[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-222":3,"related-222":69},{"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":32,"secondary_topics":38,"classification":39,"search_phrases":63,"slug":66,"view_count":52,"doi":67,"paper":8,"created_at":68},222,"数字乡村发展与农业绿色全要素生产率——来自中国的证据","https:\u002F\u002Fwww.frontiersin.org\u002Farticles\u002F10.3389\u002Ffsufs.2026.1746394\u002Ffull","基于中国30个省份2012-2022年面板数据,构建包含数字基础设施、数字服务能力和数字发展环境的多维评价框架,采用固定效应模型、中介模型和面板阈值模型考察数字乡村发展对农业绿色全要素生产率的影响。结果表明数字乡村发展显著提升AGTFP;机制分析显示数字化通过促进劳动力流动、扩大规模化经营、加强合作社发展、加快机械化与农业研发提升绿色效率;土地流转的正面效应受制度摩擦制约;阈值分析显示数字基础设施的影响在跨越临界水平后更强。",null,"## Abstract\n\nThis study investigates how digital rural development influences agricultural green total factor productivity (AGTFP) in China, with particular attention to stage characteristics and regional heterogeneity. Using panel data from 30 provinces from 2012 to 2022, we construct a multidimensional evaluation framework incorporating digital infrastructure, digital service capacity, and the digital development environment. A fixed-effects model is employed to estimate the overall impact, mediation models are used to examine the roles of factor allocation, organizational upgrading, and technology diffusion, and a panel threshold model is applied to identify nonlinear effects. The results show that digital rural development significantly enhances AGTFP, and this finding is robust to alternative measures, sample adjustments, and endogeneity tests. Mechanism analyses reveal that digitalization improves green efficiency by promoting labor mobility, expanding large-scale operations, strengthening cooperative development, and accelerating mechanization and agricultural R&D. However, the positive effect of land transfer remains constrained by institutional frictions, limiting its contribution to green transformation. Threshold analyses indicate that the impact of digital infrastructure becomes stronger once a critical level is surpassed, whereas the marginal effect of digital services weakens at higher stages of development. Regional heterogeneity further shows that the positive effects are most pronounced in eastern provinces and in non-grain-producing regions. Overall, digital rural development functions as a multidimensional driver of agricultural green transformation, offering empirical evidence and policy insights for designing differentiated digitalization strategies that support sustainable agricultural development.\n\n## 1 Introduction\n\nThe rapid expansion of the digital economy in recent years has substantially advanced rural digitalization in China. Nationwide, the targets of “gigabit county connections, 5G coverage in every township, and broadband access in every village” have largely been achieved. By 2024, over 90% of administrative villages had access to 5G networks, and rural internet penetration reached 63.8%. Meanwhile, digital applications—such as online public services, smart agriculture, and digital breeding—have become deeply embedded in rural production and governance. Globally, digital transformation is recognized as a key pathway toward sustainable food systems. From the perspective of the resource-based view, data have emerged as a new strategic resource that enhances productivity through digital technologies. The widespread adoption of these technologies in agriculture provides new opportunities to overcome resource and environmental constraints and to accelerate green transformation (; ).\n\nHowever, China’s progress in agricultural green development remains limited. The excessive use of fertilizers and pesticides continues to cause non-point source pollution, resulting in a relatively low level of agricultural green total factor productivity (AGTFP) (). This paradox—rapid rural digitalization yet lagging green transformation—raises a critical question: can digital rural development truly empower agricultural green transformation?\n\nPrevious studies provide preliminary evidence that rural digitalization enhances agricultural efficiency (; ; ). Yet, most research has focused on measuring the level of digital village development or assessing its direct effects on productivity, with limited exploration of the internal mechanisms and regional heterogeneity. Whether digitalization improves AGTFP depends on several empowerment channels, including the reduction of transaction costs, facilitation of factor mobility, upgrading of organizational structures, and diffusion of green technologies. These mechanisms may vary across development stages and regions due to differences in infrastructure, economic conditions, and environmental pressures.\n\nTo address these gaps, this study examines how digital rural development affects AGTFP, whether such effects are stage-dependent or region-specific, and through which mechanisms they operate. Using provincial panel data from 2012 to 2022, we integrate mediation and threshold models to identify key empowerment pathways and nonlinear characteristics. The findings provide theoretical insights and practical implications for promoting sustainable and regionally balanced agricultural digitalization.\n\n## 2 Literature review and theoretical framework\n\n### 2.1 Literature review\n\n#### 2.1.1 Digital economy and sectoral development\n\nThe digital economy, characterized by data as a key production factor and digital technologies as enabling tools, has been widely recognized as a fundamental driver of efficiency improvement and structural transformation across multiple sectors (; ). Existing studies consistently show that the digital economy reshapes traditional production and governance systems by reducing information asymmetry, optimizing resource allocation, and accelerating technological innovation, thereby exerting broad economic, environmental, and social impacts (; ; ; ). Similar cross-sectoral effects of the digital economy have also been documented in the tourism sector, where digitalization promotes growth through online attention allocation and transportation infrastructure, as well as in rural human settlement improvement, highlighting its role in enhancing living environments beyond purely economic outcomes (; ; ).\n\nFrom the perspective of environmental sustainability, a growing body of literature demonstrates that digital economy development significantly contributes to green outcomes. Empirical evidence from China indicates that the digital economy reduces carbon emissions and enhances carbon efficiency by promoting green technological innovation and upgrading industrial structure, with these effects exhibiting clear nonlinear characteristics and regional heterogeneity. Similar findings are reported in studies on energy systems, where digitalization alleviates energy poverty primarily through innovation-driven channels, and the positive effects only emerge after surpassing certain development thresholds (). These results highlight that the environmental dividends of the digital economy are neither automatic nor uniform, but depend critically on development stages and structural conditions.\n\nBeyond environmental outcomes, the digital economy has also been shown to exert profound influences on social governance and public service provision. Recent studies focusing on rural China reveal that digital economy development improves public security by increasing non-agricultural employment opportunities and household income, thereby indirectly enhancing social stability (). In the public health domain, interprovincial evidence suggests that the digital economy significantly raises medical and health service levels through improved resource allocation efficiency and coordinated regional development, while generating notable spatial spillover effects (). Together, these findings underscore that the digital economy functions as a systemic force that reshapes factor mobility, institutional performance, and governance capacity across sectors.\n\nMoreover, quasi-natural experimental studies further strengthen the causal interpretation of these relationships. Using the establishment of big data comprehensive pilot zones as an identification strategy, recent research confirms that digital economy development promotes high-quality economic growth through human capital accumulation and green technological innovation, with pronounced heterogeneity across regions, city sizes, and development stages (). This strand of literature emphasizes that the impacts of the digital economy are mediated by internal mechanisms and conditioned by structural environments, rather than operating through simple linear effects.\n\nOverall, the existing evidence suggests that the digital economy is not merely a sector-specific technological upgrade, but a multidimensional development system capable of influencing economic performance, environmental sustainability, and social outcomes through resource reallocation, innovation diffusion, and institutional transformation. These cross-sectoral mechanisms, including resource reallocation, innovation diffusion, and institutional upgrading, are also highly relevant to agriculture, where factor mobility and technology adoption are central to green productivity improvements (; ; ).\n\n#### 2.1.2 Determinants of agricultural green total factor productivity\n\nAgricultural green total factor productivity (AGTFP) has been widely used as a comprehensive indicator to assess the quality and sustainability of agricultural development, as it simultaneously incorporates desirable outputs and undesirable environmental impacts such as carbon emissions and non-point source pollution (; ). A growing body of literature has examined the determinants of AGTFP, indicating that agricultural green productivity is jointly shaped by factor endowments and allocation efficiency, technological progress and diffusion, as well as institutional and environmental conditions.\n\nFirst, factor endowment structure and allocation efficiency constitute fundamental determinants of AGTFP. Existing studies suggest that labor input quality, capital accumulation, and land use efficiency are closely associated with agricultural green productivity (; ). In particular, distortions in factor allocation and barriers to factor mobility tend to reduce production efficiency while intensifying environmental pressures, thereby constraining improvements in AGTFP (). Optimizing the allocation of labor and capital across regions and sectors has therefore been identified as a critical pathway for enhancing agricultural green productivity.\n\nSecond, technological progress and the diffusion of agricultural technologies represent another key driver of AGTFP. Technological innovation not only improves production efficiency but also reduces resource consumption and pollution emissions through cleaner production methods and environmentally friendly input use (). Empirical evidence indicates that green technological innovation, mechanization, and the adoption of sustainable farming practices significantly enhance AGTFP; however, the effectiveness of technological progress depends critically on the speed and scope of technology diffusion, which varies across regions and stages of development (; ).\n\nThird, institutional arrangements, environmental regulations, and external conditions also exert significant influences on AGTFP. Climate change, environmental constraints, and policy interventions affect agricultural green productivity through both direct and indirect channels. For example, adverse climate conditions—such as rising temperatures and increased precipitation variability—have been shown to exert nonlinear and regionally heterogeneous effects on China’s AGTFP (). At the same time, appropriate environmental regulations and agricultural support policies can incentivize cleaner production behavior and stimulate green technological innovation, thereby contributing to improvements in agricultural green productivity (; ).\n\nOverall, the existing literature indicates that AGTFP is the outcome of multiple interacting forces, including factor allocation efficiency, technological progress and diffusion, and institutional and environmental conditions. This body of research provides a solid foundation for examining how new development forces may further influence agricultural green productivity and underscores the importance of exploring the mechanisms through which such influences operate.\n\n#### 2.1.3 Digital rural development and agricultural green total factor productivity\n\nBuilding on the established determinants of AGTFP discussed above, recent studies have begun to explore digital rural development as a new development force that reshapes these traditional drivers rather than replacing them (; ). As an extension of the digital economy into agriculture and rural areas, digital rural development integrates digital infrastructure, digital services, and supportive institutional environments, thereby transforming agricultural production conditions and rural factor allocation systems.\n\nExisting empirical evidence suggests that digitalization-related factors can significantly enhance AGTFP through multiple channels (; ; ; ; ). In particular, studies on digital inclusive finance show that improved access to digital financial services alleviates farmers’ credit constraints, promotes investment in green technologies, and enhances production efficiency, ultimately contributing to higher AGTFP (). Similarly, recent research demonstrates that digital finance promotes AGTFP indirectly by fostering digital village development, highlighting the mediating role of rural digital infrastructure and service capacity in transmitting digital dividends to agricultural green productivity ().\n\nBeyond financial digitalization, a growing strand of literature emphasizes the importance of comprehensive digital rural development in improving agricultural green productivity. Digital infrastructure, such as broadband networks and information platforms, reduces information asymmetry and transaction costs, thereby facilitating the more efficient allocation of labor, capital, and land across agricultural activities (; ; ). Meanwhile, enhanced digital service capacity improves farmers’ access to market information, technical guidance, and public services, which in turn promotes organizational upgrading and the adoption of environmentally friendly production practices.\n\nDigital rural development also plays a crucial role in accelerating the diffusion of green technologies and knowledge spillovers in agriculture. Through digital platforms and information networks, advanced agricultural technologies and sustainable farming practices can be disseminated more rapidly across regions, shortening the lag between innovation and adoption. This process not only enhances productivity but also mitigates environmental pressures by encouraging cleaner production methods and more efficient input use.\n\nTaken together, existing studies suggest that digital rural development affects AGTFP primarily by reshaping factor allocation, facilitating organizational upgrading, and accelerating the diffusion of green technologies, although the strength and timing of these effects depend on regional development conditions and infrastructure endowments. In regions with insufficient digital infrastructure or limited human capital, the positive impacts of digitalization on agricultural green productivity may be constrained or delayed, implying the presence of nonlinear characteristics and regional heterogeneity.\n\nDespite these contributions, existing studies exhibit several important limitations. First, much of the literature focuses on single dimensions of digitalization, such as digital finance, rather than conceptualizing digital rural development as an integrated system encompassing infrastructure, service capacity, and the broader development environment. Second, the internal mechanisms through which digital rural development influences AGTFP, particularly factor allocation, organizational upgrading, and technology diffusion, remain insufficiently examined. In addition, most existing studies rely on linear specifications and thus fail to capture potential threshold effects or stage-dependent dynamics, which may obscure the conditional nature of digital rural development’s impact on agricultural green productivity. In particular, when digital infrastructure and service capacity remain below critical levels, the enabling effects of digital rural development may not fully materialize. Accordingly, this study conceptualizes digital rural development as a systemic and conditional driver of agricultural green transformation rather than a technological input. By constructing a multidimensional digital rural development index and empirically examining its effects on AGTFP through mechanism analysis, threshold modeling, and regional heterogeneity analysis, this study seeks to extend the existing literature and provide more nuanced evidence on how digital transformation contributes to sustainable agricultural development.\n\n### 2.2 Theoretical analysis and research hypotheses\n\n#### 2.2.1 Overall effect of digital rural development on agricultural green total factor productivity\n\nDigital rural development—anchored in digital infrastructure, service capacity, and the development environment—facilitates the transformation and upgrading of agricultural production through multidimensional coordination, thereby enhancing agricultural green total factor productivity (AGTFP). The construction of digital infrastructure enhances information acquisition and transmission efficiency, reduces transaction costs in production and circulation, and improves resource utilization (; ). Enhanced digital service capacity allows farmers easier access to markets and policy resources, strengthening operational flexibility and supporting the transition toward precision and green production (). Meanwhile, a supportive digital development environment—characterized by stable energy supply, efficient transport, and industrial linkages—creates favorable conditions for applying digital tools and disseminating green practices (; ).\n\n> _H1_: Digital rural development significantly enhances agricultural green total factor productivity.\n\n#### 2.2.2 Indirect effects: mediation mechanisms\n\n##### 2.2.2.1 Factor allocation efficiency\n\nInstitutional economics posits that transaction costs and information asymmetry hinder the free flow of production factors (). By advancing information infrastructure and e-commerce platforms, digital rural development improves the allocation efficiency of land and labor. The expansion of internet access and digital platforms reduces transaction costs in land transfers (), promotes off-farm employment, and facilitates large-scale operations. Transparent digital information also alleviates asymmetry, improving market matching. Furthermore, digital platforms create new employment and training opportunities, optimizing labor allocation and human capital utilization, thereby enhancing resource efficiency and AGTFP.\n\n> _H2_: Digital rural development indirectly promotes AGTFP by improving the allocation efficiency of key factors such as land and labor.\n\n##### 2.2.2.2 Organizational upgrading\n\nAccording to collective action theory, the fragmented nature of smallholders hinders coordination in green agricultural production (). The rise of digital platforms fosters collaboration among cooperatives, family farms, agribusinesses, and individual farmers (,; ; ). These new organizational forms improve market access, information sharing, and resource integration while facilitating the collective adoption of green technologies and standardized production practices. Rural e-commerce cooperatives that leverage big data and logistics systems exemplify how digitalization enhances operational efficiency and reduces resource waste. Consequently, organizational upgrading strengthens specialization, fosters economies of scale, and accelerates the adoption of green technologies.\n\n> _H3_: Digital rural development indirectly promotes AGTFP by fostering organizational upgrading and large-scale agricultural operations.\n\n##### 2.2.2.3 Technology diffusion\n\nInnovation diffusion theory emphasizes that the adoption of new technologies depends on effective information channels and social acceptance (). Digital rural development accelerates the dissemination of green and smart agricultural technologies by enhancing connectivity and service systems. Supported by public policies and socialized service platforms, technologies such as IoT-based monitor","Frontiers in Sustainable Food Systems 2026-03-17","2026-03-17T00:00:00Z","论文",10,false,74,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},20,22,18,12,2,1,"基于省级面板数据的实证研究，揭示数字乡村对农业绿色全要素生产率的提升机制与区域异质性，结论可靠，但发表时间较早，时效性不足。",[25],{"name":10,"url":6},[27,28,29,30,31],"数字乡村","Frontiers","农业绿色全要素生产率","AGTFP","阈值模型",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},"I6","digital-village","数字乡村与数据要素","\u002Ftopic\u002Fdigital-agriculture\u002Fdigital-village","I",[],{"status":40,"locked":14,"version":41,"method":42,"evidence":43},"classified","2026-10-22.2","rules",{"candidates":44,"agricultural_evidence":46},[45,50,57],{"code":33,"score":20,"supported":46,"title":47,"abstract":48,"tags":49},true,[27],[27],[27],{"code":51,"score":52,"supported":46,"title":53,"abstract":54,"tags":56},"F5",3,[],[55],"机械化",[],{"code":58,"score":52,"supported":46,"title":59,"abstract":60,"tags":62},"G2",[],[61],"土地流转",[],[64,65],"农业绿色全要素生产率 数字乡村 阈值模型 Frontiers","农业绿色全要素生产率 数字乡村","农业绿色全要素生产率数字乡村阈值模型Frontiers-222","10.3389\u002Ffsufs.2026.1746394\u002Ffull","2026-08-04T23:54:47.709265Z",{"total":70,"page":22,"page_size":70,"items":71},6,[72,116,155,227,272,330],{"id":73,"title":74,"url":75,"summary":76,"summary_zh":8,"content":77,"source_name":78,"source_url":8,"published_at":79,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":80,"score_detail":81,"sources":84,"tags":86,"primary_topic":92,"secondary_topics":93,"classification":94,"search_phrases":106,"slug":109,"view_count":110,"doi":8,"paper":111,"created_at":115},1002,"Digital villages and agricultural green total factor productivity: Evidence from a quasi-natural experiment in China","https:\u002F\u002Fm2.mtmt.hu\u002Fapi\u002Fpublication\u002F37246876","原文标题: Digital villages and agricultural green total factor productivity。作者: Guo Hui、Wang Xinyi、Xia He、Jiang Wenjie。DOI: 10.3389\u002Ffsufs.2026.1831978。本文采用super-efficiency SBM-DDF-GML模型，运用2014—2023年中国县级面板数据测算AGTFP，并以国家数字乡村试点为准自然实验，运用DID方法评估数字乡村发展对AGTFP的影响、机制与空间溢出效应。研究发现：①数字乡村发展显著提升AGTFP，效应主要源于绿色技术进步而非绿色技术效率改善；②机制上通过优化劳动力配置、激发农业创业活力、扩大消费需求；③异质性上，距省会近、地形起伏度高、路网密度低的地区政策效应更显著；④空间效应以虹吸为主，但绿色技术进步随时间推移呈现溢出迹象。","## Digital villages and agricultural green total factor productivity\n\n## Guo, HuiWang, Xinyi; Xia, He; Jiang, Wenjie\n\nAngol nyelvű Szakcikk (Folyóiratcikk) Tudományos\n\n*   SJR Scopus - Horticulture: D1\n\nEnhancing agricultural green total factor productivity (AGTFP) is essential for advancing high-quality agricultural development. This study employs a super-efficiency SBM-DDF-GML model to calculate AGTFP using county-level panel data from China (2014-2023). Leveraging national digital village pilot (NDVP) programs as a quasi-natural experiment, it further applies a Difference-in-Differences (DID) approach to evaluate the impact, underlying mechanisms, and spatial spillover effects of digital village development on AGTFP. Results indicate that digital village initiatives significantly boost AGTFP. The results remain robust across multiple robustness checks and endogeneity tests, with effects primarily driven by green technological progress rather than green technological efficiency improvements. Mechanism analysis indicates that digital village development enhances AGTFP by promoting optimal labor allocation, stimulating agribusiness entrepreneurship, and expanding consumption. Heterogeneity analysis reveals that the policy effect is more pronounced in areas near provincial capitals, with high terrain undulation, and low road network density. The spatial effect during the sample period is dominated by siphoning, with only green technological progress exhibiting signs of spillover over time. This study provides new empirical evidence on how digital village development drives agricultural green transformation and offers policy implications for optimizing policy design and promoting coordinated regional development.","Frontiers in Sustainable Food Systems 2026, 10, 1831978 · 2026-08-26","2026-08-26T00:00:00Z",80,{"impact":18,"substance":18,"depth":17,"authority":20,"freshness":82,"relevant":22,"comment":83},4,"基于全国县级面板数据与准自然实验，实证数字乡村对农业绿色全要素生产率的影响，方法严谨，结论具有政策参考价值。",[85],{"name":78,"url":75},[27,30,87,88,89,90,91],"双重差分","县级面板","绿色全要素生产率","DID","Front Sustain Food Syst",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":95},{"candidates":96,"agricultural_evidence":46},[97,101],{"code":33,"score":82,"supported":46,"title":98,"abstract":99,"tags":100},[],[27],[27],{"code":102,"score":22,"supported":14,"title":103,"abstract":104,"tags":105},"H4",[],[],[89],[107,108],"绿色全要素生产率 县级面板 双重差分 数字乡村","绿色全要素生产率 县级面板","绿色全要素生产率县级面板双重差分数字乡村-1002",7,{"doi":8,"openalex_id":8,"authors":112,"venue":8,"cited_by_count":113,"oa_url":8,"card":8,"direction":8,"ingested_from":114},[],0,"agent","2026-08-27T00:05:20.326038Z",{"id":117,"title":118,"url":119,"summary":120,"summary_zh":8,"content":121,"source_name":122,"source_url":8,"published_at":123,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":124,"score_detail":125,"sources":128,"tags":130,"primary_topic":135,"secondary_topics":136,"classification":137,"search_phrases":149,"slug":152,"view_count":52,"doi":153,"paper":8,"created_at":154},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",78,{"impact":18,"substance":126,"depth":17,"authority":20,"freshness":113,"relevant":22,"comment":127},24,"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。",[129],{"name":122,"url":119},[27,28,131,132,133,134],"阈值效应","粮食生产韧性","30省面板","新质农业生产力",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":138},{"candidates":139,"agricultural_evidence":46},[140,144],{"code":33,"score":20,"supported":46,"title":141,"abstract":142,"tags":143},[27],[27],[27],{"code":145,"score":22,"supported":14,"title":146,"abstract":147,"tags":148},"A8",[],[],[132],[150,151],"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","10.3389\u002Ffnut.2026.1901996\u002Ffull","2026-08-25T00:03:29.421132Z",{"id":156,"title":157,"url":158,"summary":159,"summary_zh":160,"content":8,"source_name":161,"source_url":158,"published_at":162,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":80,"score_detail":163,"sources":167,"tags":169,"primary_topic":174,"secondary_topics":175,"classification":176,"search_phrases":203,"slug":206,"view_count":113,"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",{"impact":19,"substance":18,"depth":19,"authority":164,"freshness":165,"relevant":22,"comment":166},13,9,"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。",[168],{"name":161,"url":158},[27,170,171,172,173],"数字素养","共同富裕","农民收入","收入不平等",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":177},{"candidates":178,"agricultural_evidence":46},[179,185,192,198],{"code":33,"score":180,"supported":46,"title":181,"abstract":182,"tags":184},8,[],[27,183],"digital village",[27,170],{"code":186,"score":70,"supported":46,"title":187,"abstract":188,"tags":191},"G6",[],[189,190],"农户收入","rural income",[],{"code":193,"score":52,"supported":46,"title":194,"abstract":195,"tags":197},"G",[],[196],"rural development",[],{"code":199,"score":22,"supported":14,"title":200,"abstract":201,"tags":202},"G4",[],[],[171],[204,205],"CFPS 数字素养 农户收入","数字素养 农村收入差距","CFPS数字素养农户收入-5581","10.3390\u002Fsu182010689",{"doi":207,"openalex_id":209,"authors":210,"venue":161,"cited_by_count":113,"oa_url":158,"card":219,"direction":223,"ingested_from":225},"W7223105459",[211,214,217],{"name":212,"orcid":213},"Yong Feng","https:\u002F\u002Forcid.org\u002F0000-0002-8151-8594",{"name":215,"orcid":216},"Shuokai  kai Wang","https:\u002F\u002Forcid.org\u002F0009-0002-7439-1764",{"name":218,"orcid":8},"Fangping Cao",{"tldr":220,"method":221,"finding":222,"direction":223,"opportunity":224},"基于CFPS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。","使用CFPS 2018-2022三期面板数据，进行机制与异质性分析。","数字素养通过扩展社会资本和缓解信贷约束显著缩小农户收入差距。","数字乡村与农业信息化","可延伸研究数字素养对不同收入来源不平等的差异化机制及动态收入流动路径。","openalex","2026-10-22T11:20:47.675827Z",{"id":228,"title":229,"url":230,"summary":231,"summary_zh":8,"content":232,"source_name":233,"source_url":8,"published_at":234,"category":235,"cover_url":8,"hotness":13,"is_selected":14,"score":236,"score_detail":237,"sources":241,"tags":243,"primary_topic":248,"secondary_topics":249,"classification":250,"search_phrases":267,"slug":270,"view_count":113,"doi":8,"paper":8,"created_at":271},5505,"广西以赛聚智赋能「AI+农业」 桂字号品牌与东盟水果IP赛道决出","https:\u002F\u002Fwww.agri.cn\u002Fzt\u002Fzhzz\u002Ftp\u002F202610\u002Ft20261019_8877223.htm","10月16日2026年广西智慧农业（数字乡村）创新大赛「桂字号」农业品牌推广赛道、中国（广西）—东盟水果贸易第一IP打造赛道决赛举行，55个项目同台竞技。自治区农业农村厅提出广西正按「北上广研发+广西集成+东盟应用」路径打造中国（广西）—东盟智慧农业先行区，去年一批项目已入选广西人工智能赋能千行百业拟补贴名单。","自治区农业农村厅党组成员、副厅长欧毅霞表示，本次大赛是落实中央一号文件关于因地制宜发展农业新质生产力、大力发展智慧农业部署的具体行动，也是推动人工智能与农业深度融合的重要举措。今年大赛聚焦“桂字号”农业品牌推广和中国（广西）—东盟水果贸易第一IP打造这两个赛道，主要基于三点考虑：一是广西农业资源丰富，“桂字号”品牌基础扎实，但数字化运营和品牌传播仍是短板，需要以赛聚智、以赛促创，借助数字化手段让好产品卖出好价钱；二是广西面向东盟的区位优势得天独厚，水果贸易体量大、潜力足，亟需打造具有国际影响力的第一IP，以数字技术赋能贸易全链条，提升通关、溯源、营销等环节效率；三是这两个赛道切口小、场景实、应用性强，能够有效打通“技术—产业—市场”闭环，推动创新链与产业链精准对接，让更多“金种子”项目在八桂大地上落地生根、开花结果。\n\n欧毅霞提出三点希望：专业组勇当产业升级“领跑者”，形成可复制、可推广的解决方案；学生组争做乡村振兴“生力军”，把创新火花带回课堂、实验室和田间地头；各方共筑智慧农业“生态圈”，推动技术、资本、人才、政策等要素集聚。\n\n广西农业职业技术大学党委书记韦波在致辞中表示，作为承办方，学校将联合广西数字乡村研究院，在赛道组委会统一领导下做好组织、服务与保障，推动赛事成果与教育教学、产业需求有效对接，发挥面向东盟农业职业教育合作优势，为“桂字号”出海、中国—东盟水果贸易双向流通搭建桥梁。\n\n据介绍，广西正按照“北上广研发+广西集成+东盟应用”路径，打造中国（广西）—东盟智慧农业先行区。智慧农业创新大赛不仅是竞技舞台，更是创新成果转化、产业赋能的重要平台。去年，广西首次举办智慧农业创新大赛并取得丰硕成果，一批新技术、新理念、新模式脱颖而出，逐步被大家看见、被市场认可；如捷佳润科技跨境农业智能决策系统、北部湾产权交易所集团AI赋能广西桑蚕茧丝绸全产业链项目入选广西人工智能赋能千行百业拟补贴项目名单；荣获“最佳潜力奖”的“桂鸟先飞—滴滴打药平台”获得地方政府投资扩容实现了升级蝶变。\n\n以赛为媒，向新而行。随着多个项目在决赛中展开比拼，一批聚焦品牌数字化运营、跨境贸易、品控溯源和IP打造的创新项目将在八桂大地加速落地。未来，广西将持续以大赛为纽带，推动技术、资本、人才、政策等要素向智慧农业集聚，促进创新链、产业链、资金链、人才链深度融合，让更多“金种子”项目生根发芽、茁壮成长，为“桂字号”香飘东盟、中国—东盟水果贸易双向流通注入新动能，助力中国（广西）—东盟智慧农业先行区建设迈上新台阶。","中国农业农村信息网 2026-10-19","2026-10-19T00:00:00Z","报道",66,{"impact":19,"substance":238,"depth":239,"authority":20,"freshness":70,"relevant":22,"comment":240},16,14,"省级智慧农业赛事报道，聚焦AI赋能品牌与东盟水果贸易，有具体路径与落地案例，但属活动通稿，时效稍滞后。",[242],{"name":233,"url":230},[27,244,245,246,247],"智慧农业","农业人工智能","桂字号","东盟水果贸易",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":251},{"candidates":252,"agricultural_evidence":46},[253,258,262],{"code":254,"score":82,"supported":46,"title":255,"abstract":256,"tags":257},"I1",[],[244],[244],{"code":33,"score":82,"supported":46,"title":259,"abstract":260,"tags":261},[],[27],[27],{"code":263,"score":22,"supported":14,"title":264,"abstract":265,"tags":266},"I2",[],[],[245],[268,269],"广西 智慧农业创新大赛","桂字号 东盟水果 IP","广西智慧农业创新大赛-5505","2026-10-22T00:06:58.572242Z",{"id":273,"title":274,"url":275,"summary":276,"summary_zh":277,"content":8,"source_name":278,"source_url":275,"published_at":279,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":280,"score_detail":281,"sources":284,"tags":286,"primary_topic":291,"secondary_topics":292,"classification":293,"search_phrases":308,"slug":311,"view_count":113,"doi":312,"paper":313,"created_at":329},5493,"Mapping Research on the Implementation of Smart Village Policies in Indonesia: A Bibliometric Study for the Years 2017-2025","https:\u002F\u002Fdoi.org\u002F10.66254\u002Fjgssvol5issue2page333-347","This research aims to analyze the role of public sector policies in the development of Smart Villages in Indonesia, focusing on the utilization of information and communication technology (ICT) to improve the quality of life in rural communities. Although the Indonesian government has launched various programs such as the Digital Village Program and Smart City, the implementation of these policies still faces various challenges, including limited infrastructure, low digital literacy, and lack of inter-agency coordination. This research uses a bibliometric approach to map relevant research and policy trends, as well as to identify challenges and opportunities in the development of Smart Villages. The research findings indicate that despite progress, existing public sector policies have not yet been fully effective in addressing the digital divide between villages and cities. This research recommends the need for more inclusive and integrated policy improvements, as well as strengthening human resource capacities in villages to support the creation of sustainable and competitive Smart Villages.","本研究旨在分析公共部门政策在印度尼西亚智慧村（Smart Villages）发展中的作用，重点关注利用信息通信技术（ICT）提升农村社区生活质量。尽管印度尼西亚政府已推出数字村计划（Digital Village Program）和智慧城市（Smart City）等多项举措，但这些政策的实施仍面临诸多挑战，包括基础设施有限、数字素养偏低以及跨机构协调不足。本研究采用文献计量学方法，梳理相关研究与政策趋势，并识别智慧村发展中的挑战与机遇。研究结果表明，尽管取得了一定进展，现有公共部门政策在弥合城乡数字鸿沟方面尚未充分发挥效用。本研究建议有必要进行更具包容性和整合性的政策改进，同时加强农村人力资源能力建设，以支持创建可持续且具有竞争力的智慧村。","Journal of Government Science Studies\u002FJournal Of Government Science Studies","2026-10-20T00:00:00Z",71,{"impact":238,"substance":19,"depth":282,"authority":164,"freshness":165,"relevant":22,"comment":283},15,"该文献计量研究系统梳理了印尼智慧乡村政策实施进展与数字鸿沟挑战，对数字乡村国际比较有参考价值，但属区域性研究，影响层级有限。",[285],{"name":278,"url":275},[27,287,288,289,290],"印尼","数字鸿沟","文献计量","智慧乡村",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":294},{"candidates":295,"agricultural_evidence":46},[296,302],{"code":297,"score":282,"supported":46,"title":298,"abstract":300,"tags":301},"I7",[299],"bibliometric",[289,299],[289],{"code":33,"score":303,"supported":46,"title":304,"abstract":305,"tags":307},11,[],[288,183,306],"digital divide",[27,288],[309,310],"印尼 智慧乡村","印尼 数字村 数字鸿沟","印尼智慧乡村-5493","10.66254\u002Fjgssvol5issue2page333-347",{"doi":312,"openalex_id":314,"authors":315,"venue":278,"cited_by_count":113,"oa_url":275,"card":324,"direction":223,"ingested_from":225},"W7223067869",[316,318,321],{"name":317,"orcid":8},"Syaifulrijal Mahulauw",{"name":319,"orcid":320},"Wahab Tuanaya","https:\u002F\u002Forcid.org\u002F0009-0001-9236-8149",{"name":322,"orcid":323},"Marno Wance","https:\u002F\u002Forcid.org\u002F0000-0001-6663-7598",{"tldr":325,"method":326,"finding":327,"direction":223,"opportunity":328},"用文献计量法梳理2017-2025年印尼智慧村政策实施研究，识别挑战与机会。","文献计量分析，覆盖2017-2025年相关文献与政策趋势。","政策尚未有效弥合城乡数字鸿沟，需更包容整合的政策与人力建设。","可延伸至跨国比较或政策效果量化评估，聚焦数字鸿沟与跨部门协同机制。","2026-10-21T23:30:33.962760Z",{"id":331,"title":332,"url":333,"summary":334,"summary_zh":335,"content":8,"source_name":336,"source_url":333,"published_at":279,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":337,"score_detail":338,"sources":341,"tags":343,"primary_topic":348,"secondary_topics":349,"classification":350,"search_phrases":375,"slug":378,"view_count":113,"doi":379,"paper":380,"created_at":393},5469,"How institutional digitalization reshapes entrepreneurial activity in new agricultural business forms: quasi-natural experimental evidence from China's Digital Village Pilot Policy","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1946061","New agricultural business forms are a key driving force for developing countries to overcome the constraints of traditional agriculture and achieve sustainability in food systems. However, it remains unclear whether institutional digitalization can effectively stimulate entrepreneurial vitality in new agricultural business forms. This study develops a theoretical framework of “opportunity creation–opportunity identification–opportunity transformation” and treats China's Digital Village Pilot Policy (DVPP), implemented in 2020, as a quasi-natural experiment. Based on panel data from 1,743 counties in China from 2014 to 2023, this study employs Double Machine Learning (DML) and Spatial Durbin Difference-in-Differences (SDM-DID) models, among other methods, to systematically identify the impact, transmission mechanisms, heterogeneous effects, and spatial spillover effects of institutional digitalization on Entrepreneurial Activity in New Agricultural Business Forms (EANA). The results reveal that: (1) DVPP significantly enhances EANA, and this finding remains robust after a series of tests, including placebo tests, Propensity Score Matching–Difference-in-Differences (PSM-DID), Synthetic Control Method–Difference-in-Differences (SCM-DID), and instrumental variable (IV) estimation; (2) agricultural technological innovation, digital infrastructure development, and digital financial development serve as mediating mechanisms; (3) the policy effects are more pronounced in counties with higher levels of digital access, better-developed transportation infrastructure, and higher levels of digital financial development, exhibiting a pattern of Central > Western > Eastern regions, and are significantly stronger in areas with advantages in characteristic agricultural products; (4) DVPP generates a significant negative spatial spillover effect on EANA in neighboring counties. This study expands theoretical understanding of the relationship between institutional digitalization and entrepreneurship in new agricultural business forms and provides empirical evidence for China and other developing countries to optimize digital rural development and foster new agricultural business forms.","农业新业态是发展中国家突破传统农业约束、实现食物系统可持续发展的关键驱动力。然而，制度数字化能否有效激发农业新业态的创业活力仍不明确。本研究构建了“机会创造—机会识别—机会转化”的理论框架，并将中国2020年实施的数字乡村试点政策（Digital Village Pilot Policy，DVPP）视为准自然实验。基于中国1743个县2014—2023年的面板数据，本研究采用双重机器学习（Double Machine Learning，DML）和空间杜宾双重差分（Spatial Durbin Difference-in-Differences，SDM-DID）等模型，系统识别制度数字化对农业新业态创业活动（Entrepreneurial Activity in New Agricultural Business Forms，EANA）的影响、传导机制、异质性效应和空间溢出效应。结果表明：（1）DVPP显著提升了EANA，且该结论在经过安慰剂检验、倾向得分匹配—双重差分（PSM-DID）、合成控制法—双重差分（SCM-DID）和工具变量（IV）估计等一系列检验后依然稳健；（2）农业技术创新、数字基础设施建设和数字金融发展发挥中介机制作用；（3）政策效应在数字接入水平较高、交通基础设施较完善和数字金融发展水平较高的县更为显著，呈现出中部>西部>东部的格局，且在特色农产品优势区显著更强；（4）DVPP对邻近县的EANA产生显著的负向空间溢出效应。本研究拓展了关于制度数字化与农业新业态创业之间关系的理论认识，为中国及其他发展中国家优化数字乡村发展、培育农业新业态提供了经验证据。","Frontiers in Sustainable Food Systems",85,{"impact":18,"substance":339,"depth":19,"authority":164,"freshness":165,"relevant":22,"comment":340},23,"基于1743个县面板数据的准自然实验，方法扎实、结论有政策参考价值，值得进入每日精选。",[342],{"name":336,"url":333},[344,27,345,346,347],"数字农业","县域经济","农业政策","农村创业",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":351},{"candidates":352,"agricultural_evidence":46},[353,357,363,367,371],{"code":33,"score":282,"supported":46,"title":354,"abstract":355,"tags":356},[183],[27,183],[27],{"code":263,"score":70,"supported":46,"title":358,"abstract":359,"tags":362},[],[360,361],"机器学习","machine learning",[],{"code":193,"score":52,"supported":46,"title":364,"abstract":365,"tags":366},[],[196],[],{"code":199,"score":21,"supported":14,"title":368,"abstract":369,"tags":370},[],[],[347,345],{"code":254,"score":22,"supported":14,"title":372,"abstract":373,"tags":374},[],[],[344],[376,377],"数字乡村试点 新农业业态","DVPP 农村创业","数字乡村试点新农业业态-5469","10.3389\u002Ffsufs.2026.1946061",{"doi":379,"openalex_id":381,"authors":382,"venue":336,"cited_by_count":113,"oa_url":333,"card":388,"direction":223,"ingested_from":225},"W7223041086",[383,385],{"name":384,"orcid":8},"Su Li",{"name":386,"orcid":387},"Changjun Yang","https:\u002F\u002Forcid.org\u002F0009-0000-6236-3489",{"tldr":389,"method":390,"finding":391,"direction":223,"opportunity":392},"用双重机器学习与空间DID评估数字乡村试点政策对新农业业态创业活力的影响。","基于1743县2014-2023面板数据，用DML与SDM-DID做准自然实验。","试点政策显著提升新农业业态创业，经技术创新、数字基建与数字金融中介，并对邻县有负向溢出。","可探究政策负向空间溢出的形成机制及跨县协同激励设计，避免创业资源虹吸。","2026-10-21T23:30:07.205317Z"]