Green Technology Innovation and Carbon Emission Performance of the Middle Reaches of the Yangtze River Urban Agglomeration: Mechanism and Spatio-Temporal Evolution

Abstract

Amid the increasingly severe global climate change situation, green technology innovation has become an important means to promote carbon reduction and achieve the transition to a low-carbon economy. This study aims to systematically analyze the relationship between green technology innovation and carbon emission performance in the urban agglomeration of the middle reaches of the Yangtze River, exploring the degree of coupling and coordination between different cities. Utilizing data from 2011 to 2021, we employ methods such as the Coupling Coordination Degree Model and fixed effects model to achieve our objectives. Our findings reveal that both green technology innovation and carbon emission performance in this region are on an upward trend; however, the growth rate of green technology innovation showed a slowdown in 2021. Notably, there are disparities in the coupling coordination degree among cities, with economically developed areas exhibiting a faster growth rate. Moreover, green technology innovation significantly enhances carbon emission performance, and heterogeneity tests indicate that this impact is even more pronounced in cities with weaker environmental regulations. Despite regional differences, the overall trend remains positive.

1. Introduction

As global climate change has become increasingly severe, carbon emissions have become a focus of attention for governments and international organizations around the world. In recent years, climate change and environmental pollution problems have continued to worsen, seriously threatening the sustainable development of human society. To address this challenge, the United Nations has set a goal in the Paris Agreement to limit the global average temperature rise to well below 2 °C above pre-industrial levels and encourages countries to take active measures to reduce greenhouse gas emissions [1]. As the world’s second-largest economy and the largest carbon emitter, China has clearly proposed the “carbon peak” and “carbon neutrality” dual carbon targets in 2020, aiming to achieve carbon peak by 2030 and carbon neutrality by 2060 [2]. In this context, green technology innovation has become an important means to promote carbon emission reduction and achieve the transition to a low-carbon economy. Green technology innovation not only includes clean energy technology and low-carbon production technology but also covers energy conservation, environmental protection, and ecological protection technologies. To address climate change, developed countries have introduced a series of relevant policies and measures. For example, the European Union has implemented a carbon pricing policy by establishing the EU Emissions Trading System (EU ETS), requiring enterprises in member states to pay carbon taxes according to their emission quotas, which has effectively promoted the innovation and application of low-carbon technologies. The United States has introduced measures such as the Clean Power Plan and vehicle emission standards, driving the development of clean energy technologies. The Japanese government has implemented a Green Innovation Strategy, focusing on supporting the research and development of renewable energy technologies, such as solar and wind power, and energy-saving technologies. The successful experiences of these countries are worth learning from and referencing for China. China’s carbon peaking and carbon neutrality targets are more grand and long-term, set for 2030 and 2060, respectively, which are more aspirational compared to the targets of the European Union, the United States, and Japan. China is vigorously developing renewable energy and improving energy efficiency, which are measures similar to other countries, but it has also formulated more targeted policies such as the carbon trading market and green finance. Unlike other countries that rely more on market-based approaches, China’s path to carbon peaking and carbon neutrality emphasizes government leadership, driving progress through systematic policy and institutional arrangements. The government plays a greater role in regulating the policy environment. According to the data from the “China Green Technology Innovation Index Report (2021)”, green technology innovation has become an important driving force for China to achieve high-quality economic development and ecological environment protection [3]. The promotion and application of green technology innovation can not only effectively improve resource utilization efficiency and reduce pollutant emissions but also drive the transformation and upgrading of related industries, thereby achieving a win–win situation between economy and environment.

The urban agglomeration in the middle reaches of the Yangtze River is located in the core economic geography of China, covering multiple provinces such as Hubei, Hunan, and Jiangxi. This region not only bears rich natural resources but also plays an important demonstrative role in the process of China’s new urbanization and regional coordinated development. In September 2016, the “Outline of Yangtze River Economic Belt Development Plan” was officially issued, establishing a new development pattern of “one axis, two wings, three poles, and multiple points” for the Yangtze River Economic Belt, with the urban agglomeration in the middle reaches of the Yangtze River as one of the three poles, shouldering the responsibility of promoting high-quality regional economic development [4]. In the “Fourteenth Five-Year Plan” released in 2021, the state further clarified the strategic direction of comprehensively promoting the development of the Yangtze River Economic Belt, emphasizing the adherence to ecological priority and green development and advocating the joint protection of the Yangtze River without large-scale development [5]. This policy aims to coordinate the protection of the ecological environment and economic development and strive to create a beautiful China model of harmonious coexistence between man and nature. Within this strategic framework, the urban agglomeration in the middle reaches of the Yangtze River needs to promote green technology innovation to achieve the coordination of economic development and ecological environmental protection and set up a replicable model of green development for the whole country. For example, as the capital of Hubei Province, Wuhan has introduced a series of supporting policies in recent years to vigorously promote the research and application of green technologies such as energy conservation, clean production, and resource recycling. Wuhan has also actively built national-level new industrialization industry demonstration bases and national-level circular economy demonstration bases, exploring experiences for the low-carbon transformation of the urban agglomeration in the middle reaches of the Yangtze River.

Although the urban agglomeration in the middle reaches of the Yangtze River has achieved some progress in green development, with the carbon emission growth rate slowing down from 8.23% to 4.85% during 2011–2020 [6], there are still significant differences among the cities in terms of green technology innovation capabilities and carbon emission performance [7]. Therefore, exploring the relationship between green technology innovation and carbon emission performance in the urban agglomeration of the middle reaches of the Yangtze River, as well as their coupling and coordinating relationship, has important theoretical and practical significance. This not only helps deepen the understanding of the regional green development mechanism but also provides a basis for improving the green transformation strategy of the Yangtze River Economic Belt.

This study aims to systematically analyze the relationship between green technology innovation and carbon emission performance within the urban agglomeration of the middle reaches of the Yangtze River and explore the coupling and coordinating degree among different cities. First, this study constructs a comprehensive evaluation indicator system to quantify the green technology innovation level and carbon emission performance of each city in the urban agglomeration. Second, the coupling and coordinating model is used to analyze the coupling and coordinating relationship between green technology innovation and carbon emission performance among the cities and reveal the key factors affecting this relationship. Finally, based on the research results, policy recommendations will be proposed to optimize the regional green technology innovation policy and improve carbon emission performance in order to provide a scientific basis for the green and low-carbon transformation of the urban agglomeration in the middle reaches of the Yangtze River. This study not only has important significance for promoting the sustainable development of the urban agglomeration in the middle reaches of the Yangtze River but also provides a useful reference for low-carbon development and green technology innovation in other regions.

2. Literature Review

In recent years, the relationship between green technology innovation and carbon emission performance has become a global focus of attention [8,9,10]. Particularly against the backdrop of rapid economic development, how to achieve the coordinated development of economic growth and carbon emissions has become a key concern for policymakers and scholars around the world [11,12]. Green technology innovation refers to the process of reducing environmental burden and improving resource utilization efficiency through technological improvements and managerial innovations. Carbon emission performance, on the other hand, measures the ability of a region or industry to reduce carbon dioxide emissions while achieving economic growth. As an important region for China’s economic development, the study of green technology innovation capability and carbon emission performance in the urban agglomeration of the middle reaches of the Yangtze River is of great significance for promoting regional sustainable development.

The concept of green technology innovation has gradually gained widespread recognition, covering a wide range of aspects from pollution control technologies to improving resource utilization efficiency. Popp’s (2010) research has shown that strict environmental policies can stimulate enterprises to engage in green technology innovation, thereby effectively reducing carbon emissions [13]. Furthermore, Effie et al. (2012) explored the driving factors of eco-innovation in British enterprises and found that different environmental regulations have varying impacts on the eco-innovation of enterprises [14]. Acemoglu et al. (2012) proposed a model that elaborates on the interaction between technological change and environmental policy, pointing out that effective policies can accelerate the research, development, and application of green technologies, thereby reducing carbon emissions [15]. In addition, the effectiveness of green technology innovation in reducing carbon emissions has been widely recognized. The “Porter Hypothesis” proposed by Porter and van der Linde (1995) suggests that environmental regulations can, through stimulating innovation, reduce pollution emissions and resource waste [16]. Lanoie et al. (2011) tested the Porter hypothesis and explored how environmental policies affect the carbon emission performance of enterprises through green innovation [17]. Overall, the effectiveness of green technology innovation in improving carbon emission performance has been widely recognized, and its role not only manifests at the technological level but also depends on the coordinated efforts of enterprises and governments in policy support and resource allocation.

Existing studies have shown that green technology innovation plays a critical role in carbon emission reduction and economic growth in different countries and regions. Cainelli et al. (2012) analyzed a sample of 555 Italian firms and found that local network relationships have a complex impact on environmental innovation, which in turn affects carbon emissions [18]. Doran et al. (2014) used a sample of 2181 companies to study the driving factors of nine different types of eco-innovation in Ireland and found that regulatory and customer pressure can significantly promote the eco-innovation of enterprises, which in turn affects the environmental performance of enterprises [19]. Zeng et al. (2022) analyzed the level of green technology innovation in 30 provinces of China from 2001 to 2019 and found that green technology innovation has significant spatial spillover effects, with more pronounced carbon emission reduction effects in underdeveloped regions [9]. Habiba et al. (2022) found that green technological innovation, financial development, and the use of renewable energy collectively inhibit the growth of carbon emissions. Among them, the effect of green technological innovation is the most significant, and it is an important means to achieve the goal of carbon emission reduction [20]. Khan et al. (2023) used dynamic panel data analysis to examine 35 “Belt and Road” countries and found a significant positive relationship between technological innovation, economic growth, and carbon emission efficiency [21]. Hong et al. (2024) analyzed panel data from 276 cities in China from 2007 to 2017 and found that the relationship between green technology innovation and carbon emissions exhibits clear regional heterogeneity [22]. These studies suggest that the effectiveness of green technology innovation varies in different geographical and economic contexts, but overall, it contributes to the development of a low-carbon economy.

The urban agglomeration in the middle reaches of the Yangtze River is one of the most vibrant city clusters in China. In recent years, its economic growth rate has been relatively fast, but it has also been accompanied by a significant increase in carbon emissions. Relevant studies have shown that in the process of rapid urbanization and industrialization, the carbon emissions of the urban agglomeration in the middle reaches of the Yangtze River exhibit obvious spatial-temporal differences, and the carbon balance is imbalanced [23]. Carbon emissions show significant spatial differences due to changes in the land use structure and intensity within the urban agglomeration [24]. Technological innovation provides a strong driving force for reducing carbon emissions. Liu et al. found that technological innovation is beneficial to the environmental protection of the urban agglomeration in the middle reaches of the Yangtze River, and its influence extends to the surrounding areas through spillover effects [25]. Tian et al. found that technological innovation has a suppressive effect on the collaborative agglomeration of pollution [26]. Ye et al. found that green technology innovation can promote the green development of the urban agglomeration in the middle reaches of the Yangtze River [27]. The urban agglomeration in the middle reaches of the Yangtze River has made certain progress in green technology innovation but still faces many challenges. There are significant differences in the efficiency of green innovation in this region, which is not as stable compared to other parts of the Yangtze River Economic Belt. There are still imbalances in green technology innovation within the urban agglomeration, and it is necessary to strengthen regional coordination to promote the improvement of innovation efficiency [28].

In summary, the impact of green technology innovation on carbon emission performance is of great research significance in the urban agglomeration of the middle reaches of the Yangtze River. Compared with the existing research, the main innovation of this paper is that the existing literature mostly focuses on the analysis of the impact of green technological innovation on carbon emissions within a single province or at the national level, lacking in-depth discussion at the scale of regional urban agglomerations. At the regional level, more empirical research is needed to analyze the differences and commonalities between different regions, especially under different policy and market environments, where the impact of green technological innovation may have significant differences. This paper selects the urban agglomeration in the middle reaches of the Yangtze River as the research object, which can better reflect the internal mechanism of green innovation and carbon emission performance under the background of regional coordinated development.

3. Analysis of the Mechanism of the Impact of Green Technology Innovation on Carbon Emission Performance

Green technology innovation plays a crucial role in addressing global climate change and promoting the transition to a low-carbon economy. Its impact on carbon emission performance can be analyzed from multiple dimensions.

First, green technology innovation effectively controls carbon emissions by improving energy efficiency, optimizing resource allocation, and driving changes in production methods. Theoretical frameworks explain this mechanism through the theories of technological advancement and efficiency improvement, where the core of technological innovation lies in enhancing energy conversion efficiency and reducing energy losses during transmission, storage, and usage. By improving energy utilization efficiency, green technologies enable the economic system to significantly reduce energy consumption and carbon emissions while maintaining established production levels or outputs [29]. Within the neoclassical economic framework, improvements in production functions are typically driven by factors such as capital, labor, and technological progress. Green technology, as a specific manifestation of technological advancement, not only optimizes the allocation of factors within the production function but also introduces constraints related to resource and environmental factors. By mitigating the “negative externalities” of environmental pollution and resource waste, green technology reduces the total social costs of economic production activities, which is specifically reflected in carbon emission performance. The emergence of green technology innovation triggers an “endogenous transformation” of the production function, optimizing production under environmental constraints, thereby shifting technological progress from merely enhancing production efficiency to advancing towards “low emissions” and “green development.”

Second, green technology innovation systematically impacts the energy structure [30]. From the perspective of sustainable development, energy transition is viewed as a core pathway from high-carbon to low-carbon or even zero-carbon energy sources, with green technology innovation serving as a vital driving force for this transition. Technological innovation can reduce carbon emissions not only by directly decreasing the proportion of high-carbon energy usage but also by enhancing the adoption rate of low-carbon technologies and lowering the marginal costs of low-carbon energy, thus optimizing the energy structure. Specifically, the energy transition involves not only a simple energy substitution but also a “paradigm shift” triggered by technological innovation, moving from an industrial model reliant on fossil fuels to a sustainable model dependent on green energy.

From the perspective of innovation diffusion theory, green technology drives the collaborative transformation of the industrial and supply chains through the dissemination and diffusion of technology, further improving overall carbon emission performance. Technological diffusion essentially represents the spillover effects of technological innovation, achieving large-scale application through market mechanisms and policy support, and generating interlinkages across different economic sectors [31]. As green technologies gradually penetrate various stages of production, transportation, and consumption, the concepts and models of low-carbon development become deeply embedded, promoting improvements in the carbon emission performance of the entire economic system. This diffusion effect can be explained through the theory of technological path dependence; once green technology becomes mainstream in a particular field, it accelerates the application of low-carbon technologies through a locking effect, creating a virtuous cycle that leads to a gradual reduction in carbon emissions across the entire system. The mechanism by which green technology innovation influences carbon emission performance is illustrated in Figure 1.

Based on the above analysis, the following research hypotheses are proposed:

H1. 

Green technology innovation significantly improves carbon emission performance.

4. Research Methods and Data Sources

4.1. Data Sources

This study selects the data of 28 prefecture-level cities in the urban agglomeration of the middle reaches of the Yangtze River (excluding Xiantao, Qianjiang, and Tianmen—three county-level cities directly under the jurisdiction of Hubei Province) from 2011 to 2021 as the research sample. The carbon emission data are calculated based on the equations and coefficients in the “IPCC National Greenhouse Gas Inventory Guidelines”, and the energy data are from the China Carbon Emission Database. Other relevant data are from the “China City Statistical Yearbook”, “Hunan Statistical Yearbook”, “Hubei Statistical Yearbook”, and “Jiangxi Statistical Yearbook” from 2011 to 2021, and some indicators are derived from the original indicators. The research area is shown in Figure 2. The map used in this article is based on the National Geographic Information Public Service Platform website (https://www.tianditu.gov.cn/, accessed on 31 August 2024). The standard map production with the approval number GS (2024) 0650 downloaded has no modifications to the boundaries of the base map.

4.2. Variable Selection

4.2.1. Explanatory Variable: Green Technology Innovation Level

Referring to the research by Wang et al. [32], this paper constructs an indicator system for green technological innovation, selecting the number of green patent applications to measure the level of green technological innovation. This includes the sum of green invention patent applications and green utility model patent applications, as shown in

Table 1. Indicator system for measuring the digital economy.

Evaluation System	Criterion Layer	Indicator Layer	Indicator Attribute
Green technological innovation system	Green invention patent applications	Number of green invention patent applications	Positive impact
		Number of green utility model patent applications	Positive impact

.

4.2.2. Dependent Variable: Carbon Emission Performance

Following the approach of Chen et al. [33], this study defines the carbon emission performance index from the perspective of ecological economics. The carbon emission performance index is defined as the minimization of energy input (i.e., carbon emissions) and the maximization of economic and social welfare output, consisting of two sub-indices: carbon economic performance and carbon welfare performance. The carbon economic performance is measured by the ratio of Gross Domestic Product (GDP) to total carbon emissions, while the carbon welfare performance is measured by the ratio of the Human Development Index (HDI) to total carbon emissions. The specific calculation equations are as follows:

$$\mathrm{CEP}={\displaystyle \frac{1}{2}}\mathrm{CEE}+{\displaystyle \frac{1}{2}}\mathrm{CSE}$$

(1)

where CEP represents the comprehensive carbon emission performance index, CEE represents the carbon emission economic performance, and CSE represents the carbon emission welfare performance.

$$\mathrm{CEE}={\displaystyle \frac{\mathrm{GDP}}{\mathrm{CE}}}$$

(2)

$$\mathrm{CSE}={\displaystyle \frac{\mathrm{HDI}}{\mathrm{CE}}}$$

(3)

In Equations (2) and (3), CE represents the carbon emissions, which measure the energy input; GDP represents the Gross Domestic Product, which measures the economic output; and HDI represents the Human Development Index, which measures the social welfare output.

$$\mathrm{HDI}={\displaystyle \frac{1}{3}}({\mathrm{H}}_1+{\mathrm{H}}_2+{\mathrm{H}}_3)$$

(4)

The Human Development Index (HDI) is a composite index consisting of three basic indicators: life expectancy, education level, and income level [3]. Since the life expectancy data are only available at the provincial level and difficult to obtain at the city level, previous studies have shown a significant correlation between medical level and life expectancy. This study uses the average number of medical beds per 10,000 residents (H1) to replace the life expectancy indicator. The education indicator (H2) is measured by the number of students enrolled per 10,000 people, and the income indicator (H3) is measured by the per capita income level. The three social welfare indicators are equally important, with equal weights, and are calculated according to Equation (4).

4.2.3. Control Variables

We selected urbanization level (UR), per capita GDP (PGDP), and energy consumption (EC) as control variables. The urbanization level has a dual impact on carbon emission performance: on the one hand, the concentration of population and economic activities increases energy demand and carbon emissions; on the other hand, improvements in infrastructure and technology may enhance energy efficiency and reduce carbon emissions. Per capita GDP reflects the economic development level of a region; higher per capita GDP is often associated with greater energy consumption and carbon emissions, while also indicating a greater capacity for investment in green technologies. Finally, energy consumption is a direct factor influencing carbon emissions. Controlling for energy consumption allows us to eliminate its direct impact on carbon emission performance, thereby enabling a more accurate assessment of the emission reduction effects of green technology innovation.

4.3. Model Construction

4.3.1. Comprehensive Evaluation Model

This study employs the entropy weight method to assign weights to the evaluation indicators of green technology innovation.

Normalization: First, standardize the indicators such as the number of green invention patent applications and the number of green utility model patent applications using the range method.

For positive impact indicators:

$$X_{ij}^{\prime }={\displaystyle \frac{X_{ij}-\min X_{\mathrm{j}}}{\max X_j-X_{ij}}}$$

(5)

For negative impact indicators:

$$X_{ij}^{\prime }={\displaystyle \frac{\max X_j-X_{ij}}{\max X_j-\min X_j}}$$

(6)

$X_{ij}^{\prime }$ and $X_{ij}$ are the values and original values of the j-th indicator in the i-th province after standardization processing, and $\max X_j$ and $\min X_j$ are the maximum and minimum values of this indicator, respectively.

Calculate the weight of the j-th indicator in the i-th year:

$$Y_{ij}={\displaystyle \frac{X_{ij}^{\prime }}{{\displaystyle \sum _{i=1}^m{X}_{ij}^{\prime }}}}$$

(7)

Calculate Information Entropy:

$$e_j=-k{\displaystyle \sum _{i=1}^m(Y_{ij}\times \ln Y_{ij})}$$

(8)

If $k={\displaystyle \frac{1}{\ln m}}$, then $0\le e_j\le 1$, and when $Y_{ij}=0$, let $Y_{ij}\times \ln Y_{ij}=0$, where m is the number of evaluation years and n is the number of indicators.

Calculation of Information Entropy Redundancy is performed as follows:

$$d_j=1-e_j$$

(9)

Determine Weights is performed as follows:

$$w_i={\displaystyle \frac{d_j}{{\displaystyle \sum _{j=1}^n{d}_j}}}$$

(10)

Calculate Green Technology Innovation is performed as follows:

$$GTI={\displaystyle \sum _{j=1}^n(X_{ij}^{\prime }\cdot w_i)}$$

(11)

4.3.2. Coupling and Coordination Degree Model

The Coupling Coordination Degree Model is a mathematical framework used to measure the interactions and coordinated development between multiple systems. Based on coupling theory, which was originally applied in the field of physics, this model describes the degree of mutual influence and interaction between two or more systems. When studying complex systems such as social, economic, and environmental frameworks, the Coupling Coordination Degree Model quantifies the strength of coupling interactions and the state of coordinated development among different systems. By introducing the concepts of Coupling Degree and Coordination Degree, this model evaluates the interactive relationships and the degree of coordinated development between systems. In this study, we employ the Coupling Coordination Degree Model to reveal the interactions and coordinated development between green technology innovation and carbon emission performance. The specific calculation process is as follows:

Calculation of Coupling Degree C is performed as follows:

$$\mathrm{C}=2\times {\left\{{\displaystyle \frac{\mathrm{f}\left(\mathrm{x}\right)\times \mathrm{g}\left(\mathrm{x}\right)}{{\left[\mathrm{f}\left(\mathrm{x}\right)+\mathrm{g}\left(\mathrm{x}\right)\right]}^2}}\right\}}^{{\displaystyle \frac{1}{2}}}$$

(12)

Calculation of T Comprehensive Coordination is performed as follows:

$$\mathrm{T}=\alpha \mathrm{f}\left(\mathrm{x}\right)+\beta \mathrm{g}\left(\mathrm{x}\right)$$

(13)

Calculation of D Coupling Coordination is performed as follows:

$$\mathrm{D}=\sqrt{\mathrm{C}\times \mathrm{T}}$$

(14)

In this context, f(x) represents the level of green technology innovation, while g(x) denotes carbon emission performance. The parameters α and β indicate the weights to be determined, reflecting the influence coefficients of green technology innovation and carbon emission performance. This study assumes that both factors are equally important, thus setting α = β = 0.5. Additionally, drawing on the research conducted by Li et al. [34], the coupling coordination degree is uniformly divided into ten intervals. The value ranges and grading standards for each level of coupling coordination degree are presented in

Table 2. Coupling coordination degree classification.

Coordination Degree Range
[0.0–0.1)	Extremely disrupted	[0.5–0.6)	Barely coordinated
[0.1–0.2)	Severely disrupted	[0.6–0.7)	Marginally coordinated
[0.2–0.3)	Moderately disrupted	[0.7–0.8)	Intermediate coordinated
[0.3–0.4)	Mildly disrupted	[0.8–0.9)	Good coordinated
[0.4–0.5)	Near disrupted	[0.9–1.0)	Excellent coordinated

.

4.3.3. Fixed Effects Model

To examine the overall impact of green technology innovation on carbon emission performance and test hypotheses H1, a fixed-effect model is constructed as a baseline regression model as follows:

CEP_it = β₀ + β₁GTI_it + β_setZ_it + λ_i + γ_t + ε_it

(15)

where i represents the city; t refers to the observation year; CEP_it represents the degree of carbon emission performance of city i associated with in year t; GTI_it represents the degree of green technology innovation of city i associated with in year t; Z_i,t represents a set of control variables; λ and γ express city fixed effect and time fixed effect, respectively; and ε_i,t is the error term. β₀ is a constant term, β₁ represents the primary term coefficient, and β_set represents the control variable coefficients.

Table 3. Variable definition table.

Variables	Definitions	Source	Cities
Green technology innovation level (GIT)	The logarithm of 1 plus the frequency of the number of green patent applications	CNRDS Database	Changde, Ezhou, Fuzhou, Hengyang, Huanggang, Huangshi, Jian, Jingmen, Jingzhou, Jingdezhen, Jiujiang, Loudi, Nanchang, Pingxiang, Shangrao, Wuhan, Xianning, Xiangtan, Xiangyang, Xiaogan, Xinyu, Yichang, Yichun, Yiyang, Yingtan, Yueyang, Changsha, and Zhuzhou
Carbon emission performance (CEP)	The minimization of energy input (i.e., carbon emissions) and the maximization of economic and social welfare output, consisting of two sub-indices: carbon economic performance and carbon welfare performance.	Statistical Yearbook of Chinese Cities
Urbanization level (UR)	Total urban population/Total population at the end of the year	National Statistics Bureau, Provincial Statistical Yearbooks, and China Statistical Yearbook
Per capita GDP (PGDP)	Total GDP/Average annual population
Energy consumption (EC)	The city-level nighttime lights were used to reverse the city-level energy consumption reference literature. ArcGIS was used to calculate the total DN of each city-level city on the Chinese mainland, and the simulated energy consumption of each city was calculated by inversion, and the inversion results were spatialized to obtain the total energy consumption data of each city in China.

shows the definitions of all variables.

5. Results Analysis

5.1. Spatio-Temporal Evolution Analysis of the Coupling and Coordination Between Green Technology Innovation Level and Carbon Emission Performance

5.1.1. Temporal Change Analysis

Figure 3 shows the changes in the average value of green technological innovation in the urban agglomeration of the middle reaches of the Yangtze River. As shown in Figure 3, the level of green technology innovation in the urban agglomeration of the middle reaches of the Yangtze River has shown a significant upward trend from 2011 to 2021, increasing from 0.0122 in 2011 to 0.0922 in 2021, an increase of about 7.6 times, indicating that the region has made significant progress in green technology R&D and application. From 2011 to 2015, the level of green technology innovation steadily increased, with an average annual growth rate of about 29.7%, reflecting the driving effect of early policy incentives and corporate investment. From 2016 to 2018, the growth rate slowed down to 21.5%, indicating that there may be a time lag between R&D and application in the technology innovation cycle. From 2019 to 2020, the growth rate rebounded to 18.1% but slightly declined in 2021, which may be affected by international situations and the pandemic. The instability of the international situation may lead to trade disruptions and supply chain interruptions, thereby affecting the R&D and application of green technologies. The impact of the pandemic may be reflected in the restrictions on economic activities and the tightening of corporate funds, thereby reducing investment in green technology innovation. Hubei Province was greatly affected by the pandemic, especially the backwardness of cities like Wuhan in Hubei, which led to a decline in the average level of green technology innovation. Overall, although the level of green technology innovation declined slightly in 2021, this is only a stage fluctuation in the development process. In the future, it is necessary to further strengthen policy support and market promotion to maintain the sustained growth of green technology innovation and promote the realization of regional green development and carbon reduction goals.

Figure 4 shows the changes in the average value of carbon emission performance in the urban agglomeration of the middle reaches of the Yangtze River. As shown in Figure 4, the carbon emission performance of the urban agglomeration in the middle reaches of the Yangtze River has shown a continuous upward trend from 2011 to 2021, increasing from 0.1136 in 2011 to 0.3206 in 2021, an increase of about 1.82 times, indicating that the region has made significant progress in reducing carbon emission intensity and promoting green development. From 2011 to 2015, the average annual growth rate of carbon emission performance was about 13.8%, showing the positive effects of early environmental policies and energy structure adjustments. From 2016 to 2018, the growth rate slowed down to 8.5%, which may be due to the increasing difficulty of further improvement under a high baseline. From 2019 to 2021, the growth rate rebounded to 9.2%, benefiting from increased investment in green technology innovation, industrial structure adjustment, and the implementation of strict environmental regulations. Overall, the improvement of carbon emission performance in this region is closely related to policy drivers, green technology application, and industrial upgrading, and future measures need to be further strengthened in these aspects to continuously improve carbon emission performance.

As shown in Figure 5, the coupling and coordination degree between green technology innovation and carbon emission performance in the urban agglomeration of the middle reaches of the Yangtze River has shown an overall upward trend from 2011 to 2021, indicating that the synergy between green technology innovation and carbon emission performance has been continuously strengthened. The coupling and coordination degree of provincial capital cities such as Wuhan, Changsha, and Nanchang have significantly improved. For example, Wuhan has risen from 0.4527 in 2011 to 0.9424 in 2021, showing the significant effects of increased investment in green technology R&D and policy support. These provincial capital cities generally have a relatively high economic level and often have more resources invested in green technology innovation, while also having stronger and more efficient policy formulation and implementation, which has led to their outstanding performance in the coordinated development of green technology innovation and carbon emission performance. Cities at the prefecture level, such as Xiangyang, Zhuzhou, and Yichang, have shown a stable growth trend, reflecting the role of green technology innovation in improving carbon emission performance. At the same time, the coupling and coordination degree of some cities, such as Changde, Huanggang, and Jingdezhen, has fluctuated. These are prefecture-level cities with relatively weak economic strength and resources, but they have also been gradually improving the coupling and coordination of green technology innovation and carbon emission performance through a stable development strategy. The fluctuations in some cities may be influenced by factors such as policy changes and economic structure adjustments. The instability of policies may affect enterprises’ investment and enthusiasm in green technology innovation, while economic structure adjustments may introduce uncertainties in carbon emission performance in the short term. Overall, although there are differences in the coupling and coordination level among different cities, future efforts are still needed to further strengthen policy support, optimize resource allocation, and enhance the coordinated development of green technology innovation and carbon emission performance.

5.1.2. Spatial Pattern Analysis

Figure 6a–d reflect the spatial patterns of the coupling and coordination degree between green technology innovation and carbon emission performance in the urban agglomeration of the middle reaches of the Yangtze River in 2011, 2014, 2017, and 2021, respectively. It can be seen that from 2011 to 2021, the coupling and coordination degree of different regions has shown spatial heterogeneity and dynamic evolution.

The coupling and coordination degree of most cities has gradually shifted from “extremely uncoordinated” and “severely uncoordinated” to “moderately uncoordinated” or higher levels, indicating that the synergy between green technology innovation and carbon emission performance in the region has improved, but there are significant differences in spatial distribution. The coupling and coordination degree of central cities such as Wuhan and Changsha has improved significantly, with Wuhan rising from the “approaching uncoordinated” state (0.4527) in 2011 to the “excellent coordination” state (0.9424) in 2021, and Changsha rising from the “approaching uncoordinated” level (0.4011) to the “moderately coordinated” state (0.7676), showing a strong coupling effect in green development and carbon reduction. In contrast, the improvement of coupling and coordination degree in many small and medium-sized cities such as Changde, Fuzhou, and Huanggang has been slow, and they are still in the “moderately uncoordinated” or “slightly uncoordinated” stage. For these cities with relatively weak coupling effects, the main reasons are as follows: First, in terms of the application of green technologies, these small and medium-sized cities may face difficulties in technology introduction and promotion, lacking sufficient financial and talent support, leading to a relatively low degree of green technology application. Second, the policy support is relatively insufficient. Compared with large cities, small and medium-sized cities may have certain lags in policy formulation and implementation and find it difficult to effectively incentivize enterprises and the public to engage in green innovation and carbon reduction. Finally, the limitation of economic development level is also an important factor. The economic strength of small and medium-sized cities is relatively weak, and they may not be able to invest enough resources in green technology R&D and infrastructure construction.

The differences in spatial patterns reflect the uneven application of green technologies, policy support intensity, and economic development levels within the region. In the future, it is necessary to strengthen the promotion and application of green technology innovation in small and medium-sized cities in order to promote the coordinated development and low-carbon transformation of the entire urban agglomeration in the middle reaches of the Yangtze River.

5.2. Impact of Green Technology Innovation on Carbon Emission Performance

5.2.1. Baseline Regression

The baseline regression results are presented in

Table 4. Regression results.

	(1)	(2)
Variable	CEP	CEP
GTI	0.287 ***	0.228 ***
	(9.199)	(6.466)
UR		0.118
		(1.335)
PGDP		0.000
		(0.695)
EC		0.000 ***
		(3.182)
_cons	0.158 ***	0.057
	(13.393)	(1.517)
Year	Yes	Yes
City	Yes	Yes
N	308	308

*, **, and *** indicate significance at the 10%, 5%, and 1% statistical levels, respectively.

. Column (1) in Table 4 shows the regression results from the model without control variables, while Column (2) presents the results after including control variables. The analysis of Column (1) provides preliminary evidence of a significant positive effect of green technology innovation on carbon emission performance (β = 0.287, p < 0.01). Column (2) shows that even when accounting for the influence of control variables, green technology innovation continues to significantly improve carbon emission performance at the 1% significance level (β = 0.228, p < 0.01). This finding confirms that green technology innovation has a robust positive impact on carbon emission performance, even after controlling for other influencing factors, thereby validating Hypothesis 1.

5.2.2. Robustness Test

To provide a more robust assessment of the impact of green technology innovation on carbon emission performance in the Middle Reaches of the Yangtze River Urban Agglomeration, this study performs several robust tests on the data, as summarized in

Table 5. Robustness test results.

	(3)	(4)	(5)	(6)
Variable	CEP	CEP	CEP	CEP
GTI	0.281 **
	(3.207)
GTI1		0.000 ***
		(6.445)
lGTI			0.805 ***	0.266 **
			(28.544)	(3.253)
UR	0.011	0.118	0.005	0.008
	(0.093)	(1.342)	(0.204)	(0.119)
PGDP	−0.000	0.000	0.000 *	−0.000
	(−0.082)	(0.745)	(2.554)	(−0.101)
EC	0.000	0.000 ***	−0.000 *	0.000 **
	(1.719)	(3.185)	(−2.154)	(2.685)
_cons	0.118 *	0.056	0.003	0.241 ***
	(2.115)	(1.496)	(0.269)	(4.552)
Year	Yes	Yes	Yes	Yes
City	Yes	Yes	Yes	Yes

*, **, and *** indicate significance at the 10%, 5%, and 1% statistical levels, respectively.

. First, to account for the impact of the COVID-19 pandemic, the regression analysis excludes data from 2020–2021. The results based on a shorter time frame indicate that green technology innovation has a significantly positive effect on urban economic resilience at the 1% significance level (β = 0.281, p < 0.01). As shown in Column (3) of Table 5, these results remain consistent.

Second, the regression is reconducted using the total number of green inventions as an alternative explanatory variable (GTI1). As indicated in Column (4) of Table 5, the positive effect of green inventions on urban carbon emission performance remains significant at the 1% level (β = 0.000, p < 0.01).

Third, to address potential endogeneity concerns, and after passing tests for identification, weak instruments, and over-identification, this study uses lagged-period explanatory variables for an instrumental variable test. As detailed in Columns (5) and (6) of Table 5, the conclusions remain robust (β = 0.805, p < 0.01).

These analyses collectively reinforce the reliability of the findings regarding the impact of green technology innovation on carbon emission performance (β = 0.266, p < 0.05).

5.2.3. Heterogeneity Test

As regulators of regional environmental performance, governments oversee local carbon emission intensity through the implementation of policies. Consequently, urban environmental regulations can enhance local carbon emission performance and act as a substitute effect for local green technology innovation. Based on this premise, we hypothesize that the impact of green technology innovation on carbon emission performance is more pronounced in cities with weaker environmental regulations.

To test this, we analyzed government work reports by calculating the frequency of environmental-related terms, including “environmental protection, pollution, energy consumption, emission reduction, pollution, ecology, green, low carbon, air, chemical oxygen demand, sulfur dioxide, carbon dioxide PM₁₀, PM_2.5”, and their proportion relative to the total word count to assess the strength of environmental regulation in each city. We then categorized the sample into two groups by using the median regulation strength as the threshold—cities with strong and weak environmental regulations.

The results of the heterogeneity test are presented in

Table 6. Heterogeneity test results.

	(7)	(8)
	CEP	CEP
GTI	0.114	0.208 ***
	(1.472)	(4.057)
UR	0.255	0.044
	(1.025)	(0.373
PGDP	0.000	0.000
	(0.822)	(1.497)
EC	0.000 **	0.000
	(2.822)	(1.563)
_cons	−0.033	0.076
	(−0.285)	(1.376)
Year	Yes	Yes
City	Yes	Yes
N	156	152

*, **, and *** indicate significance at the 10%, 5%, and 1% statistical levels, respectively.

. Column (7) shows the regression results for cities with stronger environmental regulations, where the coefficient for green technology innovation is positive but not statistically significant. In contrast, Column (8) displays the results for cities with weaker environmental regulations, where green technology innovation significantly improves carbon emission performance at the 1% significance level, aligning with our expectations.

6. Conclusions and Discussion

This study selects data from 28 prefecture-level cities in the central Yangtze River urban agglomeration from 2011 to 2021 as the research sample, constructing an evaluation system for green technology innovation and measuring carbon emission performance. The Coupling Coordination Degree Model and fixed effects model are employed to empirically test the impact of green technology innovation on carbon emission performance and the coupling relationship between the two. The results indicate that both green technology innovation and carbon emission performance in the central Yangtze River urban agglomeration are in a steadily rising phase, achieving certain progress. However, the overall coupling coordination degree across regions exhibits spatial heterogeneity, with some developed cities showing a coupling coordination degree greater than the average, reflecting the unevenness of regional development.

The fixed effects model demonstrates a positive impact of green technology innovation on carbon emission performance, a conclusion that remains valid after a series of robustness and endogeneity tests. Heterogeneity tests reveal that in cities with weaker environmental regulations, green technology innovation has a more significant impact on carbon emission performance. Furthermore, there are notable differences in the influence of green technology innovation on carbon emission performance across regions. Specifically, the development of green technology innovation in large cities significantly outpaces that of small cities, with this gap particularly evident in the disparities in green technology application, policy support, and economic development levels.

In the central Yangtze River urban agglomeration, economically developed large cities, equipped with better resource allocation capabilities and policy support, can more rapidly advance the research and application of green technologies. This has led to significant progress in green technology innovation and superior improvements in carbon emission performance. In contrast, small cities face multiple challenges, including resource shortages, insufficient policy support, and lower economic development levels, resulting in a slower pace of green technology innovation. Although these cities have made some progress in green technology application and carbon emission performance, the overall magnitude and speed of improvement lag far behind that of large cities. This differentiated development within the region reflects the complex relationship between green technology innovation and carbon emission performance, highlighting the need for targeted policy adjustments and support for the promotion and application of green technology in cities at different stages of development.

Despite these regional disparities, the overall level of green technology innovation and carbon emission performance in the central Yangtze River urban agglomeration has shown a steady upward trend in recent years. This progress indicates that cities within the region are gradually achieving results in green technology innovation and carbon emission management. To further narrow the gap between regions, policymakers need to enhance support for green technology innovation in small cities, optimize resource allocation, promote the widespread application of green technologies, and continually improve the policy environment to achieve balanced enhancement of overall carbon emission performance and sustainable development within the region.

This study also has certain limitations, as it only considers the Chinese context. However, there are significant differences in environmental policies and economic development conditions across different countries and regions. The impact of green technology innovation levels on carbon emission performance in the central Yangtze River urban agglomeration under different scenarios warrants further investigation. Future research could explore the differential effects in other regions of China and in market economies such as the United States and Japan.