Digital Economy Development, Rural Land Certification, and Rural Industrial Integration

Abstract

Rural industrial integration refers to the process of relying on technological innovation and industrial model innovation to promote the optimal allocation of factors such as land, capital, and labor in rural areas, promote the optimization of a rural industrial structure, rural property rights stability, agricultural and rural development, and ultimately achieve the extension of the agricultural industry chain and improvements in farmers’ income levels. In order to grasp the mechanism of digital economy and rural land certification on rural industrial integration, this paper analyzes the impact of digital economy development and rural land certification on rural industrial integration based on the 2011–2021 panel data of the Yangtze River Economic Belt at the municipal level. Research has shown the following. (1) The early development of the digital economy significantly promoted the integration of rural industries. After overcoming the turning point of the digital economy, the digital economy has a restraining effect on the integration of rural industries; in terms of controlling variables, the education level of rural residents, regional economic development level, per capita disposable income of rural residents, and rural power generation all significantly promote the process of rural industrial integration. (2) Rural land certification has played a positive transmission role by confirming, reviewing, and registering land ownership in accordance with the law, clarifying the ownership of land rights, providing a stable property rights foundation for rural industrial integration, and effectively promoting rural industrial integration. (3) The impact of the digital economy on rural industrial integration has a single threshold effect, and after crossing the threshold value of a rural population, the positive impact of the digital economy on industrial integration is more significant. (4) The development of the digital economy has significantly improved the integration level of rural industries in this region and neighboring areas. The above conclusions have important policy implications for further leveraging the digital economy to promote the integration of rural industries and the modernization of agriculture and rural areas.

1. Introduction

Promoting the integration of rural industries is an important way to build a modern rural industrial system and achieve the revitalization and high-quality development of rural industries. In 2024, the No. 1 central document proposed to promote rural industrial integration through industrial development, quality control, green concept guidance and industrial upstream, midstream and downstream combination, to optimize rural industrial integration development projects, and to actively cultivate new industrial integration formats such as ecotourism, forest health, leisure and camping. In recent years, the level of integrated development of rural industries in China has been increasing year by year. However, there are still problems such as low added value of agricultural products, short industrial chains, imperfect interest linkage mechanisms for industrial integration, insufficient talent for industrial integration, and low brand influence of agricultural products, which restrict the high-quality development of rural industrial integration. Therefore, exploring the mechanism and related influencing factors of rural industrial integration development is of great significance for promoting the high-quality development of rural industrial integration, rural economic transformation and upgrading, and increasing farmers’ income.

Research on the Concept and Impact of Rural Industry Integration: The concept of rural industry integration was first proposed by Imamura [1] in Japan. He highlited that modern agriculture is the integration of multiple industrial chains such as production, processing, sales, and services, and achieves the improvement of economic benefits in rural areas through close cooperation between industries. Yoffie et al. [2] believes that the driving force behind the integrated development of rural industries comes from the promotion of government policies and the innovation of traditional technologies. Beck et al. [3] believes that the support of rural financial institutions is a powerful factor in promoting the integrated development of rural industries. Koller et al. [4] believes that rural industrial integration can bring resource scale advantages, thereby improving enterprise management level and product quality, and winning higher profits for enterprises. Shen et al. [5] cited the development of the tea industry in Xiamei Town in Fujian Province, which drives online tea sales and offline tea cultural tourism through live-streaming sales, e-commerce, short videos and other forms to promote the deep integration of rural agriculture, culture and tourism industry.

The digital economy is a series of economic activities that use digital knowledge and information as key production factors and modern information networks as important carriers, and effectively use information and communication technology as an important driving force for efficiency improvement and economic structure optimization [6]. The 14th Five-Year Plan for National Informatization pointed out that “the digital economy should lead the construction of modern industrial systems, accelerate the all-round transformation of production factors, organizational forms, and business models, and comprehensively promote rural revitalization” [7]. In recent years, the Chinese government has successively issued policies such as the “Outline of Digital Rural Development”, “Digital Agriculture and Rural Development Plan (2019–2025)”, and “Guidelines for Digital Rural Construction 2.0”, aiming to promote efficient collaboration in the production, processing, and sales of rural agricultural products through the development of the digital economy, lead the all-round transformation of the rural industrial system with the digital economy, accelerate the cultivation of new forms of rural industry, and provide a practical and feasible path for the integration of rural industries in various regions.

The digital economy is accelerating the transformation of the operational mechanisms and formats of rural industries, which will have a further impact on the process of rural industry integration. First, the digital economy has accelerated the blurring of industrial boundaries [8], lowered logistics barriers [9], and reduced the information costs of rural workers [10]. Second, digital industrialization has created favorable conditions for the development of new agricultural formats: the digital economy has promoted the planning and utilization of rural spatial resources [11]. Zhang [12] research shows that digital technology is a breakthrough point for the development of rural industries, demonstrating that the reasonable application of digital economy and industry integration according to local conditions can promote the high-quality development of rural economies, enhance rural autonomy and cohesion, and assist in the integrated development of rural industries. Nishijima et al. [13] found that in the communication technology market in Brazil from 2005 to 2013, the popularization of information and communication technology effectively narrowed the digital divide, reduced the cost of rural residents accessing market and policy information, and promoted the integration of rural industries and the modernization of rural infrastructure [14]. The digital economy enhances the demand of farmers for agricultural machinery services [15], all of which provide solid support for the integration of rural industries. Third, the development of digital inclusive finance has a positive promoting effect on the integration of rural industries: Ge [16] found that digital finance effectively reduces the threshold for financial services and improves the accessibility of financial services through technological innovation and the construction of risk sharing mechanisms, effectively assisting in the integration of rural industries [17]. However, some scholars found that the development of digital economy may have some negative impact on industrial development: owing to the development of urbanization and the digital divide, the digital economy has widened the urban–rural income gap and hindered the flow of high-quality resources to rural areas [18]. Digital inclusive finance increases the risk of farmers’ excessive consumption and increases the adverse impact on farmers, especially for farmers with weak time preference [19].

Land is the fundamental resource for rural industrial integration. The large-scale operation and circulation of land will effectively improve the allocation of land resource and provide essential support for rural industrial integration. The Ministry of Agriculture and Rural Affairs and the Ministry of Natural Resources of China have successively emphasized that land planning and management are basic work for rural revitalization, and require overall planning of rural primary, secondary and tertiary industry development space, protection of rural infrastructure construction space and residential construction, and providing strong land support for rural industrial integration, the development of the digital economy provides strong technical support for the work of confirming agricultural land rights [20]. Ali et al. [21] found through their research on the national land certification reform in Rwanda that online access to land certification information ensures the security of land certification data and reduces the risk of tampering. Melese et al. [22] show that the use of land transaction platforms can effectively improve the efficiency of rural agricultural production. Additionally, land certification can enhance farmers’ sense of security regarding land ownership [23]; furthermore, land certification has stimulated the entrepreneurial spirit of farmers and helped form new industries in rural areas [24].

From the perspective of research methods, the evaluation of the integration of the digital economy and rural industries mainly uses the entropy method [25], the principal component analysis method [26], and the comprehensive index method [27] for analysis. Among them, the principal component analysis method and the comprehensive index method both have a strong qualitative analysis color and are easily influenced by individual subjective wishes. The entropy method determines the weights of each indicator through objective weighting, which is more scientific and reduces the bias caused by expert subjective judgment. Therefore, this study evaluates the integration of digital economy and rural industries using the entropy method. Research on the integration and development mechanisms of the digital economy and rural industries mainly includes theoretical methods [28], case analysis methods [29], double-fixed-effects models [30], PSM-DID models [31], and other methods. Theoretical and case studies mainly focus on qualitative analysis and make judgments based on individual subjective abilities, whereas the double-fixed-effects model and PSM-DID model are quantitative analyses. The double-fixed-effects model solves the problem of missing variables over time and regional variables, while the PSM-DID model considers the issue of policy implementation at different points. In order to enhance the objectivity and practicality of the research, this study adopted a quantitative analysis method. This study explores the impact of digital economy development on rural industrial integration in recent years. Therefore, using a spatial double-fixed-effects model is more suitable for this study. In addition, scholars have used mediation effect models to study the mediating factors of the digital economy in promoting rural industry integration [32], but few scholars have used land certification as a mediating variable. This study uses land certification as a mediating variable to enrich the study of the digital economy on rural industry integration.

In the context of a new round of socialized digital transformation, various regions have introduced policies and plans for the development of digital rural areas in various regions and new forms of digital economy continue to emerge. It should be noted that the depth of integration between the digital economy and the real economy is not enough, the rule system for the development of the digital economy is not sound, and the layout of the digital economy industry is unreasonable. Is the impact of different stages of digital economy development on the integration of rural industries the same? Is there any other intermediary mechanism between the integration of the digital economy and rural industries? Is the impact of the digital economy on rural industrial integration affected by the change of specific threshold range? Is there a spatial spillover effect of the digital economy on the integration and development of rural industries? These issues urgently need to be addressed by scholars. The contribution of this study is as follows. First, a spatial Durbin model was constructed to discuss the direct impact of the digital economy on rural industry integration. In addition to providing a new perspective for local governments to study rural industry integration, this method also enriches and improves the theory of rural industry integration. Second, this study uses land certification as a mediator variable and constructs a mediation effect model to empirically verify whether the digital economy can affect rural industry integration through land certification, which can enrich the research on the relationship between the digital economy and rural industry integration. Third, using rural population as the threshold variable, we constructed a threshold effect model to explore whether there is a threshold effect on the impact of the digital economy on rural industrial integration. Fourth, the impact of the digital economy on the integration of rural industries was discussed to determine whether there is a spatial spillover effect. This study can provide a reference for local governments to develop the digital economy and the integration of rural industries.

The rest of the article is arranged as follows: the Section 2 analyzes the mechanism of rural industrial integration, digital economic development and rural land ownership. The Section 3 measures and evaluates the development level of digital economy and rural industrial integration in various regions, as well as the data, models and variables used in this paper. The Section 4 analyzes the development trend of the digital economy and industrial integration in the Yangtze River Economic Zone and gives empirical results. The results are discussed in Section 5, and conclusions and policy recommendations are given in Section 6. The technical roadmap of this article is as follows (Figure 1):

2. Research Question

2.1. The Impact of Digital Economy Development on Rural Industry Integration

The rapid development of the digital economy will drive rural industry integration, especially in its early stages. This positive effect is particularly evident. Firstly, digital infrastructure has effectively enhanced the information circulation capacity of rural areas. Technologies such as the Internet of Things, big data, and cloud computing have been applied to agricultural production to achieve precision production; monitoring crop growth data helps farmers manage their scientifically guided cultivation practices and promotes the transformation of agricultural economic models. Secondly, digital technology innovation has facilitated the cross-border development of rural industries; the maturation of digital technologies, represented by laser communication, communication navigation, and remote sensing integration, has further improved the transmission efficiency and stability of rural geographic information data. The development of the satellite internet industry enables comprehensive data capture throughout the rural industry chain. A digital application platform integrating smart agriculture, rural environmental monitoring, rural land certification, disaster warning and real-time communication meets the all-round information needs of rural industry development; this platform objectively expedites the restructuring of rural factor resources and the integration and development process of rural industry. Furthermore, the financial innovation of digital inclusive finance has enhanced the credit evaluation and risk control capabilities of financial institutions. It has diversified financial products and services for rural entrepreneurs and farmers, promoted the optimization of the rural business environment, and provided more precise and sustainable financial support for the integrated development of rural industries [33].

When the digital economy reaches a certain stage of development, its promoting effect on rural industrial integration will undergo certain changes due to factors such as excessive investment and technological overload. First and foremost, an overabundance of investment in digital infrastructure can inhibit rural industry integration. Overinvestment in digital industries may lead to lower actual utilization efficiency of digital infrastructure compared to its design capacity. Maintenance costs resulting from underutilized equipment impose financial strains and economic losses on local governments and farmers. With the development of digital industrialization, various digital products emerge to facilitate the development of rural industries. However, there may be functional redundancy among different products; for instance, both the rural environmental monitoring big data platform and the land resource management big data platform might compile regional agricultural land ownership data. The functional overlap of data platforms may increase unnecessary data management and operation costs [34]. In addition, the development of digital innovation inevitably generates a considerable amount of invalid and low-quality information, which poses challenges for investors in their decision-making process [35]. Designers often set up multiple authentication and additional procedures to enhance the novelty of digital products, which reduces user efficiency and to some extent infringes on user data security and privacy; these factors will have adverse effects on rural industry integration.

Based on the analysis above, the following is proposed:

Hypothesis 1.

The impact of digital economy development on rural industrial integration exhibits an “inverted U-shaped” non-linear characteristic.

2.2. The Mediating Impact of Rural Land Certification in the Impact of the Digital Economy on Rural Industrial Integration

Firstly, digital technology has significantly improved the management efficiency of land information data collection and storage. Comprehensive rural data collection can be achieved through low-orbit satellites and unmanned aerial vehicle technology. Artificial intelligence technology processes and analyzes massive data to reveal land use patterns and facilitates scientifically informed land use planning based on comprehensive land and environmental information. Furthermore, blockchain technology facilitates the transaction of land information among governments, enterprises, and individuals [36]. In addition, the rural land certification platform provides online applications and query functionalities for agricultural land rights verification, helping the government improve efficiency in this regard and reduce communication costs between the government and farmers. At the same time, over-investment in the digital economy may lead to policy formulation lagging behind investment in the digital industry, and the lack of necessary policy guidance will have a serious negative impact on the efficiency of farmland tenure determination, which may lead to problems such as incomplete data collection, inaccurate analysis, and imperfect service platforms in the process of farmland tenure determination. This could increase costs and complexity, further affecting the efficiency and accuracy of rural land certification.

Rural land certification fully guarantees the rights and interests of farmers and enhances their enthusiasm for active involvement in the development of rural industries. After rural land certification, farmers are granted entire land contracting rights, which ensures their post-investment benefits and stimulates their entrepreneurial enthusiasm. The development of rural homestays, leisure agriculture, and other industries serves as a driving factor for the development of new forms of rural industries. After confirming the ownership of agricultural land, farmers can obtain complete land mortgage rights for credit financing and relatively equal status with financial institutions in credit negotiation. This enhances the availability of credit for farmers and provides sufficient financial support for rural residents to invest and thrive in rural areas. Secondly, rural land certification lays the foundation for the scale and operation of agriculture. With land rights certified, large-scale integration of land resources becomes possible, which makes it possible to address land fragmentation. This facilitates the establishment of new management entities, such as collective economies, which can lay out large-scale planting initiatives and operate modern, large-scale digital agricultural machinery facilities. All these efforts contribute to the vertical integration and high-quality development of the agricultural industry. In addition, rural land certification can help the government formulate accurate rural development plans and ensure the rational and stable utilization of land, and it assists enterprises in formulating accurate investment plans and business strategies [37].

Based on the above analysis, the following is proposed:

Hypothesis 2.

Rural land certification plays an intermediary role in the impact of the digital economy on rural industrial integration.

2.3. Threshold Effects in the Impact of the Digital Economy on Rural Industrial Integration

Variations in rural population size create threshold effects on the influence of the digital economy on rural industrial integration. When the rural population is low, the digital economy does not have a pronounced effect on rural industrial integration. However, when the rural population surpasses a certain threshold, the digital economy significantly affects rural industrial integration. The new economic geography model [38] suggests that the distribution of human capital influences the future efficiency of population clustering and regional economic capacity. As the rural population increases, the agglomeration effect becomes evident, driving rural economic growth and optimizing labor allocation [39]. This agglomeration effect impacts the inputs of production factors such as markets, labor, capital, and technological progress, further stimulating economic growth. With the expansion of various input factors, the “learning effect” of the workforce also strengthens, contributing to improved labor productivity and overall economic efficiency.

First, an increased rural population creates a substantial demand market for digital technology innovation, the testing of new products, and the implementation of new business models. This, in turn, accelerates the rapid development of the digital economy and rural industries, expediting the production and dissemination of information, technology, and knowledge. The larger population scale offers market advantages that are crucial for promoting the complementary coexistence of industries at different levels, effectively fostering rural industry integration.

Second, an expanding population scale implies an increase in labor resources, providing robust human capital support for the development of the digital economy and rural industries. This drives the acceleration and improvement of infrastructure construction, enhancing development speed and quality in areas such as transportation, electricity, water conservancy, and communication. It also provides sustained momentum for the widespread adoption of digital technology and industrial technology upgrades, thereby promoting improvements in production efficiency and the development of innovative capabilities in rural areas.

Third, a larger population scale brings greater innovation potential. With changing demands and continuous innovation in digital industries and service models, a growing population provides positive support for exploring new products and implementing new business models. As a considerable number of high-quality individuals enter rural areas, technological innovation capabilities and industrial development are further enhanced, actively contributing to the transformation and upgrading of rural industries.

Fourth, an increased population promotes the expansion and extension of the rural industrial chain. With a growing workforce in related industries, there are more interrelated effects in the production, processing, sales, and service sectors. This facilitates the formation of a more complete industrial chain and further promotes integrated development within the industry.

Based on the above analysis, the following hypothesis is proposed:

Hypothesis 3.

The Rural population exhibits a threshold effect in the impact of the digital economy on rural industrial integration.

2.4. Spatial Spillover Effects of the Impact of Digital Economy Development on Rural Industrial Integration

Firstly, the construction of rural digital infrastructure is a large-scale project that spans multiple administrative regions and requires coordinated planning. This involves the installation of optical cables, the deployment of industrial internet systems, the construction of data centers and power supplies, and the planning of digital industries, all of which extend across municipal and provincial boundaries. It is a comprehensive initiative involving various provinces and linking multiple industries, such as building materials, machinery manufacturing, postal services, finance, and public facility management. Consequently, the development of digital infrastructure not only benefits rural industrial integration locally but also has a spillover effect on the rural industrial integration in surrounding areas.

Second, in the process of digital industrial development, the widespread application of digital technology ensures that the digital economy’s influence extends beyond specific administrative boundaries. For example, a cross-regional comprehensive platform for trading agricultural products not only boosts local sales but also enhances sales in neighboring areas. Similarly, a digital rural comprehensive governance platform effectively coordinates governance efforts both locally and in surrounding regions. This platform establishes a cross-regional social security network and fosters a supportive social environment for the integration of rural industries across these areas. The experiences gained from local digital economy development provide crucial insights for improving rural industry development and management in adjacent regions. Moreover, the development of e-commerce platforms facilitates the flow of agricultural product sales, thereby integrating production, processing, and sales for holistic development. This strengthens economic exchanges and cooperation between regions, promotes complementary advantages, and fosters a collaborative development model.

Third, digital inclusive finance has low entry barriers and high efficiency. By leveraging technologies such as big data and blockchain, it can capture user information more accurately, overcoming the difficulties traditional financial institutions face in information security and risk control. Additionally, digital inclusive finance can effectively overcome the digital divide [40], which provides strong financial support for the collaborative development of rural industries across regions, enabling rural residents to accelerate the transformation and upgrading of traditional agriculture and further fostering the integrated development of rural industries. Digital inclusive finance can effectively eliminate information asymmetry, promote innovation and development of local enterprises, and support technological progress and communication in neighboring regions.

Based on the above analysis, the following hypothesis is proposed:

Hypothesis 4.

Digital economy development has a significant spatial spillover effect on rural industrial integration.

3. Methodology

3.1. Study Area Selection and Data Source

The study area comprises rural areas from 108 prefecture-level cities in 11 provinces and regions along the YREB (Figure 2). The YREB is composed of 11 provincial-level administrative regions, spanning the eastern, central, and western geographical regions of China. It holds a pivotal position in regional development and stands as China’s most significant inland economic belt. Since the beginning of the 14th Five-Year Plan, various provincial and municipal governments within the YREB have consistently promoted digital economy development, fostered the clustered development of digital industries, and created a favorable environment for the advancement of the digital economy. Investigating the mechanistic effects of the digital economy in empowering industrial integration in the YREB is crucial for exploring new pathways for the development of rural industries in China as influenced by the digital economy. This research is significant for propelling the modernization of agriculture and rural areas.

The research data were sourced from various publications, including “China Statistical Yearbook”, “China Rural Statistical Yearbook”, “The Peking University Digital Financial Inclusion Index of China (2011–2020)”, and “China Digital Rural Development Report”, as well as provincial statistical yearbooks, provincial reports on national economic and social development, the National Bureau of Statistics website, and provincial rural statistical yearbooks. In cases where specific trend data were incomplete, interpolation and aggregate growth rate methods were employed to supplement the data based on indicator characteristics and trends.

3.2. Benchmark Regression Model

Based on the theoretical analysis and literature review in Section 2, and with the aim of examining the impact of the digital economy on rural industrial integration, we followed the research approach of Haans et al. [41] for testing nonlinear relationships. We constructed two models: one incorporating explanatory variables with linear relationships (Model 1) and the other with quadratic terms added (Model 2). These models were formulated to assess the hypothesized “inverted U-shaped” relationship between the digital economy (Dig) and rural industrial integration (Ind). The models are as follows:

$$In{d}_{i,t}={\alpha }_0+{\alpha }_{1}Di{g}_{i,t}+{\alpha }_i{X}_{i,t}+u_i+v_t+{\epsilon }_{i,t}$$

(1)

$$In{d}_{i,t}={\beta }_0+{\beta }_{1}Di{g}_{i,t}+{\beta }_2{Di{g}_{i,t}}^2+{\beta }_i{X}_{i,t}+u_i+v_t+{\epsilon }_{i,t}$$

(2)

In the above models, $i$ represents prefecture-level cities, $t$ denotes the year, and $In{d}_{i,t}$ represents the industrial integration development index of city $i$ in a specific year $t$. $Di{g}_{i,t}$ is the digital economy development level for city $i$ in year $t$. $X_{i,t}$ is a set of control variables, including rural education level $Ed{u}_{i,t}$, rural economic development level $Ec{o}_{i,t}$, per capita disposable income of rural residents $\mathrm{Re}{v}_{i,t}$, and rural electricity consumption $Po{w}_{i,t}$, $u_i$, $v_t$ represents fixed effects of region and time, and ${\epsilon }_{i,t}$ represents the error term.

3.3. Mediation Effect

To examine whether rural land certification reform mediates the impact of digital economy development on rural industrial integration, we extend Equation (1) and introduce a mediation mechanism. The mediation analysis model is as follows:

$$La{n}_{i,t}={\eta }_0+{\eta }_{1}Di{g}_{i,t}+{\eta }_{2}Di{g}_{i,t}^2+{\eta }_i{X}_{i,t}+u_i+v_t+{\epsilon }_{i,t}$$

(3)

$$In{d}_{i,t}={\theta }_0+{\theta }_{1}Di{g}_{i,t}+{\theta }_{2}Di{g}_{i,t}^2+{\theta }_{3}La{n}_{i,t}+{\theta }_i{X}_{i,t}+u_i+v_t+{\epsilon }_{i,t}$$

(4)

In the above models, $La{n}_{i,t}$ serves as the mediating variable, representing rural land certification, measured by the area of rural land cultivated, ${\eta }_1$, ${\eta }_2$, ${\eta }_i$, ${\theta }_1$, ${\theta }_2$, ${\theta }_3$, ${\theta }_i$ distribution represents the elastic coefficient of $Di{g}_{i,t}$, $Di{g}_{i,t}^2$, $X_{i,t}$, $La{n}_{i,t}$, $u_i$, $v_t$ represents fixed effects of region and time.

3.4. Threshold Method

Considering the limitations of bidirectional fixed effects in testing nonlinear increasing effects, we employ a panel threshold model to analyze whether a threshold effect exists in the relationship between digital economy development and rural industrial integration. The panel threshold model is formulated as follows:

$$In{d}_{i,t}={\kappa }_0+{\kappa }_{1}Di{g}_{i,t}\times T(g{f}_{i,t}\le \phi )+{\kappa }_{2}Di{g}_{i,t}\times T(g{f}_{i,t}\ge \phi )+{\mu }_i+{\nu }_t+{\epsilon }_{i,t}$$

(5)

In the above models, $T(\cdot )$ is the threshold indicator function, and $\phi$ is the threshold variable, measured by rural population, and ${\kappa }_1$, ${\kappa }_2$ are the elastic coefficients of $Di{g}_{i,t}$.

3.5. Spatial Effects Model

Rural industrial integration often involves multi-regional linkage and the integration of various factors. Through agricultural big data processing platforms, large-scale agricultural operations and cross-regional resource allocation can be achieved, marking a new norm in rural industrial integration development. The digital economy’s impact on the reintegration of the industry chain not only positively affects local industrial integration but also promotes rural industrial integration in surrounding areas. Moran’s Index is used to measure the spatial correlations of rural industrial integration. The global Moran’s Index is calculated as follows:

$$I=\frac{n{\displaystyle \sum _{i=1}^n{\displaystyle \sum _{j=1}^n{W}_{ij}\times (in{d}_i-\overline{ind})}(in{d}_j-\overline{ind})}}{{\displaystyle \sum _{i=1}^n{\displaystyle \sum _{j=1}^n{(in{d}_i-\overline{ind})}^2}}}=\frac{n{\displaystyle \sum _{i=1}^{\mathrm{n}}{\displaystyle \sum _{j=1}^n{W}_{ij}\times (in{d}_i-\overline{ind})(in{d}_j-\overline{ind})}}}{S^2{\displaystyle \sum _{i=1}^n{\displaystyle \sum _{j=1}^n{W}_{ij}}}}$$

(6)

In the above models, $W_{i,j}$ is the spatial weight matrix, and $I$ represents Moran’s Index $I\in [-1,1]$. When $I>0$, a positive spatial correlation exists in rural industrial integration, suggesting spatial agglomeration. When $I<0$, a negative spatial correlation exists, indicating spatial dispersion. The smaller the $I$ value, the greater the spatial heterogeneity in rural industrial integration. $in{d}_i$ and $in{d}_j$ represent the levels of rural industrial integration for regions $i$ and $j$, respectively.

Based on Hypothesis 4 and relevant analysis, a spatial econometric model is established to examine whether digital economy development has spatial spillover effects on rural industrial integration, as presented in Equation (7):

$$In{d}_{i,t}={\sigma }_0+{\rho }_1{W}_{i,j}In{d}_{i,t}+{\lambda }_{i,j}{W}_{i,j}Di{g}_{i,t}+{\sigma }_{1}Di{g}_{i,t}+{\lambda }_{i,j}{W}_{i,j}Di{g}_{i,t}^2+{\sigma }_{2}Di{g}_{i,t}^2+{\sigma }_i{X}_{i,t}+{\sigma }_{3}La{n}_{i,t}+u_i+v_i+{\epsilon }_{i,t}$$

(7)

In the above models, ${\rho }_1$ represents the spatial lag coefficient, ${\sigma }_1$ is the elasticity coefficient of the core explanatory variable, and ${\lambda }_{i,j}$ is the spatial autoregressive coefficient for the core explanatory variable.

3.6. Variable Definitions

3.6.1. Explained Variables

The “Guiding Opinions of the General Office of the State Council on Promoting the Integrated Development of the Primary, Secondary and Tertiary Industries in Rural Areas” outlines clear requirements for the development of rural industrial integration. This includes the construction of comprehensive and diverse rural industrial integration models with complete industry chains. These guidelines also emphasize the promotion of market-oriented and specialized services in agriculture, exemplified by practices such as contract farming and large-scale field management. Drawing on existing research findings and considering data availability, we selected indicators for calculating the rural industry integration index from three perspectives: extension of the agricultural industry chain, utilization of agricultural multifunctionality, and integration development of agricultural services. We used the entropy method to measure and evaluate the rural industry integration index. The development index of rural industrial convergence measured by the entropy method was limited to 0–1; the higher the index is, the higher the level of integrated development of rural industries is, and when the rural industry integration index is elevated, it signifies that the distribution of weights among various indices within the comprehensive evaluation framework is more balanced and judicious. This reflects a superior performance across all relevant indices, indicating that rural industry integration has attained satisfactory development in multiple dimensions.

The specific details are outlined below:

Extending the agricultural industry chain is a crucial approach to enhancing agricultural competitiveness, boosting farmers’ income, and improving agricultural resilience. By connecting and integrating various stages of agricultural production, processing, and sales, the development of specialized services such as processing, agricultural management, and cold chain logistics for agricultural products contributes to increased efficiency and added value in the agricultural industry. In this study, we use indicators such as the proportion of the agricultural processing industry and per capita value added in the primary industry to measure this aspect.

The multifunctionality of agriculture refers to the utilization of functions beyond ensuring food supply and rural economic and social functions. It involves leveraging functions related to aspects such as ecological conservation, cultural heritage, and employment creation. Alongside ensuring food supply and rural economic functions, this involves cultivating new rural industries such as ecological agriculture, leisure agriculture, and health and wellness industries. In turn, this enhances the added value of rural resources and promotes high-quality rural development. This study measures this aspect utilizing indicators such as per capita production of major agricultural products, proportion of facility agriculture, and proportion of leisure agriculture.

The agricultural service industry is a crucial manifestation of promoting the integration of modern services with manufacturing and modern agriculture. The profound integration of agriculture with the service industry helps enhance the market value of agricultural products, optimize the rural industry structure, and improve efficiency and competitiveness, thereby achieving agricultural efficiency. This study uses the proportion of agricultural, forestry, animal husbandry, and fishery services to measure the development level of agricultural services.

3.6.2. Explanatory Variables

In accordance with the report titled “Statistical Classification of Digital Economy and Its Core Industries (2021)” compiled by the National Bureau of Statistics of China, digital economy development levels were quantified based on aspects such as digital infrastructure, digital industry development, digital innovation capabilities, and the level of digital inclusive finance, using the entropy method for measurement; the level of digital economy development was between 0 and 1, where a higher index indicates a higher level of digital economic development, and when the digital economy development index is high, the weight distribution of each index in the comprehensive evaluation is more reasonable, and the performance of each index is good, reflecting that the digital economy has achieved good development in all aspects. The indicator composition is presented in

Table 1. Evaluation index system for digital rural construction.

Target Layer	Criterion Level	Indicator Level	Indicator Meaning	Unit	Attribute	Weight
Dig (level of digital economy development)	Digital infrastructure	Broadband Internet infrastructure	Number of Internet users	(in 10,000 individuals)	+	0.01621
		Mobile Internet infrastructure	Number of mobile phone users	(in 10,000 individuals)	+	0.09672
	Development of digital industries	Information industry infrastructure	Number of computer software professionals	(in 10,000 individuals)	+	0.06525
		Postal and telecommunications industry infrastructure	Total telecommunications services volume	(in 10,000 yuan)	+	0.06651
			Total postal services volume	(in 10,000 yuan)	+	0.00239
	Digital innovation capability	Level of digital innovation output	Number of digital economy-related patents	(in units)	+	0.00715
		Support for digital innovation elements	Expenditure on science and technology	(in 10,000 yuan)	+	0.02806
	Digital inclusive finance	Breadth of coverage	Breadth of coverage of digital inclusive finance	/	+	0.3761
		Depth of usage	Depth of usage of digital inclusive finance	/	+	0.3416

Note: Constructed by the authors based on research needs.

.

The construction of digital infrastructure is a prerequisite for innovating the development model of rural industries, thereby creating conditions for smart agriculture, rural e-commerce, and other new rural industries. It serves as a link to promote the integration of agriculture with the secondary and tertiary industries. Following research by Barefoot et al. [42], the indicators used in the present study for digital infrastructure consisted of two components: broadband Internet infrastructure and mobile Internet infrastructure.

The development of digital industrialization strengthens the flow of information between rural industry operators and the market, facilitating the intensive and precise management of rural industries and effectively reducing the cost of rural industry integration. Drawing on Marshall et al. [43], the development of digital industries comprises two indicators: information industry infrastructure and postal and telecommunications industry infrastructure.

Digital innovation capabilities play a crucial role in building a modern industrial system and are a significant driving force for the socioeconomic development of rural areas. Referring to Nambisan et al. [44], digital innovation capabilities comprise two secondary indicators: level of digital innovation output and support of digital innovation elements.

Digital inclusive finance is a crucial means for improving the quality of financial services in rural areas, effectively reducing transaction costs for financial services, broadening financing channels for rural residents, and providing ample credit support for rural industry integration. Referring to Fu et al. [45], digital inclusive finance consists of two secondary indicators: coverage breadth and coverage depth. The depth of use of digital inclusive finance mainly measures the diversity of financial services provided by digital inclusive finance institutions, including payment services, credit services, insurance services, investment services and credit investigation services, mainly measured by the actual number of users of online financial services, the number of transactions per capita and the amount of transactions per capita. The coverage breadth of digital inclusive finance refers to the coverage degree of digital finance to service objects and service regions, which measures the accessibility and popularity of digital inclusive finance. Using the number of electronic accounts to measure this indicator, the popularization of electronic accounts breaks through the geographical location limitation, expands the coverage, provides the required financial services for residents in remote areas, and alleviates the geographical exclusion of financial service models.

3.6.3. Mechanism Variables

Rural land certification refers to the process of clarifying ownership, land use rights, and other property rights in a specific area through legal and policy provisions, including procedures such as land registration application, cadastral survey, ownership verification, registration, and issuance of land certificates. Rural land certification, by enhancing enthusiasm for land investment, accelerates the speed of land circulation, improves rural residents’ financing capabilities, and promotes rural labor division, thereby facilitating rural industry integration. Rural land certification reduces land transaction costs and effectively addresses issues related to unclear contract land areas, spatial problems, and registration books, addressing challenges in rural industry integration such as fragmented land and insufficient construction land. This, in turn, promotes the specialization of agricultural division of labor. The measurement is based on the area of rural land cultivated in various regions, as indicated in previous research by Song et al. [46].

3.6.4. Control Variables

Mitigating endogeneity issues resulting from omitted variables requires control for factors other than the digital economy development level that may influence rural industry integration. This study includes four control variables in the empirical analysis: the revenue of rural labor force (Rev), rural residents’ educational level (Edu), regional economic development level (Eco), and rural electricity generation (Pow).

With regard to the revenue of rural labor force, Galor et al. [47] found that the income level would affect the capital accumulation and technological innovation ability, thus affecting the level of economic growth. The higher the income level of villagers’ labor force in the same region, the higher the level of rural industrial integration and development. The income level of the labor force directly affects the purchasing power and investment enthusiasm of rural residents, driving the development of rural non-agricultural industries and rural capital accumulation. With the increase in income, farmers have more opportunities to receive education and training, which not only improves the employment competitiveness of farmers, but also attracts many talents to return to the countryside, injecting new vitality into the integration of rural industries. In addition, the increase in rural labor income helps to promote the extension of the industrial chain and the increase of industrial added value. Farmers can actively participate in the processing and sales of agricultural products to improve the added value of agricultural products. At the same time, the development of emerging industries such as rural tourism and rural e-commerce can further broaden the development space of rural industries. This study uses the per capita disposable income of rural residents to measure the income of rural labor groups. For the educational level of rural residents, Schultz’s [48] human capital theory posits that human capital plays a crucial role in economic activities. Improvement in educational levels is significant for enhancing farmers’ technical capabilities, improving agricultural production efficiency, and promoting rural industrial integration. Drawing on Galasso et al. [49], this study uses the average number of years of education for rural residents as a measure.

In terms of regional economic development level, the higher the level of economic development, the stronger the regional technological innovation capability, making cross-industry technology exchange and integration more feasible. A more well-structured industry and a relatively balanced structure across different levels of industry contribute to longer industrial chains. This study uses per capita regional gross domestic product (GDP) as a measure of regional economic development level.

Regarding rural electricity generation, the amount of electricity generated in rural areas plays a crucial role in rural infrastructure development. The higher the level of digital rural infrastructure construction, the stronger the degree of digital industrialization and innovation, contributing to the promotion of rural industrial integration. This study uses rural electricity generation as a measure of this indicator.

3.7. Descriptive Statistics

Table 2. Descriptive statistics.

Variable	Code	Maximum	Minimum	Variance	Mean	Standard Deviation	Sample Size
Industrial integration	Ind	0.803895	0.00334	0.008375	0.08854	0.091479	1188
Digital economy	Dig	0.863945	0.023259	0.008079	0.16524	0.089846	1188
Educational level	Edu	6.667984	0.410409	0.605973	2.08995	0.778115	1188
Economic development level	Eco	6,421,762	10,265	3.54 × 1010	77,182.7	188,089.7	1188
The income of rural labor force	Rev	42,189	3553	48,065,638	14,995.1	6930.02	1188
Rural electricity generation	Pow	61,973,400	0	1.21 × 1013	610,084	3,471,285	1188
Area of rural land certification	Land	10,295.13	87.3	1,876,267	1528.34	1369.192	1188
Rural population	Peop	1325	15.7194	18,034.83	205.102	134.2373	1188

presents the descriptive statistics for each variable. The industry integration level (Ind) exhibits a significant range between the maximum and minimum values, indicating substantial disparities in the level of industry integration across regions. The digital economy (Dig) shows a considerable difference in development levels across regions, with maximum and minimum values of 0.8639 and 0.0232, respectively. Variables such as per capita disposable income of rural residents (Rev), education level of rural residents (Edu), regional economic development level (Eco), and rural electricity generation (Pow) also display notable variations in their maximum and minimum values, highlighting the significant influence of various control variables on rural industry integration.

4. Results and Analysis

4.1. Analysis of the Rural Industrial Integration Index

Examining the overall development trend of rural industrial integration, we selected three time points: 2011, 2015, and 2020. To clearly illustrate the evolution of the spatial pattern, based on the rural industrial integration index of cities along the YREB from 2011 to 2021, we used an equal interval classification method and employed a consistent legend to create a colored map of rural industrial integration using ArcGIS 10.8 (Figure 3). This map shows that rural industrial integration demonstrates a trend of evolving from “point-like” to “patch-like” development.

From 2011 to 2020, the development pattern of industrial integration in the YREB underwent continuous restructuring. Zhejiang, Jiangsu, Shanghai, and Chongqing were consistently at the forefront of rural industrial integration development in the YREB, while Sichuan, Hubei, and Hunan were high-value areas for rural industrial integration development. From 2015 to 2020, the development of high-level industrial integration showed a trend of development from “blocky” to “flake”. The areas of low-level rural industrial integration decreased year by year, and the areas of high-level rural industrial integration gradually expanded, showing a positive spatial spillover effect based on the gradual expansion of the original high-level areas. In 2015, only some cities in the lower reaches of the Yangtze River, mainly concentrated in the Yangtze River Delta and its surrounding areas and Chengdu Chongqing areas, are high-level development areas of rural industrial integration. By 2020, except for a few cities, the cities affiliated to the Yangtze River Economic Zone have basically formed a high-level trend of rural industrial integration and development in the whole region.

4.2. Measurement of Digital Economy Development Level

4.2.1. Overall Trend

Based on the digital economic index system constructed in this study, the entropy method was used to measure the digital economy development index of 108 cities in the YREB. Figure 4 shows the comprehensive index of digital rural construction plotted as a line graph.

From Figure 4, it can be observed that the digital economy development level in the city areas of the YREB continuously improved from 2011 to 2021. The digital economy development index increased from 0.0063 in 2011 to 0.2354 in 2021. During the sample period, the YREB fully integrated upstream, midstream, and downstream resources and talent, steadily strengthened the foundation of digital economy development, optimized the macro-environment for digital economy development, enhanced the level of technological applications, and witnessed the flourishing development of new digital economic formats such as digital agriculture, culture, and tourism, rural e-commerce, smart agricultural equipment industries, rural Internet and related services. This created a favorable pattern for digital economy development.

4.2.2. Trend Analysis of Subsystems

Based on the digital economy development index mentioned above, the digital economies of the 108 cities in the YREB were divided into four subsystem indices. The bar chart depicting the trends is shown below (Figure 5).

In terms of subsystems, from 2011 to 2021, the development levels of digital infrastructure, digital industrialization, digital innovation capability, and digital inclusive finance in the YREB steadily increased. The level of digital inclusive finance increased from 0.3277 in 2011 to 1.1716 in 2021, digital infrastructure rose from 0.1761 in 2011 to 0.3639 in 2021, digital industrialization development increased from 0.0093 in 2011 to 0.1744 in 2021, and digital innovation capability rose from 0.0337 in 2011 to 0.0997 in 2021. Digital inclusive finance has the highest development level among the four systems, playing a positive role in narrowing the urban–rural “credit gap” and supporting rural industrial development. The digital infrastructure level showed annual increases, with a steady rise in the number of Internet domain names and continuous growth of mobile Internet users. The digital technological innovation level continuously improved, and digitalized services such as e-commerce, remote education, and telemedicine were widely promoted and applied in rural areas, providing convenient and efficient life services for rural residents. However, the synergy of digital rural construction was not strong during the sample period, and the full potential of digital technological innovation has not been fully realized. This is a key factor limiting digital economy development in the YREB at the current stage.

4.3. Benchmark Regression Results Analysis

First, the Wald and Hausman tests were employed to determine the appropriate benchmark regression model. The strong rejection of the null hypothesis in both Wald and Hausman tests indicates that the fixed effects model is superior to the ordinary least squares and random effects models. The Hausman test statistic was 62.28 and was significant at the 1% level. Therefore, the region–time double-fixed-effects model was used for the empirical analysis of the relationship between the digital economy and rural industrial integration. To address potential issues of serial correlation and heteroscedasticity, a region–cluster–robust standard error regression was employed.

Table 3. Benchmark regression results.

Variables	(1)	(2)	(3)
Dig	0.1837 ***	0.2164 ***	0.2117 ***
	(0.37)	(0.15)	(0.10)
Dig2	−0.0766 **	−0.0836 **	−0.0757 **
	(−2.36)	(−2.47)	(−2.16)
Lan			0.0038 **
			(2.48)
Edu		0.7347 ***	0.7569 ***
		(2.72)	(0.58)
Eco		0.4820 ***	0.7857 ***
		(1.01)	(0.47)
Rev		0.2849 **	0.1914 ***
		(2.40)	(0.59)
Pow		0.2839 ***	0.3702 ***
		(3.30)	(0.30)
_cons	0.0161 ***	0.0233 ***	0.1038 ***
	(1.67)	(1.89)	(1.27)
Regional fixed effects	Yes	Yes	Yes
Time fixed effects	Yes	Yes	Yes
N	1188	1188	1188
R2	0.8575	0.8733	0.8789

Standard errors in parentheses, ** p < 0.05, *** p < 0.01.

presents the estimation results of the impact of the digital economy on rural industrial integration after controlling for time and regional effects.

Table 3 reports the regression results on the impact of digital economy development on rural industrial integration. In column (1), the estimation results without considering mediator and control variables show a significant coefficient of 0.1837 for the digital economy at the 1% level. In column (2), the results with the control variables included indicate a significant promoting effect of digital economy development on rural industrial integration. Column (3) presents the results with both the control and mediator variables added, The control variables Edu, Eco, Rev, and Pow were all significant at the 1% level. The coefficient for the digital economy was 0.2117 in model 3 and significantly positive at the 1% level, preliminarily indicating an “inverted U-shaped” relationship between digital economy development and rural industrial integration.

However, according to Lind et al. [50], relying solely on the significance of the quadratic term of the explanatory variable is insufficient to determine an “inverted U-shaped” relationship. In this study, the “inverted U-shaped” three-step test proposed by Haans et al. [48] was used for a more comprehensive examination. First, the coefficient of the quadratic term for the digital economy is significantly negative, at −0.0766 in model 1. Second, calculating the slope at the lowest (dig = 0) and highest (dig = 0.8639) values of the independent variable in model 1, the slope is significantly positive at the lower limit (dy/dx = 0.1837), and significantly negative at the upper limit (dy/dx = −0.1962), with both at the 1% level. Finally, this study outputs the inflection point value (dig = 0.1578), which falls within the domain, and the maximum and minimum values of the digital economy are outside the 95% confidence interval, indicating an “inverted U-shaped” relationship between digital economy development and rural industrial integration.

Among the control variables, the regression coefficient for the education level of rural residents is 0.7374 and significant at the 1% level in model 2. This indicates that the education level of rural residents has a significant positive impact on rural industrial integration. Rural residents with higher education levels possess greater knowledge and skills, making them more likely to play an active role in the diversified process of rural industry integration. They adeptly use big data platforms for agricultural production management and online product sales, demonstrating a profound understanding of policy and industry information. This effectively contributes to the optimization of the industrial chain of rural enterprises and increases residents’ income. With the improvement of the level of economic development, rural industrial integration has received more financial and technical support, which can effectively improve agricultural production conditions and efficiency, and promote agricultural modernization and scale. The introduction of technology promotes the integration of agriculture, information technology, biotechnology and other industries, and enhances the market competitiveness of agricultural products. At the same time, economic development attracts talent inflow and provides talent support for rural industrial integration. In addition, the improvement of the level of economic development has also promoted the government’s support for the rural industrial integration policy and promoted the deep integration of various rural industries by optimizing infrastructure and strengthening policy guidance. The increase in per capita disposable income of rural residents implies that they will have more funds to invest in rural industries, thus promoting diversified development. This includes attracting capital, technology, and talent, thereby effectively improving production efficiency and product quality. It further drives the integration of traditional industries and cultivation of emerging industries in rural areas. The regression coefficient for rural electricity generation is 0.2839 and significant at the 1% level. With digital economy development, the trend in rural industrial integration becomes increasingly evident. An increase in rural electricity generation will provide stable and sufficient power, improving the investment environment and promoting industrial upgrading and transformation. This also supports the provision of stable power for agricultural mechanization, digitization, and related industries.

4.4. Mediating Effect

The results of the mechanism test are shown in

Table 4. Mediating effect test.

Variables	Ind	Lan	Ind
Dig	0.2164 ***	1.2391 **	0.2117 ***
	(0.15)	(0.08)	(0.10)
Dig2	−0.0836 **	−2.0809 ***	−0.0757 **
	(−2.47)	(−3.01)	(−2.16)
Lan			0.0038 **
			(2.48)
Edu	0.7347 ***	0.9120 ***	0.7569 ***
	(0.72)	(0.54)	(0.58)
Eco	0.4820 ***	0.7816 ***	0.7857 ***
	(0.27)	(0.43)	(0.47)
Rev	0.2849 **	0.3835 ***	0.1914 ***
	(0.40)	(0.57)	(0.59)
Pow	0.2839 ***	0.3525 ***	0.3702 ***
	(0.30)	(0.32)	(0.30)
_cons	0.0233 ***	0.1935 ***	0.1038 ***
	(1.89)	(1.13)	(1.27)
Regional fixed effects	Yes	Yes	Yes
Time fixed effects	Yes	Yes	Yes
N	1188	1188	1188
R2	0.8726	0.8733	0.8789

Standard errors in parentheses ** p < 0.05, *** p < 0.01.

. Comparing the regression results in columns (1) and (2), it can be found that the first-order coefficient of digital economy is significantly positive at the level of 1%, and the second-order coefficient of digital economy is significantly positive at the level of 5%. The absolute value of the digital economy coefficient undergoes a substantial change, indicating a positive impact of digital economy development on rural land certification.

Column (3) in Table 4 shows that the regression coefficient for rural land certification is 0.2117 and significantly positive at the 1% level. This implies that rural land certification partially mediates the impact of the digital economy on industrial integration. Rural land certification significantly promotes the development of rural industrial integration, thus supporting Hypothesis 2. Regarding the underlying reason, rural land certification enhances the status and discourse power of farmers in the integration of rural industries by safeguarding their legitimate rights and interests in the land. Rural land certification improves the efficiency of land use and provides more land resources for the integration of rural industries by clarifying the ownership relationship of land, and it encourages farmers to transfer their land use rights to enterprises with strength, technology, and market, promoting the integration of different industries in rural areas.

4.5. Endogeneity Test

First, the core explanatory variable is lagged by one period. The results are presented in column (1) of

Table 5. Endogeneity test.

Variables	Lagged First-Order Explanatory Variables	Lagged Second-Order Explanatory Variables	All Variables Lagged by One Order
Dig	0.1907 ***	0.1519 ***	0.1775 ***
	(0.06)	(0.60)	(0.08)
Dig2	−0.0465 ***	−0.0458 **	−0.0414 **
	(−2.63)	(−2.32)	(−2.09)
Edu	0.4653 ***	0.6935 ***	0.3394 ***
	(0.25)	(0.37)	(0.77)
Eco	0.6250 ***	0.5278 ***	0.1947
	(0.86)	(0.98)	(0.31)
Rev	0.3986 ***	0.5329 ***	0.4555 ***
	(0.17)	(0.27)	(0.41)
Pow	0.2986 ***	0.2006 ***	0.4300 ***
	(0.26)	(0.16)	(0.68)
_cons	0.0137	0.0103	0.0163
	(0.86)	(0.78)	(0.22)
R2	Yes	Yes	Yes
N	Yes	Yes	Yes
Regional fixed effects	0.8653	0.8581	0.8633
Time fixed effects	1188	1188	1188

Standard errors in parentheses ** p < 0.05, *** p < 0.01.

, where the coefficient for the lagged one-period digital technology impact on rural industrial integration is 0.1907 and significant at the 1% level. Incorporating the lagged one-period digital technology variable into the regression model continues to exhibit a significant promoting effect on rural industrial integration.

Next, the core explanatory variable is lagged by two periods. The results are shown in column (2) of Table 5, with the coefficient for the lagged two-period digital technology impact on rural industrial integration being 0.1519 and significant at the 1% level. Even with a two-period lag, digital technology continues to exhibit a significant promoting effect on rural industrial integration when incorporated into the regression model.

Finally, all variables were lagged for one period, as shown in column (3) of Table 5. The coefficient of the impact of the digital economy on rural industrial integration with all variables lagged for one period is 0.1775, which is significant at the 1% level. The digital economy still plays a significant promoting role in rural industrial integration.

4.6. Robustness Tests

Considering the possibility of bidirectional effects in which rural industrial integration may influence both digital economy development and the quality of rural land certification, we conducted robustness checks using four methods: reducing the sample size, narrowing the time window, replacing the model with a Tobit model, and deleting certain indicators, as shown in

Table 6. Robustness test (1).

Variables	Ind (Upstream)	Ind (Midstream)	Ind (Downstream)	Narrow Sample
Dig	0.2033 ***	0.2935 **	0.2356 ***	0.2131 ***
	(0.13)	(0.10)	(0.15)	(0.17)
Dig2	−0.1672 ***	−0.2801 **	−0.2053 **	−0.0954 ***
	(−0.12)	(−0.25)	(−0.23)	(−0.23)
Edu	0.5462 ***	0.2957 ***	0.5212 ***	0.3935 ***
	(0.02)	(0.01)	(0.01)	(0.01)
Eco	0.5882 ***	0.6353 ***	0.5343 ***	0..5263 ***
	(0.00)	(0.00)	(0.00)	(0.00)
Rev	0.4766 ***	0.2884 ***	0.4384 ***	0.3779 ***
	(0.01)	(0.01)	(0.02)	(0.00)
Pow	0.2668 ***	0.3495 ***	0.2824 ***	0.2491 ***
	(0.03)	(0.03)	(0.07)	(0.00)
_cons	0.0224	0.0058 ***	0.0825 ***	0.0660 ***
	(0.01)	(0.04)	(0.02)	(0.03)
R2	0.8482	0.8491	0.8837	0.9717
N	330	375	440	416
Regional fixed effects	Yes	Yes	Yes	Yes
Time fixed effects	Yes	Yes	Yes	Yes

Standard errors in parentheses ** p < 0.05, *** p < 0.01.

and

Table 7. Robustness test (2).

Variables	Robustness Test
	Tobit	Indicator Reduction 1	Indicator Reduction 2
Dig	0.2164 ***	0.1790 **	0.3752 **
	(0.24)	(0.23)	(0.23)
Dig2	−0.0836 ***	−0.0927 **	−0.0732 ***
	(−0.05)	(0.02)	(0.05)
Edu	0.5347 ***	0.2344 ***	0.3545 **
	(0.89)	(0.21)	(0.27)
Eco	0.4820 ***	0.5623 ***	0.5735 **
	(0.35)	(0.13)	(0.17)
Rev	0.2849 ***	0.3086 ***	0.2745 **
	(0.41)	(0.26)	(0.04)
Pow	0.2983 ***	0.2612 ***	0.3234 **
	(0.27)	(0.42)	(0.23)
_cons	−0.7579 ***	0.0237 *	0.382 ***
	(0.32)	(0.19)	(0.15)
R2	0.8362	0.8764	0.8521
N	1188	1188	1188
Regional fixed effects	Yes	Yes	Yes
Time fixed effects	Yes	Yes	Yes

Standard errors in parentheses * p < 0.1, ** p < 0.05, *** p < 0.01.

.

Reduce sample size: Tests on the upper, middle, and lower reaches of the Yangtze River were conducted separately. The time window was reduced, and data from 2013 to 2016 were selected for regression. The benchmark regression model was replaced with the Tobit model. The indicators of mobile Internet foundation, information industry foundation and post and telecommunications industry foundation that may have autocorrelation for the explanatory variables were deleted; the independent variable index was recalculated; the three subsystem indicators of agricultural product processing industry proportion, per capita output of major agricultural products and proportion of agricultural, forestry, animal husbandry and fishery services that may have autocorrelation for the explanatory variables were deleted; and the dependent variable index was recalculated. The results of the five methods of testing were basically consistent with the benchmark regression, indicating that the model has high robustness.

4.7. Threshold Effect

The baseline regression results reveal that the impact of digital economy development on rural industrial integration exhibits a non-linear “inverted U-shaped” pattern. To further validate the potential multidimensional effects of the digital economy on rural industrial integration, we introduced a rural population threshold variable and empirically examined the threshold characteristics influencing the relationship between the digital economy and rural industrial integration. Employing Hansen’s [51] “bootstrap” method with 300 iterations of repeated sampling, the threshold effect test and estimation results were obtained, as presented in

Table 8. Threshold effect test.

Explanatory Variable	Threshold Variable	Threshold Type	Threshold Value	Residual Sum of Squares (rss)	F-Statistic	p-Value	Test Results
X	Rural population threshold	Single threshold	125.3726	2.3742	45.35	0.02	Reject null hypothesis
		Double threshold	134.2347	1.4727	24.37	0.46	Accept null hypothesis
		Triple threshold	234.4728	2.1623	26.36	0.74	Accept null hypothesis

.

Table 8 shows that the p-value of the single threshold effect test was 0.0200, which is less than 0.05, with a corresponding F-value of 45.35. This indicates that the rural population, when used as a threshold variable, passes the test for single threshold significance, whereas dual and triple thresholds do not pass the significance test. Therefore, under the threshold effect of the rural population, the core explanatory variable (i.e., the impact of digital economy development on rural industrial integration) exhibits only a single threshold effect.

Table 9. Regression results of the panel threshold model.

Explanatory Variables	Edu	Eco	Rev	Pow	Thre ≤ 207.1578	Thre > 207.1578	Cons	Sample Size
Explained variables	0.012 (0.00)	0.000 (0.00)	0.000 *** (0.00)	0.000 (0.00)	0.361 *** (0.01)	0.425 *** (0.03)	0.024 * (0.02)	1188

Standard errors in parentheses *** p < 0.01, * p < 0.1.

reports the single threshold regression results. When the rural population is at a low level (thre ≤ 207.1578), the threshold regression coefficient of the digital economy on rural industrial integration is 0.361 and significant at the 1% level. When the digital economy crosses the development threshold, the regression coefficient of the digital economy on rural industrial integration is 0.425 and significant at the 1% level. This indicates that the impact of the digital economy on rural industrial integration varies significantly under different rural population conditions. With an increase in the rural population, the positive effect of the digital economy on rural industrial integration is enhanced. This suggests that the amplification effect of the digital economy is magnified under the condition of an increasing rural population, which supports Hypothesis 3.

4.8. Spatial Effects Analysis

To accurately understand whether a spatial effect of the digital economy on rural industrial integration exists, based on Hypothesis 4 and Section 3.5, Moran’s I index is employed to reveal its spatial autocorrelation. Upon calculation, under both the adjacency and geographical weight matrices, the p-values of the spatial autocorrelation model from 2011 to 2021 are all below 0.1, indicating statistical significance. This indicates a significant spatial autocorrelation in the observed values for rural industrial integration, which allows for spatial econometric models to be utilized in the regression analysis

Before conducting the regression analysis, a fixed-effects model was selected based on the results of the Hausman test. Owing to the existence of individual heterogeneity in the study sample, individual and time effects need to be controlled for in the research process. The regression results are presented in

Table 10. Results of spatial Dubin model effect decomposition of the impact of the digital economy on industrial integration.

Variables	Direct	Indirect	Total
X	1.8371 ***	0.3361 ***	2.1732 ***
	(3.37)	(3.47)	(3.25)
EX	−0.5651 ***	−0.9372 ***	−1.5023 ***
	(−0.03)	(−0.07)	(−0.45)
Control variable	Yes	Yes	Yes
R2	0.6251	0.5261	0.6373
Year	Yes	Yes	Yes
Area	Yes	Yes	Yes

t statistics in parentheses *** p < 0.01.

.

Table 10 shows that the spatial spillover effect is further decomposed into direct and indirect effects. The direct effect represents the impact of local digital economy development on local rural industrial integration, while the indirect effect reflects the average influence of the local level of digital economy development on rural industrial integration in neighboring areas. The total effect represents the combined effects of rural digital economy development in each region on local and adjacent rural industrial integration. According to the decomposition results, both the direct and indirect effects of digital economy development on rural industrial integration are significantly positive. Examining the direct effect, local digital economy development is found to be statistically significant at the 1% level, indicating that local digital economy development positively contributes to regional improvement of rural industrial integration. Regarding the indirect effect, the coefficient for the impact of the digital economy is 0.3361 and statistically significant at the 1% level. This suggests that local digital economy development not only facilitates the advancement of rural industrial integration not only locally and in the surrounding areas. Looking at the total effect, the regression coefficient for local digital economy development is 2.1732 and statistically significant at the 1% level. The above conclusions indicate that digital economy development is beneficial not only for rural industrial integration in the local area but also exerts a significant promoting effect on the rural industrial integration in the surrounding regions, fostering collaborative industrial development across regions. Digital economy development further optimizes resource allocation, thereby facilitating the free flow of information and resources and promoting positive interactions and mutual prosperity among rural industries in different regions.

5. Discussion

5.1. The Positive Impact of Digital Economy on the Integration of Rural Industries

This research has indicated that the digital economy exerts a positive impact on the integration of rural industries. This result has been verified by previous studies. Wang [30] used panel data at the prefecture level in China from 2011 to 2020, finding that the digital economy facilitates the upgrading of industrial structure, thereby driving consumer spending. It is worth noting that there is an inverted U-shaped relationship between the digital economy and rural industry integration. When the digital economy index falls below 0.4203, its development will promote the integration of rural industries, exceeding this threshold renders the digital economy less influential on rural industry integration. From an economic perspective, irrational investment decisions in the rural digital economy can lead to excessive and redundant investments in the digital industry, which hinders the integration of rural industries and creates a mismatch between the development of the digital economy and the physical industry. The phenomenon of “information technology overload” derived from the digital economy era makes it more difficult for enterprises, governments, and investors to make sound decisions based the massive quantity of data available [52]. According to Panganiban et al. [53], the term “technical expert” emerged in the construction of digital technology platforms and digital service terminals. This magnifies the autocratic characteristics of digital technology and the risk of order imbalance in the development of digital economy. Focusing on rural areas, this study extends existing research on the negative effects caused by the development of the digital economy. For example, information technology overload not only disrupts data sharing but also reduces the industrial chain synergy efficiency of rural industry development. This hinders rural industry integration.

5.2. The Mediating Effect of Rural Land Certification

Rural land certification, as a mediating variable, promotes the development of rural industrial integration and plays a significant mediating role in the impact of the digital economy on rural industrial integration. It has laid a solid foundation for promoting rural land investment, streamlining land transfer, optimizing the division of labor in rural areas, and facilitating the integration of rural industries. Park [54] studied the digital twin system, land certification, and their relationships in Jeonju City, South Korea, finding that a complete data management system and efficient operational institutions effectively facilitate the collection of land information data. Thakur studied the application of cross-chain technology in land certification activities in India and identified the cascading benefits of blockchain technology. Through the permanent linkage of land transaction records with the system, blockchain technology ensures the irreversibility and immutability of transaction records. This reduces transactional fraud and ensures the safe management and transfer of land. Song et al. [46] observed that rural land certification promotes the transfer of Rural Housing Land, laying the foundation for the integration and development of rural industries. Gao et al. [55] found that the rural land certification stabilizes land ownership, facilitating the migration of high-quality labor and land resources to more efficient places, thereby improving industrial development. Previous scholars have often used human capital and technology as intermediary variables to examine the impact of the digital economy on rural industrial integration. However, few studies employ rural land certification as an intermediary variable for empirical analysis. This paper introduces rural land certification as a variable, thereby enriching previous research. This contribution unlocks the potential of rural land elements and promotes the scientific layout of rural industries, which are both important academic issues. Specifically, the findings in this study addresses the question of how to scientifically plan land elements to coordinate rural industrial development, preserve rural ecological resources and ensure food security in the process of rural industrial integration. Furthermore, it paves the way for further research on these issues.

5.3. Spatial Spillover Effects of Digital Economy Development on Rural Industrial Integration

This study demonstrates that the digital economy exerts a spatial spillover effect on the integration of rural industries. It not only has a positively impacts on local rural industrial integration but also facilitates the development of this process in neighboring areas. Previous studies has substantiated the spatial spillover effect of the digital economy, Li et al. [56] discovered that the digital economy boosts the efficiency of industrial green innovation and produces spatial spillover effects. Yu et al. [57] observed a spatial spillover effect of the digital economy on urban carbon emissions. Deng et al. [58] examined Guangdong Province and determined that the digital economy has a significantly influences rural revitalization through spatial spillovers. This study, focusing the Yangtze River Economic Belt, contributes to the literature by enhancing understanding of the spatial spillover effects of the digital economy on rural industrial integration, offering insights for the strategic layout and effective utilization of factor resources.

6. Conclusions

6.1. Theoretical Implications

Utilizing panel data from 108 cities within the YREB for the period 2011 to 2021, this study applies the entropy method to assess the entropy method to measure the digital economy and the rural industrial integration index. It constructs spatial Durbin models, mediation effect models, threshold effect models, and spatial spillover effect models to empirically examine the mechanisms and impacts of digital economy development and rural land certification on rural industrial integration. This findings reveal that (1) rural industrial integration exhibits a developmental trajectory evolving from a “point-type” to a “patch-type” pattern. (2) Initially, the development of the digital economy development significantly fosters the process of rural industrial integration. However, in the later stages, due to insufficient assessment of rural industrial growth and the prevalence of information technology overload, the digital economy impedes the progress of rural industrial development. (3) The reform of rural land certification grants farmers control rights over the remaining land under the land contract system, thereby enhancing their motivation to expedite land transfers to capitalize on changes in land use. This indirectly contributes to the long-term stability of rural land certification in supporting rural industrial integration. (4) Rural industrial integration typically involves the amalgamation of resource elements within and across administrative boundaries, displaying pronounced spatial spillover effects. The advancement of the digital economy amplifies these effects by synchronizing the operations of upstream and downstream entities on a unified digital platform, thereby significantly improving the efficiency of industrial integration.

6.2. Managerial Implications

This study offers some insights into rural industrial integration, with an emphasis on core technologies and innovation capabilities in the digital domain. The recommendations are as follows. (1) Considering local rural resource endowments and the actual development of rural industries, selectively promote the multi-mode development of digital infrastructure construction. Build a digital infrastructure integrated system with multi-technology integration, including remote sensing technology, satellite mapping technology, unmanned aerial vehicle, ground data center, and blockchain. Use system integration of digital technologies to improve the development efficiency of digital infrastructure services for rural industries. Emphasize the technical advantages of satellite Internet and other emerging digital technologies in assisting industrial planning in remote rural areas, and explore relevant market potential. (2) Accelerate the construction of technical and market standards for digital industrialization. Increase the research on core digital technologies to reduce the cost of digital technology industrialization. (3) Enhance policy-making and process supervision in the development of the digital economy to prevent disorderly competition behaviors that may endanger public interest. (4) Stimulate the development of data market configuration and data productization. Create data products and services for different industries, regions and groups. Promote data resource sharing to support the sustainable development of digital villages.

Enhancing the mechanism of rural land certification and advancing the digital transformation and upgrade of rural land certification are critical actions. (1) Optimize the application system architecture for rural land certification, enhance the stability and applicability of the system, and provide technical training to personnel involved in rural land certification to mitigate the negative impacts of excessive investment and technological overload from the digital economy. (2) Expand reforms of the land system, legalize contracting agreements, refine the registration system for rural land certification, and consolidate and enhance long-term stability in land contracting relationships to ensure that farmers can maintain long-term control rights over their land. (3) Promote reforms that allow rural collectives operating construction land to access market, certify the rights to use homestead land, and activate the market for construction land to provide a solid guarantee for the land needed for the development of rural industry integration. (4) Clearly delineate the functional use of land, fully exploit existing land resource conditions, transform land resource advantages into asset advantages, attract the influx of various elements to rural areas, and encourage the active involvement of various societal of society in rural construction. Reform and enhance the balance between land occupation and compensation for arable land, implement arable land protection measures, and expedite the creation of large contiguous high-standard farmland to ensure the preservation of the minimum arable land area and food security.

While integrating rural industries, ecological protection and food security should be taken into account. On the one hand, the protection and improvement of the ecological environment can provide favorable natural conditions and a resource base for the development of rural industries. On the other hand, food security is crucial for social stability and rural industry development. Key recommendations include the following: (1) implement large-scale ecological protection and restoration projects to protect and restore rural ecosystems such as mountains, rivers, forests, fields, lakes and grasses, alongside the development of rural industries; (2) strengthen soil and water resources protection to avoid pollution caused by industrial development; (3) enhance the protection of cultivated land and develop high-standard farmland to improve agricultural disaster prevention and mitigation capacity; (4) improve the quality of rural industrial development, raise farmers’ incomes and digital skills, make it easier for highly skilled people to return to their hometowns to start businesses, and enrich the rural employment market.

6.3. Limitations and Suggestions for Future Research

This paper studies the impact of digital economy development on rural industrial integration, as well as the mediating role of rural land certification in this process. The limitations of the study are as follows. Firstly, the study is limited to sample data from 108 cities in the Yangtze River Economic Belt from 2011 to 2022. The research conclusions may have certain limitations, and future research can improve the accuracy of the research results by considering more micro-level data such as county-level data and a broader research scope such as a national perspective. Secondly, this article only focuses on the impact of rural land certification on rural industrial integration, without paying attention to other factors such as human resources, capital and data that may play a mechanism role in promoting rural industrial integration in the digital economy, as well as the different combinations of these factors that play a role in the development of rural industrial integration. Once again, scholars can further focus on the heterogeneity of the role of the digital economy in the integration of economically developed regions and economically underdeveloped rural industries in their research, in order to more accurately grasp the impact mechanism of the digital economy on rural industry integration and the rational layout of various factors in rural industry integration. Finally, the future construction of digital rural areas should pay special attention to the coordinated development of the digital economy and physical industries, explore the efficient collaborative mechanism between the development of the digital economy and physical industries, and avoid excessive investment in the digital economy and information technology overload.