CET 97
DOI: 10.3303/CET2297030
Paper Received: 31 May 2022; Revised: 5 September 2022; Accepted: 17 September 2022
Please cite this article as: Bencekri M., Lee D., Ku D., Lee S., 2022, Investigation of Transport Carbon Emissions in Korea, Chemical
Engineering Transactions, 97, 175-180 DOI:10.3303/CET2297030
CHEMICAL ENGINEERING TRANSACTIONS
VOL. 97, 2022
A publication of
The Italian Association
of Chemical Engineering
Online at www.cetjournal.it
Guest Editors: Jeng Shiun Lim, Nor Alafiza Yunus, Jiří Jaromír Klemeš
Copyright © 2022, AIDIC Servizi S.r.l.
ISBN 978-88-95608-96-9; ISSN 2283-9216
Investigation of Transport Carbon Emissions in Korea
Madiha Bencekria, Doyun Leeb, Donggyun Kuc, Seungjae Leec,*
aDepartment of Transportation Engineering/Department of Smart Cities, University of Seoul, Korea
bGraduate School of Environment Studies, Seoul National University, Korea
cDepartment of Transportation Engineering, University of Seoul, Korea
sjlee@uos.ac.kr
The Republic of Korea was ranked 11th globally, in 2018, in terms of greenhouse gas emissions, of which the
transportation sector contribution was estimated to be 14 % in 2017. Many policies have been drawn to enhance
transport sustainability and reduce its emissions. According to the Climate Action Tracker (CAT), the policies
and actions of South Korea to achieve the goal set in the Paris Agreement, are highly insufficient. Also, the
environmental community in Korea has claimed the weakness of the 2017 emissions strategy. To increase
policies’ efficiency an investigation of the drivers behind greenhouse gases needs to be considered. This paper
is decomposing both overall greenhouse gases and transport sector greenhouse gases, to analyse the effect of
economic growth, energy intensity, energy mix, technology, urbanization, R&D expenditure, and population.
The LMDI results showed that GDP, R&D expenditure, and energy consumption have a strong effect on
increasing carbon emissions. While population and urbanization factors have a weak effect for the analysis
period 2003-2016, due to the stable urbanization and population rate within the period. The significant
contribution of R&D expenditure in increasing GHG emissions showed an urgent need to revise the R&D
investment to boost non-carbon transportation research. Additionally, the energy structure effect was not
significant, as the reliance on fossil fuel is high despite the efforts to reduce it. This revealed that the adopted
fuel policies need strengthening measures and continuous improvement. The study deduced the vital need to
raise the use of renewable and non-carbon energy, to strengthen sustainable transportation policies and
investments, with a focus on public transportation modes, and finally to establish a good environment for a
vigorous system for the implementation and enhancement of active modes.
1. Introduction
Transportation and energy are some of the drivers of the economic growth of developed countries. Aside from
moving people, transportation is a prime mover of goods as it connects the industries to the supply chain, such
as suppliers and customers. The Republic of Korea is one country that benefits from transportation to advance
its economy. According to World Bank, the GDP per capita in 2020 is 31,327 USD/y which makes it the tenth-
largest economy in the world. While many factors were identified to have contributed to its rapid economic
growth, systematic and efficient transportation has been considered a decisive factor, and the expansion of
transport facilities has contributed to its more balanced regional development (Hwang et al., 2014). While
transportation can contribute to faster economic growth, it is a major contributor to greenhouse gas emissions,
which requires government actions to adopt low-carbon economy models that take into consideration the
country’s prevailing economic characteristics (Wang, et al., 2020). In China, for example, carbon emissions and
added value of the transport sector showed an upward trend (Wang and Wang, 2019). Likewise, the
transportation industry was found to be a large source of energy consumption and carbon emissions in the said
country. Further, the added value of transportation, energy consumption, and per capita emissions in the
transport industry was found to be the major factors in the increased carbon emissions in China (Wang et al.,
2018). Korea's total national greenhouse gas emissions in 2018 were 727.6 Mt of CO₂eq, according to Korean
Environment Corporation. It increased by 2.5 % compared to 2017 emissions and increased by 149 % compared
to the total of 292.2 Mt in 1990.
175
Internationally, Korea's total greenhouse gas emissions are estimated to be 11th in the world as of 2018, with
a share of about 1.51 %. Considering that Korea's population is 0.7 % of the world's population, studies
consider that the proportion of greenhouse gas emissions is more than twice as high as the proportion of the
population. In terms of carbon dioxide emissions per capita, Korea ranked 11, in 2018, and 17, in 2022 among
the 100 countries globally (Our World in Data, 2022). In Korea, according to the 2050 policy report of Korea,
transportation recorded accumulated greenhouse gas emissions of 101.66 Mt between 1990 and 2018.
According to data from carbon, in brief, the transportation sector comes third after power and heat and
industry in terms of carbon emissions in Korea from 2000 to 2016 (Figure 1). The transportation sector is
divided into roads, railways, shipping, and aviation, of which the road sector accounts for most of the total
energy in the transport sector (94.8 % in 2018). The transportation means in the transportation sector mainly
use fossil fuels, and except for railways, they are particularly dependent on oil. The transportation sector emits
98.1 Mt of greenhouse gases, accounting for 13.5 % of the total greenhouse gas emissions in Korea (13.7 %
including indirect emissions as of 2018). Greenhouse gas emissions in the transportation sector steadily
increased due to the spread of automobiles, expansion of road systems, and rise of cargo transportation,
increasing by about 2.8 times in 2018 from 1990.
Figure 1: Carbon emissions by sector in Korea from 2000 to 2016(Carbon Brief, 2020)
Ever since The Republic of Korea has been putting a lot of effort into reducing transportation emissions. In 2010,
Korea has adopted the “Green Transportation Movement” as one of its key policy actions reduce to reduce GHG
emissions by one-third while maintaining its competitiveness (Soojeong, 2010). While much has changed in
most recent years in the transport sector as Korea endeavors to make its transport sector cleaner and
sustainable, carbon dioxide emissions are still mainly produced in the process of economic development,
specifically in the road transportation sector in Korea. Hence, for stronger impact, the vision of the 2050 carbon
neutrality promotion strategy has been implemented in late 2020. This strategy converted Korean efforts in
reducing carbon emissions from “adaptive reduction” into “active response. The three major policy directions
are (1) adaptation, (2) opportunity, and (3) fairness. The adaptation measure focuses mainly on adapting
economic structure, accelerating energy conversion, and innovation within high-carbon industrial structures. The
second measure consists of creating a new promising low-carbon industry ecosystem. The third measure
focuses on permitting a fair transition to a carbon-neutral society to protect vulnerable industries and classes,
realize regional-centered carbon neutrality, and raise public awareness of a carbon-neutral society. Through
fiscal, green finance, R&D, and international cooperation, carbon price signals will be strengthened and the
foundation for expanding investment in the carbon-neutral sector will be established. To ensure a high level of
success and efficiency of the implemented policies and measures, it is very crucial to identify the drivers behind
carbon emissions from the transportation sector and to compare them to the drivers behind the overall carbon
emissions, which then would provide a strong theoretical basis for decision-makers. This study investigated the
drivers behind carbon emissions, by investigating the following factors: population effect, economic effect using
the GDP and the GDP growth rate, urbanization effect using the percentage of the urban population, energy
intensity effect, the energy mix effect, and the technology effect through the R&D expenditure variable. The
study adopted the decomposition analysis that is popular within similar topics related to energy and emissions,
and more specifically this research adopted the Logarithmic Mean Divisia Index (LMDI) method. Many studies
176
adopted the LMDI method in their carbon emission decomposition, Mousavi et al. (2017) exploited LMDI to
derive the factors behind carbon emissions in the energy consumption sector. Tu et al. (2019) used the method
to decompose urban passenger transport’s carbon emissions in global cities, like London and, Paris. The study
concluded lessons to be derived for developing cities, such as focusing on enhancing transit-oriented
development and strengthening active modes. The rest of the research is as follows: (2) methodology, (3)
Results and discussions, to show findings and provide interpretations, and finally (4) Conclusion.
Methodology
Decomposition analysis is a very effective and widely used methodology to identify the factors impacting energy
use and greenhouse gas emissions (Xu et al., 2016), by quantifying the influence of each of the studied factors
on greenhouse gas emissions. Generally, decomposition analysis methods are divided into two groups: the
Structural Decomposition Analysis method (SDA) and the Index Decomposition Analysis method (IDA) (Guan
et al., 2008). The SDA method is defined as an “analysis of the economic change employing a set of
comparative static changes in key parameters in an input-output table” (Rose and Casler, 1996), which allows
the evaluation of the correlation between carbon emissions and the investigated macroeconomic factors (Liu et
al., 2021). While the IDA method is often exploited to comprehend the drivers behind the emissions (Su and
Ang, 2012). The IDA method requires less data, and it is more developed and often updated than the SDA
method (Su and Ang, 2012), which makes it widely used to investigate many environmental topics, especially
those that focus on a specific energy-consumption sector (Xu et al., 2016). There are two main types within the
IDA methods: the Laspeyres index, which estimates the change percentage over time using weights, and the
Divisia index, which is a “weighted sum of logarithmic growth rate” (Xu et al., 2016). The Divisia Index is the
most popular, deals well with zero va led to an active improvement and framing of the methods by introducing
new techniques and conceiving guidelines, etc. (Wang and Wang, 2019). This study adopted the IDA method
and specifically exploited the Logarithmic Mean Divisia Index (LMDI) method, which is very popular in the
research field due to its ability to deal with the zero values and also because of the absence of residual issue,
which is present strongly in the SDA method (Wang and Wang, 2019). This paper will use the logarithmic mean
Divisia index (LMDI) to identify the influencing factors on the CO2 emissions from the road transport sector in
Korea from 2003 to 2016, and it uses seven factors, namely: energy intensity, energy mix, population,
urbanization, GDP, and R&D expenditure per GDP. The application of a Divisia decomposition analysis in
transport CO2 uses the Eq(1).
𝐶𝐶𝐶𝐶2 = �𝐶𝐶𝐶𝐶2𝑖𝑖𝑖𝑖𝑖𝑖
𝑖𝑖,𝑖𝑖
(1)
where CO2t are transported sector emissions in year t. i refers to the type of fuel source (oil, gas, other). This
equation is extended to further decompose CO2 while including the seven factors mentioned above, as shown
in the Eq(2) and Eq(3):
𝐶𝐶𝐶𝐶2 = �
𝐶𝐶𝐶𝐶2𝑖𝑖𝑖𝑖
𝐸𝐸𝑖𝑖𝑖𝑖
𝑋𝑋 �
𝐸𝐸𝑖𝑖𝑖𝑖
𝐸𝐸𝑖𝑖𝑖𝑖
𝑋𝑋 �
𝐸𝐸𝑖𝑖
𝐺𝐺𝐺𝐺𝐺𝐺𝑖𝑖
𝑋𝑋 �
𝐺𝐺𝐺𝐺𝐺𝐺𝑖𝑖
RD
𝑖𝑖
𝑋𝑋 �
RD
GDP
𝑋𝑋 �
GDP
Pop
�
Pop
𝑢𝑢𝐺𝐺𝑢𝑢𝑢𝑢
𝑖𝑖
𝑋𝑋
𝑖𝑖
𝑖𝑖𝑖𝑖𝑖𝑖
𝑢𝑢𝐺𝐺𝑢𝑢𝑢𝑢 (2)
𝐶𝐶𝐶𝐶2 = �𝐶𝐶𝐶𝐶𝑖𝑖𝑖𝑖 𝑋𝑋 𝐸𝐸𝐸𝐸𝑖𝑖𝑖𝑖 𝑋𝑋 𝐸𝐸𝐶𝐶𝑖𝑖 𝑋𝑋 𝑅𝑅𝐺𝐺𝑖𝑖𝑋𝑋 𝑅𝑅𝐺𝐺𝑐𝑐 𝑋𝑋 𝐺𝐺𝐺𝐺𝐺𝐺𝑐𝑐 𝑋𝑋 𝑈𝑈𝑢𝑢𝑢𝑢𝑢𝑢𝑟𝑟 𝑋𝑋 𝑢𝑢𝐺𝐺𝑢𝑢𝑢𝑢
𝑖𝑖
(3)
𝐶𝐶𝐶𝐶𝑖𝑖𝑖𝑖refers to the CO2 emission by energy 𝑖𝑖 in transportation sector 𝑡𝑡, 𝐸𝐸𝐸𝐸𝑖𝑖𝑖𝑖 energy 𝑖𝑖 consumption in transportation
𝑡𝑡, 𝐸𝐸𝐶𝐶𝑖𝑖 energy intensity in transportation 𝑡𝑡, 𝑅𝑅𝐺𝐺𝑖𝑖 R&D in transportation 𝑡𝑡, 𝑅𝑅𝐺𝐺𝑐𝑐 R&D expenditure per overall GDP ,
𝐺𝐺𝐺𝐺𝐺𝐺𝑐𝑐 is the GDP per capita, 𝑈𝑈𝑢𝑢𝑢𝑢𝑢𝑢𝑟𝑟 the urbanization rate, 𝑢𝑢𝐺𝐺𝑢𝑢𝑢𝑢 is the urban population.
The change in CO2 emissions can be calculated using the additive form of LMDI (Ang, 2005) as shown by
Eq(4):
∆𝐶𝐶𝐶𝐶2 = 𝐶𝐶𝐶𝐶2𝑖𝑖 − 𝐶𝐶𝐶𝐶2𝑖𝑖−1 = ∆CI + ∆𝐸𝐸𝐸𝐸 + ∆RD + ∆GDP + ∆POP + ∆Upop (4)
The decomposition of each effect between year t and t-1 is, where 𝑋𝑋𝑖𝑖𝑖𝑖 represents the factor X for the energy 𝑖𝑖
in the year 𝑡𝑡, is given by the Eq(5):
∆𝐶𝐶𝐶𝐶2 = �∆𝐶𝐶𝑖𝑖 = �𝐿𝐿(𝐶𝐶𝐶𝐶2𝑖𝑖𝑖𝑖, 𝐶𝐶𝐶𝐶2𝑖𝑖𝑖𝑖−1) ln �
𝑋𝑋𝑖𝑖𝑖𝑖
𝑋𝑋𝑖𝑖𝑖𝑖−1
�
𝑖𝑖𝑖𝑖
(5)
Given that:
177
𝐿𝐿 (𝑎𝑎, 𝑏𝑏) =
(𝑎𝑎−𝑏𝑏)
(𝑙𝑙𝑙𝑙𝑎𝑎−𝑙𝑙𝑏𝑏 𝑏𝑏)
𝑖𝑖𝑖𝑖 𝑎𝑎 ≠ 𝑏𝑏 or 𝐿𝐿 (𝑎𝑎, 𝑏𝑏) = 𝑎𝑎 𝑖𝑖𝑖𝑖 𝑎𝑎 = 𝑏𝑏
Then the next condition is:
𝐿𝐿 (𝐶𝐶𝐶𝐶2𝑖𝑖 − 𝐶𝐶𝐶𝐶2𝑖𝑖−1) = �
�𝐶𝐶𝐶𝐶2𝑖𝑖𝑖𝑖𝑖𝑖− 𝐶𝐶𝐶𝐶2𝑖𝑖𝑖𝑖𝑖𝑖−1�
�𝑙𝑙𝑙𝑙𝐶𝐶𝐶𝐶2𝑖𝑖𝑖𝑖𝑖𝑖−𝑙𝑙𝑙𝑙 𝐶𝐶𝐶𝐶2𝑖𝑖𝑖𝑖𝑖𝑖−1�
� 𝑖𝑖𝑖𝑖 𝐶𝐶𝐶𝐶2𝑖𝑖𝑖𝑖𝑖𝑖 ≠ 𝐶𝐶𝐶𝐶2𝑖𝑖𝑖𝑖𝑖𝑖−1 Or
𝐿𝐿 (𝐶𝐶𝐶𝐶2𝑖𝑖 − 𝐶𝐶𝐶𝐶2𝑖𝑖−1) = 𝐶𝐶𝐶𝐶2𝑖𝑖𝑖𝑖𝑖𝑖 𝑖𝑖𝑖𝑖 𝐶𝐶𝐶𝐶2𝑖𝑖𝑖𝑖𝑖𝑖 = 𝐶𝐶𝐶𝐶2𝑖𝑖𝑖𝑖𝑖𝑖−1
2. Results and discussion
The data is so scarce, especially the specific ones like the emissions by a source within the transportation
sector, and it is mainly collected from internet databases mainly: World Bank, Carbon Brief, Our World In Data,
Korean Statistical Information Service, Knoema cooperation, Trading Economics, and International Energy
Agency. The targeted period is between 2000 and 2022, but the full data suitable for LMDI analysis is only
between 2003 and 2016. The energy intensity (IEA. 2020), CO2 emissions by sector (Carbon Brief, 2020; WB,
2020), and transport emission were downloaded from Our World in Data database (OWID, 2020), the transport
contribution in GDP from Trading Economics database (Trading Economics, 2022), the R&D expenditure and
GDP from the World Bank database (WB, 2021), the population from Kosis institute (Kosis, 2021), urbanisation
from Knoema cooperation (Knoema, 2021), and finally the energy consumption and share of renewable energy
from IEA database (IEA, 2020). The statistics showed a steady increase in transport emissions along with the
overall CO2 emissions over the recent years, with an average growth rate of 1.14 % for transportation emissions
per year. To conduct analysis, first, the study calculated the variation of the investigated factors for the study
period (Table 1). Results showed that the changes in GDP of transportation CO2, population, and urbanization
follow the same pattern (Figure 2). Additionally, correlation analysis revealed that transport CO2 variation has a
strong positive correlation (0.99), followed by renewable energy, then the GDP. While it has a negative
correlation with R&D spending, energy intensity, gas energy share, and other energy (Table 2).
Table 1: Observed changes in transport CO2 and the investigated factors (unit: %)
Year
Transport
CO2
Population
Overall
GDP
R&D
expenditure
Renewable
Energy
Urbanizat
ion
Energy
Intensity
2002 to 2003 2.20 0.52 11.36 3.12 35.74 0.43 0.96
2003 to 2004 0.73 0.40 12.11 6.78 -11.54 0.43 -1.78
2004 to 2005 -3.29 0.21 16.44 3.23 -16.47 0.43 0.15
2005 to 2006 -0.13 0.53 11.92 7.64 -0.47 0.22 -2.75
2006 to 2007 3.07 0.51 10.74 5.37 11.26 0.12 -1.64
2007 to 2008 -5.17 0.76 -11.30 4.10 4.49 0.12 -0.43
2008 to 2009 1.06 0.51 -10.42 5.21 8.05 0.12 0.59
2009 to 2010 1.79 0.50 19.25 5.26 44.34 0.13 2.48
2010 to 2011 -1.72 0.77 9.12 7.82 28.77 -0.02 1.70
2011 to 2012 2.84 0.53 2.00 6.99 2.11 -0.09 -1.29
2012 to 2013 3.70 0.46 6.98 2.56 16.67 -0.09 -2.57
2013 to 2014 1.72 0.63 7.96 3.24 12.75 -0.09 -2.42
2014 to 2015 6.23 0.53 -1.26 -2.48 7.54 -0.10 -1.21
2015 to 2016 2.91 0.40 2.27 0.25 7.34 -0.09 -0.39
Table 2: Regression and Correlation Coefficients of Transport Emissions Variation on Factors’ Variations
Factors Populatio
n
GDP R&D
Urbanisation
Energy
Intensity
Fossil
Energy
Gas
Energy
Renewable
energy
Other
energy
Correlation -0.208 0.063 -0.450 -0.428 -0.260 0.999 -0.322 0.251 -0.113
Regression -0.677 -0.007 0.031 -0.464 -0.017 0.977 0.005 0.006 0.003
The LMDI decomposition highlighted the big effect of GDP growth on increasing transportation emissions, which
could be explained by the rise in car ownership and vehicle purchase in general. According to Statista, there
are 0.5 passenger cars per inhabitant in the Republic of Korea in 2021. The energy intensity showed a strong
effect in decreasing the transport carbon emissions, followed by the R&D expenditure which could not contribute
to the decrease until recently, since 2014. Similarly, an OLS regression showed a weak effect (0.031) of R&D,
while correlation has a significant negative coefficient (-0.450) (Table 2), which makes it a good factor, that was
178
not correctly exploited yet, to mitigate transport GHG, by investing in research such as non-CO2 energy, and a
special focus should be allocated to active modes infrastructure and sustainable transportation policies (Table
3), as it would have the best long term effect on reducing GHG (Khamphilavanh and Masui, 2021).
Figure 2: Transport CO2, population, urbanisation changes by GDP variation
The population effect is low using the LMDI decomposition, as the population knows a very weak variation the
recent years. The energy structure is a potential influencer that could be exploited to lower the CO2 emissions
from transportation. In this research, its effect was revealed to be so weak, and it is due to the high percentage
of fuel consumption (about 95 % on average of the total transportation energy consumption), very low
percentage of gas (2.5 % on average), and the modest percentage of other energies hydrogen or electric
vehicles (1.3 % on average).
Table 3: LMDI Decomposition of Transport carbon emission
Year population GDP R&D Expenditure Urbanisation Energy intensity Energy Structure
2003 0.45 9.92 2.72 0.38 0.84 0.04
2004 0.35 10.73 6.01 0.38 -1.57 0.06
2005 0.19 14.37 2.82 0.38 0.13 0.05
2006 0.45 10.25 6.57 0.19 -2.36 0.01
2007 0.44 9.37 4.68 0.11 -1.43 -0.01
2008 0.66 -9.76 3.54 0.11 -0.37 -0.03
2009 0.44 -8.82 4.41 0.10 0.50 0.05
2010 0.43 16.51 4.51 0.12 2.12 -0.05
2011 0.66 7.83 6.71 -0.02 1.46 0.00
2012 0.45 1.72 6.04 -0.07 -1.11 0.02
2013 0.41 6.22 2.29 -0.08 -2.29 0.00
2014 0.58 7.29 2.97 -0.08 -2.22 -0.00
2015 0.50 -1.20 -2.37 -0.09 -1.15 0.06
2016 0.40 2.26 0.25 -0.09 -0.39 -0.08
3. Conclusion
LMDI results showed that the GDP growth contributes strongly to the increase of transport CO2. Followed by
the effect of the R&D expenditure that contributes to an increase the transport emissions, mainly because of not
investing in research that support mitigating fossil energy use and sustainable policy investments, like
strengthening active modes. Finally, energy intensity, energy structure, population, and urbanisation displayed
a weak effect. The weak effect of energy structure and intensity revealed the need to more strong and more
efficient policies in energy mitigation and diversification. For future direction, the impact of R&D expenditure
could be explored deeply to identify the main issues and help enhance its contribution to the low carbon strategy.
Further, research needs to identify issues behind the low impact of energy structure and energy intensity in the
Korean context.
179
Acknowledgments
This work was supported by the Ministry of Education of the Republic of Korea, the National Research
Foundation of Korea, and financially supported by the Korea Ministry of Land, Infrastructure, and Transport
(MOLIT) as an ˹Innovative Talent Education Program for Smart City
References
Ang B.W., 2005, The LMDI approach to decomposition analysis: A practical guide, Energy Policy, 33(7), 867-
871.
Engo J., 2019, Decoupling analysis of CO2 emissions from the transport sector in Cameroon, Sustainable Cities
and Society, 51, 101732.
Carbon Brief, 2020, The carbon brief profile: South Korea, Carbon Brief Clear On Climate,
<www.carbonbrief.org/the-carbon-brief-profile-south-korea/> accessed 29.09.2022.
WB, 2021, Understanding drivers of decoupling of global transport CO2 emissions from economic growth, World
Bank <openknowledge.worldbank.org/bitstream/handle/10986/36427/Understanding-Drivers-of-
Decoupling-of-Global-Transport-CO2-Emissions-from-Economic-Growth-Evidence-from-145-
Countries.pdf?sequence=1&isAllowed=y> accessed 29.09.2022.
Guan D., Hubacek K., Weber C.L., Peters G.P., Reiner D.M., 2008, The drivers of Chinese CO2 emissions from
1980 to 2030, Global Environmental Change, 18(4), 626–634.
Hwang S.K., Kim G.Y., 2014, 50 Praxes for Better Transport in Korea. Seoul: The Korea Transport Institute. <
centers.ibs.re.kr/html/living_en/transport/50%20Praxes%20for%20Better%20Transport%20in%20Korea.pd
f> accessed 21.07.2022.
IEA, 2020, Korea, International Energy Agency, <www.iea.org/countries/korea> accessed 29.09.2022.
Khamphilavanh B.E., Masui T., 2021, Assessing the impacts of introducing of carbon tax and technologies for
road transportation in Laos, Chemical Engineering Transactions, 89, 103-108.
KOSIS, 2021, Population, Households and Housing Units, Korean Statistical Information Service
<kosis.kr/statHtml/statHtml.do?orgId=101&tblId=DT_1IN1502&vw_cd=MT_ETITLE&list_id=A11_2015_1&
scrId=&language=en&seqNo=&lang_mode=en&obj_var_id=&itm_id=&conn_path=MT_ETITLE&path=%25
2Feng%252FstatisticsList%252FstatisticsListIndex.do > accessed 29.09.2022.
Knoema, 2021, Republic of Korea – CO2 emissions, Knoema Corporation <knoema.com/atlas/Republic-of-
Korea/CO2-emissions> accessed 29.09.2022.
Liu M., Zhang X., Zhang M., Feng Y., Liu Y., Wen J., Liu L., 2021, Influencing factors of carbon emissions in
transportation industry based on CD function and LMDI decomposition model: China as an
example, Environmental Impact Assessment Review, 90, 106623.
Mousavi B., Lopez N.S.A., Biona J.B.M., Chiu A.S., Blesl M., 2017, Driving forces of Iran's CO2 emissions from
energy consumption: an LMDI decomposition approach, Applied Energy, 206, 804-814.
OWID, 2020, South Korea: CO2 Country Profile, Our World in Data, <ourworldindata.org/co2/country/south-
korea> accessed 29.09.2022.
Stern D.I., 2011, The role of energy in economic growth, Annals of the New York Academy of Sciences, 1219(1),
26-51.
Su B., Ang B.W., 2012, Structural decomposition analysis applied to energy and emissions: some
methodological developments, Energy Economics, 34(1), 177-188.
Trading Economics, 2022, South Korea GDP from Transport, Trading Economics <
https://tradingeconomics.com/south-korea/gdp-from-transport> accessed 29.09.2022
Tu M., Li Y., Bao L., Wei Y., Orfila O., Li W., Gruyer D., 2019, Logarithmic mean Divisia index decomposition of
CO2 emissions from urban passenger transport: An empirical study of global cities from 1960–
2001. Sustainability, 11(16), 4310.
WB, 2020, CO2 emissions (metric tons per capita) - Korea, Rep, World Bank,
<data.worldbank.org/indicator/EN.ATM.CO2E.PC?locations=KR> accessed 29.09.2022.
Wang Q., Wang S., 2019, A comparison of decomposition the decoupling carbon emissions from economic
growth in transport sector of selected provinces in eastern, central and western China. Journal of Cleaner
Production, 229, 570-581.
Wang C., Wood J., Geng X., Wang Y., Qiao C., Long X., 2020, Transportation CO2 emission decoupling:
Empirical evidence from countries along the belt and road, Journal of Cleaner Production, 263, 121450.
Xu S.C., He Z.X., Long R.Y., Chen H., 2016, Factors that influence carbon emissions due to energy
consumption based on different stages and sectors in China, Journal of Cleaner Production, 115, 139-148.
180
030.pdf
Investigation of Transport Carbon Emissions in Korea