How Does Digital Rural Construction Empower High-Quality Agricultural Development?

Abstract

Under China’s rural revitalization and agricultural modernization strategies, digital village construction overcomes resource limits to drive transformation. Using 2013–2022 provincial panel data and a case study of Lin’an, Hangzhou, this study reveals how digital villages boost high-quality agriculture. The empirical results show they significantly enhance agricultural total factor productivity via three paths: IoT-driven precision production, blockchain-enabled green value addition, and e-commerce direct sales demonstrate more pronounced effectiveness in major grain-producing regions and those characterized by balanced production and sales. Simultaneously, this study employs the instrumental variable (TI) approach to address endogeneity from reverse causality and omitted variables. Mechanism testing reveals agricultural technological innovation exerts a significant 77.5% mediating effect. Finally, digital rural construction exhibits a non-linear threshold (0.3082); surpassing it triggers a gradual slowdown in growth with decreasing marginal returns. The Lin’an case validates the empirical results while revealing structural barriers, including industrial chain penetration gaps, data silos, and factor supply constraints, leading to the formulation of targeted optimization strategies. The practical contribution of this study is the proposal of a “data-value-technology” closed loop: public brands like “Tianmu Mountain Treasures” channel premiums into R&D funds, creating a self-sustaining mechanism. The findings indicate that digital villages drive high-quality agriculture primarily through direct effects, powered by full-chain tech coordination, institutional reform, and inclusive factor supply. Finally, this study proposes a coordinated governance framework encompassing “technical synergy, institutional innovation, and factor optimization,” providing theoretical support and strategic references for optimizing the pathways of regional agricultural digital transformation.

1. Introduction

Realizing Chinese-style modernization hinges on high-quality agricultural development, and building a strong agricultural nation places this development at its core. However, Chinese agriculture currently faces multiple challenges, including tightening resource constraints, increasing ecological pressure, imbalanced industrial structures, and insufficient international competitiveness. In an era of accelerating digital economic penetration, digital technologies—exemplified by big data, artificial intelligence, and the Internet of Things (IoT)—are becoming critical engines for driving high-quality development of agriculture.

The Chinese government places significant emphasis on the empowering role of digital technology in agriculture. Since the “Digital Countryside Strategy” was first proposed in the 2018 No.1 Central Document, the state has introduced a series of guiding documents, including the Digital Countryside Development Strategy Outline and the Digital Countryside Development Action Plan (2022–2025). Taking Lin’an District of Hangzhou as an example, as one of the first pilot areas, it has notably promoted production efficiency and brand value through industrial digitization and smart platforms.

Against such a backdrop, digital rural development has emerged as a strategic imperative. While macro-level assessments indicate general efficiency improvements in pilot areas, underlying issues such as regional imbalances and insufficient depth of technology application remain prominent. Meanwhile, given the rigid constraints of resources and the reality of uneven development, current research still lacks a systematic theoretical explanation of the intrinsic interaction mechanism between the two. First, macro-level efficiency gains often overshadow latent implementation barriers, particularly the lack of verification regarding the universality of driving paths. Second, the systemic vulnerabilities of cyberspace are emerging as new concerns; any security threats to the digital chain could undermine the foundations of agricultural production. More importantly, social inequality has become a significant issue: when technological empowerment tilts disproportionately toward powerful capital entities, how to safeguard the developmental rights of smallholders and bridge the “digital divide” has emerged as a significant academic proposition in the process of high-quality agricultural transformation.

In response to these practical demands, this paper adopts an integrated framework combining quantitative empirical analysis and typical case verification. Based on 2013–2022 provincial panel data, this study attempts to reveal the interactive mechanism between digital village construction and high-quality agricultural development. At the macro level, it investigates the overall effects, dynamic evolution, and regional heterogeneities; at the micro level, by analyzing the practical trajectory of Lin’an District to identify bottlenecks in technology diffusion and their impacts on factor allocation, this study seeks to provide theoretical evidence and policy references for constructing a digital rural governance system that balances efficiency and equity.

2. Literature Review

2.1. Definition and Measurement of High-Quality Agricultural Development

At present, the existing literature on high-quality agricultural development presents divergent viewpoints from two distinct research dimensions: the connotation deconstruction perspective focuses on the multi-dimensional theoretical connotations, while the measurement methods perspective is committed to breaking through the technical bottlenecks of the indicator system.

In the field of connotation deconstruction, the academic circle has formed a core consensus: the high-quality development of agriculture is essentially a sustainable development model propelled by technological innovation, bounded by ecological security as the fundamental threshold, and realized through industrial upgrading. Among these dimensions, in terms of production efficiency, by integrating agricultural carbon emissions into the measurement system of green total factor productivity (GTFP) [1], it emphasizes the coordinated evolution of technological progress and resource efficiency under environmental constraints [2]. In the sustainability dimension, it focuses on the ecological internalization mechanism [3]. Some studies propose approaches for organic resource management and soil health maintenance [4], and others focus on the precise regulation of the crop–resource relationship to curb the overuse of chemical fertilizers and pesticides. In the industrial structure dimension, it reveals the spatial laws of factor allocation [5]. Some studies find that rural aging drives the modern transformation of production methods through capital deepening. The research on measurement methods is at a crucial stage of methodological innovation and dimensional integration. To address the subjective flaws of the traditional measurement system, the entropy method optimizes the weights of economic–ecological composite indicators based on the principle of information entropy. Meanwhile, the DEA model, leveraging its advantages in multiple inputs and multiple outputs, has given rise to improved methods such as the DEA–entropy weight combination [6] and the super-efficiency DEA + anti-entropy constraint cone [7]. In the face of the demand for multi-dimensional integration, the entropy weight AHP-DEA model [8] enhances the discrimination accuracy by integrating subjective and objective weights.

In recent years, the incorporation of digital dimensions has become an important innovative direction in measurement systems. Li and Peng (2024) [9], based on Chinese provincial panel data, constructed a digital village development index that includes dimensions such as digital industry development and agricultural digitization. They decomposed agricultural green total factor productivity using a super-efficiency global SBM model. Combined with heterogeneity analysis of dimensions such as altitude, production layout, and digital literacy, they verified the core driving role of digitization in high-quality agricultural development. At the same time, they provided an empirical example for dealing with spatial heterogeneity. Ismailov et al. (2025) [10], in an international comparative study, further proposed that the number of digital technology uses and the proportion of digital skill practitioners should be included in agricultural statistics, enriching the international perspective and social dimension connotation of the measurement system.

However, these advancements still encounter three limitations: it is difficult for static indicators to capture the dynamic spillover effects of digital technology; inadequate handling of spatial heterogeneity results in cross-regional scale deviations; and social dimension indicators are slow to respond to new contradictions such as the digital divide and data rights. The disconnection between theory and practice urgently needs to be bridged. Although the “pressure–state–response” ecological paradigm [11] and economic indicators for the integration of the agriculture, manufacturing, and service industries have been established, the quantitative linkage mechanism among technological innovation, ecological constraints, and industrial upgrading remains unclear. Moreover, there is also a lack of implementation paths for the dynamic indicators (such as algorithm transparency and data rights protection) required by the penetration of digital technologies.

2.2. Impact Mechanism of Digital Rural Construction

Currently, existing domestic and foreign studies on the impact of digital village construction on the high-quality development of agriculture mainly adopt two research perspectives: the perspective of the direct impact mechanism and the perspective of the systematic synergy effect. The former mainly analyzes how digital technologies directly contribute to the improvement of agricultural production efficiency through multiple micro-mechanisms; the latter focuses on exploring how digital infrastructure indirectly promotes the overall high-quality development of rural areas by facilitating factor flow, cultivating subjects, and coordinating policies. In the research landscape from the perspective of the direct influence mechanism, both domestic and foreign research findings have identified three key factors that affect agricultural production efficiency. These factors include technological penetration, optimization of factor allocation, and organizational change. The research has found that, apart from the application of technology itself, the ways and effects of other mechanism factors depend on dynamic contexts. These contexts include the extent to which financing constraints are alleviated, changes in the scale of productive investment, the degree of correction of resource misallocation, and the social capital conditions for the diffusion of ecological technologies.

Specifically, in the aspect of technological penetration, it is manifested in the adoption of the Internet of Things and big data technologies to conduct precise farmland monitoring and implement intelligent decision-making in agricultural production. Some scholars have suggested that sensor networks and drone technologies can optimize irrigation and fertilization plans through real-time data collection and analysis, reducing the resource waste rate by 20–30% [12]. In terms of factor allocation, digital financial support has reconstructed the flow mechanism of agricultural production factors. Some studies have shown that it promotes the adoption of green technologies by alleviating financing constraints, leading to a significant increase in farmers’ adoption rate of new technologies [13]. Another empirical study has verified that digital credit can directly promote the growth of agricultural income by expanding the scale of agricultural productive investment.

However, existing research pays insufficient attention to the social equity dimension of digital countryside construction, exhibiting a clear “technocratic” tendency. Most studies assume that the promotion of digital technology is a linear progressive process, while ignoring the power structures behind technology deployment. For example, government-led intelligent agriculture projects may preferentially favor large agricultural enterprises because they have stronger technology adaptation capabilities and data feedback capabilities [14]. Small farmers, due to insufficient digital literacy and high equipment costs, are often excluded from the dividends of technology. This collusion of “technology–capital–power” may lead to the development of digital villages becoming an “efficiency game” for a few subjects, rather than a universal social project. Furthermore, the existing literature exhibits a critical gap in exploring the conflicts of interest between large enterprises and smallholder farmers within the digital countryside. While some studies mention the integration of supply chains by digital platforms [15], they fail to thoroughly analyze how platform enterprises depress the bargaining power of smallholder farmers through data monopolies [16] or transfer risks to the production end through algorithmic rules [17]. While some academics advocate for inclusive digital governance, empirical studies on the benefit distribution mechanism between enterprises and smallholder farmers remain insufficient, which frequently results in the policy objective of empowering smallholder farmers being reduced to a mere slogan in policy formulation.

In contrast, research from the perspective of system synergy effects is still in its early stages. In general, existing relevant research can be roughly categorized into two types: one type focuses on the inadequacies in practical issues such as the urban–rural digital divide and the slow development of rural industries, highlighting the necessity and functions of digital infrastructure in activating new industries, cultivating new-type professional farmers, facilitating the flow of urban–rural elements, and optimizing the efficiency of the integration of the agriculture, manufacturing, and service industries. The other category, based on the summary of practices, puts forward principles and strategies for policy synergy optimization that should be followed, such as integrating e-commerce policies with digital agriculture and improving the digital talent cultivation system and policy framework. However, most studies of the two types merely describe phenomena or propose strategies. They lack in-depth theoretical explanations of the internal mechanism of the synergy effect and systematic empirical verification, making it difficult to effectively guide the practical scenarios of how to specifically construct and optimize the digital rural ecosystem to maximize its synergy benefits.

In terms of practical guidance, although there is a consensus on the importance of digital rural construction and some policy frameworks, it remains unclear how to clarify the logical relationship between direct mechanisms such as technology penetration, factor allocation, and organizational change and indirect effects such as the flow of urban and rural factors, subject cultivation, and policy coordination. Moreover, it is also unclear how to systematically implement the theoretical requirements of constructing a highly efficient and collaborative digital rural ecosystem to drive high-quality agricultural development through specific and operable measures. Based on the above analysis, this study constructs a multi-dimensional transmission framework through which digital villages drive the high-quality development of agriculture. Provincial panel data spanning from 2013 to 2022 are selected to analyze the macro transmission path, while a typical case from Lin’an District, Hangzhou is adopted for micro-level verification. The paper focuses on revealing the collaborative logic of technology penetration–factor allocation–organizational change and its practical transformation mechanism in efficiency improvement, ecological optimization, and industrial upgrading.

In summary, while existing studies have uncovered the direct driving mechanism of digital village construction on agricultural efficiency, the neglect of social equity and the evasion of the “enterprise–smallholder” interest conflict have resulted in deviations in the understanding of the essence of digital village development. This theoretical gap makes it difficult for policymakers to balance the dual goals of “efficiency improvement” and “equity and inclusion,” and may also lead digital village construction into a dilemma of “technological advancement but social disorder.” Based on this, this study constructs a multi-dimensional transmission framework for digital village-driven high-quality agricultural development, selects provincial panel data from 2013 to 2022 to measure the macro path, and uses the typical practice of Lin’an District, Hangzhou City as a case for micro-level verification. The study focuses on revealing the synergistic logic of technology penetration–factor allocation–organizational change and its practical transformation mechanism in efficiency improvement, ecological optimization, and industrial upgrading. Simultaneously, it pays special attention to the power relations and the protection of smallholders’ rights and interests in the application of digital technology, aiming to provide theoretical support and policy reference for a more inclusive digital village development.

3. Theoretical Analysis and Research Hypotheses

3.1. Theoretical Logic of Digital Rural Areas Driving High-Quality Agricultural Development

Digital village construction fully leverages digital innovation mechanisms to integrate digital technologies and elements into the agricultural industrial chain, value chain, and innovation chain, thereby facilitating the high-quality development of agriculture.

Digital village construction plays a pivotal role in promoting the high-quality development of agriculture through multiple interrelated channels. It contributes significantly to the improvement of agricultural efficiency and the enhancement of agricultural benefits. By integrating advanced digital technologies—including big data and blockchain—into the agricultural production system, it provides efficient, accurate, and intelligent technical support throughout the entire industrial chain of agricultural production and management. This integration facilitates technological spillover and fosters R&D collaboration within the agricultural field [18], ultimately leading to improvements in both agricultural labor productivity and land productivity. Furthermore, the optimization of the development environment for digital infrastructure and digital finance empowers farmers to access market information and secure financing with greater ease. This, in turn, promotes the widespread adoption of e-commerce and live-streaming product sales models in rural areas, which not only expands the geographical reach of agricultural product sales and broadens the consumer base but also streamlines circulation steps and reduces transaction costs [19]. Consequently, these developments collectively boost overall agricultural efficiency and increase farmers’ income.

Beyond efficiency gains, digital village construction acts as a catalyst for the transformation and upgrading of the agricultural sector. It enhances farmers’ digital literacy, which in turn boosts rural innovation and entrepreneurship, and promotes the transfer of rural labor from traditional agricultural activities to non-agricultural sectors. This labor reallocation promotes land transfer and facilitates the transition towards large-scale and intensive land management practices. Moreover, by augmenting local fiscal support and refining rural digital financial services, digital village initiatives provide targeted financial assistance for agricultural production. This infusion of resources and integration of new concepts and technologies into the agricultural factor allocation system paves the way for the gradual realization of mechanized operations alongside information-based and intelligent management in agricultural production [20], thereby driving the evolution of traditional agriculture into a more modern form.

Furthermore, economic and structural transformations, the construction of digital rural areas also strongly promotes the green and sustainable development of agriculture. By elevating the level of digital transactions, it enables farmers to establish direct connections with end-consumers. This direct linkage allows the agricultural sector to more acutely perceive and respond to the growing consumer demand for green and organic agricultural products. Simultaneously, by offering farmers more accessible and low-threshold digital financial services, it alleviates their financial constraints, which in turn heightens their awareness and motivation to engage in green and sustainable agricultural practices. Through these multifaceted mechanisms, digital village construction not only modernizes agriculture but also aligns it with environmental sustainability goals.

In addition, through the popularization and application of digital and visual technologies, digital village construction renders the whole-process traceability of green agricultural product production “visible”. This allows investors and consumers to more conveniently grasp the progress and quality control of the entire production process of green agricultural products, thereby improving the social credibility and acceptance of green agricultural product producers. Subsequently, it urges these producers to realize “precision” and “reduction” in the input of factors such as chemical fertilizers and pesticides, alleviating the damage to agricultural product quality and the natural environment [21].

In summary, digital village construction may facilitate the high-quality development of agriculture by improving agricultural efficiency and effectiveness, accelerating the transformation and upgrading of the agricultural sector, and promoting the green and sustainable development of agriculture. On the basis of the aforementioned analysis, the following research hypothesis is proposed [22]:

Hypothesis H1:

Digital village construction can significantly promote the high-quality development of agriculture.

3.2. The Mediating Effect of Technological Innovation in the Process of Digital Villages Enabling the High-Quality Development of Agriculture

3.2.1. The Theoretical Logic of How the Construction of Digital Villages Promotes Agricultural Technological Innovation

The construction of digital villages promotes agricultural technological innovation by improving the regional innovation ecosystem and alleviating the constraints on innovation financing.

First, it improves the regional innovation ecosystem. On the one hand, with the development of digital villages, local governments will focus on enhancing the construction of relevant digital infrastructure, including 5G networks, data centers, and platform systems. This will provide more comprehensive public facility support for various entities to carry out innovation activities, lower the costs and risks related to such activities, and thus promote regional agricultural technological innovation.

On the other hand, the advancement of digital village construction will also drive the digital transformation of local rural governance systems and public service delivery mechanisms, especially in areas including transactions and financial services. It will foster a more inclusive and open regional innovation policy environment and innovation-culture atmosphere. Consequently, it will contribute to the aggregation of innovation elements like talents, technologies, and capital, encourage various entities to strengthen their innovation awareness and capabilities, promote learning and communication among innovation entities as well as technology spill-over, and create a “demonstration and incentive effect” for technological innovation [23]. Second, it eases the financing constraints on innovation. On the one hand, technological innovation features high investment, long cycles, and high risks. It demands continuous and stable input of substantial financial and human resources and highly depends on external financing. However, as it is hard to rapidly convert its benefits in the short term and the future returns are uncertain, technological innovation projects often struggle to secure effective financing from traditional financial markets. Consequently, technological innovation activities face significant financing constraints [24].

Digital village construction has driven the extensive penetration and practical application of digital finance in rural contexts. Digital finance is not confined to data collection alone, but utilizes big data, cloud computing and other digital technologies to provide empowerment for financial service systems. This effectively improves the ability to collect and evaluate credit information in the agricultural sector, lowers the threshold for credit access, and achieves accurate matching of capital supply and demand. Furthermore, it guides financial resources to agricultural technology innovation projects with development potential, providing more sufficient financial support and more convenient financial services for the research and transformation activities of agricultural innovation entities. This genuinely alleviates their innovation financing constraints and promotes the steady development of agricultural technology innovation activities [25].

3.2.2. The Mediating Effect of Agricultural Technology Innovation on High-Quality Agricultural Development

Agricultural technological innovation infuses traditional agriculture with novel concepts and knowledge, elevates agricultural production efficiency and input factor utilization, curbs waste discharge and environmental pollution, and realizes the shift toward an intensive growth model characterized by low resource consumption [26]. Such transformation underpins the enhancement of agricultural development quality.

Beyond these direct impacts, agricultural technological innovation exerts an indirect impetus on high-quality agricultural development through mediating mechanisms including large-scale farmland management and rural industrial chain extension. Product innovation, germplasm innovation and related approaches enhance labor productivity, releasing substantial labor from traditional agricultural production. This drives the gradual concentration of agricultural land among new agricultural operators, such as core farmers, large-scale cultivators and professional farmers [27], enabling the orderly transfer and large-scale operation of land resources. The resultant optimization of agricultural management modes and production factor input structures further drives agricultural development toward quantitative growth, qualitative improvement and efficiency enhancement.

Agricultural technological innovation also optimizes factor allocation and intensifies inter-industry linkages in rural areas, propelling the penetration and expansion of the rural industrial chain. Traditional agriculture thus expands from a single production link to a full industrial chain covering production, marketing and services [28], fostering the upgrading of the agricultural industry and the emergence of emerging sectors such as leisure agriculture and biotechnology. On the basis of the above analysis, the following hypothesis is formulated:

Hypothesis H2:

Digital countryside development empowers the high-quality development of agriculture by facilitating agricultural technological innovation.

3.2.3. Threshold Effect of Digital Villages Enabling High-Quality Agricultural Development

Digital village construction involves substantial initial startup costs. In the early stages of development, when government support policies and digital infrastructure construction are at relatively low levels, the promotion effect of digital village construction on high-quality agricultural development may be significant. Its enabling effect is fully released by other factors that significantly drive high-quality agricultural development. However, as digital villages develop to a certain extent, digital infrastructure continues to improve, industrial digitization and life digitization gradually become widespread, and the driving effect of digital village construction on high-quality agricultural development tends to diminish. Consequently, its enabling effect may be gradually weakened.

Therefore, the promotion of high-quality agricultural development through digital village construction may exhibit a non-linear threshold effect with decreasing marginal effects. This means that the promotion effect is relatively strong when the level of digital village construction has not exceeded the threshold value. However, the promotion effect weakens significantly once the level of digital village construction surpasses the threshold value. Based on the above analysis, the following hypothesis is proposed:

Hypothesis H3:

The impact of digital rural construction on high-quality agricultural development exhibits a non-linear characteristic of diminishing marginal effects.

4. Model Specification and Variable Description

4.1. Model Specification

4.1.1. Benchmark Regression Model

Based on theoretical analyses, a benchmark regression model is employed herein to empirically examine the impact of digital rural construction on the high-quality development of agriculture in China:

$$HQD{A}_{i,t}={\alpha }_0+{\alpha }_{1}DI{V}_{i,t}+{\alpha }_{2}Control{s}_{i,t}+{\displaystyle \sum id}+{\displaystyle \sum year}+{\epsilon }_{i,t}$$

(1)

In Equation (1), HQDA_i,t denotes the level of high-quality agricultural development in province i during year t, while DIV_i,t stands for the development level of digital rural construction and Controls_i,t refers to the set of control variables, with α₁ and α₂ representing the regression coefficients corresponding to each variable, respectively. id and year denote individual fixed effects and time fixed effects, respectively, and ε_i,t is the random disturbance term.

4.1.2. Mediating Effect Model

To verify Hypothesis H2, with reference to the relevant research findings of existing scholars, this study constructs a mediating effect model to empirically test the mediating role of agricultural technological innovation in the impact of digital rural construction on the high-quality development of agriculture.

$$HQD{A}_{i,t}={\alpha }_0+{\alpha }_{1}DI{V}_{i,t}+{\alpha }_{2}Control{s}_{i,t}+{\displaystyle \sum id}+{\displaystyle \sum year}+{\epsilon }_{i,t}$$

(2)

$$T{I}_{i,t}={\beta }_0+{\beta }_{1}DI{V}_{i,t}+{\beta }_{2}Control{s}_{i,t}+{\displaystyle \sum id}+{\displaystyle \sum year}+{\epsilon }_{i,t}$$

(3)

$$HQD{A}_{i,t}={\lambda }_0+{\lambda }_{1}DI{V}_{i,t}+{\lambda }_{2}T{I}_{i,t}+{\lambda }_{3}Control{s}_{i,t}+{\displaystyle \sum id}+{\displaystyle \sum year}+{\epsilon }_{i,t}$$

(4)

Among them, β₁, β₂, λ₁, λ₂, and λ₃ respectively represent the coefficients of each variable; TI_i,t represents the agricultural technology innovation ability.

4.1.3. Threshold Effect Model

To verify hypothesis H3, with the level of digital village construction (DIV_i,t) as the threshold variable, the following threshold effect model is established to further explore whether the relationship between digital villages and high-quality agricultural development presents non-linear characteristics with changes in the level of digital village construction.

$$HQD{A}_{i,t}={\theta }_0+{\theta }_{1}DI{V}_{i,t}\times I(DIV\le f_1)+{\theta }_{2}DI{V}_{i,t}\times I(DIV>f_1)+{\theta }_{3}Control{s}_{i,t}+{\displaystyle \sum id}+{\displaystyle \sum year}+{\epsilon }_{i,t}$$

(5)

In this model, θ₁ and θ₂ are the influence coefficients of digital village construction on high-quality agricultural development when the threshold variable is in different ranges; f is the threshold value; I( ) is an indicator function, which takes a value of 1 when the condition in parentheses is met, and 0 otherwise.

4.2. Variable Description

This research establishes an evaluation index system for both digital rural construction and the high-quality development of agriculture, drawing on provincial panel data spanning the period from 2013 to 2022. It also performs logarithmic transformation on variables with relatively large orders of magnitude to empirically analyze the impact of digital rural construction on high-quality agricultural development. The data employed in this study, including the number of rural broadband access subscribers, the quantity of rural meteorological station monitoring services, the length of rural delivery routes (measured in kilometers), e-commerce sales and procurement volumes, the per capita consumption expenditure of rural residents on transportation and communication, and the total telecommunications business volume, are sourced from the China Statistical Yearbook. Data regarding the number of Taobao villages, administrative villages, and local fiscal expenditures on urban–rural community affairs are obtained from the National Bureau of Statistics. Additionally, the digital inclusive finance index data are derived from the Digital Inclusive Finance Index Report released by the Digital Finance Research Center of Peking University.

All area data used in this study are measured in mu, a Chinese municipal unit of measurement, with the statutory conversion relationship of 1 hectare equaling 15 mu (1 mu ≈ 0.0667 hectares). All monetary data denoted by “Yuan” refer to Renminbi (CNY). The conversion rates of Renminbi against the US dollar and the euro for the period from 2013 to 2022 are all based on the annual average central parity rates published by the State Administration of Foreign Exchange of China, with 1 CNY equivalent to 0.1450 US dollars and 1 euro equivalent to 7.8755 CNY in 2020.

4.2.1. Explained Variable

The dependent variable of this research is the high-quality development level of agriculture (HQDA). Based on the new development philosophy, this study establishes a set of indicators to measure the high-quality development level of agriculture. The new development philosophy emphasizes that innovative development focuses on resolving issues related to development momentum, coordinated development addresses problems of developmental imbalance, green development tackles challenges in achieving harmony between humans and nature, open development deals with issues of internal and external linkages in development, and shared development resolves matters of social fairness and justice. The new development philosophy constitutes the fundamental guideline for the high-quality development of agriculture. As a new requirement for high-quality development in the new era, it can fully reflect the level of high-quality agricultural development, conform to the characteristics of China’s agricultural development stage in the high-quality development phase, and serve as the evaluation criterion for judging the realization of high-quality development [29].

With reference to the relevant research outcomes of Shi Xiaokun [30], Zhou Li [31], and other scholars, this research defines the connotation of high-quality agricultural development from five dimensions: agricultural innovative development, coordinated development, green development, open development, and shared development. An evaluation index system for the high-quality development level of agriculture is constructed, which includes 5 primary indicators and 21 secondary indicators (see

Table 1. Indicators for Measuring the High-quality Development Level of Agriculture.

Primary Indicators	Secondary Indicators	Indicator Description	Indicator Direction	Weight
Agricultural Innovation Development	Expenditure on Three Scientific Items	Local fiscal expenditure on science and technology/Local general fiscal budget expenditure	Positive	0.035803
	Agricultural Mechanization Degree	Total agricultural machinery power/Total sown area of crops	Positive	0.030639
	Research and Development (R&D) Expenditure Intensity	Direct data	Positive	0.041329
	Number of Domestic Patent Applications Accepted	Direct data	Positive	0.083866
	Agricultural GDP Output Value	per Unit Area Gross agricultural output value/Total sown area of crops	Positive	0.027230
Agricultural Coordinated Development	Local fiscal expenditure on agriculture	forestry and water affairs/Local general fiscal budget expenditure	Positive	0.025612
	Rural Engel’s Coefficient	Food expenditure/ Total rural household	Positive	0.051774
	Rural Household Consumption Level	Per capita rural household consumption expenditure	Positive	0.060804
	Industrial Coordination Level	Value-added of the primary industry/Regional GDP	Positive	0.026928
	Agricultural Industrial Structure Adjustment Index	1-Gross agricultural output value/Gross output value of agriculture, forestry, animal husbandry and fishery	Positive	0.020145
Green Development in Agriculture	Agricultural Green Development Fertilizer Use per Unit Area	Pure-converted amount of agricultural fertilizer application/Total sown area of crops	Negative	0.030122
	Pesticide Use per Unit Area	Pesticide use amount/Total sown area of crops	Negative	0.033168
	Plastic Film Use per Unit Area	Agricultural plastic film use amount/Total sown area of crops	Negative	0.055118
	Forest Coverage Rate	Forest area/Land area	Positive	0.058452
Open Development in Agriculture	Dependence Degree of Agricultural Product Exports	Export value of agricultural products/Value added of the primary industry	Positive	0.051693
	Dependence Degree of Agricultural Product Imports	Import value of agricultural products/Value added of the primary industry	Positive	0.120835
Shared Development in Agriculture	Ratio of Urban to Rural Residents’ Income	Per capita disposable income of urban residents/Per capita disposable income of rural residents	Negative	0.028160
	Per Capita Disposable Income of Rural Residents	Direct data	Positive	0.061402
	Living Standard of Rural Residents	Per capita expenditure on culture, education and entertainment in rural areas	Positive	0.063030
	Consumption Gap between Urban and Rural Areas	Per capita consumption expenditure of urban residents/Per capita consumption expenditure of rural residents	Positive	0.024381
	Medical Level in Rural Areas	Number of village clinics	Positive	0.069509

for details). The entropy weight method is adopted to comprehensively measure the high-quality agricultural development level of each province (including provinces, autonomous regions, and municipalities directly under the Central Government; the same below).

According to the calculated results in

Table 2. Measurement Results of the High-Quality Agricultural Development Level in 31 Provinces of China from 2013 to 2022.

Region	Province	2013	2014	2015	2016	2017	2018	2019	2020	2021	2022
Eastern Region	Beijing	0.32	0.33	0.35	0.36	0.41	0.45	0.53	0.52	0.6	0.61
	Tianjin	0.21	0.21	0.21	0.21	0.21	0.21	0.23	0.25	0.23	0.22
	Hebei	0.17	0.17	0.18	0.18	0.18	0.18	0.19	0.2	0.21	0.21
	Shanghai	0.29	0.31	0.34	0.39	0.45	0.48	0.51	0.53	0.64	0.66
	Jiangsu	0.2	0.2	0.2	0.22	0.22	0.24	0.24	0.25	0.26	0.26
	Zhejiang	0.23	0.23	0.24	0.26	0.26	0.27	0.28	0.29	0.3	0.31
	Fujian	0.19	0.19	0.2	0.21	0.21	0.23	0.23	0.23	0.25	0.25
	Shandong	0.2	0.2	0.21	0.21	0.22	0.22	0.23	0.24	0.25	0.26
	Guangdong	0.2	0.2	0.22	0.25	0.27	0.3	0.31	0.32	0.33	0.34
	Hainan	0.13	0.13	0.14	0.15	0.15	0.16	0.17	0.18	0.19	0.21
Central Region	Shanxi	0.12	0.12	0.12	0.12	0.12	0.12	0.13	0.13	0.13	0.13
	Anhui	0.13	0.14	0.15	0.18	0.17	0.19	0.19	0.2	0.21	0.22
	Jiangxi	0.14	0.15	0.15	0.16	0.17	0.18	0.19	0.19	0.2	0.2
	Henan	0.15	0.15	0.16	0.16	0.17	0.18	0.19	0.2	0.21	0.22
	Hubei	0.14	0.15	0.16	0.17	0.18	0.19	0.2	0.2	0.21	0.23
	Hunan	0.15	0.16	0.17	0.18	0.18	0.19	0.2	0.21	0.22	0.23
Western Region	Inner Mongolia	0.1	0.11	0.12	0.12	0.13	0.13	0.14	0.14	0.14	0.14
	Guangxi	0.14	0.15	0.15	0.16	0.16	0.16	0.17	0.17	0.18	0.19
	Chongqing	0.1	0.11	0.12	0.13	0.13	0.14	0.14	0.15	0.16	0.16
	Sichuan	0.16	0.16	0.17	0.18	0.18	0.19	0.2	0.2	0.21	0.2
	Guizhou	0.1	0.11	0.12	0.13	0.14	0.14	0.15	0.16	0.16	0.16
	Yunnan	0.11	0.12	0.13	0.14	0.14	0.14	0.15	0.16	0.16	0.16
	Tibet	0.1	0.1	0.11	0.11	0.1	0.11	0.12	0.12	0.13	0.13
	Shaanxi	0.13	0.13	0.14	0.15	0.15	0.15	0.16	0.16	0.16	0.17
	Gansu	0.09	0.09	0.1	0.1	0.1	0.11	0.12	0.12	0.12	0.13
	Qinghai	0.09	0.09	0.09	0.1	0.1	0.11	0.11	0.11	0.12	0.11
	Ningxia	0.09	0.1	0.1	0.1	0.11	0.12	0.12	0.12	0.13	0.13
	Xinjiang	0.09	0.09	0.09	0.1	0.09	0.1	0.11	0.12	0.12	0.12
Northeastern Region	Liaoning	0.15	0.16	0.16	0.16	0.16	0.17	0.17	0.17	0.18	0.18
	Jilin	0.13	0.13	0.13	0.14	0.14	0.14	0.15	0.15	0.16	0.15
	Heilongjiang	0.13	0.13	0.14	0.15	0.15	0.15	0.16	0.16	0.17	0.17

, during the sample period from 2013 to 2022, the high-quality agricultural development level across China’s four major economic regions (eastern, central, western, and northeastern regions) presents obvious regional disparities. The eastern provinces, represented by Shanghai, Beijing, Guangdong, and Jiangsu, showed a continuous upward trajectory in their High-quality Development Level of Agriculture. Specifically, Shanghai’s score increased from 0.29 in 2013 to 0.66 in 2022, with an average annual increase of 22.8%. This growth was driven by the synergistic effects of optimized agricultural management models in the urbanization process, the in-depth application of smart agriculture technologies, and the construction of an efficient agricultural industry system. Guangdong and Jiangsu, leveraging their strong economic strength, solid technological support, and vast market potential, also saw their scores rise from 0.20 and 0.20 to 0.34 and 0.26, respectively, maintaining a steady upward trend.

Overall, the eastern region, leveraging its strengths in the digital economy, financial capital, and market channels, is driving the transformation of agriculture towards precision, branding, and integration, becoming the core leader in the High-quality Development Level of Agriculture nationwide. Central provinces, represented by Henan, Hubei, and Hunan, exhibit a steady increase in the High-quality Development Level of Agriculture, with scores generally rising from the 0.14–0.15 range to the 0.22–0.23 range, an average annual increase of approximately 5.7%. Henan, as a major grain-producing area, has achieved stable growth through large-scale planting and the upgrading of agricultural machinery and equipment. Hubei and Hunan, on the other hand, have promoted the increase in industrial added value through the integrated development of characteristic agriculture and rural tourism. However, regional differentiation still exists. Although the scores of provinces such as Anhui and Jiangxi have increased, the growth rate is relatively slow, reflecting that the agricultural transformation in the central region still faces bottlenecks such as insufficient technology penetration and limited industrial chain extension.

The western provinces exhibit a pattern of both fluctuating growth and growth stagnation. Specifically, Qinghai and Xinjiang experienced substantial data fluctuations. Qinghai’s scores fluctuated non-linearly between 0.09 and 0.12, while Xinjiang’s scores hovered between 0.09 and 0.12. This is highly correlated with exogenous factors such as natural disasters, fluctuations in agricultural product market prices, and policy adjustments. Provinces such as Gansu and Guizhou, however, showed characteristics of growth stagnation. Gansu’s scores remained stagnant in the range of 0.09 to 0.13 for an extended period, reflecting underlying issues such as weak agricultural infrastructure, insufficient technology application, and a simple industrial structure. Only Chongqing and Sichuan, provinces within the Chengdu–Chongqing economic circle, saw slight increases in scores, but their overall scores remained lower than those of the eastern and central regions, highlighting the multiple challenges facing high-quality agricultural development in the western region.

Although the northeastern region has fundamental advantages, such as abundant arable land resources and a high degree of mechanization, the high-quality development level of agriculture has shown weak growth. Heilongjiang’s score increased from 0.13 to 0.17, Jilin’s score fluctuated between 0.13 and 0.16, and Liaoning’s score hovered between 0.15 and 0.18, with some years even showing stagnant or reverse fluctuations. This phenomenon mainly stems from the path dependence of the traditional agricultural structure and the conservative characteristics of the agricultural development model, resulting in insufficient momentum for industrial upgrading. It is difficult to break through the development bottleneck of being “large but not strong.” There is an urgent need to activate growth momentum through digital technology empowerment and industrial model innovation.

4.2.2. Core Explanatory Variable

In this paper, the level of digital village construction (DIV) is chosen as the core explanatory variable. The connotation of digital villages is delineated from four dimensions: digital infrastructure development, agricultural digitization, rural digital livelihoods, and digital governance. An evaluation index system is established for digital village construction, consisting of 4 primary indicators and 25 secondary indicators, as presented in

Table 3. Measurement Indicators of the Level of Digital Rural Construction.

First-Level Indicators	Secondary Indicators	Indicator Description	Indicator Direction	Weight
Digitalization of Digital Infrastructure Development	Rural Mobile Phone Penetration Rate	Number of mobile phones owned per 100 rural households at year-end	Positive	0.008
	Rural Computer Penetration Rate	Number of computers owned per 100 rural households at year-end	Positive	0.018
	Rural Internet Penetration Rate	Number of rural broadband access users	Positive	0.070
	Rural Electricity Access Level	Rural electricity consumption	Positive	0.085
	Rural Distribution Infrastructure Construction	Rural delivery routes	Positive	0.029
	Rural Meteorological Observation Stations	Number of rural meteorological observation business stations	Positive	0.021
	Digitalization of the Financial Industry	Digitalization level index of digital finance	Positive	0.012
	Length of Optical Fiber Cables	Direct data	Positive	0.046
Digitalization of Agriculture	Effective Irrigation Area	Direct data	Positive	0.048
	Number of Large and Medium-Sized Agricultural Tractors	Direct data	Positive	0.080
	Express Delivery Volume	Direct data	Positive	0.165
	E-commerce Sales	Direct data	Positive	0.106
	E-commerce Procurement	Direct data	Positive	0.112
	E-commerce Activity Level	Proportion of enterprises with e-commerce transaction activities	Positive	0.024
Digitalization of Life	Television Penetration Rate	Rural population coverage rate of television programs	Positive	0.004
	Radio Penetration Rate	Rural population coverage rate of radio programs	Positive	0.003
	Rural Residents’ Expenditure on Transportation and Communication	Per capita expenditure on transportation and communication for rural residents	Positive	0.028
	Average Weekly Deliveries to Rural Areas	Direct data	Positive	0.011
	Rural Consumer Goods Sales Level	Rural retail sales/Total retail sales of consumer goods	Positive	0.013
	Quality of Rural Life	Engel’s coefficient of rural households	Negative	0.006
	Digital Financial Services	Coverage breadth index of digital finance	Positive	0.019
Digitalization of Governance	Level of Local Government Financial Support for Agriculture	Local government expenditure on agriculture, forestry, and water affairs	Positive	0.025
	Postal Service Coverage Rate of Administrative Villages	Percentage of administrative villages with postal service access	Positive	0.001
	Rural Digital Financial Supply	Local Government Expenditure on Urban and Rural Community Affairs/Local Government General Budget Expenditure	Positive	0.026
	Number of Students in Rural Higher Education	Number of Rural Residents with College Degree or Above	Positive	0.039

. The entropy weight method is utilized to comprehensively quantify the level of digital village construction across provinces.

The entropy weight method is adopted to measure the digital rural construction level across Chinese provinces during 2013–2022, with the corresponding results presented in

Table 4. Measurement Results of the Digital Rural Construction Level in 31 Provinces of China from 2013 to 2022.

Region	Province	2013	2014	2015	2016	2017	2018	2019	2020	2021	2022
Eastern Region	Beijing	0.13	0.15	0.19	0.19	0.22	0.22	0.25	0.26	0.28	0.3
	Tianjin	0.09	0.11	0.13	0.13	0.13	0.12	0.13	0.13	0.13	0.14
	Hebei	0.22	0.23	0.26	0.27	0.29	0.3	0.31	0.33	0.34	0.36
	Shanghai	0.14	0.18	0.21	0.23	0.23	0.25	0.26	0.23	0.27	0.3
	Jiangsu	0.32	0.34	0.4	0.4	0.42	0.45	0.47	0.43	0.45	0.47
	Zhejiang	0.22	0.24	0.28	0.29	0.32	0.36	0.4	0.39	0.44	0.47
	Fujian	0.14	0.15	0.17	0.17	0.19	0.21	0.23	0.22	0.24	0.25
	Shandong	0.26	0.28	0.32	0.34	0.37	0.39	0.39	0.42	0.44	0.47
	Guangdong	0.27	0.29	0.35	0.37	0.41	0.46	0.52	0.53	0.59	0.62
	Hainan	0.06	0.07	0.09	0.09	0.1	0.1	0.1	0.11	0.12	0.13
Central Region	Shanxi	0.12	0.13	0.16	0.15	0.16	0.17	0.18	0.19	0.2	0.2
	Anhui	0.16	0.19	0.23	0.23	0.25	0.27	0.29	0.31	0.33	0.34
	Jiangxi	0.11	0.12	0.15	0.14	0.17	0.18	0.2	0.21	0.23	0.23
	Henan	0.21	0.23	0.28	0.28	0.3	0.31	0.32	0.35	0.37	0.39
	Hubei	0.16	0.17	0.21	0.21	0.22	0.24	0.26	0.27	0.29	0.31
	Hunan	0.15	0.16	0.2	0.19	0.21	0.24	0.25	0.27	0.3	0.31
Western Region	Inner Mongolia	0.17	0.18	0.21	0.22	0.22	0.2	0.21	0.22	0.24	0.24
	Guangxi	0.11	0.12	0.14	0.15	0.16	0.18	0.21	0.22	0.23	0.24
	Chongqing	0.09	0.1	0.12	0.13	0.13	0.15	0.16	0.16	0.18	0.2
	Sichuan	0.16	0.18	0.24	0.24	0.26	0.29	0.31	0.33	0.35	0.38
	Guizhou	0.08	0.09	0.12	0.13	0.13	0.14	0.16	0.16	0.18	0.2
	Yunnan	0.13	0.14	0.17	0.17	0.18	0.18	0.2	0.22	0.23	0.23
	Tibet	0.05	0.06	0.08	0.08	0.08	0.09	0.09	0.1	0.1	0.1
	Shaanxi	0.11	0.13	0.16	0.16	0.16	0.18	0.19	0.2	0.21	0.22
	Gansu	0.11	0.12	0.14	0.14	0.15	0.16	0.16	0.17	0.18	0.19
	Qinghai	0.06	0.07	0.08	0.09	0.09	0.1	0.1	0.11	0.11	0.12
	Ningxia	0.07	0.08	0.09	0.1	0.1	0.1	0.11	0.11	0.11	0.11
	Xinjiang	0.15	0.17	0.19	0.19	0.2	0.2	0.21	0.23	0.25	0.26
Northeastern Region	Liaoning	0.14	0.16	0.18	0.18	0.19	0.19	0.18	0.19	0.2	0.21
	Jilin	0.13	0.14	0.17	0.17	0.17	0.16	0.16	0.18	0.18	0.18
	Heilongjiang	0.2	0.21	0.24	0.25	0.26	0.23	0.25	0.25	0.25	0.25

. As can be clearly seen from the table, the level of digital countryside construction in 31 provinces of China from 2013 to 2022 showed varying degrees of change during the sample inspection period, showing an overall steady upward trend. However, the development differentiation between regions was significant, and there were obvious fluctuations in the development process of some provinces. Economic foundation conditions, regional policy orientation, and the popularity of digital technology were the core factors driving the differentiation of the level of digital countryside construction in various provinces [32].

The eastern region is the core leading area of digital countryside construction in China. The construction level of each province is in the forefront of the country and shows the development characteristics of a steady increase at a high level, and some provinces have achieved rapid growth.

Among them, Guangdong Province exhibited the most remarkable growth, with its level of digital village construction climbing from 0.27 in 2013 to 0.62 in 2022, an increase of 0.35 over the decade. This made it the province with the most significant progress during the sample period. This achievement is attributed to Guangdong’s reliance on its first-mover advantage in the digital economy, continuously increasing investment in rural digital infrastructure and promoting the deep integration of e-commerce to support agriculture and intelligent agriculture. Jiangsu and Zhejiang have long maintained a leading position. Jiangsu’s construction level rose from 0.32 in 2013 to 0.47 in 2022, and Zhejiang’s increased from 0.22 to 0.47. These two provinces have achieved a continuous increase in construction levels due to their mature industrial digitization foundation, coherent rural digitization policy system, and efficient digital technology promotion network. Taking Jiangsu as an example, it adheres to the coordinated layout of urban and rural digital resources. On the one hand, it has increased investment in infrastructure such as rural network base stations and cold chain logistics. On the other hand, it has promoted the application of the internet of things and big data in scenarios such as characteristic agriculture and rural cultural tourism, forming a mature development model of “infrastructure empowerment + industrial integration,” which provides a replicable practical path for digital village construction nationwide.

Overall, the eastern region has become a benchmark for digital village construction nationwide, leading the overall development direction, thanks to its multiple advantages in economy, technology, and policy.

The digital village construction level of provinces in the central region presents a steady growth trend, with relatively balanced internal development and no obvious fluctuations or stagnation. Its overall level ranks at the national medium level, yet a certain gap remains compared with the eastern region, and some provinces still need to enhance their growth potential. Leveraging superior agricultural foundations and support from national regional coordinated development policies, central provinces have continuously improved rural digital infrastructure and promoted the initial integration of digital technology with agricultural production and rural governance, realizing steady advancement in construction levels. Constrained by weak industrial digitalization foundations, insufficient reserves of digital technology talents, and singular digital village application scenarios, the central region’s overall growth rate lags behind that of the eastern region. It has not yet formed a regionally distinctive digital village development model, and the empowering effect of digital technology on the agricultural industry has not been fully unleashed, leaving the development gap with the eastern region ineffectively narrowed. Subsequent efforts should focus on strengthening policy support, deepening technology application, and cultivating market entities to further stimulate development momentum.

The western region represents a weak area in China’s digital countryside construction. The construction level values of each province have consistently remained relatively low nationwide, and the overall growth rate is limited. The region also exhibits a clear trend of polarization. Provinces such as Qinghai, Hainan, and Ningxia have a particularly weak foundation for digital countryside construction. Constrained by the low level of local economic development and insufficient financial investment in rural digital infrastructure, the coverage of rural digital infrastructure is low. Simultaneously, there are significant shortcomings in digital technology talent reserves and the cultivation of rural digital market entities. This leads to a lack of sustained momentum for digital countryside construction, and the construction level has long remained low with slow growth. However, some relatively economically developed western provinces, such as Sichuan and Chongqing, have achieved a certain degree of growth in digital countryside construction by relying on the technological and resource radiation effects of regional central cities, becoming development highlights in the western region. Nevertheless, constrained by the overall development environment, their construction level is still far below that of eastern provinces.

Overall, the construction of digital villages in the western region is constrained by multiple factors, including the economy, resources, and talent, making development difficult. It is a key area for tackling the balanced development of digital villages nationwide.

The northeastern provinces have certain foundational advantages in the construction of digital villages. However, the overall growth has been slow during the sample period, with construction levels fluctuating slightly in the 0.20–0.25 range for a long time. In some years, growth has even stagnated, failing to achieve an effective breakthrough, and the gap with the eastern region continues to widen. Heilongjiang, Jilin, and other provinces, as important agricultural bases in China, have a certain foundation for agricultural scale and enjoy policy support such as national rural revitalization and the revitalization of old industrial bases in Northeast China. The construction of digital villages has certain development conditions. However, limited by the path dependence of traditional agricultural industries, the agricultural development model is relatively conservative, and the integration of digital technology and traditional agricultural industries is not deep enough. The policy dividends and technological potential have not been fully transformed into development momentum. Meanwhile, prominent issues in Northeast China—including the aging of rural digital infrastructure, the singularity of digital technology application scenarios, and the severe outflow of rural digital talents—further hinder the sustained improvement of digital village construction levels, trapping the Northeast region in a development predicament characterized by “having a foundation yet failing to achieve breakthroughs” in digital village construction.

In summary, China’s digital village construction level demonstrates significant regional differences among the eastern, central, western, and northeastern regions: the eastern region takes the lead in development, the central region maintains steady progress, the western region features a weak foundation, and the northeastern region faces sluggish growth. Moving forward, it is essential to further improve the regional coordinated development mechanism, promote the precise transfer of digital technologies and development experiences from the eastern region to the central, western, and northeastern regions.

Simultaneously, it is necessary to increase policy support and resource input targeting the development shortcomings of the central, western, and northeastern regions, including consolidating rural digital infrastructure, cultivating digital technology talents, and deepening the integrated application of digital technologies with agricultural industries. These measures will drive the digital village construction of all provinces toward a more balanced and high-quality development stage.

4.2.3. Mediating Variables

The natural logarithm of the number of granted Plant Variety Rights for agricultural plants in various regions is selected as the core measure of agricultural Technological Innovation (TI). This selection is primarily based on the following considerations: First, it accurately reflects the innovation characteristics of the agricultural field. One of the core achievements of agricultural technological innovation is the breeding and promotion of superior varieties. Plant Variety Rights serve as a critical innovation carrier in the agricultural sector, embodying originality, practicality, and market exclusivity. This enables the accurate capture of the genuine output of agricultural technological innovation, rather than merely resource input, effectively avoiding the efficiency loss issues that may arise from using input indicators such as research and development funding alone. Second, it comprehensively reflects effective innovation output. The number of granted Plant Variety Rights for agricultural plants is a comprehensive outcome of multiple factors, including breeding research and development investment, human capital accumulation, technological breakthroughs, and institutional support. Using it as a proxy variable can, to a considerable extent, indirectly reflect the overall strength and effective output level of regional agricultural technological innovation, which is more in line with the intrinsic requirements of high-quality agricultural development for variety innovation [33].

4.2.4. Control Variables

This study selects the urban–rural income gap (Urig), crop disaster rate (Cpd), and Engel coefficient (Rec) as control variables to exclude the interference of other factors on the high-quality development of agriculture. The urban–rural income gap is measured by the ratio of urban residents’ disposable income to that of rural residents, which may affect the high-quality development of agriculture through inducing the misallocation of economic resources and social disharmony.

The crop disaster rate is measured by the ratio of the area of irreparably damaged crops to the total affected crop area. A higher crop disaster rate corresponds to greater obstacles to the high-quality development of agriculture.

The proportion of rural residents’ food expenditure in their total consumption expenditure in each province is employed to measure the Engel’s coefficient. The Engel’s coefficient may be associated with rural residents’ living standards and the concept of green agricultural products, thereby affecting the high-quality development of agriculture.

4.3. Descriptive Statistics of Variables

Table 5. Descriptive Statistical Analysis.

Variable Types	Variables	Observations	Mean	Standard Deviation	Minimum	Maximum
Explained Variables	HQDA	310	0.1857351	0.090769	0.0872325	0.6644566
Core Explanatory Variables	DIV	310	0.212903	0.1002818	0.0499975	0.6201016
Mediating Variables	TI	310	59.46512	69.16258	0	353
Control Variables	Urig	310	2.524153	0.3643053	1.826556	3.555734
	Cpd	310	0.4717379	0.2944721	0	4.373016
	Rec	310	32.58706	5.478065	22.6149	69.93893

presents the results of descriptive statistical analysis for each variable. Over the sample period 2013–2022, the core explained variable—agricultural high-quality development level (HQDA)—had a mean value of 0.1857, with a minimum of 0.0872 and a maximum of 0.6645. This significant value range indicates that China’s overall agricultural high-quality development level remains at a low level, with prominent inter-provincial imbalances.

For the core explanatory variable, digital village construction level (DIV), the mean value was 0.2129, while its minimum and maximum values were 0.0499 and 0.6201, respectively. The large standard deviation of DIV reflects the existence of a significant digital divide in the development degree of digital villages across different provinces.

Regarding the mediating variable, agricultural technology innovation capability (TI), its mean value stood at 3292.445, yet it exhibited an extremely wide distribution range (13–16,651) and a standard deviation as high as 3218.983. This phenomenon highlights the substantial gap in agricultural science and technology resources and innovation capabilities among various regions.

Regarding the control variables, the mean value of the urban–rural income gap (Urig), at 2.5242, remains persistently high, indicating that the dual urban–rural structure problem is still prominent. The fluctuation range of the crop disaster rate (Cpd) is relatively wide, with the highest value reaching 4.373%, which suggests that agricultural production in some areas still faces high natural risks. The mean value of the Engel coefficient (Rec) is 32.587, and its inter-provincial distribution (22.615–69.939) reveals, to some extent, the heterogeneity of residents’ consumption structure and living pressure across provinces.

5. Model Estimation Results and Analysis

5.1. Analysis of Benchmark Regression Results

To examine the core impact of digital countryside construction on the High-quality Development Level of Agriculture, this study uses the High-quality Development Level of Agriculture (HQDA) as the explained variable and the level of digital countryside construction (DIV) as the core explanatory variable. Control variables, including the urban–rural income gap (Urig), crop disaster rate (Cpd), and Engel’s coefficient (Rec), are included. Parameters are estimated using both a random effects model and a two-way fixed effects model, and the optimal model is selected through the Hausman test. The test results show that the statistic is significant at the 1% level, strongly rejecting the null hypothesis that individual effects are not related to the explanatory variables. This indicates that the estimation validity of the two-way fixed effects model is significantly better than that of the random effects model. Therefore, this study uses the results of the two-way fixed effects model as the basis for the benchmark analysis. The regression results are shown in

Table 6. Basic Regression Results.

Variables	Random Effects		Two-Way Fixed Effects
	(1)	(2)	(3)	(4)
	HQDA	HQDA	HQDA	HQDA
DIV	0.540332 ***	0.5061067 ***	0.373474 ***	0.3070911 ***
	(0.0327977)	(0.0436379)	(0.0636122)	(0.0632725)
Urig		−0.0194692		0.1403139 ***
		(0.0163876)		(0.0312248)
Cpd		−0.0023324		0.0027503
		(0.0055463)		(0.0054476)
Rec		−0.0002528		−0.0003428
		(0.0003263)		(0.0003162)
Controlled Variables	No	Yes	No	Yes
Controlled Areas	No	Yes	Yes	Yes
Controlled Time	No	Yes	Yes	Yes
Constant	0.0706966 ***	0.1364637 ***	−0.2582349 ***	0.0965223 ***
	(0.0156043)	(0.0507871)	(0.0825963)	(0.0103862)
Observations	310	310	310	310
R-squared	0.2306	0.2538	0.2389	0.0001

Note: *** indicate significance at the 1% significance levels, respectively. The figures in parentheses are robust standard errors.

.

The baseline regression results indicate that digital village construction has a robust and significantly positive effect on high-quality agricultural development. When only individual and time-fixed effects are controlled for without including control variables (two-way fixed effects model 3), the regression coefficient of DIV is 0.3735, which is significant at the 1% statistical level. After further including control variables (two-way fixed effects model 4), the coefficient of DIV slightly decreases to 0.3071, but it remains statistically significant at the 1% level. This result illustrates that even after removing the interference of control variables, regional heterogeneity, and time trends, the positive enabling effect of digital village construction on high-quality agricultural development still holds true, and the degree of influence has strong stability.

Based on theoretical analysis, the reason why digital village construction can significantly and directly empower high-quality agricultural development lies in its deep penetration into the entire chain of agricultural production, operation, and circulation. The application of digital technology directly improves land productivity and labor productivity, consolidating the production foundation for high-quality agricultural development through enhanced agricultural machinery intensity and optimized land transfer efficiency. Simultaneously, digital village construction contributes to the increase in the added value of the agricultural industry, promoting the transformation of agricultural production models towards intensification and efficiency. Furthermore, digital means strengthen the information transparency of green agricultural product production, which not only enhances farmers’ awareness and ability to participate in green production but also enhances the social credibility and market acceptance of green agricultural product producers, aligning with the green and branding core requirements of high-quality agricultural development.

The cumulative effect of these multiple pathways ultimately results in a strong, positive, and direct impact of digital rural construction on high-quality agricultural development.

The Hausman test results show that the p-value < 0.05. Therefore, the null hypothesis is rejected, and the two-way fixed effects model is supported.

Regarding the regression results of the control variables, in the two-way fixed effects model (4), the estimated coefficient for the urban–rural income gap (Urig) is 0.1403, which is significantly positive at the 1% statistical level. This indicates that narrowing the urban–rural income gap significantly promotes high-quality agricultural development. The underlying logic is that reducing the urban–rural income disparity can facilitate the bidirectional flow of urban and rural factors. On the one hand, it attracts more capital, technology, and talent to agriculture and rural areas, injecting impetus into agricultural modernization. On the other hand, the increased income levels of rural residents also expand the demand for high-quality agricultural products and services, thereby driving the transformation of agricultural production toward higher quality.

The estimated coefficient for crop disaster rate (Cpd) is 0.0028, which is not statistically significant. This suggests that, within the current sample, the direct impact of crop disaster rate on high-quality agricultural development is unclear. A possible explanation is that the disaster prevention and mitigation services integrated into digital village construction, such as meteorological early warning systems and intelligent monitoring technologies, have effectively enhanced agricultural resilience, weakening the negative impact of disaster shocks on the quality of agricultural development and thus diminishing the marginal effect of the disaster rate variable.

The estimated coefficient for the Engel’s coefficient (Rec) is −0.0003, which also lacks statistical significance. This may reflect that, although the rural consumption structure has been upgraded during the sample period, the demand for high-quality agricultural products has not been fully released. Alternatively, a mismatch between the supply structure of agricultural products and the upgraded consumption demand may exist, resulting in the demand side’s failure to effectively drive high-quality agricultural development.

5.2. Robustness Test

5.2.1. Excluding Municipalities Directly Under the Central Government

Drawing on relevant research outcomes, this study conducts a robustness test by excluding samples of municipalities directly under the Central Government to verify the reliability of the baseline regression results [34]. On the basis of the original sample, samples from the four municipalities directly under the Central Government—Beijing, Shanghai, Tianjin, and Chongqing—are excluded, and the regression analysis is re-conducted. As shown in Test Result (1) of

Table 7. Robustness Test Results.

Variables	(1)	(2)
	Excluding Municipal-Level Cities	DIV Winsorization
DIV	0.3787542 ***	0.5580205 ***
	(0.0447655)	(0.1304782)
Controlled Variables	Controlled	Controlled
Controlled Areas	Yes	Yes
Controlled Time	Yes	Yes
Constant	0.1470245 ***	0.0933334
	(0.036194)	(0.0723249)
Observations	270	310
r2_a	0.7433	0.2371

Note: *** indicate significance at the 1% significance levels, respectively. The figures in parentheses are robust standard errors.

, after excluding the samples of municipalities directly under the Central Government, digital village construction still exerts a positive impact on the high-quality development of agriculture, which is significant at the 1% level. This result is consistent with that of the baseline regression, thereby confirming the reliability of the previously obtained baseline regression findings.

5.2.2. Winsorization

Given the significant regional disparities in digital village construction levels, this study conducts a robustness test using winsorization to avoid the interference of extreme values, with reference to relevant research results of scholars [35]. Specifically, the core explanatory variables are winsorized at the 1% and 99% levels, and the regression analysis is re-performed, with the results reported in Table 7. Test Result (2) shows that after winsorization, digital village construction still has a positive impact on the high-quality development of agriculture, which is significant at the 1% level. The changes in parameter estimation coefficients and their significance levels are minimal and negligible, indicating that the research results are not affected by extreme values and further verifying the relative robustness of the estimation results of the aforementioned benchmark regression model.

5.3. Endogeneity Test

A two-way causal relationship may exist between digital village construction and agricultural development quality. In other words, while digital village construction promotes the improvement of agricultural development quality, the upgrading of agricultural development quality may also exert a feedback effect on digital village construction. To mitigate the potential endogeneity problem between digital rural construction and agricultural development quality, this study adopts the instrumental variable method. Specifically, the number of landline telephones per 100 people in 1984 and the number of post and telecommunications offices per million people in 1984 are selected as instrumental variables, and regression tests are conducted using the two-stage least squares (2SLS) approach.

The use and popularization of the Internet form the foundation of digital countryside construction. The application of Internet technology and smartphones stems from the spread of landline telephones. Areas with a relatively high landline telephone penetration rate may have a solid foundation for digital countryside construction. Therefore, the number of landline telephones as an instrumental variable meets the “relevance” requirement. The post and telecommunications bureau is the entity in charge of installing fixed-line telephones. It also has a certain correlation with the popularization of the Internet and smartphones, thus meeting the “relevance” requirement. Additionally, the numbers of fixed-line telephones and post and telecommunications bureaus in 1984 have little direct impact on the current high-quality development of agriculture, so they meet the “exogeneity” requirement for instrumental variables.

Given that the number of fixed-line telephones per 100 people in 1984 and the number of post and telecommunications bureaus per million people in 1984 are historical variables with no time variation, this study constructs panel instrumental variables by integrating time-related indicators. Specifically, the interaction terms between the one-period lagged national information technology service revenue and the two historical variables—namely, the number of fixed-line telephones per 100 people in 1984 (IV1) and the number of post and telecommunications bureaus per million people in 1984 (IV2)—are employed as instrumental variables for the current digital rural construction level.

The estimation results are reported in Tests (1) and (2) of

Table 8. Endogeneity Analysis Results.

Variables	2SLS
	(1)	(2)
	DIV	HQDA
DIV		1.655325 ***
		(0.271604)
IV1	8.46 × 10−9 *
	(4.19 × 10−9)
IV2	8.15 × 10−6 ***
	(2.73 × 10−7)
Controlled Variables	Controlled	Controlled
Controlled Areas	Yes	Yes
Controlled Time	Yes	Yes
Constant	−0.199	1.270 ***
	(0.487)	(0.269)
R-squared	0.5559	−0.4628
Observations	310	310
Kleibergen–Paap rk LM statistic	50.457 [0.0000]
Kleibergen–Paap rk WaldF Statistic	24.180

Note: * and *** indicate significance at the 10% and 1% significance levels, respectively. The figures in parentheses are robust standard errors.

. Meanwhile, further validity tests are conducted using the Kleibergen–Paap rk LM statistic, whose p-value is 0.0000 (less than 0.05), and the Kleibergen–Paap rk Wald F statistic exceeds 10. These results confirm the rationality and appropriateness of the instrumental variables selected in this study. Under the condition of valid instrumental variables, the estimated coefficient of digital rural construction’s impact on high-quality agricultural development remains significantly positive, indicating that the previous research conclusions remain robust.

To sum up, this study verifies the validity of Hypothesis H1; namely, that digital rural construction can significantly promote the high-quality development of agriculture.

5.4. Heterogeneity Test

Affected by the functions and positioning of agricultural development, major grain-producing areas possess more abundant endowments of agricultural production resource elements and a more improved agricultural production system compared with non-major grain-producing areas. Furthermore, digital infrastructure construction and digital rural development may be in different stages and at varying levels among major grain-producing areas, major grain-marketing areas, and areas with balanced grain production and marketing, and their empowering effects on high-quality agricultural development may also exhibit certain differences. In view of this, this study divides China’s 31 provinces into three groups—major grain-producing areas, major grain-marketing areas, and areas with balanced grain production and marketing—to conduct regional heterogeneity tests.

According to the heterogeneity test results presented in

Table 9. Test Results of Heterogeneity.

Variables	Grain Major Producing Areas	Grain Major Consuming Areas	Balanced Grain Producing and Consuming Areas
	(1)	(2)	(3)
	HQDA	HQDA	HQDA
DIV	0.1800593 **	0.1854313	0.1266573
	0.0710023	0.2930387	0.0913877
Constant	−0.0189093	0.0417285	0.1319402
	(0.0497063)	(0.8553573)	(0.0867107)
Controlled Variables	Controlled	Controlled	Controlled
Controlled Areas	Yes	Yes	Yes
Controlled Time	Yes	Yes	Yes
Observations	130	70	110
R-squared	0.5927	0.2245	0.3901
Number of ID	13	7	11

Note: ** indicate significance at the 5% significance levels, respectively. The figures in parentheses are robust standard errors.

, the impact of digital village construction (DIV) on high-quality agricultural development (HQDA) varies significantly across different grain functional zones. In major grain-producing areas, the impact coefficient of digital village construction on high-quality agricultural development is 0.1801, which is significant at the 5% level. This finding demonstrates that digital village construction exerts a positive and stable promoting effect on high-quality agricultural development in these regions.

This outcome may be associated with the relatively solid agricultural foundation in major grain-producing areas, which allows digital technologies and information tools to be more effectively integrated into the entire agricultural production process, thereby enhancing production efficiency and quality control capabilities. In major grain-consuming areas, the impact coefficient of digital village construction is 0.1854, yet it fails to reach a statistically significant level, indicating that the direct promotional effect of digital village construction on high-quality agricultural development in these areas is relatively weak.

A plausible explanation for this is that major grain-consuming areas feature a smaller scale of agricultural production, with a focus on high-value boutique agriculture. The marginal empowering effect of digital technology is more prominently reflected in the circulation and consumption links, while its direct driving effect on the high-quality development of the production side has not been fully realized. In areas with balanced grain production and marketing, the impact coefficient of digital village construction on high-quality agricultural development is 0.1267.

Although this coefficient does not pass the conventional significance test, its absolute value lies between those of major grain-producing areas and major grain-consuming areas. The agricultural structure of areas with balanced grain production and marketing takes into account both production and circulation; digital village construction not only optimizes resource allocation during the production process but also improves the market circulation efficiency of agricultural products, reflecting its potential for comprehensive empowerment.

In summary, the promoting effect of digital village construction on high-quality agricultural development exhibits significant regional heterogeneity: the promoting effect is the most prominent and stable in major grain-producing areas, areas with balanced production and marketing have potential empowering value, while the direct effect in major grain-consuming areas has not yet achieved statistical significance.

This result implies that policy formulation should take regional functions into account: strengthening the integration of digital technology with agricultural production in major grain-producing areas, expanding the application of digital technology in agricultural product circulation and brand building in major grain-consuming areas, and addressing the shortcomings of digital infrastructure in areas with balanced production and marketing, so as to achieve differentiated and precise policy implementation.

5.5. Test of the Mediating Effect

To further examine the mediating role of agricultural technology innovation in the process of digital village construction empowering high-quality agricultural development, this paper estimates the mediation effect model, and the regression results are shown in

Table 10. Test Results of Mediating Effect.

	Main Effect	Mediating Effect
DIV	0.338387 ***	650.4177 ***	0.1781239 **
	(0.026504)	(85.03394)	(0.0689911)
TI			0.0004035 **
			(0.0001743)
Constant	0.2532651 ***	19.12161	0.2394105 ***
	(0.0317566)	(23.43239)	(0.0358221)
Controlled Variables	Controlled	Controlled	Controlled
Controlled Areas	Yes	Yes	Yes
Controlled Time	Yes	Yes	Yes
Observations	310	310	310
R-squared	0.2886	0.3821	0.3319

Note: ** and *** indicate significance at the 5%, and 1% significance levels, respectively. The figures in parentheses are robust standard errors.

. Column (2) of Table 10 shows that the impact of digital village construction (DIV) on agricultural technology innovation (TI) is positive and significant at the 1% level, with a coefficient of 650.4177, indicating that digital village construction can significantly promote agricultural technology innovation. A possible explanation is that digital village construction provides a favorable ecological environment and resource support for agricultural technology innovation by improving rural digital infrastructure, opening up channels for the circulation of innovative elements, and alleviating the financing constraints and information asymmetry of agricultural innovation entities.

Column (3) of Table 10 shows that agricultural technology innovation (TI) has a positive and significant impact on high-quality development of agriculture (HQDA) at the 1% level, with a coefficient of 0.0004035, indicating that agricultural technology innovation has a significant positive driving effect on high-quality agricultural development. Furthermore, column (3) of Table 10 reveals that, after incorporating agricultural technology innovation (TI), the impact of digital village construction on high-quality agricultural development remains positive and significant at the 1% level, with a coefficient of 0.1781, which is less than the estimated coefficient of 0.338387 in the baseline model in column (1) of Table 10. This suggests that the promotion effect of digital village construction on high-quality agricultural development is reduced after controlling for agricultural technology innovation. In addition, the total effect coefficient, the mediating path coefficient, and the coefficient of the mediating variable on the explained variable in the model are all significant and have consistent signs, indicating that agricultural technology innovation plays a partial mediating variable effect in the empowerment of digital villages to high-quality agricultural development.

The proportion of the mediating effect to the total effect is 0.775, indicating that approximately 77.5% of the promoting effect of digital village construction on high-quality agricultural development is achieved through the mediating role of agricultural technology innovation.

In summary, hypothesis H2 is supported, suggesting that digital village construction can indirectly empower high-quality agricultural development by promoting agricultural technology innovation.

5.6. Test of the Threshold Effect

To investigate whether there is a non-linear threshold effect between digital village construction and high-quality development of Chinese agriculture, a threshold effect existence test was first conducted. The Bootstrap method was used with 500 repeated samplings to test the true number of thresholds in the threshold model constructed previously. The results are shown in

Table 11. Test Results of Threshold Effect.

Threshold Number	F-Statistic	p-Value	10% Critical Value	5% Critical Value	1% Critical Value
Single Threshold	23.06	0.022	14.767	18.472	27.111
Double Threshold	16.76	0.114	18.207	24.477	33.555
Triple Threshold	35.99	0.202	88.834	118.694	161.880

. The results indicate that the threshold variable, the level of digital village construction (DIV), passed the single threshold test at a significance level of 10%, but did not pass the double threshold test or the triple threshold test. This suggests that the impact of digital village construction on high-quality agricultural development has a relatively obvious single threshold characteristic.

Next, based on the threshold effect test results, this study selects a single-threshold regression model to conduct parameter estimation. The results are shown in

Table 12. Parameter Estimation Results for the Single-Threshold Model.

Threshold Variable	Coefficient	Standard Error	t-Value	p-Value
DIV ≤ 0.3082	0.7168155	0.2077972	3.45	0.002
DIV > 0.3082	0.5990171	0.1421214	4.21	0.000

. It can be found that when the level of digital countryside construction is less than or equal to the threshold value of 0.3082, the impact of digital countryside construction on high-quality agricultural development is significantly positive. It passes the significance test at the 1% statistical level, and the coefficient value is 0.7168155. When the level of digital countryside construction is higher than the threshold value of 0.3082, its positive impact on high-quality agricultural development is still significant, and the significance remains at the 1% level, with a coefficient value of 0.5990171. This result indicates that when the level of digital countryside construction crosses this threshold value, its promoting effect on high-quality agricultural development presents a non-linear characteristic of diminishing marginal effects.

A plausible explanation for this is that, in the initial stages of digital rural construction, the establishment of digital infrastructure and the preliminary popularization of digital technology can rapidly fill the gaps in rural digital development. This leads to an explosive growth in the empowering effect on agricultural production efficiency improvement and industrial structure optimization, forming a significant positive driver. However, once the construction level surpasses the threshold of 0.3082, the digital infrastructure has reached a certain scale, and the “dividend period” of the initial digital empowerment gradually diminishes. High-quality agricultural development begins to face deeper issues, such as the deep integration of digital technology, bottlenecks in improving the digital capabilities of agricultural business entities, and insufficient innovation in digital application scenarios. These issues result in a reduced marginal promotion effect of digital rural construction on high-quality agricultural development.

Nevertheless, digital village construction consistently exerts a significant positive influence on high-quality agricultural development at the 1% level within both intervals. This fully demonstrates that digital village construction remains a core driving force for promoting high-quality agricultural development.

Therefore, this paper concludes that hypothesis H3 is valid: the impact of digital village construction on high-quality agricultural development exhibits a non-linear characteristic of diminishing marginal effects.

6. Verification of the Practice of Empowering High-Quality Agricultural Development in the Digital Rural Areas of Lin’an District, Hangzhou

6.1. Case Overview

Located in the Tianmu Mountains in northwestern Zhejiang Province, Lin’an District of Hangzhou serves as a vital ecological barrier and a specialty agricultural product supply base for the Yangtze River Delta region. As a typical example of digital village construction empowering high-quality agricultural development, Lin’an District’s practices not only directly demonstrate the driving role of digital villages in agricultural development, but also confirm the intermediary transmission path of agricultural technology innovation through its exploration in breeding innovation and technology promotion. Moreover, the distinct construction stages provide an ideal opportunity to observe threshold effects.

The district encompasses 18 towns and streets, along with 274 administrative villages. In 2022, the registered population stood at 543,000, with 38.6% engaged in agriculture. Hilly and mountainous terrain predominates, accounting for 86% of the total area, and arable land is markedly fragmented, with an average plot size of less than 0.5 Mu. The agricultural economy of this region is primarily driven by specialty crops such as hickory nuts, bamboo shoots, and tea. In 2020, Lin’an District was designated as one of the first National Digital Countryside Pilot Areas, a decision stemming from the National Development and Reform Commission’s recognition of three key qualifications: the suitability of its hilly landform gradient for addressing digitalization challenges in 70% of the country’s mountainous counties, the mature industrial base of the hickory nut industry with a total industrial output value exceeding Yuan 3 billion, and the digital infrastructure advantage reflected in a coverage density of 4.2 5G base stations per square kilometer.

Its strategic positioning is clearly focused on three aspects: undertaking the pilot task of hilly agriculture transformation as a national digital village pioneer area; anchoring the role of core area of digital villages in Zhejiang Province; and constructing a digital model of characteristic agricultural products with hickory and bamboo shoot dual industries as the core.

Concurrently, the in-depth advancement of digital countryside construction has exerted comprehensive and profound impacts on social development in Lin’an District. Regarding economic development, digitalization has promoted the quality and efficiency improvement of the entire industrial chain of characteristic agriculture, such as hickory and bamboo shoots, injecting new impetus into the regional economy. In terms of social governance, digital platforms have enhanced the refined level of grassroots governance and emergency response capabilities, strengthening public safety guarantees. Concerning public services, smart healthcare, digital government affairs, online services, and the like have narrowed the gap in urban–rural public services, improving the quality of life for rural residents. With respect to rural revitalization, digital infrastructure and applications have optimized agricultural production methods, broadened income channels, and promoted the two-way flow of urban and rural factors. Regarding ecological protection, digital monitoring and intelligent management methods have contributed to the refined management and protection of ecological resources, providing strong support for safeguarding ecological barriers and realizing Green Development.

This study selects Lin’an District, Hangzhou, as a case, mainly based on its systematic and phased typical significance in the interaction between digital villages and high-quality agricultural development. This case also has a hierarchical reference value for countries with different development backgrounds. The typicality of Lin’an District is first reflected in its representation of a leading model in which digital technologies are systematically integrated into the entire agricultural chain under the support of policies and location advantages.

The district has established an overall framework in digital infrastructure, intelligent production management, industrial chain data integration, and digital market connection. Simultaneously, its agricultural business forms cover a complete spectrum from grain production and specialty crop processing to agricultural tourism integration, providing a specific microcosm for observing how digital technology reshapes the agricultural value chain. This typical experience can provide differentiated insights for countries in different stages of development. For the vast number of developing countries in the initial stage of digitalization, Lin’an’s “lightweight” digital applications (such as traceability and e-commerce) adopted in specialty industrial segments demonstrate a feasible path to improve standardization and branding with limited investment. For developed countries facing insufficient rural vitality, its exploration of connecting urban and rural areas and activating diverse rural functions through digital platforms provides new ideas.

More importantly, the case validates the underlying logic of how digital technology drives development by reducing information costs, optimizing resource allocation, and expanding agricultural value. This mechanism has universal reference significance that transcends specific national conditions.

6.2. Case Studies and Real-World Challenges

The agricultural digital transformation practice in Lin’an District has constructed a complete transmission system that empowers the high-quality development of digital village agriculture through three-dimensional technology penetration and innovative fission. This practice not only verifies the direct positive effect of digital village construction on the high-quality development of agriculture, but also confirms the intermediary bridge role of agricultural technology innovation through the digitalization of new variety cultivation and technology promotion.

In terms of direct technology enablement, the internet of things system has realized the precise regulation of irrigation and fertilization, improved the unit yield and resource utilization efficiency of characteristic crops such as Lei bamboo shoots, and provided a technical paradigm for the intensive agricultural development in hilly areas. The blockchain traceability system covers 160 processing enterprises, providing quality endorsement for the “Tianmu Mountain Treasure” regional public brand, forming a green value-added closed loop. The 4600 village-level e-commerce service stations built in 2020 have built a direct production and sales network for agricultural products, realizing an online sales of agricultural products of 5.3 billion Yuan [33]. Demand-side data provides reverse guidance for production adjustments, promoting the transformation and upgrading of traditional agriculture to a modernized industrial system.

In terms of technology innovation transmission, Lian’an District relies on digital platforms to integrate industry–university–research resources, significantly improving the efficiency of breeding and approving new agricultural plant varieties. This vividly reflects the mediating role of digital village construction in indirectly promoting high-quality agricultural development by stimulating technological innovation. It is worth noting that, combined with the threshold effect perspective at the core of this study, the superimposed driving mode of the above direct and mediating effects shows obvious stage differences before and after the digital village construction level in Lian’an District crosses the critical threshold value of 0.3082. In the low-level construction stage (digital village construction level ≤ 0.3082), the core of construction focuses on the deployment of infrastructure such as 5G base stations and the Internet of Things, with the key to solving the “whether or not” problem of digitalization. The improvement of infrastructure not only directly reduces production and transaction costs but also provides data support for the initial research and development of new variety rights, making the marginal driving effect of digital villages on high-quality agricultural development particularly significant. For example, the unit yield and resource utilization efficiency of Lei bamboo shoots have been significantly improved, and the online sales of pecans have achieved explosive growth. When the construction level crosses the threshold value, the infrastructure has been basically improved, and the focus of construction has shifted to in-depth integration applications such as industrial brains and blockchain traceability. At this time, the positive effects of direct empowerment and technological innovation are still significant, but the marginal effect is weakened, indicating that the simple “accumulation of quantity” is difficult to maintain high-speed growth, and it is necessary to shift to “quality improvement” to release new kinetic energy through optimizing resource allocation and deepening industrial integration.

Furthermore, the profound value of innovation hubs is embodied in two major dimensions: ecological reconstruction and financial empowerment. The industrial brain platform integrates multi-dimensional data, reducing the cost of information acquisition and accelerating the promotion and application of new technologies. Relying on the business data credit evaluation system, the digital agriculture cooperative alliance provides financial support for the intelligent transformation of enterprises, giving rise to new business forms such as agricultural–catering integration and agricultural–cultural–tourism symbiosis, realizing value enhancement in all fields of the industrial chain.

Despite the remarkable achievements in the digital transformation of agriculture in Lin’an District, it still faces three major structural bottlenecks: first, the technology penetration gradient of the industrial chain experiences rupture. While the internet of things coverage in the production link is rapidly advancing, the intelligent level of processing, warehousing, and logistics links is severely lagging behind. This leads to a lack of precise control in post-production sorting, precooling, and transportation of fresh agricultural products such as bamboo shoots, resulting in high loss rates. This not only weakens the direct empowerment effect of digital villages but also hinders the market-oriented transformation of new variety technology achievements. Secondly, data silos are entrenched due to the hierarchical system. Data from departments such as agriculture and rural affairs, natural resources, and financial supervision belong to heterogeneous systems. The absence of unified standards and sharing mechanisms creates information barriers. Business entities must invest significant resources to integrate cross-departmental data. This delays decision-making efficiency and weakens the operational effectiveness of the innovation ecosystem, hindering the full realization of the intermediary role of technological innovation. Thirdly, there is a systematic deficiency in the factor allocation system. Digital financial resources are excessively skewed towards leading enterprises, while new business entities such as cooperatives and family farms face financing constraints. Simultaneously, the approval of agricultural facility land is rigid, and some primary processing plants have been forcibly closed due to land disputes. This restricts the promotion of large-scale technology applications. Consequently, after crossing the threshold of digital countryside construction, it is difficult to form inclusive and high-quality development momentum.

6.3. Optimization Path: The Theoretical Logic of Framework Repair

To address these challenges, a multi-dimensional strategy is needed, based on the three core conclusions empirically tested above. The aim is to facilitate direct empowerment channels, strengthen the intermediary function of technological innovation, and solve the problem of diminishing marginal effects after thresholds are reached. This will promote the transformation of digital countryside construction from “high-speed coverage” to “high-quality development.”

It is important to strengthen technology collaboration across the entire industrial chain to consolidate the foundation for direct empowerment and technological innovation. This includes deepening the construction of the “Tianmu Cloud Agriculture” collaborative application platform, unifying data standards, and integrating information flow across all aspects of agricultural production, processing, and distribution. A key focus should be on breaking down data barriers in the ten leading industries. Furthermore, it is necessary to promote intelligent production models such as the “Grape Super Farm,” construct a standardized digital system covering planting management, risk early warning, and quality traceability, and achieve refined management of the entire agricultural process.

Concurrently, upgrade the “Zhenong Code” blockchain traceability platform to strengthen the whole-process digital supervision of agricultural products from field to table, consolidating the technical foundation for the co-creation of industrial chain value.

At the same time, build cross-departmental institutional synergy and break through bureaucratic bottlenecks to ensure smooth mechanisms. Rely on the service mechanism innovation of the Digital Village Cooperation Alliance to integrate the three core functions of production cooperation, supply and marketing services, and credit mutual assistance into a “three-in-one” comprehensive service system; issue mandatory cross-departmental data sharing norms to break down information silos in key departments such as agriculture and rural areas, natural resources, and market supervision; and promote the real-time collection and retrieval of core agricultural data, such as meteorological, soil, and market data, in the county-level “Digital Village Command Center” to improve decision-making efficiency.

Furthermore, the establishment of agricultural data classification and grading standards clarifies the boundaries of rights and responsibilities in data collection, storage, and application. This fundamentally addresses the challenges of heterogeneous data governance and removes institutional obstacles to the rapid translation of technological innovations.

In addition, innovative factor allocation models are designed to activate inclusive development momentum after crossing thresholds. The introduction of an “internet plus agriculture plus finance” crowdfunding model attracts social capital to participate in agricultural infrastructure upgrades through digital channels, broadening the financing channels for new agricultural business entities. The implementation of a digital literacy enhancement project incorporates e-commerce operation and the use of intelligent equipment into the required content of “science and technology going to the countryside,” strengthening the digital capabilities of agricultural operators. Deepening the digital reform of rural collective construction land transfer establishes a green channel for the registration of processing and storage facilities, alleviating land constraints, paving the way for the extension of the agricultural industry chain, and ultimately achieving the sustainable drive of digital countryside construction for high-quality agricultural development.

7. Research Conclusions and Recommendations

7.1. Research Conclusions

This study analyzes and examines the impact of digital village construction and agricultural technology innovation on high-quality agricultural development, along with their mechanisms of action. Theoretical analysis indicates that digital village construction can not only directly promote high-quality agricultural development but also indirectly enable it through the mediating effect of agricultural technology innovation. Empirical tests based on Chinese provincial panel data from 2013 to 2022 lead to the following conclusions:

• The two-way fixed effects model of the benchmark regression shows that digital village construction has a significantly positive effect on the high-quality development of agriculture in China. This conclusion remains robust and reliable after a series of robustness tests, such as excluding municipality samples and continuous tail reduction processing, as well as using the instrumental variable method to mitigate endogeneity issues.
• Regional heterogeneity analysis, based on major grain-producing areas, major grain-marketing areas, and areas with balanced production and sales, indicates that the driving effect of digital village construction on high-quality agricultural development varies significantly across regions. The promotion effect is significantly positive in major grain-producing areas and areas with balanced production and sales, but not significant in major grain-marketing areas.
• The intermediary effect test, incorporating agricultural technology innovation, reveals that agricultural technology innovation plays a significant partial intermediary role between digital village construction and high-quality agricultural development. Approximately 77.5% of the promotion effect of digital village construction on high-quality agricultural development is achieved through this intermediary pathway of agricultural technology innovation.
• A single-threshold model test further confirms that the impact of digital village construction on high-quality agricultural development exhibits significant non-linear threshold characteristics, demonstrating a structural change characterized by decreasing marginal effects.

The agricultural digital transformation practice in Lin’an District further corroborates the empirical findings of this paper. This case demonstrates that digital countryside construction can directly empower high-quality agricultural development through three-dimensional technology penetration. It can also form an effective indirect transmission path relying on agricultural technology innovation. The empowerment intensity presents a phased characteristic of decreasing marginal effects before and after crossing the threshold value of 0.3082. Simultaneously, the case reveals realistic bottlenecks in the current agricultural digital transformation. These include imbalanced technology penetration in the industrial chain, solidified data silos between departments, and imperfect factor allocation mechanisms. It provides an important practical reference and experience enlightenment for similar regions nationwide to promote the deep integration of digital countryside construction and high-quality agricultural development.

This study design overcomes the limitations of a single technology or localized perspective. It also significantly enhances the robustness, scientific validity, and policy implications of the research findings through the mutual corroboration of macro statistical patterns and micro practical evidence across temporal and spatial dimensions. The conclusions provide data support and decision-making basis with both theoretical depth and practical value for scientifically evaluating the effectiveness of the digital countryside strategy, addressing the issue of unbalanced regional development, and optimizing the path of agricultural digital transformation.

7.2. Recommendations

To advance the high-quality development of agriculture, it is crucial to vigorously implement the digital rural development strategy, seize the opportunity to transform agricultural production models, and fully exert the catalytic effect of digital rural construction on the growth of the agricultural economy. This goal can be achieved through a series of specific measures. It is necessary to increase investment in rural digital infrastructure construction to accelerate its popularization and coverage, while also promoting the in-depth integration of digital technology and agriculture. By employing tools such as the Internet of Things, big data, and artificial intelligence, traditional agricultural practices can be modernized, production patterns reshaped, and production efficiency significantly improved. A comprehensive information exchange platform should be built, covering data on agricultural production materials and the supply and demand of agricultural products, so as to provide a solid information support for production planning and input management. Meanwhile, the digital transformation of agricultural product warehousing, logistics, and processing links should be vigorously pushed forward, which can drive the development of rural e-commerce, reduce post-harvest losses, extend the agricultural industrial chain, and boost the sales of agricultural products. In addition, modern information platforms should be fully utilized to carry out both online and offline digital technology training in rural areas, cultivating new types of agricultural talents and providing the necessary human capital support for the high-quality development of the agricultural economy.

Given the geographical and temporal heterogeneity in the impact of digital rural construction on the quality of agricultural development, rural development policies must be implemented in a way that adapts to local conditions. The government should accordingly coordinate and provide targeted support for digital rural initiatives across the eastern, central, and western regions, adopting differentiated and dynamic development strategies that prioritize accelerating digital construction in underdeveloped areas and increasing support to narrow regional gaps. For the central and western regions, leveraging existing policy advantages should focus on developing agricultural information service platforms, advancing the digital and low-carbon transformation of the agricultural industry, and strengthening exchanges and cooperation in digital technologies and experience with other regions to enhance the quality of agricultural development. To address temporal disparities, local governments, under the guidance of national policies, should formulate comprehensive rural digitalization strategies that encourage new agricultural business entities—including agricultural enterprises, cooperatives, and family farms—to actively adopt digital technologies. Corresponding training programs should also be carried out to improve farmers’ digital literacy and application capabilities, promoting the in-depth integration of digital villages and the agricultural industry, constructing a digital agricultural ecosystem, and ultimately upgrading the quality of agricultural development.

Complementing these efforts, a collaborative governance system must be established to overcome transmission bottlenecks. Drawing on the deep-seated contradictions revealed by Lin’an’s practice, there is an urgent need to create a linkage governance framework that integrates technological application, institutional innovation, and factor allocation. The focus should be on promoting the intelligent transformation of weak links, such as agricultural product sorting and cold-chain logistics. Data barriers throughout the entire process should be broken down using the “industrial cloud chain” model, and the post-harvest loss rate should be incorporated into the digital assessment system to ensure continuous improvement.

Compulsorily eliminate the bureaucratic data silos, formulate a list of agricultural-related data sharing, establish a county-level digital command center to integrate the authorities of multiple departments, and implement a data quality traceability and accountability mechanism to shorten the decision-making response time.

Innovate the factor supply model, promote the sustainable mechanism of using regional brand premium to replenish the technology fund, pilot the reform of the record-filing system for agricultural facility land, and use special credit with fiscal interest subsidies to specifically lower the financing costs of small-scale business entities, so as to form a collaborative governance ecosystem that integrates “technology–institution–factor”.