[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4162":3,"related-4162":60},{"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":22,"tags":24,"primary_topic":8,"secondary_topics":29,"classification":30,"search_phrases":44,"slug":47,"view_count":48,"doi":8,"paper":49,"created_at":59},4162,"公共数据开放与农业经济韧性","https:\u002F\u002Fgotu.wisvora.com\u002Farticle\u002F1100","王光荣（中国现代教育学报2026, 2(1): 101-106）。构建2012-2023年中国293个地级市面板数据，运用固定效应模型、双重差分模型等检验：1)公共数据开放对农业经济韧性具有显著正向促进作用（经稳健性检验后依然成立）；2)异质性分析表明公共数据开放的韧性提升效应在数字基础设施水平较高、农业发展水平高的地区更为凸显。",null,"中国现代教育学报2026年第1期","2026-10-01T00:00:00Z","论文",10,false,74,{"impact":16,"substance":17,"depth":16,"authority":18,"freshness":19,"relevant":20,"comment":21},18,22,13,3,1,"基于293个地级市面板数据的实证研究，方法规范、结论有新意，但发表已逾一周，时效性偏弱，适合主题聚合而非每日精选。",[23],{"name":9,"url":6},[25,26,27,28],"数字乡村","数字基础设施","农业经济韧性","公共数据开放",[],{"status":31,"locked":13,"version":32,"method":33,"evidence":34},"unclassified","2026-10-22.2","rules",{"candidates":35,"agricultural_evidence":42,"reason":43},[36],{"code":37,"score":38,"supported":13,"title":39,"abstract":40,"tags":41},"I6",2,[],[],[25,26],true,"缺少足够的主题证据",[45,46],"公共数据开放 农业经济韧性","293地级市 面板数据","公共数据开放农业经济韧性-4162",5,{"doi":8,"openalex_id":8,"authors":50,"venue":8,"cited_by_count":51,"oa_url":8,"card":52,"direction":56,"ingested_from":58},[],0,{"tldr":53,"method":54,"finding":55,"direction":56,"opportunity":57},"基于293个地级市面板数据，检验公共数据开放对农业经济韧性的促进作用。","2012-2023年地级市面板数据，固定效应与双重差分模型。","公共数据开放显著提升农业经济韧性，且在数字基础设施和农业发展水平高的地区更明显。","数字乡村与农业信息化","可探究公共数据开放影响农业韧性的微观机制及数字基础设施的门槛效应。","agent","2026-10-04T00:09:10.498452Z",{"total":61,"page":20,"page_size":61,"items":62},6,[63,107,155,226,302,352],{"id":64,"title":65,"url":66,"summary":67,"summary_zh":8,"content":8,"source_name":68,"source_url":8,"published_at":69,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":70,"score_detail":71,"sources":75,"tags":77,"primary_topic":8,"secondary_topics":81,"classification":82,"search_phrases":94,"slug":97,"view_count":51,"doi":98,"paper":99,"created_at":106},5456,"数字赋能农业产业韧性的影响与机制研究——基于浙江省县域面板数据的实证分析 (Impact and Mechanism of Digital Development on Agricultural Resilience: Evidence from County-Level Data in Zhejiang Province)","http:\u002F\u002Fzgnjhxb.niam.com.cn\u002FCN\u002F10.13733\u002Fj.jcam.issn.2095-5553.2026.10.034","基于2014—2022年浙江省县域面板数据，用系统GMM检验数字经济对农业产业韧性的影响。数字经济有效赋能农业产业韧性，农村产业融合、涉农科技创新、农业产业化为重要传导机制。非山区县促进作用明显高于山区县。","中国农机化学报 2026-10-20","2026-10-20T00:00:00Z",79,{"impact":16,"substance":17,"depth":72,"authority":18,"freshness":73,"relevant":20,"comment":74},17,9,"基于浙江县域面板数据的实证研究，方法规范、结论有新意，对数字乡村与农业韧性议题有参考价值，但属省级实证、影响范围有限。",[76],{"name":68,"url":66},[25,27,78,79,80],"县域经济","农村产业融合","涉农科技",[],{"status":31,"locked":13,"version":32,"method":33,"evidence":83},{"candidates":84,"agricultural_evidence":42,"reason":43},[85,90],{"code":86,"score":38,"supported":13,"title":87,"abstract":88,"tags":89},"G4",[],[],[79,78],{"code":37,"score":20,"supported":13,"title":91,"abstract":92,"tags":93},[],[],[25],[95,96],"浙江省 县域面板 农业韧性","数字经济 农业产业韧性","浙江省县域面板农业韧性-5456","10.13733\u002Fj.jcam.issn.2095-5553.2026.10.034",{"doi":98,"openalex_id":8,"authors":100,"venue":8,"cited_by_count":51,"oa_url":8,"card":101,"direction":56,"ingested_from":58},[],{"tldr":102,"method":103,"finding":104,"direction":56,"opportunity":105},"基于浙江县域面板数据，实证检验数字经济对农业产业韧性的赋能作用与机制。","2014—2022年浙江省县域面板数据，系统GMM估计。","数字经济显著提升农业产业韧性，农村产业融合、涉农科技创新、农业产业化是传导机制，非山区县效应更强。","可探究山区县数字赋能效应偏弱的成因与差异化路径，或拓展至跨省县域比较与韧性多维测度。","2026-10-21T00:10:07.870102Z",{"id":108,"title":109,"url":110,"summary":111,"summary_zh":8,"content":8,"source_name":112,"source_url":8,"published_at":8,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":14,"score_detail":113,"sources":116,"tags":118,"primary_topic":122,"secondary_topics":128,"classification":129,"search_phrases":143,"slug":146,"view_count":38,"doi":8,"paper":147,"created_at":154},4962,"公共数据开放、劳动力流转与农户减贫增收","http:\u002F\u002Fwww.qikanvip.com\u002Fqkml\u002F180148.html","莫馥宁(西南财经大学中国西部经济研究院;广西农商联合银行博士后科研工作站)在《财经科学》2026年第3期发表的研究以地方政府上线公共数据开放平台为准自然实验,考察其对农户减贫增收的影响。研究发现,公共数据开放显著提升农户收入,降低贫困发生率和贫困程度;机制分析表明,劳动力流转是核心路径,通过破解信息不对称、降低生产与就业风险、消除返乡顾虑,引导劳动力回流本地,实现经营性与工资性收入双增。异质性显示,数字素养高、创业意愿强、储蓄水平高的农户更明显受益。","《财经科学》2026年第3期",{"impact":16,"substance":17,"depth":16,"authority":114,"freshness":38,"relevant":20,"comment":115},14,"核心期刊实证研究，机制与异质性分析扎实，对数字乡村与数据要素惠农政策有参考价值，但发表时间较早、时效性偏弱。",[117],{"name":112,"url":110},[25,119,120,28,121],"数字素养","农户增收","劳动力流转",{"code":123,"slug":124,"name":125,"path":126,"parent_code":127},"G6","rural-integration","帮扶与城乡融合","\u002Ftopic\u002Frural-services\u002Frural-integration","G",[],{"status":130,"locked":13,"version":32,"method":33,"evidence":131},"classified",{"candidates":132,"agricultural_evidence":42},[133,139],{"code":123,"score":134,"supported":42,"title":135,"abstract":136,"tags":138},4,[],[137],"农户收入",[120],{"code":37,"score":38,"supported":13,"title":140,"abstract":141,"tags":142},[],[],[25,119],[144,145],"公共数据开放 农户减贫增收","西南财经大学 劳动力流转","公共数据开放农户减贫增收-4962",{"doi":8,"openalex_id":8,"authors":148,"venue":8,"cited_by_count":51,"oa_url":8,"card":149,"direction":56,"ingested_from":58},[],{"tldr":150,"method":151,"finding":152,"direction":56,"opportunity":153},"以公共数据开放平台上线为准自然实验，检验其对农户减贫增收的影响。","准自然实验，地方政府公共数据开放平台上线，农户收入与贫困数据。","公共数据开放显著提升农户收入、降低贫困，劳动力流转是核心机制。","可探究公共数据开放与农业物联网数据协同对农户增收的异质性作用。","2026-10-15T00:06:08.286860Z",{"id":156,"title":157,"url":158,"summary":159,"summary_zh":8,"content":8,"source_name":160,"source_url":8,"published_at":10,"category":11,"cover_url":8,"hotness":12,"is_selected":42,"score":161,"score_detail":162,"sources":166,"tags":168,"primary_topic":172,"secondary_topics":178,"classification":190,"search_phrases":214,"slug":217,"view_count":134,"doi":8,"paper":218,"created_at":225},4194,"数字乡村建设的代价？数字乡村战略对粮食安全和农业碳循环的非对称影响——来自中国的证据","https:\u002F\u002Fideas.repec.org\u002Fa\u002Fspr\u002Fagfoec\u002Fv14y2026i1d10.1186_s40100-026-00503-9.html","Weilong Gao、Qian Zhang、Ying Peng（吉林大学国家发展与安全研究院、大连理工大学商学院、长春工业大学经济管理学院）。发表于Agricultural and Food Economics 2026, 14(1): 1-22。基于989个县和1835户农户的多层次数据，将县级数字乡村发展指数与家庭层面数字采纳指数链接，运用固定效应和两阶段最小二乘法模型。结果显示：农村数字化与粮食产量显著正相关，与农业碳排放显著负相关，但对作物生物量碳吸收或净碳足迹无显著改善。机制分析表明生产收益与土地利用集约化（特别是双季种植扩张）相关，减排与减少劳动力和农药使用一致。异质性分析显示数字基础设施驱动生产率增长，数字治理和数字生活与减排关联更密切。","Agricultural and Food Economics 2026, 14(1)",88,{"impact":17,"substance":163,"depth":164,"authority":114,"freshness":73,"relevant":20,"comment":165},24,19,"基于989县1835户的全国性实证研究，揭示数字乡村对粮食安全与农业碳循环的非对称影响，数据规模大、结论有政策参考价值，值得进入每日精选。",[167],{"name":160,"url":158},[25,26,169,170,171],"粮食安全","农业碳排放","农户数字化",{"code":173,"slug":174,"name":175,"path":176,"parent_code":177},"A8","yield-food-security","单产提升与粮食安全","\u002Ftopic\u002Ffield-crops\u002Fyield-food-security","A",[179,185],{"code":180,"slug":181,"name":182,"path":183,"parent_code":184},"H4","carbon","农业碳与绿色低碳","\u002Ftopic\u002Fenvironment\u002Fcarbon","H",{"code":37,"slug":186,"name":187,"path":188,"parent_code":189},"digital-village","数字乡村与数据要素","\u002Ftopic\u002Fdigital-agriculture\u002Fdigital-village","I",{"status":130,"locked":13,"version":32,"method":33,"evidence":191},{"candidates":192,"agricultural_evidence":42},[193,200,204,208],{"code":180,"score":16,"supported":42,"title":194,"abstract":196,"tags":199},[195],"农业碳",[197,195,198],"碳排放","碳足迹",[170],{"code":37,"score":18,"supported":42,"title":201,"abstract":202,"tags":203},[25],[25],[25,26],{"code":173,"score":73,"supported":42,"title":205,"abstract":206,"tags":207},[169],[],[169],{"code":209,"score":19,"supported":42,"title":210,"abstract":211,"tags":213},"F4",[],[212],"农药",[],[215,216],"数字乡村 粮食安全 农业碳循环","吉林大学 数字乡村 农户","数字乡村粮食安全农业碳循环-4194",{"doi":8,"openalex_id":8,"authors":219,"venue":8,"cited_by_count":51,"oa_url":8,"card":220,"direction":56,"ingested_from":58},[],{"tldr":221,"method":222,"finding":223,"direction":56,"opportunity":224},"基于中国县级与农户数据，评估数字乡村战略对粮食安全和农业碳循环的非对称影响。","989县与1835户多层次数据，固定效应和两阶段最小二乘法。","数字化提升粮食产量并减少碳排放，但未改善作物碳吸收或净碳足迹。","可探究数字乡村对碳吸收无显著影响的原因，并优化数字治理以协同减排与固碳。","2026-10-05T00:04:55.880239Z",{"id":227,"title":228,"url":229,"summary":230,"summary_zh":231,"content":8,"source_name":232,"source_url":229,"published_at":233,"category":11,"cover_url":8,"hotness":12,"is_selected":42,"score":234,"score_detail":235,"sources":239,"tags":241,"primary_topic":8,"secondary_topics":245,"classification":246,"search_phrases":280,"slug":283,"view_count":252,"doi":284,"paper":285,"created_at":301},3535,"Policy coordination of public data openness and supercomputing center for agricultural chain resilience","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1882123","Strengthening agricultural supply chain resilience is critical for safeguarding food security and promoting high-quality agricultural development in the face of escalating external shocks, including climate extremes, market volatility, and geopolitical uncertainties. Although public data openness and supercomputing infrastructure represent critical pillars of digital transformation, how their coordinated deployment enhances agricultural resilience remains both theoretically underdeveloped and empirically underexamined. Using provincial panel data from 31 Chinese provinces over 2011–2024, we exploit the staggered rollout of public data platforms and supercomputing centers as quasi-natural experiments, constructing a synergistic difference-in-differences (DID) model to isolate their coordinated policy effects. The causal identification is further fortified using event-study parallel trend tests, placebo simulations, propensity score matching (PSM-DID), and double\u002Fdebiased machine learning (DDML). Our baseline estimates demonstrate that policy synergy between public data openness and supercomputing infrastructure substantially enhances agricultural supply chain resilience, with effects remaining robust across multiple specifications and economically significant. Mechanistic analysis reveals two primary transmission channels through which policy synergy strengthens resilience: (i) industrial structure rationalization, and (ii) agricultural electrification enhancement. Specifically, policy coordination (1) reduces informational barriers between agricultural and related industries, (2) facilitates optimal factor reallocation across sectors, and (3) accelerates the adoption of electricity-intensive precision agriculture technologies. Heterogeneity analysis reveals that policy effectiveness is substantially greater in central and western regions (coefficient ~0.089) compared to eastern regions (coefficient ~0.036), suggesting a ‘technology compensation effect’ whereby digital infrastructure offsets traditional regional disparities. Policy effects remain robustly positive across both major grain-producing and grain-consuming zones, indicating broad applicability. By unpacking the complementary and sequential dynamics between data factors and computing capacity, this study moves beyond the traditional “black box” treatment of the digital economy, providing critical policy insights for the coordinated deployment of digital public goods to secure modern food systems.","在气候极端事件、市场波动和地缘政治不确定性等外部冲击日益加剧的背景下，增强农业供应链韧性对于保障粮食安全和推动农业高质量发展至关重要。尽管公共数据开放和超算基础设施是数字化转型的关键支柱，但二者协同部署如何增强农业韧性，在理论上尚不成熟，在实证上也缺乏充分检验。本文利用2011—2024年中国31个省份的面板数据，将公共数据平台和超算中心的交错 rollout 作为准自然实验，构建协同双重差分（DID）模型以识别其协调政策效应。因果识别进一步通过事件研究平行趋势检验、安慰剂模拟、倾向得分匹配（PSM-DID）以及双重\u002F去偏机器学习（DDML）加以强化。基准估计表明，公共数据开放与超算基础设施之间的政策协同显著增强了农业供应链韧性，该效应在多种设定下保持稳健且具有经济显著性。机制分析揭示了政策协同增强韧性的两条主要传导渠道：（i）产业结构合理化，以及（ii）农业电气化水平提升。具体而言，政策协调（1）降低了农业与相关产业之间的信息壁垒，（2）促进了要素在部门间的优化再配置，（3）加速了电力密集型精准农业技术的采用。异质性分析表明，政策效果在中西部地区（系数约0.089）显著大于东部地区（系数约0.036），暗示存在“技术补偿效应”，即数字基础设施抵消了传统的区域差距。政策效应在粮食主产区和主销区均保持稳健为正，表明其具有广泛适用性。通过揭示数据要素与算力之间的互补性和序贯动态，本研究超越了数字经济传统的“黑箱”处理方式，为协调部署数字公共产品以保障现代粮食体系提供了关键政策启示。","Frontiers in Sustainable Food 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