1. Introduction
Global warming has attracted the attention of politicians and scholars as it has severely affected the survival and development of human beings. According to the assessment results of the UN Intergovernmental Panel on Climate Change (IPCC), greenhouse gases, specifically CO
2, are the main cause of global warming [
1,
2]. Hence, carbon emission reduction has become a consensus by the international community. As the largest carbon emitter, China has more pressure to reduce its carbon emission. According to the 2018 BP Statistical Review of World Energy, the average growth rate per annum of CO
2 emission is 3.2% in mainland China from 2006 to 2016. Its CO
2 emissions reach 9232.6 million tons in 2017, occupying 27.6 percent of the global emission amount [
3]. Based on the Global Carbon Budget 2016 released by the Global Carbon Project (GCP), the carbon dioxide emissions per unit economic outputs of China is 0.65 kg CO
2 per-year dollars, which is 1.8 times that of the United States and 2.8 times that of the European Union [
4]. Therefore, the carbon dioxide emissions per unit GDP in China is also higher than that of developed countries.
As the carbon emission reduction has become an urgent task, China pledged to reduce carbon dioxide emissions per unit of GDP at the Copenhagen Climate Change Conference in December 2009 [
5,
6]. The national government also set up a target of reducing carbon emissions per unit of GDP by 20% in its thirteenth Five-Year Social and Economic Plan [
7]. However, China has relied on an energy-intensive, heavy, industry-based developmental pattern for decades [
8,
9]. The path dependence of its economic structure makes it challenging to achieve this goal, and one suitable solution is to improve carbon emission efficiency.
However, China is a vast country with many regions, owning multifaceted features in its economic structure and resource endowment. The national government needs to formulate policies about carbon emission reduction at the regional level. The evaluation of the regional differences and influencing factors on the Chinese carbon emission efficiency has attracted the attention of scholars recently. Wang and Zhou et al. in 2013 found that CO
2 emission performance on the provincial was the higher in southeastern coastal areas but lower in central and western inland regions. They also stated that their carbon emission performance has increased by different rates after 2001 [
10]. Their evidence is also consistent with the recent finding on the regional low-carbon economic development by Chu and Geng et al. in 2019. The recent study also showed that the eastern region still had a higher degree of low-carbon economic development than the central and western regions [
11]. Zhong et al. in 2012 also discovered that China’s total-factor carbon emission performance contains significant regional characteristics, and low carbon emission performance in the central and west regions [
12].
Until now, most of the studies have divided Chinese 30 provinces into three areas (East, Central, and West regions) based on geographical position rather than on economic characteristics, which cannot accurately reflect the state of regional carbon emission efficiency in China. In 2015, the Development Research Center of the State Council (DEC) classified Chinese provinces into eight economic regions (Northern coast, Eastern coast, Southern coast, Northeast, Middle Yellow River, Middle Yangtze River, Southwest, and Northwest) based on their geographical position, economic development level, and resource endowment [
13]. This paper has chosen to investigate the disparity of carbon emission efficiency in the above eight economic regions proposed by the DRC.
As for the method to measure the carbon emission efficiency, the Charnes–Cooper–Rhodes (CCR); Banker, Chames, and Cooper Mode (BCC); or slacks-based measure (SBM) model have been widely adopted by scholars [
14,
15,
16]. However, these methods have their advantages as well as insufficiencies [
16,
17,
18,
19,
20,
21,
22,
23]. Therefore, we used the Modified Undesirable Epsilon-based measure (EBM) DEA Model to calculate the carbon emission efficiency to overcome their shortcomings [
16,
19,
23,
24]. Based on the analysis of the regional difference in the carbon emission efficiency, we verified the influencing factors by the Tobit model [
25]. This research aims to analyze the regional differences and influencing factors on China’s carbon emission efficiency from 2005 to 2015, based on the Modified Undesirable EBM DEA Model and Tobit model.
The basic structure of the rest of the paper is as follows.
Section 2 will review the definition, measurement, and influencing factors of carbon emission efficiency.
Section 3 will introduce the research method.
Section 4 will present indicator selection and its data source.
Section 5 will analyze the carbon emission efficiency in eight Chinese regions by the used the Modified Undesirable EBM DEA Model.
Section 6 will adopt a Tobit model to verify the influencing factors of carbon emission efficiency. Finally,
Section 7 will conclude and state the research limitations.
2. Literature Review
In recent years, carbon emission efficiency has become an important research issue in academic circles, and the main topics cover the definition, measurement, and influencing factors of carbon emission efficiency.
At present, there is no unified agreement on the definition of carbon emission efficiency by scholars. The definition of carbon emission efficiency stems from the method of calculation, such as the single-factor indicator method or total-factor indicator method [
6]. The dominant types of single-factor indictor method include carbon dioxide emissions per unit of GDP, carbon dioxide emissions per unit of energy consumption, and carbon dioxide emissions per capita. Specifically, Kaya and Yokobori first proposed the concept of carbon production efficiency in 1993, which is the ratio of total GDP to the total amount of carbon emission [
26]. Meilnik and Goldembergbet proposed the concept of the carbonization index in 2009, which is the ratio that the total amount of carbon emission to the total consumption of energy [
27]. Sun (2005) proposed the carbon intensity, which calculates the ratio of CO
2 emissions to GDP [
28]. Based on the concept of frontier production, the total-factor indicator method for carbon emission consists of both input and output indicators. Meanwhile, some scholars regarded carbon emission as an undesirable output indicator to evaluate carbon emission efficiency [
29,
30,
31,
32,
33].
The main methods of evaluating carbon emission efficiency include parametric and nonparametric ones. As for the parametric methods, Aigner and Lovell proposed the Stochastic Frontier Approach (SFA) by using a stochastic frontier production function to evaluate the technical efficiency in 1977 [
34]. Later, SFA has become one of the most commonly used parametric methods. The disadvantage of this parameter method is the requirement for a particular functional form assumption for the frontier. Thus, possible incorrect functional forms can cause inaccurate results [
10,
35,
36,
37,
38,
39].
As for the nonparametric method, the DEA is a common nonparametric method for evaluating the relative efficiency of several Decision-Making Units (DMUs). Unlike SFA, DEA is a deterministic method that also has many advantages, which has the capability of handling multiple inputs and outputs [
14]. In other words, the DEA keeps the input and output of DMUs unchanged by adopting the effective sample [
40]. On the other hand, DEA does not require any specification of the functional form of the frontier, therefore more scholars use the DEA approach and its various modified modes to study carbon emission efficiency. These models they adopted can be classified into three types, radial model, nonradial model, and Directional Distance Function Model (DDFM)s [
41].
(A) Radial model, namely CCR or BCC, which are the basic models of the DEA approach proposed in 1978 and 1984, respectively. After widely application in the literature, these methods have also been adopted in the Chinese context recently. Wei et al. in 2010 used the CCR model for measuring changes in total carbon emission efficiency of provinces in China from 1986 to 2008 [
42]. Zhong et al., in 2012, applied BCC model to measure static carbon emission performance of China’s 29 provinces from 1995 to 2009 [
12]. However, CCR or BCC model requires that all the inputs change in the same proportion, which ignores nonradial slacks and is contrary to reality [
17,
19,
23,
24].
(B) Nonradial model (SBM). To solve the weakness of CCR or BCC model, Tone proposed a nonradial Slacks-Based Measure that was able to calculate efficiency with slacks of input and output variables. SBM has been widely applied to evaluate carbon emissions efficiency and abatement potential recently. Chu and Geng et al. in 2019 applied the SBM to measure the carbon emission efficiency in 30 provinces of China from 2005 to 2017 [
11]. Some other scholars also have conducted relevant research using SBM DEA model [
21,
43,
44].
(C) Directional Distance Function Model (DDFM). The DDFM approach was put forward by Chung et al. (1997), which allows proportional expansion of desirable outputs and shrinking of undesirable outputs and inputs [
41]. Some scholars attempted to apply these models to calculate the carbon emissions efficiency [
45,
46].
In addition to the definition and measurement of the carbon emission efficiency, influencing factors also have attracted academic attention. The common factors adopted include industrial structure, energy structure, openness, and one of primary research methods is the measurement method. The widely adopted one is the Economic Measurement Method, which employed both the spatial econometric model and the nonspatial econometric models. The spatial econometric model includes Spatial Lag Model (SLM) [
47], Spatial Error Model (SEM) [
47], and Spatial Durbin Model (SDM) [
48], which are suitable to the case of the DUMs with obvious spatial correlations. For instance, Chuai et al. in 2012 analyzed the SLM between carbon emissions from energy consumption and their influencing factors in Chinese regions from 1997 to 2009. They stated that GDP and population are the two leading factors, which can strengthen the spatial autocorrelation of carbon emissions [
49]. Ma and Chen et al. in 2015 established the SEM to perform an empirical study on the influence factors of carbon emissions efficiency by using panel data from 30 provinces from 1998 to 2011. They argued that the economic scale, industry structure, and energy consumption structure hurt carbon emission efficiency, while opening-up, enterprise ownership structure, and government intervention play a positive role in efficiency [
50]. Cheng et al. in 2014 applied the SDM to examine the dominating factors of China’s carbon intensity from energy consumption from 1997 to 2010 [
51]. The leading approach of the nonspatial econometric model is the Tobit model [
25], also called the “limited dependent variable model”, or the “check model”. It considers the trend of continuous variable variation with limited dependent variables [
25,
26,
52]. Wang at al. in 2019, used the Tobit model to analyze the influencing factors of carbon emission performance and the technology gap ratio of carbon emission in 30 provinces in China. They stated that influencing factors have various impacts on the carbon emission efficiency in the Chinese regions [
53]. Some other scholars also investigated the carbon emission efficiency by using Tobit studies recently [
54,
55,
56].
The above studies have provided a useful reference for further study on China’s carbon emission efficiency. However, these studies also have the following limitations. From the perspective of the calculation method, Radial models, such as the CCR or BCC model, fail to consider the effect of nonradial slacks on the technical efficiency and cannot realize the factor decomposition in evaluating the efficiency, which can lead to biased estimation results. For the nonradial SBM-DEA model, the slacks are not necessarily proportional to the inputs or outputs, and the DUMs can lose the proportionality in the original inputs or outputs [
17,
19,
23,
24].
Based on the above problems, the improvements in this paper are as follows. We used the nonoriented the Modified Undesirable EBM DEA Model to investigate carbon emission efficiency. The Modified Undesirable EBM DEA Model is based on the EBM model. Tone introduced an EBM approach in 2010 to combine the radial model and nonradial model, which is more in line with reality [
23]. As the EBM model cannot solve the problem related to undesirable outputs, Li and Chiu et al. (2019) extended the EBM model into the Modified Undesirable EBM DEA Model to deal with them [
17,
19,
24].
4. Data Source and Indicator Selection
This paper investigated 30 provinces, municipalities, or autonomous regions (except Tibet) in China from 2005 to 2015. We selected the annual data of capital stock, labor force, and energy consumption as three inputs according to production processes and the prior research results [
6,
11,
21,
43,
44]. We treated gross domestic product (GDP) as a desirable output and CO
2 emission as an undesirable output. The inputs and outputs are explained as follows.
Capital stock. The paper estimated the capital stock by using the perpetual inventory method, defined as follows [
58].
where
Ki,t and
Ii,t stand for the capital stock and the gross fixed capital formation of the
ith province in the
tth year, respectively.
δ is the depreciation rate of capital stock, and is set to 9.6% in accordance with previous studies [
59]. The provincial data of capital stock were converted into the 2005 constant price. The capital stock data of provinces in 2005 was expressed as follows
All data of the gross fixed capital formation as well as the price index of fixed-asset investment in Chinese provinces were from the China Statistical Yearbook (2006–2016).
Labor force. The paper adopted the total amount of employees in three industries as the labor force variable. The data on the provincial level were collected from the statistical yearbook (2006–2016).
Energy consumption. This paper chose the energy consumption of each province as the input index. The data were collected from China’s Energy Statistical Yearbook (2006–2016).
GDP. To diminish the impact of inflation, we convert the provincial GDP into the 2005 constant price. The data came from the China Statistical Yearbook (2006–2016).
CO
2 emissions. This paper estimated the CO
2 emissions generated by the burning of fossil energy and the emissions from the process of cement production, which is consistent with the previous studies [
60,
61].
This paper calculated the carbon emissions from seven types of fossil energy, such as coal, coke, gasoline, kerosene diesel, fuel oil, and natural gas referring to the National Greenhouse Gas Emissions Inventory introduced by IPCC in 2006. The types of the fuels are classified according to the prior research results [
6,
8,
11,
20]. The formula for calculating CO
2 emissions from fossil fuels is [
62]
where
CE represents the total CO
2 emissions, ALCV stands for the average low calorific value, CCF denotes the carbon content factor, and COF is carbon oxidation factor. The number (44/12) represents the ratio of the molecular weight of CO
2 (44) to the molecular weight of carbon. The subscript
i stands for the energy source. The data of energy consumption were collected from the China Energy Statistical Yearbook (2006–2016). The data of the average low-order calorific, carbon content factor, and carbon oxidation factor were from the China Energy Statistical Yearbook and National Greenhouse Gas Emission Inventory Guide (2006) [
62] and Guidelines for Provincial Greenhouse Gas Inventories in China (2011) [
63], as shown in
Table 1.
The formula for calculating carbon emissions from the process of cement production is
In Formula (6),
CC represents the total amount of CO
2 emissions during the process of cement production, Q stands for the total amount of cement production, and
EFC represents the carbon emissions emission coefficient of cement production, the value of
EFC is 527 kgCO
2/t [
64]. The all indicators were listed in
Table 2.
5. Analysis of Regional Differences in Carbon Efficiency
5.1. Overall Characteristics of Chinese Carbon Emission Efficiency
The carbon emission efficiencies of 30 provinces of China during the period of 2005 to 2015 were calculated by Equations (1)–(3). The results are listed in
Table 3.
According to
Table 3 and
Figure 1, the carbon emission efficiency of 30 provinces was generally low from 2005 to 2015, with an average value of 0.6277. Only 11 provinces (Beijing, Shanghai, Guangdong, Tianjin, Zhejiang, Jiangsu, Fujian, Heilongjiang, Hainan, Shandong, and Hunan) performed better than the national average efficiency value. Yunnan, Xinjiang, Inner Mongolia, Shanxi, Guizhou, Qinghai, and Ningxia owned very low levels of carbon emission efficiency, which all were less than 0.5, indicating that most provinces still face the challenge of energy saving and emission reduction.
5.2. Regional Differences of Carbon Emission Efficiency: East, Central, and West
Figure 2 shows that (1) the eastern regions had the highest level of carbon emission efficiency—0.7816—followed by the central region (0.5967) and the western region (0.4964). (2) As for the eastern region, the carbon emission efficiency values for Beijing, Shanghai, and Guangdong in the eastern zone were 1, which showed that these provinces are the front runners in carbon emission efficiency and can benchmark for others. The carbon emission efficiency of the eastern provinces’ was higher than the national average, except Hebei and Liaoning. (3) For the central region, Heilongjiang had the highest level of carbon emission efficiency (0.7072), which was little below the frontier. Shanxi had the minimum level of carbon emission efficiency (0.4792), which performed slightly worse than Hebei (the one with the lowest level in the eastern region). As for the western region, Chongqing had the highest level of carbon emission efficiency in the region, but its performance was worse than that of Heilongjiang. Ningxia had the lowest average efficiency in the whole country.
5.3. Regional Differences in Carbon Emission Efficiency: A Comparison of Eight Comprehensive Economic Regions
In this subsequent analysis, Chinese provinces are divided into eight regions (
Figure 3) in line with the division of the State Council to analyze the regional differences in carbon efficiency further. The carbon emission efficiency of eight regions is shown in
Table 4.
Table 4 shows that (1) from 2005 to 2015, Northern, Eastern, and Southern coastal regions had a higher level of carbon emission efficiency than other regions, with an average efficiency of 0.7640, 0.8102, and 0.8423, respectively. Northeast and Middle Yangtze River had an average carbon emission efficiency of 0.6148 and 0.6176, respectively. Middle Yellow River, Southwest, and Northwest kept the carbon emission efficiency at a low level, and these regions’ average efficiencies were 0.5152, 0.5447, and 0.4296, respectively. (2) In a comparison of the provinces with the highest efficiency level in every region, the leading ones in Northern, Eastern, and Southern coastal regions were Beijing, Shanghai, and Guangdong, all of which are on the production frontier. Heilongjiang had the highest value of carbon emission efficiency in Northeast, with an average value of 0.7073, which was a little more than the national average while far below those of Beijing, Shanghai, and Guangdong. The provinces with higher carbon emission efficiency in the Middle Yangtze River and Southwest were Hunan and Chongqing. Those provinces’ average values were all smaller than 0.7 and far below the average level of frontier provinces. The most efficient provinces of Middle Yellow River and Northwest were Henan and Ningxia, and they held the value of more than 0.5, far from the frontier level. (3) The gap of carbon emission efficiency in the interior of the Northern coast and Southern coast was more substantial than other regions. The efficiency of Hebei and Hainan were lower than the average level of Northern and Southern coasts. The difference in carbon emission efficiency in the interior of the Middle Yangtze River was the smallest of the eight regions.
Several factors have contributed to these regional differences. First, Northern, Eastern, and Southern coastal regions are in the eastern part of China, which are the earliest areas exposed to open-up policy, and are more conducive to the introduction of foreign advanced technology, intelligence, equipment, experience, and capital. On the one hand, many universities and research institutes in the coastal regions play a crucial role in innovation activities. On the other hand, coastal regions own well-established transportation infrastructure, solid industrial foundation, and abundant human capital, which have attracted the introduction of other production factors from abroad. Therefore, a high level of production technology in the coastal regions results in high carbon emissions efficiency.
Second, many provinces in the Northeast and Middle Yangtze River belong to the Central area in China. The openness, education, and science and technology level of these provinces is lower than those of the coastal regions. As the old industrial bases in China, the Northeast region’s economic pillar is heavy industry, which is responsible for much of CO2, SO2, and COD emissions. Driven by the “Rise of Central China” Strategy (The Rise of Central China Plan is a policy to accelerate the development of its central regions in 2004, which covers six provinces: Shanxi, Henan, Anhui, Hubei, Hunan, and Jiangxi.), Middle Yangtze River has set up four national industrial transfer demonstration zones, which introduced many manufacturing industries from the coastal areas. These industries are characterized by high pollution and energy consumption, which leads to high carbon emissions inevitably.
Third, many provinces in the Middle Yellow River, Southwest, and Northwest belong to Western China. Their unfavorable geographic locations limit the introduction of foreign advanced technology, intelligence, equipment, experience, and capital. With the deepening of the reform and opening-up policy, the brain drain in these regions can be found by more talents moving to the eastern and central parts of China for better opportunities, which increase the difficulty improve the production technology and carbon emission efficiency.
7. Conclusions and Discussions
This paper presented the analysis of regional differences and influencing factors on China’s carbon emission efficiency from 2005 to 2015 by adopting the Modified Undesirable EBM DEA Model and Tobit model. The results showed that (1) most Chinese provinces generally have low carbon emission efficiencies levels. The average value of 30 provinces’ carbon emission efficiency was 0.6277. Only 11 provinces had a higher level than the national average level. Therefore, energy saving and emission reduction in China are still challenging for governments. (2) Significant differences in carbon emission efficiency exist across the three regions in China. The Eastern region ranked the top, with the average efficiency was 0.7816, followed by the Central region (0.5967) and the Western region (0.4964). (3) Northern, Eastern, and Southern coastal regions had a higher level of carbon emission efficiency, followed by Northeast and Middle Yangtze River and Middle Yellow River. The Southwest and Northwest had the lowest level of carbon emission efficiency. (4) In the results from Tobit regression, foreign trade level, foreign capital utilization level, and science and technology level all had significant positive effects on carbon emission efficiency. Government intervention and energy intensity negatively affected the carbon emission efficiency in a significant way. The relationship between industrial structure and carbon emission efficiency had a non-significant positive result. The theoretical contribution of this manuscript is to adopting the Modified Undesirable EBM DEA Model, which is more accurate than traditional methods, such as CCR, BCC or SBM model. Moreover, based on the data’s unit root testing and cointegration, the paper verified the influencing factors of carbon emission efficiency by the Tobit model, which avoids the spurious regression.
Based on the above analysis, we raised several policy implications to improve carbon emission efficiency. When making full use of foreign trade and FDI, governments can reduce the import and export of high-energy and emission products and support the foreign trade of high-tech and environmentally friendly products. The governments can also optimize the energy structure and increase investment in science and technology. The government can adjust industrial structure by promoting the tertiary sector and reduce the development of manufacturing with high pollution and emission. Additionally, governments can promote low carbon industries by providing subsidies or special findings.
As the regions have different carbon emission efficiencies, the local governments should make use of their strengths. The provincial and municipal governments of the eastern provinces should fully tap the potential of energy saving and emission reduction based on their original economic development level. As for the local governments of the central and western provinces, they can make use of their industrial legacies and combine their advantages with the import of foreign capital. In this foreign capital attraction process, the local governments should restrict pollution-intensive industries with environmental regulations, and attract high-tech industries through FDI. The provincial and municipal governments should strengthen the monitoring and supervision of pollutant emissions with improved evaluation system and punishment mechanism for carbon emission efficiency.
However, the limitations of the research remain in the data collection. We calculated the provincial capital stock and CO2 emissions due to lacking data in the provincial statistical yearbook. The accuracy of the research result can be improved if the central and local Statistical Bureau to disclose statistics on the provincial capital stock and CO2 emissions. Future research can analyze various industrial sectors’ carbon emission efficiency in the next study period, which further reflects the regional differences in carbon emission efficiency in various industrial sectors.