[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2760":3,"related-2760":93},{"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":24,"tags":26,"primary_topic":32,"secondary_topics":38,"classification":39,"search_phrases":69,"slug":72,"view_count":73,"doi":74,"paper":75,"created_at":92},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。",null,"Frontiers in Sustainable Food Systems","2026-09-16T00:00:00Z","论文",10,false,85,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},22,23,18,13,9,1,"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。",[25],{"name":10,"url":6},[27,28,29,30,31],"低碳农业","农业政策","农业碳减排","绿色技术","生态文明",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},"H4","carbon","农业碳与绿色低碳","\u002Ftopic\u002Fenvironment\u002Fcarbon","H",[],{"status":40,"locked":14,"version":41,"method":42,"evidence":43},"classified","2026-10-22.2","rules",{"candidates":44,"agricultural_evidence":47},[45,54,62],{"code":33,"score":46,"supported":47,"title":48,"abstract":49,"tags":53},11,true,[],[50,51,52],"碳排放","农业碳","carbon emission",[27,29],{"code":55,"score":56,"supported":47,"title":57,"abstract":58,"tags":61},"I2",6,[],[59,60],"机器学习","machine learning",[],{"code":63,"score":56,"supported":47,"title":64,"abstract":65,"tags":68},"I4",[],[66,67],"空间分析","spatial analysis",[],[70,71],"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","农业碳减排低碳农业农业政策生态文明-2760",0,"10.3389\u002Ffsufs.2026.1945948",{"doi":74,"openalex_id":76,"authors":77,"venue":10,"cited_by_count":73,"oa_url":84,"card":85,"direction":89,"ingested_from":91},"W7213428280",[78,80,82],{"name":79,"orcid":9},"Xiaoyu Chen",{"name":81,"orcid":9},"Xuexue Wu",{"name":83,"orcid":9},"Haohan Wang","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1945948\u002Fpdf",{"tldr":86,"method":87,"finding":88,"direction":89,"opportunity":90},"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。","农业绿色发展与碳","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。","openalex","2026-09-17T23:30:08.212229Z",{"total":56,"page":22,"page_size":56,"items":94},[95,173,229,279,332,403],{"id":96,"title":97,"url":98,"summary":99,"summary_zh":100,"content":9,"source_name":10,"source_url":98,"published_at":101,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":102,"score_detail":103,"sources":105,"tags":107,"primary_topic":112,"secondary_topics":117,"classification":119,"search_phrases":151,"slug":154,"view_count":22,"doi":155,"paper":156,"created_at":172},3531,"Extreme climate shocks and agricultural net carbon sinks: the moderating role of supply chain resilience in China","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1940685","Introduction Extreme climate shocks increasingly challenge the ability of agricultural systems to sustain crop carbon fixation while controlling production-related emissions. Methods Using panel data for 240 Chinese cities from 2002 to 2023, this study examines the relationships between four climate shocks—extreme high temperature (HTD), extreme low temperature (LTD), extreme rainfall (ERD), and extreme drought (EED)—and agricultural net carbon sinks (NCS), together with the moderating role of agricultural supply chain resilience. NCS is measured as annual crop-biomass carbon fixation net of selected agricultural production emissions, thereby providing an integrated, production-based indicator of agricultural carbon performance. Results Two-way fixed-effects estimates show that all four climate shocks are negatively associated with NCS, and the direction of these relationships remains stable across alternative accounting boundaries, sample adjustments, leave-one-province-out tests, and double machine learning specifications. Agricultural supply chain resilience significantly conditions the climate–carbon relationship. Its resistance, recovery, and reorientation capacities attenuate the adverse associations of high-temperature, extreme-rainfall, and drought shocks with NCS. The low-temperature result further indicates that the effectiveness of resilience depends on matching supply chain functions with the biological mechanisms and intervention windows of specific climate hazards. Heterogeneity analyses confirm that climate sensitivity and resilience requirements vary with regional location, agricultural productivity, modernization, and irrigation conditions. Complementary machine-learning analysis identifies nonlinear predictive patterns and reinforces the importance of differentiated adaptation. Discussion These findings extend agricultural climate research from production and emissions to an integrated carbon-balance perspective and demonstrate that hazard-specific supply chain resilience can support both climate adaptation and low-carbon agricultural development.","引言 极端气候冲击日益挑战农业系统在维持作物碳固定的同时控制生产相关排放的能力。方法 本研究利用2002年至2023年中国240个城市的面板数据，考察四种气候冲击——极端高温（HTD）、极端低温（LTD）、极端降雨（ERD）和极端干旱（EED）——与农业净碳汇（NCS）之间的关系，以及农业供应链韧性的调节作用。NCS以年度作物生物量碳固定量扣除部分农业生产排放量来衡量，从而提供一个基于生产的综合性农业碳绩效指标。结果 双向固定效应估计表明，四种气候冲击均与NCS呈负相关，且这些关系的方向在替代核算边界、样本调整、逐一剔除省份检验和双重机器学习设定下均保持稳定。农业供应链韧性显著调节气候—碳关系。其抵抗能力、恢复能力和重新定向能力减弱了高温、极端降雨和干旱冲击与NCS之间的不利关联。低温结果进一步表明，韧性的有效性取决于供应链功能与特定气候灾害的生物机制及干预窗口的匹配。异质性分析证实，气候敏感性和韧性需求因区域位置、农业生产率、现代化水平和灌溉条件而异。补充性机器学习分析识别出非线性预测模式，并强化了差异化适应的重要性。讨论 这些发现将农业气候研究从生产和排放拓展至综合碳平衡视角，并表明针对特定灾害的供应链韧性能够同时支持气候适应和农业低碳发展。","2026-09-25T00:00:00Z",80,{"impact":19,"substance":17,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":104},"基于240城22年面板数据的实证研究，方法扎实、结论稳健，对农业气候适应与低碳发展有参考价值，但属学术论文，公共传播性有限。",[106],{"name":10,"url":98},[108,109,27,110,111],"智慧农业","农业供应链","气候韧性","农业碳汇",{"code":113,"slug":114,"name":115,"path":116,"parent_code":37},"H7","climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate",[118],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":120},{"candidates":121,"agricultural_evidence":47},[122,129,135,142,146],{"code":113,"score":13,"supported":47,"title":123,"abstract":124,"tags":128},[],[125,126,127],"气候适应","干旱","drought",[110],{"code":33,"score":130,"supported":47,"title":131,"abstract":132,"tags":134},8,[],[133,51],"碳汇",[27,111],{"code":136,"score":56,"supported":47,"title":137,"abstract":138,"tags":141},"H3",[],[139,140],"灌溉","irrigation",[],{"code":55,"score":56,"supported":47,"title":143,"abstract":144,"tags":145},[],[59,60],[],{"code":147,"score":22,"supported":14,"title":148,"abstract":149,"tags":150},"I1",[],[],[108],[152,153],"中国 农业净碳汇 极端气候","农业供应链韧性 气候适应","中国农业净碳汇极端气候-3531","10.3389\u002Ffsufs.2026.1940685",{"doi":155,"openalex_id":157,"authors":158,"venue":10,"cited_by_count":73,"oa_url":98,"card":167,"direction":89,"ingested_from":91},"W7214310732",[159,161,164],{"name":160,"orcid":9},"Chunyan Zhao",{"name":162,"orcid":163},"Jiajie Xia","https:\u002F\u002Forcid.org\u002F0000-0003-3225-0154",{"name":165,"orcid":166},"Guoping Ding","https:\u002F\u002Forcid.org\u002F0000-0002-4865-5044",{"tldr":168,"method":169,"finding":170,"direction":89,"opportunity":171},"基于240个城市2002-2023年面板数据，检验四类极端气候冲击对农业净碳汇的影响及供应链韧性的调","双向固定效应模型、双重机器学习，240城市面板数据，农业净碳汇指标。","四类极端气候冲击均降低农业净碳汇，供应链韧性可显著缓解高温、暴雨和干旱的负面影响。","可探究不同气候灾害下供应链韧性功能与生物机制匹配的差异化适应策略及非线性预测。","2026-09-26T23:30:07.569768Z",{"id":174,"title":175,"url":176,"summary":177,"summary_zh":9,"content":9,"source_name":178,"source_url":9,"published_at":179,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":180,"score_detail":181,"sources":184,"tags":186,"primary_topic":191,"secondary_topics":192,"classification":193,"search_phrases":215,"slug":218,"view_count":219,"doi":9,"paper":220,"created_at":228},3447,"《农业数字化的碳减排效应：理论分析与经验证据》——基于2005—2022年省级面板数据","http:\u002F\u002Fwww.qikanzj.com\u002Fhek\u002Fhznydxxbshkxb\u002Fmulu\u002F559174.html","在测度各省农业数字化转型水平的基础上，使用扩展的STIRPAT模型和2005—2022年省级面板数据对中国农业数字化的碳减排效应及其作用机制进行检验。结果表明：数字化显著促进了农业碳减排，绿色技术创新尤其是实质性绿色技术创新是其中重要的作用机制；异质性分析发现在东部地区、人力资本水平高和数字基础设施发达的省份效果更加显著；面板门槛回归结果表明数字化对农业碳排放的影响存在基于自身发展水平的双重门槛效应。","《华中农业大学学报（社会科学版）》","2026-09-20T00:00:00Z",78,{"impact":19,"substance":17,"depth":19,"authority":182,"freshness":56,"relevant":22,"comment":183},14,"基于2005—2022年省级面板数据的实证研究，方法规范、结论有政策参考价值，但属学术论文且时效性一般，适合主题聚合而非每日精选头条。",[185],{"name":178,"url":176},[187,29,188,189,190],"数字乡村","农业数字化","省级面板数据","绿色技术创新",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":194},{"candidates":195,"agricultural_evidence":47},[196,201,206,210],{"code":33,"score":197,"supported":47,"title":198,"abstract":199,"tags":200},7,[],[50,51],[29],{"code":202,"score":22,"supported":14,"title":203,"abstract":204,"tags":205},"I",[],[],[188],{"code":147,"score":22,"supported":14,"title":207,"abstract":208,"tags":209},[],[],[188],{"code":211,"score":22,"supported":14,"title":212,"abstract":213,"tags":214},"I6",[],[],[187],[216,217],"农业数字化 碳减排 省级面板","绿色技术创新 农业碳排放","农业数字化碳减排省级面板-3447",2,{"doi":9,"openalex_id":9,"authors":221,"venue":9,"cited_by_count":73,"oa_url":9,"card":222,"direction":89,"ingested_from":227},[],{"tldr":223,"method":224,"finding":225,"direction":89,"opportunity":226},"基于2005—2022年省级面板数据，检验农业数字化的碳减排效应及其机制。","扩展STIRPAT模型、面板门槛回归与省级面板数据。","数字化显著促进农业碳减排，绿色技术创新是重要机制，且存在双重门槛效应。","可探究数字化碳减排的非线性门槛与区域异质性，识别最优数字化水平区间。","agent","2026-09-25T00:09:34.643179Z",{"id":230,"title":231,"url":232,"summary":233,"summary_zh":9,"content":9,"source_name":234,"source_url":9,"published_at":179,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":235,"score_detail":236,"sources":239,"tags":241,"primary_topic":244,"secondary_topics":245,"classification":246,"search_phrases":266,"slug":269,"view_count":270,"doi":9,"paper":271,"created_at":278},3443,"《数字赋能农业碳减排的组态效应分析》——基于河南省各省辖市2011—2022年数据","http:\u002F\u002Fwww.qikanvip.com\u002Fqkml\u002F174647.html","孟凡琳、赵冰冰、李炳军利用碳排放因子法测度2011—2022年河南省各省辖市农业碳排放量，运用模糊集定性比较分析法（fsQCA）分析数字赋能河南农业碳减排的组态效应。结果：河南省农业碳排放强度在2011—2022年总体呈下降趋势，化肥施用和翻耕是主要来源；农业碳排放强度降低是数字基础设施、技术创新、农业数字化和数字金融多因素相互耦合结果；2011—2015年完善的数字基础设施是核心驱动因素，2016—2022年农业数字化的碳减排作用更为显著。","《河南农业大学学报》2026年第2期",76,{"impact":237,"substance":17,"depth":19,"authority":182,"freshness":56,"relevant":22,"comment":238},16,"基于河南18个省辖市11年面板数据的fsQCA组态研究，方法规范、结论有新意，对数字乡村与农业低碳政策有参考价值，但属省级区域实证，影响力有限。",[240],{"name":234,"url":232},[187,29,188,242,243],"河南农业","fsQCA",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":247},{"candidates":248,"agricultural_evidence":47},[249,254,258,262],{"code":33,"score":250,"supported":47,"title":251,"abstract":252,"tags":253},15,[51],[50,51],[29],{"code":202,"score":22,"supported":14,"title":255,"abstract":256,"tags":257},[],[],[188],{"code":147,"score":22,"supported":14,"title":259,"abstract":260,"tags":261},[],[],[188],{"code":211,"score":22,"supported":14,"title":263,"abstract":264,"tags":265},[],[],[187],[267,268],"河南 农业碳排放 数字赋能","河南农业大学学报 农业碳减排","河南农业碳排放数字赋能-3443",4,{"doi":9,"openalex_id":9,"authors":272,"venue":9,"cited_by_count":73,"oa_url":9,"card":273,"direction":89,"ingested_from":227},[],{"tldr":274,"method":275,"finding":276,"direction":89,"opportunity":277},"基于河南2011-2022年数据，用fsQCA分析数字赋能农业碳减排的组态效应。","碳排放因子法测度碳排放，模糊集定性比较分析（fsQCA）。","碳强度总体下降，化肥和翻耕为主源；减排是多因素耦合，核心驱动从数字基建转向农业数字化。","可拓展至多省比较或结合面板数据，探究数字技术减排组态的时空异质性与因果机制。","2026-09-25T00:09:34.373665Z",{"id":280,"title":281,"url":282,"summary":283,"summary_zh":9,"content":9,"source_name":284,"source_url":9,"published_at":285,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":286,"score_detail":287,"sources":290,"tags":292,"primary_topic":294,"secondary_topics":295,"classification":296,"search_phrases":320,"slug":323,"view_count":219,"doi":9,"paper":324,"created_at":331},1819,"农业低碳转型视角下的数字农技服务体系：基于大晓智能无人农技服务平台的实证分析","https:\u002F\u002Fwww.cet.com.cn\u002Fitpd\u002Fitxw\u002F10537121.shtml","以上海大晓智能无人农技服务平台为研究载体，依托天-空-地-人-机多源感知体系与农业垂直大模型底层架构，系统梳理平台助力农业降碳、土壤碳平衡测算的技术路径与分层服务模式。结合大田生产、农场管理多场景实证数据量化分析平台减污降碳成效，数字农技服务平台构建起\"监测—测算—决策—减排\"闭环管理模式，既能为各地县域、规模化种植基地打造数字化低碳农业提供落地参考，也能够丰富农业碳循环与智慧农业交叉学科的实证研究样本。","中国经济新闻网2026年9月","2026-09-04T00:00:00Z",72,{"impact":19,"substance":288,"depth":19,"authority":13,"freshness":56,"relevant":22,"comment":289},20,"实证分析数字农技平台减碳成效，具行业参考价值。",[291],{"name":284,"url":282},[108,27,293,111],"数字农技",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":297},{"candidates":298,"agricultural_evidence":47},[299,304,308,315],{"code":33,"score":300,"supported":47,"title":301,"abstract":302,"tags":303},5,[],[51],[27,111],{"code":147,"score":270,"supported":47,"title":305,"abstract":306,"tags":307},[],[108],[108],{"code":309,"score":310,"supported":47,"title":311,"abstract":312,"tags":314},"H2",3,[],[313],"土壤",[],{"code":55,"score":310,"supported":47,"title":316,"abstract":317,"tags":319},[],[318],"大模型",[],[321,322],"低碳农业 农业碳汇 数字农技 智慧农业","低碳农业 农业碳汇","低碳农业农业碳汇数字农技智慧农业-1819",{"doi":9,"openalex_id":9,"authors":325,"venue":9,"cited_by_count":73,"oa_url":9,"card":326,"direction":89,"ingested_from":227},[],{"tldr":327,"method":328,"finding":329,"direction":89,"opportunity":330},"实证分析数字农技服务平台如何通过多源感知与垂直大模型助力农业降碳，构建闭环管理模式。","依托天-空-地-人-机多源感知体系与农业垂直大模型，结合多场景实证数据量化分析。","平台构建“监测—测算—决策—减排”闭环，有效减污降碳，提供落地参考。","可延伸研究数字农技服务在不同作物、区域下的碳减排差异，以及平台模式与农户采纳行为的耦合机制。","2026-09-07T00:04:41.769592Z",{"id":333,"title":334,"url":335,"summary":336,"summary_zh":337,"content":9,"source_name":338,"source_url":335,"published_at":339,"category":12,"cover_url":9,"hotness":340,"is_selected":14,"score":180,"score_detail":341,"sources":344,"tags":349,"primary_topic":352,"secondary_topics":356,"classification":358,"search_phrases":376,"slug":379,"view_count":22,"doi":380,"paper":381,"created_at":402},1652,"Digital Village Technologies and Low-Carbon Agricultural Performance in China: An Applied Evaluation Using Super-Efficiency SBM and Provincial Panel Data","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fapp16178752","Digital technologies are increasingly integrated into agricultural production, rural services, finance, and information infrastructure, creating opportunities to improve low-carbon agricultural performance. This study develops an applied evaluation framework linking rural digital technology systems with agricultural carbon emission efficiency. Using panel data from 30 Chinese provinces during 2011–2023, we construct a digital village technology index covering digital infrastructure, digital financial infrastructure, digital service platforms, and digital living scenarios. Agricultural carbon emission efficiency is measured using a super-efficiency slacks-based measure (SBM) model incorporating inputs, desirable output, and undesirable carbon emissions. Two-way fixed-effects models, instrumental-variable estimation, mediation tests, moderation analysis, and heterogeneity tests are then employed. The results show that rural digital technology systems are positively associated with agricultural carbon emission efficiency. Agricultural technological innovation and agricultural socialized services are examined as potential channels, and fiscal support for agriculture is considered as an institutional condition that may shape the DVC–ACEE relationship. The subgroup results suggest that this association is more evident in flat-terrain and large-\u002Fmedium-urban-scale regions. This study provides an applied framework for evaluating how digital infrastructure and service systems are linked to low-carbon agricultural transformation.","数字技术日益融入农业生产、农村服务、金融及信息基础设施，为提升低碳农业绩效创造了机遇。本研究构建了一个应用评估框架，将农村数字技术体系与农业碳排放效率联系起来。利用2011—2023年中国30个省份的面板数据，我们构建了涵盖数字基础设施、数字金融基础设施、数字服务平台及数字生活场景的数字乡村技术指数。农业碳排放效率采用纳入投入、期望产出与非期望碳排放的超效率松弛变量测度（SBM）模型进行衡量。随后运用双向固定效应模型、工具变量估计、中介效应检验、调节效应分析及异质性检验。结果表明，农村数字技术体系与农业碳排放效率呈正相关。农业技术创新与农业社会化服务被视为潜在传导渠道，农业财政支持则被视为可能塑造数字乡村技术—农业碳排放效率关系的制度条件。分组结果显示，该关联在平坦地形及大中城市规模地区更为显著。本研究为评估数字基础设施与服务系统如何与低碳农业转型相关联提供了应用框架。","Applied Sciences","2026-09-03T00:00:00Z",25,{"impact":288,"substance":17,"depth":19,"authority":342,"freshness":56,"relevant":22,"comment":343},12,"基于省级面板数据的实证研究，揭示数字乡村技术对低碳农业的促进作用，具有政策参考价值。",[345,346],{"name":338,"url":335},{"name":347,"url":348},"MDPI Applied Sciences 16(17):8752","https:\u002F\u002Fwww.mdpi.com\u002F2076-3417\u002F16\u002F17\u002F8752",[187,27,350,351],"农业科技","碳排放效率",{"code":211,"slug":353,"name":354,"path":355,"parent_code":202},"digital-village","数字乡村与数据要素","\u002Ftopic\u002Fdigital-agriculture\u002Fdigital-village",[357],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":359},{"candidates":360,"agricultural_evidence":47},[361,366,370],{"code":211,"score":250,"supported":47,"title":362,"abstract":364,"tags":365},[363],"digital village",[187,363],[187],{"code":33,"score":13,"supported":47,"title":367,"abstract":368,"tags":369},[],[50,51,52],[27],{"code":371,"score":310,"supported":47,"title":372,"abstract":373,"tags":375},"G2",[],[374],"社会化服务",[],[377,378],"碳排放效率 低碳农业 农业科技 数字乡村","碳排放效率 低碳农业","碳排放效率低碳农业农业科技数字乡村-1652","10.3390\u002Fapp16178752",{"doi":380,"openalex_id":382,"authors":383,"venue":338,"cited_by_count":73,"oa_url":335,"card":396,"direction":401,"ingested_from":91},"W7207843203",[384,386,388,391,394],{"name":385,"orcid":9},"Yuming Li",{"name":387,"orcid":9},"Liudi Li",{"name":389,"orcid":390},"Xiuguang Bai","https:\u002F\u002Forcid.org\u002F0000-0003-0140-208X",{"name":392,"orcid":393},"Bo Wei","https:\u002F\u002Forcid.org\u002F0000-0002-1058-5888",{"name":395,"orcid":9},"Jing Zhang",{"tldr":397,"method":398,"finding":399,"direction":89,"opportunity":400},"研究数字乡村技术对农业碳排放效率的影响，基于中国省级面板数据。","构建数字乡村技术指数，用超效率SBM模型测度碳排放效率，采用双向固定效应、工具变","数字乡村技术显著提升农业碳排放效率，技术创新和社会化服务为渠道，财政支持起调节作用。","可探索数字乡村技术在不同地形和城市规模下的差异化影响机制，或引入更多中介变量如数字素养。","数字乡村与农业信息化","2026-09-04T23:30:40.098363Z",{"id":404,"title":405,"url":406,"summary":407,"summary_zh":408,"content":9,"source_name":10,"source_url":406,"published_at":339,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":286,"score_detail":409,"sources":411,"tags":413,"primary_topic":417,"secondary_topics":418,"classification":419,"search_phrases":450,"slug":453,"view_count":219,"doi":454,"paper":455,"created_at":467},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。",{"impact":19,"substance":17,"depth":19,"authority":342,"freshness":219,"relevant":22,"comment":410},"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。",[412],{"name":10,"url":406},[414,27,415,416],"农业经济","新型农业经营主体","农业碳排放",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":420},{"candidates":421,"agricultural_evidence":47},[422,426,433,440,445],{"code":33,"score":130,"supported":47,"title":423,"abstract":424,"tags":425},[],[50,51],[27,416],{"code":427,"score":56,"supported":47,"title":428,"abstract":429,"tags":432},"G3",[],[430,431],"农业信贷","agricultural credit",[],{"code":434,"score":56,"supported":47,"title":435,"abstract":436,"tags":439},"J2",[],[437,438],"农业现代化","agricultural modernization",[],{"code":371,"score":270,"supported":47,"title":441,"abstract":442,"tags":444},[],[443],"家庭农场",[415],{"code":63,"score":310,"supported":47,"title":446,"abstract":447,"tags":449},[],[448],"空间计量",[],[451,452],"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":454,"openalex_id":456,"authors":457,"venue":10,"cited_by_count":73,"oa_url":406,"card":462,"direction":89,"ingested_from":91},"W7206169038",[458,460],{"name":459,"orcid":9},"Juan Pu",{"name":461,"orcid":9},"Li Mingliang",{"tldr":463,"method":464,"finding":465,"direction":89,"opportunity":466},"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。","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。","2026-09-03T23:30:07.743142Z"]