Study on the Decoupling Relationship and Rebound Effect between Economic Growth and Carbon Emissions in Central China

Abstract

The central area is the core region of China’s economic development. Under the current goal of carbon emission reduction, the analysis of the decoupling relationship between economic growth and carbon emissions and the carbon rebound effect will help us to formulate corresponding policies, achieve a carbon peak at an early date, and ensure high-quality economic development. Based on the energy consumption data from 2000 to 2019, the carbon emission of six provinces of the central region was calculated. The Tapio decoupling model was used to learn about the decoupling index. And then, by calculating the contribution rate of technological progress to both economic growth and carbon emission intensity, the carbon saving amount and carbon rebound amount can be calculated, and the rebound effect value of carbon emission is obtained. The results show that the economy in central China presents a trend of growth. In contrast, the carbon emission of each province shows a gradient structure with a large difference, and the economic growth and carbon emission show a weak decoupling in the past five years. We further analyzed the rebound effect of carbon emissions and found that 30% of the years in the central region have a rebound effect with values of more than one. Finally, this study puts forward policy suggestions for the early realization of carbon peaks and high-quality economic development in the central region.

1. Introduction

As the world economy grows, so does global energy demand and usage, and carbon emissions rise year after year, resulting in serious environmental pollution. China is the world’s greatest emitter of carbon dioxide [1]. In 2021, the CO₂ emissions of China hit 11.9 billion tons, which is 33% of international CO₂ emissions. The central area is the core economic development region of China. Since the “Rise of the Central Region” plan was implemented in 2004, the central region has made increasing contributions to China’s economic development, accounting for 24.6% of GDP in 2021, up from 19.2% in 2003. However, the economic development of the central region has long depended on the consumption of energy and resources. In the last two decades, the energy usage in the central region accounted for about 32% of the national energy usage. The carbon emissions caused by energy consumption continue to rise, leading to the increasingly sharp contradiction between the increase in economy and carbon emissions. In July 2021, the government issued a guideline to give an advance on how to limit carbon emissions amid rapid economic growth. The guideline calls for promoting green development in the central region, promoting green industrial upgrading, making the reduction in energy usage per unit of GDP attain the national average level, and further decreasing carbon dioxide released per unit of GDP. For central China to attain high-quality economic development, the internal conflict between economic growth and carbon emission must be solved underneath the dual constraints of decreasing carbon intensity and peaking carbon emission. Therefore, we investigated the decoupling indicator value of carbon release and economic development. On this basis, we analyzed the rebound effect of carbon emissions, which has a great practical significance to realizing better development of the economy in central China.

Based on this, the article chooses six provinces in the central area from 2000 to 2019 as the study subjects and uses the Intergovernmental Panel on Climate Change (IPCC) method to calculate each province’s carbon emissions, further assessing the decoupling degree between carbon emissions and the economy. Subsequently, we analyzed the rebound of carbon emissions by constructing the rebound effect model in order to supply a theoretical groundwork for the central region to realize the “dual carbon goal” as soon as possible and achieve high-quality economic development.

The paper’s structure is as follows: Section 2 offers a review of the papers on the decoupling of economic increase and carbon emissions, as well as the carbon emissions’ rebound effect. The following chapter introduces the methodology and data sources. Section 4 analyzes the decoupling situation of carbon release and the economic increase through time and space, as well as the rebound effect of carbon emissions. The last section is the conclusion and suggestions.

2. Review of the Literature

The word “decoupling” was initially used in the physical domain to describe the fact that there is no longer any connection between two or more variables. From the assumptions of the environmental Kuznets curve (EKC), we can know that the early stage of economic growth will cause environmental pollution. However, in the medium and long term, environmental pollution will show a trend of improvement after reaching a peak. Eventually, the process of economic development will show a strong decoupling from environmental pollution [2]. When it comes to carbon emissions, decoupling means a break of the link between economic increase and carbon emissions. The premise of carbon emissions reduction is to ensure economic growth. On the one hand, technological progress brought about by economic growth can improve energy efficiency and reduce carbon emissions. On the other hand, it will reduce the energy price, leading to the increase of energy consumption and the rise of carbon emissions. Scholars’ research on the decoupling of economic increase and carbon emissions and its rebound effect has mainly focused on the following two aspects.

2.1. Research on Decoupling between Economic Growth and Carbon Emissions

In 1990, Von first proposed the idea of “decoupling” that the economy can develop without causing any environmental harm. Subsequently, scholars began to conduct qualitative research on the decoupling situation of energy usage during economic development [3], and produced two decoupling indicators dominated by OECD and Tapio. The OECD separates decoupling into two categories: relative and absolute. By drawing on the OECD decoupling indicator and the views of other scholars, Tapio constructed a framework to evaluate the connection between the transport sector’s carbon emissions and GDP in 15 European countries from 1970 to 2001 [4]. Since then, these two methods have been widely used. (1) At the national level, scholars usually use OECD decoupling indicators [5] and the decoupling model [6] to research the linkage between economic growth, energy usage, and carbon emissions in different countries, and the specific factors causing decoupling were decomposed [7]. At the regional level, taking the global as the research background, such as in Europe and Asia, scholars assess the linkage of economic growth and environmental variables [8]; taking the country as the research background, different regions of a certain country are studied, such as The Beijing-Tianjin-Hebei region of China. At the industrial level, most scholars adopt the Tapio decoupling model for industry [9], the service industry [10], construction industry [11], and agriculture [12], and studied the decoupling linkage between industrial economic output and carbon emissions and the influencing elements of decoupling. (2) When it comes to the relationship between economic growth and carbon emissions, energy consumption is also generally studied. Since the industrial revolution, the economic increase has been accompanied by the burning of large amounts of fossil energy, and a huge quantity of carbon dioxide has been discharged into the environment [13]. Due to different measurement methods, research objects, time frames and other variables, scholars have reached different conclusions on the relationship between economic growth and carbon emissions [14]. In terms of measurement methods, scholars mostly use the environmental Kuznets curve (EKC) to expand, and through panel data, the Vector error correction model [15], Polynomial fractional logarithm model [16], and autovector regression model [17] studied the link of financial growth and carbon emissions. The conclusions are also different because of the different regions and time spans of the research objects. Some scholars believe that the impact of CO₂ emission and financial increase in developed nations is mutual, while the linkage is only one-way in developing countries [18]. In terms of time span, it mainly explores the long-term and short-term influences of economic growth on carbon dioxide release [19]. Some scholars deem that there is an “N-shaped” connection between the two in the long term [20]. Some scholars also believe that there is a significant long-term negative correlation and a significant short-term positive correlation between them. (3) In the current research, the structural decomposition analysis (SDA), index decomposition analysis (IDA) and the extensible stochastic environmental impact assessment model (STIRPAT) are often used to analyze the elements influencing the decoupling of carbon emissions. From a temporal and spatial standpoint, the influencing elements are decomposed into per capita GDP, population, energy intensity, economic output, industrial structure, urbanization, and technological level [21]. Among them, the main driving factor is the economic output effect, and the next is the population size and energy intensity; per capita GDP has a small effect on carbon release. There is a Granger causality connection relating to economic growth and carbon dioxide release [22]. The population also can accelerate carbon release [23], and the effect of population on carbon release is related to city size. The larger the city size, the greater the impact of population change or population density on carbon emissions [24]. Population changes always have a greater impact than population density. Both per capita energy usage and urbanization have a linear relationship with carbon release, and the former is positively correlated, while the relationship between per capita income and carbon release is an inverted U-shaped [25,26].

2.2. Research on the Rebound Effect

Jevons first presented the rebound effect in his book “The Coal Problem” in 1866. He found that enhancements in energy efficiency led to an increase in energy use rather than a decrease, a paradox known as the “Jevons paradox”. Based on this, scholars have thoroughly investigated the issue and have come up with many ideas, including the following aspects: (1) Definition of the carbon rebound effect. Khazzoom first proposed the energy rebound effect and described it as a phenomenon where technological development would not only lead to an increase in energy efficiency, but also to a decline in the prices of energy, thereby offsetting part of the potential energy savings [27]. In addition, an account of energy usage being significantly positively correlated with carbon dioxide release, and the energy rebound effect will also affect carbon emissions, leading to the rebound effect of carbon release [28]. Some scholars define the carbon rebound effect as a part of the decrement of underlying carbon that has not been realized due to the lower energy price and fees caused by the course of energy technology [29]. There are two types of studies on the carbon rebound effect. The first type mainly estimates the carbon rebound effect in specific fields from the microeconomic perspective, and the second focuses on the carbon rebound effect in the whole economic scope at the macroeconomic level [30]. Based on the definition of microeconomics, the majority of research focuses on the effects of energy rebound on carbon rebound in micro subjects, such as enterprises and households. The findings reveal that the former significantly affects the latter and that improving energy efficiency causes carbon emissions to rise [31]. For families, the main cause of the rebound effect is the substitution effect [32]; for enterprises, the main cause of the rebound effect is the price factor [33]. Based on the macro definition, the rebound effect is divided into short, medium, and long periods [34] in which to investigate the impact of carbon emissions coming from the energy rebound effect from different stages of urbanization, local authorities’ decision-making competition, and regional perspectives. The conclusion is that in different urbanization situations, the short period energy rebound effect has a higher impact on carbon emissions than the long, and both of them decrease when the rate of urbanization rises [35]. The impact of local authorities’ decision competition on the rebound effect of regional carbon emissions has significant area heterogeneity and spatial dependence as a result [36]. (2) The research object of the carbon rebound effect. Scholars mainly classified the research subjects into three aspects: national level, regional level, and whole industry level. At the national level, the recursive dynamic CGE model of energy economy and the endogenous theoretical model of rebound effect are constructed to model the rebound impact of increasing a country’s overall energy efficiency [37], enhancing the energy utilization efficiency and only improving the carbon emissions of high-energy-consuming industries. It is deduced that there are short period and long period rebound effects [38]. Finally, the CES production function was employed to explore the rebound impact of carbon release and its decomposition, and the conclusion was drawn that the short-term rebound effect of the carbon dioxide emission was mainly affected by energy efficiency [39], and the long-term rebound effect of carbon emissions takes into account the impact of capital changes [40], and the rebound effect value is larger, and the trend of change is more obvious. At the regional level, due to different geographical locations and industrial clusters, the rebound effect is also different, but the rebound effect of the secondary industry as a whole is the greatest [41]. At the industrial level, the rebound effect is generated by the response of the economy and behavior to the increased energy efficiency. In the course of the industry’s growth, the rebound effect of primary energy is greater than that of secondary energy [42]. Some scholars also explored the impact of rebound effect on the whole macroeconomic level based on the heterogeneity of different industries, and concluded that in the long term, the rebound effect of agriculture is the highest, up to 91%, while that of the household sector is the lowest, at 61% [41]. (3) The influencing factors of a rebound effect. Since energy usage is significantly positively linked with carbon emissions [31], the analysis of the main causes of energy rebound is useful for us to better understand the carbon rebound effect. The influencing elements mainly include: ① Energy price factors. According to the demand elasticity of price, the change in energy price has a certain influence on the energy demand. The increased energy efficiency gives rise to a decrease in the manufacturing fee and price of energy products; thus, increasing the demand for energy and affecting the rebound degree of energy [43]. ② Energy consumption structure. The rebound effect of different types of energy will be influenced by changes in the structure of energy use, where the proportion of clean energy and renewable energy will have an impact on the total rebound effect [44]. ③ Energy subsidies. The comprehensive policy of abolishing fossil fuel subsidies and enhancing new energy subsidies is conducive to reducing the energy resilience effect [45]. ④ Technical efficiency. When the technology is not improved, the absence of a rebound effect does not mean energy saving. When the rebound effect is less than 100%, the improvement of technical efficiency will save energy [46].

Through the above literature assessment, researchers’ work on the decoupling link between economic growth and carbon release is quite mature. In terms of research objects, they are mainly focused on specific countries or sectors [22,47,48,49], and few scholars have conducted decoupling analyses on a certain region. From the standpoint of research content, scholars usually investigate the link between economic growth and carbon release and the elements which impact the decoupling result through phenomenon analysis and framework guidance [11,50]. Few scholars have studied the decoupling link between them from the perspective of a carbon rebound effect. In summary, the contribution of this study is mainly reflected in the following aspects: first, based on the SFA-Malmquist index method to measure the total factor productivity, we quantitatively measure the carbon emission rebound effect based on technological progress in six central provinces; second, we describe the decoupling relationship between economic growth and carbon emissions in six central provinces, and further explores the rebound effect of carbon emissions, which extends the literature and provides a new research idea; third, the decoupling index and rebound effect value of each province in central China are calculated and the differences and commonalities are found through comparative analysis, which is helpful to provide policy suggestions for realizing carbon peak in central China as soon as possible and is a reference for other provinces to formulate relevant policies. However, the model of a rebound effect used in this paper remains in the concept of static analysis and lacks the dynamic analysis of a rebound effect at the macro level. In terms of timing, the long-term and short-term effects of the rebound effect have not been studied. At the same time, changes in social preferences, consumer behavior, production organization and other factors will also lead to changes in carbon emissions, and it is difficult to distinguish whether the increase in carbon emissions is caused by the improvement of technological innovation efficiency or other factors.

3. Materials and Methods

3.1. Calculation of Carbon Dioxide Emissions

Burning fossil energy emits large amounts of carbon dioxide into the atmosphere, and most studies estimate carbon emissions based on fossil fuel use [51]. Following the ideas and methods of most studies, this article uses the calculation method of the IPCC to estimate carbon emissions. The specific calculation formula is:

$$C=\frac{44}{12}\times {{\displaystyle \sum }}_{i}A{D}_i\times NC{V}_i\times E{F}_i\times O_i$$

(1)

where, C is the total amount of carbon dioxide discharged from fossil fuel burning at the provincial level; $i$ represents the fossil energy type; $A{D}_i$, $NC{V}_i$, $E{F}_i$ and $O_i$ represent, respectively, the fossil fuels consumption, average low calorific value, carbon content per unit calorific value, and the rate of carbon oxidation during combustion. Carbon dioxide and carbon have molecular weights of 44 and 12, respectively. The carbon emission coefficient is obtained by multiplying the three terms $NC{V}_i$, $E{F}_i$ and $O_i$, and then the carbon dioxide emission coefficient is 3.67 times the carbon emission coefficient (44/12).

3.2. Calculation of Decoupling Elasticity Coefficient

OECD first proposed the notion of “decoupling” to research the correlation between the economic increase and environmental pollution, and then “decoupling” has become a powerful tool to measure the pressure between the ecological environment and economic development. Tapio further developed the OECD decoupling theory, overcame the OECD decoupling model’s constraints in terms of base period selection, and improved the accuracy of decoupling relation measurement and the objectivity of analysis. In view of this, the decoupling model of economic increase and carbon dioxide emissions is constructed as follows:

$${\epsilon }_{C,G}=\frac{\%\Delta C}{\%\Delta G}=\Delta C\times \frac{G^0}{C^0\times \Delta G}$$

(2)

where, ${\epsilon }_{C,G}$ indicates a decoupling exponent of economic increase and carbon emissions; $\%\Delta C$ and $\%\Delta G$ express the growth rate of carbon emissions and economic development, respectively; $\Delta C$ and $\Delta G$, respectively, reflects the increase in carbon emissions, as well as the economy; $C^0$ and $G^0$, respectively, represents the carbon emission and economy level of the base period. This article takes 2000 as the base period for calculation. The decoupling state classification combined with different decoupling indexes is shown in

Table 1. Category of decoupling state in the Tapio model.

Type	Decoupling State	∆GDP	$\Delta \mathit{C}{\mathit{O}}_2$	${\mathit{\epsilon }}_{\mathit{C},\mathit{G}}$
Decoupling	Strong decoupling	(0, +∞)	(−∞, 0)	(−∞, 0)
	Weak decoupling	(0, +∞)	(0, +∞)	[0, 0.8]
	Recessive decoupling	(−∞, 0)	(−∞, 0)	(1.2, +∞)
Coupling	Expansive coupling	(0, +∞)	(0, +∞)	[0.8, 1.2]
	Recessive coupling	(−∞, 0)	(−∞, 0)	[0.8, 1.2]
Negative decoupling	Expansive negative decoupling	(0, +∞)	(0, +∞)	(1.2, +∞)
	Weak negative decoupling	(−∞, 0)	(−∞, 0)	[0, 0.8]
	Strong negative decoupling	(−∞, 0)	(0, +∞)	(−∞, 0)

:

It can be seen from Table 1, when ΔG > 0, meaning the economy is increasing, the smaller ${\epsilon }_{C,G}$ value represents the better decoupling degree, indicating that the dependence between economic growth and carbon emission is receding. On the contrary, when ΔG < 0, indicating the economic value is decreasing, the smaller ${\epsilon }_{C,G}$ value denotes the worse decoupling degree, which means economic growth depending on carbon emission is enhanced. The strong decoupling, which indicates the economic value is increasing (ΔG > 0) while carbon emission is decreasing (ΔC < 0) is the ideal status for a low-carbon economy, but, the strong negative decoupling, which denotes the economic value is decreasing (ΔG < 0) while carbon emission is increasing (ΔC > 0), goes against the low-carbon economy.

3.3. Carbon Emission Rebound Effect Model Construction

The link between technological advancement and carbon release is mainly reflected in energy usage. Technological advancement can enhance the efficiency of energy use and reduce energy usage in the manufacturing process; thus, reducing carbon emissions. However, technological progress does not just aim to reduce carbon emissions. It also boosts output, uses more energy in the process, and contributes to an increase in carbon emissions. It will offset part of the decrement of carbon emissions, resulting in a carbon emission rebound effect [52].

3.3.1. Contribution Rate of Technological Advancement to Economic Growth

In this paper, we use the SFA-Malmquist productivity index approach to compute the total factor productivity (TFP) in the central area to measure technological progress. This paper estimates the change in TFP for each province as a decision unit.

The TFP increase rate in year t + 1 is:

$$GTFP=\frac{TF{P}_{t+1}-TF{P}_t}{TF{P}_t}=\frac{TF{P}_{t+1}}{TF{P}_t}-1$$

(3)

The economy increase rate in year t + 1 is:

$$GY=\frac{Y_{t+1}-Y_t}{Y_t}\times 100\%$$

(4)

The contribution rate of technological advancement to economic increase in year t + 1 is:

$${\sigma }_{t+1}=\frac{GTFP}{GY}\times 100\%$$

(5)

3.3.2. Decomposition of Carbon Emissions Intensity Based on Logarithmic Mean Divisia Index (LMDI)

Carbon intensity is an important indicator used to measure the efficiency of carbon emissions. There was a negative correlation between the two. When carbon intensity increases, carbon emissions efficiency will decrease; otherwise, carbon emissions efficiency would increase. Two factors primarily influence the change in carbon intensity: one is technological progress and the other is energy structure [53]. Therefore, this article uses the LMDI to resolve carbon emissions intensity, and probe the effect of technological advancement on carbon emissions accurately.

The change of carbon emission intensity can be expressed as:

$$\begin{gathered} \Delta CI=\Delta C{I}_s+\Delta C{I}_T \\ =L\left(W^t,W^0\right)\ln (C{C}^t/C{C}^0)+L\left(W^t,W^0\right)\ln (E{I}^t/E{I}^0) \end{gathered}$$

(6)

where, $\Delta CI$ represents the changes in carbon intensity; $\Delta C{I}_s$ means the carbon intensity changes brought about by the effect of energy structure; $\Delta C{I}_T$ means the change in carbon emission intensity due to technological progress; CC stands for total carbon emission coefficient; EI stands for energy intensity; and $L\left(W^t,W^0\right)$ is weight coefficient, can be expressed in the following form.

$$L\left(W^t,W^0\right)=\frac{C{I}^{t+1}-C{I}^t}{\ln (\frac{C{I}^{t+1}}{C{I}^t})}$$

(7)

In this way, the contribution rate of technological advancement to the change of carbon emission intensity in year t + 1 can be written as:

$${\delta }_{t+1}=\frac{\Delta C{I}_T}{\Delta CI}$$

(8)

3.3.3. Rebound Effect of Carbon Emissions

According to the definition by scholars, the carbon rebound effect can be computed by the ratio of increase of carbon dioxide brought about by technological advancement to the carbon emissions decrement. Suppose that the GDP of an economic unit in year “t” is $Y_t$, and the carbon intensity is $C{I}_t$. Then, the carbon dioxide emission in t is $C_t=Y_t\times C{I}_t$. In year t + 1, carbon emission intensity decreases $C{I}_{t+1}$ due to technological progress, and the carbon emission saving (CS) brought about by technological progress is:

$${\delta }_{t+1}\times Y_t\times \left(C{I}_t-C{I}_{t+1}\right)$$

(9)

Similarly, technological progress can also promote economic growth, so new carbon demand will be generated, and the carbon emission rebound (CR) brought about by it is:

$${\sigma }_{t+1}\times \left(Y_{t+1}-Y_t\right)\times C{I}_{t+1}$$

(10)

Then, the carbon emission rebound effect (RE) brought about by technological progress in t + 1 year is:

$$RE=\frac{{\sigma }_{t+1}\times \left(Y_{t+1}-Y_t\right)\times C{I}_{t+1}}{{\delta }_{t+1}\times Y_t\times \left(C{I}_t-C{I}_{t+1}\right)}$$

(11)

Among them, when RE > 1, the rebound effect is the reverse effect, also known as the tempering effect. On the one hand, improvements in energy efficiency have not curbed consumption; On the other hand, the actual usage is greater than the conservation. That is, the carbon emission rebound is high, reflecting the reaction force of technological progress on carbon emission reduction.

When RE = 1, the rebound effect shows complete rebound. In other words, the carbon emissions reduced by the enhancement of energy efficiency brought about by technological advancement are equal to the rebound of carbon emissions, reflecting the inefficiency of technological advancement on carbon emission reduction.

When 0 < RE < 1, the partial rebound effect is visible. In other words, the carbon emission reduction caused by the increase in energy efficiency brought about by technological advancement is greater than the carbon emission rebound, reflecting that technological advancement is conducive to carbon reduction.

When RE = 0, there is zero rebound effect. In other words, the enhancement of energy efficiency brought about by technological advancement restrains energy consumption to a large extent, which reflects the full effectiveness of technological progress on the decrease of carbon emission.

When RE < 0, there is over storage effect. It says that technological advances can reduce energy usage and carbon emissions, reflecting the current situation of sustainable economic development.

3.4. Data Sources

The article selected the data of central China’s six provinces from 2000 to 2019 as research samples. Considering the availability and accuracy of data, we selected the energy usage of coal, oil, natural gas, and GDP data from the China Statistical Yearbook and China Energy Statistical Yearbook. By using the IPCC approach to calculate carbon dioxide emissions, the real GDP from 2000 to 2019 was calculated based on 1999. This article estimates the decoupling link between economic development and carbon emissions in central China, and the rebound effect of carbon based on technological progress is measured.

4. Results and Analysis

4.1. Analysis of the Time Evolution Trend of Carbon Dioxide and Economic Development in Central China

Through Figure 1, we can see from 2000 to 2019, the GDP of the central region increased year by year. Taking 2006 as the cut-off point, it can be divided into two periods as a whole. From 2000 to 2006, the economic growth of the central region was slow. During this period, the industrial structure in central China was unreasonable, the ratio of the primary industry was high, and the industrialization degree was low. Moreover, the national capital investment was mainly focused on the eastern and western areas, the total investment in the central region was insufficient, and the economic growth rate was slow. From 2006 to 2019, the economy of the central region maintained a steady growth trend. Under the guidance of several opinions on promoting the rise of the central area issued in 2006, the central region actively adjusted government functions, and carried out the work of attracting investment, promoting the increase of industrial investment and the development of industrial enterprises. By province, Henan saw the largest increase in GDP. The economy grew tenfold from 2000 to 2019, while Shanxi saw the smallest increase, only increasing fourfold. The reason is that the Shanxi Province has the advantage of resources and has formed a resource-oriented industrial structure under the condition of resource endowment. However, the resource-based industry structure has a large investment scale, a long investment cycle, and cumbersome government approval procedures, so it is difficult to attract foreign capital, which leads to slow economic development.

Figure 2 visually shows the changing trend of carbon emissions in six central provinces from 2000 to 2019. From the six central provinces as a whole, the total carbon emissions from 2000 to 2019 increased year by year. Carbon emissions in 2019 have nearly tripled compared to 2000. Before 2004, the growth rate of carbon emissions was relatively slow, and then, the growth rate accelerated. This is due to the 2004 “rise of central China” strategy put forward and implemented; the six provinces in the central region depend on their natural endowments and traditional energy-based industries, and strive to promote the development of heavy and chemical industries and urbanization. In this process, the demand for production energy, such as raw coal, coke, and oil, has increased significantly, and carbon dioxide emissions have increased accordingly. However, with the transformation of provincial industrial structure and the government’s emphasis on the construction of ecological civilization, the growth rate of carbon emissions in the six central provinces slowed down significantly. From the perspective of the change in each of the six central provinces, the trend of carbon emission is significantly different and shows a gradient structure. Among them, whether from the total carbon emissions or the carbon emissions growth rate, the Shanxi Province is ahead of the other five provinces. The Shanxi Province has always been based on heavy chemical industry; the economic model is extensive and carbon emissions are high. With 2011 as the cut-off point, carbon emissions in the Henan Province showed a trend of rapid growth from 2000 to 2011, along with the fast pace of industrialization. From 2011 to 2019, carbon emissions began to decline year by year, widening the gap with the Shanxi Province. It shows that the Henan Province has invested great efforts in optimizing the industrial structure and promoting energy conservation and emission reduction in recent years and achieved remarkable results. The carbon emissions of the Hubei, Hunan, and Anhui Provinces are all in the middle level. The Hubei Province, as an important industrial province in China, has been dominated by heavy industry for a long time, so it consumes a lot of energy and causes a large carbon emission. The Anhui Province is a major province of energy consumption in China. The rapid growth of industrial energy consumption from 2000 to 2013 led to a rapid increase in carbon dioxide emissions, and the growth rate slowed down after 2014. Hunan Province’s carbon dioxide output peaked in 2012, and the growth trend has been relatively stable since then. The total amount and growth rate of carbon emission in the Jiangxi Province are at the bottom of the six provinces because Jiangxi Province’s economic development level is low, so its demand for energy is lower than the other five provinces. At the same time, the forest coverage rate of the Jiangxi Province is high, and the rich forest resources make the forest carbon sink high and the carbon dioxide emission low.

4.2. Spatial-Temporal Evolution Analysis of the Decoupling Relationship between Carbon Emissions and Economic Growth in Different Provinces

Formula (2) states that the decoupling index of each province in central China is calculated, and the decoupling link between economic increase and carbon release is analyzed from the perspectives of time characteristics and spatial differences.

4.2.1. Analysis of Time Characteristics

According to the decoupling index and state of the economy and carbon release in the central region (Figure 3), we can see the decoupling elasticity in the central region is in a downwards trend. Overall, from 2000 to 2019, the Anhui, Jiangxi, and Hubei Provinces have been in a condition of decoupling for 85% of the years, while the Shanxi, Henan, and Hunan Provinces have also been in a state of decoupling for more than 50% of the years. The specific state of decoupling varies among provinces, but overall remains a weak decoupling state. From the perspective of different periods, from 2000 to 2006, the economic growth of central provinces showed a high positive correlation with carbon emissions, and the decoupling situation of six provinces was in an unstable state, which expresses the features of expansive negative decoupling and expansive coupling. The representative provinces are Shanxi, Henan and Hunan, which have been in the situation of expansive negative decoupling and expansive coupling for five years. In addition, the Hunan Province reached the highest value of 4.36 in 2005, the Henan Province at 2.84 in 2004, and the Shanxi Province at 1.75 in 2002. In this period, the economic development of central China was highly dependent on fossil fuels, and its economic increase mainly depended on energy consumption, which caused serious environmental pollution and was an extensive economic growth model. From 2006 to 2011, Shanxi, Anhui, Jiangxi, and Henan were in weak decoupling and expansive coupling turn state, and Hubei was in a decoupling situation. This stage depends on the national policy and changes in the way of economic development, and the provincial economy is in a high growth stage. Relatively, the economy’s mode shifts from extensive to intense, and the increment of carbon emissions is lower than the economic increase. The Hunan Province has experienced the evolution process of expansive negative decoupling → strong decoupling → expansive coupling → expansive negative decoupling. Since the “11th Five-Year Plan”, the Hunan Province has taken the industrial industry as the leading industry, which brought rapid economic development. It also resulted in a massive amount of resource usage and low energy utilization efficiency. From 2011 to 2019, there was a weak decoupling condition on the whole, during which the economy developed rapidly, and carbon emissions grew slowly. Only the Shanxi Province showed a condition of expansive negative decoupling in 2015, the increase in carbon release was much higher than the economic increase. In 2015, the contribution rate of the Shanxi coal industry accounted for 56.6% of the province’s economy, which is due to its over-dependence on coal resources and a relatively low ratio of new energy industries, such as hydropower and natural gas. In general, the dependence of economic growth on fossil fuels in central China has experienced a course from strong to weak, and the pattern of economic growth has progressed from extensive to relatively intensive.

4.2.2. Analysis of Spatial Characteristics

Through the decoupling state criterion and decoupling elasticity of economic increase and carbon release in the central area’s computation results, the decoupling situation of them in six provinces from 2000 to 2019 is divided into four periods: 2000–2004, 2005–2009, 2010–2014, and 2015–2019. The regional evolution pattern of decoupling degree of economic increase and carbon dioxide release in central China is intuitively evaluated using GIS spatial analysis technology. As shown in Figure 4, from 2000 to 2004, Anhui and Hubei showed weak decoupling, Shanxi and Jiangxi showed expansive decoupling, and Hunan and Henan observed expansive negative decoupling. Throughout this time, the carbon dioxide release increase rate in the Henan and Hunan Provinces was higher than the economy, and economic enhancement was at the cost of environmental pollution. The agglomeration of energy-intensive and high-polluting industries and extensive resource utilization under the traditional economic development model not only brought economic growth, but also caused the prominent contradiction of environmental pollution. From 2005 to 2009, Shanxi, Anhui, Jiangxi, Henan and Hubei all showed weak decoupling, and their economic and carbon emissions showed a positive growth trend. The decoupling situation of the Hunan Province was expansive coupling and the decoupling elasticity was 1.008. During this period, Hunan Province’s economic development and carbon emissions growth were basically synchronized, and its economic growth was still dependent on resources. From 2010 to 2014, the Hubei Province was in strong decoupling, while the other five provinces showed weak decoupling, but the decoupling index decreased to different degrees compared with that from 2005 to 2009. From 2015 to 2019, Henan showed strong decoupling, while the other five provinces showed weak decoupling. In the decade 2010–2019, it can be seen that economic growth is moving towards a low-carbon direction, but the pressure of emission reduction still exists.

In general, the degree of decoupling among different provinces from 2000 to 2009 was quite different, and the degree of decoupling among different provinces from 2010 to 2019 was similar, which was concentrated in the weak decoupling state. Facing the “dual carbon” target proposed by the state, it is obvious that there is still pressure for carbon reduction in all provinces in the central region. Therefore, it is necessary to find out the reasons why provinces have been in a weak decoupling situation for a long time and speed up the transition to a strong decoupling state.

4.3. Analysis of Rebound Effect of Carbon Emission in Central China

Thoroughly understanding the characteristics of the carbon rebound effect in the central region can help us better analyze the decoupling situation of various provinces in central China from this perspective. Through the notion of the rebound effect, this article calculates the carbon saving amount, carbon rebound amount and carbon emission rebound effect value in central China from 2000 to 2019, according to Formulas (9)–(11).

From the view of changes in total carbon emissions (

Table 2. Carbon rebound and savings of each province in central China from 2000 to 2019 (million tons).

Year	Shanxi		Anhui		Jiangxi		Henan		Hubei		Hunan
	CR	CS	CR	CS	CR	CS	CR	CS	CR	CS	CR	CS
2000	−105	318	−54.2	8.9	−14.5	6.5	−30.4	24.5	−56.8	54.9	−34.0	15.3
2001	−105	−69.3	−53.9	33.5	−21.9	15.1	−106.6	23.1	−49.2	99.3	−71.4	−6.5
2002	−325	−576	−47.6	38.5	−43.8	14.1	−184.7	3.2	−143	15.5	−48.3	1.1
2003	2940	−100	51.7	−16	22.9	−12	1672.9	111.7	1349	−19	530.0	−23.5
2004	186.4	579.6	210	69.7	189.3	−22	66.2	−292	118	6.32	80.0	−68.7
2005	173.2	−46.5	32.4	45.7	30.6	11.2	161.4	−119	70.7	10.6	46.5	−144.9
2006	−110	−128	13.3	44.1	−19.7	14.2	−145.2	35.2	0.36	10.8	−81.4	18.3
2007	−103	1043	−106	25.1	−68.6	20.7	−107.7	97.8	−133	46.5	−61.0	−38.7
2008	−247	1351	−60.1	−12	23.4	54.8	−245.8	239.1	−122	195	−68.6	132.5
2009	−340	639.7	−43.2	34.2	−51.5	36.6	−37.7	221.3	−91	67.4	−70.2	43.7
2010	2517	362.3	411	96.3	322.2	−11	1107.2	103.4	1117	16.1	567.0	34.0
2011	126	−26.8	165	69.2	50.8	12.2	347.5	44.2	103	11.9	9.2	−29.9
2012	−118	509	−40.5	78.1	13.6	54.6	−51.1	452.1	28.7	157	20.7	144.0
2013	−184	650	−121	23.0	−60.7	14.1	−265.6	219.3	−87	288	−53.0	138.4
2014	−196	258	−144	68.4	−116	41.1	−369.9	158.4	−204	73	−176	130.2
2015	0.2	−1123	−178	98.5	45.8	22.1	90.5	242.6	−84	107	131	70.6
2016	260	636	732	98.6	85.2	32.5	848.7	136.9	259	57	60.8	44.4
2017	583	164	141	45.9	254	30.3	315.3	135.4	517	34	336.4	55.3
2018	227	−57	118	41.3	−35	17.6	−84.5	83.8	−36	26.5	−57.3	44.7
2019	−440	163	−176	66.6	−16	24.3	−59.2	159.1	−121	7.65	−105	54.0

), the theoretical carbon rebound of each province in central China during 2000–2002, 2006–2009, 2013–2014, and 2018–2019 was negative, but the actual carbon emissions increased because technical advancement had a detrimental impact on economic growth in these years; there is no technological progress, that is, the technology is ineffective. From 2000 to 2002, the economic growth was mainly driven by the input of more factors, and the actual carbon emissions increased. From 2006 to 2009, the central provinces increased their energy consumption so as to achieve rapid development, as a result of the “Several Opinions on Promoting the Rise of the Central Region” issued in 2006. After the 2008 financial crisis, CNY four trillion of investment and the introduction of ten industrial revitalization plans generated growth in carbon emissions. From 2013 to 2014, although the industrial structure of each province has been adjusted, the secondary industry was still dominated by energy and heavy chemical industry, the input of capital and labor elements was primarily responsible for the economic increase, and technical advancement had a negative impact on economic growth. From 2018 to 2019, during the “13th Five-Year Plan period”, improving ecological and environmental quality was the core goal and task of the country. During this period, energy efficiency improved, but the energy structure with coal as the main fossil energy, population size, the development of secondary industry, and the promotion of urbanization led to the continuous increment of carbon emissions. In 2003 and 2011, the carbon saving amount of each province in central China was negative. This was because the impact of technological advancement on economic growth in central provinces was positive in these years, and economic growth was mainly driven by increasing input of energy and other factors. Meanwhile, the energy structure deteriorated to varying degrees or the improvement degree of energy structure was limited. As a result, the contribution of technological advancement to carbon intensity is negative, carbon intensity increases to some extent, and carbon saving is negative.

By excluding the years in which the impact of technological advancement on economic growth was negative and the carbon emission intensity was not reduced by technological progress, the scatter diagram of the effective rebound effect was obtained, as we can see in Figure 5. The rebound effect occurred in 30% of the years in the central region, mainly concentrated in 2003–2004, 2010–2012, and 2015–2017, and the rebound effect value was mostly greater than one, that is, the “tempering effect” appeared. In 2010 and 2016–2017, all provinces showed a tempering effect, because, in those years, technological progress made carbon emissions efficiency enhance, but the improvement of technological progress and efficiency made industrial enterprises increase their demand for energy in order to obtain more profits. As a consequence, the growth of carbon emissions was greater than that theoretically saved by technological advancement, and the rebound effect was greater than one. In the Shanxi Province in 2004, Anhui Province in 2006, Jiangxi, Hunan, and Hubei Provinces in 2012, and Henan Province in 2015, the carbon saving amount brought by technological advancement was greater than the carbon rebound amount caused by technological advancement, and the value of the rebound effect was between zero and one, manifesting a partial rebound. In some years, such as 2004–2005 in the Henan Province, 2010 in the Jiangxi Province and Hubei Province, and 2011 in the Shanxi Province and Hunan Province, although the impact of technological progress on economic output was positive, carbon emission efficiency did not improve, so there was no rebound effect on the carbon emission. Generally speaking, the connection between technological advancement and the carbon emission rebound effect in central China mainly exists in the following four situations: first, the carbon emission rebound effect occurred in some years, which was a partial rebound, when technological progress effectively reduced carbon emission; second, in the effective years of partial technological progress, there was a “tempering effect”; third, in the effective years of the technology, the carbon emission efficiency did not improve, and there was no carbon emission rebound effect; fourth, the technology was invalid in some years, and there was no rebound effect of carbon emissions.

5. Conclusions and Policy Recommendations

5.1. Conclusions

This article calculates CO₂ emissions in the central region by using the IPCC recommended method, and estimates the decoupling elasticity and decoupling situation of economic increase and carbon emissions in the central region from 2000 to 2019 based on the Tapio decoupling model. Subsequently, we use TFP to calculate the technological advancement of the central area and the contribution rate of economic growth, and the LMDI decomposition model for carbon emissions intensity change into technological advancement impact and energy structure effect. Subsequently, we calculate the rebound effect of carbon emission induced by technological progress in central China The following conclusions were drawn.

• Before 2006, the primary industry in central China accounted for a large proportion, the degree of industrialization was low, and the economy developed slowly. Later, each province began to develop the heavy industry through its own resource endowment, and economic growth accelerated. The Shanxi Province is affected by its coal-based resource industry structure, and economic development has been relatively slow. With economic development, the central region consumes a lot of energy, and the total carbon emission is on the rise, nearly trebling since 2000. Because of the different industrial structures and resource conditions among provinces, carbon emissions vary greatly and show a gradient structure, from high to low, of the Shanxi, Henan, Hubei, Anhui, Hunan, and Jiangxi provinces, in order.
• By summing up all kinds of decoupling index values, we conclude that economic growth in central China has roughly experienced a process from expansive coupling to expansive negative decoupling to weak decoupling from carbon emission, with slight differences among provinces. With the adjustment and upgrading of industrial structure, the proportion of the secondary industry in the Anhui, Hubei and Jiangxi provinces has declined. With the implementation of energy-saving and emission reduction policies, the growth rate of carbon dioxide emissions has been controlled and the decoupling of economic growth from carbon emissions was achieved in 85% of the years. The Henan and Shanxi provinces are large provinces of heavy industry and coal resources, respectively, and their industrial structures cannot achieve large changes in a short period of time, resulting in Henan’s economic development relying on massive resource consumption, while Shanxi’s economic growth is dominated by a rough-and-ready approach, with only 50% of the years in decoupling. The Hunan Province takes industrial industry as the leading industry, with high resource consumption and low energy efficiency, leading to a higher ratio of carbon emissions to economic growth than other provinces, and a poor decoupling situation between economic growth and carbon emissions.
• In nearly half of the years in the sample period, technical advancement contributed positively to the economic growth of central China, signifying that there is a significant positive correlation between technological advancement and economic development. However, it was negative in some years, so there was a situation of technological ineffectiveness in some years. These years of technological inefficiency mainly boosted economic growth by increasing the input of factors of production. For example, from 2000 to 2002, the central region was the center of ineffective technology. In addition, the fundamental driver of the shift in carbon emission intensity was technological advancement, showing an overall downwards trend, but in some years, such as 2003 and 2011, carbon intensity rose because of ineffective technology. According to the results of the rebound effect, most of the years of the rebound effect belong to the “tempering effect”, that is, although technological progress reduces carbon emission intensity, it increases carbon emissions by driving output growth.

5.2. Policy Suggestions

To realize the peak of carbon and carbon neutrality, economic growth must be decoupled from carbon emissions first. Currently, weak decoupling is occurring in the central region, while the economic aggregate of each province has been growing. One of the reasons for the weak decoupling state is the existence of the carbon emission rebound effect. Therefore, we offer the following policy recommendations:

First, the structure of industrial development should be adjusted, and strategic and innovative industries should be actively developed. In the beginning, we need to improve and optimize the industrial structure in an orderly manner and accelerate the development of competitive industries. To be specific, the Hunan Province should actively implement the strategy of “three high and four new”, reduce the ratio of high-energy-consuming industries in the industrial sector, drive the industrialization development with information technology, and take the road of new industrialization. The Henan Province should use innovation to promote industrial structure optimization and upgrading, and strive to build a national advanced manufacturing base. The Shanxi Province has continuously accelerated the restructuring and optimization of industrial structure, and vigorously strengthened scientific and technological innovation. Next, we should actively integrate into the surrounding economic belt and lead industrial development with scientific and technological innovation. For example, the Anhui Province can vigorously implement the eastward development, accelerate the synergetic development with the Yangtze River Delta, and build itself into an influential source of scientific and technological innovation, an emerging industry cluster, and a model area of green development. The Jiangxi Province should accelerate its integration into the pan-Pearl River Delta region, attract industrial transfer from the region, strengthen supply-side structural transformation, execute the innovation-driven development strategy, actively integrate into the national strategy, and promote high-quality development. The Hubei Province should actively integrate into the new scientific and industrial revolution, further lengthen the longboard, continuously narrow the cutting-edge technology gap with eastern regions, and accelerate the application of digital, networking, and intelligent technologies in all fields.

Second, we should formulate low-carbon development policies in light of local conditions and use scientific and technological innovation to lead low-carbon development. The central region is one of the economic centers of China, and its low-carbon economic development plan serves as an example for the rest of the country. We should actively explore economic development models suitable for each province and formulate appropriate policies for low-carbon economic development. (1) Establish and adjust low-carbon development strategies while taking into account the existing regional traditional development models. Coal-rich provinces, such as Shanxi, Anhui, and Jiangxi, should take advantage of the current round of scientific and technical revolutions and industrial transformations, accelerate low-carbon process innovation, and build a green manufacturing system. The Shanxi Province should further promote the pilot comprehensive reform of the energy revolution, play a leading role in promoting green and low-carbon technology innovation and industrial transformation, strengthen cooperation between enterprises, universities, and research institutes, promote the transformation and application of innovative achievements, highly integrate traditional industries with digital energy, and promote the establishment of green industry development ecology. On the one hand, the Anhui Province can carry out “three reform linkages” to the traditional coal industry. At the same time, increase the ratio of clean energy, electricity and wind, for instance. The Jiangxi Province should increase industrial energy-saving technology reform, accelerate the construction of a new, low-carbon energy system, and expedite the implementation of renewable energy replacement action. (2) For provinces with a large proportion of the secondary industry, the target should be concentrated on the shift of energy structure during the implementation of a low-carbon development strategy. While adjusting the energy structure, the Hunan Province can increase subsidies for new clean energy and improve the ratio of clean energy. The Henan Province should focus on enhancing energy saving, carbon decrement, and efficiency increase in traditional industries, sternly restrain the development of projects with “high energy usage and high emissions”, and speed up the cultivation and expansion of low-carbon and efficient industries. The Hubei Province needs to take targeted, scientific, and law-based pollution control measures, comprehensively transform the economy and society into green and low-carbon development, modernize its ecological and environmental governance system and capacity, and take the lead in realizing green development.

Third, the coordinated development of the central region should be promoted, and trans-regional governance of carbon emission reduction should be promoted. The economy is weakly decoupled from carbon emissions in central China, but there is still a gap between the economic aggregate and carbon emissions of provinces. Therefore, regional collaboration and connectivity must be strengthened, promoting pollution prevention and control, strengthening environmental protection, introducing more social and financial resources, saving energy and reducing emissions at a lower cost. We will strengthen international cooperation and basic research, channel technology and capital towards low-carbon development, encourage enterprises to replace old drivers of growth with new ones, force them to close down outdated production capacity, and strengthen the decoupling degree of economic increase from carbon emissions. In terms of strategy and system, on the one hand, cross-regional market-based trading of emission rights of emission indicators under total volume control can be implemented so that “increasing emissions” can truly enter into the cost decision-making of enterprises, reduce the inherent dependence of industrial industries on coal, and fundamentally reduce carbon emissions. On the other hand, consider using clean energy costs deducted on a pre-tax basis, in the policy guidance to the firms to improve the use of clean energy, such as natural gas, solar energy, wind energy, and water, especially in Henan, Shanxi, and Hubei who rely on the large province for energy, and can claim an additional deduction for a higher proportion.

Based on panel data of six central provinces from 2000 to 2019, this paper adopts the Tapio decoupling model and rebound effect model for conducting empirical analysis. The results show that carbon emissions in central China have had a rebound effect in some years, and economic growth and carbon emissions show weak decoupling in the last five years, which provides theoretical support for the formulation of relevant policies to achieve a carbon peak at an early date. However, there are still some shortcomings. First of all, in this study, we only studied the central region and did not analyze other regions. In the future, if conditions permit, we will study other regions in China, such as the Yellow River Basin, the Beijing-Tianjin-Hebei region, and the Pearl River Delta region, and analyze the differences and commonalities of different regions and put forward more targeted suggestions. Secondly, this paper only describes the decoupling of economic growth and carbon emissions and the rebound effect of carbon emissions without an in-depth exploration of its influencing factors. Therefore, further analysis of its influencing factors by using other models should become the direction of future research.