Spatio-Temporal Evolution and Influencing Factors of Coupling Coordination Degree between Urban–Rural Integration and Digital Economy

Abstract

The coupling and coordination of digital economy and urban–rural integration can help narrow the urban–rural gap and help realize comprehensive modernization. Based on the data of 31 provinces in China from 2011 to 2020, the index systems of digital economy and urban–rural integration were constructed, respectively, by using the entropy method, and the coupling coordination degree between digital economy and urban–rural integration was calculated by using the coupling coordination degree model. The spatio-temporal evolution characteristics were analyzed by using the spatial Durbin model, and the influencing factors of the coupling coordination degree were deeply decomposed. The results showed that the coupling coordination degree of digital economy and urban–rural integration has been significantly improved in the whole country during the decade, with the overall spatial distribution characteristics of “high in the east and low in the west” and “high in the south and low in the north”. The growth momentum of the backward provinces was relatively better, which have more obvious characteristics of positive spatial agglomeration. The level of economic development, government support, technological innovation and industrial structure had positive promoting effects on the coupling coordination degree. Except for the negative spatial spillover effect of government support, the three other factors all had promoting effects on the coupling coordination degree in neighboring provinces. The effect intensity of influencing factors also had regional heterogeneity.

1. Introduction

The urban–rural relationship is the most important relationship in the economic and social development of a country. With the continuous advancement of world urbanization, both developed and developing countries should explore and implement countermeasures to suit their national conditions to promote rural revitalization, reconstruction and community construction, as well as to better support the healthy development of urban construction [1]. In recent decades, the mutual flow of urban and rural resources has accelerated, resulting in changes in urban and rural structures and an increase in urban and rural residents’ income [2]. However, in the process of rapid development, problems such as unbalanced urban–rural industrial structures, restricted free flow of factors, and significant regional differences gradually emerged [3,4]. Since the beginning of the 21st century, the Chinese government has begun to solve the contradiction between urban and rural development, and it has put forward a series of policies and measures which are conducive to urban and rural development, including urban–rural integration (URI), urban–rural coordinated development and urban–rural integrated development. The coordinated development of urban–rural areas and their inseparable organic integration has always been a focus of attention [5].

With the deepening of the new round of scientific and technological revolution and industrial transformation, the new generation of information technology represented by the Internet, big data and artificial intelligence has been extensively and deeply integrated with the real economy, and human society is fully entering the era of the digital economy (DE) [6]. The DE has become an important engine for economic and social development, which is characterized by high innovation, strong penetration and wide coverage. The vigorous development of the DE has given rise to new technologies, new forms of business and new platforms, which are having a profound impact on technological innovation and industrial structure adjustment. It has become a key driving force for URI in terms of development concepts and mechanisms, equal exchange of resources, mutual flow of elements and industrial collaboration [7]. In the new journey of development, the balanced, coordinated and sustainable development of urban and rural areas is urgently required. Promoting the integrated development of urban and rural areas is an inevitable requirement to solve the problem of unbalanced and inadequate development. Digital reform and innovation is one of the important ways to promote the integrated development of urban and rural areas [8].

So, how do the DE and URI affect each other? Is the development of the DE and URI coupled and coordinated? What are the spatio-temporal evolution characteristics of the coupling coordination degree (CCD)? What factors affect the CCD between the DE and URI?

At present, scholars have conducted little research on the influencing factors of the CCD of the DE and URI. Most scholars devote themselves to studying the impact of the DE on URI or the coupling coordination relationship between them, while few scholars have studied the influencing factors of the CCD. On this basis, the current study focuses on studying the spatio-temporal evolution characteristics and influencing factors of the CCD, so as to more accurately handle the disharmony between DE and URI development faced by regions or the whole nation. The research is conducive to improving urban and rural issues, coordinating opportunities for digital development, breaking the urban–rural dual structure and realizing digital China. The study also provides practical guidance for the research of the DE and URI around the world.

The main innovations of the study include following aspects. (1) Except for an analysis of the path of the DE’s role in promoting URI, the mechanism of the impact of URI on the DE from the perspective of URI is also discussed. (2) On the basis of the original index system, the indicators of the DE and URI are further enriched, which makes the measurement more comprehensive and specific. (3) The mechanism of influencing factors of the CCD are in-depth analyzed, and the corresponding hypotheses are put forward and then verified. (4) The spatio-temporal evolution of the CCD between DE and URI is investigated dynamically from the perspective of spatio-temporal distribution and spatial correlation. (5) The spatial perspective analysis is introduced, and the spatial Durbin model is adopted to empirically study the driving factors of the CCD level, and the regional heterogeneity analysis is conducted according to the level of industrial structure.

The remainders are arranged as follows: Section 2 is the literature review, which includes three aspects of research, including DE, URI and the relationship between DE and URI. Section 3 constructs the index systems of the DE and URI, respectively, presents the calculation ways of the CCD, proposes the research hypotheses for the study of influencing factors of the CCD and constructs the spatial Durbin model. Section 4 describes the changing trend of the CCD in China from 2011 to 2020, carries out a comparative analysis in the eastern, central and western regions, and southern and northern regions, and it investigates the hierarchical changes and spatial correlation characteristics of the CCD. Section 5 empirically analyzes the factors affecting the CCD level, divides regions according to the level of industrial structure, carries out a heterogeneity analysis, and constructs different weight matrices to test the robustness of regression results. Section 6 discusses the conclusions and puts forward some corresponding countermeasures and suggestions. Section 7 gives a brief summary of current study and puts forward the deficiencies and prospects of future research.

2. Literature Review

By reviewing the existing literature, it can be found that the research closely related to current topic mainly includes three aspects: DE research, URI research and the relationship between DE and URI. Therefore, this section reviews the literature on these three respects.

2.1. Research Status of the DE

The concept of the DE was first proposed by Tapscott in 1996 and mainly applied to e-commerce and Internet fields [9]. Regarding the connotation of the DE, early studies believed that it was an economy in which products and services were directly dependent on digital technology in production, sales, supply and other links [10]. With the development of modern communication technology, Bukht and Heeks pointed out that the DE exerted an impact on social production, trade and economic activities mainly through data flow and communication technology [11]. Therefore, the DE was the product of the close combination of modern digital technology and all aspects of national economic operation. The DE represented a series of economic activities carried out on the basis of digital technology, which relied on a digital platform as the main medium and digital empowerment infrastructure as the important support [12].

Negroponte has pointed out that digital existence is a new way of existence formed by great changes in digitalization, informatization and networking on human production and existence [13]. In terms of the micro-impact effect, the DE can reduce enterprises’ search costs, marginal costs, transportation costs, tracking costs and verification costs, thus greatly reducing business friction [14,15]. The DE can also help enterprises establish relative competitive advantages, give play to the initiative of technological innovation, as well as improve the output efficiency and innovation ability of enterprises [16,17]. In terms of the macro-impact, the DE relies on digital technology to promote factor sharing and promote the transformation and upgrading of traditional industrial structure, so as to provide impetus for economic growth [18,19]. At the same time, the DE can reduce carbon emissions [20], curb energy consumption [21], and raise the level of high-quality urban development by improving human capital and promoting green technology innovation [22].

In terms of measurement indicators, Margherio et al. first discussed the boundary problem of the DE, providing a useful reference for the measurement of the DE [23]. Kahin and Braynjolfsson believed that DE is composed of five aspects: infrastructure, e-commerce, industry structure, digital labor and digital price [24]. Some scholars analyzed the connotation of the DE using data from different stages, such as 2012–2019, 2013–2018 and 2016–2020, respectively. They constructed DE indicators from the aspects of infrastructure, digital industrialization, industrial digitization and digital governance [25,26]. It can be found that the index construction of the DE had been relatively mature, but the time spans were often short. Most of the existing research is based on theoretical analyses, and the research depth needs to be improved. Based on this, we adopt the data of 31 provinces in China from 2011 to 2020, and we construct the DE index system from five dimensions: DE infrastructure, digital application, digital technology development level, digital industrialization and industrial digitalization.

2.2. Research Status of URI

The urban and rural relationship widely exists in mutual influence, mutual interaction and mutual restriction between urban and rural areas [27]. In recent years, many scholars have studied the theory and connotation of URI. Western scholars earlier carried out theoretical research on urban–rural relations, and they gradually formed some theoretical theories such as “urban bias theory”, “urban–rural dual structure theory”, “urban–rural common development theory”, “rural bias theory” and “secondary urban development strategy theory” [28]. The theory of URI represented by utopian socialism and Marxism was the most classic. It has become an important theory guiding the development of urban and rural areas in many countries. It proposed that eliminating the opposition between urban and rural areas is a historical process, and the relationship between urban and rural areas will inevitably move from separation and opposition to integration and unity [29].

From the combination of theory and practice, scholars have already defined the connotation of URI based on “flow space” and “new flow mode”. For example, Tacoli believed that URI should include human, commodity, capital and other social transactions [30]. Kenneth interpreted URI from the perspectives of food flow, resource flow, human flow, concept flow and capital flow [31]. Due to China’s long-standing urban–rural dual structure and the role of household registration barriers, its urban and rural development is very different. Therefore, some scholars believed that URI is not only the integration of urban and rural economy, but also the coupling and coordinated development of multi-dimensional subsystems such as population, space, society and ecology, which form a mutually beneficial complex urban–rural system [32]. Meanwhile, some scholars have proposed that the key to realizing URI is the integration of humanity, geography, economy, society and ecology between urban and rural areas [33,34].

With the development of economy and social culture, people have increasingly higher requirements for a better life, so the urban and rural economy, society, space, population and ecology were gradually included in the evaluation index system of URI [35]. In terms of influencing factors of URI, various subsystems, such as natural environment, space, economy, social security, culture and policies, all had an impact on URI development [36]. Specific factors such as labor, land and capital were also the main driving factors for the integrated development of urban and rural areas [37]. The actions of governments, farmers, enterprises and other entities directly affected the process of URI [38].

Considering its connotation comprehensively, scholars selected the indicators of URI from different perspectives. International scholars’ measurements were relatively simple and independent, using factors such as urban–rural economic gap [39], urban–rural human capital [40], urban–rural industrial integration [4], urban–rural agricultural development, urban–rural public service [41,42] and urban and rural welfare [43], respectively. In contrast, most Chinese scholars believed that the integration of economy, society and population was the key to the index system of URI, tending toward comprehensive evaluation [44,45]. It can be seen from the review of the relevant literature on URI that scholars have conducted few studies on URI, and the selection of integration indicators was not comprehensive enough. Moreover, empirical studies on URI rarely considered spatial factors. By taking the above factors into full consideration, we construct the index system of URI from six aspects: economic development integration, population education integration, social service integration, environmental health integration, spatial–temporal structure integration and infrastructure integration. Meanwhile, an empirical study is conducted by using spatial metrology.

2.3. Research Status of the DE and URI

In terms of the research on the relationship between DE and URI, most scholars devoted themselves to studying the impact of the DE on URI. Some scholars believed that producers can use digital technologies to break the boundary and spatial constraints between industries, so as to accelerate the digital transformation of agriculture and promote the integrated development of urban and rural industries [46]. The popularization of Internet technology had brought both opportunities and challenges to all countries in the world. It has increased the connection between urban and rural areas in terms of production and consumption, accelerated the construction of digital countryside, increased farmers’ income, as well as gradually narrowed the urban–rural income gap [47]. Digital governance can reduce transaction costs, realize urban and rural connectivity, and improve the balanced allocation and utilization efficiency of urban and rural public resources by solving the problem of the “information island” [48]. The DE enabled the mutual flow of urban and rural factors, industrial transformation and upgrading, adjustment of public service level and geospatial reconstruction [49,50]. The interactive sharing of the DE can promote the efficiency of resource allocation between urban and rural areas, narrow the income gap between urban and rural residents, and promote coordinated development between urban and rural areas [51]. Rural infrastructure construction and e-commerce development had spillover effects, which increased residents’ income and narrowed the urban–rural economic gap through “digital dividend” [52].

The development of the DE can accelerate the process of URI. The DE promoted the integration of urban and rural economy by promoting the integrated development of the primary, secondary and tertiary industries [53]. Under the background of digitalization, farmers in remote areas can receive the latest knowledge and information through the Internet, smart phones and other infrastructure, increase communication and cooperation with the outside world and promote the integration of education resources [54]. Through the application of digital technology, traditional social services can be transformed and upgraded, providing more convenient services for urban and rural residents [8]. The rapid expansion of “Internet Plus” in rural government services, medical care, rural tourism and other fields enriched people’s spiritual and cultural life, promoted high-quality life, and promoted the integration of urban and rural social services [55]. The development of digital technology integrated information resources, broken geographical restrictions, realized data sharing, and promoted the spatio-temporal integration of urban and rural areas [56]. The DE can promote the construction of new urbanization by improving the level of technological innovation and promote the intelligent transformation of traditional infrastructure such as transportation, logistics, power grid and water network; it can also drive the construction of infrastructure for people’s livelihood, so as to realize the sharing and co-construction of urban and rural digital infrastructure [57].

The accelerated development of URI can also act on the DE in reverse, forming a virtuous circle of mutual influence and interdependence between the DE and URI. The integration between urban and rural areas can provide a more perfect infrastructure, more comprehensive talents and more abundant production factors for the development of the DE; the effective flow of data resources between urban and rural areas can provide more comprehensive infrastructure support for the DE [58]. The integrated development of urban and rural areas can further improve the penetration rate of the Internet, promote the effective transmission of information, gradually overcome the digital bottleneck in agriculture and rural areas, as well as realize the integration of factors, commodities and information between urban and rural areas [59]. Rural resources, urban factor market connection and digital application level will be improved. The integrated development of urban and rural areas requires more digital talents, advanced technologies and the transformation of scientific research achievements. So, it can promote the continuous improvement of the development level of digital technology, as well as meet the requirements of the development of current circumstance. The integrated development of urban and rural areas drove the flow of factors between urban and rural areas, and it made the allocation of factors between various industries more effective, which integrated resources and promoted the development of digital industries [60].

With the development of the DE and URI, there is a trend of coupling and coordination between them. Most scholars studied the influence mechanism and path of the DE on URI, but there is little research on the coupling coordination relationship and influencing factors between the two. On this basis, we focus on the spatio-temporal evolution characteristics and influencing factors of the CCD between the DE and URI, which makes up the research gap.

3. Research Hypothesis and Model Construction

This section introduces the index system of the DE and URI and the calculation method of the CCD. The influencing factors of the CCD between the DE and URI are also analyzed, and appropriate core explanatory variables and control variables are selected. Based on correlative inspection requirements, a suitable spatial measurement model is selected.

3.1. CCD Model of URI and DE

DE index measurement. The core of the DE includes digital industrialization and industrial digitalization, which is the integration of digital technology and real economy. The guarantee of the DE is the DE infrastructure, the important support is the information and communication technology, the goal is digital application. Referring to the indicators of the DE infrastructure, digital industrialization and industrial digitalization [61] and the relevant indicators of digital technology development level [62], the current study selects five dimensions and 26 specific indicators to build a DE indicator system (Appendix A). The index is calculated using the entropy method [63].

URI index measurement. Yang et al. believed that URI is a goal, a state and, more importantly, a process. Therefore, URI is multidimensional and multifaceted. They built an indicator system for measuring URI from three dimensions (namely, foundation, driving factors and objectives) and selected indicators in a comprehensive way [64]. Qian et al. argued that the key to realizing URI is the eventual shift from “heterogeneous dual structure” to “homogeneous integration”. They claimed that URI includes the integration of five dimensions of “society”, “economy”, “culture”, “space” and “ecology” [65]. We mainly refer to the index selection method of existing relevant literature, and we consider the availability of data. The URI index system is selected with 28 specific indicators, belonging to six dimensions of economic development integration, population education integration, social service integration, environmental health integration, spatial–temporal structure integration and infrastructure integration. The indexes are listed in Appendix B, and the system is also assigned weights using entropy method.

After the comprehensive score of the DE and URI subsystem is obtained, the CCD index is calculated via the following model:

$$\left\{\begin{array}{l}{C}_{it}^{\mathrm{cx}}=2\sqrt{{\mu }_{it}^c{\mu }_{it}^x}/({\mu }_{it}^c+{\mu }_{it}^x)\\ T_{it}^{cx}=a{\mu }_{it}^c+b{\mu }_{it}^x\\ UR{C}_{it}^{cx}=\sqrt{C_{it}^{\mathrm{cx}}{T}_{it}^{cx}}\end{array}\right.$$

(1)

where $C_{it}^{cx}$ is the CCD of the DE and URI in $i$th province in $t$th year; ${\mu }_{it}^c$ represents the comprehensive score of the DE index in $t$th year of $i$th province, which is calculated by weighted summation of entropy value method; ${\mu }_{it}^x$ represents the comprehensive score of URI index in $t$th year of $i$th province; $T_{it}^{cx}$ is the inter-system comprehensive coordination index in $t$th year; and $a$ and $b$ represent the contribution degree of the DE and URI development index in the CCD, respectively. The current study considers that the development of the DE and URI has the same importance to the coupled and coordinated development of the two, so $a=b=0.5$ is taken. $UR{C}_{it}^{cx}$ is the final calculated CCD of $i$th province in $t$th year. The smaller the deviation between the DE development index and URI development index, the larger the index value, indicating the higher development level of the CCD.

The data sources for the above indicators include the China Industrial Statistics Yearbook, China Information Industry Yearbook, China Statistical Yearbook of Science and Technology, China Statistical Yearbook of Urban and Rural Construction, China Statistical Yearbook of Rural Areas, China Statistical Yearbook, China Population and Employment Yearbook, China Statistical Yearbook of Educational Expenditure, China Health Yearbook, statistical Yearbook of each province, and statistical Bulletin and annual statistical data of the State Bureau of Statistics by province. The average growth rate method is adopted to interpolate the missing year index data of some provinces.

3.2. Analysis on Influencing Factors of the CCD

(1) The economic development level reflects the scale and speed of a region’s economic development, which represents the continuous upgrading innovation process and change process of economic structure in a country or a region. With the rapid economic development, the industrial structure will be optimized and upgraded, and income distribution will become fairer and more reasonable. Meanwhile, resources will be allocated more effectively, and human, information and technological resources will be fully released. The income of urban and rural residents will also be increased, and public service facilities such as social security and infrastructure between urban and rural areas will also be improved [66]. Meanwhile, it can also provide technical support, a financial guarantee and a good environment for the development of the DE, so the coupled and coordinated development of the DE and URI cannot be separated from economic growth [67]. So, the first hypothesis is proposed As follows:

H1. 

The economic development level promotes the CCD of the DE and URI.

(2) The general budget expenditure of government finance includes the expenditure on urban and rural community affairs. The expenditure of urban and rural community affairs covers a number of points, including management affairs expenditure, planning and management expenditure, public facilities expenditure and so forth. As a special fund for urban and rural development and construction, the expenditure has a significant impact on the flow of urban and rural resource factors. Sufficient investment makes it possible for urban and rural relations to present a reciprocal, dynamic and harmonious spatial distribution pattern and integration. Under the background of digitalization, increasing the budget expenditure of digital infrastructure projects can promote the construction of digital finance and facilitate the development of the urban and rural digital infrastructure. The government can provide rural areas with land, financing, talent and other assistance, introduce Internet technology and promote the construction of “digital villages” and “new smart cities”; local governments can set up special guidance funds for “digital villages” to contribute to the realization of rural revitalization [68]. Therefore, the government can provide necessary support for the construction, improvement and innovative development of the infrastructure of the DE and URI, so as to promote the improvement of the CCD level of the DE and URI. So, the second hypothesis is proposed accordingly As follows:

H2. 

The government support positively affects the CCD.

(3) Technological innovation plays a core role in the process of modernization construction. It drives the development of digital technology, which can be widely applied in urban and rural areas, providing basic technical support for the integrated development of urban and rural areas. Digital technology can promote the integration of urban and rural industries and the integration of rural primary, secondary and tertiary industries. It can promote the integration of agricultural industrial chain and the upgrading of value chain, foster new business forms and new drivers for rural development, and improve the balance, coordination and ecological level of urban and rural industrial layout [69]. Technological innovation is the core driving force for the development of the DE, which is mainly promoted through digital industrialization, industrial digitization and governance digitization [70]. “Internet Plus”, artificial intelligence, big data, cloud computing and other digital and intelligent technologies have been gradually integrated into all aspects of URI. Technological innovation drives the updating and iteration of technology, which can promote the development of urban and rural industries to rationalize and upgrade. These will promote the optimization of rural resources, the stimulation of endogenous rural development impetus, and the narrowing of the income and consumption gap between urban and rural residents. Therefore, technological innovation can promote the CCD of the DE and URI. So, the third hypothesis is presented.

H3. 

The technological innovation positively promotes the CCD.

(4) Digital technology has high permeability. The comprehensive integration of data technology and the primary, secondary and tertiary industries can form a good trend of high-quality development of smart agriculture, intelligent manufacturing and transformation of traditional service industries. The coordinated development of the primary, secondary and tertiary industries, driven by digital supply chain, creates a new opportunity for the division of labor between urban and rural industries [71]. On the one hand, the tertiary industry is the core engine to promote the development of the DE. The optimization and upgrading of industrial structure will promote the development of the DE. On the other hand, industrial structure adjustment drives the flow of urban and rural factors, promotes the integration of urban and rural industries and reduces the income gap between urban and rural residents. The optimization and upgrading of industrial structure can promote the adjustment of inter-industry factors, strengthen the innovation level of service economy, and drive the development of digital industrialization and industrial digitalization. Furthermore, it can create new jobs, facilitate labor transfer, increase rural residents’ income and consumption, and effectively narrow the urban–rural income gap. So, the last hypothesis is presented.

H4. 

The industrial structure positively promotes the CCD.

To sum up, the core explanatory variables selected in this study are calculated as follows. Economic development level is measured by per capita GDP [72]; government support level is measured by expenditure on urban and rural community affairs [73]; technological innovation is measured by the number of domestic invention patent applications authorized per 10,000 people [74]; industrial structure is measured by the ratio of output value of the tertiary industry to that of the secondary industry [75].

The CCD may also be affected by other factors besides the four core explanatory variables. The control variables selected here are shown as follows and in

Table 1. Variable description.

Attribute	Name	Code	Description	Unit
Explained variable	CCD	digur	The calculation results of the CCD model	-
Core explanatory variables	Economic development level	pgdp	GDP per capita	Yuan
	Government support	gov	Expenditures by local governments for urban and rural community affairs	Hundred million yuan
	Technology innovation	tec	(Authorized number of domestic invention patent applications)/(total population)	Number
	Industry structure	str	(Value-added of tertiary industry)/(Value-added of secondary industry)	-
Controlled variables	Trade openness	open	(Total imports and exports)/GDP	%
	Investment openness	fdi	(Foreign direct investment)/GDP	%
	Financial development level	fin	(Financial sector added value)/GDP	%

. Trade openness: this represents the types and amounts of import and export commodities directly affecting the income of suppliers and thus affecting the income level of urban and rural residents; it can also introduce advanced technologies, thus promoting the development of the DE and influencing the CCD. So, the ratio of total imports and exports to GDP of each province and city is adopted [76]. Investment openness: in the process of China’s development, the scale and structure of foreign investment have great regional differences, which has become an important factor affecting the CCD. It is expressed as the ratio of actual utilization of foreign direct investment to GDP of each province and city [77]. There are two different views on the financial development level. One believes that the improvement of financial development level will promote the flow of urban and rural capital, expand the types of financial services, improve the effective accumulation of rural human capital and material capital, improve the ability of digital innovation and, thus, promote the development of the DE and URI. The other view holds that the expansion of financial development scale leads to the phenomenon of “individual rent-seeking” and “elite capture” due to the difference between urban and rural individuals’ access to capital, which reduces the efficiency of financial utilization and is not conducive to the development of the DE and URI. Financial development level is expressed as the ratio of financial added value to GDP of each province and city [78].

The data of the above variables are from the China Statistical Yearbook.

3.3. Spatial Measurement Model

Before the selection of the spatial model, a series of processing were carried out on the variable data. According to the descriptive statistical results, logarithmic processing was carried out on variables in order to reduce heteroscedasticity, including pgdp, gov and tec. When choosing the spatial econometric model, LM test and LR test were firstly carried out, and the test results showed that the spatial Durbin model (SDM) was worth its selection. Secondly, the Hausman test was used to determine whether the fixed-effects model or the random-effects model should be selected. The results showed that the original hypothesis of random effects was rejected at the 1% level, so the fixed-effects model was selected. Finally, the results of the likelihood ratio test reject the individual fixed effect and the time fixed effect at the 1% level, so the double fixed effect was adopted. To sum up, the spatial econometric model of the study finally selected the SDM with double fixed effects for estimation. In summary, the model is set as follows:

$$\begin{array}{l}digu{r}_{it}=\alpha +{\rho }_{}{\displaystyle \sum _{j=1}^n{w}_{ij}digu{r}_{jt}}+b_1\ln pgd{p}_{it}+b_2\ln go{v}_{it}+b_3\ln te{c}_{it}+b_{4}st{r}_{it}+b_{5}ope{n}_{it}+b_{6}fd{i}_{it}\\ +b_{7}fi{n}_{it}+{\theta }_1{\displaystyle \sum _{j=1}^n{w}_{ij}\ln pgd{p}_{jt}+}{\theta }_2{\displaystyle \sum _{j=1}^n{w}_{ij}\ln go{v}_{jt}+}{\theta }_3{\displaystyle \sum _{j=1}^n{w}_{ij}\ln te{c}_{jt}+}{\theta }_4{\displaystyle \sum _{j=1}^n{w}_{ij}st{r}_{jt}+}{\theta }_5{\displaystyle \sum _{j=1}^n{w}_{ij}ope{n}_{jt}+}\\ {\theta }_6{\displaystyle \sum _{j=1}^n{w}_{ij}fd{i}_{jt}+}{\theta }_7{\displaystyle \sum _{j=1}^n{w}_{ij}fi{n}_{jt}+{\mu }_i+{\eta }_t+{\epsilon }_{it}}\end{array}$$

(2)

where $digu{r}_{it}$ represents the CCD of the $i$th province in $t$th year t, $\rho$ is the spatial autoregressive coefficient of explained variables, $w_{ij}$ is the element of the spatial weight matrix, $b$ represents the regression coefficients of explanatory variables and control variables, $\theta$ is the spatial lag regression coefficients of explanatory variables and control variables, ${\mu }_i$ and ${\eta }_t$ represent the spatial fixed and time fixed effects, respectively, and $\epsilon it$ is the disturbance term.

4. Calculation of the CCD and Spatial–Temporal Evolution Analysis

This section aims to describe the changing trend of the CCD in China from 2011 to 2020, and it aims to conduct a comparative analysis in the eastern, central, western, southern and northern regions, as well as to investigate the hierarchical changes and spatial correlation characteristics of the CCD in China.

4.1. Spatial and Temporal Distribution Characteristics

Appendix C lists the calculation results of the CCD in 2011, 2014, 2017 and 2020. From 2011 to 2020, the average CCD has increased from 0.286 to 0.420 with an average growth rate of 46.85%, indicating that the CCD has significantly improved. Most provinces in eastern China have a higher mean value, and all provinces in China show a year-on-year growth trend. The top six provinces with a higher growth rate are Guizhou (124.68%), Yunnan (88.30%), Tianjin (82.54%), Shanxi (76.82%), Jiangxi (76.47%) and Hainan (76.43%), indicating that these later-developed provinces have relatively good growth momentum. Except for Tianjin, the CCDs in other provinces are relatively low.

Table 2. Level classification and comparison of CCD in 2011 and 2020.

Grade Standard	2011	2020
[0.8~0.9) Good Coordinate	/	Peking
[0.7~0.8) Middle Coordinate	/	Shanghai
[0.6~0.7) Basic Coordinate	/	Tianjin, Guangdong
[0.5~0.6) Slightly Coordinate	Peking, Shanghai	Jiangsu, Zhejiang, Shandong
[0.4~0.5) Slightly Disorder	Jiangsu, Zhejiang	Liaoning, Heilongjiang, Anhui, Fujian, Chongqing, Sichuan, Shanxi
[0.3~0.4) Mild Disorder	Tianjin, Liaoning, Heilongjiang, Fujian, Shandong, Guangdong	Hebei, Shanxi, Inner Mongolia, Jilin, Jiangxi, Henan, Hubei, Hunan, Guangxi, Guizhou, Yunnan, Tibet
[0.2~0.3) Middle Disorder	Hebei, Shanxi, Inner Mongolia, Jilin, Anhui, Henan, Hubei, Hunan, Guangxi, Chongqing, Sichuan, Shaanxi, Ningxia, Xinjiang	Hainan, Gansu, Qinghai, Ningxia and Xinjiang
[0.1~0.2) Severe Disorder	Jiangxi, Hainan, Guizhou, Yunnan, Tibet, Gansu, Qinghai	/

illustrates the classification and comparison of the CCD level between 2011 and 2020. It can be found that in the past decade, the level of the CCD has been improved, and serious disordered ones have been lifted. The CCD develops towards a good trend, which shows that the DE and URI promote each other. This reflects that the Strategic Plan for Rural Revitalization and the Report to the 19th National Congress have well-realized the dual drive of new urbanization and rural revitalization.

Figure 1 presents the comparison between the eastern, central and western regions of China from 2011 to 2020. The CCD levels in the eastern and western regions show an increasing trend year on year. The central region also shows an increasing trend except for a slight decline in 2012. Among them, the CCD development level in the eastern region is significantly higher than the national average, the central region is lower and the western region is significantly lower. The central and western regions should put more effort into learning from the development advantages of the eastern regions, so as to improve the CCD development rate [79].

As shown in Figure 2, the CCDs in China and the southern and northern regions are basically flat during the period 2011–2012. Since 2013, the CCD level in the southern region has gradually risen higher than the national average level, while the northern region has gradually fallen below it, indicating a widening development gap between the two. These characteristics show that the CCD level in southern and northern regions has a certain degree of unbalanced development.

In order to verify the reliability of the results, we use the entropy-TOPSIS method to calculate the indicators of the DE and URI drawing on the practice of other scholars, and then we calculate the CCD. The results are the same as the entropy method. From 2011 to 2020, the CCD in the whole country and provinces shows an increasing trend year on year. From the perspective of regional division, the overall spatial distribution features are “high in the east and low in the west” and “high in the south and low in the north” (Figure 3 and Figure 4).

4.2. Spatial Correlation Analysis of the CCD

Euclidean distance matrix and global Moran’s I index are adopted for measurement in the current study [80], and the results are shown in

Table 3. Global Moran index of the CCD.

Years	Molan’s I	p-Value
2011	0.285	0.000
2012	0.277	0.001
2013	0.278	0.001
2014	0.273	0.001
2015	0.281	0.000
2016	0.283	0.000
2017	0.236	0.002
2018	0.255	0.001
2019	0.246	0.001
2020	0.326	0.000

. It can be observed that during the decade, the Moran’s I indexes of the CCD are always positive in the range 0.236–0.326, which pass the significance test at the 1% level in all years. This indicates that there is a high degree of spatial correlation in the CCD.

In order to further investigate the local spatial agglomeration characteristics of the CCD, 2011, 2014, 2017 and 2020 are selected as typical years. Then, a local Molan index scatter plot is adopted to analyze the spatial agglomeration changes and transitions of the CCD in various provinces. The results are shown in Figure 5a–d (The numbers in the Figure represent specific provinces as shown in

Table A3. CCD between digital economy and urban–rural integration.

Code	Provincial-Level Region	Region	2011	2014	2017	2020	Time Average	Growth Rate (%)
/	Space Average	China	0.286	0.334	0.374	0.420	0.354	46.85
1	Peking	Eastern	0.578	0.675	0.787	0.895	0.729	54.84
2	Tianjin	Eastern	0.338	0.388	0.425	0.617	0.426	82.54
3	Hebei	Eastern	0.278	0.31	0.318	0.334	0.315	20.14
4	Shanxi	Central	0.257	0.276	0.277	0.353	0.293	37.35
5	Inner Mongolia	Western	0.272	0.306	0.298	0.304	0.298	11.76
6	Liaoning	Eastern	0.342	0.385	0.386	0.41	0.380	19.88
7	Jilin	Central	0.237	0.246	0.311	0.353	0.287	48.95
8	Heilongjiang	Central	0.308	0.329	0.39	0.424	0.353	37.66
9	Shanghai	Eastern	0.538	0.606	0.691	0.749	0.645	39.22
10	Jiangsu	Eastern	0.446	0.498	0.54	0.592	0.521	32.74
11	Zhejiang	Eastern	0.415	0.472	0.529	0.592	0.507	42.65
12	Anhui	Central	0.272	0.316	0.344	0.401	0.335	47.43
13	Fujian	Eastern	0.357	0.395	0.462	0.445	0.418	24.65
14	Jiangxi	Central	0.187	0.237	0.285	0.33	0.258	76.47
15	Shandong	Eastern	0.373	0.434	0.461	0.501	0.446	34.32
16	Henan	Central	0.233	0.285	0.339	0.388	0.306	66.52
17	Hubei	Central	0.291	0.328	0.354	0.38	0.348	30.58
18	Hunan	Central	0.238	0.269	0.337	0.343	0.299	44.12
19	Guangdong	Eastern	0.397	0.446	0.515	0.67	0.504	68.77
20	Guangxi	Western	0.255	0.306	0.34	0.396	0.323	55.29
21	Hainan	Eastern	0.157	0.23	0.248	0.277	0.246	76.43
22	Chongqing	Western	0.271	0.324	0.361	0.406	0.345	49.82
23	Sichuan	Western	0.273	0.306	0.369	0.411	0.348	50.55
24	Guizhou	Western	0.154	0.246	0.291	0.346	0.264	124.68
25	Yunnan	Western	0.171	0.258	0.296	0.322	0.270	88.30
26	Tibet	Western	0.189	0.235	0.279	0.304	0.251	60.85
27	Shanxi	Western	0.233	0.286	0.324	0.412	0.306	76.82
28	Gansu	Western	0.156	0.188	0.238	0.227	0.205	45.51
29	Qinghai	Western	0.197	0.239	0.261	0.275	0.239	39.59
30	Ningxia	Western	0.215	0.259	0.289	0.266	0.259	23.72
31	Xinjiang	Western	0.247	0.264	0.251	0.295	0.266	19.43

of Appendix C).

As shown in Figure 5, most provinces are in the first and third quadrants, with significant positive spatial agglomeration characteristics. Meanwhile, the number of provinces in the third quadrant is more than that in the first one, indicating that more than half of the provinces with positive spatial agglomeration are in the “low-low” agglomeration area and remain unchanged for a long time. In other words, the CCDs in these provinces are still relatively low, and there is a large room for improvement. From the perspective of quadrant distribution, the first quadrant is always dominated by Peking, Shanghai, Zhejiang, Tianjin, Shandong, Jiangsu and Fujian in the east. The third quadrant mainly includes Hunan and eleven western provinces except Inner Mongolia. In the second quadrant, Anhui, Hebei, Jilin, Inner Mongolia, Jiangxi and Henan remained unchanged. In the fourth quadrant, only Guangdong remains unchanged. Overall, the differences and regional advantages among provinces are particularly obvious.

5. Empirical Analysis of Influencing Factors of the CCD

This section empirically analyzes the factors that influencing the CCD level, divides regions according to the level of industrial structure to carry out heterogeneity analysis, and tests the robustness of regression results by constructing different weight matrices.

5.1. Baseline Regression Analysis

In order to conveniently test the robustness of variable parameter estimation, we list the estimation results of traditional panel regression, SAR, SEM and SDM models, respectively. As shown in

Table 4. Model estimation results.

Variables	oLs	SAR	SEM	SDM
lnpgdp	0.063 *** (0.011)	0.058 *** (0.017)	0.051 *** (0.019)	0.091 *** (0.020)
lngov	0.036 *** (0.005)	0.010 (0.006)	0.013 * (0.007)	0.011 * (0.006)
lntec	0.030 *** (0.005)	0.017 ** (0.007)	0.016 ** (0.007)	0.015 ** (0.007)
str	0.031 *** (0.005)	0.023 ** (0.010)	0.017 (0.010)	0.043 *** (0.011)
open	0.001 *** (0.000)	−0.001 *** (0.000)	−0.001 *** (0.000)	−0.001 *** (0.000)
fdi	−0.000 ** (0.000)	−0.000 ** (0.000)	−0.000 ** (0.000)	−0.000 ** (0.000)
fin	0.003 ** (0.001)	0.004 ** (0.002)	0.004 ** (0.002)	0.004 ** (0.002)
W × lnpgdp	-	-	-	0.075 * (0.040)
W × lngov	-	-	-	−0.065 *** (0.016)
W × lntec	-	-	-	0.041 * (0.022)
W × str	-	-	-	0.146 *** (0.033)
W × open	-	-	-	0.001 * (0.000)
W × fdi	-	-	-	0.000 *** (0.000)
W × fin	-	-	-	0.003 (0.004)
$\rho$	-	0.328 ***	0.312 ***	0.215 **
R-squared	0.9013	0.7844	0.7785	0.7971
Log-L		752.2034	749.5953	779.8938
Obs	310	310	310	310

Note: *, **, and *** indicate different significant levels of 10%, 5% and 1%, respectively.

, the $R^2$ values of SAR, SEM and SDM are all lower than that of ordinary OLS regression, possibly because the spatial metrology models increase the spatial lag term of explained variables and explanatory variables, which leads to the increase in variables in the model. Meanwhile, there is weak multicollinearity between each explanatory variable and its spatial lag term, resulting in a lower value of $R^2$ than that of the ordinary OLS model. In general, most scholars do not deal with multicollinearity [76]. According to the regression results, the positive and negative coefficients of explanatory variables of the four models are basically consistent. The spatial autoregressive coefficients of SAR, SEM and SDM models are 0.328, 0.312 and 0.215, respectively, and are significant at the level of 1% and 5%, indicating that the CCD has a positive spatial spillover effect on neighboring areas. Taking SDM model analysis as an example, the core explanatory variables such as economic development level, government support, technological innovation and industrial structure all promote the CCD at the significance level of 1%, 10% and 5%.

The optimization and upgrading of industrial structure will promote the adjustment of inter-industry factors, drive the development of digital industry, increase jobs, effectively narrow the urban–rural income gap and promote the CCD [81]. The control variable financial development level has a significant positive effect on the CCD. The degree of trade openness and investment openness has a significant inhibitory effect on the CCD. A possible reason is that with the change in our trade commodity structure, the main export products have gradually changed from agricultural products to industrial products since the beginning of opening. The transformation of the structure of traded commodities reduces the income of agriculture relative to that of industry, thus enlarging the income gap between urban and rural areas, which is not conducive to the CCD.

From the perspective of spatial spillover effect, the economic development level, technological innovation and industrial structure have a significant positive promoting effect on the CCD level in neighboring provinces. It shows that the improvement of economic development level, technological innovation and industrial structure optimization can integrate resources, narrow the development gap between provinces, improve the level of the DE and then promote the CCD on the whole. The level of government support has a significant negative effect on the CCD in neighboring provinces. The increase in government support gathers resources for the development of this province, drives technological progress, attracts outstanding talents and promotes the economic development of this region within a certain period of time. However, it is easy to drain resources from neighboring provinces, forming a “digital divide” and “development island”, which contributes to the creation of strong barriers to the improvement of the CCD in neighboring provinces.

5.2. Effect Decomposition Analysis

By using the partial differential method, the SDM model is decomposed, and the direct and indirect effects of core explanatory variables and control variables on the CCD can be obtained (

Table 5. The results of effect decomposition analysis.

Variables	Direct Effect	Indirect Effect
lnpgdp	0.096 *** (0.020)	0.114 ** (0.050)
lngov	0.009 (0.006)	−0.076 *** (0.022)
lntec	0.018 ** (0.007)	0.057 * (0.030)
str	0.049 *** (0.011)	0.193 *** (0.042)
open	−0.001 *** (0.000)	0.001 (0.001)
fdi	−0.000 ** (0.000)	0.000 ** (0.000)
fin	0.004 ** (0.002)	0.004 (0.005)

Note: *, **, and *** indicate different significant levels of 10%, 5% and 1%, respectively.

).

From the perspective of direct effect, the core explanatory variables, such as economic development level, government support, technological innovation and industrial structure, have a positive impact on the CCD, but the coefficient of government support is not significant. From the indirect effect, the economic development level, technological innovation and industrial structure have a significant positive spatial spillover effect on the CCD in neighboring provinces, and the government support will restrain the CCD in neighboring provinces.

Effect decomposition results are basically consistent with baseline regression results. The improvements in terms of economic development level, technological innovation and industrial structure not only promote the CCD level of specific province, but also drive the development of neighboring provinces, having a demonstration effect and promoting the development level of the CCD.

5.3. Further Analysis

As the baseline regression results show, it was found that the coefficient of industrial structure in the core explanatory variables is obviously larger. Considering the differences between provinces with relatively high and low industrial structure levels, there may be certain heterogeneity in the CCD of each core explanatory variable. Thus, we divide the provinces into two regions according to the level of industrial structure, namely, fifteen provinces with a relatively low industrial structure level (LISI) and the rest with a relatively high level of industrial structure level (HISI).

(1) According to the regression results, the spatial autoregressive coefficient of the LISI region is significantly negative, while the coefficient of the HISI region is significantly positive (

Table 6. The regression results of regions with different industrial structure levels.

Variables	Low-Level Regions	High-Level Regions
lnpgdp	0.092 *** (0.026)	0.073 *** (0.024)
lngov	0.020 *** (0.007)	0.005 (0.008)
lntec	−0.010 (0.012)	0.038 *** (0.011)
str	0.057 ** (0.025)	0.021 * (0.012)
open	−0.000 (0.000)	−0.001 *** (0.000)
fdi	0.001 *** (0.000)	−0.000 ** (0.000)
fin	−0.005 ** (0.002)	0.004 (0.002)
$\rho$	−0.532 ***	0.170 **
R-squared	0.8428	0.7981
Log-L	425.6587	404.9738
Obs	150	160

Note: *, **, and *** indicate different significant levels of 10%, 5% and 1%, respectively.

). A possible reason is that provinces with low industrial structure have disorderly competition with each other and that they have a great demand for talent, capital, cash technology, data and other factors. The existence of a “siphon effect” will have a negative impact on the CCD of the DE and URI in neighboring provinces. By contrast, the HISI provinces have stronger linkage. The improved CCD of the DE and URI in a province will promote the exchange and sharing of ideas, culture and technology among provinces, which is conducive to technology spillover and brings a driving effect.

(2) Among the core explanatory variables, in LISI provinces, economic development level, government support and industrial structure have a significant positive impact on the CCD, while the technological innovation coefficient is negative and has no significant impact. In such provinces, industrial output value accounts for a large proportion, the overall economic foundation is weak, and the large amount of R&D investment and financial support required for technological innovation cannot be met, so the CCD is not significantly affected. To develop these provinces, the government should play a positive role by increasing corresponding support, improving infrastructure construction, raising the level of economic development and promoting the optimization and upgrading of industrial structure. It is suggested that the government develops software, information technology and other service industries suitable for such regions, so as to play a positive role in promoting the CCD.

(3) In HISI provinces, economic development level, technological innovation and industrial structure have a significant positive promoting effect on the CCD, while government support has no significant positive effect. This shows that the industrial structure of these provinces is relatively mature, and the government does not need to invest in greater efforts to promote the CCD. With the improvement of industrial structure, the influence of technological innovation has a significant positive promoting effect. A possible reason is that the economic development of these provinces is of a higher “quality”, and they have more capital to invest in technological research and innovation. This promotes the redistribution of production factors among different industries, and the dividends of industrial structure gradually appear, thus improving the CCD.

5.4. Robustness Test

In order to investigate the robustness of the results, the current study constructs the weight matrix from two aspects for testing. With the continuous development of the DE, the spatial effect between provinces is gradually enhanced, and the development distance of the DE will also produce a spatial interaction, resulting in spatial spillover through economic activities. Therefore, the DE distance matrix is constructed, and the construction method is as follows:

$$W=\frac{1}{\left|di{g}_i-di{g}_j\right|}$$

(3)

where $di{g}_i$ and $di{g}_j$ represent the mean value of the DE indexes of $i$th and $j$th provinces during the sample investigation period, respectively.

Then, the economic geographical weight matrix nested with Euclidean inverse distance and per capita GDP is built. The construction method is as follows:

$$W_2=W_1\ast diag(\overline{X_1}/\overline{X},\overline{X_2}/\overline{X},\cdots ,\overline{X_k}/\overline{X})$$

(4)

where

$$\overline{X_i}={\displaystyle \sum _{t_1}^{t_2}{X}_{it}/(t_2-t_1+1)}$$

(5)

$$\overline{X}={\displaystyle \sum _{i=1}^k{\displaystyle \sum _{t_1}^{{}_{t_2}}{X}_{it}/k(t_2-t_1+1)}}$$

(6)

where $W_1$ is Euclidean inverse distance matrix; $X_{it}$ is the per capita GDP of $i$th province in the $t$th period; $t_1$ is the beginning year of the study period; and $t_2$ is the end year of the study period. $diag(\overline{X_1}/\overline{X},\overline{X_2}/\overline{X},\cdots ,\overline{X_k}/\overline{X})$ is the ratio of the main diagonal element equal to the average per capita GDP of $i$th Province and the average per capita GDP of all provinces (municipalities and autonomous regions) during the decade.

Table 7. Robustness test.

Variables	DE Distance Matrix	Economic Geography Nested Matrix
lnpgdp	0.059 *** (0.017)	0.099 *** (0.020)
lngov	0.006 (0.006)	0.011 * (0.006)
lntec	0.018 ** (0.008)	0.017 ** (0.007)
str	0.019 * (0.011)	0.045 *** (0.011)
open	−0.001 *** (0.000)	−0.001 *** (0.000)
fdi	−0.000 (0.000)	−0.000 ** (0.000)
fin	0.007 *** (0.002)	0.005 *** (0.002)
W × lnpgdp	−0.092 ** (0.042)	0.091 ** (0.042)
W × lngov	0.026 * (0.015)	−0.057 *** (0.017)
W × lntec	0.050 *** (0.015)	0.043 (0.028)
W × str	0.030 * (0.017)	0.146 *** (0.038)
W × open	−0.000 (0.000)	0.001 ** (0.000)
W × fdi	−0.000 *** (0.000)	0.000 *** (0.000)
W × fin	−0.003 (0.003)	0.004 (0.004)
$\rho$	0.229 ***	0.098
R-squared	0.7638	0.7970
Log-L	767.9080	774.5277
Obs	310	310

Note: *, **, and *** indicate different significant levels of 10%, 5% and 1%, respectively.

shows the regression results of the distance matrix of the DE and the nested matrix of economic geography, which are basically consistent with the regression results of the Euclidean distance matrix and generally prove the robustness of the benchmark regression structure.

6. Results and Suggestions

In this study, the entropy method was adopted to calculate the indexes of the DE and URI, the coupled coordination model was used to calculate the CCD of the DE and URI, and the spatio-temporal evolution characteristics of the development of the CCD were investigated in multiple dimensions. The driving factors and heterogeneity of the CCD level between the DE and URI were analyzed by constructing the bidirectional fixed-effect SDM.

The conclusions of this study are mainly listed in the following four aspects.

First, the measurement results showed that the CCD level has been significantly improved, and all provinces show a year-on-year growth trend. In the past decade, the level of coupling coordination between the DE and URI has changed from severe or moderately disordered to mild or near disordered, and there were no provinces that were seriously disordered. The DE and URI promoted each other, and coupled coordination presented a benign tendency.

Second, the coordinated development of the DE coupled with URI in the eastern region was significantly higher than the national average, the central region was lower than the national average, and the western region was significantly lower than the central region. The CCD level in the southern and northern regions also had a certain degree of unbalanced development.

Third, the results of spatial correlation analysis showed that there was a high degree of spatial correlation in the CCD in China. Most provinces had fallen in the first and third quadrants, and there were significant positive spatial agglomeration characteristics. For a long time, eastern provinces were the dominant high-value agglomeration area, western provinces were the dominant low-value agglomeration area, and central provinces were the secondary ones. The differences and regional advantages among provinces were particularly obvious.

Fourth, the coefficients of the core explanatory variables, including economic development level, government support, technological innovation and industrial structure, all significantly promote the CCD, which is in line with the expectation. The improvement of economic development level creates favorable conditions for the development of the DE and URI and promotes the CCD of the two. Government expenditure on urban and rural community affairs did not have a “crowding out effect”. The government provides support for infrastructure construction and improvement as well as technological innovation and development, which improves the level of the DE and URI, resulting in the promotion of the CCD level. Technological innovation drives digital innovation, stimulates the endogenous driving force of urban and rural development, and promotes the CCD level. The optimization and upgrading of the industrial structure promotes the adjustment of inter-industry factors, creates new jobs, improves rural residents’ income and consumption, effectively narrows the urban–rural income gap, and promotes the CCD. In doing so, the coefficients of economic development level, government support, technological innovation and industrial structure all significantly promoted the CCD. Among them, economic development level, technological innovation and industrial structure have a significant positive promoting effect on the CCD level of neighboring provinces, while government support had a negative spatial spillover effect. The provinces were divided into two regions according to the level of industrial structure. For LISI provinces, economic development level, government support and industrial structure had a significant positive impact on the CCD, while technological innovation had no significant impact. In HISI provinces, economic development level, technological innovation and industrial structure had a significant positive effect on the CCD, while government support had no significant positive effect.

On the basis of the above research conclusions, the following suggestions are put forward.

First, urban and rural development should be integrated, and government support should be increased. Government departments should guide the organic integration of social capital and rural revitalization through the positive role of macro-control and micro-regulation. The integration mechanism between urban and rural areas should be constantly improved. In particular, the advantages of data resources in the construction of smart cities should be given full play, the construction of rural revitalization should be constantly empowered, and the high-quality development of URI should be promised. In recent years, Peking municipal government has proposed that the departments of finance, development and reform, science and technology, economy and information technology should make overall use of financial funds and various industrial funds to increase financial support for key core areas of the DE. These measures have helped achieve good coordination between the DE and URI. All regions should learn from its successful experience and combine rural revitalization and digitalization through policies to form synergy between urban and rural areas. Efforts should be made to optimize digital governance in the process of URI, so as to improve the level and ability of modern social governance, break down the institutional obstacles standing in the way of progress on URI and development and, finally, promote URI.

Second, it is necessary to improve the level of technological innovation and strengthen the training of digital talents. The eastern region is taking the lead in all aspects. Corresponding provinces should continue to increase financial support for the innovative development of research teams and encourage researchers to start their own businesses in rural areas. The state should increase the financial support for the innovation and development of high-tech enterprises and scientific research teams, so as to improve the internal core research and development power of enterprises. In the case of important research and development results, intellectual property rights should be fully protected, which can effectively stimulate the enthusiasm of scientific and technological innovation. Technological innovation gives rise to new digital technologies, which provide new opportunities for the rational allocation of urban and rural resources and continuously break the barriers between urban and rural areas, thus providing an impetus for the development of the CCD. The coupled and coordinated development of the DE and the integration of urban and rural areas requires the leadership of innovation, which relies on the support of talents and intelligence, so it is very important to train digital talents [82]. The northeast and western regions should fully mobilize the power of the government, universities and society to attract more talents for local employment, and they should increase the training of digital and agricultural talents. For the central region, it is necessary to give full play to the positive role of various talents in digital services and strengthen the training of farmers in digital literacy in order to promote the modernization of agriculture and other rural areas.

Third, promoting the upgrading of the industrial structure and adjusting the distribution of urban and rural industrial structure are necessary. While vigorously cultivating new economic forms, their integration with traditional industries should also be encouraged and supported. This will promote the transfer of secondary and tertiary industries to rural areas, thus promoting the integration of urban and rural industries. Promoting the adjustment of inter-industry elements and realizing the matching of resource elements between urban and rural areas can also promote the development of digital industrialization. Meanwhile, attention should be paid to the development of the tertiary industry, which can then optimize the layout of urban economic development, expand the space for urban development, and improve the high quality of urban production efficiency. The development of the tertiary industry will make cities more inclusive to the migrant agricultural population, raise residents’ income and consumption level, and effectively reduce the income gap between urban and rural areas. Efforts to improve the rational level of industrial structure layout between urban and rural areas can build a picture of healthy and rapid development of the CCD. For the western region, with a low CCD level, it is necessary to guide the excess capital of big cities to actively flow into the countryside by means of “investment attraction and industrial upgrading”, so as to improve the efficiency of capital allocation and realize the real high-quality development of the DE and URI system.

Forth, mutual learning among different regions is encouraged to promote common development among them. There are some differences in the CCD level, growth rate, driving factor and size of the DE and URI between various regions in China. Such differences in space provide practical conditions for interactive complementarity between regions. As for the eastern region, with its relatively sound infrastructure, it should continue to maintain its absolute advantage and continuously strengthen the construction of its “digital brain”, so as to give full play to the driving role of cities in rural areas and further enhance the CCD level. For the central region, the construction of a new infrastructure should be accelerated to release the development dividends of the DE by promoting the mutual flow and equal exchange of data elements between urban and rural areas and then promoting the CCD. For the backward regions in western China, efforts should be made to improve the level of economic development and give full play to the financial role of the government, so as to provide support for the improvement of the CCD. All regions can learn from each other’s experience and advantages, strengthen their strongest attributes and make up for their weaknesses, strive to turn their comparative and latecomer advantages into competitive advantages, stick to reform and innovation and overcome difficulties. By focusing on advanced areas and the development frontier, and by allowing the dominant provinces to drive the latecomer provinces, the regional differences can be eliminated, and the CCD of regional the DE and URI can be promoted.

7. Summary and Prospects

The world has entered the era of the DE, and the DE has become the main driving force for the development of URI. Meanwhile, URI also provides a guarantee for the sustainable development of the DE. The CCD between the DE and URI is increasing year on year, showing a positive spatial agglomeration characteristic, with obvious regional differences. The level of economic development, government support, technological innovation and industrial structure contribute to the promotion of the CCD.

The current study not only enriched the relevant research content, but it also has theoretical value and practical significance for the DE and URI development in other countries.

First, the current study provides new ideas for the research of the DE and URI in other countries and regions. At present, international scholars’ research on the DE and URI mainly focuses on a single object, and pays little attention to the CCD relationship between the two. The DE and URI are two closely linked systems, which interact and benefit each other. To only emphasize one or another is not comprehensive enough, which sacrifices the chance of sustainable development. By studying the CCD, the characteristics of spatio-temporal evolution, influencing factors and regional differences, references for related research can be provided, and the research content is enriched in the world.

Second, it provides policy suggestions for other countries’ research on the DE and URI. Through this study, it was found that the factors affecting the CCD include the economic development level, government support, technological innovation and industrial structure. Therefore, when improving the CCD, it is necessary to focus on implementing relevant policies and give full play to the amplification role of corresponding policies. These research results can provide a policy reference for various countries around the world, which benefits the promotion of the CCD all over the world.

The current study focused on the analysis of the CCD and its influencing factors. The research was of great significance, but there were some limitations. First, although the index of multi-dimensional DE and URI was respectively constructed, due to the limitation of data availability, the index selection was still not comprehensive. In the future, the evaluation index system of the DE and URI should be supplemented, especially improve the index of the DE from the perspective of digital governance and other factors. Second, since digitalization is an emerging technology, and the time span of data related to digitalization is relatively short, the current study selects data from 2011 to 2020 for analysis, but there may be a problem of weak timeliness. In the future, we will further update the data and expand the data time span to enhance the timeliness of the research results. Third, the CCD may also be affected by other factors, and the scope needs to be further expanded. In the future, other influencing factors still need to be analyzed, so that more corresponding policies and measures can be put forward to facilitate the development of the CCD between the DE and URI.