[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3562":3,"related-3562":65},{"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":28,"search_phrases":34,"slug":37,"view_count":38,"doi":39,"paper":40,"created_at":64},3562,"Toward Common Prosperity: Spatially Heterogeneous Effects and Mechanisms of Digital Village Construction on the Urban–Rural Income Gap in Mountainous China","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fland15101801","Mountainous regions are critical to promoting inclusive growth and reducing spatial inequality worldwide. Digital village construction offers a new pathway to overcome geographical constraints and reshape urban–rural income distribution in these areas. Using county-level data for 2020 and focusing on mountainous China, this study integrates the Theil index, IV-2SLS, MGWR, and mediation models to characterize the spatial pattern of the urban–rural income gap (URIG) and identify the spatially heterogeneous effects and potential mechanisms of digital village construction across different geomorphological settings. The results indicate that: (1) Large and highly dispersed URIG are concentrated in mountainous regions. On average, the gap is approximately twice as large as that in non-mountainous regions and varies markedly across geomorphological types. URIG is highest in mountain counties, shows the greatest variation in plateau counties, and tends to be lower in hill counties. (2) Digital village construction exerts a significant negative effect on URIG in mountainous China, with the effect being stronger than in non-mountainous regions and generally increasing in magnitude from east to west. (3) The effects of different DVI dimensions on URIG exhibit substantial spatial heterogeneity and vary in their spatial scales of influence. Rural economy digitalization shows widespread and consistently negative effects and emerges as the dominant dimension across most mountainous regions, while the effect of other dimensions is more localized, particularly rural life digitalization, which shows stronger negative effects in mountain counties, plateau counties, and underdeveloped mountainous counties. (4) Agricultural operation modernization and labor allocation optimization constitute potential channels underlying the association between digital village construction and a narrower URIG, with the estimated indirect effect being more pronounced through labor allocation optimization. These findings provide empirical evidence for advancing inclusive development in mountainous regions amid China’s common prosperity agenda, with broader implications for place-based digital village construction.","山区对于促进包容性增长和缩小全球空间不平等至关重要。数字乡村建设为这些地区突破地理约束、重塑城乡收入分配格局提供了新路径。本研究以2020年县级数据为基础，聚焦中国山区，综合运用泰尔指数、IV-2SLS、MGWR和中介模型，刻画城乡收入差距（URIG）的空间格局，并识别数字乡村建设在不同地貌条件下的空间异质性效应及其潜在机制。结果表明：（1）规模较大且高度分散的URIG集中于山区。其平均值约为非山区的两倍，且在不同地貌类型间差异显著。URIG在山区县最高，在高原县变异最大，在丘陵县则趋于较低水平。（2）数字乡村建设对中国山区URIG具有显著负向影响，其效应强于非山区，且总体上呈自东向西递增态势。（3）数字乡村建设不同维度对URIG的影响表现出显著的空间异质性，且其影响的空间尺度各异。农村经济数字化呈现出广泛且稳定的负向效应，并在大多数山区成为主导维度，而其他维度的效应则更具局部性，尤其是农村生活数字化，在山区县、高原县及欠发达山区县表现出更强的负向效应。（4）农业经营现代化和劳动力配置优化构成数字乡村建设与URIG缩小之间关联的潜在渠道，其中通过劳动力配置优化的间接效应更为明显。上述发现为中国共同富裕议程下推进山区包容性发展提供了经验证据，并对因地制宜推进数字乡村建设具有更广泛的启示。",null,"Land","2026-09-25T00:00:00Z","论文",25,false,80,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},18,22,13,9,1,"基于县域数据的实证研究，方法扎实、结论对山区数字乡村政策有参考价值，但属学术论文，公共传播热度有限。",[24,25],{"name":10,"url":6},{"name":26,"url":27},"MDPI Land 15(10), 1801 2026-09-25","https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F10\u002F1801",[29,30,31,32,33],"数字乡村","县域经济","农业数字化","山区农业","城乡收入差距",[35,36],"数字乡村 城乡收入差距 山区","县域 数字乡村 泰尔指数","数字乡村城乡收入差距山区-3562",0,"10.3390\u002Fland15101801",{"doi":39,"openalex_id":41,"authors":42,"venue":10,"cited_by_count":38,"oa_url":6,"card":57,"direction":61,"ingested_from":63},"W7214349448",[43,46,49,52,54],{"name":44,"orcid":45},"Xueting Yang","https:\u002F\u002Forcid.org\u002F0000-0003-4633-2185",{"name":47,"orcid":48},"Xiaoping Qiu","https:\u002F\u002Forcid.org\u002F0000-0001-6310-5959",{"name":50,"orcid":51},"Fubiao Zhu","https:\u002F\u002Forcid.org\u002F0000-0002-4606-3336",{"name":53,"orcid":9},"Mingkai Xu",{"name":55,"orcid":56},"Yun Xu","https:\u002F\u002Forcid.org\u002F0000-0002-2442-7792",{"tldr":58,"method":59,"finding":60,"direction":61,"opportunity":62},"研究数字乡村建设对中国山区城乡收入差距的空间异质性影响及机制。","用2020年县级数据，结合泰尔指数、IV-2SLS、MGWR与中介模型。","数字乡村建设显著缩小山区城乡收入差距，效应自东向西增强，且存在地貌与维度异质性。","数字乡村与农业信息化","可深入探究不同地貌下数字乡村各维度的空间尺度差异及劳动力配置中介的微观路径。","openalex","2026-09-26T23:30:38.665617Z",{"total":66,"page":21,"page_size":66,"items":67},6,[68,98,138,164,191,230],{"id":69,"title":70,"url":71,"summary":72,"summary_zh":9,"content":9,"source_name":73,"source_url":9,"published_at":74,"category":12,"cover_url":9,"hotness":75,"is_selected":14,"score":76,"score_detail":77,"sources":80,"tags":82,"search_phrases":85,"slug":88,"view_count":38,"doi":9,"paper":89,"created_at":97},3438,"《数字乡村建设与城乡收入差距：一个U型关系》——基于县域数字乡村发展指数","http:\u002F\u002Fwww.qikanzj.com\u002Fhek\u002Fhznydxxbshkxb\u002Fmulu\u002F559280.html","李晓慧、李谷成基于偏向型技术进步理论，利用县域数字乡村发展指数与县域经济统计数据实证检验数字乡村建设与城乡收入差距之间的关系。基准回归结果显示数字乡村建设对城乡收入差距的影响呈现出先缩小后扩大的U型效应；从各维度看，数字基础设施对城乡收入差距的影响处于扩大阶段，而乡村经济数字化、乡村治理数字化及乡村生活数字化对城乡收入差距的影响处于缩小阶段；机制研究显示技能溢价是阻碍数字乡村建设缩小城乡收入差距的重要因素。","《华中农业大学学报（社会科学版）》","2026-09-20T00:00:00Z",10,78,{"impact":17,"substance":18,"depth":17,"authority":78,"freshness":66,"relevant":21,"comment":79},14,"核心期刊实证研究，基于县域指数揭示数字乡村与城乡收入差距的U型关系及技能溢价机制，结论具政策参考价值。",[81],{"name":73,"url":71},[29,83,30,33,84],"数字基础设施","技能溢价",[86,87],"数字乡村 城乡收入差距","城乡收入差距 数字基础设施 县域经济 技能溢价","数字乡村城乡收入差距-3438",{"doi":9,"openalex_id":9,"authors":90,"venue":9,"cited_by_count":38,"oa_url":9,"card":91,"direction":61,"ingested_from":96},[],{"tldr":92,"method":93,"finding":94,"direction":61,"opportunity":95},"基于县域数字乡村发展指数，实证检验数字乡村建设与城乡收入差距呈先缩小后扩大的U型关系。","偏向型技术进步理论，县域数字乡村发展指数与县域经济统计数据实证回归。","数字乡村建设对城乡收入差距呈U型效应，技能溢价是阻碍其缩小差距的重要因素。","可探究技能溢价门槛下数字乡村各维度对收入差距的异质性影响及技能培训等调节机制。","agent","2026-09-25T00:09:33.905388Z",{"id":99,"title":100,"url":101,"summary":102,"summary_zh":103,"content":9,"source_name":104,"source_url":101,"published_at":11,"category":12,"cover_url":9,"hotness":75,"is_selected":105,"score":106,"score_detail":107,"sources":111,"tags":113,"search_phrases":117,"slug":120,"view_count":38,"doi":121,"paper":122,"created_at":137},3535,"Policy coordination of public data openness and supercomputing center for agricultural chain resilience","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1882123","Strengthening agricultural supply chain resilience is critical for safeguarding food security and promoting high-quality agricultural development in the face of escalating external shocks, including climate extremes, market volatility, and geopolitical uncertainties. Although public data openness and supercomputing infrastructure represent critical pillars of digital transformation, how their coordinated deployment enhances agricultural resilience remains both theoretically underdeveloped and empirically underexamined. Using provincial panel data from 31 Chinese provinces over 2011–2024, we exploit the staggered rollout of public data platforms and supercomputing centers as quasi-natural experiments, constructing a synergistic difference-in-differences (DID) model to isolate their coordinated policy effects. The causal identification is further fortified using event-study parallel trend tests, placebo simulations, propensity score matching (PSM-DID), and double\u002Fdebiased machine learning (DDML). Our baseline estimates demonstrate that policy synergy between public data openness and supercomputing infrastructure substantially enhances agricultural supply chain resilience, with effects remaining robust across multiple specifications and economically significant. Mechanistic analysis reveals two primary transmission channels through which policy synergy strengthens resilience: (i) industrial structure rationalization, and (ii) agricultural electrification enhancement. Specifically, policy coordination (1) reduces informational barriers between agricultural and related industries, (2) facilitates optimal factor reallocation across sectors, and (3) accelerates the adoption of electricity-intensive precision agriculture technologies. Heterogeneity analysis reveals that policy effectiveness is substantially greater in central and western regions (coefficient ~0.089) compared to eastern regions (coefficient ~0.036), suggesting a ‘technology compensation effect’ whereby digital infrastructure offsets traditional regional disparities. Policy effects remain robustly positive across both major grain-producing and grain-consuming zones, indicating broad applicability. By unpacking the complementary and sequential dynamics between data factors and computing capacity, this study moves beyond the traditional “black box” treatment of the digital economy, providing critical policy insights for the coordinated deployment of digital public goods to secure modern food systems.","在气候极端事件、市场波动和地缘政治不确定性等外部冲击日益加剧的背景下，增强农业供应链韧性对于保障粮食安全和推动农业高质量发展至关重要。尽管公共数据开放和超算基础设施是数字化转型的关键支柱，但二者协同部署如何增强农业韧性，在理论上尚不成熟，在实证上也缺乏充分检验。本文利用2011—2024年中国31个省份的面板数据，将公共数据平台和超算中心的交错 rollout 作为准自然实验，构建协同双重差分（DID）模型以识别其协调政策效应。因果识别进一步通过事件研究平行趋势检验、安慰剂模拟、倾向得分匹配（PSM-DID）以及双重\u002F去偏机器学习（DDML）加以强化。基准估计表明，公共数据开放与超算基础设施之间的政策协同显著增强了农业供应链韧性，该效应在多种设定下保持稳健且具有经济显著性。机制分析揭示了政策协同增强韧性的两条主要传导渠道：（i）产业结构合理化，以及（ii）农业电气化水平提升。具体而言，政策协调（1）降低了农业与相关产业之间的信息壁垒，（2）促进了要素在部门间的优化再配置，（3）加速了电力密集型精准农业技术的采用。异质性分析表明，政策效果在中西部地区（系数约0.089）显著大于东部地区（系数约0.036），暗示存在“技术补偿效应”，即数字基础设施抵消了传统的区域差距。政策效应在粮食主产区和主销区均保持稳健为正，表明其具有广泛适用性。通过揭示数据要素与算力之间的互补性和序贯动态，本研究超越了数字经济传统的“黑箱”处理方式，为协调部署数字公共产品以保障现代粮食体系提供了关键政策启示。","Frontiers in Sustainable Food Systems",true,85,{"impact":18,"substance":108,"depth":17,"authority":78,"freshness":109,"relevant":21,"comment":110},23,8,"基于31省面板数据的准自然实验，证实公共数据开放与超算中心协同显著提升农业供应链韧性，方法严谨、结论有政策价值，值得进入每日精选。",[112],{"name":104,"url":101},[29,114,31,115,116],"农业供应链","公共数据开放","超算中心",[118,119],"公共数据开放 超算中心 农业供应链韧性","农业供应链韧性 准自然实验","公共数据开放超算中心农业供应链韧性-3535","10.3389\u002Ffsufs.2026.1882123",{"doi":121,"openalex_id":123,"authors":124,"venue":104,"cited_by_count":38,"oa_url":101,"card":132,"direction":61,"ingested_from":63},"W7214402306",[125,127,129],{"name":126,"orcid":9},"Zhaoqun Chen",{"name":128,"orcid":9},"Jiewen Zheng",{"name":130,"orcid":131},"Cheng Chi","https:\u002F\u002Forcid.org\u002F0000-0002-3823-6735",{"tldr":133,"method":134,"finding":135,"direction":61,"opportunity":136},"基于中国省级面板数据，用交错DID识别公共数据开放与超算中心协同对农业供应链韧性的因果效应。","2011–2024年31省面板数据，交错DID、PSM-DID、DDML与事件研","政策协同显著提升农业供应链韧性，经产业结构合理化与农业电气化传导，中西部效应更强。","可探究数据开放与算力协同的时序互补机制，并下沉到县域或产业链微观主体验证技术补偿效应。","2026-09-26T23:30:07.920756Z",{"id":139,"title":140,"url":141,"summary":142,"summary_zh":9,"content":9,"source_name":73,"source_url":9,"published_at":74,"category":12,"cover_url":9,"hotness":75,"is_selected":14,"score":76,"score_detail":143,"sources":145,"tags":147,"search_phrases":151,"slug":154,"view_count":38,"doi":9,"paper":155,"created_at":163},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。",{"impact":17,"substance":18,"depth":17,"authority":78,"freshness":66,"relevant":21,"comment":144},"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。",[146],{"name":73,"url":141},[29,148,31,149,150],"农业碳减排","省级面板数据","绿色技术创新",[152,153],"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",{"doi":9,"openalex_id":9,"authors":156,"venue":9,"cited_by_count":38,"oa_url":9,"card":157,"direction":161,"ingested_from":96},[],{"tldr":158,"method":159,"finding":160,"direction":161,"opportunity":162},"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。","农业绿色发展与碳","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。","2026-09-25T00:09:34.643179Z",{"id":165,"title":166,"url":167,"summary":168,"summary_zh":9,"content":9,"source_name":169,"source_url":9,"published_at":74,"category":12,"cover_url":9,"hotness":75,"is_selected":14,"score":170,"score_detail":171,"sources":174,"tags":176,"search_phrases":179,"slug":182,"view_count":38,"doi":9,"paper":183,"created_at":190},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":172,"substance":18,"depth":17,"authority":78,"freshness":66,"relevant":21,"comment":173},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。",[175],{"name":169,"url":167},[29,148,31,177,178],"河南农业","fsQCA",[180,181],"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",{"doi":9,"openalex_id":9,"authors":184,"venue":9,"cited_by_count":38,"oa_url":9,"card":185,"direction":161,"ingested_from":96},[],{"tldr":186,"method":187,"finding":188,"direction":161,"opportunity":189},"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":192,"title":193,"url":194,"summary":195,"summary_zh":196,"content":9,"source_name":104,"source_url":194,"published_at":197,"category":12,"cover_url":9,"hotness":75,"is_selected":105,"score":198,"score_detail":199,"sources":203,"tags":205,"search_phrases":209,"slug":212,"view_count":38,"doi":213,"paper":214,"created_at":229},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。","2026-09-23T00:00:00Z",89,{"impact":200,"substance":108,"depth":201,"authority":78,"freshness":20,"relevant":21,"comment":202},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。",[204],{"name":104,"url":194},[29,206,30,207,208],"空间溢出","数字乡村试点","非农就业",[210,211],"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":213,"openalex_id":215,"authors":216,"venue":104,"cited_by_count":38,"oa_url":194,"card":224,"direction":61,"ingested_from":63},"W7214093544",[217,220,222],{"name":218,"orcid":219},"Wei Yang","https:\u002F\u002Forcid.org\u002F0000-0002-0540-5934",{"name":221,"orcid":9},"Lijun Wang",{"name":223,"orcid":9},"Yizhuo Ma",{"tldr":225,"method":226,"finding":227,"direction":61,"opportunity":228},"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":231,"title":232,"url":233,"summary":234,"summary_zh":9,"content":9,"source_name":235,"source_url":9,"published_at":74,"category":12,"cover_url":9,"hotness":75,"is_selected":14,"score":236,"score_detail":237,"sources":239,"tags":241,"search_phrases":245,"slug":248,"view_count":38,"doi":9,"paper":249,"created_at":256},3319,"数字乡村发展政策如何促进县域共同富裕——基于国家数字乡村试点的准自然实验（2493个县域面板数据）","https:\u002F\u002Fhdjj.cbpt.cnki.net\u002Fportal\u002Fjournal\u002Fportal\u002Fclient\u002Fpaper\u002F9c80dbc0eb5a9c566aa948a15e08a887","《华东经济管理》2026年09月20日网络首发。何元浪、袁健红（广西大学\u002F东南大学）基于2012—2023年中国2,493个县域面板数据，以国家数字乡村试点作为准自然实验。研究发现：数字乡村发展政策能够增加农民收入，缩小城乡收入差距，促进县域共同富裕；机制分析揭示，数字乡村发展政策通过加强基础设施建设、提升农业生产效率和推动劳动力转移促进县域共同富裕；进一步分析发现，数字乡村发展政策对共同富裕的促进效应在南方地区、金融发展水平高和基层组织能力强的县域更为显著，并且该政策对邻接县域共同富裕具有显著的正向溢出效应。","《华东经济管理》2026年09月20日 网络首发",86,{"impact":18,"substance":108,"depth":201,"authority":78,"freshness":109,"relevant":21,"comment":238},"基于2493个县域面板数据的准自然实验，方法规范、数据规模大，结论对数字乡村政策评估有实质参考价值。",[240],{"name":235,"url":233},[29,242,33,243,244],"农民增收","县域共同富裕","国家试点",[246,247],"国家数字乡村试点 县域 共同富裕","数字乡村 城乡收入差距 2493个县域","国家数字乡村试点县域共同富裕-3319",{"doi":9,"openalex_id":9,"authors":250,"venue":9,"cited_by_count":38,"oa_url":9,"card":251,"direction":61,"ingested_from":96},[],{"tldr":252,"method":253,"finding":254,"direction":61,"opportunity":255},"基于2493个县域面板数据，用国家数字乡村试点准自然实验评估政策对县域共同富裕的促进效应。","2012—2023年县域面板数据，双重差分法（准自然实验）与机制、异质性、空间溢","数字乡村政策通过基建、农业生产率与劳动力转移增加农民收入、缩小城乡差距，且具正向空间溢出。","可探究数字乡村政策的空间溢出边界与衰减规律，及金融、基层组织等条件对政策效果的调节机制。","2026-09-24T00:04:02.203373Z"]