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模型测度数字技术发展与农业绿色全要素生产率,运用双向固定效应模型检验发现数字技术发展具有显著促进作用且存在双重异质性;空间上回报率呈地理位置为主导的分化,效率上呈边际效应递减;机制上要素数字化呈现「转化—产出」结构性失衡,东部强于转化弱于产出,中西部相反。","西藏大学经济与管理学院《华东经济管理》2026 年第 06 期",73,{"impact":18,"substance":91,"depth":18,"authority":154,"freshness":92,"relevant":21,"comment":155},13,"基于31省面板数据的实证研究，方法规范、结论有区域适配价值，但属学术论文且时效性弱，适合主题聚合而非每日精选。",[157],{"name":151,"url":149},[26,159,27,160,161],"数字技术","区域差异","农业全要素生产率",[],{"status":33,"locked":13,"version":34,"method":35,"evidence":164},{"candidates":165,"agricultural_evidence":63,"reason":64},[166,170,174,179],{"code":49,"score":21,"supported":13,"title":167,"abstract":168,"tags":169},[],[],[27],{"code":54,"score":21,"supported":13,"title":171,"abstract":172,"tags":173},[],[],[27],{"code":175,"score":21,"supported":13,"title":176,"abstract":177,"tags":178},"I1",[],[],[159],{"code":59,"score":21,"supported":13,"title":180,"abstract":181,"tags":182},[],[],[26],[184,185],"数字技术 农业绿色全要素生产率","SBM-GML 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resilience","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1708484","Introduction Green finance (GF) has become crucial for improving agricultural infrastructure in rural areas. This study aims to identify the role of GF-induced rural revitalisation in enhancing agricultural resilience (AR). At the same time, the association between food security and AR is determined, followed by the impact of education and environmental regulation on AR. Methods The quantitative data were collected from the “Food and Agriculture Organisation of the United Nations” for different Asian countries (Cambodia, China, Pakistan, Myanmar, India, Indonesia, Laos, Mongolia, Nepal, Sri Lanka, the Philippines, Thailand, Uzbekistan and Vietnam). The selected timeframe ranged from 2012 to 2022. The descriptive statistics, correlation analysis and multiple old regression models were applied to the data. Results The results obtained show that GF, environmental regulation, and education boost agricultural productivity ( p \u003C 0.05), whereas higher climate risk reduces it. GF and rural income significantly improve productivity ( p \u003C 0.05), leading to an enhancement of AR. This helps in decreasing food insecurity. Environmental regulation and education are both key drivers of better productivity. AR has generally increased over the years with some ups and downs. Moreover, GF levels differ widely between countries. However, agricultural leaders such as China, Thailand, Indonesia and India tend to have stronger AR and GF. Higher education scores are associated with better agricultural productivity and stronger environmental regulations to attain AR. Finally, productivity levels vary greatly between the selected countries. Discussion This study emphasises the integration of GF in agricultural infrastructure to enhance AR and food security.","引言 绿色金融（GF）已成为改善农村地区农业基础设施的关键。本研究旨在明确绿色金融驱动的乡村振兴在增强农业韧性（AR）中的作用。同时，确定粮食安全与农业韧性之间的关联，并进一步探讨教育和环境规制对农业韧性的影响。方法 定量数据收集自联合国粮食及农业组织，涵盖多个亚洲国家（柬埔寨、中国、巴基斯坦、缅甸、印度、印度尼西亚、老挝、蒙古、尼泊尔、斯里兰卡、菲律宾、泰国、乌兹别克斯坦和越南）。所选时间范围为2012年至2022年。对数据应用了描述性统计、相关性分析和多元旧回归模型。结果 所得结果表明，绿色金融、环境规制和教育提升了农业生产力（p \u003C 0.05），而较高的气候风险则降低了农业生产力。绿色金融和农村收入显著提高了生产力（p \u003C 0.05），从而增强了农业韧性。这有助于减少粮食不安全。环境规制和教育都是提高生产力的关键驱动因素。农业韧性总体上多年来有所提升，但伴有起伏。此外，各国绿色金融水平差异较大。然而，中国、泰国、印度尼西亚和印度等农业领先国家往往具有更强的农业韧性和绿色金融。较高的教育得分与更好的农业生产力和更强的环境规制相关联，从而实现农业韧性。最后，所选国家之间的生产力水平差异很大。讨论 本研究强调将绿色金融纳入农业基础设施，以增强农业韧性和粮食安全。","Frontiers in Sustainable Food Systems","2026-09-28T00:00:00Z",77,{"impact":18,"substance":17,"depth":328,"authority":154,"freshness":204,"relevant":21,"comment":329},17,"基于FAO多国面板数据的实证研究，揭示绿色金融通过乡村振兴提升农业韧性与粮食安全的机制，对农业信息化与农村金融政策有参考价值。",[331],{"name":324,"url":321},[26,333,334,29,335],"粮食安全","农业韧性","农村振兴",{"code":337,"slug":338,"name":339,"path":340,"parent_code":341},"A8","yield-food-security","单产提升与粮食安全","\u002Ftopic\u002Ffield-crops\u002Fyield-food-security","A",[],{"status":107,"locked":13,"version":34,"method":35,"evidence":344},{"candidates":345,"agricultural_evidence":63},[346,352,357,363,367],{"code":337,"score":347,"supported":63,"title":348,"abstract":350,"tags":351},15,[349],"food security",[333,349],[333],{"code":104,"score":69,"supported":63,"title":353,"abstract":354,"tags":356},[],[355],"乡村振兴",[],{"code":358,"score":69,"supported":63,"title":359,"abstract":360,"tags":362},"G6",[],[361],"rural income",[],{"code":39,"score":21,"supported":13,"title":364,"abstract":365,"tags":366},[],[],[29],{"code":59,"score":21,"supported":13,"title":368,"abstract":369,"tags":370},[],[],[26],[372,373],"绿色金融 农业韧性 粮食安全","亚洲 农业韧性 绿色金融","绿色金融农业韧性粮食安全-3766","10.3389\u002Ffsufs.2026.1708484",{"doi":375,"openalex_id":377,"authors":378,"venue":324,"cited_by_count":72,"oa_url":321,"card":385,"direction":77,"ingested_from":390},"W7214667116",[379,381,383],{"name":380,"orcid":8},"Zixiang Zhao",{"name":382,"orcid":8},"Chengcheng Lin",{"name":384,"orcid":8},"Po-Wen Tsai",{"tldr":386,"method":387,"finding":388,"direction":77,"opportunity":389},"研究绿色金融驱动的乡村振兴如何通过提升农业生产率增强农业韧性并保障粮食安全。","基于FAO 2012-2022年14个亚洲国家面板数据，用描述统计、相关分析和多","绿色金融、环境规制与教育显著提升农业生产率和农业韧性，气候风险则降低生产率。","可深入探究绿色金融影响农业韧性的微观传导机制，并比较不同国家制度环境的调节效应。","openalex","2026-09-29T23:30:07.494934Z",{"id":393,"title":394,"url":395,"summary":396,"summary_zh":397,"content":8,"source_name":324,"source_url":395,"published_at":398,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":399,"score_detail":400,"sources":402,"tags":404,"primary_topic":407,"secondary_topics":408,"classification":409,"search_phrases":437,"slug":440,"view_count":242,"doi":441,"paper":442,"created_at":466},3342,"Impact of agricultural greening on food production resilience-empirical evidence from China","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1938875","The Food Production System is generally confronted with external uncertain shocks and rigid constraints from ecological factors. How to strengthen the system’s adaptability and resilience through green transition pathways is a crucial research topic in the field of modern agricultural governance. With the provincial panel data from China, the entropy method is adopted in this paper to measure agricultural greening and food production resilience, and then the two-way fixed-effect model, the moderating-effect model, the threshold-effect model and spatial econometric models are applied for empirical testing. According to the results, the degree of agricultural greening has promoted food production resilience; at the same time, regions show heterogeneity across geographical regions and grain functional zones. Green finance and rural scientific and technological input will also be effective driving forces. There is a single threshold for economic development level; after exceeding it, the marginal promoting effect on resilience weakens, but remains significantly positive. Food production resilience also exhibits significant spatial dependence, and agricultural greening generates positive spillover effects on neighboring provinces under the adjacency-based spatial specification. This paper will provide new ideas for the theory of resilience enhancement and offer some references to help optimize the distribution of agricultural green factors and foster long-term stable grain production.","粮食生产系统普遍面临外部不确定冲击与生态因素的刚性约束。如何通过绿色转型路径增强系统的适应性与韧性，是现代农业农村治理领域的重要研究课题。本文基于中国省级面板数据，采用熵值法测度农业绿色化与粮食生产韧性，进而运用双向固定效应模型、调节效应模型、门槛效应模型及空间计量模型进行实证检验。结果表明，农业绿色化程度提升了粮食生产韧性；同时，不同地理区域与粮食功能区呈现出异质性。绿色金融与农村科技投入也是有效的驱动力量。经济发展水平存在单一门槛，跨越门槛后对韧性的边际促进效应减弱，但仍显著为正。粮食生产韧性还表现出显著的空间依赖性，在邻接空间设定下，农业绿色化对邻近省份产生正向溢出效应。本文将为韧性提升理论提供新思路，并为优化农业绿色要素配置、促进粮食长期稳定生产提供一定参考。","2026-09-23T00:00:00Z",79,{"impact":18,"substance":91,"depth":18,"authority":154,"freshness":204,"relevant":21,"comment":401},"基于中国省级面板数据的实证研究，方法扎实、结论有政策参考价值，但属学术论文而非产业级事件，适合进入主题聚合而非头条精选。",[403],{"name":324,"url":395},[27,405,406,29,99],"粮食生产韧性","农业科技投入",{"code":125,"slug":212,"name":213,"path":214,"parent_code":215},[],{"status":107,"locked":13,"version":34,"method":35,"evidence":410},{"candidates":411,"agricultural_evidence":63},[412,416,420,424,428,432],{"code":125,"score":242,"supported":63,"title":413,"abstract":414,"tags":415},[],[224],[99],{"code":337,"score":21,"supported":13,"title":417,"abstract":418,"tags":419},[],[],[405],{"code":39,"score":21,"supported":13,"title":421,"abstract":422,"tags":423},[],[],[29],{"code":49,"score":21,"supported":13,"title":425,"abstract":426,"tags":427},[],[],[27],{"code":54,"score":21,"supported":13,"title":429,"abstract":430,"tags":431},[],[],[27],{"code":433,"score":21,"supported":13,"title":434,"abstract":435,"tags":436},"J2",[],[],[406],[438,439],"农业绿色化 粮食生产韧性","绿色金融 农村科技投入","农业绿色化粮食生产韧性-3342","10.3389\u002Ffsufs.2026.1938875",{"doi":441,"openalex_id":443,"authors":444,"venue":324,"cited_by_count":72,"oa_url":395,"card":461,"direction":77,"ingested_from":390},"W7214113879",[445,447,450,452,454,456,459],{"name":446,"orcid":8},"Huimin Shao",{"name":448,"orcid":449},"Wenjuan Zhang","https:\u002F\u002Forcid.org\u002F0000-0001-5062-1292",{"name":451,"orcid":8},"Qiong Peng",{"name":453,"orcid":8},"Haichuan Liu",{"name":455,"orcid":8},"Liangyan Lu",{"name":457,"orcid":458},"Mcxin Tee","https:\u002F\u002Forcid.org\u002F0000-0001-7990-8377",{"name":460,"orcid":8},"Qing Jin",{"tldr":462,"method":463,"finding":464,"direction":77,"opportunity":465},"基于中国省级面板数据，实证检验农业绿色化对粮食生产韧性的提升作用。","熵值法测度指标，双向固定效应、调节效应、门槛效应与空间计量模型。","农业绿色化显著提升粮食生产韧性，存在区域异质性、经济门槛及正向空间溢出。","可探究绿色金融与科技投入的协同机制，及跨区域溢出路径的微观落地。","2026-09-24T23:30:07.857044Z"]