Bridging the Gap: Spatial Disparities in Coordinating New Infrastructure Construction and Inclusive Green Growth in China

Abstract

New infrastructure construction (NIC) is pivotal for advancing China’s sustainable development, yet the spatial interdependencies between NIC and inclusive green growth (IGG) remain critically underexplored. This study quantifies provincial-level NIC–IGG coordination dynamics across China (2011–2023) using a novel coupling coordination model. We further dissect regional disparities through Dagum Gini decomposition and identify causal drivers via QAP regression analysis. Key findings reveal: (1) Despite a gradual upward trend, overall NIC–IGG coordination remains suboptimal, hindering sustainable transition; (2) Regional disparities follow a “U-shaped” trajectory, primarily driven by inter-regional imbalances; (3) Uneven marketization is the dominant factor fragmenting spatial coordination. Our results expose systemic barriers to regionally integrated sustainable development and provide actionable pathways for place-based policies that synchronize NIC investment with IGG objectives.

1. Introduction

China’s economy currently faces significant downward pressure. The traditional development model has become unsustainable, and a series of “growth dilemmas”—including unbalanced and inadequate development, distorted factor pricing, and suboptimal ecological conditions—continue to constrain the nation’s high-quality development progress [1,2]. The report to the 20th National Congress of the Communist Party of China explicitly calls for “enhancing people’s wellbeing and raising quality of life” and “accelerating the green transformation of the development model.” This directive charts the course for greening China’s economy and society and fostering inclusive transformation, ultimately aiming to promote harmonious coexistence between humanity and nature [3]. However, China’s vast territory means significant disparities exist among provinces in resource endowment, industrial structure, technological level, and economic conditions [4]. These disparities lead to persistent problems of unbalanced and insufficient economic, social, and ecological development across regions. Fundamental pressure from pollution prevention and control also persists [2]. The core cause of these issues lies in the shortcomings of the traditional extensive economic growth model, specifically its lack of inclusivity and green dimensions [5]. The pursuit of sustainable and inclusive growth models has emerged as a global imperative [6]. Taking the European Union as an example, its “European Green Deal” aims to achieve climate neutrality by 2050 through systematic change, while simultaneously promoting resource efficiency, protecting biodiversity, and ensuring fairness in the transition process [7,8]. The “sustainable growth” pursued by the European Green Deal is highly aligned with the inclusive green growth (IGG) central to this paper in their core objectives—environmental sustainability, social inclusion, and economic resilience. Consequently, promoting inclusive green growth, focused on coordinated regional and urban-rural development and improved ecological environment quality, has become an inherent requirement and a crucial imperative for achieving high-quality economic development in China [9], but also resonates with the shared direction of major global economies transitioning towards sustainable development models.

Realizing this transformation—whether China’s IGG or the EU’s Green Deal—is fundamentally dependent on strategic investment in next-generation infrastructure. Against this backdrop, new infrastructure construction (NIC) emerges as a key driver for promoting IGG [10]. NIC upgrades traditional infrastructure through digitalization, networking, intelligence, and greening using next-generation information technologies, building more efficient spatiotemporal networks [11]. As a strategic platform for high-quality economic development, NIC provides the essential foundation for the digital economy and strategic emerging industries, fostering numerous innovative applications and industrial forms [12] Through platforms like information infrastructure, integrated infrastructure, and innovative infrastructure, NIC drives green innovation, enabling socioeconomic growth alongside ecologically friendly and sustainable IGG [13].

As a pivotal engine driving green transformation and inclusive growth, NIC holds critical strategic importance. However, China’s pronounced regional development imbalances necessitate an in-depth investigation into the coordinated development mechanism between NIC and IGG, while mitigating the risk that NIC may exacerbate regional disparities. Therefore, this study focuses on the following core questions: How can the development levels of NIC and IGG be objectively measured? What are the inherent mechanisms and spatiotemporal patterns of their coordinated development? Which factors can effectively narrow regional disparities? Clarifying these issues holds substantial decision-making significance for optimizing NIC deployment, fostering a regionally coordinated new paradigm of inclusive green growth, and underpinning the national high-quality development strategy.

2. Literature Reviews and Contributions

2.1. Literature Reviews

The concept of inclusive green growth (IGG) emerged after the 2012 Rio+20 Summit [14]. Following the UN’s new Sustainable Development Goals in 2016, countries and regions gradually adopted strategies targeting IGG [15,16]. Yet, a unified consensus on its precise definition remains elusive. Scholars generally interpret its connotation through two main perspectives. On one hand, it is defined from the field of development economics, where it is seen as a sustainable development approach emphasizing that economic growth must inherently possess social inclusivity and environmental friendliness [17]. On the other hand, it is defined from the perspective of welfare economics, focusing on economic growth as a means to enhance social welfare for both current and future generations [18]. Identifying factors influencing IGG is another key research direction. For instance, He et al. (2022) identify technological progress as a critical factor [19]. They argue that imbalances in infrastructure, healthcare levels, and educational equity across regions lead to low overall IGG efficiency and significant regional variations. Dong and Xu (2025) found that government attention significantly promotes IGG [14].

Therefore, the IGG paradigm integrates the core tenets of green development and inclusive development. It emphasizes pursuing social equity and environmental sustainability alongside economic growth, offering innovative solutions to current challenges. This approach is an essential pathway toward the synergistic development of the economic, social, and natural systems [6]. To achieve this goal, provinces are actively exploring specific policy solutions across multiple domains—including industrial structure optimization, energy transition, urban–rural development enhancement, transportation improvements, and sci-tech innovation empowerment—to systematically advance IGG practices [20,21].

Guided by the new development philosophy, driven by technological innovation, and based on information networks [22], NIC is an infrastructure system designed to meet high-quality development needs. It provides services like digital transformation, intelligent upgrading, integrated innovation, energy conservation, and emission reduction. NIC promotes green, low-carbon infrastructure across energy, transportation, construction, and other sectors, adapting to the demands of IGG [23]. NIC primarily encompasses three categories: information infrastructure, integrated infrastructure, and innovative infrastructure [24]. The Third Plenary Session of the 20th Central Committee proposed building planning and standard systems for the NIC and improving mechanisms for its integrated utilization. This constitutes a major strategic initiative to accelerate NIC and application, vigorously develop new quality productive forces, and solidify the advanced material foundation for building a modern socialist country [25]. As China enters the high-quality development stage, NIC’s role in practicing ecological civilization and enhancing environmental efficiency in economic development becomes increasingly vital [26]. Building on this, scholars have explored the impact of NIC on IGG from multiple angles. The first research branch concerns the relationship between NIC and economic development. Pradhan et al. (2021) established telecommunications infrastructure as critical for both short-term and long-term growth in developing economies [27]. The second branch of research focuses on the impact of NIC on inclusive development. Galperin et al. (2022) identified a positive linkage between NIC and rural income [28]. Xiang et al. (2022) further demonstrated how digital device development enhances social inclusion, including education and employment equity [29]. The third research branch explores the green characteristics of NIC. Wang and Shao (2024) empirically validated the digital infrastructure’s industrial energy efficiency enhancement through green innovation and structural advancement [30], paralleled by Zhang et al.’s (2022) findings on air quality improvement via industrial restructuring and innovation pathways [12].

Through a review of the literature, it is found that existing studies have made valuable explorations on the relationship between NIC and IGG, but there is still significant room for expansion. First, most existing literature focuses on the one-way impact of NIC on IGG, with relatively few studies on the synergistic development effects between the two. Second, there is insufficient exploration of the causes of regional synergistic differences, and a lack of theoretical support for solving the imbalance in their development. Therefore, based on provincial panel data from 2011 to 2023, this paper conducts an in-depth analysis of the causes and mechanisms behind the regional differences in the coordinated development of NIC and IGG.

2.2. Contributions

This study makes marginal contributions in three key aspects: First, adopting a coupling coordination perspective; it elucidates the coupling mechanism between NIC and IGG, clarifies their interaction mechanism, and employs a coupling coordination degree (CCD) model to scientifically measure their level of coordinated development. This facilitates a deeper understanding of the bidirectional relationship and coordinated development dynamics between NIC and IGG. Second, utilizing the Dagum Gini coefficient decomposition; it quantifies the overall disparity in NIC–IGG coordination across China. The analysis further decomposes this disparity into intra-regional (within Eastern, Central, and Western regions) and inter-regional components, revealing the multi-dimensional sources of the overall inequality. This comprehensively delineates the uneven pattern of coordinated development nationwide and summarizes its evolutionary trajectory, providing empirical grounding for policies aimed at narrowing regional disparities and achieving balanced development. Third, applying the quadratic assignment procedure (QAP) model; it systematically identifies the constraining factors affecting NIC and IGG. This offers crucial decision-making support for constructing a new development paradigm characterized by complementary advantages and synergistic symbiosis, thereby facilitating China’s transition towards inclusive development goals and green, high-quality growth.

The paper is organized as follows: Section 3 presents the research method and empirical data, including entropy value, coupling coordination degree model, Dagum Gini coefficient method, and quadratic assignment procedure model. Section 4 outlines the measurement results of the coordinated development of NIC and IGG, showing the differences among the three regions in China. Section 5 presents the analysis of regional disparities by using Dagum Gini coefficient method. Section 6 outlines the results of the formation mechanism of disparities in the CCD between NIC and IGG, using the quadratic assignment procedure model. Finally, in Section 7, we summarize the research findings of the paper, propose relevant policy recommendations, and highlight the limitations of the study as well as future prospects.

3. Research Method and Empirical Data

3.1. Study Area and Data Resources

This study utilizes panel data including 30 Chinese provinces from 2011 to 2023, (excluding Tibet due to data availability) categorized into three national strategic regions: eastern, central and western China (Figure 1). According to the National Bureau of Statistics, the eastern region includes Beijing, Tianjin, Hebei, Liaoning, Shanghai, Jiangsu, Zhejiang, Fujian, Shandong, Guangdong and Hainan; the central region includes Shanxi, Jilin, Heilongjiang, Anhui, Jiangxi, Henan, Hubei and Hunan; and the western region includes Inner Mongolia, Guangxi, Chongqing, Sichuan, Guizhou, Yunnan, Shaanxi, Gansu, Qinghai, Ningxia and Xinjiang.

3.2. Indicator Systems

NIC builds upon information networks. The continuous advancement of network infrastructure construction effectively deepens the integration of the internet with economic and social entities, fostering positive interaction between the supply and demand sides of production factors [31]. Specifically, leveraging its characteristics of intelligence, sharing, and platformization, NIC utilizes information infrastructure, integrated infrastructure, and innovative infrastructure to achieve the goals of alleviating ecological pressure and reducing carbon emissions [30,32].

According to the definition of NIC by the National Development and Reform Commission, NIC primarily encompasses three categories: information infrastructure, integrated infrastructure, and innovative infrastructure [24]. Information infrastructure refers to the infrastructure supporting information flow and data processing. It primarily encompasses the construction of communication networks, cloud computing platforms, the Internet of Things (IoT), and network security facilities. Information infrastructure exhibits strong network externalities [12]. Employing methods such as big data analytics and artificial intelligence, it can identify potential energy-saving opportunities, optimize operational plans, and provide data-driven decision support. This process promotes green technology innovation and reduces carbon emissions [33]. Integrated infrastructure emphasizes the interconnection and coordinated development of various infrastructure types. It mainly includes the construction of smart transportation, smart energy, smart cities, and integrated social services [30]. Intelligence in this context involves multiple engineering activities requiring minimal human intervention. These activities rely on supporting technologies such as interactive digital modeling and simulation, ubiquitous sensing and broadband IoT, factory manufacturing and robotic construction, artificial intelligence and decision support, as well as crucial low-carbon and ecological protection technologies [34]. Innovative infrastructure focuses on infrastructure that supports scientific research and technological innovation. Its core purpose is assisting enterprises in innovation and facilitating the transformation of innovations. Green innovation, representing innovation activities that reduce environmental impact, can significantly decrease environmental pollution [35].

Based on the categories and connotations of NIC, and following the principles of data availability, system science, objectivity, comprehensiveness, and hierarchical structure, this paper constructs an indicator system based on the classification of NIC (

Table 1. Comprehensive evaluation index system for new infrastructure construction (NIC).

System		Primary Indicators	Secondary Indicators	Attribute	Weights
New Infrastructure Construction (NIC)	Information Infrastructure Construction	Telecom Major Communication Capacity	Mobile Phone Base Stations (10,000 units)	+	0.048
			Mobile Phone Switch Capacity (10,000 units)	+	0.034
			Fiber Optic Cable Line Length (km)	+	0.047
		Main Internet Indicators Development	Number of Domain Names (10,000 units)	+	0.104
			Number of Websites (10,000 units)	+	0.169
			Internet Broadband Access Ports (10,000 units)	+	0.045
			Internet Broadband Access Users (10,000 units)	+	0.049
		Enterprise Informationization Level	Number of Enterprises (units)	+	0.074
			Number of Websites per 100 Enterprises (units)	+	0.009
		Enterprise E-Commerce Situation	Proportion of Enterprises Engaged in E-Commerce Transactions (%)	+	0.026
			E-Commerce Sales Revenue (10,000 yuan)	+	0.111
		Software and Information Services Industry Development Level	Software Business Revenue (10,000 yuan)	+	0.146
			Software Product Revenue (10,000 yuan)	+	0.136
	Integrated Infrastructure Construction	Traditional Infrastructure	Railway Operating Mileage (km)	+	0.123
			Highway Mileage (km)	+	0.119
			Mileage of High-Speed Grade Highways (km)	+	0.115
			Total Length of Public Bus and Electric Bus Operating Routes (km)	+	0.262
			Urban Bridge Density (units)	+	0.382
		Degree of Informationization	Number of Domain Names (10,000 units)	+	0.284
			Number of Websites (10,000 units)	+	0.460
			Internet Broadband Access Ports (10,000 units)	+	0.122
			Internet Broadband Access Users (10,000 units)	+	0.134
	Innovative Infrastructure Construction	Innovation Funding	R&D Expenditure (10,000 yuan)	+	0.147
			Internal Expenditure of R&D (10,000 yuan)	+	0.130
		Innovative Talent	Number of Large-Scale Industrial Enterprises with R&D Activities (units)	+	0.206
			R&D Personnel in Large-Scale Industrial Enterprises (people)	+	0.146
		Innovative Achievements	Number of New Product Development Projects (units)	+	0.186
			Number of Patent Applications (units)	+	0.185

Note: the data used were derived from China Statistical Yearbook (https://data.stats.gov.cn). “+” indicates a positive indicator (positively correlated with NIC development).

), which includes three levels: information infrastructure, integrated infrastructure, and innovative infrastructure. Both information infrastructure and innovative infrastructure have appropriate indicators for characterization, while integrated infrastructure is represented using a coupling coordination model between traditional infrastructure and new information technologies. The CCD of the two subsystems is calculated to represent integrated infrastructure.

Inclusive green growth (IGG) not only emphasizes whether economic growth possesses green and inclusive characteristics [6], but also addresses the social inclusivity of green development [36]. Belonging to the domain of sustainable development economics, IGG represents a sustainable development approach that pursues economic growth, social inclusion, and a green ecology. It strives to achieve the triple dividends of economic growth, social inclusion, and ecological sustainability [37]. In essence, IGG serves the goals of sustainable development and high-quality development. Ensuring economic growth while simultaneously improving people’s livelihoods and achieving IGG constitutes a crucial strategy for adapting to the evolution of China’s principal social contradictions. Therefore, this study constructs an IGG indicator system encompassing four dimensions: Industrial and Energy Consumption Structure Level, Technology and Carbon Sinks Level, Energy Consumption and Carbon Emissions Level, and Socioeconomic Development Level. This system is measured using 14 selected third-tier indicators (

Table 2. Comprehensive evaluation index system for inclusive green growth (IGG).

System	Primary Indicators	Secondary Indicators	Attribute	Weights
Inclusive Green Growth (IGG)	Industry and Energy Consumption Structure Level	Proportion of Tertiary Industry Added Value in GDP (%)	+	0.039
		Proportion of Industrial Added Value in GDP (%)	−	0.040
		Proportion of Coal Consumption in Energy Consumption (%)	-	0.046
	Technology and Carbon Sink Level	Transaction Volume of Technology Market in Each Region (10,000 yuan)	+	0.308
		Internal Expenditure of Science and Technology Activities in Each Region (10,000 yuan)	+	0.254
		Greening Coverage Rate (%)	+	0.014
	Energy Consumption and Carbon Emission Level	Energy Intensity (tec per 10,000 yuan)	−	0.009
		Carbon Emission Intensity (tons per 10,000 yuan)	−	0.001
		Energy Footprint (tec per person)	−	0.007
		Carbon Footprint (tons per person)	−	0.001
	Economic Development Level	Per Capita GDP (Yuan per person)	+	0.062
		Average Disposable Income of Urban Residents (Yuan per person)	+	0.060
		Proportion of Regional GDP in National GDP (%)	+	0.062
		Per Capita Net Income of Rural Households (Yuan per person)	+	0.095

Note: the data used were derived from China Statistical Yearbook (https://data.stats.gov.cn) and Carbon Emission Accounts and Datasets (https://www.ceads.net). “+” indicates a positive indicator (positively correlated with IGG development), while a “−” indicates a negative indicator (negatively correlated).

).

3.3. Entropy Value and Coupling Coordination Degree (CCD) Model

NIC builds upon information networks. The continuous advancement of network infrastructure construction effectively deepens the integration of the internet with economic and social entities, fostering positive interaction between the supply and demand sides of production factors [38]. As the infrastructure foundation for new industrialization, NIC encompasses not only the next-generation intelligent information infrastructure [39] but also various types of infrastructure related to greening [40]. The deep embedding of the Internet of Things and artificial intelligence into energy systems promotes the large-scale replacement of fossil fuels by renewable energy, achieving systematic optimization of energy production and consumption structures [41]. Green technological innovations, such as smart grids and energy management systems, leverage the synergistic effects accelerated by cross-domain collaboration networks. This synergy enhances resource utilization efficiency and industrial coordination levels, forming a comprehensive force for carbon reduction [42,43,44]. Furthermore, researchers widely consider factors such as land and energy costs, socioeconomic development levels, human capital, informatization levels, urbanization rates, and fiscal capacity as key locational determinants for NIC [45]. Studies have demonstrated that these factors can also alleviate haze pollution in local and adjacent areas, reduce urban carbon emission intensity, and enhance inclusive green growth levels.

Therefore, leveraging its characteristics of intelligence, sharing, and platformization, NIC alleviates ecological pressure and reduces carbon emissions. Simultaneously, IGG reinforces the green and low-carbon transformation of NIC through three feedback channels:

At the policy regulation level, strengthening environmental standards forces infrastructure to meet green design requirements, driving NIC to transition to low emissions [25]. At the market demand level, heightened public environmental awareness creates rigid demand for green infrastructure (e.g., green transportation, smart energy networks), stimulating the expansion and technological innovation of NIC [46]. At the technological integration level, efficient renewable energy technologies, which are key to carbon reduction, become core components of NIC, and their widespread adoption forces the integration of green technologies into facilities [25]. At the regional coordination level, the positive externalities generated by improved IGG levels facilitate the construction of cross-regional, low-carbon collaborative networks for NIC through spatial spillover effects [47,48].

In summary, there exists a bidirectional coupling relationship between NIC and IGG. On the one hand, the development of NIC can effectively promote urban IGG; on the other hand, the improvement in the level of urban IGG will also drive the further improvement of NIC. At the same time, due to differences in economic development levels and resource endowments, there are significant regional disparities in both the level of NIC and IGG, leading to regional heterogeneity in the CCD between the two in different areas.

The CCD model provides a comprehensive perspective for in-depth exploration of the coordinated development between systems, revealing the complex interactive relationships and development dynamics among them. This study focuses on 30 provinces in China and, by using the CCD model, investigates the synergistic development between NIC and the level of IGG. With the help of the coupling coordination model, the correlation and coordination degree between NIC ($U_1$) and IGG level ($U_2$) are quantitatively measured. The calculation steps are as follows:

Due to differences in dimensions and scales in the selected data, it is necessary to standardize the data. In this study, the min-max normalization method is applied for standardization, as shown below:

Positive indicator:

$$x_{ij}^t={\displaystyle \frac{x_{ij}^t-min\left\{x_{ij}^t\right\}}{max\left\{x_{ij}^t\right\}-min\left\{x_{ij}^t\right\}}},\left(i=\mathrm{1,2},\cdots ,m;j=\mathrm{1,2},\cdots ,n\right)$$

(1)

Negative indicator:

$$x_{ij}^t={\displaystyle \frac{max\left\{x_{ij}^t\right\}-x_{ij}^t}{max\left\{x_{ij}^t\right\}-min\left\{x_{ij}^t\right\}}},\left(i=\mathrm{1,2},\cdots ,m;j=\mathrm{1,2},\cdots ,n\right)$$

(2)

$max\left\{x_{ij}^t\right\}$ refers to the maximum value of the indicator in year t, $min\left\{x_{ij}^t\right\}$ refers to the minimum value of the indicator in year t, and $x_{ij}^t$ represents the dimensionless result.

After standardizing the relevant indicator data, the entropy method is employed to assign weights to the indicators. The specific calculation steps of the entropy method are as follows:

First, calculate the comprehensive development level of the subsystem, ${\omega }_{ij}$ represents the proportion of j-th indicator in the i-th year.

$${\omega }_{ij}={\displaystyle \frac{x_{ij}}{\sum _{i=1}^m x_{ij}}}$$

(3)

Next, define the indicator information entropy $e_j$ and redundancy $d_j$:

$$e_j=-{\displaystyle \frac{1}{\mathrm{l}\mathrm{n}m}}\sum _{i=1}^m \left({\omega }_{ij}\times \mathrm{l}\mathrm{n}{\omega }_{ij}\right)$$

(4)

$$d_j=1-e_j$$

(5)

where $0\le e_j\le 1$, m represents the number of years to be evaluated.

Then, calculate the weight of each indicator based on the defined information entropy and redundancy:

$${\phi }_j={\displaystyle \frac{d_j}{\sum _{j=1}^m d_j}}$$

(6)

Finally, the comprehensive score for the subsystems of NIC and CERC is determined by calculating the weights of each indicator:

$$U_i=\sum _{j=1}^m {\phi }_j\times {\omega }_{ij}\left(i=\mathrm{1,2}\right)$$

(7)

In this context, $U_i$ represents the comprehensive score of the i-th system, with the index value ranging from 0 to 1.

Based on the standardized indicators, the entropy method is used to assign weights to the indicators and calculate the system’s CCD, which is derived from both coupling degree and coordination degree.

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

(8)

Here, C represents the coupling degree between the systems, and T represents the coordination degree between the systems. The calculation formulas for these two parameters are as follows:

$$C={\left\{U_1\times U_2)/{\left(\left[U_1+U_2\right]/2\right)}^2\right\}}^{1/2}$$

(9)

$$T=\alpha \times U_1+\beta \times U_2$$

(10)

In this context, $U_1$ represents the comprehensive score for the construction of NIC, while $U_2$ represents the comprehensive score for IGG. $\alpha$ and $\beta$ are the uncertain weights assigned to NIC and IGG, respectively. In this evaluation, it is assumed that both systems are of equal importance, and therefore, both weights are set to 0.5. The synergy theory explains the dynamic coordination between two systems, progressing from low to high levels. High coordination is achieved when the coupling between the systems is strong, with minimal performance gaps [49]. To assess the coordination degree between NIC and IGG, the method in [50] is used, as shown in

Table 3. Classification criteria for the level of coupling coordination between NIC and IGG.

Value Range	Coupling Coordination Type	Value Range	Coupling Coordination Type
$\left(\mathrm{0,0.3}\right]$	Poor Coordinaton	$\left(\mathrm{0.5,0.8}\right]$	Good Coordinaton
$\left(\mathrm{0.3,0.5}\right]$	Moderate Coordination	$\left(\mathrm{0.8,1}\right]$	Excellent Coordinaton

.

3.4. Dagum Gini Coefficient Decomposition Method

This paper uses the Dagum Gini coefficient and its decomposition to examine the regional differences and sources of the degree of coordination between the coupling of NIC and IGG in China.

$$G^2=\sum _{j=1}^k \sum _{h=1}^k \sum _{i=1}^{n_j} \sum _{r=1}^{n_h} \mid y_{ji}-y_{hr}\mid /\left(2n^2\mu \right)$$

(11)

In the formula, G represents the Gini coefficient, which reflects the inequality of the CCD of NIC and IGG in Chinese provinces, k and n represent the total number of regions and provinces, respectively, j and h represent the number of regions, i and r represent the number of provinces in the region, $n_j$($n_h$) represents the number of provinces in the region of j (h), and $d_{ji}$($d_{hr}$) represents the CCD of NIC and CERC of provinces i (r) of the region j (h), and $\mu$ denotes the arithmetic mean of the CCD level.

On this basis, the degree of coupled coordination of NIC and IGG in the k regions is ranked, and it is assumed without loss of generality that ${\mu }_1\le {\mu }_2\le \cdots \le {\mu }_j\le \cdots \le {\mu }_k$.

$$G=G_w+G_{nb}+G_t$$

(12)

$G_w$ denotes intra-regional variation, reflecting the degree of imbalance in development among different provinces within the same region; $G_{nb}$ denotes inter-regional variation, reflecting the degree of inequality among different regions; and $G_t$ is the transvariation intensity, which measures the effect of cross-over between samples from different regions on the overall variation.

3.5. Quadratic Assignment Procedure (QAP)

Since regional differences form relational data when regions are viewed as action units, their inherent autocorrelation and multicollinearity problems make conventional econometric methods difficult to apply. Therefore, this study adopts the quadratic assignment procedure (QAP), which is specifically designed for relational data analysis, to reveal the driving mechanism behind the differences in the coupling coordination development of NIC and IGG levels across regions. The model is set as follows:

$$Y={\beta }_0+{\beta }_{1}X+U$$

(13)

In the equation, Y represents the difference matrix of the coupling coordination level between NIC and IGG, X represents the difference matrix of explanatory variables, ${\beta }_0$ and ${\beta }_1$ are the parameters to be estimated, U is the residual term, and all variables are n-dimensional square matrices.

4. Measurement Results of the CCD of NIC and IGG

Based on the constructed evaluation index system, the entropy method was employed to measure the levels of NIC and IGG across 30 Chinese provinces from 2011 to 2023. The analysis distinguished among the eastern, central, and western regions, enabling the creation of trend evolution charts depicting the levels of NIC and IGG for each region over the study period (Figure 2 and Figure 3).

4.1. Analysis of NIC Levels

The regional grouping results (Figure 2) reveal a pronounced upward trajectory in NIC levels across China’s eastern, central, and western regions between 2011 and 2023. Leveraging its robust economic strength, well-developed market mechanisms, and leading technological innovation capacity, the eastern region consistently maintained the highest level of NIC nationwide. The central region followed closely, demonstrating significant improvement in recent years. This advancement primarily stems from policy support under the national regional coordinated development strategy and increased infrastructure investment arising from its role as an industrial relocation hub. The widespread enhancement in regional NIC levels validates the initial success of China’s strategic initiatives to promote NIC development, revealing a distinct tiered regional progression pattern.

4.2. Analysis of IGG Levels

From 2011 to 2023, IGG levels in all three major regions (eastern, central, and western) exhibited sustained growth, albeit with marked regional disparities. The eastern region, benefiting from its advanced economic foundation, relatively optimized industrial structure, and leadership in technological innovation and environmental investment, consistently recorded the highest IGG levels, maintaining a substantial gap over others. In terms of growth rate, the central region exhibited the most remarkable performance, achieving an average annual growth rate of 9.82%. This acceleration is largely attributable to the region’s accelerated industrial restructuring, widespread adoption of green technologies, and significantly enhanced policy support. The western region followed with an average annual growth rate of 8.42%, driven primarily by optimization of its energy consumption structure and continuous improvements in infrastructure, particularly green infrastructure construction. Conversely, while leading in absolute terms, the eastern region experienced a relatively lower average annual growth rate (8.31%). This moderating growth pace may partly reflect the maturity of its industrial structure and energy consumption patterns, where the marginal gains from further emission-reduction technologies are becoming comparatively constrained.

4.3. Analysis of the CCD Between NIC and IGG

Following the calculation of the comprehensive evaluation values for NIC and IGG, this study applied the CCD model to assess the synergistic development level between these two dimensions across 30 Chinese provinces from 2011 to 2023 (detailed results in

Table 4. Coupling coordination degree of NIC and IGG in China (2011–2023).

Regions	2011	2013	2015	2017	2019	2021	2023	Average
Beijing	0.471	0.516	0.580	0.623	0.673	0.721	0.768	0.622
Tianjin	0.321	0.356	0.379	0.403	0.421	0.452	0.479	0.402
Hebei	0.343	0.363	0.387	0.418	0.453	0.484	0.526	0.424
Shanxi	0.264	0.287	0.320	0.337	0.357	0.370	0.388	0.334
Inner Mongolia	0.275	0.299	0.323	0.346	0.361	0.373	0.391	0.339
Liaoning	0.352	0.380	0.406	0.414	0.435	0.453	0.481	0.417
Jilin	0.273	0.293	0.315	0.343	0.371	0.372	0.382	0.337
Heilongjiang	0.296	0.335	0.356	0.368	0.375	0.386	0.392	0.360
Shanghai	0.417	0.445	0.486	0.520	0.555	0.601	0.654	0.525
Jiangsu	0.490	0.536	0.574	0.613	0.670	0.735	0.789	0.629
Zhejiang	0.450	0.491	0.533	0.570	0.621	0.684	0.759	0.585
Anhui	0.305	0.335	0.378	0.409	0.459	0.516	0.569	0.423
Fujian	0.348	0.371	0.416	0.473	0.494	0.516	0.535	0.451
Jiangxi	0.295	0.319	0.355	0.384	0.430	0.456	0.500	0.390
Shandong	0.427	0.480	0.500	0.532	0.561	0.630	0.702	0.546
Henan	0.337	0.360	0.404	0.431	0.473	0.502	0.539	0.435
Hubei	0.336	0.372	0.423	0.446	0.493	0.525	0.591	0.453
Hunan	0.333	0.361	0.397	0.427	0.473	0.506	0.571	0.437
Guangdong	0.502	0.549	0.587	0.646	0.726	0.809	0.860	0.667
Guangxi	0.288	0.309	0.337	0.366	0.399	0.435	0.433	0.366
Hainan	0.250	0.283	0.307	0.321	0.348	0.355	0.365	0.318
Chongqing	0.282	0.313	0.351	0.379	0.411	0.439	0.467	0.379
Sichuan	0.338	0.367	0.421	0.459	0.509	0.537	0.572	0.458
Guizhou	0.254	0.273	0.302	0.327	0.372	0.406	0.432	0.337
Yunnan	0.279	0.306	0.328	0.356	0.398	0.414	0.436	0.359
Shaanxi	0.297	0.325	0.360	0.384	0.426	0.461	0.505	0.393
Gansu	0.240	0.268	0.303	0.327	0.349	0.362	0.378	0.319
Qinghai	0.152	0.193	0.245	0.267	0.289	0.300	0.308	0.253
Ningxia	0.183	0.212	0.238	0.258	0.279	0.293	0.305	0.253
Xinjiang	0.257	0.282	0.313	0.327	0.351	0.365	0.381	0.325
Eastern Average	0.397	0.433	0.469	0.503	0.542	0.585	0.629	0.508
Central Average	0.305	0.333	0.369	0.393	0.429	0.454	0.491	0.396
Western Average	0.259	0.286	0.320	0.345	0.377	0.399	0.419	0.344
National Average	0.322	0.353	0.388	0.416	0.451	0.482	0.515	0.371

).

At the national level, the CCD between NIC and IGG displayed significant and continuous improvement. The coordination level rose from 0.322 (moderate coordination) in 2011 to 0.515 (high coordination) in 2023, representing an average annual growth rate of approximately 4%. This positive trend is primarily driven by the continuous strengthening of national strategic deployments concerning the “dual carbon” goals (carbon peak and neutrality) and NIC, the synergistic effects of these converging policies, and the accelerated application and diffusion of green technologies. Collectively, these factors have fostered deeper alignment between NIC and IGG.

Regionally, the CCD in all three areas (east, central, west) steadily increased throughout the study period, yet exhibited a gradient in development levels. In 2011, the CCD values for the eastern and central regions were 0.397 and 0.305, respectively, both classified as moderate coordination. By 2023, the eastern region advanced first to a high coordination level (0.629), closely linked to its superior economic foundation, technological innovation capacity, and policy implementation efficacy. The central region showed consistent progress but remained at a moderate coordination level (0.491). Starting from a lower base (CCD = 0.259, low coordination in 2011), the western region achieved the fastest growth rate, with an average annual growth rate of 4.1%, reaching a moderate coordination level (0.419) by 2023. This improvement underscores the catalytic effect of regional coordinated development strategies and industrial relocation on green synergistic development in the western region.

Provincially, the evolution of the coordination landscape revealed significant structural improvements between 2011 and 2023 (Figure 4). In 2011, fifteen provinces languished in the low-coordination tier, fourteen exhibited moderate coordination, and only Guangdong province attained good coordination, highlighting stark regional disparities in sustainable development capacity. By 2023, however, systemic policy interventions and strategic infrastructure investments catalyzed a remarkable transformation: no provinces remained in low coordination; moderately coordinated provinces increased to sixteen, predominantly in the central and western regions, reflecting targeted fiscal transfers and industrial relocation policies; and good-coordination provinces surged to thirteen. Notably, Guangdong province achieved a paradigm shift—elevating its coupling coordination degree (CCD) to 0.860 to enter the unprecedented “excellent coordination” tier, mostly for its early-mover advantage in deploying cutting-edge NIC, enabling real-time environmental governance and resource optimization. Moreover, agglomeration of high-tech firms (e.g., Huawei, BYD) fostered NIC–IGG feedback loops—digital tech boosted green productivity, while green demand stimulated NIC innovation.

5. Analysis of Regional Disparities

5.1. Overall Regional Disparities and Their Decomposition

This study employed the Dagum Gini coefficient to measure the regional disparities in the CCD between NIC and IGG in China from 2011 to 2023 (Figure 5). The results indicate that during the 12th Five-Year Plan period (2011–2015), the overall regional disparity in CCD showed a declining trend, signifying a narrowing gap. This initial convergence was likely attributable to proactive national policy promotion and increased funding, particularly in NIC, which fostered more balanced infrastructure development across regions alongside improvements in IGG levels. However, a slight resurgence in regional disparity emerged starting in 2015. Furthermore, since 2019, the pace of divergence accelerated, leading to a gradually widening overall gap. This reversal stems primarily from persistent structural contradictions and uneven development trajectories across different regions.

Building upon the Dagum Gini coefficient decomposition results (

Table 5. Decomposition and contribution of the Gini coefficient.

Year	Overall	Decomposition			Contribution Rate (%)
		Intra-Regional Disparity Gw	Inter-Regional Gnb	Transvariation Intensity Gt	Intra-Regional Disparity Gw	Inter-Regional Gnb	Transvariation Intensity Gt
2011	0.141	0.032	0.104	0.004	22.97	74.11	3.93
2012	0.138	0.032	0.102	0.005	23.02	73.78	4.43
2013	0.135	0.031	0.101	0.004	22.97	74.37	3.67
2014	0.133	0.031	0.098	0.004	23.31	73.46	4.30
2015	0.130	0.031	0.094	0.005	23.78	72.51	5.11
2016	0.130	0.031	0.094	0.005	23.96	71.97	5.66
2017	0.131	0.032	0.094	0.006	24.05	71.60	6.08
2018	0.132	0.033	0.093	0.007	24.68	70.39	7.01
2019	0.133	0.034	0.092	0.007	25.51	69.09	7.82
2020	0.138	0.036	0.095	0.008	25.74	68.53	8.36
2021	0.145	0.037	0.099	0.009	25.86	68.19	8.82
2022	0.151	0.039	0.103	0.010	25.78	67.99	9.26
2023	0.155	0.040	0.105	0.010	25.71	67.70	9.73
Average	0.138	0.034	0.098	0.006	24.41	71.05	6.48

), this study further dissected the sources of regional disparities in the CCD between NIC and IGG. The decomposition reveals that inter-regional disparity is the dominant contributor, accounting for an average of 71.05% of the total disparity. This predominance originates from the systemic differences in development stages, resource endowments, and policy priorities among China’s eastern, central, and western regions. For instance, the eastern region holds a significant lead in technological advancement and capital accumulation. Intra-regional disparity is the secondary source, contributing 24.41% on average. This reflects the divergence among provinces within the same region concerning development momentum, industrial structure, and policy implementation effectiveness. The contribution of transvariation density was minimal at only 6.48%, indicating that the cross-regional overlap effect exerts a relatively limited influence on the overall disparity. Dynamically, the contribution of inter-regional disparity exhibited a declining trend. This decrease may be linked to the ongoing implementation of national regional coordinated development strategies, such as the Western Development and Central Region Rise initiatives, which have progressively narrowed the development gap between regions. Conversely, the contribution rates of both intra-regional disparity and transvariation density showed upward trends. The former highlights the intensifying divergence within regions at the provincial level, while the latter potentially signifies increasing complexity in the interactions across regional boundaries.

Therefore, while spatial disparities currently stem predominantly from inter-regional imbalances, the challenge of achieving coordinated development within regions is escalating. Future policies must not only promote inter-regional synergy but also place greater emphasis on fostering balanced development within individual regions.

5.2. Intra-Regional Disparities

Although our analysis incorporates provincial and temporal fixed effects to account for key confounding factors in REP estimation, potential endogeneity concerns may persist, potentially limiting the robustness of our findings. To address this methodological challenge, we implement a dual strategy employing instrumental variable estimation and lagged variable approaches.

The preceding measurement results reveal significant regional imbalances in the CCD between NIC and IGG across China. To delve deeper into the evolution of disparities within regions, this study utilized the Dagum Gini coefficient decomposition method, focusing specifically on the eastern, central, and western regions, to analyze the dynamics of provincial-level disparities within each region from 2011 to 2023 (Figure 6).

The findings demonstrate a distinct hierarchy in intra-regional disparity levels: eastern > western > central. The eastern region exhibited the most pronounced provincial disparities, with an average Gini coefficient of 0.119. This predominance arises primarily from its substantial internal development gradient, encompassing provinces with leading CCD levels (e.g., Guangdong, Beijing, Shanghai) alongside relatively lagging provinces. Under the spatial Matthew effect, advanced provinces leverage cumulative advantages to further solidify their leading positions, while less developed provinces face increasing difficulties in catching up. This dynamic perpetuates a “rich-get-richer” divergence pattern, continuously widening the development gaps and driving high intra-regional disparity. The western region ranked second in disparity, significantly influenced by variations in geographical conditions, resource endowments, and initial development bases among its provinces. In contrast, the central region demonstrated the most balanced internal development, reflected in its lowest average Gini coefficient. This relative homogeneity suggests a more synchronized pace among central provinces in promoting the synergistic development of NIC and carbon reduction.

Regarding temporal trends, both the eastern and central regions displayed fluctuating but upward trajectories. The eastern region saw an average annual increase of 2.00% in its intra-regional Gini coefficient. The central region experienced faster growth at 5.58% per annum, with a notably accelerated pace post-2018. This surge stems from emerging differentiation among some central provinces in areas like industrial relocation absorption, adoption of green technologies, and responsiveness to policies, disrupting the previously more balanced state. Conversely, the western region exhibited a “decline followed by rise” pattern. During the 12th Five-Year Plan period, its Gini coefficient decreased annually by an average of 5.22%, indicating the initial positive impact of regional coordination policies in narrowing internal western gaps. However, the trend reversed starting in 2015, shifting to a steady annual increase of 2.42%. This reversal likely relates to growing disparities among western provinces concerning infrastructure upgrading, energy transition progress, and the types of industries they are able to attract and develop.

In summary, intra-regional disparities in CCD of NIC and IGG exhibit not only significant static hierarchical characteristics (east > west > central) but also complex regional heterogeneity in their dynamic evolution. This pattern profoundly reflects the combined influence of multiple factors, including developmental foundations, policy effectiveness, market forces, and spatial interactions.

5.3. Inter-Regional Disparities

The inter-regional disparities in the CCD between NIC and IGG from 2011 to 2023 are detailed in Figure 7. Specifically, the most significant disparities exist between the eastern and western regions and between the eastern and central regions. The underlying reason lies in the eastern region’s advantage: leveraging its economic head start and preferential policy resources, it leads in deploying NIC (e.g., 5G, data centers) and applying clean technologies. This leadership accelerates the synergy between its NIC development and carbon reduction goals. Conversely, the central and western regions face constraints such as weaker industrial foundations, insufficient capital and talent, and dependence on traditional energy pathways. These limitations result in slower NIC investment and a less rapid pace of green transformation, leading to a pronounced gap with the east. Furthermore, the disparity between the central and western regions shows an upward trend, reflecting intensifying imbalances within these broader areas. Central provinces, benefiting from geographical advantages and stronger capacity for industrial relocation (e.g., the electronics and information industries in Henan and Hubei), are accelerating the integration of NIC with green technologies. Meanwhile, some western provinces, hampered by challenging geography, fiscal constraints, and lagging energy structure transitions, demonstrate lower efficiency in coordinating NIC investment with IGG efforts.

6. Formation Mechanism of Disparities in the CCD Between NIC and IGG

The CCD between NIC and IGG embodies their bidirectional interaction. Their coordinated development is driven by multifaceted factors spanning economic, structural, technological, and market dimensions. Specifically, economically advanced provinces leverage their capital and technological advantages to invest in low-carbon technologies, enhancing emission reduction capabilities and thereby strengthening the CCD between NIC and IGG. Industrial structure transformation towards high-tech and green sectors promotes NIC deployment while reducing carbon emissions. Technological progress drives NIC advancement, improves energy efficiency, and supports low-carbon objectives, thus enhancing synergy with carbon reduction. Concurrently, the market environment accelerates demand for digital and green infrastructure, imposing higher requirements on NIC development.

The preceding measurement and decomposition of the Dagum Gini coefficient revealed significant inter-regional disparities in the CCD of NIC and IGG. To further identify the underlying causes of these disparities, this study employed quadratic assignment procedure (QAP) analysis, incorporating variables from the economic, structural, technological, and market dimensions. Specifically, the model used the disparity matrix of CCD as the dependent variable, with disparity matrices of economic development level, industrial structure, technological level, and market environment as the independent variables. Economic development was measured by the logarithm of per capita GDP; industrial structure by the share of the tertiary sector in GDP; technological level by the proportion of science and technology expenditure in total fiscal expenditure; and market environment by the ratio of general fiscal expenditure to local GDP. All variables were structured as relational data in the form of 30 $\times$ 30 matrices.

6.1. Regional Analysis

From a broader national perspective, the spatial heterogeneity in market environment, economic development, technological level, and industrial structure constitutes the core driver of provincial disparities in CCD of NIC and IGG. The standardized regression coefficients for all four factors were significantly positive (

Table 6. QAP regression results of the impact factors of CCD in NIC and IGG levels in China across different regions.

Variables	National	East	Central	West
Economic Development	0.270 **	0.205 **	0.291 **	0.314 **
(econ)	(0.014)	(0.046)	(0.035)	(0.039)
Industrial Structure	0.188 ***	0.254 **	0.151	0.117
(ind)	(0.002)	(0.027)	(0.256)	(0.141)
Technological Level	0.233 ***	0.260 **	0.252	0.306 **
(tec)	(0.000)	(0.015)	(0.205)	(0.028)
Market Environment	0.594 ***	0.692 ***	0.778 **	0.787 ***
(mar)	(0.000)	(0.001)	(0.010)	(0.003)
Adj R2	0.967 *** (0.000)	0.957 *** (0.000)	0.925 *** (0.001)	0.932 *** (0.000)
Obs	870	110	56	110
Permutations	2000	2000	2000	2000

Note: The regression coefficients in the table are standardized regression coefficients. ** and *** indicate significance levels of 5%, and 1%, respectively. The values in parentheses represent the probability that the regression coefficients generated by random permutation are not smaller than the actually observed regression coefficients.

), confirming that narrowing regional imbalances in these elements can effectively alleviate spatial disparities in NIC–IGG coordinated development. Coefficient comparison revealed the influence intensity ranking as: Market Environment > Economic Development Level > Technological Level > Industrial Structure. This result affirms the pivotal role of the market environment in coordinating NIC and IGG development.

The intensity of influencing factors on CCD disparities varied considerably across the three major regions. In the eastern region, disparities stemmed diversely from all four dimensions, with influence intensity ranked: Market Environment > Technological Level > Industrial Structure > Economic Development. This mainly reflects that the internal coordination development differences within the eastern region are primarily due to market environment differences, where government regulation and policy implementation effects may vary. For the central region, disparities originated primarily from the economic and market dimensions, with market environment differences being the dominant factor. This reflects how variations in the execution of policies—such as industrial access standards, NIC investment, and emission reduction incentives—by local governments within the region emerge as key drivers of inter-provincial divergence. In the western region, disparities were significantly influenced by differences in market environment, economic development, and technological level, while industrial structure differences showed no significant impact. This suggests that, for provinces in the western region, the current focus should be on consolidating the foundation for development.

This analysis reveals that China’s synergistic development of NIC and IGG is not only influenced by common national factors but is also critically shaped by distinct development stages and functional positioning across regions, leading to differentiated driving logics.

6.2. Temporal Evolution Analysis

Dividing the study period (2011–2023) according to China’s Five-Year Plans, this study systematically examined the dynamic evolution of factors influencing NIC–IGG synergy across three sub-periods (

Table 7. QAP regression results of the impact factors of CCD in NIC and IGG levels in China across different periods.

Variables	12th Five-Year Plan Period (2011–2015)	13th Five-Year Plan Period (2016–2020)	Since 14th Five-Year Plan (2021–2023)
Economic Development	0.261 ***	0.320 ***	0.269 ***
(econ)	(0.000)	(0.000)	(0.001)
Industrial Structure	0.228 ***	0.183 ***	0.207 ***
(ind)	(0.000)	(0.004)	(0.000)
Technological Level	0.251 ***	0.215 ***	0.181 ***
(tec)	(0.000)	(0.001)	(0.001)
Market Environment	0.597 ***	0.584 ***	0.627 ***
(mar)	(0.000)	(0.000)	(0.000)
Adj R2	0.961 *** (0.000)	0.958 *** (0.000)	0.964 *** (0.000)
Obs	870	870	870
Permutations	2000	2000	2000

Note: The regression coefficients in the table are standardized regression coefficients. *** indicate significance levels of 1%, respectively. The values in parentheses represent the probability that the regression coefficients generated by random permutation are not smaller than the actually observed regression coefficients.

): the 12th Five-Year Plan period (2011–2015), 13th Five-Year Plan period (2016–2020), and since the 14th Five-Year Plan period (2021–2023).

During both the 12th and 13th Five-Year Plan periods, the influence intensity ranking of the four dimensions was: Market Environment > Economic Development > Technological Level > Industrial Structure. Since the 14th Five-Year Plan, the ranking shifted to: Market Environment > Economic Development > Industrial Structure > Technological Level, signifying that China’s supply-side structural reform has had significant results in industrial upgrading, with the influence of industrial structure significantly increasing. Notably, the market environment has consistently maintained its dominant position, playing a core supporting role in inter-provincial coordinated development, and its foundational role in building a unified national market has become more prominent. In contrast, the impact of technological factors has continued to weaken, with the existing technological innovation system insufficient to support regional coordination. The contributions of economic development and industrial structure show periodic fluctuations: the driving force of economic development strengthened during the 13th Five-Year Plan but declined during the 14th Five-Year Plan; the influence of industrial structure significantly weakened during the 13th Five-Year Plan, but gradually regained importance since the 14th Five-Year Plan.

This evolving pattern suggests that the driving logic behind China’s regional coordinated development is shifting from a reliance on technology to a focus on structural optimization strategies, while the core position of market mechanisms continues to strengthen. These changes highlight the emerging trends in regional development: industrial upgrading as the new engine, market unification as the new cornerstone, and technological innovation in need of a new paradigm, all contributing to the construction of a dynamically adaptive regional governance system.

7. Conclusions, Policy Implications, and Limitations

7.1. Conclusions

This paper establishes a NIC–IGG evaluation framework, employing a coupling coordination model to quantify their 2011–2023 coordination across China, and dissects regional disparities and underlying mechanisms. This study discerns that: (1) NIC–IGG coordination remains suboptimal nationally but demonstrates upward progression, evolving from moderate to advanced coordination. A marked east-to-west gradient persists, with eastern regions substantially outperforming central/western zones despite the latter’s growth. (2) Aggregate regional disparities manifest a “U”-shaped trajectory, exhibiting post-2016 divergence. Inter-regional differentials constitute the dominant disparity source—superseding intra-regional variations—where eastern intra-regional variance peaks and east–west inter-regional gaps prevail. (3) Market environment differentials emerge as the principal determinant of coordination disparities. Economic development, technological capacity, and industrial structure exert heterogeneous sectoral impacts, with all four factors demonstrating temporally differentiated influence intensities.

7.2. Policy Implications

Based on the above findings, this paper proposes the following suggestions to promote the CCD of NIC and IGG in China:

Implement a differentiated regional collaborative promotion strategy: To address the gradient differences between the eastern-central and eastern-western regions, a three-level policy system of “Leading-Transformation-Catching Up” should be established. The eastern region should focus on strengthening the integration of technological innovation and market mechanisms (e.g., emulating Germany’s Regional Innovation Clusters for hydrogen transition), leveraging the demonstration effect of highly coordinated areas; the central region should emphasize the green upgrading of industrial structures, using special transfer payments to drive the green low-carbon transformation of traditional manufacturing; the western region should enhance the synergy between clean energy infrastructure and digital technology, relying on regional coordination funds to offset initial developmental disadvantages and transform growth advantages into quality advantages.

To advance market integration propelled by new infrastructure and govern market competition through inclusive green development, this study proposes a tripartite approach: firstly, construct an integrated intelligent infrastructure network embodying the computing power–logistics–energy trinity, utilizing the strategic layout of East Data West Computing hubs to establish cross-regional data trading platforms (e.g., EU’s Data Act provisions) that dismantle institutional barriers to factor mobility. Secondly, institute a three-dimensional policy assessment framework evaluating market vitality, ecological welfare, and social equity, formally incorporating carbon intensity metrics and digital inclusion indicators into fair competition review systems. Lastly, leverage the multiplier effect of new infrastructure to enhance market efficiency, reconfigure competition governance via standardized green criteria, and safeguard just transition through inclusive institutional mechanisms—culminating in a dynamic equilibrium development paradigm harmonizing efficiency, sustainability, and equity. This framework provides foundational institutional infrastructure supporting Chinese modernization trajectories.

Strengthen talent cultivation and promote the gradient flow of regional technical talents. In response to the constraint effect of the knowledge element gap on regional coordination, we believe that strengthening talent cultivation and establishing a gradient flow mechanism for technical talents between the “east–central–west” regions can effectively promote the transfer of advanced technology and management experience from high-skilled talents to underdeveloped regions, thereby fostering balanced regional technological development. This approach differs from traditional methods of increasing R&D investment, as it leverages talent as a carrier of knowledge and a medium for technology diffusion, activating technology diffusion and local learning effects between different regions, fundamentally alleviating regional imbalances caused by the technology gap.

7.3. Limitations

This paper is still deficient in the following areas.

(1) The evaluation index system and coupling coordination degree model developed in this study primarily characterize developmental outcomes, without delving into their underlying process mechanisms. Future research could employ quantitative approaches such as panel fixed-effects models and System GMM to investigate causal relationships between systems and uncover dynamic pathways of coupling coordination.

(2) By measuring the development levels and coupling coordination degrees of new infrastructure construction (NIC) and inclusive green growth (IGG) across 30 Chinese provinces, this research focuses on analyzing regional disparities and evolutionary patterns in the eastern, central, and western zones. Consequently, the proposed policy recommendations target macro-level regional coordination while overlooking potential intra-provincial disparities at the city level. Subsequent studies should refine the analytical scale to municipal units, enabling evidence-based design of differentiated policies to strengthen practical relevance.