Decoupling and Decomposition Analysis of Carbon Emissions from Industry : A Case Study from China

China has overtaken the United States as the world’s largest producer of carbon dioxide, with industrial carbon emissions (ICE) accounting for approximately 65% of the country’s total emissions. Understanding the ICE decoupling patterns and factors influencing the decoupling status is a prerequisite for balancing economic growth and carbon emissions. This paper provides an overview of ICE based on decoupling elasticity and the Tapio decoupling model. Furthermore, the study identifies the factors contributing to ICE changes in China, using the Kaya identity and Log Mean Divisia Index (LMDI) techniques. Based on the effects and contributions of ICE, we close with a number of recommendations. The results revealed a significant upward trend of ICE during the study period 1994 to 2013, with a total amount of 11,147 million tons. Analyzing the decoupling relationship indicates that “weak decoupling” and “expansive decoupling” were the main states during the study period. The decomposition analysis showed that per capita wealth associated with industrial outputs and energy intensity are the main driving force of ICE, while energy intensity of industrial output and energy structure are major determinants for ICE reduction. The largest contributing cumulative effect to ICE is per capita wealth, at 1.23 in 2013. This factor is followed by energy intensity, with a contributing cumulative effect of −0.32. The cumulative effects of energy structure and population are relatively small, at 0.01 and 0.08, respectively.


Introduction
A continuous growth in energy consumption has increased atmospheric carbon greenhouse gas emissions [1,2].As a result, carbon currently contributes approximately 63% of the gaseous radiative force contributing to climate change.Atmospheric carbon had increased to 390.5 ppm by 2011, according to the IPCC reports [3][4][5], thus exceeding pre-industrial levels by approximately 40% [6].China has surpassed the United States as the world's biggest carbon emitter.Industrial carbon emissions (ICE) accounted for approximately 65% of total emissions [7][8][9].The increased ICE from China has received significant attention in light of global warming, and there is a global consensus about the importance of reducing greenhouse gas emissions.Reducing ICE has become increasingly important for the Chinese policymakers, partly because China committed itself to lower the carbon intensity of GDP by 40% to 45% below 2005 levels by 2020.Based on the Copenhagen Climate Change Conference in 2009 [10][11][12][13][14], China has realized the importance of reducing carbon emissions [15][16][17][18].China also should pay significant attention to make emission reductions compatible with economic growth, especially for industry.
Past studies used decomposition methodologies to quantitatively identify factors on changes carbon emission, at country-, regional-, and global-level.These factors can in turn be applied in energy policymaking.For example, Shahbaz [19] and Leitão [20,21] discovered a relationship between the economy and carbon emissions from the perspective of globalization.Sun [22], Ma [23], Liao [24], Q. Wang [25], Paul [26], M. Shahbaz [27][28][29][30], and Lise [31] used a decomposition method to quantify the influencing factors of carbon emissions in China, India, Malaysia, Indonesia, Portugal, Turkey, and Brazil, respectively.Magazzino [18,[32][33][34][35] used a time series approach to examine the relationship between GDP, energy consumption and carbon emissions, and energy use in the Gulf Cooperation Council countries, EMU countries, Israel, and ASEAN-6 countries.
Wang and Yang [36] used the Beijing-Tianjin-Hebei area as example to divide the literature about the relationship between ICE and economic growth into three assessment categories: energy efficiency, environment, and economic development.Using the DEA model, some studies measured environmental and energy performance in China [37][38][39][40] and conducted a comparative analysis of China's regional energy and emission performance [41].Chung et al. [42] used the LMDI technique to evaluate the respective contributions of changes in residential energy use in Hong Kong.Leitão [43] used panel-data analysis to analyze energy consumption and foreign direct investment of Portugal.In general, researchers studying China have found that the decline in energy intensity has been the main factor associated with emissions deceleration [23,[44][45][46].Fan [47] uncovered that the change in primary energy-related carbon intensity and the material sectors' final energy-related carbon intensity mainly contributed to the decline in energy intensity.
In addition, other studies have compared energy-related carbon emissions between sectors, mostly concentrating on the rural energy and transport sectors.Wang [48] studied carbon emission from China's transport sector.Scholl [49] examined the five influencing factors on carbon emissions from passenger transport in nine OECD countries.Chipper [50] qualified the three key influencing factors on changes in energy use and carbon emissions from freight transport in 10 industrialized countries.Lakshmanan and Ha [51] reported that increased personal travel, population, and GDP contributed to changes in carbon emission from transport sector in the U.S. from 1970 to 1991 As mentioned above, previous studies usually focused two issues: (1) investigated the decoupling index of ICE and economic activities; and (2) assessed the status of decoupling.Few studies have researched the inner mechanisms of the changes of each factor.Furthermore, no research has been done on the decoupling and decomposition analysis of China's ICE using the most recent data.As the country with the most carbon emissions, China allows an effective case study for a decoupling, combine with decomposition technique.This paper is aimed to clarify the relationship between carbon emission and economic output in China's industrial sector, and then to examine these influencing factors of decoupling status.To achieve this, we developed a Tapio model based on extended Kaya identity to analysis decoupling status, and developed decoupling index based on LMDI techniques to study the contribution of different factors influencing industrial carbon emission in China from 1994-2013.

Data Sources
Data for the period of 1994 to 2013 were collected from issues of the China Statistical Yearbook [52][53][54][55].The latest data are updated in the CSY-2015; industry has increased rapidly since 1994 and policy leaders have begun to pay greater attention to both energy efficiency and environmental pressure caused by ICE.China has begun to vigorously develop clean energies, such as wind power, photovoltaic, nuclear power, and shale gas [56][57][58][59][60][61] to reduce emissions.Despite this, however, 98% of industrial energy is coal consumption, which has constantly generated carbon emissions.
The National Bureau of Statistics of the People's Republic of China publishes the CSY yearly.It is the only official agency to publish statistical data; the data are comprehensive and highly reliability.In preparation for the decomposition analysis, data were specifically collected about energy consumption by industrial sector, which mainly includes coal consumption, coke consumption, crude oil consumption, gasoline consumption, kerosene consumption, diesel oil consumption, fuel oil consumption, and natural gas consumption.Industrial output and population data were also collected.

Logarithmic Mean Divisia Index (LMDI)
There are dozens of decomposition methods that enable analysts to identify the determinants of emissions changes over intervals of time.In general, studies have found that the LMDI is the most appropriate method to decompose energy consumption and emission changes [62][63][64][65].Recently, the Logarithmic Mean Divisia Index (LMDI) approach to energy decomposition has emerged as a preferred decoupling model [66].LMDI is a calculation process proven to be a complete decomposition method, without zero-value problems [67].In this study, the decomposition technique was combined with a decoupling analysis to analyze the relationship between industrial growth and ICE.This allowed for the identification of factors that contribute to changes in China.The LMDI can be expressed as an extended Kaya identity, which was first proposed by Yoichi Kaya [68].The extended Kaya identity is as follows: In this expression, C t represents the carbon emissions in the t year, the subscript i represents energy type; the superscript t represents year.The C t i is the carbon emissions of the ith energy in the t year; E t i is the consumption of the ith energy in the t year; E t stands for total energy consumption in the t year; IOV t denotes the industrial output values.Because the study's target period was 1994 to 2013, a more recent price index was considered more appropriate.As such, we used industrial output data adjusted to 1994 prices.Total energy consumption data were then converted into standard coal consumption.P t represents the population in the t year.The r it = C it E it denotes the carbon coefficient of ith energy, the n it = E it E t illustrates the energy structure.The e t = E t IOV t represents energy intensity, and the a t = IOV t P t is the per capita wealth, reflecting the industrial scale.According to the LMDI method, the change of carbon consumption between a base year 0 and a target year t, denoted by ∆C, is 0, because the carbon emission coefficients are basically unchanged and there is no systematic monitoring of ICE in China.Thus, ∆C can be decomposed into the following determinant factors: where ∆C refers to the total changes in carbon emissions, which can be further decomposed into the following indictors: ∆C n (the effect of energy structure), ∆C e (the effect of energy intensity), ∆C a (the effect of per capita wealth), ∆C p (the population effect).If we measure the effects of determinant factors each year, we can generate figures for eight energy types.We can use the following formulae: ) To measure the effect of each factor's contribution [11], we define them as follows: where G n , G e , G a and G p indicate the effect of the contribution of energy structure, energy intensity, per capita wealth effect, and population, respectively.

Decoupling Elasticity Model
The decoupling model proposed by the Tapio model has been developed based on the OECD decoupling model, which has been widely used to analyze the relationship between economic growth and ICE [69][70][71][72].The Tapio decoupling model does not require a base year, which is more efficient and appropriate than the OECD model [73], as it mitigates the problem of choosing a base period.To probe the decoupling status in a convenient and intuitive way, a novel decoupling index is needed.In this article, based on the additive decomposition results of energy-related CO 2 emission changes, the decoupling factor ε can be measured via the ratio defined by Tapio [74] as follows: In this expression, ε is the decoupling factor, %C is the percent change in carbon emissions, and %GDP is the percent change of GDP.Carbon is the ICE for the current year, ∆carbon is the variation of ICE at the current time compared with the base period, GDP is the gross domestic product of the current year, and ∆GDP is the variation of gross domestic product at the current time compared with the base period.The results yielded eight logical possibilities, shown in Figure 1 [74].These possibilities include weak decoupling, expansive decoupling, expansive negative decoupling, strong negative decoupling, weak negative decoupling, recessive coupling, recessive decoupling, and strong decoupling.These results are often named the environmental Kuznets curve (EKC) hypothesis [75,76].
According to the IPCC method of greenhouse gas emission inventories [68], carbon emissions can be estimated via the following formula: In this formula, C represents carbon emissions, E i is the ith energy consumption, and r i (kgCO 2 /kg or kgCO 2 /m 3 ) indicates the total energy consumption and the total CO 2 emission coefficient of ith energy.SC i (tC/TJ) and O i refer to the default value of carbon content and carbon oxidation rate; K i (kJ/kg or kJ/m 3 ) indicates the average lower heating value (molecular weight of CO 2 divided by the molecular weight of carbon).Table 1 shows the default value of carbon content, carbon oxidation rate, average lower heating value, and carbon coefficient for different kinds of energy, based on the GHG Protocol Tool for Energy Consumption in China [77].

Cointegration Test
Prior to the decoupling analysis, we conducted a comprehensive analysis of the stationary data and analyzed the long-run equilibrium relationship between total carbon dioxide emissions and the effect of each factor.This involved a cointegration test [78,79], where every independent variable was assessed in a one-to-one correspondence relationship with each of the effects listed above in the LMDI decomposition.CO2 emissions was used as the dependent variable.We also conducted an Augmented Dickey-Fuller (ADF) Unite root test to assure the stationary property, subsequent to the Johansen System Cointegration Test.

Cointegration Test
Prior to the decoupling analysis, we conducted a comprehensive analysis of the stationary data and analyzed the long-run equilibrium relationship between total carbon dioxide emissions and the effect of each factor.This involved a cointegration test [78,79], where every independent variable was assessed in a one-to-one correspondence relationship with each of the effects listed above in the LMDI decomposition.CO 2 emissions was used as the dependent variable.We also conducted an Augmented Dickey-Fuller (ADF) Unite root test to assure the stationary property, subsequent to the Johansen System Cointegration Test.

Augmented Dickey-Fuller Unite Root Test
We applied the ADF Unite root test to conduct a stationary analysis of all variable quantities before the cointegration analysis.The variables (C, e, a, and p) were nondimensionalized before proceeding with ADF testing.Because there are eight kinds of energy, there are eight variables n i (i = 1, . . ., 8); these are percentages and not nondimensionalized.Following the calculation, we analyzed the ADF test by comparing the calculated result and the hypothetical ADF value.If the critical value exceeded the ADF test value, then the result was considered stationary; if not, the testing result was considered nonstationary.Table 2 shows the ADF testing results; all the variables are stationary after logarithmic function and first and second differencing, suggesting that all the variables are integrated.

Johansen System Cointegration Test
Based on unit root tests, the integrated data for the variables can be further tested for cointegration (Table 3).Table 3 shows the three cointegration relationships among the variables at the 1% level.In summary, the calculated results demonstrate that at least three cointegrating relationships exist between carbon dioxide emissions and energy intensity, per capita wealth, and population.

Descriptive Statistics and Correlation Analysis
Descriptive statistics are used to describe the basic features of the data in a study.Descriptive statistics usually include the measures of central tendency statistics, distributions of discrete variables statistics, and the degree of dispersion statistics.We use a data file containing data of 12 variables including C (total carbon emissions), P (the population), a (per capita wealth), e (energy intensity), and n i (the percentage of consumption of the ith energy on the total energy consumption; there are eight kinds of energy, i = 1, 2, 3, . . ., 8) to conduct the descriptive statistics using SPSS version 2.0.The data are all metric data and time series data.The results of the descriptive statistics are presented below (Table 4).In the results of descriptive statistics, we use the index mean to measure the central tendency of variables, use the indexes Kurtosis and Skewness of variables to reflect the distributions of discrete variables, and use the indexes Standard deviation, Variance, Minimum, and Maximum to reflect the degree of dispersion.
Correlation analysis is useful for determining the direction and strength of a relationship between two variables.In the study, we also use a data file containing data on 12 variables including C, P (the population), a, e, and n i (i = 1, 2, 3, . . ., 8) to conduct the descriptive statistics using SPSS version 2.0.The results of the correlation analysis are presented below (Table 5).The results of correlation analysis between different variables are shown in Table 5.

An Overview of Industrial Carbon Emissions
According to Equation (2), we first calculated carbon emissions from different energy types; we then calculated the industrial carbon emissions every year, shown in Table 6.In addition, we used the carbon emission coefficients of different energy types based on the GHG Protocol Tool for Energy Consumption in China [77]. Figure 2

An Overview of Industrial Carbon Emissions
According to Equation (2), we first calculated carbon emissions from different energy types; we then calculated the industrial carbon emissions every year, shown in Table 6.In addition, we used the carbon emission coefficients of different energy types based on the GHG Protocol Tool for Energy Consumption in China [77]. Figure 2 shows that the carbon emissions intensity could be analyzed in three stages: 1994-1997, 1998-1999, and 2000-2013.The ICE from industrial sectors experienced a significant upward trend during this period, reaching a total amount of 11,147 million tons in 2013.The ICE continuously increased, with the exception of 1998 and 1999.From 1994 to 1997, the ICE continued to steadily grow, with a rapid increase after 2000.The average annual growth rates of 1994-1997, 1998-1999, and 2000-2013

Decoupling Analysis
To explore the relationship between carbon emissions and economic growth, we used Equation ( 1) to calculate the decoupling elasticity using the IOV (Industrial Output Values) to replace economic growth.Table 1 shows the results; Figure 3 shows trends during the study period, comparing the environmental pressures posed by the industrial output values from 1995 to 2013.The specific values and status judgments related to decoupling elasticity are based on Figure 2 and the calculation process is shown in Table 1.Table 3 shows that the decoupling elasticity of the overall industrial sector can be divided into four states: weak decoupling, strong decoupling, expansive decoupling, and expansive negative decoupling.These coincide with Figure 3; for example, the values from 2003, 2004, and 2013 are higher in Figure 3.This indicates that the speed of ICE growth exceeded the speed of industrial output growth.
As Figure 3 and Table 6 show, the decoupling elasticity increased from 0.65 to 2.24, indicating that huge environmental pressure accompanied industrial growth.The trends associated with decoupling elasticity are different.The years 2003-2004 and 2013 were the most notable, as these years demonstrate a state of expansive negative The decoupling elasticity values fluctuated between −0.05 and 1.6 except for 2013; the values reached their lowest points at −0.45999 and −0.0471 between 1998 and 1999.These reflect the best conditions and exert the least pressure on the environment with a minimal elasticity value, while maintaining an upward trend after 1999.Based on the decoupling analysis, the overall effect on industrial decoupling was still weak.Further, the decoupling relationship indicates that "weak decoupling" and "expansive decoupling" were the main states during the study period.

Decoupling Analysis
To explore the relationship between carbon emissions and economic growth, we used Equation (1) to calculate the decoupling elasticity using the IOV (Industrial Output Values) to replace economic growth.Table 1 shows the results; Figure 3 shows trends during the study period, comparing the environmental pressures posed by the industrial output values from 1995 to 2013.The specific values and status judgments related to decoupling elasticity are based on Figure 2 and the calculation process is shown in Table 1.Table 3 shows that the decoupling elasticity of the overall industrial sector can be divided into four states: weak decoupling, strong decoupling, expansive decoupling, and expansive negative decoupling.These coincide with Figure 3; for example, the values from 2003, 2004, and 2013 are higher in Figure 3.This indicates that the speed of ICE growth exceeded the speed of industrial output growth.As Figure 3 and Table 6 show, the decoupling elasticity increased from 0.65 to 2.24, indicating that huge environmental pressure accompanied industrial growth.The trends associated with decoupling elasticity are different.The years 2003-2004 and 2013 were the most notable, as these years demonstrate a state of expansive negative decoupling.The decoupling elasticity values fluctuated between −0.05 and 1.6 except for 2013; the values reached their lowest points at −0.45999 and −0.0471 between 1998 and 1999.These reflect the best conditions and exert the least pressure on the environment with a minimal elasticity value, while maintaining an upward trend after 1999.Based on the decoupling analysis, the overall effect on industrial decoupling was still weak.Further, the decoupling relationship indicates that "weak decoupling" and "expansive decoupling" were the main states during the study period.

Decomposition Analysis
As discussed above, the decoupling analysis reflects the levels of environmental burden caused by the industrial sector.We use the decomposition technology proposed by the evaluation criterion to assesses industrial progress and identify the driving forces behind the increasing ICE [80,81].Using LMDI, the ICE were decomposed into four effects (energy structure, energy intensity, per capita wealth effect, and population) to investigate the decoupling path of industry in China.The effects and cumulative effects indicate each factor's weight and the degree to which emission reduction efforts outweigh and define the contribution of industrial output.Tables 6 and 7 and Figure 4 present

Decomposition Analysis
As discussed above, the decoupling analysis reflects the levels of environmental burden caused by the industrial sector.We use the decomposition technology proposed by the evaluation criterion to assesses industrial progress and identify the driving forces behind the increasing ICE [80,81].Using LMDI, the ICE were decomposed into four effects (energy structure, energy intensity, per capita wealth effect, and population) to investigate the decoupling path of industry in China.The effects and cumulative effects indicate each factor's weight and the degree to which emission reduction efforts outweigh and define the contribution of industrial output.Tables 6 and 7 and Figure 4 present the results of the analysis.Table 2 shows the effects of different factors of ICE year by year from 1995 to 2013.In addition, we calculated the cumulative effects of different factors of ICE and the contributions of different factors, selecting 1984 as the base year.These are shown in Table 7 and Figure 4.  6 and 7, different factors had different effects.Among the four factors, per capita wealth and energy intensity are the major factors influencing carbon emissions.Per capita wealth and population move in a consistent direction, and are always positive driving forces.Energy structure and energy intensity are mostly negative forces.Energy structure and population play a relatively unimportant role.To improve the accuracy of the results, we combined multiplicative decomposition with the calculation of effect contributions.The effects of different factors on ICE varied in China year by year are shown in Table 8.The results above suggest that both per capita wealth and population play a role in increasing carbon dioxide emissions.In contrast, the energy intensity varied from year to year, contributing to a decrease in carbon emissions.From the effect-level perspective, population effect and intensity effect contribute more to ICE, whereas energy structure and population are relatively weak.This is consistent with Figure 4.In terms of the contributions of different effects, the largest cumulative effect contribution of ICE is per capita wealth, which was 1.23 in 2013.This is followed by energy intensity, with a cumulative effect contribution of −0.32.The cumulative effect contribution of energy structure and population are relatively small, at 0.01 and 0.08, respectively.
The energy structure is the weakest factor contributing to ICE and fluctuates greatly from year to year.The effects of energy structure are negative from 1996 to 2000, and in 2009, 2010, and 2012; this indicates that the energy structure contributes to a decreased ICE.In other years, the effects caused by energy structure are positive; despite some fluctuations, energy structure effects generally rise, although they fluctuate.This caused the ICE to increase by 24.95 million tons in 2011 and 53.33 million tons in 2013.This relates to the increasing consumption ratio of energy emissions from carbon sources such as coal.Reducing the consumption of carbon-emitting energies such as coal is conducive to curbing ICE.In terms of effect contributions, the energy structure is the lowest factor.This finding confirms that China should further optimize its energy consumption structure to disincentive rapid ICE growth.
Energy intensity plays a negative role with respect to ICE.In other words, energy intensity helped decrease the ICE except in years 2003, 2004, and 9.The cumulative effects of per capita wealth and population are positive values and contribute to the increase of ICE every year.Per capita wealth is rising by year, and the contribution of this factor is also the largest.This indicates that the increase in per capita wealth is the most important factor driving ICE increase.From 1995 to 2014, the effects of per capita wealth vary every year.In the first phase, from 1995 to 1999, the effects of per capita wealth declined steadily, dropping to the lowest point of 24,213 million tons in 1999.The Asian financial crisis in 1997 led to a decline in industrial output, leading to the low contribution of ICE in China.In the second phase, from 2000 to 2011, the overall trend was stable within a specific range of fluctuations.In the third phase, there was a rapid increase during the investigated period, with the contribution reaching the highest point of 901.22 million tons in 2007.In the fourth phase, from 2008 to 2013, the carbon emissions fluctuated, but rose overall, maintaining a high contribution level.The average contribution is up to 735.48 million tons.
However, population did not significantly impact carbon dioxide emissions, even though it did contribute to increased emissions.The cumulative contribution of the population effect is very small, indicating that increased population affected ICE only weakly.The cumulative contribution of population to ICE changed only slightly, with an initial increase of 33.08 million tons in 1995.Throughout the study period, the carbon dioxide emissions decreased by 635.91 million tons in 2013.
Figure 4 shows the factors' contributions and effects more intuitively.The trends are consistent with Tables 6 and 7.The largest contributing cumulative effect on ICE is per capita wealth, which was 1.23 in 2013.This was followed by energy intensity, with a cumulative effect contribution of −0.32.The cumulative effect contribution of energy structure and population are relatively small, at 0.01 and 0.08, respectively.Industrial output was the main driving force behind ICE.This factor and the relatively high energy intensity caused the increase during this period.

Conclusions
This study analyzed data from 1994 to 2013 to provide an overview of industrial carbon emissions, based on decoupling elasticity and using a Tapio decoupling model.The Kaya identity and LMDI (Log Mean Divisia Index) methods were used to identify the factors contributing to changes in China's industrial carbon emissions.We also evaluated the accumulated effects and the contributions on ICE. Conclusions were developed based on the decoupling analysis and decomposition analysis.Proposals are made to curb the growth of carbon emissions and to balance economic development and environmental protection.
ICE from industrial sectors revealed a significant upward trend during the study period, reaching an amount of 11,147 million tons in 2013.The average annual growth rates for 1994-1997, 1998-1999, and 2000-2013 were 4.76%, −2.25%, and 9.23%, respectively.The stable low level of ICE in China can be mainly explained by the Asian financial crisis in 1997, which impacted China's economy and resulted in a slow industrial growth rate in China.
When considering the decoupling relationship, "weak decoupling" and "expansive decoupling" were the main states during the studied period.The decoupling elasticity experienced an increasing

Conclusions
This study analyzed data from 1994 to 2013 to provide an overview of industrial carbon emissions, based on decoupling elasticity and using a Tapio decoupling model.The Kaya identity and LMDI (Log Mean Divisia Index) methods were used to identify the factors contributing to changes in China's industrial carbon emissions.We also evaluated the accumulated effects and the contributions on ICE. Conclusions were developed based on the decoupling analysis and decomposition analysis.Proposals are made to curb the growth of carbon emissions and to balance economic development and environmental protection.
ICE from industrial sectors revealed a significant upward trend during the study period, reaching an amount of 11,147 million tons in 2013.The average annual growth rates for 1994-1997, 1998-1999, and 2000-2013 were 4.76%, −2.25%, and 9.23%, respectively.The stable low level of ICE in China can be mainly explained by the Asian financial crisis in 1997, which impacted China's economy and resulted in a slow industrial growth rate in China.
When considering the decoupling relationship, "weak decoupling" and "expansive decoupling" were the main states during the studied period.The decoupling elasticity experienced an increasing trend from 0.65 to 2.24.This indicates that industrial growth was accompanied by significant pressure on the environment.Based on the decoupling analysis, the overall effect on industrial decoupling was still weak.
The per capita wealth of industrial output and energy intensity are major factors that influence carbon emissions.The per capita wealth of industrial output and population move in a consistent direction, and are always positive driving forces.By contrast, energy structure and energy intensity are mostly negative forces.Energy structure and population play a relatively unimportant role.In terms of contribution effects, the largest cumulative effect on ICE is per capita wealth, which was 1.23 in 2013.This factor is followed by energy intensity, with a cumulative contribution of −0.32.The cumulative contributions of energy structure and population are relatively small, at 0.01 and 0.08, respectively.
In conclusion, some critical approaches to reducing carbon emissions are further expand industrial scales and improve industrial output.In addition, vigorously promoting optimization, upgrading traditionally high energy consuming enterprises, and cluster development may help inhibit carbon emissions growth.The energy intensity factor has been diminishing, resulting in a relative slow-down in the decrease of emissions.When compared with some developed countries, China's energy intensity with respect to industrial output remains high.New strategies and increased efforts are needed to improve management and technological practices that will reduce energy intensity.Other approaches, such as further improving thermal power technologies and clean electricity, may also reduce carbon emission coefficients.The fastest way to significantly reduce carbon emissions in the short term is to adjust energy structures and optimize a sustainable energy consumption structure.

Figure 1 .
Figure 1.Decoupling between carbon emissions from industry and economic growth.

Figure 1 .
Figure 1.Decoupling between carbon emissions from industry and economic growth.
shows that the carbon emissions intensity could be analyzed in three stages:1994-1997, 1998-1999, and 2000-2013.The ICE from industrial sectors experienced a significant upward trend during this period, reaching a total amount of 11,147 million tons in 2013.The ICE continuously increased, with the exception of 1998 and 1999.From 1994 to 1997, the ICE continued to steadily grow, with a rapid increase after 2000.The average annual growth rates of1994-1997, 1998-1999, and 2000-2013  were 4.76%, −2.25%, and 9.23%, respectively.Due to the rapid economic growth, the ICE increased to 11,147 million tons by 2013, almost 3.7 times the 3003.19 million tons in 1994.In 1998-1999, the ICE decreased, falling across both years.This decline was mainly due to the Asian financial crisis in 1997, which affected China's economy.The slow industrial growth rate led to stable, low ICE in China.Sustainability 2016, 8, x FOR PEER REVIEW 9 of 18 were 4.76%, −2.25%, and 9.23%, respectively.Due to the rapid economic growth, the ICE increased to 11,147 million tons by 2013, almost 3.7 times the 3003.19 million tons in 1994.In 1998-1999, the ICE decreased, falling across both years.This decline was mainly due to the Asian financial crisis in 1997, which affected China's economy.The slow industrial growth rate led to stable, low ICE in China.

Figure 2 .
Figure 2. ICE from the industrial sector rose during the study period 1994-2013.Figure 2. ICE from the industrial sector rose during the study period 1994-2013.

Figure 2 .
Figure 2. ICE from the industrial sector rose during the study period 1994-2013.Figure 2. ICE from the industrial sector rose during the study period 1994-2013.

Figure 3 .
Figure 3.The decoupling elasticity of the industry sector during the period 1995-2013.

Figure 3 .
Figure 3.The decoupling elasticity of the industry sector during the period 1995-2013.
2013.The cumulative effects caused by the energy intensity of industrial output rose from 1995 to 2012, causing the ICE to decline by 3369.08 million tons.When considering the effects caused by energy type, ICE increases in stage 1, decreases in stage 2, and then increases again.Despite the fluctuations, ICE rose overall, to an amount of 2299.52 million tons.We can see that the effects of energy intensity on the ICE steadily increased from 1995 to 1998 and from 2004 to 2007, and declined during 1998-2004.The level fluctuated between 2007 and 2013.

Figure 4 .
Figure 4.The contributions of different factors on ICE varied in China year by year.

Figure 4 .
Figure 4.The contributions of different factors on ICE varied in China year by year.

Table 1 .
The carbon coefficients of different kinds of energy.

Table 1 .
The carbon coefficients of different kinds of energy.

Table 3 .
The results of cointegration testing.

Table 4 .
Descriptive statistics on the variables.

Table 5 .
Correlation analysis between different variables.

Table 6 .
The carbon emissions from different kinds of energy in the industrial sector in China.

Table 6 .
The carbon emissions from different kinds of energy in the industrial sector in China.

Table 7 .
The values of decoupling elasticity.

Table 8 .
The effects of different factors on ICE varied in China year by year.
Accumulated effects of different factors on ICE from industry varied in China are shown in Table

Table 9 .
Accumulated effects of different factors on ICE varied in China.