[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-1585":3,"related-1585":79},{"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":29,"secondary_topics":35,"classification":36,"search_phrases":63,"slug":66,"view_count":67,"doi":8,"paper":68,"created_at":78},1585,"Digital Farming Slashes China's Carbon Footprint: Provincial-Level Panel Evidence from 2012 to 2023 (Frontiers in Sustainable Food Systems)","https:\u002F\u002Fagritechinsights.com\u002Findex.php\u002F2026\u002F09\u002F02\u002Fdigital-farming-slashes-chinas-carbon-footprint","由江西理工学院 Yi Zhang 领衔的研究团队在《Frontiers in Sustainable Food Systems》发表论文，运用 2012—2023 年省级面板数据，构建数字农业发展指数衡量数字基础设施、人力资本、产业支持等维度。研究发现：数字农业采纳程度较高的地区农业碳排放强度显著下降；通过提升资本配置效率（即改善投资流向，缓解对高排放机械的过度投资和对精准工具的利用不足）间接降低碳排放；空间分析显示数字农业存在跨区域溢出效应，主粮产区和东部地区效应更强，但中西部地区效益尚不显著。文章发表于 2026-09-02 综述解读。",null,"Frontiers in Sustainable Food Systems","2026-09-02T00:00:00Z","论文",10,false,78,{"impact":16,"substance":17,"depth":16,"authority":18,"freshness":19,"relevant":20,"comment":21},18,22,13,7,1,"基于省级面板数据的实证研究，揭示数字农业对碳减排的显著作用及区域差异，为政策制定提供科学依据。",[23],{"name":9,"url":6},[25,26,27,28],"数字农业","空间溢出","省级面板","农业碳排放",{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},"H4","carbon","农业碳与绿色低碳","\u002Ftopic\u002Fenvironment\u002Fcarbon","H",[],{"status":37,"locked":13,"version":38,"method":39,"evidence":40},"classified","2026-10-22.2","rules",{"candidates":41,"agricultural_evidence":44},[42,51,57],{"code":30,"score":43,"supported":44,"title":45,"abstract":47,"tags":50},15,true,[46],"carbon footprint",[48,49],"碳排放","农业碳",[28],{"code":52,"score":53,"supported":44,"title":54,"abstract":55,"tags":56},"I1",4,[],[25],[25],{"code":58,"score":53,"supported":44,"title":59,"abstract":60,"tags":62},"I4",[],[61],"空间分析",[26],[64,65],"农业碳排放 数字农业 省级面板 空间溢出","农业碳排放 数字农业","农业碳排放数字农业省级面板空间溢出-1585",3,{"doi":8,"openalex_id":8,"authors":69,"venue":8,"cited_by_count":70,"oa_url":8,"card":71,"direction":75,"ingested_from":77},[],0,{"tldr":72,"method":73,"finding":74,"direction":75,"opportunity":76},"利用省级面板数据，发现数字农业显著降低农业碳排放强度，存在区域异质性。","构建数字农业发展指数，采用省级面板数据与空间分析方法。","数字农业通过提升资本配置效率降低碳排放，主粮产区和东部效应更强。","农业绿色发展与碳","中西部数字农业减排效应不显著，可探索其制约因素及针对性政策设计。","agent","2026-09-04T00:05:34.351468Z",{"total":80,"page":20,"page_size":80,"items":81},6,[82,139,187,240,299,355],{"id":83,"title":84,"url":85,"summary":86,"summary_zh":8,"content":8,"source_name":87,"source_url":8,"published_at":88,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":89,"score_detail":90,"sources":95,"tags":97,"primary_topic":101,"secondary_topics":102,"classification":103,"search_phrases":127,"slug":129,"view_count":93,"doi":130,"paper":131,"created_at":138},1365,"基于数字化水平评估的贵州省农业碳排放影响因素分解","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fenvironmental-science\u002Farticles\u002F10.3389\u002Ffenvs.2026.1901851\u002Fabstract","基于 2013—2022 年贵州省面板数据，使用 LMDI 与 VAR 模型分解数字化对农业碳排放的驱动效应。结果显示：数字化对碳排放由强促增转为弱促增；能源强度与数字化引致的能源效率合计减排 3168 万吨；预测 2023—2030 年可净减排 925 万吨；数字化并非自动等同于减排，需以精准投入管理、畜禽粪污处理和清洁能源为支撑。","Frontiers in Environmental Science · 2026-09","2026-08-31T16:00:00Z",67,{"impact":43,"substance":91,"depth":16,"authority":92,"freshness":93,"relevant":20,"comment":94},20,12,2,"基于省级面板数据的实证研究，揭示数字化对农业碳排放的阶段性影响，为精准减排提供依据。",[96],{"name":87,"url":85},[25,28,98,99,100],"贵州","LMDI","能源效率",{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},[],{"status":37,"locked":13,"version":38,"method":39,"evidence":104},{"candidates":105,"agricultural_evidence":44},[106,111,117,123],{"code":30,"score":107,"supported":44,"title":108,"abstract":109,"tags":110},23,[48,49],[48,49],[28],{"code":112,"score":67,"supported":44,"title":113,"abstract":114,"tags":116},"C",[],[115],"畜禽",[],{"code":118,"score":67,"supported":44,"title":119,"abstract":120,"tags":122},"C6",[],[121],"畜禽粪污",[],{"code":52,"score":20,"supported":13,"title":124,"abstract":125,"tags":126},[],[],[25],[128,65],"农业碳排放 数字农业 能源效率 贵州","农业碳排放数字农业能源效率贵州-1365","10.3389\u002Ffenvs.2026.1901851\u002Fabstract",{"doi":130,"openalex_id":8,"authors":132,"venue":8,"cited_by_count":70,"oa_url":8,"card":133,"direction":75,"ingested_from":77},[],{"tldr":134,"method":135,"finding":136,"direction":75,"opportunity":137},"评估数字化水平对贵州农业碳排放的影响，预测未来减排潜力。","LMDI分解与VAR模型，基于2013-2022年面板数据。","数字化减排效应由强转弱，需精准管理等措施支撑。","可研究数字化与农业减排的耦合机制，探索不同数字化水平下的最优减排路径。","2026-09-02T00:05:06.917066Z",{"id":140,"title":141,"url":142,"summary":143,"summary_zh":8,"content":8,"source_name":144,"source_url":8,"published_at":8,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":14,"score_detail":145,"sources":148,"tags":150,"primary_topic":155,"secondary_topics":156,"classification":157,"search_phrases":175,"slug":178,"view_count":70,"doi":8,"paper":179,"created_at":186},5360,"国家农业绿色发展试点区对农业碳排放的影响:基于 284 城市 2013-2023 年面板数据","https:\u002F\u002Fm2.mtmt.hu\u002Fapi\u002Fpublication\u002F37540958","研究基于 284 个城市 2013—2023 年面板数据,以国家农业绿色发展试点区(NAGDPZ)为准自然实验,采用双重差分法(DID)系统评估政策对农业碳排放的影响。研究发现:(1)NAGDPZ 政策显著降低农业碳排放,系数为 -0.008(10% 显著);(2)机制分析表明减排效应主要通过农业生产效率提升、数字普惠金融发展和农村创新创业推动;(3)异质性分析显示南方、非粮食主产区、农业金融支持水平较高地区的碳减排效应更显著,系数分别为 -0.008、-.016、-0.015。","《中国环境科学》46(1):555-567 2026",{"impact":17,"substance":17,"depth":16,"authority":146,"freshness":93,"relevant":20,"comment":147},14,"基于284城市十年面板数据的政策评估，方法规范、结论有新意，但发表时间较早、时效性弱，适合作为研究型精选而非快讯。",[149],{"name":144,"url":142},[151,152,153,28,154],"农业绿色发展","数字普惠金融","双重差分","绿色试点区",{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},[],{"status":37,"locked":13,"version":38,"method":39,"evidence":158},{"candidates":159,"agricultural_evidence":44},[160,165,170],{"code":30,"score":161,"supported":44,"title":162,"abstract":163,"tags":164},24,[48,49],[48,49],[28,151],{"code":166,"score":20,"supported":13,"title":167,"abstract":168,"tags":169},"G3",[],[],[152],{"code":171,"score":20,"supported":13,"title":172,"abstract":173,"tags":174},"H8",[],[],[151],[176,177],"国家农业绿色发展试点区 农业碳排放","284城市 面板数据 碳减排","国家农业绿色发展试点区农业碳排放-5360",{"doi":8,"openalex_id":8,"authors":180,"venue":8,"cited_by_count":70,"oa_url":8,"card":181,"direction":75,"ingested_from":77},[],{"tldr":182,"method":183,"finding":184,"direction":75,"opportunity":185},"基于284城市面板数据，用DID评估国家农业绿色发展试点区对农业碳排放的影响。","284城市2013-2023年面板数据，双重差分法（DID）准自然实验。","试点区政策显著降低农业碳排放，主要通过生产效率、数字普惠金融和农村创新创业实现。","可探究数字普惠金融与绿色试点政策的协同减排机制，及非粮食主产区的差异化政策设计。","2026-10-20T02:47:54.150914Z",{"id":188,"title":189,"url":190,"summary":191,"summary_zh":8,"content":8,"source_name":192,"source_url":8,"published_at":193,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":194,"score_detail":195,"sources":198,"tags":200,"primary_topic":205,"secondary_topics":206,"classification":207,"search_phrases":228,"slug":231,"view_count":20,"doi":8,"paper":232,"created_at":239},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":107,"depth":16,"authority":18,"freshness":196,"relevant":20,"comment":197},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。",[199],{"name":192,"url":190},[201,202,203,28,204],"数字乡村","智慧农业","农业数字化","绿色创新",{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},[],{"status":37,"locked":13,"version":38,"method":39,"evidence":208},{"candidates":209,"agricultural_evidence":44},[210,214,218,223],{"code":30,"score":53,"supported":44,"title":211,"abstract":212,"tags":213},[],[48],[28],{"code":52,"score":93,"supported":13,"title":215,"abstract":216,"tags":217},[],[],[203,202],{"code":219,"score":20,"supported":13,"title":220,"abstract":221,"tags":222},"I",[],[],[203],{"code":224,"score":20,"supported":13,"title":225,"abstract":226,"tags":227},"I6",[],[],[201],[229,230],"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":233,"venue":8,"cited_by_count":70,"oa_url":8,"card":234,"direction":75,"ingested_from":77},[],{"tldr":235,"method":236,"finding":237,"direction":75,"opportunity":238},"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":241,"title":242,"url":243,"summary":244,"summary_zh":245,"content":8,"source_name":9,"source_url":243,"published_at":246,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":247,"score_detail":248,"sources":253,"tags":255,"primary_topic":260,"secondary_topics":261,"classification":262,"search_phrases":269,"slug":272,"view_count":70,"doi":273,"paper":274,"created_at":298},4870,"Association of energy poverty, import and export diversification with agricultural sector performance in South Asia","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1749155","This paper examines the association between energy poverty, import diversification, export diversification, and agricultural sector performance in South Asia, while controlling for carbon dioxide emissions from agriculture and employment in agriculture. Using panel data for eight South Asian countries—Afghanistan, Bangladesh, Bhutan, India, Maldives, Nepal, Pakistan, and Sri Lanka—covering the period from 2000 to 2023, the study constructs an Energy Poverty Index through Principal Component Analysis based on six energy-related indicators. The Feasible Generalized Least Squares results show that better energy conditions and lower energy poverty are positively associated with agricultural sector performance. In contrast, import diversification and carbon dioxide emissions from agriculture are negatively associated with agricultural sector performance, while export diversification is positively associated with it. Employment in agriculture shows a positive but statistically insignificant association. These findings remain robust when Panel-Corrected Standard Errors and Driscoll–Kraay standard error estimators are applied. Overall, the results suggest that improved energy access, environmentally sustainable agricultural practices, and strategic trade diversification are closely linked to agricultural sector performance in South Asia.","本文考察了南亚地区能源贫困、进口多样化、出口多样化与农业部门绩效之间的关联，同时控制了农业二氧化碳排放和农业就业。研究使用八个南亚国家——阿富汗、孟加拉国、不丹、印度、马尔代夫、尼泊尔、巴基斯坦和斯里兰卡——2000年至2023年的面板数据，基于六项能源相关指标，通过主成分分析构建了能源贫困指数。可行广义最小二乘法（Feasible Generalized Least Squares）结果显示，更好的能源条件和更低的能源贫困与农业部门绩效呈正相关。相比之下，进口多样化和农业二氧化碳排放与农业部门绩效呈负相关，而出口多样化则与其呈正相关。农业就业显示出正相关，但在统计上不显著。在应用面板校正标准误（Panel-Corrected Standard Errors）和Driscoll–Kraay标准误估计量后，这些发现仍然稳健。总体而言，结果表明，改善能源获取、环境可持续的农业实践以及战略性贸易多样化与南亚农业部门绩效密切相关。","2026-10-13T00:00:00Z",72,{"impact":92,"substance":249,"depth":250,"authority":18,"freshness":251,"relevant":20,"comment":252},21,17,9,"基于南亚八国2000—2023年面板数据的实证研究，方法规范、结论有新意，但属区域学术成果，公共政策影响有限。",[254],{"name":9,"url":243},[28,256,257,258,259],"面板数据","南亚农业","能源贫困","农业贸易",{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},[],{"status":37,"locked":13,"version":38,"method":39,"evidence":263},{"candidates":264,"agricultural_evidence":44},[265],{"code":30,"score":53,"supported":44,"title":266,"abstract":267,"tags":268},[],[48],[28],[270,271],"南亚 能源贫困","南亚 农产品 出口多元化","南亚能源贫困-4870","10.3389\u002Ffsufs.2026.1749155",{"doi":273,"openalex_id":275,"authors":276,"venue":9,"cited_by_count":70,"oa_url":243,"card":292,"direction":75,"ingested_from":297},"W7221619650",[277,280,282,284,286,288,290],{"name":278,"orcid":279},"Abdullah‐Al Mamun","https:\u002F\u002Forcid.org\u002F0000-0002-3251-4271",{"name":281,"orcid":8},"Md. Golam Kibria",{"name":283,"orcid":8},"Farian Tahrim",{"name":285,"orcid":8},"Jeremy Ko",{"name":287,"orcid":8},"Chun Kai Leung",{"name":289,"orcid":8},"Mohammad Ridwan",{"name":291,"orcid":8},"Wai-kit Ming",{"tldr":293,"method":294,"finding":295,"direction":75,"opportunity":296},"研究南亚八国能源贫困、进出口多样化与农业表现的关系。","2000-2023年面板数据，主成分分析构建能源贫困指数，FGLS回归。","能源改善和出口多样化正向关联农业表现，进口多样化和农业碳排放负向关联。","可深入探究能源贫困缓解与农业碳减排的协同机制及南亚区域异质性。","openalex","2026-10-14T23:30:06.945702Z",{"id":300,"title":301,"url":302,"summary":303,"summary_zh":304,"content":8,"source_name":305,"source_url":302,"published_at":306,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":307,"score_detail":308,"sources":310,"tags":312,"primary_topic":315,"secondary_topics":316,"classification":317,"search_phrases":334,"slug":337,"view_count":53,"doi":338,"paper":339,"created_at":354},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":18,"freshness":251,"relevant":20,"comment":309},"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。",[311],{"name":305,"url":302},[201,202,313,28,314],"农业韧性","农业产业集聚",{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},[],{"status":37,"locked":13,"version":38,"method":39,"evidence":318},{"candidates":319,"agricultural_evidence":44},[320,325,330],{"code":30,"score":16,"supported":44,"title":321,"abstract":323,"tags":324},[322],"carbon emission",[48,49,322],[28],{"code":224,"score":19,"supported":44,"title":326,"abstract":327,"tags":329},[],[201,328],"digital village",[201],{"code":52,"score":20,"supported":13,"title":331,"abstract":332,"tags":333},[],[],[202],[335,336],"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":338,"openalex_id":340,"authors":341,"venue":305,"cited_by_count":70,"oa_url":302,"card":348,"direction":353,"ingested_from":297},"W7220924322",[342,344,346],{"name":343,"orcid":8},"Jinghui Hu",{"name":345,"orcid":8},"Jun He",{"name":347,"orcid":8},"Junzhong He",{"tldr":349,"method":350,"finding":351,"direction":75,"opportunity":352},"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","2026-10-09T23:30:32.435467Z",{"id":356,"title":357,"url":358,"summary":359,"summary_zh":360,"content":8,"source_name":9,"source_url":358,"published_at":361,"category":11,"cover_url":8,"hotness":362,"is_selected":13,"score":307,"score_detail":363,"sources":365,"tags":370,"primary_topic":373,"secondary_topics":378,"classification":380,"search_phrases":406,"slug":409,"view_count":70,"doi":410,"paper":411,"created_at":429},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。","2026-10-07T00:00:00Z",25,{"impact":16,"substance":17,"depth":16,"authority":18,"freshness":251,"relevant":20,"comment":364},"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。",[366,367],{"name":9,"url":358},{"name":368,"url":369},"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",[201,202,371,28,372],"玉米","区块链",{"code":374,"slug":375,"name":371,"path":376,"parent_code":377},"A3","maize","\u002Ftopic\u002Ffield-crops\u002Fmaize","A",[379],{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},{"status":37,"locked":13,"version":38,"method":39,"evidence":381},{"candidates":382,"agricultural_evidence":44},[383,387,391,396,402],{"code":374,"score":43,"supported":44,"title":384,"abstract":385,"tags":386},[375],[371,375],[371],{"code":30,"score":92,"supported":44,"title":388,"abstract":389,"tags":390},[322],[48],[28],{"code":52,"score":19,"supported":44,"title":392,"abstract":393,"tags":395},[],[25,394],"digital agriculture",[202],{"code":397,"score":67,"supported":44,"title":398,"abstract":399,"tags":401},"G2",[],[400],"社会化服务",[],{"code":224,"score":20,"supported":13,"title":403,"abstract":404,"tags":405},[],[],[201],[407,408],"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":410,"openalex_id":412,"authors":413,"venue":9,"cited_by_count":70,"oa_url":422,"card":423,"direction":428,"ingested_from":297},"W7220869193",[414,417,419],{"name":415,"orcid":416},"Pengfei Zhou","https:\u002F\u002Forcid.org\u002F0000-0003-2117-7967",{"name":418,"orcid":8},"Zhiyi Hu",{"name":420,"orcid":421},"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":424,"method":425,"finding":426,"direction":75,"opportunity":427},"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"]