How Does the Digital Village Construction Affect the Urban–Rural Income Gap: Empirical Evidence from China

Abstract

Digital rural construction (DRC), as a crucial intersection of the rural revitalization strategy and the construction of Digital China, is a key path to addressing the imbalance and inadequacy in the urban–rural income gap (URIG). Based on provincial panel data from 2011 to 2023, this paper systematically examines the relationship and mechanism of action between the two using an econometric model. This study finds that DRC significantly reduces the URIG overall, and this effect is achieved through increasing urbanization levels, accelerating employment, and promoting social consumption. Spatial effect tests indicate that DRC has a spatial spillover effect; construction in one province reduces the URIG in neighboring provinces. Further research shows that, against the backdrop of human capital level acting as a threshold variable, the effect of DRC on the URIG exhibits an inverted “U”-shaped characteristic, first increasing and then decreasing. Therefore, this paper proposes countermeasures and suggestions, including constructing a digital-enabled urban–rural integration mechanism, promoting cross-regional coordinated development of DRC, and implementing a tiered and categorized digital literacy improvement project.

1. Introduction

Common prosperity constitutes a fundamental tenet of socialism, with the narrowing of the URIG emerging as a pivotal issue in the pursuit of this overarching goal. Following the 18th National Congress of the Communist Party of China, China’s GDP has grown from CNY 54 trillion in 2012 to nearly CNY 135 trillion in 2024, with an average annual growth rate of 6%. The per capita disposable income ratio between urban and rural residents decreased from 2.88 in 2012 to 2.34 in 2024, indicating a general trend of narrowing the URIG. However, in the same period, the ratio in some provinces still exceeded 3.0, and the absolute gap remains significant (data source: National Bureau of Statistics of China; https://www.stats.gov.cn/xxgk/jd/sjjd2020/202501/t20250117_1958335.html, accessed on 17 January 2025). Since the Central Document No. 1 in 2018 first emphasized the “digital village strategy”, to the “Outline of the DRC Strategy” clearly defining it as a “strategic direction for rural revitalization”, and further to the detailed implementation paths such as the “ DRC Guidelines”, and “ DRC Guidelines 2.0”, the national policy system has been continuously improved, and digital rural construction (DRC) has increasingly emerged as a key factor in tackling the URIG [1]. Against this backdrop, clarifying the intrinsic connection between DRC and the URIG bears profound practical value for advancing the all-around revitalization of rural areas.

Academic research on the impact of DRC on the URIG generally revolves around three aspects. First, research on DRC itself: In terms of importance, DRC is an effective tool for improving rural development while improving rural governance efficiency and promoting large-scale land management [2,3,4]. Regarding the development approach, the DRC path implemented in China’s eastern coastal provinces is characterized by organizational leadership, multi-stakeholder collaboration, and contextual driving forces, while inland regions emphasize technological innovation and external technological pressures [5]. Second, research on the URIG: Closely related to the development of digital villages is the spatiotemporal evolution of the urban–rural income gap. From a spatiotemporal distribution perspective, China’s URIG has experienced a moderate narrowing, yet the disparity remains substantial, with the URIG in underdeveloped western regions far exceeding that in developed eastern coastal areas [6]. From the perspective of influencing factors, the URIG is affected by multiple factors, including the digital divide [7], industrial digitalization [8], and digital finance [9,10], with varying effects. In terms of impact, narrowing the URIG can not only alleviate rural energy poverty [11] but also reduce carbon emissions [12,13]. Thirdly, research into the intrinsic link between DRC and the URIG is equally pertinent. DRC can narrow the URIG and inhibit its expansion into neighboring areas [14], exhibiting a “U”-shaped relationship [15]. It should be clarified that the “DRC” studied in this article has a connotation that goes beyond the general meaning of “digital economy” and includes four aspects: rural infrastructure, economy, governance, and life.

Existing studies have explored the influence exerted by DRC on the URIG, but three research gaps remain: First, most of the literature treats the digital economy as a whole, failing to focus on the specificities of DRC within rural contexts and lacking sufficient analysis of the digitalization of rural infrastructure, economy, governance, and daily life [16]. Second, mediation mechanism analyses often focus solely on employment or consumption, lacking a systematic examination that integrates urbanization levels, employment-driven growth, and social consumption into a unified analytical framework. Third, research integrating spatial perspectives and threshold effects is scarce, neglecting the cross-regional diffusion characteristics of DRC and the threshold constraint effect of human capital. Building on this foundation, this paper takes panel data of 30 provincial-level administrative regions (including autonomous regions and municipalities) nationwide over the 2011 to 2023 timeframe as the empirical research sample, constructing an evaluation system encompassing infrastructure digitalization, economic digitalization, governance digitalization, and daily life digitalization, and further adopts the entropy weight method to compute a comprehensive evaluation index of DRC. Based on the above discussion, the core objective of this study is to explore the impact of China’s DRC on the URIG and its underlying mechanisms. This study addresses four key research questions:

RQ1. 

Does DRC affect the URIG?

RQ2. 

What are the mediating mechanisms underlying this impact?

RQ3. 

What is the spatial relationship between the two?

RQ4. 

What kind of threshold role does human capital play?

The marginal contribution of this study mainly consists of two points. First, an indicator system for DRC is established, encompassing infrastructure, economy, governance, and living conditions. Second, using a combined framework of spatial metrology and thresholding, this study examines the relationship under the threshold of human capital level and explores the spatial spillover characteristics of the impact of DRC on the URIG, aiming to provide a basis for promoting DRC according to local conditions.

2. Theoretical Analysis and Research Hypotheses

2.1. The Direct Impact of DRC on the URIG

Drawing on Marx’s theory of urban–rural integration [17], the long-standing URIG stems from imbalances in development foundations, resource access, and opportunities. DRC, as the “digital engine” of rural revitalization, addresses the structural contradictions in traditional urban–rural development through digital transformation across four dimensions: infrastructure, economy, governance, and lifestyle. It directly impacts the urban–rural income distribution pattern from multiple dimensions, including income creation, cost control, and opportunity protection. Compared to single-dimensional analysis, the synergistic effect of these four digital dimensions more comprehensively reveals the inherent logic behind DRC in narrowing the URIG.

First, the urban–rural divide in traditional infrastructure is a root cause of income inequality. Rural areas suffer from underdeveloped infrastructure [18], leading to high costs for transporting agricultural products and weak resilience to economic risks, thus limiting farmers’ income growth. Digitalization of facilities is the “hardware foundation” for DRC, promoting the integration of traditional rural infrastructure with digital technologies to build a digital infrastructure system covering production, distribution, and services [19], fundamentally addressing the weakness of rural development. Second, the traditional rural economy is primarily based on primary agricultural production, resulting in low added value and weak resilience to economic risks, leading to a lack of sustainable income growth for farmers. Economic digitalization centers on advancing the in-depth integration between rural industries and digital technologies, fostering innovation in production models, extending industrial formats, and upgrading the value chain. This digital transformation of all aspects of processing and sales directly increases farmers’ operating income [20], thereby mitigating the URIG caused by differences in urban and rural industrial efficiency. Third, problems in traditional rural governance, such as delayed information dissemination, policy implementation deviations, and uneven resource allocation, often prevent low-income rural groups from enjoying the benefits of development, exacerbating income inequality. Digital governance empowers rural governance through digital technology, enabling more precise policy implementation and more efficient resource allocation, creating a fair institutional environment for increasing farmers’ income, and addressing the pain points of traditional rural governance [21]. Finally, the insufficient supply of traditional rural living services, high commodity prices, and low service quality lead to farmers having “low income and high expenditures”, further compressing their actual disposable income. Digital living services utilize digital technology to optimize the supply of rural living services, reducing living costs such as clothing, food, housing, transportation, and education, while simultaneously cultivating a rural digital consumer market, indirectly boosting farmers’ income [22]. While existing research is abundant, the relationship between digital rural development and the urban–rural income gap has not been fully verified [1]. Therefore, hypothesis H1 is proposed as follows:

H1. 

In China, DRC has a promoting effect on narrowing the URIG.

2.2. The Mechanism by Which DRC Impacts the URIG

(1)

Urbanization level: Based on the factor endowment theory, DRC takes the extension of digital infrastructure to rural areas as a breakthrough, breaking down the traditional urban–rural spatial divide. On the one hand, the integration of technologies with rural transportation and water conservancy facilities makes rural areas closer to urban areas in terms of public service supply and industrial development conditions, attracting urban capital and technology to extend downward to rural areas, promoting the construction of rural industrial parks and e-commerce clusters, accelerating rural industrial upgrading [23], and promoting urbanization transformation. At the same time, DRC empowers the construction of rural land transfer platforms, realizing the market-oriented allocation of land resources. Farmers obtain property income through land management rights, shares, and transfers while participating in nearby urbanization, thereby reducing income losses during the process. This high-quality urbanization process enhances rural productivity by balancing urban and rural factor income [24], serving as an important transmission channel for DRC to narrow the URIG. However, existing studies have not incorporated it into their analytical framework [25]. Therefore, hypothesis H2a is proposed as follows:

H2a. 

In China, urbanization level mediates the relationship between DRC and the URIG; that is, DRC narrows the URIG by improving urbanization.

(2)

Employment level: Based on the employment multiplier theory, employment is the core link between production and income. DRC builds an employment-driven mechanism from two dimensions: job creation and capacity building. At the job creation level, the integration of DRC with agriculture and service industries fosters new employment forms, creating numerous non-agricultural jobs in areas like platform operation and logistics management. This provides farmers with localized employment opportunities, enabling them to reduce the costs of cross-regional labor mobility. At the capacity-building level, digital resources such as online vocational training and remote technical guidance break down the limitations of rural educational resources, helping rural laborers quickly master digital skills and professional knowledge, improving their overall quality, and enabling them to transform from traditional agricultural workers to digital economy practitioners and social entrepreneurs [26]. This improvement in the employment levels directly increases wages and operating income, thereby narrowing the URIG and becoming an important transmission path for DRC to influence the URIG. However, existing research on the mechanisms by which employment levels play a role is insufficient [14]. Therefore, hypothesis H2b is proposed as follows:

H2b. 

In China, DRC narrows the URIG by enhancing the employment-driven effect.

(3)

Social consumption level: Consumer economics theory emphasizes that social consumption is a key link in activating rural economic vitality [27]. From the supply side, the establishment of cold chain logistics networks reduces the price of industrial products for rural residents. It broadens the sales channels for rural specialty agricultural products, achieving two-way circulation of industrial products downstream for improved quality and agricultural products upstream for increased efficiency. From the demand side, digital payments and financial tools alleviate liquidity constraints for rural households. At the same time, digital scenarios such as short videos and live streaming stimulate diversified and high-quality consumption demands from rural residents, promoting the upgrading of the social consumption structure [28], thereby driving the development of industries and creating more operating income opportunities for rural residents. The interconnection of consumption markets enables rural residents to increase their actual income through the dual effects of “selling well” and “buying cheaply”, thereby serving as an intermediary in narrowing the URIG in DRC. However, there is a gap in existing research on the mediating effect of social consumption levels on the URIG [1]. Therefore, hypothesis H2c is proposed as follows:

H2c. 

In China, social consumption plays a mediating role between DRC and the URIG; that is, DRC narrows the URIG by increasing social consumption levels.

2.3. Spatial Spillover Effect of DRC on the URIG

Based on the “polarization-trickle-down effect” theory and the new economic geography theory, the achievements of DRC extend beyond the local area. The mobility of its factors and the diffusion of its technology promote spatial spillovers, thereby affecting the URIG in neighboring regions.

First, there is the trickle-down effect of technology diffusion and knowledge spillover. Pilot areas for DRC will form “digital highlands”, and their successful technology application models, e-commerce operation experience, and digital governance solutions will spread across regions to surrounding “digital lowlands” through information networks, personnel exchanges, and enterprise cooperation. This positive spillover of knowledge and technology [29] reduces the trial-and-error costs and learning barriers for digital transformation in neighboring rural areas, allowing them to access the DRC track at a lower marginal cost. This drives an overall increase in the income level of farmers in surrounding areas, collaboratively narrowing the URIG. Second, there is the radiating effect of market integration and industrial linkage. Digital infrastructure, especially e-commerce platforms and smart logistics networks, greatly weakens market segmentation between regions. DRC, in one region, by connecting its supply and sales chains with those of surrounding areas, can integrate the specialty agriculture, rural tourism, and other resources of neighboring areas into a broader, unified market. The e-commerce service center in the core area can provide brand empowerment and sales channels for agricultural products from surrounding counties, forming a “center-periphery” collaborative development pattern. This promotes the sharing of development dividends among regions, avoids the formation of a “digital divide”, and thus generates regional linkage and income growth. Thirdly, there is the polarization-trickle-down effect of factor flows and competitive cooperation. DRC will change the spatial distribution of production factors. Initially, it may attract high-quality factors from surrounding areas to the central area with better digital industry development, generating a “siphon effect” or “polarization effect”, which may exacerbate the URIG over the short-term timeframe. However, from a long-term dynamic perspective, when the development of the central area approaches saturation, its capital and technology will seek new investment opportunities externally, thus transforming into a “diffusion effect” or “trickle-down effect”, ultimately guiding factors to achieve a better allocation among regions and narrowing the URIG. Therefore, hypothesis H3 is proposed as follows:

H3. 

In China, the impact of DRC on the URIG has spatial effects.

2.4. The Threshold Effect of DRC on the URIG

In terms of theoretical logic, according to Schultz’s human capital theory [30], human capital, as a comprehensive reflection of workers’ knowledge reserves, skill level, and digital literacy, is the core link connecting DRC and income growth. An individual’s level of human capital determines their ability to adapt to technological changes and seize emerging opportunities.

The e-commerce platforms, smart agriculture tools, and remote services upon which DRC relies inherently require users to possess basic skills. Without corresponding human capital support, the deployment of digital infrastructure fails to translate into actual benefits for ordinary farmers. Instead, it may lead to a situation in which a small group monopolizes the benefits of technology, creating a technological application gap in rural areas and further widening the URIG. In practice, the distribution of human capital in China exhibits significant regional heterogeneity. Farmers in some areas have lower levels of education and insufficient digital skills, resulting in differentiated effects of DRC across different regions. This difference in capabilities provides real-world evidence of an effect. From the perspective of threshold characteristics, when human capital levels fall below a critical threshold, farmers lack access to digital infrastructure or smart agricultural tools. This hinders the universal benefit of DRC and reduces the impetus for narrowing the URIG. However, once human capital surpasses the critical threshold, rural residents can participate equally in digital economic activities, using digital tools to participate in the market division of labor and obtain high-value-added employment opportunities, thereby sharing the benefits of development (see Figure 1). At this point, the income-equalizing effect of DRC is released, and the URIG gradually narrows. However, while existing studies have examined the threshold relationship between DRC and the URIG, they have not incorporated human capital levels into their analytical frameworks [31]. Therefore, hypothesis H4 is proposed as follows:

H4. 

In China, the human capital level exhibits a threshold effect in the relationship between DRC and the URIG, presenting an inverted U-shaped relationship.

3. Research Design

3.1. Model Design

(1)

Systemic GMM model: The justifications for the selection of the research model are outlined below. First, the explanatory variable, the URIG, exhibits dynamic dependence. The current year’s URIG level is highly correlated with the URIG in previous years, requiring the introduction of its lagged terms as explanatory variables. The systemic GMM model, by constructing level and difference equations, can effectively solve the dynamic panel bias. Second, some explanatory variables in the model may have endogeneity problems, such as bidirectional causality and omitted variables. The system GMM approach eliminates reliance on extraneous instrumental variables; instead, it leverages lagged values of variables as instruments, thereby inherently resolving endogeneity issues within the model [32]. Third, the sample utilized herein is panel data with a cross-sectional extensive structure and a time-series of short duration. The systemic GMM’s estimation robustness under small sample conditions is better than that of the differenced GMM, FE, and OLS models. Fourth, fixed-effect models struggle to address endogeneity biases, and bias-corrected least squares difference value models have limited ability to handle complex endogeneity; the system GMM model can solve this problem. The model follows the rule of “number of instrumental variables < number of cross-sectional elements, and the model is defined as follows:

$${Gap}_{i,t}={\alpha }_0+{\alpha }_1{Gap}_{i,t-1}+{\alpha }_{2}D{ig}_{i,t}+\delta {Control}_{i,t}+{\mu }_i+{\lambda }_t+{\epsilon }_{i,t}$$

(1)

In Equation (1), the explained variable Gap_i,t represents the URIG in region i during period t; Gap_i,t represents the lagged period of the dependent variable; Dig_i,t represents the level of DRC in region i during period t; control_i,t is the control variable; μ_i represents the regional fixed effect; λ_t represents the time fixed effect; and ε_i,t is the random disturbance term.

(2)

Mediation effect model: To verify the mechanism by which DRC affects the URIG, this study, referencing existing research [33], this study constructs the following mediation effect model. In Equations (2)–(4), Mid_i,t represents the mediating variable.

$${Gap}_{i,t}={\beta }_0+{\beta }_{1}D{ig}_{i,t}+{\beta }_2{Control}_{i,t}+{\epsilon }_{i,t}$$

(2)

$${Mid}_{i,t}={\gamma }_0+{\gamma }_{1}D{ig}_{i,t}+{\gamma }_2{Control}_{i,t}+{\epsilon }_{i,t}$$

(3)

$${Gap}_{i,t}={\gamma }_0+{\gamma }_{1}D{ig}_{i,t}+{\gamma }_2{Mid}_{i,t}+{\gamma }_3{Control}_{i,t}+{\epsilon }_{i,t}$$

(4)

(3)

Spatial Durbin model (SDM): Traditional fixed-effect models assume individual independence, while spatial models can separate direct and indirect effects, correcting estimation biases caused by neglecting spatial autocorrelation in traditional models [34] and improving the reliability of statistical inference. The SDM expression is as follows:

$$\begin{array}{c}{Gap}_{i,t}={\mathsf{\omega }}_0+\rho W{Y}_{i,t}+{\mathsf{\omega }}_{1}WD{ig}_{i,t}+{\mathsf{\omega }}_{2}D{ig}_{i,t}+{\mathsf{\omega }}_{1}W{Control}_{i,t}\\ +{\mathsf{\omega }}_2{Control}_{i,t}+{\mu }_i+{\lambda }_t+{\epsilon }_{i,t}\end{array}$$

(5)

In Equation (5), ρ represents the spatial autoregressive coefficient to be estimated, and ω₁ represents the spatial lag coefficient of the explanatory variable DRC, δ₁ represents the spatial lag coefficient of the control variable, and W represents the spatial weight matrix. The adjacency matrix captures the spillover effect of geographically adjacent areas by defining the “geographical adjacency” relationship. Based on existing research [35], this paper selects the adjacency matrix for matching analysis.

(4)

Threshold model: The threshold model can effectively identify inflection points and reveal the abrupt changes in the relationship between variables [36]. To examine the nonlinear relationship between DRC and the URIG, drawing on Hansen’s (1999) model construction scheme [37], this study follows the threshold model:

$${Gap}_{i,t}={{\alpha }_1+\alpha }_{2}Dig\left({\mathrm{R}\mathrm{L}}_{i,t}\le \mathsf{\gamma }\right)+{\alpha }_{3}Collect\left({\mathrm{R}\mathrm{L}}_{i,t}>\mathsf{\gamma }\right)+{{\alpha }_4{Control}_{i,t}+\epsilon }_{i,t}$$

(6)

In the above model, RL is the human capital level, and γ is a single threshold value. To obtain a single threshold conclusion, it is necessary to test the double threshold, and so on. In Equations (6) and (7), γ₁ and γ₂ are two threshold values of human capital level.

$$\begin{array}{c}{Gap}_{i,t}={\alpha }_1+{\alpha }_{2}Dig\left({\mathrm{R}\mathrm{L}}_{i,t}\le {\mathsf{\gamma }}_1\right)+{\alpha }_{3}Dig\left({\mathsf{\gamma }}_1{\mathrm{R}\mathrm{L}}_{i,t}\le {\mathsf{\gamma }}_2\right)\\ +{{\alpha }_{4}Dig\left({\mathrm{R}\mathrm{L}}_{i,t}>{\mathsf{\gamma }}_2\right)+{\alpha }_5{Control}_{i,t}+\epsilon }_{i,t}\end{array}$$

(7)

3.2. Variable Selection

(1)

Dependent variable: The Theil index incorporates sensitivity to extreme income levels and considers population factors. Given China’s country’s economic development exhibits a clear dual structure and a large population, the Theil index becomes an effective tool for measuring the URIG [38,39]. Specifically, the Theil index can accurately grasp the contribution of high-income and low-income extreme groups to the URIG, and its calculation incorporates the influence of population size, thus more comprehensively reflecting the inequality in income distribution. An elevated index value corresponds to a more pronounced URIG, and its calculation formula is presented below:

$${Theil}_{i,t}=\sum _{i=1}^2\left({\displaystyle \frac{{\mathrm{I}}_{i,t}}{{\mathrm{I}}_t}}\right)\mathrm{l}\mathrm{n}\left({\displaystyle \frac{{\mathrm{I}}_{i,t}/{\mathrm{P}}_{i,t}}{{\mathrm{I}}_t/{\mathrm{P}}_t}}\right)=\left({\displaystyle \frac{I_t^u}{{\mathrm{I}}_t}}\right)\mathrm{l}\mathrm{n}\left({\displaystyle \frac{I_t^u/P_t^u}{{\mathrm{I}}_t/{\mathrm{P}}_t}}\right)+\left({\displaystyle \frac{I_t^r}{{\mathrm{I}}_t}}\right)\mathrm{l}\mathrm{n}\left({\displaystyle \frac{I_t^r/P_t^r}{{\mathrm{I}}_t/{\mathrm{P}}_t}}\right)$$

(8)

where Theil represents the URIG; $I_t^u$ and $I_t^r$ r represent the total income of urban and rural residents in each province, respectively; I_t represents the total income of urban and rural residents in that province; and P_t represents the total population of urban and rural residents in that province

(2)

Explanatory variables: DRC: This paper expands upon existing research [40,41] by measuring DRC from four dimensions: digitalization of infrastructure (DOI), digitalization of the economy (DOE), digitalization of governance (DOG), and digitalization of life (DOL). Accounting for data gaps, 16 indicators were finally chosen for quantification, with specific details provided in

Table 1. Construction of the indicator system for DRC.

Primary Indicator	Secondary Indicator	Variable Meaning	Data Source	Weight
DOI	Rural logistics coverage rate	Rural delivery routes (km)	National Bureau of Statistics (NBOS)	0.040045
	Logistics infrastructure investment	Transport, storage, and postal sector fixed-asset investment (CNY 100 million)	China Statistical Yearbook (CSY)	0.055617
	Internet infrastructure construction	Broadband connectivity subscribers in rural areas (10,000 households)	NBOS	0.092286
	Agricultural meteorological observation stations	Agricultural meteorological observation stations (number)	CSY	0.025927
DOE	Rural e-commerce	Taobao villages (number)	Analysis by Alibaba Research Institute and Nanjing University Spatial Planning Research Center	0.347847
	Talent support for DRC	Agricultural technical professionals in public enterprises and institutions	China Science and Technology Statistical Yearbook	0.004246
	Digital technology services	Engaged personnel count in information transmission, software, and IT services	CSY	0.110207
	E-commerce infrastructure investment	Transport-related expenditure of local governments (CNY 100 million)	NBOS	0.030272
	Digital finance development level	Digital inclusive finance index	Beijing University Digital Inclusive Finance Index (2011–2020)	0.024192
DOG	Digital village governance funding supply	Local governmental expenditures for urban–rural community development	NBOS	0.050582
	E-government development level	Provincial government online government service capabilities	China E-Government Development Survey Report	0.004246
DOL	Television penetration rate	Full population coverage rate of rural TV broadcasting (%)	CSY	0.005574
	Radio penetration rate	Full population coverage rate of rural radio broadcasting services (%)	CSY	0.004412
	Information service consumption level	Per capita spending by rural dwellers on transport and communications (CNY)	China Household Survey Yearbook (CHSY)	0.03727
	Information technology services	Total telecommunications business volume (CNY 100 million)	NBOS	0.118868
	Smartphone penetration rate	Mean number of mobile devices owned by every 100 rural households (units)	CSY	0.010854

.

(3)

Mechanism variables: Urbanization level, employment level, and social consumption level: DRC, by improving urbanization levels, addresses the problem of “emphasizing agglomeration while neglecting integration” in traditional urbanization, providing a vehicle for narrowing the URIG, theoretically aligning with the “theory of integrated urban-rural development”. Employment growth fueled by DRC boosts farmers’ wage-based earnings while concurrently fostering industrial development through employment expansion, forming a virtuous cycle of income growth and becoming a core driving force for narrowing the URIG, consistent with the “employment multiplier theory”. Social consumption is a key link connecting DRC with the URIG. The interconnection of consumer markets can both improve farmers’ income levels and promote URIG convergence, consistent with the “consumption economics theory”. Hence, the present research selected these three variables to function as mechanistic variables.

(4)

Threshold variable: Human capital level (HCL): The core logic of selecting HCL as the threshold variable for the impact of DRC on the URIG lies in its crucial role as a prerequisite for transforming DRC into an income equilibrium effect, directly defining the nonlinear boundary of its impact. The critical attribute of HCL aligns with both the inherent laws of DRC and the realities of rural development, making it a reasonable threshold variable for characterizing the nonlinear impact of DRC on the URIG.

(5)

Control variables: Leveraging insights from prior research [42,43], nine control variables were selected: tax burden level, level of openness to the outside world, industrial structure, transportation infrastructure, economic level, labor force level, technological level, informatization level, and innovation level (see

Table 2. Descriptive statistics of variables.

Variable Type	Variables	Variable Definitions	Obs	Mean	Standard Deviation	Max	Min
Explanatory variable	URIG	Calculated using the Theil index	390	0.0825	0.0381	0.202	0.0159
Explanatory variable	DRC	Calculated using the entropy method	390	0.266	0.159	0.771	0.0656
Control variable	Tax burden level	Tax Revenue/Gross Regional Product	390	0.0796	0.0292	0.200	0.0354
	Level of openness to the outside world	(Total Import and Export Value of Goods × USD/CNY Exchange Rate)/Gross Regional Product	390	0.270	0.277	1.464	0.0076
	Industrial structure	Tertiary Industry Value/(Primary Industry Value + Secondary Industry Value)	390	2.403	0.123	2.846	2.132
	Transportation infrastructure level	Kilometers (Logarithm)/Total Freight Volume (Logarithm)	390	11.650	0.835	12.980	9.440
	Economic development level	GDP Per Capita	390	13,000	8300	49,000	5100
	Labor force level	Employment (Logarithm)	390	7.590	0.781	8.864	5.545
	Technology market development level	Technology Market Transaction Value/Gross Regional Product	390	0.0198	0.0323	0.195	0.0002
	Informatization level	Postal and Telecommunications Business Volume/Gross Regional Product	390	0.0632	0.135	2.513	0.0147
	Innovation level	Domestic Invention Patent Applications (Logarithm)	390	9.692	1.384	12.40	5.318
Mediator variable	Urbanization level	Urbanization Rate	390	0.599	0.131	0.896	0.228
	Employment level	Tertiary Industry Employment (Logarithm)	390	6.736	0.755	8.239	4.710
	Social consumption level	Total Retail Sales of Consumer Goods/Gross Regional Product	390	0.376	0.0699	0.538	0.183
Threshold variable	Human capital level	Number of Students Enrolled in Higher Education Institutions/Total Population	390	0.0215	0.006	0.0437	0.008

).

3.3. Data Sources and Descriptive Statistics

This paper analyzes panel data from 30 provinces in China (excluding Hong Kong, Macao, Taiwan, and Xizang due to data acquisition limitations) from 2011 to 2023. The data sources include the National Bureau of Statistics (NBOS), the China Statistical Yearbook (NSY), the China Rural Statistical Yearbook (CRSY), the China Household Survey Yearbook (CHSY), and the China Science and Technology Statistical Yearbook (CSTSY) (data source: https://www.stats.gov.cn/zs/tjwh/tjkw/tjzl/202302/t20230215_1907997.html, accessed on accessed on 12 January 2022; https://baike.baidu.com/item/%E4%B8%AD%E5%9B%BD%E4%BD%8F%E6%88%B7%E8%B0%83%E6%9F%A5%E5%B9%B4%E9%89%B4(2023)/63852221, accessed on 1 October 2023). Furthermore, a preliminary description of the relationship between DRC and the URIG is presented using a scatter plot, as shown in Figure 2. This preliminary analysis verifies the rationality of the theoretical analysis presented earlier, but further validation through econometric models is still needed. The variables for each province are shown in Table S1.

4. Empirical Analysis

4.1. Benchmark Regression Results and Robustness Tests

The variance inflation factors are all of less than 10, indicating that there is no multicollinearity. The AR (1) test result is of less than 0.1, while the AR (2) test is greater than 0.1; the Hansen test result is 0.424, which is greater than 0.1, indicating that it does not have an over-identification test (see

Table 3. Empirical estimation outcomes of DRC impact on the URIG.

Variables	OLS		FE		SYS-GMM
	(1) No Lagged Terms of the Dependent Variable	(2) Lagged Terms of the Dependent Variable	(3) No Lagged Terms of the Dependent Variable	(4) Lagged Terms of the Dependent Variable	(5)
DRC	−0.0326 *** (0.0112)	−0.0022 ** (0.0009)	−0.0749 ** (0.0362)	−0.0089 ** (0.0034)	−0.0238 ** (0.0096)
DRC is lagging by one period	-	0.9685 *** (0.0063)	-	0.9868 *** (0.0091)	0.8711 *** (0.0326)
Tax burden level	−0.0840 (0.0738)	−0.0003 (0.0031)	−0.0300 (0.0396)	−0.0034 (0.0039)	0.0204 (0.0159)
Openness level	−0.0332 *** (0.0108)	0.0013 (0.0008)	−0.0226 ** (0.0105)	−0.0007 (0.0013)	−0.0054 (0.0061)
Industrial structure	−0.0511 ** (0.0252)	0.0009 (0.0022)	−0.0493 (0.0364)	0.0038 (0.0029)	0.0133 (0.0129)
Transportation infrastructure level	−0.0120 *** (0.0021)	0.0002 (0.0002)	−0.0017 (0.0028)	0.0005 ** (0.0002)	0.0018 * (0.0010)
Economic development level	−0.0000 (0.0000)	0.0000 * (0.0000)	−0.0000 (0.0000)	−0.0000 (0.0000)	0.0000 *** (0.0000)
Labor force level	0.0030 (0.0025)	0.0008 *** (0.0003)	0.0470 ** (0.0190)	0.0037 ** (0.0015)	0.0077 ** (0.0030)
Technology market development level	−0.1224 * (0.0657)	0.0000 (0.0044)	−0.1188 (0.0895)	−0.0038 (0.0050)	−0.0075 (0.0270)
Information level	−0.0009 (0.0042)	−0.0012 (0.0012)	−0.0053 ** (0.0022)	−0.0015 (0.0013)	−0.0029 ** (0.0013)
Innovation level	−0.0255 (0.0023)	−0.0002 (0.0002)	−0.0181 *** (0.0042)	−0.0002 (0.0004)	−0.0030 ** (0.0015)
_cons	0.3583 *** (0.0624)	−0.0106 ** (0.0053)	0.0752 (0.1633)	−0.0410 ** (0.0150)	−0.0731 * (0.0395)
AR(1)	-	-	-	-	0.030
AR(2)	-	-	-	-	0.359
Hansen Tese	-	-	-	-	0.424
N	390	360	390	360	360

Note: Columns OLS (2), FE (4), and SYS-GMM (5) have added the effect of being lagged. ***, ** and * represent significance levels of 1%, 5% and 10%, respectively. The values in parentheses are robust standard errors.

). In addition, the lagged coefficient of the URIG is positive, indicating the persistence and dynamic characteristics of the URIG. As shown in Column (5) of Table 3, DRC exerts a negative effect on URIG at the 5% level. Specifically, for each additional unit of DRC growth, the URIG is capable of decreasing by 2.38%, which means that DRC can reduce the URIG, resulting in a relative decrease in urban residents’ income and a relative increase in farmers’ income. Hypothesis H1 is verified, which also shows that accelerating DRC in rural areas has important practical significance for narrowing the URIG.

For the purpose of verifying the validity of model selection, the present research carried out robustness checks by means of comparison across multiple models. OLS and FE models were used for regression under conditions of no dependent variable lag term and with a dependent variable lag term, respectively. Table 3’s columns (1) and (3) show the regression results without a dependent variable lag term, while columns (2) and (4) show the results with a dependent variable lag term. The coefficients of the URIG lag term were all significant at the 5% level, further demonstrating that the change in URIG is a dynamic process, which verifies the robustness of the GMM model.

4.2. Mechanism Testing

The Sobel test findings demonstrate that the Sobel coefficients of the level of urbanization, employment-induced growth, and level of social consumption reach statistical significance, which attests to the presence of a mediating effect with high statistical credibility. Meanwhile, after 1000 samplings in the Bootstrap test, the quantile confidence intervals for the indirect effects of urbanization level, employment level, and social consumption level at the 95% confidence level are [−0.0480, −0.0074], [−0.0676, −0.0245], and [−0.0635, −0.0184], respectively, none of which include 0. Furthermore, the values of repeated sampling estimates (_bs_1) are significant at the 1%, 5%, and 1% levels, further illustrating that this study has a mediating effect.

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The mediating role of urbanization level: Column (1) in

Table 4. Mediating effect test of DRC on the URIG.

Variables	(1) Urbanization Level	(2) URIG	(3) Employment Level	(4) URIG	(5) Social Consumption Level	(6) URIG
DRC	0.1382 *** (0.0244)	−0.0277 *** (0.0089)	0.3103 ** (0.1447)	−0.0461 *** (0.0090)	0.3690 *** (0.0681)	−0.0409 *** (0.0098)
Urbanization Level		−0.1902 *** (0.0189)
Employment Level				−0.0256 *** (0.0033)
Social Consumption Level						−0.0354 *** (0.0075)
Sobel	−0.0263 *** (0.0053)		−0.0079 ** (0.0038)		−0.0013 *** (0.0037)
_bs_1 95% Confidence Interval (P)	[−0.0380, −0.0146]		[−0.0158, −0.0000]		[−0.0212, −0.0049]
_bs_1 95% Confidence Interval (BC)	[−0.0480, −0.0074]		[−0.0676, −0.0245]		[−0.0635, −0.0184]
_bs_1 p-value	0.002		0.049		0.002
Control Variables	Controlled		Controlled		Controlled
N	390		390		390
F	619.10 ***		556.99 ***		506.13 ***

Note: *** and ** represent significance levels of 1%, 5% and, respectively. The values in parentheses are robust standard errors.

demonstrates that the impact of DRC on the urbanization level is positive. Column (2) shows that after adding urbanization level and DRC to the model together, the impact of both urbanization level and DRC on the URIG is negative. This indicates that DRC can reduce the URIG by improving the urbanization level, thus verifying hypothesis H2a. The reason for this is that, on the one hand, DRC breaks geographical spatial limitations through digital infrastructure, driving the effective clustering of rural-based production factors in urbanized towns. On the other hand, the agglomeration of factors in urbanization forms a “radiation effect”, with urban technology and management experience penetrating rural areas through digital platforms. Rural factors achieve added value in the integration with urban factors, thereby narrowing the disparity in the return rate of urban and rural factors and inhibiting the widening of the URIG. On this basis, the rural digital economy spawned by DRC complements the urban digital industry chain, and rural areas become the supply chain terminal of urban digital industries, gradually narrowing the URIG.

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The mediating role of employment level: Column (3) shows that the impact of DRC on employment is positive, while in column (4), both employment level and DRC are negative. Evidence derived from the analysis demonstrates that DRC narrows the URIG by improving the employment level, thus verifying hypothesis H2b. The reason for this is that DRC has emerged as a live streaming and smart agriculture platform, providing rural laborers with high-income and flexible employment options. At the same time, online recruitment reduces the information asymmetry of rural labor employment, facilitates the high-efficiency mobility of the rural surplus labor force toward non-agricultural sectors or urban regions, and improves employment matching efficiency. It can be said that DRC transforms the rural employment structure from traditional agriculture to digital service industries and modern agriculture, not only improving the stability of rural labor employment but also improving the quality of employment and farmers’ income level, narrowing the URIG with urban employment. This result shows that DRC can not only “create employment” but also “improve the quality of employment”, providing a new practical path for breaking the employment structure.

(3)

The mediating role of social consumption level: Column (5) demonstrates that the impact of DRC on social consumption level is significantly positive, while the coefficients of social consumption and DRC in column (6) are both negative. The regression outcomes validate that social consumption can serve as an intermediate path for DRC to narrow the URIG, thus verifying hypothesis H2c. The reasons for this are that DRC improves the rural e-commerce logistics system, such as cold chain logistics networks, reducing the time and money costs of rural consumption, expanding the scale of the rural consumer market, spurring the advancement of relevant industrial sectors such as farm produce processing and rural tourism, and leisure, thereby elevating rural households’ earnings. Consumption upgrading forces rural industries to transform towards branding and high added value, enhancing the profitability and income-generating capacity of rural industries. This conclusion supplements the research on the mediating role of DRC on the URIG from a consumption perspective, breaks through the traditional analytical framework that focuses on the production end, verifies the mitigating effect of social consumption level on the URIG, and provides a new entry point for the study of balanced development of social consumption and income.

4.3. Spatial Effect Test

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Spatial autocorrelation test (SAT): This paper uses the global Moran’s I index calculated based on the spatial adjacency weight matrix for this test. Overall, all values in

Table 5. Results of SAT.

Year	Moran’s I	Z	p	Year	Moran’s I	Z	p
2011	0.429 ***	4.191	0.000	2018	0.414 ***	4.077	0.000
2012	0.427 ***	4.182	0.000	2019	0.402 ***	3.975	0.000
2013	0.426 ***	4.178	0.000	2020	0.389 ***	3.867	0.000
2014	0.427 ***	4.189	0.000	2021	0.390 ***	3.872	0.000
2015	0.432 ***	4.225	0.000	2022	0.388 ***	3.863	0.000
2016	0.427 ***	4.179	0.000	2023	0.382 ***	3.821	0.000
2017	0.423 ***	4.145	0.000

Note: *** represents significance levels of 1%. The values in parentheses are robust standard errors.

are significant, suggesting that the URIG has spatial autocorrelation among provinces, allowing for the construction of this model for analysis.

Figure 3 presents Moran scatter plots of the URIG in 2011, 2015, 2019, and 2023. The spatial distribution of predominantly selected research samples demonstrates distinct local characteristics of high–high (HH) and low–low (LL). Specifically, in 2011, the sample size of HH clustering accounted for 43.3%, and the sample size of LL clustering accounted for 46.7%, indicating that the URIG in most provinces of China was at a relatively low level. From 2011 to 2023, the number of low–low clustering areas remained basically unchanged, but the count of high–high agglomeration regions decreased by 3, while the quantity of low–high and high–low clustering areas increased by 2. This reflects that the barriers to the two-way flow of factors of production between urban and rural areas have not been eliminated, and there is still a certain imbalance in the URIG. By 2023, compared with 2011, the URIG in the third quadrant showed a trend of concentration, with a decrease in dispersion and no significant change in quantity. Only the gap within the agglomeration area was decreasing, indicating that the relative size of the URIG in the low-agglomeration area was shrinking.

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Spatial spillover effect test and decomposition: To select a suitable spatial model for analyzing the impact of DRC on the URIG, the LM test was performed on each variable. The statistical results in

Table 6. Results of LM and LR tests.

Test Method	Statistic	p-Value
LM-error	144.843	0.004
Robust LM-error	29.437	0.000
LM-lag	196.852	0.000
Robust LM-lag	81.446	0.000
LR (sar)	96.30	0.000
LR (sem)	89.09	0.000

reveal that the statistics for LM(error) and LM(lag) are 144.843 and 196.852, respectively, both significant, indicating that the SDM should be selected for analysis. To test the robustness of the model selection, the LR test was further performed. The results for LR (sar) and LR (sem) were both significant, demonstrating strong model stability. Simultaneously, a fixed-effect-type test was performed.

Table 6 demonstrates that the DRC regression coefficient stands at −0.0188, exerting a negative impact on URIG. Columns (3)–(5) in

Table 7. Spatial spillover effect and decomposition.

Variables	(1)	(2)	(3)	(4)	(5)
	Main	Wx	DE	IE	TE
DRC	−0.0188 *** (0.0073)	−0.0545 *** (0.0124)	−0.0221 *** (0.0073)	−0.0731 *** (0.0168)	−0.0953 *** (0.0190)
Control variables	YES	YES	YES	YES	YES
rho	0.2325 *** (0.0650)
sigma2_e	0.0000 *** (0.0000)
N	390

Note: *** represents significance levels of 1%. The values in parentheses are robust standard errors.

show that DRC negatively affects the URIG under the direct (DE), indirect (IE), and total effects (TE). This finding verifies that the impact of DRC on the URIG has a spatial effect, thus verifying hypothesis H3. Specifically, the following was observed:

The direct effect demonstrates that the improvement in DRC in a region can reduce the URIG. First, by improving the digital skills of farmers, creating local digital jobs, and stimulating the rural consumer market, it directly increases the income level of farmers. Second, by leveraging the digital empowerment of rural public services, it alleviates income inequality. The indirect effect is negative, which verifies that the improvement in DRC in a region will significantly narrow the URIG in its neighboring regions. Rural characteristic industries spurred by local DRC may form industrial chain divisions with neighboring regions, improving the overall rural income level of the region through industrial synergy. At the same time, DRC reduces the flow costs of labor, technology, and information, and the digitally skilled labor force cultivated by local DRC may flow to neighboring regions, driving the improvement in rural employment quality in neighboring regions and narrowing the URIG. The total effect is negative, indicating that the “local effect” and “spillover effect” work together in the overall impact of DRC on the URIG. From a numerical perspective, the significance and coefficient of the total effect mean that, from a spatial correlation perspective, the impact of DRC on the URIG is a joint effect. Not only does the investment in the local area generate direct returns, but the spillover effect on surrounding areas further expands the impact.

4.4. Threshold Inspection

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Threshold model validation: To verify the stationarity of the data and prevent spurious regression, this paper uses the LLC test method for validation.

Table 8. Unit root test.

Variables	LLC	p	Conclusion	Variables	LLC	p	Conclusion
URIG	−4.9196	0.0000	Stable	Economic development level	−3.0809	0.0010	Stable
DRC	−5.7058	0.0000	Stable	Labor force level	−5.3735	0.0000	Stable
Tax burden level	−3.8301	0.0001	Stable	Technology market development level	3.9870	0.0000	Stable
Level of openness to the outside world	−7.8730	0.0000	Stable	Information level	−13.7471	0.0000	Stable
Industrial structure	−1.7012	0.0444	Stable	Innovation level	−3.6753	0.0001	Stable
Level of transportation infrastructure	−5.2733	0.0000	Stable	Human capital	−1.6502	0.0495	Stable

shows that DRC, the URIG, threshold variables, and control variables all have no unit roots, verifying that the selected data is stable.

The impact of DRC on the URIG may exhibit a non-linear relationship.

Table 9. Threshold effect test.

Explained Variable	Threshold Variables	Number of Thresholds	F	p	Bootstrap Times	Critical Value
						10%	5%	1%
URIG	Human Capital Level	Single Threshold	58.87 **	0.0483	600	49.0793	58.6409	83.0222
		Double Threshold	51.26 **	0.0317	600	38.6486	46.0503	64.0659
		Triple Threshold	32.15	0.8000	600	77.5492	87.7515	117.0301

Note: ** represents significance levels of 5%. The values in parentheses are robust standard errors.

shows that the F-values are 58.87 and 51.26, respectively, and are significant. Nevertheless, the threefold threshold test fails to attain statistical significance, which demonstrates that it exerts a dual threshold effect. Accordingly, a dual threshold model is adopted for estimation.

Table 10. Estimation outcomes of threshold values.

Explained Variable	Threshold Variables	Threshold Number	Threshold Value	95% Confidence Interval
Double Threshold Test	URIG	First Threshold Variable	0.0139	[0.0134, 0.0139]
		Second Threshold Variable	0.0232	[0.0230, 0.0232]

demonstrates that the two threshold values corresponding to DRC influence on the URIG are 0.0139 and 0.0232, respectively, reflecting a relatively notable effectiveness of threshold identification.

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Threshold model regression results (see

Table 11. Threshold regression results.

Variable	(1)		(2)
	URIG	p-Value	URIG	p-Value
Human capital level ≤ 0.0139	0.0771 *** (0.0251)	0.002	0.0815 *** (0.0281)	0.004
0.0139 < Human capital level < 0.0232	−0.0392 ** (0.0168)	0.021	−0.0497 ** (0.0199	0.013
Human capital level ≥ 0.0232	−0.0805 *** (0.0167)	0.000	−0.0845 *** (0.0199)	0.000
Constant term	0.1295 (0.0840)	0.124	0.0482 (0.0887)	0.587
Control variable	Controlled		Controlled
Observed value	390		360

Note: *** and ** represent significance levels of 1%, 5% and, respectively. The values in parentheses are robust standard errors.

): When the HCL is lower than the first threshold value of 0.0139, the coefficient of DRC is positive, which verifies that DRC will widen the URIG when the HCL is low. When HCL is between the two threshold values, i.e., greater than 0.0139 and less than 0.0232, the coefficient of DRC is −0.0392, significantly negatively affecting the URIG at the 5% level, indicating that DRC will begin to narrow the URIG when the HCL is high. When the HCL surpasses the second threshold parameter of 0.0232, DRC’s regression coefficient further diminishes to −0.0805 and attains significance. Relative to the findings in the second threshold interval, the coefficient in this range is smaller, indicating that DRC has a greater effect on reducing the URIG at a high HCL than at a low HCL. In summary, the impact of DRC on the URIG presents an inverted “U”-shaped relationship, thus verifying hypothesis H4.

The reasons for this are as follows: In the early stages of DRC, the focus was primarily on infrastructure development [3], exhibiting a clear skill bias. Rural laborers lacked sufficient education and digital skills, making it difficult for them to master technologies such as e-commerce operations and smart agricultural management. In contrast, highly skilled urban workers could quickly connect with digital industries, leading to income disparity. Simultaneously, the urban–rural “digital divide” persisted, with rural residents primarily using digital tools for basic social and entertainment purposes, failing to translate them into productive and managerial capabilities. At this stage, the income-generating effects of DRC were concentrated among urban and rural elites, further widening the URIG. However, once HCL reaches the first threshold, some laborers acquire basic digital skills through skills training and begin participating in non-agricultural employment or online sales of agricultural products. Farmers can optimize their planting structure and reduce operational risks through DRC, gradually loosening the information barriers between areas, and the URIG begins to narrow. Once HCL crosses the second threshold and digital skills become widespread, new professional farmers, returning entrepreneurs, and other groups can deeply utilize technologies to significantly improve agricultural labor productivity and enter the stage of industrial integration in DRC, thereby further bridging the URIG.

Furthermore, this paper lags the threshold variable, HCL. It can be seen that after lagging the HCL by one period, the impact of DRC on URIG remains significant and is affected by the double threshold of the HCL, consistent with the above conclusions, verifying the stability of the double threshold regression.

5. Discussion, Conclusions and Recommendations

5.1. Discuss

The innovations of this study are as follows: First, it constructs a more comprehensive indicator system for DRC based on existing research. Second, it examines the mechanism by which DRC affects the URIG from three aspects: urbanization level, employment level, and social consumption, refining and supplementing the existing literature on this mechanism. Third, it examines the inverted “U”-shaped relationship under the human capital threshold, expanding upon the existing literature’s research on linear and positive “U”-shaped relationships. Fourth, it examines the spatial spillover effect of DRC on the URIG, making up for the shortcomings of existing studies that neglect the spatial connections and spillover relationships between regions. All hypotheses stated in this paper are confirmed.

However, this study also has some limitations, particularly its insufficient focus on its role in grassroots governance and cultural heritage protection. Future research could focus on the multifaceted roles of DRC in areas such as grassroots governance innovation and local cultural protection.

5.2. Research Conclusions

This paper focuses on the impact and mechanism of DRC on the URIG. Based on theoretical analysis, using 30 provinces across China from 2011 to 2023 as a research sample, it constructs an indicator system for DRC. Using the econometric model, the analysis examines the impact and mechanism of DRC on URIG, providing empirical support for addressing the problem of URIG. The conclusions are as follows: First, DRC can narrow the URIG, with urbanization level, employment, and social consumption playing important mediating roles. Second, DRC has a spatial spillover effect; DRC in one province can narrow the URIG in neighboring provinces. Third, under the threshold influence of human capital, the influence of DRC on the URIG shows a distinct inverted “U”-type trait.

5.3. Countermeasures and Recommendations

Based on the preceding analysis, this paper proposes the following countermeasures and suggestions:

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Construct a digital mechanism to empower urban–rural integration in China. Addressing the intermediary role of urbanization, employment, and consumption levels in hypotheses H2a, H2b, and H2c, digital technology is needed to remove bottlenecks in the transmission process. First, in the urbanization field, establish a digital service platform for rural migrants, integrating modules such as job recruitment, children’s education, and medical security to enable online processing of the entire process of household registration, employment, and social security. Simultaneously, use digital maps to mark the distribution of urban and rural public service resources, promoting the downward flow of education and medical services. Second, regarding employment, collaborate with e-commerce platforms and universities to offer digital skills training, providing specialized training for rural laborers in areas such as agricultural product photography and cross-border e-commerce, establishing a closed loop of training-certification-employment. Third, at the social consumption level, support the upgrading of rural convenience stores into “digital purchasing points”, promote the “online ordering + offline delivery” model, and create regional public brands, enhancing the quality the added value of agricultural products.

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Promote China’s cross-regional collaboration and benefit sharing in DRC across eastern, central, and western regions. Based on hypothesis H3, the spatial interconnectedness of DRC breaks down administrative barriers. First, the eastern provinces with developed digital villages should take the lead in establishing a digital village collaborative development alliance with central and western provinces. This alliance will create a technology sharing database and experience exchange mechanism, regularly hold cross-provincial digital agriculture field observation meetings, and promote mature technologies such as smart irrigation and digital pest and disease monitoring. Second, a cross-regional digital circulation platform for agricultural products should be built to integrate cold chain logistics resources across provinces, achieve data interoperability, and reduce losses during agricultural product circulation. Third, a regional digital governance coordination mechanism should be established to unify e-commerce standards and traceability systems for agricultural products, solve the qualification certification problems in cross-regional transactions, and allow the digital development dividends of developed provinces to be transmitted to less developed areas through the industrial chain.

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Stratification and classification to improve China’s human capital level: Based on the inverted “U”-shaped threshold characteristic of human capital in hypothesis H4, it precisely matches the digital skill training content. First, for remote rural areas with low human capital levels, conduct digital infrastructure popularization actions, providing hands-on training through pairing village cadres with volunteers to teach basic smartphone operation, online payment, medical insurance reimbursement, and other practical skills. Second, for areas with moderate human capital levels, focus on advanced digital skills enhancement, offering courses such as agricultural data analysis, e-commerce platform operation, and live-streaming script design, establishing practical training bases in conjunction with local enterprises, and encouraging trainees to participate in e-commerce project practice. Third, for areas with high human capital levels, focus on cultivating digital innovation talent, collaborating with universities to offer cutting-edge majors such as agricultural artificial intelligence and rural digital governance, setting up a digital entrepreneurship support fund dedicated to young returnees starting businesses in their hometowns, and supporting the development of digital application scenarios suitable for rural areas.