[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4520":3,"related-4520":89},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":23,"tags":25,"primary_topic":31,"secondary_topics":37,"classification":38,"search_phrases":64,"slug":67,"view_count":68,"doi":69,"paper":70,"created_at":88},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。",null,"Systems","2026-10-08T00:00:00Z","论文",10,false,80,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},18,22,13,9,1,"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。",[24],{"name":10,"url":6},[26,27,28,29,30],"数字乡村","智慧农业","农业韧性","农业碳排放","农业产业集聚",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"H4","carbon","农业碳与绿色低碳","\u002Ftopic\u002Fenvironment\u002Fcarbon","H",[],{"status":39,"locked":14,"version":40,"method":41,"evidence":42},"classified","2026-10-22.2","rules",{"candidates":43,"agricultural_evidence":45},[44,52,59],{"code":32,"score":17,"supported":45,"title":46,"abstract":48,"tags":51},true,[47],"carbon emission",[49,50,47],"碳排放","农业碳",[29],{"code":53,"score":54,"supported":45,"title":55,"abstract":56,"tags":58},"I6",7,[],[26,57],"digital village",[26],{"code":60,"score":21,"supported":14,"title":61,"abstract":62,"tags":63},"I1",[],[],[27],[65,66],"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",4,"10.3390\u002Fsystems14101263",{"doi":69,"openalex_id":71,"authors":72,"venue":10,"cited_by_count":79,"oa_url":6,"card":80,"direction":86,"ingested_from":87},"W7220924322",[73,75,77],{"name":74,"orcid":9},"Jinghui Hu",{"name":76,"orcid":9},"Jun He",{"name":78,"orcid":9},"Junzhong He",0,{"tldr":81,"method":82,"finding":83,"direction":84,"opportunity":85},"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。","农业绿色发展与碳","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",{"total":90,"page":21,"page_size":90,"items":91},6,[92,145,225,277,338,393],{"id":93,"title":94,"url":95,"summary":96,"summary_zh":9,"content":9,"source_name":97,"source_url":9,"published_at":98,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":99,"score_detail":100,"sources":104,"tags":106,"primary_topic":109,"secondary_topics":110,"classification":111,"search_phrases":132,"slug":135,"view_count":21,"doi":9,"paper":136,"created_at":144},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":18,"substance":101,"depth":17,"authority":19,"freshness":102,"relevant":21,"comment":103},23,8,"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。",[105],{"name":97,"url":95},[26,27,107,29,108],"农业数字化","绿色创新",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":112},{"candidates":113,"agricultural_evidence":45},[114,118,123,128],{"code":32,"score":68,"supported":45,"title":115,"abstract":116,"tags":117},[],[49],[29],{"code":60,"score":119,"supported":14,"title":120,"abstract":121,"tags":122},2,[],[],[107,27],{"code":124,"score":21,"supported":14,"title":125,"abstract":126,"tags":127},"I",[],[],[107],{"code":53,"score":21,"supported":14,"title":129,"abstract":130,"tags":131},[],[],[26],[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":9,"openalex_id":9,"authors":137,"venue":9,"cited_by_count":79,"oa_url":9,"card":138,"direction":84,"ingested_from":143},[],{"tldr":139,"method":140,"finding":141,"direction":84,"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。","agent","2026-10-18T00:05:30.735414Z",{"id":146,"title":147,"url":148,"summary":149,"summary_zh":150,"content":9,"source_name":151,"source_url":148,"published_at":152,"category":12,"cover_url":9,"hotness":153,"is_selected":14,"score":15,"score_detail":154,"sources":156,"tags":161,"primary_topic":164,"secondary_topics":169,"classification":171,"search_phrases":201,"slug":204,"view_count":79,"doi":205,"paper":206,"created_at":224},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":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":155},"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。",[157,158],{"name":151,"url":148},{"name":159,"url":160},"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",[26,27,162,29,163],"玉米","区块链",{"code":165,"slug":166,"name":162,"path":167,"parent_code":168},"A3","maize","\u002Ftopic\u002Ffield-crops\u002Fmaize","A",[170],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":14,"version":40,"method":41,"evidence":172},{"candidates":173,"agricultural_evidence":45},[174,179,184,190,197],{"code":165,"score":175,"supported":45,"title":176,"abstract":177,"tags":178},15,[166],[162,166],[162],{"code":32,"score":180,"supported":45,"title":181,"abstract":182,"tags":183},12,[47],[49],[29],{"code":60,"score":54,"supported":45,"title":185,"abstract":186,"tags":189},[],[187,188],"数字农业","digital agriculture",[27],{"code":191,"score":192,"supported":45,"title":193,"abstract":194,"tags":196},"G2",3,[],[195],"社会化服务",[],{"code":53,"score":21,"supported":14,"title":198,"abstract":199,"tags":200},[],[],[26],[202,203],"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":205,"openalex_id":207,"authors":208,"venue":151,"cited_by_count":79,"oa_url":217,"card":218,"direction":223,"ingested_from":87},"W7220869193",[209,212,214],{"name":210,"orcid":211},"Pengfei Zhou","https:\u002F\u002Forcid.org\u002F0000-0003-2117-7967",{"name":213,"orcid":9},"Zhiyi Hu",{"name":215,"orcid":216},"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":219,"method":220,"finding":221,"direction":84,"opportunity":222},"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":226,"title":227,"url":228,"summary":229,"summary_zh":9,"content":9,"source_name":230,"source_url":9,"published_at":231,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":232,"score_detail":233,"sources":235,"tags":237,"primary_topic":240,"secondary_topics":241,"classification":246,"search_phrases":265,"slug":268,"view_count":119,"doi":9,"paper":269,"created_at":276},2495,"China's Digital Farming Push Cuts Carbon Emissions:13 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":17,"substance":18,"depth":17,"authority":19,"freshness":102,"relevant":21,"comment":234},"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。",[236],{"name":230,"url":228},[26,27,29,238,239],"农业社会化服务","农业绿色低碳",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[242],{"code":53,"slug":243,"name":244,"path":245,"parent_code":124},"digital-village","数字乡村与数据要素","\u002Ftopic\u002Fdigital-agriculture\u002Fdigital-village",{"status":39,"locked":14,"version":40,"method":41,"evidence":247},{"candidates":248,"agricultural_evidence":45},[249,253,257,261],{"code":32,"score":101,"supported":45,"title":250,"abstract":251,"tags":252},[49,50],[49,50],[29],{"code":53,"score":180,"supported":45,"title":254,"abstract":255,"tags":256},[26],[26],[26],{"code":191,"score":21,"supported":14,"title":258,"abstract":259,"tags":260},[],[],[238],{"code":60,"score":21,"supported":14,"title":262,"abstract":263,"tags":264},[],[],[27],[266,267],"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 农业绿色低碳","农业社会化服务农业绿色低碳农业碳排放数字乡村-2495",{"doi":9,"openalex_id":9,"authors":270,"venue":9,"cited_by_count":79,"oa_url":9,"card":271,"direction":84,"ingested_from":143},[],{"tldr":272,"method":273,"finding":274,"direction":84,"opportunity":275},"基于13年省级面板数据，研究数字乡村技术对农业碳排放效率的影响。","超效率SBM模型量化期望与非期望产出，省级面板数据分析。","数字融合通过优化资源与数据决策提升碳效率，技术创新与合作社平台起桥梁作用。","可探究数字技术对碳效率影响的非线性阈值及不同作物系统的异质性机制。","2026-09-15T00:04:27.428323Z",{"id":278,"title":279,"url":280,"summary":281,"summary_zh":9,"content":9,"source_name":282,"source_url":9,"published_at":283,"category":12,"cover_url":9,"hotness":13,"is_selected":45,"score":284,"score_detail":285,"sources":290,"tags":292,"primary_topic":296,"secondary_topics":301,"classification":304,"search_phrases":326,"slug":329,"view_count":68,"doi":9,"paper":330,"created_at":337},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":18,"substance":286,"depth":287,"authority":288,"freshness":20,"relevant":21,"comment":289},24,19,14,"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。",[291],{"name":282,"url":280},[26,293,294,29,295],"数字基础设施","粮食安全","农户数字化",{"code":297,"slug":298,"name":299,"path":300,"parent_code":168},"A8","yield-food-security","单产提升与粮食安全","\u002Ftopic\u002Ffield-crops\u002Fyield-food-security",[302,303],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"code":53,"slug":243,"name":244,"path":245,"parent_code":124},{"status":39,"locked":14,"version":40,"method":41,"evidence":305},{"candidates":306,"agricultural_evidence":45},[307,312,316,320],{"code":32,"score":17,"supported":45,"title":308,"abstract":309,"tags":311},[50],[49,50,310],"碳足迹",[29],{"code":53,"score":19,"supported":45,"title":313,"abstract":314,"tags":315},[26],[26],[26,293],{"code":297,"score":20,"supported":45,"title":317,"abstract":318,"tags":319},[294],[],[294],{"code":321,"score":192,"supported":45,"title":322,"abstract":323,"tags":325},"F4",[],[324],"农药",[],[327,328],"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":9,"openalex_id":9,"authors":331,"venue":9,"cited_by_count":79,"oa_url":9,"card":332,"direction":86,"ingested_from":143},[],{"tldr":333,"method":334,"finding":335,"direction":86,"opportunity":336},"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":339,"title":340,"url":341,"summary":342,"summary_zh":9,"content":9,"source_name":343,"source_url":9,"published_at":344,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":345,"score_detail":346,"sources":348,"tags":350,"primary_topic":354,"secondary_topics":355,"classification":362,"search_phrases":381,"slug":384,"view_count":21,"doi":9,"paper":385,"created_at":392},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","2026-09-20T00:00:00Z",78,{"impact":17,"substance":18,"depth":17,"authority":288,"freshness":90,"relevant":21,"comment":347},"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。",[349],{"name":343,"url":341},[26,351,352,29,353],"财政支农","粮食主产区","省级面板数据",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[356],{"code":357,"slug":358,"name":359,"path":360,"parent_code":361},"G3","rural-finance","农村金融与保险","\u002Ftopic\u002Frural-services\u002Frural-finance","G",{"status":39,"locked":14,"version":40,"method":41,"evidence":363},{"candidates":364,"agricultural_evidence":45},[365,369,373,377],{"code":32,"score":101,"supported":45,"title":366,"abstract":367,"tags":368},[49,50],[49,50],[29],{"code":357,"score":180,"supported":45,"title":370,"abstract":371,"tags":372},[351],[351],[351],{"code":297,"score":21,"supported":14,"title":374,"abstract":375,"tags":376},[],[],[352],{"code":53,"score":21,"supported":14,"title":378,"abstract":379,"tags":380},[],[],[26],[382,383],"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":9,"openalex_id":9,"authors":386,"venue":9,"cited_by_count":79,"oa_url":9,"card":387,"direction":84,"ingested_from":143},[],{"tldr":388,"method":389,"finding":390,"direction":84,"opportunity":391},"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":394,"title":395,"url":396,"summary":397,"summary_zh":398,"content":9,"source_name":399,"source_url":396,"published_at":400,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":232,"score_detail":401,"sources":403,"tags":405,"primary_topic":409,"secondary_topics":414,"classification":416,"search_phrases":465,"slug":468,"view_count":79,"doi":469,"paper":470,"created_at":496},2325,"Carbon Farming in the Digital Era: Soil Carbon Sequestration, Permanence, Measurement, Reporting and Verification","https:\u002F\u002Fdoi.org\u002F10.9734\u002Fjabb\u002F2026\u002Fv29i104418","Carbon farming is increasingly promoted as a way to rebuild soil organic carbon while creating verifiable climate benefits and new farm income streams. Its credibility, however, depends on a chain of conditions that extends well beyond whether a practice can increase soil carbon at an experimental site. This critical narrative review evaluates the biophysical basis of agricultural soil carbon sequestration, the distinction between molecular persistence and project-level permanence, the measurement of stock change, and the rapidly developing role of digital technologies in measurement, reporting and verification (MRV). Literature published from 2000 to 6 July 2026 was examined, with earlier conceptual material considered only where necessary. Evidence was prioritised from peer-reviewed field studies, meta-analyses, methodological studies and authoritative technical frameworks. The evidence supports the capacity of cover crops, organic amendments, diversified rotations and some biochar applications to raise soil carbon under appropriate conditions, but effect sizes are strongly conditioned by baseline stocks, climate, texture, depth, carbon inputs, management history and system boundaries. Apparent gains can also arise from altered depth distribution or transferred organic matter rather than additional atmospheric carbon removal. Permanence is therefore a management and accounting property, not simply a property of chemically resistant carbon. Direct remeasurement remains the evidentiary anchor for stock-change assessment, yet high spatial variability and slow accumulation make short project periods statistically difficult. Fixed-depth accounting can bias comparisons when bulk density changes; equivalent-soil-mass approaches and explicit uncertainty analysis are more defensible. Remote sensing, digital soil mapping, spectroscopy, machine learning and process-based models can reduce transaction costs and improve stratification, activity monitoring and extrapolation, but they do not remove the need for field calibration, independent validation and periodic soil remeasurement. High-integrity carbon farming consequently requires hybrid MRV, conservative uncertainty treatment, transparent counterfactual baselines, reversal provisions, leakage and non-carbon greenhouse-gas accounting, auditable data lineage and safeguards for equitable participation. Digitalisation can make carbon farming more scalable, but only if it strengthens rather than substitutes for biophysical and governance integrity.","碳农业日益被推广为一种在重建土壤有机碳的同时创造可验证气候效益和新型农业收入来源的途径。然而，其可信度取决于一系列条件，这些条件远不止于某项实践能否在试验点增加土壤碳。本批判性叙事综述评估了农业土壤碳固存的生物物理基础、分子持久性与项目层面永久性之间的区别、碳储量变化的测量，以及数字技术在测量、报告与核查（MRV）中迅速发展的作用。本文考察了2000年至2026年7月6日发表的文献，仅在必要时参考更早的概念性材料。证据优先取自同行评议的田间研究、荟萃分析、方法学研究及权威技术框架。证据支持覆盖作物、有机改良剂、多样化轮作及部分生物炭应用在适当条件下提高土壤碳的能力，但效应量强烈受基线碳储量、气候、质地、深度、碳输入、管理历史和系统边界的影响。表观增益也可能源于深度分布的改变或有机物的转移，而非额外的大气碳去除。因此，永久性是一种管理与核算属性，而不仅仅是化学抗性碳的属性。直接再测量仍是碳储量变化评估的证据锚点，但高空间变异性和缓慢积累使短项目周期在统计上难以奏效。当容重变化时，固定深度核算可能使比较产生偏差；等效土壤质量方法和明确的不确定性分析更具辩护力。遥感、数字土壤制图、光谱学、机器学习和过程模型可以降低交易成本，改善分层、活动监测和外推，但它们并不能消除田间校准、独立验证和定期土壤再测量的需要。因此，高完整性碳农业需要混合MRV、保守的不确定性处理、透明的反事实基线、逆转条款、泄漏和非碳温室气体核算、可审计的数据溯源以及公平参与的保障措施。数字化可以使碳农业更具可扩展性，但前提是它加强而非替代生物物理","Journal of Advances in Biology & Biotechnology","2026-09-11T00:00:00Z",{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":102,"relevant":21,"comment":402},"系统评述数字技术支撑土壤碳汇MRV的方法与治理条件，对农业碳汇数字化与碳交易机制建设有实质参考价值。",[404],{"name":399,"url":396},[26,27,406,407,408],"遥感监测","碳农业","土壤固碳",{"code":410,"slug":411,"name":412,"path":413,"parent_code":36},"H2","soil","土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil",[415],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":14,"version":40,"method":41,"evidence":417},{"candidates":418,"agricultural_evidence":45},[419,424,430,437,444,451,457,461],{"code":410,"score":175,"supported":45,"title":420,"abstract":421,"tags":423},[411],[422,411],"土壤",[408],{"code":32,"score":425,"supported":45,"title":426,"abstract":428,"tags":429},11,[427],"carbon sequestration",[427],[],{"code":431,"score":54,"supported":45,"title":432,"abstract":433,"tags":436},"I3",[],[434,435],"遥感","remote sensing",[406],{"code":438,"score":90,"supported":45,"title":439,"abstract":440,"tags":443},"B7",[],[441,442],"温室","greenhouse",[],{"code":445,"score":90,"supported":45,"title":446,"abstract":447,"tags":450},"I2",[],[448,449],"机器学习","machine learning",[],{"code":452,"score":192,"supported":45,"title":453,"abstract":454,"tags":456},"A7",[],[455],"轮作",[],{"code":60,"score":21,"supported":14,"title":458,"abstract":459,"tags":460},[],[],[27],{"code":53,"score":21,"supported":14,"title":462,"abstract":463,"tags":464},[],[],[26],[466,467],"土壤固碳 数字乡村 智慧农业 遥感监测","土壤固碳 数字乡村","土壤固碳数字乡村智慧农业遥感监测-2325","10.9734\u002Fjabb\u002F2026\u002Fv29i104418",{"doi":469,"openalex_id":471,"authors":472,"venue":399,"cited_by_count":79,"oa_url":396,"card":490,"direction":495,"ingested_from":87},"W7212265915",[473,476,478,480,482,484,486,488],{"name":474,"orcid":475},"Samarpan Chakraborty","https:\u002F\u002Forcid.org\u002F0000-0002-2740-8722",{"name":477,"orcid":9},"Anusmita Goswami",{"name":479,"orcid":9},"Ritam Dhar",{"name":481,"orcid":9},"Kasturi Mandal",{"name":483,"orcid":9},"Tamalika Mondal",{"name":485,"orcid":9},"Sancharee Paul",{"name":487,"orcid":9},"Priya Sarawgi",{"name":489,"orcid":9},"Sujan Biswas",{"tldr":491,"method":492,"finding":493,"direction":84,"opportunity":494},"综述数字时代碳农业的土壤固碳、持久性与MRV，强调混合验证与保守核算。","2000-2026年文献批判性综述，聚焦田间研究、荟萃分析与技术框架。","数字技术可降本增效，但无法替代田间校准与定期土壤复测，需混合MRV。","可研究遥感与模型融合的混合MRV框架，量化不确定性并保障小农公平参与。","农业遥感与作物表型","2026-09-13T23:30:23.245745Z"]