Coupling Coordination and Influencing Factors Between Digital Village Development and Agricultural and Rural Modernization: Evidence from China

Abstract

With the increasing application of new digital technologies in rural areas, digital village development has become a crucial pathway for achieving agricultural and rural modernization. This study develops a comprehensive measurement index system to assess the levels of digital village development and agricultural modernization. Then, the coupling coordination model is adopted to measure the coupling coordination development levels of digital villages and agricultural modernization across 266 prefecture-level cities in China from 2014 to 2020. Additionally, the driving mechanisms of the coupling coordination degree are also explored based on the geographic detector model. The results show that the overall levels of digital village development and agricultural and rural modernization in China are on an upward trend. The coupling coordination degree of the two systems has shifted from being on the verge of imbalance to primary coordination. Additionally, absolute regional differences have widened, while relative differences have narrowed. Ecological livability, management systems, living standards, informatization, and mechanization levels are identified as the key factors driving the coupling coordination between digital village development and agricultural modernization. These results offer valuable insights for both theoretical research and practical applications in advancing digital villages and agricultural modernization efforts.

1. Introduction

Agricultural and rural modernization forms the foundation for national modernization and sustainable development [1]. It plays a crucial role in enhancing food security, reducing poverty, improving the livelihoods of those dependent on agriculture, strengthening the resilience of rural systems, narrowing the urban–rural gap, and achieving sustainable development [2,3,4]. As advanced technologies, including big data, cloud computing, artificial intelligence, and the Internet of Things, become more integrated into rural societies, significant changes are taking place in the ways rural areas produce and live. The construction of the digital village, leveraging these digital technologies, is viewed as a critical opportunity and key measure for achieving agricultural and rural modernization and sustainable development [5,6]. The digital village is a process that naturally arises from the modernization and transformation of agriculture and rural areas, driven by integrating digital technologies and networks into agricultural and rural development, alongside efforts to enhance farmers’ digital skills [7,8]. Therefore, based on previous studies, this study defines the digital village as the development process aimed at achieving modernization in rural areas using digital technologies as tools for the digital transformation and upgrading of the production and living conditions of rural regions. Many countries around the world have recognized the importance of digital village development. For example, the European Union has developed the Smart Villages initiative, India launched the “Digital India” program, South Korea implemented the “Information Network Village Project”, and China proposed the “digital village Strategy”. Thus, digital village construction has become an effective approach to addressing challenges in agricultural and rural development and achieving agricultural and rural modernization [9].

Building a digital village can significantly boost the potential of digital technologies. It promotes the consolidation of data elements and enables their conversion into greater rural productivity, which in turn brings fresh momentum to the modernization of agriculture and rural communities [10]. Specifically, the development of the digital economy, represented by rural e-commerce and digital-inclusive finance, has significantly improved the income levels of rural residents [11,12,13]. The application of technologies such as big data, the Internet of Things, and digital twins has greatly enhanced agricultural production efficiency [14,15]. Additionally, the use of digital and intelligent measures has improved rural governance and facilitated the high-quality development of public services [16]. However, in practice, several factors limit the effectiveness of digital village initiatives in advancing agricultural and rural modernization, such as the insufficient empowerment of rural industries by data elements, underdeveloped digital governance platforms, and data-sharing mechanisms [17,18,19]. Studies indicate that farmers’ digital literacy directly influences their ability to use digital tools, and this lack of ability often results in the failure of many digital village projects to achieve their desired outcomes [20]. In rural areas, there is often a significant gap between the penetration of digital technologies and the skill level of farmers [21]. The lack of digital literacy remains one of the main barriers to the effective implementation of digital villages in practice, and addressing this issue through systematic education and training is essential for the full realization of digital village initiatives [22]. Enhancing digital literacy in rural areas has thus become a critical task in promoting the full implementation of digital villages and narrowing the development gap between urban and rural areas [23].

Although many regions have started digital village construction, the levels of agricultural and rural modernization vary widely due to differences in development stages, foundational resources, and other regional disparities. How do digital villages and agricultural and rural modernization interact and evolve together? Does the coupling between digital village development and agricultural and rural modernization change over time and across geographical spaces, and what spatiotemporal evolution characteristics does it exhibit? What are the key factors influencing these coupling coordination degrees? Consequently, exploring the current state of coordinated development between digital villages and agricultural and rural modernization, and analyzing the underlying mechanisms, has become a significant topic in both theoretical research and practical exploration. Existing research has explored key issues related to the concept, development assessment, and impact effects of the digital village [18,24,25,26,27]. Scholars have also systematically studied various aspects of agricultural and rural modernization, including theoretical frameworks, the measurement of development levels, and spatiotemporal characteristics [1,28,29,30]. While existing studies have touched upon related areas of digital village development and agricultural and rural modernization, there is a noticeable gap in the scientific assessment and in-depth analysis of the coupling relationship between these two domains. As a result, the current state, evolution characteristics, and influencing factors of coordinated development between digital villages and agricultural and rural modernization have not been comprehensively assessed. To address this gap, this study aims to develop an evaluation system for digital village development and agricultural and rural modernization, analyzing the spatiotemporal characteristics and exploring the potential influencing factors of their coupling coordination degree. By enriching the research on the interaction between these two areas, this study seeks to provide valuable insights for promoting the coordinated development of digital villages and agricultural and rural modernization.

The remainder of this manuscript is structured as follows: Section 2 reviews the related literature on digital villages and agricultural and rural modernization. Section 3 presents the theoretical framework for the coupling coordination of digital villages and agricultural and rural modernization. Section 4 details the research methodology, including the construction of an indicator system, research methods, and data sources. Section 5 explains the main results of the analysis. Finally, Section 6 provides conclusions and discussions, including the main conclusions, limitations, and future research directions.

2. Literature Review

2.1. Digital Village

As a strategic overlay of digital transformation and rural revitalization, the construction of a digital village has become a significant topic in both theoretical research and practical exploration. Scholars have conducted systematic investigations into its connotations, development measurement, and impacts.

Firstly, concerning the concept and meaning of a digital village, the European Union describes it as a process that leverages existing rural resources and communication technologies to strengthen traditional networks and create new channels for urban–rural interaction, fostering innovative growth opportunities for rural areas [31]. Sutriadi [8] views the digital village as a sustainable innovation for urban–rural development, achieved by improving rural human capital and utilizing information technologies, supported by national development planning systems. Wang and Tang [26] suggest that the digital village represents a novel model focused on advancing agricultural development and rural modernization by employing digital technologies to transform rural agriculture, lifestyles, ecology, culture, and social structures.

Secondly, as for the measurement and evaluation of digital village development, most scholars measure construct indicator systems from various dimensions: (1) Infrastructure dimension, which is the foundation of the digital village, including information infrastructure and financial infrastructure. Some scholars also consider human resources as crucial infrastructure for a digital village [7,26,32]. (2) Agricultural digitization dimension, which emphasizes the digital transformation of agriculture, involving agricultural digital technology, agricultural production informatization, and agricultural management informatization [18,19,33]. (3) Rural digitization dimension, which includes aspects such as rural industries, living services, and governance. Among these, rural industrial digitization is a core indicator of focus [16], while the importance of the digitalization of rural living services is also highlighted [34]. (4) Farmer digitization dimension, where farmers are the primary actors and service recipients in digital village construction, directly impacting the level of construction of a digital village, with a particular focus on farmers’ digital literacy [7,18].

Thirdly, there are the impacts of digital village construction. For the macro level, digital village construction can effectively promote high-quality agricultural development, reduce agricultural carbon emission intensity, and enhance farmer income [18,32,35]. Liu and Liu [36] indicated that digital village construction can narrow the urban–rural income gap. For the micro level, it can foster rural household consumption, encourage entrepreneurial activities among farmers, reduce the probability of rural households facing disruptive changes to their stable conditions, and enhance the resilience of rural families [37,38,39]. Some studies have pointed out that digital village construction can stimulate entrepreneurial activities among farmers and retiring farmers and thereby alleviate relative poverty in rural China [34].

2.2. Agricultural and Rural Modernization

Scholars have approached the study of agricultural and rural modernization from various perspectives, with existing research primarily focusing on two key aspects: theoretical interpretation and development measurement.

First is the theoretical interpretation of agricultural and rural modernization. Agricultural and rural modernization is understood as the process of advancing traditional agricultural production methods, technologies, and organizational structures. It also represents a comprehensive transformation of rural society, encompassing three dimensions: agriculture, rural areas, and farmers [4]. This concept highlights the integration of agricultural and rural development with industrialization and urbanization, emphasizing a balanced and coordinated state where agricultural modernization supports rural modernization and vice versa [1,28]. It underscores the multi-dimensional coordinated development of political, economic, social, cultural, and ecological aspects in rural areas, marking a shift from quantity to quality, from static cultivation to dynamic interaction, and from a single-dimensional focus to a multi-dimensional balanced approach [30].

Second is the measurement of agricultural and rural modernization. Scholars have developed various evaluation indicator systems to measure the level of agricultural and rural modernization, generally focusing on the three dimensions of agriculture, rural areas, and farmers. Specifically, agricultural modernization primarily involves specific indicators such as agricultural industry systems, production systems, management systems, and sustainable development. Rural modernization includes aspects like rural infrastructure, public services, governance capabilities, and social development [40,41]. Farmer modernization focuses on indicators related to the mindset and quality of life of rural residents and their wealth and access to digital services, and the urban–rural consumption ratio [17,30]. Additionally, some scholars argue that ecological livability and rural cultural vitality are also important indicators for assessing the development of agricultural and rural modernization [1,38].

2.3. Digital Villages and Agricultural and Rural Modernization

The construction of digital villages contributes significantly to advancing agricultural and rural modernization. Scholars have explored the relationship between the two from various perspectives. From the economic development perspective, scholars have argued that the digital economy, exemplified by rural e-commerce, can effectively promote green development in agriculture and rural areas. This includes restructuring rural economies, adjusting industrial structures, transforming employment models, and altering the composition of farmers’ income, all of which are crucial for achieving agricultural and rural modernization [40,42]. From the rural industrial development perspective, digital village construction fosters innovation in new rural industries and business models. It helps extend rural industrial chains, enhances the level of integrated rural industrial development, promotes the growth of high-value industries in rural areas, and modernizes rural value chains [29,33,43,44,45]. From the rural governance perspective, the construction of digital villages leverages modern information technology to address specific governance scenarios related to ecology, culture, and livelihoods. It enables the participation of multiple stakeholders, building a governance system characterized by diverse participation, thereby enhancing the modernization of rural governance levels and capabilities [2,46]. Scholars also proved that rural infrastructure construction can promote rural living environment governance and rural economic development [47].

The construction of digital villages not only plays an important role in promoting agricultural and rural modernization but also plays a key role in promoting sustainable development. A comprehensive understanding of this process requires a return to the stage theory of Adam Smith, John Mill, and Marx from the perspective of classical political economy. This historical and trans-historical perspective provides a deep theoretical foundation for exploring sustainable development, particularly emphasizing the importance of economic growth, social class change, and historical context for sustainability studies [48]. On this basis, scholars further elaborated on how sustainable development has evolved in the modern context and pointed out that its core has always been the balance between economy, society, and environment. Although the concept of sustainable development has undergone many adjustments and adaptations around the world, its basic goal remains the same, that is, to meet contemporary needs without sacrificing the resources and opportunities of future generations. Through this continuous evolution, modern society has made great progress in the application of green technology, resource management, and social equity, providing a practical framework for contemporary sustainable development [49].

In this context, digital transformation is emerging as a key tool to drive sustainable development. Through digital technologies such as the Internet of Things (IoT), artificial intelligence (AI), and big data (BD), digital villages have been built to significantly improve the efficiency of resource utilization, as well as to achieve significant results in terms of environmental protection and social equity. The application of these technologies has not only boosted agricultural productivity, but has also created more possibilities for achieving a balance between the economic, social, and environmental triple bottom line. In particular, in the process of promoting sustainable rural–urban development, digital technologies have narrowed the digital divide between urban and rural areas and enhanced social inclusiveness and economic resilience [35]. Thus, the digital village is not only a driver of modernization, but is also an important vehicle for promoting balanced social development through its sustainability function. Specifically, the application of digital technologies, such as artificial intelligence, big data analytics, the Internet of Things, and cloud computing, has played a significant role in environmental management, efficient resource utilization, and carbon emission reduction in rural areas [50]. Thus, digital rural development not only promotes the modernization of agriculture and rural areas, but also accelerates the transition to sustainable development.

2.4. Research Space

In summary, existing research has systematically explored issues related to digital villages and agricultural and rural modernization, providing valuable references for this study. However, several gaps remain. On the one hand, current studies tend to emphasize specific aspects of either digital villages or agricultural and rural modernization. Most discussions revolve around the unidirectional impact of digital villages on agricultural and rural modernization, with relatively few studies offering a comprehensive and scientific analysis of the coupling relationship between the two systems. On the other hand, most of the research on the relationship between digital villages and agricultural and rural modernization is predominantly theoretical discussion, lacking corresponding quantitative empirical studies.

Given these gaps, this study seeks to expand the existing research in the following ways: (1) By integrating digital villages and agricultural and rural modernization into a unified framework, this manuscript constructs an evaluation index system to assess the coupling and coordination between the two systems, which aims to provide a reference point for future research. (2) This study analyzes the overall development of digital villages and agricultural and rural modernization, as well as the spatiotemporal characteristics of their coupling and coordination levels. By examining regional differences, this study aims to provide a comprehensive picture of the development of digital villages and agricultural and rural modernization. (3) This study utilizes the geographical detector model to explore the key factors that support the enhancement of coupling and coordination levels. It aims to identify the sources and contributions of overall disparities, providing practical insights for reducing regional differences and supporting the formulation of policies for coordinated regional development.

3. Coupling Analysis Between Digital Villages and Agricultural and Rural Modernization

Digital villages, underpinned by digital technologies, play a transformative role in restructuring and revitalizing rural productivity, production relations, and production factors, thereby driving the modernization of agriculture and rural areas. The application of digital technology across rural industries, livelihoods, and governance is significantly accelerating the digital transformation of both rural areas and agriculture [10]. In digital agriculture, tools like the Internet of Things and big data play a crucial role in driving agricultural modernization. These innovations support the development of advanced agricultural management systems, allowing for comprehensive oversight and decision making throughout the entire production chain, from initial planning and investment to cultivation, harvesting, processing, and distribution. This approach not only reduces costs, but also enhances resource efficiency, boosts productivity, improves product quality, and fosters more sustainable ecological environment [14,15,24]. As rural digital infrastructure continues to improve, new business models such as rural e-commerce and live-streaming are emerging, enriching the rural industrial system. These advancements are propelling the shift of traditional agricultural production, management, and trade toward digital systems, fostering the digital growth of rural industries, boosting farmers’ incomes, and supporting rural economic prosperity [11,16,27]. In terms of rural governance, digital technologies are being utilized to develop governance platforms that enhance the efficiency of public administration. For instance, government policies can be communicated to residents via WeChat, video conferencing tools can create “mobile meeting rooms” for village affairs, and integrated e-government services can streamline the management of public matters [9,34,51].

The modernization of agriculture and rural areas establishes a strategic direction for agricultural and rural development by integrating advanced technologies and modern equipment, thereby laying a foundational framework for the development of digital villages. China’s Digital Village Strategic Outline explicitly emphasizes the need to strengthen the construction of digital infrastructure in agriculture and rural areas, identifying the consolidation of the digital development foundation in rural areas as a primary task. This robust digital infrastructure serves as an essential condition for the progress of agricultural and rural modernization, providing critical support for the advancement of digital villages [52]. Furthermore, as agricultural and rural modernization progresses, practices such as intensive agriculture, green agriculture, and sustainable agriculture continue to evolve. This ongoing development enhances the integration of digital technologies with the agricultural industry, not only accelerating the digitalization of agriculture, but also injecting new momentum into the construction of digital villages, offering additional models and successful case studies [42,43]. In this context, emerging farmers and new types of agricultural entities play a crucial role. They bring high-quality human resources to rural areas and provide significant impetus for the construction of digital villages, thereby strengthening the innovation capacity of rural digital transformation. The advancement of agricultural and rural modernization also underscores the importance of top-level design, which guides digital transformation and expands digital application scenarios, further promoting comprehensive rural development [53]. Additionally, a well-functioning rural governance system, integral to agricultural and rural modernization, fosters institutional innovation, thereby offering sound policy guidance for rural digital transformation [1,46].

Based on the aforementioned theoretical analysis, digital villages represent a critical strategy for advancing agricultural and rural modernization. They are deeply embedded in the agricultural and rural modernization process, which, in turn, lays the groundwork and serves as a crucial basis for the growth of digital villages. The relationship between digital villages and agricultural and rural modernization is closely interlinked, with their coupling process being dynamic and continuously evolving. This relationship undergoes ongoing self-adjustment, ultimately leading to a stage of coordinated development characterized by mutual enhancement and a win-win outcome. Therefore, examining the coupling and coordination between digital villages and agricultural and rural modernization is crucial for accurately addressing the deeper issues within agricultural and rural modernization and for leveraging digital villages to enhance their development. To further elucidate the theoretical and logical relationship between digital villages and agricultural and rural modernization, a coupling coordination mechanism flowchart has been constructed, as shown in Figure 1.

4. Research Design

This study aims to explore the coupling and coordination characteristics between the development of digital villages and agricultural and rural modernization, with the goal of guiding digital village construction and advancing agricultural and rural modernization. First, based on existing research, this study develops a measurement index system for digital villages and agricultural and rural modernization development, employing the entropy weight method to calculate the development levels. Next, the coupling coordination model is applied to examine the spatial and temporal dynamics of the relationship between digital village development and agricultural and rural modernization. Finally, the geographic detector model is used to investigate the influencing factors and specific mechanisms underlying this relationship.

4.1. Construction of the Indicator System

To investigate the coupling and coordination between digital villages and agricultural and rural modernization, this section develops evaluation index systems for both digital villages and agricultural modernization, and calculates their coupling coordination indices. Following the principles of scientific validity, systematic coverage, and data availability, a comprehensive evaluation index system is constructed for both digital villages and agricultural and rural modernization. In line with the concept of digital villages and referencing related research [7,26,34,54,55,56,57], this study selects four secondary indicators—digital infrastructure, the digitalization of rural life, the digitalization of rural industries, and rural governance—to build the comprehensive evaluation index system for digital villages, as shown in

Table 1. Evaluation indicator system of digital villages.

Indicator	Type of Indicator	Content of Indicator
Digital Village	Digital Infrastructure	Percentage of Villages with Access to Internet Broadband Services Cable Television Coverage Rate Digital Infrastructure Index
	Digitalization of Rural Life	Per Capita Disposable Income of Farmers Digital Financial Usage Level Number of Rural Cultural Centers
	Digitalization of Rural Industries	Proportion of Workforce in the Primary Industry Total Output Value of Agriculture, Forestry, Animal Husbandry, and Fisheries Digitalization Level of Inclusive Finance
	Level of Rural Governance	Proportion of Villages that Handle Household Waste Proportion of Villages that Treat Domestic Sewage Proportion of Village Heads and Party Secretaries Holding Both Positions

.

Due to the broad scope of the field and the lack of a unified definition of agricultural and rural modernization among scholars, this study, referencing existing research [1,30,33,38], constructs an index system to evaluate the level of agricultural and rural modernization development. This index system incorporates dimensions such as the modernization of the agricultural industry system, agricultural production systems, agricultural management systems, rural infrastructure and public services, and the quality of life for rural residents. The specific indicators are detailed in

Table 2. Measurement system of agricultural and rural modernization.

Indicator	Type of Indicator	Content of Indicator
Agricultural and Rural Modernization	Modernization of the Agricultural Industry System	Per Capita Vegetable Production Main Business Revenue of Agricultural Product Processing Enterprises Above Designated Size Comprehensive Grain Production Capacity
	Modernization of the Agricultural Production System	Comprehensive Utilization Rate of Livestock and Poultry Manure Per Capita Total Agricultural Machinery Power Pesticide and Fertilizer Application Volume
	Modernization of the Agricultural Management System	Proportion of the Primary Industry in Regional GDP Agricultural Labor Productivity Inclusive Finance Composite Index
	Modernization of Rural Infrastructure and Public Services	Village Road Paving Rate Rural Green Coverage Rate Number of Health Technicians per 1000 Rural Residents Percentage of Full-Time Teachers with a Bachelor’s Degree or Higher in Rural Compulsory Education Schools
	Quality of Life for Rural Residents	Urban–Rural Income Ratio Engel’s Coefficient for Rural Residents Percentage of Rural Residents’ Expenditure on Education, Culture, and Recreation Average Years of Schooling of Rural Residents
	Modernization of Rural Governance Systems and Capacities	Urbanization rate Incidence of Rural Poverty Percentage of Administrative Villages that have Prepared Village Plans Percentage of Administrative Villages that have Carried out Village Improvement

.

4.2. Research Methods

4.2.1. Entropy Weight-TOPSIS Method

This study utilizes the entropy weight method to assess the developed indicator systems for digital village development and agricultural and rural modernization. This method combines the entropy approach from objective weighting techniques with the TOPSIS method for evaluating advantages and disadvantages. It provides an objective and accurate reflection of the influence of variables on the composite results and ranks evaluation subjects based on their proximity to the ideal target. Additionally, this method is unaffected by sample size or the choice of reference sequences, making it well suited for the objectives of this study.

4.2.2. Coupling Coordination Model

To assess the association and extent of impact between digital villages and agricultural and rural modernization, this study, based on the comprehensive evaluation of the development levels of digital villages and agricultural and rural modernization, constructs a coupling degree model. The formula for calculating the coupling degree is as follows.

$$C=2{\{U_1\times U_2/(U_1+U_2{)}^2\}}^{1/2}$$

In the formula, C represents the coupling degree between the development levels of digital villages and agricultural and rural modernization, with C ranging from 0 to 1. U₁ and U₂ denote the comprehensive development indices for digital villages and agricultural and rural modernization, respectively. A higher coupling degree is indicated by a C value closer to 1, signifying a strong synergy between the two elements. Conversely, a lower C value suggests a weaker coupling, reflecting an imbalance and lack of stability between the development levels of digital villages and agricultural and rural modernization.

Since the coupling degree model only reflects the strength of the coupling relationship between systems and does not capture the degree of coordination between them, we construct a coupling coordination degree model to further analyze the quality of the coupling between the systems.

$$T=\alpha U_1+\beta U_2$$

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

In the formula, D represents the coupling coordination degree between digital villages and agricultural and rural modernization. T reflects the comprehensive coordination index of the synergistic effects between digital village development and agricultural and rural modernization. α and β are coefficients to be estimated, with their sum equal to 1. In this study, considering the equal importance of both components in the relationship between digital village development and agricultural and rural modernization, we set α = β = 0.5.

4.2.3. Geographical Detector

Geodetector models are particularly suitable for exploring the spatial heterogeneity of phenomena, which is one of the key elements of this study [58]. Unlike traditional methods of spatial analysis (e.g., spatial autocorrelation analysis or regression modeling), geodetectors do not assume a linear relationship between the independent and dependent variables and are therefore able to analyze complex nonlinear relationships between influencing factors more flexibly [59]. Rural development and digital transformation typically involve complex interactions of multiple factors, and the geodetector’s nonlinear analysis methods can more accurately reflect these dynamics. At the same time, geodetector models excel in identifying and quantifying the explanatory power of multiple factors and their interactions, not only measuring the independent effects of each factor on spatial differentiation, but also exploring synergistic effects among multiple factors. This ability makes it superior to other spatial analysis methods such as ordinary least squares (OLS) or geographically weighted regression (GWR) when dealing with multivariate interactions. Hence, his study employs the geographical detector model, utilizing factor detection and interaction detection methods to quantitatively analyze the influencing factors and their interactions concerning the coupling and coordination between digital village development and agricultural and rural modernization. The specific calculation formulas are as follows.

$$P_{D,v}=1-{\displaystyle \frac{1}{n{\sigma }_v^2}}{\displaystyle \sum _{i=1}^m{n}_{D,i}}{\sigma }_{v_{D,i}}^2$$

In the formula, P represents the detection power value of factor D on the coordination level between digital villages and agricultural and rural modernization development. $n_{D,i}$ denotes the number of samples in the overall region, and m indicates the number of sub-regions. ${\sigma }_{\mathrm{v}}^2$ refers to the variance of the coupling and coordination level for the entire region, while ${\sigma }_{{\mathrm{v}}_{D,i}}^2$ denotes the variance within the sub-regions. The value ranges from [0, 1], with a higher value indicating a greater influence of factor D on the coordination degree.

4.3. Data Sources

Considering the availability, continuity, and applicability of the data, this study selects the period from 2014 to 2020 and focuses on 266 prefecture-level cities in China as the research subjects. The choice of 2014–2020 as the time period was made because more data related to the evaluation indicators of digital villages were missing before 2014, and because the way of counting some of the data in some of the cities’ statistical yearbooks differed before and after 2014, and the reliability of the data was poor, so 2014 was taken as the starting year. Additionally, 2020 is an important point for China to build a moderately affluent society in all aspects and achieve its first 100-year goal. The period is not only a critical stage for digital village construction to help modernize the countryside, but is also closely linked to the country’s ambitious goals of poverty alleviation, economic restructuring, and upgrading rural living standards. Therefore, the 2014–2020 timeframe is an ideal window to study the synergistic development of digital village development and agricultural modernization, reflecting the effects of policy implementation and technological progress. The data primarily originate from provincial and municipal statistical yearbooks, statistical bulletins, and the “Peking University Digital Inclusive Finance Index” (2011–2020). This study removed samples with a high proportion of missing values that could not be obtained. For samples with fewer missing values, we retrieved relevant data by manually searching the bulletins of various cities. Finally, we obtained the sample data required for this study.

5. Results of the Analysis

5.1. Evaluation of Digital Villages and Agricultural and Rural Modernization

This study evaluates the levels and growth rates of digital village development and agricultural and rural modernization across 266 prefecture-level cities in China from 2014 to 2020, with the results presented in Figure 2. During this period, China’s digital village development steadily progressed, with the composite index increasing from 0.241 in 2014 to 0.350 in 2020, reflecting a 6.47% yearly growth on average. When analyzing the development stages, the growth rate showed a downward trend from 2014 to 2018, dropping from 10.84% in 2014–2015 to 2.72% in 2017–2018. However, starting from 2018, the growth rate of digital village development began to recover, reaching 5.32% in 2020. Similarly, the level of agricultural and rural modernization showed a consistent upward trend, with the composite index increasing from 0.309 in 2014 to 0.401 in 2020, and an average annual growth rate of 4.4%. The growth rate of agricultural and rural modernization declined from 5.87% in 2014–2015 to 3.37% in 2018–2019, before rebounding to 3.76% in 2020.

Additionally, this study employs the natural break method to classify the levels of digital village development and agricultural and rural modernization across 266 prefecture-level cities in China from 2014 to 2020. The classifications are divided into five categories: low, relatively low, medium low, relatively high, and high levels. This classification framework is then used to analyze the spatial evolution characteristics of digital village development and agricultural and rural modernization.

The spatiotemporal characteristics of digital village development are illustrated in Figure 3. Between 2014 and 2020, the standard deviation of digital village development levels increased from 0.064 to 0.076, indicating an expansion in absolute regional disparities. Conversely, the coefficient of variation decreased from 0.266 to 0.218, suggesting a reduction in relative regional disparities. During this period, the proportion of cities with high levels of digital village development rose from 1.5% to 13.91%, those with relatively high levels increased from 2.63% to 39.1%, and those with medium levels grew from 20.3% to 29.32%. Meanwhile, the proportion of cities with relatively low levels dropped from 46.62% to 13.91%, and those with low levels decreased from 28.95% to 3.76%.

From the temporal and spatial perspective, in 2014, only Guangzhou, Shijiazhuang, Shenyang, and Harbin were categorized as high-level cities in terms of digital village development, with relatively high-level cities including Fuzhou, Yancheng, Jining, Weifang, Qingdao, Tangshan, and Dalian. Low-level cities were primarily concentrated in Jilin, Inner Mongolia, Shanxi, Henan, Anhui, Jiangxi, and the southwestern regions, while most other cities fell into the relatively low-level category. In 2016 and 2018, the cities classified as high-level remained unchanged, with many low-level cities transitioning to relatively low and medium levels. Cities that were initially at medium levels gradually shifted to relatively high levels, leading to a shift in the dominant city types from relatively low and medium levels to medium and relatively high levels. By 2020, medium and relatively high levels became the predominant distribution types for digital village development. Relatively high-level cities were widely distributed across Heilongjiang, Hebei, Shandong, Jiangsu, Zhejiang, Fujian, Guangdong, Hunan, Hubei, and Shaanxi, with some cities in these regions gradually advancing from relatively high to high levels. In Henan, which had many low-level cities in 2014, the digital village development level significantly improved by 2020. However, the overall development level in the southwestern region still lagged considerably behind other regions.

The spatiotemporal characteristics of agricultural and rural modernization are depicted in Figure 4. From 2014 to 2020, the standard deviation of agricultural and rural modernization levels increased from 0.098 to 0.115, indicating an expansion in absolute regional disparities. However, the coefficient of variation decreased from 0.315 to 0.283, reflecting a reduction in relative regional disparities. During this period, the proportion of cities with high levels of agricultural and rural modernization rose from 1.5% to 2.63%, while those with relatively high levels increased significantly from 2.26% to 53.38%. Conversely, the proportion of cities at medium levels decreased from 55.64% to 13.53%. Additionally, the proportion of cities with relatively low levels slightly decreased from 27.07% to 25.94%, and those with low levels declined from 13.53% to 4.51%.

From the temporal and spatial perspective, in 2014, only Guangzhou, Shijiazhuang, Shenyang, and Harbin were categorized as high-level cities in terms of agricultural and rural modernization, while relatively high-level cities included Nanjing, Lanzhou, Langfang, Tangshan, Panjin, and Tieling. Cities with low and relatively low levels were mainly concentrated in Jilin, Inner Mongolia, Shanxi, Henan, Ningxia, Anhui, Jiangxi, and the southwestern regions, with most other regions being at medium levels. In 2016 and 2018, regional disparities in agricultural and rural modernization levels became more pronounced, and spatial differentiation gradually increased. Most cities that were initially at low and relatively low levels still struggled to improve, while the dominant types of cities shifted from mainly medium levels to a combination of medium and relatively high levels. By 2020, relatively high-level cities had become the predominant type in agricultural and rural modernization. Additionally, Hezhou and Jinan emerged as new high-level cities. Overall, the provinces of Heilongjiang, Liaoning, Hebei, Shandong, Jiangsu, Zhejiang, Fujian, Guangdong, Hunan, Hubei, Shaanxi, and Gansu showed relatively high levels of development.

5.2. Analysis of Coupling Coordination Development of Digital Villages and Agricultural and Rural Modernization

Based on the results of the digital village development and agricultural and rural modernization levels calculated in the previous sections, the coupling coordination analysis was conducted, with the findings presented in Figure 5. The results indicate that, between 2014 and 2020, the coupling coordination degree between digital villages and agricultural and rural modernization development in China increased from 0.475 to 0.570. According to the classification standards for coupling coordination, the overall coupling coordination level across the country improved from being on the verge of imbalance in 2014 to a state of primary coordination by 2020. During the period from 2014 to 2018, the growth rate of the coupling coordination degree exhibited a downward trend, decreasing from a growth rate of 5.22% in 2014–2015 to 1.4% in 2018. However, starting in 2018, the growth rate of the coupling coordination degree began to recover, reaching 2.3% by 2020.

Based on the calculated coupling coordination index, this study identifies the coupling coordination levels between digital village development and agricultural and rural modernization, with the results presented in

Table 3. Classification of coupling coordination level.

Coupling Coordination Level	2014	2015	2016	2017	2018	2019	2020
Severe Imbalance	3.01%	1.88%	1.13%	1.50%	1.13%	0	0.38%
On the Verge of Imbalance	60.15%	43.23%	32.33%	22.93%	22.18%	16.54%	12.41%
Primary Coordination	34.96%	50.75%	55.64%	60.53%	56.02%	57.89%	51.88%
Intermediate Coordination	1.88%	3.76%	10.15%	14.29%	19.92%	24.44%	32.71%
High-Quality Coordination	0	0.38%	0.75%	0.75%	0.75%	1.13%	2.63%

. In 2014, cities classified under the “on the verge of imbalance” category accounted for 60.15%, while those in the “primary coordination” category made up 34.96%. By 2020, the proportion of cities in the “on the verge of imbalance” category had significantly decreased, and the proportion of cities in the “primary coordination” category initially showed an upward trend before declining. Meanwhile, the proportions of cities in the “intermediate coordination” and “high-quality coordination” categories steadily increased. The dominant categories shifted from “on the verge of imbalance” and “primary coordination” in 2014 to “primary coordination” and “intermediate coordination” by 2020. Overall, the coupling coordination between digital villages and agricultural and rural modernization development in Chinese cities showed a steady upward trend from 2014 to 2020.

To further explore the spatial and temporal distribution patterns of coupling and coordination between digital village development and agricultural and rural modernization across Chinese cities, this study utilizes the coupling coordination degree index for the years 2014, 2016, 2018, and 2020. Based on these indices, spatiotemporal distribution maps were generated for each city, as shown in Figure 6. The results indicate that, in 2014, the overall level of coupling coordination between digital villages and agricultural and rural modernization was relatively low. Only a few cities, such as Guangzhou, Weifang, Shijiazhuang, Dalian, Shenyang, and Harbin, achieved a medium level of coordination, while most cities across the country were on the verge of imbalance. By 2016, cities with medium coordination levels began to emerge in provinces such as Liaoning, Hebei, Shandong, Jiangsu, Zhejiang, Fujian, and Hubei, with most cities still on the verge of imbalance or attaining a medium level of coordination. In 2018, the majority of cities across the country reached a primary level of coordination, with an increase in the number of cities achieving medium coordination. By 2020, cities with medium coordination were widely distributed across the central south and eastern regions, with a few also present in the northeast and northwest regions. Overall, most cities were categorized as having primary or medium levels of coordination.

5.3. Analysis of the Driving Mechanism of the Spatiotemporal Differentiation of Coupling Coordination

The findings showed a clear differentiation in the spatiotemporal evolution of the coupling and coordination between digital village development and agricultural and rural modernization, though the internal driving factors remain unverified. To identify the factors influencing these spatial and temporal variations, this study applies the geodetector model to assess the degree of coupling coordination and examine the key drivers behind these evolving trends.

5.3.1. Influencing Factors

Based on the coupling logic and intrinsic mechanisms of the development of digital villages and agricultural and rural modernization [9,13,26], this study selects nine factors, as shown in

Table 4. Detection results of influencing factors for the coupling coordination degree.

Indicators	All Periods	2014	2016	2018	2020
Informatization Level (X1)	0.754	0.753	0.746	0.759	0.760
Income Level (X2)	0.173	0.181	0.175	0.189	0.148
Agricultural Scale (X3)	0.212	0.246	0.205	0.201	0.195
Ecological Liability (X4)	0.760	0.769	0.751	0.759	0.760
Agricultural Management (X5)	0.758	0.762	0.748	0.760	0.761
Mechanization Level (X6)	0.637	0.644	0.634	0.637	0.634
Financial Inclusion Level (X7)	0.188	0.121	0.223	0.232	0.176
Standard of Living (X8)	0.756	0.757	0.749	0.761	0.759
Urbanization Level (X9)	0.111	0.110	0.141	0.111	0.083

, and applies the factor detection method of the geodetector to assess the driving force of each factor on the coupling coordination degree between digital villages and agricultural and rural modernization during different periods. Specifically, the selected factors include the following: Informatization level (X1): as a key driver in the digital transformation of villages; informatization empowers rural development by providing advanced technology and information resources. This is represented by the coverage rate of cable television. Income level (X2): income represents the total economic returns gained by rural residents through diversified economic activities and reflects the achievements of digital villages and agricultural and rural modernization. This is measured by rural per capita disposable income. Agricultural scale (X3): the expansion of agricultural scale directly affects agricultural output efficiency, which in turn influences the degree of digital villages and agricultural and rural modernization. This is measured by the gross output value of agriculture, forestry, animal husbandry, and fishery. Ecological livability (X4): the ecological environment is the foundation of sustainable development, aligning with the green, low-carbon, and circular development concepts emphasized in agricultural and rural modernization. This is represented by the proportion of domestic sewage treatment. Management system (X5): this factor mainly involves the modernization of production methods, the extension of industrial chains, and the expansion of market scope, represented by the main business income of agricultural product processing enterprises above a designated scale. Mechanization level (X6): technological innovation and the popularization of intelligent agricultural services can accelerate the development of digital villages and agricultural and rural modernization. This is measured by the total agricultural machinery power per capita. Financial inclusion level (X7): financial inclusion can promote rural modernization by enhancing the accessibility and convenience of financial services in rural areas. This is represented by the comprehensive financial inclusion index. Standard of living (X8): changes in the consumption structure of rural residents are an important indicator of agricultural and rural modernization. This is measured by the Engel coefficient of rural residents. Urbanization level (X9): the advancement of urbanization promotes urban–rural integration, aiding in the development of digital villages and agricultural and rural modernization. This is measured by the urbanization rate.

5.3.2. Single-Factor Detection Analysis

The factor detection results are presented in Table 4. In 2014, the most significant driving factors, ranked by explanatory power, were ecological livability (0.769) > management system (0.762) > standard of living (0.757) > informatization level (0.753) > mechanization level (0.644). By 2020, these five factors remained significant, but their ranking had shifted to management system (0.761) > informatization level (0.760) = ecological livability (0.760) > standard of living (0.759) > mechanization level (0.634). Between 2014 and 2016, there was a notable increase in the driving effects of financial inclusion level (X7) and urbanization level (X9). This indicates that financial services in rural areas expanded in coverage and depth, and that urban–rural integration contributed to improvements in rural infrastructure and public services. The relatively small changes in values between 2018 and 2020 suggest that the impact of various factors on the spatial differentiation of the coordinated development of digital villages and agricultural and rural modernization had stabilized.

Throughout the study period, the overall ranking of the influence of various factors is as follows: ecological livability (0.760) > management system (0.758) > standard of living (0.756) > informatization level (0.754) > mechanization level (0.637) > agricultural scale (0.212) > financial inclusion level (0.188) > income level (0.173) > urbanization level (0.111). Among the significant factors, ecological livability is the most dominant, followed by management system, standard of living, informatization level, and mechanization level.

5.3.3. Factor Interaction Detection Analysis

Additionally, this study conducted factor interaction detection using the nine selected indicators. The results, as shown in Figure 7, indicate that the interaction effects between the driving factors are all greater than the effects of individual factors acting independently. The interaction effects manifest as both two-factor enhancement and nonlinear enhancement, suggesting that the spatial differentiation in the development of digital villages and agricultural and rural modernization is the result of multiple driving factors interacting with each other.

A comparison between 2014 and 2020 reveals a general decline in the driving force of interactions between factors. This suggests that, as the advancement of digital villages and agricultural and rural modernization in China progresses, the influence of factor interactions on the coupling coordination degree of the two systems decreases year by year. This trend indicates a gradual reduction in the spatial differentiation of the coupling coordination between digital villages and agricultural and rural modernization in China.

5.3.4. Driving Mechanism Analysis

Based on the results of both the single-factor detection and the factor interaction detection, this study identifies five key factors as the main driving forces influencing the coupling and coordination between digital villages and agricultural and rural modernization: ecological livability, management system, standard of living, informatization level, and mechanization level.

Ecological liability: In advancing the coupling process between digital villages and agricultural and rural modernization, the enhancement of pollution control systems ensured the sustainability of rural ecological environments, laying a foundation for livable rural settings. With strong policy support and the widespread application of digital technologies, pollution control facilities and management levels in rural areas significantly improved. The deployment of smart environmental protection systems enabled the real-time monitoring and precise management of environmental monitoring and pollution control, effectively reducing the impact of pollutants on water, soil, and air. This not only improves the quality of life for rural residents, but also establishes a solid foundation for agricultural and rural modernization.

Management system: The advancement of the agricultural management system showcases the digital transformation and enhancement of the agricultural product processing sector, propelling the modernization of the agricultural value chain. Specifically, the application of digital technologies such as big data analytics, artificial intelligence, and the Internet of Things not only improved production efficiency and quality control in agricultural product processing, but also optimized supply chain management and marketing strategies. The integrated use of these technologies enables processing enterprises to achieve refined management, reduce production costs, increase product added value, and enhance market competitiveness. By increasing the added value of agricultural products and extending the agricultural value chain, these advancements promoted the integration of primary, secondary, and tertiary industries, thereby accelerating the process of agricultural modernization.

Standard of living: The living standards of rural residents, particularly changes in their consumption patterns, highlight the improvement in their quality of life and inject vitality into the consumer market of digital villages. From an economic perspective, a decrease in the Engel coefficient indicates that the proportion of income spent on food is decreasing, reflecting an increase in income levels and an optimization of the consumption structure. This trend is especially pronounced in rural areas. As the Engel coefficient declines, rural residents’ consumption preferences are shifting towards higher-quality and more diversified goods and services. The expansion of the rural consumer market not only stimulates rural economic development, but also provides businesses with extensive market opportunities, thereby fostering the integration of urban and rural areas.

Informatization level: The development of informatization and digitalization in rural areas enhanced the information literacy of rural residents, enriched their cultural lives, and promoted rural informatization infrastructure. The expansion of cable television in rural areas objectively drove the improvement and upgrading of rural communication networks and power facilities, fostering the development of rural informatization platforms and advancing the high-quality development of digital villages. Through the internet, farmers can more easily access various types of information, including agricultural production techniques, market trends, and regulatory policies. The widespread use of e-commerce platforms has also enabled rural residents to more conveniently purchase a wide range of goods and services, further driving changes in consumption patterns. This not only improves farmers’ decision-making capabilities in modern agricultural production, but also enhances their market awareness and responsiveness.

Mechanization level: Mechanization and automation are crucial for transforming agricultural production methods and achieving agricultural modernization. The improvement in agricultural mechanization significantly boosts production efficiency, reduces labor intensity, and facilitates the transition from traditional to modern agriculture. As mechanization becomes more widespread, farmers can complete large-scale field operations in a shorter time, greatly increasing land use efficiency and crop yields. Additionally, the enhancement of total agricultural machinery power promotes the development of the agricultural product processing industry, increases the added value of agricultural products, and extends the agricultural value chain. This enables more farmers to be liberated from agricultural production and engage in other industries and services, fostering the diversification of the rural economy. The development of agricultural mechanization also provides new opportunities for rural infrastructure construction. Mechanized production imposes higher demands on infrastructure such as roads, storage, electricity, and water resources, thereby objectively advancing the modernization of rural infrastructure.

The analysis shows that the interaction between the drivers is stronger than the independent effect of a single factor, which is manifested in two-factor enhancement and nonlinear amplification. This implies that the spatial heterogeneity of digital countryside and agricultural rural modernization is the result of multi-factor interaction. However, from 2014 to 2020, with the development of the digital countryside and agricultural rural modernization, different regions and periods have different development stages. In more developed areas, digital infrastructure and agricultural modernization are more mature, while in most lagging areas, the speed and degree of the development of digitization and agrarian modernization are inconsistent, leading to an increasing or decreasing trend in the interaction between the two factors. Specifically, the factors with decreasing interaction power include informatization level (X1), ecological livability (X4), agricultural management (X5), mechanization level (X6), and standard of living (X8). For developed regions, digital infrastructure and agricultural modernization have stabilized, and the synergies between the drivers have weakened from earlier times. This is manifested in the following ways: the interactions between informatization level and factors such as ecological livability, agricultural management, and mechanization level have weakened their driving force on the overall coupling and coordination, and the interactions between ecological livability and agricultural management, mechanization level, and standard of living have also declined. This is because, with the gradual improvement of technology, management, and ecosystems, the differences between regions narrow and spatial heterogeneity gradually decreases; thus, the interaction between the two factors has a relatively weaker effect on the coupling coordination degree.

Moreover, the factors with rising interaction power include informatization level (X1), mechanization level (X6), and standard of living (X8). Despite the overall trend of decreasing interaction power, the synergistic effect between certain factors still shows an increasing trend. In particular, this is the case for the interaction between the level of informatization and the standard of living, where the enhancement of information technology enables rural residents to access more resources and improve their living conditions, thus further promoting the application of information technology. The synergy between the mechanization level and the standard of living has also increased, with advances in mechanization increasing production efficiency, improving income levels, and enhancing agricultural production and the quality of rural life. The two-way enhancement of these factors drives the degree of coupled harmonization within the region.

6. Conclusions and Discussion

6.1. Conclusions

Based on panel data of 266 prefecture-level cities in China from 2014 to 2020, this study constructs an indicator system to measure the development levels of digital villages and agricultural and rural modernization, and employs the coupling coordination model to calculate the degree of coordinated development between the two systems. In addition, this study utilizes the geodetector model to analyze the spatiotemporal differences in the coupling coordination degree and explores the underlying driving mechanisms. The main conclusions are as follows.

From 2014 to 2020, China experienced a general rise in digital village development, with growth rates initially decreasing before accelerating again, signaling rapid progress in the construction of digital villages. However, regional disparities became more pronounced, with central and eastern areas seeing significant improvements, while the western regions lagged, resulting in an expanding regional divide. The level of agricultural and rural modernization also showed a continuous upward trend, with growth rates following a similar pattern of initial decline followed by gradual acceleration. However, significant regional differences in agricultural and rural modernization levels are evident, with spatial disparities becoming increasingly pronounced. Regions with higher levels of agricultural and rural modernization are primarily concentrated in central and eastern areas, including Jiangsu, Zhejiang, Fujian, Guangdong, Shandong, Hebei, Hunan, and Hubei. In contrast, the western regions have generally lower levels of development and slower growth, further widening the gap with the central and eastern regions.

The overall coupling coordination degree between digital villages and agricultural and rural modernization has been steadily increasing, rising from 0.475 to 0.570. This indicates a transition from a state of on the verge of imbalance to primary coordination, with a stable upward trend. In 2014, the coupling coordination level between digital villages and agricultural and rural modernization was generally low, with most cities across the country being on the verge of imbalance. By 2016, most cities moved to being on the verge of coordination or medium coordination. In 2018, the majority of cities reached primary coordination, with an increasing number of cities achieving intermediate coordination. By 2020, most cities progressed to either primary coordination or intermediate coordination. Overall, the coupling coordination degree between the development of digital villages and agricultural and rural modernization is in a grinding phase, gradually advancing towards a more coordinated stage.

The coupling coordinated development of digital villages and agricultural and rural modernization in China is the result of the combined influence of multiple factors. Ecological livability is identified as the most critical factor promoting the coupling development of the two systems. Additionally, the management system, standard of living, informatization level, and mechanization play crucial roles in driving their coordinated development. From the temporal perspective, the influence of income levels, agricultural scale, and urbanization on the coordinated development of digital villages and agricultural and rural modernization is declining. Conversely, the impact of informatization level and financial inclusion on their coordinated development is increasing over time.

6.2. Contribution to Theory

The theoretical significances of this study are as follows: (1) As for the research perspective, this study focuses on the two-way coupling relationship between digital countryside and agricultural and rural modernization, which complements the existing research perspective that mainly explores one-way influence. The digital countryside is intrinsically linked to the modernization process, and agricultural modernization provides support and application scenarios for the digital countryside, so the analysis from the perspective of coupling and coordination provides a new perspective for understanding the relationship between the two. (2) For the research object, this study focuses on the development of digital villages and agricultural and rural modernization at the municipal level, providing a complementary perspective to existing research that predominantly centers on the provincial level. In practice, municipal governments play a crucial role in advancing these initiatives by implementing policies, providing financial support, and developing specific plans. Therefore, by focusing on the municipal level, this study offers a more granular and detailed understanding of the development status of digital villages and agricultural and rural modernization. (3) Regarding the research content, this study integrates digital villages and agricultural and rural modernization into a unified analytical framework. While existing research often constructs indicator systems from various dimensions to measure the development levels of digital villages and agricultural and rural modernization, there is limited analysis of the coupling coordination mechanisms and the underlying influencing factors between them. This study provides theoretical insights for better understanding the internal driving mechanisms that promote the coordinated development of digital villages and agricultural and rural modernization. (4) In terms of the research paradigm, although existing studies have made preliminary explorations into the relationship between digital villages and agricultural and rural modernization, most of these are normative theoretical analyses lacking empirical quantitative evidence. This study adopts the entropy weight method, coupled coordination model, and geodetector model, which provide empirical support and expand the understanding of the complex relationship between digital countryside and agricultural and rural modernization.

6.3. Contribution to Practice

The practical implications of this study are as follows. This study reveals the uneven development of different regions, pointing out that the south-central and eastern regions are developing faster, while the western regions are lagging behind, relatively. By analyzing the application of digital technology in agriculture, this paper emphasizes the necessity of strengthening digital infrastructure construction and promoting the digital transformation of agriculture, and proposes that the construction of digital villages is key to driving the modernization of agriculture and rural areas, which is of great practical guidance significance. This study also shows that ecological livability, management system, living standard, informatization level, and mechanization level are the key driving factors affecting the coupling and coordination of digital countryside and agricultural and rural modernization, the development of which not only enhances the efficiency of rural production, but also promotes the sustainable development of the ecological environment and the extension of the agricultural value chain. Together, these factors contribute to the diversification of the rural economy and the coordinated development of the region.

6.4. Recommendations

The government should formulate corresponding development strategies according to the differences between regions in order to promote the coordinated development of digital villages and the modernization of agriculture and rural areas. (1) From the perspective of regional development, in the western region, priority should be given to the development of digital infrastructure and the promotion of the in-depth integration of digital technology and agriculture, especially in making breakthroughs in ecological livability and mechanization levels. Improving the rural ecological environment through smart environmental technologies will not only help improve agricultural productivity, but also lay the foundation for sustainable development. In the central and eastern regions, the government should build model cities for the coupled development of digital countryside and agricultural modernization, and give full play to its leading role in modernizing the management system and improving living standards, so as to drive the common development of the surrounding areas and promote inter-regional coordination and linkage. (2) From the perspective of governmental hierarchical structure, governments at all levels should strengthen synergy and cooperation. The central government should formulate targeted policies according to the resource endowment of each region, focusing on promoting the popularization and deepening of informatization and mechanization. Provincial governments should play a key role in facilitating the free flow of capital, technology, and talent, promoting the digitalization and upgrading of agricultural management, extending the agricultural value chain, and enhancing the added value of agricultural products. Municipal governments, on the other hand, should accelerate the construction of big data platforms for agriculture and rural areas, improve information sharing and decision support capabilities, help farmers access market information in a timely manner, optimize agricultural production and business decisions, promote the diversification of the rural economy, and contribute to the process of urban–rural integration.

6.5. Limitations and Future Work

This study also presents several limitations and leaves a few avenues for future research. Firstly, in terms of data acquisition, this study faced challenges due to limitations in data availability across different regions. Consequently, the measurement of digital villages and agricultural and rural modernization development was not as comprehensive as desired. Some regions lacked sufficient data, making it difficult to fully capture the development landscape across all prefecture-level cities. Secondly, this study focused on the development of digital villages and agricultural and rural modernization at the prefecture level, without delving into a more granular analysis at the county level. Future research should aim to address this by exploring more detailed dimensions, such as county-level administrative divisions and regional typologies, to provide a more nuanced understanding. Thirdly, this study focuses on the development of digital villages and agricultural and rural modernization in China, with limited attention to applications in other countries, lacking a summary and comparison of international experiences. Considering China’s unique context and differences from other nations, this study aims to summarize the process, characteristics, and current status of the coupling development of digital villages and agricultural and rural modernization in China, providing a reference for the digital transformation and development of rural areas in other countries. In future research, we will place greater emphasis on comparing different countries and summarizing international experiences, thereby offering a more comprehensive perspective on digital villages and rural development. Fourth, the measurement indicators used in this study are all derived from existing research. Although these indicators have a certain degree of validity, they may also have limitations and may not fully reflect the variables being measured. In future research, we will make greater efforts to address this issue by selecting more appropriate measurement indicators through relevant existing studies and practical research in order to measure the variables more accurately.