[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3338":3,"related-3338":54},{"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,"search_phrases":32,"slug":35,"view_count":36,"doi":37,"paper":38,"created_at":53},3338,"Public education investment and agricultural structural transformation: evidence from prefecture-level cities in China's grain-producing provinces","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1942783","The decline of agriculture's relative output weight as non-farm sectors expand is a defining regularity of development, yet its local public-investment correlates within a single economy remain unclear. This study relates local public education-investment intensity—annual education expenditure over city gross domestic product (GDP), a spending flow rather than a human-capital stock—to agricultural structural transformation in 172 prefecture-level cities across China's 13 major grain-producing provinces, using two-way fixed-effects models that compare each city with itself over time. The primary-sector output share is the direct measure of intersectoral composition (3,249 city-year observations, 2005–2023); primary value added per registered resident is a complementary scale outcome that avoids the shared GDP denominator (3,080 observations, 2005–2022). On the share margin the association is negative in the reference specification but not robust, changing sign when city-specific trends and lagged controls are imposed jointly. On the scale margin it is negative in every control set examined, including lagged controls and city trends, and placebo fiscal ratios do not reproduce it; it is nevertheless indistinguishable from zero under province-by-year fixed effects and 13-province few-cluster inference. The three sector-share estimates form an accounting decomposition whose receiving-sector sign reverses across specifications, so no services-led claim is made. All estimates are conditional within-city associations, not causal effects. For grain-producing regions, they motivate coordinating education budgets with agricultural productivity and capacity safeguards rather than reading a falling primary-sector share as modernization.","农业相对产出比重的下降是非农部门扩张过程中的一个典型发展规律，但其在单一经济体内部与地方公共投资的关联仍不明确。本研究将地方公共教育投资强度——即年度教育支出占城市国内生产总值（GDP）的比重，这是一种支出流量而非人力资本存量——与中国13个粮食主产省172个地级市的农业结构转型相联系，采用双向固定效应模型对每个城市进行自身随时间变化的比较。第一产业产出份额是部门间构成的直接度量（2005—2023年，3,249个城市—年份观测值）；按户籍居民计算的第一产业增加值是一个互补性的规模结果变量，可避免使用共同的GDP分母（2005—2022年，3,080个观测值）。在份额边际上，基准设定中的关联为负但不稳健，当同时加入城市特定趋势和滞后控制变量后符号发生改变。在规模边际上，在所有考察的控制变量组合中——包括滞后控制变量和城市趋势——关联均为负，且安慰剂财政比率无法重现该结果；然而在省份—年份固定效应和13省少数聚类推断下，该关联与零无法区分。三个部门份额估计构成一个核算分解，其接收部门的符号在不同设定间发生反转，因此不提出服务业主导的论断。所有估计均为城市内部的条件关联，而非因果效应。对于粮食主产区而言，这些结果提示应将教育预算与农业生产率和产能保障相协调，而非将第一产业份额下降解读为现代化。",null,"Frontiers in Sustainable Food Systems","2026-09-23T00:00:00Z","论文",10,false,78,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},16,22,18,13,9,1,"基于172个地级市2005—2023年面板数据的实证研究，方法规范、结论审慎，对粮食主产区教育投入与农业结构转型的协调具有参考价值，但属学术论文、影响面偏细分领域。",[25],{"name":10,"url":6},[27,28,29,30,31],"县域经济","粮食主产区","农业经济研究","农业结构转型","教育投入",[33,34],"粮食主产区 地级市 教育投入","农业结构转型 主产区 面板数据","粮食主产区地级市教育投入-3338",0,"10.3389\u002Ffsufs.2026.1942783",{"doi":37,"openalex_id":39,"authors":40,"venue":10,"cited_by_count":36,"oa_url":6,"card":46,"direction":50,"ingested_from":52},"W7214101078",[41,43],{"name":42,"orcid":9},"Kewen Hou",{"name":44,"orcid":45},"Xiuzhi Wang","https:\u002F\u002Forcid.org\u002F0000-0002-3770-3231",{"tldr":47,"method":48,"finding":49,"direction":50,"opportunity":51},"研究中国13个粮食主产省172个地级市教育投入强度与农业结构转型的关联。","双向固定效应模型，2005-2023年地级市面板数据，教育支出占GDP比重。","教育投入与农业产出份额关联不稳健，与人均农业增加值负相关但非因果。","数字乡村与农业信息化","可探究教育投入通过人力资本与数字技术采纳影响农业转型的机制，并做因果识别。","openalex","2026-09-24T23:30:07.572572Z",{"total":55,"page":22,"page_size":55,"items":56},6,[57,96,136,166,195,224],{"id":58,"title":59,"url":60,"summary":61,"summary_zh":62,"content":9,"source_name":63,"source_url":60,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":64,"score_detail":65,"sources":70,"tags":72,"search_phrases":77,"slug":80,"view_count":36,"doi":81,"paper":82,"created_at":95},3365,"The Impact of Agricultural Digitization on Green Total Factor Productivity in Agriculture: Evidence from 30 Provinces in China","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fsu18199758","Amid tighter resource and environmental limits in China, the integration of digitization and greening gives new momentum to sustainable agriculture; however, its methods and structural conditions remain unclear. This study utilizes provincial panel data from 2011–2023 and the super-efficiency SBM-GML model to estimate agricultural green total factor productivity (AGTFP). It empirically examines the green empowerment effect of agricultural digitization, its transmission pathways, and structural boundaries. Unlike existing studies that rely on input-oriented or rural-level digitalization measures and qualitative regional groupings, this study constructs an agriculture-specific index, identifies agricultural socialized services (ASS) as an organizational channel, examines the moderating role of environmental regulation, and uses a continuous threshold variable to identify the structural breakpoint at which digitization’s green effect changes. The findings indicate that digitization considerably enhances AGTFP, and this association remains stable across a range of robustness checks. Mechanism analysis indicates that ASS partially mediates this effect, while environmental regulation intensity positively moderates the relationship. Heterogeneity and threshold analyses show that the empowering effect is only significant in the eastern region, but not in the central and western regions; it is significant in the main grain-producing areas. Moreover, a tentative structural breakpoint is identified when the grain-sown area proportion reaches 0.486. Since this threshold is statistically significant only at the 10% level, it should be regarded as preliminary evidence. Accordingly, we recommend regionally differentiated digital agriculture strategies, strengthening the socialized agricultural service system, and enhancing synergy between environmental regulation and digital empowerment to effectively advance the green transformation of China’s agriculture.","在中国资源与环境约束趋紧的背景下，数字化与绿色化的融合为可持续农业注入了新动力；然而，其实现路径与结构性条件仍不明确。本研究利用2011—2023年省级面板数据和超效率SBM-GML模型测算农业绿色全要素生产率（AGTFP），实证检验了农业数字化的绿色赋能效应、传导路径及结构边界。与现有研究依赖投入导向或农村层面数字化指标以及定性区域分组不同，本研究构建了农业专属指数，识别出农业社会化服务（ASS）作为组织化渠道，考察了环境规制的调节作用，并使用连续门槛变量识别数字化绿色效应发生变化的结构性断点。研究发现，数字化显著提升了AGTFP，且该关联在一系列稳健性检验中保持稳定。机制分析表明，ASS在其中发挥部分中介作用，而环境规制强度正向调节该关系。异质性与门槛分析显示，赋能效应仅在东部地区显著，在中西部地区不显著；在粮食主产区显著。此外，当粮食播种面积占比达到0.486时，识别出一个初步的结构性断点。由于该门槛仅在10%水平上统计显著，应将其视为初步证据。据此，我们建议采取区域差异化的数字农业策略，强化农业社会化服务体系，并增强环境规制与数字赋能之间的协同效应，以有效推进中国农业绿色转型。","Sustainability",85,{"impact":18,"substance":66,"depth":19,"authority":67,"freshness":68,"relevant":22,"comment":69},23,14,8,"基于30省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。",[71],{"name":63,"url":60},[73,74,28,75,76],"农业绿色发展","农业数字化","农业社会化服务","环境规制",[78,79],"农业数字化 绿色全要素生产率","农业社会化服务 环境规制","农业数字化绿色全要素生产率-3365","10.3390\u002Fsu18199758",{"doi":81,"openalex_id":83,"authors":84,"venue":63,"cited_by_count":36,"oa_url":60,"card":89,"direction":50,"ingested_from":52},"W7214071788",[85,87],{"name":86,"orcid":9},"Zhaojuan Meng",{"name":88,"orcid":9},"Guixiang Lin",{"tldr":90,"method":91,"finding":92,"direction":93,"opportunity":94},"基于2011–2023年省级面板数据，检验农业数字化对农业绿色全要素生产率的赋能效应、机制与结构边界","超效率SBM-GML测算AGTFP，构建农业专属数字化指数，中介、调节与连续门槛","数字化显著提升AGTFP，农业社会化服务部分中介，环境规制正向调节，效应仅东部和主产区显著。","农业绿色发展与碳","可深入探究中西部数字化绿色效应缺失的组织与市场条件，并验证粮食播种面积占比0.486的结构断点。","2026-09-24T23:30:25.917176Z",{"id":97,"title":98,"url":99,"summary":100,"summary_zh":101,"content":9,"source_name":10,"source_url":99,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":102,"score":103,"score_detail":104,"sources":108,"tags":110,"search_phrases":115,"slug":118,"view_count":36,"doi":119,"paper":120,"created_at":135},3336,"Prosper or beggar thy neighbor? non-farm employment effects and spatial spillovers of China's National Digital Village Pilot Program","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1950504","Introduction Counties are a key spatial unit linking cities and villages in China's rural transformation, yet many still lack the industrial base, market scale, and non-farm job opportunities needed to absorb rural labor. This study examines whether China's National Digital Village Pilot Program expands rural non-farm employment and whether its effects extend beyond pilot counties. Methods Using a panel of 1,450 Chinese counties and 24,111 county-year observations from 2007 to 2024, this study exploits the staggered implementation of the 2020 and 2024 pilot cohorts as a quasi-natural experiment. The policy effect is estimated using a multi-period difference-in-differences model and cross-validated with stacked DID and the Callaway-Sant'Anna estimator. Robustness is further assessed through propensity score matching, inverse probability weighting, entropy balancing, placebo tests, and alternative samples and specifications. Transmission channels, heterogeneity, and spatial effects are also examined. Results The National Digital Village Pilot Program increased rural non-farm employment in pilot counties by approximately 25% relative to non-pilot counties, and the result remains robust across alternative identification and estimation strategies. The two-step transmission-channel analysis provides evidence consistent with two potential channels: enhanced county-level innovation and improved mobile network access capability. The policy effect is significantly stronger in central-region counties than in western counties and stronger in smaller counties than in larger ones, with weaker evidence of a larger effect in counties with lower human capital. Spatial analysis shows a positive direct effect on pilot counties but a negative spillover effect on neighboring counties. Discussion The findings indicate that digital village development can strengthen local non-farm employment absorption, but part of the local gain may reflect a spatial reallocation of economic activity rather than net regional job creation. Effective implementation therefore requires not only county-level digital development but also cross-county resource coordination and regional policy integration.","引言 县域是中国城乡转型中连接城市与乡村的关键空间单元，但许多县域仍缺乏吸纳农村劳动力所需的产业基础、市场规模和非农就业机会。本研究考察中国国家数字乡村试点项目是否扩大了农村非农就业，以及其效应是否超出试点县范围。方法 本研究使用2007年至2024年间1，450个中国县域、24，111个县—年观测值的面板数据，将2020年和2024年试点批次的分批实施作为准自然实验。政策效应采用多期双重差分模型估计，并通过堆叠双重差分和Callaway-Sant'Anna估计量进行交叉验证。稳健性进一步通过倾向得分匹配、逆概率加权、熵平衡、安慰剂检验以及替代样本和设定加以评估。研究还考察了传导渠道、异质性和空间效应。结果 国家数字乡村试点项目使试点县的农村非农就业相对于非试点县增加了约25%，且该结果在替代性识别和估计策略下保持稳健。两步传导渠道分析提供的证据与两个潜在渠道相一致：县域创新能力增强和移动网络接入能力改善。政策效应在中部地区县域显著强于西部地区县域，在较小县域强于较大县域，而在人力资本较低县域效应更大的证据较弱。空间分析显示，政策对试点县具有正向直接效应，但对邻近县域产生负向溢出效应。讨论 研究结果表明，数字乡村发展能够增强本地非农就业吸纳能力，但部分本地收益可能反映的是经济活动的空间再配置，而非区域净就业创造。因此，有效实施不仅需要县域层面的数字发展，还需要跨县域资源协调和区域政策整合。",true,89,{"impact":105,"substance":66,"depth":106,"authority":67,"freshness":21,"relevant":22,"comment":107},24,19,"基于1450个县2007-2024年面板数据的准自然实验研究，证实数字乡村试点使县域非农就业提升约25%，但存在对邻县的负向溢出，对政策协调具有实质参考价值。",[109],{"name":10,"url":99},[111,112,27,113,114],"数字乡村","空间溢出","数字乡村试点","非农就业",[116,117],"国家数字乡村试点 非农就业","县域 数字乡村 空间溢出","国家数字乡村试点非农就业-3336","10.3389\u002Ffsufs.2026.1950504",{"doi":119,"openalex_id":121,"authors":122,"venue":10,"cited_by_count":36,"oa_url":99,"card":130,"direction":50,"ingested_from":52},"W7214093544",[123,126,128],{"name":124,"orcid":125},"Wei Yang","https:\u002F\u002Forcid.org\u002F0000-0002-0540-5934",{"name":127,"orcid":9},"Lijun Wang",{"name":129,"orcid":9},"Yizhuo Ma",{"tldr":131,"method":132,"finding":133,"direction":50,"opportunity":134},"评估国家数字乡村试点对县域非农就业的影响及空间溢出效应。","2007-2024年1450个县面板数据，多期DID与Callaway-Sant","试点县非农就业增约25%，但邻县出现负向溢出，部分为就业空间再分配。","可探究数字乡村试点的跨县协同机制与负溢出治理，及不同区域差异化政策设计。","2026-09-24T23:30:07.434384Z",{"id":137,"title":138,"url":139,"summary":140,"summary_zh":9,"content":9,"source_name":141,"source_url":9,"published_at":142,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":143,"score_detail":144,"sources":146,"tags":148,"search_phrases":152,"slug":155,"view_count":36,"doi":156,"paper":157,"created_at":165},3003,"数字乡村建设对农业经济韧性的影响研究——基于2014—2023年30个省份面板数据","https:\u002F\u002Fxb.ynau.edu.cn\u002Fjwk_sk\u002Fcn\u002Farticle\u002Fpdf\u002Fpreview\u002F10.12371\u002Fj.ynau(s).202603058.pdf","安徽建筑大学潘和平等基于2014—2023年中国30个省份面板数据，运用双向固定效应模型和中介效应模型系统考察数字乡村建设对农业经济韧性的影响与传导机制。研究结果表明：数字乡村建设显著提升农业经济韧性，且这种正向效应在自然灾害频发区、粮食主产区及高数字乡村试点区表现更为突出；其传导机制主要通过促进农业技术创新实现。","云南农业大学学报(社会科学)2026,20(0):1-8","2026-09-15T00:00:00Z",80,{"impact":19,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":145},"基于30省十年面板数据的实证研究，方法规范、结论明确，对数字乡村政策评估有参考价值，但属学术论文，公共传播性有限。",[147],{"name":141,"url":139},[111,149,28,150,151],"农业经济韧性","农业技术创新","面板数据",[153,154],"数字乡村 农业经济韧性","潘和平 安徽建筑大学 数字乡村","数字乡村农业经济韧性-3003","10.12371\u002Fj.ynau(s).202603058.pdf",{"doi":156,"openalex_id":9,"authors":158,"venue":9,"cited_by_count":36,"oa_url":9,"card":159,"direction":50,"ingested_from":164},[],{"tldr":160,"method":161,"finding":162,"direction":50,"opportunity":163},"基于30省面板数据，实证检验数字乡村建设对农业经济韧性的提升效应及传导机制。","2014—2023年30省面板数据，双向固定效应与中介效应模型。","数字乡村建设显著提升农业经济韧性，且在灾害频发区、粮食主产区、高试点区更突出，农业技术创新为中介。","可进一步探究数字乡村不同维度（如电商、治理数字化）对韧性的异质性影响及空间溢出效应。","agent","2026-09-20T00:03:08.396757Z",{"id":167,"title":168,"url":169,"summary":170,"summary_zh":9,"content":9,"source_name":171,"source_url":9,"published_at":172,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":173,"score_detail":174,"sources":177,"tags":179,"search_phrases":183,"slug":186,"view_count":36,"doi":9,"paper":187,"created_at":194},2403,"乡村治理数字化对农民增收的影响研究——基于县域数字乡村指数与CFPS微观调查数据","http:\u002F\u002Fwww.qikanvip.com\u002Fqkml\u002F127622.html","统计与决策2026年第02期。付莎、王军将《县域数字乡村指数》和CFPS微观调查数据进行匹配，实证探究乡村治理数字化对农民增收的影响。结果表明：乡村治理数字化有助于促进农民增收，具有益贫效应（在农村未接入互联网和低收入群体中增收效应更强）；通过提高农民数字素养、提升农民对本地官员的信任度来促进农民增收；对高学历农民的增收效应更显著。","统计与决策 | 2026-09-09","2026-09-09T00:00:00Z",76,{"impact":19,"substance":18,"depth":19,"authority":20,"freshness":175,"relevant":22,"comment":176},5,"核心期刊实证研究，将县域数字乡村指数与CFPS微观数据匹配，揭示乡村治理数字化的益贫效应与信任机制，对数字乡村政策有参考价值。",[178],{"name":171,"url":169},[111,180,181,182,27],"乡村治理","数字素养","农民增收",[184,185],"乡村治理 农民增收 县域经济 数字乡村","乡村治理 农民增收","乡村治理农民增收县域经济数字乡村-2403",{"doi":9,"openalex_id":9,"authors":188,"venue":9,"cited_by_count":36,"oa_url":9,"card":189,"direction":50,"ingested_from":164},[],{"tldr":190,"method":191,"finding":192,"direction":50,"opportunity":193},"匹配县域数字乡村指数与CFPS数据，实证检验乡村治理数字化对农民增收的影响。","县域数字乡村指数与CFPS微观调查数据匹配，实证回归分析。","乡村治理数字化促进农民增收且具益贫效应，通过提升数字素养与官员信任实现。","可探究治理数字化益贫效应的长期动态及不同治理场景下的异质性机制。","2026-09-14T00:06:33.536950Z",{"id":196,"title":197,"url":198,"summary":199,"summary_zh":9,"content":9,"source_name":200,"source_url":9,"published_at":201,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":173,"score_detail":202,"sources":205,"tags":207,"search_phrases":212,"slug":215,"view_count":36,"doi":9,"paper":216,"created_at":223},2217,"《数字经济参与与农场扩张:来自中国主要粮食产区农地租赁市场进入和经营耕地面积的证据》","https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1677","Caihua Xu等(长安大学、西北农林科技大学)基于安徽、河南、河北、山东四省1025户农户调查数据,考察农民在数字化农业活动中的参与是否与中国主要粮食产区的农场扩张相关。数字经济参与通过采购、生产、供销、金融四个农业价值链阶段衡量,农场扩张通过进入农地租赁市场和经营耕地面积对数两个结果衡量。采用双重\u002F去偏机器学习框架灵活调整观察到的家庭、地方和县级协变量及潜在非线性关系。数字经济参与与两个结果均呈正相关。","Land 2026年9月10日","2026-09-10T00:00:00Z",{"impact":17,"substance":203,"depth":19,"authority":20,"freshness":68,"relevant":22,"comment":204},21,"基于四省1025户调查与双重机器学习方法，为数字农业参与促进农地租赁与规模经营提供了实证证据，对数字乡村与适度规模经营政策有参考价值。",[206],{"name":200,"url":198},[208,209,28,210,211],"机器学习","数字经济","土地流转","农场扩张",[213,214],"粮食主产区 农场扩张 土地流转 数字经济","粮食主产区 农场扩张","粮食主产区农场扩张土地流转数字经济-2217",{"doi":9,"openalex_id":9,"authors":217,"venue":9,"cited_by_count":36,"oa_url":9,"card":218,"direction":50,"ingested_from":164},[],{"tldr":219,"method":220,"finding":221,"direction":50,"opportunity":222},"基于四省1025户调查，研究农户数字经济参与是否促进农场扩张。","安徽等四省1025户调查数据，双重\u002F去偏机器学习框架。","数字经济参与与农地租赁市场进入和经营耕地面积均呈正相关。","可探究数字经济参与促进农场扩张的因果机制及不同价值链阶段的异质性效应。","2026-09-12T00:06:40.676162Z",{"id":225,"title":226,"url":227,"summary":228,"summary_zh":9,"content":9,"source_name":229,"source_url":9,"published_at":230,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":231,"score_detail":232,"sources":234,"tags":236,"search_phrases":239,"slug":242,"view_count":36,"doi":243,"paper":244,"created_at":251},2109,"《Digital villages and agricultural green total factor productivity》 数字乡村试点DID评估AGTFP","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1831978\u002Ffull","Guo Hui、Wang Xinyi、Xia He、Jiang Wenjie基于2014—2023年中国县级面板数据,采用超效率SBM-DDF-GML模型测算AGTFP,以国家数字乡村试点为准自然实验,运用双重差分法评估数字乡村建设对AGTFP的影响、机制与空间溢出效应。结果显示数字乡村显著提升AGTFP,主要通过绿色技术进步而非绿色技术效率改善;机制上通过优化劳动力配置、激发农业经营创业、扩大消费实现;政策效应在靠近省会城市、地形起伏大、路网密度低的地区更显著。","Frontiers in Sustainable Food Systems 10:1831978","2026-09-05T00:00:00Z",82,{"impact":18,"substance":66,"depth":19,"authority":20,"freshness":55,"relevant":22,"comment":233},"基于县级面板与试点准自然实验，方法规范、结论明确，对数字乡村政策评估有参考价值。",[235],{"name":229,"url":227},[111,27,73,237,238],"政策评估","全要素生产率",[240,241],"全要素生产率 农业绿色发展 县域经济 政策评估","全要素生产率 农业绿色发展","全要素生产率农业绿色发展县域经济政策评估-2109","10.3389\u002Ffsufs.2026.1831978\u002Ffull",{"doi":243,"openalex_id":9,"authors":245,"venue":9,"cited_by_count":36,"oa_url":9,"card":246,"direction":50,"ingested_from":164},[],{"tldr":247,"method":248,"finding":249,"direction":50,"opportunity":250},"基于县级面板数据，用DID评估数字乡村试点对农业绿色全要素生产率的影响。","超效率SBM-DDF-GML测算AGTFP，双重差分法，2014—2023年县级","数字乡村显著提升AGTFP，主要靠绿色技术进步，通过劳动力配置、创业和消费实现。","可探究数字乡村对AGTFP的空间溢出边界及绿色技术效率滞后原因。","2026-09-11T00:04:22.864968Z"]