Evaluation of Regional Carbon Emission Reduction Capacity and Complex Collaborative Development: An Empirical Study of the Yangtze River Delta Region

Abstract

Rapid economic development has exacerbated environmental degradation, particularly because of carbon dioxide emissions. To address these issues, China has proposed economic transformation from high-speed to high-quality development to achieve carbon peak and neutrality. Regional collaborative carbon emission reduction is critical for sustainability. Therefore, measuring regional carbon emission reduction capacity and the collaborative development level is imperative. This study employed provincial- and city-level data (2014–2023) from the Yangtze River Delta to assess regional collaborative carbon emission reduction capacity. Evaluation model of carbon emission reduction capacity was constructed based on five perspectives: economic development, carbon emission, carbon transfer, carbon sink, and industrial development. The entropy weighting method was employed to assign index weights, which was then integrated with a composite system synergy degree model. The subsystem order parameters and the composite system’s order degree were utilized to assess carbon emission reduction and collaborative trends. Results revealed that (1) overall carbon emission reduction capacity in the Yangtze River Delta constantly improved; (2) provincial economic development strengthened while carbon emissions declined; (3) carbon transfer fluctuations decreased; (4) technology and carbon sinks improved; (5) industrial development fluctuated or declined; and (6) interregional carbon emission reduction cooperation remained stable and improved. This research offers a theoretical and scientific reference for formulating low-carbon development strategies in similar regions.

1. Introduction

Decades of rapid global economic development have increased environmental degradation, much of which is caused by carbon dioxide emissions [1,2,3]. Global CO₂ emissions from fossil fuels increased from 2.26 billion tons in 1990 to 3.68 billion tons in 2023 [4]. Global GDP grew by about 80% between 2000 and 2019, while energy-related CO₂ emissions increased by 43% over the same period [5]. The global average temperature in 2023 is 1.45 ± 0.12 °C above pre-industrial levels, the highest in 100,000 years [6]. The oceans absorb about 30% of anthropogenic CO₂, causing the pH of surface water to decline at a rate of 0.002 per year [7]. China has established the objective of reaching a carbon peak and achieving carbon neutrality by the year 2030. Realization of the “double carbon” goal requires attention to “carbon reduction” and “carbon sequestration”, in which reducing carbon emissions is fundamental. The characteristics of regional economic integration in China are remarkable. China’s economic regional integration refers to promoting the formation of a closely linked economic community among different regions through policy coordination, industrial synergy, infrastructure connectivity, and market integration. For example, Beijing-Tianjin-Hebei coordinated development, Yangtze River Delta integration, Guangdong-Hong Kong-Macao Greater Bay Area, Chengdu-Chongqing area, and other integrated development strategies continue to promote infrastructure connectivity, industrial coordination, division of labor (from competition to complementarity), and market integration (free flow of elements and unified big market). Carbon emissions involve carbon transfer between regions, industries, and the upstream and downstream of industrial chains. Carbon reduction is not a problem that can be solved by a single region or industry. Therefore, with the regional economic integration development, carbon emissions also need to be integrated and coordinated. The Yangtze River Delta region stands as one of China’s most economically dynamic and innovative areas, making its efforts in carbon emission reduction crucial for setting a benchmark both domestically and internationally. Conducting an empirical study on the capacity for regional carbon emission reduction and collaborative development in this area holds substantial importance.

Because carbon emissions are a serious problem faced by all countries globally, many researchers have focused on carbon emission reduction.

1.1. Research on Influencing Factors of Carbon Emission and Related Case Studies

Using an extended input-output model alongside a multi-objective optimization framework, Lochot et al. [8] analyzed the carbon dioxide emissions trajectory resulting from economic production in France and studied the feasibility and impact of energy substitution. They found that relying solely on clean energy to achieve both carbon dioxide reduction and GDP maximization simultaneously has limitations in the French economy. Energy efficiency and sobriety were also important. By analyzing carbon emissions in the Yangtze River Delta metropolitan area, Wang [9] suggested optimizing the energy consumption structure and advancing regional low-carbon integrated development through the application of low-carbon technologies. Wang et al. [10] developed a predictive model for carbon emissions in the Yangtze River Delta region with grey regression combined model. They predicted that carbon emissions in this region will still increase significantly from 2023 to 2025. Yu et al. [11] determined that trade between China and ASEAN (Association of Southeast Asian Nations) could limit carbon emission intensity. Sikder et al. [12] examined data from the 16 member countries of the Regional Comprehensive Economic Partnership (RCEP), one of Asia’s largest emerging global trading blocs, as well as the South Asian Association for Regional Cooperation (SAARC). Their findings revealed that renewable energy can counteract the negative effects of logistics services on low-carbon transportation and foster economic growth through international trade opportunities. Hu et al. [13] employed the input-output model combined with social network analysis to conduct an empirical study on the industrial correlation effect. They found that sectors with lower export embodied carbon productivity have a higher degree of forward and backward correlation in the industry, playing a dual role of carbon uptake and carbon spillover. Carbon conduction mainly radiated and diffused from the energy and materials industries to other sectors. The difference in research and development investment funds was the main reason for the differentiation in export embodied carbon productivity among various departments. Through empirical research, Wu et al. [14] found that digital economy development can significantly promote industrial carbon production efficiency, with regional heterogeneity. He et al. [15] contended that the clustering of agricultural industries contributes positively to the reduction of carbon emissions. Meng et al. [16] conducted an empirical analysis of the impact mechanism of the digital economy on agricultural carbon emissions, revealing that the digital economy plays a role in sustaining agricultural carbon emissions; however, regional characteristics remain. Zeng [17] discovered that digital trade plays a significant role in driving carbon emission reduction within the circulation industry. Jiao et al. [18] conducted an empirical analysis of the influence of the digital economy on carbon emissions in the logistics industry, uncovering a notable positive correlation between the two, and proposed that carbon reduction in different industries needs to implement differentiated strategies based on the characteristics of their own low-entropy effect and consumption-increasing effect. Shi et al. [19] developed an index system to evaluate carbon emission reduction and high-quality development, calculated a comprehensive index for these factors, and empirically analyzed the driving factors of coupling synergy. Chen et al. [20] demonstrated that the digital economy significantly enhances carbon emission efficiency and generates a spillover effect on adjacent regions. Wang et al. [21] utilized provincial panel data to investigate the influence of new quality productivity on carbon emission reduction, showing that it effectively reduces carbon emission intensity. Additionally, they separately examined the effects of green productivity, scientific and technological productivity, and innovation productivity on carbon emission reduction. Hai et al. [22] applied social network analysis to empirically study the spatial correlation features of carbon emissions across 30 provinces. They revealed that economically developed provinces and less developed provinces form a “core-edge structure” of the network, with each exhibiting spillover effects.

1.2. Research on Cooperative Degree Model of Composite System

The coordination of a composite system refers to the harmonious coexistence between subsystems to achieve an overall effect [23]. The composite system synergy degree model is the most extensively utilized framework in synergy theory [24]. Many studies have applied this model to evaluate the relationship between two or more systems. Huang et al. [25] established a synergy degree model to quantitatively measure the synergy between independent innovation and FDI (Foreign Direct Investment) utilization in the Yangtze River Delta region. According to the result, they proposed to strengthen the joint research and development work between enterprises, research institutions, universities, and multinational companies, constantly optimizing the environment to provide complete basic conditions for the coordinated development of independent innovation and FDI utilization in the Yangtze River Delta region. Liu et al. [26] employed a composite system synergy degree model to assess the synergistic relationship between technological innovation and industrial structure optimization within the Yangtze River Economic Belt. Through empirical research, it was found that the regional innovation capacity of the Yangtze River Economic Belt has been continuously enhanced, while the order degree of industrial structure optimization fluctuates significantly. Chen et al. [27] utilized a complex system synergy degree model to empirically evaluate and analyze the level of collaborative development among innovation factors in the Beijing-Tianjin-Hebei region. They constructed an evaluation index system that included the innovation environment, innovation input, and innovation output. The result showed that the order degree of innovation elements and the integration level of innovation elements among the three regions have generally shown an increasing trend year by year. Wu C. [28] developed a synergy degree model to examine the relationship between low-carbon development and socio-economic development, conducting an empirical analysis of their synergistic effects. They found that while China had taken measures for energy conservation, emission reduction, and addressing climate change, it had achieved rapid and high-quality economic growth. Ma [29] and Wang et al. [30] assessed the collaborative economic development of the Beijing-Tianjin-Hebei region by applying a composite system synergy degree model. They respectively constructed a coordinated development evaluation index system, including “per capita GDP, added value of the primary, secondary, and tertiary industries, per capita disposable income of urban/rural residents”, and “economic development, innovation and opening up, coordinated opening up, social comprehensiveness, resources and environment”. Wang et al. [31] conducted a quantitative evaluation of the synergistic effects of pollution reduction and carbon mitigation in the Yangtze River Economic Belt. The evaluation index system mainly included economy, resources, environment, etc. They found that the coordinated situation of pollution reduction and carbon emission reduction from 2006 to 2021 roughly showed an inverted U-shaped development trend over time. And it was concluded that low-carbon development and green development could become new engines for promoting economic growth, ultimately achieving a coordinated and win-win situation for the economy, society, and the environment.

1.3. Research on Cooperation and Coordination in Carbon Mitigation

William Nordhaus [32] did theoretical and empirical analysis and found that in the absence of sanctions against free-riders, climate coalitions inevitably collapse to minimal participation and abatement. Yukun C. et al. [33] established a game model to analyze whether retailers invest in carbon emission for manufacturers or not. They found that a collaborative, integrated supply chain between manufacturers and retailers enabled optimization, delivering both environmental sustainability and higher profits. Sun H. et al. [34] constructed a game model for carbon emission reduction cooperation between the government and enterprises. The results showed that under collaborative cooperation, the benefits of the government, enterprises, and the system were all optimal. Li X.Y. et al. [35] developed a dynamic integrated climate-economic regional collaboration model to project carbon emission pathways and estimate the social cost of carbon across China’s regions, and identified the impact of central-local coordination on climate governance. Using multi-agent modeling, Sun X.M. et al. [36] simulated China’s provincial carbon emission reduction coordination and projected emission reduction trajectories. They concluded that coordinated low-carbon approaches optimally reconcile economic growth with emission mitigation objectives. Brita Hermelin et al. [37] employed a case study to analyze the governance efficacy of subnational governmental entities acting as territorial facilitators in climate governance networks. The investigation specifically explored the institutional embeddedness of these collaborative mechanisms within distinct local governance contexts. They found that local governments’ place-leadership emerges from political-administrative collaboration, with its effectiveness contingent on alignment with local institutional environments.

Prior research has significantly enhanced the understanding of influencing factors of carbon emission, carbon emission measurement, the relationship between the digital economy and carbon emission reduction, the spatial dynamics of carbon emissions, the complexities of collaborative development, cooperation, and coordination in carbon mitigation, which offered valuable insights and references. However, few studies have investigated regional collaborative carbon reduction with a synergy model of composite systems. Meanwhile, there is a lack of research on the construction of a complete evaluation index system for carbon emission reduction capacity. How is the regional carbon emission reduction capacity? And the status of the coordinated development of carbon emission reduction among different regions is an issue worthy of in-depth study. In this study, an evaluation index system for assessing regional carbon emission reduction capacity was developed, with the Yangtze River Delta region serving as the empirical basis for measuring carbon emission reduction capabilities. The level of collaborative carbon emission reduction capacity between three provinces and one city was evaluated. This study has theoretical and practical significance.

Theoretically, it provided a new research perspective for low-carbon development and regional integration development, expanded research methods of the low-carbon economy, and enriched theories related to carbon emissions.

Practically, it can offer valuable insights for formulating low-carbon development policies and a scientific basis for relevant government departments to formulate regional integrated development strategies and games in integrated development.

2. Methodology

Regional carbon emission reduction capacity is an important indicator of low-carbon economic development in a region, which is affected by many factors in the region. The synergy degree of the composite system is to fully consider the influence of the evolution of various elements in the system on the whole system. If the statistical caliber is consistent, each region can be regarded as a carbon emission reduction subsystem, and the organic combination of these regions can be regarded as a carbon emission reduction composite system. Therefore, we introduced the composite system synergy model to study the issue of regional carbon synergy emission reduction, which is conducive to in-depth thinking and discussion of carbon emission issues from the perspectives of systems theory and synergy theory. To assess regional carbon emission reduction capacity, this study developed an evaluation index system for carbon emission reduction capability and applied the entropy weight method to determine the weight of each indicator, and constructed carbon emission reduction ability order parameter and regional carbon emission reduction composite system cooperation degree models.

2.1. Carbon Emission Reduction Capacity Assessment Index System

As for the evaluation of the level of low-carbon development, many researchers have achieved rich results [38,39,40], which established the evaluation index system of low-carbon development. According to the research purpose of this paper, and considering the actual situation, such as the availability of data, we construct the evaluation index system of regional carbon emission reduction ability from five dimensions: economic development ability, carbon emission ability, carbon transfer ability, technology and carbon sink, and industrial development ability. Economic development is the most important driving force for the development of a country or region, as well as the main cause of carbon emissions today. Moreover, many indicators of low-carbon development are calculated by indicators related to economic development, so it is reasonable to use economic development ability as an indicator. Carbon transfer is divided into different regional transfers within a country, including transnational transfers. In combination with the purpose and scope of our research, we mainly choose cross-border transfer indicators. Carbon sink absorption is an important way of achieving carbon neutrality, and low-carbon technologies will also affect carbon emissions, so we take technology and carbon sink as one of the indicators. Different industries have different carbon emissions, among which the secondary industry and the tertiary industry are the main sources of carbon emissions. At present, in China, high-tech industries are often low-carbon industries, which can also reflect low-carbon development. Therefore, this paper takes industrial development as one of the indicators. The third-level indicators in the evaluation index system are also designed based on the above related research results, combined with data availability and research purposes. Compared to these research results, we add carbon transfer capacity (cross-border transfer), technology, and carbon sinks in the second-level indicators, as well as carbon footprint, export competitive advantage, intensity of regional R&D expenditure, et al. in the third-level indicators. The specific indicator system is shown in

Table 1. Assessment framework for regional carbon emission reduction capacity.

Primary Index	Secondary Index	Tertiary Index	Definition of the Indicator	Positive/Negative
Regional carbon reduction capacity	Economic development ability	Per capita GDP	Regional output creation capacity per capita (Yuan/person)	+
		Share of regional GDP in the national total	Regional economic status in the country (%)	+
		Per capita disposable income of urban residents	Disposable income of urban residents (Yuan/person)	+
		Per capita disposable income of rural residents	Disposable income of rural residents (Yuan/person)	+
	Carbon emission capacity	Energy intensity	Energy consumption per unit of GDP (tons of standard coal/10,000 yuan)	−
		Carbon emission intensity	Carbon emissions per unit of GDP (tons of standard coal/10,000 yuan)	−
		Carbon footprint	Carbon emissions per capita (tons/person)	−
		Share of fossil energy in total energy consumption	Fossil energy consumption capacity (%)	−
	Carbon transfer capacity (cross-border transfer)	Share of FDI in GDP	Foreign investment capacity (%)	+
		Share of import and export trade in GDP	Carbon transfer capacity (%)	+
		Export competitive advantage	Balance of import and export trade proportion of total import and export trade (%)	+
	Technology and carbon sinks	Intensity of regional R&D expenditure	Share of R&D expenditure in GDP (%)	+
		Urban green coverage rate	Carbon sink capacity (%)	+
		Urbanization rate	Sustainable capacity of carbon sink (%)	+
	Industrial development ability	Share of secondary industry in GDP	Secondary industry development capacity (%)	−
		Share of tertiary industry in GDP	Tertiary industry development capacity (%)	+
		Share of main revenue of high-tech industries in GDP	High-tech industry development capability (%)	+

.

2.2. Carbon Emission Calculation

Carbon dioxide emissions are mainly produced by the consumption of fossil fuels. Eight types of fossil fuels, including “coal, coke, crude oil, fuel oil, gasoline, kerosene, diesel, and natural gas”, were selected in this study [41,42].

We used the carbon emission calculation method published by the IPCC in 2006 and related coefficients to calculate carbon emissions. The calculation formulas and coefficients are shown in Formula (1) and Table 1, respectively.

$$C={\sum }_{i=1}^8{E}_i{\times SCC}_{ i}\times {CEF}_i$$

(1)

C is carbon emission. $E_i$ is fossil fuel consumption of type i. ${SCC}_i$ and $C{EF}_i$ are the standard coal coefficient and carbon emission coefficient of fossil fuel i. the value of ${SCC}_i$ and $C{EF}_i$ are shown in

Table 2. SCC and CEF of eight fossil energy.

Fossil Fuel	SCC	CEF
Coal	0.7143	0.7559
Coke	0.9714	0.855
Crude oil	1.4286	0.5538
Fuel oil	1.4286	0.5857
Gasoline	1.4714	0.5921
Kerosene	1.4714	0.5714
Diesel	1.4571	0.6185
Natural gas	1.33	0.4483

Data derived from IPCC Guidelines for National Greenhouse Gas Emission Inventory.

.

2.3. Entropy Weight Method

The determination of weight is key in the assessment, and its accuracy directly affects the evaluation effect. The weights of evaluation indicators are commonly determined using methods such as the entropy weight method [43,44] and the analytic hierarchy process [45]. The entropy weight method prevents index failure, reflects the difference between different indicators, and fully utilizes raw data within the context of a limited sample size to minimize the influence of subjective human factors [46]. Therefore, the entropy weight method was employed in this study.

(1) Data were standardized, and the dimensions of different indicators were eliminated. The mean value method was adopted for data standardization. That is, ${s^{\prime }}_{ij}={\displaystyle \frac{s_{ij}}{\overline{s_i}}}$, where $\overline{s_i}$ represents the mean value of the i-th index, $s_{ij}$ denotes the unnormalized value, and ${s^{\prime }}_{ij}$ is the normalized value.

$${\mathit{p}}_{\mathit{j}}=-{\sum }_{i=1}^n{h}_{\mathrm{ij}}\times \ln \left(1-{\mathrm{h}}_{ij}\right)$$

(2)

where ${\mathit{h}}_{\mathit{i}\mathit{j}}={\mathit{s}}_{\mathit{i}\mathit{j}}/{\sum }_{\mathit{i}-\mathbf{1}}^{\mathit{n}}{\mathit{s}}_{\mathit{i}\mathit{j}}$.

$${\mathit{W}}_{\mathit{j}}={\mathit{P}}_{\mathit{j}}/{\sum }_{\mathit{j}=1}^{\mathit{n}}{\mathit{P}}_{\mathit{j}}$$

(3)

where ${\mathit{P}}_{\mathit{j}}$ denotes the inverse entropy of the j-th index, and ${\mathit{W}}_{\mathit{j}}$ represents the weight of the j-th index determined by the anti-entropy weight method.

2.4. Order Parameter of Regional Carbon Emission Reduction Capacity

Regional carbon emission reduction was regarded as a system, and its capability for carbon emission reduction can be assessed using order parameters. The value of the i-th order parameter is represented by $e_i$.

$$e_i={\sum }_{j=1}^m{w}_j\times {s^{\prime }}_{ij}$$

(4)

where $e_i$ ranges from 0 to 1; a higher value indicates a more orderly system and a greater capacity for carbon emission reduction.

2.5. Cooperation Degree Model of Regional Carbon Emission Reduction Complex System

Regional carbon emission reduction was regarded as a composite system, and a single regional carbon emission reduction system was considered a subsystem of the composite system. Based on previous studies [23,39], a computational model for assessing the coordination degree of regional carbon emission reduction systems was developed.

2.5.1. Subsystem Order Degree Model

Let $e_{ij}=\left(e_{\mathrm{i}1},e_{\mathrm{i}2},e_{\mathrm{i}3},\dots ,e_n\right)$ represent the order parameter of the subsystem, and ${\alpha }_{ij}\le e_{ij}\le {\beta }_{ij}, {\alpha }_{ij}, {\beta }_{ij}$ are the lower and upper bounds of the order parameters, respectively. If a variable contributes positively to the system’s development, its order degree is calculated as:

$$\begin{gathered} U_i\left(e_{ij}\right)=\left(e_{ij}-{\alpha }_{ij}\right)/\left({\beta }_{ij}-{\alpha }_{ij}\right) \\ j\in \left[1,m\right] \end{gathered}$$

(5)

If the variable has a negative impact on the subsystem’s development, its order degree is determined as:

$$\begin{gathered} U_i\left(e_{ij}\right)=\left({\beta }_{ij}-e_{ij}\right)/\left({\beta }_{ij}-{\alpha }_{ij}\right) \\ j\in \left[m+1,n\right] \end{gathered}$$

(6)

where $U_i\left(e_{ij}\right)\in \left[\mathrm{0,1}\right]$. The closer $U_i\left(e_{ij}\right)$ is to 1, the higher its contribution to the order degree; conversely, the closer it is to 0, the lower its contribution to the order degree.

$$\begin{gathered} U_i(e_i)={\sum }_{j=1}^8{W}_{ij}{U}_i\left(e_{ij}\right) \\ {W}_j\ge 0, \sum W_j=1; \end{gathered}$$

(7)

where $U_i\in \left[\mathrm{0,1}\right]$ represents the order degree of carbon emission subsystem $U_i$. A value of $U_i$ closer to 1 indicates a higher contribution to the order degree of the regional carbon emission system, while a value closer to 0 signifies a lower contribution.

2.5.2. Cooperative Degree Model of the Composite System

Assuming that the regional carbon emission system has i subsystems, then:

$$U=\left\{U_1,U_2{,U}_3\dots \dots U_i\right\}$$

$$U_i=\left\{U_{i1},U_{i2}{,U}_{i3},...,U_{in}\right\}$$

where $U_i$ varies over time. If the initial time of the subsystem is 0, the order degree of the subsystem at time “0” is $U_i^0\left(e_i\right)$, and the order degree of the subsystem at time “t” is $U_i^t\left(e_i\right)$, then the cooperation degree U of the composite system that changes dynamically with time is:

$$U=\theta {\sum }_{i=1}^n{\lambda }_i\left[\left|U_i^t\left(e_i\right)-U_i^0\left(e_i\right)\right|\right]$$

(8)

where $\theta ={\displaystyle \frac{\underset{i}{\min }\left[U_i^t\left(e_i\right)-U_i^0\left(e_i\right)\ne 0\right]}{\left|\underset{i}{\min }\left[U_i^t\left(e_i\right)-U_i^0\left(e_i\right)\ne 0\right]\right|}},{\lambda }_i\ge 0,\sum _{i=1}^n{\lambda }_i=1$.

The overall cooperation degree of regional carbon emission is $U\in \left[-\mathrm{1,1}\right]$. A higher value indicates a greater level of coordination within the regional carbon emission system, while a lower value reflects a reduced level of coordination.

3. Empirical Research

To assess the model’s efficacy, statistical data spanning from 2014 to 2023 were gathered from the statistical yearbook of Anhui, Jiangsu, Zhejiang, and Shanghai within the Yangtze River Delta region, following the established index system. The statistical yearbooks were available directly from the statistical offices’ websites in each of the four regions. Some of the indicators were directly available in the statistical yearbook. The other part of the data was calculated according to the definition of the tertiary index in the index system shown in Table 1. The model was employed to evaluate the order degree of the carbon emission reduction system in the three provinces and one municipality, as well as to assess the synergy degree of the carbon emission reduction composite system across the Yangtze River Delta region. Consequently, the regional collaborative carbon emission reduction mechanism was investigated.

3.1. Overview of the Yangtze River Delta Region and Its Carbon Emission Challenges

In May 2019, the Central Committee of the Communist Party of China officially designated the integrated development of the Yangtze River Delta as a national-level strategic initiative. The Yangtze River Delta region comprises Anhui, Jiangsu, Zhejiang, and Shanghai, spanning a total area of 358,000 square kilometers. As of the end of 2023, the Yangtze River Delta region had a permanent population of 238 million and a combined GDP of 30.5 trillion yuan. It stands as one of China’s most economically dynamic regions, boasting the highest level of openness and the strongest innovation capabilities in the country. Despite its high level of economic development, the Yangtze River Delta region also generates the largest amount of carbon emissions in China. The total carbon emissions, per capita carbon emissions, and carbon emission intensity of the three provinces and one municipality in the Yangtze River Delta are illustrated in Figure 1, Figure 2 and Figure 3:

As shown in Figure 1, Jiangsu Province’s total carbon emissions were considerably higher compared to the other three regions. While Anhui and Zhejiang provinces demonstrated an upward trend in carbon emissions, Jiangsu and Shanghai showed a consistent decline. From 2014 to 2023, per capita carbon emissions in Jiangsu and Shanghai remained stable initially, followed by a declining trend (Figure 2). In contrast, per capita carbon emissions in Anhui and Zhejiang showed an upward trend. By 2022, Zhejiang’s per capita carbon emissions had exceeded those of Anhui. After 2022, the overall per capita carbon emissions were highest in Shanghai, followed by Jiangsu, Zhejiang, and Anhui. The carbon emission intensity in the Yangtze River Delta gradually decreased (Figure 3). Anhui exhibited the highest carbon emission intensity, followed by pre-2022 levels in Jiangsu. After 2021, the carbon emission intensity in Jiangsu, Zhejiang, and Shanghai decreased. The differences between each province and the city may be caused by regional heterogeneity between economic development and carbon emissions. The economic volume of Anhui is relatively low; however, it has the highest carbon emission intensity owing to industrial transfer within the Yangtze River Delta region. As a long-developed area, Shanghai’s economy is dominated by a high proportion of service industries, resulting in the lowest carbon emission intensity in the region. Promoting coordinated carbon emission reduction across the Yangtze River Delta is essential to ensuring fairness and scientific rigor in policy formulation. Additionally, it serves as a model for carbon reduction efforts in other regions of China and globally.

3.2. Weight Calculation of Evaluation Index for Carbon Emission Reduction Capacity in the Yangtze River Delta Region

By applying the collected data to the entropy weight method, the weights for each indicator were determined (

Table 3. Weight of carbon emission reduction capability evaluation index.

Index/Province	Anhui	Jiangsu	Zhejiang	Shanghai
Per capita GDP	0.0563	0.0546	0.0532	0.0538
Share of regional GDP in the national total	0.0387	0.0512	0.0734	0.0703
Per capita disposable income of urban residents	0.0538	0.0568	0.0535	0.0461
Per capita disposable income of rural residents	0.0606	0.0530	0.0550	0.0472
Energy intensity	0.0653	0.0702	0.06147	0.0553
Carbon emission intensity	0.0627	0.0667	0.0653	0.0630
Carbon footprint	0.0390	0.0240	0.0693	0.0803
Share of fossil energy in total energy consumption	0.0563	0.0717	0.0588	0.0664
Share of FDI in GDP	0.1060	0.0995	0.0243	0.0434
Share of import and export trade in GDP	0.0528	0.0833	0.0730	0.0489
Export competitive advantage	0.0461	0.0537	0.0513	0.0651
Intensity of regional R&D expenditure	0.0528	0.0388	0.0412	0.0527
Urban green coverage rate	0.0396	0.0680	0.0479	0.0904
Urbanization rate	0.0543	0.0513	0.0582	0.0596
Share of secondary industry in GDP	0.0612	0.0678	0.0464	0.0573
Share of tertiary industry in GDP	0.0953	0.0544	0.0638	0.0471
Share of main revenue of high-tech industries in GDP	0.0592	0.0350	0.1039	0.0531

Remark: Considering that the data is a percentage value, it is usually enough to retain the percentage after 2 decimal places, so the rounding method is used to retain the corresponding value. The following data is processed in the same way.

).

The top three carbon emission reduction capacity evaluation indices in Anhui Province were the proportions of FDI in GDP, tertiary industry in GDP, and secondary industry in GDP. This demonstrated that attracting investment, particularly foreign investment, significantly influences the carbon emission reduction capacity of Anhui Province. The growth of secondary and tertiary industries also plays a significant role in shaping the carbon emission reduction capacity. The top three indicators for evaluating carbon emission reduction capability in Jiangsu Province were the proportions of FDI in GDP, import and export trade in GDP, and fossil energy in total energy consumption. This revealed that as a large economic province, Jiangsu must focus on attracting foreign investment, and foreign trade also has an important impact on carbon emission reduction in this region. Moreover, Jiangsu Province should focus on the application of new energy. The top three indicators for evaluating carbon emission reduction capability in Zhejiang Province were the proportion of high-tech industry revenue to GDP, the share of regional GDP in national GDP, and the ratio of import and export trade to GDP. The data further reflects the actual situation of Zhejiang Province as a region with strong technological innovation ability and import and export trade ability. The top three indices of Shanghai’s carbon emission reduction capability evaluation were urban green coverage, carbon footprint, and the proportion of regional GDP in the country. As a super metropolis with high population density, Shanghai should prioritize urban greening and per capita carbon emission in its economic development efforts more than other regions.

3.3. Calculation and Analysis of Sequence Parameters of the Regional Carbon Emission Reduction Subsystem in the Yangtze River Delta

3.3.1. Subsystem Order Parameter

To uncover the development trend of each index of the four subsystems, the data were substituted into the formula. Additionally, the order parameters of the secondary indices of the four subsystems of carbon reduction in Anhui, Jiangsu, Zhejiang, and Shanghai were calculated (

Table 4. Sequence parameters of the carbon emission reduction subsystem in Anhui Province.

Year	Economic Development Ability	Carbon Emission Capacity	Carbon Transfer Capacity	Technology and Carbon Sinks	Industrial Development Ability
2014	0.1313	0.2691	0.2291	0.1246	0.2041
2015	0.1514	0.2493	0.2051	0.1275	0.1823
2016	0.1677	0.2398	0.2489	0.1352	0.2171
2017	0.1824	0.2360	0.2113	0.1432	0.2219
2018	0.2012	0.2287	0.2145	0.1472	0.2252
2019	0.2134	0.2219	0.2164	0.1510	0.2335
2020	0.2298	0.2096	0.1857	0.1531	0.2420
2021	0.2495	0.2008	0.1960	0.1574	0.2241
2022	0.2732	0.1923	0.1751	0.1603	0.2035
2023	0.2949	0.1854	0.16718	0.1663	0.2033

,

Table 5. Sequence parameters of the carbon emission reduction subsystem in Jiangsu Province.

Year	Economic Development Ability	Carbon Emission Capacity	Carbon Transfer Capacity	Technology and Carbon Sinks	Industrial Development Ability
2014	0.1525	0.2766	0.3035	0.1495	0.1629
2015	0.1676	0.2729	0.2938	0.1490	0.1620
2016	0.1809	0.2603	0.2976	0.1534	0.1626
2017	0.1928	0.2481	0.2640	0.1562	0.1616
2018	0.2056	0.2338	0.2352	0.1579	0.1600
2019	0.2206	0.2254	0.2115	0.1591	0.1581
2020	0.2345	0.2209	0.1988	0.1616	0.1573
2021	0.2517	0.2047	0.1902	0.1628	0.1538
2022	0.2669	0.1932	0.1854	0.1644	0.1491
2023	0.2824	0.1900	0.1855	0.1677	0.1447

,

Table 6. Sequence parameters of the carbon emission reduction subsystem in Zhejiang Province.

Year	Economic Development Ability	Carbon Emission Capacity	Carbon Transfer Capacity	Technology and Carbon Sinks	Industrial Development Ability
2014	0.1774	0.3122	0.1521	0.1330	0.2203
2015	0.1910	0.2991	0.1511	0.1360	0.2112
2016	0.2013	0.2804	0.1506	0.1418	0.2091
2017	0.2127	0.2684	0.1543	0.1446	0.2098
2018	0.2235	0.2549	0.1603	0.1474	0.2120
2019	0.2377	0.2465	0.1545	0.1490	0.2109
2020	0.2515	0.2344	0.1497	0.1518	0.2121
2021	0.2682	0.2299	0.1426	0.1524	0.2113
2022	0.2853	0.2141	0.1382	0.1565	0.2195
2023	0.3031	0.2092	0.1325	0.1602	0.2245

and

Table 7. Sequence parameters of the Shanghai carbon emission reduction subsystem.

Year	Economic Development Ability	Carbon Emission Capacity	Carbon Transfer Capacity	Technology and Carbon Sinks	Industrial Development Ability
2014	0.1724	0.3266	0.1086	0.1977	0.1870
2015	0.1797	0.3126	0.1242	0.2037	0.1792
2016	0.1866	0.2966	0.1296	0.2047	0.1711
2017	0.1937	0.2916	0.1391	0.2052	0.1627
2018	0.2046	0.2593	0.1525	0.2062	0.1583
2019	0.2165	0.2514	0.1662	0.2047	0.1532
2020	0.2332	0.2392	0.1742	0.2017	0.1443
2021	0.2482	0.2329	0.1915	0.2022	0.1439
2022	0.2626	0.2201	0.1962	0.2001	0.1395
2023	0.2760	0.2202	0.1922	0.2012	0.1351

).

Anhui Province’s economic development ability index steadily increased, carbon emission capacity gradually declined, carbon transfer capacity exhibited a fluctuating decreasing trend, technology and carbon sink capacity continuously increased, and industrial development capacity was the lowest in 2015, whereas it remained stable in other years (Table 4).

The economic development capacity of Jiangsu Province continuously increased, its carbon emission capacity continuously decreased, and its carbon transfer capacity fluctuated between 2015 and 2016 but exhibited a decreasing trend in other years (Table 5). Technology and carbon sequestration capacity have increased since 2016. The industrial development capacity showed a fluctuating trend between 2014 and 2017 and declined after 2018.

The economic development ability index of Zhejiang Province continuously increased, carbon emission capacity continuously decreased, and carbon transfer capacity exhibited a fluctuating trend before 2018 but continuously decreased after 2018 (Table 6). Technology and carbon sequestration capacity continuously improved, and the capacity of industrial development exhibited a stable fluctuating trend.

As can be seen from Table 7, Shanghai’s economic development capacity continuously increased, whereas its carbon emission capacity gradually decreased but recovered slightly in 2023 (Table 7). Technology and carbon sink capacity were relatively stable at approximately 0, and industrial development ability exhibited a decreasing trend.

3.3.2. Subsystem Order Degree

To uncover the trends in carbon emission reduction capability across the four subsystems using the subsystem order degree model, the order degrees of the carbon emission reduction subsystems for the three provinces and one municipality were calculated (

Table 8. Order degrees of carbon emission reduction subsystems.

Year/Province	Anhui	Jiangsu	Zhejiang	Shanghai
2014	0.2505	0.3774	0.2012	0.3830
2015	0.2995	0.3326	0.2296	0.3982
2016	0.2886	0.3124	0.2211	0.3236
2017	0.2539	0.3306	0.2419	0.2981
2018	0.3722	0.3879	0.2985	0.3319
2019	0.3481	0.4148	0.3546	0.3687
2020	0.3927	0.4289	0.3664	0.3928
2021	0.4889	0.4526	0.5050	0.4270
2022	0.6663	0.6223	0.7140	0.6533
2023	0.7322	0.6923	0.8576	0.6696

and Figure 4).

The carbon emission reduction capacity of Anhui and Shanghai showed a fluctuating decline from 2014 to 2017, subsequently increasing gradually (Table 8 and Figure 4). Zhejiang demonstrated a consistent upward trend from 2014 to 2020, followed by a sharp increase after 2020. The carbon emission reduction capacity of Jiangsu exhibited a steady rise from 2014 to 2021, followed by a rapid increase after 2021. Prior to 2020, Zhejiang had the lowest carbon emission reduction capacity in the Yangtze River Delta region, but it experienced significant growth after 2020. From 2021, Zhejiang Province exhibited the highest carbon emission reduction capacity. Shanghai’s carbon emission reduction capacity was the lowest in 2023. Overall, the carbon emission reduction capacity in the Yangtze River Delta region displayed a fluctuating upward trend.

3.4. Cooperation Degree of Regional Carbon Reduction System in the Yangtze River Delta

To reveal the cooperative development of carbon emission reduction in the Yangtze River Delta region, the pairwise composite cooperation degree of each subsystem and the overall composite cooperation degree between the study areas were calculated (

Table 9. Cooperation degree of composite system of carbon emission reduction capacity in the Yangtze River Delta region.

Year	Anhui–Jiangsu	Anhui– Zhejiang	Anhui– Shanghai	Jiangsu– Zhejiang	Jiangsu– Shanghai	Zhejiang– Shanghai	Overall Cooperation Degree
2015	0.0469	0.0373	0.0273	0.0357	0.0261	0.0208	0.0074
2016	0.0498	0.0276	0.0476	0.0360	0.0622	0.0344	0.0235
2017	0.0128	0.0119	0.0172	0.0437	0.0630	0.0588	0.0560
2018	0.0357	0.1089	0.0789	0.0319	0.0232	0.0706	0.1305
2019	0.0604	0.1224	0.0374	0.0758	0.0232	0.0469	0.2521
2020	0.0856	0.1533	0.0372	0.0923	0.0224	0.0401	0.3618
2021	0.1340	0.2692	0.1024	0.1512	0.0575	0.1156	0.4247
2022	0.3191	0.4618	0.3352	0.3544	0.2573	0.3723	0.7557
2023	0.3895	0.5623	0.3716	0.4547	0.3004	0.4337	0.9402

and Figure 5).

Before 2017, the pairwise composite cooperation degree of carbon emissions in the Yangtze River Delta region was low and fluctuated (Table 9 and Figure 5). The degree of cooperation between Jiangsu and Shanghai was low before 2020 and began to increase in 2021; however, the level was still the lowest among the study areas. The pairwise collaboration degree in other provinces and cities has increased rapidly since 2020. After 2018, the degree of collaboration between Anhui and Zhejiang was consistently the highest. After 2018, the cooperation degree of carbon emission reduction in the study area exhibited a rapidly increasing trend, which was constantly higher than the collaboration between the two. Particularly in 2023, the synergy degree between the study areas in the composite system peaked at 0.94, indicating that the overall coordinated development of carbon emission reduction capacity in the Yangtze River Delta region was good.

4. Findings and Discussions

Empirical research and analysis on the statistical data of the Yangtze River Delta region in the past decade revealed the following:

4.1. Overall Regional Carbon Emission Reduction Capacity Trend Is Good

Except from 2014 to 2016, the carbon emission reduction capacity of Anhui and Shanghai showed a slow decreasing trend, whereas Jiangsu and Zhejiang demonstrated an upward trend. After 2016, the carbon emission reduction capacity of the study areas showed a significant upward trend, which was particularly significant after 2020. The carbon emission reduction capacity of Zhejiang and Anhui increased from the bottom two to the top two in the past decade, and Shanghai moved from the highest to the lowest.

In recent years, Anhui Province leveraged the strategic opportunity of integrated development in the Yangtze River Delta, developed 10 emerging industries, and promoted the low-carbon transformation and upgrading of manufacturing. This series of policies and measures allowed Anhui Province to achieve remarkable results in low-carbon development. Zhejiang Province successively formulated the “13th Five-Year” and “14th Five-Year” low-carbon development plans, formulated specific emission reduction targets and detailed emission reduction indicators, decomposed emission reduction tasks, and ensured an increase in their carbon emission reduction capacity from the top design of the government. As a global megacity, Shanghai is responsible for many nodes in the global manufacturing supply chain, which may lead to a downward trend in its carbon emission reduction capacity. As a large economic province, Jiangsu has been affected by the low-carbon transformation in its economic development, which may lead to fluctuations in the carbon emission reduction capacity of these two regions.

4.2. Economic Development Capacity Continuously Increased and Carbon Emission Capacity Continuously Decreased

As the most expansive and dynamic region underpinning China’s economic growth, the Yangtze River Delta exhibited strong vitality. Following the integration of the Yangtze River Delta into the national development strategy, the economic development capacity of the region showed a strong development momentum. As a negative indicator, carbon emission capacity reflects the effectiveness of low-carbon development—the lower the emissions, the better the progress. With ongoing industrial restructuring and the adoption of low-carbon technologies and renewable energy, carbon emissions in the Yangtze River Delta region have steadily declined, showcasing a positive trend toward improved low-carbon development.

4.3. Fluctuation in Carbon Transfer Capacity Decreased

In Anhui, Jiangsu, and Zhejiang provinces, carbon transfer capacity exhibited a decreasing trend, whereas Shanghai’s carbon transfer capacity exhibited an increasing trend. The raw data of various indicators revealed that although the import and export trade volume of various regions increased, their proportion in GDP continuously decreased. Foreign investment fluctuated significantly, and its share in GDP also exhibited a significant decreasing trend. As the national and possibly global economic center, Shanghai is an important node and integrated city in the global industrial chain, and its carbon transfer capacity has exhibited an increasing trend in the past 10 years. Carbon transfer, as defined in this study, refers to cross-border transfer. The change in carbon transfer capacity requires the attention of relevant government departments. In recent years, owing to the deceleration in global economic development, Western countries, led by the United States, have provoked trade frictions and implemented trade barriers, which may also explain the change in carbon transfer capacity.

4.4. Technology and Carbon Sequestration Capacity Continuously Improved

Anhui, Jiangsu, and Zhejiang provinces continuously enhanced their technological and carbon sequestration capabilities, whereas Shanghai maintained a relatively stable state. With the continuous deepening of high-quality development, new quality productivity has rapidly formed and improved. All regions have increased investment in research and development, significantly enhancing the advancement and implementation of low-carbon technologies. Over the past 10 years, urbanization has accelerated in China, and it continues to increase. By the end of 2023, the urbanization rates of Anhui, Jiangsu, Zhejiang, and Shanghai were 61.51%, 75%, 74.2%, and 89.5%, respectively. During urbanization, the green concept was implemented, and the urban green coverage rate continuously improved. As a megacity with a limited regional area, Shanghai achieved a high urbanization rate earlier than other provinces. In this index, Anhui, Jiangsu, and Zhejiang provinces have significant potential for improvement.

4.5. Industrial Development Ability Fluctuated or Declined

In the past 10 years, the industrial development ability of the study area exhibited a fluctuating or decreasing trend. The secondary industry constituted a larger share of GDP in the Yangtze River Delta region, with all areas except Shanghai exceeding 40%. In contrast, developed countries or regions have a tertiary industry share of around 80%, highlighting a significant gap of approximately 50% in the proportion of the tertiary industry in this region. Although the service industry was highly developed in Shanghai, the highest proportion was only 74%. The share of high-tech industry income in GDP exhibited a decreasing trend. High-tech industries require substantial upfront investment and have a prolonged return cycle. With the acceleration of the new industrial revolution and the rapid pace of technological innovation in recent years, these factors are expected to significantly influence the revenue dynamics of high-tech industries.

4.6. Degree of Interregional Cooperation on Carbon Emission Reduction Was Stable and Increasing

From 2014 to 2020, the pairwise collaboration degree of carbon emission reduction capacity was stable but at a relatively low level. After 2020, the pairwise collaboration increased rapidly. Particularly in Anhui and Zhejiang, the degree of synergy in 2023 peaked at 0.56. In November 2018, the Chinese government announced its support for elevating the integrated development of the Yangtze River Delta to a national strategy [47]. By 2019, Anhui province was incorporated into this regional integration initiative. Subsequently, the integrated development of the Yangtze River Delta accelerated, and interregional cooperation further strengthened, which was significantly reflected in the rapid improvement in collaborative carbon emission reduction capacity. The level of collaborative carbon emission reduction in the Yangtze River Delta region has seen a significant rise since 2017. This growth was catalyzed by the release of the “Yangtze River Delta City Cluster Development Plan” in 2016, which included Anhui, Jiangsu, Zhejiang, and Shanghai, marking the beginning of advanced and rapid coordinated development in the area. By 2023, the synergy level of the region’s carbon emission reduction efforts reached its highest point at 0.94. This result is remarkable. Although the degree of cooperation between the two in the region has shown a trend of increasing after 2020, the growth rate is far less than the increase in the overall degree of cooperation in the Yangtze River Delta region. The overall cooperation degree of the Yangtze River Delta region increased from 0.0074 in 2015 to 0.056 in 2017. In this interval, the changes are relatively stable and at a low level of coordination. However, from 2018 to 2023, the overall cooperation degree of the Yangtze River Delta region increased from 0.1305 to 0.9402, that is from a low level to a very high level, and the overall cooperation degree of the Yangtze River Delta region was much higher than the cooperation degree between the two in the region. To give a reasonable explanation for this result, we first consulted the literature and found that the cooperation degree of the overall composite system can be greater than that of different subsystems, and this situation often happens [48]. We believe that this result may be caused by the following reasons: First, the strong promotion of national strategy: In 2018, the integration of the Yangtze River Delta rose to a national strategy, bringing a higher level of policy support and coordination mechanism. Second, the networking effects of infrastructure and technology: Connectivity of transport and information is more conducive to multilateral synergy. Third, the formation of industrial and innovation networks: The division of labor in industrial and innovation chains has expanded from bilateral to multilateral. Fourth, the deepening of market integration: The flow of factors and the construction of a unified market have integral characteristics. Fifth, the nonlinear growth of synergies: Network effects and threshold effects promote the acceleration of synergies. Sixth, the catalyst of external environment and emergencies: External factors such as international competition and epidemics have strengthened the need for synergy. Seventh, in contrast, pairwise growth is limited by the scope of cooperation, competition, and lack of overall coordination, so the growth rate is slower. The jump in the overall cooperation degree reflects the qualitative change from “point-to-point cooperation” to “network cooperation” in the Yangtze River Delta region.

5. Conclusions

In this study, an evaluation index system for regional carbon emission reduction capacity was developed, with the entropy weight method applied to assign weights to the indices. A carbon emission reduction capacity order parameter model and a regional carbon emission reduction composite system cooperation degree model were constructed. Compared to the prior research results, we added carbon transfer capacity (cross-border transfer), technology, and carbon sinks in the second-level indicators, as well as carbon footprint, export competitive advantage, intensity of regional R&D expenditure, et al. in the third-level indicators. This will contribute to making the evaluation of carbon emission reduction capabilities more scientific and reasonable. Meanwhile, we introduced the synergy model of the composite system into the field of regional carbon emission reduction research, which will expand the research methods related to low-carbon and sustainable development. The validity of these models was confirmed using data from the Yangtze River Delta region from 2014 to 2023. The findings indicated that the cooperation degree of carbon emission reduction in the Yangtze River Delta region remained consistently high, demonstrating clear characteristics of regional low-carbon development. However, the carbon transfer capacity and industrial development capacity showed a decreasing trend.

The Yangtze River Delta region serves as a model for regional integration development. Its coordinated progress offers valuable insights and a reference framework for the integrated development of other regions or nations. The results of this study provided a theoretical reference for regional economic integrated development and low-carbon development. Moreover, it provided a scientific basis for low-carbon development strategies in different provinces and cities in the Yangtze River Delta region, or on a national level. Additionally, it provided a decision-making reference for regional coordinated development policies.

This study has several limitations. Low-carbon development is influenced by numerous factors, and the index system for assessing carbon emission reduction capacity requires further enrichment and refinement. The analysis was based on data from only three provinces and one city in the Yangtze River Delta over a 10-year period, which is relatively short. Additionally, the Yangtze River Delta region is highly developed economically, raising questions about the applicability of the findings to less economically advanced regions. Future research should aim to address these limitations.