energies
Article
Estimation of Total Transport CO2 Emissions
Generated by Medium- and Heavy-Duty Vehicles
(MHDVs) in a Sector of Korea
Jigu Seo 1 , Junhong Park 2 , Yunjung Oh 1 and Sungwook Park 3, *
1
2
3
*
Graduate School of Hanyang University, 222 Wangwimni-ro, Seongdong-gu, Seoul 04763, Korea;
[email protected] (J.S.); [email protected] (Y.O.)
National Institute of Environmental Research, Hwangyong-ro 42, Seo-gu, Incheon 22689, Korea;
[email protected]
Department of Mechanical Engineering, Hanyang University, 222 Wangwimni-ro, Seongdong-gu,
Seoul 04763, Korea
Correspondence: [email protected]; Tel.: +82-2-2220-0430; Fax: +82-2-2220-4588
Academic Editor: S. Kent Hoekman
Received: 19 March 2016; Accepted: 5 August 2016; Published: 13 August 2016
Abstract: In order to mitigate carbon dioxide (CO2 ) emissions, policy action that addresses vehicle
emissions is essential. While many previous studies have focused on light-duty vehicles (LDV), little is
known about medium- and heavy-duty vehicles (MHDV). This study lays the groundwork for future
MHDV investigations in the Republic of Korea by developing an MHDV CO2 emissions inventory.
The bottom-up approach was used to calculate national CO2 emissions. Simulation methods that
calculated the CO2 emissions of each vehicle and statistical data, such as vehicle miles traveled
(VMT) and the number of registered vehicles were used to predict CO2 emissions. The validity of
this simulation model was examined by comparing it with the chassis dynamometer test results.
The results of this study showed that the CO2 emissions of MHDV in 2015 were 24.47 million tons,
which was 25.5% of the total road transportation CO2 emissions, despite only comprising 4.2% of
the total vehicles. Trucks emitted 69.6% and buses emitted 30.4% of the total MHDV CO2 emissions.
Using the results between 2012 and 2015, the level of business-as-usual (BAU) CO2 emissions will be
25.37 million tons in 2020.
Keywords: carbon dioxide emissions; medium and heavy-duty vehicle (MHDV); vehicle simulation;
carbon dioxide emission inventory
1. Introduction
The emission of greenhouse gases (GHG) is a significant, long-term threat to the global
environment, due to its role in global warming. Global warming will increase the temperature
on Earth by approximately 3 ◦ C to 5 ◦ C by the year 2100, and the global mean sea level (GMSL) will
continue to rise [1]. The components of greenhouse gases are carbon dioxide (CO2 , 76%), methane
(CH4 , 16%), nitrous oxide (N2 O, 6%), hydrofluorocarbons (HFCs, 2%) [2]. The reduction of greenhouse
gases was discussed internationally during the United Nations Framework Convention on Climate
Change. (FCCC). At this conference, each country suggested their GHG reduction target and reduction
strategy based on the Intended Nationally Determined Contribution (INDC). The U.S. plans to reduce
GHG emissions by 26%–28% compared to its 2005 level by 2025 [3], and the EU has set a target
of at least a 40% domestic reduction in GHG emissions by 2030 in comparison to those of 1990 [4].
According to the INDC submitted by Korea, the plan is to reduce its greenhouse gas emissions by 37%
from business-as-usual (BAU) levels by 2030 [5], and to set a detailed reduction target for each field
of industry.
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Within the economic sector, the transportation section makes up a 14% share of global GHG
emissions. It is reported that 95% of transportation emissions caused by road transport [2]. The CO2
emissions from road transportation in Korea are 83.1 million tons/year, which comprises 13.5% of the
total CO2 emissions of Korea [6]. To reduce the total CO2 emissions generated by the transportation
sector in Korea, mandatory CO2 emission regulations for passenger vehicles and light-duty trucks are
being enforced until 2020. The average CO2 emission standard was 140 g/km in 2015, and will be
97 g/km in 2020 [7]. Many policy researchers have focused on light-duty vehicles (LDVs) to find ways
to mitigate CO2 emissions [8,9].
However, there are no such regulations and standards for CO2 emissions of medium- and
heavy-duty vehicles (MHDV), and there have only been a few studies on MHDVs in comparison to
those on LDVs. CO2 regulation for MHDVs is still in its nascent stages. The estimated implementation
timeline for MHDV efficiency standards of each country [10] is shown in Table 1. In Korea, permissible
levels of exhaust emissions only exist for oxides of nitrogen and hydrocarbons, but not for CO2 .
However, in order to achieve the target reductions proposed by the INDC, it is necessary to regulate
CO2 emissions emitted by MHDVs. Table 2 shows the percentage of MDHV number and the percentage
of CO2 emissions which is generated by MHDVs in the transportation sector [11]. The percentage of
MHDVs relative to the total vehicle registration numbers was only 5%–20%. However, the percentage
of CO2 emissions emitted by MHDVs increased to 30%–70% of the total CO2 emissions generated in
the road transportation sector.
Table 1. Estimated implementation timelines for medium- and heavy-duty vehicle (MHDV) CO2
emission standards [10].
Country
Japan
U.S.
Canada
China
2012
2013
Phase 1
2014
2015
2016
2017
2018
2019
2020
Phase 1
Phase 1
Phase 1
Phase 2
2021
2022
Monitoring,
reporting
E.U.
India
Mexico
S. Korea
2023
2024
2025
Phase 2
Phase 2
Phase 2
Phase 3
Phase 1
Phase 1
Phase 1
Phase 1
Table 2. Medium- and heavy-duty vehicle (MHDV) contributions to road transportation CO2
emissions [11].
Country
Percent of MHDV
Percent of CO2 Emissions from MHDV
China
U.S.
EU
Japan
Brazil
India
Russia
Canada
Global
10%
5%
11%
19%
4%
5%
14%
15%
11%
65%
30%
37%
43%
61%
71%
54%
42%
46%
A chassis dynamometer is widely used to measure CO2 emissions. The regulation standards for
LDVs are based on chassis dynamometer test results. A chassis dynamometer test is an effective way to
measure emissions by simulating road driving in a laboratory. However, since MHDVs vary in weight
(5–40 tons), length (5–15 m), and driveline (4 × 2, 4 × 4, 6 × 2, 6 × 4, 6 × 6 axle), it is expensive and
time consuming to test each vehicle on a case-by-case basis. From this point of view, the simulation
method is useful to compensate for the limitations of the test method. Some countries, such as the U.S.,
E.U., and Japan, have suggested computer simulation programs to measure CO2 emission which is
Energies 2016, 9, 638
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generated by MHDVs. The U.S. developed a simulation program called Greenhouse Emission Model
(GEM), and E.U. has also developed a vehicle-dynamic-based simulation program called the Vehicle
Energy Calculation Tool (VECTO).
In the process of conducting vehicle simulations, acquiring accurate input data is essential to
reproduce the real world performance of a vehicle. The most significant factors affecting the calculation
result are the fuel consumption map, maximum torque curve, driving resistance coefficient, and the test
driving cycle. In addition, some studies that have considered other parameters, such as temperature,
driving style, and road surface conditions [12]. It is desirable to provide accurate input data, however,
but generic data are allocated according to a vehicle class if specific data are not available.
A large amount of CO2 emission rate data from MHDVs is predicted and used to develop a
detailed CO2 emission inventory for the MHDV sector of Korea. There are two types of approaches to
develop a CO2 emission inventory: top-down and the bottom-up approaches [13–16]. The top-down
approach focuses on fuel market interactions in order to estimate the energy consumption of each
economic sector. This type of methodology is effective to analyze the energy consumption ratio of
each economic sector in a short time; however, this methodology has a weakness in implementing
detailed analysis in inventory data since it does not contain an engineering perspective. The bottom-up
approach, on the other hand, focuses on technological details by using emission models of each vehicle,
VMT and vehicle registration statistics. This methodology has an advantage in reflecting detailed
technological data and technological development scenarios and models [17].
The previous method used to assess CO2 emissions in Korea was a fuel-based, top-down model,
which used domestic fuel consumption [6]. However, the fuel-based method, which is based on
sales volumes, cannot reflect the CO2 emissions of each vehicle class, and it is difficult to use when
conducting a detailed analysis. To overcome these limitations, the bottom-up approach was used in
this study to develop the emission inventory of MHDVs in Korea.
The aim of this study was to develop a CO2 emission inventory of MHDVs in Korea by using
bottom-up approach and simulation method. To validate the prediction accuracy, the simulated results
were compared with fuel-based results, which predicted CO2 emissions based on fuel consumption.
Finally, the developed inventory provided useful suggestions to meet the GHG reduction target and to
establish CO2 emission regulations for MHDVs.
2. Methodology
To calculate the MHDV CO2 emissions (kg/year), simulation results of emission factors, VMT
of each vehicle type, and vehicle registration statistics were used. The calculation of CO2 emissions
(kg/year) is as follows:
i =n
ECO2 =
∑ Ci × Ri × Di
(1)
i =1
where ECO2 is the total CO2 emissions of MHDVs, Ci is the emission factor of the vehicle model i,
which is calculated using a simulation (kg/km), Ri is the registration quantity of a vehicle model i in
Korea (-), and Di is the VMT of vehicle model i (km/year).
2.1. Simulations
The vehicle-dynamic-based simulation model was composed of the engine, transmission, chassis,
and driving cycle component modules. The proposed model calculated the fuel consumption and CO2
emissions based on a backward type calculation, as shown in Figure 1.
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Figure
(MHDV) simulation.
simulation.
Figure 1.
1. Calculation
Calculation flow
flow of
of the
the mediummedium- and
and heavy-duty
heavy-duty vehicle
vehicle (MHDV)
In the chassis subsystem module, all of the forces that act on a vehicle, including the resistance forces
In the chassis subsystem module, all of the forces that act on a vehicle, including the resistance
and the acceleration force, were taken into consideration. The relationship of the forces is as follows:
forces and the acceleration force, were taken into consideration. The relationship of the forces is
𝐹𝑣 = 𝐹𝑟.𝑟 + 𝐹𝑎𝑖𝑟.𝑟 + 𝐹𝑎 + 𝐹𝑔
(2)
as follows:
Fv = Fr.r + Fair.r + Fa + Fg
(2)
where 𝐹𝑣 is the net force acting on the vehicle (N), 𝐹𝑟.𝑟 is the rolling resistance force (N), 𝐹𝑎𝑖𝑟.𝑟 is the
air resistance
(N),acting
𝐹𝑎 isonthe
(N),rolling
and 𝐹resistance
to
where
Fv is the force
net force
theacceleration
vehicle (N), force
Fr.r is the
force (N), Fforce
the air
𝑔 is the resistance
air.r isdue
inclination
(N).
The
resistance
forces
𝐹
and
𝐹
are
given
as
follows:
resistance force (N), Fa is the acceleration
force (N),𝑟.𝑟and Fg is the resistance force due to inclination (N).
𝑎𝑖𝑟.𝑟
The resistance forces Fair.r and Fr.r are given as follows: 2
(3)
𝐹𝑎𝑖𝑟.𝑟 = μ𝑎 𝐴𝑒 𝑉
2
Fair.r
𝐹𝑟.𝑟==µμ
eV
aA
𝑟𝑊
(3)
(4)
where μ𝑎 is the air resistance coefficient (N/m
/(km/h)
Fr.r 2=
µr W 2), 𝐴𝑒 is the effective frontal area, V is the
(4)
vehicle speed (km/h), μ𝑟 is the rolling resistance
coefficient
(-),
and
W
is
the
vehicle
weight
(N).
where µa is the air resistance coefficient (N/m2 /(km/h)2 ), Ae is the effective frontal area, V is the
Since it is difficult to derive the drag and rolling resistance coefficients for all types of MHDVs,
vehicle speed (km/h), µr is the rolling resistance coefficient (-), and W is the vehicle weight (N).
the approximated resistance force equations are used in this study. Equations (5) and (6) have been
Since it is difficult to derive the drag and rolling resistance coefficients for all types of MHDVs,
integrated in the Japanese HDV simulation model. The air resistance coefficient is determined as a
the approximated resistance force equations are used in this study. Equations (5) and (6) have been
function of vehicle’s height and width. The rolling resistance coefficient is determined based on the
integrated in the Japanese HDV simulation model. The air resistance coefficient is determined as a
vehicle weight [18,19]. In this study, the air resistance coefficient and rolling resistance coefficients of
function of vehicle’s height and width. The rolling resistance coefficient is determined based on the
each MHDV were calculated as follows:
vehicle weight [18,19]. In this study, the air resistance coefficient and rolling resistance coefficients of
(5)
μ𝑎 𝐴 = 0.00299𝐵 ∙ 𝐻 − 0.000832
each MHDV were calculated as follows:
17.6
μ𝑟0.00299B
= 0.00513
µa A =
·H +
− 0.000832
𝑊
(6)
(5)
where A is the front projection area (m2), B is the full width
17.6(m), H is the full height (m), and W is the
µ = 0.00513 +
(6)
vehicle weight (N). For the air resistancer coefficient of aWbus, the approximation noted above was
multiplied
correction
factorarea
of 0.68.
where
A is by
theafront
projection
(m2 ), B is the full width (m), H is the full height (m), and W is
In the weight
transmission
subsystem,
engine speed
and of
torque
calculated, which
were
used
as
the vehicle
(N). For
the air resistance
coefficient
a bus,were
the approximation
noted
above
was
input
data
of
the
engine
subsystem.
Considering
that
the
vehicle
followed
the
target
velocity
profile
multiplied by a correction factor of 0.68.
exactly,
thetransmission
engine speedsubsystem,
can be calculated
follows:
In the
engineasspeed
and torque were calculated, which were used as
input data of the engine subsystem. Considering
followed the target velocity profile
𝑉𝑛 that
× 𝐺𝑔the
× 𝐺vehicle
𝑓
𝑁=
(7)
exactly, the engine speed can be calculated as follows:
𝐶𝑡
where 𝑁 is the engine speed (RPM), Vn is theVvehicle
velocity
(km/h), 𝐺𝑔 is the gear ratio (-), 𝐺𝑓 is
Gf
n × Gg ×
N=
(7)
the final gear ratio (-), and 𝐶𝑡 is the circumference
of
the
tire
(m). If a vehicle uses an automatic
Ct
transmission, then the speed loss coefficient is applied to reflect rotational speed loss. The engine
where
is be
thecalculated
engine speed
(RPM), Vn is the vehicle velocity (km/h), Gg is the gear ratio (-), G f is
torque N
can
as follows:
the final gear ratio (-), and Ct is the circumference of the tire (m). If a vehicle uses an automatic
𝐹𝑡 × 𝑉
𝑇=
(8)
2π × 𝑁 × 𝐸𝑡
Energies 2016, 9, 638
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transmission, then the speed loss coefficient is applied to reflect rotational speed loss. The engine
torque can be calculated as follows:
Ft × V
T=
(8)
2π × N × Et
where T is the engine torque (N·m), Fv is the net force acting on the vehicle (N), V is the vehicle velocity
(km/h), and Et is the torque transmission efficiency (-).
In addition to calculating engine speed and engine torque, the transmission subsystem includes
gear shifting, which is determined by considering gear ratios, the transmission efficiency of each gear,
and the gear shifting mechanism. Gear shifting is determined by considering the residual driving
power and the engine speed of each gear. If residual driving power is sufficient to drive the vehicle,
then the gear is shifted to the next one; if not, the gear is downshifted.
In an engine subsystem, the CO2 emissions are calculated using a fuel consumption map. A fuel
consumption map is three-dimensional and is comprised of engine speed (x axis, RPM), engine Brake
Mean Effective Pressure (BMEP, y axis, bar), and fuel consumption rate (z axis, kg/h). Based on the
calculated engine speed and torque, the fuel consumption rate was calculated for each time step.
After adding all of the fuel consumption for each time step, the calculation of the CO2 emission was
conducted as follows:
Ci = EFi × Fuel j
(9)
where Ci is the CO2 emissions (g/km), EFi is the CO2 emission factor of fuel (g/L), and Fuel j is the
vehicle’s fuel consumption rate (L/km).
The main input variables were vehicle weight, rolling and air resistance coefficient, frontal area,
maximum torque curve, fuel consumption map, gear ratio, idle condition, tire radius, transmission
efficiency, and driving cycle. Since the average loading ratio in Korea was reported to be 50% [20], the
vehicle weight for testing was half the loaded condition, as follows:
Wt = Wt.curb +
Wmax.load
+ 65 (human body weight of one person)
2
(10)
Np
× 65 (human body weight of one person)
2
(11)
Wb = Wb.curb +
where Wt is the test weight of the truck (kg), Wt.curb is the curb weight of the truck (kg), Wmax.load is the
maximum loading capacity of the truck (kg), Wb is the test weight of the bus (kg), Wb.curb is the curb
weight of the bus (kg) and Np is the passenger capacity of the bus (-). The passenger capacity of a truck
is one, which is the driver, and the bus takes half of its maximum passenger capacity. The average
passenger weight of a Korean person is set to 65 kg.
2.2. Driving Cycle Weighting Factor
In a previous report, it was revealed that the World Harmonized Vehicle Cycle (WHVC) is
appropriate for representing real driving patterns of MHDVs in Korea [21]. However, some of the
MHDVs with gross vehicle weight (GVW) is greater than 40 tons cannot fully follow the original
WHVC because the too much power is required in rapid acceleration regions. For these reasons, the
Korean-World Harmonized Vehicle Cycle (K-WHVC), which is a slightly modified version of the
WHVC, was used in this study.
The K-WHVC is composed of three phases (urban, rural, and motorway) and the differences
in the velocity profiles between WHVC and K-WHVC are presented in Figure 2. Since the driving
pattern of the vehicles varied depending on the class of vehicle operation, a weighting factor for each
phase was applied. The weighting factors of all of the phases are shown in Table 3. Fuel efficiency
was highest in the motorway phase and lowest in the urban phase. The idle time, acceleration, and
deceleration losses were the reasons for the fuel efficiency differences in each phase. Certified CO2
emission rates of MHDVs are calculated as follows:
ECO2 ,MHDV = WFurban · ECO2 ,urban + WFrural · ECO2 ,rural ·WFmotorway · ECO2 ,motorway
(12)
Energies 2016, 9, 638
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where ECO2, MHDV is the weighting applied CO2 emissions (g/km), WFurban is the weighting factor of
the urban phase (-), ECO2, urban is the CO2 emissions of the urban phase (g/km), WFrural is the weighting
Energies 2016, 9, 638
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factor of the rural phase (-), ECO2, rural is the CO2 emissions of the rural phase (g/km), WFmotorway is the
weighting
motorway
phase (-) and ECO2,
is the(g/km),
CO2 emissions
of
the ruralfactor
phase of
(-),the
ECO2,
rural is the CO2 emissions of
themotorway
rural phase
WFmotorwayof
is the
the motorway
weighting
phase
(g/km).
factor of the motorway phase (-) and ECO2, motorway is the CO2 emissions of the motorway phase (g/km).
Velocity (km/h)
100
WHVC
80
K-WHVC
Urban
60
Motorway
Rural
40
20
0
0
200
400
600
800
1000
1200
1400
1600
1800
Time (s)
Figure 2.
Comparisonofof the
the World
World Harmonized
Harmonized Vehicle
Vehicle Cycle
Cycle (WHVC)
(WHVC) and
and Korean-World
Korean-World
Figure
2. Comparison
Harmonized
Vehicle
Cycle
(K-WHVC)
[21].
Harmonized Vehicle Cycle (K-WHVC) [21].
Table 3.
3. Weighting
Weighting factors
Table
factors of
of the
the driving
driving cycles.
cycles.
Vehicle Type
Vehicle Type
Factor
WeightingWeighting
Factor (Urban:Rural:Motorway)
City bus
Ordinary busCity bus
Truck (1 < loading capacity
< 5 tons)
Ordinary
bus
Truck (5 <
loading
< 25
tons) < 5 tons)
Truck
(1 capacity
< loading
capacity
Truck (loading capacity > 25 tons)
Truck (5 < loading capacity < 25 tons)
Truck (loading capacity > 25 tons)
2.3. Vehicle Miles Traveled and Vehicle Statistics
(Urban:Rural:Motorway)
9:1:0
9:1:0
1:2:7
2:4:4
1:2:7
1.5:3.5:5
2:4:4
1:3:6
1.5:3.5:5
1:3:6
2.3. Vehicle
Traveled
and model
Vehicle was
Statistics
In thisMiles
study,
the object
a truck with a GVW greater than 3.5 tons, and a bus with a
passenger
greater
16. was
Dueatotruck
the wide
of greater
MHDVthan
sizes,
trucks
In thiscapacity
study, the
objectthan
model
with range
a GVW
3.5the
tons,
andwere
a busdivided
with a
into
two
groups:
Medium-sized
with
a
GVW
smaller
than
10
tons,
and
heavy-sized
with
a
GVW
of
passenger capacity greater than 16. Due to the wide range of MHDV sizes, the trucks were divided
more
than
10
tons.
The
buses
were
also
classified
into
two
groups:
city
buses
and
ordinary
buses.
into two groups: Medium-sized with a GVW smaller than 10 tons, and heavy-sized with a GVW of
This classification
standard
waswere
in agreement
with the
national
database.
to the
tremendous
range
more
than 10 tons.
The buses
also classified
into
two groups:
cityDue
buses
and
ordinary buses.
of
vehicle
types
and
applications,
MHDV
models
are
complex,
especially
when
compared
to
LDV,
and
This classification standard was in agreement with the national database. Due to the tremendous
2349 vehicle
models
were
for the simulations.
For example,
the same
engine could
different
range
of vehicle
types
andused
applications,
MHDV models
are complex,
especially
whenhave
compared
to
gear
boxes
and
axles;
4
×
2,
4
×
4,
6
×
2,
6
×
4,
and
6
×
6
axle
types;
different
tires
for
each
axle;
and
LDV, and 2349 vehicle models were used for the simulations. For example, the same engine could
singledifferent
or twin gear
tires.boxes
Evenand
if these
cannot
all ofdifferent
the models
Korea,
have
axles;2349
4 × 2,vehicle
4 × 4, 6models
× 2, 6 × 4,
and 6represent
× 6 axle types;
tiresinfor
each
the share
of these
is more
90%2349
of all
of the models
registered
MHDVs
in Korea.
is
axle;
and single
ormodels
twin tires.
Eventhan
if these
vehicle
cannot
represent
all ofTherefore,
the modelsitin
reasonable
that
these
models
are
sufficient
to
represent
MHDV
specifications
in
Korea.
Korea, the share of these models is more than 90% of all of the registered MHDVs in Korea. Therefore,
The statistical
estimated
by the
KoreantoAutomobile
Manufacturer’s
Association
[22] for each
it is reasonable
thatdata
these
models are
sufficient
represent MHDV
specifications
in Korea.
vehicle
used, such
the vehicle
The proportions
of each
type of vehicle
in
Thewere
statistical
dataasestimated
byregistration
the Koreanstatistics.
Automobile
Manufacturer’s
Association
[22] for
Korea
are
shown
in
Figure
3.
The
truck
and
bus
share
was
19.7%,
which
are
including
light-duty
truck
each vehicle were used, such as the vehicle registration statistics. The proportions of each type of
and bus,
a total
number
of 4,364,000
vehicles.
theare
MHDV
sharelightwas
vehicle
inwith
Korea
are shown
in Figure
3. Thesuch
truck
and busAfter
shareexcluding
was 19.7%,LDV,
which
including
4.2%,
and
the
total
vehicle
number
was
858,000.
duty truck and bus, with a total number of 4,364,000 such vehicles. After excluding LDV, the MHDV
vehicle
classnumber
were important
data for predicting the total CO2 emissions
shareThe
wasVMTs
4.2%, for
andeach
the total
vehicle
was 858,000.
generated
in thefor
MHDV
sector of
Korea.
Korea, the
VMT was
Korean
The VMTs
each vehicle
class
were In
important
datavehicle
for predicting
theestimated
total CO2by
emissions
Transportation
Safety
Authority
(TS).
The
VMT
of
each
vehicle
(total
7,272,000
entries)
were
retrieved
generated in the MHDV sector of Korea. In Korea, the vehicle VMT was estimated by Korean
annually duringSafety
inspections
at vehicle
inspection
and (total
these 7,272,000
data wereentries)
classified
into
several
Transportation
Authority
(TS). The
VMT of stations,
each vehicle
were
retrieved
categories.
In thisinspections
study, VMT
classified
by vehicle
class
(bus
or data
truck)
andclassified
GVW were
used
[23].
annually
during
atdata
vehicle
inspection
stations,
and
these
were
into
several
categories. In this study, VMT data classified by vehicle class (bus or truck) and GVW were used [23].
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1313
7 of
Heavy Truck
18.9%
City Bus
5.0%
Ordinary Bus
18.3%
LDV
95.80%
MHDV
4.20%
Medium Truck
57.8%
Figure
The
ratio
MHDV
total
road
vehicles.
Figure
3.3.
The
ratio
ofof
MHDV
toto
total
road
vehicles.
2.4.Modification
ModificationFactor
FactorforforReal
RealWorld
WorldEmissions
Emissions
2.4.
general,certified
certifiedfuel
fuelefficiency
efficiencyand
andCO
CO
emissionsofofLDVs
LDVsare
aredetermined
determinedbased
basedonon
InIngeneral,
2 2emissions
certification
testing.
However,
it
was
revealed
that
the
certified
CO
emission
data,
acquired
2
certification testing. However, it was revealed that the certified CO2 emission
data, acquired by theby
the chassis
dynamometer
test and
real-world
show
discrepancies
[24].
real-worlddriving
driving
chassis
dynamometer
test and
real-world
teststests
show
discrepancies
[24].
In Inreal-world
conditions,
vehicle
performance
can
be
affected
by
a
variety
of
external
factors
(air
density,
wind
speed,
conditions, vehicle performance can be affected by a variety of external factors (air density, wind
road gradient,
etc.). On
the On
otherthe
hand,
these
variables
controlled
the chassis
speed,
road gradient,
etc.).
other
hand,
these are
variables
are in
controlled
indynamometer
the chassis
test. For this reason,
the certified
real world
CO
has
2 emissions
dynamometer
test. Forthe
thisaverage
reason,discrepancy
the average between
discrepancy
betweenand
the certified
and
real
world CO
2
increased
to
25
percent
in
recent
years
[24].
The
frequency
of
additional
accessories
used
in
cars
is
one
emissions has increased to 25 percent in recent years [24]. The frequency of additional accessories
of the
for the
increased
of CO
Given
these circumstances,
it is
2 emissions. of
used
in main
cars isreasons
one of the
main
reasons discrepancies
for the increased
discrepancies
CO2 emissions.
Given these
obvious
that
K-WHVC
based
simulation
results
have
to
be
revised
in
order
to
reduce
the
relative
error
circumstances, it is obvious that K-WHVC based simulation results have to be revised in order to
between
type error
approval
test and
conditions.
this reason,
all of the
CO
2 emissions
reduce
the the
relative
between
the real-world
type approval
test and For
real-world
conditions.
For
this
reason,
study arederived
increased
percent
compared
the original
allderived
of the in
COthis
2 emissions
in by
this30study
arewhen
increased
by 30topercent
whensimulation
compareddata.
to the
original simulation data.
2.5. Scenario Setting
2.5. Scenario
Setting
In order
to predict the level of CO2 emissions, a BAU-scenario-based prediction was conducted.
TheIntotal
emissions
generated
by the MHDV
sector is calculated
by trend
which
orderCO
to 2predict
the level
of CO2 emissions,
a BAU-scenario-based
prediction
was lines,
conducted.
are
generated
using
2012–2015
data.
BAU-scenario-based
prediction
assumes
that
there
are
The total CO2 emissions generated by the MHDV sector is calculated by trend lines, which areno
advanced-technology
appliances
of use in future vehicles.
generated
using 2012–2015
data. BAU-scenario-based
prediction assumes that there are no advanced-
technology appliances of use in future vehicles.
3. Results
Using the methodology described above, the CO2 emission rates of 2349 case vehicle models were
3. Results
calculated, considering all of their specifications. In order to analyze the prediction accuracy of the
Using the methodology described above, the CO2 emission rates of 2349 case vehicle models
simulation results, comparative analyses between chassis dynamometer test data (six types of trucks
were calculated, considering all of their specifications. In order to analyze the prediction accuracy of
and two types of buses) and simulation data were conducted. Eight types of MHDVs were tested
the simulation results, comparative analyses between chassis dynamometer test data (six types of
under the same simulation conditions, half-loaded conditions, and using the K-WHVC driving mode.
trucks and two types of buses) and simulation data were conducted. Eight types of MHDVs were
The predicted CO2 emission rates of the MHDVs are illustrated in Figure 4.
tested under the same simulation conditions, half-loaded conditions, and using the K-WHVC driving
As can be seen in Figure 4, the CO emissions were proportional to the GVW. The range of GVW
mode. The predicted CO2 emission rates2of the MHDVs are illustrated in Figure 4.
was approximately five tons to 40 tons, and the range of CO2 emissions was approximately 200 g/km
As can be seen in Figure 4, the CO2 emissions were proportional to the GVW. The range of GVW
to 900 g/km. Since it is too complicated to show all of the simulation results for the 2349 vehicles,
was approximately five tons to 40 tons, and the range of CO2 emissions was approximately 200 g/km
we omitted some of the data points. By comparing the test and the simulation data, there were no
to 900 g/km. Since it is too complicated to show all of the simulation results for the 2349 vehicles, we
significant differences between chassis dynamometer test results and the simulation results.
omitted some of the data points. By comparing the test and the simulation data, there were no
Figures 5 and 6 indicate the stepped levels of the emission factors. The straight line is categorized
significant differences between chassis dynamometer test results and the simulation results.
by vehicle class, which is based on weight. The emission factors of the trucks showed a constant
Figures 5 and 6 indicate the stepped levels of the emission factors. The straight line is categorized
tendency to be proportional to GVW, with the exception of MHDVs of which the gross vehicle weight
by vehicle class, which is based on weight. The emission factors of the trucks showed a constant
is lower than 10 tons. On the other hand, the emissions of the buses had wide differences between
tendency to be proportional to GVW, with the exception of MHDVs of which the gross vehicle weight
city bus and ordinary bus. City buses generally drive in inner cities and have long idle times and low
is lower than 10 tons. On the other hand, the emissions of the buses had wide differences between
average speeds, which result in low fuel economy and large amounts of CO emissions. On the other
city bus and ordinary bus. City buses generally drive in inner cities and have 2long idle times and low
average speeds, which result in low fuel economy and large amounts of CO2 emissions. On the other
Energies 2016, 9, 638
8 of 13
Energies 2016, 9, 638
8 of 13
hand, ordinary buses have lower CO2 emissions than city buses. For this reason, city buses usually
hand, ordinary buses have lower CO2 emissions than city buses. For this reason, city buses usually
emit
more2016,
CO2 than ordinary buses, which is reflected in our results.
Energies
8 of 13
emit
more CO9,2638
than ordinary buses, which is reflected in our results.
1200have lower CO2 emissions than city buses. For this reason, city buses usually
hand, ordinary buses
emit more CO2 than ordinary buses,
which is reflected in our results.
Simulation Result
Chassis Dynamometer Test
800
1000
Simulation Result
Chassis Dynamometer Test
CO2 emission rate (g/km)
CO2 emission rate (g/km)
1000
1200
600
800
600
400
400
200
200
0
0
5,000
0
0
10,000 15,000 20,000 25,000 30,000 35,000 40,000 45,000
Vehicle weight (half-loaded, kg)
5,000
10,000 15,000 20,000 25,000 30,000 35,000 40,000 45,000
Figure 4.
4. Comparison
Comparison of
of the
the chassis
chassis
dynamometer
test results
results
and the
the simulation
simulation results.
results.
Vehicle
weight (half-loaded,
kg)and
Figure
dynamometer
test
800
Figure 4. Comparison
of the chassis dynamometer test results and the simulation results.
CO2 emission (g/km)
800
CO2 emission rate (g/km)
700
CO2 emission (g/km)
700
CO2 emission rate (g/km)
600
600
500
500
400
400
300
300
200
0
5000
200
0
5000
800
700
CO2 emission rate (g/km)
CO2 emission rate (g/km)
800
700
10000
15000
20000
25000
30000
35000
Vehicle
10000
15000 weight
20000
(half-loaded,
25000 30000 kg)
35000
Vehicle weight
(a)(half-loaded, kg)
(a)
40000
40000
45000
45000
City Bus: CO2 emission (g/km)
Ordinary
2 emission
(g/km)
City Bus:Bus:
CO2CO
emission
(g/km)
Ordinary Bus: CO2 emission (g/km)
600
600
500
500
400
400
300
300
200
200
4000
4000
6000
6000
8000
8000
10000
10000
12000
12000
14000
14000
16000
16000
18000 20000
20000
18000
Vehicle
(half-loaded,kg)
kg)
Vehicle weight
weight (half-loaded,
(b)
(b)
Figure
5. 5.
(a)(a)
The
and(b)
(b)the
thestepped
steppedlevel
levelofof
bus
CO
2 emissions.
Figure
Thestepped
steppedlevel
levelof
oftruck
truckCO
CO22 emissions;
emissions; and
bus
CO
2 emissions.
Figure 5. (a) The stepped level of truck CO2 emissions; and (b) the stepped level of bus CO2 emissions.
Figure6 6shows
showsthe
theinfluence
influence of
of loading
loading conditions
conditions on
were
Figure
on trucks.
trucks.Three
Threeloading
loadingconditions
conditions
were
tested:
empty,
half-loaded,
and
fully
loaded.
In
contrast
to
passenger
vehicles,
the
loading
conditions
tested: empty, half-loaded, and fully loaded. contrast to passenger vehicles, the loading conditions
Energies 2016, 9, 638
9 of 13
Figure 6 shows the influence of loading conditions on trucks. Three loading conditions were
Energies 2016, 9, 638
9 of 13
tested: empty, half-loaded, and fully loaded. In contrast to passenger vehicles, the loading conditions
of the MHDVs had considerable influence. For example, maximum payload of a 40-ton truck (heaviest
of the MHDVs had considerable influence. For example, maximum payload of a 40-ton truck
truck) was 25 tons which takes 62% of its total weight. The average CO2 emissions of a fully-loaded
(heaviest truck) was 25 tons which takes 62% of its total weight. The average CO2 emissions of a fullyHDV were 12% more than those of the half-loaded conditions, and 25% more than those of the empty
loaded HDV were 12% more than those of the half-loaded conditions, and 25% more than those of
conditions. However, the influence of the loading capacity of a bus was not significant compared to
the empty conditions. However, the influence of the loading capacity of a bus was not significant
that of a truck. For example, the maximum payload of the heaviest bus takes 15% of its total weight.
compared to that of a truck. For example, the maximum payload of the heaviest bus takes 15% of its
For this reason, the CO2 emission rates of the bus did not show a distinct gap, regardless of the
total weight. For this reason, the CO2 emission rates of the bus did not show a distinct gap, regardless
loading conditions.
of the loading conditions.
1200
Empty
Half loaded
Full loaded
CO2 emission rate (g/km)
1000
800
600
400
200
0
0
5000
10000
15000
20000
25000
30000
35000
40000
45000
Gross vehicle weight (kg)
Figure 6.
6. The
The CO
CO2 emissions
according to loading conditions.
Figure
2 emissions according to loading conditions.
CO2 emission (g/km)
Figure 7 shows the predicted CO2 emission results of MHDVs divided by loading capacity.
Figure 7 shows the predicted CO emission results of MHDVs divided by loading capacity.
These results are useful to analyze how2many grams of CO2 emission are generated to move one ton
These results are useful to analyze how many grams of CO2 emission are generated to move one ton of
of payload (g/km·ton). The Environmental Protection Agency (EPA, U.S.) uses gallons per ton·mile
payload (g/km·ton). The Environmental Protection Agency (EPA, U.S.) uses gallons per ton·mile for
for MHDV emission units. It is desirable to express CO2 emission data as a unit of g/km·ton than
MHDV emission units. It is desirable to express CO2 emission data as a unit of g/km·ton than g/km
g/km to evaluate freight efficiency. Due to the tremendous range of vehicle weights, trucks were
to evaluate freight efficiency. Due to the tremendous range of vehicle weights, trucks were divided
divided into four groups and buses into two groups, according to weight. The CO2 emission values
into four groups and buses into two groups, according to weight. The CO2 emission values in Figure 7,
in Figure 7, which are marked on the center horizontal line, are the average emissions of each class.
which are marked on the center horizontal line, are the average emissions of each class. Since fixed
Since fixed fuel consumption is needed to operate a vehicle, a more heavily-loaded vehicle has less
fuel consumption is needed to operate a vehicle, a more heavily-loaded vehicle has less relative CO2
relative CO2 emissions, which means it is more cost effective. The averaged CO2 emissions of the
emissions, which means it is more cost effective. The averaged CO2 emissions of the lightest truck
lightest truck group (5 tons < GVW < 10 tons) emitted 119 g/km·ton, but the averaged CO2 emissions
group (5 tons < GVW < 10 tons) emitted 119 g/km·ton, but the averaged CO2 emissions of heaviest
of heaviest truck group (30 tons < GVW < 40 tons) is only 29 g/km·ton. From an economical freight
truck group (30 tons < GVW < 40 tons) is only 29 g/km·ton. From an economical freight perspective,
perspective, the heavier truck is more cost effective with respect to freight efficiency, and this
the heavier truck is more cost effective with respect to freight efficiency, and this tendency can also be
tendency can also be seen in buses.
seen in buses.
800
Figure 8 shows the percentage of the Korea MHDV sector CO2 emissions by source. This result was
668
derived by using a700
simulation model, VMT, and vehicle registration
statistics data. Trucks contributed
612
a large portion to the
600 total CO2 emissions, 69.6% of the total MHDV emissions, 14.76 million tons/year.
502
In Figure 3, the number of medium trucks is three times more than heavy trucks; however, more CO2
500
emissions are generated by heavy trucks than medium trucks. This is because heavy trucks have
340
longer VMT than 400
medium trucks.
Bus emissions accounted for the rest of the emissions at 30.4% or
300
6.45 million tons/year. The total CO2 emissions of the MHDVs were 21.21 million tons/year, and this
accounted for 25.5%
200 of road transportation CO2 emissions in Korea.
100
0
0
5,000
10,000
15,000
20,000
25,000
Gross vehicle weight (kg)
(a)
30,000
35,000
40,000
in Figure 7, which are marked on the center horizontal line, are the average emissions of each class.
Since fixed fuel consumption is needed to operate a vehicle, a more heavily-loaded vehicle has less
relative CO2 emissions, which means it is more cost effective. The averaged CO2 emissions of the
lightest truck group (5 tons < GVW < 10 tons) emitted 119 g/km·ton, but the averaged CO2 emissions
of heaviest truck group (30 tons < GVW < 40 tons) is only 29 g/km·ton. From an economical freight
Energies
2016, 9, 638the heavier truck is more cost effective with respect to freight efficiency, and 10
of 13
perspective,
this
tendency can also be seen in buses.
800
668
700
612
CO2 emission (g/km)
600
502
500
340
400
300
200
100
0
0
5,000
10,000
15,000
20,000
25,000
30,000
35,000
40,000
Gross vehicle weight (kg)
Energies 2016, 9, 638
10 of 13
(a)
CO2 emission (g/km·ton)
200
160
119
120
74
80
41
29
40
0
0
5,000
10,000
15,000
20,000
25,000
30,000
35,000
40,000
Gross vehicle weight (kg)
(b)
800
CO2 emission (g/km)
700
609
600
500
400
360
300
200
100
0
2,500
5,000
7,500
10,000
12,500
15,000
17,500
Gross vehicle weight (kg)
(c)
600
CO2 emission (g/km·ton)
500
·
400
300
292
239
200
100
0
2,500
5,000
7,500
10,000
12,500
15,000
17,500
Gross vehicle weight (kg)
(d)
Figure
the
averageCO
CO2emissions
emissions of
of trucks
trucks (g/km);
(g/km); (b)
2 emissions of trucks
Figure
7. 7.
(a)(a)
the
average
(b)the
theaverage
averageCO
CO
2
2 emissions of trucks
(g/km∙ton); (c) the average CO2 emissions of buses (g/km); and (d) the average CO2 emissions of buses
(g/km·ton); (c) the average CO2 emissions of buses (g/km); and (d) the average CO2 emissions of
(g/km∙ton).
buses (g/km·ton).
Figure 8 shows the percentage of the Korea MHDV sector CO2 emissions by source. This result
was derived by using a simulation model, VMT, and vehicle registration statistics data. Trucks
Energies 2016, 9, 638
11 of 13
Energies 2016, 9, 638
11 of 13
It is estimated that CO2 emissions will increase from 24.47 million tons in 2015 to 25.37 million tons
in 2020, and that the amount of CO2 emissions will increase 0.8% annually.
City Bus,
9.9%
Energies 2016, 9, 638
11 of 13
It is estimated that CO2 emissions will increase from 24.47 millionOrdinary
tons in 2015 to 25.37 million tons
Heavy
20.5%
in 2020, and that the amount of
CO2 emissions will increase 0.8%Bus,
annually.
Truck,
43.8%
City Bus,
9.9%
Ordinary
Medium Bus, 20.5%
Truck,
25.8%
Heavy
Truck,
43.8%
Figure 8.
8. The
The share
share of
of Korea
Korea MHDV
MHDV sector
sector CO
CO2 emissions
by source.
Figure
2 emissions by source.
40
emission (million tons/year)
CO2
tons/year)
CO2 emission (million
Medium
Using the VMT and vehicle
registration
statistics from
the national database between 2012 and
y = 0.214x
+ 23.44
Truck,
35
R²
=
0.9646
2015, the BAU level of CO2 emissions, from 2016 to 2020, were calculated and are shown in Figure 9.
25.8%
It is estimated that CO2 emissions will increase from 24.47 millionBAU
tons in 2015 to 25.37 million tons in
30
2020, and that the amount
of
CO
emissions
will
increase
0.8%
annually.
2
Figure 8. The share
of Korea MHDV sector CO2 emissions by source.
25
40
20
35
15
30
10
25
5
20
0
15
10
23.77
23.81
23.85
24.47
24.51
24.72
24.94
25.15
25.37
25.15
25.37
y = 0.214x + 23.44
R² = 0.9646
BAU
23.77
23.81
23.85
24.47
24.51
24.72
24.94
Year
Figure 9. The business-as-usual level of CO2 emissions from 2016 to 2020.
4. Conclusions
5
0
In order to meet the GHG emission reduction target in the transportation sector, mandatory
Year In contrast to LDVs, which use the chassis
regulation of MHDV CO2 emissions is necessary.
dynamometer test
to
measure
CO
2 emissions, the simulation method is being adopted in many
Figure 9. The business-as-usual level of CO2 emissions from 2016 to 2020.
Figure 9.sector.
The business-as-usual
of CO2 emissions
2016
to 2020. driveline model,
countries in the MHDV
Therefore, thelevel
simulation
method from
using
a vehicle
based
on vehicle dynamics, was used in this study to predict the CO2 emissions of 2349 MHDVs in
4. Conclusions
4.
Conclusions
Korea. In the proposed model, a weighting factor was applied to the driving cycle in order to reflect
In order to meetfor
the GHG
emission
reduction
target in the
transportation
sector,
mandatory
real driving
type
of vehicle.
After calculating
thetransportation
emission factors
of each
type of
In orderpatterns
to meet theeach
GHG
emission
reduction
target in the
sector,
mandatory
regulation
of
MHDV
CO
2 emissions is necessary. In contrast to LDVs, which use the chassis
vehicle
using
the
bottom-up
method,
which
uses
the
vehicle
VMT
and
registration
statistics,
the
regulation of MHDV CO2 emissions is necessary. In contrast to LDVs, which use the chassis
dynamometer
test
to
measure
CO
2 emissions, the simulation method is being adopted in many
emission inventory
of MHDVs
Korea
was developed and used to predict the BAU CO2 emissions.
dynamometer
test to
measure in
CO
2 emissions, the simulation method is being adopted in many
countries
in the shows
MHDVthat
sector.
Therefore,
the
simulation
method
using
vehicle
driveline
model,
The
result
MDHVs
make
up simulation
4.2% of total
vehicles,
butaa they
emit
25.5% of
road
countries in the MHDV sector. Therefore, the
method
using
vehicle
driveline
model,
based
on
vehicle
dynamics,
was
used
in
this
study
to
predict
the
CO
2 emissions of 2349 MHDVs in
transportation
CO
2
emissions.
The
CO
2
emissions
generated
in
the
MHDV
sector
were
concentrated
based on vehicle dynamics, was used in this study to predict the CO2 emissions of 2349 MHDVs in
Korea.
In the
proposed
model,
aduty
weighting
factor
was0.8%
applied to the
drivingbut
cycle
in order
to
reflect
in
specific
vehicle
types.
Heavy
trucks
are was
only
vehicles,
account
forreflect
11.2%
of
Korea.
In the
proposed
model,
a weighting
factor
appliedoftototal
the driving
cycle in
order to
real
real
driving
patterns
for
each
type
of
vehicle.
After
calculating
the
emission
factors
of
each
type
of
road
transportation
CO
2
emissions.
This
is
because
each
MHDV
emits
more
CO
2
than
an
LDV,
and
driving patterns for each type of vehicle. After calculating the emission factors of each type of vehicle
vehicle
usingVMT
the bottom-up
which
uses of
the vehicle
VMT and
registration
statistics,
the
the
average
of method,
MHDVsmethod,
is longer
than
However,
heavier
vehicles
were
more
using
the bottom-up
which
uses
the that
vehicle LDVs.
VMT and
registration
statistics,
the
emission
emissionthan
inventory
MHDVs in Korealess
wasCO
developed
used
to payload
predict the
BAU
COIn
2 emissions.
efficient
lighterofvehicles,
2 to used
moveand
ton of
(g/km·
ton).
addition,
inventory
of MHDVs
in Korea emitting
was developed
and
toone
predict
the BAU CO
emissions.
The result shows that MDHVs make up 4.2% of total vehicles, but they2 emit 25.5% of road
transportation CO2 emissions. The CO2 emissions generated in the MHDV sector were concentrated
in specific vehicle types. Heavy duty trucks are only 0.8% of total vehicles, but account for 11.2% of
road transportation CO2 emissions. This is because each MHDV emits more CO2 than an LDV, and
the average VMT of MHDVs is longer than that of LDVs. However, heavier vehicles were more
efficient than lighter vehicles, emitting less CO2 to move one ton of payload (g/km·ton). In addition,
Energies 2016, 9, 638
12 of 13
The result shows that MDHVs make up 4.2% of total vehicles, but they emit 25.5% of road
transportation CO2 emissions. The CO2 emissions generated in the MHDV sector were concentrated
in specific vehicle types. Heavy duty trucks are only 0.8% of total vehicles, but account for 11.2% of
road transportation CO2 emissions. This is because each MHDV emits more CO2 than an LDV, and the
average VMT of MHDVs is longer than that of LDVs. However, heavier vehicles were more efficient
than lighter vehicles, emitting less CO2 to move one ton of payload (g/km·ton). In addition, when the
CO2 emissions were classified according to loading, fully loaded trucks emitted 25% more CO2 (g/km)
than empty trucks.
There are some other issues that should be considered in future studies. The use of additional
equipment, such as air conditioning, and idle stops, are important factors that affect CO2 emissions.
Alternative fuels, such as compressed natural gas (CNG), also have to be considered in future works.
Author Contributions: The authors contributed equally to this work.
Conflicts of Interest: The authors declare no conflict of interest.
Abbreviations
GHG
GMSL
BMEP
IPCC
UNFCCC
BAU
LDV
MHDV
ICCT
VMT
GEM
VECTO
WHVC
K-WHVC
GVW
TS
Greenhouse Gas
Global Mean Sea Level
Brake Mean Effective Pressure
Intergovernmental Panel on Climate Change
United Nations Framework Convention on Climate Change
Business as Usual
Light Duty Vehicle
Medium and Heavy Duty Vehicle
International Council on Clean Transportation
Vehicle miles traveled
Greenhouse Emission Model
Vehicle Energy Calculation Tool
World Harmonized Vehicle Cycle
Korean World Harmonized Vehicle Cycle
Gross Vehicle Weight
Korean Transportation safety authority
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2.
3.
4.
5.
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Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change;
Intergovernmental Panel on Climate Change (IPCC): Geneva, Switzerland, 2014.
Climate Change 2014: Mitigation of Climate Change. Contribution of Working Group III to the Fifth Assessment
Report of the Intergovernmental Panel on Climate Change IPCC (Intergovernmental Panel on Climate Change);
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