[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3444":3,"related-3444":87},{"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":30,"secondary_topics":36,"classification":43,"search_phrases":72,"slug":75,"view_count":20,"doi":8,"paper":76,"created_at":86},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。",null,"《河南农业大学学报》2026年第3期","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。",[23],{"name":9,"url":6},[25,26,27,28,29],"数字乡村","财政支农","粮食主产区","农业碳排放","省级面板数据",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},"H4","carbon","农业碳与绿色低碳","\u002Ftopic\u002Fenvironment\u002Fcarbon","H",[37],{"code":38,"slug":39,"name":40,"path":41,"parent_code":42},"G3","rural-finance","农村金融与保险","\u002Ftopic\u002Frural-services\u002Frural-finance","G",{"status":44,"locked":13,"version":45,"method":46,"evidence":47},"classified","2026-10-22.2","rules",{"candidates":48,"agricultural_evidence":51},[49,57,62,67],{"code":31,"score":50,"supported":51,"title":52,"abstract":55,"tags":56},23,true,[53,54],"碳排放","农业碳",[53,54],[28],{"code":38,"score":58,"supported":51,"title":59,"abstract":60,"tags":61},12,[26],[26],[26],{"code":63,"score":20,"supported":13,"title":64,"abstract":65,"tags":66},"A8",[],[],[27],{"code":68,"score":20,"supported":13,"title":69,"abstract":70,"tags":71},"I6",[],[],[25],[73,74],"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":77,"venue":8,"cited_by_count":78,"oa_url":8,"card":79,"direction":83,"ingested_from":85},[],0,{"tldr":80,"method":81,"finding":82,"direction":83,"opportunity":84},"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。","农业绿色发展与碳","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。","agent","2026-09-25T00:09:34.446164Z",{"total":19,"page":20,"page_size":19,"items":88},[89,144,203,282,344,390],{"id":90,"title":91,"url":92,"summary":93,"summary_zh":8,"content":8,"source_name":94,"source_url":8,"published_at":95,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":96,"score_detail":97,"sources":101,"tags":103,"primary_topic":107,"secondary_topics":108,"classification":109,"search_phrases":132,"slug":135,"view_count":20,"doi":8,"paper":136,"created_at":143},5227,"China's farm digitization reduces agricultural carbon emissions but the relationship is nonlinear (U-shaped)","https:\u002F\u002Fcommonplace.workforcefutures.net\u002Fpaper\u002Fopenalex:W7221809067","Gao、Feng、Yuan、Sun 在 OpenAlex 2026-10-16 索引的最新研究,基于中国31个省份2010—2023年平衡面板数据,采用双向固定效应面板与SEM中介模型,发现农业数字技术通过精准管理与间接融资\u002F绿色创新两条路径降低碳排放,但呈U型非线性,转折点为数字化指数0.540,劳动生产率提升可推迟转折点,数字化在区域、规模、排放来源维度呈现异质性。","OpenAlex W7221809067(2026-10-16)","2026-10-16T00:00:00Z",84,{"impact":17,"substance":50,"depth":16,"authority":98,"freshness":99,"relevant":20,"comment":100},13,8,"基于31省2010—2023年面板数据的全国性实证研究，揭示数字化与农业碳排放的U型关系及0.540转折点，结论新颖且具政策参考价值，值得进入每日精选。",[102],{"name":94,"url":92},[25,104,105,28,106],"智慧农业","农业数字化","绿色创新",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":44,"locked":13,"version":45,"method":46,"evidence":110},{"candidates":111,"agricultural_evidence":51},[112,117,123,128],{"code":31,"score":113,"supported":51,"title":114,"abstract":115,"tags":116},4,[],[53],[28],{"code":118,"score":119,"supported":13,"title":120,"abstract":121,"tags":122},"I1",2,[],[],[105,104],{"code":124,"score":20,"supported":13,"title":125,"abstract":126,"tags":127},"I",[],[],[105],{"code":68,"score":20,"supported":13,"title":129,"abstract":130,"tags":131},[],[],[25],[133,134],"农业数字化 碳排放 U型","数字技术 农业碳减排","农业数字化碳排放U型-5227",{"doi":8,"openalex_id":8,"authors":137,"venue":8,"cited_by_count":78,"oa_url":8,"card":138,"direction":83,"ingested_from":85},[],{"tldr":139,"method":140,"finding":141,"direction":83,"opportunity":142},"基于中国31省2010—2023年面板数据，发现农业数字化与碳排放呈U型非线性关系。","双向固定效应面板模型与SEM中介模型，31省平衡面板数据。","数字化通过精准管理和绿色创新降碳，但存在0.540的U型转折点。","可探究转折点后数字化增碳的机制，及劳动生产率等调节因素如何推迟拐点。","2026-10-18T00:05:30.735414Z",{"id":145,"title":146,"url":147,"summary":148,"summary_zh":149,"content":8,"source_name":150,"source_url":147,"published_at":151,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":152,"score_detail":153,"sources":156,"tags":158,"primary_topic":161,"secondary_topics":162,"classification":163,"search_phrases":181,"slug":184,"view_count":113,"doi":185,"paper":186,"created_at":202},4520,"Agricultural Industry Agglomeration and Carbon Emission Intensity: A Systems Perspective on Resilience and Digital Development","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fsystems14101263","Decarbonizing agriculture depends not only on the efficiency of individual production activities but also on coordination among production, processing, and service activities. From a systems perspective, this study treats provincial agriculture as an interconnected industrial system and examines the relationship between agricultural industry agglomeration and agricultural carbon emission intensity. Moving beyond conventional measures of production concentration, agricultural industry agglomeration is measured as coordination between horizontal agglomeration in agricultural production and vertical agglomeration across production, processing, and agricultural services. Based on a balanced panel of 390 observations across 30 Chinese provinces from 2011 to 2023, the study uses province and year fixed effects, mediation and moderation analyses, robustness checks, and an endogeneity check. Results show that agricultural industry agglomeration is significantly associated with lower agricultural carbon emission intensity. The mediation results are consistent with a partial intermediary role for agricultural industry resilience in this relationship. The interaction and simple slope results show that the positive association between agricultural industry agglomeration and agricultural industry resilience is larger at higher levels of digital village development. These findings connect subsystem coordination, adaptive capacity, digital conditions, and carbon performance, thereby extending research on agricultural transformation toward lower carbon intensity from a systems perspective.","农业脱碳不仅取决于单个生产活动的效率，还取决于生产、加工和服务活动之间的协调。本研究从系统视角出发，将省级农业视为一个相互关联的产业系统，考察农业产业集聚与农业碳排放强度之间的关系。本研究超越传统的生产集中度衡量方式，将农业产业集聚界定为农业生产横向集聚与生产、加工及农业服务纵向集聚之间的协调。基于2011年至2023年中国30个省份390个观测值的平衡面板数据，本研究采用省份和年份固定效应、中介与调节分析、稳健性检验以及内生性检验。结果表明，农业产业集聚与较低的农业碳排放强度显著相关。中介结果表明，农业产业韧性在这一关系中发挥部分中介作用。交互效应和简单斜率结果表明，在数字乡村发展水平较高时，农业产业集聚与农业产业韧性之间的正向关联更强。这些发现将子系统协调、适应能力、数字条件与碳绩效联系起来，从而从系统视角拓展了农业向低碳强度转型的研究。","Systems","2026-10-08T00:00:00Z",80,{"impact":16,"substance":17,"depth":16,"authority":98,"freshness":154,"relevant":20,"comment":155},9,"基于30省2011-2023年面板数据，从系统视角揭示农业产业集聚通过韧性降低碳排放强度，并受数字乡村发展调节，方法规范、结论有政策参考价值。",[157],{"name":150,"url":147},[25,104,159,28,160],"农业韧性","农业产业集聚",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":44,"locked":13,"version":45,"method":46,"evidence":164},{"candidates":165,"agricultural_evidence":51},[166,171,177],{"code":31,"score":16,"supported":51,"title":167,"abstract":169,"tags":170},[168],"carbon emission",[53,54,168],[28],{"code":68,"score":172,"supported":51,"title":173,"abstract":174,"tags":176},7,[],[25,175],"digital village",[25],{"code":118,"score":20,"supported":13,"title":178,"abstract":179,"tags":180},[],[],[104],[182,183],"农业产业集聚 碳排放强度","数字乡村 农业韧性","农业产业集聚碳排放强度-4520","10.3390\u002Fsystems14101263",{"doi":185,"openalex_id":187,"authors":188,"venue":150,"cited_by_count":78,"oa_url":147,"card":195,"direction":200,"ingested_from":201},"W7220924322",[189,191,193],{"name":190,"orcid":8},"Jinghui Hu",{"name":192,"orcid":8},"Jun He",{"name":194,"orcid":8},"Junzhong He",{"tldr":196,"method":197,"finding":198,"direction":83,"opportunity":199},"从系统视角研究中国省级农业产业集聚对农业碳排放强度的影响及韧性与数字化的作用。","30省2011-2023年面板数据，固定效应、中介与调节分析。","农业产业集聚显著降低碳排放强度，产业韧性部分中介，数字乡村发展正向调节。","可探究产业集聚不同模式与数字化协同对碳减排的非线性及空间溢出效应。","数字乡村与农业信息化","openalex","2026-10-09T23:30:32.435467Z",{"id":204,"title":205,"url":206,"summary":207,"summary_zh":208,"content":8,"source_name":209,"source_url":206,"published_at":210,"category":11,"cover_url":8,"hotness":211,"is_selected":13,"score":152,"score_detail":212,"sources":214,"tags":219,"primary_topic":222,"secondary_topics":227,"classification":229,"search_phrases":258,"slug":261,"view_count":78,"doi":262,"paper":263,"created_at":281},4416,"Agricultural blockchain readiness environment and carbon emission intensity in maize production: evidence from provincial panel data in major maize-producing areas of China","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1942225","China’s major maize-producing regions face the joint challenge of sustaining food production and reducing agricultural emissions. Existing research on digital agriculture often uses broad digitalization measures and therefore does not distinguish the regional conditions that may enable information management, traceability, and verification applications associated with blockchain. This study constructs an Agricultural Blockchain Readiness Environment Index (ABRE Index, block_index) using entropy weighting across digital infrastructure, the agricultural industrial base, and policy support. The index measures regional readiness and enabling conditions rather than realized blockchain adoption. Using 180 province-year observations for 12 major maize-producing provinces from 2010 to 2024, we estimate two-way fixed-effects models with province-clustered inference and conduct supplementary robustness analyses. The ABRE Index is negatively associated with carbon emissions per unit of maize-sown area, with the baseline coefficient statistically significant at the 10% level under conventional clustered inference and supported by supplementary inference procedures. The coefficient remains negative in all 12 leave-one-out specifications, is statistically significant at the 10% level or better in 10 specifications, and the wild cluster bootstrap test yields p = 0.0415. The index is positively correlated with blockchain-related patent activity ( r = 0.1883, p = 0.0114), although this correlation does not establish that the index measures realized adoption. The mechanism analysis is consistent with an organizational pathway through agricultural socialized services: the ABRE Index is positively associated with service_ratio (0.1099, p \u003C 0.01), and agricultural socialized services are negatively associated with ce_area (−2.2765, p \u003C 0.05). The interaction with the high-development indicator is negative but does not reach the conventional 10% significance threshold (−0.1499, p = 0.103). Overall, the evidence supports an associational relationship between blockchain-readiness conditions and maize-production carbon-emission intensity; it does not establish a strictly causal effect of blockchain adoption.","中国主要玉米产区面临保障粮食生产与减少农业排放的双重挑战。现有数字农业研究多采用宽泛的数字化指标，因而未能区分可能支撑与区块链相关的信息管理、追溯和验证应用的区域条件。本研究基于数字基础设施、农业产业基础和政策支持三个维度，采用熵权法构建了农业区块链就绪环境指数（ABRE Index，block_index）。该指数衡量的是区域就绪程度和使能条件，而非已实现的区块链采用。利用2010年至2024年12个主要玉米生产省份的180个省份—年份观测值，我们估计了双向固定效应模型，采用省份聚类推断，并进行了补充稳健性分析。ABRE指数与单位玉米播种面积碳排放呈负相关，基准系数在常规聚类推断下于10%水平上统计显著，并得到补充推断程序的支持。在全部12个逐一剔除（leave-one-out）设定中，该系数均保持为负，其中10个设定在10%或更优水平上统计显著，野生聚类自举检验（wild cluster bootstrap test）得出p = 0.0415。该指数与区块链相关专利活动呈正相关（r = 0.1883，p = 0.0114），尽管这一相关性并不能证明该指数衡量的是已实现的采用。机制分析与通过农业社会化服务发挥作用的组织路径相一致：ABRE指数与service_ratio呈正相关（0.1099，p \u003C 0.01），农业社会化服务与ce_area呈负相关（−2.2765，p \u003C 0.05）。与高发展指标的交互项为负，但未达到常规10%显著性阈值（−0.1499，p = 0.103）。总体而言，证据支持区块链就绪条件与玉米生产碳排放强度之间存在关联关系；但并未确立区块链采用的严格因果效应。","Frontiers in Sustainable Food Systems","2026-10-07T00:00:00Z",25,{"impact":16,"substance":17,"depth":16,"authority":98,"freshness":154,"relevant":20,"comment":213},"基于12个玉米主产省2010—2024年面板数据构建农业区块链就绪环境指数，发现其与玉米碳排放强度负相关，方法规范、数据规模可观，但结论仅为关联性而非因果，属细分领域实证进展。",[215,216],{"name":209,"url":206},{"name":217,"url":218},"Frontiers in Sustainable Food Systems 2026 年 10 月 07 日 Volume 10 Sec. Land, Livelihoods and Food Security","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1942225\u002Ffull",[25,104,220,28,221],"玉米","区块链",{"code":223,"slug":224,"name":220,"path":225,"parent_code":226},"A3","maize","\u002Ftopic\u002Ffield-crops\u002Fmaize","A",[228],{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},{"status":44,"locked":13,"version":45,"method":46,"evidence":230},{"candidates":231,"agricultural_evidence":51},[232,237,241,247,254],{"code":223,"score":233,"supported":51,"title":234,"abstract":235,"tags":236},15,[224],[220,224],[220],{"code":31,"score":58,"supported":51,"title":238,"abstract":239,"tags":240},[168],[53],[28],{"code":118,"score":172,"supported":51,"title":242,"abstract":243,"tags":246},[],[244,245],"数字农业","digital agriculture",[104],{"code":248,"score":249,"supported":51,"title":250,"abstract":251,"tags":253},"G2",3,[],[252],"社会化服务",[],{"code":68,"score":20,"supported":13,"title":255,"abstract":256,"tags":257},[],[],[25],[259,260],"农业区块链 玉米 碳排放","玉米主产区 区块链 碳强度","农业区块链玉米碳排放-4416","10.3389\u002Ffsufs.2026.1942225",{"doi":262,"openalex_id":264,"authors":265,"venue":209,"cited_by_count":78,"oa_url":274,"card":275,"direction":280,"ingested_from":201},"W7220869193",[266,269,271],{"name":267,"orcid":268},"Pengfei Zhou","https:\u002F\u002Forcid.org\u002F0000-0003-2117-7967",{"name":270,"orcid":8},"Zhiyi Hu",{"name":272,"orcid":273},"Yang Shen","https:\u002F\u002Forcid.org\u002F0000-0002-6781-6915","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1942225\u002Fpdf",{"tldr":276,"method":277,"finding":278,"direction":83,"opportunity":279},"构建农业区块链就绪环境指数，分析其与玉米生产碳排放强度的关联。","熵权法构建ABRE指数，用2010-2024年12省面板数据做双向固定效应模型。","ABRE指数与玉米单位播种面积碳排放负相关，机制可能通过农业社会化服务实现。","可探究区块链就绪环境通过社会化服务减碳的因果机制，并拓展到其他主粮作物或区域。","智慧农业 \u002F 农业物联网","2026-10-08T23:30:07.668939Z",{"id":283,"title":284,"url":285,"summary":286,"summary_zh":8,"content":8,"source_name":287,"source_url":8,"published_at":288,"category":11,"cover_url":8,"hotness":12,"is_selected":51,"score":289,"score_detail":290,"sources":294,"tags":296,"primary_topic":300,"secondary_topics":304,"classification":310,"search_phrases":332,"slug":335,"view_count":113,"doi":8,"paper":336,"created_at":343},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)","2026-10-01T00:00:00Z",88,{"impact":17,"substance":291,"depth":292,"authority":18,"freshness":154,"relevant":20,"comment":293},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。",[295],{"name":287,"url":285},[25,297,298,28,299],"数字基础设施","粮食安全","农户数字化",{"code":63,"slug":301,"name":302,"path":303,"parent_code":226},"yield-food-security","单产提升与粮食安全","\u002Ftopic\u002Ffield-crops\u002Fyield-food-security",[305,306],{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},{"code":68,"slug":307,"name":308,"path":309,"parent_code":124},"digital-village","数字乡村与数据要素","\u002Ftopic\u002Fdigital-agriculture\u002Fdigital-village",{"status":44,"locked":13,"version":45,"method":46,"evidence":311},{"candidates":312,"agricultural_evidence":51},[313,318,322,326],{"code":31,"score":16,"supported":51,"title":314,"abstract":315,"tags":317},[54],[53,54,316],"碳足迹",[28],{"code":68,"score":98,"supported":51,"title":319,"abstract":320,"tags":321},[25],[25],[25,297],{"code":63,"score":154,"supported":51,"title":323,"abstract":324,"tags":325},[298],[],[298],{"code":327,"score":249,"supported":51,"title":328,"abstract":329,"tags":331},"F4",[],[330],"农药",[],[333,334],"数字乡村 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年省级面板数字乡村技术对农业碳排放效率影响(西北农林)","https:\u002F\u002Fagritechinsights.com\u002Findex.php\u002F2026\u002F09\u002F12\u002Fchinas-digital-farming-push-cuts-carbon-emissions","西北农林科技大学 Yuming Li 在《Applied Sciences》发表 13 年省级数据研究,构建首个量化数字乡村技术(DVT)对农业碳排放效率影响的框架,使用超效率 SBM 模型同时量化期望产出(如作物产量)和非期望产出(如碳排放),发现数字融合可通过简化资源使用、启用数据驱动决策来提高农业碳效率。技术创新和社会化农业服务(如合作社平台)充当数字采纳与排放降低之间的桥梁。在地形平坦、农业集约化地区和靠近中大型城市的地区观察到最强效益。","MDPI Applied Sciences 2026-09-12","2026-09-11T16:00:00Z",79,{"impact":16,"substance":17,"depth":16,"authority":98,"freshness":99,"relevant":20,"comment":399},"基于13年省级面板数据构建数字乡村技术影响农业碳排放效率的量化框架，方法新颖、结论有政策参考价值，但属学术论文层面，影响范围限于细分领域。",[401],{"name":395,"url":393},[25,104,28,403,404],"农业社会化服务","农业绿色低碳",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[407],{"code":68,"slug":307,"name":308,"path":309,"parent_code":124},{"status":44,"locked":13,"version":45,"method":46,"evidence":409},{"candidates":410,"agricultural_evidence":51},[411,415,419,423],{"code":31,"score":50,"supported":51,"title":412,"abstract":413,"tags":414},[53,54],[53,54],[28],{"code":68,"score":58,"supported":51,"title":416,"abstract":417,"tags":418},[25],[25],[25],{"code":248,"score":20,"supported":13,"title":420,"abstract":421,"tags":422},[],[],[403],{"code":118,"score":20,"supported":13,"title":424,"abstract":425,"tags":426},[],[],[104],[428,429],"农业社会化服务 农业绿色低碳 农业碳排放 数字乡村","农业社会化服务 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