Carbon Pressure and Economic Growth in the Urban Agglomeration in the Middle Reaches of the Yangtze River: A Study on Decoupling Effect and Driving Factors

Abstract

Coordination between regional economic development and carbon pressure is essential for sustainable regional development. However, existing research on carbon pressure and studies on the relationship between economic growth and carbon pressure needs to be more comprehensive. This study analyzes the decoupling impact of economic growth and carbon pressure in different regions of urban agglomeration in the middle reaches of the Yangtze River by revealing the evolution of the geographical and temporal characteristics of carbon pressure from 2000 to 2020. We investigate the drivers of carbon pressure in the middle reaches of the Yangtze River urban agglomeration using the LMDI index decomposition model. The results show that (1) the carbon pressure in the urban agglomeration of the middle reaches of the Yangtze River and its three sub-city agglomerations shows a declining tendency at the beginning and later exhibits an increasing tendency; (2) from 2000 to 2020, the carbon pressure of the majority of cities increased, with Wuhan and Ezhou in the high carbon pressure area and Ji’an, Fuzhou, and Shangrao in the carbon sink surplus area; (3) the rate of decoupling climbs from 45% to 96% over time, then declines to 67%, and reaches 90% by 2020; and (4) the most prominent influence on carbon pressure in the 31 cities is energy consumption, followed by economic expansion. The research in this paper is beneficial for cities to explore solutions to coordinate economic development and carbon pressure despite the constraints of imposed by the two.

1. Introduction

Amongst the several environmental concerns of today, global climate change caused by carbon dioxide—the primary greenhouse gas—has become a major international concern [1]. As a worldwide leader in environmental regulation, China places a high priority on the issue of climate change. China has embraced green development as one of its five new development concepts and is aggressively implementing low-carbon development and environmental protection measures [2]. In 2020, China projected carbon dioxide emissions to peak by 2030 and to be carbon neutral by 2060. To reach the dual carbon objectives, academic research on carbon peaking and carbon neutrality has accelerated [3], and its study orientation includes policy [4,5], industry transformation requirements [6,7], and technical design [8] of carbon neutrality.

2. Literature Review

Carbon pressure is the ratio between the carbon emissions of regional energy consumption and carbon carrying capacity, which directly represents the impact of carbon emissions on the regional ecological environment [9]. In the extant literature, carbon pressure-related terms include “carbon footprint” [10,11], “carbon deficit” [12,13], and others. Among them, the “carbon footprint ecological pressure index” [14] and “carbon footprint breadth” [15] based on “carbon footprint” are most similar to the concept of carbon pressure. Unfortunately, the contemporary academic community measures carbon footprints in various ways [16,17], making it impossible to compare research findings. To clarify the notion of the equilibrium between carbon emissions and carbon carrying capacity, Liang and Xu (2017) [9] proposed the term “carbon pressure”. The measurement of carbon pressure contains two components: the calculation of carbon emissions and carbon carrying capacity. Current research on carbon emission accounting focuses primarily on national and provincial levels [18,19]. Several studies have demonstrated that cities are the primary source of energy consumption and greenhouse gas emissions [20]. However, most extant city-level research measures carbon emissions in municipalities [21], provincial capitals, and select developed cities. The two most popular approaches for measuring carbon emissions are the Independent Police Complaints Commission (IPCC)’s reference method and the Defense Meteorological Satellite Program/Operational Linescan System(DMSP/OLS) nighttime lighting data method [22,23]. The IPCC technique is the simplest, most straightforward, and easiest to comprehend. It has a mature accounting formula and activity data [24], making it the method of choice for many academics. Carbon carrying capacity refers to the carbon sequestration ability of green vegetation. There is no universal standard for calculating carbon-carrying capacity, and the measuring scope consists mainly of the carbon-carrying capacity of forests, grasslands, farmlands, and wetlands [25,26].

Investigating the carbon pressure drivers is vital to further improve emission reduction plans, however, till date there has been no study on the drivers of carbon pressure. On the other hand, many domestic and international academics have conducted a great deal of study on the drivers of carbon footprint and carbon emission, and the methods of carbon footprint and carbon emission driver analysis [27] primarily use the factor decomposition approach and the econometric method. On the basis of the application of Tapio index [28], Kang (2012) [29] utilized the IPAT equation which is a quantitative relational model that represents the impact of human activities on the environment and log-averaged Divisia index decomposition model (LMDI technique) to refine the elements impacting the decoupling relationship between carbon emissions and economic development. Engo (2019) [30] utilized the extended Kaya constant equation and LMDI decomposition technique to measure and explain the effects of population size, energy intensity, fossil fuel substitution, and renewable energy penetration on Cameroon’s carbon emissions. Peters (2007) [31] and others investigated the primary reasons for the growth in CO₂ emissions in China using structural analysis while Xu and Dietzenbacher (2014) [32] analyzed the reasons for the changes in implied carbon emissions in bilateral commerce between nations from 1995 to 2009. In conclusion, carbon pressure investigations have made some progress. However, there are still limitations: (1) Need for more research at the city level. Cities are important regions for energy conservation and emission reduction. However, due to the availability of urban energy consumption data, most existing studies related to carbon pressure are conducted at the national or provincial scale. (2) The period of existing studies is relatively short, making it difficult to comprehensively explore regional carbon pressure patterns and thus develop more reasonable emission reduction plans. (3) The research on carbon pressure still needs to be improved, and there needs to be more research on its driving factors.

The emphasis of this study lies in finding a solution for coordinating economic development and carbon pressure despite the dual constraints imposed by the two. We take the middle reaches of the Yangtze River urban agglomeration as the research region, use the relevant data from 2000 to 2020, and adopt the “top-down” decomposition method to calculate the consumption of each energy type at the city level through the provincial energy balance sheet, and use this as the basis to measure the carbon emissions of each city in the region. This paper synthesizes carbon emissions and carbon carrying capacity to construct a carbon stress index to objectively reflect the carbon stress level of the cities. The Tapio decoupling model is used to study the decoupling of carbon pressure and economic development. Moreover, the LMDI model is used to investigate the drivers of carbon pressure in order to offer a scientific basis for coordinating regional economic development and carbon pressure.

3. Research Area

According to the “Development Plan of the urban agglomeration in the middle reaches of the Yangtze River” approved by the Chinese government in 2015, the urban agglomeration in the middle reaches of the Yangtze River covers thirteen cities in Hubei Province, including Wuhan, Huangshi, Ezhou, Huanggang, Xiaogan, Xianning, Xiantao, Qianjiang, Tianmen, Xiangyang, Yichang, Jingzhou and Jingmen; eight cities in Hunan Province, including Changsha, Zhuzhou, Xiangtan, Yueyang, Yiyang, Changde, Hengyang, and Loudi; and ten cities in Jiangxi Province, including Nanchang, Jiujiang, Jingdezhen, Yingtan, Xinyu, Yichun, Pingxiang, Shangrao, Fuzhou, and Ji’an (Figure 1). Urban agglomeration in the middle reaches of the Yangtze River are a specific ecologically sensitive area in China with a land area of about 317,000 square kilometers. As one of the most densely populated area in the Yangtze River basin with the highest intensity of resource exploitation, it has numerous industrial bases, a firm reliance on resources for economic development, and severe environmental damage issues [33]. Though a mega-city cluster encouraging the development of central China, the urban agglomeration in the middle reaches of the Yangtze River still lacks an adequate inter-provincial consultation and collaboration mechanism, has a low level of integrated development, is affected by inadequate radiation push from the central cities to the surrounding areas, has a comparatively slow growth of secondary cities, and a shaky foundation for pollutant control [34]. Urban agglomeration in the middle reaches of the Yangtze River is at a crucial stage of accelerated industrialization and urbanization. As such, it is of great practical importance for the government to scientifically identify the level of carbon pressure in this region and its driving factors and the decoupling effect with economic development to achieve high-quality regional development.

4. Research Methodology and Data Sources

4.1. Research Methodology and Data Sources

4.1.1. Carbon Pressure Measurement

• Urban carbon emission accounting

The IPCC reference method is used to calculate carbon emissions from energy use, with the following calculation formula:

$$CE={\sum }_i{AD}_i\times {NCV}_i\times {EF}_i\times O_i, i \in [1, 17]$$

(1)

In the above equation, CE represents the carbon emissions from urban fossil energy consumption; ${AD}_i$ represents the consumption of the ith fossil fuel in the city; ${NCV}_i$ represents the calorific value of fossil fuel i; ${EF}_i$ represents the emission factor of fossil fuel i; and $O_i$ represents the oxidation efficiency of fossil fuel i. The consumption of urban sub-variety fossil fuels is not directly available, and this paper refers to the study of Jing Qiaonan et al. (2019) [35], which uses a top-down decomposition method based on provincial energy balance sheets to convert.

$${AD}_{i,j}^c={AD}_{i,j}^p\times a_j$$

(2)

$$a_j=\frac{I_j^c}{I_j^p}$$

(3)

In the above equation, j represents the row in the provincial energy balance sheet in the category of energy consumption; ${AD}_{i,j}^c$ represents the consumption of fossil fuel i in category j of the target city; ${AD}_{i,j}^p$ represents the consumption of fossil fuel i in category j of the province to which the target city belongs; a_j is the allocation coefficient in row j; $I_j^c$ represents the value of the allocation index in row j of the target city; and $I_j^p$ represents the value of the allocation index in row j of the province to which the target city belongs.

2.

Urban carbon carrying capacity measurement

According to Fang, J.Y., et al. [36], construction land and cropland are used as carbon sources, and carbon sinks are mainly calculated for forest land, grassland, water and unused land (

Table 1. Land use types and corresponding carbon sink coefficients.

Land Use Type		Carbon Sink Factor	Reference Sources
Woodland	With woodland	$0.87 \mathrm{t} {\mathrm{h}\mathrm{m}}^{-2}{\mathrm{a}}^{-1}$	Fang et al. [38], Tang et al. [39]
	Shrubland	$0.23 \mathrm{t} {\mathrm{h}\mathrm{m}}^{-2}{\mathrm{a}}^{-1}$
	Open woodland	$0.58 \mathrm{t} {\mathrm{h}\mathrm{m}}^{-2}{\mathrm{a}}^{-1}$
	Other woodland	$0.2327 \mathrm{t} {\mathrm{h}\mathrm{m}}^{-2}{\mathrm{a}}^{-1}$
Grassland	High cover grassland	$0.138 \mathrm{t} {\mathrm{h}\mathrm{m}}^{-2}{\mathrm{a}}^{-1}$	Piao et al. [40], Fang et al. [36]
	Medium cover grassland	$0.046 \mathrm{t} {\mathrm{h}\mathrm{m}}^{-2}{\mathrm{a}}^{-1}$
	Low-cover grassland	$0.021 \mathrm{t} {\mathrm{h}\mathrm{m}}^{-2}{\mathrm{a}}^{-1}$
Water area	River and canal	$0.671 \mathrm{t} {\mathrm{h}\mathrm{m}}^{-2}{\mathrm{a}}^{-1}$	Kong et al. [41]
	Lakes	$0.303 \mathrm{t} {\mathrm{h}\mathrm{m}}^{-2}{\mathrm{a}}^{-1}$
	Reservoir ponds	$0.303 \mathrm{t} {\mathrm{h}\mathrm{m}}^{-2}{\mathrm{a}}^{-1}$
	Mudflats	$0.567 \mathrm{t} {\mathrm{h}\mathrm{m}}^{-2}{\mathrm{a}}^{-1}$
	Beachland	$0.567 \mathrm{t} {\mathrm{h}\mathrm{m}}^{-2}{\mathrm{a}}^{-1}$
Unused land	Unused land	$0.0005 \mathrm{t} {\mathrm{h}\mathrm{m}}^{-2}{\mathrm{a}}^{-1}$	Li et al. [42]

). The carbon sink estimation model can be expressed as [37].

$$cc=\sum S_j={\sum }_{j=1}^n{B}_j{\gamma }_j$$

(4)

In the above equation, CC is the carbon carrying capacity; $S_j$ is the carbon sink generated by the jth land use type; $B_j$ is the area of (forest land, grassland, water, and unused land) of the jth land use type, and ${\gamma }_j$ is the carbon sink coefficient of the jth land use type.

Based on the calculated carbon emissions and carbon carrying capacity of each city, the carbon pressure index (CBI) is constructed.

$$\mathit{CBI}=\frac{CE}{CC}$$

(5)

If the carbon pressure index CBI > 1, the city’s carbon emissions are greater than its carbon carrying capacity, and the city is in a state of carbon pressure overload; if the carbon pressure index CBI < 1, the city’s carbon emissions are within the acceptable range of its carbon absorption capacity, and the urban ecosystem has a carbon surplus; and if the carbon pressure index CBI = 1, the city’s carbon emissions are equal to its carbon carrying capacity.

4.1.2. Tapio Decoupling Analysis

“Decoupling” relates to economic expansion with reduced environmental impact or resource usage. To completely characterize the volatility of the decoupling state of dynamic data and the objective accuracy of decoupling prediction, incremental data are added to the Tapio index model in order to develop a decoupling model defining the relationship between carbon emissions and GDP growth.

$$e_n\left(CBI,GDP\right)= (\frac{\Delta CBI}{{CBI}_{n-1}})/(\frac{\Delta GDP}{{GDP}_{n-1}})$$

(6)

In the above equation, $e_n(CBI,GDP)$ is the decoupling elasticity coefficient between carbon pressure and GDP, ΔCBI is the amount of change in carbon pressure, ΔGDP is the amount of change in GDP, $\frac{\Delta CBI}{{CBI}_{n-1}}$ is the rate of change in carbon emissions in year n relative to the previous year, and $\frac{\Delta GDP}{{GDP}_{n-1}}$ is the rate of change in GDP in year n relative to the previous year. The decoupling state can be classified into 8 types according to the values of ΔCBI, ΔGDP, $e_n(CBI,GDP)$. The specific classifications, their characteristics and significance are shown in

Table 2. Decoupling status division.

Decoupling Type	Decoupling Status	$\frac{\Delta \mathit{C}\mathit{B}\mathit{I}}{{\mathit{C}\mathit{B}\mathit{I}}_{\mathit{n}-1}}$	$\frac{\Delta \mathit{G}\mathit{D}\mathit{P}}{{\mathit{G}\mathit{D}\mathit{P}}_{\mathit{n}-1}}$	e
Connections	Expansion Connection	+	+	[0.8, 1.2)
	Recession Connection	−	−	[0.8, 1.2)
Decoupling	Strong decoupling	−	+	(−∞, 0)
	Weak decoupling	+	+	[0, 0.8)
Negative decoupling	Recession decoupling	−	−	[1.2, +∞)
	Strong negative decoupling	+	−	(−∞, 0)
	Weak negative decoupling	−	+	[0, 0.8)
	Expansion negative decoupling	+	+	[1.2, +∞)

.

4.1.3. LMDI Index Decomposition Model

The decoupling model can effectively measure the correlation between the economy and carbon pressure, but it cannot account for the mechanism of the influence of carbon pressure. In this paper, to further explore the drivers of carbon pressure changes in the middle reaches of Yangtze River urban agglomeration, factor decomposition of carbon emission changes in each period is needed. Hence, this paper uses the LMDI factor decomposition model to decompose and analyze the factors influencing carbon pressure of the middle reaches of Yangtze River urban agglomeration from four aspects: energy structure, energy consumption, economic development, and population size [43], where energy structure is expressed as carbon pressure divided by total energy consumption, energy consumption is expressed as total energy consumption divided by GDP, economic development is expressed as GDP per capita, and population size is expressed as total population. The model is constructed as:

$$CBI={\sum }_i\frac{{CBI}_i}{{ENERGY}_i}\times \frac{{ENERGY}_i}{{GDP}_i}\times \frac{{GDP}_i}{{POP}_i}\times {POP}_i={\sum }_i({CBI}_i\times E_i\times G_i\times P_i)$$

(7)

In the above equation, CBI, POP, GDP, and ENERGY represent carbon pressure, total population, GDP, and total energy consumption, respectively, and the change in carbon pressure from period “0” to period “t” is decomposed by applying the product decomposition and sum decomposition. The equation is as follows:

$${CBI}_t-{CBI}_o=\Delta c+\Delta e+\Delta g+\Delta p$$

(8)

The model can be decomposed as follows:

$$\begin{gathered} \Delta c={\sum }_i\frac{{CBI}_{i,t}-{CBI}_{i,o}}{\ln {CBI}_{i,t}-\ln {CBI}_{i,o}}·\ln \frac{c_{i,t}}{c_{i,o}} \\ \Delta e={\sum }_i\frac{{CBI}_{i,t}-{CBI}_{i,o}}{\ln {CBI}_{i,t}-\ln {CBI}_{i,o}}·\ln \frac{e_{i,t}}{e_{i,o}} \\ \Delta g={\sum }_i\frac{{CBI}_{i,t}-{CBI}_{i,o}}{\ln {CBI}_{i,t}-\ln {CBI}_{i,o}}·\ln \frac{g_{i,t}}{g_{i,o}} \\ \Delta p={\sum }_i\frac{{CBI}_{i,t}-{CBI}_{i,o}}{\ln {CBI}_{i,t}-\ln {CBI}_{i,o}}·\ln \frac{p_{i,t}}{p_{i,o}} \end{gathered}$$

(9)

The equations for calculating the contribution of different factors are:

$$m_1=\frac{\Delta c}{\Delta CBI}\times 100\%, m_2=\frac{\Delta e}{\Delta CBI} \times 100\%, m_3=\frac{\Delta g}{\Delta CBI} \times 100\%, m_4=\frac{\Delta p}{\Delta CBI} \times 100\%$$

(10)

4.2. Data Sources

Provincial energy balance sheet data were obtained from the China Energy Statistical Yearbook (2000–2020). Land use data were obtained from the Resource and Environment Science Data Center of the Chinese Academy of Sciences (https://www.resdc.cn/ (accessed on 15 January 2023)), with five periods of Landsat remote sensing images in 2000, 2005, 2010, 2015, and 2020 as the primary data source, with an accuracy of 30 m $\times$ 30 m. These data were obtained by manual visual interpretation. GDP, total population, urban population, rural population, industrial output value, agriculture, forestry, animal husbandry and fishery output value, construction industry output value, service industry output value, transportation and postal service, wholesale, retail, accommodation, and catering industry data were obtained from the China Urban Statistical Yearbook 2001–2021. Data from the statistical yearbooks of the Hubei, Hunan and Jiangxi provinces were also included in the study.

5. Analysis of Results

5.1. Spatial and Temporal Characteristics of Carbon Pressure of the Urban Agglomeration in the Middle Reaches of the Yangtze River

5.1.1. Analysis of Carbon Pressure Temporal Evolution of the Urban Agglomeration in the Middle Reaches of the Yangtze River

The carbon pressure value of the urban agglomeration in the middle reaches of the Yangtze River increased from 0.991 to 2.139 between 2000 and 2020 (Figure 2). The amount of carbon pressure had been growing from 2000 to 2015 but was slightly lower in 2020 than in 2015, while the carrying capacity underwent a nominal changed and was somewhat lower in 2020. The change in carbon pressure was dominated by the trend of carbon emission, which dropped in the latter peri-periods. From 2000 to 2005, the carbon pressure exhibited a rapid growth trend, with an average annual growth rate of 12%. The rapid economic development and the rapid growth of energy consumption in this region during this period led to the increase in carbon emissions and the subsequent amplification of carbon pressure. The growth rate of carbon pressure decreased from 2005 to 2015, and there was also a decreasing trend from 2015 to 2020. This may be because, since 2012, China’s economic development has entered a new normal, with a gradual shift from high-speed growth to high-quality intensive development, nominal increase in energy consumption in urban agglomerations, and a decrease in carbon pressure. At the same time, carbon emissions were reduced due to the impact of the New Crown Pneumonia outbreak, resulting in a significant reduction in carbon pressure in 2020.

From the standpoint of three sub-city groups, the carbon pressure in different places varies significantly. The size of the carbon pressure demonstrates the following: Wuhan urban agglomeration > urban agglomeration around Changsha-Zhuzhou-Xiangtan > urban agglomeration encircling Poyang Lake. The carbon pressure of both the urban agglomeration encircling Poyang Lake and the urban agglomeration around Changsha-Zhuzhou-Xiangtan increased between 2000 and 2015. However, it decreased significantly in 2020 compared to 2015. The Wuhan urban agglomeration shows an inverted “U” shape, and the carbon pressure displays a decreasing trend. However, the overall carbon pressure is higher than the other two urban agglomerations. In 2020, the carbon pressure of Wuhan urban agglomeration, the urban agglomeration encircling Poyang Lake and the urban agglomeration around Changsha-Zhuzhou-Xiangtan was 3.14, 2.18 and 1.49, respectively, with an increase of 35%, 281% and 205%, respectively compared with 2000. Wuhan urban agglomeration had the highest carbon pressure and the lowest average annual growth rate. In contrast, urban agglomeration encircling Poyang Lake and urban agglomeration around Changsha-Zhuzhou-Xiangtan had a lower carbon pressure level; however, the annual increase rate was greater. This indicates that they will face a greater carbon increment pressure in the future.

5.1.2. Analysis of the Spatial Evolution of Carbon Pressure of Urban Agglomeration in the Middle Reaches of the Yangtze River

Significant regional carbon pressure differences existed between urban agglomeration in the middle reaches of the Yangtze River between 2000 and 2020. This paper, using the natural breakpoint method, classified the 31 cities in the region into four categories based on the magnitude of carbon pressure: high carbon pressure zone, medium carbon pressure zone, low carbon pressure zone, and carbon sink surplus zone (Figure 3). During 2000–2020, the carbon pressure in the low carbon pressure zone and the carbon sink surplus zone increased the most. Among the seventeen cities where the leap occurred, eight leaped from the carbon sink surplus zone to the low carbon pressure zone, five cities leaped from the carbon sink surplus zone to the medium carbon pressure zone, and one city jumped directly from the medium carbon pressure zone to the high carbon pressure zone. Generally, the carbon pressure level in regions with high carbon pressure is declining. In contrast, the carbon pressure level in the areas with carbon sink surplus and low carbon pressure is rising.

In 2000, the high carbon pressure area was mainly Wuhan and Ezhou, with carbon pressure values up to 10 or more, in a highly high-pressure state of regional carbon ecology. The medium carbon pressure zone includes seven cities, such as Jingzhou and Nanchang, whose carbon pressure value is around 5 in general, also in a severe carbon pressure overload state. The low carbon pressure zone is a region with a carbon pressure value greater than 1, mainly including four cities, such as Xiangyang, Jingmen, and Huanggang (1 < CBI < 2). The carbon surplus zone includes 18 cities, such as Yichang, Changde, and Changsha, all with carbon pressure values less than 1. Nanchang jumped from the middle carbon pressure zone to the high carbon pressure zone between 2000 and 2005. Huanggang City jumped from a low carbon pressure zone to a carbon sink surplus zone. Changsha, Yueyang, and five other cities jumped from a carbon sink surplus area to a medium carbon pressure area. Hengyang, Zhuzhou, and six other cities jumped from the carbon sink surplus area to the low carbon pressure area. Between 2005 and 2010, Nanchang jumped from a high carbon pressure area to a medium carbon pressure area. Three cities, including Huanggang and Jingdezhen, moved from a carbon surplus zone to a low carbon pressure zone. Xiangyang jumped from a low carbon pressure zone to a medium carbon pressure zone. Between 2010 and 2015, Nanchang jumped from a medium carbon pressure zone to a high carbon pressure zone. Three cities, including Jiujiang and Yichun, jumped from the carbon sink surplus area to the low carbon pressure area. Xiantao leaps from a medium carbon pressure zone to a low carbon pressure zone. Two cities, Xiantao and Jiujiang, leaped from a low carbon pressure zone to a medium carbon pressure zone between 2015 and 2020. Xianning leapt from a medium carbon pressure zone to a low carbon pressure zone.

There are three main reasons for the changes of carbon pressure zones in cities. The first reason is high emissions and poor carrying capacity. With the annual increase in carbon pressure, the city’s carbon sink remains low and stable, leading to a further rise in carbon pressure. Second reason is low emissions and insufficient carrying capacity. Although the carbon pressure in Xiantao and Qianjiang is moderate compared to other cities in the urban agglomeration, their carbon sequestration resource endowment is limited, and their carbon carrying capacity is inferior, resulting in a high carbon pressure level. The third reason is low emission and high carrying capacity. For instance, cities such as Fuzhou have substantial carbon sequestration resources, allowing them to remain in the carbon sink surplus area or quiet carbon pressure area for an extended period.

5.2. Decoupling Effect of Carbon Pressure of Urban Agglomeration in the Middle Reaches of the Yangtze River

The decoupling elasticity coefficient, CO₂ and GDP values of each city in urban agglomeration in the middle reaches of the Yangtze River during 2000–2020 are calculated, and the decoupling status of each urban agglomeration is evaluated based on Table 2; the results are presented in

Table 3. Decoupling state of carbon pressure and economic growth in the urban agglomeration in the middle reaches of the Yangtze River.

Year	Urban Ag-Glomeration	Carbon Pressure Change Rate	GDP Change Rate	Decoupling Index	Decoupling Type
2000–2005	Urban agglomeration encircling Poyang Lake	0.6274	1.1750	0.5339	Weak decoupling
	Wuhan urban agglomeration	0.2813	0.5307	0.5301	Weak decoupling
	Urban agglomeration around Changsha-Zhuzhou-Xiangtan	2.3847	0.8744	2.7272	Expansion negative decoupling
	Urban agglomeration in the middle reaches of the Yangtze River	0.7352	0.7701	0.9547	Expansion Connection
2005–2010	Urban agglomeration encircling Poyang Lake	0.1459	1.3855	0.1053	Weak decoupling
	Wuhan urban agglomeration	0.1738	1.4268	0.1218	Weak decoupling
	Urban agglomeration around Changsha-Zhuzhou-Xiangtan	0.0157	1.5752	0.0099	Weak decoupling
	Urban agglomeration in the middle reaches of the Yangtze River	0.1120	1.4675	0.0763	Weak decoupling
2010–2015	Urban agglomeration encircling Poyang Lake	0.7336	0.7900	0.9285	Expansion Connection
	Wuhan urban agglomeration	−0.0007	0.9789	−0.0007	Strong decoupling
	Urban agglomeration around Changsha-Zhuzhou-Xiangtan	0.2225	0.8625	0.2579	Weak decoupling
	Urban agglomeration in the middle reaches of the Yangtze River	0.2187	0.8926	0.2450	Weak decoupling
2015–2020	Urban agglomeration encircling Poyang Lake	−0.0447	0.4817	−0.0928	Strong decoupling
	Wuhan urban agglomeration	−0.0997	0.3980	−0.2506	Strong decoupling
	Urban agglomeration around Changsha-Zhuzhou-Xiangtan	−0.0931	0.3845	−0.2421	Strong decoupling
	Urban agglomeration in the middle reaches of the Yangtze River	−0.0829	0.4120	−0.2012	Strong decoupling

. Between 2000 and 2020, the decoupling rate of urban agglomeration in the middle reaches of the Yangtze River is reasonably excellent; only from 2000 to 2005 is the expansion connection significant. Wuhan urban agglomeration has the best decoupling status, either solid or weak decoupling. (The amount of carbon pressure decreases with economic growth. The amount of carbon pressure increases with economic growth, but the growth rate of carbon pressure is lower than the economic growth rate). The decoupling states of urban agglomeration encircling Poyang Lake and urban agglomeration around Changsha-Zhuzhou-Xiangtan are also relatively ideal. The only undesirable state (economic growth and increased amount of carbon pressure) for the urban cluster around Poyang Lake is the expansion connection from 2010 to 2015. Urban agglomeration encircling Poyang Lake has a negative decoupling state of growth from 2000 to 2005.

Regarding the temporal characteristics of the urban agglomeration in the middle reaches of the Yangtze River, the state of decoupling between 2000 and 2020 is relatively excellent. From 2000 to 2005, the decoupling rate of urban agglomeration in the middle reaches of the Yangtze River was only 45%, with the decoupling rate of the urban agglomeration encircling Poyang Lake being 40%, the decoupling rate of the Wuhan urban agglomeration being 76%, and all cities in the urban agglomeration surrounding Changsha-Zhuzhou-Xiangtan being in an undesirable state. The decoupling rate of urban agglomeration in the middle reaches of the Yangtze River from 2005 to 2010 was 96%, with 100% decoupling rates for urban agglomeration encircling Poyang Lake and urban agglomeration around Changsha-Zhuzhou-Xiangtan and 92% for Wuhan urban agglomeration. From 2010 to 2015, the decoupling rate of intermediate urban agglomeration in the middle reaches of the Yangtze River was 67%, the decoupling rate of Wuhan urban agglomeration was 100%, and the decoupling rate of urban agglomeration encircling Poyang Lake was 0%. From 2015 to 2020, the decoupling rate of urban agglomeration in the middle reaches of the Yangtze River was 90%, the decoupling rate of urban agglomeration surrounding Changsha-Zhuzhou-Xiangtan and Poyang Lake was 100%, and the decoupling rate of Wuhan urban agglomeration was 77%.

From the geographical evolution of each city, there are numerous unsatisfactory decoupling states in these years in six cities: Nanchang, Pingxiang, Jiujiang, Xinyu, Yingtan, and Fuzhou, except for growth connection, which is primarily a growth-negative decoupling state (economic growth and carbon pressure amount increases, and carbon pressure growth rate is higher than economic growth rate) (Figure 4). Fourteen cities have only one unfavorable period, and the decoupling condition is generally favorable. Ten cities, including Yichang, Xiangyang, Jingmen, and Changsha, are in perfect status except for 2000–2005. Besides 2005–2010, where decoupling condition in Huanggang was unsatisfactory, the other years were good. In 2015–2020, Xiantao, Qianjiang, and Xiantao were the only three cities having an undesirable status. Overall, the carbon pressure of urban agglomeration in the middle reaches of the Yangtze River is relatively stable, with a significant decrease in carbon pressure and a more desirable decoupling state. The results indicate that this urban agglomeration has made substantial strides in promoting economic and social green development.

5.3. Carbon Pressure Drivers and Decomposition for Urban Agglomeration in the Middle Reaches of the Yangtze River

As shown in Figure 5, the overall carbon pressure in the middle reaches of the Yangtze River urban agglomeration in 2020 is 1.147 more than that in 2000, and the basin-wide carbon pressure change amount became negative after 2015. The trend of carbon pressure change in the urban agglomeration surrounding Changsha-Zhuzhou-Xiangtan is consistent with the middle reaches of the Yangtze River city group. In contrast, the carbon pressure change in the urban agglomeration surrounding Poyang Lake is sometimes positive and sometimes hostile, with no apparent pattern. The carbon pressure in the Wuhan urban agglomeration has decreased over time. Even though the amount of change in each region’s carbon emissions is inconsistent, the difference in carbon pressure has been negative in recent years.

The LMDI factor decomposition model was used to decompose the factors influencing energy and carbon pressure in urban agglomeration in the middle reaches of the Yangtze River from 2000 to 2020. The contribution rates of four factors, namely energy structure, energy consumption, economic growth, and population size, were measured, and the results are presented in Figure 5. The absolute value of the contribution rate of the four factors is as follows: economic growth > energy consumption > energy structure > population size. The sum of the contribution rate of the four factors from 2000 to 2020 is more significant than zero, indicating that carbon pressure is increasing. Additionally, Economic growth is the primary driver of carbon pressure increase, and the energy consumption is the primary driver of carbon pressure decline from 2000–2020. With the publication of the Development Plan for the Middle spans of urban agglomeration in the middle reaches of the Yangtze River after 2015, the driving influence of energy consumption is also gradually diminished as the Yangtze River Economic Belt prioritizes the effect of enormous protection.

In the specific analysis of each city group, the order of absolute value of the factor contribution rate for urban agglomeration encircling Poyang Lake is economic growth > energy consumption > energy structure > population scale. In contrast, the total value of factor contribution rate for Wuhan urban agglomeration and urban agglomeration around Changsha-Zhuzhou-Xiangtan is energy consumption > economic growth > energy structure > population scale. The impact of the population size factor is relatively small. In two time periods, 2005–2010 and 2015–2020, the total contribution rate of the four elements in the urban agglomeration encircling Poyang Lake is less than zero, promoting the decrease in carbon pressure. Among these, energy consumption accounts for a relatively high percentage, which is 77%. The 2000–2005 and 2010–2015 intervals are more extensive than zero, promoting the growth of carbon pressure. Economic development represents a more significant proportion in these two periods, with the absolute maximum amount reaching 77%. From 2000 to 2015, the sum of the contributions of the four factors in Wuhan urban agglomeration is negative. However, in 2015–2020, the sum of the assistance of the factors was positive, and carbon pressure rose. During 2000–2020, energy consumption has the most excellent contribution rate with the highest absolute value, 83%. Before 2005, the sum of the contribution rates of the four factors in the urban agglomeration surrounding Changsha-Zhuzhou-Xiangtan was more significant than 0, and all factors contributed positively, which promoted the increase in carbon pressure. After 2005, the sum of contribution rates is less than 0, and all factors contribute negatively, which supports the decrease in carbon pressure. From 2000 to 2020, energy consumption contributes the most to carbon pressure, with a maximum absolute contribution rate of 66%.

As shown in Figure 6, from 2000 to 2020, the energy consumption and economic development of the urban agglomeration in the middle reaches of the Yangtze River accounted for156% and 215% of the total effect, respectively. These two are the main factors driving the increase in carbon pressure in the urban agglomeration in the middle reaches of the Yangtze River. Energy consumption and economic development are also the main driving factors for the increase in carbon pressure in the urban agglomeration around Poyang Lake and the urban agglomeration around Chang-Zhu-Tan. The energy consumption and economic development of the urban agglomeration around Poyang Lake accounted for 104% and 176% of the total effect, respectively, and the two factors of the urban agglomeration around Changsha-Zhuzhou-Xiangtan accounted for 143% and 116% of the actual impact, respectively. The energy consumption of Wuhan urban agglomeration accounts for 301% of the full impact, which is the main factor driving the reduction of carbon pressure. According to the calculation results, each urban agglomeration should try to reduce the driving effect with a high contribution rate, transform the product with a low contribution rate into the effect driving carbon emission reduction as much as possible, and promote the coordinated development of carbon pressure and economy.

6. Conclusions and Suggestions

6.1. Research Conclusions

The objective of this research is to reveal whether the economic development of urban agglomeration in the middle reaches of the Yangtze River and carbon pressure change simultaneously. This paper measures the carbon pressure of 31 cities in the region from 2000 to 2020, analyzes them from the perspective of the region as a whole and from the perspective of each city, and determines the decoupling status of each city for the same time period by means of the decoupling model. Again for the same time period, the LMDI factor decomposition model is used to deconstruct the factors affecting carbon pressure of the 31 cities. The following conclusions are derived.

(1) The carbon emissions of the urban agglomeration in the middle reaches of the Yangtze River showed an overall increasing trend from 2000 to 2015, but it decreased from 2015 to 2020. The amount of carbon sink is relatively stable, with only a little change. The carbon pressure of three urban agglomeration showed an increasing trend at first and then exhibited a decreasing trend. Among them, the carbon pressure of the urban agglomeration around Poyang Lake and around Changsha-Zhuzhou-Xiangtan increased from 2000 to 2015. It is slightly lower in 2020 than in 2015. However, Wuhan urban agglomeration presents an inverted “U” shape, implying that its carbon pressure has decreased. The overall carbon pressure of the Wuhan urban agglomeration is higher than that of the other two urban agglomeration.

(2) The carbon pressure of most cities is on the rise, particularly in Wuhan and Ezhou as their carbon pressure value is 10 or more and thus are in the highly high-pressure state of regional carbon ecology. In contrast, Ji’an, Fuzhou, and Shangrao are in the carbon-sink surplus area. There are three essential characteristics in evaluating the causes of the changes in urban pressure zones: The first is high emissions and low carrying capacity; the second is low emissions and very low carrying capacity and the third is low emission and high carrying capacity.

(3) The decoupling state of economic development and carbon pressure is relatively ideal. Wuhan urban agglomeration have the ideal decoupling state, followed by urban agglomeration encircling Poyang Lake and Changsha-Zhuzhou-Xiangtan. The rate of decoupling in the entire region climbs from 45% to 96% over time, then declines to 67%, and reaches 90% by 2020.

(4) In 2010–2020, the main effect of carbon pressure on the 31 cities was energy consumption, followed by economic growth, which accounted for 156% and 215% of the overall effect, respectively. The contribution of factors such as energy structure and population size to carbon pressure is negligible, and the change in carbon pressure caused by energy structure and energy intensity is negative. However, the effects of economic growth and population size are positive.

6.2. Policy Suggestion

This research aids local governments in understanding the causes of carbon pressure and in discerning the relationship between carbon pressure and economic growth. The findings of the research may allow governments to offer a scientific foundation for the local formulation of diversified carbon emission reduction plans and they can more effectively accomplish the coordinated development of carbon pressure and economic growth. Based on the aforementioned findings, the government must assist and develop carbon reduction programs to mitigate considerable carbon pressure inequalities. First, in order to promote the flow of talent and advanced technologies and to alleviate the spatially divergent patterns of carbon pressure in cities caused by population and technology differences, the government must provide more financial and policy support to relatively underdeveloped cities and formulate carbon emission reduction policies based on local circumstances. Second, because carbon pressure is geographically correlated, the carbon pressure of cities is not only significantly correlated with their carbon pressure level but is also affected by the carbon pressure of surrounding cities. Hence, when establishing carbon reduction measures, governments must examine the economic, social, and environmental aspects of their own and surrounding cities.

Present regional disparities in carbon pressure in the middle reaches of the Yangtze River urban agglomeration are primarily attributable to energy consumption and economic expansion. Energy consumption is the most influential in reducing overall carbon pressure. The economic intensity impact is the most significant factor contributing to the rise in carbon pressure. The government must take certain steps to tackle the increasing carbon pressure. First, the government should change the industrial structure to lower the secondary industry’s energy consumption in the middle reaches of the Yangtze River urban agglomeration. Second, the government can reduce its use of traditional fossil fuels and increase investment in scientific and technological research and development to improve energy efficiency. Third, the government can lessen the reliance on high-carbon industries for economic growth by fostering the fast development of the tertiary industry and emphasizing the impact of high-tech industries on economic development.

At the same time, the government can also strengthen the protection and restoration of the ecosystem in the middle reaches of the Yangtze River urban agglomeration to improve the carbon absorption capacity of the urban agglomeration, thus effectively reducing carbon pressure. Local governments can improve the carbon adsorption capacity of regional ecological nature by adjusting the forest planting structure and increasing the forest stock. Each urban cluster should protect grasslands and wetlands, expand woodlands, and develop unused land to increase carbon storage and reduce emissions. To encourage these practices, the government should also vigorously promote the development of carbon capture, CCS, and utilization and sequestration technologies to provide technical support for reducing carbon pressure.

6.3. Limitation

(1) This paper only discusses the decoupling relationship between carbon pressure and economic development and does not discuss the relationship between carbon pressure and natural factors, social factors, and so on. Future research can comprehensively consider the decoupling relationship between natural and socio-economic factors and carbon pressure. (2) In the analysis of drivers, although this paper analyzes the role of four factors (energy structure, energy consumption, economic growth, and population size) on carbon pressure, it lacks the study of the influence of different natural, economic, and social conditions on carbon pressure. Issues such as how to develop more detailed carbon reduction strategies based on the regional characteristics of the middle reaches of the Yangtze River urban agglomeration have not been explored due to the slow research progress. (3) Due to the limited availability of data and the length of the paper, the differential distribution of the decomposition of carbon pressure drivers at finer spatial scales within the urban agglomerations and their dynamic evolution at different times require more regional practice to test and improve.