[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-5498":3,"related-5498":98},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":8,"published_at":11,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":24,"tags":26,"primary_topic":32,"secondary_topics":38,"classification":39,"search_phrases":92,"slug":95,"view_count":96,"doi":8,"paper":8,"created_at":97},5498,"江西省「十五五」农业农村现代化规划印发 部署智慧农业与四季接续","https:\u002F\u002Fjx.ifeng.com\u002Fc\u002F8xPB4YCdgGF","《江西省「十五五」农业农村现代化规划》（赣府发〔2026〕16号）围绕32项重点任务展开，明确推进信息技术与农业深度融合，加快农业人工智能技术创新与场景拓展，建设农业农村大数据平台。规划还部署井冈山国家农业科技园区升建国家农高区，推广「村播」「田播」电商直播带货，并开展农产品碳足迹管理与农业碳交易。",null,"（一）提升粮食等重要农产品供给保障水平。坚持产量产能、生产生态、增产增收一起抓，深入实施藏粮于地、藏粮于技战略，落实新一轮粮食产能提升行动，实施粮油作物大面积单产提升行动，建设吨粮片、高产片，促进良田良种良机良法集成增效。优化农产品种植结构，稳定水稻、油菜生产，大力发展油茶产业，提升大豆产能。优化生猪产能综合调控机制，推进牛羊产业扩量提质，促进畜禽养殖节本提质增效。推动农产品品种培优和品质提升，增加营养、健康、风味农产品供给。完善价格、补贴、保险“三位一体”的种粮农民收益保障机制，建立健全与周边省份粮食产销协作机制，强化粮食产购储加销协同保障。\n\n（二）构建多元化食物供给体系。践行大食物观，坚持农林牧渔并举，全方位、多用途开发食物资源。提升“菜篮子”产业质量和效益，推动蔬菜、水果、茶叶、中药材产业优质发展。丰富畜产品供给，发展蜂、兔、肉鸽、鹌鹑等特色养殖。积极发展大水面生态渔业，科学推进稻渔综合种养。发展林下经济，壮大林药和森林食品。发展富硒食品与功能食品，开发新型食品资源。\n\n（三）加强耕地保护与质量提升。严格落实耕地保护党政同责，严守耕地保护红线。统筹农用地布局优化，严格耕地占补平衡管理，规范补充耕地质量验收。科学开展红壤改良利用，推进酸化耕地治理，分阶段实施耕地有机质提升行动。分区分类高质量推进高标准农田建设，对新建和改造提升实行差异化投入，优先在鄱阳湖平原、赣抚平原、吉泰盆地等区域推进整区域建设，逐步把具备条件的永久基本农田建成高标准农田。完善高标准农田立项、建设、验收和管护机制，编制高标准农田建设全流程工作规范，统一建设标准、施工工艺、管理流程，压实管护责任。开展高标准农田整治提升行动，按照“集中修复一批、改造提升一批、疏通灌溉一批、置换退出一批”分类施策，逐步补齐设施短板、消除存量问题。健全完善问题发现响应处置机制。实施大中型灌区现代化建设与改造，推动井冈灌区、赣抚平原灌区等项目建设，强化高标准农田渠系与灌区骨干工程衔接。\n\n（四）提升农业防灾减灾救灾能力。发展韧性农业，完善农业气象、水文、地质灾害等监测预警机制。推进防洪排涝抗旱设施建设，持续开展农田沟渠修复整治、中小河流和平原涝区治理。完善农机应急救灾设施装备条件,健全和落实救灾机具配置、救灾备荒种子储备和应急调用机制，加快补齐烘干、仓储等设施装备短板。强化病虫疫情和重大动物疫病监测预警、联防联控和应急处置，建强县乡动植物疫病防控专业队伍。","凤凰网江西 2026-10-21","2026-10-21T00:00:00Z","政策",10,false,80,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},22,23,18,8,9,1,"省级「十五五」农业农村现代化规划全文，条款与数字细节充实，涉及智慧农业、高标准农田、粮食产能等实质部署，值得进入每日精选。",[25],{"name":10,"url":6},[27,28,29,30,31],"智慧农业","高标准农田","十五五规划","大食物观","粮食产能",{"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":48},[45,53,58,65,72,77,82,87],{"code":46,"score":47,"supported":48,"title":49,"abstract":51,"tags":52},"J2",11,true,[50],"农业农村现代化",[50],[],{"code":54,"score":21,"supported":48,"title":55,"abstract":56,"tags":57},"I1",[27],[],[27],{"code":33,"score":59,"supported":48,"title":60,"abstract":61,"tags":64},6,[],[62,63],"农业碳","碳足迹",[],{"code":66,"score":67,"supported":48,"title":68,"abstract":69,"tags":71},"I2",3,[],[70],"农业人工智能",[],{"code":73,"score":22,"supported":14,"title":74,"abstract":75,"tags":76},"A8",[],[],[31],{"code":78,"score":22,"supported":14,"title":79,"abstract":80,"tags":81},"E2",[],[],[30],{"code":83,"score":22,"supported":14,"title":84,"abstract":85,"tags":86},"H1",[],[],[28],{"code":88,"score":22,"supported":14,"title":89,"abstract":90,"tags":91},"J1",[],[],[29],[93,94],"江西 十五五 农业农村现代化规划","江西 高标准农田 鄱阳湖平原","江西十五五农业农村现代化规划-5498",0,"2026-10-22T00:06:58.106992Z",{"total":59,"page":22,"page_size":59,"items":99},[100,159,216,295,366,442],{"id":101,"title":102,"url":103,"summary":104,"summary_zh":8,"content":8,"source_name":105,"source_url":8,"published_at":106,"category":107,"cover_url":8,"hotness":13,"is_selected":14,"score":108,"score_detail":109,"sources":112,"tags":114,"primary_topic":119,"secondary_topics":120,"classification":121,"search_phrases":145,"slug":148,"view_count":22,"doi":8,"paper":149,"created_at":158},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","论文",84,{"impact":17,"substance":18,"depth":19,"authority":110,"freshness":20,"relevant":22,"comment":111},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。",[113],{"name":105,"url":103},[115,27,116,117,118],"数字乡村","农业数字化","农业碳排放","绿色创新",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":122},{"candidates":123,"agricultural_evidence":48},[124,130,135,140],{"code":33,"score":125,"supported":48,"title":126,"abstract":127,"tags":129},4,[],[128],"碳排放",[117],{"code":54,"score":131,"supported":14,"title":132,"abstract":133,"tags":134},2,[],[],[116,27],{"code":136,"score":22,"supported":14,"title":137,"abstract":138,"tags":139},"I",[],[],[116],{"code":141,"score":22,"supported":14,"title":142,"abstract":143,"tags":144},"I6",[],[],[115],[146,147],"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":150,"venue":8,"cited_by_count":96,"oa_url":8,"card":151,"direction":155,"ingested_from":157},[],{"tldr":152,"method":153,"finding":154,"direction":155,"opportunity":156},"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。","农业绿色发展与碳","可探究转折点后数字化增碳的机制，及劳动生产率等调节因素如何推迟拐点。","agent","2026-10-18T00:05:30.735414Z",{"id":160,"title":161,"url":162,"summary":163,"summary_zh":164,"content":8,"source_name":165,"source_url":162,"published_at":166,"category":107,"cover_url":8,"hotness":13,"is_selected":14,"score":15,"score_detail":167,"sources":169,"tags":171,"primary_topic":174,"secondary_topics":175,"classification":176,"search_phrases":194,"slug":197,"view_count":125,"doi":198,"paper":199,"created_at":215},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",{"impact":19,"substance":17,"depth":19,"authority":110,"freshness":21,"relevant":22,"comment":168},"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。",[170],{"name":165,"url":162},[115,27,172,117,173],"农业韧性","农业产业集聚",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":177},{"candidates":178,"agricultural_evidence":48},[179,184,190],{"code":33,"score":19,"supported":48,"title":180,"abstract":182,"tags":183},[181],"carbon emission",[128,62,181],[117],{"code":141,"score":185,"supported":48,"title":186,"abstract":187,"tags":189},7,[],[115,188],"digital village",[115],{"code":54,"score":22,"supported":14,"title":191,"abstract":192,"tags":193},[],[],[27],[195,196],"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":198,"openalex_id":200,"authors":201,"venue":165,"cited_by_count":96,"oa_url":162,"card":208,"direction":213,"ingested_from":214},"W7220924322",[202,204,206],{"name":203,"orcid":8},"Jinghui Hu",{"name":205,"orcid":8},"Jun He",{"name":207,"orcid":8},"Junzhong He",{"tldr":209,"method":210,"finding":211,"direction":155,"opportunity":212},"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","openalex","2026-10-09T23:30:32.435467Z",{"id":217,"title":218,"url":219,"summary":220,"summary_zh":221,"content":8,"source_name":222,"source_url":219,"published_at":223,"category":107,"cover_url":8,"hotness":224,"is_selected":14,"score":15,"score_detail":225,"sources":227,"tags":232,"primary_topic":235,"secondary_topics":240,"classification":242,"search_phrases":271,"slug":274,"view_count":96,"doi":275,"paper":276,"created_at":294},4416,"Agricultural blockchain readiness environment and carbon emission intensity in maize production: evidence from provincial panel data in major maize-producing areas of China","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1942225","China’s major maize-producing regions face the joint challenge of sustaining food production and reducing agricultural emissions. Existing research on digital agriculture often uses broad digitalization measures and therefore does not distinguish the regional conditions that may enable information management, traceability, and verification applications associated with blockchain. This study constructs an Agricultural Blockchain Readiness Environment Index (ABRE Index, block_index) using entropy weighting across digital infrastructure, the agricultural industrial base, and policy support. The index measures regional readiness and enabling conditions rather than realized blockchain adoption. Using 180 province-year observations for 12 major maize-producing provinces from 2010 to 2024, we estimate two-way fixed-effects models with province-clustered inference and conduct supplementary robustness analyses. The ABRE Index is negatively associated with carbon emissions per unit of maize-sown area, with the baseline coefficient statistically significant at the 10% level under conventional clustered inference and supported by supplementary inference procedures. The coefficient remains negative in all 12 leave-one-out specifications, is statistically significant at the 10% level or better in 10 specifications, and the wild cluster bootstrap test yields p = 0.0415. The index is positively correlated with blockchain-related patent activity ( r = 0.1883, p = 0.0114), although this correlation does not establish that the index measures realized adoption. The mechanism analysis is consistent with an organizational pathway through agricultural socialized services: the ABRE Index is positively associated with service_ratio (0.1099, p \u003C 0.01), and agricultural socialized services are negatively associated with ce_area (−2.2765, p \u003C 0.05). The interaction with the high-development indicator is negative but does not reach the conventional 10% significance threshold (−0.1499, p = 0.103). Overall, the evidence supports an associational relationship between blockchain-readiness conditions and maize-production carbon-emission intensity; it does not establish a strictly causal effect of blockchain adoption.","中国主要玉米产区面临保障粮食生产与减少农业排放的双重挑战。现有数字农业研究多采用宽泛的数字化指标，因而未能区分可能支撑与区块链相关的信息管理、追溯和验证应用的区域条件。本研究基于数字基础设施、农业产业基础和政策支持三个维度，采用熵权法构建了农业区块链就绪环境指数（ABRE Index，block_index）。该指数衡量的是区域就绪程度和使能条件，而非已实现的区块链采用。利用2010年至2024年12个主要玉米生产省份的180个省份—年份观测值，我们估计了双向固定效应模型，采用省份聚类推断，并进行了补充稳健性分析。ABRE指数与单位玉米播种面积碳排放呈负相关，基准系数在常规聚类推断下于10%水平上统计显著，并得到补充推断程序的支持。在全部12个逐一剔除（leave-one-out）设定中，该系数均保持为负，其中10个设定在10%或更优水平上统计显著，野生聚类自举检验（wild cluster bootstrap test）得出p = 0.0415。该指数与区块链相关专利活动呈正相关（r = 0.1883，p = 0.0114），尽管这一相关性并不能证明该指数衡量的是已实现的采用。机制分析与通过农业社会化服务发挥作用的组织路径相一致：ABRE指数与service_ratio呈正相关（0.1099，p \u003C 0.01），农业社会化服务与ce_area呈负相关（−2.2765，p \u003C 0.05）。与高发展指标的交互项为负，但未达到常规10%显著性阈值（−0.1499，p = 0.103）。总体而言，证据支持区块链就绪条件与玉米生产碳排放强度之间存在关联关系；但并未确立区块链采用的严格因果效应。","Frontiers in Sustainable Food Systems","2026-10-07T00:00:00Z",25,{"impact":19,"substance":17,"depth":19,"authority":110,"freshness":21,"relevant":22,"comment":226},"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。",[228,229],{"name":222,"url":219},{"name":230,"url":231},"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",[115,27,233,117,234],"玉米","区块链",{"code":236,"slug":237,"name":233,"path":238,"parent_code":239},"A3","maize","\u002Ftopic\u002Ffield-crops\u002Fmaize","A",[241],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":243},{"candidates":244,"agricultural_evidence":48},[245,250,255,261,267],{"code":236,"score":246,"supported":48,"title":247,"abstract":248,"tags":249},15,[237],[233,237],[233],{"code":33,"score":251,"supported":48,"title":252,"abstract":253,"tags":254},12,[181],[128],[117],{"code":54,"score":185,"supported":48,"title":256,"abstract":257,"tags":260},[],[258,259],"数字农业","digital agriculture",[27],{"code":262,"score":67,"supported":48,"title":263,"abstract":264,"tags":266},"G2",[],[265],"社会化服务",[],{"code":141,"score":22,"supported":14,"title":268,"abstract":269,"tags":270},[],[],[115],[272,273],"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":275,"openalex_id":277,"authors":278,"venue":222,"cited_by_count":96,"oa_url":287,"card":288,"direction":293,"ingested_from":214},"W7220869193",[279,282,284],{"name":280,"orcid":281},"Pengfei Zhou","https:\u002F\u002Forcid.org\u002F0000-0003-2117-7967",{"name":283,"orcid":8},"Zhiyi Hu",{"name":285,"orcid":286},"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":289,"method":290,"finding":291,"direction":155,"opportunity":292},"构建农业区块链就绪环境指数，分析其与玉米生产碳排放强度的关联。","熵权法构建ABRE指数，用2010-2024年12省面板数据做双向固定效应模型。","ABRE指数与玉米单位播种面积碳排放负相关，机制可能通过农业社会化服务实现。","可探究区块链就绪环境通过社会化服务减碳的因果机制，并拓展到其他主粮作物或区域。","智慧农业 \u002F 农业物联网","2026-10-08T23:30:07.668939Z",{"id":296,"title":297,"url":298,"summary":299,"summary_zh":300,"content":8,"source_name":301,"source_url":298,"published_at":302,"category":107,"cover_url":8,"hotness":13,"is_selected":14,"score":303,"score_detail":304,"sources":309,"tags":311,"primary_topic":315,"secondary_topics":316,"classification":317,"search_phrases":339,"slug":342,"view_count":67,"doi":343,"paper":344,"created_at":365},4219,"Cognitive Causal Modeling of Blockchain Adoption in Agri-Food Supply Chains: A Fuzzy-DEMATEL Approach","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs12559-026-10660-0","Background \u002F Introduction: Blockchain can improve agri-food supply-chain transparency, but adoption decisions are cognitively complex because managers judge interdependent criteria under uncertainty. This study frames adoption as cognitive causal decision modeling, aligning with cognitive computation through expert judgment, linguistic uncertainty, causal reasoning, and explainable decision support. Methods: We applied Fuzzy Decision-Making Trial and Evaluation Laboratory (Fuzzy-DEMATEL) to judgments from 20 experts. Eight technology-adoption criteria were modeled using triangular fuzzy numbers, and an impact relationship map was used to classify criteria by outgoing influence (R), incoming influence (C), prominence (R + C), and net causality (R − C). Results: Smart Contracts showed the strongest net causal influence (R = 3.46; R − C = + 0.78). Food Traceability was the most prominent criterion (R + C = 6.40) and the most influenced by other criteria (C = 3.17). Smart Contracts, Payment Transactions, Food Traceability, Fraud Prevention, and Secure Storage were net causes, whereas Food Waste Prevention, Carbon Emission Detection, and Power Consumption were net effects. Sensitivity analysis confirmed a stable cause\u002Feffect partition across threshold and prominence-cutoff variation, with Fraud Prevention the only criterion whose sign reverses at the lower fuzzy bound. Payment Transactions and Secure Storage sit close to the core-driving boundary. Conclusions: The findings support an explainable prioritization logic: transactional and information-security capabilities should be addressed before downstream sustainability outcomes. Because the evidence is expert-elicited rather than longitudinal field performance data, the results should be interpreted as structured decision-support guidance.","背景\u002F引言：区块链可以提高农业食品供应链的透明度，但其采纳决策在认知上较为复杂，因为管理者需要在不确定性下判断相互依赖的标准。本研究将采纳行为界定为认知因果决策建模，通过专家判断、语言不确定性、因果推理和可解释决策支持与认知计算相契合。方法：我们采用模糊决策试验与评价实验室方法（Fuzzy Decision-Making Trial and Evaluation Laboratory，Fuzzy-DEMATEL）对20位专家的判断进行分析。使用三角模糊数对八项技术采纳标准进行建模，并利用影响关系图按输出影响（R）、输入影响（C）、中心度（R + C）和净因果性（R − C）对标准进行分类。结果：智能合约表现出最强的净因果影响（R = 3.46；R − C = + 0.78）。食品追溯是最突出的标准（R + C = 6.40），也是受其他标准影响最大的标准（C = 3.17）。智能合约、支付交易、食品追溯、欺诈预防和安全存储为净原因，而食品浪费预防、碳排放检测和电力消耗为净结果。敏感性分析证实，在阈值和中心度截断值变化下，原因\u002F结果划分保持稳定，欺诈预防是唯一在较低模糊边界下符号发生反转的标准。支付交易和安全存储接近核心驱动边界。结论：研究结果支持一种可解释的优先排序逻辑：交易和信息安全能力应先于下游可持续性结果加以处理。由于证据来自专家 elicitation 而非纵向实地绩效数据，结果应被解释为结构化的决策支持指导。","Cognitive Computation","2026-10-02T00:00:00Z",71,{"impact":251,"substance":305,"depth":306,"authority":307,"freshness":20,"relevant":22,"comment":308},20,17,14,"核心期刊论文，用模糊DEMATEL方法对20位专家判断建模，识别智能合约与食品溯源为区块链采纳的关键驱动因素，方法新颖、结论有决策参考价值，但属专家主观判断而非实地数据，产业影响偏细分领域。",[310],{"name":301,"url":298},[27,312,313,234,314],"农业供应链","农产品溯源","决策支持",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":318},{"candidates":319,"agricultural_evidence":48},[320,324,330,335],{"code":33,"score":59,"supported":48,"title":321,"abstract":322,"tags":323},[],[128,181],[],{"code":325,"score":125,"supported":48,"title":326,"abstract":327,"tags":329},"E4",[],[328],"food traceability",[313],{"code":66,"score":125,"supported":48,"title":331,"abstract":332,"tags":334},[],[333],"decision support",[314],{"code":54,"score":22,"supported":14,"title":336,"abstract":337,"tags":338},[],[],[27],[340,341],"区块链 农产品供应链 溯源","Fuzzy-DEMATEL 区块链","区块链农产品供应链溯源-4219","10.1007\u002Fs12559-026-10660-0",{"doi":343,"openalex_id":345,"authors":346,"venue":301,"cited_by_count":96,"oa_url":358,"card":359,"direction":293,"ingested_from":214},"W7216251074",[347,350,353,356],{"name":348,"orcid":349},"Ali Ala","https:\u002F\u002Forcid.org\u002F0000-0003-4552-4732",{"name":351,"orcid":352},"Behnam Malmir","https:\u002F\u002Forcid.org\u002F0000-0003-1771-9751",{"name":354,"orcid":355},"Hamed Baziyad","https:\u002F\u002Forcid.org\u002F0000-0002-5770-7766",{"name":357,"orcid":8},"Vladimir Simic","https:\u002F\u002Flink.springer.com\u002Fcontent\u002Fpdf\u002F10.1007\u002Fs12559-026-10660-0.pdf",{"tldr":360,"method":361,"finding":362,"direction":363,"opportunity":364},"用模糊DEMATEL对20位专家判断建模，分析农业食品供应链区块链采纳的因果优先级。","模糊DEMATEL、三角模糊数、20位专家判断、敏感性分析","智能合约净因果影响最强，食品溯源最突出；交易与信息安全能力应先于可持续性结果。","农业人工智能与决策模型","可结合纵向田间绩效数据验证专家因果结论，并研究交易安全能力向碳减排等可持续结果的传导机制。","2026-10-05T23:30:12.085269Z",{"id":367,"title":368,"url":369,"summary":370,"summary_zh":371,"content":8,"source_name":222,"source_url":369,"published_at":372,"category":107,"cover_url":8,"hotness":13,"is_selected":14,"score":15,"score_detail":373,"sources":375,"tags":377,"primary_topic":381,"secondary_topics":386,"classification":388,"search_phrases":420,"slug":423,"view_count":22,"doi":424,"paper":425,"created_at":441},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",{"impact":19,"substance":17,"depth":19,"authority":110,"freshness":21,"relevant":22,"comment":374},"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。",[376],{"name":222,"url":369},[27,312,378,379,380],"低碳农业","气候韧性","农业碳汇",{"code":382,"slug":383,"name":384,"path":385,"parent_code":37},"H7","climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate",[387],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":389},{"candidates":390,"agricultural_evidence":48},[391,398,403,410,416],{"code":382,"score":13,"supported":48,"title":392,"abstract":393,"tags":397},[],[394,395,396],"气候适应","干旱","drought",[379],{"code":33,"score":20,"supported":48,"title":399,"abstract":400,"tags":402},[],[401,62],"碳汇",[378,380],{"code":404,"score":59,"supported":48,"title":405,"abstract":406,"tags":409},"H3",[],[407,408],"灌溉","irrigation",[],{"code":66,"score":59,"supported":48,"title":411,"abstract":412,"tags":415},[],[413,414],"机器学习","machine learning",[],{"code":54,"score":22,"supported":14,"title":417,"abstract":418,"tags":419},[],[],[27],[421,422],"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":424,"openalex_id":426,"authors":427,"venue":222,"cited_by_count":96,"oa_url":369,"card":436,"direction":155,"ingested_from":214},"W7214310732",[428,430,433],{"name":429,"orcid":8},"Chunyan Zhao",{"name":431,"orcid":432},"Jiajie Xia","https:\u002F\u002Forcid.org\u002F0000-0003-3225-0154",{"name":434,"orcid":435},"Guoping Ding","https:\u002F\u002Forcid.org\u002F0000-0002-4865-5044",{"tldr":437,"method":438,"finding":439,"direction":155,"opportunity":440},"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":443,"title":444,"url":445,"summary":446,"summary_zh":447,"content":8,"source_name":222,"source_url":445,"published_at":448,"category":107,"cover_url":8,"hotness":13,"is_selected":14,"score":449,"score_detail":450,"sources":452,"tags":454,"primary_topic":459,"secondary_topics":460,"classification":461,"search_phrases":476,"slug":479,"view_count":96,"doi":480,"paper":481,"created_at":501},2863,"Smart energy transition in agricultural industrial parks for sustainable food systems: policy coordination pathways and dynamic evolution simulation","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1885233","Introduction Based on the profound evolution of global “dual carbon” goals and sustainable food systems, agri-food parks are key nodes with high energy consumption and high carbon emissions in the food supply chain. Their smart energy systems provide a feasible path for low-carbon transition, but face governance obstacles such as policy fragmentation, departmental silos, and instrument conflicts, which constrain sustainable food production. Methods This study constructs a system dynamics model encompassing four subsystems: energy technology, digital infrastructure, policy market, and environmental goals. It identifies six core feedback loops and, through simulation for the period 2025 to 2040, reveals the mechanisms of policy coordination, and use sensitivity analysis to ensure robustness. Meanwhile, the study adopts the micro-perspective of the agri-food park, incorporating the entire process of policy coordination into a systematic endogenous feedback structure. Results The results show that the smart energy system of agri-food park possesses endogenous growth potential. The cumulative installed capacity of smart energy increased from 50 MW to 885.269 MW, representing a nearly 16.7 fold growth over the 15 year period, with an average annual compound growth rate of approximately 20.6%.Compared with the no policy intervention scenario, the multi policy coordination scenario boosted installed capacity by about 229.8%, indicating that strong policy support is essential to overcome initial development thresholds; strengthening goal coordination, tool optimization, and departmental integration can reduce the technology lock in effect from 0.683 to 0.300, a decline of 56.1%, and lower carbon emission intensity from 2.498 tons\u002F10,000 yuan to 1.737 tons\u002F10,000 yuan, thereby effectively breaking through existing technological lock-in and institutional barriers; and an increase in the carbon emission factor of smart energy alters the trajectory of new investments in carbon-based energy, significantly curbing the expansion of high-carbon energy. Discussion The study confirms that policy coordination is a core lever for activating the system’s endogenous dynamics and driving the green transformation and upgrading of agri-food parks, thereby providing systematic scientific decision-making support for building a more resilient sustainable food supply chain and advancing the “dual carbon” goals.","引言 基于全球“双碳”目标与可持续食物系统的深刻变革，农业食品园区是食物供应链中高能耗、高碳排放的关键节点，其智慧能源系统为低碳转型提供了可行路径，但面临政策碎片化、部门壁垒与工具冲突等治理障碍，制约了可持续食物生产。方法 本研究构建涵盖能源技术、数字基础设施、政策市场与环境目标四个子系统的系统动力学模型，识别六条核心反馈回路，并通过2025—2040年的仿真模拟揭示政策协同的作用机制，同时采用敏感性分析保证稳健性。同时，本研究采用农业食品园区的微观视角，将政策协同的全过程纳入系统内生反馈结构。结果 结果表明，农业食品园区智慧能源系统具有内生增长潜力。智慧能源累计装机容量从50 MW增至885.269 MW，15年间增长近16.7倍，年均复合增长率约为20.6%。与无政策干预情景相比，多政策协同情景使装机容量提升约229.8%，表明强有力的政策支持对于跨越初期发展门槛至关重要；加强目标协同、工具优化与部门整合可将技术锁定效应从0.683降至0.300，下降56.1%，并将碳排放强度从2.498吨\u002F万元降至1.737吨\u002F万元，从而有效突破现有技术锁定与制度壁垒；智慧能源碳排放因子上升会改变碳基能源新增投资轨迹，显著抑制高碳能源扩张。讨论 研究证实，政策协同是激活系统内生动力、推动农业食品园区绿色转型升级的核心杠杆，从而为构建更具韧性的可持续食物供应链和推进“双碳”目标提供系统性科学决策支持。","2026-09-17T00:00:00Z",79,{"impact":19,"substance":17,"depth":19,"authority":110,"freshness":20,"relevant":22,"comment":451},"系统动力学模拟农业园区智慧能源政策协同，数据详实、结论有决策参考价值，但属学术论文、时效性一般，可入精选。",[453],{"name":222,"url":445},[27,455,456,457,458],"低碳转型","系统动力学","政策协同","农业园区",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":462},{"candidates":463,"agricultural_evidence":48},[464,468,472],{"code":33,"score":185,"supported":48,"title":465,"abstract":466,"tags":467},[],[128,181],[455],{"code":54,"score":22,"supported":14,"title":469,"abstract":470,"tags":471},[],[],[27],{"code":66,"score":22,"supported":14,"title":473,"abstract":474,"tags":475},[],[],[456],[477,478],"农业产业园 智慧能源 低碳转型","农食园区 政策协同 双碳","农业产业园智慧能源低碳转型-2863","10.3389\u002Ffsufs.2026.1885233",{"doi":480,"openalex_id":482,"authors":483,"venue":222,"cited_by_count":96,"oa_url":445,"card":496,"direction":155,"ingested_from":214},"W7213496319",[484,487,490,492,494],{"name":485,"orcid":486},"Zhong Li","https:\u002F\u002Forcid.org\u002F0000-0001-9869-006X",{"name":488,"orcid":489},"Yixuan Chen","https:\u002F\u002Forcid.org\u002F0000-0002-4879-7165",{"name":491,"orcid":8},"Simiao Tong",{"name":493,"orcid":8},"Huabin Wu",{"name":495,"orcid":8},"Caijuan Hu",{"tldr":497,"method":498,"finding":499,"direction":155,"opportunity":500},"构建系统动力学模型，模拟农业产业园智慧能源转型的政策协同路径与动态演化。","系统动力学建模，含四个子系统与六个反馈回路，2025-2040年仿真及敏感性分析","多政策协同使智慧能源装机增229.8%，技术锁定降56.1%，碳排放强度降30.5%。","可将园区级政策协同模型扩展到区域农业供应链，量化部门壁垒对减碳的阻滞效应。","2026-09-18T23:30:07.001188Z"]