[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3447":3,"related-3447":45},{"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,"search_phrases":30,"slug":33,"view_count":34,"doi":8,"paper":35,"created_at":44},3447,"《农业数字化的碳减排效应：理论分析与经验证据》——基于2005—2022年省级面板数据","http:\u002F\u002Fwww.qikanzj.com\u002Fhek\u002Fhznydxxbshkxb\u002Fmulu\u002F559174.html","在测度各省农业数字化转型水平的基础上，使用扩展的STIRPAT模型和2005—2022年省级面板数据对中国农业数字化的碳减排效应及其作用机制进行检验。结果表明：数字化显著促进了农业碳减排，绿色技术创新尤其是实质性绿色技术创新是其中重要的作用机制；异质性分析发现在东部地区、人力资本水平高和数字基础设施发达的省份效果更加显著；面板门槛回归结果表明数字化对农业碳排放的影响存在基于自身发展水平的双重门槛效应。",null,"《华中农业大学学报（社会科学版）》","2026-09-20T00:00:00Z","论文",10,false,78,{"impact":16,"substance":17,"depth":16,"authority":18,"freshness":19,"relevant":20,"comment":21},18,22,14,6,1,"基于2005—2022年省级面板数据的实证研究，方法规范、结论有政策参考价值，但属学术论文且时效性一般，适合主题聚合而非每日精选头条。",[23],{"name":9,"url":6},[25,26,27,28,29],"数字乡村","农业碳减排","农业数字化","省级面板数据","绿色技术创新",[31,32],"农业数字化 碳减排 省级面板","绿色技术创新 农业碳排放","农业数字化碳减排省级面板-3447",0,{"doi":8,"openalex_id":8,"authors":36,"venue":8,"cited_by_count":34,"oa_url":8,"card":37,"direction":41,"ingested_from":43},[],{"tldr":38,"method":39,"finding":40,"direction":41,"opportunity":42},"基于2005—2022年省级面板数据，检验农业数字化的碳减排效应及其机制。","扩展STIRPAT模型、面板门槛回归与省级面板数据。","数字化显著促进农业碳减排，绿色技术创新是重要机制，且存在双重门槛效应。","农业绿色发展与碳","可探究数字化碳减排的非线性门槛与区域异质性，识别最优数字化水平区间。","agent","2026-09-25T00:09:34.643179Z",{"total":19,"page":20,"page_size":19,"items":46},[47,74,100,128,168,199],{"id":48,"title":49,"url":50,"summary":51,"summary_zh":8,"content":8,"source_name":52,"source_url":8,"published_at":10,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":53,"score_detail":54,"sources":57,"tags":59,"search_phrases":62,"slug":65,"view_count":34,"doi":8,"paper":66,"created_at":73},3443,"《数字赋能农业碳减排的组态效应分析》——基于河南省各省辖市2011—2022年数据","http:\u002F\u002Fwww.qikanvip.com\u002Fqkml\u002F174647.html","孟凡琳、赵冰冰、李炳军利用碳排放因子法测度2011—2022年河南省各省辖市农业碳排放量，运用模糊集定性比较分析法（fsQCA）分析数字赋能河南农业碳减排的组态效应。结果：河南省农业碳排放强度在2011—2022年总体呈下降趋势，化肥施用和翻耕是主要来源；农业碳排放强度降低是数字基础设施、技术创新、农业数字化和数字金融多因素相互耦合结果；2011—2015年完善的数字基础设施是核心驱动因素，2016—2022年农业数字化的碳减排作用更为显著。","《河南农业大学学报》2026年第2期",76,{"impact":55,"substance":17,"depth":16,"authority":18,"freshness":19,"relevant":20,"comment":56},16,"基于河南18个省辖市11年面板数据的fsQCA组态研究，方法规范、结论有新意，对数字乡村与农业低碳政策有参考价值，但属省级区域实证，影响力有限。",[58],{"name":52,"url":50},[25,26,27,60,61],"河南农业","fsQCA",[63,64],"河南 农业碳排放 数字赋能","河南农业大学学报 农业碳减排","河南农业碳排放数字赋能-3443",{"doi":8,"openalex_id":8,"authors":67,"venue":8,"cited_by_count":34,"oa_url":8,"card":68,"direction":41,"ingested_from":43},[],{"tldr":69,"method":70,"finding":71,"direction":41,"opportunity":72},"基于河南2011-2022年数据，用fsQCA分析数字赋能农业碳减排的组态效应。","碳排放因子法测度碳排放，模糊集定性比较分析（fsQCA）。","碳强度总体下降，化肥和翻耕为主源；减排是多因素耦合，核心驱动从数字基建转向农业数字化。","可拓展至多省比较或结合面板数据，探究数字技术减排组态的时空异质性与因果机制。","2026-09-25T00:09:34.373665Z",{"id":75,"title":76,"url":77,"summary":78,"summary_zh":8,"content":8,"source_name":79,"source_url":8,"published_at":10,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":14,"score_detail":80,"sources":82,"tags":84,"search_phrases":88,"slug":91,"view_count":34,"doi":8,"paper":92,"created_at":99},3444,"《数字经济和财政支农对农业碳排放双控的影响》——基于2013—2021年30省级面板数据","http:\u002F\u002Fwww.qikanvip.com\u002Fqkml\u002F174665.html","基于2013—2021年中国30个省级面板数据（西藏和港、澳、台除外），采用熵值法对农业碳排放双控水平进行评估，随后通过面板Tobit模型探究数字经济对农业碳排放双控的影响，最后分析财政支农在该影响中的调节效应。结果：中国农业碳排放双控水平呈波浪式上升趋势，粮食主销区水平相对较高、主产区相对较低；数字经济对农业碳排放双控具有显著正向影响，财政支农发挥正向调节作用；在粮食主销区促进作用更明显。","《河南农业大学学报》2026年第3期",{"impact":16,"substance":17,"depth":16,"authority":18,"freshness":19,"relevant":20,"comment":81},"基于30省面板数据的实证研究，方法规范、结论明确，对数字乡村与农业绿色低碳政策有参考价值，但属学术论文、时效性一般，适合主题聚合而非每日精选头条。",[83],{"name":79,"url":77},[25,85,86,87,28],"财政支农","粮食主产区","农业碳排放",[89,90],"数字经济 农业碳排放 财政支农","河南农业大学学报 农业碳排放双控","数字经济农业碳排放财政支农-3444",{"doi":8,"openalex_id":8,"authors":93,"venue":8,"cited_by_count":34,"oa_url":8,"card":94,"direction":41,"ingested_from":43},[],{"tldr":95,"method":96,"finding":97,"direction":41,"opportunity":98},"基于30省面板数据，研究数字经济与财政支农对农业碳排放双控的影响及调节效应。","熵值法评估碳排放双控水平，面板Tobit模型分析，2013—2021年省级数据。","数字经济显著促进农业碳排放双控，财政支农正向调节，粮食主销区效应更明显。","可探究数字经济影响农业碳排放的空间溢出效应及财政支农的门槛非线性特征。","2026-09-25T00:09:34.446164Z",{"id":101,"title":102,"url":103,"summary":104,"summary_zh":8,"content":8,"source_name":52,"source_url":8,"published_at":10,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":105,"score_detail":106,"sources":109,"tags":111,"search_phrases":115,"slug":118,"view_count":34,"doi":8,"paper":119,"created_at":127},3436,"《数字经济赋能农业高质量发展的实证分析》——基于2012—2022年省级面板数据","http:\u002F\u002Fwww.qikanvip.com\u002Fqkml\u002F174646.html","耿俊婷、徐梓钦、张朝辉基于2012—2022年省级面板数据，运用熵值法测算数字经济、农业新质生产力并用SBM-GML模型测算农业高质量发展，使用双向固定效应模型、中介效应模型等方法探究数字经济对农业高质量发展的影响及农业新质生产力的作用机制。研究发现：数字经济在推动农业实现高质量发展方面具有显著的正向效应且存在显著的时间异质性；农业新质生产力是数字经济赋能农业迈向高质量发展阶段的重要机制；在粮食主销区和产销平衡区存在显著正向影响。",81,{"impact":16,"substance":17,"depth":16,"authority":18,"freshness":107,"relevant":20,"comment":108},9,"基于2012—2022年省级面板数据的实证研究，方法规范、结论有新意，对数字乡村与农业高质量发展政策制定有参考价值。",[110],{"name":52,"url":103},[25,112,113,114,28],"农业新质生产力","数字经济","农业高质量发展",[116,117],"数字经济 农业高质量发展","农业新质生产力 中介效应","数字经济农业高质量发展-3436",{"doi":8,"openalex_id":8,"authors":120,"venue":8,"cited_by_count":34,"oa_url":8,"card":121,"direction":125,"ingested_from":43},[],{"tldr":122,"method":123,"finding":124,"direction":125,"opportunity":126},"基于2012—2022年省级面板数据，实证检验数字经济对农业高质量发展的影响及作用机制。","熵值法测度数字经济与农业新质生产力，SBM-GML测度农业高质量发展，双向固定效","数字经济显著正向促进农业高质量发展且具时间异质性，农业新质生产力为重要中介机制，效应在粮食主销区和产","数字乡村与农业信息化","可细化数字经济子维度（如数字基础设施、电商）对农业高质量发展的差异化路径，并补充微观农户或县域层面证据。","2026-09-25T00:09:33.774119Z",{"id":129,"title":130,"url":131,"summary":132,"summary_zh":133,"content":8,"source_name":134,"source_url":131,"published_at":135,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":105,"score_detail":136,"sources":139,"tags":141,"search_phrases":145,"slug":148,"view_count":34,"doi":149,"paper":150,"created_at":167},2627,"How does agricultural digitalization drive green total factor productivity? Evidence from rural industrial integration, data factor allocation, and spatial effects in China","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1852421","Introduction Digital empowerment accelerates cross-sectoral integration within the rural economy, driving profound structural shifts in agricultural development. Methods Using a provincial-level panel dataset from China spanning 2012 to 2022, this study constructs comprehensive indices to assess agricultural digitalization, rural industrial integration, and data factor allocation. By employing mediation, moderation, and spatial Durbin models, this research investigates the underlying mechanisms through which agricultural digitalization affects agricultural green total factor productivity. Results The empirical results demonstrate that agricultural digitalization significantly enhances AGTFP, a finding that is robust to a battery of specification checks. Mechanism analysis reveals that rural industrial integration serves as a crucial partial mediator in this relationship. Furthermore, optimizing data factor allocation positively moderates and thereby amplifies the impact of agricultural digitalization on AGTFP. Spatial analysis indicates that agricultural digitalization generates positive spatial spillover effects, boosting AGTFP in both local and neighboring regions. Finally, heterogeneity analyses reveal that this positive effect is particularly pronounced in the Eastern region and in areas with high levels of urban-rural integration, and it remains robust across both major and non-major grain-producing areas. Discussion Ultimately, this study deepens the understanding of the digitalization-sustainability nexus in agriculture, underscoring the vital role of advancing rural industrial integration and optimizing data factor allocation in driving green productivity.","引言 数字赋能加速了农村经济内部的跨部门融合，推动农业发展发生深刻的结构性变革。方法 本研究利用中国2012年至2022年的省级面板数据集，构建了农业数字化、农村产业融合和数据要素配置的综合指数。通过采用中介模型、调节模型和空间杜宾模型，本研究探讨了农业数字化影响农业绿色全要素生产率的潜在机制。结果 实证结果表明，农业数字化显著提升了农业绿色全要素生产率（AGTFP），这一发现在一系列设定检验中保持稳健。机制分析揭示，农村产业融合在这一关系中起到了关键的部分中介作用。此外，优化数据要素配置对这一关系产生了正向调节作用，从而放大了农业数字化对AGTFP的影响。空间分析表明，农业数字化产生了正向空间溢出效应，提升了本地及邻近地区的AGTFP。最后，异质性分析显示，这一正向效应在东部地区和城乡融合水平较高的地区尤为显著，且在粮食主产区和非主产区均保持稳健。讨论 最终，本研究深化了对农业数字化与可持续性关系的理解，凸显了推进农村产业融合和优化数据要素配置在推动绿色生产力方面的重要作用。","Frontiers in Sustainable Food Systems","2026-09-16T00:00:00Z",{"impact":16,"substance":17,"depth":16,"authority":137,"freshness":12,"relevant":20,"comment":138},13,"基于2012—2022年省级面板数据的实证研究，揭示农业数字化通过农村产业融合与数据要素配置提升农业绿色全要素生产率并具空间溢出效应，方法规范、结论有政策参考价值，值得进入每日精选。",[140],{"name":134,"url":131},[25,142,27,143,144],"数据要素","绿色全要素生产率","农村产业融合",[146,147],"绿色全要素生产率 农村产业融合 农业数字化 数字乡村","绿色全要素生产率 农村产业融合","绿色全要素生产率农村产业融合农业数字化数字乡村-2627","10.3389\u002Ffsufs.2026.1852421",{"doi":149,"openalex_id":151,"authors":152,"venue":134,"cited_by_count":34,"oa_url":131,"card":161,"direction":41,"ingested_from":166},"W7213309985",[153,155,158],{"name":154,"orcid":8},"Yijia Zhou",{"name":156,"orcid":157},"Jun He","https:\u002F\u002Forcid.org\u002F0000-0003-4839-3950",{"name":159,"orcid":160},"Jun Chen","https:\u002F\u002Forcid.org\u002F0000-0001-7397-2714",{"tldr":162,"method":163,"finding":164,"direction":41,"opportunity":165},"基于中国省级面板数据，揭示农业数字化通过产业融合与数据要素配置提升农业绿色全要素生产率。","2012-2022年省级面板数据，构建综合指数，用中介、调节与空间杜宾模型。","农业数字化显著提升AGTFP，农村产业融合起部分中介作用，数据要素配置正向调节，且具正向空间溢出。","可深入微观地块或县域尺度，探究数据要素配置的阈值效应及跨区域溢出衰减机制。","openalex","2026-09-16T23:30:07.645013Z",{"id":169,"title":170,"url":171,"summary":172,"summary_zh":8,"content":8,"source_name":173,"source_url":8,"published_at":174,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":175,"score_detail":176,"sources":181,"tags":183,"search_phrases":187,"slug":190,"view_count":34,"doi":8,"paper":191,"created_at":198},2402,"数字乡村建设对农民收入水平的影响研究——基于2015—2023年省级面板数据","https:\u002F\u002Fxb.ynau.edu.cn\u002Fjwk_sk\u002Farticle\u002Flatest_all","云南农业大学学报（社会科学）2026, 20(0):1-7。张庆红、贺纯、刘振峰基于2015-2023年省级面板数据，构建评价指标体系测度和分析各省份数字乡村建设水平，借助固定效应、中介效应与门槛效应等模型实证检验。结果表明：数字乡村建设不仅直接促进农民增收，还通过劳动力转移产生积极作用；在东部、中部、东北部地区表现突出，在高经济水平与人力资本高潜力地区更为明显。","云南农业大学学报 | 2026-09-08","2026-09-08T00:00:00Z",75,{"impact":16,"substance":177,"depth":178,"authority":18,"freshness":179,"relevant":20,"comment":180},21,17,5,"基于2015—2023年省级面板数据的实证研究，方法规范、结论明确，对数字乡村建设与农民增收的政策制定有参考价值，但属学术论文且时效性一般，适合进入主题聚合而非每日精选头条。",[182],{"name":173,"url":171},[25,184,185,28,186],"农民增收","区域差异","农村劳动力转移",[188,189],"农村劳动力转移 省级面板数据 农民增收 区域差异","农村劳动力转移 省级面板数据","农村劳动力转移省级面板数据农民增收区域差异-2402",{"doi":8,"openalex_id":8,"authors":192,"venue":8,"cited_by_count":34,"oa_url":8,"card":193,"direction":125,"ingested_from":43},[],{"tldr":194,"method":195,"finding":196,"direction":125,"opportunity":197},"基于2015—2023年省级面板数据，实证检验数字乡村建设对农民收入的影响及机制。","构建数字乡村评价指标体系，用固定效应、中介效应与门槛效应模型分析省级面板数据。","数字乡村建设直接促进农民增收，并通过劳动力转移间接起作用，区域与禀赋异质性明显。","可深入微观农户数据，探究数字乡村增收效应的空间溢出与数字鸿沟调节机制。","2026-09-14T00:06:33.480192Z",{"id":200,"title":201,"url":202,"summary":203,"summary_zh":8,"content":8,"source_name":204,"source_url":8,"published_at":205,"category":206,"cover_url":8,"hotness":12,"is_selected":13,"score":207,"score_detail":208,"sources":212,"tags":214,"search_phrases":218,"slug":221,"view_count":34,"doi":8,"paper":8,"created_at":222},2227,"广东发布《关于推进\"四好\"农产品市场体系建设行动方案》","https:\u002F\u002Fwww.crnews.net\u002F2026\u002F09\u002F0910687.html","广东省政府新闻办举行专题新闻发布会,介绍推进\"四好\"农产品市场体系建设行动方案(2026-2030年),提出到2030年建成国家级农产品产地市场5个、省级20个、市级50个,实现产地市场交易额突破3000亿元,带动全省农产品流通数字化率超过75%。","广东省政府新闻办","2026-09-07T16:00:00Z","政策",79,{"impact":17,"substance":209,"depth":178,"authority":210,"freshness":19,"relevant":20,"comment":211},23,11,"省级五年行动方案，含产地市场数量与交易额、数字化率等硬指标，政策条款与数据增量扎实，值得进入每日精选。",[213],{"name":204,"url":202},[25,27,215,216,217],"农产品流通","产地市场","市场体系",[219,220],"农业数字化 农产品流通 产地市场 市场体系","农业数字化 农产品流通","农业数字化农产品流通产地市场市场体系-2227","2026-09-13T00:03:59.351554Z"]