[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4194":3,"related-4194":97},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":8,"source_name":9,"source_url":8,"published_at":10,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":14,"score_detail":15,"sources":23,"tags":25,"primary_topic":31,"secondary_topics":37,"classification":50,"search_phrases":81,"slug":84,"view_count":85,"doi":8,"paper":86,"created_at":96},4194,"数字乡村建设的代价？数字乡村战略对粮食安全和农业碳循环的非对称影响——来自中国的证据","https:\u002F\u002Fideas.repec.org\u002Fa\u002Fspr\u002Fagfoec\u002Fv14y2026i1d10.1186_s40100-026-00503-9.html","Weilong Gao、Qian Zhang、Ying Peng（吉林大学国家发展与安全研究院、大连理工大学商学院、长春工业大学经济管理学院）。发表于Agricultural and Food Economics 2026, 14(1): 1-22。基于989个县和1835户农户的多层次数据，将县级数字乡村发展指数与家庭层面数字采纳指数链接，运用固定效应和两阶段最小二乘法模型。结果显示：农村数字化与粮食产量显著正相关，与农业碳排放显著负相关，但对作物生物量碳吸收或净碳足迹无显著改善。机制分析表明生产收益与土地利用集约化（特别是双季种植扩张）相关，减排与减少劳动力和农药使用一致。异质性分析显示数字基础设施驱动生产率增长，数字治理和数字生活与减排关联更密切。",null,"Agricultural and Food Economics 2026, 14(1)","2026-10-01T00:00:00Z","论文",10,true,88,{"impact":16,"substance":17,"depth":18,"authority":19,"freshness":20,"relevant":21,"comment":22},22,24,19,14,9,1,"基于989县1835户的全国性实证研究，揭示数字乡村对粮食安全与农业碳循环的非对称影响，数据规模大、结论有政策参考价值，值得进入每日精选。",[24],{"name":9,"url":6},[26,27,28,29,30],"数字乡村","数字基础设施","粮食安全","农业碳排放","农户数字化",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"A8","yield-food-security","单产提升与粮食安全","\u002Ftopic\u002Ffield-crops\u002Fyield-food-security","A",[38,44],{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},"H4","carbon","农业碳与绿色低碳","\u002Ftopic\u002Fenvironment\u002Fcarbon","H",{"code":45,"slug":46,"name":47,"path":48,"parent_code":49},"I6","digital-village","数字乡村与数据要素","\u002Ftopic\u002Fdigital-agriculture\u002Fdigital-village","I",{"status":51,"locked":52,"version":53,"method":54,"evidence":55},"classified",false,"2026-10-22.2","rules",{"candidates":56,"agricultural_evidence":13},[57,65,70,74],{"code":39,"score":58,"supported":13,"title":59,"abstract":61,"tags":64},18,[60],"农业碳",[62,60,63],"碳排放","碳足迹",[29],{"code":45,"score":66,"supported":13,"title":67,"abstract":68,"tags":69},13,[26],[26],[26,27],{"code":32,"score":20,"supported":13,"title":71,"abstract":72,"tags":73},[28],[],[28],{"code":75,"score":76,"supported":13,"title":77,"abstract":78,"tags":80},"F4",3,[],[79],"农药",[],[82,83],"数字乡村 粮食安全 农业碳循环","吉林大学 数字乡村 农户","数字乡村粮食安全农业碳循环-4194",4,{"doi":8,"openalex_id":8,"authors":87,"venue":8,"cited_by_count":88,"oa_url":8,"card":89,"direction":93,"ingested_from":95},[],0,{"tldr":90,"method":91,"finding":92,"direction":93,"opportunity":94},"基于中国县级与农户数据，评估数字乡村战略对粮食安全和农业碳循环的非对称影响。","989县与1835户多层次数据，固定效应和两阶段最小二乘法。","数字化提升粮食产量并减少碳排放，但未改善作物碳吸收或净碳足迹。","数字乡村与农业信息化","可探究数字乡村对碳吸收无显著影响的原因，并优化数字治理以协同减排与固碳。","agent","2026-10-05T00:04:55.880239Z",{"total":98,"page":21,"page_size":98,"items":99},6,[100,151,206,277,320,383],{"id":101,"title":102,"url":103,"summary":104,"summary_zh":8,"content":8,"source_name":105,"source_url":8,"published_at":106,"category":11,"cover_url":8,"hotness":12,"is_selected":52,"score":107,"score_detail":108,"sources":111,"tags":113,"primary_topic":117,"secondary_topics":118,"classification":120,"search_phrases":138,"slug":141,"view_count":76,"doi":142,"paper":143,"created_at":150},3051,"数字乡村建设赋能粮食安全：机制、区域异质性与政策启示","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1748737\u002Fpdf","华中师范大学Muzi Li、中国农科院农业经济与发展研究所Shuai Hao、北京工业大学Haoqi Chang、江苏科技大学Xiaoyuan Zhao合作发表。使用2011-2023年中国31省面板数据采用双向固定效应模型评估数字乡村建设对粮食安全水平（FSL）影响及其空间效应。结果显示：数字乡村建设显著提升本地粮食安全水平，在粮食主产区效应尤其突出，但跨区域溢出效应有限。机制分析表明该效应通过农业技术培训覆盖率与土地生产率两个主要渠道发挥作用，政府规模发挥正向调节作用。","Frontiers Sustain Food Syst 10:1748737","2026-09-20T00:00:00Z",87,{"impact":16,"substance":109,"depth":58,"authority":19,"freshness":12,"relevant":21,"comment":110},23,"基于31省11年面板数据的实证研究，机制与异质性结论清晰，对数字乡村政策有参考价值，值得进入每日精选。",[112],{"name":105,"url":103},[26,28,114,115,116],"区域异质性","农业技术培训","土地生产率",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[119],{"code":45,"slug":46,"name":47,"path":48,"parent_code":49},{"status":51,"locked":52,"version":53,"method":54,"evidence":121},{"candidates":122,"agricultural_evidence":13},[123,128,132],{"code":32,"score":124,"supported":13,"title":125,"abstract":126,"tags":127},12,[28],[28],[28],{"code":45,"score":124,"supported":13,"title":129,"abstract":130,"tags":131},[26],[26],[26],{"code":133,"score":76,"supported":13,"title":134,"abstract":135,"tags":137},"G1",[],[136],"技术培训",[],[139,140],"华中师范大学 数字乡村 粮食安全","中国农科院 数字乡村 粮食安全","华中师范大学数字乡村粮食安全-3051","10.3389\u002Ffsufs.2026.1748737\u002Fpdf",{"doi":142,"openalex_id":8,"authors":144,"venue":8,"cited_by_count":88,"oa_url":8,"card":145,"direction":93,"ingested_from":95},[],{"tldr":146,"method":147,"finding":148,"direction":93,"opportunity":149},"基于2011-2023年31省面板数据，评估数字乡村建设对粮食安全的影响及空间效应。","双向固定效应模型，中国31省面板数据，机制与空间溢出分析。","数字乡村建设显著提升本地粮食安全，主产区效应突出，但跨区域溢出有限。","可探究数字乡村跨区域溢出受限的成因，及如何通过协同政策增强主产区外溢效应。","2026-09-21T00:04:39.562305Z",{"id":152,"title":153,"url":154,"summary":155,"summary_zh":8,"content":8,"source_name":156,"source_url":8,"published_at":157,"category":11,"cover_url":8,"hotness":12,"is_selected":52,"score":158,"score_detail":159,"sources":162,"tags":164,"primary_topic":168,"secondary_topics":169,"classification":171,"search_phrases":192,"slug":195,"view_count":196,"doi":8,"paper":197,"created_at":205},2495,"China's Digital Farming Push Cuts Carbon Emissions:13 年省级面板数字乡村技术对农业碳排放效率影响(西北农林)","https:\u002F\u002Fagritechinsights.com\u002Findex.php\u002F2026\u002F09\u002F12\u002Fchinas-digital-farming-push-cuts-carbon-emissions","西北农林科技大学 Yuming Li 在《Applied Sciences》发表 13 年省级数据研究,构建首个量化数字乡村技术(DVT)对农业碳排放效率影响的框架,使用超效率 SBM 模型同时量化期望产出(如作物产量)和非期望产出(如碳排放),发现数字融合可通过简化资源使用、启用数据驱动决策来提高农业碳效率。技术创新和社会化农业服务(如合作社平台)充当数字采纳与排放降低之间的桥梁。在地形平坦、农业集约化地区和靠近中大型城市的地区观察到最强效益。","MDPI Applied Sciences 2026-09-12","2026-09-11T16:00:00Z",79,{"impact":58,"substance":16,"depth":58,"authority":66,"freshness":160,"relevant":21,"comment":161},8,"基于13年省级面板数据构建数字乡村技术影响农业碳排放效率的量化框架，方法新颖、结论有政策参考价值，但属学术论文层面，影响范围限于细分领域。",[163],{"name":156,"url":154},[26,165,29,166,167],"智慧农业","农业社会化服务","农业绿色低碳",{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},[170],{"code":45,"slug":46,"name":47,"path":48,"parent_code":49},{"status":51,"locked":52,"version":53,"method":54,"evidence":172},{"candidates":173,"agricultural_evidence":13},[174,178,182,187],{"code":39,"score":109,"supported":13,"title":175,"abstract":176,"tags":177},[62,60],[62,60],[29],{"code":45,"score":124,"supported":13,"title":179,"abstract":180,"tags":181},[26],[26],[26],{"code":183,"score":21,"supported":52,"title":184,"abstract":185,"tags":186},"G2",[],[],[166],{"code":188,"score":21,"supported":52,"title":189,"abstract":190,"tags":191},"I1",[],[],[165],[193,194],"农业社会化服务 农业绿色低碳 农业碳排放 数字乡村","农业社会化服务 农业绿色低碳","农业社会化服务农业绿色低碳农业碳排放数字乡村-2495",2,{"doi":8,"openalex_id":8,"authors":198,"venue":8,"cited_by_count":88,"oa_url":8,"card":199,"direction":203,"ingested_from":95},[],{"tldr":200,"method":201,"finding":202,"direction":203,"opportunity":204},"基于13年省级面板数据，研究数字乡村技术对农业碳排放效率的影响。","超效率SBM模型量化期望与非期望产出，省级面板数据分析。","数字融合通过优化资源与数据决策提升碳效率，技术创新与合作社平台起桥梁作用。","农业绿色发展与碳","可探究数字技术对碳效率影响的非线性阈值及不同作物系统的异质性机制。","2026-09-15T00:04:27.428323Z",{"id":207,"title":208,"url":209,"summary":210,"summary_zh":211,"content":8,"source_name":212,"source_url":209,"published_at":213,"category":11,"cover_url":8,"hotness":214,"is_selected":52,"score":215,"score_detail":216,"sources":219,"tags":224,"primary_topic":228,"secondary_topics":229,"classification":231,"search_phrases":250,"slug":253,"view_count":21,"doi":254,"paper":255,"created_at":276},1652,"Digital Village Technologies and Low-Carbon Agricultural Performance in China: An Applied Evaluation Using Super-Efficiency SBM and Provincial Panel Data","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fapp16178752","Digital technologies are increasingly integrated into agricultural production, rural services, finance, and information infrastructure, creating opportunities to improve low-carbon agricultural performance. This study develops an applied evaluation framework linking rural digital technology systems with agricultural carbon emission efficiency. Using panel data from 30 Chinese provinces during 2011–2023, we construct a digital village technology index covering digital infrastructure, digital financial infrastructure, digital service platforms, and digital living scenarios. Agricultural carbon emission efficiency is measured using a super-efficiency slacks-based measure (SBM) model incorporating inputs, desirable output, and undesirable carbon emissions. Two-way fixed-effects models, instrumental-variable estimation, mediation tests, moderation analysis, and heterogeneity tests are then employed. The results show that rural digital technology systems are positively associated with agricultural carbon emission efficiency. Agricultural technological innovation and agricultural socialized services are examined as potential channels, and fiscal support for agriculture is considered as an institutional condition that may shape the DVC–ACEE relationship. The subgroup results suggest that this association is more evident in flat-terrain and large-\u002Fmedium-urban-scale regions. This study provides an applied framework for evaluating how digital infrastructure and service systems are linked to low-carbon agricultural transformation.","数字技术日益融入农业生产、农村服务、金融及信息基础设施，为提升低碳农业绩效创造了机遇。本研究构建了一个应用评估框架，将农村数字技术体系与农业碳排放效率联系起来。利用2011—2023年中国30个省份的面板数据，我们构建了涵盖数字基础设施、数字金融基础设施、数字服务平台及数字生活场景的数字乡村技术指数。农业碳排放效率采用纳入投入、期望产出与非期望碳排放的超效率松弛变量测度（SBM）模型进行衡量。随后运用双向固定效应模型、工具变量估计、中介效应检验、调节效应分析及异质性检验。结果表明，农村数字技术体系与农业碳排放效率呈正相关。农业技术创新与农业社会化服务被视为潜在传导渠道，农业财政支持则被视为可能塑造数字乡村技术—农业碳排放效率关系的制度条件。分组结果显示，该关联在平坦地形及大中城市规模地区更为显著。本研究为评估数字基础设施与服务系统如何与低碳农业转型相关联提供了应用框架。","Applied Sciences","2026-09-03T00:00:00Z",25,78,{"impact":217,"substance":16,"depth":58,"authority":124,"freshness":98,"relevant":21,"comment":218},20,"基于省级面板数据的实证研究，揭示数字乡村技术对低碳农业的促进作用，具有政策参考价值。",[220,221],{"name":212,"url":209},{"name":222,"url":223},"MDPI Applied Sciences 16(17):8752","https:\u002F\u002Fwww.mdpi.com\u002F2076-3417\u002F16\u002F17\u002F8752",[26,225,226,227],"低碳农业","农业科技","碳排放效率",{"code":45,"slug":46,"name":47,"path":48,"parent_code":49},[230],{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},{"status":51,"locked":52,"version":53,"method":54,"evidence":232},{"candidates":233,"agricultural_evidence":13},[234,240,245],{"code":45,"score":235,"supported":13,"title":236,"abstract":238,"tags":239},15,[237],"digital village",[26,237],[26],{"code":39,"score":12,"supported":13,"title":241,"abstract":242,"tags":244},[],[62,60,243],"carbon emission",[225],{"code":183,"score":76,"supported":13,"title":246,"abstract":247,"tags":249},[],[248],"社会化服务",[],[251,252],"碳排放效率 低碳农业 农业科技 数字乡村","碳排放效率 低碳农业","碳排放效率低碳农业农业科技数字乡村-1652","10.3390\u002Fapp16178752",{"doi":254,"openalex_id":256,"authors":257,"venue":212,"cited_by_count":88,"oa_url":209,"card":270,"direction":93,"ingested_from":275},"W7207843203",[258,260,262,265,268],{"name":259,"orcid":8},"Yuming Li",{"name":261,"orcid":8},"Liudi Li",{"name":263,"orcid":264},"Xiuguang Bai","https:\u002F\u002Forcid.org\u002F0000-0003-0140-208X",{"name":266,"orcid":267},"Bo Wei","https:\u002F\u002Forcid.org\u002F0000-0002-1058-5888",{"name":269,"orcid":8},"Jing Zhang",{"tldr":271,"method":272,"finding":273,"direction":203,"opportunity":274},"研究数字乡村技术对农业碳排放效率的影响，基于中国省级面板数据。","构建数字乡村技术指数，用超效率SBM模型测度碳排放效率，采用双向固定效应、工具变","数字乡村技术显著提升农业碳排放效率，技术创新和社会化服务为渠道，财政支持起调节作用。","可探索数字乡村技术在不同地形和城市规模下的差异化影响机制，或引入更多中介变量如数字素养。","openalex","2026-09-04T23:30:40.098363Z",{"id":278,"title":279,"url":280,"summary":281,"summary_zh":8,"content":282,"source_name":283,"source_url":8,"published_at":284,"category":11,"cover_url":8,"hotness":12,"is_selected":52,"score":285,"score_detail":286,"sources":288,"tags":290,"primary_topic":295,"secondary_topics":296,"classification":298,"search_phrases":315,"slug":318,"view_count":160,"doi":8,"paper":8,"created_at":319},197,"Digital Village Development and Agricultural Carbon Reduction in China: Evidence from Provincial Panel Data(中国数字乡村发展与农业碳减排:来自省级面板数据的证据)","https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F14\u002F7482","基于2014-2023年中国30个省份平衡面板数据,文章考察了数字乡村发展与农业投入品相关碳排放的关系。研究构建了由8个标准化指标组成的复合数字乡村指数,采用双向固定效应模型进行实证估计。结果显示,在控制经济、财政、技术、监管等多重因素后,数字乡村发展与农业碳排放显著负相关,使用农村电商代理变量和替代性指标检验后结论依然稳健。异质性分析表明,在农村受教育水平较高、中部地区及粮食主产区,负相关关系更强。研究认为数字乡村建设能够支撑农业低碳转型,前提是数字基础设施转化为有效的产业应用、公共服务、人力资本开发和生产组织改善。","## 1. Introduction\n\nClimate change, ecological degradation, and the continued growth of greenhouse gas (GHG) emissions continue to place considerable pressure on global sustainable development. Carbon mitigation is not only an environmental issue but also a strategic development goal for China. The country has announced that before 2030, it will reach peak carbon dioxide emissions, and before 2060, it will become carbon neutral [[1](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F14\u002F7482#B1-sustainability-18-07482)]. While emissions from sectors based primarily on CO 2 combustion from fossil fuels are different from agricultural emissions, this means that achieving national climate targets will not be possible without mitigation measures in all parts of the economy, not just in heavy industry, power generation and transport [[2](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F14\u002F7482#B2-sustainability-18-07482)]. It is, hence, essential for policies concerning agricultural and rural progress to be incorporated into the holistic framework of climate governance in China.\n\nWhen compared with manufacturing, electricity generation, transportation or construction, agriculture is sometimes considered a relatively low-carbon industry. However, this perception might not consider the significant carbon footprint of today’s food production. The current agricultural system relies on chemical fertilisers, pesticides, plastic mulch, machinery powered by diesel, and irrigation and tillage systems. These inputs and practices are sources of emissions during their manufacture and distribution and also during their use on-farm; they are significant contributors to agricultural carbon emissions [[3](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F14\u002F7482#B3-sustainability-18-07482)]. Fossil energy is also required during irrigation and land management operations, which can also have an impact on the soil carbon cycle and consequently increase the climate change footprint of agriculture [[4](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F14\u002F7482#B4-sustainability-18-07482)]. The spatial distribution of agricultural emissions in China is uneven, and high emissions are frequently found in the north. This trend is due to variations in natural resource endowments, crop composition, production technologies, intensities of production inputs, and local policies [[5](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F14\u002F7482#B5-sustainability-18-07482)]. Earlier empirical studies have also revealed that the performance of emissions varies widely across regions, suggesting that mitigating policies should be flexible and locally tailored instead of managed at the national level [[6](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F14\u002F7482#B6-sustainability-18-07482)]. Technological progress and the change of the organisation of agricultural production have been important means of achieving a low-carbon transition in agriculture in this context [[7](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F14\u002F7482#B7-sustainability-18-07482)].\n\nRural economies are changing in the context of digitalisation all over the world. The collection, transfer, interpretation and utilisation of agricultural information is transforming through big data analysis, Internet of Things (IoT) equipment, mobile communication networks, remote sensing technologies, cloud-based platforms, and digital service systems. Evidence from the international literature shows that data-intensive farming can lead to better resource allocation and a reduction in the waste of resources [[8](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F14\u002F7482#B8-sustainability-18-07482)]. Smart farming systems can help to better manage water, fertilisers, pesticides and machinery [[9](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F14\u002F7482#B9-sustainability-18-07482)]. However, rural digital transformation is not merely the introduction of new equipment into agricultural production. It also transforms agricultural extension, institutions at the local level, market linkages, farmers’ behaviour in managing business, and the linkages among farmers, service providers, firms and consumers [[10](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F14\u002F7482#B10-sustainability-18-07482)].\n\nThe impacts of digital agriculture are quite varied at the national and regional levels. In many high-income countries, sensor-based systems, IoT applications and automated control technologies have been adopted for large-scale commercial farming. In these situations, digital solutions have shown the potential to optimise input usage and create tangible gains in emissions reductions [[11](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F14\u002F7482#B11-sustainability-18-07482)]. On the other hand, there is still a significant gap in agriculture mechanisation, according to data from many low- and middle-income economies. Digitalisation is limited by weak rural infrastructure, fragmented farms, limited financial access and a lack of technical support to achieve environmental gains [[12](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F14\u002F7482#B12-sustainability-18-07482)]. Such disparities reveal that the climatic effects brought by digital farming hinge on plot scale, institutional arrangement, human capital stock and matched service infrastructures. The digital village strategy in China has emerged in an agricultural landscape dominated by smallholders. It is not the “smart-farm” model that has been adopted by many advanced economies, nor is it the more nascent digital agriculture model in many low-income economies. It, thus, serves as a valuable case study to explore the linkages between rural digitalisation and agricultural carbon mitigation in the context of a developing country.\n\nIn China, digital village development is not limited to access to the internet or the construction of hardware. It is a holistic rural transformation approach integrating digital infrastructure, digital industry, public services, financial inclusion, logistics systems and e-governance. According to comparisons among different provinces, it is known that the development of the digital village is still in an imbalanced state in China [[13](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F14\u002F7482#B13-sustainability-18-07482)]. Recent evaluation studies also indicate a close relationship between digital rural development results and rural revitalisation outcomes and governance capacity [[14](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F14\u002F7482#B14-sustainability-18-07482)]. The advancement of the rural digital economy has further facilitated industrial upgrading and the restructuring of local production and organisational patterns [[15](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F14\u002F7482#B15-sustainability-18-07482)]. Digital platforms have made the farmers’ market more accessible, brought agricultural products into circulation faster and have helped rural household income and welfare [[16](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F14\u002F7482#B16-sustainability-18-07482)].\n\nHowever, the environmental implications of digital village development remain insufficiently understood. From a macro perspective, the digital economy promotes green total factor productivity through three channels: knowledge spillover, optimised resource allocation and industrial structure transformation [[17](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F14\u002F7482#B17-sustainability-18-07482)]. In China, internet development has impacted energy consumption in three ways: energy use efficiency, the nature of energy consumption, and industrial transformation [[18](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F14\u002F7482#B18-sustainability-18-07482)]. International evidence also suggests that digitalisation can contribute to energy transition, but the impact is contingent on the quality of local governance and institutional capacity [[19](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F14\u002F7482#B19-sustainability-18-07482)]. Based on the empirical studies conducted in China, energy intensity and emissions may be reduced by digitalisation if the conditions are suitable [[20](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F14\u002F7482#B20-sustainability-18-07482)]. Digitalisation in agriculture has the potential to lower carbon intensity through structural change and technological innovation [[21](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F14\u002F7482#B21-sustainability-18-07482)]. Digital village development can also enhance information flow and the land use of cultivated lands, which can help greener land use transformation [[22](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F14\u002F7482#B22-sustainability-18-07482)]. There is recent research evidence that digitalisation in the countryside can enhance the green performance of agricultural production; however, direct evidence on input-related agricultural carbon emissions remains limited [[23](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F14\u002F7482#B23-sustainability-18-07482)].\n\nInternational studies have highlighted the importance of institutional arrangements and rural human capital in determining the potential of turning digital tools into low-carbon farming practices [[24](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F14\u002F7482#B24-sustainability-18-07482),[25](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F14\u002F7482#B25-sustainability-18-07482)]. Many developing economies are confronted with two pressures: on the one hand, rural digitalisation remains underdeveloped, while on the other hand, agricultural production relies on carbon-intensive inputs. The lessons learned from China suggest that digitalisation can contribute to emission reduction and green transition if it is integrated into relevant policy systems, service systems, and production systems; thus, the country is able to offer empirical references for nations confronting similar development challenges [[20](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F14\u002F7482#B20-sustainability-18-07482),[21](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F14\u002F7482#B21-sustainability-18-07482),[26](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F14\u002F7482#B26-sustainability-18-07482)].\n\nDespite growing interest in the environmental consequences of digitalisation, several gaps remain. First, much of the existing literature examines the general digital economy, urban digitalisation, or isolated digital–agricultural technologies, while fewer studies focus on digital village development as a multidimensional rural transformation process that integrates infrastructure, industrial applications, public services, digital finance, logistics, and rural e-commerce. Second, agricultural carbon emissions from the input side and the field-operation side have been less directly discussed in many studies, while agricultural green total factor productivity, carbon intensity, and pollution indicators are used as outcome variables. Third, there is scarce evidence regarding the circumstances in which digital village development is more strongly connected to agricultural carbon mitigation, including its relationship to rural education, the region in which it is implemented, grain-functional areas, and mechanisation. Fourth, there is a need for increased transparency in the construction of the composite digital village indicators, including indicator selection, PCA loadings, and the contribution of various digital dimensions.\n\nThe following question guides this study: is digital village development associated with lower agricultural carbon emissions across China? A multidimensional digital village development index is constructed from eight standardised indicators to answer this question. PCA diagnostics and a PCA-based alternative index are further used to examine the validity and sensitivity of the index construction. To estimate the relationship between digital village development and agricultural carbon emissions, this study utilises balanced panel datasets covering 30 provincial regions from 2014 to 2023. Empirical analysis consists of two-way fixed-effects models, multiple robustness tests, heterogeneity analysis, and an additional instrumental-variable exercise (reported in the [Appendix A](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F14\u002F7482#app1-sustainability-18-07482)) and exploratory subgroup analysis with regard to potential mechanisms.\n\nThis study contributes to the literature in three ways. First, it links digital village development with agricultural climate governance by examining input-related agricultural carbon emissions rather than only rural income, industrial upgrading, or agricultural green total factor productivity. Unlike recent prefecture-level or county-level studies that usually focus on the rural digital economy or agricultural green productivity, this study treats the digital village as a multidimensional policy and development framework and directly examines input-related agricultural carbon emissions. Second, it constructs a multidimensional digital village index from eight standardised indicators and reports additional PCA diagnostics, including component loadings, variance contribution, and the KMO statistic. It also uses a recomputed PCA-based index as a sensitivity check, thereby improving the transparency and reproducibility of index construction. Third, it examines heterogeneous associations across rural education levels, regions, grain-functional areas, and mechanisation conditions and provides mechanism-related evidence based on different agricultural carbon-emission sources.\n\nChina’s digital village strategy has a distinctive institutional context. It has developed in a smallholder-dominated agricultural system but is supported by national rural revitalisation policies, public investment in digital infrastructure, the rapid expansion of rural e-commerce, digital inclusive finance, and logistics networks. This differs from the capital-intensive smart-farming model in many high-income economies and from more fragmented digital–agriculture initiatives in some low-income economies. Therefore, the findings should not be interpreted as mechanically generalisable to all countries. Rather, they provide analytical evidence for developing economies seeking to combine rural digital inclusion, agricultural modernisation, and low-carbon transition under smallholder conditions.\n\nThe remainder of this paper is organised as follows. [Section 2](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F14\u002F7482#sec2-sustainability-18-07482) reviews the related literature. [Section 3](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F14\u002F7482#sec3-sustainability-18-07482) develops the theoretical framework and research hypotheses. [Section 4](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F14\u002F7482#sec4-sustainability-18-07482) describes the data, variables, and empirical models. [Section 5](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F14\u002F7482#sec5-sustainability-18-07482) presents the empirical results. [Section 6](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F14\u002F7482#sec6-sustainability-18-07482) discusses the findings. [Section 7](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F14\u002F7482#sec7-sustainability-18-07482) concludes the paper and provides policy implications.\n\n## 2. Literature Review\n\n### 2.1. Digital Village Development and Rural Transformation\n\nDigital village development is a comprehensive process of rural transformation which involves the connectivity of infrastructure, digital industrial activities, service delivery and the modernisation of governance. Digital tools improve agricultural production efficiency through real-time data gathering, remote farm supervision, precision agricultural management and data-driven decision-making [[27](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F14\u002F7482#B27-sustainability-18-07482)]. A special characteristic of digitalisation in the rural areas of China is the fast development of rural e-business, especially the growth of the ‘Taobao villages’ linked to big online platforms [[28](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F14\u002F7482#B28-sustainability-18-07482)]. These new rural economic models are examples of the way that digital marketplaces can connect geographically dispersed producers with national consumer markets and encourage local entrepreneurship [[28](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F14\u002F7482#B28-sustainability-18-07482),[29](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F14\u002F7482#B29-sustainability-18-07482)]. Advancement in the development of digital villages helps bridge the income gap between urban and rural residents. It enables rural populations to gain easier access to information, employment opportunities, financial services, logistics systems and public service facilities [[29](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F14\u002F7482#B29-sustainability-18-07482)]. New evidence from the provinces shows that digital village development can also help to foster agricultural green growth [[30](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F14\u002F7482#B30-sustainability-18-07482)].\n\nThe majority of studies published so far agree that digital village development can bring about economic and social benefits and that the impact on the environment is more complex [[17](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F14\u002F7482#B17-sustainability-18-07482),[21](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F14\u002F7482#B21-sustainability-18-07482),[22](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F14\u002F7482#B22-sustainability-18-07482)]. Increased digital infrastructure can, on the one hand, lead to a higher electricity demand and a higher use of digital equipment in rural areas. Conversely, there are applications in the fields of precision agriculture, rural logistics, e-commerce and extension that could help to decrease the overuse of agricultural inputs, enhance coordination and lower agricultural carbon emissions [[22](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F14\u002F7482#B22-sustainability-18-07482)].\n\nThe environmental impact overall will hinge on how the implementation of digital infrastructure is followed by productive applications and service innovations that impact decisions in agricultural production. International experiences demonstrate that digitalisation in rural areas can take several forms in different countries. In the high-income context of Western Europe, digital technologies are frequently used in large mechanised agriculture, but their use is still dependent on the social, political and institutional situation in rural communities [[31](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F14\u002F7482#B31-sustainability-18-07482)]. In many of the settings in which digital benefits are explored in developing contexts, these services are also critical, including mobile money, input markets, extension networks, and reliable infrastructure. Smallholder dominance might also constrain the environmental benefits of applications of digital technology, unless service systems can be developed to address scale constraints [[32](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F14\u002F7482#B32-sustainability-18-07482)]. These differences indicate that the size of the farm, institutional conditions and service capacity play a key role in whether or not rural digitalisation can make a positive contribution to carbon reduction.\n\n### 2.2. Agricultural Carbon Emissions: Measurement, Drivers, and Mitigation\n\nIn the agricultural carbon-accounting literature in China, emissions are often estimated based on the inputs and field operations of agricultural production processes [[3](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F14\u002F7482#B3-sustainability-18-07482),[5](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F14\u002F7482#B5-sustainability-18-07482),[7](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F14\u002F7482#B7-sustainability-18-07482)]. The use of chemical fertilisers, pesticides and plastic mulch films continues to be a significant source of the carbon footprint of Chinese agriculture [[33](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F14\u002F7482#B33-sustainability-18-07482)]. Other practices such as diesel, irrigation and tillage are also important because they are closely related to energy use and field management. Agricultural emissions of carbon are highly location- and time-dependent and are influenced by land resources, planting systems, mechanisation, environmental policies and input levels [[34](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F14\u002F7482#B34-sustainability-18-07482)].\n\nDigitalisation has increasingly been regarded as a potential pathway for greener agricultural development. Digital technologies offer the potential to better allocate reso","Sustainability 18(14):7482 2026-07-22","2026-07-22T00:00:00Z",74,{"impact":217,"substance":16,"depth":58,"authority":124,"freshness":196,"relevant":21,"comment":287},"基于省级面板数据实证研究数字乡村对农业碳减排的影响，方法严谨，结论具有政策参考价值，但发表于近两周前，时效性较低。",[289],{"name":283,"url":280},[26,291,292,293,294],"农业碳减排","省级面板","双向固定效应","可持续发展",{"code":45,"slug":46,"name":47,"path":48,"parent_code":49},[297],{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},{"status":51,"locked":52,"version":53,"method":54,"evidence":299},{"candidates":300,"agricultural_evidence":13},[301,305,309],{"code":45,"score":217,"supported":13,"title":302,"abstract":303,"tags":304},[26,237],[26],[26],{"code":39,"score":235,"supported":13,"title":306,"abstract":307,"tags":308},[60],[62,60],[291],{"code":310,"score":76,"supported":13,"title":311,"abstract":312,"tags":314},"E5",[],[313],"农村电商",[],[316,317],"双向固定效应 农业碳减排 可持续发展 数字乡村","双向固定效应 农业碳减排","双向固定效应农业碳减排可持续发展数字乡村-197","2026-08-04T01:15:47.389860Z",{"id":321,"title":322,"url":323,"summary":324,"summary_zh":8,"content":325,"source_name":326,"source_url":8,"published_at":327,"category":328,"cover_url":8,"hotness":12,"is_selected":52,"score":107,"score_detail":329,"sources":331,"tags":333,"primary_topic":337,"secondary_topics":338,"classification":340,"search_phrases":377,"slug":380,"view_count":381,"doi":8,"paper":8,"created_at":382},134,"中国农科院王斌:创新种植业技术助力\"双碳\"与粮食安全协同发展——基于现有技术可实现种植业11%-24%减排幅度","https:\u002F\u002Fwww.caas.cn\u002Fxwzx\u002Fmtxw\u002F55c76d07d9bd4daf93f878e676784db7.htm","7月31日,\"绿色低碳农食系统实践者平台\"2025年会暨农食系统绿色低碳转型发展机遇与实践创新研讨会在北京召开,中国农业科学院农业环境与可持续发展研究所王斌提出通过技术创新实现粮食安全与温室气体减排的协同路径。针对水稻甲烷减排,介绍四大关键技术:节水灌溉(间歇灌溉\u002F中期晒田)、专用减排肥料(甲烷抑制剂\u002F硝化抑制剂\u002F硫酸盐)、秸秆科学还田(旱季还田\u002F堆腐后还田\u002F菌剂加速分解)、品种改良(高收获指数\u002F节水抗旱型);旱地氧化亚氮减排则采用减氮优化、缓控释肥、水肥一体化及微生物菌剂等技术。王斌呼吁加强农业减排区域适用性技术推广,完善农业碳排放统计核算体系,构建\"政策-技术-市场\"协同机制。","7月31日，由绿色创新发展研究院（iGDP）主办，北京低碳农业协会协办的“绿色低碳农食系统实践者平台”2025年会暨农食系统绿色低碳转型发展机遇与实践创新研讨会在北京召开，中国农业科学院农业环境与可持续发展研究所王斌提出通过技术创新实现粮食安全与温室气体减排的协同路径，为我国农业绿色低碳转型提供科学支撑。\n\n王斌指出，水稻种植和氮肥施用是农业领域甲烷（CH₄）和氧化亚氮（N₂O）两大温室气体的主要来源。国家温室气体排放清单数据显示，我国水稻甲烷排放量2017年最高，随后逐渐下降，而氧化亚氮排放则因“十三五”期间化肥减量增效行动显著下降，“农业排放是基础性、生存性排放，必须通过技术创新在保障产量的同时实现减排。”\n\n针对水稻甲烷减排，王斌介绍了四大关键技术。首先是节水灌溉技术，通过间歇灌溉或中期晒田减少厌氧环境，可大幅降低甲烷排放，但需平衡干旱胁迫、杂草增长和土壤碳汇能力下降的风险；第二是专用减排肥料，添加甲烷抑制剂、硝化抑制剂或硫酸盐的肥料可实现甲烷与氧化亚氮协同减排，并减少面源污染，但成本较高；三是秸秆科学还田，旱季还田、堆腐后还田或使用菌剂加速分解，避免淹水期直接还田导致的排放激增；四是品种改良，选育高收获指数、节水抗旱型水稻品种，从源头降低排放潜力。\n\n在旱地氧化亚氮减排方面，王斌团队验证了减氮优化、缓控释肥、水肥一体化及微生物菌剂等技术的效果。王斌指出，“2012年后我国氧化亚氮排放下降，正是减肥减药政策与技术进步协同的结果。”\n\n王斌表示，基于现有技术保守预估，我国种植业在保障粮食安全前提下，通过有机替代、秸秆科学还田、氮盈余控制、灌溉优化等技术路径，可实现绿色低碳转型，预计达到11%-24%的减排幅度。王斌呼吁加强农业减排区域适用性技术推广，完善农业碳排放统计核算体系，并通过政策引导技术创新与农户收益的衔接。“农业低碳发展不能‘单打独斗’，需构建‘政策-技术-市场’协同机制，让绿色技术真正落地生根，实现粮食丰产、生态低碳与农民增收的共赢。”","新京报 2026-07-31 中国农科院官网","2026-07-31T00:00:00Z","报道",{"impact":214,"substance":16,"depth":58,"authority":66,"freshness":20,"relevant":21,"comment":330},"国家级科研机构专家提出种植业减排具体路径与数据，对农业绿色低碳转型有重要参考价值。",[332],{"name":326,"url":323},[28,334,335,225,336],"中国农科院","双碳","种植业",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[339],{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},{"status":51,"locked":52,"version":53,"method":54,"evidence":341},{"candidates":342,"agricultural_evidence":13},[343,347,352,359,365,371],{"code":32,"score":124,"supported":13,"title":344,"abstract":345,"tags":346},[28],[28],[28],{"code":39,"score":12,"supported":13,"title":348,"abstract":349,"tags":351},[],[62,60,350],"甲烷减排",[225],{"code":353,"score":98,"supported":13,"title":354,"abstract":355,"tags":358},"F3",[],[356,357],"肥料","水肥一体化",[],{"code":360,"score":76,"supported":13,"title":361,"abstract":362,"tags":364},"A1",[],[363],"水稻",[],{"code":366,"score":76,"supported":13,"title":367,"abstract":368,"tags":370},"B7",[],[369],"温室",[],{"code":372,"score":76,"supported":13,"title":373,"abstract":374,"tags":376},"H3",[],[375],"灌溉",[],[378,379],"中国农科院 低碳农业 粮食安全 种植业","中国农科院 低碳农业","中国农科院低碳农业粮食安全种植业-134",11,"2026-08-01T06:01:54.157050Z",{"id":384,"title":385,"url":386,"summary":387,"summary_zh":388,"content":8,"source_name":389,"source_url":386,"published_at":390,"category":11,"cover_url":8,"hotness":12,"is_selected":52,"score":285,"score_detail":391,"sources":395,"tags":397,"primary_topic":401,"secondary_topics":402,"classification":403,"search_phrases":434,"slug":437,"view_count":88,"doi":438,"paper":439,"created_at":456},5491,"Analysis of the Impact of Digital Technology on the Resilience of Anhui’s Agricultural Industry Chain: Threshold Effect of Relative Grain Yield","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fsu182010656","Improving the resilience of the agricultural industry chain to frequent external shocks and the intertwined agricultural production risks is critical for ensuring national food security and sustainable agricultural development. Taking the 16 prefecture-level cities in Anhui Province—a major grain-producing region in China—as the research sample, two composite proxy indices were constructed in this study; namely, an agricultural industry chain resilience index built upon aggregate official statistics (rather than direct observed shock-response outcomes) and an index capturing broad rural digital development instead of purely agriculture-specific digital adoption. Based on balanced panel data from 2015 to 2024, two-way fixed-effect, mediation-effect, and panel-threshold models were adopted in this study to examine the association, mediating mechanism, regional heterogeneity, and threshold characteristics conditioned on the relative grain yield. The empirical results show that the rural-oriented digital development index is significantly and robustly positively associated with our composite agricultural industry chain resilience proxy. Although agricultural TFP exhibits a statistically detectable partial mediating pattern in this correlational relationship, the economic magnitude of this indirect channel is extremely modest. As such, this finding indicates a potential theoretical transmission pathway rather than providing definitive empirical evidence for a causal mechanism. The promotion effect exhibits clear regional heterogeneity, with the strongest effect in Northern Anhui, followed by Central Anhui, while the coefficient is positive but insignificant in Southern Anhui. Furthermore, a single-threshold effect of the relative grain yield is identified; that is, the positive association between rural-oriented digital development and resilience proxy cannot be statistically detected when the grain yield remains below the threshold. Only after the agricultural grain yield increases can digital-related rural development release its resilience-related empowering dividends. We note that both composite indices are subject to measurement constraints linked to the local agricultural economic scale and broad rural digital conditions; these limitations are further discussed in the paper. This study provides empirical correlational evidence for the implementation of differentiated digital agriculture policies in major grain-producing provinces.","提升农业产业链对频繁外部冲击与交织叠加的农业生产风险的韧性，对保障国家粮食安全和农业可持续发展至关重要。本研究以中国粮食主产省安徽省的16个地级市为研究样本，构建了两个复合代理指标：一是基于官方统计数据总量（而非直接观测的冲击—响应结果）构建的农业产业链韧性指数，二是刻画农村数字化广泛发展（而非单纯农业领域数字化应用）的指数。基于2015—2024年平衡面板数据，本研究采用双向固定效应模型、中介效应模型和面板门槛模型，考察二者之间的关联、中介机制、区域异质性以及以相对粮食产量为条件变量的门槛特征。实证结果表明，农村导向的数字化发展指数与本研究的农业产业链韧性复合代理指标之间存在显著且稳健的正向关联。尽管农业全要素生产率在这一相关关系中呈现出统计上可检测的部分中介模式，但该间接渠道的经济量级极为有限。因此，这一发现指示的是一种潜在的理论传导路径，而非为因果机制提供确凿的实证证据。其促进效应表现出明显的区域异质性，皖北效应最强，皖中次之，皖南系数为正但不显著。此外，还识别出相对粮食产量的单一门槛效应，即当粮食产量低于门槛值时，农村导向的数字化发展与韧性代理指标之间的正向关联在统计上无法被检测到；只有在农业粮食产量提高之后，数字相关农村发展才能释放其韧性赋能红利。我们注意到，两个复合指数均受到与地方农业经济规模和农村数字化总体状况相关的测度约束；这些局限性在文中进一步讨论。本研究为粮食主产省实施差异化数字农业政策提供了实证相关性证据。","Sustainability","2026-10-20T00:00:00Z",{"impact":392,"substance":217,"depth":393,"authority":66,"freshness":160,"relevant":21,"comment":394},16,17,"基于安徽16市面板数据的实证研究，方法规范、结论有区域异质性价值，但属省级学术论文，公共影响有限，适合作为主题页聚合素材而非头条精选。",[396],{"name":389,"url":386},[398,26,28,399,400],"数字农业","农业产业链韧性","安徽农业",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":51,"locked":52,"version":53,"method":54,"evidence":404},{"candidates":405,"agricultural_evidence":13},[406,412,417,424,430],{"code":32,"score":407,"supported":13,"title":408,"abstract":409,"tags":411},7,[],[28,410],"food security",[28],{"code":188,"score":407,"supported":13,"title":413,"abstract":414,"tags":416},[],[398,415],"digital agriculture",[398],{"code":418,"score":98,"supported":13,"title":419,"abstract":420,"tags":423},"G",[],[421,422],"农村发展","rural development",[],{"code":425,"score":76,"supported":13,"title":426,"abstract":427,"tags":429},"J1",[],[428],"农业政策",[],{"code":45,"score":21,"supported":52,"title":431,"abstract":432,"tags":433},[],[],[26],[435,436],"安徽 农业产业链韧性 数字技术","安徽 粮食产量 数字乡村","安徽农业产业链韧性数字技术-5491","10.3390\u002Fsu182010656",{"doi":438,"openalex_id":440,"authors":441,"venue":389,"cited_by_count":88,"oa_url":386,"card":451,"direction":93,"ingested_from":275},"W7223005655",[442,445,447,449],{"name":443,"orcid":444},"Lan Ma","https:\u002F\u002Forcid.org\u002F0000-0003-2468-9917",{"name":446,"orcid":8},"Yuyan Li",{"name":448,"orcid":8},"张国亮",{"name":450,"orcid":8},"Yaoyao Li",{"tldr":452,"method":453,"finding":454,"direction":93,"opportunity":455},"以安徽16市面板数据检验农村数字发展对农业产业链韧性的影响及粮食产量门槛效应。","2015—2024年安徽16市平衡面板，双向固定效应、中介效应与面板门槛模型。","数字发展显著提升产业链韧性，存在粮食相对产量单门槛，且皖北效应最强、皖南不显著。","可细化数字技术指标、引入真实冲击响应韧性测度，并探究门槛形成机制与皖南失效原因。","2026-10-21T23:30:29.219265Z"]