In recent years, the Chinese government has taken the construction of rural digitalization areas as an important part of the digital China strategy, issued a series of policy documents of strategic significance, and gradually built a systematic framework and policy system. China’s digital village policy is a key component of the state’s strategy to promote rural revitalization, with the aim of empowering the modernization of agriculture and rural areas through digital technology, narrowing the digital divide between urban and rural areas, and promoting common prosperity. In 2024, the Digital Countryside Construction Guide 2.0 was jointly released by the Central Internet Information Office and six other departments. The principles of adapting measures to local conditions and implementing differentiated policies, emphasizing scientific planning and implementation, and preventing blind expansion and waste of resources were proposed. In 2023, the Overall Layout Plan for the Construction of Digital China systematically designed a framework for the construction of digital China at the national level, focusing on the construction of digital infrastructure and data resource systems, and promoting the in-depth integration of digital technology with various fields, such as agriculture, education, and healthcare. In 2022, the Action Plan for the Development of Digital Villages (2022–2025) specified the core tasks of digital rural construction in the 14th Five-Year Plan period, including the upgrading of rural digital infrastructure, the innovative development of smart agriculture, and the enhancement of digital capacity in rural governance. The twentieth report emphasized “accelerating the construction of a strong agricultural country and solidly promoting the revitalization of the countryside,” and clearly pointed out the key role of digital technology in the modernization of agriculture and rural areas. The above policy system profoundly reflects the strategic importance that the state attaches to the construction of rural digitalization areas. In theory, it provides a top-level design for the development of rural digitalization areas and, in practice, it promotes the transformation of the rural economy and the optimization of the social governance model. These policy measures have enhanced the effectiveness of rural governance and improved rural livelihoods by empowering agricultural production; they have injected strong impetus into the modernization of agriculture and rural areas, and also provide important reference for the international community’s research and practice in the field of rural digitalization.

The core of high-quality growth in the agricultural economy lies in improving the efficiency, sustainability, and stability of growth; optimizing the agricultural structure; and improving the welfare level of farmers (Njuguna et al., 2025). In addition, rural digitalization—as a core driving force that promotes the high-quality development of the agricultural economy—reconstructs the development paradigm of traditional agriculture through the systematic integration of modern information technology (Shadkam and Irannezhad, 2025). This transformation process has the fundamental objectives of enhancing total-factor productivity in agriculture and focusing on identifying the key bottlenecks constraining the development of the agricultural economy. Its core values lie in optimizing resource allocation efficiency, innovating industrial forms, and upgrading development concepts through digital means, forming a path of agricultural modernization with Chinese characteristics (Lin et al., 2025).

At the factor allocation level, digital technologies profoundly change the intrinsic structure of the agricultural production function (Dibbern et al., 2024). By enhancing land-use efficiency through precise spatial information management, relying on mobile internet technology to break through the constraints of human capital development, and optimizing the flow of capital factors with the help of financial science and technology, these changes have significantly improved the ways in which factors can be combined in traditional agricultural production (Yang et al., 2024). This digital reconstruction at the factor level provides new basic conditions for the agricultural economy to overcome resource and environmental constraints and achieve intensive development.

From the perspective of business entities, digital transformation shows distinct characteristics of classified advancement. Small farmers effectively connect to the large market through e-commerce platforms, and new business subjects use intelligent equipment to improve production efficiency. Agricultural enterprises can build a digital supply chain management system, and different subjects—on the basis of their own characteristics—can explore different development paths (Chen and Li, 2025). This multilevel and diversified pattern of transformation has created a gradient of quality, efficiency, and power changes in the agricultural economy.

With respect to functional realization, rural digitalization promotes the formation of a new paradigm of development that synergistically enhances economic, ecological, and social benefits (Li and Wang, 2025). In the economic dimension, it has promoted sustained improvements in industrial efficiency. In the ecological dimension, a new model of green development has been constructed. In the social dimension, it has provided innovation to the rural governance system. These systemic changes have enabled agricultural economic development to overcome the limitations of a single production function. Through the deep integration of digital technology with agriculture and rural areas, a more efficient, inclusive, and sustainable modern agricultural economic system is being built.

As a core engine for the high-quality development of the agricultural economy, rural digitalization systematically promotes the modernization of traditional agriculture through the three-dimensional drive of factor reconstruction, empowerment, and functional upgrading. This development path with Chinese characteristics has cracked the double dilemma of resource and environmental constraints and market inefficiency. Furthermore, the deep integration of digital technology and institutional innovation has contributed practical solutions that can be drawn on for the modernization of global agriculture. Future research should further explore the differentiated performance of the digital empowerment effect under different regional conditions, in order to provide a new empirical basis for constructing more inclusive and adaptive theoretical frameworks for agricultural digital transformation. Hence, the following hypothesis is made in this study:

Rural digitalization provides new ideas and methods for high-quality agricultural development. This has become a driving force for enhancing agricultural productivity (Hu et al., 2024). Increasing agricultural productivity is another important mechanism for modernizing agriculture through rural digitalization (Cao and Wang, 2024). The application of digital technology not only improves the productivity of land, labor, and capital, but also significantly reduces the waste of resources in agricultural production (Jiang et al., 2024).

First, digital solutions optimize agricultural resource management. The adoption of remote sensing, drone systems, and IoT devices enables real-time monitoring of field conditions and crop dynamics (Wang and Li, 2024). For example, smart agricultural systems use sensors and big data analysis to guide farmland irrigation, fertilization, and pest control accurately, thereby dramatically increasing the output efficiency per unit of land. This technology-enabled refinement not only reduces production costs, but also reduces the negative impact on the environment (Wen et al., 2024b).

Second, rural digitalization improves the productive capacity of the rural labor force through knowledge sharing and technology dissemination. Online education and distance training platforms enable farmers to access the latest agricultural technologies and market information (Kitole et al., 2024). For example, the Ministry of Agriculture in Rural China, in collaboration with a number of science and technology enterprises, has launched “Farmer Training on the Cloud,” which helps farmers to master e-commerce operation skills and intelligent farming techniques. This knowledge empowerment not only improves the overall ability of farmers, but also provides intellectual support to increase agricultural productivity.

Finally, digital technologies allow for innovation in agricultural production methods. For example, the “order agriculture” model achieves accurate matching of the supply and demand of agricultural products through digital platforms, avoiding farmers’ blind cultivation and the problem of mismatch between supply and demand in the market (Wen et al., 2024a). In addition, digital platforms have promoted the development of an agricultural sharing economy; for example, shared agricultural machinery and land hosting services have further improved the efficiency and scale of agricultural production.

Future rural digitalization necessitates tripartite strategic prioritization: (1) Accelerated deployment of broadband infrastructure to resolve spatial connectivity asymmetries; (2) Scaled implementation of certified digital literacy programs targeting agricultural labor forces; (3) Ecosystem expansion for applied technologies, particularly parametric agricultural insurance and digital microfinance solutions, to catalyze systemic productivity transformation. In summary, the following hypothesis is made:

With its core qualities of innovation-driven system integration, in-depth penetration, and open sharing, rural digitalization is highly compatible with the development strategy of new-type urbanization, and it has injected new development momentum into the urbanization process. The construction of the digital countryside promotes the synergistic development of industries and the construction of a unified national market, improves the infrastructure and public service system, strengthens the digital transformation of grassroots governance, and optimizes the allocation of the flow of production factors by promoting the synergistic development of industries. This can effectively facilitate the implementation of the rural revitalization strategy, promote the common prosperity of society, and accelerate the modernization and development process with Chinese characteristics (Chen et al., 2025).

Through the synergistic mechanism of industrial empowerment, governance upgrading, and factor optimization, rural digitalization accelerates the process of new urbanization and, thus, strongly promotes the development of the agricultural economy (Hu M. et al., 2025). At the industrial level, the digital economy is centered on the whole industrial chain system of “digital agriculture, intelligent processing, and e-commerce logistics,” and the total-factor productivity of agriculture in pilot counties has significantly improved (Lu et al., 2024). At the same time, it has spawned artificial intelligence, smart agriculture, and other convergent industries, promoting the continued expansion of the scale of the digital economy in these counties. Rural digitalization effectively reduces the degree of resource mismatch through the cross-domain flow of data elements, alleviating the contradiction between resource constraints in SMEs and the hollowing out of rural areas under the siphoning effect of large cities. Moreover, the innovation-driven urbanization development paradigm injects technology and market momentum into agriculture (Radoine and Nahiduzzaman, 2025). At the governance level, relying on the “one network,” “city brain,” and other technological structure concepts, the depth of the administrative process is re-engineered to promote the governance model for intelligent decision making, significantly improving the efficiency and quality of public service provision, while also reducing running costs. Moreover, algorithm optimization strengthens urban management refinement and emergency response capabilities and promotes the balanced allocation of public resources. The fruits of urbanization more equitably benefit urban and rural residents, reducing systemic transaction costs and increasing development vitality for agricultural business entities (Zhao et al., 2023). At the factor level, the construction of rural digitalization areas is characterized by information transparency to break down traditional barriers to factor allocation. Accurately matching human resources through the urban and rural talent information platform and cross-platform data sharing can enhance the effectiveness of docking capital and land elements. This not only forms a virtuous cycle of synergy between supply and demand, but also introduces modern production factors for agriculture through the factor integration function of urbanization and promotes the transformation of agriculture from single production to the integration of “production + service” (Jiang et al., 2022). These three mechanisms are nested within one another. Industrial innovation injects core kinetic energy into urbanization, governance optimization guarantees the stability of transformation, and factor circulation strengthens the foundation of development. Eventually, through the acceleration of the urbanization process (Wang et al., 2021) and the diffusion effect of digital technology, the synergistic effect of governance and the aggregation effect of factors will be transformed into a new kinetic energy source for the digitization of the agricultural economy and the upgrading of intensification, achieving the sustainable development pattern of “bringing the countryside to the city and promoting agriculture with numbers.”

The construction of rural digitalization areas can significantly improve the level of scientific and technological innovation and enhance the entrepreneurial activity of the county, thereby promoting the development of the agricultural economy (Tang et al., 2025). By building a county innovation ecosystem, rural digitalization systematically promotes the transformation and upgrading of the agricultural economy, and its mechanism of action is characterized by a threefold innovation-driven pathway. First, policy-guided digital infrastructure construction and smart technology application support create an environment of institutional security for agricultural technology iteration (Wang et al., 2025). Second, the Internet of Things and big data platforms are deeply embedded in the agricultural production system, significantly optimizing the technology research and development process and overcoming the bottleneck of traditional production efficiency. The digital circulation system promotes the market-oriented transformation of agricultural scientific and technological achievements, forming a benign interaction between technology application and economic benefits (Dimitrijević, 2023). This innovation-driven model effectively improves the allocation efficiency of agricultural production factors, promotes value upgrading in the industrial chain, and accelerates the transformation of traditional agriculture into intelligent agriculture. Through the construction of cross-regional technical collaboration networks, rural digitalization promotes the spatial overflow of knowledge elements, forms a regional synergistic innovation pattern, and injects sustained kinetic energy into the high-quality development of the agricultural economy.

The theoretical framework is depicted in Figure 1.

To investigate the impact of rural digitalization on agricultural economic development, a study was carried out based on the research hypotheses proposed above. In this study, the implementation of China’s digital village pilot policy is viewed as a quasi-natural experiment, and the following baseline model is set up via a difference-in-differences (DID) model:

where Agric _ Ec o it is an explanatory variable that represents the level of economic development of rural county i in China during year t. The difference-in-differences term DID _ D V it indicates whether rural county i is a digital rural construction pilot area in year t. The coefficient β 1 is the core coefficient of interest in this paperstudy. Control s it is the control variable, α 1 is a constant term, η t is a year fixed effect, and μ i is a county fixed effect, and ε it is a randomized disturbance term.

This study explores the impact of rural digitalization on agricultural economic development via 2011–2022 Chinese county panel data from the 2011–2022 China County Statistical Yearbook. The descriptive statistics of the variables are shown in Table 2. In this study, 23,868 annual observations from 1989 counties in rural China were finally obtained via routine cleaning of the data and elimination of areas with many missing data.

The Stata17 software was used to analyze the data in this study based on Equation 1. Table 3 presents the results of the benchmark regression of rural digitalization on agricultural economic development via a progressive regression strategy, where columns (1), (3), and (5) are the cases where control variables are not included. The coefficients of the core explanatory variables are 0.6244, 0.4078, and 0.2519, respectively, after adding year and county fixed effects step by step. Column (1) is significant at the 1% level, and columns (3) and (5) are significant at the 5% level. Columns (2), (4), and (6) include control variables, for which year and county fixed effects are added stepwise, and the results are all significant at the 1% level. The results in columns (2) and (6) of Table 3 indicate that the coefficient of the core explanatory variable is 0.2547 when the year and county fixed effects are combined and all control variables are included. The results of the benchmark regression show that rural digitalization and construction can significantly contribute to the development of the agricultural economy. The core explanatory variables under different forms of model setting are all significant, and the coefficients are all positive. The results of the positive impact of rural digitalization and construction on agricultural economic development are relatively robust, and Hypothesis 1 is preliminarily verified.

Parallel trends are necessary for double-difference estimation. This implies that, prior to the implementation of the digital village policy, these two distinct groups of counties did not differ significantly in their level of agricultural economic development; i.e., the common trend hypothesis was met. For this purpose, the following model was developed.

The core explanatory variables are befor e i , t and afte r i , t , which are dummy variables indicating the year of implementation of the digital village policy; befor e i , t takes a value of 1 if the pilot area is in year t before the policy is implemented, and 0 otherwise; afte r i , t is the same. The policy was not released until 2020, and there was a lag in data collection. Parallel trend tests were conducted using Equation 2, choosing 6 years before and 2 years after the policy implementation as forward-looking and lagged indicators, respectively. The results are shown in Figure 2, where the confidence intervals of the estimated coefficients for the pre-pilot digital village policy intersect the horizontal axis, with a smooth trend. This suggests that there was no significant difference in agricultural economic development between these two types of counties prior to digital countryside rural policy intervention. However, after the implementation of the policy, the level of agricultural economic development in the pilot counties was significantly greater than that in the non-pilot counties. This further validates the hypothesis that the digital village policy can have a positive effect on agricultural economic development.

To verify that unobservable omitted variables did not affect the baseline regression results, and drawing on previous methods for avoiding shocks from un-observable omitted variables, this study implemented a placebo test by randomly replacing the sample counties in the treatment group. Of the 1989 counties in the study sample, 100 counties were randomly selected and set as the experimental group, while the remaining areas were set as the control group. The 1,000 regressions assumed that the year of implementation of the digital village policy was 2020. As shown in Figure 3, the coefficients generated by the spurious policy shocks follow a normal distribution of approximately 0, whereas the p values are mostly far from 0, and the placebo test is passed. This further proves the robustness of the model.

To avoid bias in the regression results due to sample selection bias, this study adopted the double-difference method of propensity score matching (PSM) for robustness testing. Three methods—1:1 nearest-neighbor matching, kernel matching, and caliper matching—were also used for testing. The impact of the digital village policy on agricultural economic development was then re-estimated, and the specific results are shown in Table 4. As shown in columns (2), (4), and (6) of the table, the DID_DV coefficients are significantly positive for all three methods, which is consistent with the hypothetical case of this study and the results of the benchmark regression. This further confirms the reliability of the findings of this study.

This text argues that there may be confounding factors from other policies in the course of the study that prevent the policy effects from being easily identifiable. First, the comprehensive demonstration policy for e-commerce in rural areas mainly promotes agricultural economic development by improving the rural e-commerce infrastructure and expanding financing and agricultural product sales channels (Peng et al., 2021). Therefore, this study constructed a dummy variable based on whether the county in which the sample is located implements the comprehensive demonstration policy of e-commerce in rural areas. If the county where the sample is located is a comprehensive demonstration county for e-commerce in rural areas, the value is 1; otherwise, it is 0. Second, the new urbanization pilot promotes the flow of resource factors through urban–rural integration, industrial upgrading, and infrastructure improvement, driving increases in rural employment and entrepreneurship and farmer income (Ma and Shi, 2023). If the county where the sample is located is in the new urbanization pilot area, it is assigned a value of 1; otherwise, it is 0. Third, the pilot reform of the rural collective property rights system can create a good economic environment for county entrepreneurship by promoting agglomeration, driving employment, optimizing the structure, and other channels. This, in turn, has a positive effect on rural entrepreneurship (Xu G. et al., 2024). Similarly, if the sample is located in the area of the rural collective property rights system, reform pilot counties are assigned a value of 1; otherwise, they are assigned a value of 0. On this basis, this study incorporates the three variables of e-commerce into a rural comprehensive demonstration policy, a new urbanization pilot policy, and a rural collective property rights system reform pilot in the model. As can be seen from the results in column (6) of Table 5, the regression coefficients of the model for the digital village policy on agricultural economic development are all significantly positive at the 1% level after the introduction of the dummy variables for the above three policies. This is consistent with the results of the benchmark regression.

This study also used two other methods for robustness testing: First, some samples were excluded. The digitization and economic levels of the four municipalities of Beijing, Shanghai, Tianjin, and Chongqing differ significantly from those of other counties, and their levels are in the leading position. Therefore, to ensure the robustness of the findings, this study excludes data from these municipalities; it also re-examines the impact of digital village policies on agricultural economic development. According to the results in column (3) of Table 6, the regression coefficients of digital village policies on the rural economy after re-moving the sample of municipalities are still significantly positive at the 5% level. This further enhances the credibility and stability of this study. Second, the explanatory variables were replaced. In this study, on the basis of the original foundation and referring to the literature, the gross agricultural output value is used to measure agricultural economic development (Gollin, 2010), which was included as a new explanatory variable in the regression model. The results in column (6) of Table 6 show that the regression coefficient of the digital village policy on agricultural economic development remains significantly positive at the 5% level. This result is consistent with the previous benchmark regression results. Thus, the robustness of the regression results is again confirmed.

The theoretical derivation in the previous section demonstrated that the agricultural productivity, degree of urbanization, and innovation ability are important factors affecting the level of agricultural economic development in a county. The previous section demonstrated, in several ways, that digital village policies can significantly boost the agricultural economy; however, there is a need to further test whether the implementation of digital village policies can further boost the agricultural economy by increasing agricultural productivity, accelerating urbanization, and improving innovation capacity. To address the possible estimation bias of the mediated effects models widely used in the literature (Dell, 2010), the following model was constructed.

where Mechanis m it are present the mechanism variables, namely, agricultural productivity, the urbanization level, and innovation ability, and the rest other parameters are consistent with the baseline regression model. Specifically, this study paper measures agricultural productivity as the ratio of total agricultural, forestry, livestock, and fisheries production to the total crop production area. The urbanization rate is measured as the ratio of the number of people in towns to the total population of the county. Innovation capacity is measured by taking the logarithm of the number of utility model patent applications filed in the county during the year. Following Equation 3, the impact of the digital village policy on the agricultural economy is presented in Table 7 through the regression results. As seen from the perspective of agricultural productivity, columns (2) show that DID_DV is significant at the 10% level. This indicates that the digital village policy can significantly increase agricultural productivity. From the analysis of the urbanization rate, the results in columns (3) and (4) also show that the estimated coefficient of DID_DV is significantly positive at the 1% level. This implies that the digital village policy has the same significant effect on the urbanization rate. From the innovation ability perspective, the results in columns (5) and (6) continue to indicate that the estimated coefficients of DID_DV are significantly positive at 10% levels, respectively. Thus, Hypotheses 2, 3, and 4 are tested and confirmed.

Our empirical findings on the three transmission channels resonate with, yet contextually extend, the existing body of knowledge. The identified positive mediating role of urbanization empirically validates and extends the theoretical linkage proposed by Jiang et al. (2022), who highlighted the synergistic potential between digital agriculture and urban–rural integration. Our results from Chinese counties demonstrate that the digital village policy does not merely correlate with, but actively accelerates urbanization through structured factor optimization and governance digitization. Regarding agricultural productivity, our conclusion aligns with evidence from other developing contexts, such as the digital transformation observed in Tanzanian agriculture Kitole et al. (2024). However, a key distinction emerges: within China‘s integrated “digital infrastructure–platform–governance” policy framework, productivity gains are systematically orchestrated alongside institutional innovations, rather than arising from isolated technological adoption. Finally, the significant role of enhanced innovation capacity underscores a distinctive feature of China‘s model. This suggests that top-down, coordinated policy pilots can effectively stimulate local innovation ecosystems—a nuance that adds a governance dimension to the literature often focused on market-driven or spontaneous technology diffusion.

To test the regional heterogeneity of the effects of rural digitalization areas on agricultural economic development, the sample was divided into three regions (east, central, and west) for fixed-effects regression. The results show that the positive effect of rural digitalization on the agricultural economy is significant in the west, while it was not significant in the east and central regions. As shown in Table 8, the coefficient of the core explanatory variable for the western region was 0.3469, and all of the coefficients were significant at the 5% level. In contrast, neither the eastern nor the central regions passed the test of significance. This regional heterogeneity may stem from two sources: first, the western region relies on digital technology to overcome traditional production factor constraints. The agricultural industry chain has been upgraded through the application of e-commerce to help farmers, the implementation of intelligent irrigation, and other scenarios. Second, the better foundation of agricultural digitization in East China and Central China has entered a phase of diminishing marginal benefits. As such, growth in these areas needs to be driven by technological integration and innovation rather than scale expansion.

The pronounced regional heterogeneity—where western China exhibits the strongest positive effects—provides a critical spatial perspective to the literature on the digital economy‘s uneven impacts. This finding directly engages with the concerns raised by Hu et al. (2024) regarding the spatially divergent consequences of digital economic development. Contrary to scenarios where market forces alone might exacerbate regional disparities, our counter-intuitive result—that the largest policy dividends accrued to the less-developed west—suggests that well-structured national policies can harness digitalization as a “great equalizer” for lagging regions. It indicates that targeted digital infrastructure and services can help overcome traditional factor constraints in less-developed areas. Therefore, our analysis highlights the pivotal, and often underemphasized, role of proactive and spatially-sensitive governance in steering digital transformation toward regional convergence, offering a crucial insight for developing countries grappling with significant internal spatial inequalities.