[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3123":3,"related-3123":46},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":8,"source_name":9,"source_url":8,"published_at":10,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":14,"score_detail":15,"sources":23,"tags":25,"search_phrases":31,"slug":34,"view_count":35,"doi":8,"paper":36,"created_at":45},3123,"Turning Data into Sustainability: Agricultural Big Data Policy, Capital Composition, and Rural Eco-Environmental Sustainability in China（数据转化为可持续性：中国农业大数据政策、资本构成与农村生态环境可持续性）","https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F18\u002F9478","湖南第一师范学院马克思主义学院 Zhumiao Wang 等联合吉林大学、韩国圆光大学在《Sustainability》18(18): 9478 发表论文（2026-09-16 发表）。农村环境退化是分散的、监测薄弱的、难以治理的，是可持续发展目标（SDGs）最持久的障碍之一。以 2010-2024 年中国 271 个城市面板数据为基础，以 2016 年农业农村大数据政策（ABDP）试点为\"准自然实验\"，采用交错双重差分（DID）设计估计政策对农村生态环境可持续性（RES）的效应。结果显示 ABDP 使 RES 提升 0.0015 个指数点（相当于样本均值 2.91%、分布 6.8 个百分位点）；效应规模取决于政策所面临的资本存量：随农业机械化（AM）上升而下降并在高机械化水平下变得不显著，随数字资本（DC）上升而上升；田间\"做什么\"的资本替代信息，向田间\"传递信息\"的资本补充信息。增益集中在生产—环境维度。",null,"MDPI Sustainability","2026-09-16T00:00:00Z","论文",10,false,84,{"impact":16,"substance":17,"depth":18,"authority":19,"freshness":20,"relevant":21,"comment":22},22,23,18,13,8,1,"基于271城面板数据的交错DID实证，量化农业大数据政策对农村生态可持续性的效应及资本构成的调节作用，方法规范、结论有政策参考价值。",[24],{"name":9,"url":6},[26,27,28,29,30],"数字乡村","农业大数据","农业政策","双重差分","农村生态环境",[32,33],"农业大数据政策 农村生态环境","Zhumiao Wang 农业大数据","农业大数据政策农村生态环境-3123",0,{"doi":8,"openalex_id":8,"authors":37,"venue":8,"cited_by_count":35,"oa_url":8,"card":38,"direction":42,"ingested_from":44},[],{"tldr":39,"method":40,"finding":41,"direction":42,"opportunity":43},"以2016年农业大数据政策试点为准自然实验，评估其对中国农村生态环境可持续性的影响。","2010-2024年271个城市面板数据，交错双重差分（DID）设计。","政策使农村生态可持续性提升约2.91%，效应随数字资本上升、随机械化上升而减弱。","数字乡村与农业信息化","可探究数字资本与机械资本的替代\u002F互补机制，及政策效应在非生产维度的异质性。","agent","2026-09-22T00:05:38.376954Z",{"total":47,"page":21,"page_size":47,"items":48},6,[49,75,97,121,149,172],{"id":50,"title":51,"url":52,"summary":53,"summary_zh":8,"content":54,"source_name":55,"source_url":8,"published_at":56,"category":57,"cover_url":8,"hotness":12,"is_selected":13,"score":58,"score_detail":59,"sources":64,"tags":66,"search_phrases":70,"slug":73,"view_count":35,"doi":8,"paper":8,"created_at":74},2080,"山东定陶区\"一体三链多场景\"数字农业发展体系成型 入选省级数字乡村试点","https:\u002F\u002Fwww.toutiao.com\u002Farticle\u002F7683000671758598671","山东菏泽定陶区作为省级数字农业发展先行县、省级数字乡村试点地区,创新构建\"一体三链多场景\"数字农业发展体系,以农业大数据一张图为核心载体,搭建多维度智慧运维网络,将数字化智能化手段贯穿农业生产、管理、溯源全流程。同步推行的能评、水保\"无干预智能审批\"服务模式将项目审批时限由2个工作日压缩至分钟级,审批效率提升98%以上,打造可复制可推广的政务数字化改革\"定陶样本\"。","数字赋能启新程，数实融合促发展。近年来，定陶区紧抓数字强省、数字强市建设机遇，深耕数字政务改革与智慧农业发展两大重点领域，以数字化、智能化变革破解发展痛点、激活发展动能，持续释放数字改革红利，推动政务服务提质增效、乡村产业迭代升级，为区域经济社会高质量发展注入强劲数字活力。\n\n![Image 1](https:\u002F\u002Fp3-sign.toutiaoimg.com\u002Ftos-cn-i-axegupay5k\u002Fd8f11c1cff3d4cc5890467c944da0e92~tplv-tt-origin-web:gif.jpeg?_iz=58558&from=article.pc_detail&lk3s=953192f4&x-expires=1789689971&x-signature=m7FIiLyjBKSAM%2FaIjaOjU28gDR4%3D)\n\n聚焦政务服务提质增效，定陶区靶向破解工程建设项目审批流程繁琐、环节较多、耗时较长等行业痛点，创新推行能评、水保“无干预智能审批”服务模式。通过全面重构审批流程、深化跨部门数据共享、优化系统智能核验功能，构建起以信用承诺为基础、系统自动校验为支撑、全程网办、无感办理为特色的智能化审批体系，彻底改变传统人工审批模式。依托全新审批机制，相关项目审批时限由原先2个工作日压缩至分钟级，审批效率提升98%以上，实现了极简申报、智能秒批、全程无感的政务服务新突破。\n\n![Image 2](https:\u002F\u002Fp3-sign.toutiaoimg.com\u002Ftos-cn-i-6w9my0ksvp\u002Ffb6a738632ab4dac90349fe0f70da7d0~tplv-tt-origin-web:gif.jpeg?_iz=58558&from=article.pc_detail&lk3s=953192f4&x-expires=1789689971&x-signature=hD%2BXgUjN6wUyt%2BXQ4exmKX0ISG8%3D)\n\n在提速增效的同时，定陶区坚持放管结合、提质固本，同步搭建“承诺—审批—监管”全链条闭环管理机制。审批办结后，系统自动推送审批数据至各行业监管部门，实现信息实时共享、业务联动衔接，全面强化事中事后精准监管、全流程监管，有效杜绝“重审批、轻监管”问题。该新模式自2025年10月落地实施以来，已高效保障8个重点项目顺利落地，企业满意度达100%，打造出可复制、可推广的政务数字化改革“定陶样本”，为全市数字政府建设积累了宝贵经验。\n\n定陶区行政审批服务局副局长马洪彬表示，下一步，将持续拓展智能审批应用场景，丰富涉企高频服务事项，持续擦亮“无感审批、有感服务”政务服务品牌，全方位优化全域营商环境，让企业和群众尽享数字化改革红利，为数字强市建设贡献审批服务力量。\n\n![Image 3](https:\u002F\u002Fp3-sign.toutiaoimg.com\u002Ftos-cn-i-6w9my0ksvp\u002F5c8cb91e21b14c5f82ba8e526e91064f~tplv-tt-origin-web:gif.jpeg?_iz=58558&from=article.pc_detail&lk3s=953192f4&x-expires=1789689971&x-signature=X8%2BKQYvVy84A9NXk9XucD%2FrlgTw%3D)\n\n政务数字化改革迭代升级的同时，定陶区智慧农业建设蹄疾步稳、成效凸显。作为省级数字农业发展先行县、省级数字乡村试点地区，定陶区立足本地特色农业资源优势，创新构建“一体三链多场景”数字农业发展体系，以农业大数据一张图为核心载体，配套搭建多维 度智慧运维网络，将数字化、智能化手段贯穿农业生产、管理、溯源全流程，有效破解传统农业粗放式经营、精细化程度不足等短板，全力推动传统农业向数字化、智能化、标准化转型升级。\n\n![Image 4](https:\u002F\u002Fp3-sign.toutiaoimg.com\u002Ftos-cn-i-6w9my0ksvp\u002Fa74a4cb090b148978f011ce87f9d907f~tplv-tt-origin-web:gif.jpeg?_iz=58558&from=article.pc_detail&lk3s=953192f4&x-expires=1789689971&x-signature=7bg%2BQ2tuJ4f44VI9pB%2FL15bzggc%3D)\n\n“我们把智能管控体系融入农业全产业链条，不断丰富数字农业应用场景，以数字技术赋能农业提质、增效、降本，为乡村全面振兴注入源源不断的数字动能。”定陶区农业农村局产业信息科科长邱爱兰说道。\n\n如今，定陶区数字农业建设成果丰硕、成效斐然。全区已建成智慧农业应用基地4万余亩，物联网监测技术应用面积达3万余亩，数字化智能精准施肥覆盖5000余亩，累计培育14个省级智慧农业应用基地。一组组亮眼数据，直观展现了定陶区深耕农业数字化转型的扎实成效，生动勾勒出数字赋能乡村产业振兴的崭新图景。\n\n定陶区大数据中心主任王善栋表示，下一步，将依托数字强省宣传月契机，持续深耕数智化应用场景建设，全力打通数据共享壁垒，深化数字经济与实体经济深度融合，以常态化、长效化数字化转型成效，持续赋能区域高质量发展。\n\n（记者：赵化林）","菏泽广电","2026-09-08T00:00:00Z","报道",57,{"impact":60,"substance":61,"depth":62,"authority":47,"freshness":20,"relevant":21,"comment":63},16,15,12,"省级数字乡村试点落地报道，含智慧农业基地规模与智能审批提速等实质数据，但主体为地方政务与农业数字化成效宣传，全国性增量有限。",[65],{"name":55,"url":52},[26,67,68,27,69],"智慧农业","山东","智能审批",[71,72],"农业大数据 数字乡村 智慧农业 智能审批","农业大数据 数字乡村","农业大数据数字乡村智慧农业智能审批-2080","2026-09-11T00:04:18.569724Z",{"id":76,"title":77,"url":78,"summary":79,"summary_zh":8,"content":8,"source_name":80,"source_url":8,"published_at":81,"category":82,"cover_url":8,"hotness":12,"is_selected":13,"score":83,"score_detail":84,"sources":87,"tags":89,"search_phrases":92,"slug":95,"view_count":35,"doi":8,"paper":8,"created_at":96},1159,"福建省印发十五五加快推进农业农村现代化规划 构建1+3+N数智农业体系","https:\u002F\u002Fwww.ptxy.gov.cn\u002Fztzl\u002Fsnzx\u002Fcyzd\u002F202608\u002Ft20260828_2231484.htm","福建省人民政府印发《福建省十五五加快推进农业农村现代化规划》,提出推动数智赋能农业农村发展,建设1个省级农业大数据中心、3个现代农业农村运行一张图、N个智慧农业应用场景,构建1+3+N数智农业体系。实施智慧林业123工程,建设一体化大融合海上福建平台一期项目。建设数字乡村先行区,加快5G网络与千兆光网向乡村延伸,推进智慧广电乡村建设,推广十个小微数字乡村应用场景。强化农业农村防灾减灾,完善农业气象灾害监测预警体系,构建智能化农作物病虫疫情监测网络,确保粮食作物病虫危害损失率控制在5%以内。","福建省人民政府 2026-08-28","2026-08-28T02:00:00Z","政策",83,{"impact":85,"substance":16,"depth":18,"authority":19,"freshness":47,"relevant":21,"comment":86},24,"福建省级规划明确数智农业体系，含具体建设内容与量化目标，信息增量足，权威性高。",[88],{"name":80,"url":78},[26,67,90,91,27],"十五五规划","福建",[93,94],"农业大数据 十五五规划 数字乡村 智慧农业","农业大数据 十五五规划","农业大数据十五五规划数字乡村智慧农业-1159","2026-09-01T00:03:36.299602Z",{"id":98,"title":99,"url":100,"summary":101,"summary_zh":8,"content":102,"source_name":103,"source_url":8,"published_at":104,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":105,"score_detail":106,"sources":111,"tags":113,"search_phrases":116,"slug":119,"view_count":35,"doi":8,"paper":8,"created_at":120},1120,"Smart Agriculture Development: How Can Rural Digital Transformation Enhance the Resilience of Food Security? (Foods 2026, 15(3), 426)","https:\u002F\u002Fwww.mdpi.com\u002F2304-8158\u002F15\u002F3\u002F426","Yingjie Song、Yi Song、Qiusu Wang 发表论文。基于2012—2023年中国粮食主产县面板数据,将2020年\"国家数字乡村试点项目\"作为准自然实验,采用双重差分模型评估该政策对粮食安全韧性的影响及其机制。结果显示:数字乡村发展对粮食安全韧性具有显著驱动效应,在南方地区、劳动资源丰富地区、经济发展水平较低地区效应更显著。机制分析表明,数字乡村发展通过规模化粮食经营和农业技术进步增强粮食安全韧性;资源配置效率和财政透明度对数字乡村发展影响粮食安全韧性起正向调节作用。","Logical Operator Operator\n\nSearch Text\n\nSearch Type\n\n_add\\_circle\\_outline_\n\n_remove\\_circle\\_outline_\n\n[![Image 1: foods-logo](https:\u002F\u002Fpub.mdpi-res.com\u002Fimg\u002Fjournals\u002Ffoods-logo.png?a8f99d50b4c10c98)](https:\u002F\u002Fwww.mdpi.com\u002Fjournal\u002Ffoods)\n\n## Article Menu\n\nFont Type:\n\n_Arial_ _Georgia_ _Verdana_\n\nFont Size:\n\nAa Aa Aa\n\nLine Spacing:\n\n__ __ __\n\nColumn Width:\n\n__ __ __\n\nBackground:\n\nOpen Access Article\n\nby \nYingjie Song\n\n, \nYi Song\n\n[](mailto:sy230218tax@163.com) and \nQiusu Wang\n\n *[](mailto:wqiusu824@163.com)[![Image 2: ORCID](https:\u002F\u002Fpub.mdpi-res.com\u002Fimg\u002Fdesign\u002Forcid.png?0465bc3812adeb52?1788001719)](https:\u002F\u002Forcid.org\u002F0009-0004-9043-4175)\n\nSchool of Finance, Shandong Technology and Business University, Yantai 264000, China\n\n*\n\nAuthor to whom correspondence should be addressed.\n\nSubmission received: 6 December 2025 \u002F Revised: 14 January 2026 \u002F Accepted: 22 January 2026 \u002F Published: 24 January 2026\n\n## Abstract\n\nThe essential prerequisite for the state to ensure the stable production and supply of grain and other key agricultural products is to enhance food security resilience and transform traditional agricultural production and management models. This study utilizes panel data from major grain-producing counties in China from 2012 to 2023. Adopting the 2020 “National Digital Rural Pilot Program” as a quasi-natural experiment, it applies a difference-in-differences (DID) model to assess the program’s impact on food security resilience and its underlying mechanisms. The results demonstrate that digital rural development has a significant driving effect on food security resilience, with more pronounced effects observed in Southern regions, areas endowed with abundant labor resources, and regions with lower economic development levels. Mechanism analyses indicate that digital rural development plays a role in enhancing food security resilience through scaled grain operations and agricultural technological progress. Furthermore, resource allocation efficiency and fiscal transparency exert a positive regulatory effect in impacting food security resilience through digital rural development. This study elucidates the mechanism through which digital rural development enhances food security resilience, offering valuable policy insights for the coordinated advancement of rural revitalization and agricultural digitization.\n\n## 1. Introduction\n\nFood is the cornerstone of national security, food security is the fundamental guarantee for the stability and sustainable development of the national economy and society, and food security resilience serves as the critical support for withstanding risks and consolidating the baseline of food security. Together, these three components constitute the material foundation and security guarantee for national stability, social development, and public well-being. Currently, a confluence of multiple risk factors is synergistically exacerbating threats to global food security, including increasingly severe global climate change [[1](https:\u002F\u002Fwww.mdpi.com\u002F2304-8158\u002F15\u002F3\u002F426#B1-foods-15-00426)], a resurgence of geopolitical conflicts [[2](https:\u002F\u002Fwww.mdpi.com\u002F2304-8158\u002F15\u002F3\u002F426#B2-foods-15-00426)], and heightened volatility in international food trade [[3](https:\u002F\u002Fwww.mdpi.com\u002F2304-8158\u002F15\u002F3\u002F426#B3-foods-15-00426)]. Some low-income countries are even facing food shortages. Food security resilience, which denotes the capacity of food systems to withstand shocks and stresses while maintaining essential functions, constitutes a fundamental pillar for ensuring stable food production and sustainable supply. As the world’s most populous nation, China has been making sustained and substantive contributions to global food security and its resilience through a series of effective and systematic measures. During the 20th National Congress of the Communist Party of China, the government articulated the strategy to “comprehensively consolidate the foundation of food security”, delineating a systematic framework to contribute to food security resilience. Correspondingly, the 2025 Central Document No. 1 explicitly emphasizes the imperative to “continuously enhance the supply security capacity of grain and other key agricultural products”. By implementing targeted measures, including “stabilizing cultivated areas while enhancing yields, reinforcing technological support, and improving income safeguards”, it seeks to efficiently operationalize capacity-building programs that help bolster the resilience of the grain system to risks and ensure a stable supply.\n\nDespite a sustained average annual growth rate of 1.45 percent in China’s grain output over the past decade, signifying long-term steady progress, significant disparities persist in grain production efficiency and cultivated land quality relative to developed nations. These gaps are further amplified by the international context, resulting in accumulating external pressures on China’s food security. Frequent extreme climate events, such as droughts, floods, and typhoons across various regions, have substantially disrupted the normal growth cycles of grain crops, impeded biomass accumulation, and consequently diminished yields. This situation underscores the structural vulnerabilities within China’s food security system when exposed to abrupt external environmental shocks [[4](https:\u002F\u002Fwww.mdpi.com\u002F2304-8158\u002F15\u002F3\u002F426#B4-foods-15-00426)]. Meanwhile, the reallocation of land away from grain production amid urbanization and agricultural modernization [[5](https:\u002F\u002Fwww.mdpi.com\u002F2304-8158\u002F15\u002F3\u002F426#B5-foods-15-00426)], combined with the low profitability of grain cultivation that discourages farmers’ planting enthusiasm, has created systemic incentive distortions [[6](https:\u002F\u002Fwww.mdpi.com\u002F2304-8158\u002F15\u002F3\u002F426#B6-foods-15-00426)]. These internal factors collectively undermine the endogenous stability of food security. This indicates that ensuring China’s food security extends beyond maintaining total grain output and necessitates a transition toward greater resilience.\n\nIn 2020, China officially launched the “National Digital Rural Pilot Program”, a strategic measure designed to contribute to the resilience of its food security system. Since its implementation, the digital rural pilot policy has contributed to agricultural modernization [[7](https:\u002F\u002Fwww.mdpi.com\u002F2304-8158\u002F15\u002F3\u002F426#B7-foods-15-00426)] by leveraging advanced technological innovations to increase grain yield and quality [[8](https:\u002F\u002Fwww.mdpi.com\u002F2304-8158\u002F15\u002F3\u002F426#B8-foods-15-00426)], improve land productivity [[9](https:\u002F\u002Fwww.mdpi.com\u002F2304-8158\u002F15\u002F3\u002F426#B9-foods-15-00426)], and augment human capital levels [[10](https:\u002F\u002Fwww.mdpi.com\u002F2304-8158\u002F15\u002F3\u002F426#B10-foods-15-00426),[11](https:\u002F\u002Fwww.mdpi.com\u002F2304-8158\u002F15\u002F3\u002F426#B11-foods-15-00426)]. The adoption of digital agricultural technologies, particularly integrated water–fertilizer irrigation and precision farming systems, has increased grain yield per unit area, accounting for over 70 percent of the total growth in grain production. The application of digital technology and smart agriculture is associated with the advancement of traditional agricultural production through technological innovation [[12](https:\u002F\u002Fwww.mdpi.com\u002F2304-8158\u002F15\u002F3\u002F426#B12-foods-15-00426)]. By integrating agricultural production factors, it contributes to reducing grain production losses in quantitative terms [[13](https:\u002F\u002Fwww.mdpi.com\u002F2304-8158\u002F15\u002F3\u002F426#B13-foods-15-00426)] and enhancing resource allocation efficiency in qualitative terms [[14](https:\u002F\u002Fwww.mdpi.com\u002F2304-8158\u002F15\u002F3\u002F426#B14-foods-15-00426)]. This approach strengthens the stability and adaptability of the grain supply chain, thereby reinforcing the system’s resilience to external shocks and providing solid support for stable production capacity. Concurrently, as the digital rural initiative advances, over 90% of China’s administrative villages have achieved 5G coverage. Agricultural big data platforms, established through digital technologies, facilitate the real-time monitoring of national cropland cultivation status. This effectively curbs the conversion of farmland to non-agricultural and non-grain uses [[15](https:\u002F\u002Fwww.mdpi.com\u002F2304-8158\u002F15\u002F3\u002F426#B15-foods-15-00426)], thereby fortifying a solid defense for the stability of grain planting areas. Evidently, the implementation of digital rural development not only effectively mitigates external risks and internal structural contradictions within the grain industry but also constitutes a core pathway for enhancing food security resilience.\n\nSince resilience thinking was introduced into agricultural research, food security resilience has been predominantly defined as the capacity of a food system to sustain its essential functions and structural integrity when confronted with diverse disturbances [[16](https:\u002F\u002Fwww.mdpi.com\u002F2304-8158\u002F15\u002F3\u002F426#B16-foods-15-00426)]. Existing research defines food security resilience as the ability of food systems to withstand shocks while preserving systemic stability [[17](https:\u002F\u002Fwww.mdpi.com\u002F2304-8158\u002F15\u002F3\u002F426#B17-foods-15-00426)]. The new era necessitates building a sustainable food security system that balances quantity, quality, and ecological sustainability, marking a strategic shift from a yield-centric paradigm toward multidimensional coordination [[18](https:\u002F\u002Fwww.mdpi.com\u002F2304-8158\u002F15\u002F3\u002F426#B18-foods-15-00426)]. Building upon this foundation, the core essence of food security resilience discussed in this study lies in the fundamental enhancement of a food system’s risk resistance capacity. This is achieved through structural adjustments and functional optimization in response to internal and external disturbances such as extreme weather and market volatility. Such resilience not only ensures stable food supply but also improves food quality, thereby realizing a “qualitative transformation”. It is not merely about immediate growth in quantitative indicators like food production volume or yield per unit area to achieve “quantitative expansion” [[19](https:\u002F\u002Fwww.mdpi.com\u002F2304-8158\u002F15\u002F3\u002F426#B19-foods-15-00426)]. Existing studies indicate that although the overall resilience of national food security has exhibited a fluctuating upward trajectory [[20](https:\u002F\u002Fwww.mdpi.com\u002F2304-8158\u002F15\u002F3\u002F426#B20-foods-15-00426)], significant regional disparities persist, with resilience levels being higher in central and eastern regions and lower in the western region, particularly more pronounced in major grain-producing areas [[21](https:\u002F\u002Fwww.mdpi.com\u002F2304-8158\u002F15\u002F3\u002F426#B21-foods-15-00426)]. Concurrently, the development of the current food system encounters a complex landscape shaped by multiple intertwined factors. Externally, uncertainties such as climate change [[22](https:\u002F\u002Fwww.mdpi.com\u002F2304-8158\u002F15\u002F3\u002F426#B22-foods-15-00426),[23](https:\u002F\u002Fwww.mdpi.com\u002F2304-8158\u002F15\u002F3\u002F426#B23-foods-15-00426)], geopolitical conflicts [[24](https:\u002F\u002Fwww.mdpi.com\u002F2304-8158\u002F15\u002F3\u002F426#B24-foods-15-00426)], and economic cyclical fluctuations [[25](https:\u002F\u002Fwww.mdpi.com\u002F2304-8158\u002F15\u002F3\u002F426#B25-foods-15-00426)] continue to impose persistent impacts. Internally, key elements including the foundational conditions for agricultural production [[26](https:\u002F\u002Fwww.mdpi.com\u002F2304-8158\u002F15\u002F3\u002F426#B26-foods-15-00426)], the efficacy of scientific and technological innovation conversion [[27](https:\u002F\u002Fwww.mdpi.com\u002F2304-8158\u002F15\u002F3\u002F426#B27-foods-15-00426)], the implementation outcomes of policy subsidies [[28](https:\u002F\u002Fwww.mdpi.com\u002F2304-8158\u002F15\u002F3\u002F426#B28-foods-15-00426),[29](https:\u002F\u002Fwww.mdpi.com\u002F2304-8158\u002F15\u002F3\u002F426#B29-foods-15-00426)], and the structure of the supply chain system still require substantial improvement [[30](https:\u002F\u002Fwww.mdpi.com\u002F2304-8158\u002F15\u002F3\u002F426#B30-foods-15-00426)]. Compounded by the inherent vulnerabilities of the grain industry and structural contradictions derived from imbalanced internal incentive mechanisms [[31](https:\u002F\u002Fwww.mdpi.com\u002F2304-8158\u002F15\u002F3\u002F426#B31-foods-15-00426)], this situation underscores the fact that enhancing resilience cannot be accomplished through internal self-adjustment and optimization alone. It urgently demands external support and systemic reforms to create synergistic effects. Digital technologies, encompassing big data, cloud computing, and artificial intelligence, are propelling a profound paradigm shift in agriculture. This transformation derives from the robust data analysis and processing capabilities, as well as intelligent decision-support functionalities, inherent in digital technologies [[32](https:\u002F\u002Fwww.mdpi.com\u002F2304-8158\u002F15\u002F3\u002F426#B32-foods-15-00426)]. It injects a revolutionary driving force into the systematic enhancement of food security resilience. Unlike traditional approaches that primarily focus on short-term productivity gains, digital rural development achieves intelligent cultivation models through the deep integration of digital technologies across the entire agricultural value chain. This enhances the risk response capabilities of agricultural producers, thereby providing systematic external support for strengthening food security resilience. At this critical juncture of agriculture’s transition towards digitalization, informatization, and intelligent transformation, major grain-producing regions carry the vital responsibility of safeguarding national food security [[19](https:\u002F\u002Fwww.mdpi.com\u002F2304-8158\u002F15\u002F3\u002F426#B19-foods-15-00426)]. Hence, advancing the deep integration of digital rural development with agricultural production has emerged as a pivotal strategic pathway, one that contributes to the overall competitiveness of agriculture and guarantees stable supply in major grain-producing areas [[33](https:\u002F\u002Fwww.mdpi.com\u002F2304-8158\u002F15\u002F3\u002F426#B33-foods-15-00426)].\n\nTo achieve this, this study concentrates on major grain-producing areas as its research focus. It establishes an evaluation framework for food security resilience grounded in three core dimensions: resistance capacity, recovery capacity, and transformation capacity [[34](https:\u002F\u002Fwww.mdpi.com\u002F2304-8158\u002F15\u002F3\u002F426#B34-foods-15-00426)]. Utilizing this framework, the study examines the impact of the “National Digital Rural Pilot Program” policy on food security resilience and investigates its underlying mechanisms. The primary contributions of this research are threefold: (1) Perspective innovation—digital rural development represents a crucial policy instrument for promoting agricultural modernization. However, its potential effect on food security resilience has not yet undergone systematic empirical assessment. This study offers a novel perspective for utilizing digital rural initiatives to enable resilience governance and ensure food security. (2) Methodological advancement—methodologically, this study diverges from prior approaches that often relied on subjective comprehensive evaluation methods to measure digital rural development levels. By conceptualizing the “National Digital Rural Pilot Program” as a quasi-natural experiment and applying the difference-in-differences method, it mitigates the subjectivity inherent in evaluating digital rural initiatives, thereby furnishing more objective empirical evidence for national digital rural development. (3) Content enrichment—regarding research content, this study delves into the actual transmission mechanisms of digital rural development policies and elucidates their pathways for enhancing food security resilience. Specifically, from the standpoints of large-scale grain operations and agricultural technological advancement, it clarifies the mechanism through which digital rural development contributes to food security resilience, separately verifying the mediating roles of large-scale operations and agricultural technological progress. Furthermore, considering issues such as information asymmetry and the urban–rural digital divide, it investigates the moderating effects of resource allocation efficiency and fiscal transparency within the process of digital rural development enhancing food security resilience.\n\n## 2. Theoretical Analysis\n\nThe essence of digital rural development lies in utilizing digital technology as the core driving force to bridge the urban–rural digital divide, thereby promoting the transformation of rural areas from traditional models towards digitalization, intelligence, and modernization. This approach addresses fundamental challenges in rural development, such as information asymmetry and inefficient resource allocation, and plays a critical role in enhancing food security resilience. The theoretical analytical framework illustrating the impact mechanism of digital rural development on food security resilience is presented in [Figure 1](https:\u002F\u002Fwww.mdpi.com\u002F2304-8158\u002F15\u002F3\u002F426#fig_body_display_foods-15-00426-f001).\n\n**Figure 1.** Theoretical analysis framework.\n\n### 2.1. The Direct Impact of Digital Rural Development on Food Security Resilience\n\nFrom the perspective of new growth theory, technological progress constitutes the core driver of economic growth. In contrast to the “extensive growth” model driven by disordered resource inputs, the decisive force propelling sustainable economic development resides in achieving optimal resource allocation, transforming production methods, and enhancing productivity through endogenous technological progress [[35](https:\u002F\u002Fwww.mdpi.com\u002F2304-8158\u002F15\u002F3\u002F426#B35-foods-15-00426)]. Digital rural development leverages technologies such as the Internet, big data, cloud computing, and artificial intelligence to foster the deep integration of digital technologies with agricultural production practices, thereby providing comprehensive technological support for food security. By utilizing big data and information technology, agricultural resource inputs can be precisely identified and controlled, resulting in a reduction in the application of agricultural chemicals, including pesticides, plastic mulch, and fertilizers [[36](https:\u002F\u002Fwww.mdpi.com\u002F2304-8158\u002F15\u002F3\u002F426#B36-foods-15-00426)]. This approach not only optimizes grain production models and diminishes reliance on resource endowments but also enhances grain production efficiency and quality, consequently contributing to the foundation for sustainable food security. Meanwhile, grounded in transaction cost theory, digital rural development systematically reduces transaction costs throughout the entire grain supply chain. This addresses the pain points inherent in traditional grain systems, such as information asymmetry, opaque processes, and inadequate oversight, directly mitigating market friction and information gaps [[37](https:\u002F\u002Fwww.mdpi.com\u002F2304-8158\u002F15\u002F3\u002F426#B37-foods-15-00426)]. Consequently, it enhances the grain system’s operational efficiency and flexibility in risk response, bolsters the grain industry chain’s capacity to buffer against market fluctuations, and consolidates its overall resilience in risk management [[38](https:\u002F\u002Fwww.mdpi.com\u002F2304-8158\u002F15\u002F3\u002F426#B38-foods-15-00426)]. Based on the foregoing analysis, the first research hypothesis is proposed.\n\n**Hypothesis****1.**\n\nDigital rural development is conducive to ensuring food security resilience.\n\n### 2.2. The Indirect Impact of Digital Rural Development on Food Security Resilience\n\n(1)\nThe mediating effect of scale operations in grain production and agricultural technological progress:\n\nLarge-scale grain operations represent a pivotal pathway for modern agricultural development, signifying the transformation and upgrading of traditional smallholder farming models towards modernized and intensive practices. Grounded in the theory of economies of scale, replacing fragmented farming with centralized and standardized management can effectively address the inherent issues of low production efficiency and high production costs in traditional smallholder agriculture. Digital rural development vigorously propels the advancement of large-scale grain operations by enhancing agricultural productivity, establishing integrated agricultural industrial systems, and adv","Foods (MDPI)","2026-02-18T01:00:00Z",81,{"impact":107,"substance":16,"depth":18,"authority":108,"freshness":109,"relevant":21,"comment":110},25,14,2,"基于中国县域面板数据与准自然实验，实证数字乡村试点对粮食安全韧性的影响，机制清晰，政策启示明确，但发表于半年前，时效性较低。",[112],{"name":103,"url":100},[26,29,114,115],"粮食安全韧性","粮食主产县",[117,118],"粮食安全韧性 粮食主产县 双重差分 数字乡村","粮食安全韧性 粮食主产县","粮食安全韧性粮食主产县双重差分数字乡村-1120","2026-08-30T00:11:11.526983Z",{"id":122,"title":123,"url":124,"summary":125,"summary_zh":8,"content":126,"source_name":127,"source_url":8,"published_at":128,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":129,"score_detail":130,"sources":134,"tags":136,"search_phrases":142,"slug":145,"view_count":35,"doi":8,"paper":146,"created_at":148},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":16,"substance":16,"depth":131,"authority":62,"freshness":132,"relevant":21,"comment":133},20,4,"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。",[135],{"name":127,"url":124},[26,137,29,138,139,140,141],"AGTFP","县级面板","绿色全要素生产率","DID","Front Sustain Food Syst",[143,144],"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",{"doi":8,"openalex_id":8,"authors":147,"venue":8,"cited_by_count":35,"oa_url":8,"card":8,"direction":8,"ingested_from":44},[],"2026-08-27T00:05:20.326038Z",{"id":150,"title":151,"url":152,"summary":153,"summary_zh":8,"content":154,"source_name":155,"source_url":8,"published_at":8,"category":11,"cover_url":8,"hotness":107,"is_selected":13,"score":105,"score_detail":156,"sources":158,"tags":163,"search_phrases":167,"slug":170,"view_count":35,"doi":8,"paper":8,"created_at":171},338,"数字乡村建设对农村居民收入增长的影响——来自国家数字乡村试点的证据","https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002FY2026\u002FV8\u002FI3\u002F147","余泽田、张世博、吴俣、彭华、董晓霞在《智慧农业(中英文)》2026年第8卷第3期发表文章,基于2014—2023年全国727个县域面板数据,将2020年国家数字乡村试点政策视为准自然实验,构建双重差分模型识别数字乡村建设对农村居民收入增长的影响。研究发现:国家数字乡村试点政策显著促进农村居民收入增长,但未对城乡收入差距产生显著影响;机制分析表明政策主要通过促进劳动力配置调整和提高创业活跃度推动增收,且经济基础、数字金融普惠水平和工业化程度会正向调节增收效应。后续应继续推进数字乡村建设,围绕优化劳动力配置、提升创业活跃度增强政策传导能力,统筹配套条件,强化数字红利普惠共享。DOI: 10.12133\u002Fj.smartag.SA202602009。","欢迎您访问《智慧农业（中英文）》官方网站！[English](https:\u002F\u002Fwww.smartag.net.cn\u002FEN\u002F2096-8094\u002Fhome.shtml)\n\n[![Image 1](https:\u002F\u002Fwww.smartag.net.cn\u002Fimages\u002F2096-8094\u002Fimages\u002Fbanner.png)](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002F2096-8094\u002Fhome.shtml)\n\n[![Image 2](https:\u002F\u002Fwww.smartag.net.cn\u002Fimages\u002F2096-8094\u002Fimages\u002Fbanner_mobile.png)](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002F2096-8094\u002Fhome.shtml)\n\n[![Image 3](https:\u002F\u002Fwww.smartag.net.cn\u002Fimages\u002F2096-8094\u002Fimages\u002Fyyzkj.png)](https:\u002F\u002Fpano.bookan.com.cn\u002F?id=60704&cid=352)\n\nToggle navigation[智慧农业](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002FY2026\u002FV8\u002FI3\u002F147)\n\n*   [网站首页](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002F2096-8094\u002Fhome.shtml)\n*   [期刊介绍](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002Fcolumn\u002Fcolumn133.shtml)\n*   [编委会](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002Fcolumn\u002Fcolumn153.shtml)\n*   [投稿指南](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002Fcolumn\u002Fcolumn135.shtml)\n*   [期刊服务](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002Fcolumn\u002Fcolumn124.shtml)\n*   [下载中心](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002Fcolumn\u002Fcolumn126.shtml)\n*   [联系我们](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002Fcolumn\u002Fcolumn127.shtml)\n\n[Smart Agriculture](https:\u002F\u002Fwww.smartag.net.cn\u002F) ›› [2026](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002Farticle\u002FshowTenYearVolumnDetail.do?nian=2026), [Vol. 8](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002Farticle\u002FshowTenYearVolumnDetail.do?nian=2026) ›› [Issue (3)](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002Fvolumn\u002Fvolumn_841.shtml): 147-158.doi: [10.12133\u002Fj.smartag.SA202602009](https:\u002F\u002Fdoi.org\u002F10.12133\u002Fj.smartag.SA202602009)\n\n• 专刊--数字技术赋能与农业经济范式转型 •[上一篇](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002Fabstract\u002Fabstract22762.shtml)[下一篇](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002Fabstract\u002Fabstract22736.shtml)\n\n### 数字乡村建设对农村居民收入增长的影响——来自国家数字乡村试点的证据\n\n余泽田, 张世博, 吴俣, 彭华, 董晓霞([![Image 4](https:\u002F\u002Fwww.smartag.net.cn\u002Fimages\u002Femail.png)](mailto:dongxiaoxia@caas.cn))\n\n1.   中国农业科学院农业信息研究所，北京 100081，中国\n\n*   `收稿日期:`2026-02-03 `出版日期:`2026-05-30 \n*   `基金项目:`国家重点研发计划项目(2024YFD1700505); 农业农村部政府购买服务项目(08250132) \n*   `作者简介:`. 余泽田，博士研究生，研究方向为农业经济。E-mail：[yu_zetian2024@163.com](mailto:yu_zetian2024@163.com)； 张世博，硕士研究生，研究方向为农业经济。E-mail：[15524007001@163.com](mailto:15524007001@163.com) 余泽田、张世博为并列第一作者 \n*   `通信作者:`董晓霞，博士，研究员，研究方向为农业经济。E-mail：[dongxiaoxia@caas.cn](mailto:dongxiaoxia@caas.cn) \n\n### The Impact of Digital Rural Development on Rural Residents' Income Growth: Evidence from the National Digital Rural Pilot Program\n\nYU Zetian, ZHANG Shibo, WU Yu, PENG Hua, DONG Xiaoxia([![Image 5](https:\u002F\u002Fwww.smartag.net.cn\u002Fimages\u002Femail.png)](mailto:dongxiaoxia@caas.cn))\n\n1.   Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China\n\n*   `Received:`2026-02-03 `Online:`2026-05-30 \n*   `Foundation items:`National Key Research and Development Program of China(2024YFD1700505); Government Procurement Service Project of the Ministry of Agriculture and Rural Affairs(08250132) \n*   `About author:`YU Zetian, E-mail: [yu_zetian2024@163.com](mailto:yu_zetian2024@163.com); ZHANG Shibo, E-mail:[15524007001@163.com](mailto:15524007001@163.com) \n*   `Corresponding author:`DONG Xiaoxia, E-mail: [dongxiaoxia@caas.cn](mailto:dongxiaoxia@caas.cn) \n\n## [在线阅读 4](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002FY2026\u002FV8\u002FI3\u002F147#)\n\n## [知网下载](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002FY2026\u002FV8\u002FI3\u002F147)\n\n## [本地下载 24](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002FY2026\u002FV8\u002FI3\u002F147#1)\n\n[_![Image 6](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002FY2026\u002Fimages\u002Fknowledge\\_map1.png)_ ## 可视化 0](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002FY2026\u002FV8\u002Flexeme\u002FshowArticleByLexeme.do?articleID=22765)\n\n#### [摘要\u002FAbstract](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002FY2026\u002FV8\u002FI3\u002F147)\n\n**摘要：**\n\n**【目的\u002F意义】** 为识别数字乡村建设是否能够促进农村居民增收，并厘清其作用机制，以国家数字乡村试点政策为准自然实验，系统考察数字乡村建设对农村居民收入增长的影响。研究有助于为科学评估数字乡村政策成效、优化农民增收路径提供经验证据。 **【方法】** 基于2014—2023年全国727个县域面板数据，将2020年国家数字乡村试点政策视为准自然实验，构建双重差分模型识别数字乡村建设对农村居民收入增长的影响，并通过平行趋势检验、安慰剂检验、PSM-DID等方法进行稳健性检验，进一步结合机制检验和调节效应模型分析其作用路径。 **【结果和讨论】** 国家数字乡村试点政策显著促进了农村居民收入增长，但未对城乡收入差距产生显著影响。机制分析表明，政策主要通过促进劳动力配置调整和提高创业活跃度推动农村居民增收，且经济基础、数字金融普惠水平和工业化程度会正向调节增收效应。 **【结论】** 数字乡村建设是提升农村居民收入的有效路径，后续应持续推进，并围绕优化劳动力配置、提升创业活跃度增强政策传导能力，统筹配套条件，强化数字红利普惠共享。\n\n**关键词:**[数字乡村,](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002FY2026\u002FV8\u002FI3\u002F147#)[农村居民增收,](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002FY2026\u002FV8\u002FI3\u002F147#)[城乡包容性增长,](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002FY2026\u002FV8\u002FI3\u002F147#)[双重差分模型,](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002FY2026\u002FV8\u002FI3\u002F147#)[调节效应模型,](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002FY2026\u002FV8\u002FI3\u002F147#)[数字技术](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002FY2026\u002FV8\u002FI3\u002F147#)\n\n**Abstract:**\n\n**[Objective]** Digital rural construction has become an important strategy for promoting rural revitalization and breaking the long-standing urban-rural dual structure in China. Under the goals of common prosperity and agricultural modernization, sustained income growth for rural residents is a key policy concern. Existing studies have explored the economic effects of digital rural development, but several gaps remain. Most research focuses on a single outcome rather than jointly considering income growth and broader inclusive development. Many studies also rely on composite indices that may be endogenous to local conditions. In addition, the channels through which digital rural policies affect rural income have not been fully clarified. To address these issues, the National Digital Rural Pilot Policy was used as a quasi-natural experiment in this research to examine whether digital rural construction raises rural residents' income, the transmission mechanisms of this effect and its effect on the urban-rural income gap. **[Methods]** Using the county-level panel data for 727 counties in China from 2014 to 2023, the 2020 launch of the National Digital Rural Pilot Policy was treated as a quasi-natural experiment, and a difference-in-differences model was applied to identify the policy's effect on rural residents' per capita disposable income.The analysis controlled for economic foundation, fiscal expenditure, financial development, population density, savings level, industrialization, service-sector development, and education. To test robustness, parallel trend tests, placebo tests, the exclusion of special samples, controls for other concurrent policies, and propensity score matching combined with DID estimation were conducted. Mechanism tests were used to examine whether the policy works through labor allocation optimization and enhanced entrepreneurial activity, while moderating effect models assessed whether county economic foundation, digital financial inclusion, and industrialization strengthen the income-enhancing effect. **[Results and Discussions]** The results showed that the National Digital Rural Pilot Policy significantly increased rural residents' income. After county and year fixed effects as well as other relevant factors were controlled for, the policy led to a significant increase in rural per capita disposable income, and this finding remained robust across a series of tests. Dynamic analysis showed no significant difference in pre-policy trends between pilot and non-pilot counties, while the positive effect emerged after policy implementation and strengthened over time, indicating a sustained and cumulative policy impact. Mechanism analysis identified two main channels through which the policy promoted income growth. First, digital rural construction improved labor allocation by fostering new forms of rural economic activity, such as digital agriculture and rural e-commerce, reducing labor market frictions, and improving the matching efficiency between labor and employment opportunities. Second, it enhanced entrepreneurial activity by lowering market entry and operating costs, increasing the vitality of agriculture-related business entities, and creating more opportunities for local business development and income generation. The income-enhancing effect was more pronounced in counties with a stronger economic foundation, higher levels of digital financial inclusion, and greater industrialization. However, although the policy significantly increased rural residents' income, it did not significantly reduce the urban-rural income gap, suggesting that absolute income growth did not necessarily lead to relative distributional convergence. **[Conclusions]** Digital rural construction is an effective pathway for increasing rural residents' income in China. By exploiting the National Digital Rural Pilot Policy as a quasi-natural experiment, this study provides more credible evidence on the income effects of digital rural development and clarifies its main transmission mechanisms. Future policy efforts should continue to advance digital rural construction while focusing on improving labor allocation, enhancing entrepreneurial activity, and strengthening county-level supporting conditions. Greater attention should also be paid to digitally disadvantaged areas and vulnerable rural groups in order to promote the inclusive sharing of digital dividends and foster more balanced urban-rural development.\n\n**Key words:**[digital rural construction,](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002FY2026\u002FV8\u002FI3\u002F147#)[rural residents' income growth,](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002FY2026\u002FV8\u002FI3\u002F147#)[urban-rural inclusive growth,](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002FY2026\u002FV8\u002FI3\u002F147#)[difference-in-differences model,](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002FY2026\u002FV8\u002FI3\u002F147#)[moderating?effect model,](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002FY2026\u002FV8\u002FI3\u002F147#)[digital technology](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002FY2026\u002FV8\u002FI3\u002F147#)\n\n**中图分类号:**\n\n*   [F320.3](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002FY2026\u002FV8\u002FI3\u002F147#)\n\n#### 引用本文\n\n余泽田, 张世博, 吴俣, 彭华, 董晓霞. 数字乡村建设对农村居民收入增长的影响——来自国家数字乡村试点的证据[J]. 智慧农业(中英文), 2026, 8(3): 147-158.\n\nYU Zetian, ZHANG Shibo, WU Yu, PENG Hua, DONG Xiaoxia. The Impact of Digital Rural Development on Rural Residents' Income Growth: Evidence from the National Digital Rural Pilot Program[J]. Smart Agriculture, 2026, 8(3): 147-158.\n\n#### 使用本文\n\n**[[收藏文章](javascript:;)](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002FY2026\u002FV8\u002FI3\u002F147#) \u002F[推荐](javascript:void(null))**\n**导出引用管理器**[EndNote](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002Farticle\u002FgetTxtFile.do?fileType=EndNote&id=22765)|[Ris](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002Farticle\u002FgetTxtFile.do?fileType=Ris&id=22765)|[BibTeX](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002Farticle\u002FgetTxtFile.do?fileType=BibTeX&id=22765)\n\n**链接本文:**[https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002F10.12133\u002Fj.smartag.SA202602009](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002F10.12133\u002Fj.smartag.SA202602009)\n\n[https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002FY2026\u002FV8\u002FI3\u002F147](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002FY2026\u002FV8\u002FI3\u002F147)\n\n#### [图\u002F表 12](javascript:;)\n\n[![Image 7](https:\u002F\u002Fwww.smartag.net.cn\u002Fimages\u002Ftable-icon.gif)](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002FY2026\u002FV8\u002FI3\u002F147#inline_content14977)\n\n**表1**\n\n数字乡村增收效应研究主要变量的描述性统计结果\n\n| 变量 | 说明 | 样本量 | 均值 | 标准差 | 最小值 | 最大值 |\n| :---: | :---: | :---: | :---: | :---: | :---: | :---: |\n| 农村居民收入 | 农村居民人均可支配收入\u002F万元 | 7 270 | 1.288 | 0.515 | 0.510 | 3.562 |\n| 数字乡村试点政策 | 见2.2节 | 7 270 | 0.018 | 0.134 | 0.000 | 1.000 |\n| 经济基础 | 县区人均GDP\u002F万元 | 7 270 | 3.497 | 2.336 | 0.802 | 17.218 |\n| 财政水平 | 人均地方财政一般预算支出\u002F万元 | 7 270 | 0.206 | 0.192 | 0.031 | 1.746 |\n| 金融水平 | 人均年末金融机构各项贷款余额\u002F万元 | 7 270 | 2.546 | 1.985 | 0.352 | 21.043 |\n| 人口规模 | 年末总人口\u002F行政区域总面积\u002F（万人\u002Fkm 2） | 7 270 | 0.031 | 0.033 | 0.000 | 0.425 |\n| 储蓄水平 | 人均城乡居民储蓄存款余额\u002F万元 | 7 270 | 2.655 | 1.297 | 0.340 | 11.250 |\n| 工业化程度 | 第二产业增加值\u002FGDP | 7 270 | 0.387 | 0.153 | 0.058 | 0.772 |\n| 服务化程度 | 第三产业增加值\u002FGDP | 7 270 | 0.430 | 0.108 | 0.164 | 0.838 |\n| 教育水平 | 普通中学在校学生数\u002F年末总人口 | 7 270 | 0.058 | 0.017 | 0.011 | 0.104 |\n\n 表1 \n\n[![Image 8](https:\u002F\u002Fwww.smartag.net.cn\u002Fimages\u002Ftable-icon.gif)](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002FY2026\u002FV8\u002FI3\u002F147#inline_content14978)\n\n**表2**\n\n数字乡村试点对农村居民收入影响的基准回归结果\n\n| 变量 | 农村居民收入 |\n| :---: | :---: |\n| （1） | （2） | （3） |\n| 数字乡村试点 | 0.477*** | （0.022） | 0.052*** | （0.019） | 0.049*** | （0.019） |\n| 经济基础 |  |  |  |  | -0.059*** | （0.017） |\n| 财政水平 |  |  |  |  | -0.143*** | （0.039） |\n| 金融水平 |  |  |  |  | 0.091*** | （0.020） |\n| 人口规模 |  |  |  |  | 1.136 | （0.710） |\n| 储蓄水平 |  |  |  |  | -0.144*** | （0.030） |\n| 工业化程度 |  |  |  |  | 0.090 | （0.074） |\n| 服务化程度 |  |  |  |  | 0.185** | （0.086） |\n| 教育水平 |  |  |  |  | 0.412* | （0.224） |\n| 年份固定效应 |  |  | 已控制 |  | 已控制 |  |\n| 县区固定效应 |  |  | 已控制 |  | 已控制 |  |\n| 常数项 | 1.279*** | （0.016） | 0.918*** | （0.004） | 0.930*** | （0.072） |\n| 样本量 | 7 270 |  | 7 270 |  | 7 270 |  |\n| _R_ 2 | 0.037 |  | 0.915 |  | 0.921 |  |\n\n 表2 \n\n[![Image 9](https:\u002F\u002Fwww.smartag.net.cn\u002Ffileup\u002F2096-8094\u002FFIGURE\u002F2026-8-3\u002FImages\u002F2096-8094-2026-8-3-147\u002Fthumbnail\u002FC46A5376-C865-4676-8D6A-6D5BDC96B1D2-F001.jpg)](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002FY2026\u002FV8\u002FI3\u002F147#figureClass14979)\n\n**图1**\n\n农民收入平行趋势检验结果 注：以2019年为基准期，在95%的置信区间下绘制。\n\n![Image 10](https:\u002F\u002Fwww.smartag.net.cn\u002Ffileup\u002F2096-8094\u002FFIGURE\u002F2026-8-3\u002FImages\u002F2096-8094-2026-8-3-147\u002FC46A5376-C865-4676-8D6A-6D5BDC96B1D2-F001.jpg)\n\n 图1 \n\n[![Image 11](https:\u002F\u002Fwww.smartag.net.cn\u002Ffileup\u002F2096-8094\u002FFIGURE\u002F2026-8-3\u002FImages\u002F2096-8094-2026-8-3-147\u002Fthumbnail\u002FC46A5376-C865-4676-8D6A-6D5BDC96B1D2-F002.jpg)](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002FY2026\u002FV8\u002FI3\u002F147#figureClass14980)\n\n**图2**\n\n效应基准回归的伪政策年份安慰剂检验结果\n\n![Image 12](https:\u002F\u002Fwww.smartag.net.cn\u002Ffileup\u002F2096-8094\u002FFIGURE\u002F2026-8-3\u002FImages\u002F2096-8094-2026-8-3-147\u002FC46A5376-C865-4676-8D6A-6D5BDC96B1D2-F002.jpg)\n\n 图2 \n\n[![Image 13](https:\u002F\u002Fwww.smartag.net.cn\u002Ffileup\u002F2096-8094\u002FFIGURE\u002F2026-8-3\u002FImages\u002F2096-8094-2026-8-3-147\u002Fthumbnail\u002FC46A5376-C865-4676-8D6A-6D5BDC96B1D2-F003.jpg)](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002FY2026\u002FV8\u002FI3\u002F147#figureClass14981)\n\n**图3**\n\n基准回归的随机置换安慰剂检验结果\n\n![Image 14](https:\u002F\u002Fwww.smartag.net.cn\u002Ffileup\u002F2096-8094\u002FFIGURE\u002F2026-8-3\u002FImages\u002F2096-8094-2026-8-3-147\u002FC46A5376-C865-4676-8D6A-6D5BDC96B1D2-F003.jpg)\n\n 图3 \n\n[![Image 15](https:\u002F\u002Fwww.smartag.net.cn\u002Fimages\u002Ftable-icon.gif)](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002FY2026\u002FV8\u002FI3\u002F147#inline_content14982)\n\n**表3**\n\n基准回归的稳健性检验：剔除特殊样本\n\n| 变量 | 农村居民收入 |\n| :---: | :---: |\n| 数字乡村试点 | 0.471*** | 0.052** | 0.053** |\n|  | （0.026） | （0.023） | （0.022） |\n| 控制变量 |  |  | 已控制 |\n| 时间固定效应 |  | 已控制 | 已控制 |\n| 县区固定效应 |  | 已控制 | 已控制 |\n| 样本量 | 6 640 | 6 640 | 6 640 |\n| _R_ 2 | 0.034 | 0.913 | 0.920 |\n\n 表3 \n\n[![Image 16](https:\u002F\u002Fwww.smartag.net.cn\u002Fimages\u002Ftable-icon.gif)](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002FY2026\u002FV8\u002FI3\u002F147#inline_content14983)\n\n**表4**\n\n基准回归的稳健性检验：排除其他政策干扰\n\n| 变量 | 是否是信息进村入户工程试点 | 是否是革命老区县 | 是否为少数民族自治县 |\n| :---: | :---: | :---: | :---: |\n| 数字乡村试点 | 0.048*** | 0.049*** | 0.049*** |\n|  | （0.019） | （0.019） | （0.019） |\n| 控制变量 | 已控制 | 已控制 | 已控制 |\n| 时间固定效应 | 已控制 | 已控制 | 已控制 |\n| 县区固定效应 | 已控制 | 已控制 | 已控制 |\n| 样本量 | 7 270 | 7 270 | 7 270 |\n| _R_ 2 | 0.921 | 0.921 | 0.921 |\n\n 表4 \n\n[![Image 17](https:\u002F\u002Fwww.smartag.net.cn\u002Fimages\u002Ftable-icon.gif)](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002FY2026\u002FV8\u002FI3\u002F147#inline_content14984)\n\n**表5**\n\n基准回归的稳健性检验：更换回归模型\n\n| 变量 | 农村居民收入 |\n| :---: | :---: |\n| 数字乡村试点 | 0.049*** |\n|  | （0.019） |\n| 控制变量 | 已控制 |\n| 时间固定效应 | 已控制 |\n| 县区固定效应 | 已控制 |\n\n 表5 \n\n[![Image 18](https:\u002F\u002Fwww.smartag.net.cn\u002Fimages\u002Ftable-icon.gif)](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002FY2026\u002FV8\u002FI3\u002F147#inline_content14985)\n\n**表6**\n\n数字乡村试点对农村居民收入影响的异质性检验结果\n\n| 变量 | 数字基础 | 经济基础 | 地理区域 | 地形起伏度 |\n| :---: | :---: | :---: | :---: | :---: |\n| 数字乡村试点 | 0.002 | -0.030* | 0.095*** | 0.078** |\n|  | （0.023） | （0.018） | （0.031） | （0.034） |\n| 数字乡村试点*数字基础 | 0.077** |  |  |  |\n|  | （0.032） |  |  |  |\n| 数字乡村试点*经济基础 |  | 0.096*** |  |  |\n|  |  | （0.026） |  |  |\n| 数字乡村试点*地理区域（以东部为基准） |  |  |  |  |\n| 中部 |  |  | -0.073 |  |\n|  |  |  | （0.047） |  |\n| 西部 |  |  | -0.049 |  |\n|  |  |  | （0.038） |  |\n| 数字乡村试点*地形起伏度 |  |  |  | -0.048 |\n|  |  |  |  | （0.039） |\n| 控制变量 | 已控制 | 已控制 | 已控制 | 已控制 |\n| 时间固定效应 | 已控制 | 已控制 | 已控制 | 已控制 |\n| 县区固定效应 | 已控制 | 已控制 | 已控制 | 已控制 |\n| 样本量 | 7 270 | 7 270 | 7 270 | 7 270 |\n| _R_ 2 | 0.921 | 0.921 | 0.921 | 0.921 |\n\n 表6 \n\n[![Image 19](https:\u002F\u002Fwww.smartag.net.cn\u002Fimages\u002Ftable-icon.gif)](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002FY2026\u002FV8\u002FI3\u002F147#inline_content14986)\n\n**表7**\n\n数字乡村试点影响农村居民收入的中介机制检验结果\n\n| 变量 | 劳动力配置 | 创业活跃度 |\n| :---: | :---: | :---: |\n| 数字试点政策 | 0.033** | 7.848* |\n|  | （0.014） | （4.664） |\n| 控制变量 | 已控制 | 已控制 |\n| 时间固定效应 | 已控制 | 已控制 |\n| 县区固定效应 | 已控制 | 已控制 |\n| 样本量 | 2 420 | 6 910 |\n| _R_ 2 | 0.220 | 0.216 |\n\n 表7 \n\n[![Image 20](https:\u002F\u002Fwww.smartag.net.cn\u002Fimages\u002Ftable-icon.gif)](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002FY2026\u002FV8\u002FI3\u002F147#inline_content14987)\n\n**表8**\n\n数字乡村试点影响农村居民收入的调节效应检验结果\n\n| 变量 | 农村居民收入 |\n| :---: | :---: |\n| 数字乡村试点政策 | -0.275*** | （0.042） | -1.088*** | （0.225） | -0.062** | （0.026） |\n| GDP | -0.062*** | （0.017） |  |  |  |  |\n| 数字乡村试点政策*GDP | 0.193*** | （0.027） |  |  |  |  |\n| 北大数字金融普惠指数 |  |  | -0.004*** | （0.000） |  |  |\n| 数字乡村试点政策*北大数字金融普惠指数 |  |  | 0.010*** | （0.002） |  |  |\n| 工业化水平 |  |  |  |  | 0.075 | （0.074） |\n| 数字乡村试点政策*工业化水平 |  |  |  |  | 0.280*** | （0.069） |\n| 控制变量 | 已控制 |  | 已控制 |  | 已控制 |  |\n| 时间固定效应 | 已控制 |  | 已控制 |  | 已控制 |  |\n| 县区固定效应 | 已控制 |  | 已控制 |  | 已控制 |  |\n| 样本量 | 7 270 |  | 6 967 |  | 7 270 |  |\n| _R_ 2 | 0.922 |  | 0.927 |  | 0.921 |  |\n\n 表8 \n\n[![Image 21](https:\u002F\u002Fwww.smartag.net.cn\u002Fimages\u002Ftable-icon.gif)](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002FY2026\u002FV8\u002FI3\u002F147#inline_content14988)\n\n**表9**\n\n进一步分析：对城乡包容性增长的影响\n\n| 变量 | 城乡收入差距 |\n| :---: | :---: |\n| 数字试点政策 | 0.041 |\n|  | （0.033） |\n| 控制变量 | 已控制 |\n| 时间固定效应 | 已控制 |\n| 县区固定效应 | 已控制 |\n| 样本量 | 7 270 |\n| _R_ 2 | 0.417 |\n\n 表9 \n\n[](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002FY2026\u002FV8\u002FI3\u002F147)\n#### [参考文献 31](javascript:;)\n\n[1]穆克瑞. 新发展阶段城乡融合发展的主要障碍及突破方向[J]. 行政管理改革, 2021(1): 79-85.\nMU K R. 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Journal of Business Venturing Insights, ","智慧农业(中英文) 2026, 8(3): 147-158",{"impact":107,"substance":16,"depth":18,"authority":108,"freshness":109,"relevant":21,"comment":157},"基于国家试点政策与县域面板数据的实证研究，结论可靠，对数字乡村政策评估有重要参考价值，但发表时间较早，时效性不足。",[159,160],{"name":155,"url":152},{"name":161,"url":162},"智慧农业(中英文) 2026年第3期 147-158","https:\u002F\u002Fwww.toutiao.com\u002Farticle\u002F7676495154286576128\u002F",[26,29,164,165,166],"农村居民收入","国家数字乡村试点","智慧农业8",[168,169],"国家数字乡村试点 农村居民收入 智慧农业8 双重差分","国家数字乡村试点 农村居民收入","国家数字乡村试点农村居民收入智慧农业8双重差分-338","2026-08-09T23:54:49.546174Z",{"id":173,"title":174,"url":175,"summary":176,"summary_zh":8,"content":177,"source_name":178,"source_url":8,"published_at":179,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":35,"score_detail":180,"sources":182,"tags":184,"search_phrases":189,"slug":192,"view_count":193,"doi":8,"paper":8,"created_at":194},247,"Driving digital village development for rural modernization: The evidence of China","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS0743016726002428","2026年8月《Journal of Rural Studies》上线的研究基于中国数字乡村发展实践,实证检验数字乡村建设对乡村现代化的驱动效应。研究运用2015-2023年省级面板数据,采用双重差分模型、空间计量模型与中介效应模型考察数字乡村建设对农业生产率、农民收入、乡村治理、城乡融合等多维目标的影响。研究发现,数字乡村建设显著提升农业生产率与农民收入水平,通过促进农村电商发展、推动数字普惠金融与完善乡村治理体系三个机制实现乡村现代化;东部地区效应大于中西部,粮食主产区效应显著;数字乡村建设具有显著空间溢出效应,周边地区发展滞后将削弱本地红利释放。文章为发展中国家通过数字乡村建设推动乡村现代化提供了中国经验与政策启示。","[![Image 1: Elsevier logo](blob:http:\u002F\u002Flocalhost\u002F84fae110a9934890163c7653d951a57a)](https:\u002F\u002Fwww.sciencedirect.com\u002F)\n\n*   Help   \n\n# Are you a robot?\n\nPlease confirm you are a human by completing the captcha challenge below.\n\nVerification successful. 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