Decomposition and Decoupling Analysis of Factors Affecting Carbon Emissions in China’s Regional Logistics Industry

Abstract

This paper selected the data from 2010 to 2020 to measure the carbon emissions of the logistics industry in different regions of China, decomposed the influencing factors of carbon emissions in China’s logistics industry based on the LMDI model, and, finally, conducted a decoupling analysis of carbon emissions and the development of the logistics industry. The conclusions are as follows: (1) China’s carbon emission levels vary greatly from region to region, with the highest distribution pattern in the east and the lowest in the west, while the growth rate in the east is also the highest. (2) The level of economic development has the greatest impact on carbon emissions, and it has the effect of promoting carbon emissions in three regions; logistics development effects have the characteristics of first driving and then restraining emissions in the three major regions. The effect of energy intensity has great volatility. The effect of intensity in the eastern region dropped sharply in 2015, with negative effects after that year. Development of the logistics industry has limited the inhibition of carbon emissions in the central and western regions. Although the effect of the energy structure is negative, it failed the significance test. The effects of the energy structure began to show a downward trend in three regions after 2015. (3) The decoupling analysis showed that only 3 provinces are strongly decoupled, 20 provinces are weakly decoupled, and the regional carbon emissions are quite different.

1. Introduction

Since the industrial revolution, the economies of various countries have achieved rapid development. At the same time, environmental problems have become increasingly prominent, especially the continuous increase in greenhouse gas carbon emissions and global warming [1], which have a serious impact on production and life. Rapid economic growth has accelerated carbon dioxide emissions, and the environmental and climate issues caused by carbon emissions have gradually attracted the attention of the international community. Countries around the world are also making active efforts to reduce carbon emissions and slow global warming [2,3].

China’s development model will definitely have a profound impact on the world. The balanced development of energy security issues, living environment issues, and economic development issues are problems and dilemmas faced by the government, constantly testing the wisdom of Chinese leaders. Against the dual background of energy shortages and climate deterioration, countries all over the world are rushing to change their development methods and pursue a new sustainable development model with low emissions [4]. To achieve the difficult goal of controlling carbon emissions, China needs to reduce energy consumption. This path is full of difficulties, as it needs various industries across the country, such as manufacturing, energy, and foreign trade to make adjustments to reduce carbon emissions in concert [5,6].

Faced with the requirements of reducing carbon emissions, the logistics industry, as a high-carbon emission field, has assumed most of the responsibility for reducing carbon emissions. Based on the report by the International Climate Organization, the energy consumed by the transportation industry in 2006 accounted for more than 20% of the world’s energy consumption. The reason for the high carbon emissions of the transportation industry is its higher energy consumption; therefore, it needs effective measures to reduce energy consumption. The Chinese logistics industry is a long way from the international level and is in a period of rapid development. There is huge room for development in terms of the scale of development and technological improvements, which also provides the possibility of reducing carbon emissions across the country. At present, the development of regional logistics is still in the expansion stage, and the input–output structure and development scale are unreasonable. While the logistics has achieved rapid development, the logistics cost is relatively high. Especially with the rapid development of regional logistics, the environmental pollution caused by logistics activities is increasing. From the research on industry carbon emissions, compared with the decline in carbon emissions in agriculture, industry, construction, and other industries, the carbon emissions of the logistics industry have shown an upward trend, becoming one of the few companies whose carbon emissions continue to increase [7]. However, due to differences in development level, system, and location factors, there are differences in logistics carbon emissions in different regions. In the context of vigorously carrying out energy conservation and emission reduction, reducing logistics carbon emissions and improving logistics operation efficiency are the key and focus of Chinese development of energy conservation and emission reduction, and also an important way to achieve China’s emission reduction goals [8]. The main content is what is the status quo of regional logistics carbon emissions, whether the level of logistics carbon emissions in various regions is balanced, what are the factors that affect regional logistics carbon emissions, and how to formulate regional logistics carbon emissions control strategies based on the influencing factors.

The main significance of this study is, on the one hand, with the increasingly serious problems of energy crisis, climate warming and environmental deterioration, the sustainable development of economy has been paid more and more attention. As a major source of energy consumption and carbon emissions, logistics plays an important role in sustainable economic development. Reducing the carbon emission of logistics and promoting the low-carbon development of logistics are the objective requirements to improve the sustainable ability of logistics and are also an effective way to speed up the transformation of the economic development mode. On the other hand, the differences and influencing factors of regional logistics carbon emission levels are analyzed and studied, and a regional logistics carbon emission control strategy is given based on the influencing factors, which provides reference for the control of regional logistics carbon emissions and the formulation of carbon emission reduction policies and is helpful for improving regional logistics. The level of sustainable development of logistics is of strategic significance.

Therefore, based on the LMDI decomposition method, this paper decomposed the carbon emission factors of China’s regional logistics industry from 2010 to 2020, found out the main driving factors of regional logistics industry carbon emissions and determined their degree of effect, and then, studied the decoupling effect of regional carbon emissions. The influence of influencing factors on the carbon emissions of logistics in different regions was obtained, and the decoupling of logistics carbon emissions in different regions was analyzed at the same time. The main research framework of this paper is as follows: The second part is a literature review, which mainly analyzes the main contributions of existing research in this field and hopes to provide theoretical reference for this research. The third part is the method introduction, which mainly explains the rationality of the methods used in this paper. The fourth part is the empirical analysis results and discussion, focusing on the contribution of the results obtained. The last part is the conclusions and shortcomings of this study.

2. Literature Review

2.1. Carbon Emissions of the Logistics Industry

The logistics industry’s carbon emissions research started earlier. For example, Akbostanc et al. [9] used energy consumption to measure carbon dioxide emissions and studied the carbon emissions of 57 factories from 1995 to 2001. Mahony et al. [10] studied carbon emissions in Ireland during 1990–2007, in order to find the influencing factors. Andreoni and Galmarini [11] applied the decoupling theory to evaluate the relationship between economic growth and carbon dioxide emissions. They divided the impact of carbon dioxide emissions into five parts: namely, agriculture, industry, the electric heating sector, the transportation industry, and the service industry. Miranda Pinto et al. [12] focused on mitigating global climate problems and studied the transportation modes of logistics. They believed that combined road–rail transportation is a means of low-carbon logistics and could alleviate climate problems. Jr and D’Agosto [13] took the ethanol transportation in Brazilian ports as their object of study and conducted investigations from both economic and social perspectives. They found that pipeline transportation was the most effective for reducing environmental pollution, while road transportation was the least effective.

In China, Zhu et al. [14] found that the logistics industry in China’s Yellow River Basin has also been showing a steady upward trend, but while economic growth caused pressure on the environment, its carbon emission levels showed a significant increase. Cao and Zhou [15] found that the carbon emissions of the logistics industry in the Yangtze River Delta region are gradually increasing. The average level of carbon emissions in the logistics industry in Shanghai is the highest and the lowest is in the Anhui region. Hu et al. [16] showed that the carbon emissions of cold chain logistics for fruits and vegetables have been increasing year by year, up to 4,101,100 tons in 2020, which was not conducive to achieving China’s carbon emission reduction targets, and the coupling policy had the best effect for controlling carbon emissions. Zhang et al. [17] believed that the development of rural logistics has a strong stimulating effect on rural economic growth. The development of rural logistics has a significant nonlinear impact on rural economic growth and carbon emissions. Rural economic growth and the development of rural logistics development, and carbon emissions are mutually constrained. Zhu et al. [18] explored carbon emissions from energy consumption. Song [19] used the LMDL method to explore the carbon emission problem of Shandong Province’s logistics industry. He and Liu [20] studied the carbon emission intensity at different economic development stages within the country and applied corresponding models to explore the rate of carbon emission intensity reductions. Chen [21] studied the carbon dioxide emissions and energy consumption data of China’s industrial value—added from 1980 to 2008—and found the reasons for carbon emissions.

2.2. Driving Factors of Carbon Emissions in the Logistics Industry

Scholars at home and abroad also mainly use the following methods to study the influencing factors of carbon emissions in the logistics industry: the LMDI index decomposition method and the STIRPAT model.

The carbon emission factor decomposition method is a quantitative analysis method that specifically determines the effect value of each influencing factor change on the overall change of the research object. It can quantitatively study the changes or differences in the energy or environmental performance of one or more research objects. The contribution of influencing factors to changes or differences in energy or environmental indicators is mainly based on index decomposition analysis (IDA). The index decomposition method can decompose and analyze the changes or differences of energy and environmental indicators at the industrial level year by year: that is, chain decomposition can be realized. Among them, the logarithmic mean diesel index decomposition method (LMDI) can be completely decomposed and does not produce residual errors, so it is widely used in many fields. For example, Ang [22] used LMDI to analyze the main influencing factors of CO₂ emissions in eight industrial sectors in China and concluded that industrial added value and industry energy intensity are the main factors affecting carbon emissions. Baležentis et al. [23] used the LMDI analysis method to study the change of energy intensity in Lithuania from 1995 to 2009 and obtained the result that energy efficiency would decline during economic recession. Ma and Wang [24] analyzed the influencing factors of carbon emissions in China’s logistics industry from 1991 to 2010 based on LMDI, and the results showed that economic growth was the main driving force of the logistics industry. Moutinho et al. [25] used the LMDI method to analyze the influencing factors of carbon emissions in European countries and found that the reason for the decline in carbon emissions in the post-Kyoto era was the use of mixed energy and clean energy. Liu Bowen et al. [26] used the LMDI method and the Tapio decoupling indicator to analyze the decoupling elasticity and decoupling effort of China’s regional industrial growth and carbon dioxide emissions. Maruf and Wu [27] applied the LMDI method to three different future emission scenarios by studying the historical carbon dioxide emissions of the Bangladesh power sector from 1979 to 2018 and concluded that carbon dioxide intensity and electricity intensity had a negative impact on reducing carbon emissions. Ma and Stern [28] used LMDI technology to decompose the change of energy intensity in China from 1980 to 2003 and found that technological innovation was the main cause of the decline in energy intensity; Sheinbaum-Pardo et al. [29] analyzed the manufacturing industry change trend of CO₂ emissions, using LMDI to analyze the influencing factors of manufacturing carbon emissions, and concluded that energy structure and energy intensity are the main factors affecting the manufacturing carbon emissions in Mexico.

In the application of the STIRPAT model, Zhao and Zhang [30] used the STIRPAT model to explore the influencing factors of regional urbanization on carbon emissions, factors that have an impact on carbon emissions and the degree of their respective influences. Yao et al. [31] studied the factors affecting the carbon emissions of rural logistics through the STIRPAT stochastic model and used the Tapio model to analyze the carbon emissions of rural logistics and decoupling trend from regional economic growth from the perspective of eight major economic regions in China.

2.3. Decoupling Analysis of the Carbon Emissions of Logistics

Tapio et al. [32] conducted a study decoupling the transport industry in Europe and decoupling the transport industry in Finland. Hu et al. [33] showed that the logistics industry’s carbon emissions and economic development had been weakly decoupled in most years. It was recommended to improve the policy system for reducing the logistics industry’s carbon emissions, establish a logistics information platform for the Yangtze River Economic Belt, and adjust the energy structure and other low-carbon paths to achieve ecological priorities and the goal of green development. Deng and Li [34] showed that the decoupling state of the logistics industry and carbon emissions in the Yangtze River Delta region from 2000 to 2016 was divided into three stages: 2001–2007 showed expansive negative decoupling, 2008–2012 showed expansive connection, and 2013–2016 showed weak decoupling. Yu et al. [35] showed that the growth rate of energy consumption in the logistics industry in the Yangtze River Delta region was slightly higher than the growth rate of carbon emissions, the total decoupling of the logistics industry was basically a weak decoupling state, energy-saving decoupling was basically a weak decoupling state and an expansive coupling state, and emission reduction decoupling was basically an expansive coupling state. Liu [36] found that the carbon emissions of the logistics industry in Shanxi province exhibited a certain decoupling effect and, during five periods, the decoupling effect has undergone strong and weak changes.

In summary, by combing through the abovementioned literature, it can be seen that scholars have achieved certain results in carbon emissions research in different fields, but there are still some shortcomings. First of all, the research on carbon emissions in the logistics industry has not been studied in the regional field; secondly, the literature combining driver analysis and decoupling analysis is also somewhat insufficient. This article used the LMDI model to decompose the factors influencing carbon emissions, and Tapio’s decoupling model was selected to verify the decoupling state of the logistics industry’s carbon emissions in 30 provinces. Moreover, we used an empirical analysis to understand the degree of difference in carbon emissions among regions to provide a theoretical reference for the realization of coordinated regional development and green development.

3. Material and Methods

3.1. Measuring Carbon Emissions

The commonly used methods for measuring carbon emissions include the actual measurements and the coefficient method. The actual measurement method uses a special measuring instrument combined with a scientific measurement method to monitor and calculate the flow rate and the concentration of the detected gas. This method has the characteristics of accurate measurement and high accuracy, but onsite measurements are relatively cumbersome to implement, and the monitoring cost is very high. It requires high technical standards for operators and poor practicability. Therefore, this method is only suitable for measuring carbon emissions in a small area. The carbon emission coefficient method was proposed by the IPCC in the first panel report of 1990. The carbon emission coefficient represents the amount of carbon emissions formed by a unit of energy during the consumption of various types of energy. According to the climate change committee’s assumptions, it can be determined that the carbon emission coefficient of a certain energy type is fixed: that is, in a hypothetical production environment, the amount of carbon dioxide emitted by a certain energy type can be calculated from the consumption of that type of energy. The carbon emission coefficient of each energy source can be directly checked from the IPCC’s report. The energy sources consumed by China’s logistics industry include crude oil, gasoline, kerosene, diesel, fuel oil, liquefied petroleum gas, natural gas, and electricity. Therefore, this study used the consumption of each of these energy types multiplied by the respective standard coal coefficient, then multiplied this by the respective carbon emission coefficient to calculate the carbon emissions of the logistics industry. The specific formula is as follows:

(1)$C={\displaystyle \sum }_i{C}_i={\displaystyle \sum }_i{\delta }_i{\theta }_i{E}_i$

where C is the carbon emissions, i represents the energy type i, $C_i$ represents the carbon emissions of energy type i, ${\delta }_i$ is the coefficient of the energy type i, ${\theta }_i$ represents the conversion factor of the ith energy type shown in Table 1 and $E_i$ represents the consumption of the ith energy type. The carbon emission of the ith energy is equal to the product of the carbon emission coefficient of this energy and the amount of converted standard coal, which is also equal to the product of the carbon emission coefficient of this energy, the coefficient of converted standard coal, and the energy consumption.

This study selected the energy consumption of China’s transportation, warehousing, and postal industries to represent the energy consumption of the logistics industry. The energy consumption data were taken from the China Energy Statistical Yearbook. As the latest statistics were 2020 data, the time frame of this research was 2010–2020. In the process of converting the various energy sources into standard coal, the conversion of electricity is unique. There are two main methods of converting electricity: one is the electric heat equivalent calculation method; the other is the method of calculating coal consumption for power generation. The primary energy composition and energy conversion rate cannot be distinguished in the conversion process of the electric heating equivalent calculation method. The power generation coal consumption calculation method is closely related to the energy structure, and carbon emissions are also related to the energy structure. Therefore, this study adopted the power generation coal consumption calculation method, and the conversion coefficients are shown in Table 1.

3.2. LMDI Decomposition Method

Factor decomposition is proposed to analyze and study the influencing factors of changes in indicators such as economy, society, and environment. There are two most commonly used decomposition methods [37,38]: structural decomposition method (SDA) and exponential decomposition method (IDA). The SDA method is mainly based on a large amount of data, establishes an input–output model, and decomposes and analyzes the research objects; while the IDA method only requires a single industry data and the data is easy to collect. Compared with the SDA method, the IDA method requires a small amount of data, and the continuity of the data can facilitate the study of the changing trend of the subject in a period of time [39].

In exponential decomposition analysis, the most commonly used methods are the Laspeyres and the logarithmic mean divisia exponential decomposition (LMDI) methods. The former is expressed in the form of percentage change, while the latter is based on logarithm, which studies the weight change of the influencing factors to the total change. Compared with the Laspeyres exponential decomposition method, LMDI is easy to model and has additivity [40]; the decomposition process can be expressed in the form of multiplication and addition, and the conversion is easier; the residual of the decomposition result is 0. Combined with the characteristics of the research object of this paper, logistics as an industry, it was difficult to obtain input–output data, and it was difficult to establish an input–output model, and one of the purposes of this paper was to study the changes in the influencing factors of regional logistics carbon emissions within a certain period of time. Considered comprehensively, the LMDI method is more suitable [41]. Compared with other decomposition methods, LMDI has many advantages: (1) Multiple influencing factors can be decomposed, and the factor decomposition result does not contain residual items that cannot be explained, and the residual is zero. (2) Multiplicative decomposition and additive decomposition are consistent, and the results obtained by these two methods can be converted into each other. (3) The results of applying the LMDI decomposition method, since the results of the sum of utility and the total effect of the decomposition of each department are the same, the decomposition does not affect the overall results. This feature is very important in the multifactor AHP.

In the literature, the factors affecting changes in the carbon emissions of the logistics industry are energy factors (including the energy structure and energy intensity), carbon emission intensity, logistics factors (including logistics industry development, the utilization rate of logistics facilities, and the methods of transporting goods), economic development factors (including per capita GDP, urbanization level, etc.), population size, and relevant national laws and regulations, systems, and other factors [42]. Among these factors, it is very difficult to quantify factors such as the utilization rate of facilities and equipment or relevant national laws and regulations, and the accuracy of their quantification is limited. This study therefore selected the following factors for research:

• Economic development: When studying carbon emissions-related issues, many scholars found that regional economic development affects the development level and scale of logistics in a region, which is the internal demand driving force that promotes the development of the logistics industry and has a decisive influence on the scale and efficiency of logistics [43,44]. This article selected regional GDP from 2010 to 2020 and used 2010 as the base period to deflate it.

• Logistics development level: The logistics industry is the most direct source of carbon emissions. The logistics industry continues to expand. Its relationship with the development of various industries in the regional economy is growing closer, which is important in economic development. The change trend of industrial value-added logistics and carbon emissions during the period is mainly an upward trend. The development of the logistics industry has the most direct impact on its carbon emissions [45]. The study selected the added value of the transportation, storage, and postal industry, and used 2010 as the base period to deflate it.

• Energy structure: Different energy types have different carbon emission coefficients. Table 1 shows that the carbon emission coefficients of different types of energy are quite different. In this article, the proportion of certain types of fossil energy consumed by the logistics industry within total energy consumption was used to express the energy structure [46,47,48].

• Energy intensity: The logistics industry’s energy intensity has a strong correlation with carbon emission intensity. Energy consumption intensity reflects the energy utilization efficiency of the logistics industry, and changes in energy intensity also have an important impact on its carbon emission levels [49,50]. Here, the energy consumption required by the logistics industry per unit of value added was used to express the energy intensity.

By referring to how the selected factors were divided in the literature on carbon emissions, we used LMDI to decompose the logistics industry’s carbon emissions in year t, which divides the influencing factors into carbon factor effects (CI), energy structure effects (ES), energy intensity effects (EI), logistics development effects (D), and economic scale effects (G). For these, the decomposition process is as follows:

(2)${CO}_2{e}_i^t={\displaystyle \sum }_j{\displaystyle \sum }_s\frac{C{O}_2{e}_{ijd}^t}{E_{ijd}^t}\times \frac{E_{ijd}^t}{E_{ij}^t}\frac{E_{ij}^t}{G_{ij}^t}\times \frac{G_{ij}^t}{G_i^t}\times G_i^t$

(3)$\begin{array}{c}\Delta C{O}_2{e}_i=C{O}_2{e}_i^t-C{O}_2{e}_i^0\hfill \\ ={\displaystyle {\displaystyle \sum }_j}{\displaystyle {\displaystyle \sum }_d}C{I}_{ijd}^t{E}_{ijd}^{t}E{I}_{ij}^t{D}_{ij}^t{G}_i^t-{\displaystyle {\displaystyle \sum }_j}{\displaystyle {\displaystyle \sum }_d}C{I}_{ijd}^0{E}_{ijd}^{0}E{I}_{ij}^0{D}_{ij}^0{G}_i^0\hfill \\ =C{I}_{effrct}^i+E{S}_{effrct}^i+E{I}_{effrct}^i+D_{effrct}^i+G_{effrct}^i\hfill \end{array}$

(4)$C{I}_{effrct}^i={\displaystyle \sum }_j{\displaystyle \sum }_s\left[\left(C_{ijd}^t-C_{ijd}^0\right)/\left(ln{C}_{ijd}^t-ln{C}_{ijd}^0\right)\right]ln\left(C_{ijd}^t/C_{ijd}^0\right)$

(5)$E{S}_{effrct}^i={\displaystyle \sum }_j{\displaystyle \sum }_s\left[\left(C_{ijd}^t-C_{ijd}^0\right)/\left(ln{C}_{ijd}^t-ln{C}_{ijd}^0\right)\right]ln\left(E{S}_{ijd}^t/E{S}_{ijd}^0\right)$

(6)$E{I}_{effrct}^i={\displaystyle \sum }_j{\displaystyle \sum }_s\left[\left(C_{ijd}^t-C_{ijd}^0\right)/\left(ln{C}_{ijd}^t-ln{C}_{ijd}^0\right)\right]ln\left(E{I}_{ij}^t/E{I}_{ij}^0\right)$

(7)$D_{effrct}^i={\displaystyle \sum }_j{\displaystyle \sum }_s\left[\left(C_{ijd}^t-C_{ijd}^0\right)/\left(ln{C}_{ijd}^t-ln{C}_{ijd}^0\right)\right]ln\left(D_{ij}^t/D_{ij}^0\right)$

(8)$G_{effrct}^i={\displaystyle \sum }_j{\displaystyle \sum }_s\left[\left(C_{ijd}^t-C_{ijd}^0\right)/\left(ln{C}_{ijd}^t-ln{C}_{ijd}^0\right)\right]ln\left(G_i^t/G_i^0\right)$

In this formula, i is the type of energy consumption, t is the year, 0 is the base year data, this article represents 2010, and d represents different regions; $C{I}_{effrct}^i$ represents the contribution of carbon factors to the logistics industry’s carbon emissions, $E{S}_{effrct}^i$ represents the contribution of changes in the energy structure to the logistics industry’s carbon emissions, $E{I}_{effrct}^i$ is the contribution of the change in the energy intensity to the logistics industry’s carbon emissions, $D_{effrct}^i$ is the contribution of development on the logistics industry’s carbon emissions, and $G_{effrct}^i$ is the contribution of the economic scale on the logistics industry’s carbon emissions. The logistics industry is a composite industry that integrates transportation, warehousing, postal services, and information services and is an important part of the national economy. Considering the principle of availability of index data, this paper did not include the index of "information platform" in the evaluation index system, because the information platform not only serves the logistics industry but also serves other industries in the entire regional economy, and the statistical yearbook does not include the index of "information platform". There is no information data divided into various industries, so the data of the information platform will not be analyzed for the time being. According to the data of "China Tertiary Industry Statistical Yearbook", the transportation industry, warehousing, and postal industry account for more than 92% of the logistics industry which can basically represent the overall development of the logistics industry. The logistics industry mentioned in this article refers to the transportation, warehousing, and postal industry. The data of the article were taken from the "Statistical Yearbook", "China Tertiary Industry Statistical Yearbook", and "China Energy Statistical Yearbook" of various provinces and cities in China over the years. In order to exclude the impact of price changes in the development of the regional logistics industry, the output value of the logistics industry was calculated at the constant price in 2010.

3.3. Decoupling Model

The LMDI decomposition model can decompose and analyze the driving factors of carbon emissions in the logistics industry but cannot specifically measure the relationship between regional carbon emission reduction and the development of the logistics industry. Therefore, "decoupling" is an effective tool to study the relationship between economic development and material energy consumption. At this stage, there are mainly two representative decoupling models: the first category is the economic cooperation and development group.

According to the decoupling factor model proposed by the OECD, the decoupling results mainly depend on the initial and final values; the OECD divides decoupling into relative decoupling and absolute decoupling [51]. The second category is the decoupling index model proposed by Tapio, whose decoupling results mainly depend on the increase or decrease of decoupling elasticity. The Tapio decoupling model first introduced the concept of decoupling elasticity. Tapio [32] defined the decoupling elasticity when studying the relationship between transportation and carbon dioxide emissions, which is the ratio of the percentage change in traffic volume to the percentage change in GDP over a specific time period. Compared with the OECD decoupling model, the advantages of the Tapio decoupling model are more obvious [52]. First of all, the Tapio decoupling model considers two indicators, the relative amount change and the total amount change, which avoids the error caused by the OECD decoupling model due to the selection of gene stages, and improves the objectivity and accuracy of the decoupling measurement. Secondly, the phase division of the decoupling state of the Tapio decoupling model is more detailed, and it is not affected by the change of the statistical dimension, which can better reflect the relationship between variables. This paper adopted the Tapio decoupling model to study the decoupling relationship between the development of the logistics industry and carbon emissions. In this paper, the following formula was set as the Tapio decoupling index.

(9)$e=\frac{\frac{\Delta C}{C}}{\frac{\Delta D}{D}}$

In the formula, e represents the decoupling index, C represents the total carbon emissions of the logistics industry, and D represents the total output value of the logistics industry. The decoupling state can be divided into eight states (Table 2), in which if the decoupling is stronger, the carbon emissions will be lower.

4. Results and Discussion

4.1. Carbon Emissions Measurement Results

Based on the analysis method described above, the article calculated the carbon logistics industry emissions shown in Table 3 and Figure 1.

Table 3 and Figure 1 show the logistics industry’s carbon emissions in China as a whole and in three regions and show that the three regions are quite different: the eastern region has the highest emissions of the three regions, mainly related to the economic aggregate of the region. The larger the economic aggregate, the greater the total carbon emissions. The rate of increase in the eastern region is also the largest. This is related to the development of the logistics industry in each region. The eastern region has a more developed economy, and the logistics industry has developed rapidly, so the total carbon emissions have increased rapidly. The total carbon emissions of the logistics industry have tended to increase in China, which also shows that carbon emissions are consistent with the evolutionary trend of the level of economic development.

4.2. Decomposition of Factors Affecting Carbon Emissions in the Logistics Industry

4.2.1. Overall Decomposition of Factors Affecting Carbon Emissions in the Logistics Industry

We selected the LMDI decomposition method to decompose the factors affecting the logistics industry’s carbon emissions. ΔES, ΔEI, ΔD, and ΔG, respectively, represent the change in the value of carbon emissions in the logistics industry caused by the energy structure, energy intensity, development of logistics, and economic development.

Table 4 shows that the total effect of the influencing factors is the same as the change trend of the logistics industry’s carbon emissions, with an overall upward trend, indicating that the LMDI method is reasonable and robust. Within the decomposition results, economic development had the greatest effect compared with the other indicators. The effects of specific factors are as follows:

• Economic development: Economic development is the biggest factor influencing the logistics industry’s carbon emissions, and it is greatly increasing the total carbon emissions. Since 2010, economic development has been a stimulating effect, and its contribution rate has also been increasing. With economic growth, the total energy consumption of the logistics industry has also increased. In 2001, China joined the WTO, its economy developed rapidly, and people’s lives have also undergone tremendous changes. As a bridge connecting social life, the logistics industry inevitably develops. At the same time, the logistics industry is also a large energy consumer, which inevitably consumes more energy and increases carbon emissions. Between 2010 and 2020, the logistics industry has been driven by economic development. The rapid development of energy sources and, consequently, the demand for energy soared, resulting in greater carbon emissions.

• Logistics development: The coefficient of the logistics industry development is positive between 2010 and 2015 and become negative after 2015, indicating that the development of the logistics industry promoted carbon emissions before 2015. However, the increase in carbon emissions was suppressed after 2015. The main reason for this is that the mode of the logistics industry changed. In recent years, the modernization of the logistics industry has been relatively high, which has improved the energy utilization efficiency of logistics, especially through information technology. In the context of development, the logistics industry’s carbon emissions have been declining, which has resulted in negative effects after 2015.

• Energy structure: From 2010 to 2015, the energy consumption structure restrained the logistics industry’s carbon emissions. This may be because, during this period, the logistics industry was in its infancy, its scale was small and scattered, and the economic aggregate was small. The demand for energy was not large, so the energy structure’s restraining effect on carbon emissions was mainly because of imperfect development of the logistics industry itself; from 2016 to 2020, the energy structure affected the logistics industry, carbon emissions had a driving effect and have been gradually increasing.

• Energy intensity: From 2010 to 2015, the effects of energy intensity showed a positive effect, promoting carbon emissions. During this period, the development of the logistics industry was relatively extensive, and its energy utilization efficiency was low, which increased carbon emissions. From 2016 to 2020, the changes in the energy intensity showed obvious negative effects on the contribution rate of carbon emissions in the logistics industry and have played a role in restraining carbon emissions. In other words, the energy efficiency of the logistics industry has increased significantly, which has had a positive impact on reducing carbon emissions.

4.2.2. Decomposition of Factors in Different Regions

We broke down the influencing factors by region, mainly divided into the eastern, central, and western regions of China. Table 5, Table 6, and Table 7 show the results of each, respectively.

Compared with other indicators, the economic development indicator has the greatest impact on the logistics industry’s carbon emissions. The impacts of economic development on the three regions all changed in the same direction. Other scholars have also reached the same conclusions; for example, Liu and Pan [53] found that with the further development of China’s economy, the continuous increase in the total carbon emissions of the logistics industry was difficult to change in the short term; Ma and Wang’s [24] research showed that economic growth was the driving force of logistics. The main driving force for the growth of industrial carbon emissions is an exponential growth trend during the study period. The central region showed the least impact. The main reason for this is that the eastern region has a relatively high level of economic development. The highly developed e-commerce sector has promoted the rapid development of the logistics industry and has also changed people’s consumption patterns. People buy more and more products through e-commerce platforms, which has made the logistics industry develop further by speeding up, which, in turn, has led to increasing the carbon emission intensity of the logistics industry. The economic development of the central and western regions is lagging, and the development of the logistics industry is limited; moreover, the western region is dominated by mountains instead of plains. Therefore, it is difficult to construct a logistics network, which restricts the development of the logistics industry. Therefore, the logistics industry’s carbon emissions in the western region are low. In the east, the development of the logistics industry in the western region and the eastern region has been similar. The main reason is that both of them were based on primary energy consumption, and the resulting carbon emissions of logistics are higher than those in the central region. After 2015, it has flattened and maintained at a low level, but it is still a positive relationship, and economic growth had significantly promoted logistics carbon emissions.

The effects of development of the logistics industry show the characteristics of first pulling and then restraining carbon emissions in different regions, this is inconsistent with the conclusions drawn by some scholars. Most scholars have drawn a single pulling or inhibiting conclusion. For example, scholar Yang [54] found that developing the logistics industry and reducing the logistics cost per unit of GDP have a significant impact on reducing carbon emissions. Scholar Yang [55] pointed out that the development of the logistics industry itself is the main driving factor of energy consumption. The development of advanced logistics methods can support the production methods and lifestyles under the low-carbon economy, and the low-carbon economy needs the support of the modern logistics industry. This paper shows that China’s logistical efficiency has improved during the study period. Logistics originate from storage and transportation. An important feature of warehousing and transportation is that it must be above a certain scale to be effective. With the gradual expansion of the scale of the logistics industry, the utilization rate and turnover rate of logistics facilities and equipment continue to increase. Energy efficiency has improved and has an important role in inhibiting carbon emissions. Therefore, China should further promote the development of the logistics industry in the direction of larger scale and specialization by exerting scale effects, increasing the proportion of the logistics industry’s output value within GDP, improving the efficiency of logistics, and reducing the cost of the logistics industry. This will be conducive to the reduction of carbon emissions in China’s logistics industry.

The effects of energy intensity have large volatility and are mainly negative. The region with the greatest negative impact is the eastern region of China. In 2015, the effects of energy intensity for all regions in China had an inflection point, mainly in the context of sustainable development, which pursues both green development and low-carbon development. China has also introduced corresponding policies and measures to maintain the level of carbon emissions of the logistics industry. After 2015, the effects in the central region and the western region were higher than the effects in the eastern region. The eastern region began to show a rapid decline in 2015, and the effects of energy intensity were less than 0, indicating that carbon emission reductions were most obvious in the eastern region, and the central and western regions showed limited reducing effects due to their economic development and technical level. The significant negative impact of energy intensity is consistent with the conclusions of domestic and foreign scholars. For example, Timilsina et al. [56] used LMDI to analyze the influencing factors of carbon dioxide emissions from the transportation industry in 12 Asian countries and found that the inhibitory effect was limited; domestic scholar Cao [57] found that China’s energy consumption intensity and the development level of the logistics industry have an opposite relationship. The development of the logistics industry can promote the improvement of energy efficiency in the industry, thereby reducing carbon emissions. This is consistent with the results obtained in the eastern region of this paper.

The negative impact of the effects of energy structure is not obvious: the logistics industry is an energy-intensive industry, and the effects of energy structure are affected by the macroeconomic form and energy policy. In the early stage of industrial development before 2015, the inhibitory effect of the energy structure was poor. There was a turning point in the energy structure’s effect from 2015 to 2020. Since 2015, the Chinese government has emphasized the goal of reducing carbon emissions and put forward the "target of reducing carbon emissions per unit of GDP by 40–50%." The energy structure’s effects have begun to show a downward trend. The macroenergy policy has played a role in structural adjustment. However, since 2015, the energy structure’s effect on China’s three major regions has been around zero, and the negative impact is not obvious. The main reason is that coal and diesel dominate the main effects. The industry is over-reliant on petroleum fuels such as diesel and kerosene, and there is no other clean energy alternative for the time being. This is consistent with the research results of most scholars. For example, scholars Tang and Lu [58] found that the optimization of energy structure helps to reduce carbon emission intensity, and the energy structure dominated by fossil energy was not conducive to the reduction of carbon emissions in the logistics industry. Li [59] also indicated that the direct impact of the energy structure on the carbon emissions of the logistics industry was a negative drive.

4.3. Analysis of the Decoupling Effects

The decoupling formula was used to measure the logistics industry’s carbon emissions, and then calculate the average value and rate of change in carbon emissions for each province over 11 years. We calculated the carbon emission decoupling coefficient of the logistics industry in 30 provinces. The results are shown in Table 8.

From the results of carbon emission decoupling, the 3 provinces of Beijing, Shanghai, and Zhejiang are in a state of strong decoupling, and 20 provinces, including Hebei, Tianjin, Sichuan, and Guangdong, are in a state of weak decoupling. The provinces with strong and weak decoupling account for 76.67% of the country’s total, indicating that the growth rate of the logistics industry’s carbon emissions in these provinces is slower than the growth rate of the output value of the logistics industry in these regions. The emission reduction work in these regions during 2007–2013 had achieved some initial results, which has guiding and reference significance for emissions control in other regions. Scholar Zhang [60] also came to the same conclusion, believing that the decoupling indicators of the relationship between energy consumption, carbon dioxide emissions, and industry development of the logistics industry in various provinces, municipalities, and autonomous regions in China have generally shown a trend of continuous optimization, indicating that, since 2009, the national intensive introduction of a series of logistics policies has played a certain role. Shanxi and Hainan provinces are in a state of expansion and connection. Heilongjiang, Anhui, Henan, and Yunnan are in a state of negative decoupling of expansion: this means that while the logistics industry’s output value in these areas has increased, carbon emissions are also growing rapidly, and their growth rate is far greater than the output value. With an increased growth rate, these regions will face greater pressure to reduce emissions in the future. From 2010 to 2020, the output value and carbon emissions of China’s 30 provinces have shown an increasing trend. The standard deviation coefficients for calculating the ΔC/C and ΔGDP/GDP indicators of the 30 provinces are 66.21% and 34.45%, respectively. The dispersion of the average growth rate of the logistics industry’s carbon emissions is greater than the average growth rate of the output value, indicating that the differences in the industry’s emission reduction capabilities between provinces are expanding.

5. Conclusions and Implications

5.1. Conclusions

This study estimated the carbon emissions of the logistics industry according to the IPCC’s (2006) method, then used the LMDI model to explore the factors affecting carbon emissions in China’s logistics industry, and, finally, conducted a decoupling analysis of carbon emissions and the development of the logistics industry. The conclusions are as follows:

• (1). Results of LMDL decomposition

The total carbon emissions in the east are higher than those in the central and western regions. This is mainly related to the economic aggregate of the region. The larger the economic aggregate, the greater the total carbon emissions. The increase in the eastern region has been greater than that of the central and western regions. In the west, this is related to logistics industry development in the region. From the perspective of China as a whole, the total carbon emissions of the logistics industry have been tending to increase, which further shows that China’s economy has had a long-term growth trend, which is consistent with the evolutionary trend of the level of economic development.

Compared with other indicators, the economic development has the greatest impact on the logistics industry’s carbon emissions. The impact of economic development on the three regions all changed in the same direction. At the same time, the impact of economic development on the central region was the smallest. Nevertheless, the effects of logistics output are similar across the three regions, showing the characteristics of first pulling and then restraining. The effects of energy intensity show large volatility and are mainly negative. The region with the greatest negative impact is the eastern region of China. After 2015, the energy intensity of the central and western regions was higher than that of the eastern region, and the eastern region began to decline rapidly in 2015, until the effects of energy intensity were less than zero. The central and western regions showed smaller restraining effects. The negative impact of the energy structure’s effect is not obvious, and the logistics industry is an energy-intensive industry. The structural effects are affected by macroeconomic forms and energy policies. In the early stage of industrial development before 2015, the inhibitory effect of the energy structure was poor. From 2016 to 2020, the energy structure’s effect had an inflection point. Since 2016, the energy structure’s effects began to show a downward trend, but the negative impact of the energy structure’s effect in the three major regions is not obvious.

• (2). The decoupling analysis

From the results of the decoupling effects, it can be seen that the only provinces in the country that are strongly decoupled are Beijing, Shanghai, and Zhejiang, indicating that the development of the regional logistics industry and the environment have formed a benign interaction. There are 20 provinces in a state of weak decoupling, and the strong decoupling and weak decoupling provinces accounted for 76.67% of the country, indicating that most provinces in China have achieved definite results in reducing carbon emissions and should continue to maintain this trend and increase the implementation of carbon emission reduction efforts. It is recommended to strive to achieve a complete decoupling of carbon emissions from the output value of the logistics industry. At present, 13.33% of provinces are in a state of expansive negative decoupling, and 6.67% of provinces are in a state of connection with growth, indicating that these provinces should be the focus of future emission reduction efforts. The differences in carbon emissions for this industry across the country have clearly widened and differentiated low-carbon logistics development policies need to be formulated in accordance with local conditions.

5.2. Policy Suggestion

In the context of international emission reduction, China and the United States jointly issued the "Sino-US Joint Statement on Climate Change" and proposed that China’s carbon dioxide emissions will peak by 2030, and carbon dioxide emissions per unit of GDP will be reduced by 60% to 65% compared with 2005. This paper analyzed the influencing factors of logistics carbon emissions in different regions, which has important implications for China and other countries to reduce carbon emissions in the logistics industry, improve the operational efficiency of the logistics industry, and control carbon emissions to achieve low-carbon economic transformation. Combining the results of the empirical analysis, this article proposes suggestions for promoting the reduction of carbon emissions in the logistics industry from the following perspectives:

• (1). Improve energy efficiency.

From the perspective of energy efficiency, energy efficiency is a key factor in curbing carbon emissions in the logistics industry. Energy efficiency can be improved from two aspects. One is the introduction of foreign advanced logistics technology and the use of advanced logistics technology to improve energy efficiency; the second is independent innovation, including innovation of the management system of logistics enterprises and the innovation of logistics operation processes. Reducing the costs of logistics and improving the utilization rate of logistics energy would help achieve the carbon emission reduction goals of the logistics industry.

• (2). Formulate policies to promote low-carbon logistics development.

There are obvious regional differences in Chinese logistics industry. The formulation of carbon emission reduction policies should be more targeted. For western regions with small differences in interprovincial carbon emissions, a unified carbon emission reduction policy can be considered, while for regions with large interprovincial carbon emission differences, the process of formulating policies should vary from place to place. Local governments can combine the development level of the local logistics industry and the actual situation of carbon emissions and participate in the formulation of suitable rules.

• (3). Actively adjust the energy structure of the logistics industry.

A reasonable energy structure is an effective measure to reduce the intensity of carbon emissions. At present, the energy structure of the logistics industry is mainly based on high-carbon fossil energy such as coal and oil. Energy intensity is the main reason for the decline in the carbon emission intensity of the logistics industry in Fujian Province. The rate of contribution of the energy structure is very small, increasing the carbon emissions. Therefore, it is necessary to accelerate the adjustment of the energy structure, promote the use of clean energy, increase the proportion of new energy in the total energy consumption, and reduce the carbon emission intensity of the logistics industry through reasonable adjustment of the energy structure and promoting the development of a low-carbon logistics industry.

• (4). Strengthen informatization.

Informatization is an effective means to improve the efficiency of logistics operations, and it also provides important technical support and guarantee for the development of low-carbon logistics. The public information platform of logistics can effectively realize the transmission of information throughout the main body of the logistics supply chain, realize the integration and reasonable allocation of logistic resources, and improve the operational efficiency of enterprises. The construction of a public information platform for logistics would provide conditions for the realization of the common distribution of logistics enterprises. Logistics enterprises jointly use logistics facilities and equipment through the information platform, which would reduce the empty load rate during transportation, and reduce repeated transportation and ineffective transportation. In turn, this would reduce energy consumption and carbon emissions through rationalization and efficiency in all aspects of logistic activities.

5.3. Research Values and Limitations

On the one hand, the research of this paper promotes the integration of economics, sociology, and management, which is conducive to the further development of management; on the other hand, it expands and deepens the research field of energy and environmental issues and enriches the research perspective. This paper took the logistics industry as the research object, which is conducive to improving the research framework of energy and environmental science. Finally, this paper adopted the national and regional research on the emission of logistics industry, which is conducive to excavating and discovering the actual characteristics of energy and environmental problems in different regions and provinces, making policy recommendations more pertinent, to improve the operability of the policy.

The issue of regional logistics carbon emissions has been a hot research topic in the academic circles in recent years. This paper carried out preliminary research on it. It is in the preliminary and superficial research stage. Although some research conclusions have been obtained, due to the limitation of personal level and ability, the research on the differences and influencing factors of regional logistics carbon emissions still needs to be further explored, and needs to be improved and perfected from the following aspects:

1. When analyzing the carbon emission characteristics of the logistics industry, this paper selected the carbon emission intensity, energy structure, economic development, and the development of the logistics industry to analyze its carbon emissions. There is a lack of interaction mechanism between measurement indicators and carbon emission characteristics. The path is explained indepth.

2. Lack of indepth research on industry logistics. The logistics industry involves a very wide range, and every industry in the national economy has logistics. Due to industry differences, there are many differences in energy consumption and carbon dioxide emissions in logistics in different industries. This article was based on the logistics industry from a broad perspective and does not reflect industry differences. In the future research, it is necessary to conduct indepth research in combination with industry logistics.

3. The optimization and adjustment of regional logistics itself is very important to improve the level of low-carbon development, but it also requires the cooperation of the industry, which requires the impact mechanism and degree of influence of different industries on the logistics industry in each region, so as to reach the purpose of the interindustry common development.

Author Contributions

Conceptualization, T.Z.; methodology, T.Z.; software, R.W.; validation, R.W.; formal analysis, T.Z.; investigation, R.W.; resources, R.W.; data curation, R.W.; writing—original draft preparation, R.W.; writing—review and editing, R.W.; visualization, G.X.; supervision, G.X.; project administration, G.X.; funding acquisition, G.X. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data used to support the findings of this study are available from the corresponding author upon request.

Conflicts of Interest

The authors declare no conflict of interest.

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Figure and Tables

Figure 1. Changes in carbon emissions in various regions.

Table 1

Carbon emission coefficients and standard coal conversion coefficients.

Energy Variety	Average Low Calorific Value (kJ/kg)	Conversion Factor of Standard Coal (kg Standard Coal)	Carbon Emission Factor (t Carbon/tce)
Coal (kg)	20,908 kJ/kg	0.7143	0.7467
Diesel oil (kg)	42,652 kJ/kg	1.4571	0.5913
Gasoline (kg)	43,070 kJ/kg	1.4714	0.5532
Kerosene (kg)	43,070 kJ/kg	1.4714	0.3416
Fuel oil (kg)	41,816 kJ/kg	1.4286	0.6176
Coke (kg)	28,435 kJ/kg	0.9714	0.1128
Electricity (kW·h)	3596 kJ/kW·h	0.1229	2.2132
Natural gas (m3)	38,931 kJ/kg	1.3300	0.4479

Table 2

Decoupling state division.

State	ΔCO2	ΔD	Elasticity e
Expansive negative decoupling	>0	>0	>1.2
Strong negative decoupling	>0	<0	<0
Weak negative decoupling	<0	<0	0 < e < 0.8
Weak decoupling	>0	>0	0 < e < 0.9
Strong decoupling	<0	>0	<0
Recession decoupling	<0	<0	>1.2
Expansive connection	<0	>0	0.8 < e < 1.2
Decaying connection	<0	<0	0.8 < e < 1.2

Table 3

The logistics industry’s carbon emissions in various regions.

Region	2010	2011	2012	2013	2014	2015	2016	2017	2018	2019	2020
East Region	2.1234	2.2457	2.2986	2.3316	2.3675	2.4324	2.5435	2.6435	2.7895	3.0965	3.3421
Central Region	1.4573	1.4674	1.4786	1.4875	1.4998	1.5232	1.7342	2.0213	2.1455	2.2213	2.4435
Western Region	0.9986	1.1123	1.1245	1.1287	1.1453	1.1764	1.2095	1.2476	1.2764	1.4533	1.7432
China	4.5793	4.8254	4.9017	4.9478	5.0126	5.132	5.4872	5.9124	6.2114	6.7711	7.5288

Table 4

Decomposition results of influencing factors at the national level.

Year	Energy Intensity		Energy Structure		Logistics Development		Economic Growth		Total Effect
	DEI	ΔCEI	DES	ΔCES	DD	ΔCD	DG	ΔCG	Dtot	ΔCtot
2010	1.001	−110	0.912	−12	0.945	35	1.023	512	0.8825	425
2011	1.003	−156	0.922	−35	0.934	223	1.113	3135	0.9613	3167
2012	1.014	−187	0.943	−59	0.856	568	1.117	4563	0.9143	4885
2013	1.036	−113	0.956	−85	0.823	645	1.236	6990	1.0075	7437
2014	1.039	−103	1.002	−142	0.846	348	1.367	9542	1.2040	9744
2015	1.046	−98	1.004	−123	0.909	287	1.562	11,345	1.4911	11,411
2016	1.059	89	1.012	36	0.811	−123	2.109	11,876	1.8330	11,878
2017	1.064	94	1.052	87	0.812	−223	2.167	12,332	1.9696	12,290
2018	1.077	109	1.058	98	0.804	−345	2.198	12,785	2.0137	12,647
2019	1.089	159	1.074	123	0.765	−457	2.246	12,983	2.0096	12,808
2020	1.112	176	1.094	156	0.746	−554	2.345	13,446	2.1282	13,224

Table 5

Decomposition results for the eastern region.

Year	Energy Intensity		Energy Structure		Logistics Development		Economic Growth		Total Effects
	DEI	ΔCEI	DES	ΔCES	DD	ΔCD	DG	ΔCG	Dtot	ΔCtot
2010	1.145	1123	0.967	98	0.917	45	1.112	1545	1.1290	2811
2011	1.245	1156	0.946	156	0.945	564	1.234	3456	1.3734	5332
2012	1.256	1478	0.955	178	0.865	1123	1.412	5678	1.4650	8457
2013	1.243	1113	0.962	256	0.812	1562	1.546	7893	1.5011	10,824
2014	1.197	1035	1.012	277	0.856	1236	1.763	9945	1.8281	12,493
2015	0.768	88	1.013	134	0.901	768	1.872	11,235	1.3122	12,225
2016	0.786	−82	0.993	87	0.895	−12	2.133	13,458	1.4900	13,451
2017	0.765	−165	0.954	32	0.875	−205	2.136	14,532	1.3640	14,194
2018	0.768	−154	0.947	12	0.871	−198	2.142	16,789	1.3569	16,449
2019	0.735	−159	0.933	8	0.866	−188	2.146	19,875	1.2744	19,536
2020	0.721	−134	0.931	−4	0.823	−204	2.154	21,346	1.1900	21,004

Table 6

Decomposition results for the central region.

Year	Energy Intensity		Energy Structure		Logistics Development		Economic Growth		Total Effects
	DEI	ΔCEI	DES	ΔCES	DD	ΔCD	DG	ΔCG	Dtot	ΔCtot
2010	1.012	234	0.911	103	0.866	67	1.022	985	0.8160	1389
2011	1.014	245	0.923	145	0.876	123	1.034	1125	0.8477	1638
2012	1.017	567	0.922	167	0.898	457	1.123	3425	0.9456	4616
2013	1.023	1134	0.963	178	0.903	1463	1.125	4457	1.0008	7232
2014	1.046	789	1.012	189	0.899	1145	1.236	6789	1.1762	8912
2015	1.057	346	1.014	235	0.901	764	1.245	8793	1.2023	10,138
2016	1.076	145	1.076	147	0.898	−18	1.334	9833	1.3869	10,107
2017	1.088	123	1.114	145	0.884	−206	1.342	13,242	1.4379	13,304
2018	1.093	87	1.198	132	0.856	−199	1.332	15,645	1.4930	15,665
2019	1.098	80	1.099	112	0.879	−234	1.328	17,643	1.4086	17,601
2020	1.112	82	0.998	56	0.869	−308	1.345	18,764	1.2971	18,594

Table 7

Decomposition results for the western region.

Year	Energy Intensity		Energy Structure		Logistics Development		Economic Growth		Total Effect
	DEI	ΔCEI	DES	ΔCES	DD	ΔCD	DG	ΔCG	Dtot	ΔCtot
2010	1.012	556	0.943	456	0.734	45	1.124	1123	0.7873	2180
2011	1.034	986	0.956	467	0.785	59	1.231	1678	0.9552	3190
2012	1.067	1478	0.952	489	0.789	126	1.302	4574	1.0435	6667
2013	1.078	1896	0.969	433	0.804	345	1.345	7563	1.1296	10,237
2014	1.098	1675	1.013	498	0.814	454	1.457	9755	1.3192	12,382
2015	1.135	986	1.015	343	0.824	234	1.569	11,456	1.4894	13,019
2016	1.147	956	1.095	213	0.846	−123	1.789	14,569	1.9009	15,615
2017	1.153	945	1.123	123	0.854	−125	1.788	15,698	1.9771	16,641
2018	1.167	923	1.049	45	0.865	−235	1.803	17,832	1.9092	18,565
2019	1.168	934	1.083	12	0.877	−265	1.812	19,043	2.0102	19,724
2020	1.178	903	1.006	6	0.894	−276	1.845	21,346	1.9547	21,979

Table 8

Decoupling coefficient of each province.

Province	ΔC	ΔD	Elasticity e	Decoupling State	Province	ΔC	ΔD	Elasticity e	Decoupling State
Beijing	−26.01	66.35	−0.65	Strong	Jiangxi	19.02	53.21	0.64	Weak
Tianjin	4.32	66.32	0.12	Weak	Henan	43.12	71.22	1.33	Expansive negative
Hebei	14.13	210.09	0.33	weak	Hubei	15.34	99.43	0.12	Weak
Liaoning	22.14	129.01	0.28	Weak	Sichuan	11.23	33.45	0.36	Weak
Shanghai	−11.28	33.39	−0.22	Strong	Guizhou	23.44	104.29	0.39	Weak
Jiangsu	56.32	257.02	0.55	Weak	Yunnan	33.21	11.24	1.45	Expansive negative
Zhejiang	−35.21	93.29	−0.68	Strong	Shaanxi	18.66	55.19	0.23	Weak
Fujian	33.21	86.23	0.44	Weak	Gansu	21.99	25.39	0.55	Weak
Shandong	22.31	238.91	0.22	Weak	Qinghai	4.12	5.22	0.77	Weak
Guangdong	54.12	223.49	0.34	weak	Ningxia	1.23	23.91	0.14	Weak
Hainan	10.03	8.63	0.88	Expansive connection	Xinjiang	18.23	29.03	0.43	Weak
Shanxi	37.77	75.23	0.88	Expansive connection	Nei Monggol	27.88	127.39	0.41	Weak
Jilin	13.44	33.21	0.65	Weak	Guangxi	7.22	55.02	0.32	Weak
Heilongjiang	44.25	42.14	1.22	Expansive negative	Chongqing	23.33	47.29	0.67	Weak
Anhui	40.23	36.99	2.14	Expansive negative	Hunan	32.13	111.29	0.62	Weak

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