Coupling Coordination and Sustainable Improvement Path of Digital Village and Rural Economic Resilience at County Level in Hunan Province

Abstract

Rural sustainable development is a core component of the global Sustainable Development Goals, and building digital villages and enhancing the resilience of rural economies are key pathways for underdeveloped regions to achieve rural sustainable development. The coordination and synergy between these two areas are central to rural revitalization. Taking 122 counties in Hunan Province as research units and using 2013–2023 spatial panel data, this study employs an improved coupling coordination model, spatial autocorrelation analysis and geographically weighted regression to explore their spatiotemporal evolution, clustering patterns and driving factors. The results show that both systems improved steadily: digital villages expanded from core areas, while economic resilience developed more balancedly. The coupling coordination evolved from near-disorder to a pattern characterized by regional equilibrium. The coupling coordination degree displayed significant positive spatial autocorrelation, forming an “High-High (H-H)” cluster in the Changsha-Zhuzhou-Xiangtan-Dongting Lake Plain and an “Low-Low (L-L)” cluster in western Hunan. Driving factors showed marked spatial heterogeneity. These findings provide empirical support for differentiated digital village policies in Hunan.

1. Introduction

Digital village development is a key lever for both Digital China and rural revitalization, while rural economic resilience represents the capacity of rural areas to withstand risks and achieve adaptive transformation. The “Digital Village” is a central research topic in the field of global rural sustainable development. The international academic community generally defines it as a development model that uses digital infrastructure as its foundation and digital technology as its core to drive the end-to-end digital transformation of agricultural production, rural governance, and residents’ daily lives [1]. International research indicates that there is a significant correlation between rural digitalization and the resilience of the rural economy. Digital technologies can optimize the allocation of production factors, extend agricultural industrial chains, and improve risk early-warning systems, thereby strengthening the rural economic system’s ability to withstand, adapt to, and transform external shocks. Conversely, continuously enhanced rural economic resilience provides a stable economic foundation and application space for the deployment of digital infrastructure and the widespread adoption of digital technologies [2]. This theoretical framework offers core support for policy design regarding rural digital transformation across the globe.

The coupling coordination between the two is therefore an important pathway toward high-quality rural development. China’s rural development currently faces a series of quantifiable challenges, which are also the primary drivers behind the government’s supportive policies: According to official monitoring data from the National Bureau of Statistics and the Ministry of Agriculture and Rural Affairs for 2013–2023, the number of migrant workers nationwide has consistently remained above 160 million, and the rate of rural depopulation in counties across central and western China generally exceeds 25%. At the same time, structural labor shortages and an aging population are prominent issues. In 2023, agricultural labor productivity in China was only 23.8% of that in the secondary sector. Traditional smallholder farming models remain dominant, and the transition to modernization has lagged behind. At the same time, the urban-rural digital divide has persisted for a long time, with the rural internet penetration rate lagging behind that of urban areas by more than 20 percentage points. Channels for digital empowerment of rural development remain inadequate, and the rural economy’s resilience to external shocks such as market fluctuations and natural disasters is notably weak.

The 2018 Central No. 1 Document, Opinions of the CPC Central Committee and the State Council on Implementing the Rural Revitalization Strategy, explicitly introduced the concept of the “digital village,” marking the formal elevation of rural digital transformation to the level of national strategy. The 2023 Central No. 1 Document, Opinions of the CPC Central Committee and the State Council on Comprehensively Advancing Key Work for Rural Revitalization in 2023, further emphasized that the rural economy should possess the capacities of “resistance-adaptation-transformation” in responding to external shocks such as natural disasters and market fluctuations [2]. The 2024 Central No. 1 Document, Opinions of the CPC Central Committee and the State Council on Effectively Advancing Comprehensive Rural Revitalization by Learning from the Experience of the “Thousand Villages Demonstration, Ten Thousand Villages Renovation” Project, together with the 2024 Digital Village Development Work Priorities, further focused on “digitally empowering the improvement of rural economic resilience.” These policy documents explicitly call for stronger rural digital infrastructure, smart agriculture, and better rural e-commerce and logistics systems, thereby providing policy guidance for the deep integration of digital technology and rural economic resilience [3].

Hunan Province is both a major economic province in central China and a key region for rural revitalization, making the interaction between digital village development and economic resilience highly representative. Hunan comprises 14 prefecture-level cities and autonomous prefectures and 122 counties, which can be divided into four major economic subregions: the Changsha-Zhuzhou-Xiangtan region, the Dongting Lake region, southern Hunan, and western Hunan. There are significant disparities in digital infrastructure, economic foundations and industrial structure. For example, the Changsha-Zhuzhou-Xiangtan region has already achieved full county-level 5G coverage, whereas some counties in western and southern Hunan still face problems such as inadequate broadband quality and lagging agricultural digital transformation. At the same time, Hunan is a major agricultural province, and the strong dependence of the rural economy on agriculture exposes weaknesses in resilience. Digital technologies are therefore needed to optimize factor allocation, extend industrial chains, and improve risk-prevention systems. At present, Hunan has already incorporated digital village development into its key rural revitalization projects. However, there is still a lack of systematic county-level research on how to solve problems such as addressing regional imbalance and weak risk resistance through the coupling coordination of digital village development and economic resilience [4,5]. Therefore, this study uses the 122 counties in Hunan Province as a sample and employs an improved coupled coordination model and spatial econometric methods to reveal the patterns of evolution and underlying drivers of these two factors. This study not only helps to uncover the underlying mechanisms through which digital technologies empower rural development but also holds significant theoretical and practical implications for implementing the United Nations 2030 Agenda for Sustainable Development and advancing China’s rural revitalization strategy.

2. Literature Review

2.1. Digital Village Development and Rural Economic Resilience

Digital village development is grounded in digital infrastructure, centered on digital technologies, and supported by digital services, promoting the comprehensive digital transformation of agricultural production, rural governance, and farmers’ daily lives [1]. Existing studies mainly focus on three aspects. First, in terms of measurement, many studies construct evaluation systems from the three dimensions of digital infrastructure, digital application, and digital services to quantify the level of regional digital village development [6]. Second, in terms of spatiotemporal differentiation, previous research shows that digital village development in China exhibits a spatial pattern characterized by “high in the east and low in the west, core agglomeration, and peripheral lagging,” with marked provincial and municipal disparities [7]. Third, in terms of influencing factors, economic development, fiscal input, digital literacy, and topography are identified as the main drivers [8].

Rural economic resilience refers to the capacity of rural economic systems to resist, adapt and transform in response to external shocks such as market fluctuations, natural disasters, and policy adjustments, which is a core indicator for measuring rural sustainability. Existing studies generally build evaluation frameworks from the three dimensions of resistance, adaptability, and transformation capacity, focusing on the mechanisms through which industrial structure, human capital, resource endowment, and locational conditions shape economic resilience. They confirm that regions with diversified industries, stable incomes, and active innovation tend to have stronger economic resilience [9]. However, most existing work concentrates on urban economic resilience; specialized research on county-level rural economic resilience remains limited, and the linkage with digital transformation has not been sufficiently explored [10].

2.2. Research Methods, Spatial Scale, and Research Content of Existing Studies

In terms of research methods, many studies adopt advanced econometric and spatial analytical tools to characterize the spatiotemporal dynamics and spatial patterns of coupling coordination. In addition to the traditional coupling coordination model, kernel density estimation (KDE) and Markov chains are often used to analyze dynamic evolutionary trends [11]. Local spatial autocorrelation analysis (LISA), Moran’s I, and exploratory spatial data analysis (ESDA) are used to identify spatial agglomeration patterns (e.g., High-High and Low-Low) and spatial differentiation. Spatial econometric models, such as the spatial Durbin model (SDM) and the spatial error model (SEM), are employed to reveal influencing factors and their spatial spillover effects, whether positive radiation or negative siphoning. The Dagum Gini coefficient is used to decompose the sources of regional disparity, while gray forecasting models (GM(1,1)), trend-surface analysis, and standard deviational ellipse models are used to predict trends and describe the directional features of spatial distributions [7]. Methods such as geographically weighted regression (GWR) and the optimal parameter-based geographical detector (OPGD) are further used to detect the spatial heterogeneity of driving factors [12]. This multi-method research paradigm enables a more comprehensive and fine-grained depiction of coupling coordination.

In terms of research scale, studies have covered multiple levels, including the national scale, economic belts, urban agglomerations, provinces, and counties. Studies generally find strong spatial imbalance, such as “high east and low west”, “core-edge”, “gradient distribution” and other patterns [13]. Scholars emphasize the need to take into account differences in geographical conditions, resource endowments, stages of development, and policy contexts, so policy recommendations tend to favor tailored and differentiated approaches [14].

In terms of research content, existing studies not only identify the key factors influencing coupling coordination degree, such as economic development, industrial structure, government support, technological innovation, human capital, infrastructure, and environmental regulation, but also pay increasing attention to their direct effects, spatial spillover effects, and interactive effects [15]. These studies show that interactions among factors often display “bivariate enhancement” or “nonlinear enhancement” [16]. Internal system factors are often stronger than external environmental factors [17]. The identification of spatial spillover effects, such as the coexistence of radiating and siphoning effects from central cities, provides an important basis for cross-regional collaborative planning [18].

Digital village development and rural economic resilience exhibit a bidirectionally reinforcing coupling relationship. Digital technologies directly empower rural economic resilience by optimizing factor allocation, extending industrial chains, strengthening risk early warning, and broadening transaction channels [2]. As the resilience of the rural economy grows, it provides a stable economic foundation and practical applications for the implementation of digital infrastructure. Thereby, the resilience of the rural supports the development of digital villages [19].

2.3. Review of the Literature

Existing studies have preliminarily established the theoretical framework linking digital village development and rural economic resilience, confirmed their bidirectional reinforcing relationship, and formed a mainstream research paradigm centered on “measurement-spatiotemporal differentiation-driving factors,” thereby providing a solid basis for this study. However, a systematic review of studies on Hunan and related research nationwide reveals three major gaps that still need to be addressed. First, existing Hunan-related studies on rural digitalization mostly focus on the provincial or municipal scale and primarily examine the coupling relationships between digital village development and rural e-commerce or urban-rural integration. No study has yet taken Hunan’s 122 counties as micro-level units to systematically investigate the coupling coordination between digital village development and rural economic resilience. Second, existing studies on Hunan generally combine the traditional coupling coordination model with ordinary panel regression, which suffers from two important limitations: the traditional coupling coordination model tends to overestimate coordination and therefore cannot accurately reflect the true synergistic relationship between systems, and these studies ignore spatial heterogeneity and dynamic transition characteristics, failing to analyze the dynamic evolution of coupling coordination. Third, insufficient verification of the regional generalizability of core conclusions means that Hunan-specific patterns have not been fully identified. Therefore, a systematic investigation of the spatiotemporal patterns and driving factors of the coupling coordination between county-level digital village development and economic resilience in Hunan can provide an empirical basis for tailoring digital village policies to local conditions and strengthening rural economic resilience in the province.

3. Study Area and Research Methods

3.1. Overview of the Study Area

Hunan Province is located in south-central China, in the middle reaches of the Yangtze River. It is an important intersection between the Yangtze River Economic Belt and the Rise of Central China strategy. Its geographic coordinates range from 108°47′ to 114°15′ E and from 24°38′ to 30°08′ N (Figure 1). It is adjacent to Jiangxi in the east, Guangdong and Guangxi in the south, Guizhou and Chongqing in the west, and Hubei in the north. The province has a horseshoe-shaped topography that is enclosed by mountains on three sides and opens to the north; mountains and hills account for more than 80% of its total area, which intensifies regional development imbalance. As one of China’s major agricultural provinces, Hunan consistently contributes about 6.5% of national grain output, and agriculture plays a significant role in supporting county-level economic resilience. Meanwhile, digital village development has accelerated: the Changsha-Zhuzhou-Xiangtan region has relatively well-developed digital infrastructure, whereas remote areas such as western and southern Hunan still face clear deficiencies. This regional divergence in both digital and economic development provides a typical case for studying the coupling coordination between digital village development and economic resilience [20].

3.2. Data Sources and Evaluation Indicators

3.2.1. Evaluation Index System for Digital Village Development

Drawing on existing studies and the main dimensions of digital village development, this study measures the level of digital village development from three dimensions: digital infrastructure, digital application level, and digital service support (

Table 1. Evaluation Index System for Digital Village Development.

Dimension	Criterion	Indicator	Unit	Weight
Digital infrastructure	Rural telephone penetration rate	Average mobile phones per 100 rural households	sets/100 households	0.155049
	Rural fiber-optic accessibility	Rural broadband users/total rural population	%	0.187142
Digital application level	Rural digital transaction level	Per capita rural e-commerce transaction value	CNY/person	0.305247
	Rural online payment penetration rate	Rural residents able to use online payments/total rural population	%	0.174992
Digital service support	Rural digital service level	Density of rural digital service stations	stations/1000 persons	0.17757

) [7]. The data are sourced from the National Bureau of Statistics of China, local statistical yearbooks, and agricultural and rural statistical yearbooks. These data were collected using web crawlers. Digital infrastructure construction is the prerequisite for the construction of digital villages. It is mainly characterized by the rural telephone penetration rate and the rural optical fiber access rate. The corresponding measurement indicators are the average mobile phone ownership per 100 households of rural residents and the proportion of rural broadband access users to the total rural population. Digital application level is the core carrier of digital technology and is measured by rural digital transaction level and rural online payment penetration, corresponding to per capita rural e-commerce transaction value and the proportion of rural residents able to use online payments. Digital service support provides the basis through which rural residents can access digital services; it is measured by the density of rural digital service stations, which reflects the spatial accessibility and coverage intensity of digital services within a county and offers service support for the downward diffusion of digital technologies in rural areas.

3.2.2. Evaluation Index System for Rural Economic Resilience

From an evolutionary perspective, this study constructs an evaluation index system for rural economic resilience (

Table 2. Evaluation Index System for Rural Economic Resilience.

Dimension	Criterion	Indicator	Unit	Weight
Resistance	Agricultural land scale	Per capita cultivated land area	1000 hm2/10,000 persons	0.14877
	Farm household economic status	Per capita disposable income of rural residents	CNY	0.232527
Adaptability	Share of non-agricultural industries	Output of secondary and tertiary industries/GDP	%	0.052532
	Rural labor force scale	Rural employed population/total rural population	%	0.139993
Transformation capacity	Rural educational development level	Average years of schooling in rural areas	years	0.185148
	Rural technological innovation	Agricultural intellectual property creation index	1–100	0.24103

) comprising three dimensions: resistance, adaptability, and transformation capacity [9]. The data was also obtained from the National Bureau of Statistics, the China Local Statistical Yearbook, and the Agricultural and Rural Statistical Yearbook. This data was collected using web crawlers. Resistance is the precondition for maintaining the basic functioning of the county economy. This dimension is represented by two criterion indicators, namely the scale of agricultural land and farm household economic conditions, measured by per capita cultivated land area and per capita disposable income of rural residents. They both support the fundamental capacity of the county economy to withstand shocks [13]. Adaptability is the elastic support for county-level economic adjustment. This dimension is represented by the share of non-agricultural industries and rural labor force size, measured by the ratio of output value of the secondary and tertiary sectors to GDP and the ratio of the rural employed population to the total rural population, respectively. These indicators reflect the degree of industrial diversification and the flexibility of labor allocation in supporting employment and structural adjustment aftershocks [14]. Transformation is the long-term upgrading potential of the economic system. This dimension is represented by rural educational development and rural technological innovation, measured by average years of schooling in rural areas and the agricultural intellectual property creation index, which together capture the technological upgrading potential of the agricultural sector and promote long-term improvements in economic resilience [21].

3.3. Research Methods

3.3.1. Vertical and Horizontal Scatter Degree Method

On the basis of normalizing the raw data, this study uses the vertical and horizontal scatter degree method to determine indicator weights and then applies a linear weighted method to measure both digital village development and rural economic resilience. It can not only compare the temporal differences in digital village development or rural economic resilience within a province but also compare the cross-sectional differences in the distribution of these two indices across provinces in a given year [22].

3.3.2. Improved Coupling Coordination Model

To overcome the systematic overestimation bias of the traditional model, this study adopts an improved coupling model to measure the coupling degree between digital village development and rural economic resilience [23].The traditional coupling coordination model tends to produce inflated values [24]. The traditional coupling model only focuses on the relative synchrony of the system. It has no punishment effect on the development imbalance. It cannot distinguish between high and low levels of coordination. When extreme imbalance or low levels are similar, it is easy to output false high coupling values, and there is systematic overestimation deviation. This study adopts an improved coupling model to measure the coupling degree between digital village development and rural economic resilience [23].

On this basis, the coupling coordination model is used to calculate the degree of coordination between the two systems. The specific formulas are as follows:

$$\begin{array}{c}C=\sqrt{\left[1-\sqrt{{\left(U_2-U_1\right)}^2}\right]\times {\displaystyle \frac{U_1}{U_2}}}\end{array}$$

(1)

$$C=\sqrt{\left[1-\left(U_2-U_1\right)\right]\times {\displaystyle \frac{U_1}{U_2}}}$$

(2)

$$T={\alpha }_1{U}_1+{\alpha }_2{U}_2$$

(3)

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

(4)

Equation (2) is a simplified version of Equation (1), where $U_1$ indicates the lower value of the level of digital rural construction and the level of rural economic resilience, $U_2$ is the higher value of the two. $C$ is the coupling degree, $T$ is the harmonic index, and $D$ is the coupling coordination degree. Because the two systems are assumed to be equally important in this study, both ${\alpha }_1$ and ${\alpha }_2$ are set to 0.5. Following the classic classification standards widely used in coupling coordination studies and considering the actual development characteristics of county-level digital village development and rural economic resilience in Hunan Province, the coupling coordination degree $D$ (ranging from 0 to 1) is divided into four levels: 0 ≤ $D$ < 0.4 indicates that the systems have begun to form a benign interactive relationship, but the synergistic effect has not yet been fully released; 0.6 ≤ $D$ < 0.8 indicates intermediate coordination, meaning that the two systems are developing relatively synchronously and their mutual support has increased significantly; and 0.8 ≤ $D$ ≤ 1.0 indicates advanced coordination, meaning that the two systems are deeply integrated and have formed a virtuous cycle of mutual promotion and coordinated enhancement.

3.3.3. Spatial Autocorrelation

Spatial autocorrelation is used mainly to analyze the interdependence among regional variables. Global spatial autocorrelation evaluates, at the overall level, the degree of association or clustering among geographical units and is commonly measured by Moran’s I and Moran scatterplots. Local Spatial Autocorrelation (LISA) further identifies local clustering patterns within each spatial unit, which are specifically classified into four categories: High-High (H-H) clustering: both the unit’s own observations and those of its neighboring units are at high levels, forming high-value hotspots; Low-Low clustering (L-L): both the unit’s own and its neighbors’ observed values are at low levels, forming a low-value cold spot; Low-High outlier (L-H): the unit’s own observed value is low, while its neighbors’ observed values are high, representing a spatially heterogeneous area where low values are surrounded by high values; and High-Low Anomaly (High-Low, abbreviated as H-L): the unit’s own observation value is high, while the observation values of adjacent units are low, forming a spatially heterogeneous zone where high values are surrounded by low values [24,25]. The formula is as follows:

$$\begin{array}{c}I={\displaystyle \frac{N}{{\displaystyle {\sum }_{i,j=1}^N}{w}_{\mathrm{ij}}}}\times {\displaystyle \frac{{\sum }_{i=1}^N{\sum }_{j=1}^N{w}_{\mathrm{ij}}\left(x_i-\overline{x}\right)\left(x_j-\overline{x}\right)}{{\sum }_{i=1}^N{\left(x_i-\overline{x}\right)}^2}}\end{array}$$

(5)

where N is the number of spatial units in the study area; $w_{ij}$ is the spatial weight, taking the value 1 when regions $i$ and $j$ are adjacent and 0 otherwise; $x_i$ and $x_j$ are the observed values for regions $i$ and $j$, respectively; and $\overline{x}$ is the mean of the observed values.

The value of Moran’s I lies within a bounded interval. When Moran’s I is greater than 0, the observed values exhibit positive spatial autocorrelation, meaning that neighboring areas tend to have similar characteristics. When Moran’s I is less than 0, the observed values exhibit negative spatial autocorrelation, meaning that neighboring areas tend to have dissimilar characteristics. When Moran’s I equals 0, there is no spatial autocorrelation and the spatial distribution is random [26].

3.3.4. Spatial Panel Model

The geographically weighted regression (GWR) model is used to explore spatial heterogeneity in spatial data and allows regression coefficients to vary across locations [12]. The formula is as follows:

$$\begin{array}{c}{V}_i={\beta }_0\left(u_i,v_i\right)+{\displaystyle \sum _{j=1}^p}{\beta }_j\left(u_i,v_i\right)x_{ij}+{\epsilon }_i\end{array}$$

(6)

In the formula, ${\beta }_0$($u_i$, $v_i$) is the intercept at spatial location ($u_i$, $v_i$); ${\beta }_j$($u_i$, $v_i$) is the coefficient of the explanatory variable at spatial location ($u_i$, $v_i$); $x_{ij}$ is the value of the $j$th explanatory variable for the $i$th observation; and ${\epsilon }_i$ is the error term for the ith observation.

4. Results Analysis

4.1. Spatiotemporal Evolution of Digital Village Development and Rural Economic Resilience

This study classifies both digital village development and rural economic resilience into five levels. Figure 2 and Figure 3 present the spatiotemporal distribution patterns of county-level digital village development and rural economic resilience in Hunan Province in 2013, 2018, and 2023. Based on the composite scores of the two systems calculated by the vertical and horizontal scatter degree method, these figures clearly reveal the evolutionary trajectories, spatial differentiation characteristics, and regional disparities of the two core systems across Hunan’s counties over the past decade, which lays an empirical foundation for the subsequent analysis of their coupling coordination.

From the perspective of the spatiotemporal evolution of digital village development, the province exhibits a pattern of “steady province-wide improvement, diffusion led by the core, and high in east and low in west” as a whole. In 2013, digital village development in Hunan was still in an initial and lagging stage. The minimum county score in the province was only 0.008014, and the vast majority of counties were concentrated in the low-value range below 0.2. Only a few counties around the Changsha-Zhuzhou-Xiangtan area in eastern Hunan entered the middle-value range of 0.2–0.6, with no high-value agglomeration and only limited interregional disparity under a generally low overall level. By 2018, digital village development had achieved a stage of clear improvement. The minimum county score rose to 0.19595, the low-value range shrank substantially, and the 0.2–0.6 middle-value range spread widely into central and southern Hunan. A contiguous medium-to-high-value cluster emerged around the Changsha-Zhuzhou-Xiangtan area in eastern Hunan, and the feature of gradient diffusion began to appear. By 2023, the province had achieved a leap in digital village development, with the minimum county score reaching 0.452372. All counties had entered at least the medium-value range, and the Changsha-Zhuzhou-Xiangtan core area formed a high-value agglomeration belt above 0.8, revealing a pronounced pattern of gradient decline from east to west centered on the Changsha-Zhuzhou-Xiangtan area. This is highly consistent with the spatial differentiation characteristics of digital village development in Hunan identified by previous studies.

From the perspective of the spatiotemporal evolution of rural economic resilience, the province shows a development pattern characterized by “inclusive and balanced improvement, overall province-wide uplift, and moderate polarization in the core”, which stands in clear contrast to the evolutionary logic of digital village development [10]. In 2013, rural economic resilience across Hunan was dominated by low-value areas. The minimum county score was 0.163843, and high- and low-value counties were distributed in an interspersed and scattered manner, without obvious spatial clustering. This indicates that the province’s rural economic capacity to withstand risk was generally weak and had not yet differentiated significantly across regions. By 2018, rural economic resilience had risen across the whole province, with the minimum county score increasing to 0.242137. Low-value areas had basically disappeared, and most counties entered the middle-value range of 0.2–0.6, indicating a more balanced overall pattern. By 2023, the inclusive improvement of rural economic resilience had become even more evident. The minimum county score reached 0.641631, and except for a very small number of counties, the entire province entered the medium-to-high-value range above 0.4. Only the Changsha-Zhuzhou-Xiangtan core area formed an extremely high-value agglomeration above 0.8. Regional disparities continued to narrow, and the tendency toward balanced development was much stronger than that observed for digital village development [27].

Overall, both systems achieved steady province-wide improvement over the past decade and took the Changsha-Zhuzhou-Xiangtan area as their core high-value region, which is highly compatible with Hunan’s regional economic development pattern of “one core, two sub-centers, three belts, and four zones”. However, clear differences also exist between them. The spatial gradient of digital village development is more pronounced, and the development gap between core and peripheral areas has continued to widen. By contrast, rural economic resilience shows a much stronger feature of inclusive improvement, with interregional disparities continuing to narrow. This differentiated spatiotemporal pattern also determines the regionally differentiated basis for the coordinated development of the two systems.

4.2. Spatiotemporal Evolution of the Coupling Coordination Between Digital Village Development and Economic Resilience

Coupling coordination types are divided into near disorder, primary coordination, intermediate coordination and advanced coordination [28]. Analysis of county-level type transition trajectories shown in Figure 4 indicates that in 2013 the coupling coordination degree across Hunan’s counties was generally at a low level. Most counties were concentrated in the near-disorder type, and only the core counties in the Changsha-Zhuzhou-Xiangtan area formed isolated high-value points, belonging to the leading tier despite relatively small regional disparities. By 2018, with the advancement of digital village development, areas such as the Dongting Lake Plain began to absorb spillover effects from the core region, and the coupling coordination degree started to improve. The number of low-level counties declined, regional disparities narrowed temporarily, and the probability of upward transition to primary coordination reached 35%. However, no county crossed two or more categories consecutively, mainly because rural digital infrastructure develops incrementally and farmers’ digital literacy improves gradually [28,29]. By 2023, after the digital village strategy had been implemented more deeply, the Changsha-Zhuzhou-Xiangtan area and the Dongting Lake Plain formed contiguous clusters of advanced coordination, and the probability of upward transition to intermediate coordination reached 42%. Counties in the Dongting Lake area, in particular, became the core area of upward transition thanks to their strong agricultural base and policy support. Together with the Changsha-Zhuzhou-Xiangtan area, parts of southern Hunan also formed contiguous clusters of advanced coordination. By contrast, due to terrain constraints and weak digital infrastructure, most counties in western Hunan remained in the intermediate coordination category, and there was still a regional imbalance [30].

4.3. Spatial Clustering Characteristics

To reveal the spatial distribution pattern of the coupling coordination degree between digital village development and rural economic resilience across Hunan’s counties, this study uses GeoDa to construct a Queen contiguity spatial weight matrix and measures the spatial agglomeration characteristics of coupling coordination by combining univariate Moran’s I (global spatial autocorrelation), local univariate Moran’s I (local spatial autocorrelation), and Moran scatterplots (Figure 5) [31].

To uncover the dynamic evolution of the spatial agglomeration of the coupling coordination degree between county-level digital village development and rural economic resilience in Hunan Province, this study calculates the global Moran’s I for 2013, 2018, and 2023 based on the Queen contiguity spatial weight matrix. All values pass the 95% significance test (p < 0.05). The results show a slight decline from 0.126 in 2013 to 0.108 in 2018, with the value remaining at 0.108 from 2018 to 2023; this indicates that the coupling coordination degree consistently exhibited significant positive spatial autocorrelation over the decade. In 2013, digital village development was still in its initial stage, and the gap between core and peripheral areas was substantial. Spatial clustering showed a clear pattern of “contiguous high values in the northeast, contiguous low values in the west, and scattered transitional areas in the center.” Fourteen H-H clustered counties were concentrated in the core of the Changsha-Zhuzhou-Xiangtan metropolitan area and the northern Dongting Lake Plain, while 16 L-L clustered counties were distributed contiguously in western Hunan. Only one L-H county and two H-L counties were scattered in the transitional belt of central Hunan, and 73% of counties showed no obvious spatial clustering. During 2013–2018, with the full implementation of rural broadband expansion, digital infrastructure sank across counties, improving development levels in peripheral areas and narrowing the core-periphery gap; spatial clustering weakened accordingly. The number of H-H counties declined to 13, with some counties in eastern southern Hunan exiting the cluster; the number of L-L counties fell to 14, and the western low-value zone contracted slightly; the number of L-H counties rose to 2; the number of H-L counties remained unchanged; and the share of non-significant counties increased to 74.6%. After the elevation of the digital village strategy to the level of national strategy in 2018, the policy concentration and agglomeration effects in the core region reached a dynamic balance with the catch-up effect in peripheral areas, and the spatial differentiation pattern became relatively stable. The number of H-H counties remained at 13, showing a pattern of stability in the north and expansion in the south. Three counties in eastern Chenzhou, southern Hunan, rejoined the high-value cluster and formed the province’s second high-value growth pole. Meanwhile, the number of L-L counties rose back to 15, and some counties that had previously exited the low-value zone fell back into contiguous low-value lock-in. The number and locations of L-H and H-L counties remained largely unchanged, and the share of non-significant counties fell slightly to 73.8%. Overall, geographical location, economic foundations, and resource endowments remain the core determinants of the coupling coordination level. The rise of specialty agricultural e-commerce in southern Hunan has broken the assumption that high-value areas are found only in economically developed regions, but the low-value lock-in effect in western Hunan has continued to deepen. The radiating influence of the core region on surrounding counties remains limited, and a long-term mechanism for regional coordinated development has yet to be formed [9].

5. Analysis

5.1. Baseline Regression Analysis Based on Panel OLS

Drawing on the analytical framework for the driving factors of digital village development, this study selects four core explanatory variables from the dimensions of digital foundations, economic foundations, resource endowments, and labor structure: online payment penetration rate, per capita income, per capita cultivated land area, and labor structure. Taking the coupling coordination degree between county-level digital village development and rural economic resilience as the dependent variable, the study first conducts a global baseline regression using ordinary least squares (OLS) (

Table 3. Estimation Results of the Panel OLS Model.

Dimension	Variable	Coefficient	Std. Error	t Statistic	p	VIF
Digital foundation	Online payment penetration rate	0.4078066	0.0088977	45.83	0.000	2.68
Economic foundation	Per capita income	0.1740875	0.0161126	10.80	0.000	2.73
Labor scale	Labor structure	0.0390527	0.0229445	1.7	0.09	1.21
Resource endowment	Per capita cultivated land area	−0.0290475	0.0085487	−3.4	0.001	1.9
	Intercept	0.3097422	0.0088228	35.11	0.000	-

) to identify the core drivers and their global effects [32]. The key reason lies in its ability to clearly present the global average effects of driving factors across the entire sample, thereby providing a unified benchmark for subsequent spatial heterogeneity analysis using geographic weighted regression (GWR). Preliminary tests using a panel model revealed that neither individual nor time fixed effects were statistically significant, eliminating the need to introduce additional fixed- or random-effects models.

After introducing the Geographically Weighted Regression (GWR) model, we deconstructed the spatial non-stationarity characteristics of each driving factor, revealing regional differences in the mechanisms driving the degree of coupling and coordination across different counties. And at the same time together with the spatiotemporal distribution maps of standardized GWR residuals from 2013 to 2023, verify model fit and the spatiotemporal evolution of driver effects. The results provide empirical support for differentiated policy design aimed at county-level coordinated development in Hunan Province [33].

5.2. Spatial Effect Analysis Based on the GWR Model

Prior to the spatial heterogeneity analysis, the validity of the GWR models is examined.

Table 4. Validity Test of GWR Models: Summary of Key Performance Indicators (2013–2023).

Year	Model	Bandwidth	${\mathit{R}}^2$	${\mathit{R}}^2$ Adjust	AICc
2013	Online payment penetration rate	0.56	0.94	0.90	−473.17
2018		1.20	0.98	0.98	−667.50
2023		1.96	0.77	0.76	−417.54
2013	Per capita income	0.57	0.93	0.90	−391.18
2018		0.57	0.94	0.91	−487.45
2023		0.58	0.94	0.91	−509.17
2013	Labor structure	0.56	0.92	0.87	−366.52
2018		0.56	0.94	0.90	−473.17
2023		0.56	0.94	0.90	−491.51
2013	Per capita cultivated land area	1.40	0.97	0.97	−571.21
2018		1.12	0.95	0.95	−582.90
2023		1.35	0.94	0.94	−587.42

presents the key performance indicators, including optimal bandwidth, R², adjusted $R^2$, and AICc, for each driving factor in different years. The results show that the adjusted $R^2$ of all models is above 0.76, indicating good overall fitting performance. Meanwhile, the Moran’s I test of residuals is not significant for all models (p > 0.05), confirming that the GWR models effectively eliminate spatial autocorrelation in residuals. The model specifications are thus reliable for subsequent spatial heterogeneity analysis. GWR allows regression coefficients to vary by location and can therefore effectively capture the spatial non-stationarity of explanatory effects. Considering the dynamic evolution of the driving mechanism over the study period and to avoid potential collinearity among variables, this study constructs single-factor GWR models for each year of 2013, 2018 and 2023 for the four driving factors of online payment penetration rate, per capita income, labor structure, and per capita cultivated land area. The optimal bandwidth of each model is automatically determined by the default AICc (corrected Akaike information criterion) setting in ArcGIS 10.8. Global spatial autocorrelation tests of the overall residuals show that Moran’s I for all model residuals in the three years falls between −0.03 and 0.03, with p-values all greater than 0.05, indicating that the residuals do not exhibit significant spatial autocorrelation and that the spatial dependence problem in the OLS model has been effectively addressed. After model estimation, standardized residual maps for each explanatory variable are generated (Figure 6, Figure 7, Figure 8 and Figure 9) to visualize the spatiotemporal evolution of the overall effects of the driving factors [34]. The following discussion analyzes, one by one, the spatial heterogeneity and spatiotemporal evolution of each factor’s effect on the coupling coordination degree, with all interpretations linked to the development characteristics of the four major regions of Hunan: the Changsha-Zhuzhou-Xiangtan area, the Dongting Lake Plain, western Hunan, and southern Hunan.

(1) As a core indicator of digital foundations, online payment penetration exerts a positive driving effect on the coupling coordination degree that follows a gradient spatial pattern characterized by “a strong core in the Changsha-Zhuzhou-Xiangtan-Dongting Lake area and weak margins in the mountainous areas of western and southern Hunan.” In terms of residual distribution, in 2013 high-residual areas were scattered across the province, whereas low-residual areas appeared only as isolated points in core counties of the Changsha-Zhuzhou-Xiangtan region, such as Changsha County and Liuyang. At that time, digital financial infrastructure was still underdeveloped, and the driving effect of online payment penetration was scattered and unstable. By 2018, low-residual areas had expanded toward the Dongting Lake Plain, and the Changsha-Zhuzhou-Xiangtan-Dongting Lake Plain formed a contiguous zone with good model fit, while high-residual areas began to cluster in the mountainous regions of western and southern Hunan. This indicates that the driving effect of online payment penetration had started to concentrate in economically developed areas. By 2023, low-residual areas covered the entire Changsha-Zhuzhou-Xiangtan region and most counties in the Dongting Lake Plain, while high-residual areas remained only in a few counties in southwestern and western Hunan and southeastern and southern Hunan. This suggests that the driving effect of online payment penetration had formed a core high-value cluster in the Changsha-Zhuzhou-Xiangtan-Dongting Lake Plain. In terms of regional driving mechanisms, counties in the Changsha-Zhuzhou-Xiangtan region and the Dongting Lake Plain benefit from sound digital financial infrastructure and relatively mature new forms of business such as rural e-commerce and digital agriculture. Online payment has been deeply embedded in scenarios such as agricultural production and transactions, as well as household consumption and financial management, allowing the empowering effect of digital inclusive finance to be fully released; consequently, its positive effect on the coupling coordination degree is particularly significant [15,34]. By contrast, counties in the mountainous regions of western and southern Hunan face high costs and limited coverage in digital infrastructure development, and rural residents there generally have lower levels of digital literacy. As a result, digital finance has not effectively empowered agricultural industrialization and scaling-up, and the driving effect remains weak, leading to relatively large model deviations.

(2) The positive driving effect of per capita income on the coupling coordination degree displays a reverse gradient pattern of “strong in western and southern Hunan, weak in the Changsha-Zhuzhou-Xiangtan-Dongting Lake area,” forming a clear contrast with the spatial pattern of online payment penetration. This pattern remained stable throughout 2013–2023. In terms of residual distribution, low-residual areas for per capita income were consistently concentrated in the central part of western Hunan and the western part of southern Hunan during 2013–2023, whereas high-residual areas were concentrated in the core counties of the Changsha-Zhuzhou-Xiangtan region and the eastern Dongting Lake Plain. The spatial pattern of model fit therefore closely matches the spatial pattern of the driver effect. This feature reflects regional differentiation in the marginal contribution of income growth [35]. In the Changsha-Zhuzhou-Xiangtan region and the Dongting Lake Plain, per capita income is already at a relatively high level, and the marginal contribution of further income growth to the coupling coordination degree has diminished. Improvement in coupling coordination there increasingly depends on higher-order factors such as industrial upgrading and digital technological innovation rather than on income growth alone. In contrast, counties in the mountainous regions of western and southern Hunan have relatively low income bases. Supported in recent years by policies such as rural revitalization and western development, rural residents’ incomes have increased steadily, and this improvement has directly enhanced households’ willingness and ability to purchase digital devices and acquire digital skills. A virtuous cycle has thus emerged from income growth to digital empowerment and then to higher coupling coordination, making the marginal contribution of income growth more pronounced in these areas [36]. This result is consistent with previous findings that improvements in the economic foundations of less-developed regions exert a more significant driving effect on digital village development [37].

(3) The positive driving effect of labor structure on the coupling coordination degree between digital village development and rural economic resilience is spatially scattered and does not show any obvious province-wide regional agglomeration. From the spatiotemporal distribution of the factor itself, the high, medium, and low-value areas of county-level labor structure in Hunan were interspersed across the province throughout 2013–2023. Neither a large contiguous high-value core cluster nor a stable contiguous low-value peripheral cluster emerged, and the overall spatial pattern did not undergo any disruptive change. This laid the spatial foundation for the scattered distribution of its driver effect. In terms of the relationship between regional differentiation and driving effects, counties in the Changsha-Zhuzhou-Xiangtan region and the Dongting Lake Plain generally show a trend toward non-agricultural labor transformation and improved digital literacy among workers, which provides a labor basis for enhancing the coupling coordination degree in principle. However, some of these counties also face outflows of rural labor to core cities, leading to an insufficient local rural labor supply and weakening the positive driving role of labor-structure optimization. In some counties of western and southern Hunan, labor outmigration is even more severe, and the problem of “hollow villages” is prominent. The remaining local labor force generally has limited digital literacy and skill levels and cannot meet the needs of digital village development and enhanced rural economic resilience, making it difficult for the positive effect of labor structure to be released. Only a few counties in central Hunan, such as Hengyang County and Qiyang County, have achieved coordinated development among labor-structure optimization, digital village development and improved rural economic resilience, so the positive effect of labor structure on the coupling coordination degree is only modestly visible there. Overall, Hunan’s counties widely face problems such as labor outflow, structural differentiation, and poor alignment between labor quality and rural development needs. These issues lead the positive driving effect of labor structure on the coupling coordination degree to remain scattered rather than forming large-scale or regionally concentrated promotion effects, and its driving mechanism has not yet developed a stable spatial pattern at the county scale [38].

(4) The effect of per capita cultivated land area on the coupling coordination degree shows a spatial pattern of “strongly negative in southern and western Hunan, weakly negative or even slightly positive in the Dongting Lake Plain.” In terms of residual distribution, in 2013 high-residual areas were concentrated in counties in eastern and southern Hunan and southwestern and western Hunan, while low-residual areas appeared only in a few counties in the northern Dongting Lake Plain. In 2018, the low-residual area in the Dongting Lake Plain expanded into a contiguous zone, while the range of high-residual areas in southern and western Hunan contracted. By 2023, most counties in the Dongting Lake Plain had become low-residual areas, whereas high-residual areas in southern and western Hunan remained only in counties with severe cultivated-land fragmentation, indicating that the explanatory power of cultivated land resources in the Dongting Lake Plain continued to improve [39]. This pattern can be explained by the fact that the Dongting Lake Plain is Hunan’s core cultivated-land area, where farmland is relatively contiguous and large-scale operation is more advanced. In recent years, the development of digital and smart agriculture has promoted the deep integration of technologies such as the Internet of Things and big data with cultivated land resources, substantially improving both production efficiency and economic value. As a result, the negative constraint of per capita cultivated land area has weakened markedly there, and some counties have even shifted to a slightly positive effect. By contrast, in the mountainous counties of southern and western Hunan, cultivated land is highly fragmented and digital agricultural technologies are difficult to implement. Some counties also remain overly dependent on traditional farmland-based agriculture and have a relatively single industrial structure, which strengthens the negative constraint of per capita cultivated land area and leads to larger model deviations [40]. From 2013 to 2023, with the advance of high-standard farmland construction and agricultural digital transformation in Hunan, the overall negative constraint of per capita cultivated land area gradually weakened, reflecting initial progress in the digital transformation of cultivated-land resource endowments [41].

6. Conclusions and Policy Implications

6.1. Conclusions

This study systematically reveals the spatiotemporal evolution, spatial clustering characteristics, and driving mechanisms of the coupling coordination between digital village development and rural economic resilience. The main conclusions and related discussion are as follows:

From the perspective of spatiotemporal evolution and spatial clustering, both digital village development and rural economic resilience show steady improvement; their evolutionary features and spatial patterns are mutually related [42]. Digital village development takes the Changsha-Zhuzhou-Xiangtan region as the core and displays a spatial pattern of “core-led growth and gradient diffusion.” By 2023, the Changsha-Zhuzhou-Xiangtan-Dongting Lake Plain had become the highest-level agglomeration zone, whereas the mountainous areas of western and southern Hunan remained at a medium level, which is highly consistent with the previously identified east-to-west decline in digital village development across Hunan. Rural economic resilience, by contrast, exhibits a trend toward regionally balanced development. Supported by policy intervention and optimized resource endowments, western and southern Hunan have gradually narrowed their gap with the core region. These findings reflect the progressive nature of both rural digital transformation and the cultivation of economic resilience, with the Dongting Lake Plain emerging as an area of latecomer advantage [4].

From the perspective of driving factors, the direction and intensity of the effects of the four core drivers on the coupling coordination degree exhibit significant spatial differentiation and are shaped by multi-factor interactions [43]. Online payment penetration displays a positive gradient pattern that is stronger in the east and weaker in the west. The Changsha-Zhuzhou-Xiangtan area and the Dongting Lake Plain possess relatively complete digital financial infrastructure and mature new forms of business such as digital agriculture, so their empowering effects are fully released. The mountainous areas of western Hunan and southern Hunan are limited by topography, the cost of digital infrastructure is high, the coverage is insufficient, and the driving effect is weak. There are regional differences in the supporting role of digital foundations on the development of digital villages [44]. Per capita income shows a reverse gradient that is stronger in the west and weaker in the east. This reverse gradient is directly related to Hunan Province’s targeted support policies for underdeveloped areas.

Since 2004, there has been a special industrial development fund for Western Hunan. From 2013 to 2020, there was a provincial-level special poverty alleviation fund for the Wuling Mountain area. These funds helped build a regular income-increasing support system for underdeveloped counties in Western and Southern Hunan. This system significantly strengthened the marginal driving effect of local income growth on the coupling coordination of the two systems. In the mountainous areas of western and southern Hunan, the income base is relatively low, so income growth significantly promotes the purchase of digital devices and the acquisition of digital skills, creating a benign cycle. Labor structure shows no obvious regional clustering pattern; only a few counties in central Hunan have achieved synergy between labor-structure optimization and digital village development. Across the province, rural areas generally face labor outflows and mismatches in digital literacy, especially in the mountainous areas of western and southern Hunan, where the “hollow village” phenomenon is prominent, preventing the positive effect of labor structure from being released at scale. Per capita cultivated land area exhibits a “strong south, weak north” negative-constraint pattern, although this constraint has gradually weakened. In the Dongting Lake Plain, contiguous farmland and a high degree of scaled operation, combined with digital agricultural technologies, have enabled a shift from negative constraints to a slightly positive effect. In the mountainous areas of southern and western Hunan, cultivated land fragmentation and the difficulty of adapting digital technologies maintain a significant negative constraint. However, the promotion of high-standard farmland construction and agricultural digital transformation has gradually weakened the negative effect of this indicator throughout the province [45]. These findings can provide precise empirical support for major agricultural provinces in central China to formulate tailored digital rural development policies and promote the sustainable development of the rural economy at the county level.

6.2. Discussion

Existing research has confirmed that there is a core bidirectional relationship between digital villages and rural economic resilience. At the same time, previous studies have preliminarily indicated that Hunan Province exhibits regional disparities in digital development, with higher levels in the east and lower levels in the west. These findings provide a solid theoretical foundation and empirical support for this study. The core innovation and added value of this study stem from the unique empirical findings derived through systematic modeling.

This study is the first to use counties in Hunan Province as the unit of analysis, filling a gap in research on the coupling and coordination between digital villages and rural economic resilience at the county level within the province. The study employed an improved coupling and coordination model, which corrected the systematic bias in traditional models that tended to overestimate coordination levels. The results are more closely aligned with the actual development situation in Hunan’s counties.

It overcomes the limitations of previous studies, which only provided a general understanding of the “digital development gap between eastern and western Hunan.” Through spatial econometric analysis, this study precisely identifies the spatial pattern of coupling coordination characterized by “high-value clustering of H-H in the Changsha-Zhuzhou-Xiangtan region and the Dongting Lake Plain and low-value locking of L-L in Western and Southern Hunan,” as well as the significant spatial heterogeneity of the four major driving factors. The differentiated policy recommendations proposed in this study are all strictly grounded in the aforementioned empirical findings.

6.3. Policy Implications

Based on the above findings, combined with the Hunan’s regional development realities and policy orientation toward digital village development, the following differentiated policy recommendations are proposed [46]:

Strengthen the demonstration and leading role of core regions and release spatial spillover effects. The Changsha-Zhuzhou-Xiangtan-Dongting Lake Plain should focus on the deep integration of digital technologies and agriculture, build model projects such as smart agriculture demonstration parks and rural e-commerce industry clusters, and establish paired assistance mechanisms with western and southern Hunan through technology transfer, personnel training, and resource sharing so as to promote the diffusion of digital dividends from the core to peripheral areas [47].

Address the development shortcomings of peripheral areas and break the low-value lock-in. The mountainous areas of western and southern Hunan should prioritize the strengthening of digital infrastructure by upgrading fiber-optic networks, expanding 5G base-station coverage, and improving the layout of digital service stations. At the same time, practical training in digital literacy and skills should be provided for farmers. Relying on local specialty agricultural resources, these areas should develop niche and high-quality forms of digital agriculture so as to effectively connect “small farmers” with the “digital economy” [48].

Activate the development momentum of central Hunan and promote gradient linkage. Counties in central Hunan should build on their agricultural foundations and locational advantages to advance large-scale farmland operation and the adaptive application of digital technologies and cultivate regionally distinctive digital agricultural industries [45]. They should also improve rural employment services and skills training systems so as to attract migrant workers back to their hometowns to start businesses and thus build an intermediate bridge for coupling coordination development [49,50].

6.4. Research Limitations and Future Prospects

This study systematically reveals the core patterns of the coupling coordination between county-level digital village development and rural economic resilience in Hunan Province, but some limitations remain. For example, the indicator system does not fully exhaust all dimensions of digital village development and economic resilience; dimensions such as digital governance and practical responses to risk are not yet sufficiently included. In addition, the analysis of the interaction mechanisms among driving factors remains relatively macro and does not quantify the strength of synergy or constraint effects. This study has not yet conducted a systematic analysis by situating its research within the theoretical frameworks of evolutionary resilience theory or complex adaptive systems, the intrinsic dynamic processes and bidirectional feedback mechanisms underlying the coupling and coordination between digital villages and rural economic resilience. The two-way feedback mechanisms of this relationship also require in-depth interpretation. The failure to effectively address the endogeneity issues arising from the bidirectional causality may have some impact on the rigor of the core estimation results.

Future research can be extended in four directions: First, optimize the indicator system by adding dimensions such as digital governance and risk response, and combine it with micro-survey data to improve the comprehensiveness and precision of the evaluation. Second, extend the temporal span of the data and use longer panel data to explore the long-term evolution and mutation mechanisms of coupling coordination, thereby deepening the identification of policy intervention effects. Third, introduce spatial econometric interaction models to quantify the interactive effects among driving factors and provide more refined empirical support for more targeted coordinated development policies.

Meanwhile, this study does not address the bidirectional causal endogeneity between digital village development and rural economic resilience. Future research can use instrumental variable methods to further identify their causal relationship. The instrumental variables include the historical internet penetration rate in counties at the beginning of the study period and the county terrain ruggedness index. These variables are highly correlated with the development of digital villages and are determined by historical or natural geographical endowments. They are not subject to the reverse influence of current rural economic resilience and possess exogeneity, which can effectively mitigate estimation bias.