[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3535":3,"related-3535":56},{"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":55},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。",null,"Frontiers in Sustainable Food Systems","2026-09-25T00:00:00Z","论文",10,true,85,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},22,23,18,14,8,1,"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。",[25],{"name":10,"url":6},[27,28,29,30,31],"数字乡村","农业供应链","农业数字化","公共数据开放","超算中心",[33,34],"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",0,"10.3389\u002Ffsufs.2026.1882123",{"doi":37,"openalex_id":39,"authors":40,"venue":10,"cited_by_count":36,"oa_url":6,"card":48,"direction":52,"ingested_from":54},"W7214402306",[41,43,45],{"name":42,"orcid":9},"Zhaoqun Chen",{"name":44,"orcid":9},"Jiewen Zheng",{"name":46,"orcid":47},"Cheng Chi","https:\u002F\u002Forcid.org\u002F0000-0002-3823-6735",{"tldr":49,"method":50,"finding":51,"direction":52,"opportunity":53},"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。","数字乡村与农业信息化","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。","openalex","2026-09-26T23:30:07.920756Z",{"total":57,"page":22,"page_size":57,"items":58},6,[59,110,157,186,213,251],{"id":60,"title":61,"url":62,"summary":63,"summary_zh":64,"content":9,"source_name":65,"source_url":62,"published_at":11,"category":12,"cover_url":9,"hotness":66,"is_selected":67,"score":68,"score_detail":69,"sources":73,"tags":78,"search_phrases":82,"slug":85,"view_count":36,"doi":86,"paper":87,"created_at":109},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。","Land",25,false,80,{"impact":19,"substance":17,"depth":19,"authority":70,"freshness":71,"relevant":22,"comment":72},13,9,"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。",[74,75],{"name":65,"url":62},{"name":76,"url":77},"MDPI Land 15(10), 1801 2026-09-25","https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F10\u002F1801",[27,79,29,80,81],"县域经济","山区农业","城乡收入差距",[83,84],"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","10.3390\u002Fland15101801",{"doi":86,"openalex_id":88,"authors":89,"venue":65,"cited_by_count":36,"oa_url":62,"card":104,"direction":52,"ingested_from":54},"W7214349448",[90,93,96,99,101],{"name":91,"orcid":92},"Xueting Yang","https:\u002F\u002Forcid.org\u002F0000-0003-4633-2185",{"name":94,"orcid":95},"Xiaoping Qiu","https:\u002F\u002Forcid.org\u002F0000-0001-6310-5959",{"name":97,"orcid":98},"Fubiao Zhu","https:\u002F\u002Forcid.org\u002F0000-0002-4606-3336",{"name":100,"orcid":9},"Mingkai Xu",{"name":102,"orcid":103},"Yun Xu","https:\u002F\u002Forcid.org\u002F0000-0002-2442-7792",{"tldr":105,"method":106,"finding":107,"direction":52,"opportunity":108},"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。","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。","2026-09-26T23:30:38.665617Z",{"id":111,"title":112,"url":113,"summary":114,"summary_zh":115,"content":9,"source_name":116,"source_url":113,"published_at":117,"category":12,"cover_url":9,"hotness":13,"is_selected":67,"score":118,"score_detail":119,"sources":121,"tags":123,"search_phrases":127,"slug":130,"view_count":36,"doi":131,"paper":132,"created_at":156},3492,"Compliance Algorithm for Socio-Environmental Safeguards in Legal Amazon: A Traceability and Risk Assessment Model for Agricultural Commodities Chain under Regulation (EU) 2023\u002F1115","https:\u002F\u002Fdoi.org\u002F10.66104\u002F5gcafj90","Regulation (EU) 2023\u002F1115 requires agricultural commodities placed on the European market to be deforestation-free from 31 December 2020, produced in line with laws in its country of origin, and covered by a due diligence statement identifying production plots by geolocation. Demonstrating compliance depends on a unit of analysis—rural property and plot—that falls between municipal-scale trade-flow models and pixel-scale remote sensing products, while relying on heterogeneous and consistently incomplete databases. This article presents COMPLY-SOY-MA, a model for assessing traceability, risk, and socio-environmental compliance across agricultural commodities supply chains in Legal Amazon. Its main contribution is a credibility estimator that separates two often-confused dimensions: substantive compliance, measured through a composite index of thirty-one indicators aggregated using power means with controlled compensability, and supporting-evidence quality, measured through a five-indicator index. Risk is estimated by shrinking observed deficiency toward a territorial prior, weighted by the power of the evidence index, so opaque units are not assessed using averages from units that disclose information. Under isolated changes in evidence, providing more evidence reduces risk only for genuinely compliant units. Traceability is modelled as a mass-flow-weighted multigraph, using origin entropy and a formal distinction between exposure and contamination index. Scope is stated clearly: the study delivers a formal model and reference implementation, with results based on mathematical derivation and domain characterization. It specifies but does not execute a validation protocol, and its risk score is not a calibrated probability.","欧盟第2023\u002F1115号条例要求，投放欧洲市场的农产品自2020年12月31日起不得涉及毁林，须依照原产国法律生产，并须通过尽职调查声明予以涵盖，该声明需以地理定位方式标明生产地块。证明合规取决于一个分析单元——农村地产与地块——它介于市级贸易流模型与像素级遥感产品之间，同时依赖于异质且始终不完整的数据库。本文提出COMPLY-SOY-MA，一个用于评估合法亚马逊地区农产品供应链可追溯性、风险及社会环境合规性的模型。其主要贡献在于一个可信度估计器，该估计器区分了两个常被混淆的维度：实质性合规，通过由三十一项指标构成的综合指数衡量，该指数采用可控补偿性的幂均值进行聚合；以及支撑性证据质量，通过一个五项指标指数衡量。风险估计通过将观测到的缺陷向地域先验收缩来实现，并以证据指数的幂进行加权，从而避免对不透明单元使用披露信息单元的平均值进行评估。在证据发生孤立变化的情况下，提供更多证据仅对真正合规的单元降低风险。可追溯性被建模为质量流加权的多重图，使用来源熵以及暴露指数与污染指数之间的形式化区分。研究范围已明确界定：本研究提供一个形式化模型及参考实现，其结果基于数学推导与领域刻画。它规定但并未执行验证方案，其风险评分并非经过校准的概率。","Revista Multidisciplinar do Nordeste Mineiro","2026-09-24T00:00:00Z",63,{"impact":21,"substance":17,"depth":19,"authority":57,"freshness":71,"relevant":22,"comment":120},"提出面向欧盟零毁林法规的农产品供应链可追溯与风险评估模型，方法新颖但仅属形式化模型、未经验证，且来源为地方性期刊，影响力有限。",[122],{"name":116,"url":113},[27,28,124,125,126],"农产品溯源","遥感","欧盟法规",[128,129],"EU 2023 1115 农产品 溯源","Legal Amazon 大豆 供应链","EU20231115农产品溯源-3492","10.66104\u002F5gcafj90",{"doi":131,"openalex_id":133,"authors":134,"venue":116,"cited_by_count":22,"oa_url":113,"card":149,"direction":155,"ingested_from":54},"W7214225231",[135,138,140,142,144,147],{"name":136,"orcid":137},"Felipe Oliveira Carvalho","https:\u002F\u002Forcid.org\u002F0000-0002-7540-286X",{"name":139,"orcid":9},"Allan Kardec Duailibe Barros Filho",{"name":141,"orcid":9},"José Artur Lima Cabral Marques",{"name":143,"orcid":9},"Rosângela Maria Guimarães Rosa",{"name":145,"orcid":146},"Paulo Fortes Neto","https:\u002F\u002Forcid.org\u002F0000-0001-5837-8450",{"name":148,"orcid":9},"Silvio Carlos Leite Mesquita",{"tldr":150,"method":151,"finding":152,"direction":153,"opportunity":154},"提出COMPLY-SOY-MA模型，评估合法亚马逊农产品供应链的可追溯性、风险与社会环境合规性。","构建可信度估计器，用31项指标复合指数与证据质量指数，结合质量流多重图与熵。","区分实质合规与证据质量，证据增加仅降低真正合规单元的风险，避免不透明单元被平均化。","农业绿色发展与碳","可延伸至多品类供应链验证，并开发实证校准的风险概率模型与验证协议。","农业遥感与作物表型","2026-09-25T23:30:30.374587Z",{"id":158,"title":159,"url":160,"summary":161,"summary_zh":9,"content":9,"source_name":162,"source_url":9,"published_at":163,"category":12,"cover_url":9,"hotness":13,"is_selected":67,"score":164,"score_detail":165,"sources":167,"tags":169,"search_phrases":173,"slug":176,"view_count":36,"doi":9,"paper":177,"created_at":185},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。","《华中农业大学学报（社会科学版）》","2026-09-20T00:00:00Z",78,{"impact":19,"substance":17,"depth":19,"authority":20,"freshness":57,"relevant":22,"comment":166},"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。",[168],{"name":162,"url":160},[27,170,29,171,172],"农业碳减排","省级面板数据","绿色技术创新",[174,175],"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":178,"venue":9,"cited_by_count":36,"oa_url":9,"card":179,"direction":153,"ingested_from":184},[],{"tldr":180,"method":181,"finding":182,"direction":153,"opportunity":183},"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。","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。","agent","2026-09-25T00:09:34.643179Z",{"id":187,"title":188,"url":189,"summary":190,"summary_zh":9,"content":9,"source_name":191,"source_url":9,"published_at":163,"category":12,"cover_url":9,"hotness":13,"is_selected":67,"score":192,"score_detail":193,"sources":196,"tags":198,"search_phrases":201,"slug":204,"view_count":36,"doi":9,"paper":205,"created_at":212},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":194,"substance":17,"depth":19,"authority":20,"freshness":57,"relevant":22,"comment":195},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。",[197],{"name":191,"url":189},[27,170,29,199,200],"河南农业","fsQCA",[202,203],"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":206,"venue":9,"cited_by_count":36,"oa_url":9,"card":207,"direction":153,"ingested_from":184},[],{"tldr":208,"method":209,"finding":210,"direction":153,"opportunity":211},"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":214,"title":215,"url":216,"summary":217,"summary_zh":218,"content":9,"source_name":10,"source_url":216,"published_at":219,"category":12,"cover_url":9,"hotness":13,"is_selected":67,"score":220,"score_detail":221,"sources":223,"tags":225,"search_phrases":229,"slug":232,"view_count":22,"doi":233,"paper":234,"created_at":250},2627,"How does agricultural digitalization drive green total factor productivity? Evidence from rural industrial integration, data factor allocation, and spatial effects in China","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1852421","Introduction Digital empowerment accelerates cross-sectoral integration within the rural economy, driving profound structural shifts in agricultural development. Methods Using a provincial-level panel dataset from China spanning 2012 to 2022, this study constructs comprehensive indices to assess agricultural digitalization, rural industrial integration, and data factor allocation. By employing mediation, moderation, and spatial Durbin models, this research investigates the underlying mechanisms through which agricultural digitalization affects agricultural green total factor productivity. Results The empirical results demonstrate that agricultural digitalization significantly enhances AGTFP, a finding that is robust to a battery of specification checks. Mechanism analysis reveals that rural industrial integration serves as a crucial partial mediator in this relationship. Furthermore, optimizing data factor allocation positively moderates and thereby amplifies the impact of agricultural digitalization on AGTFP. Spatial analysis indicates that agricultural digitalization generates positive spatial spillover effects, boosting AGTFP in both local and neighboring regions. Finally, heterogeneity analyses reveal that this positive effect is particularly pronounced in the Eastern region and in areas with high levels of urban-rural integration, and it remains robust across both major and non-major grain-producing areas. Discussion Ultimately, this study deepens the understanding of the digitalization-sustainability nexus in agriculture, underscoring the vital role of advancing rural industrial integration and optimizing data factor allocation in driving green productivity.","引言 数字赋能加速了农村经济内部的跨部门融合，推动农业发展发生深刻的结构性变革。方法 本研究利用中国2012年至2022年的省级面板数据集，构建了农业数字化、农村产业融合和数据要素配置的综合指数。通过采用中介模型、调节模型和空间杜宾模型，本研究探讨了农业数字化影响农业绿色全要素生产率的潜在机制。结果 实证结果表明，农业数字化显著提升了农业绿色全要素生产率（AGTFP），这一发现在一系列设定检验中保持稳健。机制分析揭示，农村产业融合在这一关系中起到了关键的部分中介作用。此外，优化数据要素配置对这一关系产生了正向调节作用，从而放大了农业数字化对AGTFP的影响。空间分析表明，农业数字化产生了正向空间溢出效应，提升了本地及邻近地区的AGTFP。最后，异质性分析显示，这一正向效应在东部地区和城乡融合水平较高的地区尤为显著，且在粮食主产区和非主产区均保持稳健。讨论 最终，本研究深化了对农业数字化与可持续性关系的理解，凸显了推进农村产业融合和优化数据要素配置在推动绿色生产力方面的重要作用。","2026-09-16T00:00:00Z",81,{"impact":19,"substance":17,"depth":19,"authority":70,"freshness":13,"relevant":22,"comment":222},"基于2012—2022年省级面板数据的实证研究，揭示农业数字化通过农村产业融合与数据要素配置提升农业绿色全要素生产率并具空间溢出效应，方法规范、结论有政策参考价值，值得进入每日精选。",[224],{"name":10,"url":216},[27,226,29,227,228],"数据要素","绿色全要素生产率","农村产业融合",[230,231],"绿色全要素生产率 农村产业融合 农业数字化 数字乡村","绿色全要素生产率 农村产业融合","绿色全要素生产率农村产业融合农业数字化数字乡村-2627","10.3389\u002Ffsufs.2026.1852421",{"doi":233,"openalex_id":235,"authors":236,"venue":10,"cited_by_count":36,"oa_url":216,"card":245,"direction":153,"ingested_from":54},"W7213309985",[237,239,242],{"name":238,"orcid":9},"Yijia Zhou",{"name":240,"orcid":241},"Jun He","https:\u002F\u002Forcid.org\u002F0000-0003-4839-3950",{"name":243,"orcid":244},"Jun Chen","https:\u002F\u002Forcid.org\u002F0000-0001-7397-2714",{"tldr":246,"method":247,"finding":248,"direction":153,"opportunity":249},"基于中国省级面板数据，揭示农业数字化通过产业融合与数据要素配置提升农业绿色全要素生产率。","2012-2022年省级面板数据，构建综合指数，用中介、调节与空间杜宾模型。","农业数字化显著提升AGTFP，农村产业融合起部分中介作用，数据要素配置正向调节，且具正向空间溢出。","可深入微观地块或县域尺度，探究数据要素配置的阈值效应及跨区域溢出衰减机制。","2026-09-16T23:30:07.645013Z",{"id":252,"title":253,"url":254,"summary":255,"summary_zh":9,"content":9,"source_name":256,"source_url":9,"published_at":257,"category":258,"cover_url":9,"hotness":13,"is_selected":67,"score":259,"score_detail":260,"sources":264,"tags":266,"search_phrases":270,"slug":273,"view_count":36,"doi":9,"paper":9,"created_at":274},2227,"广东发布《关于推进\"四好\"农产品市场体系建设行动方案》","https:\u002F\u002Fwww.crnews.net\u002F2026\u002F09\u002F0910687.html","广东省政府新闻办举行专题新闻发布会,介绍推进\"四好\"农产品市场体系建设行动方案(2026-2030年),提出到2030年建成国家级农产品产地市场5个、省级20个、市级50个,实现产地市场交易额突破3000亿元,带动全省农产品流通数字化率超过75%。","广东省政府新闻办","2026-09-07T16:00:00Z","政策",79,{"impact":17,"substance":18,"depth":261,"authority":262,"freshness":57,"relevant":22,"comment":263},17,11,"省级五年行动方案，含产地市场数量与交易额、数字化率等硬指标，政策条款与数据增量扎实，值得进入每日精选。",[265],{"name":256,"url":254},[27,29,267,268,269],"农产品流通","产地市场","市场体系",[271,272],"农业数字化 农产品流通 产地市场 市场体系","农业数字化 农产品流通","农业数字化农产品流通产地市场市场体系-2227","2026-09-13T00:03:59.351554Z"]