[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-1518":3,"related-1518":101},{"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":30,"secondary_topics":36,"classification":37,"search_phrases":80,"slug":83,"view_count":20,"doi":84,"paper":85,"created_at":100},1518,"Do new agricultural business entities reduce the marginal abatement cost of agricultural carbon emissions? Evidence from China","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1922569","Introduction The development of new agricultural business entities has become an important driver of agricultural modernization and rural transformation. Their growing role in promoting green agricultural development and carbon mitigation highlights the need to assess not only their emission-reduction potential but also the economic costs of carbon mitigation. However, whether these entities can reduce the marginal abatement cost of agricultural carbon emissions remains unclear. Methods Using panel data from 30 provinces in China from 2011 to 2023, this study empirically examines the impact of new agricultural business entities on the marginal abatement cost of agricultural carbon emissions and its underlying mechanisms by employing a two-way fixed effects model, mediation analysis, and spatial econometric models. Results The results show that the development of new agricultural business entities significantly reduces the marginal abatement cost of agricultural carbon emissions. Further analysis indicates that farmer cooperatives and family farms play a particularly important role in reducing carbon abatement costs, with stronger effects in major grain-producing regions and areas with greater governance capacity. Mechanism tests suggest that new agricultural business entities reduce marginal abatement costs by easing agricultural credit constraints, improving land allocation efficiency, and promoting agricultural technological progress. In addition, the spatial econometric results reveal significant spatial spillover effects, indicating that the development of new agricultural business entities in neighboring regions also contributes to lower local agricultural carbon abatement costs. Discussion These findings provide new evidence on the economic mechanisms through which new agricultural business entities contribute to agricultural carbon mitigation and offer policy implications for promoting the low-carbon transformation of agriculture in China and other developing countries.","引言 新型农业经营主体的发展已成为推动农业现代化与农村转型的重要动力。其在促进农业绿色发展与碳减排方面日益凸显的作用，要求我们不仅评估其减排潜力，还需考量碳减排的经济成本。然而，这些主体能否降低农业碳排放的边际减排成本，目前尚不明确。方法 基于2011至2023年中国30个省份的面板数据，本研究运用双向固定效应模型、中介效应分析及空间计量模型，实证考察了新型农业经营主体对农业碳排放边际减排成本的影响及其内在机制。结果 研究结果显示，新型农业经营主体的发展显著降低了农业碳排放的边际减排成本。进一步分析表明，农民专业合作社与家庭农场在降低碳减排成本方面发挥着尤为重要的作用，且在粮食主产区及治理能力较强的地区效果更为显著。机制检验提示，新型农业经营主体通过缓解农业信贷约束、改善土地配置效率及促进农业技术进步等途径，降低了边际减排成本。此外，空间计量结果揭示了显著的空间溢出效应，表明邻近地区新型农业经营主体的发展亦有助于降低本地区农业碳减排成本。讨论 上述发现为新型农业经营主体助力农业碳减排的经济机制提供了新的证据，并对中国及其他发展中国家推动农业低碳转型具有政策启示意义。",null,"Frontiers in Sustainable Food Systems","2026-09-03T00:00:00Z","论文",10,false,72,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},18,22,12,2,1,"研究新型农业经营主体对农业碳排放边际减排成本的影响，数据详实，结论具有政策参考价值。",[24],{"name":10,"url":6},[26,27,28,29],"农业经济","低碳农业","新型农业经营主体","农业碳排放",{"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",[],{"status":38,"locked":14,"version":39,"method":40,"evidence":41},"classified","2026-10-22.2","rules",{"candidates":42,"agricultural_evidence":45},[43,51,59,66,73],{"code":31,"score":44,"supported":45,"title":46,"abstract":47,"tags":50},8,true,[],[48,49],"碳排放","农业碳",[27,29],{"code":52,"score":53,"supported":45,"title":54,"abstract":55,"tags":58},"G3",6,[],[56,57],"农业信贷","agricultural credit",[],{"code":60,"score":53,"supported":45,"title":61,"abstract":62,"tags":65},"J2",[],[63,64],"农业现代化","agricultural modernization",[],{"code":67,"score":68,"supported":45,"title":69,"abstract":70,"tags":72},"G2",4,[],[71],"家庭农场",[28],{"code":74,"score":75,"supported":45,"title":76,"abstract":77,"tags":79},"I4",3,[],[78],"空间计量",[],[81,82],"新型农业经营主体 农业碳排放 低碳农业 农业经济","新型农业经营主体 农业碳排放","新型农业经营主体农业碳排放低碳农业农业经济-1518","10.3389\u002Ffsufs.2026.1922569",{"doi":84,"openalex_id":86,"authors":87,"venue":10,"cited_by_count":92,"oa_url":6,"card":93,"direction":97,"ingested_from":99},"W7206169038",[88,90],{"name":89,"orcid":9},"Juan Pu",{"name":91,"orcid":9},"Li Mingliang",0,{"tldr":94,"method":95,"finding":96,"direction":97,"opportunity":98},"研究新型农业经营主体对农业碳排放边际减排成本的影响及机制。","使用2011-2023年中国省级面板数据，采用双向固定效应、中介效应和空间计量模","新型农业经营主体显著降低边际减排成本，存在空间溢出效应，合作社和家庭农场作用更明显。","农业绿色发展与碳","可进一步研究新型经营主体降低减排成本的具体技术路径，或在不同区域尺度下的差异化政策设计。","openalex","2026-09-03T23:30:07.743142Z",{"total":53,"page":21,"page_size":53,"items":102},[103,141,189,243,300,357],{"id":104,"title":105,"url":106,"summary":107,"summary_zh":9,"content":108,"source_name":109,"source_url":9,"published_at":110,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":111,"score_detail":112,"sources":115,"tags":117,"primary_topic":121,"secondary_topics":122,"classification":123,"search_phrases":136,"slug":139,"view_count":75,"doi":9,"paper":9,"created_at":140},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 2](https:\u002F\u002Fnews.ebiotrade.com\u002FQR_ebiotradeweibo.png)生物通新浪微博\n\n微信\n\n新浪微博\n\n*   搜索\n*   国际\n*   国内\n*   人物\n*   产业\n*   热点\n*   科普\n\n[今日动态](https:\u002F\u002Fwww.ebiotrade.com\u002Fnewsf\u002F) | [人才市场](https:\u002F\u002Fwww.ebiotrade.com\u002Fjobf\u002F) | [新技术专栏](https:\u002F\u002Febiotech.ebiotrade.com\u002F) | [中国科学人](https:\u002F\u002Fscientist.ebiotrade.com\u002F) | [云展台](https:\u002F\u002Finstrument.ebiotrade.com\u002F) | [BioHot](https:\u002F\u002Fwww.ebiotrade.com\u002FBioHot) | [云讲堂直播](https:\u002F\u002Fwww.ebiotrade.com\u002Fspeech\u002F) | [会展中心](https:\u002F\u002Fmeetings.ebiotrade.com\u002F) | [特价专栏](https:\u002F\u002Fwww.ebiotrade.com\u002Fpromotion\u002F) | [技术快讯](https:\u002F\u002Fwww.ebiotrade.com\u002Fjsqk\u002F) | [免费试用](https:\u002F\u002Fwww.ebiotrade.com\u002Fpromotion\u002F#tejia3)\n\n版权所有 生物通\n\nCopyright© eBiotrade.com, All Rights Reserved\n\n联系信箱：![Image 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",74,{"impact":17,"substance":18,"depth":113,"authority":19,"freshness":75,"relevant":21,"comment":114},19,"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。",[116],{"name":109,"url":106},[118,27,29,119,120],"数字经济","规模效应","效率效应",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":14,"version":39,"method":40,"evidence":124},{"candidates":125,"agricultural_evidence":45},[126,131],{"code":31,"score":127,"supported":45,"title":128,"abstract":129,"tags":130},24,[48,49],[48,49],[27,29],{"code":132,"score":21,"supported":14,"title":133,"abstract":134,"tags":135},"I6",[],[],[118],[137,138],"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":142,"title":143,"url":144,"summary":145,"summary_zh":9,"content":9,"source_name":146,"source_url":9,"published_at":9,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":147,"score_detail":148,"sources":151,"tags":153,"primary_topic":158,"secondary_topics":159,"classification":160,"search_phrases":176,"slug":179,"view_count":92,"doi":9,"paper":180,"created_at":188},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",78,{"impact":18,"substance":18,"depth":17,"authority":149,"freshness":20,"relevant":21,"comment":150},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。",[152],{"name":146,"url":144},[154,155,156,29,157],"农业绿色发展","数字普惠金融","双重差分","绿色试点区",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":14,"version":39,"method":40,"evidence":161},{"candidates":162,"agricultural_evidence":45},[163,167,171],{"code":31,"score":127,"supported":45,"title":164,"abstract":165,"tags":166},[48,49],[48,49],[29,154],{"code":52,"score":21,"supported":14,"title":168,"abstract":169,"tags":170},[],[],[155],{"code":172,"score":21,"supported":14,"title":173,"abstract":174,"tags":175},"H8",[],[],[154],[177,178],"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":9,"openalex_id":9,"authors":181,"venue":9,"cited_by_count":92,"oa_url":9,"card":182,"direction":97,"ingested_from":187},[],{"tldr":183,"method":184,"finding":185,"direction":97,"opportunity":186},"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。","agent","2026-10-20T02:47:54.150914Z",{"id":190,"title":191,"url":192,"summary":193,"summary_zh":9,"content":9,"source_name":194,"source_url":9,"published_at":195,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":196,"score_detail":197,"sources":201,"tags":203,"primary_topic":208,"secondary_topics":209,"classification":210,"search_phrases":231,"slug":234,"view_count":21,"doi":9,"paper":235,"created_at":242},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":198,"depth":17,"authority":199,"freshness":44,"relevant":21,"comment":200},23,13,"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。",[202],{"name":194,"url":192},[204,205,206,29,207],"数字乡村","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":38,"locked":14,"version":39,"method":40,"evidence":211},{"candidates":212,"agricultural_evidence":45},[213,217,222,227],{"code":31,"score":68,"supported":45,"title":214,"abstract":215,"tags":216},[],[48],[29],{"code":218,"score":20,"supported":14,"title":219,"abstract":220,"tags":221},"I1",[],[],[206,205],{"code":223,"score":21,"supported":14,"title":224,"abstract":225,"tags":226},"I",[],[],[206],{"code":132,"score":21,"supported":14,"title":228,"abstract":229,"tags":230},[],[],[204],[232,233],"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":236,"venue":9,"cited_by_count":92,"oa_url":9,"card":237,"direction":97,"ingested_from":187},[],{"tldr":238,"method":239,"finding":240,"direction":97,"opportunity":241},"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":244,"title":245,"url":246,"summary":247,"summary_zh":248,"content":9,"source_name":10,"source_url":246,"published_at":249,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":250,"sources":255,"tags":257,"primary_topic":262,"secondary_topics":263,"classification":264,"search_phrases":271,"slug":274,"view_count":92,"doi":275,"paper":276,"created_at":299},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",{"impact":19,"substance":251,"depth":252,"authority":199,"freshness":253,"relevant":21,"comment":254},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。",[256],{"name":10,"url":246},[29,258,259,260,261],"面板数据","南亚农业","能源贫困","农业贸易",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":14,"version":39,"method":40,"evidence":265},{"candidates":266,"agricultural_evidence":45},[267],{"code":31,"score":68,"supported":45,"title":268,"abstract":269,"tags":270},[],[48],[29],[272,273],"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":275,"openalex_id":277,"authors":278,"venue":10,"cited_by_count":92,"oa_url":246,"card":294,"direction":97,"ingested_from":99},"W7221619650",[279,282,284,286,288,290,292],{"name":280,"orcid":281},"Abdullah‐Al Mamun","https:\u002F\u002Forcid.org\u002F0000-0002-3251-4271",{"name":283,"orcid":9},"Md. Golam Kibria",{"name":285,"orcid":9},"Farian Tahrim",{"name":287,"orcid":9},"Jeremy Ko",{"name":289,"orcid":9},"Chun Kai Leung",{"name":291,"orcid":9},"Mohammad Ridwan",{"name":293,"orcid":9},"Wai-kit Ming",{"tldr":295,"method":296,"finding":297,"direction":97,"opportunity":298},"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。","2026-10-14T23:30:06.945702Z",{"id":301,"title":302,"url":303,"summary":304,"summary_zh":305,"content":9,"source_name":306,"source_url":303,"published_at":307,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":308,"score_detail":309,"sources":311,"tags":313,"primary_topic":316,"secondary_topics":317,"classification":318,"search_phrases":336,"slug":339,"view_count":68,"doi":340,"paper":341,"created_at":356},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":17,"substance":18,"depth":17,"authority":199,"freshness":253,"relevant":21,"comment":310},"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。",[312],{"name":306,"url":303},[204,205,314,29,315],"农业韧性","农业产业集聚",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":14,"version":39,"method":40,"evidence":319},{"candidates":320,"agricultural_evidence":45},[321,326,332],{"code":31,"score":17,"supported":45,"title":322,"abstract":324,"tags":325},[323],"carbon emission",[48,49,323],[29],{"code":132,"score":327,"supported":45,"title":328,"abstract":329,"tags":331},7,[],[204,330],"digital village",[204],{"code":218,"score":21,"supported":14,"title":333,"abstract":334,"tags":335},[],[],[205],[337,338],"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":340,"openalex_id":342,"authors":343,"venue":306,"cited_by_count":92,"oa_url":303,"card":350,"direction":355,"ingested_from":99},"W7220924322",[344,346,348],{"name":345,"orcid":9},"Jinghui Hu",{"name":347,"orcid":9},"Jun He",{"name":349,"orcid":9},"Junzhong He",{"tldr":351,"method":352,"finding":353,"direction":97,"opportunity":354},"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":358,"title":359,"url":360,"summary":361,"summary_zh":362,"content":9,"source_name":10,"source_url":360,"published_at":363,"category":12,"cover_url":9,"hotness":364,"is_selected":14,"score":308,"score_detail":365,"sources":367,"tags":372,"primary_topic":375,"secondary_topics":380,"classification":382,"search_phrases":409,"slug":412,"view_count":92,"doi":413,"paper":414,"created_at":432},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":17,"substance":18,"depth":17,"authority":199,"freshness":253,"relevant":21,"comment":366},"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。",[368,369],{"name":10,"url":360},{"name":370,"url":371},"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",[204,205,373,29,374],"玉米","区块链",{"code":376,"slug":377,"name":373,"path":378,"parent_code":379},"A3","maize","\u002Ftopic\u002Ffield-crops\u002Fmaize","A",[381],{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},{"status":38,"locked":14,"version":39,"method":40,"evidence":383},{"candidates":384,"agricultural_evidence":45},[385,390,394,400,405],{"code":376,"score":386,"supported":45,"title":387,"abstract":388,"tags":389},15,[377],[373,377],[373],{"code":31,"score":19,"supported":45,"title":391,"abstract":392,"tags":393},[323],[48],[29],{"code":218,"score":327,"supported":45,"title":395,"abstract":396,"tags":399},[],[397,398],"数字农业","digital agriculture",[205],{"code":67,"score":75,"supported":45,"title":401,"abstract":402,"tags":404},[],[403],"社会化服务",[],{"code":132,"score":21,"supported":14,"title":406,"abstract":407,"tags":408},[],[],[204],[410,411],"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":413,"openalex_id":415,"authors":416,"venue":10,"cited_by_count":92,"oa_url":425,"card":426,"direction":431,"ingested_from":99},"W7220869193",[417,420,422],{"name":418,"orcid":419},"Pengfei Zhou","https:\u002F\u002Forcid.org\u002F0000-0003-2117-7967",{"name":421,"orcid":9},"Zhiyi Hu",{"name":423,"orcid":424},"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":427,"method":428,"finding":429,"direction":97,"opportunity":430},"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"]