Investigating Provincial Coupling Coordination Between Digital Infrastructure and Green Development in China

Abstract

Digital technologies could facilitate green development by enhancing energy efficiency. However, existing research on coupling coordination between digital infrastructure and green development remains scarce. To fill this research gap, this study analyzes the spatio-temporal variations and barriers of coupling coordination. An evaluation index system is established and then the coupling relationship and the barrier factors between digital infrastructure and green development are analyzed. A provincial analysis is conducted by using data from China. The results in the study indicate (1) coupling coordination between digital infrastructure and green development exhibits a relatively low state, characterized by an overall upward trend; (2) noteworthy disparities are observed in the spatio-temporal pattern of the coupling coordination degree, reflecting the overall evolutionary trend from low to high coupling coordination, along with the characteristics of positive spatial correlation and high spatial concentration; and (3) obstacle factors are analyzed from the aspects of digital infrastructure and green development, emphasizing the construction of mobile phone base stations and investment in pollution control, among other aspects. This study contributes valuable insights for improvement paths for digital infrastructure and green development, offering recommendations for optimizing strategies to promote their coupled development.

1. Introduction

The world’s economic and social structures are being reshaped by the digital revolution and the concept of green development [1,2]. Digital infrastructure, as the cornerstone of the digital economy, is a main driver of economic activities, information flow, and social service enhancement [3,4]. The growing environmental challenges, though, have led to a widespread international consensus on the importance of green development [4]. To address such challenges, a number of countries have set out to pursue a commitment to realigning their economies towards green, low-carbon, and circular economies, aiming to achieve a balance between economic growth and environmental protection [5,6,7]. In China, the interaction between digital infrastructure and green development has gained significant attention. The development of digital infrastructure has generated enormous economic benefits, but its operation process may have adverse environmental impacts in terms of energy consumption and resource utilization [8]. Nevertheless, digital technology also provides new opportunities and channels for green development [9].

Despite the existence of studies addressing the dynamic changes in digital infrastructure and green development, systematic studies on the degree of coupling coordination between the two are still scarce, especially within the context of China. In order to fill this research gap, we propose three specific research questions and corresponding testable hypotheses that guide our study: (1) What is the degree of coupling coordination between digital infrastructure and green development across Chinese provinces from 2013 to 2022? (2) How do spatio-temporal patterns and disparities in coupling coordination evolve over this period? (3) What are the primary barrier factors hindering effective coupling coordination? The corresponding research hypotheses are as follows:

H1. 

Coupling coordination degree is generally low but shows an upward trend over time, driven by policy and technological advancements.

H2. 

Significant regional disparities exist in coupling coordination, with eastern provinces exhibiting higher coordination due to advanced infrastructure.

H3. 

Specific factors, such as mobile phone base station density and pollution control investment share, are key barriers to coordination.

By answering these three research questions, the objectives of this study are the following: (1) In terms of research content, a quantitative analysis is conducted in this study to analyze the relationship between digital infrastructure and green development based on a Chinese perspective from both temporal and spatial dimensions. It addresses the gap in the existing research by analyzing the two-way relationship between the two. (2) In terms of research methodology, based on the establishment of an evaluation index system, the study employs coupling coordination models, the spatial Moran’s Index, and obstacle factor models. These methods enhance the reliability of the research conclusions. (3) In terms of research significance, from a theoretical perspective, this study enriches the current literature on digital infrastructure and green development, while from a practical perspective, this study proposes strategies to improve the energy efficiency of digital infrastructure and deepen green development. These suggestions offer valuable insights for future deep integration and positive interaction between digital infrastructure construction and green development.

In the remainder of this paper, a brief literature review is provided in Section 2. Then this study presents the research methods in Section 3. We report the results and discuss spatial and temporal variability and barrier factors of coupling coordination between digital infrastructure and green development in Section 4. Finally, we provide recommendations and the conclusions of this study in Section 5 and Section 6, respectively.

2. Literature Review

In order to better understand coupling coordination between digital infrastructure and green development, this section will review the Environmental Kuznets Curve (EKC) theory and related research. It will explore the interrelationship between digital infrastructure and green development, laying the theoretical foundation for subsequent analysis.

2.1. Theoretical Basis

Based on EKC theory, this study explains the relationship between digital infrastructure and green development. The EKC hypothesis suggests that in the early stages of development, environmental quality deteriorates as economic growth increases. However, once economic development reaches a certain level, a high-quality environment becomes a growing demand, and awareness of environmental protection and governance gradually increases [10].

Building upon EKC theory, many scholars have attempted to determine whether the relationship between “digitalization and green development” exhibits a relevant correlation. For example, Zhong observed a double exponential curve between digital finance and the environment while also finding that digital finance can improve the environment [11]. Nasser and Abdelkaoui obtained the EKC-type relationship between digitalization itself and emissions by empirical evidence [12]. Zhang and Meng also attained similar conclusions [13]. Overall, scholars are increasingly focusing on the relationship between digitalization and the environment.

In China, with the rise in economic development levels, the implementation of a series of sustainable development policies such as “high-quality green development” and the growing public awareness of environmental protection, there is an increasing demand for high-quality environmental standards. Coupling refers to the phenomenon where two or more systems or modes of motion interact and influence each other through various interactions [14]. Given the nature of the relationship between digital infrastructure and green development, analyzing the coupling relationship between the two will provide effective support for promoting the coordinated evolution of digital infrastructure and environmental development.

2.2. Digital Infrastructure

As a key component of the new infrastructure, digital infrastructure plays a pivotal role and has been studied from different perspectives by scholars from all walks of life since its emergence. Digital infrastructure has already exerted a positive influence on myriad aspects of social development. It has emerged as a pivotal foundation for promoting high-quality economic development [15,16]. At the ecological level, the implementation of digital infrastructure has been shown to promote green and low-carbon development, foster green growth in the manufacturing industry, and positively impact the transformation of the energy structure [17,18]. From a technological perspective, the impact of digital infrastructure on improving the efficiency of urban green innovation is more significant in larger cities with higher levels of economic development. Furthermore, the enhancement of green innovation has been demonstrated to lead to a reduction in urban CO₂ emissions [19,20]. At the enterprise level, digital infrastructure has been shown to promote and stimulate enterprise innovation, enhance enterprise information acquisition capabilities, and expand enterprise innovation boundaries [21,22]. It also significantly promotes digital transformation, driving carbon and emission reduction efforts in enterprises [23].

Early scholars’ definitions of digital infrastructure were more focused on communication information and network telecommunications [24,25,26]. In recent years, scholars have redefined digital infrastructure. Compared with a single indicator, a composite indicator can integrate the characteristics of several indicators and more comprehensively reflect the characteristics and nature of digital infrastructure. Jin Jing et al. posit that digital infrastructure refers to an infrastructure that evolves alongside the new generation of information technology [27], encompassing both traditional information infrastructure and emerging digital infrastructure [28]. Some studies, adopting an international perspective, contend that the countries of the Shanghai Cooperation Organization [9] are used as the evaluation object for digital infrastructure construction [29,30]. They constructed an index system based on six dimensions: development environment, market demand, network facilities, computing power facilities, service capabilities, and growth trends.

2.3. Green Development

Conceptualization and conceptual framing of the term “green development” is an evolving process, which has grown and intensified in meaning increasingly and simultaneously with the social development globally [31]. It can be posited that the notion of “green development” emerged from the concepts of the “circular economy” and “green economy” that gained prominence during the 20th century, in addition to the notion of “sustainable development” [32]. Following the beginning of the 21st century, academic circles commenced to ascribe novel connotations to “green development” [33].

China’s investigation into green development commenced comparatively recently, and the concept of deep green development continues to evolve [34]. However, a unified definition of the concept has yet to be established. The progression of green development can be categorized into three distinct phases [35]. The initial phase is an “ecologically oriented” development model, which focuses on reducing carbon emissions during economic development, with the primary goal of addressing climate change. The second stage introduces an “ecological/economic system”-oriented development model advocating for an economic growth model that is efficient and economical and emphasizes the mutually advantageous scenario of ecological and economic benefits [36]. The third stage is a development model that focuses on the “ecological/economic/social system.” This model highlights the importance of social integration, asserting that a harmonious social system is essential for green development [37,38]. It emphasizes the preservation and usage of ecological resources, as well the construction of an ecological environment.

2.4. Relationship Between Digital Infrastructure and Green Development

The relationship between digital infrastructure and green development should be approached from the standpoint of the influence of digital infrastructure on the environment. Digital infrastructure has been demonstrated to have the capacity to significantly reduce the emission of industrial dust, sulfur dioxide, and exhaust gas, thereby enhancing environmental quality [39,40]. Nelson argues that digital infrastructure significantly contributes to the reduction in carbon emissions [41], particularly in regions characterized by minimal carbon emissions and elevated digitalization and regions unaffected by resource constraints. The promotion of regional industrial upgrading, the easing of resource dependence, and the promotion of technological innovation have been identified as the primary mechanisms through which to achieve emission reduction [42,43]. In the digital era, stakeholders should seize the opportunities for green development presented by digital infrastructure.

The continuous advancement of digital infrastructure drives green development. Conversely, green development also plays a role in promoting the development of digital infrastructure to some extent [44]. Firstly, The transition of energy from fossil fuels to renewable energy can effectively promote green development, which is highly dependent on the support of digital technologies and digital infrastructure [45]. Moreover, green development fosters new business types, organizations, and models, which in turn accelerates industrial digitalization and promotes the development of digital infrastructure [46,47]. Specifically, some high-pollution, high-emission enterprises, under policy pressure, must transform to low carbon to achieve sustainable development. To successfully achieve low-carbon transformation, the widespread application of digital infrastructure is needed to alleviate cost pressures and enhance environmental efficiency and production productivity.

2.5. Research Gap

From the above literature review, the definitions of digital infrastructure and green development vary significantly across the globe, reflecting the unique characteristics that arise from different stages of development in various countries. However, there remains a lack of a comprehensive indicator system specifically adapted to the current China. Although independent research on digital infrastructure and green development is relatively abundant, scholars also have begun to explore the relationship between digitalization and green development. Nevertheless, quantitative analysis of the coupling coordination degree between digitalization and green development is still lacking, as well as an in-depth exploration of the spatial and temporal characteristics and dynamic evolution trends. Based on this, this study focuses on China from 2012 to 2022 and constructs a comprehensive evaluation system for the coupling coordination degree between digital infrastructure and green development. The study uses the coupling coordination degree model, entropy weight TOPSIS method, Moran’s I index, and obstacle degree model to analyze the coupling coordination degree, spatial differences, and obstacle factors between digital infrastructure and green development.

3. Research Methods

This section will provide a detailed explanation of the data sources and selection criteria, explaining why these data effectively support the research objectives of this study, and will preprocess the data to lay the foundation for subsequent analysis.

3.1. Data Sources

In consideration of data availability, the present study has selected 30 provinces (municipalities directly under the central government and autonomous regions) as the observation sample from 2013 to 2022, with Tibet, Hong Kong, Macao, and Taiwan excluded from the analysis for the time being. The green application patent information is gathered from the Chinese Research Data Services Platform (CNRDS), based on the “International Patent Classification Green List” published by the World Intellectual Property Organization (WIPO). The additional data is sourced from the China Statistical Yearbook, China Energy Statistical Yearbook, Guotai An database (Shenzhen GTA Education Tech Co., Ltd., Shenzhen, China), etc. Due to the adjustment of the statistical system in 2022, the data for the registered unemployment rate (G2) is unavailable. The missing values were estimated by using interpolation. To validate the robustness of the imputed data, we conducted a 5-fold cross-validation. The results, as shown in

Table 1. Results of the robustness test.

Variable	(1)	(2)	(3)	(4)	(5)
G2	0.339 ***	0.359 ***	0.365 ***	0.364 ***	0.372 ***
Constant	2.190 ***	2.123 ***	2.094 ***	2.117 ***	2.089 ***
R-squared	0.338	0.374	0.371	0.354	0.3582
F	125.37	147.20	144.79	134.63	137.30

*** p < 0.01, ** p < 0.05, * p < 0.1.

, indicate that the regression coefficients are significant in all models (p < 0.01). Furthermore, the mean absolute error (MAE) exhibits minimal variation across different data partitions, demonstrating that the interpolation data exhibits good stability and reliability.

Based on the knowledge of the notion of digital infrastructure, this study, drawing on the relevant research by scholars such as Henfridsson and Bygstad [48], Lewis and Byrd [49], Zhang and Sun [50], and Omelianenko et al. [51] constructs an evaluation index system following the principles of scientific rigor, systematization, representativeness, and data availability. Starting from the two components of digital infrastructure, this study establishes two criterion layers, Internet Infrastructure and Communication Infrastructure, which are further refined into six indicators in the indicator layer.

Green development is a multidimensional concept. In conjunction with the definition of green development discussed earlier and drawing on the indicator systems established by scholars such as Shinkevich et al. [52] and Mushafiq [53], this paper develops a green development indicator system. From the perspectives of ecological condition, economic development, governance capacity, innovation capability, and social stability, five criterion layers are established: Green Ecology, Green Economy, Green Governance, Green Innovation, and Green Society. Corresponding indicators are selected from these five perspectives to assess the degree of green development.

3.2. Data Preprocessing

To ensure the reliability and consistency of the data, this section will elaborate on the data preprocessing process, including how indicators are standardized and how missing data are handled.

3.2.1. Measurement of Development Level

This research employs the entropy weight-TOPSIS approach to evaluate the level of digital infrastructure and green development, which serve as the foundation for calculating the coupling degree and coupling coordination degree in the subsequent analysis. The core idea of the weight-TOPSIS method is based on the standardized data of each indicator, calculating the information entropy reflected by the data of each indicator and then performing quantitative ranking using the TOPSIS method. Compared to other traditional methods (such as AHP and Delphi), the weight-TOPSIS method has significant advantages in terms of not relying on subjective judgment, avoiding multicollinearity, and handling large datasets effectively [54,55]. The entropy weight approach is an objective enhancement technique that can circumvent errors caused by human factors and is highly operable [56]. The TOPSIS method is based on the results of the entropy weight method and then carries out the next step of calculation [57,58]. Specifically, it involves the ranking of the proximity of the selected evaluation object to the “ideal solution,” where the closer the “ideal solution,” the higher the level of the evaluation object [59]. The particular calculating stages are as follows:

(1) Construct judgment matrix, where m represents the number of provinces and n denote the number of evaluation indicators, the original matrix $X$,

$$\begin{array}{c}{X}_{ij}={\left(x_{ij}\right)}_{m\times n}(i=\mathrm{1,2},3,\dots \dots ,m;j=\mathrm{1,2},3,\dots \dots ,n)\end{array}$$

(1)

$X_{ij}$ denotes the $j$-th evaluation indicator for the $i$-th province.

(2) Standardization of indicators. To standardize the indicators, the following equations are applied:

For positive indicators:

$$\begin{array}{c}{Y}_{ij}={\displaystyle \frac{X_{ij}-min{X}_{ij}}{max{X}_{ij}-min{X}_{ij}}}\end{array}$$

(2)

For negative indicators:

$$Y_{ij}={\displaystyle \frac{max{X}_{ij}-X_{ij}}{max{X}_{ij}-min{X}_{ij}}}$$

(3)

After normalization, the standardized matrix $Y$ is obtained:

$$Y={\left(Y_{ij}\right)}_{m\times n}(i=\mathrm{1,2},3,\dots \dots ,m;j=\mathrm{1,2},3,\dots \dots ,n)$$

(4)

$Y$ ($0<Y_{ij}<1$) denotes the $j$-th evaluation indicator for the $i$-th province.

(3) The entropy $E_j$ of each indicator is calculated as follows:

$$E_j=-{\displaystyle \frac{1}{\ln m}}{\sum }_{i=1}^m{P}_{ij}\ln P_{ij}, 0\le E_j\le 1$$

(5)

where $P_{ij}={\displaystyle \frac{Y_{ij}}{{\sum }_{i=1}^m{Y}_{ij}}}$ is the proportion of the $i$-th indicator’s normalized value in the $j$-th column. This calculation gives the entropy value $E_j$ for each indicator, which reflects the amount of information it carries.

(4) The entropy weights $W_j$ are then computed as

$$W_j={\displaystyle \frac{1-E_j}{{\sum }_{j=1}^n(1-E_j)}}$$

(6)

(5) The weighted decision matrix $R$ is computed by multiplying the normalized matrix $Y_{ij}$ by the entropy weights $W_j$, as follows:

$$R={\left(r_{ij}\right)}_{m\times n}, r_{ij=}\left(W_j\times Y_{ij}\right)$$

(7)

(6) The optimal solution $Q_j^{+}$ and the suboptimal solution $Q_j^{-}$ are determined as follows:

$$Q_j^{+}=(\max r_{1j}, \max r_{2j},\dots \dots ,\max r_{mj})$$

(8)

$$Q_j^{-}=(\min r_{1j}, \min r_{2j},\dots \dots ,\min r_{mj})$$

(9)

(7) The Euclidean distance between each province and the ideal solution $d_i^{+}$ and the negative ideal solution $d_i^{-}$ is calculated using the following equations:

$$d_i^{+}=\sqrt{\sum _{j=1}^m{(Q_i^{+}-r_{ij})}^2}$$

(10)

$$d_i^{-}=\sqrt{\sum _{j=1}^m{(Q_i^{-}-r_{ij})}^2}$$

(11)

(8) The relative closeness to the ideal solution is computed as follows:

$$P_i={\displaystyle \frac{d_i^{-}}{d_i^{+}+d_i^{-}}}(0\le P_i\le 1)$$

(12)

where the value of $P_i$ closer to 1 indicates that the evaluation object is closer to the ideal solution, meaning the digital infrastructure development level in province $i$ is higher. Conversely, a value closer to 0 suggests a lower level of digital infrastructure and green development.

This calculation reflects the relative weight of each indicator and criterion layer, as given in

Table 2. Evaluation index system for digital infrastructure and green development.

	Criterion Layer		Indicator Layer
	Index	Weight	Index	Attribute	Weight
Digital Infrastructure	Internet Infrastructure	0.37	(A1) Internet Domain Density (per 100 people)	+	0.324
			(A2) Internet Webpage Density (per 100 people)	+	0.011
			(A3) Broadband Access Port Density (per 100 people)	+	0.035
	Communication Infrastructure	0.63	(B1) Mobile Phone Base Station Density (per km2)	+	0.350
			(B2) Mobile Phone Penetration Rate (per 100 people)	+	0.010
			(B3) Fiber-Optic Line Density (per km2)	+	0.270
Green Development	Green Ecology	0.3306	(C1) CO2 Emission per GDP Unit (per 100 tons/billion)	−	0.0014
			(C2) Industrial Smoke and Dust Emissions per Unit GDP	−	0.1737
			(C3) Industrial Wastewater Discharge per Unit GDP	−	0.0025
			(C4) Energy Consumption per Unit GDP (tons per 10,000 RMB)	−	0.0011
			(C5) Per Capita Green Space (m2)	+	0.1421
			(C6) Green Coverage Rate in Built-up Areas	+	0.0018
			(C7) Per Capita Water Resources (m3 per person)	+	0.008
	Green Economy	0.0076	(D1) GDP per Capita (RMB)	+	0.0004
			(D2) Average Wage of Urban Employed Persons (RMB)	+	0.0002
			(D3) Tertiary Industry Share of GDP	+	0.0061
			(D4) Local Fiscal Revenue per Capita (RMB)	+	0.0009
	Green Governance	0.3891	(E1) Comprehensive Utilization Rate of Industrial Solid Waste	+	0.0218
			(E2) Daily Sewage Treatment Capacity	+	0.1394
			(E3) Domestic Waste Harmless Treatment Rate	+	0.0018
			(E4) Pollution Control Investment as a Percentage of GDP	+	0.2261
	Green Innovation	0.1805	(F1) Proportion of R&D Personnel in the Population	+	0.1723
			(F2) R&D Expenditure	+	0.0022
			(F3) Green Patent Application Count	+	0.006
	Green Society	0.0923	(G1) Natural Population Growth Rate	+	0.0349
			(G2) Registered Unemployment Rate	−	0.0119
			(G3) Per Capita Disposable Income	+	0.0004
			(G4) Number of Hospital Beds per 10,000 People	+	0.0081
			(G5) Number of Buses per 10,000 People	+	0.0112
			(G6) Per Capita Road Area	+	0.0226
			(G7) Number of Urban Basic Medical Insurance Participants	+	0.0032

.

3.2.2. Coupling Measurements

The theory of coupling, originally developed in the field of physics, is a framework for describing and measuring the influence of elements between two or more systems through interactions [60]. The term “coupling” signifies the phenomenon in which two or more systems, modes of motion, or entities are interconnected through interaction and mutual influence, resulting in a harmonious relationship. In a coupled system, the interaction between the subsystems is positive, enabling them to align and promote dynamic associations. The strength of the coupling is directly proportional to the magnitude of interaction between the systems. Furthermore, the system typically evolves into a structured order as the level of coupling intensifies [61].

This study calculates the coupling degree as a basis for determining the coupling coordination degree. The coupling degree is determined using the subsequent formula

$$C=2\sqrt{{\displaystyle \frac{P_1\times P_2}{(P_1+P_2{)}^2}}}$$

(13)

where $C$ represents the coupling degree, with a value range of $\left[\mathrm{0,1}\right]$. The closer $C$ is to 1, the smaller the degree of dispersion between the systems, indicating a higher degree of coupling between them. $P_1$ represents the level of digital infrastructure development, and $P_2$ represents the level of green development.

The coupling degree of digital infrastructure and green development in 30 provinces in China from 2013 to 2022 is obtained through the coupling calculation formula, and the results are shown in the coupling degree heat map in Figure 1. Referring to the division of the coupling degree value $C$ by many scholars, the coupling degree is divided into four intervals, $0<C\le 0.3$, $0.3<C\le 0.5$, $0.5<C\le 0.8$, and $0.8<C\le 1$, which represent low levels of the coupling stage, antagonistic stage, and grinding stage and a high level of the coupling stage, respectively.

The coupling degree for each province across different years is represented through varying colors, with red indicating a high coupling degree (close to 1) and blue indicating a low coupling degree (close to 0.6). The heatmap shows that most provinces exhibit a high coupling degree, especially in recent years, indicating an increasing integration of digital infrastructure and green development. However, some provinces still display relatively low coupling degrees, suggesting that in certain regions, the coordination between digital infrastructure and green development remains at the antagonistic or grinding stage.

3.3. Coupled Coordination Degree Modeling

The coupling degree is subject to certain limitations, namely its incapacity to measure the influence of subsystem development on the degree of coupling [62]. Consequently, the introduction of the coupling coordination degree becomes imperative, which is essential, serving as a comprehensive evaluative metric that integrates both the coupling degree and the coordination degree. This index indicates the overall quality and effect of interactions between systems, as well as the level of coordination between subsystems within the system. It is important to note that a high degree of coupling does not inherently indicate a high degree of coupling coordination, as the interactions between systems may not be coordinated. Similarly, low coupling does not necessarily imply low coupling coordination, as the interactions between systems may be very well coordinated [63,64].

The coupling coordination degree is calculated by the following formulas [50]:

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

(14)

$$T=\alpha P_1+\beta P_2$$

(15)

$T$ represents the comprehensive coordination index between digital infrastructure and green development; $\alpha$ and $\beta$ are weighting parameters, where $\alpha +\beta =1$. In this study, $\alpha =\beta =0.5$, indicating equal importance between the two systems. The value of $D$ reflects the coordination degree, with values in the range of $\left[\mathrm{0,1}\right]$. The closer $D$ is to 1, the higher the level of coordination between the two systems. For further details, refer to the coordination standards outlined in

Table 3. Criteria for classifying the degree of coupling coordination.

Coupled Coordination Degree	Rating Standard
(0,0.2)	Serious disorder
[0.2,0.3)	Moderate disorder
[0.3,0.4)	Low disorder
[0.4,0.5)	Marginal coordination
[0.5,0.6)	Low coordination
[0.6,0.7)	Moderate coordination
[0.7,0.8)	Well coordination
[0.8,1.0)	High coordination

[65].

3.4. Spatial Correlation and Spatial Clustering Characteristics

This study employs both the global Moran’s I statistic and the local Moran scatter plot to explore the spatial correlation and spatial clustering characteristics of digital infrastructure and green development [66].

The calculation for the global Moran’s Index is as follows:

$$I={\displaystyle \frac{\sum _{i=1}^n\sum _{j=1}^n{w}_{ij}(y_i-\overline{y)}(y_j-\overline{y})}{S^2\sum _{i=1}^n\sum _{j=1}^n{w}_{ij}}}$$

(16)

where $n$ is the number of provinces, $S^2$ is the sample variance, and $w_{ij}$ represents the spatial weight matrix. The value of global Moran’s Index is in the range $\left[-\mathrm{1,1}\right]$. A value $I>0$ indicates positive spatial correlation, a value $I<0$ indicates negative spatial correlation, and $I=0$ represents spatial randomness.

The calculation for local Moran’s I is given by

$$I_i={\displaystyle \frac{(y_i-\overline{y})\sum _{j=1}^n{w}_{ij}(y_i-\overline{y})}{S^2}}$$

(17)

where $I_i$ represents the local spatial autocorrelation for province $i$, indicating whether province $i$ has high (or low) spatial clustering. A positive $I_i$ indicates high [67] clustering, whereas a negative $I_i$ suggests low (high) clustering.

Using Equation (16), Stata 16 (StataCorp LLC, College Station, TX, USA) software is applied to calculate the spatial correlation for the 30 provinces (including municipalities and autonomous regions) based on the historical digital infrastructure data.

3.5. Barrier Modeling

The Barrier Model primarily involves three key indicators: the contribution of factors ($F_j$), the indicator deviation ($I_{ij}$), and the barrier degree ($O_j$). Specifically, $F_j$ represents the contribution of factor $j$, indicating the impact of each individual indicator on the total goal. The weight of indicator $j$ to the total goal is represented by $W_j$. The indicator deviation ($I_{ij}$) reflects the disparity of the values of each individual indicator and the normalized value of the system goal, which is between 1 and 0. The barrier degree ($O_j$) indicates the extent to which indicator $j$ affects the system. An increased value of $O_j$ suggests a greater obstacle to the integration of digital infrastructure and sustainable growth. This model is based on the study by Hao et al. [47], with the following calculation formulas:

$$F_j=W_j$$

(18)

$$I_{ij}=1-{X^{\prime }}_{ij}$$

(19)

$$O_j={\displaystyle \frac{F_j\times I_{ij}}{{\sum }_{j=1}^n(F_j\times I_{ij})}}\times 100\mathrm{\%}$$

(20)

In the equations, ${X^{\prime }}_{ij}$ refers to the standardized value obtained from the calculation method for the indicators. The number $n$ represents the total number of individual indicators. Upon calculating the indicator deviation, the next step is to compute the system’s overall barrier degree ($U_i$), with a larger value indicating that the indicator’s barrier to the coordination of digital infrastructure and green development is higher, as shown in the following formula:

$$U_i=\sum O_{ij}$$

(21)

4. Results and Discussion

This section will use quantitative analysis to reveal the evolving trends of the coupling coordination degree, further exploring the factors influencing the regional differences in coupling coordination degree.

4.1. Spatial and Temporal Variability in Coupling Coordination

This section analyzes the coordinated coupling degree of digital infrastructure and green development across 30 provinces in China. It will explore annual trends, evaluate the performance of various regions at different time points, and analyze key factors influencing the coordinated coupling degree.

4.1.1. Coupling Coordination

The coupling coordination degrees of digital infrastructure and green development for 30 Chinese provinces (municipalities and autonomous regions) were calculated quantitatively for the period 2013–2022 by Formula 14, shown in Figure 2. The overall coupling coordination degree in China exhibited a steady annual increase, with the coordination type gradually transitioning from low disorder to marginal coordination. However, considerable heterogeneity was evident among the provinces. In the majority of provinces, the level of coupling coordination improved by one stage, while in certain provinces, the improvement reached two levels during the observation period. For instance, Jiangsu advanced from the marginal coordination stage to the well coordination stage, while the provinces of Hunan, Guangxi, Chongqing, and Sichuan shifted from the moderate disorder stage to the marginal coordinated stage.

This signifies that China has achieved substantial progress in the integration and coordination of digital infrastructure and green development over the past decade. Evidently, China’s policy initiatives and strategic framework for advancing the harmonious development of the digital economy and ecological civilization are progressively yielding favorable outcomes. Recently, China has vigorously advocated for the coordinated development of the digital economy and ecological civilization by implementing a succession of policies, including the Digital New Infrastructure Investment Plan and subsidies for the digital upgrading of green industries nationwide. These experiences have motivated the various regions to promote the integration of digital infrastructure construction and green growth and hence improve the degree of coupling coordination. However, the diffusion effect of technological innovation should not be overlooked. The maturation and dissemination of digital technologies (e.g., 5G, big data, and artificial intelligence) have accelerated the coupling process.

Regional disparities primarily originate from differences in the economic foundations of various areas. Economically developed regions such as Jiangsu and Zhejiang possess well-established industrial systems, substantial capital accumulation, and robust technological research and development capabilities. These attributes enable a rapid response to national policy directives and facilitate the deep integration of digital technologies into green industrial development, thereby achieving leapfrogging improvements. In contrast, certain and western regions, with relatively weaker economic bases, encounter multiple constraints—ranging from capital and technology to talent—during the initial stages of digital infrastructure development, which impedes a swift enhancement in the coupling coordination degree. Furthermore, significant variations in regional industrial structures contribute to these disparities. The eastern coastal regions, which are predominantly characterized by high-technology industries and modern service sectors, naturally are in coordination with digital technologies. This facilitates their capacity for digital transformation and upgrading, especially within the context of green development. In contrast, regions dominated by traditional or resource-intensive industries typically exhibit a more homogeneous industrial structure with extensive adjustment challenges, resulting in a comparatively slower improvement in the degree of coupling coordination. Moreover, the uneven distribution of human resources is another critical factor. Areas such as Beijing, Shanghai, and Jiangsu concentrate numerous higher education institutions, research centers, and high-caliber professionals, thereby offering substantial intellectual support and technological innovation impetus for both digital infrastructure construction and green development. Conversely, some frontier or economically underdeveloped regions are short of talent attraction and professional elites. This divergence in talent availability hinders breakthrough progress in the coordination of digitalization and green development, ultimately leading to conspicuous differences in the level of coupling coordination among regions.

4.1.2. Temporal Evolution of Coupling Coordination Degree

The Moran’s Index test was employed with Stata 16 software to assess the coupling coordination degree of China’s digital infrastructure and green development in 30 provinces from 2013 to 2022. The findings, illustrated in

Table 4. 2013–2022 Overall Moran’s Index of coupling coordination of digital infrastructure and green development in 30 provinces and cities.

Year	Moran’s Index	p-Value	Z-Value	Significance Level
2013	0.472	0.000	4.196	0.01
2014	0.466	0.000	4.143	0.01
2015	0.450	0.000	4.004	0.01
2016	0.418	0.000	3.724	0.01
2017	0.464	0.000	4.094	0.01
2018	0.460	0.000	4.050	0.01
2019	0.471	0.000	4.142	0.01
2020	0.446	0.000	3.926	0.01
2021	0.441	0.000	3.876	0.01
2022	0.432	0.000	3.800	0.01

, reveal that the global Moran’s index of the coupling coordination degree of is positive, and the significance test is passed at the 1% level for all years, indicating a substantial positive spatial correlation between China’s digital infrastructure and green development.

On this basis, a local Moran scatter plot of the coupling coordination degree was drawn. As illustrated in the Figure 3, in the four-year periods of 2013, 2016, 2019, and 2022, the number of provinces and cities located in the first and third quadrants significantly surpasses that of those situated in the second and fourth quadrants. This indicates that the coupling coordination exhibits distinct characteristics of “high-high aggregation” and “low-low aggregation.”

From the spatial agglomeration perspective, provinces such as Qinghai, Ningxia, and Xinjiang exhibited significant high-high agglomeration in the observation years, which means that the coupling coordination is relatively high, thereby creating a strong spatial agglomeration effect. Conversely, provinces such as Sichuan, Shandong, Henan, and Hunan exhibited low-low agglomeration in 2016 and 2019, suggesting a lag in their respective fields and a considerable disparity with neighboring regions. Developed provinces like Guangdong and Beijing have undergone a gradual shift from high to low agglomeration, reflecting increasing internal heterogeneity. The low-low agglomeration of provinces such as Jilin and Heilongjiang further underscores the imbalance in regional development, underscoring the necessity for more expeditious narrowing of the gap between these regions in terms of digital infrastructure and green development.

4.1.3. Spatial Evolution of Coupling Coordination Degree

In order to explore the spatial distribution features of the coupling coordination level of digital infrastructure and green development in each province in China, data on the coupling coordination degree in each province in China in 2013, 2016, 2019, and 2022 was used. ArcGIS 10.8 software was used to classify the data according to the previously set coupling coordination degree classification standard to visualize the coupling coordination degree, as shown in Figure 4. According to the level standard, the degree is divided into eight categories, from serious disorder to high coordination. The coupling coordination degree level increases sequentially, and the darker the color on the graph, the higher the coupling coordination degree level.

The degree of coupling coordination between China’s digital infrastructure and green development exhibits a distinct pattern, with higher levels in the eastern regions and lower levels in the western regions. In 2013, the majority of provinces in the western region of China were experiencing serious disorder, while Zhejiang and Beijing had only just reached the low disorder, and Guangdong and Jiangsu in the coastal areas had even attained the low coordination stage. The central region and the three provinces in the northeast were both in the moderate disorder and low disorder. By 2016, only Qinghai, Ningxia, and Gansu were still in a serious disorder stage, Zhejiang Province had entered marginal coordination, and Jiangsu Province had reached moderate coordination. At this time, most provinces in the central and western region had similarly been upgraded to a status of low disorder. During the year 2019, the eastern coastal regions, including Beijing and Guangdong provinces and cities, exhibited a high degree of coordination, with Jiangsu also reaching a well coordination. The majority of provinces in the central and western regions have also reached the marginal coordination stage, while Yunnan, Guizhou, and most provinces in the northwest remain in a state of moderate disorder. Notably, Qinghai continues to be in the stage of serious disorder. In 2022, with the exception of Hainan, Shanxi, and Jiangxi, all other provinces in the eastern and central regions of China have attained the coordination level. Qinghai remains in a state of serious disorder, while Ningxia, Gansu, and Inner Mongolia are experiencing moderate disorder. Chongqing and Guangxi have also entered marginal coordination. Overall, the level of coupling coordination between the country’s digital infrastructure and green development has shown significant improvement since 2013, when only five provinces and municipalities were in the coordination stage. Presently, half of the country’s provinces have entered the coordination stage. Nevertheless, considerable opportunities for enhancement persist with regard to the comprehensive degree of coupling coordination.

The spatiotemporal differences appear to be influenced by a complex interplay of socioeconomic, technological, and policy factors. As a result, the eastern region, with its relatively mature economy and stronger industry, has achieved digital infrastructure development in conjunction with green development due to earlier technological development, which enabled them to simultaneously develop 5G and artificial intelligence [54]. In contrast, the western and central regions with their weaker economy and industry have lagged behind in terms of technology and policy. In addition, these regions also have lower population density and more infrastructure challenges, which have impeded the integration of digital infrastructure with green development [68]. Moreover, there are significant differences in the timing and degree of policy implementation. The eastern region, due to earlier policy implementation, has achieved digital infrastructure development in conjunction with green development. In contrast, the western region faces more challenges in terms of allocation and policy implementation [69].

4.2. Barrier Degree Diagnosis

In order to explore the obstacles that limit the improvement of the coupling coordination level of digital infrastructure and green development in China’s provinces (municipalities and autonomous regions) and to propose corresponding countermeasures in a targeted manner, this paper further diagnoses the obstacle degree at the indicator level of digital infrastructure and green development. First, the indicator layer of digital infrastructure is diagnosed. Due to the substantial number of indicators in the indicator layer and space constraints, only the top three obstacle factors in terms of obstacle degree for the 30 provinces and cities in China at the four-time nodes of 2013, 2016, 2019, and 2022 are listed here, as shown in

Table 5. Barrier at the indicator layer of digital infrastructure and green development.

	Digital Infrastructure				Green Development
Province	2013	2016	2019	2022	2013	2016	2019	2022
Beijing	A1,B1,B3	A1,B1,B3	A1,B1,B3	A3,B1,B3	C2,E2,E4	C2,E4,F1	C2,E4,F1	C2,E4,F1
Tianjin	A1,B1,B3	A1,B1,B3	A1,B1,B3	A1,B1,B3	C2,E2,E4	C2,C5,E4	C2,E2,E4	C2,E2,E4
Hebei	A1,B1,B3	A1,B1,B3	A1,B1,B3	A1,B1,B3	C5,E4,F1	C5,E4,F1	C2,E4,F1	C2,E4,F1
Shanghai	A1,A3,B3	A1,B1,B3	A1,B1,B3	A1,A1,B3	C2,C5,E4	C2,C5,E4	C2,C5,E4	C2,E2,E4
Jiangsu	A1,B1,B3	A1,B1,B3	A1,B1,B3	A1,B1,B3	C2,E4,F1	C2,E2,E4	C2,E2,E4	C2,E2,E4
Zhejiang	A1,B1B3	A1,B1,B3	A1,B1,B3	A1,B1,B3	C2,C5,E4	C2,C5,E4	C2,C5,E4	C2,C5,E4
Fujian	A1,B1,B3	A1,B1,B3	A1,B1,B3	A1,B1,B3	C2,E2,E4	C5,E2,E4	C2,E2,E4	C2,E2,E4
Shandong	A1,B1,B3	A1,B1,B3	A1,B1,B3	A1,B1,B3	C2,E4,F1	C5,E4,F1	C2,E4,F1	C2,E4,F1
Guangdong	A1,B1,B3	A1,B1,B3	A1,B1,B3	A1,B1,B3	C2,C5,E4	C5,E4,F1	C2,C5,E4	C2,C5,E4
Hainan	A1,B1,B3	A1,B1,B3	A1,B1,B3	A1,B1,B3	C2,E4,F1	C2,E4,F1	C2,E4,F1	C2,E4,F1
Shanxi	A1,B1,B3	A1,B1,B3	A1,B1,B3	A1,B1,B3	E2,E4,F1	C5,E4,F1	C2,E4,F1	C2,E4,F1
Anhui	A1,B1,B3	A1,B1,B3	A1,B1,B3	A1,B1,B3	C2,E4,F1	E2,E4,F1	C5,E4,F1	C2,C5,E4
Jiangxi	A1,B1,B3	A1,B1,B3	A1,B1,B3	A1,B1,B3	C2,E4,F1	E2,E4,F1	C2,E4,F1	C2,E4,F1
Henan	A1,B1,B3	A1,B1,B3	A1,B1,B3	A1,B1,B3	E2,E4,F1	C5,E4,F1	C2,E4,F1	C2,E4,F1
Hubei	A1,B1,B3	A1,B1,B3	A1,B1,B3	A1,B1,B3	C2,E4,F1	C5,E4,F1	C2,E4,F1	C2,C5,E4
Hunan	A1,B1,B3	A1,B1,B3	A1,B1,B3	A1,B1,B3	C2,E4,F1	C5,E4,F1	C2,E4,F1	C2,E4,F1
Inner Mongolia	A1,B1,B3	A1,B1,B3	A1,B1,B3	A1,B1,B3	C5,E2,F1	E2,E4,F1	E2,E4,F1	E2,E4,F1
Guangxi	A1,B1,B3	A1,B1,B3	A1,B1,B3	A1,B1,B3	C2,E4,F1	C5,E4,F1	C2,E4,F1	C2,E4,F1
Chongqing	A1,B1,B3	A1,B1,B3	A1,B1,B3	A1,B1,B3	C2,E4,F1	C2,E4,F1	C2,C5,E4	C2,C5,E4
Sichuan	A1,B1,B3	A1,B1,B3	A1,B1,B3	A1,B1,B3	C2,E4,F1	C5,E4,F1	C2,E4,F1	C2,E4,F1
Guizhou	A1,B1,B3	A1,B1,B3	A1,B1,B3	A1,B1,B3	C2,E4,F1	E2,E4,F1	C2,E4,F1	C2,E4,F1
Yunnan	A1,B1,B3	A1,B1,B3	A1,B1,B3	A1,B1,B3	C2,E4,F1	C5,E4,F1	C5,E4,F1	C2,E4,F1
Shaanxi	A1,B1,B3	A1,B1,B3	A1,B1,B3	A1,B1,B3	C5,E4,F1	C5,E4,F1	C2,E4,F1	C2,E4,F1
Gansu	A1,B1,B3	A1,B1,B3	A1,B1,B3	A1,B1,B3	C2,E4,F1	C5,E4,F1	E2,E4,F1	C2,E4,F1
Qinghai	A1,B1,B3	A1,B1,B3	A1,B1,B3	A1,B1,B3	C2,E4,F1	C5,E4,F1	C2,E4,F1	C2,E4,F1
Ningxia	A1,B1,B3	A1,B1,B3	A1,B1,B3	A1,B1,B3	C2,E2,F1	C2,E2,F1	C2,E4,F1	C2,E4,F1
Xinjiang	A1,B1,B3	A1,B1,B3	A1,B1,B3	A1,B1,B3	E2,E4,F1	E2,E4,F1	C5,E4,F1	E2,E4,F1
Liaoning	A1,B1,B3	A1,B1,B3	A1,B1,B3	A1,B1,B3	C5,E4,F1	C5,E4,F1	C5,E4,F1	C2,E4,F1
Jilin	A1,B1,B3	A1,B1,B3	A1,B1,B3	A1,B1,B3	C2,E4,F1	C5,E4,F1	C2,E4,F1	C2,E4,F1
Heilongjiang	A1,B1,B3	A1,B1,B3	A1,B1,B3	A1,B1,B3	C2,E4,F1	C5,E4,F1	C2,E4,F1	C2,E4,F1

.

A comprehensive analysis reveals that, among the top three factors in terms of obstacle degree in the digital infrastructure indicator layer for the period from 2013 to 2022, a total of four obstacle factors hinder the advancement of digital infrastructure in China’s 3 0 provinces and cities, which are mobile phone base station density (per km²) (B1), Internet domain density (A1), fiber-optic line density (B3), and broadband access port density (A3).

From a national perspective, the primary obstacle factor for virtually every province is centered on B1, and this has remained constant over the 10-year period. B1 has consistently been the top obstacle factor hindering the coupling coordination of digital infrastructure and green development in 30 provinces and cities across the country. The most significant obstacle indicators in the central region provinces were A1 and B1 in 2013. However, over the course of the observation period, B1 gradually replaced A1 as the primary obstacle indicator for all central region provinces. This suggests that the quantity of cell phone base stations is an important obstacle factor hindering the coupling coordination of digital infrastructure and green development in the central region provinces, and indeed, all the provinces and municipalities in the country. The secondary obstacle indicators, conversely, were predominantly concentrated in A1, while the other secondary obstacle indicators of Beijing, Tianjin, Henan, and Chongqing fell in B3 or B1 in 2013. However, over time, the indicators have increasingly moved towards a stable situation, with A1 acting as the secondary obstacle. In the meantime, B3 emerges as the tertiary obstacle, signifying an enhancement in the fiber-optic cable line density, i.e., the density of Internet domain names, within the central region provinces. This indicates that the density of Internet domain names persists as a significant impediment, constraining the national secondary obstacle to the coupling coordination of digital infrastructure and green development.

In the subsequent analysis, the obstacle degree of the indicator layer of green development is examined. This analysis involves the calculation of the obstacle degree of different indicators in the indicator layer to green development. The top three obstacle factors of the obstacle degree of the 30 provinces and cities in China in 2013, 2016, 2019, and 2022 are identified. The results are presented in Table 5. Overall, among the factors in the top three regarding in terms of barrier degree size, there are five main barrier factors to green development in the 30 provinces, which are industrial soot and dust emissions per unit of GDP (C2), green space per capita (C5), sewage treatment capacity per day (E2), the share of investment in pollution control projects in GDP (E4), and the share of R&D personnel in the population (F1). Among them, the first obstacle factor is mainly E4, the second obstacle factors are mainly (C2) and (F1), and the third obstacle factors appear in (C2), (C5), (E2), and (F1).

In economically developed provinces such as Beijing, Shanghai, Zhejiang, and Guangdong, the primary barrier factor for 2013–2022 is predominantly E4. However, the secondary and tertiary barrier factors change from F1 and C5 to C2 over time. The shift also indicates a shift in the nature of quality in green development within these locations as the focal point from policy implementation issues towards issues related to the implementation of technology and structure within the economy. In contrast, western provinces such as Inner Mongolia, Gansu, Shaanxi, and Ningxia exhibited a consistent E4 as the primary barrier at all four observation points. However, the secondary barrier varied, from F1 between 2013 and 2016 to C2 by 2022. This reflects the dynamics of green development in these provinces from policy implementation problems to technical application and economic structure issues. The evolution of barriers to green development in the three northeastern provinces is quite obvious. The first barrier factor has always been E4, the second barrier factor has evolved from F1 to C2, and the third barrier factor has shown different changes in different years, but it still maintains a high degree of barrier, indicating that these regions face greater challenges to green development, especially in terms of policy implementation and technical issues.

The findings in Table 5 indicate that the primary impediment to the integration and coordination of China’s digital infrastructure and green development, as measured by the green development index, is the proportion of investment in pollution control projects within GDP. This proportion has remained constant during the observed period, suggesting that the investment in pollution control is inadequate across all provinces. Green development is inseparable from effective environmental governance. Absent effective governance, environmental concerns become mere rhetoric. Identifying industrial dust and R&D personnel as the second and third major obstacles to the coordinated development of the system underscores the deficiencies in pollution emission control and the number of R&D personnel across all provinces, impeding the process of coordinated system improvement. To this end, it is vital to emphasize the improvement of environmental governance and technological innovation with the implementation of targeted measures: an appropriate adjustment in the severity of environmental regulation, the active involvement of research and development institutions and enterprises, the reduction in taxes, the provision of subsidies for innovative talents, and the comprehensive improvement of the environmental governance system and technological innovation capabilities.

5. Recommendations

Based on the research findings and analysis, the accompanying countermeasures can be proposed to enhance the high-quality integrated development of digital infrastructure and green development.

It is essential for all parties—industry, local governments, and the central government—to engage in multifaceted collaboration. Industry players in digital infrastructure should leverage their technological strengths to facilitate cross-regional cooperation and resource sharing, particularly by exporting the advanced technologies and experiences accumulated in the eastern regions. Eastern enterprises can take the lead in combining green technology and digital innovations, offering technical support to the western and central regions to enhance their digital infrastructure levels while simultaneously increasing investment in green technology R&D, especially in areas like green data centers and energy-saving technologies, thereby driving the synergy between digitalization and green development.

Local governments should tailor their development strategies according to their regional characteristics, integrating digital infrastructure construction with green development objectives. Specifically, in central and western regions, local governments must increase their support for technological investment to ensure the parallel progression of digitalization and green development goals. Additionally, governments should promote regional exchanges and technological collaboration, with eastern regions playing a leading role in the green transformation process, fostering innovation, and sharing successful experiences in green development.

At the central government level, policies and financial support should focus on enhancing digital infrastructure in underdeveloped regions, particularly for mobile base stations and fiber optic networks, ensuring these areas are not left behind in the digital transformation. The government should also encourage enterprises to innovate in green technologies related to environmental protection, energy conservation, and pollution reduction. Supporting green technology R&D and applications, as well as facilitating investments in ecological restoration projects, will further integrate digital technologies and green development, creating a balanced approach to economic, environmental, and social sustainability.

6. Conclusions

From 2013 to 2022, the average coupling coordination degree of digital infrastructure and green development in Chinese provinces and the four major economic regions remained relatively low but showed an upward trend, indicating that the synergistic effect between digital infrastructure and green development has steadily improved. From the viewpoint of spatial agglomeration, the coupling coordination degree in most provinces is distributed in the first and third quadrants, which belongs to the situation of “high-high agglomeration” and “low-low agglomeration”, reflecting a strong spatial dependence. The eastern coastal region, being the most economically developed, has invested more time in digital infrastructure construction, with its development level and coupling coordination degree higher than those of other regions. In contrast, the western region exhibits the lowest coupling coordination degree, primarily due to its remote geographical location, limited resources, underdeveloped talent pool, and outdated production styles. As a result, digital development in the western provinces lags behind others regions and has not aligned with the requirements of green development. However, the western and central regions have maintained strong growth rates, indicating that the recent policies such as the “Rise of the Central Region,” “Western Development,” and “Eastern Data to Western Computing” have proven effective. These policies have directed additional human, material, and financial resources into the information infrastructure development of the western and central regions. Therefore, these policies lead to rapid growth in digital infrastructure construction in the west, with noticeable improvements in the central region as well. For the northeast region, the low coupling coordination degree in the early years can be attributed to the high proportion of resource-based cities and the long-standing dominance of resource-based industries. The northeast region’s deep historical roots, influenced by economic transformation and industrial structure adjustments, have hindered its ability to rapidly meet the new demands of green development.

This suggests that the level of economic development remains the strongest influencing factor on the level of digital infrastructure and green development. Advanced technologies and equipment are more likely to be deployed first in the eastern regions, and higher economic development levels correlate with higher demands from residents for living environments and ecological construction.

The density of mobile phone base stations emerges as the primary obstacle factor affecting the coupling coordination of digital infrastructure. The density of Internet domain names and the frequency of optical cable lines are the second and third obstacle factors affecting the coupling coordination of the two systems, respectively. Despite China’s advanced mobile technology and Internet Infrastructure, the per capita number of mobile base stations and the Internet Infrastructure remain inadequate due to China’s substantial population. This limitation hinders the efficiency of people’s digital lives and is a significant factor impeding the harmonization of digital infrastructure and green development. In terms of green development indicators, the top three obstacles are the proportion of investment in pollution control projects to GDP, industrial dust and smoke emissions per unit of GDP, and the proportion of R&D personnel to the population. The third obstacle in some provinces is the per capita green space or the daily sewage treatment capacity. It is evident that from the perspective of green development, the ability to control pollution and the level of ecological construction are still shortcomings compared to economic development and social security, with substantial room for improvement. This is because China has rapid, resource-intensive development; particularly, the environmental and ecological repercussions of this growth are substantial, necessitating a comprehensive and expeditious remediation effort to restore environmental quality. Green innovation has emerged as a pivotal catalyst in this process, contributing to the enhancement of environmental quality.

Although this study provides valuable insights, it still has certain limitations and uncertainties. These limitations include the focus solely on provincial-level data, lacking a detailed comparative study at the city level, and the failure to explore the issue more profoundly. Furthermore, the numerous measurement perspectives and complex indicators make it impossible to consider all aspects. Therefore, future research should develop a more comprehensive evaluation framework and methods to assess the coupling coordination between digital infrastructure and green development.