Geographical Patterns and Influencing Mechanisms of Digital Rural Development Level at the County Scale in China

Abstract

Digital rural development has become an emerging dynamic force for high-quality rural development in China. This paper constructs the “environmental-economic-social” analysis framework for digital rural development, analyzes the spatial variation of the digital rural development level (DRDL) in Chinese counties in 2020, and conducts the factor detection and interaction detection of its influencing factors. It is found that: (1) digital rural development has its own unique spatial differentiation mechanism, which can be analyzed from three dimensions: environmental system, economic system, and social system, which play a fundamental role, decisive role, and a magnifying effect on digital rural development, respectively. (2) The DRDL in China’s counties has significant spatial distribution, spatial correlation, and spatial clustering characteristics. The DRDL in general shows a decreasing distribution trend from coastal to inland regions, and the overall differences in DRDL mainly come from intra-regional differences rather than inter-regional differences. The rural infrastructure digitalization dimension has stronger spatial correlation while the spatial correlation of the rural governance digitalization dimension is weaker. There are obvious hotspot and coldspot areas in the DRDL, with large differences between the coldspot and hotspot areas of different sub-dimensions. (3) The spatial divergence of the DRDL is closely related to geographical elements and is the result of the combined effect of several geographical factors. The factor detection results show that the dominant factors within the four regions are significant different. The interaction detection results show that the driving force of the two-factor interaction is stronger than that of the single-factor interaction and that the interaction among the factors further deepens the spatial differentiation of the DRDL.

1. Introduction

With the continuous advancement of China’s agricultural and rural informatization process, digital rural construction has become an important grasp of China’s rural development. The application of digital technology in rural areas has greatly improved the digitalization level of rural regions and plays an important role in promoting rural transformation, implementing rural revitalization, and promoting urban–rural integration.

On the one hand, the development of the digital economy provides a power source for digital rural construction. With the rapid development of science and technology and the Internet, the digital economy has become an important engine to drive economic growth and an emerging way to drive industrial transformation and upgrading [1,2], which is an important grasp to promote high-quality economic development and common prosperity in China [3,4]. At the same time, along with the industrial upgrading and modernization transformation of the rural, the pace of integration between the digital economy and the rural economy and society has also accelerated significantly [1]. Some scholars point out that the integration of the digital economy and rural economy can promote the upgrading of agriculture, rural progress, and farmers’ incomes in many aspects [5,6], which becomes an effective path to promote digital rural construction. In 2018, Document No. 1 of the Central Committee of the Communist Party of China first proposed “implementation of the digital rural strategy”, which clarifies the general requirements for digital rural construction at the national level. In 2019, the General Office of the CPC Central Committee and the General Office of the State Council officially issued the Outline of Digital Rural Development Strategy (ODRDS), pointing out that digital rural development is both the process of the development and transformation of agricultural and rural modernization endogenous to the application of networking, informatization, and digitization in agricultural and rural economic and social development as well as the improvement of farmers’ modern information skills [7]. The ODRDS takes digital rural construction as the strategic direction of rural revitalization and the construction of digital China. The ODRDS also puts forward specific construction tasks in terms of developing the rural digital economy, promoting the modernization of the rural governance capacity, and coordinating and promoting the integrated development of urban and rural informatization, which is a guiding platform for promoting the digital rural development of China. In 2020, the Cyberspace Administration of China and seven other departments jointly issued the Notice on the National Rural Digital Pilot Work, officially starting the deployment of a national digital rural pilot. Issued in 2021, the Digital Rural Construction Guide 1.0 puts forward the general architecture design and typical application scenarios of digital rural construction, providing important references for local conditions and classification to explore digital rural development modes. Issued in January 2022, the Digital Rural Development Action Plan (2022–2025) clarifies the new stage of digital rural development goals, key tasks, and guarantee measures, promoting digital rural construction to a new stage [8].

On the other hand, the rural revitalization strategy provides new opportunities for digital rural construction. (2017) The 19th National Congress of the Communist Party of China put forward the rural revitalization strategy. It specifies the general requirements for prosperous industry, ecological livability, civilized rural effective governance, and affluent living. Among these, prosperous industry is the cornerstone of rural revitalization, ecological livability is the guarantee to improve the quality of rural development, civilized rural development is the soul of rural construction, effective governance is the core of good governance in rural areas, and affluent living is the goal of rural revitalization. Digital rural development provides powerful power and advanced means for the implementation of a rural revitalization strategy, is an important tool for implementing the general requirements of rural revitalization [9], and plays an active role in promoting rural revitalization. At the same time, the new generation of information and communication technology plays an important role in promoting regional economic growth and digital transformation. Information technology has become a new engine for rural development. This can help activate rural labor, land, capital, and other development elements, driving technology flow, capital flow, talent flow, and material flow to rural areas with information flow and enhancing the digital production capacities and governance abilities of rural areas. In addition, the improvement of the rural digitalization level can help promote the optimization of the allocation of resources in rural areas and the improvement of the total factor productivity of rural regions, which can effectively make up for the shortcomings of rural development and boost the development of the rural economy, society, culture, ecology, and other fields [9]. Thus, building digital rural is an important path to narrow the regional differences in rural development and an important measure to weaken the digital divide between urban and rural areas [10,11,12]. It has a positive effect on promoting the implementation of a rural revitalization strategy, promoting the construction of new urbanization, and coordinating regional coordinated development and urban–rural coordinated development.

Digital technology brings new opportunities for rural development [13,14,15,16]. Building digital rural is an urgent need to realize comprehensive rural revitalization and an effective way to promote the integrated development of urban and rural areas [17], and it is also an important means to narrow regional disparities and promote common development in the east, middle region, and west [18]. The experience of digital rural development in Europe and the United States is relatively mature. The digitalization of rural areas and the impact it brings to rural development has been widely explored. Relevant research has focused on the digital divide in the rural regions [19,20,21,22,23], the resilience of the digital rural [15,24,25], digital rural policy [26], digital economic development in the rural regions [27,28,29], and digital public services in the rural regions [30,31,32], etc. For example, Park (2017) points out that socio-demographic factors such as education level and employment status exacerbate the digital divide in the rural regions [33], and Salemink (2017) proposes that the development of the rural regions in the digital era should fully consider its connectivity and inclusiveness [34], which has made a positive contribution to the advancement of research on the digital rural. For China, studies on the digital rural have mostly been based on digital economy development and rural revitalization strategy, and they started relatively late. At present, most of the studies focus on the level measurement of digital rural development [35,36,37], digital rural governance [38,39,40], digital rural construction [8,17,41], digital rural public service [42], and the digital rural development model [43,44,45]. Related research shows that rural infrastructure construction and industrial development can help narrow the urban–rural digital divide [35,37] and boost the digital transformation of the rural regions to realize the modernization and intelligent development of the rural regions [46,47].

Through the literature, we can find that most of the existing studies have explored the theoretical and practical research on the digital rural from the perspectives of political science, economics, management, and other disciplines, but not enough attention has been paid to the research topic of digital rural development from the perspective of geography. The geographical pattern and spatial differentiation of digital rural development have not been clarified, and the influence factors and the strength of their spatial differentiation also need to be explored. In addition, the county scale is the basic unit of integrated urban–rural development [48], and it is also a suitable research scale for new urbanization and rural revitalization strategies [49], but few studies have paid attention to the issues related to digital rural development at this scale. Therefore, it is important to explore the digital rural development status at the county scale in order to effectively promote the rural revitalization strategy and dovetail with the development of county urbanization. Based on this, this paper explores the geographical pattern and spatial variation of digital rural development level (DRDL) at the county scale in China from the perspective of geography and probes and analyzes the influencing factors of their spatial divergence. In this paper, we aim to grasp the regional differences and variation characteristics of digital rural development in China and to provide some reference for the construction and development of the digital rural.

2. Theoretical Foundation and Analytical Framework

2.1. Rural Digital Transformation and Rural Regional System

As digital technology continues to penetrate into all aspects of rural production and living, a series of reconfigurations have taken place in the rural regions [50,51,52], focusing on spatial, economic, and social aspects [53,54,55], and concentrating on the digital transformation of the rural regions [56]. At the same time, rural digital empowerment provides new dynamic energy for rural society development [10], which promotes the digital revolution of the rural regions and triggers changes in production, living, and ecological and social governance in rural areas. As they constitute a complex regional system of human–land relations, the digital transformation of the rural regions is gradually changing the human–land relations in the rural regions, making the rural human–land relations emerge some new characteristics, which are concentrated in society, economy, and natural environment, and the three are interrelated and influence each other.

Integrity and regionalism are geography research characteristics [57,58,59], and understanding and analyzing the digital rural’s heterogeneity from the perspective of geography is helpful to systematically understand the mechanism of interaction between the digital rural and geographical environment. The regional system of human–land relationship is the core of geography research [60]. As a significant sub-discipline of geography, rural geography’s research core is the regional system of rural human–land relations [61]. The rural regional system in the context of digitalization is a complex system with certain functions and structures that is composed of the interaction of geographical location, ecological environment, resource endowment, economic development, policy system, public facilities, and other elements in a specific rural area (Figure 1). ① The environment system, which is composed of “land” as the core element, characterizes the influence of location conditions, topography, altitude difference, and other factors on digital rural development and reflects the relationship between digital rural development and the natural geographical environment. ② The economic system, with “industry” as the core element, portrays the role of economic development, the industrial base, and industry structure in digital rural development, and reflects the relationship between digital rural development and regional economic development. ③ The social system, with “human” as the core element, indicates the influence of the policy system, public services, and demographic characteristics on digital rural development and illustrates the relationship between digital rural development and policies/social services. Digital rural development is a concentrated expression of the coupling and coordination of the three core elements of “human”, “land”, and “industry” in the process of development and evolution of rural regional systems.

2.2. “Environmental-Economic-Social” Analysis Framework for Digital Rural Development

Digitization’s multidimensionality determines the complexity of digital rural development [62,63]. In terms of digital rural development and evolution, this complexity is manifested in the diversity of development elements. It is also manifested in the interactivity of action paths and the multidimensionality of digital rural representations. From the viewpoint of the elements of digital rural development, the geographical elements affecting digital rural development can be divided into two categories: natural geographical elements, including topography, climate, hydrology, biology, soil, etc., and human geographical elements, including population, location, transportation, industry, technology, capital, policy, social services, etc. [64]. From the viewpoint of the action path, digital rural development is not the result of the independent action of individual geographical elements within the rural regional system. Instead, it is the result of the joint action of multiple geographical elements between and within regions. Digital rural development representation includes three dimensions: the environmental, economic, and social dimensions (Figure 1).

(1) Digital rural development—the environmental system: The natural geographical environment of a specific region, which is innately present and inherent [65], is difficult to change and plays a fundamental role in digital rural development [66], and belongs to the first level. The influence of the environmental system on digital rural development is more stable and constant in the long run. This is mainly reflected in both the surface natural environmental conditions, such as topographic relief, surface steepness, and elevation difference, as well as the geographical location conditions in the rural regions. ① Topographic relief characterizes the general condition of a region’s terrain. The greater the topographic relief, the more difficult it is to build transportation and communication facilities and the more difficult it is to promote the free flow and sharing of factors in the region, which restricts the development and modernization of rural areas and plays an innate restrictive role. ② The steepness of the ground surface indicates the difference in elevation within a region. The more gentle the surface is, the more conducive to the construction and use of public service facilities, and the more it can promote the popularization of information and communication, the Internet, and other technical facilities in rural areas, promoting the modernization and informatization process in rural areas. ③ Average altitude characterizes the altitude of an area, which directly affects temperature, precipitation, and other natural geographical factors in a region. The higher the altitude, the worse the natural environment is in terms of development conditions compared to the same region, and the more unfavorable the construction of infrastructure and public services becomes, thus affecting the digital development of the rural in the region. ④ Geographical location is crucial to a region’s influence [67]. Areas far from economic, political, and cultural centers and transportation hubs have higher costs for the flow of goods, services, and various economic and social development factors between regions. These factors are less driven by the radiation of centers at all levels, and their development and driving effect on rural areas is even weaker.

(2) Digital rural development—the economic system: The economic system is the material basis and source of funds for digital rural development [68]. It plays a decisive role in digital rural development and belongs to the second level. The influence of economic system elements in the digital rural is mainly reflected in the general economic development level, industrial development base, agricultural modernization level, and service industry development level. ① In terms of overall economic development level, if the overall level of economic development of a region is better, the flow of economic and social development factors such as capital, technology, and information between urban and rural areas will be smoother, and high-quality development factors from urban areas such as networked, informatized, and digital development factors will flow into rural areas, which can have greater radiation-driven effect on rural areas. ② The industrial development base has a greater role in enhancing the economic growth capacity and digitalization level of a region. It will effectively promote the development of information technology, thus promoting the digital transformation of rural areas. ③ The use of agricultural science and technology and big data internet, etc. makes agricultural development gradually move from traditional agriculture to modern agriculture, and the use of new technology also makes agricultural production, management, and sales change, which facilitates the digital transformation of agricultural production in rural areas. ④ In addition, the rapid development of service industry, especially of transportation and communication and information networks, has led to the rapid development of service industry in rural areas, and a series of new rural service businesses such as rural tourism and rural e-commerce have flourished, greatly promoting the change of service industry in rural areas and enhancing the digitalization of rural areas.

(3) Digital rural development—the social system: The social system has an amplifying effect on digital rural development. It plays a crucial role in supporting and guaranteeing the sustainable and solid development of the digital rural [69], and belongs to the third level. The influence of the social system on digital rural development is mainly reflected in policy guidance, social services, and individual residents. ① In terms of policy guidance, the government’s guidance and support is a strong support for digital rural development. The government has strongly promoted digital rural construction by investing in digital infrastructure and placing resources such as information networks in rural areas. Reasonable and effective system design and institutional mechanism construction provide strong guarantees for digital rural development. ② In terms of social services, well-developed basic public services in rural regions play active roles in promoting digital rural construction. Digital rural development depends not only on the construction of digital infrastructure but also on the improvement of the quality of public services. For example, information and communication services provide strong guarantees for digital rural development, professional technical services provide professional technical talents for the digital transformation of rural regions, basic education services provide potential talent reserves for the continuous transformation and in-depth development of the digital rural, and the combined effect of social public services provides solid social security for the construction of digital rural. ③ In terms of individual residents, digital rural construction should adhere to the concept of people-oriented construction and development [17]. Improving rural residents’ information literacy and skills are important parts of digital rural construction, and thus the personal characteristics of rural residents also play significant subjective roles in digital rural development. The higher the income level is, the stronger the ability to purchase digital facilities is; at the same time, the quality of the population determines, to a great extent, the use of digital equipment and facilities, and the higher the education level is, the stronger the ability to learn and use new technologies is, and the more conducive to the digital transformation of rural areas [70].

3. Data and Methods

3.1. Data Source and Processing

The digital rural index data used in this paper comes from the County Digital Rural Index (2020), jointly published by the Institute for New Rural Development of Peking University and the Ali Research Institute [71]. The index system of the county digital rural index includes 4 primary indicators and dimensions—the rural infrastructure digitalization index, rural economy digitalization index, rural governance digitalization index, and rural living digitalization index—and 12 secondary indicators: information infrastructure index, digital financial infrastructure index, digital production index, governance means index, etc. There are also 33 specific indicators, such as the number of mobile devices per 10,000 people, the breadth of digital financial infrastructure coverage, the depth of digital financial infrastructure usage, etc. Due to the length of the paper, the detailed index system and its calculation are not described here. Please refer to reference [71] for details. The index fully considers the new digital phenomenon in rural development and builds a digital rural index system that is more suitable for “agriculture, rural areas and farmers (the three rural issues)” in four aspects—rural digital infrastructure, rural economy, rural governance, and rural living—that can comprehensively reflect the digital development level of rural areas today [71]. The study area comprises 2481 county-level administrative units, including 699 municipal districts, 328 county-level cities, and 1454 counties. Some county units are treated as having “no data” in the following section because of missing statistics.

By considering factors such as scientificity, representativeness, and accessibility, and avoiding overlap with the county digital rural index, this paper constructs an index system that is based on the “environmental-economic-social” analysis framework for digital rural development. This index system is based on the three dimensions of the environmental system, economic system, and social system. Fourteen indicators such as average elevation, per capita GDP, and per capita public budget expenditure are selected to characterize the influencing factors of the digital rural development level (DRDL), and to comprehensively build a system of indicators to identify the spatial variation of the DRDL in Chinese counties (

Table 1. Index system of factors influencing the spatial differentiation of county DRDL in China. Source: author’s compilation based on relevant literature.

System Dimension	Specific Indicators	Representational Meaning	Calculation Method	Properties
Environmental Systems	X1: Average elevation (m)	Average surface elevation	Extracting the average elevation value of the county	−
	X2: Average slope (°)	Steepness of the ground surface	Extracting the average slope value of the county	−
	X3: Terrain undulation (m)	Surface elevation difference	Extracting the topographic relief of the county	−
	X4: Distance from the capital city of the province to which it belongs (km)	Geographical distance	Distance from the county administrative center to the provincial capital administrative center	−
Economic Systems	X5: GDP per capita (RMB 10,000)	Overall economic level	GDP/number of resident population	+
	X6: Value added of tertiary industry as a proportion of GDP (%)	Service industry development level	Value added of tertiary industry/GDP	+
	X7: Number of industrial enterprises above the scale per capita	Industrial economic base	Number of industrial enterprises above the scale/number of resident population	+
	X8: Percentage of facility agriculture area (%)	Agricultural modernization level	Area of facility agriculture/total area of administrative region	+
Social Systems	X9: Public budget expenditure per capita (RMB 10,000)	Government support	Government public budget expenditure/number of resident population	+
	X10: Number of fixed telephone subscribers (10,000 families)	Communication service level	Number of fixed telephone subscribers	+
	X11: Number of students in primary and secondary schools (persons)	Human capital reserve	The sum of the number of students enrolled in general secondary schools and primary school	+
	X12: Number of IT service industry personnel (persons)	Professional and technical talents	Total number of information technology services and related employees	+
	X13: Savings deposit balance per capita (RMB 10,000)	Resident savings income	Household savings deposit balance/number of resident population	+
	X14: Average education years for the population (years)	Regional population quality	Average years of education of residents	+

). The environmental system is the negative indicator, economic system is the positive indicator, and all indicators are discrete. The original data of the indicators are listed in Table 1; the base year is 2020. DEM data have been obtained from the geospatial data cloud (http://www.gscloud.cn/ (accessed on 30 May 2022)), the average elevation, average slope, and topographic relief of each county and city have been extracted through slope and neighborhood analysis, and the distance to the capital city of the province to which they belong has been obtained by calculating the distance from the administrative center of the county to the administrative center of the capital city. The rest of the socio-economic data have been obtained from the 2021 China Statistical Yearbook (County-level), Tabulation On 2020 China Population Census By County, and the statistical yearbooks and national economic and social development statistical bulletins of counties and cities of China; a few missing values have been supplemented by interpolation.

3.2. Research Methodology

3.2.1. Thiel Index and Its Decomposition

The Thiel index is an important indicator of income disparity between individuals or regions. In this paper, we use the Thiel index to analyze the differences between the DRDLs within and between regions in China and measure overall national differences, inter-regional differences, intra-regional differences, and related contribution rates. The specific formula is as follows [35]:

$$T=\frac{1}{k}{\displaystyle \sum _{q=1}^k(\frac{S_q}{\overline{S}}}\times \ln \frac{S_q}{\overline{S}})$$

(1)

$$T_p=\frac{1}{k}{\displaystyle \sum _{q=1}^{k_p}(\frac{S_{pq}}{{\overline{S}}_p}}\times \ln \frac{S_{pq}}{{\overline{S}}_p})$$

(2)

$$T=T_w+T_b={\displaystyle \sum _{p=1}^4(\frac{k_p}{k}}\times \frac{{\overline{S}}_p}{\overline{S}}\times T_p)+{\displaystyle \sum _{p=1}^4(\frac{k_p}{k}}\times \frac{{\overline{S}}_p}{\overline{S}}\times \ln \frac{{\overline{S}}_p}{\overline{S}})$$

(3)

In Equation (1), T denotes the Thiel index of the overall differences between the DRDLs, and its size is in [0, 1]; a smaller T indicates a smaller overall difference in the DRDLs, and the opposite indicates a larger overall difference. q denotes the county, k denotes the number of counties, S_q denotes the DRDL of county q, and S denotes the average of the national DRDLs. In Equation (2), T_p indicates the overall difference Thiel index of region p, k_p indicates the number of counties in region p, S_pq indicates the DRDL of county q in region p, and ${\overline{S}}_p$ indicates the average of the DRDL in region p. In Equation (3), the overall differences Thiel index of the DRDL is further decomposed into intra-regional differences Thiel index T_w and inter-regional differences Thiel index T_b. In addition, T_w/T and T_b/T are defined as the contribution of intra-regional differences and inter-regional differences to the overall differences, respectively, (S_p/S) × (T_p/T) is the contribution of each region to the overall differences within the region, S_p denotes the sum of the DRDL of each county in region p, and S denotes the sum of national DRDL.

3.2.2. Spatial Autocorrelation Analysis

Spatial autocorrelation analysis is mainly applied to analyze spatial data interdependence, including two parts: global spatial autocorrelation and local spatial autocorrelation [72]. Moran’s I is used to measure the global spatial association characteristics of the research object. Local spatial autocorrelation is mainly used to measure the local spatial association characteristics of the research object, expressed by the local Getis-Ord-Gi* index in this paper [73], which is used to identify the spatial distribution location of the similar clustering areas of the DRDL and classify them into coldspots and hotspots so as to facilitate the observation of the spatial difference degree of the DRDL between the studied county and the surrounding counties. The formula is shown below:

$$Moran’s^{ }I=\frac{n{\displaystyle {\sum }_i^n{\displaystyle {\sum }_j^n{w}_{ij}(y_i-\overline{y})(y_j-\overline{y})}}}{{\displaystyle {\sum }_i^n{\displaystyle {\sum }_j^n{w}_{ij}{\displaystyle {\sum }_i^n{(y_j-\overline{y})}^2}}}}$$

(4)

$$G_i=\frac{{\displaystyle \sum _{j=1}^n{w}_{ij}{x}_j}}{{\displaystyle \sum _{j=1}^n{x}_j}}\begin{array}{cc}& (j\ne i)\end{array}$$

(5)

In Equation (4), n is the total number of spatial units in the study area, Y_i and Y_j indicate the DRDL in spatial units i and j, $\overline{y}$ is the mean value of the DRDL, W_ij is the spatial weight matrix, and Z_i and Z_j are the normalized values of the observed values in spatial units i and j, respectively. In Equation (5), w_ij is the spatial weight, and Σ_j = lnw_ij = 1. If the G_i value is significantly positive, it indicates a high value agglomeration area around region i. If the G_i value is significantly negative, it indicates a low value agglomeration area around region i.

3.2.3. Geodetector Model

The Geodetector method is an innovative statistical method for detecting spatial heterogeneity and revealing the driving factors behind it. It includes four detectors: heterogeneity and factor detection, interaction detection, risk detection, and ecological detection [74]. In this paper, we draw on the factor detection and interaction detection functions in the Geodetector model. We explore the factors influencing the spatial differentiation of the DRDL and their interactions. The formula is as follows:

$$q=1-\frac{{\displaystyle \sum _{h=1}^L{N}_h{\sigma }_h{}^2}}{N{\sigma }^2}=1-\frac{SSW}{SST}\begin{array}{cc}& \\ & \end{array}(h=1,2,\cdot \cdot \cdot ,L)$$

(6)

In Equation (6), h is the stratification of variable Y or factor X; N_h and N are the number of units in square h and the whole region, respectively; σ_h² and σ² are the variances of Y values in square h and the whole region, respectively; SSW and SST are the Within Sum of Squares and the Total Sum of Squares, respectively; the value range of q is [0, 1], and a larger value of q indicates that the spatial heterogeneity of Y is more obvious. Interaction detection was also used to identify interactions between different levels of determinants. A comparison of q(X1), q(X2), and q(X1∩X2) was used to determine whether the deterministic effects of any two indicators of X1 and X2 on the DRDL were independent or whether they increased or decreased the explanatory power when acting together, and five types were classified according to the comparison [74] (

Table 2. Expressions for interaction detection. Source: reference [74].

Expressions	Type of Action
q(X1∩X2) < Min(q(X1), q(X2))	Non-linear weakening
min(q(X1), q(X2)) < q(X1∩X2) < max(q(X1), q(X2))	Single-factor non-linear attenuation
q(X1∩X2) = q(X1) + q(X2)	The two factors are independent of each other
q(X1∩X2) > Max(q(X1), q(X2))	Two-factor enhancement
q(X1∩X2) > q(X1) + q(X2)	Non-linear enhancement

).

4. Spatial Divergence Characteristics of the DRDL in China’s Counties

4.1. Overall Distribution Characteristics

With the help of the ArcGIS 10.2 software, the county DRDL data was divided into five categories according to the quantile classification method for visual expression (Figure 2), and the blue→red legends in the figure indicate the development stages of the low level, lower level, medium level, higher level, and high level respectively. According to Figure 2, the DRDL in general shows a decreasing distribution trend from the coast to the inland, showing a spatial distribution pattern of “high in the east and low in the west”, and the middle- and high-level areas are mainly distributed in the area east of the Hu Huanyong population line (Hu line), but the distribution pattern of development levels in different dimensions reveals certain differences.

Specifically, in terms of the overall DRDL, the high-level and higher-level areas are mainly concentrated on the east side of Hu line, distributed in “clusters” in Hebei, Shandong, Zhejiang, Fujian, and other provinces, while the low-level and lower-level areas are primarily concentrated in the vast western region and northeastern region. In terms of rural infrastructure digitalization dimension, the high-level areas are also mainly located on the east side of the Hu line, such as Zhejiang, Anhui, Henan, and other provinces. However, it is worth noting that there are still more areas in the western region with high and higher levels of distribution, such as central, northern, and southeastern Tibet and northeastern Xinjiang. In the rural economy digitalization dimension, the “Hu line effect” is more obvious, with areas of medium level and above being mainly distributed to the east of the Hu line, while areas of higher level to the west of the Hu line are only “dotted” and most of them are low-level areas. In the rural governance digitalization dimension, besides Shandong, Zhejiang, and Hubei to the east of the Hu line, Ningxia, and Inner Mongolia to the west of the Hu line also show cluster-like distributions of high- and higher-level areas. In the rural living digitalization dimension, high-level areas are concentrated in Jiangxi, Fujian, Zhejiang, Shandong, and other provinces; in addition, the northern part of the northeast region and some parts of eastern Xinjiang also show cluster-like distributions.

4.2. Characteristics of Regional Differences

Based on the Thiel index, the characteristics of regional differences in DRDL in Chinese counties were carved (

Table 3. Thiel index and contribution rate of county DRDL in four major regions of China. Source: authors; values were derived by calculating the Thiel index.

Types	Thiel Index	Inter-Regional Differences and Contribution Rate (%)	Intra-Regional Differences and Contribution Rate (%)
			Overall China	Eastern Region	Central Region	Western Region	Northeast Region
Digital rural index	0.0315	0.0122 (38.58%)	0.0194 (61.42%)	0.0206 (19.33%)	0.0103 (8.96%)	0.0260 (28.70%)	0.0170 (4.44%)
Rural infrastructure digitalization index	0.0252	0.0078 (30.84%)	0.0175 (69.16%)	0.0088 (9.60%)	0.0070 (7.67%)	0.0323 (46.85%)	0.0160 (5.03%)
Rural economy digitalization index	0.068	0.0204 (30.03%)	0.0476 (69.97%)	0.0621 (29.17%)	0.0306 (12.01%)	0.0526 (25.22%)	0.0280 (3.57%)
Rural governance digitalization index	0.0849	0.0082 (9.70%)	0.0766 (90.29%)	0.0433 (14.74%)	0.0567 (17.73%)	0.1134 (48.39%)	0.0959 (9.42%)
Rural living digitalization index	0.0625	0.0152 (24.35%)	0.0473 (75.65%)	0.0551 (26.19%)	0.0376 (16.96%)	0.0458 (25.07%)	0.0599 (7.45%)

Note: The eastern region includes Hebei, Beijing, Tianjin, Shandong, Jiangsu, Shanghai, Zhejiang, Fujian, Guangdong, and Hainan; the central region includes Shanxi, Henan, Hubei, Hunan, Anhui, and Jiangxi; the western region includes Inner Mongolia, Xinjiang, Tibet, Shaanxi, Ningxia, Gansu, Qinghai, Yunnan, Guizhou, Sichuan, Chongqing, and Guangxi; the northeast region includes Heilongjiang, Jilin, and Liaoning.

). In terms of overall differences, the Thiel index of the digital rural overall level was 0.0315, which was relatively small compared with the rural economy digitalization dimension, rural governance digitalization dimension, and rural living digitalization dimension (Table 3). Among these, the Thiel index of the rural economy digitalization level, the rural governance digitalization level, and the rural living digitalization level were all higher than 0.6, indicating that the regional differences of these three types of index are larger and the regional development imbalances are more prominent. In contrast, the Thiel index of the rural digital infrastructure dimension was the smallest (0.0252), indicating that the regional differences in the development level of rural digital infrastructure are relatively small and that the level of regional development is more balanced.

To explore the intra- and extra-regional differences in DRDL, the overall differences Thiel index is further decomposed into the intra-regional differences Thiel index and inter-regional differences Thiel index. From the decomposition results, the contribution rates of intra-regional differences in DRDL and its four sub-dimensions are all greater than 50%, i.e., the contribution rate of intra-regional differences is greater than that of inter-regional differences, indicating that the overall differences in DRDL in China’s countries predominantly originate from intra-regional differences. From the overall level of the DRDL and its sub-dimensions, the highest contribution rates of intra-regional differences are mainly in the western and eastern regions while the contribution rates of intra-regional differences in the central and northeastern regions are relatively low, indicating that the intra-regional differences in DRDL in China are mainly in the western and eastern regions.

Specifically, in terms of the overall DRDL, intra-regional differences are mainly concentrated in the western and eastern regions, with relatively small differences within the northeastern and central regions, indicating that there are significant regional differences in the overall DRDL while large differences exist within the western and eastern regions. In the rural infrastructure digitalization dimension, the main difference originates from the western region, whose contribution rate is as high as 46.85%, indicating that there are large differences in the construction of rural digital infrastructure in the western region, while development within the eastern, central, and northeastern regions is relatively balanced. In the rural economy digitalization dimension, the higher contribution rate of intra-regional differences is the eastern and western regions and the sum of their contribution rates is more than 50%, indicating that there are large differences in rural economy digitalization development levels in the eastern and western regions. In the rural governance digitalization dimension, the contribution rate of intra-regional differences is as high as 90.29%, indicating that most of the regional differences in the rural governance digitalization level in China’s counties are caused by intra-regional differences, among which intra-regional differences in the western region dominate (with a contribution rate of 48.39%). In the rural living digitalization dimension, intra-regional differences in the eastern region (contribution rate 26.19%) and intra-regional differences in the western region (contribution rate 25.07%) are relatively similar, and both are the main sources of intra-regional differences, mainly due to uneven development among the internal counties.

4.3. Spatial Correlation and Spatial Clustering Characteristics

The global spatial correlation characteristics of DRDL in Chinese counties were measured with the help of the Moran Index (Moran’s I) and their spatial correlation was analyzed (

Table 4. Global Moran index of county DRDL in China. Source: authors; values were derived by calculating the Moran index.

Category	Moran’s I	Z-Value	p-Value
Digital rural index	0.472	4.238	0.000
Rural infrastructure digitalization index	0.475	4.261	0.000
Rural economy digitalization index	0.435	3.910	0.000
Rural governance digitalization index	0.321	2.885	0.004
Rural living digitalization index	0.358	3.216	0.001

). According to the results in Table 4, the Moran’s I values of the DRDL and its sub-dimensions are all greater than 0, and the p-values all pass the significance test, showing strong spatial correlation. There are some differences in the spatial correlation of different dimensions, and the Moran’s I values show that the rural infrastructure digitalization level > total level of the DRDL > rural economy digitization level > rural living digitization level > rural governance digitization level, indicating that the spatial correlation nature of the rural infrastructure digitalization level has a stronger spatial correlation compared with other sub-dimensions while the rural governance digitalization level has a weaker spatial correlation.

According to the Moran’s I results, there is a strong spatial correlation between the DRDLs of Chinese counties. However, the specific correlation areas and clustering areas are unclear and need to be analyzed in more depth. Thus, with the help of the coldspot and hotspot analysis tool, we analyzed the DRDL of Chinese counties and identified the coldspot and hotspot distribution areas of the total DRDL and each of its dimensions so as to better understand the spatial clustering characteristics of DRDL (Figure 3). Through Figure 3, it can be found that there are obvious hotspot areas and coldspot areas in the DRDL and that there are differences in the coldspot and hotspot areas in different dimensions.

Specifically, in terms of the total level of DRDL, the hotspot areas are mainly distributed in the vast eastern region, with a gradual transition from the east to the west, and the hotspot significant areas are concentrated in the eastern provinces of Hebei, Shandong, Henan, Jiangsu, Zhejiang, Jiangxi, Fujian, and Guangdong; the insignificant areas are mostly concentrated in the central region, and also include parts of Xinjiang; the coldspot areas are mainly concentrated in both the vast western region as well as the northeast region. Regarding the rural infrastructure digitalization dimension, its hotspot areas are similar to the hotspot areas of the total level of the DRDL, being mainly distributed in the eastern provinces, but the coldspot areas are smaller in scope, being mainly concentrated in the northeast region, Yunnan, Qinghai, western Sichuan, western Xinjiang, etc. Regarding the rural economy digitalization dimension, the hotspot areas are distributed in a “piece” shape in Hebei, Shandong, Jiangsu, Zhejiang, Fujian, and other provinces, while the coldspot areas are concentrated in the vast western region, and the transition area between the coldspot areas and the hotspot areas is not significant. Regarding the rural governance digitalization dimension, the hotspot areas are mostly concentrated in the areas north of the Yangtze River, including Hebei, Shandong, Jiangsu, Henan, Hubei, and other provinces, and the areas south of the Yangtze River are mainly distributed in Zhejiang, southern Guangdong, and eastern Guangxi; the coldspot areas are mainly distributed in the vast southwestern region, while there are also local coldspot areas distributed in the northern and southern parts of northeast China and southern Hunan. Regarding the rural living digitalization dimension, the hotspot areas are still mainly concentrated in the eastern provinces, the coldspot areas are mainly distributed in the southwest region, northeast region, Shaanxi, and Gansu, and the rest of the areas are contiguous transition areas.

5. Detection of Influence Factors in the Spatial Divergence of County DRDL in China

5.1. Factor Detection and Dominant Factor Analysis

By geographically detecting the influencing factors for the spatial divergence of DRDL, we found that the intensities of the effects of different influencing factors on different scale spaces varied, showing certain scale differences and spatial differentiation characteristics, and so, they need to be discussed separately (

Table 5. Factor detection results of geographical differentiation of county DRDL in China. Source: obtained by the authors using the Geodetector software.

	Overall China		Eastern Region		Central Region		Western Region		Northeast Region
	q-Value	Contribution Rate	q-Value	Contribution Rate	q-Value	Contribution Rate	q-Value	Contribution Rate	q-Value	Contribution Rate
X1	0.3127	12.85%	0.0719	6.66%	0.1125	12.49%	0.2624	10.32%	0.1853	13.34%
X2	0.0956	3.93%	0.0230	2.13%	0.0626	6.95%	0.0514	2.02%	0.0136	0.98%
X3	0.2651	10.90%	0.0279	2.59%	0.0969	10.76%	0.2586	10.17%	0.1673	12.04%
X4	0.0362	1.49%	0.0151	1.40%	0.0634	7.04%	0.0897	3.53%	0.0018	0.13%
X5	0.0779	3.20%	0.0524	4.86%	0.0438	4.86%	0.0593	2.33%	0.0797	5.74%
X6	0.0198	0.81%	0.0285	2.64%	0.0130	1.44%	0.0113	0.44%	0.0574	4.13%
X7	0.2264	9.31%	0.1752	16.24%	0.0230	2.55%	0.1363	5.36%	0.1130	8.13%
X8	0.1269	5.22%	0.0704	6.52%	0.0312	3.46%	0.1117	4.39%	0.1592	11.46%
X9	0.2272	9.34%	0.0297	2.75%	0.0549	6.10%	0.3334	13.11%	0.0607	4.37%
X10	0.1869	7.68%	0.1652	15.31%	0.1121	12.45%	0.2371	9.32%	0.1020	7.34%
X11	0.2291	9.42%	0.1084	10.05%	0.0854	9.48%	0.2096	8.24%	0.1025	7.38%
X12	0.2948	12.12%	0.1854	17.18%	0.1265	14.05%	0.3407	13.39%	0.1753	12.62%
X13	0.1778	7.31%	0.0809	7.50%	0.0256	2.84%	0.2554	10.04%	0.0815	5.87%
X14	0.1563	6.42%	0.0451	4.18%	0.0497	5.52%	0.1869	7.35%	0.0900	6.48%

).

5.1.1. The National Scale

On a national scale, the factors influencing the spatial differentiation of DRDL in China’s counties vary significantly. The intensities of effects presented by different factors vary greatly. Five factors—average elevation (X1), employees in the information service industry (X12), topographic relief (X3), number of students in primary and secondary schools (X11), and per capita public budget expenditure (X9)—have an explanatory power contribution of 54.63%, and are the main influencing factors of regional differences in DRDL.

Among them, in terms of natural environment, average altitude (X1) has the strongest effect, with a q-value of 0.3127, and terrain undulation (X3) also has a strong effect, with a q-value of 0.2651, indicating that on a national scale, spatial variation in DRDL is more restricted by natural factors and that the higher the average altitude is, and the greater the terrain undulation, the lower the DRDL will be. This is mainly because digital rural development requires certain digital infrastructure. However, with the increase of altitude and undulation, the construction of digital facilities such as information communication and mobile network becomes more difficult and the construction cost increases gradually, both of which make the construction of digital rural geographically restricted and further restrict the improvement of rural economy digitalization, living digitalization, and governance digitalization, and thus, to a greater extent, cause regional differences in DRDL.

Meanwhile, in terms of social environment, IT service industry practitioners (X12) and the number of school students in primary and secondary schools (X11) also influence digital rural construction to a greater extent. Digital-related professional and technical talents can provide the necessary human support and intellectual guarantee for digital rural construction. A certain number of school students provide a talent reserve for the cultivation of professional and technical talents, and are the reserve force of talent for digital rural construction. Therefore, specialized technical personnel and their reserve force can promote digital rural development to a greater extent, resulting in regional differences in DRDL. In addition, the influence of per capita public budget expenditure (X9) on the DRDL is also high (with q-value of 0.2272 and contribution of 9.34%). The DRDL is closely related to digital infrastructure investment, which is mainly financed by the government’s public financial expenditure. The more public financial expenditure is made, the more sufficient funds are available for digital rural construction, and the more conducive the situation to the improvement of the DRDL, thus resulting in the differences in its distribution patterns.

Compared with the above factors, the explanatory power of factors such as the proportion of the value added by tertiary industry to GDP (X6) and the distance from the capital city of the province to the place which it belongs (X4) is lower, probably because due to the development of digitalization and networking in the rural regions, digital technology has broken through the constraints of geographical space to a certain extent, and the role of geographical distance for digital rural development has been relatively weakened. At the same time, with the gradual promotion of industrial transformation and development in various places, the proportion of service industry output value in each region has gradually increased. Its effect on digital rural development has been relatively weakened.

5.1.2. Regional Scale

In the eastern region, the sum of the explanatory power of four factors, namely, the number of personnel in IT service industry (X12), the number of industrial enterprises above the scale per capita (X7), the number of fixed telephone subscribers (X10), and the number of students in primary and secondary schools (X11), reaches 58.77%, which is the main influencing factor for the regional variation of the DRDL in the eastern region. Compared with the overall situation in China, the number of IT service industry personnel plays a stronger role in the regional variation of the DRDL in the eastern region, and its influence contribution reaches 17.18%, indicating that professional and technical talents play an important role in the construction and development of the digital rural in the eastern region. The total number of information technology service personnel in eastern regions such as Zhejiang and Jiangsu is leading in China, and professional and technical talent can provide solid human support for digital rural construction and promote digital rural development, making the DRDLs in eastern coastal regions such as Zhejiang and Jiangsu relatively high. At the same time, the number of industrial enterprises above the scale per capita, as the basis of industrial economic development, also lays the economic foundation for digital rural development. Together with the developed communication services, this makes the rural infrastructure digitalization level, the rural economy digitalization level, and the rural governance digitalization level in the eastern region stay at a high level, making it a hotspot area of DRDL in China.

For the central region, the explanatory power of five factors on the DRDL reaches 59.23%, and these factors include the number of IT service industry personnel (X12), the average elevation (X1), the number of fixed-line telephone subscribers (X10), the topographic relief (X3), and the number of primary and secondary school students (X11). Similar to the eastern region, factors such as professional and technical personnel, communication service level, and human resource reserves also have strong explanatory power for the DRDL in the central region. However, unlike the eastern region, the two natural environmental factors of average elevation and terrain undulation are more prominent, mainly because some provinces in the central region straddle the second and third steps (e.g., Shanxi, Henan, Hubei, and Hunan provinces) and the differences in average elevation and terrain undulation within the provinces are relatively large, imposing different degrees of constraints on digital rural construction and thus affecting the development of the digital rural, resulting in spatial differences between their development levels.

In the western region, natural environment factors influence the DRDL more. Among them, the q-values of average elevation (X1) and topographic relief (X3) are 0.2624 and 0.2586, respectively, and the sum of their explanatory power occupies 20.48% of the contribution, indicating that the DRDL in the western region is influenced by the natural geographical environment to a greater extent. Meanwhile, the q-values of the number of personnel in the IT service industry (X12), per capita public budget expenditure (X9), and per capita savings deposit balance (X13) contribute 13.39%, 13.11%, and 10.04% of the explanatory power, respectively, indicating that professional and technical talent, government support, and people’s income level play important roles in digital rural construction in the western region. Therefore, upgrading professional talent team construction, increasing government financial investment, and increasing people’s income level are important ways to enhance the DRDL in the western region.

For the northeast region, the explanatory power of five factors—average elevation (X1), number of IT service industry personnel (X12), topographic relief (X3), percentage of facility agriculture area (X8), and number of industrial enterprises above the scale per capita (X7)—reaches 57.59%, making these the main factors influencing the DRDL in the northeast region. Notably, the contribution of agricultural modernization level to the DRDL in the northeast region (11.46%) is much higher than that at the national (5.22%) level and in the eastern (6.52%), central (3.46%), and western (4.39%) regions. The northeast region is an important commodity grain base in China, and its agricultural modernization and mechanization rate is in the leading position in China. The comprehensive mechanization degree in the northeast region has reached 95.05%, ranking first in China, and the comprehensive mechanization rate of agriculture in Heilongjiang Province is as high as 98% [75]. Moreover, the improvement of the agricultural modernization level can greatly promote the rural economy digitalization level, which becomes an important factor influencing the development of the digital rural in the northeast region.

5.2. Interaction Detection Analysis

Digital rural development is often the result of the combined effect of multiple factors, and the results of the combined effect of different factors may differ from the results of single factors. Therefore, this paper explores the interactions of factors influencing DRDL in Chinese counties on the basis of factor detection. For comparison, the top 10 factors in terms of the q-value of interactions were selected for analysis in the four major regions of China in order to explore the relationships among the influencing factors. The interaction detection results showed (Figure 4,

Table 6. Interaction detection results of the geographical divergence of county DRDL in the four regions of China. Source: Geodetector results collated by the authors.

Rank	Eastern Region		Central Region		Western Region		Northeast Region
	Interaction	q-Value	Interaction	q-Value	Interaction	q-Value	Interaction	q-Value
1	X7∩X11	0.3656	X1∩X14	0.2250	X12∩X13	0.4915	X10∩X14	0.4378
2	X7∩X12	0.3446	X1∩X12	0.2118	X9∩X13	0.4899	X3∩X12	0.4315
3	X6∩X7	0.3050	X12∩X14	0.2036	X11∩X13	0.4789	X1∩X11	0.4262
4	X2∩X12	0.3010	X10∩X12	0.2031	X9∩X14	0.4575	X3∩X11	0.4141
5	X7∩X10	0.2982	X1∩X4	0.2001	X1∩X12	0.4425	X1∩X10	0.4057
6	X7⋂X9	0.2801	X9⋂X10	0.1981	X3⋂X12	0.4399	X12⋂X14	0.4001
7	X1⋂X7	0.2793	X1⋂X10	0.1973	X9⋂X12	0.4360	X1⋂X12	0.3890
8	X2⋂X7	0.2748	X11⋂X14	0.1969	X12⋂X14	0.4302	X11⋂X14	0.3693
9	X7⋂X8	0.2676	X10⋂X14	0.1934	X7⋂X9	0.4253	X1⋂X9	0.3675
10	X3⋂X7	0.2671	X4⋂X12	0.1919	X5⋂X9	0.4212	X11⋂X13	0.3672

Note: The light blue part indicates that the interaction type is non-linear enhancement and the light yellow part indicates that the interaction type is two-factor enhancement.

) that the driving force of the two-factor interaction was stronger than that of the single-factor action and that the type of interaction showed either two-factor enhancement or non-linear enhancement. Compared with the single-factor effect, the q-values of each factor when acting together with other factors were all increased to different degrees, indicating that the explanatory power of the interactions among factors on the differences of the DRDL was always greater than that of the single-factor effect, thus further deepening the spatial differentiation of DRDL.

5.2.1. National Scale

Overall, nationally, the type of interaction is mainly two-factor enhanced, indicating that for most of the influencing factors, the interaction of a single factor with any other factor is greater than its own individual effect (Figure 4). Among them, the number of industrial enterprises above the scale per capita (X7) and the number of primary and secondary school students (X11) have the strongest interaction forces, indicating that the combined effect of industrial economic base and human capital reserve plays an important role in the regional differences in DRDL across the country. It is noteworthy that the q-values of the interactions between X7 and X11, X7 and X12, and X11 and X13 are significantly higher than the highest values of the q-values of the single-factor detection XI interacting with other factors, which indicates that in the case of interaction, the strength of the effect of natural environmental factors is less influential on the regional differences in DRDL while the interaction of socio-economic factors plays a dominant role in the regional differences in DRDL. It can be inferred that, on a national scale, although natural environmental factors have a greater constraint on the DRDL from a single-factor perspective, digital rural development is more of a socio-economic phenomenon and is more constrained by socio-economic conditions. Therefore, by improving the regional socio-economic conditions, it is still possible to compensate for the hindrance caused by natural environmental conditions and realize the catch-up development of the digital rural in areas with poor natural environments.

5.2.2. Regional Scale

From the eastern region, the type of interaction showed mainly non-linear enhancement (Table 6). The number of industrial enterprises above the scale per capita (X7) had a strong interaction with nine factors, including the number of students in primary and secondary schools (X11) and the number of workers in the information technology service industry (X12), and mainly showed a non-linear enhancement. It is shown that the economic development base, together with the vast majority of factors, has a greater influence than the sum of each factor individually. This indicates that economic development, as the material base of digital rural construction, plays a key role in digital rural development. It is noteworthy that the number of primary and secondary school students (X11) ranks 4th in influence in the single factor detection (q = 0.1084), but the q-value increases significantly after interacting with the number of industrial enterprises above the scale per capita (X7), which enhances the influence on digital rural development. This finding indicates that the influence degrees of individual factors of education service level on digital rural development is not significant. The rule of basic education lies in the long period, slow effect, and strong after-effects [76], which mainly shows an indirect influence on digital rural development. Therefore, when education service level is combined with other factors such as the economic development base, it can significantly promote digital rural development.

The types of factor interactions in the central region show both two-factor enhancement and non-linear enhancement, and the number of both interaction types is roughly equal, but the influence in the central region is weaker than in the east, west, northwest, and nation as a whole, and the maximum q-value of the interaction is only 0.2250. It is noteworthy that the single factor power of the average years of education of the population (X14) ranks relatively low, but the interactions with average elevation (X1), IT service industry personnel (X12), number of primary and secondary school students (X11), and number of fixed-line telephone subscribers (X10) result in significant increases in its q-value; in particular, the interaction with average elevation (X1) has its influence jumped to first place (q = 0.2250). This indicates that regional population quality has great potential for digital rural development in the central region and that its power can be fully realized when interacting with factors such as mean elevation, communication services, education services, and professional and technical services.

Western region interactions are mainly two-factor enhanced, indicating that they have a greater impact than each factor alone. The first-ranked single-factor interaction, IT service industry personnel (X12, q = 0.3407), continues its first-ranked influence in the interaction and has the strongest influence (q = 0.4915) in the interaction with per capita savings deposit balance (X13). Meanwhile, the influence of the interaction of IT service industry personnel (X12) with average elevation (X1), topographic relief (X3), public budget expenditure per capita (X9), and average years of education of the population (X14) is also strong. This indicates that for the western region, professional and technical personnel play a key role in digital rural development, either as a single factor or in interaction with other factors, and that this factor is more likely to influence regional variability in the DRDL when combined with socio-economic factors such as residents’ savings and income, government support, regional population quality, and natural factors such as altitude and elevation.

The types of interactions in the northeast region were all non-linearly enhanced, indicating that the influence of the two-factor interaction in the northeast region was significantly greater than the sum of the two factors alone. It is noteworthy that the average elevation (X1) factor is the most influential factor (q = 0.1853) when acting as a single factor, but its influence decreases when interacting with other factors. The single-factor effects of the number of students in primary and secondary schools (X10) and the average years of education of the population (X14) were not significant. However, the interaction between the two factors increased significantly and the q-value jumped to first place (q = 0.4378). This indicates that the combined effect of the level of communication services and the regional population quality is significant, that the improvement of communication services combined with the improvement of population quality can significantly enhance people’s awareness of digital technology and their ability to use digital devices, and that the superimposed effect of the two can effectively promote digital rural development and influence the regional differences in DRDL.

6. Discussion

6.1. Response to Previous Studies

Through its study of the DRDLs in Chinese counties, this paper has found that public services in rural regions constitute an important factor affecting DRDL. The improvement of public service in rural areas will contribute greatly to the digitalization of rural areas, which echoes the viewpoints of Real (2014) and other scholars [30]. At the same time, the digital divide is one of the key factors affecting the development of the digital rural and urban–rural integration and plays an important role in promoting the sustainable development of the digital rural, which is also the focus of scholars such as Rooksby (2002), Fong (2009), and Philip (2017) [11,19,21]. However, what needs to be highlighted is that this paper, based on the previous studies, further advances and improves the regional differences and influence mechanisms of digital rural development at the theoretical level, which has not been covered in the previous studies, and that this will help improve the theoretical studies on digital rural development. In addition, this paper focuses the study of the digital rural on the geographical perspective, which makes up for the lack of attention paid by previous studies to the regional differences in digital rural development under the geographical perspective, and at the same time identifies the influencing factors of the regional differences between the DRDL in different regions, thus providing references and guidance on the strategies and directions of the development of the digital rural in different regions, which have been lacking in the studies conducted by other disciplines.

6.2. Revelations and Recommendations

Nowadays, the organic combination and deep integration of digital economy strategy and rural revitalization strategy has provided an emerging dynamic energy for high-quality rural development, promoting the continuous optimization and enhancement of the functions of rural regional systems and bridging the digital divides both between urban and rural areas as well as between regions. On the one hand, the digital economy has penetrated into all aspects of socio-economic development and urban–rural integration, and the deep integration of digital economy and rural resources is an important way to realize rural revitalization, which has become an important force to promote the integration of rural spatial structure, industrial transformation and upgrading, governance innovation and optimization, and cultural inheritance and activation. On the other hand, promoting digital rural construction is an effective way to narrow the digital divide between urban and rural areas. The digital development of the rural regions can greatly improve rural informationization and intelligence level, accelerate the flow of elements between urban and rural areas, and promote the modernization process of the rural regions and the integration process involving urban and rural areas. At the same time, on the national level, promoting digital rural construction and enhancing DRDL is also an important way to narrow the imbalance of regional development in the rural regions and an effective path to realize rural development in the central and western regions to “catch up” with the rural development in the eastern regions. For example, the National Big Data Center established in Guizhou Province, through the empowerment of big data and digital technology, has greatly promoted the digital transformation and upgrading of rural governance in Guizhou Province, and is forming the “Guizhou experience” of rural digital governance in China, which has become a typical representative of rural development in the western region to catch up with the eastern region and achieve “overtaking” [77].

In addition, based on the interaction detection of the factors influencing the spatial differentiation of DRDL in Chinese counties, this paper has found that digital rural development in four major regions in China is not a single factor acting independently, but a factor, interacting with other factors, that can play a “1 + 1 > 2” superposition effect. Therefore, the results of this study can be used to further improve digital rural development in different regions by implementing different policy measures and human interventions to take advantage of their unique interactions. Specifically, for the western region, the factors of government support, professional and technical talent, and residents’ savings and income show obvious two-factor enhancement effects when interacting with other factors. Therefore, in the western region, we should focus on increasing the government’s investment in digital infrastructure in rural areas and providing technical support with corresponding professional and technical talents, and, more importantly, increasing residents’ income levels to achieve the “catch-up” of digital rural development in the western region and narrow the digital divide of rural development between the eastern and western regions. For the central region, the two factors of professional and technical talents and regional population quality interact significantly with other factors, showing obvious superposition effects, and so, we should focus on improving the cultivation of digital technical talent, vigorously developing basic education, and improving the level of population quality. For the northeast region, the interactions of human capital reserve and communication service level with the remaining factors are significant. However, due to the severe outflow of population in the northeast region in recent years, its population has been shrinking extensively [78]. Based on this, while increasing the supply of communication services, a population development strategy should be formulated scientifically to curb the continuous shrinkage of population in the northeast region so as to ensure a sufficient population to support the construction and development of the digital rural. For the eastern region, the industrial and economic base is the most prominent factor in its interaction; thus, steady economic growth should be maintained in the eastern region while increasing industrial investment to provide solid and stable material support for digital rural development.

7. Conclusions

The research in this paper will help to deepen the theoretical knowledge of China’s digital rural development and its regional heterogeneity, clarify the regional pattern and geographical differences between the DRDLs in Chinese counties, and identify the geographical factors affecting the differences in DRDL so as to provide scientific references for digital rural construction in China. The main research conclusions are as follows:

• The DRDL is an important representation of digital technology to promote rural development and transformation and has its unique spatial differentiation mechanism. Digital rural development can be analyzed from two elements and three dimensions, namely the natural and human elements and the three dimensions of environmental system, economic system, and social system. Among these, the environmental system plays a fundamental role in the process of digital village development, which is mainly reflected in the natural environmental conditions of the earth’s surface such as in topography topographic relief, surface steepness, altitude difference, and the geographical location conditions in which the rural regions are located. The economic system plays a decisive role in digital rural development, which is mainly reflected in the overall level of economic development, industrial development foundation, agricultural modernization level, and service industry development level. The social system plays an important role in guaranteeing and supporting the sustainable and solid development of the digital rural, mainly in terms of policy guidance, social services, and individual residents.
• The DRDL data for China’s counties has significant spatial distribution, spatial correlation, and spatial clustering characteristics. In terms of spatial distribution, the DRDL shows a decreasing distribution trend from the coastal to inland regions, with the high-value area generally distributed in the area east of the Hu Line, but the distribution pattern of different sub-dimensions shows certain differences. In terms of regional differences, the overall regional differences between the DRDLs are relatively small while the regional differences in each sub-dimension are relatively large, and the contribution rate of intra-regional differences is larger than that of inter-regional differences. In terms of spatial correlation, compared with other sub-dimensions, the rural infrastructure digitalization dimension has a stronger spatial correlation. In terms of spatial clustering, the hotspot regions are primarily concentrated in the eastern region, and the coldspot regions are mainly concentrated in the western region and the northeastern region, but there are large differences in the hotspot and coldspot regions of different sub-dimensions.
• The spatial variation of DRDL is closely related to geographical factors and is the result of the combined effect of several geographical factors. The factor detection results show that average surface elevation, surface elevation difference, government support, human resource reserve, and professional and technical talent are the main influencing factors of the spatial variation of DRDL at the national level. The dominant factors vary within the four regions. Among these factors, average surface elevation, communication service level, and residents’ saving incomes have stronger influence on DRDL within the four regions in general. The interaction detection results show that the driving force of the two-factor interaction is stronger than that of the single-factor action, indicating that the explanatory power of the interaction among the factors on the DRDL is always greater than that of single-factor action, further deepening the regional differences between DRDLs.

It should be noted that due to the limitations of research scale and data acquisition, this paper has only analyzed the DRDLs of Chinese counties in 2020—a choice that has had certain limitations in terms of time scale and spatial scale. The following two directions can be explored and extended in the future. First, one may expand the time scale of the study. Based on the availability of data, one may do a long-time series study of ten years, or even of twenty years, to explore the spatial and temporal evolution characteristics of DRDL and to analyze the reasons for changes. The second direction for future research is to refine the spatial scale of the study. In the future, we can further focus the research scale on the village scale, select typical villages to do case studies, and combine qualitative research methods to conduct qualitative analyses to further deepen and supplement this study.