybrid vehicles and each other vehicle type. CV=conventional vehicle.
Scenario Charging
Probability damages are larger than
for HEVs
Mean change in life cycle damages com-
pared to HEVs
CVrecent grid 100% $650
PHEV-10recent grid Uncontrolled 81% $210
Controlled 98% $1100
PHEV-35recent grid Uncontrolled 95% $1200
Controlled 99% $4200
BEV-265recent grid Uncontrolled 99% $4800
Controlled 99% $8400
CVfuture grid 99% $580
PHEV-35future grid Uncontrolled 4% $420
Controlled 28% $60
PHEV-35future grid with
high wind
Uncontrolled 17% $150
Controlled 18% $150
Table 6. Summary of ndings from sensitivity analysis.
Sensitivity scenario Purpose of scenario Finding
AP2 2011 damage estimates Test more recent estimates of damages per
ton emissions from each location
Higher estimated damages, but key ndings are robust.
Increased potential for PEVs to lower damages ver-
sus HEVs in future grid
EASIUR damage estimates Test an alternative method for estimating
damages
Higher estimated damages, but key ndings are robust.
PEVs have comparable damages versus HEVs in
future grid
High efciency long-
range BEV
Test the life cycle damages of a BEV with
comparable performance to the other
vehicles
Lower estimated damages but key ndings are robust.
BEVs have higher damages than CVs, HEVs, and
PHEVs in the recent grid
Future with expensive coal,
cheap natural gas
Test effect of possible future fuel prices PEVs slightly increase damages versus HEVs when
uncontrolled, reduce damages when controlled
Future with cheap coal, expen-
sive natural gas
Test effect of possible future fuel prices PEVs slightly reduce damages versus HEVs whether
uncontrolled or controlled
Additional spinning reserves
required for high wind case
Test effect of higher reserve requirements for
intermittent wind
Key ndings are robust
No transmission constraints Test effect of transmission constraints and
location of loads and responding
generators
Key ndings are robust
PEVs in urban areas only Test effect of location of vehicles Key ndings are robust
SO
2
cap Test effect of a binding SO
2
cap SO
2
damages likely reduced, but not necessarily to zero
since PEV charging may affect location of SO
2
emis-
sions. PEVs likely reduce damages
CAFE leakage effect Test effect of vehicle eet-wide emissions
enabled by PEV sales due to CAFE incen-
tives for AFV adoption
PEVs increase overall US damages versus HEVs even in
future grid and even with high wind penetration
10
Environ. Res. Lett. 11 (2016)024009
lower damages than HEVs in a future grid and whether
controlled charging increases or decreases emissions,
and (2)when the effect of US PEV adoption on
automaker eet emissions is accounted for, PEV
adoption could increase emissions in all scenarios due
to leakage effects in federal fuel economy standards
[29,3436]. The SI includes a discussion about the
limitations of the model beyond the scope of the
sensitivity analysis.
Our results for the recent PJM grid are consistent
with those from Tessum et al using the 2007 electricity
mix [2]: PEVs have higher damages than gasoline vehi-
cles in the recent grid. Michalek et al [1]found that
PEVs with larger batteries cause more damage, which
we also observe in our recent grid scenarios. None of
the PEVs in our study, regardless of battery size, have
lower damages than the HEV in the recent grid due to
the large amounts of coal on the margin in PJM com-
pared to the average mix used in Michalek et al [1]. The
future of the grid past 2018, which is relevant for
future PEV adoption, is expected to be lower-emitting
than the recent grid, but consequential emissions from
a grid far into the future are difcult to meaningfully
project. Both Michalek et al [1]and Tessum et al [2]
nd that PEVs can reduce damages if charged with
zero-emission electricity. This is a useful bounding
case but not a scenario likely to occur soon, since even
if wind, solar, nuclear, and hydroelectric power make
up a much larger portion of the grid mix in the future,
the consequential effect of PEV charging (the differ-
ence between grid operations when PEVs are present
versus absent)is still primarily to increase generation
from fossil fuel plants. Since most wind, solar, nuclear,
and hydroelectric power is fully used in the absence of
PEV load, PEV adoption will not cause an increase in
generation from these plants. Only when low-emis-
sion plants would have been curtailed in the absence of
PEVs can PEV adoption result in increased use of these
plants. Thus, the consequential emissions of PEV
charging are affected more by the mix of coal and nat-
ural gas plants in a region than by the amount of
renewable or low-emission generation capacity.
The difference between coal and natural gas gen-
eration is signicant. We show that even in one of the
power systems in the country with the highest coal
generation, PEVs could reduce transportation health
and environmental damages in the near future, long
before a zero-carbon electricity mix is achieved, due
primarily to substitution of natural gas for coal on the
margin.
While PEVs can double or triple air emission
damages in the recent grid relative to HEVs, they could
reduce damages in a future grid. However, we estimate
that near future (2018)potential air emissions bene-
ts from PEV adoption in PJM are small relative to
HEVs (or even negative when considering the net
effect on the automakerseet under federal fuel econ-
omy policy). Nevertheless, electrication may offer a
promising long term option to signicantly reduce air
emissions from the transportation sector compared to
some other alternative transportation fuels, including
biofuels and natural gas, that have been shown to offer
small-to-no reductions in GHG emissions and could
have unintended consequences like higher global food
prices [30,31]. Indeed, the logistics of regulating emis-
sions from individual vehicles over their functional
lives are more difcult than regulation of power plant
emissions [37].
Continued regulation of the electricity system can
increase the benets of vehicle electrication, and con-
sequential air emissions implications of PEV charging
are already lower in many regions than in PJM [6].
While near-term benets of PEV adoption in PJM are
estimated to be small or negative, a transition of the
transportation system could lead to long-term benets
outside the scope of this analysis, including greater
benets in other regions and future emissions savings
enabled by a transition to electric vehicles as the elec-
tricity grid becomes cleaner and as public policy
adjusts [32,33].
Acknowledgments
We would like to thank to Nick Muller for his support
with the APEEP and AP2 models as well as Peter
Adams and Jinhyok Heo for their help with the
EASIUR model. The Doris Duke Charitable Founda-
tion, the Richard King Mellon Foundation, the Electric
Power Research Institute, and the Heinz Endowment
provided support for this research through the Renew-
Elec project (www.renewelec.org). The National
Science Foundation also provided nancial support
through a Graduate Research Fellowship, and Toyota
Motor Corporation provided support through a
nancial gift. Findings and conclusions are the sole
responsibility of the authors and may not represent the
views of the funding entities.
References
[1]Michalek J et al 2011 Valuation of plug-in vehicle life-cycle air
emissions and oil displacement benets Proc. Natl Acad. Sci.
USA 108 165548
[2]Tessum C, Hill J and Marshall J 2014 Life cycle air quality
impacts of conventional and alternative light-duty
transportation in the United States Proc. Natl Acad. Sci. USA
111 184905
[3]Eckelman M 2013 Life cycle assessment in support of
sustainable transportation Environ. Res. Lett. 8021004
[4]Samaras C and Meisterling K 2008 Life cycle assessment of
greenhouse gas emissions from plug-in hybrid vehicles:
implications for policy Environ. Sci. Technol. 42 31706
[5]Hawkins T, Singh B, Majeau-Bettez G and Strømman A H
2013 Comparative environmental life cycle assessment of
conventional and electric vehicles J. Ind. Ecology 17 5364
[6]Tamayao M, Michalek J J, Hendrickson C and Azevedo I 2015
Regional variability and uncertainty of electric vehicle life cycle
CO
2
emissions across the United States Environ. Sci. Technol.
49 884455
[7]Silas-Evans K, Azevedo I and Morgan M G 2012 Marginal
emissions factors for the US electricity system Environ. Sci.
Technol. 46 47428
11
Environ. Res. Lett. 11 (2016)024009
[8]Graff Zivin J, Kotchen M and Mansur E 2014 Spatial and
temporal heterogeneity of marginal emissions: implications
for electric cars and other electricity-shifting policies J. Econ.
Behav. Organ. 107 24868
[9]Sioshansi R and Denholm P 2010 The value of plug-in hybrid
electric vehicles as grid resources Energy J. 31 110
[10]Peterson S B, Whitacre J F and Apt J 2011 Net air emissions
from electric vehicles: the effect of carbon price and charging
strategies Environ. Sci. Technol. 45 17927
[11]Choi D G, Kreikebaum F, Thomas V and Divan D 2013
Coordinated EV adoption: double-digit reductions in
emissions and fuel use for $40/vehicle-year Environ. Sci.
Technol. 47 1070310707
[12]Weis A, Michalek J, Jaramillo P and Lueken R 2015 Emissions
and Cost implications of controlled electric vehicle charging
in the US PJM interconnection Environ. Sci. Technol. 49
58139
[13]Ma H, Balthasar F, Tait N, Riera-Palou X and Harrison A 2012
A new comparison between the life cycle greenhouse gas
emissions of battery electric vehicles and internal combustion
vehicles Energy Policy 44 16073
[14]Thomas S 2012 US marginal electricity grid mixes and EV
greenhouse gas emissions Int. J. Hydrog. Energy 37 1923140
[15]Onat N C, Kucukvar M and Tatari O 2015 Conventional,
hybrid, plug-in hybrid or electric vehicles? State-based
comparative carbon and energy footprint analysis in the
United States Appl. Energy. 150 3649
[16]Hadley W S and Tsvetkova A 2008 Potential Impacts of Plug-in
Hybrid Electric Vehicles on Regional Power Generation Oak
Ridge, Tennessee (http://web.ornl.gov/info/ornlreview/
v41_1_08/regional_phev_analysis.pdf)
[17]FERC (http://ferc.gov/market-oversight/mkt-electric/
pjm.asp)
[18]Lueken R and Apt J 2014 The effects of bulk electricity storage
on the PJM market Energy Syst. 5677704
[19]EPA 2010 National Electric Energy Data System (NEEDS)
v.4.10 (www.epa.gov/airmarkets/integrated-planning-
model-ipm-base-case-v410)
[20]Environmental Protection Agency 2018 Parsed Results
Detailed Output Files (http://epa.gov/airmarkets/progsregs/
epa-ipm/BaseCasev513.html)
[21]NREL Eastern Wind Dataset(http://nrel.gov/electricity/
transmission/eastern_wind_dataset.html)(accessed 7
July 2011)
[22]Argonne National Laboratory 2013 GREET 1 (https://greet.es.
anl.gov/)
[23]Argonne National Laboratory 2013 GREET 2 (https://greet.es.
anl.gov/)
[24]EPA 2012 Tesla Model S,fueleconomy.gov
[25]US Department of Transportation, Federal Highway
Administration 2009 National Household Travel Survey 2009
(http://nhts.ornl.gov)(accessed 6 October 2011)
[26]Weis A, Jaramillo P and Michalek J 2014 Estimating the
potential of controlled plug-in hybrid electric vehicle charging
to reduce operational and capacity expansion costs for electric
power systems with high wind penetration Appl. Energy 115
190204
[27]Interagency Working Group on the Social Cost of Carbon
Technical Support Document:Technical Updated of the
Social Cost of Carbon for Regulatory Impact AnalysisUnder
Executive Order 12866 (http://whitehouse.gov/sites/default/
les/omb/assets/inforeg/technical-update-social-cost-of-
carbon-for-regulator-impact-analysis.pdf)
[28]Muller N AP2 (APEEP)Model (https://sites.google.com/site/
nickmullershomepage/home/ap2-apeep-model-2)
[29]Jenn A, Michalek J J and Azevedo I L Unintended
consequences: Why US alternative fuel vehicle adoption
increases gasoline consumption and greenhouse gas emissions
(in review)
[30]Mullins K A, Grifn W M and Matthews H S 2011
Policy Implications of uncertainty in modeled life-cycle
greenhouse gas emissions of biofuels Environ. Sci. Technol. 45
1328
[31]Tong F, Jaramillo P and Azevedo I M L 2015 Comparison of life
cycle greenhouse gases from natural gas pathways for light-
duty vehicles Energy Fuels 29 600818
[32]Greene D, Park S and Liu C 2013 Analyzing the Transition to
Electric Drive in California The Howard H. Baker Jr Center for
Public Policy (www.theicct.org/sites/default/les/
publications/Transition-to-Electric-Drive-2013-report.
FINAL_.pdf)
[33]National Research Council of the National Academies 2013
Transitions to Alternative Vehicles and Fuels (www.nap.edu/
catalog/18264/transitions-to-alternative-vehicles-and-fuels)
[34]Gecan R, Kile J and Beider P 2012 Effects of Federal Tax Credits
for the Purchase of Electric Vehicles US Congressional Budget
Ofce pp 136 (www.cbo.gov/sites/default/les/cboles/
attachments/09-20-12-ElectricVehicles_0.pdf)
[35]Goulder L, Jacobsen M and Van Benthem A 2012 Unintended
consequences from nested state and federal regulations: the
case of the Pavley greenhouse-gas-per-mile limits J. Environ.
Econ. Manage. 63 187207
[36]Jenn A 2014 Advanced and alternative fuel vehicle policies:
regulations and incentives in the United States PhD
Dissertation Department of Engineering and Public Policy,
Carnegie Mellon University, Pittsburgh, PA
[37]Beaton S P et al 1995 On-road vehicle emissionsregulations,
costs, and benets Science 268 9913
12
Environ. Res. Lett. 11 (2016)024009
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