[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-1243":3,"related-1243":77},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":8,"source_name":9,"source_url":6,"published_at":10,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":14,"score_detail":15,"sources":23,"tags":28,"primary_topic":8,"secondary_topics":33,"classification":34,"search_phrases":52,"slug":55,"view_count":21,"doi":56,"paper":57,"created_at":76},1243,"From traditional production to low-carbon transformation: the impact of the digital economy on agricultural carbon emissions","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1891832","Introduction The rapid development of the digital economy is reshaping traditional agricultural production models, providing new opportunities for low-carbon agricultural transformation. However, the mechanisms through which the digital economy affects agricultural carbon emissions remain underexplored. Methods Based on panel data from 30 Chinese provinces during 2011–2021, this study employs a fixed-effects model, the SYS-GMM model, and a Spatial Durbin Model (SDM) to systematically examine the impact of DE on agricultural carbon emissions (ACE) from both local mitigation and spatial spillover perspectives. Results DE significantly reduces ACE within the region, with agricultural technological innovation and improvements in agricultural total factor productivity serving as important transmission mechanisms through which the emission reduction is achieved. Furthermore, the development of DE also exerts a significant inhibitory effect on carbon emissions in neighboring regions, thereby promoting coordinated regional emission reduction. Moreover, the carbon reduction effect exhibits significant regional heterogeneity, with stronger total effects observed in eastern and central regions as well as in areas with higher levels of large-scale agricultural operation. Discussion This study reveals the dual role of DE in facilitating local emission reduction and regional low-carbon coordination, thereby extending the research perspective on the relationship between DE and agricultural lowcarbon transformation. The findings also provide important policy implications for promoting green and low-carbon agricultural development and strengthening regional collaborative governance.",null,"Frontiers in Sustainable Food Systems","2026-08-25T00:00:00Z","论文",25,false,74,{"impact":16,"substance":17,"depth":18,"authority":19,"freshness":20,"relevant":21,"comment":22},20,22,18,12,2,1,"基于省级面板数据的实证研究，揭示数字经济对农业碳减排的本地与空间溢出效应，为低碳农业政策提供依据。",[24,25],{"name":9,"url":6},{"name":26,"url":27},"Computers and Electronics in Agriculture","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112389",[29,30,31,32],"数字经济","低碳农业","农业碳减排","区域协同",[],{"status":35,"locked":13,"version":36,"method":37,"evidence":38},"unclassified","2026-10-22.2","rules",{"candidates":39,"agricultural_evidence":50,"reason":51},[40,45],{"code":41,"score":20,"supported":13,"title":42,"abstract":43,"tags":44},"H4",[],[],[30,31],{"code":46,"score":21,"supported":13,"title":47,"abstract":48,"tags":49},"I6",[],[],[29],true,"缺少足够的主题证据",[53,54],"农业碳减排 低碳农业 区域协同 数字经济","农业碳减排 低碳农业","农业碳减排低碳农业区域协同数字经济-1243","10.3389\u002Ffsufs.2026.1891832",{"doi":56,"openalex_id":58,"authors":59,"venue":9,"cited_by_count":67,"oa_url":68,"card":69,"direction":73,"ingested_from":75},"W7204170711",[60,63,65],{"name":61,"orcid":62},"Fuchun Yang","https:\u002F\u002Forcid.org\u002F0000-0002-5734-327X",{"name":64,"orcid":8},"Chen Luo",{"name":66,"orcid":8},"Jinbo Du",0,"https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1891832\u002Fpdf",{"tldr":70,"method":71,"finding":72,"direction":73,"opportunity":74},"研究数字经济对农业碳排放的影响，发现其能显著降低本地及邻域碳排放。","基于2011-2021年中国省级面板数据，采用固定效应、SYS-GMM和空间杜宾","数字经济通过农业技术创新和全要素生产率提升降低碳排放，且具有空间溢出效应和区域异质性。","农业绿色发展与碳","可进一步探究数字经济影响农业碳排放的行业细分机制，或结合微观农户数据验证其作用路径。","openalex","2026-09-01T04:03:09.387695Z",{"total":78,"page":21,"page_size":78,"items":79},6,[80,152,189,237,282,427],{"id":81,"title":82,"url":83,"summary":84,"summary_zh":85,"content":8,"source_name":9,"source_url":83,"published_at":86,"category":11,"cover_url":8,"hotness":87,"is_selected":13,"score":88,"score_detail":89,"sources":94,"tags":96,"primary_topic":100,"secondary_topics":105,"classification":106,"search_phrases":132,"slug":134,"view_count":67,"doi":135,"paper":136,"created_at":151},2760,"Ecological civilization demonstration zones and sustainable low-carbon agricultural transformation: Quasi-natural evidence from China","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1945948","Promoting sustainable low-carbon agricultural transformation is essential for building climate-smart and sustainable food systems, particularly in countries where agricultural production remains closely associated with resource consumption and environmental pressure. This study examines whether China’s National Ecological Civilization Demonstration Zones (ECDZ) reduce agricultural carbon emission intensity (ACEI) and thereby support greener agricultural production. Using panel data from 30 Chinese provinces during 2003–2024, we treat ECDZ construction as a quasi-natural experiment and estimate its effects using double machine learning (DML) and a spatial difference-in-differences (SDID) model. The results show that ECDZ construction significantly reduces ACEI, and this conclusion remains robust to parallel-trend tests, the exclusion of concurrent policy shocks, and alternative machine-learning algorithms. Mechanism tests indicate that the reduction operates through agricultural green technological progress and agricultural industrial structure upgrading. Spatial analysis further shows significant spillover effects, suggesting that ECDZ construction reduces ACEI not only locally but also in neighboring regions. These findings provide empirical evidence that ecological civilization policies can support sustainable low-carbon agricultural transformation and contribute to climate-oriented food system governance. They also highlight the need to strengthen green technology diffusion, agricultural structural upgrading, and regional policy coordination.","推动可持续低碳农业转型，对于建设气候智慧型和可持续的粮食体系至关重要，尤其是在农业生产仍与资源消耗和环境压力密切相关的国家。本研究考察中国国家生态文明建设示范区（ECDZ）是否能够降低农业碳排放强度（ACEI），从而支持更绿色的农业生产。基于2003—2024年中国30个省份的面板数据，我们将ECDZ建设视为一项准自然实验，并采用双重机器学习（DML）和空间双重差分（SDID）模型估计其效应。结果表明，ECDZ建设显著降低了ACEI，且该结论在平行趋势检验、排除同期政策冲击以及替代机器学习算法后依然稳健。机制检验表明，这一减排效应通过农业绿色技术进步和农业产业结构升级发挥作用。空间分析进一步显示出显著的溢出效应，表明ECDZ建设不仅在本地降低ACEI，也降低了邻近地区的ACEI。这些发现提供了经验证据，表明生态文明政策能够支持可持续低碳农业转型，并有助于面向气候目标的粮食体系治理。研究还强调，需要加强绿色技术扩散、农业结构升级和区域政策协调。","2026-09-16T00:00:00Z",10,85,{"impact":17,"substance":90,"depth":18,"authority":91,"freshness":92,"relevant":21,"comment":93},23,13,9,"基于30省2003—2024年面板数据，用双机器学习与空间双重差分实证生态文明示范区对农业碳排放强度的抑制效应及溢出，方法新颖、结论可靠，对低碳农业政策有参考价值。",[95],{"name":9,"url":83},[30,97,31,98,99],"农业政策","绿色技术","生态文明",{"code":41,"slug":101,"name":102,"path":103,"parent_code":104},"carbon","农业碳与绿色低碳","\u002Ftopic\u002Fenvironment\u002Fcarbon","H",[],{"status":107,"locked":13,"version":36,"method":37,"evidence":108},"classified",{"candidates":109,"agricultural_evidence":50},[110,118,125],{"code":41,"score":111,"supported":50,"title":112,"abstract":113,"tags":117},11,[],[114,115,116],"碳排放","农业碳","carbon emission",[30,31],{"code":119,"score":78,"supported":50,"title":120,"abstract":121,"tags":124},"I2",[],[122,123],"机器学习","machine learning",[],{"code":126,"score":78,"supported":50,"title":127,"abstract":128,"tags":131},"I4",[],[129,130],"空间分析","spatial analysis",[],[133,54],"农业碳减排 低碳农业 农业政策 生态文明","农业碳减排低碳农业农业政策生态文明-2760","10.3389\u002Ffsufs.2026.1945948",{"doi":135,"openalex_id":137,"authors":138,"venue":9,"cited_by_count":67,"oa_url":145,"card":146,"direction":73,"ingested_from":75},"W7213428280",[139,141,143],{"name":140,"orcid":8},"Xiaoyu Chen",{"name":142,"orcid":8},"Xuexue Wu",{"name":144,"orcid":8},"Haohan Wang","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1945948\u002Fpdf",{"tldr":147,"method":148,"finding":149,"direction":73,"opportunity":150},"以中国生态文明示范区为准自然实验，检验其能否降低农业碳排放强度。","2003–2024年30省面板数据，双机器学习与空间双重差分。","示范区显著降低农业碳排放强度，并通过绿色技术进步与产业结构升级起作用，存在空间溢出。","可探究示范区政策工具组合与绿色技术扩散路径，及跨区域协同减排的微观机制。","2026-09-17T23:30:08.212229Z",{"id":153,"title":154,"url":155,"summary":156,"summary_zh":8,"content":157,"source_name":158,"source_url":8,"published_at":159,"category":11,"cover_url":8,"hotness":87,"is_selected":13,"score":14,"score_detail":160,"sources":164,"tags":166,"primary_topic":170,"secondary_topics":171,"classification":172,"search_phrases":184,"slug":187,"view_count":162,"doi":8,"paper":8,"created_at":188},416,"数字经济发展对农业碳排放的影响:来自规模效应与效率效应的证据","https:\u002F\u002Fwww.ebiotrade.com\u002Fnewsf\u002F2026-8\u002F20260807000145187.htm","发表于《Frontiers in Sustainable Food Systems》。基于中国2011-2022年省级面板数据,从规模效应(scale effect)与效率效应(efficiency effect)双视角考察农业数字化如何影响农业碳排放与碳排放效率。以双向固定效应模型为主分析方法,研究表明农业数字化显著改善农业碳绩效:农业数字化水平每提升1%,农业碳排放总量下降15.72%,碳排放效率提升3.97%,规模效应与效率效应同时成立。机制证据表明,现阶段减排主要通过增量创新而非突破式创新实现;南方地区与数字化水平较高省份减排效应更强,且数字化存在空间溢出效应。结论提示加快农业数字化、强化区域差异化支持与创新激励可助推低碳农业转型。","数字经济发展对农业碳排放的影响：来自规模效应与效率效应的证据\n\n《Frontiers in Sustainable Food Systems》：Impact of digital economy development on agricultural carbon emissions: evidence from scale and efficiency effects\n\n_【字体： 大 中 小 】_ _时间：2026年08月07日_ _来源：Frontiers in Sustainable Food Systems 4.1_\n\n**编辑推荐：**\n\n农业数字化日益被视为农业现代化与低碳转型的关键路径。研究人员基于中国2011—2022年省级面板数据，从规模效应（scale effect）与效率效应（efficiency effect）双视角考察农业数字化如何影响农业碳排放（agricultural car\n\n农业数字化日益被视为农业现代化与低碳转型的关键路径。研究人员基于中国2011—2022年省级面板数据，从规模效应（scale effect）与效率效应（efficiency effect）双视角考察农业数字化如何影响农业碳排放（agricultural carbon emissions）与碳排放效率（carbon emission efficiency）。研究以双向固定效应模型（two-way fixed-effects model）为主分析方法，结果表明农业数字化显著改善农业碳绩效：农业数字化水平每提升1%，农业碳排放总量下降15.72%，碳排放效率提升3.97%，规模效应与效率效应同时成立。机制证据表明，现阶段减排主要通过增量创新（incremental innovation）而非突破式创新（breakthrough innovation）实现；南方地区与数字化水平较高省份减排效应更强，且数字化存在空间溢出效应（spatial spillover effects）。结论提示，加快农业数字化、强化区域差异化支持与创新激励，可助推低碳农业转型。\n\n研究背景方面，全球气候变化背景下农业作为温室气体重要源，其排放具分散源、工艺复杂、技术替代受限等特征，在保粮食安全前提下减排难度大。既有文献对数字化与农业碳排放关系存在两派观点：一派认为精准投入与绿色技术扩散降低强度，另一派认为市场连通与规模机械化扩张推高能源需求。已有研究多孤立看排放总量或效率，难以判断是外延扩张还是内涵改进驱动转型。为此，研究人员将农业数字化作为核心解释变量，同时以碳排放总量与碳排放效率（基于Super-SBM模型测度，含非期望产出）刻画碳绩效，并区分规模效应（改变产出Y与高碳投入）与效率效应（改变单位产出强度θ），从增量创新与突破式创新异质性、阈值非线性、空间溢出三方面展开。\n\n样本与数据来自2011—2022年中国31个省级行政区平衡面板，融合?《中国统计年鉴》?《中国农村统计年鉴》、专利数据库、进出口统计、区域投入产出表及Ran等（2025）突破式创新测度；农业数字化（DVI）以农业数字经济增加值占农业增加值比为主指标，辅以农村宽带\u002F电脑\u002F手机普及率等七指标变异系数法合成DVI1；农业碳排放按IPCC因子法聚合水稻甲烷、耕地N 2 O、化肥农药农膜、灌溉用电、农机柴油及猪牛羊肠道发酵与粪管甲烷\u002FN 2 O；控制变量含人均GDP及其平方、人口、城镇化、农业开放度、财政支农、种植结构、生产集聚等。关键技术方法包括：Super-SBM（含非期望产出、VRS）测度碳效率；双向固定效应面板回归；Tobit机制检验；面板阈值模型；空间自回归（SAR）\u002F空间误差（SEM）\u002F空间杜宾（SDM）模型配地理邻接权重并做经济距离稳健。\n\n研究结果部分，理论分析提出四组假设：H1指数字化通过要素配置优化提升效率但总量方向不确定；H2指增量创新主作用于效率、突破式创新兼影响规模与效率；H3指存在数字化阈值，过阈后减排显化；H4指空间溢出经效率扩散与规模再配置两通道，净效应符号不定。实证主回归显示DVI对lnC负向显著（弹性-0.1572）、对lnCE正向显著（弹性0.0397），即规模与效率双效应成立。机制检验中DVI显著提升增量创新（ln(1+人均绿色农业专利)）而未显著撬动突破式创新占比，印证现阶段靠增量创新减排。阈值模型估计出单\u002F多阈值，低于阈时减排不显著，跨阈后负向弹性增强。空间SDM表明本地数字化降本地排放、提升本地效率，且对邻省效率有正间接效应、对邻省总量排放呈负向间接效应，支持效率扩散主导而非规模虹吸。异质性上南方、高数字基础省弹性更大。\n\n讨论与结论翻译：农业数字化通过优化生产要素配置、生产决策与过程控制改善农业碳效率；其对总排放的影响在理论上不确定，因效率提升可与生产规模扩张并存。数字化经异质创新渠道作用，增量创新主增效率收益，突破式创新可同时影响效率与规模。数字化对碳排放的影响具阈值特征，跨临界值后减排方显。数字化不仅影响本地碳绩效，亦经效率扩散与规模再配置影响邻域，净溢出符号因变量而异。研究人员建议加快农业数字化并配区域差异化支持与创新激励，以推进低碳农业转型。该文发表于《Frontiers in Sustainable Food Systems》。\n\n相关新闻\n\n![Image 1](https:\u002F\u002Fnews.ebiotrade.com\u002FQR_ebiotrade.jpg)生物通微信公众号\n\n![Image 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3](https:\u002F\u002Fwww.ebiotrade.com\u002Fimages\u002Fservice_com.gif)\n\n粤ICP备09063491号","Frontiers in Sustainable Food Systems 2026-08-07","2026-08-07T00:00:00Z",{"impact":18,"substance":17,"depth":161,"authority":19,"freshness":162,"relevant":21,"comment":163},19,3,"基于省级面板数据的实证研究，揭示农业数字化减排机制，对政策制定有参考价值，但时效性稍差。",[165],{"name":158,"url":155},[29,30,167,168,169],"农业碳排放","规模效应","效率效应",{"code":41,"slug":101,"name":102,"path":103,"parent_code":104},[],{"status":107,"locked":13,"version":36,"method":37,"evidence":173},{"candidates":174,"agricultural_evidence":50},[175,180],{"code":41,"score":176,"supported":50,"title":177,"abstract":178,"tags":179},24,[114,115],[114,115],[30,167],{"code":46,"score":21,"supported":13,"title":181,"abstract":182,"tags":183},[],[],[29],[185,186],"农业碳排放 低碳农业 效率效应 数字经济","农业碳排放 低碳农业","农业碳排放低碳农业效率效应数字经济-416","2026-08-11T23:57:03.209009Z",{"id":190,"title":191,"url":192,"summary":193,"summary_zh":8,"content":8,"source_name":194,"source_url":8,"published_at":195,"category":11,"cover_url":8,"hotness":87,"is_selected":13,"score":196,"score_detail":197,"sources":202,"tags":204,"primary_topic":8,"secondary_topics":209,"classification":210,"search_phrases":222,"slug":225,"view_count":67,"doi":226,"paper":227,"created_at":236},5458,"数字经济对农业高质量发展的影响——以新疆14地州市为例 (Impact of Digital Economy on the High-Quality Development of Agriculture: Evidence from the 14 Prefectures and Cities of Xinjiang)","http:\u002F\u002Fzgnjhxb.niam.com.cn\u002FCN\u002F10.13733\u002Fj.jcam.issn.2095-5553.2026.10.041","基于2010—2022年新疆14地州市面板数据，熵值法测度双指数，中介与固定效应模型分析。数字经济对农业高质量发展影响系数0.132且显著，农业产业结构升级中介效应0.0649。区域异质性为东疆最强、南疆次之、北疆最弱。","中国农机化学报 2026-10-20","2026-10-20T00:00:00Z",78,{"impact":198,"substance":199,"depth":18,"authority":200,"freshness":92,"relevant":21,"comment":201},16,21,14,"基于新疆14地州市面板数据的实证研究，方法规范、结论具体，对区域数字农业政策有参考价值，但属区域性学术成果，影响层级有限。",[203],{"name":194,"url":192},[29,205,206,207,208],"农业高质量发展","区域异质性","农业产业结构","新疆农业",[],{"status":35,"locked":13,"version":36,"method":37,"evidence":211},{"candidates":212,"agricultural_evidence":50,"reason":51},[213,217],{"code":46,"score":21,"supported":13,"title":214,"abstract":215,"tags":216},[],[],[29],{"code":218,"score":21,"supported":13,"title":219,"abstract":220,"tags":221},"J2",[],[],[205],[223,224],"新疆 数字经济 农业高质量发展","新疆 14地州市 面板数据","新疆数字经济农业高质量发展-5458","10.13733\u002Fj.jcam.issn.2095-5553.2026.10.041",{"doi":226,"openalex_id":8,"authors":228,"venue":8,"cited_by_count":67,"oa_url":8,"card":229,"direction":233,"ingested_from":235},[],{"tldr":230,"method":231,"finding":232,"direction":233,"opportunity":234},"基于新疆14地州市面板数据，实证检验数字经济对农业高质量发展的影响及中介机制。","2010—2022年新疆14地州市面板数据，熵值法测度指数，中介与固定效应模型。","数字经济显著促进农业高质量发展（系数0.132），农业产业结构升级起中介作用，效应东疆最强、南疆次之","数字乡村与农业信息化","可延伸至县域尺度、微观农户数据及数字基础设施异质性下的机制识别与政策分区研究。","agent","2026-10-21T00:10:08.003418Z",{"id":238,"title":239,"url":240,"summary":241,"summary_zh":8,"content":8,"source_name":242,"source_url":8,"published_at":243,"category":11,"cover_url":8,"hotness":87,"is_selected":13,"score":244,"score_detail":245,"sources":248,"tags":250,"primary_topic":8,"secondary_topics":253,"classification":254,"search_phrases":269,"slug":272,"view_count":67,"doi":273,"paper":274,"created_at":281},5300,"张捷\u002F李林飞《中国林业经济》:数字经济政策对农业高质量发展的影响研究——基于国家大数据综合试验区的准自然实验","https:\u002F\u002Fzglyjj.nefu.edu.cn\u002Farticle\u002Fdoi\u002F10.13691\u002Fj.cnki.cn23-1539\u002Ff.2026.04.001","东北林业大学经济管理学院张捷、李林飞基于2011-2023年中国31个省级行政区面板数据,以国家大数据综合试验区建设为准自然实验,运用多期双重差分模型评估数字经济政策对农业高质量发展的影响;研究发现数字经济政策显著促进农业高质量发展且具滞后性和累积性;对本地区农业高质量发展具显著正向直接效应,但对其他地区产生负向空间溢出,呈现\"本地促进、外部抑制\"特征;非粮食主产区估计效应相对更强。","中国林业经济(2026,4)","2026-10-15T00:00:00Z",80,{"impact":18,"substance":17,"depth":18,"authority":200,"freshness":246,"relevant":21,"comment":247},8,"基于31省面板数据与国家大数据综合试验区的准自然实验，方法规范、结论有政策参考价值，但属学术论文且非时效热点，可入精选备选。",[249],{"name":242,"url":240},[29,205,251,252],"空间溢出","大数据试验区",[],{"status":35,"locked":13,"version":36,"method":37,"evidence":255},{"candidates":256,"agricultural_evidence":50,"reason":51},[257,261,265],{"code":126,"score":21,"supported":13,"title":258,"abstract":259,"tags":260},[],[],[251],{"code":46,"score":21,"supported":13,"title":262,"abstract":263,"tags":264},[],[],[29],{"code":218,"score":21,"supported":13,"title":266,"abstract":267,"tags":268},[],[],[205],[270,271],"国家大数据综合试验区 农业高质量发展","数字经济政策 双重差分","国家大数据综合试验区农业高质量发展-5300","10.13691\u002Fj.cnki.cn23-1539\u002Ff.2026.04.001",{"doi":273,"openalex_id":8,"authors":275,"venue":8,"cited_by_count":67,"oa_url":8,"card":276,"direction":233,"ingested_from":235},[],{"tldr":277,"method":278,"finding":279,"direction":233,"opportunity":280},"用多期DID评估国家大数据综合试验区对农业高质量发展的影响。","2011-2023年31省面板数据，多期双重差分模型。","政策显著促进本地农业高质量发展，但对其他地区有负向空间溢出。","可探究政策负向溢出的机制与跨区协同路径，及非粮食主产区的差异化政策设计。","2026-10-19T00:07:48.514777Z",{"id":283,"title":284,"url":285,"summary":286,"summary_zh":287,"content":8,"source_name":288,"source_url":285,"published_at":289,"category":11,"cover_url":8,"hotness":87,"is_selected":13,"score":290,"score_detail":291,"sources":293,"tags":295,"primary_topic":8,"secondary_topics":300,"classification":301,"search_phrases":353,"slug":356,"view_count":20,"doi":357,"paper":358,"created_at":426},4803,"Reducing Uncertainty in Cradle‐to‐Farmgate Carbon Intensity for Feedstock Using Model‐Data Fusion","https:\u002F\u002Fdoi.org\u002F10.1111\u002Fgcbb.70179","ABSTRACT Biofuel carbon intensity (CI) quantifies greenhouse gas (GHG) emissions across the biofuel supply chain, providing a transparent metric guiding renewable fuel policies and enabling market‐based mechanisms to reduce GHG emissions. In this study, we focus on the cradle‐to‐farmgate feedstock segment of biofuel CI, the portion attributable to feedstock production from cradle to farm gate and directly influenced by agricultural management decisions. We quantify field‐level cradle‐to‐farmgate CI of corn and soybean feedstocks by integrating a process‐based model ( ecosys ) with a life cycle assessment (LCA) tool (GREET R&D FD‐CIC). We first identified CI uncertainty sources at a “supersite,” a representative farm in Illinois with comprehensive GHG measurements, using a Monte Carlo sampling framework that varied soil properties, management practices, and plant trait parameters across plausible ranges to quantify each source's contribution to CI uncertainty. Unknown plant trait parameters were the largest CI uncertainty source for both crops, with fertilizer information also important for corn because these inputs affect yield, N 2 O emissions, and soil organic carbon (SOC) stocks. We revealed widespread field‐level variability in cradle‐to‐farmgate CI within the same county, demonstrating the need for field‐level assessment. This variability was most strongly associated with SOC change for soybean (total‐effect index, 38%) and N 2 O emissions for corn (43%). While inherent uncertainty is inevitable, we demonstrated that a Model‐Data Fusion (MDF) approach, which integrates observational data including in situ flux measurements and satellite‐based remote sensing to constrain model parameters with known processes, can substantially reduce cradle‐to‐farmgate CI uncertainty. Specifically, we used crop photosynthesis and yield data for plant trait parameter calibration, and incorporated tillage and cover crop information as model inputs, achieving a 41%–52% CI uncertainty reduction. This work establishes a CI quantification framework composed of process‐based models, an LCA tool, MDF, and satellite data, enabling scalable, cost‐effective, field‐level cradle‐to‐farmgate CI quantification across regions.","摘要 生物燃料碳强度（CI）量化了生物燃料供应链中的温室气体（GHG）排放，为可再生燃料政策提供了透明的衡量指标，并使基于市场的机制能够减少温室气体排放。本研究聚焦于生物燃料碳强度中从摇篮到农场门的原料生产环节，即从摇篮到农场门阶段归属于原料生产且直接受农业管理决策影响的部分。我们通过将过程模型（ecosys）与生命周期评价（LCA）工具（GREET R&D FD-CIC）相结合，量化了玉米和大豆原料的田块尺度从摇篮到农场门碳强度。我们首先在一个“超级站点”——位于伊利诺伊州的一个具有全面温室气体测量数据的代表性农场——采用蒙特卡洛采样框架，在合理范围内变化土壤属性、管理措施和植物性状参数，以量化各来源对碳强度不确定性的贡献。未知植物性状参数是两种作物碳强度不确定性的最大来源，肥料信息对玉米同样重要，因为这些投入会影响产量、N₂O排放和土壤有机碳（SOC）储量。我们揭示了同一县内从摇篮到农场门碳强度存在广泛的田块尺度变异性，表明有必要进行田块尺度评估。这种变异性与大豆的土壤有机碳变化关联最强（总效应指数为38%），与玉米的N₂O排放关联最强（43%）。尽管固有不确定性不可避免，我们证明了一种模型-数据融合（MDF）方法可以大幅降低从摇篮到农场门碳强度不确定性，该方法整合了包括原位通量测量和卫星遥感在内的观测数据，以已知过程约束模型参数。具体而言，我们利用作物光合作用和产量数据校准植物性状参数，并将耕作和覆盖作物信息作为模型输入，实现了41%–52%的碳强度不确定性降低。本研究建立了一个由过程模型、生命周期评价工具、模型-数据融合和卫星数据组成的碳强度量化框架，能够实现跨区域可扩展、成本效益高的田块尺度从摇篮到农场门碳强度量化。","GCB Bioenergy","2026-10-12T00:00:00Z",81,{"impact":18,"substance":90,"depth":18,"authority":200,"freshness":246,"relevant":21,"comment":292},"将过程模型、LCA与遥感数据融合，实现田块级原料碳强度量化并降低不确定性，方法新颖、结论可靠，对农业碳核算与低碳政策有参考价值。",[294],{"name":288,"url":285},[31,296,297,298,299],"遥感监测","生物燃料","生命周期评价","玉米大豆",[],{"status":302,"locked":13,"version":36,"method":37,"evidence":303},"needs_review",{"candidates":304,"agricultural_evidence":50,"reason":352},[305,313,320,327,334,341,348],{"code":306,"score":307,"supported":50,"title":308,"abstract":309,"tags":312},"A3",7,[],[310,311],"玉米","corn",[299],{"code":314,"score":307,"supported":50,"title":315,"abstract":316,"tags":319},"I3",[],[317,318],"遥感","remote sensing",[296],{"code":321,"score":78,"supported":50,"title":322,"abstract":323,"tags":326},"A4",[],[324,325],"大豆","soybean",[],{"code":328,"score":78,"supported":50,"title":329,"abstract":330,"tags":333},"B7",[],[331,332],"温室","greenhouse",[],{"code":335,"score":78,"supported":50,"title":336,"abstract":337,"tags":340},"F3",[],[338,339],"肥料","fertilizer",[],{"code":342,"score":78,"supported":50,"title":343,"abstract":344,"tags":347},"H2",[],[345,346],"土壤","soil",[],{"code":41,"score":20,"supported":13,"title":349,"abstract":350,"tags":351},[],[],[31,298],"多个产业证据相当，需要确认主归属",[354,355],"ecosys GREET 生物燃料碳强度","玉米 大豆 农田碳排放 模型数据融合","ecosysGREET生物燃料碳强度-4803","10.1111\u002Fgcbb.70179",{"doi":357,"openalex_id":359,"authors":360,"venue":288,"cited_by_count":67,"oa_url":419,"card":420,"direction":425,"ingested_from":75},"W7221531583",[361,364,367,370,373,376,378,381,384,387,390,393,396,399,402,404,407,410,413,416],{"name":362,"orcid":363},"Ziqi Qin","https:\u002F\u002Forcid.org\u002F0000-0001-5854-0323",{"name":365,"orcid":366},"Kaiyu Guan","https:\u002F\u002Forcid.org\u002F0000-0002-3499-6382",{"name":368,"orcid":369},"Yalin Li","https:\u002F\u002Forcid.org\u002F0000-0002-8863-4758",{"name":371,"orcid":372},"Wang Zhou","https:\u002F\u002Forcid.org\u002F0000-0002-8432-9828",{"name":374,"orcid":375},"Xinyu Liu","https:\u002F\u002Forcid.org\u002F0000-0003-4683-0272",{"name":377,"orcid":8},"Hao Cai",{"name":379,"orcid":380},"Bin Peng","https:\u002F\u002Forcid.org\u002F0000-0002-7284-3010",{"name":382,"orcid":383},"Tongxi Hu","https:\u002F\u002Forcid.org\u002F0000-0003-4773-1022",{"name":385,"orcid":386},"R. F. Grant","https:\u002F\u002Forcid.org\u002F0000-0002-8890-6231",{"name":388,"orcid":389},"Jinyun Tang","https:\u002F\u002Forcid.org\u002F0000-0002-4792-1259",{"name":391,"orcid":392},"Zhenong Jin","https:\u002F\u002Forcid.org\u002F0000-0002-1252-2514",{"name":394,"orcid":395},"Jeremy S. Guest","https:\u002F\u002Forcid.org\u002F0000-0003-2489-2579",{"name":397,"orcid":398},"Lexuan Ye","https:\u002F\u002Forcid.org\u002F0009-0005-1841-7175",{"name":400,"orcid":401},"Kuang‐Yu Chang","https:\u002F\u002Forcid.org\u002F0000-0002-7859-5871",{"name":403,"orcid":8},"Zhangliang Chen",{"name":405,"orcid":406},"DoKyoung Lee","https:\u002F\u002Forcid.org\u002F0000-0003-1401-9661",{"name":408,"orcid":409},"Andrew J. Margenot","https:\u002F\u002Forcid.org\u002F0000-0003-0185-8650",{"name":411,"orcid":412},"Carl J. Bernacchi","https:\u002F\u002Forcid.org\u002F0000-0002-2397-425X",{"name":414,"orcid":415},"Elizabeth A. Ainsworth","https:\u002F\u002Forcid.org\u002F0000-0002-3199-8999",{"name":417,"orcid":418},"William Eddy","https:\u002F\u002Forcid.org\u002F0000-0003-2215-9680","https:\u002F\u002Fonlinelibrary.wiley.com\u002Fdoi\u002Fpdfdirect\u002F10.1111\u002Fgcbb.70179",{"tldr":421,"method":422,"finding":423,"direction":73,"opportunity":424},"用模型-数据融合降低玉米大豆原料从摇篮到农场门的碳强度不确定性。","ecosys过程模型与GREET LCA耦合，蒙特卡洛分析，卫星与通量数据融合。","植物性状参数是最大不确定性源，模型-数据融合使碳强度不确定性降低41%–52%。","可将该碳强度量化框架扩展至更多作物与区域，结合遥感实现大范围田块级碳核算。","智慧农业 \u002F 农业物联网","2026-10-13T23:30:08.889814Z",{"id":428,"title":429,"url":430,"summary":431,"summary_zh":432,"content":8,"source_name":9,"source_url":430,"published_at":433,"category":11,"cover_url":8,"hotness":87,"is_selected":13,"score":434,"score_detail":435,"sources":438,"tags":440,"primary_topic":8,"secondary_topics":445,"classification":446,"search_phrases":468,"slug":471,"view_count":20,"doi":472,"paper":473,"created_at":489},4410,"Digital capability and supply chain resilience in rural food tourism ventures: the role of food supply chain integration","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1976172","Introduction Rural food tourism ventures coordinate perishable local supplies, food-service operations and fluctuating tourist demand. How digital capability and food supply chain integration relate to resource-saving practices and enterprise-level resilience remains unclear. Methods A four-wave matched survey of 467 women operators was analyzed using covariance-based structural equation modeling, bootstrap tests of statistical indirect associations and an ordinal-estimator sensitivity analysis. Public user-generated content was analyzed separately to characterize platform discourse. Results Digital capability was positively associated with food supply chain integration and enterprise-level supply chain resilience. Integration was positively associated with both the low-carbon-oriented and resource-saving food practices index and resilience. The direct association between the practices index and resilience was not statistically significant. Bootstrap estimates supported the indirect association between digital capability and the practices index through integration, but not the serial indirect association extending to resilience. Only the resilience text classifier met the prespecified reporting threshold; the remaining labels were retained as exploratory semantic cues. Discussion The findings distinguish resource-saving practices from disruption resilience and identify supply chain integration as an organizational correlate of both. Future research should test these associations in probability-based and gender-comparative samples and validate reported practices against measured energy use, food loss and emissions.","引言 乡村美食旅游企业需要协调易腐的本地供应、餐饮服务运营与波动的游客需求。数字能力与食品供应链整合如何与资源节约型实践及企业层面的韧性相关联，目前尚不清楚。方法 对467名女性经营者的四轮匹配调查数据，采用基于协方差的结构方程模型、统计间接关联的Bootstrap检验以及有序估计量敏感性分析进行分析。公开的用户生成内容另行分析，以刻画平台话语特征。结果 数字能力与食品供应链整合及企业层面的供应链韧性呈正相关。整合与低碳导向及资源节约型食品实践指数和韧性均呈正相关。实践指数与韧性之间的直接关联在统计上不显著。Bootstrap估计支持数字能力通过整合与实践指数之间的间接关联，但不支持延伸至韧性的序列间接关联。仅韧性文本分类器达到预设的报告阈值；其余标签作为探索性语义线索保留。讨论 研究结果将资源节约型实践与中断韧性区分开来，并确定供应链整合是二者的组织关联因素。未来研究应在基于概率的样本和性别比较样本中检验这些关联，并依据实测能源使用、食品损耗和排放数据验证所报告的实践。","2026-10-07T00:00:00Z",67,{"impact":246,"substance":16,"depth":436,"authority":91,"freshness":92,"relevant":21,"comment":437},17,"基于467名女性经营者四轮匹配调查的实证研究，揭示数字能力通过供应链整合关联资源节约实践与企业韧性，方法规范、结论有增量，但属细分领域学术成果，公共影响有限。",[439],{"name":9,"url":430},[441,30,442,443,444],"数字乡村","农业数字化","乡村文旅","农产品供应链",[],{"status":35,"locked":13,"version":36,"method":37,"evidence":447},{"candidates":448,"agricultural_evidence":50,"reason":51},[449,454,459,464],{"code":450,"score":21,"supported":13,"title":451,"abstract":452,"tags":453},"E",[],[],[444],{"code":455,"score":21,"supported":13,"title":456,"abstract":457,"tags":458},"I",[],[],[442],{"code":460,"score":21,"supported":13,"title":461,"abstract":462,"tags":463},"I1",[],[],[442],{"code":46,"score":21,"supported":13,"title":465,"abstract":466,"tags":467},[],[],[441],[469,470],"乡村食品旅游 数字能力 供应链韧性","农产品供应链 农业数字化 乡村文旅 低碳农业","乡村食品旅游数字能力供应链韧性-4410","10.3389\u002Ffsufs.2026.1976172",{"doi":472,"openalex_id":474,"authors":475,"venue":9,"cited_by_count":67,"oa_url":483,"card":484,"direction":233,"ingested_from":75},"W7220839382",[476,479,481],{"name":477,"orcid":478},"Wei Li","https:\u002F\u002Forcid.org\u002F0000-0003-4665-4643",{"name":480,"orcid":8},"Peng Yin",{"name":482,"orcid":8},"Ruirui Zhou","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1976172\u002Fpdf",{"tldr":485,"method":486,"finding":487,"direction":233,"opportunity":488},"研究数字能力与食品供应链整合如何影响乡村食品旅游企业的资源节约与韧性。","对467名女性经营者四轮匹配调查，用结构方程模型和Bootstrap检验。","数字能力正向关联供应链整合与韧性；整合关联节约实践与韧性，但实践与韧性直接关联不显著。","可在概率样本与性别比较中验证，并用实测能耗、食物损耗与排放校准自报实践。","2026-10-08T23:30:07.258973Z"]