The decomposition of energy-related carbon emission and its decoupling with economic growth in China

Full text

The decomposition of energy-related carbon emission
and its decoupling with economic growth in China
Yue-Jun Zhang
b,c,
n
, Ya-Bin Da
a,d
a
School of Management and Economics, Beijing Institute of Technology, Beijing 100081, PR China
b
Business School of Hunan University, Changsha 410082, PR China
c
Center for Resource and Environmental Management, Hunan University, Changsha 410082, PR China
d
Center for Energy and Environmental Policy Research, Beijing Institute of Technology, Beijing 100081, PR China
article info
Article history:
Received 29 December 2013
Received in revised form
21 July 2014
Accepted 17 September 2014
Available online 8 October 2014
Keywords:
Carbon emissions
Carbon emission intensity
LMDI
Decoupling index
abstract
In order to find the efficient ways to reduce carbon emission intensity in China, we utilize the LMDI
method to decompose the changes of China's carbon emissions and carbon emission intensity from 1996
to 2010, from the perspectives of energy sources and industrial structure respectively. Then we introduce
the decoupling index to analyze the decoupling relationship between carbon emissions and economic
growth in China. The results indicate that, on the one hand, economic growth appeared as the main driver
of carbon emissions increase in the past decades, while the decrease of energy intensity and the cleaning
of final energy consumption structure played significant roles in curbing carbon emissions; meanwhile, the
secondary industry proved the principal source of carbon emissions reduction among the three industries
and had relatively higher potential. On the other hand, when the decoupling relationship is considered,
most years during the study period saw the relative decoupling effect between carbon emissions and
economic growth, which indicated that the reduction effect of inhibiting factors of carbon emissions was
less than the driving effect of economic growth, and the economy grew with increased carbon emissions;
there appeared the absolute decoupling effect in 1997, 2000 and 2001, which implied that the economy
grew while carbon emissions decreased; whereas no decoupling effect was identified in 2003 and 2004.
&2014 Elsevier Ltd. All rights reserved.
Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1256
2. Related literature review. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1256
3. Methodologies and data definitions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1259
3.1. Energy-related CO
2
emissions estimation approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1259
3.2. CO
2
emission change decomposition approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1259
3.3. Carbon emission intensity change decomposition approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1260
3.4. The decoupling measurement between CO
2
emissions and economic growth . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1260
3.5. Data definitions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1260
4. Empirical results and analyses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1261
4.1. Decomposition results of CO
2
emission changes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1261
4.2. Decomposition results of carbon emission intensity changes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1263
4.3. Analyses of the decoupling between CO
2
emissions and economic growth. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1264
4.4. Policy recommendations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1265
5. Conclusions and future work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1265
Acknowledgments. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1265
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1265
Contents lists available at ScienceDirect
journal homepage: www.elsevier.com/locate/rser
Renewable and Sustainable Energy Reviews
http://dx.doi.org/10.1016/j.rser.2014.09.021
1364-0321/&2014 Elsevier Ltd. All rights reserved.
n
Corresponding author at: Business School of Hunan University, Changsha 410082, PR China. Tel./fax: þ86 731 88822899.
E-mail address: zyjmis@126.com (Y.-J. Zhang).
Renewable and Sustainable Energy Reviews 41 (2015) 1255–1266

1. Introduction
In recent years, the international community has paid enor-
mous attention to addressing the climate change issues which are
mainly caused by the emission of greenhouse gases (GHG) [1].
Based on the carbon emitting speed now, it is predicted that the
global temperature will increase on average 1.3 1C compared to the
level during the pre-industrial revolution period [2]. In view of
the immense pressure to slow down global warming, more and
more countries have joined into the carbon reduction procession.
Since the reform and opening up in late 1970s, China's economy
has experienced sustained take-off, which has made China the
second largest economy in the world but also driven China's
carbon emissions to lead the world. China has surpassed the US
and became the largest CO
2
emitter in the world wide since 2008,
and in 2012, China's CO
2
emission reached 9.21 billion tons, which
accounted for 26.7% of the total emissions around the world [3].
It should be noted that as a responsible country, China has
made tremendous efforts to reduce carbon emission intensity
(carbon emissions per unit of gross domestic product (GDP));
specifically, China's carbon emission intensity dropped from
3.59 kg/US dollar in 2005 prices in 1990 to 1.79 in 2010 [4] with
a 71% decrease in total and a 3.8% annual decrease rate on average;
during the same time period, China's energy intensity dropped
58% with an annual decline rate of 4.2%. Meanwhile, before the
Copenhagen climate change conference in 2009, China promised a
target to reduce carbon emission intensity by 40–45% by 2020
compared with the level in 2005. During the 12th Five-Year Plan
period (2011–2015), China also proposed a quantitative target to
drop carbon emission intensity by 17%.
In order to achieve these ambitious targets in the wake of steady
economicgrowth,itprovesurgentforChinatofind the effective and
efficient ways to control carbon emissions, coordinate the relation-
ship between carbon emissions and economic growth, and ensure a
continual decline in carbon emission intensity. This is the major
motivation of this paper. On the other hand, the paper appears as a
good supplement for the CO
2
emissions decomposition analysis in
China, not only the research contents but also the research methods,
which is another motivation here. Therefore, in this paper, we
decompose the changes of energy-related CO
2
emissions and carbon
emission intensity in China during 1996–2010, which is in line with
China's 9th, 10th and 11th Five-Year Plan periods. In the meantime,
based on the decomposition results, we introduce the decoupling
index to analyze the decoupling relationship between China's carbon
emissions and economic growth. And then some policy recommen-
dations are provided to support the decision-making of China's
government to achieve carbon intensity targets.
As for the contribution in this paper, three aspects can be
obtained. First, in view of decomposition object, except for the
decomposition of CO
2
emissions changes, this paper also decom-
poses the changes of carbon emissions intensity during 1996–
2010. Second, in view of the methodology, this paper extends the
common LMDI decomposition analysis in CO
2
emissions changes,
through examining the effect of final energy consumption struc-
ture on CO
2
emissions changes from the perspectives of energy
sources and industrial structure simultaneously. Third, based on
the decomposition results of CO
2
emissions changes, we introduce
the decoupling index to investigate the decoupling relationship
between CO
2
emissions and economic growth in China on the
national level; and we also explore the affecting factors of the
decoupling relationship.
The rest of the paper is organized as follows. Section 2 gives the
related literature review. Section 3 presents the research meth-
odologies and data definitions. Section 4 discusses the empirical
results and puts forward some policy recommendations, while
Section 5 concludes the paper.
2. Related literature review
It has been confirmed that CO
2
, as the main type of greenhouse
gases (GHG), contributes about 60% of total greenhouse effect in
the world [5,6]. As a result, climate change issues have received
more and more attention by academics, practitioners and politi-
cians since 1980s especially in the recent decade. In particular, an
increasing number of studies start to decompose the changes of
CO
2
emissions to explore their driving factors.
As for the approaches of CO
2
emissions decomposition, there
are primarily three categories, i.e., the structural decomposition
analysis (SDA), the index decomposition analysis (IDA) and the
production-theoretical decomposition analysis (PDA). The SDA
approach is based on the input–output model in quantitative
economics to decompose carbon emission changes by using the
input–output tables in specific years. Chang and Lin [7] employed
structural decomposition analysis to examine emission trends and
effects for industrial CO
2
emission changes in Taiwan during 1981–
1991. Results indicate that the level of domestic final demand and
exports is the primary factor for the increase of CO
2
emission. On
the other hand, the effect of decreasing industrial CO
2
intensity is a
main reducing factor. Chang et al. [8] used the structural decom-
position method to identify the major factors and industries
contributing to the CO
2
emission changes in Taiwan during
1984–2004. The results show that the highway, petrochemical
materials, steel and iron are primary industries affecting CO
2
emissions in Taiwan. The level of exports and the level of domestic
final demand are the largest contributors to the carbon emission
increment. The major decreasing effect comes from industrial
energy coefficient and the structure of domestic final demand.
Tian et al. [9] conducted structural decomposition analysis to
quantify the contribution of technological and socio-economic
factors to the CO
2
growth in Beijing from 1995 to 2007. The results
indicate that final demand level and production structure change
led to carbonizing Beijing significantly, while energy intensity
improvement is Beijing's sole prominent source on decarbonizing
its economic development. In order to uncover driving forces for
provincial CO
2
emission in China, Geng et al. [10] undertook a case
study on the CO
2
emission growth in Liaoning province of China
during 1997–2007 by using the structural decomposition analysis.
Research outcomes indicate that rapid increase of per capita
consumption activities is the main driver for emission increment,
while energy intensity and energy structure partly offset the CO
2
emission increase. Nevertheless, because of the dependence on the
input–output tables, the decomposition can only be performed
additively, which constrains the extensive use of SDA in empirical
analyses [11].
Compared to the SDA approach, the application of IDA proves
more widely, which mainly contains the Laspeyres and Divisia
index approaches. The Laspeyres index approach is easier to
understand without the “zero-value”problem, but the decom-
position results have large residual terms. Based on the traditional
Laspeyres index, Sun [12] proposed “the complete decomposition
model”also called the refinement of Laspeyres index, which can
settle the residual problem very well, but its decomposition
formulae appears very complicated when the number of impact
factors exceeds three [11]. Paul and Bhattacharya [13] adopted “the
complete decomposition model”to investigate the factors influen-
cing the energy-related CO
2
emissions changes in India during the
period 1980–1996. The results show that economic growth has the
largest positive effect in CO
2
emissions changes in all the major
economic sectors. Emissions of CO
2
in industrial and transport
sectors show a decreasing trend due to improved energy efficiency
and fuel switching. By using the refined Laspeyres method,
Kumbaroğlu [14] carried out a decomposition analysis on CO
2
emission changes in Turkish over 1990–2007 at sectoral level
Y.-J. Zhang, Y.-B. Da / Renewable and Sustainable Energy Reviews 41 (2015) 1255–12661256

based on disaggregated data. As a result, it is found that the scale
effect (production activity) is a major source of emission increase
in the electricity, manufacturing and transport sectors. Manufac-
turing and transport sectors emissions decrease mainly due to
energy intensity improvement, while compositional changes
prove effective in reducing emissions in the electricity sector.
Moreover, the Divisia index mainly includes the arithmetic
mean Divisia index (AMDI) and log mean Divisia index (LMDI),
which use the arithmetic mean weight function and log mean
weight function respectively. It should be noted that the AMDI not
only contains residual problem but also cannot solve the “zero-
value”problem in the data. Then Ang and Choi [15] put forward a
refined Divisia index method using logarithmic mean weight
functions in 1997, which could well settle the residual and “zero-
value”problems and satisfy other conditions of “perfect decom-
position approaches”simultaneously. We consider this method as
the early decomposition form of LMDI. Then Ang et al. [16]
extended the above approach which was based on decomposition
of an aggregate index, and proposed another refined Divisa index
method based on logarithmic mean weight functions and decom-
position of a different quantity in 1998. The appellation of LMDI I
and LMDI II first appeared in 2001, when Ang and Liu [17]
presented a new energy decomposition method, called the Log-
Mean Divisia Index Method I (LMDI I). The method has the
desirable properties of perfect decomposition and is consistent
in aggregation. Ang and Liu [17] also pointed out that the early
LMDI decomposition form proposed by Ang and Choi [15] in 1997
was not consistent in aggregation. They referred the early one in
1997 as the Log-Mean Divisia Index Method II (LMDI II) for easy
reference. In 2003, Ang et al. [18] further indicated that the LMDI I
was closely related to the LMDI II, and the method proposed by
Ang et al. [16] in 1998 was the additive form of LMDI I, while
method by Ang and Liu [17] in 2001 was the multiplicative form.
Therefore, we can figure out that the LMDI II was proposed in 1997
and the LMDI I was proposed in 2001 (as the appellation first
appeared in 2001). In fact, the LMDI I method has become one of
the most extensively used methods in the decomposition of CO
2
emission changes, as meeting all those constraints. Tunc et al. [5]
tried to identify the factors that contribute to changes in CO
2
emissions for the Turkish economy by utilizing the LMDI method.
Their analysis shows that economic activity is the main compo-
nent determining the CO
2
emissions changes, while intensity
effect is another significant influencing factor. Focusing on Turkish
manufacturing industry, Akbostanci et al. [19] used the LMDI
method to decompose the changes in the CO
2
emissions into five
components. It is also found that changes in total industrial
activity and energy intensity are the primary factors determining
CO
2
emissions changes in Turkish manufacturing industry during
the study period. Jeong and Kim [20] decomposed Korean indus-
trial manufacturing CO
2
emissions changes during 1991–2009
using the LMDI method, both multiplicatively and additively. The
results indicate that the structure effect (industrial activity mix)
and intensity effect (sectoral energy intensity) play significant
roles in reducing CO
2
emissions, and the structure effect plays a
bigger role than the intensity effect. Malla [21] used the LMDI
method to examine the three defined factors affecting the evolu-
tion of CO
2
emissions from electricity generation in seven coun-
tries, which are electricity production, electricity generation
structure and energy intensity of electricity generation. The find-
ings indicated that production effect was the major factor respon-
sible for the rise in CO
2
emissions during the period 1990–2005.
The generation structure effect also contributed to CO
2
emissions
increase, while the energy intensity effect was responsible for the
modest reduction in CO
2
emissions during this period. Employing
the LMDI method, Mahony et al. [22] analyzed the driving forces of
CO
2
emissions in eleven final energy consuming sectors in Ireland.
The results illustrate that scale effects predominate in acting to
increase emissions in the economic and transport sectors, while
improvements in energy intensity are notable in the economic
sectors and in the residential sector.
In addition to the SDA and IDA approaches, with the increasing
application of production theory, distance functions and data
envelopment analysis (DEA) technique in energy and environ-
mental research area, the production-theoretical decomposition
analysis (PDA) approach is gradually developed. Zhou and Ang [23]
presented an alternative approach to decompose the change of
aggregate CO
2
emissions over time using two Shephard input
distance functions and the environmental data envelopment
analysis technology in production theory into contributors from
seven factors, and firstly call this method as the PDA approach, to
the best of our knowledge. Besides defining two input distance
functions for input and undesirable output, Zhang et al. [24]
extended Zhou and Ang [23] to decompose the CO
2
emissions
changes of 20 developing countries into the contributors from
nine factors by defining the Shephard output distance function for
the desirable output. Empirical results indicate that the economic
(GDP) growth is the most important contributor to CO
2
emissions
increase, while good output technical change is the most impor-
tant component for CO
2
emissions reduction. Zhang and Da [25]
employed the PDA approach combined with environmental DEA
technique and distance functions to decompose China's CO
2
emissions changes at the provincial and regional levels during
the 11th Five-Year Plan period. The results show that economic
growth and energy consumption are the two main drivers of CO
2
emissions increase during the study period, while the improve-
ment of carbon abatement technology and the reduction in energy
intensity play significant roles in curing CO
2
emissions. Overall,
compared to the other two decomposition approaches, the most
significant advantage of PDA is that it can reveal the effect of
carbon emission factors related with production technology and
efficiency; and it also has the drawback that it cannot reflect the
effect of structure components (such as industrial structure,
energy consumption structure) as SDA and IDA do [23].
As one of the biggest carbon emitters around the world, China's
carbon emissions have received more and more attention, and an
increasing number of studies use the LMDI approach to decom-
pose China's carbon emission changes (see Table 1). For instance,
Wang et al. [26] attributed the changes of aggregated CO
2
in China
during 1957–2000 to carbon emission coefficient, energy con-
sumption structure, energy intensity, GDP per capita and popula-
tion level based on the LMDI approach, and indicated that the
improved energy intensity proves the main factor curbing carbon
emissions. In addition, fuel switching and renewable energy
penetration also exert positive effects on CO
2
decrease. Liu et al.
[27] utilized the LMDI approach to decompose carbon emission
changes of Chinese industrial sectors during 1998–2005 into
carbon emission coefficient of heat and electricity, energy inten-
sity, industrial structural shift, industrial activity and final fuel
shift, and argue that the industrial activity and energy intensity are
the overwhelming contributors to carbon emission changes. Lin
and Moubarak [28] examined the influencing factors of energy-
related carbon emissions in Chinese textile industry during 1986–
2010. By separating the study interval into five subintervals, they
contrast the variation of carbon emission impact factors in each
subinterval and find that textile industrial activity is the major
factor that contributes to the increase of CO
2
emissions while
energy intensity has volatile reduction effect along the study
period. Zhang et al. [29] analyzed the carbon emission changes
of electricity generation in China during 1991–2009, and the main
results imply that coal product is the main fuel type for thermal
power generation, which accounts for more than 90% of CO
2
emissions from electricity generation, and economic activity
Y.-J. Zhang, Y.-B. Da / Renewable and Sustainable Energy Reviews 41 (2015) 1255–1266 1257

is the most important contributor to the increase of carbon
emissions, while the improvement of electricity generation effi-
ciency plays the dominant role in decreasing CO
2
emissions. Wang
et al. [30] used the LMDI approach to investigate the driving forces
of energy-related CO
2
emissions in Jiangsu province during the
period 1995–2009 and hold that economic activity is the critical
factor of carbon emissions growth and the energy intensity effect
plays the dominant role in the decreasing way.
Meanwhile, there also appears a body of literature considering
the decomposition of carbon emission intensity changes in China
(see Table 2). For instance, Zhang et al. [31] applied “the complete
decomposition model”proposed by Sun [12] to decompose China's
carbon emission intensity changes during 1991–2006 into energy
intensity, carbon emission coefficient and industrial structure. The
results indicate that the decline of energy intensity is the main
promoting factor to carbon emission intensity reduction, while the
effect of carbon emission coefficient and industrial structure
proves weaker. Based on the Adaptive Weighting Divisia (AWD)
method, Fan et al. [32] quantitatively investigated the driving
forces of China's primary energy-related carbon intensity and
measured the material production sectors' final energy-related
carbon intensity during 1980–2003, and found that the over-
whelming contributor to the decline of carbon emission intensity
is the reduction in real energy intensity. Tan et al. [33] examined
the forces to reduce China's CO
2
emission intensity between 1998
and 2008 using the LMDI approach and pointed out that the
electric power industry contributes 59.59% of carbon emission
intensity reduction, which mainly benefits from the decrease of
energy intensity in power generation.
In addition, the rapid growth of CO
2
emissions in emerging
giant countries raises public attention to sustainable economic
development for those countries including China. In fact, in recent
years, a growing number of studies have been focused on the
decoupling relationship between carbon emissions or environ-
mental pressure and economic growth across the world (see
Table 3). For example, Zhang [34] introduced the decoupling index
into resource and environmental field in 2000 [35] and OECD
regards the decoupling effect as disconnecting the relationship
between economic growth and environmental pressure and
divides it into the absolute and relative decoupling effect [36].
Freitas and Kaneko [35] utilized the decoupling analysis method
proposed by OECD to investigate the decoupling relationship
between economic growth and CO
2
emissions in Brazil during
the period 2004–2009 and found that there existed absolute
decoupling effect in 2009. Also based on the OECD method, Lu
et al. [37] examined the decoupling effects among economic
growth, transport energy demand and CO
2
emissions in Germany,
Japan, Korea and Taiwan and found that the growth of economy
and vehicle ownership are the most important factors for the
increased CO
2
emissions. In order to investigate the degree of
decoupling effect in EU, Tapio [38] introduced the elasticity theory
into the decoupling index and created the Tapio decoupling
analysis theoretical framework. Diakoulaki and Mandaraka [39]
focused on the CO
2
emission changes in the EU manufacturing
sectors during 1990–2003 and combined “the complete decom-
position model”proposed by Sun [12] with the decoupling index
not only to discover the influencing factors of CO
2
emission
changes but also evaluated the contribution made by these factors
on the decoupling between CO
2
emissions and industrial growth.
And Wang et al. [30] combined the LMDI decomposition approach
with the decoupling index to examine the decoupling effect
between carbon emissions and economic growth in Jiangsu
province during 1995–2009, and found that carbon emissions
and economic growth demonstrated the absolute decoupling
effect in 1997 and 2001, no decoupling effect during 2003–2005
while relative decoupling effect in the rest periods. Moreover, the
decrease of energy intensity and the cleaning of energy consump-
tion structure are the main contributors to the decoupling effect
between carbon emissions and economic growth in Jiangsu
province. These previous studies provide helpful references for
the decoupling analyses in this paper.
Based on the previous studies, we may decompose the changes
of final energy-related CO
2
emissions and carbon emission inten-
sity in China from 1996 to 2010 (the 9th, 10th and 11th Five-Year
Table 2
Representative literature for decomposition of carbon emissions intensity in China.
Authors Research object Time interval Research conclusions
Zhang et al. Carbon emission intensity changes 1991–2006 The decline of energy intensity is the main promoting factor to carbon
emission intensity reduction, while the effect of carbon emission
coefficient and industrial structure proves weaker.
Fan et al. Primary energy-related carbon intensity and the
material production sectors' final energy-
related carbon intensity
1980–2003 The reduction in real energy intensity is the overwhelming
contributor to the decline of carbon emission intensity.
Tan et al. Carbon emissions intensity changes 1998–2008 The electric power industry contributes 59.59% of carbon emission
intensity, which mainly benefits from the decrease of energy intensity
in power generation.
Table 1
Representative literature for decomposition of CO
2
emissions changes in China.
Authors Research object Time interval Research conclusions
Wang et al. The changes of China's
aggregated CO
2
1957 –2000 Improved energy intensity, fuel switching and renewable energy penetration are the
main factors curbing carbon emissions.
Liu et al. Carbon emission changes of
Chinese industrial sectors
1998–2005 The industrial activity and energy intensity are the overwhelming contributors to
carbon emission changes.
Lin and Moubarak Energy-related carbon
emissions in Chinese textile
industry
1986–2010 Industrial activity is the major factor that contributes to the increase of CO
2
emissions
while energy intensity has volatile reduction effect.
Zhang et al. The carbon emission changes of
electricity generation In China
1991 –2009 Economic activity is the most important contributor to the increase of carbon
emissions, while the improvement of electricity generation efficiency plays the
dominant role in decreasing CO
2
emissions.
Wang et al. Energy-related CO
2
emissions in
Jiangsu province
1995–2009 Economic activity is the critical factor of carbon emissions growth and energy
intensity effect plays the dominant role in the decreasing way.
Y.-J. Zhang, Y.-B. Da / Renewable and Sustainable Energy Reviews 41 (2015) 1255–12661258

Plan periods) by using the LMDI approach. This period owns the
highest economic growth rates, so the empirical results will be
good references for China's government to formulate scientific and
realistic carbon reduction policies under the on-going develop-
ment pattern. Moreover, except for the decomposition of CO
2
emissions changes, this paper also decomposes the changes of
carbon emissions intensity during this period. To the best of our
knowledge, the research on the decomposition of carbon emis-
sions intensity changes in China, no matter using the LMDI
method or other decomposition approaches, appears not enough.
Under this circumstance, we employ the LMDI method to decom-
pose carbon emissions intensity changes in China into energy
intensity, industrial structure and energy consumption structure.
Moreover, as the extension of common decomposition, we may
examine the effect of final energy consumption structure on CO
2
emission changes from the perspectives of energy sources and
industrial structure. Then, based on the decomposition results, we
may investigate the decoupling relationship between CO
2
emis-
sions and economic growth on the national level in China through
the combination of LMDI approach with decoupling index. In brief,
currently, there is little literature discussing the decoupling effect
between China's carbon emissions and economic growth on the
national level through the combination of decomposition
approaches with decoupling index. We also test the effect of
energy intensity, industrial structure and energy consumption
structure on the decoupling progress.
3. Methodologies and data definitions
3.1. Energy-related CO
2
emissions estimation approach
We apply the approach proposed by the 2006 IPCC Guidelines
for National Greenhouse Gas Inventories [40] to estimate CO
2
emissions related to the final consumption of coal, oil and natural
gas in energy balance tables, as shown in the following equation:
C
t
¼∑
i;j
C
t
ij
¼∑
i;j
E
t
ij
F
j
44
12 ð1Þ
where C
t
denotes the total CO
2
emissions in year tand is quoted in
ten thousand tons; C
t
ij
means the CO
2
emissions related to energy
source jconsumed by industry iin year t, while i¼1;2;3 denotes
the primary, secondary and tertiary industries respectively; and
j¼1;2;3 indicates coal, oil and natural gas respectively; E
t
ij
means
the use of energy source jof industry iin year t; while F
j
denotes
the carbon emission coefficient of energy source j, which is
proposed by China Sustainable Energy and Carbon Emissions
Scenario Analysis Comprehensive Report released by Energy
Research Institute, National Development and Reform Commission
of China [41]. Specifically, the carbon emission coefficients of coal,
oil and natural gas are 0.7476, 0.5825 and 0.4435 respectively. And
44/12 indicates the conversion coefficient from carbon to carbon
dioxide.
3.2. CO
2
emission change decomposition approach
In order to decompose the energy-related CO
2
emissions, we
rewrite Eq. (1) as follows:
C
t
¼∑
i;j
C
t
ij
¼∑
i;j
E
t
ij
E
t
i
E
t
i
Y
t
i
Y
t
i
Y
t
Y
t
F
j
44
12 ð2Þ
where E
t
i
means the total final energy consumption of industry iin
year t;Y
t
i
denotes the added value of industry iin year t; and Y
t
indicates the GDP in year t.Wedefine CS
t
i
¼ðE
t
ij
Þ=ðE
t
i
Þ, which
indicates the final energy consumption structure of industry i
and similarly appears in Hammond and Norman [42] and Jeong
and Kim [20];EI
t
i
¼ðE
t
i
Þ=ðY
t
i
Þ, which means the energy intensity of
industry i; and IS
t
i
¼ðY
t
i
Þ=ðY
t
Þ, which implies the industrial struc-
ture in year t. Therefore, Eq. (2) can be expressed as follows:
C
t
¼∑
i;j
CS
t
i
EI
t
i
IS
t
i
Y
t
F
j
44
12 ð3Þ
It should be noted that the final energy consumption structure
CS
t
i
in Eq. (3) is based on the perspective of the three industries
(i.e., the primary, secondary and tertiary industries). Moreover, we
can also obtain the final energy consumption structure based on
the perspective of the three energy sources (i.e., coal, oil and
natural gas).To this end, we rewrite Eq. (2) as
C
t
¼∑
j
E
t
j
E
t
E
t
F
j
44
12 ð4Þ
where E
t
¼∑
i
ðE
t
i
Þ=ðY
t
i
ÞðY
t
i
Þ=ðY
t
ÞY
t
, indicating the total final
energy consumption in year t, while E
t
j
means the total final
energy consumption of energy source j. Then the energy-related
CO
2
emissions can be obtained from the following equation:
C
t
¼∑
i;j
CS
t
j
EI
t
i
IS
t
i
Y
t
F
j
44
12 ð5Þ
where CS
t
j
¼ðE
t
j
Þ=ðE
t
Þ, indicating the final energy consumption
structure based on the three energy sources, which similarly
appears in Wang et al. [30].
No matter which decomposition form above is adopted, the
changes of CO
2
emission can be decomposed into those caused by
economic growth, energy intensity, industrial structure and final
energy consumption structure. Since the study period in this paper
is not very long, we presume that the carbon coefficients of coal,
oil and natural gas maintain steady across the whole period. Then
Table 3
Representative literature for environmental decoupling analyses.
Authors Research object Time interval Research conclusions
Freitas and Kaneko The decoupling relationship between
economic growth and CO
2
emissions in Brazil
2004–2009 The decoupling effect was highlighted when economic activity and CO
2
emissions moved in opposite directions in 2009.
Lu et al. The decoupling effects among economic
growth, transport energy demand and CO
2
emissions in four regions
1990–2002 Energy conservation performance and CO
2
mitigation in each country
are strongly correlated with environmental pressure and economic
driving force, except for Germany in 1993 and Taiwan during 1992–
1996.
Diakoulaki and
Mandaraka
Evaluating the progress made in EU countries
in decoupling emissions from industrial
growth
1990–2003 Most EU countries make a considerable but not always sufficient
decoupling effort, while no significant acceleration is observed in the
post-Kyoto period.
Wang et al. The decoupling effect between carbon
emissions and economic growth in Jiangsu
province
1995–2009 Carbon emissions and economic growth demonstrated the absolute
decoupling effect in 1997 and 2001, no decoupling effect during 2003–
2005 while relative decoupling effect in the rest periods.
Y.-J. Zhang, Y.-B. Da / Renewable and Sustainable Energy Reviews 41 (2015) 1255–1266 1259

according to the LMDI approach proposed by Ang [18] and the
decomposition forms in Eqs. (3) and (5), we decompose the CO
2
emission changes from C
0
in the base year to C
t
in year tas
follows:
ΔC
t
¼ΔC
t
gdp
þΔC
t
ei
þΔC
t
is
þΔC
t
cs
ð6Þ
ΔC
t
gdp
¼∑
i;j
wC
t
ij
;C
0
ij

ln Y
t
Y
0
 ð6aÞ
ΔC
t
ei
¼∑
i;j
wC
t
ij
;C
0
ij

ln EI
t
i
EI
0
i
! ð6bÞ
ΔC
t
is
¼∑
i;j
wC
t
ij
;C
0
ij

ln IS
t
i
IS
0
i
! ð6cÞ
ΔC
t
cs
¼∑
i;j
wC
t
ij
;C
0
ij

ln CS
t
i;j
CS
0
i;j
! ð6dÞ
wC
t
ij
;C
0
ij

¼C
t
ij
C
0
ij
ln C
t
ij
ln C
0
ij
ð6eÞ
where ΔC
t
means the CO
2
emission changes from C
0
to C
t
;ΔC
t
gdp
,
ΔC
t
ei
,ΔC
t
is
and ΔC
t
cs
represent CO
2
emission changes caused by
economic growth, energy intensity, industrial structure and final
energy consumption structure respectively. It should be noted that
the final energy consumption structure can be further decom-
posed from the perspectives of industrial structure and energy
sources as mentioned below.
3.3. Carbon emission intensity change decomposition approach
Based on the definitions above, carbon emission intensity often
means the CO
2
emissions per unit of GDP, which can be decom-
posed as follows:
I
t
¼C
t
Y
t
¼∑
i
E
t
i
Y
t
i
Y
t
i
Y
t
C
t
i
E
t
i
ð7Þ
where I
t
means the carbon emission intensity in year t;C
t
i
represents the final energy-related CO
2
emissions of industry i;
the denotations of other symbols are the same as above. And the
last item in the right side ðC
t
i
Þ=ðE
t
i
Þ

can be rewritten as follows:
FS
t
i
¼C
t
i
E
t
i
¼
∑
j
C
t
ij
E
t
i
¼
∑
j
E
t
ij
F
j

44
12
E
t
i
ð8Þ
Because of the assumption of stable carbon coefficient F
j
,we
define FS
t
i
as the final energy consumption structure. Thus Eq. (7)
can be expressed as follows:
I
t
¼∑
i
EI
t
i
IS
t
i
FS
t
i
ð9Þ
According the LMDI approach, the change of carbon emission
intensity from I
0
in the base year to I
t
in year tcan be decomposed
as follows:
ΔI
t
¼ΔI
t
ei
þΔI
t
is
þΔI
t
fs
ð10Þ
ΔI
t
ei
¼∑
i
wC
t
i
=Y
t
;C
0
i
=Y
0

ln EI
t
i
EI
0
i
! ð10aÞ
ΔI
t
is
¼∑
i
wC
t
i
=Y
t
;C
0
i
=Y
0

ln IS
t
i
IS
0
i
! ð10bÞ
ΔI
t
fs
¼∑
i
wC
t
i
=Y
t
;C
0
i
=Y
0

ln FS
t
i
FS
0
i
! ð10cÞ
wC
t
i
=Y
t
;C
0
i
=Y
0

¼C
t
i
=Y
t
C
0
i
=Y
0
ln C
t
i
=Y
t

ln C
0
i
=Y
0
 ð10dÞ
where ΔI
t
means the change of carbon emission intensity; ΔI
t
ei
,
ΔI
t
es
and ΔI
t
fs
indicate carbon emission intensity changes caused by
energy intensity, industrial structure and final energy consump-
tion structure respectively.
3.4. The decoupling measurement between CO
2
emissions and
economic growth
Based on the decomposition results of final energy-related CO
2
emission changes, we combine the LMDI approach with the
decoupling index as Diakoulaki and Mandaraka [39] adopted to
analyze the decoupling relationship between CO
2
emissions and
economic growth in China. During the research period (1996–
2010), according to the previous literature, China's economy
experienced rapid growth, which eventually contributed to the
increase of CO
2
emissions. On the other hand, the decrease of
energy intensity, the upgrading of industrial structure and the
cleaning of energy consumption structure in the past decades may
directly or indirectly reduce CO
2
emissions. Therefore, we use ΔF
t
to represent the total inhibiting effect on CO
2
emissions as follows:
ΔF
t
¼ΔC
t
ΔC
t
gdp
¼ΔC
t
ei
þΔC
t
is
þΔC
t
cs
ð11Þ
Then the decoupling index is defined as follows:
D
t
¼ ΔF
t
ΔC
t
gdp
¼D
t
ei
þD
t
is
þD
t
cs
ð12Þ
where D
t
means the total decoupling index, while D
t
ei
,D
t
is
and D
t
cs
mean the influence of energy intensity, industrial structure and
final energy consumption structure on the decoupling between
CO
2
emissions and economic growth respectively. If D
t
Z1, which
denotes the absolute decoupling effect, we can say that the total
carbon reduction effect of those inhibiting factors is greater than
the driving effect of economic growth; putting it in another way,
China's economy grows while its CO
2
emission declines. If
14D
t
40, which indicates the relative decoupling effect, we can
say that the carbon reduction effect appears weaker than the
driving effect; putting it in another way, China's economy grows
with the increased CO
2
emissions. And if D
t
r0, which implies that
there is no decoupling effect, we can say that the possible
inhibiting factors do not play significant roles in decreasing CO
2
emissions and increasing CO
2
emissions instead. As for D
t
ei
,D
t
is
and
D
t
cs
, if their values are greater than 0, we can say that there exists
inhibiting effect on CO
2
emissions caused by energy intensity,
industrial structure and final energy consumption structure
respectively, and they make contribution to the decoupling
between CO
2
emissions and economic growth. Otherwise, there
exists promoting effect on CO
2
emissions, and they have no
contribution to the decoupling progress.
3.5. Data definitions
In this paper, the study period ranges from 1996 to 2010,
covering the 9th, 10th and 11th Five-Year Plan periods in China. As
for the economic data, we adopt the GDP and added value of the
primary, secondary and tertiary industries, which are quoted in
100 million yuan in 1978 constant RMB price and come from China
Statistical Yearbook 1997–2011 [43]. As for the energy data, we use
the final consumption of coal, oil and natural gas in the three
industries, which are quoted in ten thousand tons of coal
Y.-J. Zhang, Y.-B. Da / Renewable and Sustainable Energy Reviews 41 (2015) 1255–12661260

equivalent and come from China Energy Statistical Yearbook 1997–
2011 [44].
1
Note that here the primary industry mainly includes
the farming, forestry, animal husbandry and fishery conservancy;
the secondary industry mainly consists of industry and construc-
tion; and the tertiary industry mainly covers transport, storage and
post, wholesale, retail trade and hotel, restaurants and others.
Besides, all the calculations are conducted in the Excel software.
4. Empirical results and analyses
4.1. Decomposition results of CO
2
emission changes
The decomposition results of final energy-related CO
2
emission
changes are shown in Table 4, and several findings are identified as
follows.
First, CO
2
emission almost kept increasing over the period
1996–2010 except some years such as 1997, 2000 and 2001.
Specifically, carbon emissions have grown 1.218 billion tons with
an annual growth rate of 4.3%. In particular, the growth rates
reached 17% and 20.8% in 2003 and 2004 respectively, which were
the highest rates during 1996–2010.
Second, economic growth appeared the main contributor to the
increase of CO
2
emissions during 1996–2010, which is in line with
the results of an array of previous literature [31,45]. According to
the decomposition results, ΔC
gdp
, which means the CO
2
emission
changes from economic growth, was increased by 2.679 billion
tons during the period of 1996–2010, which is 2.2 times of the
total CO
2
emission changes. Over the past decades, China's
economy has experienced sustained take-off and the average
growth rate during 1996–2010 reached 9.6%. Even during the
period of the international financial crisis in 2009, the growth rate
still achieved 9.2%. However, it cannot be ignored that the enviable
economic growth was supported by tremendous energy consump-
tion. During 1996–2010, the total final energy consumption of
China increased from 1.29 billion tons coal equivalent to 3.05
billion tons. Currently, China stands on its critical stage of
industrialization and urbanization, which may continually con-
sume more energy and emit more carbon dioxide, and the top
priority has been given to ensure stable and sustained economic
growth. Therefore, it is expected that economic growth remains a
main contributor to the increase of carbon emissions in the long
future.
Third, energy intensity proved the major inhibiting factor for
CO
2
emissions as far as the factors are concerned in this paper,
followed by final energy consumption structure. As the ratio of
energy consumption and GDP, the decrease of energy intensity
often indicates the improvement of energy use efficiency. During
the period of 1996–2010, energy intensity in China has declined
from 6.39 tce per ten thousand yuan to 4.06 tce per ten thousand
yuan with an average annual decline rate of 2.97%. The decreased
energy intensity caused 1.097 billion tons of carbon emissions
reduction accumulatively (i.e., ΔC
ei
), which accounted for 90.1% of
total CO
2
emission changes. In particular, the reduction effect of
energy intensity even exceeded the promoting effect of economic
growth in 1997 and 1998, because of the shutting down of a group
enterprises characterized by “high energy consumption, heavy
pollution and low efficiency”. On the other hand, because of the
cleaning of energy consumption structure during the period of
1996–2010, the CO
2
emission caused by energy consumption
structure adjustment (i.e., ΔC
cs
) was reduced by 555 million tons,
which accounted for 45.6% of the total changes. And the inhibiting
effect of final energy consumption structure was even greater than
that of energy intensity in 2005 and 2006. As a matter of fact, the
energy consumption structure in China proves cleaner gradually.
During 1996–2010, the proportion of natural gas in the total
energy consumption has increased from 1.8% to 4.4%, while the
proportion of energy like hydro power and nuclear power has
increased from 6.0% to 7.8%. These decomposition findings are
consistent with the arguments of Asian/World Energy Outlook
2013 proposed by the Institute of Energy Economic of Japan (IEEJ)
recently, which predicts that between the reference scenario and
advanced technology scenario, the reduction of China's CO
2
emis-
sions may be accounted for 47% by energy saving and 29% by fuel
switching (and 24% by CCS) during 2011–2040.
Finally, there exists some promoting effect of industrial struc-
ture on CO
2
emission increase. During the period of 1996–2010,
the CO
2
emission resulting from industrial structure adjustment
(i.e., ΔC
is
) was increased by 191 million tons accumulatively, which
only accounted for 15% of the total CO
2
emission changes and was
less than 8% of CO
2
emissions growth contributed by economic
growth. It should be noted that the overall industrial structure did
not change a lot during the study period. Specifically, although the
ratio of the tertiary industry, which is featured by lower energy
consumption and lighter carbon emissions, increased gradually in
the national economy from 32.8% in 1996 to 43.1% in 2010, the
secondary industry with higher energy consumption and carbon
emissions still accounted for the major part, i.e., 46.8% in 2010. The
enormous CO
2
emissions increment of the secondary industry
offsets the carbon reduction effect coming from the adjustment of
industrial structure. Therefore, in the “Work Plan of Controlling
Greenhouse Gas Emissions during the 12th Five-Year Plan period”,
Chinese central government proposes to speed up the adjustment
of industrial structure and pay more attention to the development
of service and strategic emerging industries with the targeted
shares 47% and 8% in the national economy by 2015 respectively.
Moreover, we further explore the effect of energy intensity and
industrial structure changes on CO
2
emissions. The results are
shown in Table 5, from which several points are gained.
(1) The carbon reduction effect coming from the decreased energy
intensity in the secondary industry accounts for about 80% of
the total reduction effect contributed by energy intensity
decrease, with 886 million tons of CO
2
emissions reduced
Table 4
The decomposition of final energy-related CO
2
emission changes.
Time period ΔC
tot
ΔC
gdp
ΔC
ei
ΔC
is
ΔC
cs
1996–1997 3953 13,213 13,925 1383 4619
1997 –1998 1936 11,139 11,498 1107 1188
1998–1999 3012 11,025 6382 839 2470
1999–2000 1018 12,233 8100 1199 6350
2000–2001 3060 11,890 7582 501 7869
2001–2002 6243 13,100 5635 923 2145
2002–2003 26,207 15,914 5721 2426 2147
2003–2004 37,429 19,035 10,201 1160 7034
2004–2005 7312 23,690 2900 1132 14,610
2005–2006 9606 27,396 7393 1195 11,593
2006–2007 8336 31,591 14,440 1535 10,350
2007–2008 13,134 22,924 14,372 533 4051
2008–2009 11,449 23,052 11,736 1130 997
2009–2010 5120 26,799 15,920 2337 8106
1996–2010 121,754 267,903 109,695 19,061 55,521
Note: The time period ‘1996–1997’in the table means that from the year 1996 to
1997, and other time periods have the similar meaning. ΔC
tot
means the total CO
2
emission changes over time; ΔC
gdp
,ΔC
ei
,ΔC
is
and ΔC
cs
indicate the CO
2
emission
changes caused by economic growth, energy intensity, industrial structure and final
energy consumption structure respectively and all of them are quoted in ten
thousand tons.
1
All the original data sets can be obtained in the Excel format from the authors
upon request.
Y.-J. Zhang, Y.-B. Da / Renewable and Sustainable Energy Reviews 41 (2015) 1255–1266 1261

during the period of 1996–2010. Among the three major
industries in national economy, the energy intensity of the
secondary industry owns the highest total decrease and
average annual decrease rates, which declines from 6.98 tce
per ten thousand yuan to 3.9 tce per ten thousand yuan with a
total decrease of 44.08% and an average annual decrease of
3.8%. The secondary industry mainly includes industrial and
construction sectors, which often have higher energy con-
sumption and heavier carbon emissions. The data from China
Energy Statistical Yearbook showed that the secondary indus-
try consumed about 71.4% of total final energy use and emitted
72.3% of total CO
2
emissions in 2010; therefore the improve-
ment of energy use efficiency and carbon reduction technol-
ogy in the secondary industry plays vital roles in curbing CO
2
emissions.
(2) The decreased energy intensity in the tertiary industry con-
tributed about 20% of the total reduction effect of energy
intensity and reduced 213 million tons of CO
2
emissions
accumulatively during the period of 1996–2010. The energy
intensity of the tertiary industry declined from 3.05 tce per ten
thousand yuan to 1.91 tce per ten thousand yuan with a total
decrease of 37.43% and an average annual decrease of 3.08%. As
mentioned above, the energy intensity of the tertiary industry
has significant decrease rates, but the proportion of the
secondary industry in national economy always proves the
largest. Compared to the secondary industry, the tertiary
industry has less reduction potential because of its less energy
usage and more consumption of cleaner energy like electricity
and heat. Specifically, compared to the secondary industry's
2.18 billion tons of final energy consumption and 0.5 billion
tons of coal consumption in 2010, the case for the tertiary
industry was 0.46 and 0.03 respectively. On the other hand,
the primary industry often has the least energy consumption
among the three industries, so its energy intensity change may
only slightly affect CO
2
emissions, with 1.72 million tons of CO
2
emissions increased during the study period. In fact, the
energy intensity of the primary industry remains stable
basically with 1.4 tce per ten thousand yuan.
(3) The promoting effect of industrial structure on CO
2
emissions
changes mainly comes from the secondary industry, with the
driving force accounting for more than 90% of the total effect
of industrial structure. The secondary industry in China has
always played the most important role in the development of
economic growth and also is characterized by higher energy
consumption and heavier CO
2
emissions. The tertiary industry
has been increasing gradually in the national economy but
contributes less to CO
2
emissions increase, which benefits
from the consumption of cleaner energy and higher energy
use efficiency. Moreover, the primary industry, whose added
value accounted for 10.1% in the national economy in 2010, has
pretty weak inhibiting effect with only 4.6 million tons of CO
2
emissions decreased.
In addition, we further analyze the influence of final energy
consumption structure changes on CO
2
emissions. The results are
shown in Table 6, and some findings are obtained as follows.
(1) In view of energy sources, the reduction effect of final energy
consumption structure mainly comes from coal. Benefited
from the improvement of coal use efficiency and coal clean
technology, the CO
2
emissions caused by coal consumption
(i.e., ΔC
cse1
) was reduced by 618 million tons during 1996–
2010. On the other hand, because of the increasing usage of oil
and natural gas, 63 million tons of CO
2
emission was pro-
moted. Comparatively, it can be found that the carbon reduc-
tion potential of coal is immense. As the principal energy
source in China, coal is given the strategic role in the economic
growth of the country. According to the official data from
China's National Bureau of Statistics, the coal consumption
accounted for about 68% of the total energy consumed in
China in 2010. Because of China's rich resource endowment of
coal and relatively cheaper coal price, coal will continue to be a
key component of the primary energy mix in China in the long
future [46]. But in order to reach the national carbon emissions
reduction targets, China needs to set a quantitative cap for coal
consumption as soon as possible and continue to improve the
coal use efficiency and coal clean technology; meanwhile the
consumption of clean energy like hydro power, wind power
and solar power should be enhanced. In China's recent “Atmo-
spheric Pollution Prevention Action Plan”, the government
proposes a goal to drop the rate of coal usage in the total
energy consumption below 65% by 2017.
(2) In view of industrial structure, the reduction effect of final
energy consumption structure mainly comes from the second-
ary industry. During the period of 1996–2010, the cleaning of
final energy consumption structure in the secondary industry
Table 5
The influence of energy intensity and industrial structure changes on CO
2
emissions.
Time period ΔC
ei
ΔC
ei1
ΔC
ei2
ΔC
ei3
ΔC
is
ΔC
is1
ΔC
is2
ΔC
is3
1996–1997 13925 143 10903 2879 1383 234 1231 386
1997 –1998 11498 153 9231 2113 1107 173 113 3 147
1998–1999 6382 10 5121 1251 839 190 554 474
1999–2000 8100 46 6671 1475 1199 238 1059 378
2000–2001 7582 96 5607 2071 501 222 147 576
2001–2002 5635 101 4347 1390 923 262 769 415
2002–2003 5721 562 3284 1875 2426 363 2963 175
2003–2004 10,201 488 7193 2520 1160 217 1388 11
2004–2005 2900 92 2311 680 1132 410 1145 3 97
2005–2006 7393 54 5523 1815 1195 551 1071 675
2006–2007 14,440 406 10,189 3845 1535 732 1369 897
2007–2008 14,372 635 10,242 3495 532 287 403 416
2008–2009 11,736 16 9582 2139 1130 344 1276 197
2009–2010 15,920 48 15,414 458 2337 440 3188 412
1996–2010 109,695 172 88,561 21,305 19,061 4600 18,580 5080
Note: The time period ‘1996–1997’in the table means that from the year 1996 to 1997, and other time periods have the similar meaning. ΔC
ei
and ΔC
is
indicate CO
2
emissions
changes caused by energy intensity and industrial structure respectively; ΔC
ei1
,ΔC
ei2
and ΔC
ei3
denote CO
2
emissions changes caused by energy intensity in the three
industries, i.e., C
t
eiðiÞ
¼∑
i;j
wC
t
ij
;C
0
ij

ln ðEI
t
i
Þ=ðEI
0
i
Þ

ði¼1;2;3Þ; and ΔC
is1
,ΔC
is2
and ΔC
is3
mean CO
2
emissions changes caused by the added value of the three industries, i.e.,
ΔC
t
isðiÞ
¼∑
i;j
wC
t
ij
;C
0
ij

ln ðIS
t
i
Þ=ðIS
0
i
Þ

ði¼1;2;3Þ. The unit is ten thousand tons.
Y.-J. Zhang, Y.-B. Da / Renewable and Sustainable Energy Reviews 41 (2015) 1255–12661262

decreased 496 million tons of CO
2
emissions (i.e., ΔC
csi2
),
which accounted for 89.3% of the total CO
2
emissions reduced
by the cleaning of final energy consumption structure (i.e.,
ΔC
cs
). The reason is obvious. By the end of 2010, the added
value of the secondary industry accounted for 46.8% in the
national economy, as the dominant industry, and the second-
ary industry consumed about 71.4% of total final energy
consumption. Therefore, the inhibiting effect on CO
2
emissions
coming from the secondary industry played a very important
role during the carbon reduction process. Moreover, during the
research period, the cleaning of final energy consumption
structure in the tertiary industry produced weak suppression
effect on CO
2
emissions with 61 million tons of CO
2
emissions
decreased accumulatively.
In brief, economic growth still proves the major contributor to
CO
2
emissions increase while the decrease of energy intensity and
the cleaning of final energy consumption structure play significant
roles in the carbon reduction process, which are consistent with
the results by Wang [26]. Moreover, in some individual years, the
carbon reduction effect of final energy consumption structure is
greater than that of energy intensity. In addition, the secondary
industry is the key object to achieve China's carbon emission
reduction goals and the government should strictly control the cap
of coal consumption, continually drop the ratio of coal consump-
tion in the total primary energy consumption and strenuously
develop the mechanisms for clean coal production and efficient
coal use.
4.2. Decomposition results of carbon emission intensity changes
The decomposition results of carbon emission intensity
changes are shown in Table 7. Several findings are identified as
follows.
(1) The carbon emission intensity in China dropped significantly
during the research period and similar results appear in the
study of Geng et al. [47]. Specifically, carbon emission intensity
in China decreased from 0.748 kg/yuan in 1996 to 0.363
kg/yuan in 2010 with an annual decreasing rate of 5.03%.
Especially in 1997 and 2001, the decrease rate even reached
10.91% and 9.54% respectively.
(2) The decrease of energy intensity is the primary inhibiting
factor for carbon emission intensity and drops carbon emission
intensity about 0.287 kg/yuan, which accounts for 74.43% of
the total changes. Therefore, energy intensity can not only curb
CO
2
emissions but also contribute to the decrease of carbon
emission intensity, just as Zhang et al. [31] mentioned. At
present, since coal is still the major energy consumption
source in China, the improvements of coal use efficiency and
coal clean technology are the key outlets to drop carbon
emission intensity.
(3) The effect of cleaning final energy consumption structure is
also remarkable to drop carbon emission intensity about
0.148 kg/yuan, which accounts for 38.5% of the total changes
and denotes the second most significant inhibiting factor
following energy intensity overall. More use of clean and
renewable energy is the long-lasting trend for China's energy
consumption structure adjustment. In 2010, non-fossil energy
like hydro power and nuclear power accounted for 7.8% of total
energy consumed in China. In order to further reduce CO
2
emissions and carbon emission intensity, the government put
forward a target to increase the rate of non-fossil energy in
primary energy consumption to 11.4% by the year of 2015 and
15% by 2020.
(4) Industrial structure always played a relatively weaker role in
promoting carbon emission intensity, with an increase of
0.05 kg/yuan accumulatively during 1996–2010. Now the sec-
ondary industry bears relatively higher energy consumption
and heavier CO
2
emissions, but it is still the dominant industry
in China, which accounted for 46.8% in the national economy
in 2010. Therefore, in order to sharply decrease carbon emis-
sion intensity, China has to put the upgrading of industrial
structure at the first place and suppress or eliminate the excess
capacities in the secondary industry, which is often character-
ized by higher energy consumption and serious pollution, and
improves the energy use efficiency. On the other hand, China
also needs to pay great attention to developing IT, financial
and service sectors, which have lower energy consumption
and less CO
2
emissions.
To sum up, in order to achieve the carbon emission intensity
reduction targets, China needs to focus on the two most important
inhibiting factors, i.e., energy intensity and final energy consump-
tion structure, and aim at the breakthrough in the secondary
Table 6
The influence of final energy consumption structure changes on CO
2
emissions.
Time period ΔC
cs
ΔC
cse1
ΔC
cse2
ΔC
cse3
ΔC
csi1
ΔC
csi2
ΔC
csi3
1996–1997 4619 7588 3349 380 10 6 3778 735
1997 –1998 1188 375 1498 64 45 1679 447
1998–1999 2470 5088 2237 381 201 2125 144
1999–2000 6350 8510 1951 2 10 16 6080 253
2000–2001 7869 7582 592 305 15 5 7179 535
2001–2002 2145 3227 1008 74 181 2549 223
2002–2003 2147 5827 3860 181 183 2762 798
2003–2004 7034 8092 801 258 467 6026 541
2004–2005 14,610 10,279 4470 140 343 14,225 728
2005–2006 11,593 10,816 1385 608 134 10,614 845
2006–2007 10,350 10,227 829 706 379 9702 269
2007–2008 4051 4143 1283 1191 20 4887 817
2008–2009 997 2366 3108 255 51 13 1060
2009–2010 8106 13,392 6630 13 44 9 8 7306 898
1996–2010 55,521 61,815 4025 2268 164 49,556 6129
Note: The time period ‘1996199 7’in the table means that from the year 1996 to 1997, and other time periods have the similar meaning. ΔC
cs
means the CO
2
emissions
changes caused by final energy consumption structure changes; ΔC
cse1
,ΔC
cse2
and ΔC
cse3
indicate the CO
2
emissions changes caused by coal, oil and natural gas consumption
respectively, i.e., ΔC
t
cseðjÞ
¼∑
i;j
wC
t
ij
;C
0
ij

ln ðCS
t
j
Þ=ðCS
0
j
Þ

ðj¼1;2;3Þ; and ΔC
csi1
,ΔC
csi2
and ΔC
csi3
show the CO
2
emissions changes caused by the final energy consumption
structure changes in the primary, secondary and tertiary industries respectively, i.e., ΔC
t
csiðiÞ
¼∑
i;j
wC
t
ij
;C
0
ij

ln ðCS
t
i
Þ=ðCS
0
i
Þ

ði¼1;2;3Þ. The unit is ten thousand tons.
Y.-J. Zhang, Y.-B. Da / Renewable and Sustainable Energy Reviews 41 (2015) 1255–1266 1263

industry by improving energy use efficiency, upgrading industrial
structure, reducing the consumption of fossil fuel especially coal
and developing the clean and renewable energy.
4.3. Analyses of the decoupling between CO
2
emissions and
economic growth
The total decoupling index between CO
2
emissions and eco-
nomic growth and the influence of energy intensity, industrial
structure and final energy consumption structure changes on the
decoupling progress are shown in Table 8. And some insightful
results are acquired.
First, the total decoupling index ranges during 0–1 in most
years, which indicates the relative decoupling effect between CO
2
emissions and economic growth and the carbon reduction effect
coming from inhibiting factors is less than the driving effect
contributed by economic growth. It means that when the economy
grows, CO
2
emissions increase too. However, it should be noted
that the total decoupling indices were 1.299, 1.083 and 1.257 in
1997, 2000 and 2001 respectively, all greater than 1, which shows
the absolute decoupling effect. In fact, in the three years, the
economic growth accompanies with the reduction of CO
2
emis-
sions. In other words, the reduction effect of inhibiting factors like
energy intensity on CO
2
emissions appears more than the driving
effect of economic growth. This mainly results from the Asian
financial crisis in 1997 and the rare flood disaster in 1998, which
hinder the energy consumption in China heavily. The growth rates
of energy consumption were 0.40% and 0.51% respectively in 1997
and 1998, while the average growth rate was 6.47% during 1996–
2010. Moreover, the government has shut down a group of
enterprises characterized by “high energy consumption, heavy
pollution, and low efficiency”simultaneously. Besides, the total
decoupling indices were negative in 2003 and 2004 with 0.647
and 0.966 respectively, which means there is no decoupling
effect between the rapid growth of economy and CO
2
emissions.
Putting it in another way, those inhibiting factors including energy
intensity, industrial structure and final energy consumption struc-
ture changes did not play significant roles in the carbon reduction
process and turned into contributors to CO
2
emissions increase
together with economic growth. Specifically, in 2003 and 2004,
the growth rate of CO
2
emissions reached 17% and 20.8% respec-
tively while the average during 1996–2010 was only 4.3%.
Second, the effect of energy intensity change on the decoupling
progress (i.e., D
ei
) is greater than zero in most years even greater
than one in some years, which implies the promoting effect on the
decoupling between CO
2
emissions and economic growth. And
D
ei
overall accounted for 75.1% of the total decoupling index (i.e., D)
during 1996–2010. In particular, the contribution of energy inten-
sity decrease to the decoupling process, which is mainly benefited
from its apparent carbon reduction effect, appears more significant
in those years with the absolute decoupling effect than that in
other years. For example, in the absolute decoupling year 1997, the
decrease of energy intensity reduced 1.39 billion tons of CO
2
emissions, even greater than that increased by economic growth,
i.e., 1.32 billion tons, which is also the reason that D
ei
is greater
than one in 1997. Meanwhile, as the most important inhibiting
factor to carbon emissions, energy intensity proves the biggest
contributor to the decoupling relationship.
Third, the effect of industrial structure change on the decou-
pling progress (i.e., D
is
) was always negative over the study period,
which indicates that industrial structure does not make contribu-
tion to the decoupling between CO
2
emissions and economic
growth. Similar to the decomposition results of CO
2
emissions
changes above, the effect of industrial structure change on the
decoupling proves relatively weaker, which only accounted for
13.04% of the total decoupling index on average during 1996–2010.
Fourth, the effect of final energy consumption structure change
on the decoupling progress (i.e., D
cs
) was almost over zero during
1996–2010 except for a few years, which means it is another
contributor to the decoupling between CO
2
emissions and eco-
nomic growth. Similar to energy intensity, the contribution mainly
comes from the carbon reduction effect of cleaning energy con-
sumption structure. In some particular years, the promoting effect
of final energy consumption structure on the decoupling progress
is even higher than that of energy intensity. For example, D
ei
was
0.122 and 0.270 in 2005 and 2006 respectively while D
cs
was 0.617
and 0.423 respectively.
Finally, there appears the relative decoupling effect between CO
2
emissions and economic growth in China in most years during
1996–2010, accounting for two-thirds (10/15) as shown in the last
column in Table 8. The absolute decoupling effect only exists in
three periods. And there is no decoupling effect in two periods, i.e.,
the year of 2003 and 2004. Moreover, the large share of the relative
decoupling effect overall reflects the fact that although the decrease
of energy intensity and the cleaning of final energy consumption
structure play important roles in promoting the decoupling
Table 7
The decomposition of carbon emission intensity changes.
Time period ΔI
tot
ΔI
ei
ΔI
fs
ΔI
is
1996–1997 0.816 0.662 0.220 0.066
1997 –1998 0.403 0.503 0.052 0.048
1998–1999 0.326 0.259 0.100 0.034
1999–2000 0.498 0.305 0.239 0.045
2000–2001 0.519 0.263 0.273 0.017
2001–2002 0.219 0.180 0.068 0.029
2002–2003 0.299 0.167 0.062 0.071
2003–2004 0.486 0.270 0.186 0.031
2004–2005 0.392 0.069 0.349 0.027
2005–2006 0.380 0.158 0.248 0.026
2006–2007 0.438 0.272 0.195 0.029
2007–2008 0.165 0.242 0.068 0.009
2008–2009 0.178 0.180 0.015 0.017
2009–2010 0.304 0.223 0.113 0.033
1996–2010 3.852 2.867 1.483 0.498
Note: The time period ‘1996–1997’in the table means that from the year 1996 to
1997, and other time periods have the similar meaning. ΔI
tot
means the total
changes of carbon emission intensity; ΔI
ei
,ΔI
fs
and ΔI
is
indicate the carbon emission
intensity changes caused by energy intensity, final energy consumption structure
and industrial structure respectively. The unit is kg/yuan.
Table 8
The decoupling between CO
2
emissions and economic growth.
Time period DD
ei
D
is
D
cs
Decoupling effect
1996–1997 1.299 1.054 0.105 0.350 Absolute
1997 –1998 0.826 1.032 0.099 0.107 Relative
1998–1999 0.727 0.579 0.076 0.224 Relative
1999–2000 1.083 0.662 0.098 0.519 Absolute
2000–2001 1.257 0.638 0.042 0.662 Absolute
2001–2002 0.523 0.430 0.070 0.164 Relative
2002–2003 0.647 0.359 0.152 0.135 No decoupling
2003–2004 0.966 0.536 0.061 0.370 No decoupling
2004–2005 0.691 0.122 0.048 0.617 Relative
2005–2006 0.649 0.270 0.044 0.423 Relative
2006–2007 0.736 0.457 0.049 0.328 Relative
2007–2008 0.427 0.627 0.023 0.177 Relative
2008–2009 0.503 0.509 0.049 0.043 Relative
2009–2010 0.809 0.594 0.087 0.302 Relative
1996–2010 0.546 0.409 0.071 0.207 Relative
Note: The time period ‘1996–1997’in the table means that from the year 1996 to
1997, and other time periods have the similar meaning. Dmeans the total
decoupling index (effect); D
ei
,D
is
and D
cs
indicate the effect of energy intensity,
industrial structure and final energy consumption structure changes on the
decoupling progress respectively.
Y.-J. Zhang, Y.-B. Da / Renewable and Sustainable Energy Reviews 41 (2015) 1255–12661264

progress through their significant carbon reduction effect, it still
appears weaker than the promoting effect of economic growth.
Therefore, in order to break the connection between CO
2
emis-
sions and economic growth and achieve China's carbon emission
intensity target, the government should still take effective mea-
sures to further decrease energy intensity and cleanse energy
consumption especially coal consumption.
4.4. Policy recommendations
Based on the empirical analyses above, in order to achieve the
target to reduce 40–50% of carbon emission intensity by 2020
compared to the level in 2005 and 17% by 2015 compared to the
level in 2010, we put forward some policy recommendations for
China's government as follows.
(1) The government is expected to continually transform eco-
nomic growth pattern and upgrade industrial structure by
innovative technologies. In particular, some effective policies
(such as tax, subsidy) should be adopted to decrease the
proportion of the industries with higher energy consumption
and heavier carbon emissions and strive to deploy those low-
carbon industries instead. On the one hand, the government
can promote the marketing process of energy like coal, oil,
natural gas and electricity. On the other hand, the government
can impose energy tax and environmental tax to some specific
industries depending on their energy consumption and CO
2
emissions conditions. The government can also create appro-
priate energy-saving and carbon reduction subsidies to accel-
erate the development of those low-carbon industries like the
modern service industries.
(2) The government is advised to persistently improve the effi-
ciency of energy consumption and vigorously expand the
reduction potential of the secondary industry through elim-
inating backward production capacities and advancing the use
efficiency and clean technology. It should also be noted that, in
the wake of industry transformation especially in the urbani-
zation process, the sectors in the tertiary industry with lower
energy consumption and less carbon emissions should be
enhanced further, such as the IT, financial sectors. The govern-
ment can improve the efficiency of energy consumption
through the cooperation between local enterprises and foreign
advanced technologies, combining with some mandatory
energy-saving and carbon reduction policies. As mentioned
above, the development of the tertiary industry especially the
modern service industry can be supported by macro-control
means like tax and subsidy.
(3) The government ought to adjust the energy use structure in
the secondary industry by dropping coal consumption,
improving coal use efficiency and increasing the utilization
of clean coal technologies. The government should encourage
or help the secondary industry to adjust the energy use
structure through drafting rewarding policies. Specific mea-
sures like dropping coal consumption and increasing the use
of clean energy like natural gas and electricity. Improving coal
use efficiency and increasing the utilization of clean coal
technologies should be backed by the cooperation mentioned
above. Besides, the market competition can also force the
enterprises in the secondary industry to make those changes.
In conclusion, in order to achieve the promised carbon emis-
sions intensity target, China needs to make full use of the two
major inhibiting factors, i.e., energy intensity and final energy
consumption structure, and strive for breakthrough in the sec-
ondary industry and coal consumption. Overall, China now is on
the way of urbanization, industrialization and agricultural
modernization, and its economic policies should be more closely
combined with the energy development policies, environmental
protection policies and climate change policies.
5. Conclusions and future work
In this paper, we decompose the changes of final energy-
related CO
2
emissions and carbon emission intensity in China
during the period of 1996–2010 to figure out the main affecting
factors by using the LMDI approach. Meanwhile, we introduce the
decoupling index to analyze the decoupling relationship between
CO
2
emissions and economic growth. Several conclusions are
obtained as follows.
(1) Economic growth proves the main contributor to increased
CO
2
emissions during 1996–2010. Over the study period,
China's CO
2
emissions caused by economic growth were
increased by 2.679 billion tons, which is 2.2 times of the total
CO
2
emission changes.
(2) Energy intensity and final energy consumption structure play
significant roles in decreasing CO
2
emissions and carbon
emission intensity, with a reduction of 1.652 billion tons of
CO
2
emissions and 4.35 kg/yuan carbon emissions intensity.
Moreover, the secondary industry proves the primary source
of the reduction effect and still contains enormous reduction
potential. When considering the energy use structure, the
reduction effect of the decrease of coal consumption, the
improvement of coal use efficiency and coal clean technology
also cannot be ignored.
(3) In two-thirds of the study period, there appears the relative
decoupling effect between final energy-related CO
2
emissions
and economic growth in China. There was absolute decoupling
effect in 1997, 2000 and 2001, whereas no decoupling effect
was identified during 2003–2004. Moreover, the decrease of
energy intensity and the cleaning of final energy consumption
structure are the main facilitators to the decoupling between
CO
2
emissions and economic growth in China.
This paper uses the LMDI approach to decompose China's CO
2
emissions and carbon emission intensity; however, the approach
cannot reveal the effect from the changes of production technol-
ogy and efficiency. Therefore, we may bring the PDA approach into
use to incorporate the role of production technology and efficiency
in the future, not only for the decomposition of CO
2
emissions but
also for the decoupling between carbon emissions and economic
growth.
Acknowledgments
We gratefully acknowledge the financial support from the
National Natural Science Foundation of China (Nos. 71001008,
71273028, 71322103, and 71431008) and the Basic Research Fund
of Beijing Institute of Technology (No. 20122142008).
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