An Analysis of the Spatial–Temporal Evolution and Influencing Factors of the Coupling Coordination Degree Between the Digital and Real Economies in China

Abstract

The digital economy (DE) and real economy (RE) are dual pillars of the modern economic system. The deep integration of the digital economy and real economy (IDR) has emerged as a pivotal strategic trend. IDR not only can enhance international competitiveness but also contributes to sustainable development goals. This work collects DE and RE data from 30 provinces in China between 2012 and 2022. The entropy weight method and the coupling coordination degree (CCD) model are employed to measure the level of IDR. Furthermore, the Dagum Gini coefficient, Kernel density estimation, the spatial autocorrelation model, and the geographically and temporally weighted regression (GTWR) model are utilized to analyze the spatial–temporal evolution and influencing factors of CCD. The following conclusions are drawn: (1) During the study period, CCD shows an upward trend, but the value is relatively low. (2) There are significant spatial differences in CCD, and the inter-regional difference is the primary cause. (3) The regional differences in CCD are continuously widening. (4) CCD shows an obvious global spatial agglomeration feature, and the spatial agglomeration degree of CCD has been enhanced from 2012 to 2022. (5) The policy intensity, digital infrastructure, industrial structure, human capital, technological innovation, and market environment have significant impacts on CCD. The obtained findings provide important theoretical support for the coordinated development of DE and RE.

1. Introduction

Against the backdrop of a slowing global economy and challenges to China’s economic recovery, the advancement of the digital economy (DE) has provided new momentum for economic growth. However, the real economy (RE), which serves as the foundation of economic development, faces multiple issues such as insufficient developmental momentum, structural imbalances in supply and demand, and regional development differences. The accelerated progress of digital technological revolution and industrial transformation has facilitated the IDR [1]. This integration has evolved into a powerful catalyst for economic development and can propel the economy toward the pinnacle of high-quality development [2]. Consequently, it is crucial to facilitate the development of IDR.

DE represents a novel economic paradigm that has emerged alongside the iterative advancement and application of digital technologies. It is recognized as the third significant economic model, succeeding the agricultural and industrial economies [3]. Tapscott first introduced the DE and defined it as an economic model that facilitates the digital representation of information flow [4]. Due to varying research perspectives, a unified and precise definition of the DE has not been established. The definition of the DE can be categorized into narrow and broad dimensions. In the narrow sense, it can be summarized as economic activities fundamentally driven by digital elements. While in the broad sense, it encompasses economic activities significantly enhanced by digital elements, with this enhancement primarily stemming from digital industrialization and industrial digitalization.

After the financial crisis of 2008, the Federal Reserve classified the real estate and financial sectors as part of the non-real economy. The existing literature seldom defines the RE specifically, but with the deepening of related research, the conceptual definition of the real economy has gradually become more unified. In its broad sense, the RE refers to all economic activities related to the production, distribution, exchange, and consumption of tangible products and services. It encompasses all stages from raw material extraction, processing, and manufacturing to sales and consumption. The broad definition of RE includes not only manufacturing, agriculture, and construction but also sectors within the service industry that provide material support to society. On the other hand, the narrow definition of the RE focuses more specifically on economic sectors that directly create material wealth and tangible products. Typically, the narrow definition of the RE emphasizes traditional industries and actual production activities [5].

IDR is a product of the continuous development of economic society. Some scholars considered that IDR is essentially the enhancement and innovation of traditional industries through the new elements from the DE, including advanced technologies, data elements, and platforms [6]. Sun et al. stated that IDR signified the extensive application of advanced digital technologies within the real economy sector [7]. This integration fosters a synergistic relationship between the DE and RE and forms a virtuous circle. Specifically, the RE underpins the development of the DE by providing a robust foundation of talent, technology, capital, and infrastructure, thereby creating a conducive environment for the DE’s sustainable growth. Concurrently, the DE empowers traditional industries by leveraging digital technologies to enhance the efficiency of processing, production, circulation, and consumption within the RE. This empowerment drives the digital transformation and upgrading of the RE, leading to improved operational efficiency, optimized product quality, and technological innovation. This deep integration generates synergistic effects, boosting national and regional competitiveness. The growth of the DE is closely linked to the foundational support of the RE, and the modernization of the RE is reliant on its digital transformation. Thus, IDR establishes an interdependent relationship that propels the continuous advancement of both the DE and RE. The promotion of IDR is conducive to fully releasing developmental dividends of the DE, enhancing the modernization level of the RE, strengthening the vitality and resilience of economic development, and facilitating the formation of a new development paradigm.

However, the development of IDR in China faces multiple challenges. At this stage, there are still some constraints hindering the IDR, including digital divides across regions, an incomplete market system for data elements, bottlenecks in critical core technologies, a significant shortage of digital talent, and lagging legal frameworks for intellectual property protection. Consequently, it is imperative to address the following questions: How can IDR in China be systematically, reasonably, and scientifically measured? What are the spatial and temporal trends of IDR? Are there regional differences in its development? What are the underlying driving factors? A comprehensive analysis of these issues will significantly contribute to the profound integration of the DE and RE.

On a global scale, the coordinated development of the DE and RE has become a key issue in the economic transformation of various countries. European nations, particularly those within the European Union, have leveraged the DE to drive the transformation of traditional industries, achieving significant economic growth. Brynjolfsson and McAfee emphasized the profound impact of technology on productivity and employment patterns [8], while Choudary analyzed how the platform economy has facilitated the IDR in the United States [9]. Rodríguez-González et al. found that in a developing economy like Mexico, fostering a digital organizational culture can strengthen the company’s ability to acquire, assimilate, and apply external knowledge and resources, thereby enhancing its ability to absorb new information [10].

For China, the existing literature mainly emphasizes the impact of the DE on RE on the micro level, with limited exploration of their interactive relationship from a meso-level perspective. For instance, Jiang et al. empirically analyzed the overall effects, conditionalities, and stage-specific characteristics of the DE’s impact on the RE, revealing a significantly negative impact and a discernible “crowding-out effect” [11]. Liu et al. examined the micro-level implications of DE development on the investment efficiency of the RE, further discussing potential heterogeneities related to property rights, regions, and industries [12]. Yang et al. demonstrated that the development of digital transformation enhances intrapreneurship in traditional enterprises by altering labor input. Additionally, the execution of digital strategies exhibits a positive relationship with innovation and the progression of cutting-edge technologies [13]. Jiang et al. indicated that the adoption of digital technology significantly boosts the efficiency of enterprise investments, particularly in privately owned enterprises, firms with a high equity concentration, and those operating in technology-intensive sectors [14]. Cheng et al. conducted an empirical analysis to explore how digital transformation influences the total factor productivity of enterprises in the RE [15].

Currently, the research concerning the evaluation of the level of IDR remains relatively limited, with existing studies primarily concentrating on two methodological approaches. The first is the input–output method, which has been employed by a limited number of scholars to assess the level of IDR within specific industries at a meso level, thereby uncovering variations in integration levels across different sectors within those industries [16]. However, while the input–output method effectively measures IDR at the industrial level, it falls short in accurately capturing integration at non-industrial levels. The second method is the coupling coordination degree method (CCDM), which is derived from a comprehensive analysis of the dynamic interdependence and interaction between subsystems [17]. It has been widely used in various fields, including economics, ecology, geography, and others [18]. The CCD serves as an indicator to assess whether a composite system is evolving toward a higher order, signifying sustainable development. Research on CCDM has predominantly focused on economic development [19], the ecological environment [20], new urbanization [21], socioeconomics [22], and technological innovation [23], among others. With the ongoing advancement of the DE, CCDM has been widely applied to studies involving the DE, including the relationship between the DE and carbon environment governance [24], DE and tourism development and the ecological environment [25], digital infrastructure and inclusive green growth [26], DE and high-quality energy development [27], and DE and higher education [28]. In essence, the CCDM effectively captures the extent of mutual influence and coordinated development between two subsystems. Given that IDR represents a long-term, complex process of mutual penetration and interaction between the DE and the real economy (RE), it aligns closely with the conceptual underpinnings of the CCDM. Therefore, drawing on relevant research [29], this work employs CCDM to measure the level of IDR.

Regarding underlying mechanisms, extensive research has investigated the effects of IDR, but the study focusing on its influencing factors remains limited. The influence of IDR has been widely examined in areas such as urban green total factor productivity [24], carbon emission efficiency [13], energy productivity [16], industrial green transformation [6], the risk of stock price collapse [7], and enterprise green innovation [2]. Additionally, Xin et al. highlighted that the enhanced integration level between the DE and the industry is beneficial for the improvements in the development of the sustainable environment, the efficiency of industrial energy utilization, and the consumption structure [30].

Through the review of the literature, it can be observed that existing studies have explored the coordination development of the DE and RE from various perspectives, leading to certain theoretical achievements. However, since the development of the digital-real economy’s integration in China is still in its early stages, there is currently a lack of more in-depth and systematic analyses. Based on this, this work conducts a comprehensive analysis of the spatiotemporal evolution and influencing factors of CCD. Firstly, evaluation index systems for the DE and RE are constructed based on existing research, and the entropy weight method is utilized to evaluate their levels. Secondly, the CCDM is utilized to assess the level of IDR across provinces and four major regions. Thirdly, the Dagum Gini coefficient is utilized to measure regional differences and their sources in the level of CCD. Fourthly, the kernel density estimation method is adopted to investigate the temporal evolution trend of IDR. Finally, the spatial autocorrelation model is employed to identify spatial agglomeration patterns, followed by the application of GTWR to examine the influencing factors of IDR.

The marginal contributions of this study are mainly reflected in the following aspects: The first contribution is the improvement of the indicator system. As a relatively mature economic form, the DE has a rich variety of measurement methods. However, a unified conclusion has not yet been reached. Based on the summary and generalization of existing research on the indicator system, this study adds the innovation dimension to build it. The second contribution is the supplementation of empirical analysis. Previous studies have used various models to analyze the spatial–temporal evolution of the CCD. However, the influencing factors of the integration level and their underlying mechanisms have not been systematically analyzed. This represents a significant gap in the existing theoretical framework and an important breakthrough for advancing research in this field. One goal of this work is to enrich the theoretical framework of CCD between the DE and RE; another objective is to provide actionable recommendations for the IDR in China, offering policy suggestions and practical guidance for local governments to formulate more targeted regional development strategies and facilitate coordinated development across regions.

The subsequent structure is as follows: Section 2 states a detailed description of the evaluation index selection, research methods, and data sources; Section 3 clarifies the results; Section 4 discusses the underlying reasons of results and policy suggestions; Section 5 states the conclusions.

2. Materials and Methods

2.1. Evaluation Index System

Currently, there is no consensus on standardized evaluation indicators for measuring the development levels of both the DE and RE. This work conceptualizes the DE through four key dimensions: digital infrastructure, digital industrialization, industrial digitalization, and digital technology innovation. Firstly, digital infrastructure serves as a critical enabler for the advancement of the DE. As data constitute the primary production factor in the digital economy, its acquisition predominantly relies on digital facilities, including networked software and digital hardware equipment. Consequently, the timeliness, effectiveness, and accessibility of data acquisition are fundamentally based on the robustness of digital infrastructure. Secondly, digital industrialization is the core component of the DE. It mainly refers to the commercial development and market-oriented trading activities of data elements, forming new digital sectors. Thirdly, industrial digitalization is a pivotal embodiment of the DE. It refers to the transformation of traditional industries by applying the digital technologies, thereby fostering the evolution of new industrialization [31]. Lastly, digital technology innovation serves as the core driver propelling the advancement of the DE [32], it can unleash its amplification, superposing, and multiplicative effects on economic growth.

When measuring the development level of RE, this work adopts a three-dimensional framework encompassing the development scale, economic benefits, and innovation ability. The development scale of the RE serves as the foundational basis for its expansion. Economic benefits, which measure the comprehensive outcomes and contributions of these activities, are a critical indicator of the RE’s success and a core requirement for achieving high-quality economic development. It underscores the real economy’s role in fostering sustainable growth. Meanwhile, innovation ability within the RE is pivotal for driving economic transformation, upgrading, and long-term sustainability. It enables the adoption of advanced technologies, processes, and business models, thereby enhancing competitiveness and resilience in a rapidly evolving global landscape.

Following the principles of scientific rigor, rationality, data availability, and continuity, this work constructs the following two index systems for the DE (

Table 1. Index system for the DE.

Primary Indicators	Secondary Indicators	Units	Weights	Attributes
Digital infrastructure	The length of long-distance cable per unit area	km/km2	0.1088	+
	Density of internet broadband access ports	Ports/km2	0.1418	+
	Number of mobile internet users	In ten thousand	0.0430	+
	Number of mobile phone base stations	In ten thousand	0.0510	+
Digital industrialization	Proportion of software and information technology services revenue to GDP	%	0.1171	+
	Proportion of telecommunications revenue to GDP	%	0.0266	+
	Proportion of electronic information manufacturing revenue to GDP	%	0.0762	+
	Proportion of employees in the digital industry	%	0.0819	+
Industrial digitization	Digital financial inclusion index	/	0.0210	+
	Proportion of enterprises with e-commerce transactions	%	0.0260	+
	E-commerce transaction volume	In billion yuan	0.1166	+
	Number of websites per 100 enterprises	Units	0.0089	+
Digital technology innovation	Number of patents granted	Units	0.1248	+
	Proportion of R&D expenditure to GDP	%	0.0562	+

) and RE (

Table 2. Index system for the RE.

Primary Indicators	Secondary Indicators	Units	Weights	Attributes
Development scale	Proportion of value-added of the real economy to GDP	%	0.0250	+
	Proportion of fixed asset investment to GDP	%	0.0573	+
	Number of industrial enterprises above the designated size	Units	0.2390	+
Economic benefits	Proportion of total profits to operating income of industrial enterprises above the designated size	%	0.0028	+
	Asset–liability ratio of industrial enterprises above the designated size	%	0.0344	+
	Proportion of total retail sales of consumer goods to GDP	%	0.0297	+
Innovation capability	Full-time equivalent of R&D personnel in industrial enterprises above the designated size	Person-years	0.3213	+
	R&D expenditure of industrial enterprises above the designated size	In ten thousand yuan	0.2905	+

). All indicators below are positive.

2.2. Methods

2.2.1. The Entropy Weight Method

According to the above evaluation index systems, this work employs an entropy weight method to evaluate the DE and RE. To eliminate the impact of varying units and enhance the comparability of the data, all indicators are standardized and normalized using the extreme value method [27,33]. This process transforms the indicators into dimensionless values, ensuring uniformity in their measurement. For positive indicators, the standardization equation is expressed as

$$y_{ij}={\displaystyle \frac{x_{ij}-\min (x_{ij})}{\max (x_{ij})-\min (x_{ij})}},$$

(1)

where $x_{ij}$ is the original value of the indicator; for $i=1,2,3,\dots ,m$, $m$ is the number of provinces; for $j=1,2,3,\dots ,n$, $n$ is the number of indicators in each index system; and $y_{ij}$ is the standardized index.

Then, the entropy of each indicator is constructed as

$$e_j=-{\displaystyle \frac{1}{\ln n}}{\displaystyle \sum _{i=1}^m{y}_{ij}}\ln y_{ij},$$

(2)

where $e_j$ is the entropy of indicator $j$.

The weight of the indicator $j$ is expressed as

$$w_j={\displaystyle \frac{1-e_j}{{\displaystyle {\sum }_{j=1}^n(1-e_j)}}}.$$

(3)

Finally, the comprehensive index is expressed as

$$Z={\displaystyle \sum _{j=1}^m{w}_j}{y}_{ij}.$$

(4)

2.2.2. The Coupling Coordination Degree Model (CCDM)

Coupling first came from the field of physics [34]. It can describe the level of interdependence among two or more subsystems and is also a measure of their independence. It is expressed as

$$C=2\times {\left\{{\displaystyle \frac{Z_1\times Z_2}{(Z_1+Z_2)/2}}\right\}}^{\frac{1}{2}},$$

(5)

where $C$ is the coupling degree, and the higher the $C$ is, the closer the connection between the two systems is; $Z_1$ is the comprehensive level of the DE; and $Z_2$ is the comprehensive level of the RE.

CCD reflects the overall system’s coordination and consistency [18], and it can be expressed as

$$T=\alpha Z_1+\beta Z_2,$$

(6)

$$D=\sqrt{CT},$$

(7)

where $T$ is the comprehensive coordination index of the DE and RE; $\alpha$ is the significance of the DE; $\beta$ is the significance of the RE; and $D$ is the CCD, which belongs to [0, 1]. Here, we consider that the DE and RE are equally important, so we take $\alpha =\beta =0.5$. Referring to related studies, CCD is divided into ten levels [35], and

Table 3. Classification of CCD.

CCD	Stage	CCD	Stage
[0.0, 0.1)	Extreme imbalance	[0.5, 0.6)	Barely coordinated
[0.1, 0.2)	Serious imbalance	[0.6, 0.7)	Primary coordination
[0.2, 0.3)	Moderate imbalance	[0.7, 0.8)	Intermediate coordination
[0.3, 0.4)	Mild imbalance	[0.8, 0.9)	Good coordination
[0.4, 0.5)	Near imbalance	[0.9, 1.0]	Excellent coordination

shows the classification standards.

2.2.3. Dagum Gini Coefficient

In order to research the regional differentiation of CCD, this work utilizes the Dagum Gini coefficient, which is presented based on the subgroup decomposition technique [36]. The Dagum Gini coefficient provides an accurate illustration of the source of regional differences while also considering the cross-overlap among samples [24,37]. The overall Gini coefficient $G$ is expressed as

$$G={\displaystyle \frac{{\displaystyle {\sum }_{j=1}^k{\displaystyle {\sum }_{h=1}^k{\displaystyle {\sum }_{i=1}^{n_j}{\displaystyle {\sum }_{r=1}^{n_k}\left|y_{ji}-y_{hr}\right|}}}}}{2n^2\mu }},$$

(8)

where $y$ is CCD; $n$ denotes the number of provinces; $\mu$ is each province’s mean CCD level; $k$ is the number of regions; $y_{ji}$ and $y_{hr}$ are the CCD of province $i$ in region $j$ and that of province $r$ in region $h$, respectively. To obtain more rigorous measurement results, it breaks down the overall difference into three components [38], which are expressed as

$${\mathrm{G}}_{jj}={\displaystyle \frac{1}{2{\mu }_j{n}_j^2}}{\displaystyle {\sum }_{i=1}^{n_j}{\displaystyle {\sum }_{r=1}^{n_j}\left|y_{ji}-y_{jr}\right|}},$$

(9)

$$G_w={\displaystyle {\sum }_{j=1}^k{G}_{jj}}{p}_j{s}_j,$$

(10)

$$G_{jh}={\displaystyle \frac{{\displaystyle {\sum }_{i=1}^{n_j}{\displaystyle {\sum }_{j=1}^{n_h}\left|y_{ji}-y_{hr}\right|}}}{n_j{n}_h({\mu }_j+{\mu }_h)}},$$

(11)

$$G_{nb}={\displaystyle {\sum }_{j=2}^k{\displaystyle {\sum }_{h=1}^{j-1}{G}_{jh}}}(p_j{s}_h+p_h{s}_j)D_{jh},$$

(12)

$$G_t={\displaystyle {\sum }_{j=2}^k{\displaystyle {\sum }_{h=1}^{j-1}{G}_{jh}}}(p_j{s}_h+p_h{s}_j)(1-D_{jh}),$$

(13)

where $G_w$ is the intra-regional difference, $G_{nb}$ is the inter-regional difference, and $G_t$ is the hypervariable density, which fulfills $G=G_w+G_{nb}+G_t$; $p_j=n_j/n$, $s_j=n_j{\mu }_j/n_j{\mu }_j$, and $j=1,2,3,\dots ,k$; $D_{jh}$ is the relative impact of CCD among regions, which is expressed as

$$D_{jh}={\displaystyle \frac{d_{jh}-p_{jh}}{d_{jh}+p_{jh}}},$$

(14)

$$d_{jh}={\displaystyle {\int }_0^{\infty }d}{F}_j(y){\displaystyle {\int }_0^y(y-x)d{F}_h}(x),$$

(15)

$$p_{jh}={\displaystyle {\int }_0^{\infty }d{F}_h}(y){\displaystyle {\int }_0^y(y-x)d{F}_j}(y),$$

(16)

where $d_{jh}$ is the weighted average of all positive differences in CCD; $p_{jh}$ is the weighted average of the absolute values of all negative differences; $F_h(\cdot )$ and $F_j(\cdot )$ are the cumulative distribution function for regions $h$ and $j$, respectively.

2.2.4. The Kernel Density Estimation Method

The Dagum Gini coefficient indicates the regional differences in CCD. However, this approach is limited in its ability to capture the dynamic evolution of absolute disparities across different regions over time [39]. To address this limitation and to provide a more comprehensive analysis, this study draws on the methodologies employed by existing scholars in characterizing spatial–temporal dynamics at the regional level [24]. Specifically, the kernel density estimation method is utilized to explore the dynamic temporal characteristics of CCD. The kernel density function $f(X)$ is expressed as

$$f(X)={\displaystyle \frac{1}{nh}}{\displaystyle {\sum }_{i=1}^{n}K(\frac{X_i-\overline{X}}{h})},$$

(17)

where $K(\cdot )$ denotes the Gaussian kernel function; $h$ is the bandwidth; and $X_i$ is the CCD of province $i$.

2.2.5. The Spatial Autocorrelation Model

This study employs global and local spatial autocorrelation analysis to examine the spatial agglomeration patterns of CCD in China [40]. Global Moran’s I index is employed to evaluate the overall spatial clustering or dispersion trends of CCD [41], and it is expressed as

$$I={\displaystyle \frac{{\displaystyle {\sum }_{i=1}^n{\displaystyle {\sum }_{j=1}^n{W}_{ij}(D_i-\overline{D})(D_j-\overline{D})}}}{S^2{\displaystyle {\sum }_{i=1}^n{\displaystyle {\sum }_{j=1}^n{W}_{ij}}}}},$$

(18)

where $D_i$ and $D_j$ denote the CCD of provinces $i$ and $j$, respectively; $\overline{D}$ is the mean value of $D$; and $W_{ij}$ is the spatial weight matrix. $I\in [-1,1]$ and $I>0$ illustrate that the space unit is positively correlated, and $I<0$ illustrates a random distribution.

Local Moran’s I is a statistical metric employed to identify the local spatial patterns of CCD [42]. It is capable of quantifying the spatial aggregation degree of each local area and adjacent area [43], and it is expressed as

$$I_i={\displaystyle \frac{n^2(D_i-\overline{D}){\displaystyle {\sum }_{j=1}^n{W}_{ij}(D_j-\overline{D})}}{{\displaystyle {\sum }_{j=1}^n{W}_{ij}{\displaystyle {\sum }_{j=1}^n{(D_j-\overline{D})}^2}}}},$$

(19)

where $I_i$ is the local autocorrelation coefficient.

A spatial agglomeration pattern is divided into four types: High–High, High–Low, Low–High, and Low–Low cluster [26]. To present the results visually, this paper uses ArcGIS to create LISA clustering maps.

2.2.6. The Geographically and Temporally Weighted Regression (GTWR) Model

A traditional geographically weighted regression (GWR) model is a spatially localized regression approach that constructs an independent regression equation for each sample point within a given spatial range [44]. Building upon this framework, the temporal dimension is introduced into the GWR model, thereby proposing the GTWR model [45]. Compared with GWR, the GTWR model provides a more comprehensive representation of spatiotemporal heterogeneity in driving factors [46]. By associating research objects with distinct spatial coordinates across different time periods, GTWR effectively reduces parameter estimation deviation and model errors arising from coordinate overlap. Consequently, GTWR is better suited for analyzing complex spatiotemporal datasets, yielding more precise and reliable estimation results. It is expressed as

$$Y_i={\beta }_0(u_i,v_i,t_i)+{\displaystyle {\sum }_{k=1}^m{\beta }_k}(u_i,v_i,t_i)X_{ik}+{\epsilon }_i,$$

(20)

where $Y_i$ represents CCD; $(u_i,v_i,t_i)$ is the spatial–temporal location; and ${\beta }_j(u_i,v_i,t_i)$ is the estimated parameter of each variable at position $i$.

2.3. Study Area and Data Sources

2.3.1. Study Area

This work collects data from 30 provinces in China between 2012 and 2022 to assess the DE and RE. Considering the availability and completeness of data, Taiwan, Macao, Hong Kong, and Tibet are not in this work. To facilitate a comparative analysis of CCD between the DE and RE across different regions, the 30 provinces are categorized into four regions, namely northeast, east, central, and west, based on the classification framework provided by the National Bureau of Statistics of China.

2.3.2. Data Sources

This study utilizes official statistical data from authoritative Chinese government publications, including the Chinese Statistical Yearbook, China’s Statistical Yearbook of The Tertiary Industry, the Yearbook of China’s Information Industry, China’s Industry Statistical Yearbook, and provincial statistics yearbooks. Additionally, missing data were supplemented via interpolation and fitting techniques.

3. Results

3.1. The Level of the DE and RE

To provide a clear visualization of the developmental trends of the DE and RE, line charts are constructed to illustrate their respective development levels at a nationwide level and in four main regions. Figure 1 shows the comprehensive development level of the DE, which is composed of four dimensions: digital infrastructure, digital industrialization, industrial digitization, and digital technology innovation.

The level of DE exhibits a steady increase, with its mean value rising from 0.0966 to 0.1985. However, regional differences in development are evident. The east exhibits the most advanced development of DE, coupled with the fastest growth rate, while the central region follows closely. The northeastern region trails significantly, showing the least progress in the DE. Notably, the DE levels in the central, west, and northeast all fall below the national mean, highlighting significant imbalance in these regions.

The comprehensive development level of the RE is composed of three dimensions: the development scale, economic benefits, and innovation ability. Figure 2 indicates that the national average of the RE level demonstrates an overall upward trend, but significant regional differences are evident. The east achieves the highest level of the RE, accompanied by the fastest growth rate. The central region follows, though its development level remains marginally lower than that of the nation. In contrast, the northeast records the lowest level of the RE, exhibiting a mild declining trend. Meanwhile, the western region displays a relatively stable trend, surpassing the northeastern region after 2013.

3.2. The CCD Between the DE and RE

3.2.1. At the Provincial Level

As depicted in Figure 3, the CCD between the DE and RE in each province displays an overall upward trajectory from 2012 to 2022, indicating that the DE and RE in these provinces are developing in a more coordinated trend. However, significant disparities in CCD are observed among provinces. Based on the mean ranking of provincial CCD values, Guangdong, Jiangsu, Zhejiang, Shandong, and Shanghai are the leading provinces, exhibiting the most advanced levels. They constitute the first tier of CCD levels, with average coordination values exceeding 0.5, and are mainly located in the east. Conversely, provinces such as Inner Mongolia, Heilongjiang, Hainan, Gansu, Qinghai, Ningxia, and Xinjiang recorded the lowest average CCD values, all below 0.3, and are primarily distributed across the west and northeast. The majority of provinces exhibit average CCD values ranging between 0.3 and 0.5.

The spatial patterns of the CCD for the years 2012, 2015, 2018, and 2022 are visualized using ArcGIS. The results are presented in Figure 4. Overall, the CCD levels exhibited a significant improvement from 2012 to 2022. Provinces with the most lagging development are primarily concentrated in the northeast and west. The proportion of provinces in the moderate imbalance stage decreased from 43.33% to 23.33%, while provinces transitioning into the mild imbalance and near imbalance stages emerged. Specifically, compared to 2012, Yunnan, Guizhou, Guangxi, Jilin, and Shanxi progressed from the moderate imbalance stage to the mild imbalance stage, while Jiangxi advanced to the near imbalance stage. Additionally, several provinces in the east, central, and west, including Beijing, Tianjin, Hebei, Fujian, Chongqing, Sichuan, Hubei, Hunan, and Anhui, transitioned from the mild imbalance stage to the near imbalance stage. Provinces with the highest CCD levels are predominantly concentrated in the east, including Shandong, Jiangsu, Shanghai, Zhejiang, and Guangdong, which also exhibit notable stage transitions. Among these, Guangdong Province stands out as particularly remarkable, progressing from barely coordinated in 2012 to good coordination in 2022, representing a rapid development in the coordinated development between the DE and RE.

3.2.2. At the Regional Level

To better illustrate the trends in the CCD between the DE and RE across the four major regions, this work presents the average CCD values for these regions as well as the national average in a line chart, as depicted in Figure 5.

At the national level, the CCD exhibits a sustained upward trend during the period, rising from 0.336 (mild imbalance) to 0.430 (near imbalance), with an average yearly increase of 2.49%. Among the regions, the eastern region consistently maintains the highest integration level, reaching the barely coordinated stage (0.549) in 2022, with an average yearly increase of 2.68%. The central region ranks second, with its CCD level rising from 0.322 (mild imbalance) in 2012 to 0.447 (near imbalance) in 2022, achieving an average yearly increase of 3.32%. The northeastern region initially exhibits a higher CCD level than the western region. However, its growth is relatively slow, and it remains in the mild imbalance stage throughout the period. After 2016, its CCD reaches a similar level to that observed in the west. Meanwhile, the western region, though lagging behind other regions, demonstrates a positive trend, advancing from 0.276 (moderate imbalance) in 2012 to 0.339 (mild imbalance) in 2022, with an average yearly increase of 2.08%.

3.3. Spatial Differences in CCD and the Sources of Differences

3.3.1. Overall Spatial Differences

The analysis above reveals the presence of regional heterogeneity in the CCD. To further quantify the difference and identify the source of it, this work uses the Dagum Gini coefficient to evaluate the overall differences in the CCD level from 2012 to 2022 (

Table 4. Dagum Gini coefficient decomposition.

Year	Gini Coefficient				Contribution Rate (%)
	G	Gw	Gnb	Gt	Gwr	Gnbr	Gtr
2012	0.143	0.029	0.098	0.015	20.68%	69.07%	10.25%
2013	0.139	0.029	0.097	0.014	20.64%	69.33%	10.03%
2014	0.139	0.029	0.096	0.014	20.84%	69.01%	10.15%
2015	0.140	0.029	0.097	0.014	20.84%	69.28%	9.88%
2016	0.138	0.029	0.094	0.014	21.07%	68.45%	10.48%
2017	0.143	0.031	0.096	0.016	21.66%	67.27%	11.07%
2018	0.156	0.034	0.105	0.017	21.78%	67.29%	10.92%
2019	0.160	0.035	0.107	0.018	21.93%	66.60%	11.47%
2020	0.166	0.037	0.110	0.019	21.99%	66.30%	11.71%
2021	0.171	0.037	0.114	0.020	21.82%	66.77%	11.40%
2022	0.178	0.039	0.117	0.022	21.93%	65.51%	12.56%

). In this table, G is the overall Gini coefficient; G_w, G_nb, and G_t are the intra-regional difference, the inter-regional difference, and the hypervariable density, respectively; and G_wr, G_nbr, and G_tr are the contribution rates of G_w, G_nb, and G_t, respectively.

The overall Gini coefficient decreases from 0.143 in 2012 to 0.138 in 2016, and subsequently increases to 0.178 in 2022, with an annual average of 0.152. This illustrates that the CCD level across Chinese provinces exhibits significant spatial heterogeneity, with the degree of differences initially narrowing and then expanding. Notably, 2016 marks the turning point in this trend of divergence.

The mean annual values of intra-regional, inter-regional, and hypervariable density Gini coefficients are 0.033, 0.103, and 0.017, respectively. Correspondingly, the average contribution rates stand at 21.38%, 67.72%, and 10.90%, in the same order. It is evident that the primary source of difference comes from the inter-regional difference. In terms of the contribution rates, both intra-regional and hypervariable density contribution rates exhibit a consistent upward trend, albeit with different fluctuation magnitudes, showing annual average growth rates of 0.59% and 2.05%, respectively. Conversely, an upward trajectory with fluctuations characterizes the inter-regional contribution rate, which decreases by an average of 0.53% per year.

3.3.2. Intra-Regional Differences at the CCD Level

Figure 6 represents the trend of the intra-regional difference in the CCD level across the four major regions from 2012 to 2022. The annual average Gini coefficients of four regions exhibit a trend characterized by eastern > western > northeastern > central regions. Specifically, the eastern region persistently exhibits a high-level Gini coefficient (0.149), which is slightly lower than the overall difference, primarily due to the significant inter-provincial differences within this region. The western region ranks second, displaying a mean value of 0.084. The northeastern and central regions rank last, with average values of 0.055 and 0.052, respectively. In addition, the magnitude of intra-regional differences varies significantly. The eastern region consistently exhibits the highest level of difference, with an upward fluctuation, increasing from 0.145 in 2012 to 0.172 by 2022, at a mean yearly increasing rate of 1.78%. In contrast, the central region, despite having relatively smaller differences, exhibits the fastest growth rate of 5.30%, rising from 0.066 to 0.108 over the research period. The western region followed closely, rising at an average yearly rate of 5.05%. Notably, the northeastern region stands out as the only region where the difference exhibits a downward trajectory, decreasing at a mean yearly rate of 1.01%.

3.3.3. Inter-Regional Differences at the CCD Level

Figure 7 depicts the evolution of inter-regional differences in the CCD levels from 2012 to 2022. Based on the annual average Gini coefficients, the most significant inequality is identified between the eastern and western regions, with the average value of 0.227. The inter-regional difference between the eastern and northeastern regions ranks second. However, since 2017, it has gradually surpassed that of the east–west, emerging as the highest average inequality. The annual average Gini coefficients between central–west and central–northeast are close, at 0.119 and 0.110, respectively. The least pronounced difference is observed between northeast and west, with an average value of 0.079.

Regarding difference evolution, after 2012, the trends of CCD levels in the northeastern and western regions are similar, so the inter-regional difference between the northeast and the west is relatively minor. The inter-regional difference between the central and eastern region exhibits a trend of first narrowing and then expanding, but it decreases slightly in general. In contrast, inter-regional differences between other regions display an expanding trend, with significant regional heterogeneity. Among these, the difference between the central and northeastern regions demonstrates the most rapid increase at an average yearly rate of 8.59%. This is followed by that of northeast–west and east–west, which show respective growth rates of 5.57% and 3.37%. Additionally, the differences in northeast–west and east–west display the slowest expansion, with average yearly growth rates of 1.64% and 1.47%, respectively.

3.4. The Temporal Evolution Trend of CCD

To analyze the temporal evolution of the CCD level, the kernel density estimation method is applied, and the years 2012, 2015, 2018, and 2022 are selected as time nodes to draw the kernel density curves. Figure 8 shows the evolution traits of the kernel density for the CCD level at the national and regional levels. During the sample period, the distribution curves exhibit a slight rightward shift, revealing a progressive enhancement in the CCD level.

3.4.1. Temporal Evolution Characteristics at the Regional Level

Figure 8a–d show the existence of significant regional differences in the dynamics of the CCD level over the years 2012, 2015, 2018, 2022. Specifically, in terms of the distribution pattern, the main peaks in east, central, and west have experienced a consistent downward trend in height alongside a gradual expansion in width, reflecting a steady increase in differences in CCD levels across these regions. In contrast, the northeastern region demonstrates a dynamic evolution of its main peak, characterized by a pattern of ‘upward-downward-upward’, yet it maintains a generally upward trend. Regarding distribution extension, the right tails of the distribution curves in the western and eastern regions have been elongated, signifying a widening distribution ductility in these regions. This implies that the proportion of provinces with high CCD levels has increased in both the western and eastern regions, with some provinces significantly outperforming others. Conversely, in the central region, the leftward disparity has expanded, indicating that certain provinces in this region have notably lower CCD levels compared to others. In the northeastern region, the width of the distribution curve remains relatively stable, gradually exhibiting a symmetrical trend. Regarding distribution polarization, the kernel density curves of the four regions consistently maintain a single main peak.

3.4.2. Temporal Evolution Characteristics at the National Level

As shown in Figure 8e, concerning the distribution pattern, the height of the primary peak at the nationwide level exhibits a gradual decline, while its width correspondingly broadens, indicating that the difference in CCD level across the nation is gradually expanding. Regarding distribution extension, the curve exhibits an obvious rightward trailing change, with the distribution ductility showing a widening trend. This indicates the presence of provinces with high levels of the CCD, such as Guangdong and Jiangsu, where the gap between these provinces and the national average level is significantly large. Furthermore, compared to 2012 and 2015, a marked extension trend on the right side of the curve is observed in 2018 and 2022, demonstrating an increase in the proportion of high-value provinces in the CCD level. As for the polarization characteristics, the national kernel density curve exhibits a unimodal distribution over the research period, demonstrating that the CCD levels in most provinces of China are relatively similar. In general, despite the continuous advancement in the CCD level in China, regional differences are progressively expanding.

3.5. Spatial Autocorrelation Analysis of CCD

The global Moran’s I index of CCD between the DE and RE across Chinese provinces from 2012 to 2022 is calculated by using Stata 17.

Table 5. Global Moran’s index.

Year	I	Sd (I)	Z	p-Value *
2012	0.266	0.119	2.529	0.006
2013	0.265	0.120	2.505	0.006
2014	0.299	0.120	2.767	0.003
2015	0.334	0.121	3.057	0.001
2016	0.327	0.120	3.003	0.001
2017	0.322	0.120	2.975	0.001
2018	0.330	0.119	3.049	0.001
2019	0.311	0.119	2.900	0.002
2020	0.321	0.119	2.976	0.001
2021	0.330	0.120	3.051	0.001
2022	0.321	0.120	2.968	0.002

Note: * represents the significance level at 10%.

shows that the global Moran’s I indices are all greater than zero and statistically significant at the 10% level. The results reveal a significant positive spatial autocorrelation in the CCD, implying that the CCD level in China displays pronounced global spatial clustering characteristics. Additionally, the global Moran’s index increases from 0.266 in 2012 to 0.321 in 2022, displaying a fluctuating yet upward trajectory. This trend underscores the progressive strengthening of spatial clustering intensity in the CCD over time.

The LISA agglomeration map shows the local agglomeration patterns of the CCD (Figure 9). High–High and Low–High cluster emerge as the predominant types, and from 2012 to 2022, notable changes occur in the cluster categories of certain provinces. Specifically, the High–High cluster is primarily concentrated in the east, including provinces such as Jiangsu, Zhejiang, Shanghai, and Fujian, with Anhui being added in 2022, indicating that these regions exhibit higher levels of CCD. The spatial patterns of the Low–High clusters and High–Low clusters remain relatively stable over the study period. In contrast, the Low–Low clusters are less stable, with Gansu classified as such in 2012 and Qinghai in 2022.

3.6. Influencing Factors of CCD

3.6.1. Influencing Factor Selection

The CCD between the DE and RE is influenced by multiple factors. Building on existing research, this work attempts to examine the influencing factors of CCD from the perspectives of economic development, policy intensity, the industrial structure, human capital, technological innovation, and the market environment. This work takes the influencing factors as explanatory variables and the CCD as the dependent variable. The specific variables and indicators are as shown in

Table 6. Evaluation metrics for variables.

	Variable	Variable Symbols	Variable Measurement
Explained variable	Coupling coordination degree (Y)	CCD	Calculation results of coupling coordination
Explanatory variables	Economic development level (X1)	pgdp	GDP per capita
	Policy intensity (X2)	pol	Digital economy-related keyword frequency in provincial government work reports
	Industrial structure (X3)	ind	The ratio of value added of the tertiary industry to value added of the secondary industry
	Human capital (X4)	hum	Proportion of digital technology employment
	Technological innovation (X5)	tech	Proportion of science and technology financial expenditure to general financial expenditure
	Market environment (X6)	mar	Marketization index

.

3.6.2. Model Evaluation

Considering the temporal variability and spatial positive correlation of CCD, the GTWR model is applied to demonstrate the influencing factors of CCD. Prior to applying the GTWR model, all variables need to be standardized to prevent the occurrence of spurious regression during the regression analysis [47]. Subsequently, a test of multicollinearity is performed on the normalized explanatory variables, and the results reveal that all variance inflation factor (VIF) values remain under five, confirming the absence of significant multicollinearity.

3.6.3. Analysis of Results

The influencing factors are evaluated by using ArcGIS, and the model fitting results are presented in

Table 7. Relevant parameters of GTWR.

Variable	Regression Results
	Coe.	Std.	t-Value	p-Value	Sig.
pgdp	−0.000	0.000	−0.444	0.657
pol	0.001	0.000	3.187	0.002	**
ind	−0.052	0.009	−6.051	0.000	***
hum	0.009	0.004	2.046	0.042	*
tech	3.615	0.340	10.624	0.000	***
mar	0.028	0.003	10.118	0.000	***
AIC	−979.089
R2	0.786
Adj-R2	0.782

Note: *, **, and *** mean that they are significant at the 10%, 5%, and 1% levels, respectively.

. The R² and adjusted R² values reach 0.786 and 0.782, respectively, demonstrating a high level of model fit. Based on the overall regression results, the impact of the economic development level is not statistically significant. Factors including policy intensity, human capital, technological innovation, and the market environment exhibit positive effects on CCD, with their average regression coefficients being 0.001, 0.009, 3.615, and 0.028, respectively. In contrast, the industrial structure has a negative effect on the CCD, with an average regression coefficient of −0.052. Additionally, the statistical results reveal that the degree of influence of these factors on the CCD varies across different regions, underscoring the importance of considering their spatiotemporal heterogeneity at a local scale.

Furthermore, the spatial heterogeneity of influencing factors is shown in Figure 10. The results reveal that the factors affecting the CCD exhibit significant spatial heterogeneity.

(1)

The overall impact of the economic development level is not significant, but it shows a spatial differentiation. The regression coefficients for eastern regions such as Shanghai, Jiangsu, and Zhejiang, as well as central regions like Anhui and Hubei, are positive, while the remaining provinces exhibit negative coefficients. Therefore, it is evident that the relationship between the economic development level and the CCD varies across different regions.

(2)

The impact of policy intensity on CCD demonstrates a significant positive correlation, with a regression coefficient of 0.001. Among the provinces, Jiangsu’s regression coefficient is negative, while the coefficients for other provinces are positive. This indicates that stronger policy support promotes the synergistic development of both, but there may be specificities in Jiangsu regarding the gap between policy implementation and actual development outcomes.

(3)

The optimization of the industrial structure has a significant negative impact on CCD, with an overall regression coefficient of −0.052, and the regression coefficients for all provinces are negative. This suggests that although adjustments in industrial structure have some effect on CCD, overall, changes in the industrial structure may not necessarily promote the deep integration of the DE and RE.

(4)

Human capital is significantly positively correlated with CCD, with an overall regression coefficient of 0.009, and the regression coefficients for all provinces are positive. This means that the higher the human capital, the smoother the coordinated development between the DE and RE, with talent playing a driving role in their integration.

(5)

Technological innovation significantly promotes the improvement of CCD, with an overall regression coefficient of 3.615, and the regression coefficients for all provinces are positive. These findings suggest that increased investments in technological innovation fosters improved coordination between the DE and RE.

(6)

The impact of the market environment is also significantly positive on CCD, with an overall regression coefficient of 0.028, and the regression coefficients for all provinces are positive. This indicates that for most regions, a higher marketization index is associated with an enhanced CCD between the DE and RE.

4. Discussion

4.1. Interpretation of the Spatial–Temporal Evolution of CCD Between the DE and RE

The CCD in China exhibits an overall positive development trend, while it keeps at a relatively low level. A limited number of provinces, primarily located in remote western regions such as Xinjiang, Qinghai, and Gansu, demonstrate the lowest CCD level. While a few regions, particularly coastal provinces in the east, show the highest levels of CCD. In all, the issue of uneven development is particularly prominent.

The east has maintained the highest CCD level, demonstrating a steady annual growth rate of 2.68%. This reflects the region’s remarkable effect in fostering the CCD. The underlying reasons may lie in the fact that the east leads the nation in terms of infrastructure development, talent pool, innovation, and economic development, thereby possessing a solid foundation for both the DE and the RE [48]. This foundation exerts a strong driving force in promoting the coordinated development of the DE and RE.

Provinces ranked lower in the CCD level are primarily concentrated in the west and northeast, which collectively face multiple development challenges. These regions exhibit notable deficiencies in some aspects, such as economic vitality and digital infrastructure development, which hinder the efficient flow of information and the deep exploitation of value [26]. Such limitations limit the realization of their integration. Among these regions, the northeast has experienced the slowest growth rate in CCD. Prior to 2015, the northeast led the western region in CCD levels due to its robust heavy industrial base. However, its late-mover advantage has diminished in recent years due to slow economic restructuring, aging industrial structures, brain drain, and technological lag. In contrast, the western region has gradually surpassed the northeast in integration levels, benefiting from opportunities brought by the Western Development Strategy and the Belt and Road Initiative [29].

The CCD level across Chinese provinces exhibits significant spatial heterogeneity. From 2012 to 2016, the degree of difference narrowed, but from 2016 to 2022, it gradually widened. A plausible explanation is that prior to 2016, big data technology is in its nascent development stage, with relatively limited applications primarily concentrated in industries such as the internet and telecommunications, resulting in lower CCD levels and less pronounced regional disparities. In March 2014, big data is first included in the Government Work Report, marking its formal recognition at the national level. Subsequently, the Big Data Industry Development Plan (2016–2020) is officially issued in 2016. In 2021, the 14th Five-Year Plan for Big Data Industry Development is subsequently released [49], leading to the accelerate advancement of the big data industry. In addition, due to disparities in economic development levels, infrastructure, and resource allocation across regions, coupled with the difficulty for low-level provinces to catch up with high-level provinces in the short term, the disparity in coordinated development between the DE and RE gradually widened. This signifies the ongoing challenges in achieving the coordinated integration of the DE and RE.

In additions, the sources of spatial difference in the CCD level across Chinese provinces are analyzed. The results reveal that the inter-regional difference constitutes the primary factor driving the overall difference, suggesting that to achieve integration, priority should be given to reducing relative disparities between regions and promoting the CCD across different regions, followed by narrowing disparities among provinces. Meanwhile, the contribution rates of intra-regional differences and hypervariable density have exhibited a rising trend. This suggests that the effective implementation of strategies, including the Rise of Central China Plan and the Western Development Strategy, has substantially mitigated relative inter-regional differences [24].

The CCD between the DE and RE exhibits regional variations, a phenomenon commonly observed worldwide. Differences in digital infrastructure, technological applications, and policy environments across countries and regions all influence the level of CCD. However, the characteristic of “higher levels in the eastern regions and lower levels in the western regions” is closely tied to China’s unique factors, such as regional development imbalances, policy support, and economic structure.

4.2. Analysis of Influencing Factors

This paper examines the influencing factors of CCD from six perspectives: the economic development level, policy intensity, the industrial structure, human capital, technological innovation, and the market environment. According to the results, the overall impact of the economic development level is not significant, but there are regional differences. Policy strength, human capital, technological innovation, and the market environment have significant positive effects on CCD, while the industrial structure exerts an inhibitory influence. This work further explores the differences in the impact of various factors across different regions and development stages, as well as the possible underlying reasons through typical case studies and regional practices.

From the perspective of the economic development level, the overall impact is not statistically significant, indicating that a high per capita GDP cannot be simply regarded as a guarantee for IDR. However, provincial analysis reveals that some eastern provinces (Jiangsu, Shanghai, and Zhejiang) and a few central provinces (Anhui and Hubei) still show a positive impact. These provinces usually have abundant research institutions, high-quality talent resources, and a well-established open environment, which allow per capita GDP to be better converted into the demand and conditions for IDR. Specifically, Shanghai has made significant investments in digital reform, with the resources derived from its high per capita GDP being more effectively channeled into the digital upgrading of industries, resulting in a positive contribution to CCD. In recent years, Zhejiang Province has not only maintained a high level of economic development but also actively promoted the internet economy and digital transformation, particularly achieving remarkable success in e-commerce and digital payment. Some traditional high-GDP regions, however, show negative coefficients in the CCD, indicating that industries relying solely on scale or extensive expansion may not smoothly integrate into the digital technology wave, leading to negative or insignificant impacts. China’s economic transformation stage is relatively complex and constrained by factors such as local government development strategies and industrial structure adjustments, which differs from the experiences of some developed countries.

The differentiated impact of DE policy intensity is more noteworthy. Overall, the intensity of digital economy policies has a positive effect, indicating that government support remains a crucial factor in facilitating the transformation of old and new driving forces and promoting the IDR. However, the negative coefficient for Jiangsu suggests that, in more developed digital industry clusters, the “overlap” of policies may lead to issues such as resource waste or redundant specialization. In the past, Jiangsu experienced instances of redundant construction and a lack of coordination in some high-tech industrial parks. Although DE-related policies are frequently mentioned in government reports, they have not been effectively translated into sustainable drivers of integration with the RE. This highlights the need for more refined policy-making and implementation to avoid diminishing policy effectiveness due to excessive concentration or a lack of coordination. In contrast, Guangdong Province has fully leveraged the policy dividends of the Guangdong–Hong Kong–Macau Greater Bay Area, with parallel investments in DE development and institutional safeguards. Cities such as Shenzhen have adopted a multi-pronged approach, including industrial support, government procurement, tax reductions, and the facilitation of investment and financing, thereby creating a robust support system for the flourishment of the DE and facilitating its positive integration with the RE. This finding specifically refers to issues related to local policy implementation and coordination between local governments in China, and it may not be universally applicable on a global scale.

The negative impact of industrial structure on CCD is worthy of attention. As the proportion of the tertiary industry continues to rise in China, if the service sector remains dominated by low value-added, labor-intensive, or traditional services, it will be difficult to achieve breakthroughs in the IDR. The internal imbalance within the tertiary industry, with certain traditional services (such as wholesale and retail, accommodation, and catering) having relatively limited demand for IDR, needs to be addressed. Instead, the focus should be on the role of digital transformation in the secondary industry, particularly manufacturing, which drives overall progress. Moreover, the adjustment of the industrial structure may involve a lag effect. For example, in Shanxi Province, the industrial structure is heavily reliant on coal and heavy industries, and this traditional industrial structure has slowed down the IDR. The provincial government has mainly applied digital technologies to the intelligent transformation of the coal industry. However, due to the relatively low-level digitalization needs of traditional industries and the long cycle required for industrial structural transformation, the integration process of the DE and RE in Shanxi has been relatively delayed.

Human capital and technological innovation both exhibit a strong positive driving effect, reflecting that the core of the digital transformation process lies in sustained investment in talent and technology. This is generally applicable worldwide, as many countries, such as the United States and Germany, exhibit similar phenomena. The presence of highly skilled digital talent enhances the effective integration of technical elements and data elements. Both the digital transformation of traditional sectors and the emerging digital industrial clusters depend heavily on skilled digital professionals to drive progress [50], along with stable and sustainable funding to support research activities and technological iterations. Beijing, with its abundance of universities and research institutions, has attracted a large number of high-level research and technical talents in fields such as the internet, artificial intelligence, and big data, serving as a crucial foundation for the rapid development of the DE. This, in turn, has fostered the collaborative development of sectors such as manufacturing and cultural creativity. Technological innovation serves as the foremost productive force. Shenzhen, renowned for its high research and development (R&D) investment and innovative atmosphere, continues to invest heavily in areas such as 5G, artificial intelligence, and biomedicine. The rapid transformation of technological achievements into industrial products and service applications demonstrates a high degree of integration between the digital economy and the real economy. In comparison to regions such as Beijing, Shanghai, and Shenzhen, which are hubs for technological and educational resources, the high efficiency of integration is evident. It is clear that the upgrading of the talent structure and R&D investments have a “multiplier effect” in promoting the synergy between the digital and real economies. Some provinces in the central and western regions, although actively building digital infrastructure, lag behind their eastern counterparts in terms of the speed of digital integration with traditional industries, largely due to severe talent outflow or relatively weak research environments.

A conducive market environment signifies an efficient resource allocation mechanism, which optimizes market functionality, accelerates the efficiency of digital transformation in real enterprises, and facilitates the coordinated development of the DE and RE. Regions with a higher degree of marketization usually have a more open investment environment, sound laws and regulations, and a fairer competition environment, providing sufficient policy and social conditions for the IDR. In addition, marketization helps to break down administrative barriers and promote the free flow of technology, capital, talent, and other elements, thus enhancing the depth and breadth of DE applications. Guangdong and Zhejiang have shown strong market vitality in digital payment, internet finance, e-commerce, and other fields, which are inseparable from the relatively sound local market environment. In recent years, driven by the economic circle in the Chengdu–Chongqing area, Chongqing has gradually improved its market mechanism and actively attracted internet enterprises and technology-based enterprises to settle down, which led to the combination of the RE and digital platforms.

Overall, it is evident that the CCD in China exhibits significant spatiotemporal variations. These differences are influenced by a range of factors, including the foundation of economic development, the industrial structure, policy implementation, talent reserve, and the degree of marketization, all of which interact to shape the observed outcomes. By deeply analyzing the differences and underlying causes across regions, this can provide valuable insights for formulating more region-specific development strategies for China in the future.

5. Conclusions and Findings

5.1. Conclusions

This work systematically evaluated the spatial–temporal evolution and influencing factors of the CCD between the DE and RE in China from 2012 to 2022. The principal findings are summarized below:

• The overall trend of CCD has shown an upward trajectory, although the levels remain relatively low, with significant spatial disparities observed across regions. Eastern provinces have consistently led in CCD levels. In contrast, western and northeastern regions, facing challenges such as insufficient economic vitality and weak digital infrastructure, have struggled with slower integration processes, particularly in the northeast region where industrial structure and talent outflow have hindered progress.
• Regional differences in CCD have been widening over time, particularly after 2016 when big data technologies began to gain prominence. The study identifies inter-regional disparities as the primary driver of the overall differences, underscoring the need for targeted policies to promote cross-regional coordination.
• The intensity of DE policies, human capital, technological innovation, and the market environment significantly contribute to CCD, while the industrial structure exerts a negative influence. In addition, the impact of the economic development level is not statistically significant.

5.2. Policy Suggestions

Based on the analysis, the following suggestions are proposed in this work:

To enhance the coordinated development of the DE and RE across diverse regions, specific strategies should be formulated based on local characteristics to address spatial differences and imbalances. Local governments should leverage regional development features and comparative advantages to advance the IDR. In particular, for remote western provinces such as Xinjiang, Qinghai, and Gansu, it is imperative to intensify the construction of digital infrastructure to enhance both the efficiency of information flow and the potential for value creation. Simultaneously, strengthening policy coordination between eastern, central, and western regions, especially in areas such as technological innovation and talent attraction, is essential for promoting resource sharing and leveraging regional complementarities, thereby facilitating interregional coordination in CCD development. For the northeastern region, efforts should focus on accelerating the digital economic transformation to mitigate the constraints imposed by outdated industrial structures and to foster the rapid growth of emerging industries. Given that interregional disparities remain the primary driver of uneven CCD levels, the implementation of differentiated policies at the national level, which are tailored to the specific conditions of each region, will be instrumental in narrowing provincial development gaps and enhancing the IDR.

Furthermore, it is essential to strengthen the local government’s capacity for precise implementation of DE policies. The experience of Jiangsu demonstrates that an over-reliance on digital industry clusters may lead to resource waste and redundant construction. Therefore, local governments should focus on the meticulous design and coordination of policies to prevent overlaps and inefficiencies in implementation. This approach will ensure the optimal realization of the benefits derived from the IDR.

Enhancing the development of digital human capital is essential. Region should align with their specific developmental requirements by establishing a diversified talent system to create effective mechanisms for cultivating and attracting digital professionals. Simultaneously, emphasis should be placed on emerging digital fields such as artificial intelligence and the Internet of Things, with targeted training programs to elevate the practical and innovative capabilities of digital talent. Areas with limited digital talent pools should implement more appealing recruitment policies, incorporating financial incentives and housing benefits. Additionally, increased investment in technological research is crucial, supporting innovation-driven projects and stimulating enterprise creativity through tax exemptions and funding subsidies, thereby fostering an optimized innovation environment. The successful experiences of regions such as Beijing, Shanghai, and Shenzhen indicate that a strong talent pool and an innovative technological environment are key to the development of the DE. These regions can serve as models for policy formulation.

Finally, it is crucial to enhance the market environment to promote the IDR. Policies should focus on advancing market-oriented reforms to improve resource allocation efficiency, optimize market mechanisms, and strengthen market competitiveness. Additionally, efforts should be made to dismantle administrative barriers and facilitate the free flow of technological, capital, and human resources. This will contribute to a higher level of CCD. Furthermore, refining the investment environment and legal frameworks and ensuring fair competition will create a more favorable ecosystem for the development of both the DE and RE.

In summary, through a combination of measures such as regional coordination, industrial optimization, talent acquisition, and market-oriented reforms, the IDR can be effectively promoted. These strategies offer more precise and feasible development pathways for different regions of China.

5.3. Deficiency and Prospect

Currently, there is no consensus on the definition, boundaries, and measurement criteria for IDR, which significantly limits the comparability and generalizability of research findings. Existing studies predominantly rely on macro-level statistical data, while high-quality micro-level data at the enterprise level remain limited. In all, the intricate dynamics between the DE and RE remain a pivotal domain for in-depth investigation. Future research should prioritize theoretical innovation, methodological advancements, policy evaluation, and the application of emerging technologies, thereby facilitating research that promotes the coordinated development of the DE and RE.