[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-1002":3,"related-1002":66},{"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":23,"tags":25,"primary_topic":33,"secondary_topics":39,"classification":40,"search_phrases":56,"slug":59,"view_count":60,"doi":8,"paper":61,"created_at":65},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。",null,"## 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","论文",10,false,80,{"impact":17,"substance":17,"depth":18,"authority":19,"freshness":20,"relevant":21,"comment":22},22,20,12,4,1,"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。",[24],{"name":10,"url":6},[26,27,28,29,30,31,32],"数字乡村","AGTFP","双重差分","县级面板","绿色全要素生产率","DID","Front Sustain Food Syst",{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},"I6","digital-village","数字乡村与数据要素","\u002Ftopic\u002Fdigital-agriculture\u002Fdigital-village","I",[],{"status":41,"locked":14,"version":42,"method":43,"evidence":44},"classified","2026-10-22.2","rules",{"candidates":45,"agricultural_evidence":47},[46,51],{"code":34,"score":20,"supported":47,"title":48,"abstract":49,"tags":50},true,[],[26],[26],{"code":52,"score":21,"supported":14,"title":53,"abstract":54,"tags":55},"H4",[],[],[30],[57,58],"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":62,"venue":8,"cited_by_count":63,"oa_url":8,"card":8,"direction":8,"ingested_from":64},[],0,"agent","2026-08-27T00:05:20.326038Z",{"total":67,"page":21,"page_size":67,"items":68},6,[69,129,176,216,272,314],{"id":70,"title":71,"url":72,"summary":73,"summary_zh":8,"content":8,"source_name":74,"source_url":8,"published_at":75,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":76,"score_detail":77,"sources":82,"tags":84,"primary_topic":88,"secondary_topics":89,"classification":90,"search_phrases":115,"slug":118,"view_count":20,"doi":119,"paper":120,"created_at":128},4198,"促进农业技术创新的数字力量——基于数字乡村建设视角","https:\u002F\u002Fnyxdhyj.isa.ac.cn\u002Farticle\u002Fdoi\u002F10.13872\u002Fj.1000-0275.2025.1035","黄建碧、马春艳、李睿琪、熊航（华中农业大学经济管理学院\u002F数字农业研究院）。发表于《农业现代化研究》2026年第47卷第2期325-338页。从数字乡村建设视角系统分析其对农业技术创新的促进作用，基于数字乡村试点数据构建多维评价指标体系，结合双重差分模型与机制检验考察数字乡村建设对农业技术创新的影响路径与作用机理。研究发现数字乡村建设通过促进农业科技创新要素集聚、提升农业科技创新效率、推动农业科技创新成果转化等机制显著促进农业技术创新。得到国家自然科学基金项目(72173050)、教育部哲学社会科学研究重大专项(2024JZDZ060)资助。","农业现代化研究2026, 47(2)","2026-09-01T00:00:00Z",75,{"impact":78,"substance":17,"depth":78,"authority":79,"freshness":80,"relevant":21,"comment":81},18,14,3,"核心期刊论文，方法规范、结论明确，但发表已逾一月，时效性偏弱，适合主题聚合而非每日精选。",[83],{"name":74,"url":72},[26,28,85,86,87],"农业技术创新","华中农业大学","国家自然科学基金",{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},[],{"status":41,"locked":14,"version":42,"method":43,"evidence":91},{"candidates":92,"agricultural_evidence":47},[93,97,103,109],{"code":34,"score":19,"supported":47,"title":94,"abstract":95,"tags":96},[26],[26],[26],{"code":98,"score":80,"supported":47,"title":99,"abstract":100,"tags":102},"G1",[],[101],"成果转化",[],{"code":104,"score":80,"supported":47,"title":105,"abstract":106,"tags":108},"I1",[],[107],"数字农业",[],{"code":110,"score":80,"supported":47,"title":111,"abstract":112,"tags":114},"J2",[],[113],"农业现代化",[],[116,117],"华中农业大学 数字乡村 农业技术创新","数字乡村试点 双重差分 农业技术创新","华中农业大学数字乡村农业技术创新-4198","10.13872\u002Fj.1000-0275.2025.1035",{"doi":119,"openalex_id":8,"authors":121,"venue":8,"cited_by_count":63,"oa_url":8,"card":122,"direction":126,"ingested_from":64},[],{"tldr":123,"method":124,"finding":125,"direction":126,"opportunity":127},"基于数字乡村试点数据，实证检验数字乡村建设对农业技术创新的促进作用及机制。","数字乡村试点数据、多维评价指标体系、双重差分模型与机制检验。","数字乡村建设通过要素集聚、效率提升和成果转化显著促进农业技术创新。","数字乡村与农业信息化","可探究数字乡村建设对不同区域、不同农业技术类型创新影响的异质性及空间溢出效应。","2026-10-05T00:04:56.164123Z",{"id":130,"title":131,"url":132,"summary":133,"summary_zh":8,"content":8,"source_name":134,"source_url":8,"published_at":135,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":136,"score_detail":137,"sources":142,"tags":144,"primary_topic":148,"secondary_topics":149,"classification":150,"search_phrases":164,"slug":167,"view_count":67,"doi":8,"paper":168,"created_at":175},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。","MDPI Sustainability","2026-09-16T00:00:00Z",84,{"impact":17,"substance":138,"depth":78,"authority":139,"freshness":140,"relevant":21,"comment":141},23,13,8,"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。",[143],{"name":134,"url":132},[26,145,146,28,147],"农业大数据","农业政策","农村生态环境",{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},[],{"status":41,"locked":14,"version":42,"method":43,"evidence":151},{"candidates":152,"agricultural_evidence":47},[153,157],{"code":34,"score":13,"supported":47,"title":154,"abstract":155,"tags":156},[145],[],[145,26],{"code":158,"score":67,"supported":47,"title":159,"abstract":160,"tags":163},"F5",[],[161,162],"农业机械","机械化",[],[165,166],"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",{"doi":8,"openalex_id":8,"authors":169,"venue":8,"cited_by_count":63,"oa_url":8,"card":170,"direction":126,"ingested_from":64},[],{"tldr":171,"method":172,"finding":173,"direction":126,"opportunity":174},"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。","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。","2026-09-22T00:05:38.376954Z",{"id":177,"title":178,"url":179,"summary":180,"summary_zh":8,"content":181,"source_name":182,"source_url":8,"published_at":8,"category":12,"cover_url":8,"hotness":183,"is_selected":14,"score":184,"score_detail":185,"sources":188,"tags":193,"primary_topic":197,"secondary_topics":198,"classification":199,"search_phrases":211,"slug":214,"view_count":60,"doi":8,"paper":8,"created_at":215},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*   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•[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. The main barriers and breakthrough direction of urban-rural integration during the new development stage[J]. Administration Reform, 2021(1): 79-85.\n[2]陈高威, 温铁军. 破局乡村振兴——中国式农业农村现代化的11个思考[M]. 重庆: 重庆出版社, 2023.\n[3]LI F L, HE K. Internet use as a catalyst for high-quality nonfarm employment among rural laborers in China[J]. Review of Development Economics, 2025, 29(4): 2258-2275.\n[4]张勋, 万广华, 张佳佳, 等. 数字经济、普惠金融与包容性增长[J]. 经济研究, 2019, 54(8): 71-86.\nZHANG X, WAN G H, ZHANG J J, et al. Digital economy, financial inclusion, and inclusive growth[J]. Economic Research Journal, 2019, 54(8): 71-86.\n[5]DEICHMANN U, GOYAL A, MISHRA D. Will digital technologies transform agriculture in developing countries [J]. Agricultural Economics, 2016, 47(S1): 21-33.\n[6]SALEMINK K, TOWNSEND L, CHAPMAN P. The remapping of rural digitalisation: A just-rural narrative review[J]. Journal of Rural Studies, 2025, 113: 103499.\n[7]崔华滨, 王鹏程. 数字乡村建设推动城乡融合发展的作用机制与实现路径[J]. 南京农业大学学报(社会科学版), 2025, 25(4): 152-163.\nCUI H B, WANG P C. The mechanism and path for promoting urban-rural integration through digital villages development[J]. Journal of Nanjing Agricultural University (Social Sciences Edition), 2025, 25(4): 152-163.\n[8]平卫英, 王瑶华, 张谊瑞. 数字乡村建设、农村居民增收与城乡收入差距——基于国家数字乡村试点的准自然实验[J]. 农业技术经济, 2025(8): 80-104.\nPING W Y, WANG Y H, ZHANG Y R. Digital village construction, rural residents' income increase and rural-urban income gap: Quasi-natural experiment based on the national digital village pilots[J]. Journal of Agrotechnical Economics, 2025(8): 80-104.\n[9]HE H Y, LESZCZYNSKI A. Digital rurality: A three-fold model and research agenda[J]. Journal of Rural Studies, 2026, 122: 103958.\n[10]霍鹏, 肖荣美, 马九杰. 数字乡村建设的底层逻辑、功能价值与路径选择[J]. 改革, 2022(12): 57-65.\nHUO P, XIAO R M, MA J J. The underlying logic, functional value and path choice of digital village construction[J]. Reform, 2022(12): 57-65.\n[11]LYTHREATIS S, SINGH S K, EL-KASSAR A N. The digital divide: A review and future research agenda[J]. Technological Forecasting and Social Change, 2022, 175: 121359.\n[12]罗廷锦, 茶洪旺. \"数字鸿沟\"与反贫困研究——基于全国31个省市面板数据的实证分析[J]. 经济问题探索, 2018(2): 11-18, 74.\n[13]HJORT J, TIAN L. The economic impact of internet connectivity in developing countries[J]. Annual Review of Economics, 2025, 17(1): 99-124.\n[14]QIN Q X, GUO H D, SHI X J, et al. Rural E-commerce and county economic development in China[J]. China & World Economy, 2023, 31(5): 26-60.\n[15]高雅, 邬红梅. 数字乡村建设对县域经济发展影响的实证检验[J]. 统计与决策, 2025, 41(10): 102-107.\nGAO Y, WU H M. An empirical test on the impact of digital village construction on county-level economic development[J]. Statistics & Decision, 2025, 41(10): 102-107.\n[16]SU F, WU B R, ZHANG H M, et al. Examining digital village construction's impact on farmers' common prosperity in China: Dynamic and spatial perspectives[J]. Chinese Geographical Science, 2025, 35(6): 1342-1358.\n[17]BERGHOLZ C, FÜNER L, LUBCZYK M, et al. Infrastructure required, skill needed: Digital entrepreneurship in rural and urban areas[J]. Journal of Business Venturing Insights, ","智慧农业(中英文) 2026, 8(3): 147-158",25,81,{"impact":183,"substance":17,"depth":78,"authority":79,"freshness":186,"relevant":21,"comment":187},2,"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。",[189,190],{"name":182,"url":179},{"name":191,"url":192},"智慧农业(中英文) 2026年第3期 147-158","https:\u002F\u002Fwww.toutiao.com\u002Farticle\u002F7676495154286576128\u002F",[26,28,194,195,196],"农村居民收入","国家数字乡村试点","智慧农业8",{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},[],{"status":41,"locked":14,"version":42,"method":43,"evidence":200},{"candidates":201,"agricultural_evidence":47},[202,206],{"code":34,"score":19,"supported":47,"title":203,"abstract":204,"tags":205},[26],[26],[26],{"code":104,"score":80,"supported":47,"title":207,"abstract":208,"tags":210},[],[209],"智慧农业",[],[212,213],"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":217,"title":218,"url":219,"summary":220,"summary_zh":8,"content":221,"source_name":222,"source_url":8,"published_at":223,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":63,"score_detail":224,"sources":226,"tags":228,"primary_topic":233,"secondary_topics":234,"classification":235,"search_phrases":267,"slug":270,"view_count":60,"doi":8,"paper":8,"created_at":271},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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0===o(e,t)}},q=!!b.postMessage,X={postMessage:function(e,t,a){var n=1;t&&(q?a.postMessage(e,t.replace(\u002F([^:]+:\\\u002F\\\u002F[^\\\u002F]+).*\u002F,\"$1\")):t&&(a.location=t.replace(\u002F#.*$\u002F,\"\")+\"#\"+ +new Date+n+++\"&\"+e))},receiveMessage:function(e,t){var a;try{q&&(e&&(a=function(a){if(\"string\"==typeof t&&a.origin!==t||\"[object Function]\"===Object.prototype.toString.call(t)&&!1===","Journal of Rural Studies 2026-08-04","2026-08-04T00:00:00Z",{"impact":63,"substance":63,"depth":63,"authority":63,"freshness":63,"relevant":21,"comment":225},"正文为验证码页面，无实际内容，无法评估。",[227],{"name":222,"url":219},[26,229,28,230,231,232],"空间溢出","乡村现代化","中国证据","Journal of Rural Studies",{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},[],{"status":41,"locked":14,"version":42,"method":43,"evidence":236},{"candidates":237,"agricultural_evidence":47},[238,243,249,255,261],{"code":34,"score":19,"supported":47,"title":239,"abstract":241,"tags":242},[240],"digital village",[26],[26],{"code":244,"score":20,"supported":47,"title":245,"abstract":246,"tags":248},"I4",[],[247],"空间计量",[229],{"code":250,"score":80,"supported":47,"title":251,"abstract":252,"tags":254},"E5",[],[253],"农村电商",[],{"code":256,"score":80,"supported":47,"title":257,"abstract":258,"tags":260},"G5",[],[259],"乡村治理",[],{"code":262,"score":80,"supported":47,"title":263,"abstract":264,"tags":266},"G6",[],[265],"城乡融合",[],[268,269],"乡村现代化 中国证据 双重差分 数字乡村","乡村现代化 中国证据","乡村现代化中国证据双重差分数字乡村-247","2026-08-05T23:53:02.437856Z",{"id":273,"title":274,"url":275,"summary":276,"summary_zh":8,"content":277,"source_name":278,"source_url":8,"published_at":279,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":280,"score_detail":281,"sources":283,"tags":285,"primary_topic":289,"secondary_topics":290,"classification":291,"search_phrases":308,"slug":311,"view_count":80,"doi":312,"paper":8,"created_at":313},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。","## 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",74,{"impact":18,"substance":17,"depth":78,"authority":19,"freshness":186,"relevant":21,"comment":282},"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。",[284],{"name":278,"url":275},[26,286,287,27,288],"Frontiers","农业绿色全要素生产率","阈值模型",{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},[],{"status":41,"locked":14,"version":42,"method":43,"evidence":292},{"candidates":293,"agricultural_evidence":47},[294,298,302],{"code":34,"score":19,"supported":47,"title":295,"abstract":296,"tags":297},[26],[26],[26],{"code":158,"score":80,"supported":47,"title":299,"abstract":300,"tags":301},[],[162],[],{"code":303,"score":80,"supported":47,"title":304,"abstract":305,"tags":307},"G2",[],[306],"土地流转",[],[309,310],"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",{"id":315,"title":316,"url":317,"summary":318,"summary_zh":319,"content":8,"source_name":320,"source_url":317,"published_at":321,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":15,"score_detail":322,"sources":325,"tags":327,"primary_topic":332,"secondary_topics":333,"classification":334,"search_phrases":358,"slug":361,"view_count":63,"doi":362,"paper":363,"created_at":380},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":78,"substance":17,"depth":78,"authority":139,"freshness":323,"relevant":21,"comment":324},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。",[326],{"name":320,"url":317},[26,328,329,330,331],"数字素养","共同富裕","农民收入","收入不平等",{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},[],{"status":41,"locked":14,"version":42,"method":43,"evidence":335},{"candidates":336,"agricultural_evidence":47},[337,341,347,353],{"code":34,"score":140,"supported":47,"title":338,"abstract":339,"tags":340},[],[26,240],[26,328],{"code":262,"score":67,"supported":47,"title":342,"abstract":343,"tags":346},[],[344,345],"农户收入","rural income",[],{"code":348,"score":80,"supported":47,"title":349,"abstract":350,"tags":352},"G",[],[351],"rural development",[],{"code":354,"score":21,"supported":14,"title":355,"abstract":356,"tags":357},"G4",[],[],[329],[359,360],"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":362,"openalex_id":364,"authors":365,"venue":320,"cited_by_count":63,"oa_url":317,"card":374,"direction":126,"ingested_from":379},"W7223105459",[366,369,372],{"name":367,"orcid":368},"Yong Feng","https:\u002F\u002Forcid.org\u002F0000-0002-8151-8594",{"name":370,"orcid":371},"Shuokai  kai Wang","https:\u002F\u002Forcid.org\u002F0009-0002-7439-1764",{"name":373,"orcid":8},"Fangping Cao",{"tldr":375,"method":376,"finding":377,"direction":126,"opportunity":378},"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。","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"]