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Driving Forces of CO2 Emissions in Emerging Countries: LMDI Decomposition Analysis on China and India’s Residential Sector

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  • Seoul National University, Seoul, South Korea

Abstract and Figures

The main objective of this paper is to identify and analyze the keyriversehind changes of CO2missions in the residential sectors of themergingconomies, China and India. For the analysis, we investigate to whatxtent changes in residentialmissions areue to changes innergymissions coefficients,nergy consumption structure,nergy intensity, household income, and population size. Weecompose the changes in residential CO2missions in China and India into these five contributing factors from 1990 to 2011y applying the Logarithmicean Divisia Index (LMDI) method. Our results show that the increase in per capita income level was theiggest contributor to the increase of residential CO2missions, while thenergy intensityffect had the largestffect on CO2missions reduction in residential sectors inoth countries. This implies that investments fornergy savings, technological improvements, andnergyfficiency policies wereffective in mitigating CO2missions. Our results alsoepict that the change in CO2mission coefficients for fuels which includeothirect and indirectmission coefficients slowedown the increase of residentialmissions. Finally, our resultsemonstrate that changes in the population andnergy consumption structurerove the increase in CO2missions.
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Sustainability 2015, 7, pagepage; doi:10.3390/ www.mdpi.com/journal/sustainability
Article
Driving Forces of CO2 Emissions in Emerging
Countries: LMDI Decomposition Analysis on China
and India’s Residential Sector
Yeongjun Yeo, Dongnyok Shim *, Jeong-Dong Lee and Jörn Altmann
Received: 5 October 2015; Accepted: 27 November 2015; Published: date
Academic Editor: Giuseppe Ioppolo
Technology Management, Economics, and Policy Program, College of Engineering,
Seoul National University, Seoul 151-742, Korea; yyj913@snu.ac.kr (Y.Y.); leejd@snu.ac.kr (J.-D.L.);
altmann@snu.ac.kr (J.A.)
* Correspondence: sk4me@snu.ac.kr; Tel.: +82-2-880-8386; Fax: +82-2-873-7229
Abstract: The main objective of this paper is to identify and analyze the key drivers behind changes
of CO2 emissions in the residential sectors of the emerging economies, China and India. For the
analysis, we investigate to what extent changes in residential emissions are due to changes in energy
emissions coefficients, energy consumption structure, energy intensity, household income, and
population size. We decompose the changes in residential CO2 emissions in China and India into
these five contributing factors from 1990 to 2011 by applying the Logarithmic Mean Divisia Index
(LMDI) method. Our results show that the increase in per capita income level was the biggest
contributor to the increase of residential CO2 emissions, while the energy intensity effect had the
largest effect on CO2 emissions reduction in residential sectors in both countries. This implies that
investments for energy savings, technological improvements, and energy efficiency policies were
effective in mitigating CO2 emissions. Our results also depict that the change in CO2 emission
coefficients for fuels which include both direct and indirect emission coefficients slowed down the
increase of residential emissions. Finally, our results demonstrate that changes in the population
and energy consumption structure drove the increase in CO2 emissions.
Keywords: CO2 emissions; emerging economy; residential sector; Logarithmic Mean Divisia Index
(LMDI) method
1. Introduction
Global warming has been regarded as one of the most important environmental problems of our
age. The ever-increasing amount of CO2 emissions, which are partly to blame for the greenhouse
effect, is aggravating climate change. In an effort to mitigate climate change, the international society
has been undertaking numerous efforts to reduce CO2 emissions at the national level. Given their
high growth potential, emerging economies significantly affect future emission levels. Indeed, among
the worlds emerging economies, China and India are the largest CO2 emitters, accounting for almost
one-third of the worlds CO2 emissions [1,2]. Accordingly, in recent years, rapid economic growth
accompanied by increasing CO2 emissions has focused the worlds attention on China and India in
the context of emissions reduction.
The rapid economic growth experienced by China and India in recent years has led to an
exponential increase in the countries energy consumption [3]. Over the last few years, China and
India have been ranked significantly high in terms of energy demand; China was ranked the largest
global energy consumer in 2011, followed by the United States, Russia, and India [48]. As the energy
consumption structures of both countries are heavily dependent on coal, it is highly likely that the
spurt in economic growth will cause their CO2 emissions levels to rise even further in the coming
Sustainability 2015, 7, pagepage
2
years [9]. Furthermore, more than three-quarters of the worlds energy-related CO2 emissions growth
is expected to come from China and India [10], while it is predicted that the share of the other
industrialized countries will fall continuously.
The recent impressive economic growth in China and India have intensified their demand for
energy across all sectors, including the residential sector. The residential sector in China accounts for
23% of total energy consumption, the second largest share in its economy. Indias residential sector
consumes 36% of its total final energy and constitutes the largest sector in this respect. More
importantly, with increasing household income and rapid electrification in these countries, the
residential sector is recognized as being key toward mitigating CO2 emissions at the national level.
Trends in CO2 emissions are closely tied to economic growth and energy consumption. Therefore,
the main objective of this paper is to identify and understand the drivers behind CO2 emissions from
the residential sector in China and India, so as to draw implications in terms of challenges and
opportunities with regard to their countries energy policies.
To address this objective, we use the Logarithmic Mean Divisia Index (LMDI) decomposition
technique to analyze how socio-economic factors contribute to changes of CO2 emissions in the
residential sector over time (from 1990 to 2011). The LMDI approach has been used previously to
analyze the determinants of CO2 emissions in the residential sector [1115]. However, these studies
are limited to a single country and do not conduct a comparative analysis between countries. By
comparing the factors affecting the trends of CO2 emissions in these countries, we discuss possible
energy policy implications for other developing countries.
The rest of this paper is organized as follows. Section 2 introduces the LMDI methodology and
datasets used for the decomposition analysis. Section 3 presents the main results from the
decomposition analysis on residential CO2 emissions in China and India. Section 4 concludes and
draws policy implications from the analysis.
2. Methods and Data
2.1. The Logarithmic Mean Divisia Index (LMDI) Method
Index decomposition analysis (IDA) has been widely applied to studies that focus on analyzing
the driving factors of energy consumption and greenhouse gas (GHG) emission trends. Index
decomposition starts by defining the various factors associated with the aggregate variable (e.g., industrial
energy consumption and industrial energy-related carbon emissions). Using these defined factors,
different methods can be formulated to quantify the impacts of changes in the factors on the
aggregate variable [14,15]. The IDA approach can be divided into two groups: methods linked to the
Laspeyres index and methods linked to the Divisia index. Since 2000, the LMDI method, classified as
a Divisia index approach, has been the most popular IDA approach [15,16]. The LMDI approach has
advantages in terms of practical implementation; it does not contain an unexplained residual term
and is consistent in aggregation [1719]. Furthermore, the literature notes that the LMDI method
can be used to investigate changes in GHG emissions both in the theoretical and practical
contexts [15,19,20]
This paper investigates the evolving patterns of CO2 emissions in the residential sectors of China
and India from 1990 to 2011 using the LMDI approach. Given their rapid rates of economic growth,
these countries have been experiencing a sharp increase in energy demand. Rampant urbanization
has triggered high construction activity in the worlds top two populous nations. Therefore, household
share of total CO2 emissions will continue to grow as these emerging economies are expected to make
transitions to higher industrialization and higher income levels [21]. Given the strong prospects for
continued rapid economic growth and higher future CO2 emissions, a number of studies have
explored key factors influencing changes of CO2 emissions in the residential sector, with a special
focus on China and India [1113,22,23].
In the case of China, Wang et al. [22] decomposed Chinas CO2 emissions at the regional level
(focusing on Tianjin city) and concluded that income and population effects were the dominant
positive factors affecting the growth in CO2 emissions for all sectors. Zhao et al. [13] concluded that
Sustainability 2015, 7, pagepage
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price effects resulting from price deregulation in the energy sector contributed to reduction of energy
consumption, and thus slowed down CO2 emissions. Zha et al. [11] estimated and compared CO2
emissions trends and key factors influencing these trends for the urban and rural residential sectors.
They found that energy intensity and income effects, respectively, contributed the most to the decline
and the increase of residential CO2 emissions in both urban and rural areas. Xu et al. [23] also reported
that an increase in per capita energy usage contributed to rising emissions in the residential sector,
while the effects of the shifts in the energy mix and emission coefficients on the change in CO2
emissions were marginal. For the Indian residential sector, Das et al. [24] identified that income level,
energy consumption expenditure level, and population size were the main factors responsible for
increasing CO2 emissions at the household level. Pachauri [25] analyzed the variations in the pattern
of household energy consumption using micro level survey data. The econometric analysis showed
that income level and age of the household head were important explanatory variables for variation
in energy requirements across households. As one of the analysis tools, the LMDI approach has been
employed in a number of practical applications pertaining to the analysis of the determinants
associated with the change of CO2 emissions in the residential sector.
2.2. Model Construction
The LMDI method can be either additive or multiplicative form [14,15]. In our analysis, we use
the additive form of the LMDI approach to analyze the driving forces contributing to changes in CO2
emissions from the residential sectors in China and India. Annual aggregate residential CO2
emissions can be decomposed into five factors as follows (Equation (1)):







(1)
As shown in the Equation (1),  refers to each country’s aggregate CO2 emissions from the
residential sector, and 
represents the CO2 emissions arising from consumption of fuel type
i (e.g., coal, oil, gas, and electricity). In calculating those values, both direct CO2 emissions from
residential fuel use and indirect CO2 emissions arising from the electricity and heat consumption are
considered. In addition, we define the following variables for year t: , the residential energy
consumption level for fuel i; , total energy consumption in the residential sector; , Gross
Domestic Product (GDP) level; , population size in each country. Hence, the Equation (1) can
be expressed as an Equation (2):



(2)
In the Equation (2),  (=
) term is the emission factor of each fuel in the residential
sector, which is CO2 emission coefficient effect. This effect evaluates the fuel quality [11], and this
covers both direct emission coefficients and indirect emission coefficients.  (=
) captures the
energy consumption share of fuel i relative to the total energy consumption, which indicates the
energy substitution effect. In addition,  (=
) term presents energy intensity of the residential
sector, which is related to investments for energy savings, technological improvements, and energy
efficiency policies.  (= 
), and  are, respectively, per capita income, and population
effect associated with the change in the residential CO2 emissions.
Based on this structure, we can decompose the observed changes in CO2 emissions (
from the residential sector from a base year (t-1) to a target year (t) into five different factors as
mentioned above: the change in emission factors in energy consumption (CO2 emission
coefficient effect), the change in energy consumption structure (energy substitution effect),
the change in residential energy intensity (energy intensity effect), the change in the income
of residents (income effect), and the change in population size (population effect).
Sustainability 2015, 7, pagepage
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Hence, the changes of aggregate residential CO2 emissions between year (t-1) and year (t) can be
expressed as the following equation (Equation (3)).
    
(3)
In addition, those five contributing factors can be derived from Equation (4) to Equation (8).
Those factors give information upon relative contributions to changes in residential CO2 emissions.
Each of those five effects is isolated by measuring changes in residential CO2 emissions associated
with the change in the corresponding variable, while fixing the other variables constant with
respective values in base year [26,27].
 







(4)
 







(5)
 







(6)
 







(7)
 







(8)
where , , , , and  denote the changes in CO2 emissions
from the residential sector arising from the CO2 emission coefficient effect, energy substitution effect,
energy intensity effect, income effect, and population effect, respectively.
2.3. Data Collection
In order to apply above mentioned additive form of the LMDI approach, we collect the data
related to the energy consumption and CO2 emissions from the residential sectors in China and India.
Data for the residential CO2 emissions levels from 1990 to 2011 in both countries are collected from
the International Energy Agencys (IEA) datasets [1,2,28]. The values of energy consumption in the
residential sectors from 1990 to 2011 are sourced from other IEA datasets [6,7].
For China, we divide energy sources into 11 categories according to [6,7], including coal
(bituminous coal, patent fuel, coke oven coke, gas works gas, and coke oven gas), oil (liquefied
petroleum gases (LPG), kerosene, and gas/diesel oil), natural gas, electricity, and heat. For India,
seven categories of energy sources, including coal (coking coal, bituminous coal, and brown coal
briquettes (BKB)), oil (LPG, and kerosene), natural gas, electricity, and heat are considered.
Furthermore, according to the accounting methodology for CO2 emissions proposed by the
Intergovernmental Panel on Climate Change (IPCC) [28], biofuels is not considered in assessments
of CO2 emissions from fuel combustion.
By constructing datasets, both direct CO2 emissions from fuel combustion and indirect emissions
associated with the electricity and heat consumption have been considered to understand the
residential CO2 emissions. CO2 emissions from electricity and heat generation have been allocated to
residential sectors in proportion to the electricity and heat consumed. In case of direct emissions from
fuel use, emission factor (coefficient) by fuel type is calculated by dividing CO2 emissions arising
from consumption of fuel i by consumption level of fuel i in the residential sector. As with direct
emission factor, indirect emission factor is derived from dividing indirect CO2 emissions by the
amount of electricity and heat demand from the residential sector. The population and GDP levels
during the study period are obtained from the statistics collected by the Organization for Economic
Co-operation and Development (OECD) [29,30].
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3. Analysis Results
3.1. Residential CO2 Emissions and Energy Consumption Structure
The CO2 emissions from the residential sector in China, including both direct emissions and
indirect emissions) increased by 136%, from 398.9 million tonnes CO2 equivalent (MtCO2e) in 1990 to
942.1 MtCO2e in 2011, during the study period (20092011). India also shows a clear trend of a rapid
increase between 1990 (79 MtCO2e) and 2011 (278 MtCO2e). When comparing the absolute emissions
levels between two countries, China shows higher values, as it is the largest CO2 emitter in the world.
Moreover, residential CO2 emissions in India show a steady increase from 1990 to 2011 (Figure 1).
Figure 1. Residential CO2 emissions from China and India (Unit: MtCO2e).
The energy consumption structures associated with residential CO2 emissions in both countries
show the structural changes in fuel switching (Tables 1 and 2) and the quantity of energy used (Figure 2).
Residential energy consumption in China is more than triple that of India in terms of the aggregate
level. However, both countries experienced rapid increase in rates of energy consumption in the
residential sectors from 1990 to 2011, mainly due to rapid urbanization, industrialization, and
economic growth [31]. Those factors contributed to the accelerated growth of energy demand in their
residential sectors.
Figure 2. Trends of energy consumption level in China and India (Unit: kTOE).
The structural transitions of energy consumption by the residential sectors of both countries are
reported in Table 1 and Table 2. As shown in Table 1, coal and peat products accounted for the largest
proportion of energy consumption during the whole study period. However, it is also pertinent to
Sustainability 2015, 7, pagepage
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note that the residential sector in China shifted from using the most carbon-intensive energy source
(coal, including bituminous coal, patent fuel, etc.) to oil products, natural gas, and electricity. In the
case of China, the share of coal and peat products in energy consumption by the residential sector
declined from 90.3% in 1990 to 34.5% in 2011. On the other hand, the shares of oil products, natural
gas, and electricity grew by 13.1%, 11.1%, and 23.6%, respectively, between 1990 and 2011.
Table 1. Final energy consumption structure of Chinas residential sector (Unit: %).
1990
1995
2000
2005
2010
2011
Coal/
Peat
Bituminous Coal
89.09%
73.92%
48.82%
38.42%
31.71%
29.75%
Patent Fuel
0.00%
4.59%
5.63%
5.48%
2.43%
2.35%
Coke Oven Coke
0.19%
0.79%
1.02%
0.49%
0.18%
0.16%
Gas Works Gas
0.29%
0.90%
2.54%
2.08%
1.00%
1.40%
Coke Oven Gas
0.78%
0.92%
1.90%
1.65%
1.27%
0.85%
Oil
LPG
1.65%
5.70%
11.38%
12.82%
10.49%
10.82%
Kerosene
0.83%
0.64%
0.86%
0.23%
0.13%
0.14%
Gas/Diesel Oil
0.00%
0.15%
2.01%
3.33%
4.72%
5.12%
Natural Gas
1.35%
1.44%
2.99%
5.34%
11.39%
12.40%
Electricity
3.97%
8.27%
16.73%
20.22%
27.02%
27.64%
Heat
1.85%
2.68%
6.13%
9.93%
9.66%
9.38%
Table 2. Final energy consumption structure of Indias residential sector (Unit: %).
1990
1995
2000
2005
2010
2011
Coal/
Peat
Coking Coal
2.44%
1.17%
0.86%
0.00%
0.04%
0.04%
Bituminous Coal
16.86%
13.89%
11.27%
10.23%
9.14%
8.53%
BKB
2.29%
1.77%
0.91%
0.65%
0.68%
0.81%
Oil
LPG
12.21%
15.56%
21.56%
33.24%
34.70%
34.38%
Kerosene
50.27%
46.87%
41.56%
29.90%
22.66%
19.23%
Natural Gas
0.24%
0.66%
0.97%
0.19%
0.06%
3.62%
Electricity
15.69%
20.07%
22.85%
25.79%
32.72%
33.41%
For India, it is found that the residential sector in India also experienced a structural transition
in terms of the relative importance of different types of energy sources (Table 2). In 1990, the majority
of Indias energy demand was fulfilled by coal (21.6%) and oil products (62.5%), respectively.
However, shares of coal and oil products in total final energy consumption in the residential sector
declined to 9.4% and 53.6%, respectively, in 2011. These declines were accompanied by increased
shares of natural gas and electric power during this period. In summary, like Pachauri and Jiang [31],
we find that both China and India have undergone transitions with regard to residential energy
consumption, which are described by their shifting away from low efficiency solid fuels to more
efficient liquid and gaseous fuels and electric power.
It is also essential to look into the electricity generation mix by fuel type for each country, as
indirect emissions arising from the electricity consumption are taken into consideration when
analyzing changes of residential CO2 emissions. In China, the electricity consumption by the
residential sector grew rapidly from 53.4 TWh (approximately 4.0% of final energy consumption in
the residential sector) in 1990, to 573.1 TWh (27.6% of total final energy consumption) in 2011
(Table 1). In addition, as shown in Figure 3, Chinas electricity sector is predominantly reliant on
fossil fuels, especially via coal-fired generation [32,33]. It is found that total electricity output from
coal-fired power plants with high emission coefficients grew significantly, from 600.3 TWh in 1992 to
3751.0 TWh in 2012. Dominance of coal-fueled power plants in China can be attributed to its large
proven coal reserves [34], firmly holding the first place among coal producing countries.
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Figure 3. Generation mix by fuel type in China (Unit: TWh)
As with China, the demand for greater electricity generation is found for Indias residential
sector during the study period. Indias residential sector also showed rapid rates of electrification,
and the share of electricity in final energy consumption grew by 7.9% annually, increasing from 15.7%
in 1990 to 33.4% in 2011 (Table 2). Indias electricity sector is also heavily dependent on fossil fuels,
especially coal, because it is the least expensive fossil fuel for power generation in the Indian economy
(Figure 4). This can be understood by the Indias domestic coal reserves (the third largest coal reserves
in the world), and relatively easy access to affordable imported coal [34]. From 1990 to 2011, increased
electricity generation was accompanied by increased coal-fired generation [32,33]. It is also found
that total electricity generation output grew from 229.3 TWh in 1992 to 714.9 TWh in 2011.
Figure 4. Generation mix by fuel type in India (Unit:TWh).
3.2. Main Results from the LMDI Methodology
This section presents the main results of the year-by-year decomposition analyses of China’s and
India’s residential CO2 emissions from 1990 to 2011. We use the equations and datasets mentioned in
Section 2 to investigate the degree of contribution of socio-economic factors to trends in CO2
emissions. The decomposition analyses allow us to understand the impact of those factors driving
these trends in each country. Furthermore, we find common features in the identified contributing
factors and accordingly draw policy implications for major CO2 emitting countries such as, China
and India.
Figures 5 and 6 present the LMDI decomposition results from 1990 (the base year) to 2011 (the
target year) for China and India, respectively. In the case of China (Figure 5), the emission coefficient
effect ( ) and the energy substitution effect () accounted for a decrease of 43.0
MtCO2e and an increase of 234.5 MtCO2e, respectively, in CO2 emissions from the residential sector.
Furthermore, the intensity effect () contributed to a decrease of 697.6 MtCO2e, whereas
the income effect () and population effect () caused increases of 965.5 MtCO2e and
83.7 MtCO2e, respectively.
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Figure 5. Results of LMDI decomposition from 1990 to 2011 in China (Unit: MtCO2e).
Figure 6. Results of LMDI decomposition from 1990 to 2011 in India (Unit: MtCO2e).
When analyzing the corresponding factors for India (Figure 6), similar trends are observed from
1990 to 2011. The CO2 emission coefficient effect was responsible for a decrease of 14.4 MtCO2e in
residential CO2 emissions, while changes in the fuel mix pertaining to the energy consumption
structure accounted for an increase of 60.7 MtCO2e. In addition, the change in the population drove
increases in CO2 emissions by 65.9 MtCO2e. The income effect was the main contributory factor
raising CO2 emissions from Indias residential sector, as it drove an increase of 173.0 MtCO2e. On the
other hand, it is found that the change in energy intensity was mainly responsible for slowing down
residential CO2 emissions, causing a decrease of 86.1 MtCO2e between 1990 and 2011.
Accordingly, it can be stated that the income effect was the major contributory factor leading to
increased CO2 emissions in both countries during the study period. The changes in energy
consumption structure and population also drove increase both countries CO2 emissions from their
residential sectors, while CO2 emission coefficient effect slowed down the residential CO2 emissions.
In addition, the energy intensity effect was mainly responsible for the CO2 emissions reduction in the
residential sectors of China and India from 1990 to 2011.
Besides analyzing the overall trends from the decomposition analysis, it is essential to analyze
year-by-year trends of factors contributing to residential CO2 emissions. Figures 7 and 8 highlight
several characteristics of the decomposition analysis of residential CO2 emissions since 1991. It is
worthwhile to identify the forces responsible for residential CO2 emissions and the differences
between the two countries in terms of policy interventions associated with this issue. We discuss each
of the five contributing factors in the following sub-sections.
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Figure 7. Year-by-year decomposition analysis for China (Unit: MtCO2e)
Figure 8. Year-by-year decomposition analysis for India (Unit: MtCO2e).
3.2.1. CO2 Emission Coefficient Effect
Figure 7 shows that the CO2 emission coefficient effect ( contributed the least toward
changes in residential CO2 emissions in China. In fact, during the entire period under study, the CO2
emission coefficient effect on the changes of residential CO2 emissions is found to be negative.
In addition, the results from the annual decomposition analysis show that this effect on residential
CO2 emissions tends to fluctuate. Given that we consider both the direct emissions from fuel
combustion and the indirect emissions from the consumption of electricity and heat, the CO2 emission
coefficient effect ( is associated with direct emission factors (which is calculated by
dividing direct CO2 emissions by fuel use), and indirect emission factors (which is defined as indirect
emissions from the electricity consumed per unit of electricity consumed). In this context, the impact
of emission coefficients on residential CO2 emissions is mainly determined by the fluctuation of
indirect emission coefficients, while the direct emission coefficient is fuel specific, and relatively
constant over the study period [35]. The electricity emission coefficient, and the indirect emission
coefficient could vary depending on the fuel mix used to generate the electricity, and electricity
generation efficiency.
In the case of China, it is evident that, barring the period from 2003 to 2005, these changes slowed
down residential CO2 emissions since 1999. As shown in Figure 3, Chinas electricity sector is
predominantly reliant on fossil fuels, especially via coal-fired generation, and total electricity output
from coal-fired power plants with high emission coefficients grew significantly. Therefore, it can be
inferred that the CO2 emission coefficient effect ( on reducing Chinas residential CO2
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emissions could be attributed to increased electricity production and transmission efficiency. These
efficiency gains reduced the indirect emission coefficients, partly offsetting the increased electricity
generation from coal-fired power plants. As shown in the Figure 9, the indirect emission coefficient
for Chinas residential sector showed a decreasing trend with the exception of the period from 2003
to 2005, which implies a strong relationship between the CO2 emission coefficient effect (
and the indirect emission coefficient. Furthermore, it is also found that CO2 intensity of coal-fired
generation (CO2 emissions per KWh from electricity generation using coal) in China decreased from
1,102 in 1990 to 950 in 2011 [32]. Efficiency improvements in the power sector can be understood by
retracing the relevant policy implementation during this period. As mentioned above, the CO2
emission coefficient effect ( had contributed to the decrease of residential CO2 emissions
since 1999 in China. Therefore, it is worthwhile to look into relevant policies to improve the overall
efficiency in the electricity power generation which were implemented at that time (around 1999).
Figure 9. Trends of indirect emission coefficients in China (Unit: MtCO2e/GWh).
A scrutiny of the regulation based on policy implementation shows that Chinas government
has made substantial efforts to increase the efficiency of the electricity production and transmission
process by transforming power sectors market structure. In fact, the Electric Power Law legislated
in 1996 was brought into force, which aimed to regulate the generation, distribution, and
consumption of electricity [36]. For instance, small-scale power plants were prohibited, and small
coal-fired power plants with inefficient facilities had been continuously closed down through 2002.
However, closure of small and inefficient power plants stopped completely from 2003 to 2004, due to
the power supply shortages experienced in China at that time [37]. In addition, closing of small plants
with outdated capacity was again accelerated from 2005, showing the average growth rate of closure
capacity of 115% from 2005 to 2008, as reported by [37]. Therefore, it can be inferred that the annual
fluctuations in the CO2 emission coefficient effect ( were partly driven by the small plant
closures which had been implemented since 1999.
In addition, in 1997 the Chinese government took steps to reform the electricity sector by
establishing the State Power Corporation. This action involved separating business operations and
management from government authorities in order to resolve the power industrys structural
problem [36]. Starting with this action, the Chinese government accelerated market reforms in the
electricity sector by transforming the monopoly system of the countrys planned economy to a
market-based economy. In 2002, Chinese government dismantled the State Power Corporation
legislated by the Scheme of the Reform for Power Industry, and set up 11 new companies in a move
to end the corporations monopoly of the power industry, which was expected to improve efficiency
and lower costs. Thus, the restructuring process with breaking up the monopoly in the industry and
introducing the concept of competition helped improve the overall electricity production (including,
the fuel use efficiency) and transmission efficiency in Chinas power sector. Xu and Chen [36]
Sustainability 2015, 7, pagepage
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estimated that the coal consumption for power supply was reduced with improvements of fuel use
in the power generation process after the market reforms in 2002. In addition, Zha et al. [38] also
reported that transmission losses in Chinas power sector declined from 7% in 1990 to 6.5% in 2004.
Figure 8 shows that the emission coefficient factor generally drove the increase in CO2 emissions
from Indias residential sector until 2004. Conversely, this factor drove CO2 emissions reduction in
the residential sector since 2004. Indias electricity sector is also heavily dependent on fossil fuels,
especially coal, as shown in the Figure 4. From 1990 to 2011, increased electricity generation was
accompanied by increased coal-fired generation with high emission coefficients. Similar to Chinas
case, the CO2 emissions coefficient effect ( on reducing Indias residential CO2 emissions
from 2004 onwards could be attributed to gradual improvements in power generation and
transmission efficiencies. As shown in the Figure 10, the indirect emission coefficient for Indias
residential sector showed an increasing trend from 1990 to 2002, while it showed a decreasing trend
over the period from 2002 to 2011. It is also confirmed that there is a strong relationship between the
CO2 emission coefficient effect ( and the indirect emission coefficient.
Figure 10. Trends of indirect emission coefficients in India (Unit: MTCO2e/GWh).
Indeed, Indias government implemented the Electricity Act of 2003 to create a market-based
regime in the electricity sector to ensure a stable supply of electricity [3840]. This legislation entailed
open access to the transmission and distribution system, introduction of competition in the power
generation sector, and facilitating an electricity trading market [41]. In particular, this Act aimed to
improve efficiency and customer service standards by promoting competition among various players
within the electricity sector [38]. Furthermore, Indias short-term energy policies were executed based
on Five-Year Plans [41]. During the 9th and 10th Five-Year Plans (19962001 and 20022007,
respectively), approximately 138 units at 29 power stations were taken up for renovation and
modernization in order to improve their technological performance and efficiency [4]. Thus, its efforts
to enhance efficiency in power generation helped India’s power sector take steps for promoting
positive technology changes related to energy conversion efficiency [42,43].
As discussed above, we can figure out that both countries power sector reforms had promoted
competition within these sectors, and improved the overall efficiency in electricity production and
transmission process. Furthermore, the annual fluctuations in the CO2 emission coefficient effect
( can be partly understood by each countrys relevant policy implementations as
mentioned above. In this context, differences of annual s trends between China and India,
and those fluctuations can be liked with timing of relevant policies implemented, as shown in
Figure 11.
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Figure 11. Annual trends of  and relevent policies in China and India.
3.2.2. Energy Substitution Effect
The energy substitution effect ( contributed to increases in CO2 emissions for most of
the study period for both China (234.52 MtCO2e) and India (60.65 MtCO2e). This energy substitution
effect () captures to what extent changes in residential emissions are due to changes in the
share of energy consumption by fuel type. Investigating the trend of the energy substitution effect
requires an analysis of the energy consumption structure, as discussed in section 3.1. For China, the
proportion of coal products in residential energy consumption decreased substantially from 1990 to
2011, as shown in the Table 1. On the other hand, the share of the electricity and heat, both of which
have high emission coefficients, increased from 5.8% in 1990 to 37.0% in 2011. Therefore, it can be
understood that even though there was a reduction in the direct emissions from most
carbon-intensive energy sources, the indirect emissions from electricity and heat consumption
increased. Accordingly, it can be inferred that the increased share of electricity largely based on the
coal-fired generation substituted for other fuels, resulted in the increase of residential CO2 emissions.
A similar trend in energy consumption structure of the residential sector, is also detected for
India (Table 2). In 1990, oil products (e.g., LPG and kerosene) accounted for the largest share of energy
consumption associated with emissions from the residential sector, followed by coal products
(e.g., coking coal and bituminous coal). However, in 2011, while oil products continued to dominate
other fuels, electricity took the second largest share (33.41%) in the final energy consumption. In other
words, the share of coal in the energy consumption structure of Indias residential sector was
relatively reduced while that of electricity rose. Accordingly, it can be understood that electricity
consumption which has high emission coefficients, substituted demands for other energy sources,
resulting in increases of residential CO2 emissions in both countries.
In 2000, India alone accounted for more than 35% of the worlds population without electricity
access, which was the largest contributor in the world [44,45]. To counter this, the Indian government
launched several programs to provide households access to electricity in India. In 2000, the Prime
Ministers Village Development Program was launched with a focus on providing basic services,
including the rural electricity supply, to villages with investments in new generation capacity [46,47].
In addition, the Rajiv Gandhi Grameen Vidyutikran Yojana (RGGVY), funded by the Rural
Electrification Corporation (REC), was launched in 2005, which aimed to electrify all villages and
habitations in India. The REC designed a couple of policy programs to provide free supply of
electricity to households below the poverty line and invested in fundamental infrastructure, such as
electricity distribution transformers and distribution lines, to fulfill this goal [45,48].
Chinas central government had also undertaken many initiatives to expand citizens access to
electricity. From 1990 to 2002, over 900 million rural residents had benefitted from these initiatives,
Sustainability 2015, 7, pagepage
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and the country had achieved an electricity access rate of as high as 98% in 2002 [49,50]. In 1996, the
Chinese government launched the Brightness Program and Developing Rural Power through Wind
to improve the living conditions of populations in remote areas by providing electricity from
decentralized sources [51]. In fact, the institutional management of rural electricity was operated by
a multi-level administrative system until the late 1990s. From 1998 to 2002, China implemented and
launched a series of policy instruments aimed at reforming rural electricity management, renovating
rural power grid and leveling rural and urban electricity tariffs [50]. Moreover, the central
government facilitated commercial operations in the utility market by letting the local electricity
supply free from the control of local governments. Furthermore, the rural and urban electricity
systems have been merged to form a uniform and integrated nationwide system after 2002.
As described above, China and Indias governments made efforts to expand households access
to electricity in order to stimulate economic and social development, and decrease disparities
between rural and urban residents. However, contrary to Indias relatively low level of the electricity
access (62.3% of population), China showed higher level of the electricity access (98.0% of population)
in 2000. In this context, in the early 2000s Indias government mainly took into consideration
promoting access to electricity nation wide with investments in new generation capacity and
fundamental infrastructure by implementing various programs as mentioned above. On the other
hand, the Chinese government focused on renovating rural power grids, and improving the existing
systems to facilitate efficient delivery of electricity to remote areas. Accordingly, we can figure out
different approaches between China and India to expand households’ access to electricity during the
study period. Furthermore, we can infer that those programs implemented by each countrys
government to promote electricity consumption partially drove changes of , as shown in
Figure 12.
Figure 12. Annual trends of  and relevant policies in China and India.
3.2.3. Energy Intensity Effect
The energy intensity effect () contributed to emissions reductions over 1990 to 2011 in
both China and India. It was the main driving factor responsible for slowing down residential CO2
emissions, as it cancelled out the positive effects arising from the other factors. This energy intensity
effect (  ) captures the effectiveness of investments for energy savings, technological
improvements, and energy efficiency policies. Figure 5 shows that about 697.58 MtCO2e of CO2
emissions reduction were associated with the change in energy intensity of Chinas residential sector
over the whole study period. In addition, we detected that  had the largest impact in 1998,
driving a decline of 101.86 MtCO2e in CO2 emissions. For India, about 86.08 MtCO2e of CO2 emissions
reduction was driven by the energy intensity effect throughout the study period (Figure 6), while it
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had the largest impact in 2010, contributing to emissions reductions of 22.83 MtCO2e. Impacts of the
energy intensity effects on residential emissions reductions in both countries can be attributed to each
countrys efforts to improve energy efficiency by promoting technological progress and regulating
standards for product energy efficiency levels. Therefore, the energy intensity effect can be
interpreted as the decrease in energy consumption from energy efficiency gains, partly offsetting the
increase energy consumption from economic growth [11].
China has undertaken wide-ranging efforts toward improving energy efficiency. Since the 1990s,
it has been encouraging citizens to replace their old energy-inefficient home appliances [52]. In 1990,
the Chinese government unveiled its first program on energy efficiency standards for appliances such
as air conditioners and washing machines [5355]. After modernizing the energy efficiency standard
system in mid 1990s, the Energy Conservation Law was formulated in 1998 to introduce mandatory
minimum efficiency standards and energy efficiency labeling [53]. Accordingly, new standards and
labeling requirements (such as voluntary energy efficiency labeling) were implemented in 1999,
inducing appliance manufacturers to invest in technological progress [53,56]. Furthermore, the
energy efficiency labeling system was formally established in China in 2005 in order to help
consumers make more informed choices while buying appliances [11,57].
India also recognized the importance of energy efficiency. The government passed the Energy
Conservation Act in 2001 in order to reduce the energy intensity of the economy [58]. In addition, the
Bureau of Energy Efficiency (BEE), established in 2002 under the Ministry of Power [58,59], initiated
a number of energy efficiency initiatives in the areas of household lighting, standards, labeling of
appliances, and so on. The establishment of the BEE was a turning point for articulating national
initiatives for energy efficiency. In 2006, the BEE launched the Standards and Labeling Program for
residential and commercial appliances/equipment, including 19 categories of products such as air
conditioners, refrigerators, color TVs, washing machines, and so on. Furthermore, in 2008, India
announced the National Action Plan on Climate Change, which emphasized climate change
mitigation. Indias government included the National Mission for Enhanced Energy Efficiency
(NMEEE) as one of the missions under this Plan. Based on this initiative, India is making efforts to
accelerate the shift to more energy efficient products and is using fiscal instruments to promote their
development [60,61].
In summary, China and India have long perceived the importance of energy efficiency and
adopted energy efficiency standards and labeling systems. However, these policies were initiated at
different times, from the 1990s for China and from the 2000s for India. Therefore, we can infer that
this difference in the timing of policy implementation between two countries shows up in the
observed trends in their energy intensity effects (Figure 13).
Figure 13. Annual trends of  and relevent policies in China and India.
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3.2.4. Income Effect
The income effect () had the largest effect on increases of CO2 emissions from the
residential sectors in China and India. Figure 5 shows that an increase of 965.50 MtCO2e of emissions
was attributed to the change in the income level in China during the whole study period, while the
corresponding value for India was 172.96 MtCO2e (Figure 6). In addition, the trends for this factor
show a pronounced increase from 1990 to 2011 in both countries. China and India are the worlds
fastest expanding large economies. From 1990 to 2011, China and India experienced rapid economic
growth by means of privatization and trade liberalization, as evidenced by their average GDP growth
rates of 9.1% and 6.2%, respectively. In addition, it is found that in China, the proportion of expense
on food to total consumption expenditure decreased from 54.2% in 1990 to 36.3% in 2011, while the
share of expense on transport and communications rose from 3.2% in 1990 to 14.2% in 2011 [62]. For
India, the share of food expenditure had also steadily fallen from 60-70% in 1990 to 40-50% in 2011
[63]. We can infer that the rapid growth of energy demand is associated with high and stable
economic expansion evidenced in both countries, higher household income levels, and improved
access to electricity encouraged households to use more electric home appliances.
In fact, the electrification rates of China and India increased from 94.2% and 50.9% in 1990 to
100% and 78.7% in 2012, respectively, [64]. Bearing in mind that access to energy is essential for
economic development and a better quality of life, the Chinese and Indian governments launched a
variety of programs to support household electrification in their countries. In the 1990s, China made
great strides in enhancing rural electrification. Chinese power companies owned by the public sector
enacted several programs to spur rural electrification using renewable energy such as solar and wind
energy [65,66] (e.g., Serving Agriculture & Serving Peasants, Serving Rural Economic Development,
and Project for Reducing Poverty and Simultaneously Enriching Rural and Urban Households by
Electrification). In addition, in 1998 the Chinese government launched the Rural Network
Development and Upgrade Program, which involved reforms in rural electricity systems, including
leveling tariffs across networks and promoting technological advances in the electricity supply
system [67].
India also executed a variety of programs to fulfill the energy and electricity demand of its rural
population from the early 1990s by covering a wide range of technology and fuel options [45,66]. For
example, Indias government initiated the Rural Electricity Supply Technology Mission (REST) in
2002 in order to provide decentralized electricity generation options in all villages using local
renewable energy sources instead of depending on the centralized electricity supply system. This
initiative included identifying and adopting technological solutions and providing financial support
to rural areas [68,69].
Consequently, the electrification rates in both countries improved between 1990 and 2011. With
better access to the electricity, the number of home appliances owned by the residential sector with
higher income level increased drastically in China and India [12,70]. Therefore, it shows that people
with better access to electricity experienced significant improvements in living standards, which led
to consuming more energy to sustain their comfortable lives.
3.2.5. Population Effect
The population effect () contributed to increasing CO2 emissions in China (83.65
MtCO2e) and India (65.85 MtCO2e) during the whole study period. From 1990 to 2011, population
grew steadily, with average annual growth rate of 0.77% in China and 1.74% in India. The significant
population size and its expanding growth rate drove rapid energy consumption in the residential
sectors of both countries. Furthermore, the urbanization rate (which refers to the share of population
living in urban areas) increased from 26% in 1990 to 51% and 31% in 2011 in China and India,
respectively [71]. With rapid rates of urbanization, China now has the largest urban population
(758 million), followed by India (410 million). In fact, as a part of family planning policy, China had
implemented the one child policy for last two decades to control the population. However, from the
decomposition analysis it is found that the population effect in China was relatively constant over
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the study period. Hence, it can be inferred that, while one child policy had somewhat slowed down
Chinas population growth, rapid rates of urbanization had made rural residents move to urban areas
with higher living standards, resulting in higher demands for energy sources. Therefore, it can be
understood that the process of urbanization, along with the already large population size, drove the
increase of CO2 emissions in the residential sectors of both countries [72].
4. Conclusions and Discussions
Emerging economies can significantly influence future emission levels on a global scale, given
their present emission levels and high growth potentials. Trends and changes in CO2 emissions in
such countries are closely associated with their economic growth and changes in energy consumption.
Among the emerging economies, China and India are the worlds largest CO2 emitting countries,
together accounting for almost one-third of global CO2 emissions. Moreover, their recent impressive
economic growth has intensified their energy demand in all sectors, including the residential sector.
The residential sector in China accounts for 23% of the countrys total final energy consumption,
comprising the second largest share in its economy. Indias residential sector is responsible for 36%
of the nations total final energy consumption, which is the largest sector in its economy. More
importantly, increasing households income and rapid rates of electrification in these countries point
to enhanced focus on the residential sector as an important segment in mitigating CO2 emissions at
the national level.
Therefore, it is essential to analyze trends of residential CO2 emissions in those countries, and
investigate key contributing factors behind them. After identifying these determinants, we can
investigate the challenges and opportunities pertaining to those countries existing energy policies.
In this paper, we analyzed changes of residential CO2 emissions in China and India using LMDI
decomposition analysis. Five socio-economic factors were considered as key contributing factors
affecting changes of residential CO2 emissions: CO2 emission coefficient effect (), energy
substitution effect ( ), energy intensity effect (  ), income effect (  ), and
population effect (  ). Along with analyzing relative contributions of each factor, we
investigated relevant policies implemented by those countries, which were related to changes of five
factors (, , , , and ).
We found that during the period 1990-2011, the increase in per capita income level contributed
to the most to the increase in CO2 emissions. This implies that rapid growth of energy demand is
associated with high and stable economic expansion evidenced in both countries. In addition, we
found that the energy intensity effect had the largest effect on CO2 emissions reduction in both
countries residential sectors, which implies that investments for energy savings, technological
improvements, and energy efficiency policies were effective in mitigating CO2 emissions. It is also
found that the change in CO2 emission coefficients for fuels which is the CO2 emissions per unit of
fuel uses slowed down the increase of residential emissions. This can be understood by both countries
power sector reforms which promoted competition within power sectors, and improved the overall
efficiency in electricity production and transmission process. Furthermore, our results demonstrate
that changes in the population and energy consumption structure drove the increase in CO2
emissions. Those results are largely associated with rapid rates of electrification and better access to
electricity experienced by those countries residential sectors, resulting in increases of indirect
emissions from the higher levels of electricity consumption.
The increased population, household income level, and urbanization indicate that the residential
sectors of both countries must receive greater attention when planning CO2 emissions mitigation
efforts at the national level. Environmental sustainability is challenged by rapid urbanization and
consumption patterns that prevail in urban settings. Owing in part to their higher incomes, urban
populations tend to consume more energy resources than rural populations [73,74]. Yet many
governments are not well prepared to cope with the speed at which their urban populations are
growing. Therefore, it is essential for governments, including China and India who are experiencing
rapid urbanization, to be well equipped with skillful planning and management to handle residential
CO2 emissions [75]. Furthermore, policies which aim to mitigate residential CO2 emissions should be
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prepared from various perspectives. Based on the results of our research, national level strategies and
policies for low carbon economy in China and India are presented as follows:
(1) Technology policy
Despite prior policy implementation to enhance the overall efficiency in electricity sectors
(as discussed in Section 3.2), the energy efficiency of thermal power generation in China and India is
consistently poor compared to that of other countries (e.g., the United States, Japan, Australia,
Germany, and Italy) [76]. Thus, there is considerable potential to reduce CO2 emissions via energy
efficiency improvements in the electricity sector. The analysis shows that the electricity emission
coefficients and indirect emission coefficient decreased recently for both countries. Therefore, both
China and India need to further reduce the CO2 emission intensity of electricity by adopting advanced
technologies to enhance energy efficiency in their existing power generation systems to cope with
further increases in electricity demand.
To be specific, China and Indias electricity sectors are predominantly reliant on fossil fuels,
especially via coal-fired generation due to large proven coal reserves. It is also found that total
electricity output from coal-fired power plants with high emission coefficients grew significantly.
Therefore, it is essential for both countries to reduce the CO2 intensity of electricity and improve
efficiency by applying mature and advanced technologies and enhancing the overall coal-fired power
generation technology.
In addition, hybrid power systems which combine coal-fired power plants with renewable
energy power generation systems can be another option for the power sector to reduce CO2 intensity
in China and India. The introduction of wind energy, solar energy, and other renewables in electricity
generation can be attractive options to reduce the dependence on fossil fuel power generation.
However, electricity production from these sources is largely intermittent and unstable, which are
strongly influenced by seasonal and regional characteristics. Therefore, it could be an attractive
option for China and Indias power sectors to develop hybrid power systems which integrate
renewables with stable coal-fired power plants. However, this approach poses substantial
developmental and operational challenges. Hence, there should be higher level of investments in
research and development on related technologies, supported by governments.
(2) Industry policy
China and Indias governments made great efforts to make power sector reforms by introducing
competition in the power sector and phasing out small plants with inefficient facilities. Those
approaches aimed to increase the overall efficiency in the power generation and transmission process,
implement effective incentive mechanisms, and deliver electricity with lower costs. Price et al. [37]
estimated that Chinas power market reforms with closures of small plants and outdated generation
capacities resulted in significant amounts of savings from final energy and primary energy
consumption. In addition, Xu and Chen [36] estimated that the coal consumption for power supply
in China was reduced with improvements of fuel use in the power generation process after the market
reforms in 2002. Furthermore, Thakur et al. [38] also reported that transmission losses in Chinas
power sector declined from 7% in 1990 to 6.5% in 2004, after the market reforms.
As mentioned above, it can be inferred that structural changes in China and India electricity
sectors enhanced efficiency in power generation and transmission process, and reduced the carbon
intensity of the power sectors. However, these measures are not enough to complete the desired
structural shift away from carbon intensive and low efficiency systems. As mentioned by Price et al. [37],
additional measures are needed, such as adoption of international practices in energy management
and technologies to encourage efficiency gains, and energy pricing reform (including, energy or
carbon taxes) to incentivize economic agents (electricity producers and consumers) to reduce energy
consumption with efficiency gains.
Furthermore, China and India must continue their efforts toward improving the energy
efficiency of home appliances. The growing population and increased household income levels led
to a drastic increase in the ownership of home appliances in both countries. Fortunately, our analysis
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found that there was a decoupling of GDP growth from energy consumption growth, resulting in the
observed effects of energy intensity on residential CO2 emissions reduction in both countries.
Therefore, efforts to reduce residential energy consumption should be continuously made with
continuous and timely revisions to energy efficiency standards and labeling systems. The home
appliances market is rapidly changing with the rise of the middle class and dramatic technological
progress. Therefore, China and India should capture dynamic changes in the appliance market and
reflect those in newly updated energy efficiency standards and labeling systems.
(3) Population policy
Population policy can be divided into direct (or explicit) policy and indirect (or implicit) policy.
Direct policy refers to government actions taken for the purpose of controlling fertility rates and
maintaining the size of the population. Indirect policy is associated with policy implementation
indirectly influencing individual and family decisions. In terms of direct policy, China implemented
the one child policy for the last two decades as a part of family planning policy. However, from the
decomposition analysis it is found the population effect in China relatively constant over the study
period. Hence, it can be inferred that, while the one child policy had somewhat slowed down Chinas
population growth, rapid rates of urbanization had made rural residents move to urban areas with
higher living standards, resulting in higher demands for energy sources.
As the urban population increases at a rapid rate, managing energy consumption and CO2
emissions from urban populations will become a greater priority in China and India. Rather than
direct population policy such as controlling fertility rates by governmental regulation, more attention
should be given to indirect policy with education programs. Increasing awareness of the urbanization
will have to include the impact of cities on the environment and their contribution to global
environmental solutions [77]. In addition, government officials of both countries need to note that
urban populations in China and India are now experiencing significant increases in per capita income
through rapid urbanization and economic development, offering vibrant new consumer markets for
businesses to serve [74]. In fact, private consumption nowadays plays a significant role in India and
Chinas economy [75]. In this regard, population policy should pay more attention to educating
citizens about purchasing environmentally friendly products, consuming less energy in daily life
which would ultimately lead to residential CO2 emissions reduction in both countries.
Acknowledgments: This work was supported by the National Research Foundation of Korea Grant, funded by
the Korean Government (NRF- 2015027596).
Author Contributions: All four authors contributed to the completion of the research. Yeongjun Yeo contributed
to the concept and design of the paper, and data analysis. Dongnyok Shim contributed to result analysis and
modified the draft. Jeong-Dong Lee and Jörn Altmann contributed policy implication and discussion. Yeongjun
Yeo and Dongnyok Shim were in charge of the final version of the paper. All authors read and approved the
final manuscript.
Conflicts of Interest: The authors declare no conflict of interest.
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... Population was treated as the activity effect driving the energy consumption. Yeo et al. (2015) looked into the CO 2 emission from the residential sector in China and India. They considered different energy types and included the gross domestic product into analysis. ...
... Thus, the latter variable is assumed to be the carrier of the CO 2 emission. Our model is similar to that used by Yeo et al. (2015) in this regard. However, we resort to physical indicators describing the lifestyle of the population as well as the prevailing technologies rather than economic indicators of affluence. ...
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In this article, we decomposed Korean industrial manufacturing greenhouse gas (GHG) emissions using the log mean Divisia index (LMDI) method, both multiplicatively and additively. Changes in industrial CO2 emissions from 1991 to 2009 may be studied by quantifying the contributions from changes in five different factors: overall industrial activity (activity effect), industrial activity mix (structure effect), sectoral energy intensity (intensity effect), sectoral energy mix (energy-mix effect) and CO2 emission factors (emission-factor effect). The results indicate that the structure effect and intensity effect played roles in reducing GHG emissions, and the structure effect played a bigger role than the intensity effect. The energy-mix effect increased GHG emissions, and the emission-factor effect decreased GHG emissions. The time series analysis indicates that the GHG emission pattern was changed before and after the International Monetary Fund (IMF) regime in Korea. The structure effect and the intensity effect had contributed more in emission reductions after rather than before the IMF regime in Korea. The structure effect and intensity effect have been stimulated since the high oil price period after 2001.
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In order to better understand sectoral greenhouse gas (GHG) emissions in China, this study utilized a logarithmic mean Divisia index (LMDI) decomposition analysis to study emission changes from a sectoral perspective. Based on the decomposition results, recently implemented policies and measures for emissions mitigation in China were evaluated. The results show that for the economic sectors, economic growth was the dominant factor in increasing emissions from 1996 to 2011, whereas the decline in energy intensity was primarily responsible for the emission decrease. As a result of the expansion of industrial development, economic structure change also contributed to growth in emissions. For the residential sector, increased emissions were primarily driven by an increase in per-capita energy use, which is partially confirmed by population migration. For all sectors, the shift in energy mix and variation in emission coefficient only contributed marginally to the emissions changes. The decomposition results imply that energy efficiency policy in China has been successful during the past decade, i.e., Top 1000 Priorities, Ten-Key Projects programs, the establishment of fuel consumption limits and vehicle emission standards, and encouragement of efficient appliances. Moreover, the results also indicate that readjusting economic structure and promoting clean and renewable energy is urgently required in order to further mitigate emissions in China.