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Well-to-wheel analysis of greenhouse gas emissions for electric vehicles based on electricity generation mix: A global perspective

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In the transport sector, electric vehicles (EVs) are widely accepted as the next technology paradigm, capable of solving the environmental problems associated with internal combustion engine vehicles (ICEVs). However, EVs also have environmental impacts that are directly related to the country’s electricity generation mix. In countries without an environmentally friendly electricity generation mix, EVs may not be effective in lowering greenhouse gas (GHG) emissions. In this study, we analyzed the extent to which the GHG emissions associated with EVs differs among 70 countries in the world, in relation to their domestic electricity generation mix. Then, we compared the results with the GHG emissions from the ICEVs. Countries with a high percentage of fossil fuels in their electricity generation mix showed high GHG emissions for EVs, and for some of these countries, EVs were associated with more GHG emissions than ICEVs. For these countries, policies based on the positive environmental impact of EVs may have to be reconsidered. In addition, different policies may need to be considered for different vehicle types (compact car, SUV, etc.), because the ability of EVs to reduce GHG emissions compared to that of ICEVs varies by vehicle type.
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Well-to-wheel analysis of greenhouse gas emissions for electric vehicles based on electricity
generation mix: A global perspective
JongRoul Wooa, Hyunhong Choib, Joongha Ahnc,
a PhD Candidate, Technology Management, Economics, and Policy Program, College of Engineering, Seoul National University, San
56-1, Sillim-Dong, Kwanak-Gu, Seoul 151-742, South Korea; Tel: +82-2-880-83 86; Fax: +82-2-873-7229; Email: jroul86@snu.ac.kr
b PhD Student, Technology Management, Economics, and Policy Program, College of Engineering, Seoul National University, San 56-
1, Sillim-Dong, Kwanak-Gu, Seoul 151-742, South Korea; Tel: +82-2-880-8386; Fax: +82-2-873-7229; Email: hongchoi@snu.ac.kr
c Research Fellow, Samsung Economic Research Institute, 29th Floor, Samsung Life Seocho Tower, Seocho 2-dong, Seocho-gu, Seoul,
137-955, South Korea; Tel: +82-2-3780-8024; Fax: +82-2-3780-8006; Email: joongha.ahn@gmail.com
Note: This document is a pre-press version of the article, published in 2017 by
Transportation Research Part D: Transport and Environment, Vol. 51,
pages 340-350,
doi: https://doi.org/10.1016/j.trd.2017.01.005
The article on the journal website is at:
https://www.sciencedirect.com/science/article/pii/S1361920916301973
There may be slight formatting and editorial differences from
The published version
Please cite as:
Woo, J., Choi, H., & Ahn, J. (2017). Well-to-wheel analysis of greenhouse gas
emissions for electric vehicles based on electricity generation mix: A global
perspective. Transportation Research Part D: Transport and Environment, 51, 340-
350.
Corresponding author. E-mail: joongha.ahn@gmail.com; Tel.: +82-2-3780-8024; Fax: +82-2-3780-8006
1
Abstract
In the transport sector, electric vehicles (EVs) are widely accepted as the next technology paradigm,
capable of solving the environmental problems associated with internal combustion engine vehicles
(ICEVs). However, EVs also have environmental impacts that are directly related to the country’s
electricity generation mix. In countries without an environmentally friendly electricity generation
mix, EVs may not be effective in lowering greenhouse gas (GHG) emissions. In this study, we
analyzed the extent to which the GHG emissions associated with EVs differs among 70 countries in
the world, in relation to their domestic electricity generation mix. Then, we compared the results
with the GHG emissions from the ICEVs. Countries with a high percentage of fossil fuels in their
electricity generation mix showed high GHG emissions for EVs, and for some of these countries,
EVs were associated with more GHG emissions than ICEVs. For these countries, policies based on
the positive environmental impact of EVs may have to be reconsidered. In addition, different policies
may need to be considered for different vehicle types (compact car, SUV, etc.), because the ability of
EVs to reduce GHG emissions compared to that of ICEVs varies by vehicle type.
Keywords: Well-to-wheel, greenhouse gas emission, electric vehicle, electricity generation mix
2
1. Introduction
Since the latter part of the 20th century, climate change has become a global issue. Governments and
people around the world noticed the negative effects that excessive greenhouse gas (GHG) emissions
can have on our nature and society. Consequently, they are continuing their efforts to reduce the use
of fossil fuels, which are considered the main source of emission of GHGs and other pollutants.
The transport sector is a major contributor to the world’s fossil fuel consumption and GHG
emissions. In 2013, the energy spent in the transport sector comprised 27.6% of the total energy
consumption in the world and 92.6% of this amount was based on the consumption of oil products
(IEA, 2016). In addition, CO2 emissions generated by the transport sector were 22.9% of the total
CO2 emissions in the world (IEA, 2015a). As a consequence, there is now a tacit consensus
worldwide on the need to change the technological paradigm in the transport sector to improve the
air quality and reduce GHG emissions. Given this situation, EVs are considered as the energy
efficient solution to the environmental problems associated with the conventional Internal
Combustion Engine Vehicles (ICEVs) because the former produce zero tail pipe emissions.
Market penetration of BEVs has been very slow and restricted until recently, because of their
shortcomings such as a short traveling distance, long charging time, unaffordability, and under-
developed or non-existent charging infrastructure (Larminie and Lowry, 2003; Nilsson, 2011; Bishop
et al., 2014; Wikström, et al., 2014; Donateo et al., 2015). However, the annual sales of EVs have
been increasing rapidly in the recent years. With a significant reduction in the prices of EVs, in
addition to governmental regulations related to GHG (mainly CO2) and fuel efficiency all around the
world, the market demand for EVs is growing notably. In 2011, the annual global BEV sales were
approximately 36,000 units. This number increased by over 50% in 2012, exceeding 55,000 units. In
2013, the annual sales were beyond 100,000 units, and in 2014, they exceeded 160,000 units with the
global BEV stock of over 350,000 units (IEA, 2013; IEA 2015c). In Norway, more than 10% of the
annual car sales in 2014 comprised BEVs (IEA, 2015c). In addition, global car sales are expected to
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rise by about 40% from 2013 (83 million units) to 2020 (117 million units) and 41% of the increased
demand (14 million units) is expected to consist of Plug-in Hybrid Electric Vehicles (PHEVs) and
BEVs (JD Power, 2013).
Although EVs are now considered as the next technology paradigm in the transport sector,
their actual effect on the environment is directly related to the electricity generation mix used in a
particular country. Therefore, for countries that do not have an environmentally friendly (in terms of
GHG emissions) electricity generation mix, some argue that EVs may not be very effective in
reducing GHG emissions (Faria et al., 2013; Freire and Marques, 2012; Doucette and McCulloch,
2011; Tomic and Kempton, 2007; Huo et al. 2009; Wu et al., 2007; Granvskii et al., 2006; Helms et
al., 2010; Varga, 2013; Nichols et al., 2015; Bickert et al., 2015; Huo et al., 2015; Rangaraju et al.,
2015; Tamayao et al., 2015; Jochem et al., 2016).
Some studies linking the GHG emissions associated with EVs to a particular country’s
electricity generation mix have been conducted recently. Varga (2013) analyzed the CO2 emissions of
EVs and ICEVs considering Romania’s electricity generation mix. He pointed out that even if the EV
market penetration were increased in accordance with the Romanian government’s EV supply policy,
it would not lead to a reduction in the CO2 emissions in the country. Faria et al. (2013) selected three
countries, each of which depends heavily on a particular type of energy, namely fossil fuels (Poland),
nuclear energy (France), and renewable energy (Portugal), in their electricity generation mix and
compared the GHG emissions of EVs in these countries. Hawkins et al. (2013) claimed that
considering the average electricity generation mix, EVs in Europe will help reduce the GHG
emissions by 10-24% compared to ICEVs. Onat et al. (2013) considered the electricity generation
mix of the 51 states in the United States, and compared the GHG emissions of ICEVs, hybrid electric
vehicles (HEVs), PHEVs, and BEVs. The results showed that, according to the average electricity
generation mix scenario (EPA, 2009), the GHG emissions calculated for BEVs were the lowest in 24
states and according to the near-future marginal electricity generation mix scenario (Hadley and
4
Tsvetkova, 2009; Thomas 2012), BEVs were not associated with the lowest emissions of GHGs in
any of the states. According to Onat et al. (2013), a high market penetration of BEVs in the near
future would be an “unwise” strategy based on the existing and near-future scenarios.
One of the limitations of the above-mentioned studies is that their analyses are based on a
single country, mainly a European or a North American country. To clarify whether or to what extent
BEVs could help solve global GHG emission-related problems, such as climate change, these studies
will need to be extended to more countries to obtain a global viewpoint. Furthermore, to compare the
GHG emissions associated with EVs with those of ICEVs from a global viewpoint, the entire supply
chain of the power source for different vehicular technologies (electricity or oil) must be considered.
Although studies evaluating the environmental effect of EVs from a global viewpoint have
not yet been actively pursued, Doucette and McCulloch (2011) compared the CO2 emissions of
ICEVs and those associated with BEVs in the United States, China, India, and France by considering
their electricity generation mix. However, these researchers did not use the actual EV data, instead,
they used the estimatedspecifications of the hypothetical EVs. This was because their study was
carried out in 2011, and at that time, there were not many commercialized EVs in circulation.
Moreover, even though their study included some major countries, it still falls short of obtaining a
global-level implication of the environmental effects of BEVs.
In this study, the vehicle technology of interest is conventional motorization, as represented
by both gasoline and diesel ICEVs, and the electric vehicle technology represented by Battery
Electric Vehicles (BEVs). The study covers the top 70 countries, including those from Central &
South America, Africa, Middle East, and Asia & Pacific, in terms of their CO2 emissions in 2012
(EIA, 2016). These countries together generate 97% of the world’s total CO2 emissions and 96.7% of
the world’s total electricity. By comparing the GHG emissions from BEVs and ICEVs in these
countries, we will assess the environmental effects of BEVs from a global viewpoint. Also, by
dividing these countries into seven regional categories (Africa, Asia & Pacific, Europe, Eurasia,
5
Middle East, North America, South & Central America), we will identify some regional differences
in the environmental impacts of BEVs. For the list of countries studied and the regional divisions,
see Appendix A.
The objective of this study is to evaluate the extent to which the GHG emissions associated
with EVs change according to each country’s electricity generation mix and the differences between
the GHG emissions associated with EVs and ICEVs in each country by performing a well-to-wheel
analysis. The study utilizes the specifications of EVs and ICEVs that are currently being sold in the
market, which makes the analysis more realistic and reliable than the previous studies that used the
estimated or virtual specifications for vehicles. The findings will have significant implications on the
environmental impacts of BEVs at the country-, regional-, and global-level.
The remainder of this paper is organized as follows. Section 2 presents the methodology and
data used for the analysis. Section 3 contains the results and discussion of the analysis. Finally,
section 4 presents the conclusions and some policy implications of this study.
2. Methodology
2.1. Analysis Method
The term “well-to-wheelrefers to the entire process of energy flow, from the mining of the energy
source to a vehicle being driven. Specifically, the well-to-wheel process of ICEVs is a seven-step
process consisting of: 1) extraction (well), 2) transport, 3) refining, 4) distribution, 5) engine
combustion, 6) power delivery system, and 7) wheel. On the other hand, the well-to-wheel process of
BEVs includes nine steps: 1) extraction (well), 2) transport, 3) refining, 4) distribution, 5) power
generation, 6) power transmission and distribution, 7) charging, 8) motor, and 9) wheel. We
implement this approach to compare the GHG emissions associated with EVs with those of ICEVs to
consider the entire supply chain of the power source for each vehicle technology.
6
The well-to-wheel process of ICEVs largely consists of two major processes. The first is the
process of mining the energy source, transporting it, and storing the energy in the car (well-to-tank),
and the other is the process of driving the car using the stored energy (tank-to-wheel). Thus, the
GHG emissions of ICEVs from the well-to-wheel viewpoint is the sum of the GHG emissions of the
combined processes of well-to-tank and tank-to-wheel. This can be calculated by using eqn (1) below.
_GHG ( )
WtW WtT TtW
ICEV GHG GHG FE=+×
(1)
In eqn (1),
_GHG
WtW
ICEV
is the total GHG an ICEV emits from the well-to-wheel
viewpoint and it is measured in units of gCO2eq/km. The terms
WtT
GHG
and
TtW
GHG
represent
the total GHG emitted in the well-to-tank and tank-to-wheel processes, respectively, and are
measured in units of gCO2eq/L.
FE
refers to the fuel efficiency of an ICEV, which is measured in
L/km.
The well-to-wheel process of BEVs consists of two major steps. The first is the process of
mining the energy source and transporting it to the power plant (well-to-power plant), and the other
is the process of transmitting the electricity to the car and driving the car using that electricity (power
plant-to-wheel). Thus, the well-to-wheel GHG emissions from a BEV is the sum of the GHG
emissions of the well-to-power plant and power plant-to-wheel processes. This can be calculated by
using eqn (2) below.
( )
,, , ,
_GHG
{U.S.,China,U.K.,Germany,France,South Korea,...}
{Coal,Gas,Nuclear,Hydro,Wind,Biomass,Solar}
WtW i e i e WtPP e PPtW
e
EV P GHG GHG VE
i
e

=×+ ×


=
=
(2)
7
where,
is the total GHG emitted by country
i
from the well-to-wheel viewpoint
and it is measured in gCO2eq/km.
,ei
P
is the ratio of the power source
e
in the electricity
generation mix of country
i
.
,e WtPP
GHG
and
,e PPtW
GHG
are the GHG emissions by the power
source
e
in the well-to-power plant and power plant-to-wheel processes, respectively, and both are
measured in gCO2eq/kWh.
VE
refers to the electricity efficiency of BEVs and is measured in
kWh/km.
2.2. Data
For this study, we chose the car models representative of different vehicular technologies to be able
to compare the GHG emissions associated with BEVs and ICEVs in multiple countries from the
well-to-wheel perspective. In order to derive a sound basis for policy recommendations, we first
defined four representative vehicle categories (subcompact, compact, full-size luxury, SUV)
according to the European Union (EU) and the U.S. standards (EC, 1999; EPA, 2016a). Vehicle
categories were selected based on the availability of competitive and popular BEV models. Then, we
chose the representative car models from each vehicle category, first by selecting BEVs for each
vehicle category, and then choosing the corresponding ICEVs. This is because BEVs have the least
number of models available in the market. Finally, one to three models of BEVs and three models of
ICEVs were selected for each category (Table 1). The classification system based on the EU and U.S.
standards and the specific fuel economy information for each car model are given in Appendix B and
Appendix C.
Table 1. Selected car models for each vehicle category
Subcompact
Compact
Full-sized luxury
SUV
ICEV
Ford Fiesta
KIA Rio
Volkswagen Polo
BMW 3 Series
Hyundai i30
Volkswagen Golf
AUDI A8
BMW 7 Series
JAGUAR XJ
BMW X6
Porsche Cayenne
VOLVO XC90
8
BEV
KIA Soul
Nissan Leaf
Renault Zoe
BMW i3
Volkswagen e-Golf Tesla Model S Tesla Model X
The data needed to calculate the GHG emissions of ICEVs are the fuel economy data and the
emission factor for each fuel type (gasoline and diesel). For the fuel economy data, we used the
imperial combined fuel consumption data from a single independent source, the Vehicle Certification
Agency (VCA)1, which is an executive government agency of the United Kingdom Department for
Transport. The VCA provides the fuel economy data (imperial fuel consumption) for various types of
vehicles, including ICEVs (diesel-based and gasoline-based) and BEVs that will allow us to compare
the fuel economy of each vehicle in the same line2.
For the emission factors for ICEVs, we used JEC’s well-to-wheel CO2-equivalent emissions
data (JRC, 2014), which is probably the newest and the most reliable source available. JEC is a
research collaboration of JRC (Joint Research Center of European Commission), EUCAR (European
Council for Automotive R&D), and CONCAWE (CONservation of Clean Air and Water in Europe).
According to JEC, the total value of the well-to-wheel GHG emissions is 3,241.3 gCO2eq/L (well-to-
tank: 2,676.9, tank-to-wheel: 564.4) for diesel fuel and 2,778.2 gCO2eq/L (well-to-tank: 2,314.4,
tank-to-wheel: 463.8) for gasoline fuel. We used these emission factors to calculate GHG emissions
for ICEVs and HEVs.
There are two sets of data necessary to calculate the well-to-wheel GHG emissions of BEVs
using eqn (2). First is the GHG emission data of each power source in the well-to-wheel process, and
the other is the electricity generation mix data for each country. The GHG emissions data of each
power source in the well-to-wheel process are presented in Table 2. We utilized Turconi et al.
1 We have also considered using other databases from different sources, such as US EPA. However, US EPA’s database
severely lacks data for diesel fuel vehicles (since not many diesel ICEVs are being sold in US), which makes it not
appropriate for this study.
2 Fuel economy data for every vehicles are collected from the VCA except for Tesla’s Model X. This is because Model
X is a new model and its fuel economy data is not available in VCA yet. Therefore, we approximated the fuel economy
of Model X using the fuel economy data of Tesla Model S from the VCA and official fuel economy data of Model S and
Model X provided by Tesla homepage
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(2013)’s review of 167 previous studies involving the life cycle assessment of GHG emissions
related to the power sources as our data source.
Table 2. GHG emissions (gCO2eq/kWh) for each power source in the well-to-wheel process
The number of data sources
Min
Median
Max
Average
Coal
43
660
960
1,370
942.33
Natural gas
23
380
490
1,000
533.17
Oil
10
530
779
890
773.80
Nuclear
10
3.1
7.3
35
12.23
Hydropower
12
2
4.9
20
8.22
Solar PV
22
13
53
190
65.05
Wind
22
3
12
41
17.63
Biomass
25
1
39
130
51.02
Source: Turconi et al. (2013)
The electricity generation mix data used to calculate the well-to-wheel GHG emissions for
each country are listed in Table 3. Due to space limitations, data for 20 countries are presented in
Table 3, but in the analysis, the electricity generation mix of 70 countries was used.
10
Table 3. Electricity generation mix in each country (2014)
Country
Total Net
Electricity
Generation
(Billion kWh)
Coal
(%)
Natural
Gas
(%)
Oil
(%) Nuclear
(%) Hydro
(%) Wind
(%) Biomass
(%) Solar
(%)
China
4,768
72
2
0
3
20
1
3
0
United
States 4,048 38 30 1 19 6 4 2 0
India
1,052
72
8
1
3
12
3
0
1
Russia
1,012
14
49
3
17
17
0
0
0
Japan
966
30
43
12
0
8
1
4
2
Germany
585
45
10
2
16
3
10
8
6
Korea,
South 500 42 23 4 29 1 0 0 0
Iran
239
0
65
27
2
6
0
0
0
Saudi
Arabia 255 0 62 38 0 0 0 0 0
Canada
616
12
10
1
16
58
2
1
0
Brazil
538
3
9
4
3
69
1
11
0
United
Kingdom 336 32 30 1 19 2 8 7 1
South
Africa 239 92 0 1 6 0 0 0 0
Indonesia
185
49
20
23
0
7
0
0
0
Mexico
279
11
49
19
3
14
2
1
0
Australia
235
67
20
1
0
6
4
1
1
France
533
3
3
1
76
11
3
1
1
Ukraine
187
35
10
0
51
4
0
0
0
Egypt
155
0
73
17
0
9
1
0
0
Norway
145
0
2
0
0
94
2
1
1
Source: IEA (2015b), EIA (2015b), World Bank (2016)
3. Results and discussion
3.1. GHG emissions of ICEVs
First, using the fuel economy data of the models selected for each vehicle category (Appendix C) and
the GHG emission factors for gasoline and diesel fuel presented in section 2.2 (JRC, 2014), we
calculated the amount of GHG emissions for each vehicle category using eqn (1). The results
presented in Table 4 show that ICEVs that use gasoline have higher GHG emissions than those using
11
diesel on average. Even though gasoline vehicles emit less GHG than diesel vehicles for the same
amount of fuel combusted, the fuel efficiency of diesel engines is much higher than gasoline engine.
Table 4. Well-to-wheel GHG emissions (gCO2eq/kWh) of ICEVs
Vehicle category
Fuel type
Well-to-tank
Tank-to-wheel
Well-to-wheel
Subcompact
Gasoline
84.5
16.9
101.4
Diesel
74.2
15.6
89.8
Compact
Gasoline
99.7
20.0
119.7
Diesel
79.4
16.7
96.1
Full-size luxury
Gasoline
156.2
31.3
187.5
Diesel
118.2
24.9
143.1
SUV
Gasoline
175.0
35.1
210.1
Diesel
135.9
28.7
164.6
Note: For each vehicle category, the average value is presented if multiple models are considered.
3.2. GHG emissions of BEVs when the electricity is supplied by a sole power source
We first calculate the GHG emissions of BEVs when the electricity is supplied by a sole power
source to compare it with the emissions of ICEVs. The specifications of the BEVs in Appendix C
and the GHG emissions data of the Well-to-Wheel processes in Table 2 enable us to calculate the
GHG emissions associated with BEVs when the electricity is supplied exclusively by each power
source. For the emissions data of each power source, the median value in Table 2 was used. The
results are presented in Table 5.
Table 5. GHG emissions (g
CO2eq/km) of BEVs driven with electricity generated solely with
each power source
Coal
Natural
Gas Oil Nuclear Hydro Wind Biomass Solar
Subcompact car
142.0
72.5
115.2
1.1
0.7
1.8
5.8
7.8
Compact car
123.0
62.8
99.8
0.9
0.6
1.5
5.0
6.8
Full-size luxury car
180.8
92.3
146.7
1.4
0.9
2.3
7.3
10.0
SUV
206.8
105.5
167.8
1.6
1.1
2.6
8.4
11.4
Note: GHG emissions of BEVs in bold is higher than corresponding ICEV using diesel. GHG emissions of BEVs in
underline is higher than corresponding ICEV using gasoline.
The results show that the GHG emissions associated with BEVs using electricity generated
by fossil fuels such as coal, natural gas, and oil considerably exceeds the amount of GHGs emitted
when the electricity is generated by other power sources.
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The GHG emissions of ICEVs (both using gasoline and diesel) were higher than those of
BEVs using electricity generated by natural gas, nuclear, and renewable energy sources in all four
vehicle categories. Therefore, if the electricity for BEVs is generated by natural gas, nuclear, or
renewable energy sources, BEVs have less negative impacts on the environment than ICEVs.
However, BEVs that use electricity generated by coal or oil always had higher GHG
emissions than diesel ICEVs. BEVs using electricity generated by coal in subcompact and compact
car categories and BEVs using electricity generated by oil in the subcompact car category had higher
emissions than their corresponding gasoline-based ICEVs. Therefore, one should not assume that
BEVs are more environmentally friendly (in terms of GHG emissions) than ICEVs without
considering the electricity generation mix.
Furthermore, using the results in Table 5, we can estimate the GHG emissions of BEVs
based on the marginal electricity mix if the last power plant dispatched in a given hour for a specific
region is known. For example, in California, marginal electricity generators are usually natural gas-
fired, so we know that GHG emissions of BEVs based on the marginal electricity mix in California
come from natural gas-fired power plants (McCarthy and Yang, 2010).
3.3. GHG emissions of BEVs considering each country’s electricity generation mix
We calculated the GHG emissions of BEVs by considering the electricity generation mix of each
country using the data presented in Table 2 and Table 3, in order to compare it with the GHG
emissions of the corresponding ICEVs for each category (Table 4). The results for BEVs for 20
representative countries, the average value of each region, and the average of all 70 countries are
presented in Table 6. For each case, the values calculated using the median emission factors from
Table 2 are presented at the top and values calculated using the minimum and maximum emission
factor values are presented inside the parentheses.
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Table 6. GHG emissions (g
CO2eq/km) of BEVs considering generation mix
Region
Country
Subcompact
Compact
Full-size luxury
SUV
Global average (70 countries)
78.1 a
(54.9
b
, 121.3
c
)
67.6
(47.5, 105.0)
99.3
(69.8, 154.3)
113.6
(79.9, 176.5)
Asia & Pacific
China
102.7
(70.6, 148.3)
88.9
(61.1, 128.4)
130.7
(89.8, 188.7)
149.5
(102.7, 215.8)
India
108.1
(74.7, 158.1)
93.6
(64.7, 136.9)
137.5
(95.1, 201.1)
157.3
(108.8, 230.1)
Japan
88.1
(63.0, 141.9)
76.3
(54.5, 122.9)
112.1
(80.2, 180.5)
128.2
(91.7, 206.5)
South Korea
82.7
(58.2, 128.0)
71.6
(50.4, 110.8)
105.2
(74.1, 162.9)
120.4
(84.7, 186.3)
Indonesia
110.7
(77.2, 159.5)
95.8
(66.8, 138.0)
140.8
(98.2, 202.9)
161.1
(112.3, 232.1)
Australia
111.1
(77.5, 167.7)
96.2
(67.1, 145.2)
141.3
(98.7, 213.3)
161.7
(112.9, 244.0)
Asia & Pacific average (17 countries)
98.7
(68.5, 146.3)
85.4
(59.3, 126.6)
125.5
(87.2, 186.1)
143.6
(99.7, 212.9)
North America United States
77.3
(54.9, 124.6)
67.0
(47.5, 107.8)
98.4
(69.8, 158.5)
112.6
(79.9, 181,3)
Canada
26.1
(18.4, 43.3)
22.6
(15.9, 37.5)
33.3
(23.4, 55.1)
38.0
(26.8, 63.0)
North America average (2 countries)
70.6
(50.1, 113.8)
61.1
(43.4, 98.5)
89.8
(63.7, 144.8)
102.7
(72.9, 165.7)
Europe
Germany
74.8
(51.5, 113.4)
64.8
(44.6, 98.2)
95.2
(65.5, 144.3)
108.9
(74.9, 165.1)
United Kingdom
69.2
(49.1, 113.8)
59.9
(42.5, 98.5)
88.1
(62.4, 144.7)
100.7
(71.4, 165.6)
France
8.7
(5.8, 16.8)
7.5
(5.1, 14.6)
11.1
(7.4, 21.4)
12.7
(8.5, 24.5)
Norway
2.3
(1.4, 6.3)
2.0
(1.3, 5.5)
2.9
(1.8, 8.1)
3.4
(2.1, 9.2)
Europe average (22 countries)
51.0
(35.7, 82.0)
44.1
(30.9, 71.0)
64.8
(45.4, 104.3)
74.2
(51.9, 119.3)
Eurasia Russia
59.2
(43.7, 106.2)
51.2
(37.8, 92.0)
75.3
(55.6, 135.2)
86.1
(63.6, 154.6)
Ukraine
57.6
(40.1, 88.5)
49.8
(34.7, 76.6)
73.2
(51.0, 112.6)
83.8
(58.3, 128.8)
Eurasia average (6 countries)
63.2
(46.1, 109.9)
54.7
(39.9, 95.1)
80.4
(58.7, 139.8)
91.9
(67.1, 159.9)
Middle East Iran
78.3
(57.8, 132.0)
67.8
(50.0, 114.3)
99.7
(73.5, 168.0)
114.0
(84.1, 192.2)
Saudi Arabia
88.8
(64.7, 141.8)
76.9
(56.0, 122.8)
112.9
(82.3, 180.4)
129.2
(94.1, 206.4)
Middle East average (9 countries)
83.9
(61.8, 141.3)
72.6
(53.5, 122.3)
106.7
(78.6, 179.8)
122.1
(89.9, 205.7)
Central & South America Brazil
16.6
(11.4, 29.0)
14.4
(9.8, 25.1)
21.1
(14.5, 37.0)
24.1
(16.5, 42.3)
Mexico
74.0
(53.8, 122.2)
64.1
(46.6, 105.8)
94.2
(68.5, 155.5)
107.7
(78.4, 177.9)
Central & South America average
(8 countries)
39.1
(28.1, 65.6)
33.9
(24.3, 56.8)
49.8
(35.7, 83.5)
56.9
(40.9, 95.5)
Africa South Africa
133.4
(91.7, 190.2)
115.5
(79.4, 164.7)
169.7
(116.6, 242.0)
194.1
(133.4, 276.8)
Egypt
72.6
(54.4, 130.8)
62.9
(47.1, 113.2)
92.4
(69.2, 166.4)
105.7
(79.2, 190.3)
Africa average (6 countries)
102.4
(72.7, 159.7)
88.6
(62.9, 138.2)
130.2
(92.5, 203.1)
149.0
(105.8, 232.4)
Gasoline ICEV
101.4
119.7
187.5
210.1
Diesel ICEV
89.8
96.1
143.1
164.6
a Value using the median emission factor, b value using the minimum emission factor, c value using the maximum emission factor
d Average values presented in this table are weighted average by the country’s total net electricity generation
e Bold: Higher than corresponding ICEV using diesel
f Underline: Higher than corresponding ICEV using gasoline
Considering the results calculated using the median emission factors for power sources used
14
for generation, BEVs generally have lower GHG emissions than their corresponding ICEVs. This
GHG reduction effect was the largest in the full-size luxury category and the smallest in the
subcompact category (full-size luxury > SUV > compact > subcompact). Excluding the subcompact
category, BEVs had less GHG emissions than ICEVs in most countries and regions. Considering the
results that used the maximum emission factors (right side of the parenthesis for each case), there are
quite a few cases of BEVs that had higher GHG emissions than their corresponding diesel ICEVs
(marked with bold) or even gasoline ICEVs (marked with underline). The rest of this section will
focus on the results of the country-level, regional-level, and global-level emissions. Unless
mentioned otherwise, the descriptions will be based on the results that used the median emission
factors.
The GHG emissions calculated for BEVs for each country are strongly related to the
electricity generation mix of the country. The GHG emissions calculated for BEVs were the highest
in South Africa, which has a high ratio of fossil fuels (93%) in its electricity generation mix,
specifically 102.7 to 149.5 gCO2eq/km. Similarly, countries with high fossil fuel ratios in their
generation mix such as Australia (88%), India (81%), and China (74%) also had high GHG emissions.
However, for countries such as Russia, which has 66% fossil fuels in their mix, GHG emissions were
lower because the majority of the fossil fuels used was natural gas, which emits much less GHG than
coal or oil.
On the other hand, countries with high nuclear power and renewable energy ratios in their
generation mix had lower GHG emissions for BEVs. For countries like Norway (94% hydropower),
Canada (58% hydropower), and France (76% nuclear power), the GHG emissions calculated for
BEVs were considerably lower than for both types of ICEVs. In these countries, BEVs are certainly
a more viable option to reduce the GHG emissions than traditional ICEVs. Norway had the lowest
GHG emissions for BEVs, 2.3 to 3.4 gCO2eq/km, which is less than 10% of GHG emitted by the
corresponding ICEVs. In comparison, BEVs in South Africa were associated with about 40 times
15
higher GHG emissions than BEVs in Norway. This emphasizes the importance of an environmentally
friendly electricity generation mix to reduce the GHG emissions from BEVs. The results suggesting
that the GHG emissions from BEVs for different countries vary considerably according to their
electricity generation mix are in agreement with the results of the previous studies (Faria et al. 2013;
Doucette and McCulloch 2011; Varga 2013).
In addition to the country-level analysis, we conducted a regional-level analysis by dividing
70 countries into 7 regional categories to determine the electricity generation mix of each region and
ascertain the regional characteristics. Studying the regional characteristics is important because
different regions have different resources and environmental characteristics. For example, Central &
South America have abundant natural gas reserves and its natural environment is appropriate for
electricity generation with alternative power sources such as hydropower. In fact, about half of
Central & South America’s electricity is produced by hydroelectric power plants (World Bank, 2016).
The results of the regional-level analysis showed significant differences in GHG emissions of BEVs
for each vehicle category for each region (Figure 1). Among seven regions, Africa ranked worst and
Central & South America ranked first in terms of GHG reductions by BEVs. Specifically, GHGs
associated with BEVs were less in all vehicle categories, regardless of the emission factors used in
South & Central America and Europe. For the other regions, emissions associated with BEVs
compared to ICEVs varied by the car category and the emission factor used. For the full-size luxury
vehicle category, BEVs had lower GHG emissions than the corresponding gasoline ICEVs regardless
of the emission factors in all regions except Africa. On the other hand, for the subcompact vehicle
category, Europe and Central & South America were the only regions with lower GHG emissions
from BEVs than gasoline ICEVs.
16
Figure 1.
(1.1) Sub-compact
(1.3) Full-size luxury
(1.2) Compact
(1.4) SUV
17
On the global-level, the results for BEVs with median emission factors revealed significantly
lower GHG emissions compared to ICEVs (both diesel and gasoline). However, the results differ by
the choice of the emission factor. For the subcompact category, GHG emissions for both types of
ICEVs are inside the range of emissions for BEVs depending on the choice of the emission factor
(Figure 2). Therefore, on the global scale, for this category, BEVs not necessarily have a better GHG
reduction effect compared to ICEVs (both gasoline and diesel) depending on the emission factor.
However, for the other three vehicle categories, gasoline ICEVs had higher GHG emissions than
BEVs even if the maximum emission factor was used. Globally, for the compact, full-size luxury,
and SUV vehicle categories, lower GHG emissions were attributed to BEVs than to gasoline ICEVs
regardless of the choice of emission factor, while diesel ICEVs may or may not have lower GHG
emissions than BEVs depending on the emission factor.
Figure 2.
18
4. Conclusions and Policy Implications
This study analyzed the differences in GHG emissions associated with BEVs in multiple countries
according to their electricity generation mix from a Well-to-Wheel perspective, and compared these
results with the GHG emissions of ICEVs. We selected four comparable vehicle types that represent
BEVs and ICEVs; subcompact, compact, full-size luxury, and SUV, since these are the categories
where BEVs are most competitive on. Then, the representative car models were selected for each
vehicle category for the analysis.
First, GHG emissions from ICEVs were calculated for the Well-to-Wheel process. Then, we
analyzed how much GHG emissions were associated with BEVs when the electricity is generated
solely by each power source (coal, natural gas, oil, nuclear or renewables) for the Well-to-Wheel
process. The results showed that the GHG emissions attributed to BEVs that use electricity generated
by fossil fuels were considerably higher than the emissions attributed to BEVs that use electricity
generated by nuclear or renewable sources. We also established that BEVs that use electricity
generated with coal or oil may be associated with higher GHG emissions than ICEVs. These findings
confirm that it is necessary to consider the electricity generation mix to study the environmental
impacts of BEVs versus ICEVs.
Next, we calculated the GHG emissions associated with BEVs for each country based on its
electricity generation mix and compared it with those from the ICEVs of the same type. The results
show that in general, the GHG emissions from BEVs in countries with a high fossil fuel ratio
(especially coal and oil) in their electricity generation mix were higher, and the difference between
the countries was quite large. Similar results were observed for the regional-level analysis.
Finally, we analyzed the global effects of BEV and ICEV technologies on GHG emissions
by taking the average of the GHG emissions from BEVs for 70 countries weighted by their total net
electricity generation. The results showed that for the subcompact category, BEVs may or may not be
associated with lower GHG emissions depending on the choice of the emission factors. On the other
19
hand, for the other three categories, gasoline ICEVs had higher GHG emissions than BEVs
regardless of the emission factors chosen.
BEVs have recently been attracting attention as a solution capable of reducing GHG
emissions and alleviating problems such as global warming. However, the trend of downsizing cars
running on gasoline and diesel (ICEVs) together with the advances in exhaust reduction technologies
have considerably reduced the GHG emissions of ICEVs. In countries with high coal dependency in
their electricity generation mix, BEVs may be associated with increased emissions of GHGs
compared to the conventional ICEVs. Therefore, the ratio of coal in the electricity generation mix
should be lowered for the BEVs to be effective in alleviating GHG emission problems. The United
States is currently aiming to lower its dependency on coal and to increase the ratio of power sources
with low or no GHG emissions with its Clean Power Plan (EPA, 2016b). China also announced a
plan to reduce the ratio of coal in its electricity generation mix to below 65% by 2017 (C2ES, 2015).
Furthermore, China has been implementing the Renewable Portfolio Standard (RPS) since 2012, and
has undertaken to increase the ratio of renewables in its electricity generation mix by 15% by 2020
(RENE21, 2013). In the European Union (EU), EU63, which strictly regulates tail emissions, is in
effect. Moreover, the EU has decided to increase the ratio of renewables in their electricity
generation mix by more than 27% by 2030 (EC, 2014). The continued market penetration of BEVs,
together with enthusiastic governmental policies to improve the electricity generation mix will
significantly enhance the beneficial environmental effects of BEVs. For example, Norway is one of
the most enthusiastic countries in promoting EVs. In 2014, their annual BEV sale was 10% of total
vehicle sales. Since Norway is a country with an environmentally friendly generation mix, they can
achieve an enormous amount of GHG reductions from BEVs.
Based on the results of this study we recommend a detailed review to confirm whether BEVs
3 EU6 is an emissions regulation for vehicles being driven in EU. Compliance of a vehicle is determined by running the
vehicle in a standardized test cycle and checking its emissions. Non-compliant vehicles cannot be sold in the EU. EU6
has been in effect since September 2014.
20
would ultimately reduce GHG emissions. GHG emission reductions may vary by the electricity
generation mix of each country, the choice of emission factors for each power source, vehicle type,
and fuel type of the competing ICEVs. Thus, enabling BEVs to practically contribute to mitigating
global warming and air pollution would require the ratio of fossil fuels (especially coal) in the
electricity generation mix to be lowered. In other words, the beneficial environmental effects of EVs
will be boosted when combined with governments’ strong will to lower the coal dependency in their
electricity generation mix.
Moreover, since the ability of BEVs to reduce GHG emissions compared to that of ICEVs
varies by vehicle category, separate policies should be considered for different types of vehicles. For
example, since the GHG emission reduction effect of BEVs compared to the corresponding ICEVs is
the smallest for the subcompact category, promotional efforts (subsidies, tax incentives) can focus
more on other vehicle categories where the GHG reduction effect of BEV is greater. Also, different
regulations or policies should be applied to gasoline and diesel fuel vehicles reflecting their distinct
effect on the environment. The results of this study suggest that GHG emissions for a particular
distance driven are the highest for gasoline-based ICEVs, which always had higher emissions than
diesel-based ICEVs in the analysis, and for most cases had higher GHG emissions than BEVs
regardless of the emission factor chosen. Therefore, countries whose objective is to reduce GHG
emissions from their transport sector could consider discouraging the purchase of gasoline fuel
vehicles and encouraging the other types of vehicles analyzed in this study.
21
Appendix A. List of countries considered and their regional division
Region (Number of Countries)
Countries
Asia & Pacific (17)
China, India, Japan, South Korea, Indonesia, Australia, Taiwan,
Thailand, Singapore, Malaysia, Pakistan, Vietnam, Hong Kong,
Philippines, North Korea, Bangladesh, New Zealand
North America (2)
United States, Canada
Europe (22)
Germany, United Kingdom, Italy, France, Spain, Turkey, Poland,
Netherlands, Belgium, Czech Republic, Greece, Romania,
Austria, Portugal, Sweden, Bulgaria, Hungary, Finland,
Switzerland, Serbia, Norway, Denmark
Eurasia (6)
Russia, Ukraine, Kazakhstan, Uzbekistan, Belarus, Turkmenistan
Middle East (9)
Iran, Saudi Arabia, United Arab Emirates, Iraq, Kuwait, Qatar,
Israel, Oman, Syria
Central & South America (8)
Brazil, Mexico, Argentina, Venezuela, Chile, Colombia, Peru,
Trinidad and Tobago
Africa (6)
South Africa, Egypt, Algeria, Libya, Morocco
22
Appendix B. Car classifications based on European and US standards
Vehicle Category
Euro Market Segment*
US EPA Size Class**
Subcompact
B-segment small cars
Subcompact
Compact
C-segment medium cars
Compact
Full-size luxury
F-segment luxury cars
N/A
SUV
J-segment sport utility cars
Standard sport utility vehicle
*Source: EC (1999), **Source: EPA (2016a)
23
Appendix C. Selected models and their fuel economy
Vehicle category
BEV
ICEV
Model
BEV Fuel
Economy
(km/kWh)
Model
Gasoline Fuel
Economy
(km/L)
Diesel Fuel
Economy
(km/L)
Subcompact
KIA Soul
6.76
Ford Fiesta
28.3
38.0
Nissan Leaf
6.60
KIA Rio
25.8
36.8
Renault Zoe 6.92
Volkswagen
Polo
29.6 33.8
Compact BMW i3 7.72
BMW 3
Series
22.5 31.9
Hyundai i30
20.3
33.7
Volkswagen
e-Golf
7.89
Volkswagen
Golf
28.3 35.7
Full-size luxury Tesla Model S 5.31
AUDI A8
12.9
21.3
BMW 7
Series
17.8 25.8
JAGUAR XJ
14.4
21.3
SUV Tesla Model
X 4.64
BMW X6
12.5
20.3
Porsche
Cayenne
12.3 17.8
VOLVO
XC90
15.2 21.3
Source: VCA (2016)
24
Figure Captions
Figure 1. GHG emissions of each region by each vehicle category
Figure 2. Global GHG emissions by each vehicle category
25
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... One initiative that has been proposed to reduce these emissions is the adoption of electric vehicles (EVs), rather than internal combustion engine vehicles (ICEVs). Depending on the electricity generation mix, this may be effective in reducing emissions (Axsen et al. 2015a, Woo et al. 2017. ...
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We present a new model for finding the optimal placement of electric vehicle charging stations across a multi-period time frame so as to maximise electric vehicle adoption. Via the use of advanced discrete choice models and user classes, this work allows for a granular modelling of user attributes and their preferences in regard to charging station characteristics. Instead of embedding an analytical probability model in the formulation, we adopt a simulation approach and pre-compute error terms for each option available to users for a given number of scenarios. This results in a bilevel optimisation model that is, however, intractable for all but the simplest instances. Using the pre-computed error terms to calculate the users covered by each charging station allows for a maximum covering model, for which solutions can be found more efficiently than for the bilevel formulation. The maximum covering formulation remains intractable in some instances, so we propose rolling horizon, greedy, and GRASP heuristics to obtain good quality solutions more efficiently. Extensive computational results are provided, which compare the maximum covering formulation with the current state-of-the-art, both for exact solutions and the heuristic methods. Keywords: Electric vehicle charging stations, facility location, integer programming, discrete choice models, maximum covering
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... Thus, EVs are energy-efficient, and are quiet vehicles reducing noise and local air pollution when compared to ICEVs (Grauers et al., 2013). However, it is important to note, that the overall reduction of emissions is dependent on the country-specific electricity generation mix (Varga, 2013;Hawkins et al., 2013;Liu and Santos, 2015;Woo et al., 2017). ...
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Governments provide policy incentives to increase adoption rates of electric vehicles and achieve sustainability goals. This paper investigates the impact of UK financial purchase incentives on new registrations of electric fleets whilst moderating for change in gross domestic product (GDP). We use a unique dataset of new registrations of electric fleets in the UK for a 20-year period (1999–2019). Our results show that financial purchase incentives positively impact new registrations of electric fleets, and this effect is positively moderated by GDP change. Marginal analysis reveals three categories of adopters that purchase electric fleets at different levels of policy and GDP change: insensitive adopters, standard adopters, and sensitive adopters. Overall, financial incentives may be more effective in fostering the adoption of electric fleets when set to levels that are conditioned on the level of GDP change. In particular, financial purchase incentives are most effective when GDP change and incentives are above their average level.
... Numerous studies have been used to investigate the emission levels between ICEVs and BEVs (including plug-in hybrid electric vehicles (PHEVs) and hybrid electric vehicles (HEVs)), with results varying based on model used and differences for countries/regions [81]. These studies have all highlighted that the deep decarbonisation of the electricity network away from fossil fuels towards renewables will be necessary [82], otherwise the environmental benefits of electric transport will be negated [83]. For example, Varga (2013) analysed and compared the CO 2 emissions from BEVs and ICEVs in Romania, considering the electricity generation mix [84]. ...
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Even with technological advances, internal combustion engine vehicles (ICEVs) are unlikely to meet net zero targets, whilst emitting high levels of greenhouse gas emissions in addition to impacting public health. Technological improvements of ICEVs are not enough to meet targets. Therefore, phasing out and banning the sale of new ICEVs as soon as possible could provide a stronger impetus to reduce transport emissions. The integration of low emission vehicles including battery electric vehicles, plug-in hybrid electric vehicles and hybrid electric vehicles is often seen as a method to reduce transport emissions. Although these vehicles are often considered ‘zero emission’ at their point of use, their true environmental impact is dependent on the carbon intensity of electricity used to ‘fuel’ the vehicle. Therefore, without the decarbonisation of electricity generation, environmental benefits of low emission transport will be diminished. This chapter focuses on private vehicles and shows that transitioning to low emission transport faces many barriers including cost, range anxiety and charging infrastructure distribution, which need to be overcome for an effective transition to low emission vehicles. This has resulted in numerous monetary and non-monetary incentives being introduced to encourage this transition. However even with this transition, emission levels will remain high per person per kilometre travelled and other low carbon alternatives need to be considered.
... This needs that the major part of the electrical energy is produced by renewable sources or nuclear power plants. It is also known that this does not happen in a great part of Countries [8]. Moreover, these technologies still have high costs [9]. ...
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Electric vehicles have the potential to lower emissions in the mobility sector, but especially high costs might hinder their market development. This paper aims to access environmental and economic impacts and potentials by comparing CO2-emissions and costs of small vehicles. Considering actual data it is analysed, if and under which conditions electric vehicles are financially competitive for private consumers and under which conditions emissions can be saved. For this, a multiple-stage approach is focusing on (1) emissions during production and operation, (2) private costs and (3) external costs of emissions. A model of total cost of ownership is applied for the analysis of private and external costs. Results show that emissions of electric vehicles exceed emissions of combustion engine vehicles in the production phase, but electric vehicles cause fewer emissions during operation. Total emissions can be saved by electric vehicles even with low annual driving distances (2500–5500 km/a today). Results highly depend on the form of electricity production. Today, private costs of electric vehicles exceed the costs of combustion engine vehicles. Due to cost decreases electric vehicles can gain financial advantages in the future. External costs are high, especially for combustion engine vehicles (up to 15% of private costs), but in none of the considered cases high enough to give electric vehicles a financial advantage today. This picture will change in the future.
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We evaluated the fuel-cycle emissions of greenhouse gases (GHGs) and air pollutants (NOx, SO2, PM10, and PM2.5) of electric vehicles (EVs) in China and the United States (U.S.), two of the largest potential markets for EVs in the world. Six of the most economically developed and populated regions in China and the U.S. were selected. The results showed that EV fuel-cycle emissions depend substantially on the carbon intensity and cleanness of the electricity mix, and vary significantly across the regions studied. In those regions with a low share of coal-based electricity (e.g., California), EVs can reduce GHG and air pollutant emissions (except for PM) significantly compared with conventional vehicles. However, in the Chinese regions and selected U.S. Midwestern states where coal dominates in the generation mix, EVs can reduce GHG emissions but increase the total and urban emissions of air pollutants. In 2025, EVs will offer greater reductions in GHG and air pollutant emissions because emissions from power plants will be better controlled; EVs in the Chinese regions examined, however, may still increase SO2 and PM emissions. Reductions of 60–85% in GHGs and air pollutants could be achieved were EVs charged with 80% renewable electricity or the electricity generated from the best available technologies of coal-fired power plants, which are futuristic power generation scenarios.