[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4481":3,"related-4481":104},{"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":31,"secondary_topics":37,"classification":38,"search_phrases":76,"slug":79,"view_count":80,"doi":81,"paper":82,"created_at":103},4481,"Climate policy uncertainty and agricultural digital-intelligent transformation in China: evidence from provincial panel data","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1909691","Introduction Climate policy uncertainty can affect long-term investment decisions in agriculture, yet its influence on agricultural digital-intelligent transformation remains insufficiently understood. This study examines how lagged climate policy uncertainty affects agricultural digital-intelligent transformation and whether rural finance and agricultural service policies can moderate this relationship. Methods Using panel data from 31 Chinese provinces from 2013 to 2022, we construct an agricultural digital-intelligent transformation index. A two-way fixed effects model, instrumental variable estimation, heterogeneity analysis, and moderation tests are adopted for empirical analysis. Results The results show that climate policy uncertainty significantly inhibits agricultural digital-intelligent transformation, and this negative effect remains evident after addressing potential endogeneity. Heterogeneity analysis indicates that the inhibitory effect is significant only in regions with moderately developed digital finance and in major grain-consuming areas. Further analysis shows that loans to agriculture, forestry, animal husbandry and fishery industries and the pilot policy for full-process socialized agricultural services positively moderate this relationship and weaken the adverse effect of climate policy uncertainty. Discussion These findings contribute to the literature on climate policy uncertainty and sustainable agricultural transformation by linking policy expectations, agriculture-related loans, and digital-smart agriculture development. The results suggest that stabilizing climate policy expectations, improving rural credit support, and strengthening agricultural service systems are important for promoting climate-resilient and regionally differentiated digital transformation in agriculture.","引言 气候政策不确定性会影响农业的长期投资决策，但其对农业数字化智能化转型的影响尚缺乏充分认识。本研究考察滞后气候政策不确定性如何影响农业数字化智能化转型，以及农村金融和农业服务政策能否调节这一关系。方法 基于2013—2022年中国31个省份的面板数据，构建农业数字化智能化转型指数，采用双向固定效应模型、工具变量估计、异质性分析和调节效应检验进行实证分析。结果 结果表明，气候政策不确定性显著抑制农业数字化智能化转型，且在处理潜在内生性后这一负向效应依然明显。异质性分析表明，该抑制效应仅在数字金融中等发达地区及粮食主销区显著。进一步分析显示，农、林、牧、渔业贷款和农业生产全程社会化服务试点政策对这一关系具有正向调节作用，削弱了气候政策不确定性的不利影响。讨论 这些发现通过将政策预期、涉农贷款与数字智慧农业发展联系起来，为气候政策不确定性与农业可持续转型的文献作出了贡献。结果表明，稳定气候政策预期、完善农村信贷支持、健全农业服务体系，对于促进农业气候韧性型和区域差异化的数字化转型具有重要意义。",null,"Frontiers in Sustainable Food Systems","2026-10-08T00:00:00Z","论文",10,false,81,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},18,22,14,9,1,"基于31省十年面板数据的实证研究，揭示气候政策不确定性对农业数字化智能化转型的抑制效应及农村金融、农业社会化服务的调节作用，方法规范、结论有政策参考价值，值得进入每日精选。",[24],{"name":10,"url":6},[26,27,28,29,30],"数字乡村","智慧农业","农村金融","农业社会化服务","气候政策",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"G3","rural-finance","农村金融与保险","\u002Ftopic\u002Frural-services\u002Frural-finance","G",[],{"status":39,"locked":14,"version":40,"method":41,"evidence":42},"classified","2026-10-22.2","rules",{"candidates":43,"agricultural_evidence":46},[44,51,57,64,71],{"code":32,"score":45,"supported":46,"title":47,"abstract":48,"tags":50},7,true,[],[28,49],"rural finance",[28],{"code":52,"score":45,"supported":46,"title":53,"abstract":54,"tags":56},"I1",[],[27,55],"smart agriculture",[27],{"code":58,"score":59,"supported":46,"title":60,"abstract":61,"tags":63},"G2",4,[],[62],"社会化服务",[29],{"code":65,"score":66,"supported":46,"title":67,"abstract":68,"tags":70},"D",3,[],[69],"渔业",[],{"code":72,"score":21,"supported":14,"title":73,"abstract":74,"tags":75},"I6",[],[],[26],[77,78],"气候政策不确定性 农业数字化 省级面板","农业数字化智能化转型 农村金融","气候政策不确定性农业数字化省级面板-4481",2,"10.3389\u002Ffsufs.2026.1909691",{"doi":81,"openalex_id":83,"authors":84,"venue":10,"cited_by_count":94,"oa_url":6,"card":95,"direction":101,"ingested_from":102},"W7221013071",[85,88,90,92],{"name":86,"orcid":87},"Wenwen Wang","https:\u002F\u002Forcid.org\u002F0000-0003-1547-5714",{"name":89,"orcid":9},"Yuanyi Yang",{"name":91,"orcid":9},"Yanru Yu",{"name":93,"orcid":9},"Yanwei Zhang",0,{"tldr":96,"method":97,"finding":98,"direction":99,"opportunity":100},"研究气候政策不确定性如何抑制中国农业数字化智能化转型，并检验农村金融与服务政策的调节作用。","2013-2022年31省面板数据，构建农业数字化智能化转型指数，用双向固定效应","气候政策不确定性显著抑制农业数字化智能化转型，该效应仅在数字金融中等发达地区和粮食主销区显著。","数字乡村与农业信息化","可深入探究气候政策预期稳定机制与数字金融、农业社会化服务的协同路径，及区域差异化转型策略。","智慧农业 \u002F 农业物联网","openalex","2026-10-09T23:30:06.185798Z",{"total":105,"page":21,"page_size":105,"items":106},6,[107,170,234,293,329,371],{"id":108,"title":109,"url":110,"summary":111,"summary_zh":9,"content":9,"source_name":112,"source_url":9,"published_at":113,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":114,"score_detail":115,"sources":117,"tags":119,"primary_topic":124,"secondary_topics":130,"classification":132,"search_phrases":156,"slug":159,"view_count":21,"doi":9,"paper":160,"created_at":169},3444,"《数字经济和财政支农对农业碳排放双控的影响》——基于2013—2021年30省级面板数据","http:\u002F\u002Fwww.qikanvip.com\u002Fqkml\u002F174665.html","基于2013—2021年中国30个省级面板数据（西藏和港、澳、台除外），采用熵值法对农业碳排放双控水平进行评估，随后通过面板Tobit模型探究数字经济对农业碳排放双控的影响，最后分析财政支农在该影响中的调节效应。结果：中国农业碳排放双控水平呈波浪式上升趋势，粮食主销区水平相对较高、主产区相对较低；数字经济对农业碳排放双控具有显著正向影响，财政支农发挥正向调节作用；在粮食主销区促进作用更明显。","《河南农业大学学报》2026年第3期","2026-09-20T00:00:00Z",78,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":105,"relevant":21,"comment":116},"基于30省面板数据的实证研究，方法规范、结论明确，对数字乡村与农业绿色低碳政策有参考价值，但属学术论文、时效性一般，适合主题聚合而非每日精选头条。",[118],{"name":112,"url":110},[26,120,121,122,123],"财政支农","粮食主产区","农业碳排放","省级面板数据",{"code":125,"slug":126,"name":127,"path":128,"parent_code":129},"H4","carbon","农业碳与绿色低碳","\u002Ftopic\u002Fenvironment\u002Fcarbon","H",[131],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":14,"version":40,"method":41,"evidence":133},{"candidates":134,"agricultural_evidence":46},[135,142,147,152],{"code":125,"score":136,"supported":46,"title":137,"abstract":140,"tags":141},23,[138,139],"碳排放","农业碳",[138,139],[122],{"code":32,"score":143,"supported":46,"title":144,"abstract":145,"tags":146},12,[120],[120],[120],{"code":148,"score":21,"supported":14,"title":149,"abstract":150,"tags":151},"A8",[],[],[121],{"code":72,"score":21,"supported":14,"title":153,"abstract":154,"tags":155},[],[],[26],[157,158],"数字经济 农业碳排放 财政支农","河南农业大学学报 农业碳排放双控","数字经济农业碳排放财政支农-3444",{"doi":9,"openalex_id":9,"authors":161,"venue":9,"cited_by_count":94,"oa_url":9,"card":162,"direction":166,"ingested_from":168},[],{"tldr":163,"method":164,"finding":165,"direction":166,"opportunity":167},"基于30省面板数据，研究数字经济与财政支农对农业碳排放双控的影响及调节效应。","熵值法评估碳排放双控水平，面板Tobit模型分析，2013—2021年省级数据。","数字经济显著促进农业碳排放双控，财政支农正向调节，粮食主销区效应更明显。","农业绿色发展与碳","可探究数字经济影响农业碳排放的空间溢出效应及财政支农的门槛非线性特征。","agent","2026-09-25T00:09:34.446164Z",{"id":171,"title":172,"url":173,"summary":174,"summary_zh":9,"content":175,"source_name":176,"source_url":9,"published_at":177,"category":178,"cover_url":9,"hotness":13,"is_selected":46,"score":179,"score_detail":180,"sources":186,"tags":188,"primary_topic":194,"secondary_topics":195,"classification":196,"search_phrases":228,"slug":231,"view_count":232,"doi":9,"paper":9,"created_at":233},2694,"加快农业保险高质量发展实施方案印发——2030年保险深度1.9%、密度1200元\u002F人","https:\u002F\u002Fjcs.moa.gov.cn\u002Fgzdt\u002F202609\u002Ft20260915_6487691.htm","财政部、农业农村部、金融监管总局、国家林草局联合印发《关于加快农业保险高质量发展的实施方案》,到2030年农业保险深度达到1.9%、密度达到1200元\u002F人、科技投入强度达到1%,稻谷、小麦、玉米三大粮食作物覆盖率稳定在90%左右,综合费用率不高于20%。","各省、自治区、直辖市人民政府，新疆生产建设兵团，国务院有关部委、有关直属机构：\n\n经国务院同意，现将《关于加快农业保险高质量发展的实施方案》印发给你们，请抓好贯彻落实。\n\n财政部 农业农村部\n\n金融监管总局 国家林草局\n\n2026 年 9 月 8 日\n\n**关于加快农业保险高质量发展的实施方案**\n\n为进一步加快农业保险高质量发展，稳定农户收益，服务保障国家粮食安全和农业生产能力，经国务院同意，制定本实施方案。\n\n一、总体要求\n\n坚持以习近平新时代中国特色社会主义思想为指导，深入贯彻党的二十大和二十届历次全会精神，认真落实四中全会部署，全面贯彻习近平总书记关于“三农”工作的重要论述，完整准确全面贯彻新发展理念，加快构建新发展格局，着力推动高质量发展，遵循政府引导、市场运作、自主自愿、协同推进、改革创新的原则，充分发挥农业保险在价格、补贴、保险“三位一体”农业支持政策体系中的重要一环作用。\n\n到 2030 年，基本建成与农户风险保障需求相契合、与现代农业发展相适应、中央与地方分工负责的多层次农业保险体系，总体达到国际先进水平，实现补贴有效率、产业有保障、农民得实惠、机构可持续的多赢格局，政策更加普惠，发展更加均衡。农业保险深度（保费\u002F第一产业增加值）达到 1.9%，农业保险密度（保费\u002F第一产业就业人数）达到 1200 元\u002F人，农业保险科技投入强度（农业保险机构科技创新投入\u002F保费）达到 1%。稻谷、小麦、玉米三大粮食作物农业保险覆盖率稳定在 90%左右，提升特色农业保险对特色农业产业的保障水平，更好发挥富农强农作用。农业保险综合费用率不高于 20%。\n\n二、聚焦农业保险发展重点\n\n**（一）健全多层次农业保险体系。**坚持中央与地方共同推动、各部门分工负责，构建复合、多元、多层次的农业保险体系。加强对关系国计民生和粮食安全的大宗农产品以及地方特色农业的保障，稳步推进物化成本保险、完全成本保险、收入保险等险种发展，加强对小农户和农民合作社、家庭农场等新型农业经营主体的保障，强化对防止返贫致贫对象的支持。建立健全投保人、直保端、再保端等多方参与的风险分散链条，发挥好大灾风险准备金作用。\n\n**（二）增强种植业农产品风险保障。**加强主粮、棉花、油料、糖料、橡胶等大宗农产品风险保障，提高区域间农业保险发展均衡性，完善省以下保费分担机制。逐步降低产粮大县农业保险县级保费补贴承担比例，推动扩大稻谷、小麦、玉米、大豆完全成本保险和种植收入保险投保面积。完善对马铃薯、油菜、花生、甜菜、青稞等农产品以及稻谷、小麦、玉米制种的保险支持保障。\n\n**（三）促进养殖业保险规范发展。**完善养殖企业和养殖场（户）养殖保险模式，采用电子耳标、生物识别等技术加强养殖数量监控。动态调整能繁母猪、育肥猪、奶牛等养殖业保险保障水平，鼓励根据不同养殖方式提供差异化风险保障方案。做好牦牛、藏系羊等涉藏特定品种保险支持保障。\n\n**（四）推动林草保险提质增效。**坚持预防与保障并重，与现代林草业发展相适应，推动构建林草综合保险体系。完善公益林保险和商品林保险政策，探索适合相关林业保险险种特点的承保理赔模式。保险机构要合理确定森林保险防灾减损费用计提比例，据实列支。鼓励保险机构开展草原、湿地、国家公园、碳汇等保险试点，丰富林草保险产品。\n\n**（五）支持地方特色发展。**鼓励各地结合实际制定特色农业保险发展政策，加强相关资金统筹，加大支持力度，扩大保险范围，积极发展蔬菜、水果、肉牛、肉羊、禽类、渔业、林下经济等特色农业保险。\n\n**（六）完善产品供给体系。**健全“政策险+商业险”、“基本险+补充险”的农业保险产品供给体系。鼓励各地结合实际在物化成本保险等提供基本保险保障的险种基础上，设计开发相关补充保险，形成更高保障。鼓励各地因地制宜发展农业气象指数保险。支持保险机构发展商业性农业保险，开发便利农户投保的综合型保险保障方案。\n\n**（七）推广“农业保险+”模式。**深化农业保险与信贷、担保、期货等金融工具协同，探索保单质押、联合分担风险等模式。发挥农业保险增信功能，提高投保农户信用等级，促进涉农数据、科技、资产等要素资源融合，引导加大金融资源投入。\n\n**（八）逐步拓宽服务领域。**鼓励保险机构根据农业发展需要以市场化方式提供覆盖农林牧渔、涉农产业上下游、农民人身安全等方面风险的保险产品，探索开展农产品加工、仓储、运输、质量保险及高标准农田工程质量保险，为农业对外合作提供保险服务。鼓励地方和保险机构提供大棚、农房、农机、仓库等农业生产设施设备保险，以及农业绿色发展、智慧农业、民族村寨等领域保险。\n\n三、优化农业保险运行机制\n\n**（九）优化完善承保管理。**财政、发展改革、农业农村、保险监管、林业草原等部门及保险机构加强信息互通，共享农（林）地确权、流转、目标价格补贴面积以及农产品成本调查、畜禽存出栏量、病死猪无害化处理等农业生产经营数据。逐步推行“一户一单”承保模式。保险监管部门指导保险机构制定农业保险精准承保规范，提升可操作性。保险机构应加强农业保险承保精准性管理，开展验标和承保信息审核，集体投保类业务逐步提高抽查验标比例，规模经营主体投保类业务实现全部验标。\n\n**（十）加强财政补贴管理。**强化保费补贴资金管理，加强补贴资金所涉投保信息与农业生产经营数据、历史投保数据的比对核验，提高财政资金使用效益。及时向保险机构拨付保费补贴资金，优化资金拨付方式。鼓励各地实行省级财政部门与保险机构省级分公司“省级对省级”结算等模式。\n\n**（十一）完善查勘定损制度。**农业农村、保险监管、林业草原等部门结合当地种植、养殖、林业生产实际，指导保险机构制定分类或分险种查勘定损操作规范，明确查勘、抽样、定损等工作流程和标准。农业农村、林业草原等部门配合做好灾后查勘和灾损核定工作。\n\n**（十二）提升理赔服务质效。**保险监管部门指导保险机构制定完善农业保险理赔实施规范，提升理赔效率和精准度。保险机构应完善基层服务网络，简化手续、优化流程、赔款到户，建立农业大灾快速理赔响应机制，针对重大自然灾害和事故合理预付部分赔偿金，支持农户恢复生产。保险机构应通过银行转账等非现金方式将保险赔款直接支付给被保险人。\n\n**（十三）鼓励开展风险减量管理。**健全风险减量管理机制，增强事前防灾减灾、事中救灾减损、事后及时赔付相结合的一体化服务能力，推动农业保险从单一事后理赔向全流程风险管理转型。保险机构可与农业农村、林业草原、气象等领域社会化服务机构合作，开展防灾减损工作。\n\n**（十四）健全监督管理机制。**健全农业保险领域常态化监督机制，财政、农业农村、保险监管、林业草原等部门加强联动，加大监督力度。对虚构虚增保险标的、虚假理赔、骗取套取保费补贴等违法违规行为，加大打击和惩戒力度，依法依规予以处理。\n\n四、夯实农业保险发展基础\n\n**（十五）强化信息化支撑。**持续推进全国农业保险数据信息系统、全国农业保险信息管理平台建设。统一农业保险数据标准，加强部门间、中央与地方间农业保险数据信息共享。加快推动农业保险行业数字化转型，促进业务流程智能化、标准化。\n\n**（十六）推进风险区划工作。**推进三大粮食作物、主要畜产品等重要农产品保险风险区划工作，适时发布风险区划结果和保险费率参考。探索开展地方特色农业保险风险区划工作。\n\n**（十七）加强科技应用。**保险机构应加大科技投入强度，合理应用无人机、遥感、物联网、人工智能、区块链、云计算等技术工具，提高保险服务精准度。鼓励保险机构探索开发农业灾害风险模型。制定农业保险科技应用规范或标准。保险机构应开展承保理赔信息实名校验和身份认证。\n\n**（十八）加强遴选管理。**按照适度竞争原则，结合服务能力、风控能力合理确定农业保险承保机构。各省份根据保费规模等因素统筹确定承保机构数量，承保机构一经确定应保持相对稳定，服务有效期一般不少于 3 年。开展承保机构绩效评价，强化绩效评价结果应用，做好承保机构动态调整工作。审慎评估农业保险市场环境和保险机构服务能力，加强经营资格管理，优化准入退出机制。\n\n**（十九）健全政策法规。**适应农业保险发展态势和高质量发展需要，推动适时修订《农业保险条例》，进一步加强农业保险法治建设。\n\n五、健全农业保险大灾风险分散机制\n\n**（二十）完善大灾风险准备金管理。**科学设置农业保险各险种大灾风险准备金计提比例，完善大灾风险准备金触发使用标准。保险机构按照独立运作、因地制宜、分级管理、统筹使用的原则，加强大灾风险准备金使用规范性管理，进一步发挥大灾风险准备金作用。\n\n**（二十一）大力发展农业再保险。**按照约定分保和市场化分保相结合的发展模式，健全农业再保险制度。完善约定分保机制和流程，优化大灾超赔保障。鼓励发展商业性再保险，逐步提高市场化业务比重。推动完善农业再保险机构治理机制，提高运行规范性和发展可持续性。\n\n**（二十二）健全风险分散机制。**研究建立农业保险大灾风险基金，完善农业保险大灾风险分散链条。加强农业保险赔付资金与政府救灾资金协同运用。\n\n六、做好组织实施工作\n\n各地区、各有关部门要高度重视农业保险工作，夯实工作基础，形成工作合力，更好满足“三农”主体风险保障需求。各省级人民政府加强组织领导，统筹推进本行政区域内农业保险规划发展、资源统筹、政策宣传等各项工作，发挥农业保险工作小组作用，组织有关部门和保险机构加强协同配合，确保各项任务落实见效。各有关部门按职责分工做好政策设计、资金安排、监督管理等工作。保险机构切实提高服务质效，可根据业务需要设立协保员，为承保理赔等服务提供便利。","农业农村部计划财务司 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农业遥感","农业保险农业遥感数字乡村粮食安全-2694",5,"2026-09-17T00:04:35.464456Z",{"id":235,"title":236,"url":237,"summary":238,"summary_zh":9,"content":9,"source_name":239,"source_url":9,"published_at":240,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":241,"score_detail":242,"sources":245,"tags":247,"primary_topic":251,"secondary_topics":252,"classification":253,"search_phrases":281,"slug":284,"view_count":59,"doi":9,"paper":285,"created_at":292},1508,"金融支持推进乡村全面振兴助力加快农业强国建设——第十九届中国农村金融发展论坛会议综述","https:\u002F\u002Fh.mffb.com.cn\u002Fa\u002F475184.html","综述显示第十九届中国农村金融发展论坛围绕数字金融与共同富裕主题形成以下发现：银行机构数字普惠金融水平对共同富裕具有显著促进作用、金融监管在其中发挥了正向调节效应；契约链主要通过促进社会化服务与缓解信贷约束增收、数字链则通过提升数字素养与扩大金融参与增收；农户参与数字经济通过生产智能化、供销平台化、金融普惠化三维协同机制显著促进财富积累。数字普惠金融通过促进非农就业与提升金融可得性不仅增加了农民收入，还推动了低收入群体收入阶层跃迁。","农业信息网","2026-09-01T00:00:00Z",77,{"impact":243,"substance":243,"depth":17,"authority":143,"freshness":45,"relevant":21,"comment":244},20,"论坛综述提炼了数字普惠金融促进共同富裕的实证结论，对农村金融研究有参考价值。",[246],{"name":239,"url":237},[248,249,250,28],"乡村振兴","共同富裕","数字普惠金融",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":254},{"candidates":255,"agricultural_evidence":46},[256,261,268,272,276],{"code":32,"score":257,"supported":46,"title":258,"abstract":259,"tags":260},13,[28],[28],[28,250],{"code":262,"score":263,"supported":46,"title":264,"abstract":266,"tags":267},"J2",8,[265],"农业强国",[],[],{"code":58,"score":66,"supported":46,"title":269,"abstract":270,"tags":271},[],[62],[],{"code":36,"score":21,"supported":14,"title":273,"abstract":274,"tags":275},[],[],[248],{"code":277,"score":21,"supported":14,"title":278,"abstract":279,"tags":280},"G4",[],[],[249],[282,283],"数字普惠金融 乡村振兴 共同富裕 农村金融","数字普惠金融 乡村振兴","数字普惠金融乡村振兴共同富裕农村金融-1508",{"doi":9,"openalex_id":9,"authors":286,"venue":9,"cited_by_count":94,"oa_url":9,"card":287,"direction":99,"ingested_from":168},[],{"tldr":288,"method":289,"finding":290,"direction":99,"opportunity":291},"综述农村金融论坛，探讨数字金融促进共同富裕的机制与效应。","论坛综述，归纳实证研究结论。","数字普惠金融促进共同富裕，金融监管正向调节，多维机制增收。","可深入研究数字普惠金融在农业细分领域的差异化效应及监管适配机制。","2026-09-03T00:06:44.980923Z",{"id":294,"title":295,"url":296,"summary":297,"summary_zh":9,"content":298,"source_name":299,"source_url":9,"published_at":300,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":114,"score_detail":301,"sources":303,"tags":305,"primary_topic":310,"secondary_topics":311,"classification":312,"search_phrases":324,"slug":327,"view_count":105,"doi":9,"paper":9,"created_at":328},421,"Debt financing and agricultural income among large-scale grain producers: evidence from Jiangsu Province, China","https:\u002F\u002Fwww.ebiotrade.com\u002Fnewsf\u002F2026-8\u002F20260806165737778.htm","发表于《Frontiers in Sustainable Food Systems》。基于江苏省LSGPs(大规模粮食生产者)调查数据,构建债务融资—要素投入—农业收入理论框架,从融资规模与融资结构双视角展开实证。研究核心问题:大规模粮食生产者仍面临组织能力弱、生产成本上升与经营效率低等约束,其根源在于自有资本有限与规模经营资本需求之间的错配严重制约农业收入持续增长。高效农村金融市场通过改善投资能力、促进农业现代化与增强风险韧性缓解资本约束,但中国农村金融市场仍存在发展不均与服务供给不足。论文发表于Frontiers in Sustainable Food Systems。","《Frontiers in Sustainable Food Systems》：Debt financing and agricultural income among large-scale grain producers: evidence from Jiangsu Province, China\n\n**编辑推荐：**\n\n实现大规模粮食生产者（large-scale grain producers, LSGPs）农业收入增长对保障粮食安全与促进农业可持续发展至关重要。基于2022年江苏省645户LSGPs调查数据，研究人员采用普通最小二乘法（ordinary least squ\n\n实现大规模粮食生产者（large-scale grain producers, LSGPs）农业收入增长对保障粮食安全与促进农业可持续发展至关重要。基于2022年江苏省645户LSGPs调查数据，研究人员采用普通最小二乘法（ordinary least squares, OLS）、两阶段最小二乘法（two-stage least squares, 2SLS）与中介效应模型，考察债务融资对农业收入的影响、机制及规模边界。结果显示，总信贷、传统信贷、民间信贷与贸易信贷对农业收入呈倒U形影响，而数字信贷（digital credit）具有显著正向影响。机制分析表明，债务融资主要通过转入耕地规模、机械投资与基础设施投资提升农业收入；传统信贷与数字信贷经全部三条渠道起效，贸易信贷经转入耕地规模与机械投资起效，民间信贷主要经转入耕地规模起效。债务融资对农业收入的影响亦存在教育异质性：相较初中及以下学历农户，高中及以上学历农户的总信贷与贸易信贷拐点更高，且数字信贷的正向收入效应更显著。研究发现，金融政策应有序推进数字信贷发展、将债务融资控制在适度区间、强化对生产性投资的支持，并按农户受教育程度提供差异化金融服务。\n\n研究背景方面，增加农业收入是现代农业发展核心目标，对保障粮食安全与推进乡村振兴具有根本意义。规模化经营成为农业现代化关键路径，但大规模粮食生产者（LSGPs）仍面临组织能力弱、生产成本上升与经营效率低等约束，其根源在于自有资本有限与规模经营资本需求之间的错配严重制约农业收入持续增长。高效农村金融市场通过改善投资能力、促进农业现代化与增强风险韧性缓解资本约束，但中国农村金融市场仍存在发展不均与服务供给不足，LSGPs融资需求未根本解决。既有文献沿农户债务融资行为、债务融资对要素投入决策影响、债务融资对农业收入影响三条线索展开，但对规模经营下债务融资是否存在适度边界、不同渠道异质效应及作用机制缺乏系统检验。为此，研究人员利用江苏省LSGPs调查数据构建债务融资—要素投入—农业收入理论框架，从融资规模与融资结构双视角展开实证，论文发表于《Frontiers in Sustainable Food Systems》。\n\n关键技术方法上，研究人员基于2022年江苏省常州、无锡、南通、泰州、淮安、徐州、宿迁七市分层随机抽取645户有效LSGPs样本（以50亩为大规模经营阈值），以农业净收入对数为主被解释变量，以总信贷及传统信贷、数字信贷、民间信贷、贸易信贷四类渠道为解释变量，以转入耕地规模、机械投资、基础设施投资为中介变量，控制户主年龄、教育年限、健康、家庭人口、非农收入、技术示范户、自有耕地、地权稳定性等变量并加入城市固定效应。估计策略依次采用OLS基准回归（含融资一次项与平方项捕捉倒U形）、以前期信贷及其平方项为工具变量的2SLS处理双向因果与遗漏变量偏误、utest与边际效应分析严格检验倒U形、逐步回归结合bootstrap（500次）的中介效应模型，以及按户主受教育年限以9年为切分的异质性分组回归。\n\n研究结果部分，第4.1节基准回归表明，总信贷对农业收入呈倒U形，拐点约121万元，94.88%样本低于拐点；传统信贷、民间信贷、贸易信贷同样倒U形，拐点分别约65万、52万、53万元；数字信贷线性显著为正。第4.2节内生性与稳健性检验中，2SLS以去年信贷及其平方项为工具变量，Hausman与DWH检验确认内生性，一阶段F均超10，结论与OLS一致；utest显示区间内斜率由正转负且极值落Fieller置信区间；边际效应在P25—P90为正、样本最大值转负；对农业收入1%缩尾及控制其他信贷渠道后结果稳健。第4.3节机制分析显示，转入耕地规模对全部四类渠道均具中介效应；机械投资中介传统、贸易、数字信贷，不中介民间信贷；基础设施投资仅中介传统与数字信贷。第4.4节教育异质性表明，总信贷倒U形在两组的拐点分别为117.57万与122.44万元；贸易信贷拐点由49.54万升至60.34万；数字信贷在初中及以下组不显著，在高中及以上组线性显著为正。\n\n讨论与结论翻译部分，研究人员基于江苏省645户LSGPs田野调查数据，使用OLS、2SLS与中介效应模型考察债务融资对农业收入的影响，发现总信贷呈倒U形，数字信贷正向，传统、民间、贸易信贷倒U形，超90%样本低于拐点。机制上转入耕地规模中介全部渠道，机械投资中介传统、贸易、数字信贷，基础设施投资仅中介传统与数字信贷。教育异质性显示高中及以上组总信贷与贸易信贷拐点更高，数字信贷正效应集中于该组。政策含义包括：信贷供给应控于适度区间；有序推进数字信贷并提升LSGPs使用能力；规范民间与贸易信贷互补作用；优化对转入耕地、机械与基础设施的生产性信贷支持；按教育程度提供差异化金融服务。研究局限为截面数据难以捕捉动态长期效应、工具变量排除限制未完全验证、样本仅限江苏需谨慎外推。","Frontiers in Sustainable Food Systems 2026-08-06","2026-08-06T00:00:00Z",{"impact":17,"substance":182,"depth":243,"authority":19,"freshness":80,"relevant":21,"comment":302},"基于江苏645户数据，系统考察债务融资对农业收入的影响，方法严谨，结论具有政策参考价值，但时效性稍差。",[304],{"name":299,"url":296},[306,307,308,309,28],"江苏","债务融资","大规模粮食生产者","农业收入",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":313},{"candidates":314,"agricultural_evidence":46},[315,319],{"code":32,"score":59,"supported":46,"title":316,"abstract":317,"tags":318},[],[28],[28],{"code":262,"score":66,"supported":46,"title":320,"abstract":321,"tags":323},[],[322],"农业现代化",[],[325,326],"大规模粮食生产者 债务融资 农业收入 农村金融","大规模粮食生产者 债务融资","大规模粮食生产者债务融资农业收入农村金融-421","2026-08-11T23:57:03.342056Z",{"id":330,"title":331,"url":332,"summary":333,"summary_zh":9,"content":9,"source_name":334,"source_url":9,"published_at":335,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":336,"score_detail":337,"sources":340,"tags":342,"primary_topic":345,"secondary_topics":346,"classification":347,"search_phrases":358,"slug":361,"view_count":94,"doi":362,"paper":363,"created_at":370},5463,"中国粮食生产现代化时空演变特征及影响因素分析 (Spatiotemporal Evolution and Influencing Factors of Grain Production Modernization in China)","http:\u002F\u002Fzgnjhxb.niam.com.cn\u002FCN\u002F10.13733\u002Fj.jcam.issn.2095-5553.2026.10.042","基于2011—2023年省级面板数据，六维构建粮食生产现代化指标，用核密度、空间自相关与GTWR分析。水平稳步提升，呈主产区＞主销区＞产销平衡区梯度；山东、江苏、安徽形成稳定高—高集聚区。数字普惠金融与财政支农正向。","中国农机化学报 2026-10-20","2026-10-20T00:00:00Z",79,{"impact":17,"substance":338,"depth":17,"authority":257,"freshness":20,"relevant":21,"comment":339},21,"基于省级面板数据构建六维指标并采用GTWR方法，结论对粮食生产现代化区域差异与数字普惠金融作用有参考价值，但属学术论文，公共影响有限。",[341],{"name":334,"url":332},[190,120,250,343,344],"粮食生产现代化","空间集聚",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":348},{"candidates":349,"agricultural_evidence":46},[350,354],{"code":32,"score":232,"supported":46,"title":351,"abstract":352,"tags":353},[],[120],[250,120],{"code":148,"score":21,"supported":14,"title":355,"abstract":356,"tags":357},[],[],[190],[359,360],"粮食生产现代化 时空演变","数字普惠金融 粮食生产","粮食生产现代化时空演变-5463","10.13733\u002Fj.jcam.issn.2095-5553.2026.10.042",{"doi":362,"openalex_id":9,"authors":364,"venue":9,"cited_by_count":94,"oa_url":9,"card":365,"direction":99,"ingested_from":168},[],{"tldr":366,"method":367,"finding":368,"direction":99,"opportunity":369},"构建六维指标测度2011—2023年中国粮食生产现代化水平并分析其时空演变与影响因素。","省级面板数据，六维指标体系，核密度、空间自相关与GTWR模型。","现代化水平稳步提升，呈主产区>主销区>产销平衡区梯度，山东、江苏、安徽为稳定高—高集聚区。","可探究数字普惠金融与财政支农的协同机制，并细化到县域尺度检验空间溢出效应。","2026-10-21T00:10:08.522187Z",{"id":372,"title":373,"url":374,"summary":375,"summary_zh":9,"content":9,"source_name":376,"source_url":9,"published_at":377,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":378,"score_detail":379,"sources":381,"tags":383,"primary_topic":388,"secondary_topics":389,"classification":395,"search_phrases":411,"slug":414,"view_count":80,"doi":415,"paper":416,"created_at":423},4730,"政策型农业信贷担保能否激励农户采纳秸秆还田技术","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1948304\u002Ffull","基于微观数据与变量法,实证评估政策型农业信贷担保对农户采纳秸秆还田技术的影响。研究发现该政策显著促进采纳,主要通过推动社会化服务购买与绿色信贷期限匹配生产周期实现,对数字素养较高、正式社会资本匮乏的农户效应更强","Frontiers in Sustainable Food Systems 2026-10-09","2026-10-09T00:00:00Z",80,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":263,"relevant":21,"comment":380},"基于微观数据的实证研究，揭示政策型信贷担保通过社会化服务与绿色信贷期限匹配促进秸秆还田，结论具政策参考价值。",[382],{"name":376,"url":374},[62,384,385,386,387],"秸秆还田","农业信贷担保","绿色金融","农户技术采纳",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[390],{"code":391,"slug":392,"name":393,"path":394,"parent_code":129},"H5","waste","面源污染与废弃物利用","\u002Ftopic\u002Fenvironment\u002Fwaste",{"status":39,"locked":14,"version":40,"method":41,"evidence":396},{"candidates":397,"agricultural_evidence":46},[398,403,407],{"code":32,"score":257,"supported":46,"title":399,"abstract":401,"tags":402},[400],"农业信贷",[400],[385,386],{"code":391,"score":143,"supported":46,"title":404,"abstract":405,"tags":406},[384],[384],[384],{"code":58,"score":59,"supported":46,"title":408,"abstract":409,"tags":410},[],[62],[62],[412,413],"农业信贷担保 秸秆还田","绿色信贷 农户 技术采纳","农业信贷担保秸秆还田-4730","10.3389\u002Ffsufs.2026.1948304\u002Ffull",{"doi":415,"openalex_id":9,"authors":417,"venue":9,"cited_by_count":94,"oa_url":9,"card":418,"direction":166,"ingested_from":168},[],{"tldr":419,"method":420,"finding":421,"direction":166,"opportunity":422},"实证评估政策型农业信贷担保对农户采纳秸秆还田技术的影响。","基于微观数据与变量法进行实证分析。","政策显著促进采纳，通过社会化服务购买与绿色信贷期限匹配实现。","可探究数字素养与信贷担保协同机制，及对异质性农户的精准政策设计。","2026-10-12T00:11:14.617567Z"]