[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-221":3,"related-221":58},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":8,"published_at":11,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":24,"tags":26,"primary_topic":8,"secondary_topics":32,"classification":33,"search_phrases":51,"slug":54,"view_count":55,"doi":56,"paper":8,"created_at":57},221,"农业数字化与农民收入增长——来自中国的证据","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1776288\u002Ffull","基于中国30个省份2011-2022年平衡面板数据,构建涵盖数字基础设施、赋能条件、数字化生产经营、数字服务的多维农业数字化指数,采用双向固定效应模型分析数字化对农民收入的影响。研究发现:农业数字化显著提高农村居民人均可支配收入;边际收入增长在数字化水平较低阶段较大,跨越阈值后仍为正向但效应减弱;人力资本具有强化阈值效应,一旦农村教育水平超过临界值,收入增长明显增强;中西部、经济欠发达地区和粮食主产区效应更大。",null,"## Abstract\n\nAgricultural digitalization has become an important driver of rural revitalization and agricultural modernization. Using a balanced panel of 30 Chinese provinces from 2011 to 2022, this study constructs a multidimensional Agricultural Digitalization Index covering digital infrastructure, enabling conditions, digitalized production and operation, and digital services. We estimate baseline two-way fixed-effects models and conduct complementary analyses to address endogeneity, test nonlinearities, and examine the conditioning role of urbanization. The results show that agricultural digitalization is associated with higher per capita disposable income of rural residents, and the finding is robust to alternative index construction, lag structures, and sample adjustments. Threshold estimates reveal stage-dependent returns: marginal income gains are larger at lower levels of digital development and remain positive but weaker beyond the estimated threshold. Human capital exhibits a reinforcing threshold, with markedly stronger income gains once rural education surpasses a critical level. The effects are larger in central and western provinces, economically less developed regions, and major grain-producing areas, and urbanization further amplifies the income effect, underscoring the role of complementary market and service environments. Income-component results indicate that gains are concentrated in wage and operating income. Overall, agricultural digitalization can raise farmers' income, but the magnitude of benefits depends on development stage, human-capital constraints, and local market connectivity.\n\n## 1 Introduction\n\nThe rapid advancement of digital technologies, including the Internet of Things (IoT), artificial intelligence (AI), big data analytics and blockchain, has fundamentally reshaped agricultural production systems and global agri-food value chains (; ). A growing body of research indicates that digital agriculture has become an important driver of agricultural sustainability by improving resource allocation efficiency, enhancing risk management and strengthening resilience to climate and market shocks (; ). In developing countries where smallholders constitute the backbone of the agricultural economy, digital tools can reduce information frictions, lower transaction costs and expand market access, thereby improving household welfare and income (; ). In response to these global trends, China has accelerated its rural digital transformation through policies such as the Digital Village Strategy, accompanied by extensive investment in digital infrastructure, smart agriculture and digital agricultural services (; ; ). However, the agricultural sector remains dominated by small-scale farmers, and substantial disparities in economic development, human capital and digital infrastructure persist across regions, which are closely related to regional disparities in digital infrastructure and rural development (; ; ). At the same time, raising farmers' income is a central policy objective for advancing rural revitalization and achieving common prosperity in China. Against this policy background, whether agricultural digitalization (AD) can effectively promote farmers' income has become a critical research question. It is equally important to understand under what conditions this effect may be strengthened or weakened. In this study, agricultural digitalization (AD) refers to the sector-specific digital transformation of agriculture, encompassing digital infrastructure, enabling environments, digitalized production, digital management, and digital services. Unlike the broader concept of the digital economy, AD focuses exclusively on the agricultural sector. Rather than capturing micro-level adoption by individual farmers, we conceptualize AD as a province-level development process reflecting the overall digital capacity and application intensity within agriculture. This definition forms the theoretical basis for constructing the multi-dimensional AD index used in the empirical analysis.\n\nAlthough research on digital transformation has expanded rapidly, empirical studies that directly examine agricultural digitalization (AD) remain relatively limited (; ; ). Existing studies on China often rely on broad proxies such as internet penetration, broadband coverage, or composite indices of digital development, rather than directly measuring digital applications and services embedded in agricultural production, management, and marketing (). This inconsistency between the conceptual focus on AD and the use of broad digital-development indicators may obscure the sector-specific mechanisms through which digital technologies affect farmers' income in rural areas (; ). In particular, failing to distinguish agriculture-oriented digital infrastructure, platform services, and rural service systems from broader digital development may hinder an accurate assessment of how digital technologies reshape production modes, transaction patterns, and income structures in the agricultural sector (). There is therefore a clear need to construct a sector-specific and multidimensional Agricultural Digitalization Index tailored to the Chinese context and to use it as the core explanatory variable in empirical analysis, while also examining the channels and conditions through which its income effects may vary.\n\nBeyond the average effect, the income impact of agricultural digitalization is likely to be conditional and nonlinear. From a theoretical perspective, agricultural digitalization can influence farmers' income through multiple channels, including improving production decisions, expanding employment opportunities, facilitating financial inclusion, and enhancing access to agricultural and public services (; ; ). However, the income gains generated by agricultural digitalization are unlikely to evolve linearly along the development path. Related studies on digital development suggest that its effects on income distribution and urban–rural disparities may become nonlinear once certain development stages are reached (; ; ). In the early stages of digital development, income gains may be constrained by infrastructural gaps, limited service coverage, and adoption barriers (). As digital infrastructure and agricultural applications become more widespread, network externalities, learning effects, and business model innovation may strengthen the income-enhancing effects of agricultural digitalization (; ). At higher development levels, diminishing marginal returns may emerge as basic digital services become saturated and new forms of digital divide begin to appear (; ). These stage-based dynamics suggest that the income effects of agricultural digitalization may exhibit threshold characteristics that require explicit empirical testing. Human capital further shapes these effects because farmers need basic education and digital literacy to access, understand, and effectively use digital tools and services (; ; ; ). If rural human capital is insufficient, the benefits of agricultural digitalization may be significantly constrained or may accrue mainly to better-equipped households; once education levels surpass a critical point, however, the income-enhancing effects of AD may become markedly stronger (). Urbanization also constitutes a key structural condition that may alter the relationship between agricultural digitalization and farmers' income. Related studies suggest that digital development can reshape urban–rural income relations, urbanization processes, and urban–rural integration by reallocating labor, capital, and information across space (; ; ; ; ). In this sense, higher levels of urbanization may provide a more favorable external environment for agricultural digitalization to generate stable and sustained income gains.\n\nTaken together, these considerations suggest that the income effects of agricultural digitalization may vary systematically with development stage, human capital, and local market conditions. Against this background, this study constructs a provincial-level Agricultural Digitalization Index for China and empirically examines the impact of AD on farmers' income using panel data for 30 provincial-level regions in China from 2011 to 2022. A two-way fixed-effects model is employed as the baseline framework, and potential endogeneity is addressed through instrumental-variable estimation. Furthermore, panel threshold regressions are used to identify possible development-stage thresholds of AD and human capital thresholds, while moderation analysis is applied to investigate how urbanization shapes the income effects of AD. Specifically, this study addresses four core research questions: (1) Does AD significantly increase farmers' income? (2) Does the income effect of AD exhibit stage-based threshold characteristics along the digital development path? (3) Does rural human capital also present a threshold that conditions the income-enhancing effect of AD? (4) Does urbanization moderate the relationship between AD and farmers' income? In addition, the empirical analysis further examines whether the income effects of AD differ across eastern, central, and western regions with distinct economic structures, industrial foundations, and resource endowments. This extension provides additional evidence on possible digital divide effects.\n\nThis paper makes three main contributions. First, by developing a sector-specific Agricultural Digitalization Index, this study provides a more suitable empirical basis for examining how agricultural digitalization is associated with farmers' income growth. Moving beyond broad digital-economy proxies helps better identify the sector-specific channels through which digital technologies affect rural income. Second, the paper shows that the income effect of agricultural digitalization is conditional rather than uniform. By examining development-stage thresholds, the threshold role of human capital and the moderating role of urbanization, the study clarifies the conditions under which agricultural digitalization can be translated more effectively into income growth. Third, the paper further reveals substantial heterogeneity across regional and functional contexts, offering additional evidence on the differentiated pathways through which agricultural digitalization supports rural income growth. The remainder of this paper is structured as follows. Section 2 reviews the related literature. Section 3 develops the theoretical framework and research hypotheses. Section 4 describes the data and variable construction. Section 5 outlines the empirical methodology. Section 6 reports the empirical results, including the baseline estimates, robustness checks, endogeneity tests, threshold effects, heterogeneity analysis, moderating effects and further analysis. Section 7 discusses the main findings. Section 8 concludes with policy implications.\n\n## 2 Literature review\n\n### 2.1 Agricultural digitalization and farmers' income\n\nAgricultural digitalization is commonly understood as the embedding of digital technologies such as sensors, the Internet of Things (IoT), big data analytics, and artificial intelligence into agricultural production, management, and value chains, thereby enabling data-driven decisions at both farm and system levels (; ). These technologies support more precise input use, real-time monitoring, and predictive decision-making, which can improve productivity, reduce risk, and enhance environmental performance across the agri-food system (; ).\n\nA growing body of empirical research has examined whether agricultural digitalization and related digital technology adoption can translate into higher household income. At the micro level, studies for low- and middle-income countries find that digital agriculture solutions and platforms can improve smallholders' access to price information, weather forecasts, extension services and buyers, thereby increasing farm revenues and, in some cases, off-farm earnings (; ). Recent evidence from Vietnam and ethnic minority regions of China suggests that digital technology adoption significantly raises farm and total household income, particularly in remote rural areas with limited traditional market access (; ). Other work highlights that digital infrastructure, including broadband and e-commerce logistics, facilitates the expansion of farm scale and market-oriented production, which can further enhance income levels ().\n\nHowever, these benefits are not automatic or evenly distributed. Some studies emphasize that digital agriculture may reduce the demand for certain types of manual labor and restructure employment within the rural economy, with ambiguous implications for low-skilled workers (). In addition, heterogeneous access to digital tools and services can generate a “digital dividend” for connected farmers while reinforcing a “digital divide” for others, especially where infrastructure, finance and skills are unevenly distributed (). Recent evidence also shows that digital applications can significantly improve family farm income, with stronger income-enhancing effects observed for smaller-scale and lower-income farms, further supporting the income-improving potential of agricultural digitalization (). Overall, the literature suggests that agricultural digitalization has considerable potential to raise farmers' income, but its realized effects depend strongly on local development conditions and complementary factors such as infrastructure, finance, and human capital.\n\n### 2.2 Nonlinear and threshold effects\n\nBeyond average effects, recent studies stress that the impact of agricultural digitalization on income distribution and inequality is often nonlinear and may exhibit threshold characteristics. For China, several papers find that the development of agricultural digitalization affects the urban–rural income or consumption gap in a U-shaped or inverted U-shaped pattern along the digitalization path (; ; ). At low levels of agricultural digitalization, infrastructure constraints, limited coverage, and digital exclusion tend to reinforce existing disparities, leading to widening income and consumption gaps. As coverage and usage expand, inclusive applications and economies of scale enable agricultural digitalization to narrow these gaps, before potential re-widening occurs when high-end services and advanced sectors become concentrated in leading regions (; ).\n\nRelated evidence on digital finance and rural agricultural digitalization also points to nonlinear effects. Studies using national panel data for China document that digital finance can initially increase income inequality, particularly where access is biased toward higher-income or urban residents, but may reduce inequality once coverage becomes sufficiently broad and inclusive (; ). At the regional level, agricultural digitalization is found to promote urban–rural integration in a nonlinear way, with clear dual-threshold effects driven by differences in economic development (). At the local level, rural agricultural digitalization significantly affects intra-rural income distribution and has not yet led to inclusive growth in many areas (). These findings suggest that agricultural digitalization may generate different “phases” of impact along the development trajectory and that modeling it with linear specifications may obscure important stage-based dynamics. However, most of this work assesses the digital economy as a whole. More recent evidence on digital rural development further identifies significant stage characteristics, threshold effects, and regional heterogeneity in agricultural performance, reinforcing the need to consider nonlinear effects in the analysis of agricultural digitalization outcomes (). Whether agricultural digitalization displays similar nonlinear patterns in its effects on farmers' income remains largely unexplored.\n\n### 2.3 Conditioning factors: human capital and urbanization\n\nHuman capital is widely recognized as a key determinant of whether farmers can access, understand, and effectively utilize agricultural digital technologies. Empirical studies show that education, digital literacy, and training significantly raise the probability that rural households use digital platforms, adopt agricultural digital technologies, and participate in e-commerce, thereby increasing both agricultural and total household income (; ; ). Evidence from China further indicates that digital literacy has a positive and significant effect on farmers' income, both directly and indirectly through improved information acquisition, technology adoption and participation in digital finance (; ; ).\n\nFrom a broader perspective, policy reports and analytical work emphasize that strengthening farmers' digital skills and general education is necessary to prevent agricultural digitalization from amplifying existing inequalities and to ensure that rural residents can share in the benefits of digital innovation (; ). Inadequate human capital can create “capability bottlenecks” that limit adoption even when infrastructure is present and may lead to a situation where better-educated farmers capture most of the digital gains, while low-skilled farmers are left behind (; ). Despite this consensus on the complementary role of human capital, relatively few studies have examined whether there exists a threshold level of rural human capital beyond which the income effects of digitalization, particularly agricultural digitalization, become significantly stronger. This represents an important gap in the literature on digitalization and rural development.\n\nUrbanization fundamentally reshapes the environment in which agricultural digitalization diffuses and affects rural incomes. For China, a large literature documents that agricultural digitalization interacts with urban–rural structures, influencing both the level and the spatial pattern of income gaps (; ; ). Several studies show that the impact of agricultural digitalization on the urban–rural income gap displays spatial heterogeneity, with stronger narrowing effects in central and western regions and weaker or ambiguous effects in more developed eastern regions (; ). Rural agricultural digitalization has also been found to play a role in promoting new-type urbanization by reallocating labor and capital and by improving access to digital public services ().\n\nAt the same time, research on rural agricultural digitalization and income distribution suggests that digitalization can generate new forms of inequality within rural areas, particularly when digital infrastructure and services are concentrated in peri-urban zones or county seats (). Urban–rural integration studies indicate that agricultural digitalization can facilitate factor mobility and service equalization across space, yet these effects are conditioned by regional fiscal capacity, industrial structure, and institutional arrangements (; ). Despite these insights, most existing work focuses on the overall digital economy or digital finance. Few studies explicitly examine how urbanization moderates the relationship between agricultural digitalization and farmers' income, or how this relationship varies across eastern, central and western regions with distinct industrial bases and resource endowments. Understanding this moderating and heterogeneous pattern is crucial for designing differentiated policies that align agricultural digitalization promotion with regional development strategies.\n\n### 2.4 Research gaps\n\nOverall, the existing literature provides valuable insights into how agricultural digitalization and the broader digital economy affect rural development, income distribution, and spatial structures. Nevertheless, several important research gaps remain. First, there is a measurement gap. Most empirical studies for China construct indices of the general digital economy, whereas agriculture-specific measur","Frontiers in Sustainable Food Systems 2026-06-12","2026-06-12T00:00:00Z","论文",10,false,78,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},22,23,18,13,2,1,"基于省级面板数据的实证研究，揭示农业数字化对农民收入的非线性影响及条件因素，对政策制定有参考价值，但发表时间较早，时效性不足。",[25],{"name":10,"url":6},[27,28,29,30,31],"Frontiers","区域异质性","农业数字化","农民收入","阈值效应",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":37},"unclassified","2026-10-22.2","rules",{"candidates":38,"agricultural_evidence":49,"reason":50},[39,44],{"code":40,"score":22,"supported":14,"title":41,"abstract":42,"tags":43},"I",[],[],[29],{"code":45,"score":22,"supported":14,"title":46,"abstract":47,"tags":48},"I1",[],[],[29],true,"缺少足够的主题证据",[52,53],"农业数字化 区域异质性 农民收入 阈值效应","农业数字化 区域异质性","农业数字化区域异质性农民收入阈值效应-221",7,"10.3389\u002Ffsufs.2026.1776288\u002Ffull","2026-08-04T23:54:47.685242Z",{"total":59,"page":22,"page_size":59,"items":60},6,[61,108,157,227,273,330],{"id":62,"title":63,"url":64,"summary":65,"summary_zh":8,"content":66,"source_name":67,"source_url":8,"published_at":68,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":15,"score_detail":69,"sources":75,"tags":77,"primary_topic":82,"secondary_topics":87,"classification":88,"search_phrases":101,"slug":104,"view_count":105,"doi":106,"paper":8,"created_at":107},938,"数字乡村建设对粮食生产韧性的影响 基于30个中国省份面板数据的阈值效应分析","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fnutrition\u002Farticles\u002F10.3389\u002Ffnut.2026.1901996\u002Ffull","原文标题：The impact of digital rural construction on grain production resilience。使用2012-2023年30个中国省份面板数据，构建数字乡村建设与粮食生产韧性的综合指标，采用双向固定效应模型、中介模型与面板阈值模型检验直接效应、传导机制与财政支撑边界条件。结果显示数字乡村建设显著提升粮食生产韧性，新质农业生产力通过升级农业劳动力、生产工具与要素配置部分中介该关系；阈值分析揭示两个财政支撑阈值0.0699和0.1236，低于第一阈值时效应最强，两阈值之间效应减弱，高于第二阈值效应不显著。本研究为通过数字乡村发展与精准财政支撑强化粮食与营养安全提供政策证据。","## Abstract\n\nEnsuring resilient grain production is essential for food availability, price stability, and nutrition security under growing climatic, market, and geopolitical uncertainty. However, limited evidence explains how digital rural construction strengthens resilience specifically at the grain-production stage. Using panel data from 30 Chinese provinces from 2012 to 2023, this study constructs composite indices of digital rural construction and grain production resilience and applies a two-way fixed effects model, a mediation model, and a panel threshold model to examine its direct effect, transmission mechanism, and fiscal boundary condition. The results show that digital rural construction significantly improves grain production resilience, and this conclusion remains robust after sample adjustments, alternative variable measurement, and endogeneity tests. Heterogeneity analysis indicates stronger effects in western China, major grain-producing areas, and production-consumption balanced areas. Mechanism analysis shows that new quality agricultural productive forces partially mediate this relationship by upgrading agricultural labor, production tools, and factor allocation. The threshold results reveal two fiscal-support thresholds, 0.0699 and 0.1236: the effect of digital rural construction is strongest below the first threshold, weakens between the two thresholds, and becomes statistically insignificant above the second threshold. These findings provide policy evidence for strengthening food and nutrition security through digital rural development and more efficient fiscal support.\n\n## 1 Introduction\n\nGlobal food and nutrition security is increasingly exposed to multiple, overlapping sources of uncertainty. In recent years, climate change, extreme weather events, geopolitical conflicts, fluctuations in international grain prices, and disruptions to global supply chains have shifted the food security agenda from a narrow concern with aggregate output to a broader concern with stable access to sufficient, safe, and nutritious food. The State of Food Security and Nutrition in the World 2024 indicates that global progress toward ending hunger, food insecurity, and all forms of malnutrition remains insufficient and uneven, and that billions of people still lack reliable access to healthy diets (). For China, a developing country with a population of more than 1.4 billion, resilient grain production is closely related not only to domestic economic stability and basic livelihood protection but also to the affordability and availability of staple foods that underpin dietary energy supply and nutrition security. Unlike ordinary industrial goods, grain production is jointly shaped by natural conditions, resource endowments, factor inputs, technological capacity, and institutional arrangements. It is therefore cyclical, vulnerable, and partly public-good oriented.\n\nAgainst this background, food security can no longer be understood solely as a static question of output sufficiency. It increasingly depends on whether the production system can maintain stable supply, recover rapidly from shocks, adapt continuously, and support long-term access to nutritionally essential foods. Recent evidence from China’s forest carbon sink research further shows that climate-response capacity is shaped not only by ecological endowments but also by research and development intensity, industrial structure upgrading, governance intervention, and regional spillover effects (). This reinforces the need to examine grain production resilience within a broader framework that links climate pressure, ecological governance, technological innovation, and food and nutrition security capacity.\n\nAt the same time, China has continued to promote the application of digital technologies in agricultural and rural modernization. The Action Plan for Digital Rural Development (2022–2025) emphasizes digital infrastructure upgrading, smart agriculture innovation, and digital governance capacity, with the aim of accelerating the digital transformation of agricultural production and management and achieving initial progress in smart agriculture by 2025 (). The 2024 Central No. 1 Document further calls for continued implementation of the digital rural development strategy, the development of smart agriculture, the narrowing of the urban–rural digital divide, the construction of regional big data platforms, and stronger coordination and sharing of agricultural information. The National Smart Agriculture Action Plan (2024–2028) also proposes that, by the end of 2028, information technology should play a visible role in reducing costs, increasing yields, and improving efficiency in grain, oil, and other important agricultural products (). These policy developments suggest that digital rural construction is becoming an important support for risk warning, resource allocation, and system recovery in grain production. The concept of resilience was first used to explain the capacity of ecological systems to maintain structural and functional stability under external disturbance (). It was later introduced into food systems and agricultural economics to analyze the ability of food systems to sustain supply, nutrition security, and social welfare under shocks and stresses (, ). From this perspective, grain production resilience is not a simple extension of output level. Rather, it captures the integrated capacity of a grain production system to ensure stable supply, respond to risks, stabilize food prices, and maintain the production-side foundation for adequate dietary intake in complex and uncertain environments.\n\nExisting studies on grain production resilience have mainly addressed three issues: measurement, spatiotemporal evolution, and determinants. One strand of research focuses on evaluation systems and regional differences, arguing that grain production resilience in China displays clear spatial disparities and is influenced by agricultural resource endowments, technological conditions, stages of economic development, and infrastructure (). A second strand extends the object of analysis from production alone to the broader food system, emphasizing that food security depends not only on production capacity but also on circulation, consumption, governance, and risk regulation (). A third strand examines the formation of food resilience from the perspectives of digital inclusive finance, labor migration, and farmland operation, suggesting that financial access, factor mobility, and scale operation affect the stability of food systems and grain production by improving resource allocation (, ). These studies provide an important foundation for understanding grain production resilience. However, relatively little attention has been paid to how digital rural construction, as a comprehensive digital and institutional arrangement, may strengthen the production-side basis of food availability, price stability, and nutrition security.\n\nDigital rural construction is a concrete extension of the Digital China strategy to agriculture and rural areas and an important pathway for agricultural and rural modernization. Unlike a narrow focus on information infrastructure, digital rural construction encompasses digital infrastructure, rural industrial digitalization, digital finance, rural e-commerce, digital governance, and the development of agricultural data resources. Its essence lies in embedding digital technologies, data elements, and platform-based organizations into the economic and social operation of rural areas (). The integration of the digital economy and agriculture can promote agricultural modernization by improving infrastructure, optimizing the behavior of agricultural operators, and reshaping industrial processes (). Related evidence from Chinese listed firms shows that digital transformation can reduce supply chain concentration by improving information transparency and information acquisition capability, thereby supporting supply-chain diversification and robustness (). Although this evidence is drawn from the corporate sector, it is consistent with the argument that digital technologies reshape inter-organizational relationships and resource coordination, which is relevant to the resilience of agricultural production and circulation systems. The rural digital economy can also increase agricultural total factor productivity through multiple pathways, including production upgrading, management transformation, and industrial improvement, thereby enhancing the quality of agricultural development (). International research similarly shows that smart farming and digital agriculture can improve agricultural efficiency and sustainability through precision management, resource conservation, risk identification, and knowledge sharing (, ). Taken together, these findings indicate that digital technology is moving from an external auxiliary tool to a key input in the agricultural production function, with deeper implications for production, management, governance, and food-system resilience.\n\nFrom the perspective of grain production resilience, digital rural construction may operate through at least three channels that are relevant to nutrition-oriented food security. First, digital infrastructure improves rural information accessibility and alleviates information asymmetry in traditional grain production. This enables agricultural operators to obtain meteorological information, input prices, market demand, and policy support in a more timely manner, thereby improving risk anticipation and production decision-making. More stable grain production can reduce the likelihood of supply disruption and sharp price fluctuations in staple foods, which are important determinants of household food access. Second, industrial digitalization introduces intelligent agricultural machinery, remote sensing, the agricultural Internet of Things, agricultural big data, and digital extension services into grain production. These technologies improve precision in sowing, irrigation, plant protection, harvesting, and post-disaster recovery. Smart agriculture can strengthen the shock resistance and recovery capacity of grain production systems through intelligent forecasting, disaster identification, and factor allocation (). Third, digital finance, rural e-commerce, and digital governance tools can improve farmers’ financing capacity, market connectivity, and access to public services, thereby enhancing the adaptability of grain production systems under market fluctuations and disaster shocks. Previous research has shown that digital rural construction strengthens the resilience of China’s food system, with rural digital finance and digital service platforms playing relatively stable enabling roles (). Incorporating digital rural construction into the analytical framework of grain production resilience therefore helps explain how the upstream production capacity required for food and nutrition security is formed in the era of digital transformation.\n\nIt is also important to recognize that the effect of digital rural construction on grain production resilience may not be purely linear. On the one hand, the extent to which digital technologies empower agriculture depends on whether productive forces can be upgraded. As the concept of new quality productive forces is extended to agriculture, increasing emphasis has been placed on the coordinated advancement of scientific and technological innovation, data elements, intelligent equipment, and human capabilities. New quality agricultural productive forces are dynamic and regionally differentiated, and their formation is closely associated with agricultural technological progress, the upgrading of production tools, and the reorganization of production factors (). The digital economy can enhance grain production resilience through technological innovation capacity and agricultural product marketing capacity, while digital inclusive finance can strengthen grain production resilience through agricultural technological innovation (, ). This suggests that digital rural construction does not merely improve grain production conditions directly. It may also enhance the stable supply capacity and risk response capacity of grain production systems by upgrading agricultural labor, means of production, and objects of labor. At the same time, the role of digital rural construction is constrained by public resource allocation. Fiscal support for agriculture can provide public investment for agricultural infrastructure, extension services, and risk-prevention systems, all of which are closely related to the capacity of local food systems to maintain stable supply under shocks. Yet if fiscal resources are inefficiently allocated or poorly structured, the enabling effect of digitalization may weaken. Local fiscal expenditure on agriculture, forestry, and water affairs is therefore an important condition for digitalization to strengthen agricultural resilience, and the effect of agricultural informatization on agricultural total factor productivity may also exhibit threshold characteristics (, ).\n\nOverall, the existing literature provides a useful foundation for studying digital rural construction and grain production resilience, but three gaps remain. First, studies of digital rural construction have largely focused on high-quality agricultural development, agricultural total factor productivity, farmers’ income, and rural governance, while systematic evidence on its impact on grain production resilience remains limited. Second, research on food resilience often adopts a food-system or supply-chain perspective, leaving the stability, adaptability, and transformative capacity of the production stage underexplored, even though production shocks are transmitted downstream to food availability, affordability, and diet quality. Third, the mechanisms and boundary conditions through which digital rural construction affects grain production resilience have not been sufficiently clarified, particularly the roles of agricultural productivity upgrading and fiscal support for agriculture.\n\nTo address these gaps, this study uses panel data from 30 Chinese provinces from 2012 to 2023 to examine how digital rural construction affects grain production resilience. After constructing composite indices of digital rural construction and grain production resilience, this study applies a two-way fixed effects model, a mediation model, and a panel threshold model to identify the direct effect, transmission mechanism, and fiscal boundary condition of digital rural construction. The study contributes to the literature in three respects. First, while existing research has mainly examined food-system resilience or the broader impact of the digital economy on agriculture, this study focuses specifically on the grain-production stage, thereby clarifying how digital rural construction contributes to the stability, recovery, and adaptive capacity of staple-food production. This provides production-side evidence for understanding nutrition security, especially the availability and affordability of staple foods in developing-country contexts. Second, beyond estimating the average effect of digital rural construction, this study introduces new quality agricultural productive forces as a mechanism variable, providing evidence that rural digital transformation enhances grain production resilience by upgrading agricultural laborers, production tools, and factor allocation. Third, by incorporating fiscal support for agriculture as a threshold variable, this study identifies the public-finance conditions under which digital rural construction is more or less effective, offering policy insights into how fiscal support can be better aligned with digital rural development to strengthen grain production resilience and, ultimately, the upstream foundation of food and nutrition security.\n\n## 2 Theoretical analysis and research hypotheses\n\n### 2.1 Direct effect of digital rural construction on grain production resilience\n\nGrain production resilience refers to the comprehensive capacity of a grain production system to maintain stable supply, recover rapidly, and continue adapting and upgrading under multiple shocks, including natural disasters, market fluctuations, resource constraints, and changes in the institutional environment. Drawing on digital technologies, digital platforms, and data elements, digital rural construction systematically reshapes agricultural production, operation, service provision, and governance. It thus provides new technological and organizational foundations for improving grain production resilience. The key to digitally enabled high-quality agricultural development is to overcome the constraints of inadequate information, inefficient resource allocation, and weak risk response in traditional agriculture through information sharing, technology diffusion, and factor reconfiguration (, ).\n\nFirst, digital rural construction can strengthen the risk-resistance capacity of grain production systems. Grain production is highly dependent on natural conditions and is characterized by strong seasonality. Weather changes, the spread of pests and diseases, fluctuations in input prices, and shifts in market demand can all affect production stability. Traditional agricultural operators often rely on experience-based judgment and have limited access to information, making it difficult to identify external shocks in a timely manner. By expanding rural broadband networks, mobile communication, agricultural information platforms, digital financial services, and digital extension services, digital rural construction improves agricultural operators’ access to meteorological, market, input, policy, and technical information. More efficient information transmission enables grain producers to adjust planting schedules, input use, and risk-prevention strategies more quickly, thereby enhancing system stability under external disturbance.\n\nSecond, digital rural construction can improve the recovery and adjustment capacity of grain production systems. Resilience is reflected not only in pre-shock protection but also in post-shock recovery. The introduction of remote sensing, the agricultural Internet of Things, intelligent machinery, drone-based field inspection, and digital extension services can improve the speed of disaster identification, machinery dispatch, input delivery, and technical support. The development of digital finance, rural e-commerce, and agricultural socialized service platforms can also ease the financial, market, and service constraints faced by agricultural operators during post-disaster recovery. Digital technology-driven high-quality agricultural and rural development is not simply the replacement of one technology by another; rather, it involves the overall upgrading of production organization, public services, and governance capacity (). In this sense, digital rural construction can shorten the time required for a grain production system to move from a shocked state back to a stable state.\n\nThird, digital rural construction can promote structural optimization in grain production systems. A higher level of resilience does not simply mean returning to the original state after a shock; it also implies upgrading production modes under pressure. By incorporating data elements into agricultural production, digital rural construction shifts grain production from experience-based judgment to data-driven decision-making, from extensive input use to precision management, and from single-link optimization to coordinated optimization across production, circulation, finance, and governance. Digital technologies empower agricultural development not only by improving production efficiency but also by strengthening agricultural value-chain coordination and reorganizing business models (). In the grain se","Frontiers in Nutrition 2026-08-05","2026-08-05T00:00:00Z",{"impact":17,"substance":70,"depth":71,"authority":72,"freshness":73,"relevant":22,"comment":74},24,20,12,0,"基于30省面板数据的实证研究，揭示数字乡村建设对粮食生产韧性的影响及财政支持阈值，结论新颖，对政策制定有参考价值，但发表于20天前，时效性不足。",[76],{"name":67,"url":64},[78,27,31,79,80,81],"数字乡村","粮食生产韧性","30省面板","新质农业生产力",{"code":83,"slug":84,"name":85,"path":86,"parent_code":40},"I6","digital-village","数字乡村与数据要素","\u002Ftopic\u002Fdigital-agriculture\u002Fdigital-village",[],{"status":89,"locked":14,"version":35,"method":36,"evidence":90},"classified",{"candidates":91,"agricultural_evidence":49},[92,96],{"code":83,"score":72,"supported":49,"title":93,"abstract":94,"tags":95},[78],[78],[78],{"code":97,"score":22,"supported":14,"title":98,"abstract":99,"tags":100},"A8",[],[],[79],[102,103],"新质农业生产力 粮食生产韧性 数字乡村 阈值效应","新质农业生产力 粮食生产韧性","新质农业生产力粮食生产韧性数字乡村阈值效应-938",3,"10.3389\u002Ffnut.2026.1901996\u002Ffull","2026-08-25T00:03:29.421132Z",{"id":109,"title":110,"url":111,"summary":112,"summary_zh":8,"content":113,"source_name":114,"source_url":8,"published_at":115,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":15,"score_detail":116,"sources":119,"tags":121,"primary_topic":126,"secondary_topics":132,"classification":133,"search_phrases":151,"slug":154,"view_count":13,"doi":155,"paper":8,"created_at":156},269,"Digital agricultural technology innovation and agricultural green total factor productivity: county-level evidence from China","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1898854\u002Fabstract","2026年8月3日Frontiers in Sustainable Food Systems上线的县级面板实证研究,基于2014-2021年中国县级平衡面板数据,结合县级社会经济统计与农业专利记录,开发县级数字农业技术创新(DATI)测量指标,系统考察DATI对农业绿色全要素生产率(AGTFP)的影响。研究发现:第一,DATI显著提升AGTFP,但其效应因区域、数字基础设施禀赋、农业发展条件而异;第二,DATI对AGTFP的影响呈现显著阈值特征,数字基础设施与财政支农力度是关键阈值变量,一旦跨越相关阈值,估计效应由负转正;第三,DATI呈现显著空间依赖性,但其空间溢出效应有限,表明数字农业技术创新所产生的绿色生产率增益主要呈现本地化特征。研究为通过加强农村数字基础设施、深化数字技术在农业生产中的应用、因地制宜推进数字化与绿色化协同转型提供县级证据支持。","## Abstract\n\nDigital agricultural technology innovation (DATI) has become an important driver of agricultural modernization and green transformation. By integrating county-level socioeconomic statistics with agricultural patent records, this study develops a county-level measure of DATI and provides more fine-grained evidence on how digital agricultural technology innovation affects agricultural green total factor productivity in China. Using a balanced panel of Chinese counties data from 2014 to 2021, we examine the effect of DATI on agricultural green total factor productivity (AGTFP). The results reveal three main findings. First, DATI significantly improves AGTFP, although its effects differ across regions, digital infrastructure endowments, and agricultural development conditions. Second, the effect of DATI on AGTFP displays pronounced threshold characteristics, with digital infrastructure and fiscal support for agriculture acting as key threshold variables; once the relevant thresholds are crossed, the estimated effect turns from negative to positive. Third, DATI exhibits significant spatial dependence, but its spatial spillover effects are limited, indicating that the green productivity gains generated by digital agricultural technology innovation remain largely localized. These findings provide fine-grained county-level evidence on the role of DATI in promoting green agricultural development and underscore the need to strengthen rural digital infrastructure, deepen the application of digital technologies in agricultural production, and tailor the coordinated digital and green transformation of agriculture to local conditions.\n\n## 1 Introduction\n\nAgricultural green total factor productivity is widely recognized as a key indicator of agricultural green development and sustainability. Enhancing AGTFP is essential not only for improving resource-use efficiency and environmental performance but also for advancing agricultural modernization and rural revitalization. In recent years, the rapid integration of digital technologies into agricultural production has created new opportunities for agricultural green transformation. Existing studies suggest that digital technologies can improve information acquisition, expand market access, and enhance resource allocation efficiency, thereby promoting agricultural performance and rural development (; ; ). Meanwhile, the proliferation of digital platforms, e-commerce networks, and information and communication technology (ICT) services has significantly expanded market opportunities and improved agricultural productivity, particularly in rural areas where traditional information channels remain limited (; ; ). Digital agricultural technology innovation, represented by big data, artificial intelligence (AI), smart agricultural machinery, and digital management platforms, has emerged as a critical force driving agricultural green development.\n\nThe relationship between digitalization and AGTFP has attracted increasing attention in the agricultural economics. Existing evidence indicates that digital technologies can promote AGTFP through technological innovation, industrial upgrading, and improvements in production efficiency (; ; ). From green development perspective, technological innovation is widely regarded as a fundamental driver of green productivity growth, as it enhances resource-use efficiency, reduces environmental costs, and facilitates environmentally directed technological change (; ). These findings suggest that digital agricultural technology innovation is not only an important manifestation of agricultural digital transformation but also a potential pathway for simultaneously achieving productivity growth and environmental sustainability.\n\nHowever, the effectiveness of digital agricultural technology innovation depends on the successful adoption and diffusion of new technologies. Existing studies emphasize that learning processes, social interactions, and absorptive capacities are crucial in determining whether technological advances can be translated into productivity gains (; ; ). As digital technologies become increasingly embedded in agricultural production systems, digital agricultural technology innovation can accelerate the diffusion of green technologies, improve production efficiency, reduce production risks, and facilitate agricultural modernization (; ). Nevertheless, its impacts are unlikely to be uniform across regions, as they are conditioned by differences in digital infrastructure, institutional environments, and local absorptive capacities. Existing evidence further shows that high-quality innovation can significantly enhance green productivity and strengthen the environmental benefits of technological progress (; ). Moreover, innovation activities often generate knowledge diffusion and interregional learning effects, enabling technological benefits to extend beyond local jurisdictions (; ). Given that knowledge spillovers and factor mobility frequently transcend administrative boundaries, innovation activities tend to exhibit significant spatial dependence and spillover effects (; ). Therefore, digital agricultural technology innovation may not only improve local AGTFP but also promote agricultural green development in neighboring regions through technology diffusion and knowledge spillovers.\n\nOverall, existing studies have highlighted the positive role of digitalization in agricultural green development. However, several important gaps remain. First, most existing studies rely on provincial- or prefecture-level data, while county-level evidence remains relatively scarce despite counties being the primary units for agricultural production and technology adoption in China. In China, counties constitute a key spatial unit for agricultural production, technology extension, infrastructure provision, and rural policy implementation, and a large share of agricultural activities is organized at the county level. Compared with analyses based on provincial or prefecture-level data, a county-level approach can more precisely capture local heterogeneity in agricultural resource endowments, digital infrastructure, technology diffusion capacity, and policy implementation. This finer spatial scale is therefore particularly suitable for examining the effect of digital agricultural technology innovation on agricultural green total factor productivity. By combining county-level panel data with an SBM-based measure of AGTFP that accounts for undesirable outputs, this study provides a more disaggregated empirical assessment of the green productivity effects of digital agricultural technology innovation. In doing so, it extends the literature on digital agriculture and agricultural green development by moving beyond broad measures of digitalization and by identifying the role of agricultural digital innovation at the spatial level where agricultural technologies are adopted and rural policies are implemented.\n\nDigitalization is typically measured using broad indicators such as digital economy indices, internet penet ration rates, or digital finance indicators, whereas relatively few studies directly capture digital agricultural technology innovation from the perspective of agricultural digital innovation and agricultural technology patents. Third, although increasing attention has been paid to threshold effects and spatial dependence, a unified analytical framework integrating digital agricultural technology innovation, threshold effects, spatial spillovers, and AGTFP remains underdeveloped. To address these gaps, this study employs Chinese county-level panel data and incorporates digital agricultural technology innovation, threshold models, and spatial Durbin models into a unified analytical framework to systematically examine the impact of digital agricultural technology innovation on AGTFP and its underlying mechanisms. This study provides new county-level evidence on how digital agriculture contributes to agricultural green transformation and sustainable development.\n\nThis study contributes to the existing literature in three principal respects. First, it shifts the analytical focus from general digitalization to digital agricultural technology innovation. Using county-level agricultural patent information, we capture the creation and accumulation of digital technological knowledge in agriculture and examine how agricultural digital technologies affect county-level AGTFP. Second, the study provides county-level evidence and thereby addresses the relative scarcity of fine-grained empirical evidence in the existing literature. Counties are key spatial units for agricultural production, technology adoption, infrastructure provision, and rural policy implementation in China; thus, a county-level perspective allows for a more precise identification of local heterogeneity in the effect of digital agricultural technology innovation on AGTFP. Third, within an integrated framework linking digital agricultural technology innovation and green productivity, this study examines whether DATI improves AGTFP, thereby deepening our understanding of the mechanisms through which digital agriculture contributes to green agricultural transformation.\n\n## 2 Theoretical analysis and research hypotheses\n\n### 2.1 Digital economy and agricultural green total factor productivity\n\nDigital economy has become an important driver of agricultural green transformation. find that digital inclusive finance significantly improves AGTFP by promoting agricultural technological innovation and industrial structure optimization. emphasize that digital finance promotes AGTFP partly through digital village development. explored the impact of digital transformation on supply chain resilience among Chinese listed agricultural firms. The results demonstrate that digital transformation significantly enhances supply chain resilience. Overall, this strand of literature suggests that the digital economy improves AGTFP through digital infrastructure, financial inclusion, agricultural innovation, industrial upgrading, and rural digital transformation. Recent studies have extended this line of research from the perspectives of digitalization, digital technology adoption, and digital village development. shows that the use of digital technologies can effectively improve agricultural productivity across EU countries. further indicate that ICT adoption contributes to higher land and labor productivity in agriculture. provide recent evidence that agricultural and rural digitalization significantly increases agricultural green total factor productivity (AGTFP), while show that digital village development affects AGTFP through both supply-side and demand-side mechanisms. Taken together, the existing literature suggests that the digital economy can improve AGTFP through digital infrastructure, financial inclusion, agricultural innovation, industrial upgrading, ICT diffusion, and rural digital transformation. Based on the above analysis, we propose research hypothesis 1 as follows:\n\n> _H1_: Digital technology applications significantly promote AGTFP.\n\n### 2.2 Digital technology applications and agricultural green total factor productivity\n\nAnother strand of literature focuses on the application of digital technologies in agricultural production. show that big data in smart farming improves decision-making efficiency across agricultural production and the food supply chain. show that digital agriculture development significantly contributes to AGTFP improvement. demonstrate that deep learning can be applied to crop monitoring, disease detection, yield prediction, and agricultural automation. shows that meaningful participation in digital technologies varies across actors, and that differences in technological perceptions and interpretations influence the diffusion and adoption of digital agricultural technologies. emphasize that farmers’ technology adoption behavior determines whether precision agricultural technologies can effectively improve production efficiency. Therefore, digital technology applications promote AGTFP mainly by improving input-use efficiency, reducing agricultural pollution, strengthening monitoring capacity, and improving production decision-making.\n\nDigital agricultural technologies are not merely general ICT applications, but are embedded in agricultural production systems, farm management, and technology adoption decisions. discuss the opportunities and potential dilemmas associated with agricultural digitalization, suggesting that digital transformation in agriculture should be evaluated in relation to production efficiency, inclusiveness, and sustainability. emphasizes that digital transformation of the agricultural sector can reshape production practices through data collection, monitoring, decision support, and precision input application. further show that the digital revolution in agriculture is driven not only by farmers’ demand but also by supply-side actors, including input firms, software providers, digital service platforms, and technology start-ups. argues that digital innovations can contribute to sustainable and resilient agricultural systems by improving productivity, reducing environmental pressure, and strengthening adaptive capacity. also show that farmers’ preferences and perceived technology attributes are important determinants of precision farming adoption. In addition, agricultural technology innovation and agricultural patents provide an important foundation for understanding DATI. examine China’s agricultural patents and show that agricultural patent value has changed amid the rapid expansion of patent applications, indicating that agricultural patents can reflect important dimensions of agricultural technological innovation. Related studies highlight the roles of environmental regulation, digital technologies, and technological progress in agricultural green development. shows that environmental regulation promotes agricultural green innovation and AGTFP, emphasize the potential of agricultural IoT for monitoring, precision management, and data-driven decision-making. further demonstrate that green innovation, human capital, and infrastructure are important drivers of agricultural green productivity growth.\n\nIn this study, the role of agricultural technological innovation is conceptualized as a transmission mechanism rather than a moderating effect. A moderating effect would imply that agricultural technological innovation changes the strength or direction of the relationship between digital technology applications and AGTFP. However, our theoretical framework emphasizes that digital agricultural technology applications affect AGTFP through specific transmission channels. More specifically, digital agricultural technologies improve agricultural production efficiency, enhance agricultural value creation, facilitate technology adoption and diffusion, and improve rural development conditions. These changes subsequently contribute to the improvement of AGTFP. Therefore, the mechanism is examined through channel variables that capture productivity enhancement and rural development improvement. Based on the above analysis, we propose research hypothesis 2 as follows:\n\n> _H2_: Digital agricultural technology applications promote AGTFP by enhancing agricultural value creation and improving rural development conditions.\n\n### 2.3 Spatial effects of digital agricultural technology innovation on agricultural green total factor productivity\n\nAccording to the theories of new economic geography and spatial spillovers, knowledge, technology, and production factors can flow across regional boundaries, thereby generating significant spatial interactions. Digital agricultural technology innovation is characterized by strong openness, connectivity, and diffusibility. Consequently, it may not only improve local AGTFPbut also influence on neighboring regions through knowledge spillovers, technology diffusion, and factor mobility. Existing studies have documented significant spatial spillover effects of digitalization. found that the digital economy promotes agricultural green development and generates positive effects on neighboring regions through spatial interaction mechanisms. further demonstrated that the digital economy exerts significant spatial spillover effects on green total factor productivity, primarily through green technological innovation and industrial upgrading. showed that carbon trading policies not only improve AGTFP in pilot regions but also generate spillover effects on surrounding areas through technology diffusion and policy transmission. found that the digital economy significantly enhances green total factor productivity and exhibits strong spatial dependence. further confirmed that the digital economy improves green productivity through technological innovation diffusion and cross-regional factor mobility, thereby generating significant spatial spillover effects. Therefore, the knowledge spillover, technology diffusion, and resource allocation effects generated by digital agricultural technology innovation may extend beyond administrative boundaries and influence AGTFP in neighboring regions. Digital agricultural technology innovation may not only promote local agricultural green transformation but also facilitate agricultural green development in surrounding regions through interregional learning and information sharing. Based on the above analysis, the following hypothesis is proposed:\n\n> _H3_: Digital agricultural technology innovation exhibits spatial correlation and generate positive spatial spillover effects on agricultural green total factor productivity.\n\nTo clarify the theoretical basis of [Figure 1](https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1898854\u002Fabstract#fig1), this study constructs the analytical framework by integrating technological innovation theory, innovation diffusion theory, green total factor productivity theory, and agricultural sustainable development theory. Digital agricultural technology innovation (DATI) refers to the innovation, diffusion, and application of digital technologies in agricultural production, agricultural resource allocation, rural governance, and agricultural industrial chains. It includes not only the generation of digital agricultural technologies, such as digital patents and intelligent agricultural equipment, but also their practical application in agricultural production and rural development.\n\nFigure 1\n\nBased on this conceptual framework, DATI affect AGTFP through three main pathways. First, the application of digital technologies in agriculture promotes agricultural digitalization and intelligent transformation, improves the allocation and utilization efficiency of agricultural resources, and thereby facilitates the green transformation of agricultural production. Second, DATI accelerates agricultural technology diffusion and innovation, promotes the modernization of agricultural industrial chains, and enhances agricultural technological progress and industrial upgrading. Third, DATI supports rural digitalization and green transformation, improves the rural ecological environment and factor mobility, and thus creates favorable conditions for the growth of AGTFP. [Figure 1](https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1898854\u002Fabstract#fig1) illustrates these theoretical mechanisms.\n\n## 3 Data, variables and methodology\n\n### 3.1 Empirical model\n\nThe empirical models are organized according to the research hypotheses. [Equation 1](https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1898854\u002Fabstract#E1) is used to estimate the baseline effect of DATI on AGTFP. [Equation 2](https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1898854\u002Fabstract#E2) is used","Frontiers in Sustainable Food Systems 2026-08-03","2026-08-03T00:00:00Z",{"impact":19,"substance":17,"depth":71,"authority":20,"freshness":117,"relevant":22,"comment":118},5,"基于县级数据的实证研究，揭示数字农业技术创新对绿色全要素生产率的影响及门槛效应，方法新颖，结论可靠，对政策制定有参考价值。",[120],{"name":114,"url":111},[27,122,31,123,124,125],"空间溢出","农业绿色全要素生产率","数字农业技术创新","县级面板",{"code":127,"slug":128,"name":129,"path":130,"parent_code":131},"G3","rural-finance","农村金融与保险","\u002Ftopic\u002Frural-services\u002Frural-finance","G",[],{"status":89,"locked":14,"version":35,"method":36,"evidence":134},{"candidates":135,"agricultural_evidence":49},[136,141,146],{"code":127,"score":105,"supported":49,"title":137,"abstract":138,"tags":140},[],[139],"财政支农",[],{"code":45,"score":105,"supported":49,"title":142,"abstract":143,"tags":145},[],[144],"数字农业",[],{"code":147,"score":22,"supported":14,"title":148,"abstract":149,"tags":150},"I4",[],[],[122],[152,153],"农业绿色全要素生产率 数字农业技术创新 县级面板 空间溢出","农业绿色全要素生产率 数字农业技术创新","农业绿色全要素生产率数字农业技术创新县级面板空间溢出-269","10.3389\u002Ffsufs.2026.1898854\u002Fabstract","2026-08-07T00:18:31.266403Z",{"id":158,"title":159,"url":160,"summary":161,"summary_zh":162,"content":8,"source_name":163,"source_url":160,"published_at":164,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":165,"score_detail":166,"sources":169,"tags":171,"primary_topic":175,"secondary_topics":176,"classification":177,"search_phrases":203,"slug":206,"view_count":73,"doi":207,"paper":208,"created_at":226},5581,"How Does Digital Literacy Promote Inclusive Rural Growth? Micro-Level Evidence from Income Inequality Among Farm Households","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fsu182010689","Against the backdrop of the coordinated advancement of digital village construction and the common prosperity strategy, the widespread improvement in rural households’ digital literacy is emerging as a key force in reshaping rural income distribution and enabling sustainable rural development. Whether digital dividends can equitably benefit farm households with heterogeneous endowments has a direct bearing on the inclusive growth outcomes of digital village construction and the sustainable advancement of the common prosperity goal. Using three waves of panel data from the China Family Panel Studies (CFPS) covering 2018 to 2022, this paper empirically examines the impact of digital literacy on income inequality among rural households. The findings indicate that improvements in digital literacy significantly reduce rural household income inequality, and this conclusion remains robust across a series of robustness checks. Mechanism analysis reveals that digital literacy mainly curbs the widening of rural household income gaps through two core channels: expanding social capital and alleviating credit constraints. Heterogeneity analysis shows that the inequality-reducing effect of digital literacy is more pronounced among households that depend on non-agricultural income, have lower levels of human capital, or fall into relatively lower income brackets. Further analysis demonstrates that digital literacy significantly reduces inequalities in wage income, business income, and property income across rural households, whereas its mitigating effect on transfer income inequality is not statistically significant. Meanwhile, digital literacy significantly raises the probability of upward income mobility among rural households, thereby alleviating income inequality from a dynamic perspective. On the basis of these findings, this paper proposes policy implications in several areas: building a hierarchical and categorized rural digital literacy cultivation system, fostering a virtuous cycle of digital empowerment, equal opportunity, and income convergence, and optimizing income distribution structures and dynamic mobility mechanisms, with the aim of accelerating progress toward common prosperity.","在数字乡村建设与共同富裕战略协同推进的背景下，农户数字素养的普遍提升正成为重塑乡村收入分配格局、实现乡村可持续发展的重要力量。数字红利能否公平惠及禀赋异质性农户，直接关系到数字乡村建设的包容性增长成效与共同富裕目标的可持续推进。本文利用中国家庭追踪调查（CFPS）2018—2022年三期面板数据，实证检验数字素养对农户收入不平等的影响。研究发现，数字素养的提升显著降低了农户收入不平等，这一结论在一系列稳健性检验后依然成立。机制分析表明，数字素养主要通过拓展社会资本与缓解信贷约束两条核心渠道抑制农户收入差距的扩大。异质性分析显示，数字素养的减不平等效应在依赖非农收入、人力资本水平较低或处于相对较低收入区间的农户中更为明显。进一步分析发现，数字素养显著缩小了农户工资性收入、经营性收入和财产性收入的不平等，而对转移性收入不平等的缓解作用在统计上不显著。同时，数字素养显著提高了农户收入向上流动的概率，从而从动态视角缓解了收入不平等。基于上述发现，本文从构建分层分类的农户数字素养培育体系、形成数字赋能—机会均等—收入收敛的良性循环、优化收入分配结构与动态流动机制等方面提出政策启示，以期为加快共同富裕进程提供参考。","Sustainability","2026-10-21T00:00:00Z",80,{"impact":19,"substance":17,"depth":19,"authority":20,"freshness":167,"relevant":22,"comment":168},9,"基于CFPS三期面板数据的微观实证，方法规范、结论有新意，对数字乡村与共同富裕政策有参考价值，值得进入每日精选。",[170],{"name":163,"url":160},[78,172,173,30,174],"数字素养","共同富裕","收入不平等",{"code":83,"slug":84,"name":85,"path":86,"parent_code":40},[],{"status":89,"locked":14,"version":35,"method":36,"evidence":178},{"candidates":179,"agricultural_evidence":49},[180,186,193,198],{"code":83,"score":181,"supported":49,"title":182,"abstract":183,"tags":185},8,[],[78,184],"digital 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