Next Article in Journal
The Effect of Free-Atmosphere Stratification on Boundary-Layer Flow and Power Output from Very Large Wind Farms
Previous Article in Journal
Comparative Study of Dynamic Programming and Pontryagin’s Minimum Principle on Energy Management for a Parallel Hybrid Electric Vehicle
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Energies
Volume 6
Issue 5
10.3390/en6052319
energies-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Decomposition Analysis of Energy-Related Industrial CO2 Emissions in China

by
Liang Chen
,
Zhifeng Yang
* and
Bin Chen
*
State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Beijing Normal University, Beijing 100875, China
*
Authors to whom correspondence should be addressed.
Energies 2013, 6(5), 2319-2337; https://doi.org/10.3390/en6052319
Submission received: 31 December 2012 / Revised: 7 April 2013 / Accepted: 7 April 2013 / Published: 25 April 2013
Browse Figures
Versions Notes

Abstract

:
Based on the logarithmic mean Divisia index (LMDI) approach, this paper presents a decomposition analysis of China’s energy-related industrial CO2 emissions from 1985 to 2007, as well as a comparative analysis of differential influences of various factors on six sectors. Via the decomposition, five categories of influencing factors are included: (1) Per capita GDP (PCG) was the largest positive driving factor for industrial CO2 emissions growth for all sectors in China, with the largest cumulative contribution value; Population (P), economic structure (YS) and energy structure (ES) also played a positive driving role, but with weak contributions. As the only negative inhibiting factor, energy intensity (EI) significantly reduced the energy-related CO2 emissions from industrial sectors. Meanwhile, CO2 emissions reduction based on the efficiency of energy use still held a large space. (2) Various influencing factors imposed differential impacts on CO2 emissions of six sectors.

1. Introduction

Intensive use of fossil fuels can be cited among the main reasons of the significant increase in anthropogenic greenhouse gases (GHG) that lead to climate change. The long term reduction of greenhouse gases (GHG’s) and low-carbon development has long been identified by the Intergovernmental Panel on Climate Change (IPCC) as an appropriate response to climate change. At the end of 2009, the Chinese government put forward the intensity target that CO2 emissions per GDP should be reduced by 40%–50% in 2020, which has been included in the long-term program for national economy and social development. In the 12th Five-Year Plan Outline, China planned to boost the value-added proportion of the GDP accounted for by the service sector by 4%, and to reduce energy consumption by 16% and carbon emissions by 17% per unit of GDP in 2015 [1]. However, controlling and mitigating energy-related carbon emissions require careful analysis of the factors that influence these emissions so that appropriate remedies can be developed.
Decomposition analysis is one of the most effective tools for investigating the mechanisms influencing energy consumption and its environmental side effects. In recent decades, index decomposition analysis (IDA) has been widely accepted as a decomposition methodologies based on the Laspeyres and the Divisia indices that are well known in the fields of economics and statistics [2]. One basic drawback of the conventional Laspeyres and Divisia index methods was the large residual term found in most applications, leaving a significant part of the examined changes unexplained [3]. However, this problem has been effectively solved through the improved variants [4,5].
The logarithmic mean Divisia index (LMDI) method has been identified as the preferred approach in energy use and CO2 emission analyses under the umbrella of IDA [2,6,7], because of its robust theoretical foundations, strong adaptability to a range of situations, and ability to provide perfect decomposition; that is, no unexplained residual terms [8]. LMDI was first used in 1998 to study the factor decomposition for the CO2 emissions of energy consumption from China’s industrial sectors [9]. It has since broadened its scope to include analysis of energy supply and demand, energy-related emissions, material flow and dematerialization, monitoring of national energy efficiency trends and making cross-country comparisons of energy performance [10,11].
Recently, energy-related carbon emissions have been studied by adopting LMDI approach in different scale levels. At the global level, the driving forces for the changes of energy consumption and carbon emissions are decomposed in multiple scales [12,13,14]. On top of this, the secondary decomposition is further conducted for intensity factors of CO2 emissions from sectors like manufacturing and transportation [15,16]. These studies generally considered different kinds of energy (such as coal, oil, natural gas, and electricity) and sectors (such as primary industry, secondary industry, and tertiary industry). Case studies at the national level include the research of main factors driving changes in CO2 emissions of different time intervals in different countries, such as South Korea [17,18], Thailand [19], India [20], Brazil [21,22], Turkey [23], The United Kingdom [24], and Greece [25]. Studies on the cyclical fluctuation and driving forces of energy-related CO2 emissions are also applied to China [26,27,28,29,30,31,32,33,34,35]. These studies considered a relatively small number (four or fewer) of types of energy, thereby decreasing the accuracy of the accounting. In addition, more detailed information about each sector’s carbon emissions and the proportion of total emissions accounted for by each sector that could guide management planning was often not provided [33]; at an urban level, most of the literature focuses on a province/city [36,37,38,39], discussing the mechanism of main factors affecting regional CO2 emissions, and coming up with alternative policies [40,41,42].
As can be seen, the index decomposition technique has good performances on identifying the magnitude of some predetermined driving factors of changes in observed indicators. It has been not only conducted at different scale levels, but also for studies on energy consumption and energy-related CO2 emissions in a specific industry or sector. These studies estimated and evaluated observed indicators to examine the factors, structural, and technological changes at industry level [10,43,44,45,46,47]. Many scholars also suggested the use of IDA to specifically focus on China’s industry and give a disaggregated sectoral decomposition often dealing with three or four sectors [34,48,49,50,51].
Since the industrial sector is a major consumer of energy in China, the total industrial CO2 emissions absolutely represents a dominant share. Investigating the intrinsic driving factors of China’s industrial CO2 emissions growth in time series is necessary to understand the trends in energy use, to forecast future energy demand and carbon emissions and to measure the effectiveness of energy-related policies. This paper hereby offers a year-by-year decomposition analysis of the cumulative effects of influencing factors for China’s energy-related industrial CO2 emissions from 1985 to 2007 and compares differential influences of all factors on the cumulative effects of six sectors.

2. Data Set and Estimation

The time interval of data sample ranged from 1985 to 2007. The GDP for each sector were obtained from the China Statistical Yearbook [52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74], and the economic output and income data are adjusted according to the constant price of 1985, which serves as the reference period. According to the industrial structure specified in China Statistical Yearbook, we divided the national economy into six sectors: agriculture (farming, forestry, animal husbandry, fisheries, and water conservation), industry, construction, transport, storage, and postal services, urban and rural households and business (wholesale, retail, trade, hotel, and restaurants; and other tertiary sectors). According to the classification of industrial internal sectors in China Energy Statistical Yearbook, there are three major sectors, including mining, manufacturing and electric power, gas and water production and supply.
Energy consumption data were obtained from the China Energy Statistical Yearbook [75,76,77,78,79,80,81,82,83,84], including eight types of fossil fuels: coal (raw coal, cleaned coal, other washed coal, briquettes), crude oil, gasoline, kerosene, diesel oil, fuel oil, coke, and natural gas. Using the reference method provided in “2006 IPCC Guidelines for National Greenhouse Gas Inventories” [85], we have also estimated the amounts of CO2 emission in China over the years based on the fossil fuel classification and sector classification. The carbon emissions coefficients for estimation of different energy resources are given in Table 1.
Table
Table 1. The carbon emission coefficients of different energy resources. tce = ton of standard coal equivalent.
Table 1. The carbon emission coefficients of different energy resources. tce = ton of standard coal equivalent.
Energy TypeIPCC coefficients (kgC/GJ)Adopted coefficients (tC/tce)
Coal25.80.7552
Coke29.20.8547
Crude Oil20.00.5854
Gasoline18.90.5913
Kerosene19.60.5737
Diesel Oil20.20.5913
Fuel Oil21.10.6176
Natural Gas15.30.4479
According to the estimation results, the energy-related industrial CO2 emissions increased gradually with the rapid industrialization process and underwent different stages (Figure 1) from “steadily rising” of 1985–1996 to “stable with a decline” of 1997–2001, and then to “rapid growth” of 2002–2007. The fluctuation intensity significantly increased after 1996. In addition, the substantial differences of energy-related CO2 emissions existed among different sectors in China. From 1985 to 2007, the total energy-related industrial CO2 emissions absolutely represented a dominant share and always accounted for around 80%. Thus, it was the largest carbon emitter in China. The total CO2 emissions of other sectors only accounted for around 20% of the total emission and gradually decreased.
As can be seen from the variables of CO2 emissions in three major industrial internal sectors (Figure 2), from 1985 to 2007, manufacturing was the largest carbon emitter in China, accounting on average for 60% of total industrial CO2 emissions, and dropping gradually from 1996; the next was electric power, gas and water production and supply, which accounted for 30% and maintained stable growth; mining was involved in the minimum CO2 emissions, which accounted for 10%. In spite of the relatively low CO2 emissions, the relative proportion still decreased slowly.
Energies 06 02319 g001 1024
Figure 1. The total amount of energy-related CO2 emissions in six different sectors.
Figure 1. The total amount of energy-related CO2 emissions in six different sectors.
Energies 06 02319 g001
Energies 06 02319 g002 1024
Figure 2. Contribution of energy-related CO2 emissions in three industrial internal sectors.
Figure 2. Contribution of energy-related CO2 emissions in three industrial internal sectors.
Energies 06 02319 g002
As an example, the detailed distribution of carbon emissions from three major industrial internal sectors in 2007 indicated that the major sources of emissions inside industry were due to heavy manufacturing and production and distribution of electric power and heat power. Therein, heavy manufacturing had an overall high carbon emissions level by including high energy consumption sectors, especially the petroleum, chemical products, iron and steel, electrolytic aluminum. In addition, the proportion of carbon emissions from light manufacturing, such as foods, tobacco, furniture etc, were relatively low in the industrial total level (Figure 3).
Energies 06 02319 g003 1024
Figure 3. The distribution of carbon emissions from industrial internal sectors in 2007.
Figure 3. The distribution of carbon emissions from industrial internal sectors in 2007.
Energies 06 02319 g003

3. Methodology

Selecting the industry sector in China as a case study, we aimed to conduct a year-by-year decomposition analysis of the cumulative effects of influencing factors for China’s industrial CO2 emissions from 1985 to 2007 and also a comparative analysis of differential influences of various factors on different sectors.

3.1. Decomposition Model for CO2 Emissions in Industrial Internal Sectors

The total energy-related industrial CO2 emissions can be expressed in the following way:
Energies 06 02319 i001
where index i = 1, 2, denote the three major industrial internal sectors, including mining, manufacturing, and electric power, gas and water production and supply, respectively; index j = 1, 2,…, 8 denotes eight types of fossil fuels, i.e., coal, coke, crude oil, gasoline, kerosene, diesel oil, fuel oil and natural gas, respectively (considering energy transformation sectors, such as the power sector, are included in the industrial sector, electricity and heat consumption in the final energy demand sectors cannot be considered in the current framework of decomposition to avoid duplicate calculations), consumed by each sector. The meanings of other variables in Equation (1) are described in Table 2.
Table
Table 2. Implication of each variable in Equation (1).
Table 2. Implication of each variable in Equation (1).
VariableImplicationVariableImplication
CTotal amount of CO2 emissionsPTotal population
CijThe amount of CO2 emitted by fossil fuel j consumed in sector iCIijCIij = Cij/Eij, carbon emissions coefficient for fossil fuels j in sector i
EiTotal amount of fossil fuel consumed in sector iESijESij = Eij/Ei, ratio of fossil fuel j to total fossil fuels in sector i
EijThe amount of fossil fuel j consumed in sector iEIiEIi = Ei/Yi, energy intensity of sector i
YTotal industrial outputYSiYSi = Yi/Y, share of economic output in sector i in total industrial output
YiEconomic output of sector iPCGPer capita GDP
The changes in CO2 emissions from 0 (base year) to T (target year) can be expressed as follows:
Energies 06 02319 i002
Therein, ∆C denotes the total effect of CO2 emissions, ∆CES denotes energy structure effect, ∆CYS denotes economic structure effect, ∆CEI denotes energy intensity effect, ∆CPCG denotes economic activity effect, and ∆CP denotes population effect (CI represents the CO2 emissions coefficient for various fossil fuels and it is usually a constant and not considered as an investigation factor in this paper). According to the definition of logarithmic mean function, introducing into the weighting function Wij(t*) [86], Equation (2) can be carried out as follows:
Energies 06 02319 i003
These components are written as:
Energies 06 02319 i004
In Equations (3) and (4), the driving force of growth in energy-related industrial CO2 emissions was decomposed into 5 influencing factors (ES, EI, YS, PCG, P).

3.2. Decomposition Model for CO2 Emissions in Different Sectors

The energy-related CO2 emissions in six sectors can be expressed in the following way:
Energies 06 02319 i005
In Equation (5), the index k = 1, 2,…, 6 respectively denote agriculture, industry, construction, transport, storage, and postal services, urban and rural households and business. The meanings of other variables and the decomposition process are the same as those in the model.

4. Results and Discussion

4.1. Decomposition Analysis of Energy-Related Industrial CO2 Emissions

The present paper offers a year-by-year decomposition analysis of cumulative effects of influencing factors for CO2 emissions in industrial sectors in China from 1985 which serves as the reference period (Table 3).
The decomposition results show that, till 2007, the cumulative contribution values of all influencing factors varied a lot. Therein, the positive driving factors for industrial CO2 emissions growth included ES, YS, PCG and P, with cumulative contribution of 16 million metric tons, 269 million metric tons, 1.428 billion metric tons and 141 million metric tons respectively. PCG was the dominant positive driving factor; EI was the only negative driving factor with cumulative contribution value of −744 million metric tons and the cumulative contribution rate of −67.10%. Except for PCG, the negative inhibiting effects of EI far outweighed the four other positive inhibiting factors for industrial CO2 emissions growth. This point further indicates that EI has been a significant control factor to effectively reduce China’s industrial energy-related CO2 emissions. Energy conservation by both technical and non- technical changes (e.g., behavior and lifestyle) will be the main ways to control the continuous growth of China’s industrial energy-related CO2 emissions.
Table
Table 3. Decomposition result of energy-related industrial CO2 emissions from 1985 to 2007.
Table 3. Decomposition result of energy-related industrial CO2 emissions from 1985 to 2007.
YearTotalEnergy structureEnergy intensityEconomic structureEconomic activityPopulation
ΔCdtotalΔCESdESΔCEIdEIΔCYSdYSΔCPCGdPCGΔCPdP
19862,416.20100%57.012.36%−1,174.05−48.59%282.3811.69%2,652.51109.78%598.3424.76%
19875,995.85100%168.942.82%−2,847.62−47.49%872.7814.56%6,517.65108.70%1,284.1021.42%
19889,236.44100%189.692.05%−6,064.83−65.66%2,465.3826.69%10,652.69115.33%1,993.5121.58%
198911,955.49100%277.492.32%−5,813.45−48.63%2,923.0024.45%11,856.0499.17%2,712.4222.69%
199013,212.04100%389.532.95%−6,302.61−47.70%2,724.5220.62%12,977.4998.22%3,423.1325.91%
199116,378.96100%430.752.63%−10,190.11−62.21%5,149.2731.44%16,890.79103.12%4,098.2625.02%
199219,997.38100%469.182.35%−17,316.68−86.59%8,459.0942.30%23,640.69118.22%4,745.0923.73%
199324,271.84100%511.202.11%−23,982.48−98.81%11,560.6147.63%30,757.59126.72%5,424.9222.35%
199429,403.84100%554.321.89%−30,009.41−102.06%14,771.6450.24%37,943.97129.04%6,143.3120.89%
199534,887.18100%765.712.19%−33,776.88−96.82%16,671.2547.79%44,357.42127.15%6,869.6819.69%
199638,888.68100%733.061.89%−38,884.55−99.99%18,435.5247.41%50,921.97130.94%7,682.6719.76%
199738,827.83100%281.590.73%−46,021.17−118.53%19,668.2250.65%56,512.94145.55%8,386.2421.60%
199834,837.26100%316.930.91%−50,164.52−144.00%19,016.6954.59%57,222.29164.26%8,445.8724.24%
199935,357.59100%526.961.49%−50,832.35−143.77%18,567.0652.51%58,549.40165.59%8,546.5024.17%
200037,411.77100%399.361.07%−56,460.74−150.92%19,739.9952.76%64,529.73172.49%9,203.4224.60%
200139,171.90100%466.911.19%−60,644.40−154.82%19,984.1851.02%69,673.59177.87%9,691.6124.74%
200243,716.09100%397.080.91%−63,503.75−145.26%20,666.9947.28%75,948.26173.73%10,207.4923.35%
200359,430.41100%715.151.20%−61,253.47−103.07%23,368.3539.32%85,610.91144.05%10,989.4618.49%
200474,223.47100%793.931.07%−58,328.83−78.59%24,796.7133.41%95,307.64128.41%11,654.0215.70%
200586,430.03100%1,308.801.51%−59,671.34−69.04%25,185.5029.14%107,238.75124.08%12,368.3214.31%
200699,639.60100%1,524.021.53%−63,542.53−63.77%25,640.3625.73%122,860.88123.31%13,156.8513.20%
2007110,951.62100%1,601.771.44%−74,444.77−67.10%26,899.9124.24%142,804.16128.71%14,090.5212.70%
Notes: ∆CX: The cumulative contributionn value of factor X (104 metric tons); dX: The cumulative contribution rate of factor X (%).
The curve of cumulative contribution variation of influencing factors in Figure 4 shows that, since 1996, the growth of ∆CPCG has been slowed down influenced by the southeastern financial crisis [27,30] and then accelerated again in the latter years. The positive driving effects from economic activity for CO2 emissions growth were largely impaired in China, which caused ∆C growing stably with a decline and then a rise in the short term accordingly. In addition, Figure 4 and Figure 5 show that all influencing factors contributed differently to the industrial energy-related CO2 emissions growth in different years and got involved in largely differential influence in respect of time series.
Energies 06 02319 g004 1024
Figure 4. Cumulative contribution values of influencing factors of the energy-related industrial CO2 emissions.
Figure 4. Cumulative contribution values of influencing factors of the energy-related industrial CO2 emissions.
Energies 06 02319 g004
Energies 06 02319 g005 1024
Figure 5. Cumulative contribution rates of influencing factors of the energy-related industrial CO2 emissions.
Figure 5. Cumulative contribution rates of influencing factors of the energy-related industrial CO2 emissions.
Energies 06 02319 g005

4.1.1. Energy Structure Effect

From 1985 to 2007, ES showed a weak positive effect on China’s industrial CO2 emissions growth year by year with a low fluctuation. Therein, its contribution value was 570,100 metric tons in 1986, with the contribution rate of 2.36%; the cumulative contribution value was increased to 16.0177 million metric tons in 2009, with the contribution rate decreased to 1.44%. Within the context of the rapid economic development in China, the improvement of industrialization would definitely arouse the surging demand for various fossil fuels such as coal, petroleum and natural gas in different industrial sectors. However, the fuel structure dominated by coal due to the fact that China's natural resources endowment was hard to change obviously in short term. Meanwhile, the energy consumption structure in industrial sectors is obviously not optimized, the proportion of high-carbon energy consumption shows slight growth, i.e., coal consumption rate increased from 80.30% in 1985 to 85.87% in 2007, while petroleum consumption rate decreased from 16.91% in 1985 to 9.42% in 2007. Hence, with increasingly swift industrialization process, the total industrial energy consumption growth and high-carbon energy consumption growth are major causes that ES plays a positive driving effect on China’s industrial CO2 emissions. Therefore, one significant measure to reduce industrial CO2 emissions in the future is involved in continually enhancing adjustment and optimization of energy consumption structure in industrial sectors, increasing low-carbon energy consumption ratio and improving energy use efficiency.

4.1.2. Energy Intensity Effect

As the only negative driving factor for China’s industrial energy-related CO2 emissions growth, EI extremely reduces the total industrial CO2 emissions. From 1985 to 2001, the negative driving effects of EI were sharply increased in general, with the cumulative contribution value of −606 million metric tons and the cumulative contribution rate of −154.82%; from 2002 to 2004, the negative driving effects of EI for industrial CO2 emissions growth were decreased to a certain extent, with the cumulative contribution value rebounding from −635 million metric tons in 2002 to 583 million metric tons in 2004 and the cumulative contribution rate rebounding from −145.26% to −78.59%. Since 2005, the negative driving effects of EI had been gradually increased, with the cumulative contribution value of −744 million metric tons in 2007.
EI, which represents the input/output characteristics of energy system through the energy consumptions per GDP, is closely bound to many factors such as industrial structure, energy structure and technological progress. In this paper, the energy consumption per industrial output value is used for weighing the energy intensity of industrial sectors to further reflect the overall efficiency of energy economic activities in the industrial sectors. From 1980 to 2007, the energy intensity of all industries was in a sharp fall and the average energy intensity of all industries dropped from 3.78 tce/¥10,000 to 1.31 tce/¥10,000, with the reduction rate of 65.35%. The energy intensity of industrial sectors calculated based upon the constant price of 1985 in the paper indicated that the energy intensity of China’s industrial sectors was 15.12 tce/¥10,000 in 1985 and decreased to 4.50 tce/¥10,000 in 2007, with the reduction rate of 70.24% which was more than the average of energy intensity reduction in China. It is thus clear that China’s efficiency of energy use has made substantial progress in recent three decades, but the adjustment of industrial structure and energy structure is still quite slow. Within the rapid development of industrialization in China, the growth of total energy consumption is expected to continue. Therefore new technology breakthroughs are preferred, rather than a direct reduction of energy consumption to improve China’s efficiency of energy use.

4.1.3. Economic Structure Effect

The economic share effect is primarily displayed as the proportion of industrial output value in GDP which affects CO2 emissions. The decomposition results show that, YS played a positive driving role in China’s energy-related industrial CO2 emissions growth, with the contribution rate in a relatively slow growth. The cumulative effects of YS surged before the middle and late 1990s, with 197 million metric tons and the cumulative contribution rate of 50.65% in 1997. Later, its positive driving effects were gradually lessened, with the cumulative effects increased to 269 million metric tons while the cumulative contribution rate decreased to 24.24% in 2007. In recent years, the industrial structure has been adjusted in China to a certain extent, but it still failed to restrain industrial CO2 emissions growth due to slow progress. China’s gross industrial output value accounted for 38.25% of GDP in 1985 and exceeded half of GDP in 2007 [87,88], as China was in that very period of rapid industrialization so that the industry scale was continually expanded and the industrial adjustment lagged behind the demand of economic development for continual industrial expansion. Even if the industrial share kept unchanged, the expansion would still lead to a high energy demand so that CO2 emission was increased.

4.1.4. Economic Activity Effect

As calculated based upon the constant price in 1985, China’s per capita GDP was ¥851.76 in 1985, which increased to ¥6524.45 in 2007, corresponding to a growth rate of 604.88%. However, the rapid economic development in China brought with it a huge quantity of energy consumption so that economic development also became the foremost driving factor for industrial CO2 emissions growth. Viewed from the decomposition results, PCG was the largest positive driving factor for China’s industrial CO2 emissions. In terms of time series, the cumulative contribution rate was almost over 100% (Figure 5) with the maximum of 177.87% in 2001, and then suffered from a slow decline and dropped to 128.71% at the end of 2007, excluding special years. Moreover, the cumulative contribution value of PCG for China’s industrial CO2 emissions was always rapidly increased (Figure 5). The cumulative contribution value was increased from 26.5251 million metric tons in 1986 to 1.428 billion metric tons in 2007, with annually average increment of 63.7053 million metric tons of CO2 emissions. As can be seen from the above, China’s continuous economic development was a major decisive factor for industrial CO2 emissions growth, of which the contribution to CO2 emissions growth was also on the rise. As the largest developing country, China shall not always sacrifice economic development to reduce CO2 emissions, but coordinate various factors and balance the relationship between emission reduction and development so that normal economic development can be guaranteed and a win-win outcome in both development and emission reduction can be reached.

4.1.5. Population Effect

P also played a positive driving role in China’s industrial CO2 emissions growth. China’s population growth rate has declined since 1987, even reaching the lowest point of 0.52% in 2007 due to family planning policy, but the net growth of population was about 7.69 million people on the average from 2000 to 2007, for the huge population base in China. With the improvement of the population-urbanized ratio, growth of household consumption and variation of consumption mode, China’s absolutely rapid population growth promoted the continuous growth of economic output and corresponding growth of energy consumption to become the essential conditions to meet the basic demand for national people’s survival and development, thus, the energy-related CO2 emissions were gradually increased. In 1986, the cumulative contribution value of scale effect in China was 5.9834 million metric tons, exceeding 100 million metric tons in 2002 due to continuous population growth and increased to 141 million metric tons at the end of 2009. On the contrary, the cumulative contribution rate of population size underwent the continuous decline year by year, decreasing from 24.76% in 1986 to 12.70% in 2007. The evaluation of all this data over time further indicates that continuous industrial CO2 emissions growth is one consequence of population growth, but such influence is gradually reduced.

4.2. Decomposition Analysis of Industry Discrepancy

To further discuss the differential influences of various factors for the industry and other sectors, the paper presents a decomposition analysis of the cumulative effects of influencing factors for energy-related CO2 emissions of six sectors from 1985 to 2007 and also a comparative analysis of differential influences of various factors on the industry. The decomposition results are as follows (Table 4).
Table
Table 4. Decomposition results of CO2 emissions of six sectors from 1985 to 2007.
Table 4. Decomposition results of CO2 emissions of six sectors from 1985 to 2007.
SectorAgricultureIndustryConstructionTransport, storage, and postal servicesBusinessUrban and rural households
TotalΔC1,215.83110,951.621,226.418,503.051,070.7−793.19
dtotal100%100%100%100%100%100%
Energy structureΔCES−124.241,601.77−54.62−490.48−167.46−1,348.6
dES−10.22%1.44%−4.45%−5.77%−15.64%170.02%
Energy intensityΔCEI−554.67−74,444.77−494.5−711.04−360.42−22,515.99
dEI−45.62%−67.10%−40.32%−8.36%−33.66%2,838.65%
Economic structureΔCYS−2,746.7126,899.91−46.0561.36−218.63,100.75
dYS−225.91%24.24%−3.76%0.72%−20.42%−390.92%
Economic activityΔCPCG4,176.32142,804.161,659.798,806.691,647.8317,786.79
dPCG343.49%128.71%135.34%103.57%153.90%−2,242.43%
PopulationΔCP465.1414,090.52161.79836.52169.342,183.84
dP38.26%12.70%13.19%9.84%15.82%−275.32%
Notes: ∆CX: The cumulative contribution value of factor X (104 t), dX: The cumulative contribution rate of factor X (%).
For the cumulative effects of factors for CO2 emissions from all sectors, both PCG and P played a significantly positive driving role in CO2 emissions growth; EI on the other hand represented a negative driving effect; except for positive driving effect in the industrial sectors, ES played a negative driving role in the rest five sectors (Figure 6), for which the continuous growth of fossil fuel consumption such as coal and petroleum in industrial sectors acted as the major reason; besides, energy consumption was increased with the rapid development of the industry, transportation and residential sector. YS played a positive driving role in those three sectors, but it played a negative driving role in three other sectors which were developed slowly to some extent.
Seen from the cumulative contribution values of all driving factors for different sectors, PCG, which offered the largest contribution value in all sectors, was the largest positive driving factor for CO2 emissions growth; EI was the largest inhibiting factor for CO2 emissions growth in five sectors excluding farming, forestry, animal husbandry and fishery; ES reflected the substitution of energy consumption. Except for the industry, CO2 emission was efficiently reduced through the adjustment of energy structure in other sectors. Therein, the residential sector made the most contribution, with the cumulative contribution value of −13.4860 million metric tons (Table 4, Figure 6). For a long time, China’s industrial energy consumption was constituted by coal and petroleum in dominance. Compared with other energy resources, coal and petroleum were of relatively high CO2 emission coefficients so that ES could have a positive driving effect on industrial CO2 emission growth; P produced the maximum cumulative contribution value of 141 million metric tons for the industry, but the minimum cumulative contribution value of 1.6179 million metric tons was reserved for the construction sector. In respect of transportation, the cumulative contribution value of P was 8.3652 million metric tons which surpassed the cumulative contribution value of −7.1104 million metric tons of EI as a negative inhibiting factor (Table 4, Figure 6). Apart from economic development, the rapid population growth also intensified the burden of transportation and became the second largest driving force for industrial CO2 emission growth.
Energies 06 02319 g006 1024
Figure 6. Cumulative contribution value of influencing factors of CO2 emissions in six sectors from 1985 to 2007.
Figure 6. Cumulative contribution value of influencing factors of CO2 emissions in six sectors from 1985 to 2007.
Energies 06 02319 g006
Furthermore, PCG and EI were fairly obvious in the cumulative effects of residential CO2 emissions growth. The negative driving effects of EI outweighed the positive driving effects of PCG. Since the residential energy consumption was closely associated to people’s living habits, consumption patterns and climate change in general, the energy demand growth got slow somewhat and many new energy resources such as solar energy and methane were increasingly used in people’s living, effectively substituted for fossil fuels and also inhibited CO2 emissions growth to a certain extent. Thus, EI was involved in a negative driving effect on the total effect of residential CO2 emission, that is, CO2 emissions varied to finally reach the decrement of 7.9319 million metric tons. Meanwhile, the residential sector was the unique whose emission reduction was realized in six sectors from 1985 to 2007.

5. Conclusions and Policy Prescriptions

From 1997 to 2001, China’s industrial CO2 emissions underwent some different stages ranging from “steadily rising” to “stable with a decline”, and then to “rapid growth” but always held an absolutely dominant place in the total CO2 emissions of six sectors, which was the biggest carbon emitter in China; in respect of major industrial sectors, the manufacturing got involved in maximum CO2 emissions, with rapid growth rate; followed by the electric power, gas and water production and supply, such sector was often steadily increased; however, CO2 emissions from the mining was relatively low and slowly decreased.
In terms of all influencing factors for China’s industrial CO2 emissions, the positive driving factors included ES, PCG, YS and P. Therein, PCG was the foremost positive driving factor for China’s industrial CO2 emissions growth, so the control of energy-related CO2 emissions was the grand challenge which China’s industrial sectors were confronted with, given that economic development was guaranteed; P and YS also boosted China’s industrial CO2 emissions growth. Although the decomposition results showed that P and YS had small contribution values respectively, the population size and industrial structure were highly related to industrial CO2 emissions so that corresponding driving effects could not be neglected. It is an inevitable trend that continuous population growth and advanced industrialization will cause residential and industrial CO2 emissions growth; additionally, it is difficult to radically change the overall energy structure with the present dominance of coal, so the energy consumption structure in industrial sectors obviously fails to be optimized and ES is also displayed as a slightly positive driving effect. Therefore, with economic development, controlling population growth and optimizing industrial structure are major ways to control China’s industrial CO2 emissions growth.
EI was the only one which played a negative driving role in China’s industrial CO2 emissions growth, largely reducing total industrial CO2 emissions. Moreover, China’s efficiency of energy use has made a substantial progress in recent three decades, but the industrial structure and energy structure adjustment is still quite slow. Along with the rapid development of industrialization, China’s industrial sectors still have large space for energy intensity decreases and CO2 emissions reduction. China shall vigorously boost technical progress to further improve efficiency of energy use and low-carbon development to maximize output in low intensity branches in the future.
All influencing factors had a differential bearing on CO2 emissions from six sectors. PCG always played a positive driving role in all sectors, with the maximum cumulative contribution value; EI had a negative driving effect, and other factors presented different effects and contributions according to different sectors. PCG and EI played a decisive role in driving differential influences of various factors on six sectors.
As seen from the decomposition results, reduction of the energy-related industrial CO2 emissions is a hard and complex project, related to different factors such as economy, technology, industrial structure and population. The paper presents the following policy recommendations for the development of China’s industrial CO2 emissions reduction in the future:
First and foremost, the control strength of environmental policies shall be intensified in different stages to control continuous CO2 emissions growth under the industrialization. It is required to establish and implement CO2 emissions laws and regulations, technical standards, entry threshold for the enterprises of carbon trade and CO2 emissions, energy conservation and emission reduction and other policies and measures for CO2 emission reduction.
Furthermore, the industrial structure shall be positively optimized and adjusted. Based on the principle of “Reduction, Re-use, and Recycling” and the requirements for new industrialized road, the effective measures shall be taken to further improve industrial structure and reduce CO2 emissions. It is required to vigorously develop the tertiary and high-tech industry, especially environment-friendly industry, to make industrial structure develop toward energy conservation and advancement.
In third place, it is necessary to optimize energy consumption structure and strengthen energy conservation. The CO2 emission reduction technology shall be actively implemented to increase thr efficiency of energy use. The low-carbon energy and renewable energy shall be developed to improve the energy structure.
Finally, it is needed to strengthen publicity and advocate a low-carbon lifestyle. Slowing down global warming and reducing greenhouse gas emissions are associated to lifestyle and values change. Therefore, a continuous education and publicity programme should be conducted to convert people’s values for the popularization of a low-carbon lifestyle and low-carbon consumption habits, which are also an effective way for CO2 emission reduction.

Acknowledgements

This study was supported by Fund for Creative Research Groups of the National Natural Science Foundation of China (No. 51121003).

References

  1. The State Council of the People’s Republic of China (SCPRC). The 12th Five-Year Plan Outline of National Economy and Social Development of People’s Republic of China. Available online: http://news.xinhuanet.com/politics/2011-03/16/c_121193916.htm (accessed on 16 March 2011).
  2. Ang, B.W. Decomposition analysis for policymaking in energy: Which is the preferred method? Energy Policy 2004, 32, 1131–1139. [Google Scholar]
  3. Steenhof, P.; Woudsma, C.; Sparling, E. Greenhouse gas emissions and the surface transport of freight in Canada. Transp. Res. Part D Transp. Environ. 2006, 11, 369–376. [Google Scholar] [CrossRef]
  4. Ang, B.W. Decomposition analysis applied to energy. In Encyclopedia of Energy; Cleveland, C.J., Ed.; Elsevier: Amsterdam, The Netherlands, 2004; pp. 761–769. [Google Scholar]
  5. Ang, B.W.; Liu, N. Energy decomposition analysis: IEA model versus other methods. Energy Policy 2007, 35, 1426–1432. [Google Scholar] [CrossRef]
  6. Ang, B.W.; Zhang, F.Q. A survey of index decomposition analysis in energy and environmental studies. Energy 2000, 25, 1149–1176. [Google Scholar] [CrossRef]
  7. Ang, B.W.; Liu, F.L. A new energy decomposition method: Perfect in decomposition and consistent in aggregation. Energy 2001, 26, 537–548. [Google Scholar] [CrossRef]
  8. O’Mahony, T.; Zhou, P.; Sweeney, J. The driving forces of change in energy-related CO2 emissions in Ireland: A multi-sectoral decomposition from 1990 to 2007. Energy Policy 2012, 44, 256–267. [Google Scholar] [CrossRef]
  9. Ang, B.W.; Zhang, F.Q.; Choi, K. Factorizing changes in energy and environmental indicators through decomposition. Energy 1998, 23, 489–495. [Google Scholar] [CrossRef]
  10. Liu, N.; Ang, B.W. Factors shaping aggregate energy intensity trend for industry: Energy Intensity versus product mix. Energy Econ. 2007, 29, 609–635. [Google Scholar] [CrossRef]
  11. Ang, B.W.; Mu, A.R.; Zhou, P. Accounting frameworks for tracking energy efficiency trends. Energy Econ. 2010, 32, 1209–1219. [Google Scholar] [CrossRef]
  12. Peters, G.P.; Hertwich, E.G. CO2 embodied in international trade with implications for global climate policy. Environ. Sci. Technol. 2008, 42, 1401–1407. [Google Scholar] [CrossRef] [PubMed]
  13. Greening, L.A.; Ting, M.; Davis, W.B. Decomposition of aggregate carbon intensity for freight: Comparison of declining trends from 10 OECD countries for the period 1971–1993. Energy Econ. 1999, 21, 331–361. [Google Scholar] [CrossRef]
  14. Pani, R.; Mukhopadhyay, U. Identifying the major player behind increasing global carbon dioxide emissions: A decomposition analysis. Environmentalist 2010, 30, 183–205. [Google Scholar] [CrossRef]
  15. Greening, L.A.; Ting, M.; Krackler, T.J. Effects of changes in residential end-uses and behavior on aggregate carbon intensity: Comparison of 10 OECD countries for the period 1970–1993. Energy Econ. 2001, 23, 153–178. [Google Scholar] [CrossRef]
  16. Greening, L.A. Effects of human behavior on aggregate carbon intensity of personal transportation: Comparison of 10 OECD countries for the period 1970–1993. Energy Econ. 2004, 26, 1–30. [Google Scholar] [CrossRef]
  17. Choi, K.H.; Ang, B.W. A time-series analysis of energy-related carbon emissions in Korea. Energy Policy 2001, 29, 1155–1161. [Google Scholar] [CrossRef]
  18. Oh, I.; Wehrmeyer, W.; Mulugetta, Y. Decomposition analysis and mitigation strategies of CO2 emissions from energy consumption in South Korea. Energy Policy 2010, 38, 364–377. [Google Scholar] [CrossRef]
  19. Bhattacharyya, S.C.; Ussanarassamee, A. Decomposition of energy and CO2 intensities of Thai industry between 1981 and 2000. Energy Econ. 2004, 26, 765–781. [Google Scholar] [CrossRef]
  20. Paul, S.; Bhattacharya, R.N. CO2 Emission from energy use in India: A decomposition analysis. Energy Policy 2004, 32, 585–593. [Google Scholar] [CrossRef]
  21. Luciano, C.F.; Shinji, K. Decomposing the decoupling of CO2 emissions and economic growth in Brazil. Ecol. Econ. 2011, 70, 1459–1469. [Google Scholar] [CrossRef]
  22. Luciano, C.F.; Shinji, K. Decomposition of CO2 emissions change from energy consumption in Brazil: Challenges and policy implications. Energy Policy 2011, 39, 1495–1504. [Google Scholar] [CrossRef]
  23. Tunc, G.I.; Türüt-Asık, S.; Akbostancı, E.; Gipek, T.; Serap, T.A.; Elif, A. A decomposition analysis of CO2 emissions from energy use: Turkish case. Energy Policy 2009, 37, 4689–4699. [Google Scholar] [CrossRef]
  24. Kwon, T.H. Decomposition of factors determining the trend of CO2 emissions from car travel in Great Britain (1970–2000). Ecol. Econ. 2005, 53, 261–275. [Google Scholar] [CrossRef]
  25. Hatzigeorgiou, E.; Polatidis, H.; Haralambopoulos, D. CO2 Emissions in Greece for 1990–2002: A decomposition analysis and comparison of results using the arithmetic mean divisia index and logarithmic mean divisia index techniques. Energy 2011, 33, 492–499. [Google Scholar] [CrossRef]
  26. Wu, L.B.; Kaneko, S. Driving forces behind the stagnancy of Chinas energy-related CO2 emissions from 1996 to 1999: The relative importance of structural change, intensity change and scale change. Energy Policy 2005, 33, 319–335. [Google Scholar] [CrossRef]
  27. Wu, L.B.; Kaneko, S.; Matsuoka, S. Dynamics of energy-related CO2 emissions in China during 1980 to 2002: The relative importance of energy supply-side and demand-side effects. Energy Policy 2006, 34, 3549–3572. [Google Scholar] [CrossRef]
  28. Xu, G.Q.; Liu, Z.Y.; Jiang, Z.H. Decomposition model and empirical study of carbon emissions for China, 1995–2004 [in Chinese]. China Popul. Resour. Environ. 2006, 16, 158–161. [Google Scholar]
  29. Song, D.Y.; Lu, Z.B. The factor decomposition and periodic fluctuations of carbon emission in China [in Chinese]. China Popul. Resour. Environ. 2009, 3, 18–24. [Google Scholar]
  30. Fan, Y.; Liu, L.C.; Wu, G.; Tsai, H.T.; Wei, Y.M. Changes in carbon intensity in China: Empirical findings from 1980–2003. Ecol. Econ. 2007, 62, 683–691. [Google Scholar] [CrossRef]
  31. Zhang, M.; Mu, H.L.; Ning, Y.D.; Song, Y.C. Decomposition of energy-related CO2 emission over 1991–2006 in China. Ecol. Econ. 2009, 68, 2122–2128. [Google Scholar] [CrossRef]
  32. Sun, J.W.; Zhao, R.Q.; Huang, X.J.; Chen, Z.G. Research on carbon emission estimation and factor decomposition of China from 1995 to 2005. J. Nat. Resour. 2010, 25, 1284–1295. [Google Scholar]
  33. Zhang, Y.; Zhang, J.Y.; Yang, Z.F.; Li, S.S. Regional differences in the factors that influence China’s energy-related carbon emissions, and potential mitigation strategies. Energy Policy 2011, 39, 7712–7718. [Google Scholar] [CrossRef]
  34. Wu, F.; Fan, L.W.; Zhou, P.; Zhou, D.Q. Industrial energy efficiency with CO2 emissions in China: A nonparametric analysis. Energy Policy 2012, 49, 164–172. [Google Scholar] [CrossRef]
  35. Wang, S.S.; Zhou, D.Q.; Zhou, P.; Wang, Q.W. CO2 Emissions, energy consumption and economic growth in China: A panel data analysis. Energy Policy 2011, 39, 4870–4875. [Google Scholar] [CrossRef]
  36. Wang, H.T.; Mu, S.R. The characteristics and trend of carbon emissions in Beijing. Urban Studies 2010, 17, 55–61. [Google Scholar]
  37. Zhou, S.Y.; Chen, H.; Li, S.C. Resources use and greenhouse gas emissions in urban economy: Ecological input–output modeling for Beijing 2002. Commun. Nonlinear Sci. Numer. Simul. 2010, 15, 3201–3231. [Google Scholar] [CrossRef]
  38. Liu, C.C. An extended method for key factors in reducing CO2 emissions. Appl. Math. Comput. 2007, 189, 440–451. [Google Scholar] [CrossRef]
  39. Zhao, M.; Zhang, W.G.; Yu, L.Z. Carbon emissions from energy consumption in Shanghai city. Res. Environ. Sci. 2009, 22, 984–989. [Google Scholar]
  40. Brownstone, D.; Golob, T.F. The impact of residential density on vehicle usage and energy consumption. J. Urban Econ. 2010, 65, 91–98. [Google Scholar] [CrossRef]
  41. Glaser, E.L.; Kahn, M.E. The greenness of cities: Carbon dioxide emissions and urban development. J. Urban Econ. 2010, 26, 51–75. [Google Scholar]
  42. Dhakal, S. Urban energy use and carbon emissions from cities in China and policy implications. Energy Policy 2009, 37, 4208–4219. [Google Scholar] [CrossRef]
  43. Diakoulaki, D.; Mandaraka, M. Decomposition analysis for assessing the progress in decoupling industrial growth from CO2 emission in the EU manufacturing sector. Energy Econ. 2007, 29, 636–664. [Google Scholar] [CrossRef]
  44. Freeman, S.L.; Niefer, M.J.; Roop, J.M. Measuring industrial energy intensity: Practical issues and problems. Energy Policy 1997, 25, 703–714. [Google Scholar] [CrossRef]
  45. Ang, B.W.; Choi, K.H. Decomposition of Aggregate energy and gas emission intensities for industry: A refined divisia index method. Energy J. 1997, 18, 59–73. [Google Scholar] [CrossRef]
  46. Timilsina, G.R.; Shrestha, A. Factors affecting transport sector CO2 emissions growth in Latin American and Caribbean countries: An LMDI decomposition analysis. Int. J. Energy Res. 2009, 33, 396–414. [Google Scholar] [CrossRef]
  47. Ediger, V.S.; Huvaz, O. Examining the sectoral energy use in Turkish economy (1980–2000) with the help of decomposition analysis. Energy Convers. Manag. 2006, 47, 732–745. [Google Scholar] [CrossRef]
  48. Zhang, M.Y.; Huang, X.J. Effects of industrial restructuring on carbon reduction: An analysis of Jiangsu Province, China. Energy 2012, 44, 515–526. [Google Scholar] [CrossRef]
  49. Liu, L.; Fan, Y.; Wu, G.; Wei, Y. Using LMDI method to analyze the change of China’s industrial CO2 emissions from final fuel use: An empirical analysis. Energy Policy 2007, 35, 5892–5900. [Google Scholar] [CrossRef]
  50. Ren, S.G.; Fu, X.; Chen, X.H. Regional variation of energy-related industrial CO2 emissions mitigation in China [in Chinese]. China Econ. Rev. 2012, 23, 1134–1145. [Google Scholar] [CrossRef]
  51. Zhao, M.; Tan, L.R.; Zhang, W.G.; Ji, M.H.; Liu, Y.; Yu, L.Z. Decomposing the influencing factors of industrial carbon emissions in Shanghai using the LMDI method. Energy 2010, 35, 2505–2510. [Google Scholar] [CrossRef]
  52. National Bureau of Statistics of China. China Statistical Yearbook 1986; China Statistics Press: Beijing, China, 1986.
  53. National Bureau of Statistics of China. China Statistical Yearbook 1987; China Statistics Press: Beijing, China, 1987.
  54. National Bureau of Statistics of China. China Statistical Yearbook 1988; China Statistics Press: Beijing, China, 1988.
  55. National Bureau of Statistics of China. China Statistical Yearbook 1989; China Statistics Press: Beijing, China, 1989.
  56. National Bureau of Statistics of China. China Statistical Yearbook 1990; China Statistics Press: Beijing, China, 1990.
  57. National Bureau of Statistics of China. China Statistical Yearbook 1991; China Statistics Press: Beijing, China, 1991.
  58. National Bureau of Statistics of China. China Statistical Yearbook 1992; China Statistics Press: Beijing, China, 1992.
  59. National Bureau of Statistics of China. China Statistical Yearbook 1993; China Statistics Press: Beijing, China, 1993.
  60. National Bureau of Statistics of China. China Statistical Yearbook 1994; China Statistics Press: Beijing, China, 1994.
  61. National Bureau of Statistics of China. China Statistical Yearbook 1995; China Statistics Press: Beijing, China, 1995.
  62. National Bureau of Statistics of China. China Statistical Yearbook 1996; China Statistics Press: Beijing, China, 1996.
  63. National Bureau of Statistics of China. China Statistical Yearbook 1997; China Statistics Press: Beijing, China, 1997.
  64. National Bureau of Statistics of China. China Statistical Yearbook 1998; China Statistics Press: Beijing, China, 1998.
  65. National Bureau of Statistics of China. China Statistical Yearbook 1999; China Statistics Press: Beijing, China, 1999.
  66. National Bureau of Statistics of China. China Statistical Yearbook 2000; China Statistics Press: Beijing, China, 2000.
  67. National Bureau of Statistics of China. China Statistical Yearbook 2001; China Statistics Press: Beijing, China, 2001.
  68. National Bureau of Statistics of China. China Statistical Yearbook 2002; China Statistics Press: Beijing, China, 2002.
  69. National Bureau of Statistics of China. China Statistical Yearbook 2003; China Statistics Press: Beijing, China, 2003.
  70. National Bureau of Statistics of China. China Statistical Yearbook 2004; China Statistics Press: Beijing, China, 2004.
  71. National Bureau of Statistics of China. China Statistical Yearbook 2005; China Statistics Press: Beijing, China, 2005.
  72. National Bureau of Statistics of China. China Statistical Yearbook 2006; China Statistics Press: Beijing, China, 2006.
  73. National Bureau of Statistics of China. China Statistical Yearbook 2007; China Statistics Press: Beijing, China, 2007.
  74. National Bureau of Statistics of China. China Statistical Yearbook 2008; China Statistics Press: Beijing, China, 2008.
  75. National Bureau of Statistics of China. China Energy Statistical Yearbook 1986; China Statistics Press: Beijing, China, 1987.
  76. National Bureau of Statistics of China. China Energy Statistical Yearbook 1989; China Statistics Press: Beijing, China, 1990.
  77. National Bureau of Statistics of China. China Energy Statistical Yearbook 1991–1996; China Statistics Press: Beijing, China, 1998.
  78. National Bureau of Statistics of China. China Energy Statistical Yearbook 1997–1999; China Statistics Press: Beijing, China, 2001.
  79. National Bureau of Statistics of China. China Energy Statistical Yearbook 2000–2002; China Statistics Press: Beijing, China, 2004.
  80. National Bureau of Statistics of China. China Energy Statistical Yearbook 2004; China Statistics Press: Beijing, China, 2005.
  81. National Bureau of Statistics of China. China Energy Statistical Yearbook 2005; China Statistics Press: Beijing, China, 2006.
  82. National Bureau of Statistics of China. China Energy Statistical Yearbook 2006; China Statistics Press: Beijing, China, 2007.
  83. National Bureau of Statistics of China. China Energy Statistical Yearbook 2007; China Statistics Press: Beijing, China, 2008.
  84. National Bureau of Statistics of China. China Energy Statistical Yearbook 2008; China Statistics Press: Beijing, China, 2008.
  85. Intergovernmental Panel on Climate Change (IPCC). 2006 Intergovernmental Panel on Climate Change Guidelines for National Greenhouse Gas Inventories, Volume 2 Energy. Available online: http://www.ipcc-nggip. iges.or.jp/public/2006gl/vol2.html (accessed on 1 April 2007).
  86. Ang, B.W.; Choi, K. Decomposition of aggregate energy and gas emission intensities for industry: A refined divisia index method. Energy J. 1997, 18, 59–73. [Google Scholar] [CrossRef]
  87. National Bureau of Statistics of China (NBSC). Statistical Communiqué of the People’s Republic of China on the 1985 National Economic and Social Development [in Chinese]. In Gazette State Council of the People’s Republic of China; NBSC: Beijing, China, 1986; Volume 6, pp. 131–134. [Google Scholar]
  88. National Bureau of Statistics of China (NBSC). Statistical Communiqué of the People’s Republic of China on the 2007 national economic and social development [in Chinese]. In China Stat.; 2008; 3, pp. 4–9. [Google Scholar]

Share and Cite

MDPI and ACS Style

Chen, L.; Yang, Z.; Chen, B. Decomposition Analysis of Energy-Related Industrial CO2 Emissions in China. Energies 2013, 6, 2319-2337. https://doi.org/10.3390/en6052319

AMA Style

Chen L, Yang Z, Chen B. Decomposition Analysis of Energy-Related Industrial CO2 Emissions in China. Energies. 2013; 6(5):2319-2337. https://doi.org/10.3390/en6052319

Chicago/Turabian Style

Chen, Liang, Zhifeng Yang, and Bin Chen. 2013. "Decomposition Analysis of Energy-Related Industrial CO2 Emissions in China" Energies 6, no. 5: 2319-2337. https://doi.org/10.3390/en6052319

APA Style

Chen, L., Yang, Z., & Chen, B. (2013). Decomposition Analysis of Energy-Related Industrial CO2 Emissions in China. Energies, 6(5), 2319-2337. https://doi.org/10.3390/en6052319

Article Metrics

Back to TopTop