[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2495":3,"related-2495":88},{"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":22,"tags":24,"primary_topic":30,"secondary_topics":36,"classification":43,"search_phrases":72,"slug":75,"view_count":76,"doi":8,"paper":77,"created_at":87},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。",null,"MDPI Applied Sciences 2026-09-12","2026-09-11T16:00:00Z","论文",10,false,79,{"impact":16,"substance":17,"depth":16,"authority":18,"freshness":19,"relevant":20,"comment":21},18,22,13,8,1,"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。",[23],{"name":9,"url":6},[25,26,27,28,29],"数字乡村","智慧农业","农业碳排放","农业社会化服务","农业绿色低碳",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},"H4","carbon","农业碳与绿色低碳","\u002Ftopic\u002Fenvironment\u002Fcarbon","H",[37],{"code":38,"slug":39,"name":40,"path":41,"parent_code":42},"I6","digital-village","数字乡村与数据要素","\u002Ftopic\u002Fdigital-agriculture\u002Fdigital-village","I",{"status":44,"locked":13,"version":45,"method":46,"evidence":47},"classified","2026-10-22.2","rules",{"candidates":48,"agricultural_evidence":51},[49,57,62,67],{"code":31,"score":50,"supported":51,"title":52,"abstract":55,"tags":56},23,true,[53,54],"碳排放","农业碳",[53,54],[27],{"code":38,"score":58,"supported":51,"title":59,"abstract":60,"tags":61},12,[25],[25],[25],{"code":63,"score":20,"supported":13,"title":64,"abstract":65,"tags":66},"G2",[],[],[28],{"code":68,"score":20,"supported":13,"title":69,"abstract":70,"tags":71},"I1",[],[],[26],[73,74],"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":78,"venue":8,"cited_by_count":79,"oa_url":8,"card":80,"direction":84,"ingested_from":86},[],0,{"tldr":81,"method":82,"finding":83,"direction":84,"opportunity":85},"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。","agent","2026-09-15T00:04:27.428323Z",{"total":89,"page":20,"page_size":89,"items":90},6,[91,157,228,271,319,373],{"id":92,"title":93,"url":94,"summary":95,"summary_zh":8,"content":8,"source_name":96,"source_url":8,"published_at":97,"category":11,"cover_url":8,"hotness":12,"is_selected":51,"score":98,"score_detail":99,"sources":105,"tags":107,"primary_topic":111,"secondary_topics":117,"classification":120,"search_phrases":143,"slug":146,"view_count":147,"doi":8,"paper":148,"created_at":156},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。","Agricultural and Food Economics 2026, 14(1)","2026-10-01T00:00:00Z",88,{"impact":17,"substance":100,"depth":101,"authority":102,"freshness":103,"relevant":20,"comment":104},24,19,14,9,"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。",[106],{"name":96,"url":94},[25,108,109,27,110],"数字基础设施","粮食安全","农户数字化",{"code":112,"slug":113,"name":114,"path":115,"parent_code":116},"A8","yield-food-security","单产提升与粮食安全","\u002Ftopic\u002Ffield-crops\u002Fyield-food-security","A",[118,119],{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},{"code":38,"slug":39,"name":40,"path":41,"parent_code":42},{"status":44,"locked":13,"version":45,"method":46,"evidence":121},{"candidates":122,"agricultural_evidence":51},[123,128,132,136],{"code":31,"score":16,"supported":51,"title":124,"abstract":125,"tags":127},[54],[53,54,126],"碳足迹",[27],{"code":38,"score":18,"supported":51,"title":129,"abstract":130,"tags":131},[25],[25],[25,108],{"code":112,"score":103,"supported":51,"title":133,"abstract":134,"tags":135},[109],[],[109],{"code":137,"score":138,"supported":51,"title":139,"abstract":140,"tags":142},"F4",3,[],[141],"农药",[],[144,145],"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":149,"venue":8,"cited_by_count":79,"oa_url":8,"card":150,"direction":154,"ingested_from":86},[],{"tldr":151,"method":152,"finding":153,"direction":154,"opportunity":155},"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。","2026-10-05T00:04:55.880239Z",{"id":158,"title":159,"url":160,"summary":161,"summary_zh":162,"content":8,"source_name":163,"source_url":160,"published_at":164,"category":11,"cover_url":8,"hotness":165,"is_selected":13,"score":166,"score_detail":167,"sources":170,"tags":175,"primary_topic":179,"secondary_topics":180,"classification":182,"search_phrases":201,"slug":204,"view_count":20,"doi":205,"paper":206,"created_at":227},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":168,"substance":17,"depth":16,"authority":58,"freshness":89,"relevant":20,"comment":169},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。",[171,172],{"name":163,"url":160},{"name":173,"url":174},"MDPI Applied Sciences 16(17):8752","https:\u002F\u002Fwww.mdpi.com\u002F2076-3417\u002F16\u002F17\u002F8752",[25,176,177,178],"低碳农业","农业科技","碳排放效率",{"code":38,"slug":39,"name":40,"path":41,"parent_code":42},[181],{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},{"status":44,"locked":13,"version":45,"method":46,"evidence":183},{"candidates":184,"agricultural_evidence":51},[185,191,196],{"code":38,"score":186,"supported":51,"title":187,"abstract":189,"tags":190},15,[188],"digital village",[25,188],[25],{"code":31,"score":12,"supported":51,"title":192,"abstract":193,"tags":195},[],[53,54,194],"carbon emission",[176],{"code":63,"score":138,"supported":51,"title":197,"abstract":198,"tags":200},[],[199],"社会化服务",[],[202,203],"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":205,"openalex_id":207,"authors":208,"venue":163,"cited_by_count":79,"oa_url":160,"card":221,"direction":154,"ingested_from":226},"W7207843203",[209,211,213,216,219],{"name":210,"orcid":8},"Yuming Li",{"name":212,"orcid":8},"Liudi Li",{"name":214,"orcid":215},"Xiuguang Bai","https:\u002F\u002Forcid.org\u002F0000-0003-0140-208X",{"name":217,"orcid":218},"Bo Wei","https:\u002F\u002Forcid.org\u002F0000-0002-1058-5888",{"name":220,"orcid":8},"Jing Zhang",{"tldr":222,"method":223,"finding":224,"direction":84,"opportunity":225},"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":229,"title":230,"url":231,"summary":232,"summary_zh":8,"content":233,"source_name":234,"source_url":8,"published_at":235,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":236,"score_detail":237,"sources":239,"tags":241,"primary_topic":246,"secondary_topics":247,"classification":249,"search_phrases":266,"slug":269,"view_count":19,"doi":8,"paper":8,"created_at":270},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":168,"substance":17,"depth":16,"authority":58,"freshness":76,"relevant":20,"comment":238},"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。",[240],{"name":234,"url":231},[25,242,243,244,245],"农业碳减排","省级面板","双向固定效应","可持续发展",{"code":38,"slug":39,"name":40,"path":41,"parent_code":42},[248],{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},{"status":44,"locked":13,"version":45,"method":46,"evidence":250},{"candidates":251,"agricultural_evidence":51},[252,256,260],{"code":38,"score":168,"supported":51,"title":253,"abstract":254,"tags":255},[25,188],[25],[25],{"code":31,"score":186,"supported":51,"title":257,"abstract":258,"tags":259},[54],[53,54],[242],{"code":261,"score":138,"supported":51,"title":262,"abstract":263,"tags":265},"E5",[],[264],"农村电商",[],[267,268],"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":272,"title":273,"url":274,"summary":275,"summary_zh":8,"content":8,"source_name":276,"source_url":8,"published_at":277,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":278,"score_detail":279,"sources":281,"tags":283,"primary_topic":286,"secondary_topics":287,"classification":288,"search_phrases":307,"slug":310,"view_count":20,"doi":8,"paper":311,"created_at":318},5227,"China's farm digitization reduces agricultural carbon emissions but the relationship is nonlinear (U-shaped)","https:\u002F\u002Fcommonplace.workforcefutures.net\u002Fpaper\u002Fopenalex:W7221809067","Gao、Feng、Yuan、Sun 在 OpenAlex 2026-10-16 索引的最新研究,基于中国31个省份2010—2023年平衡面板数据,采用双向固定效应面板与SEM中介模型,发现农业数字技术通过精准管理与间接融资\u002F绿色创新两条路径降低碳排放,但呈U型非线性,转折点为数字化指数0.540,劳动生产率提升可推迟转折点,数字化在区域、规模、排放来源维度呈现异质性。","OpenAlex W7221809067(2026-10-16)","2026-10-16T00:00:00Z",84,{"impact":17,"substance":50,"depth":16,"authority":18,"freshness":19,"relevant":20,"comment":280},"基于31省2010—2023年面板数据的全国性实证研究，揭示数字化与农业碳排放的U型关系及0.540转折点，结论新颖且具政策参考价值，值得进入每日精选。",[282],{"name":276,"url":274},[25,26,284,27,285],"农业数字化","绿色创新",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":44,"locked":13,"version":45,"method":46,"evidence":289},{"candidates":290,"agricultural_evidence":51},[291,295,299,303],{"code":31,"score":147,"supported":51,"title":292,"abstract":293,"tags":294},[],[53],[27],{"code":68,"score":76,"supported":13,"title":296,"abstract":297,"tags":298},[],[],[284,26],{"code":42,"score":20,"supported":13,"title":300,"abstract":301,"tags":302},[],[],[284],{"code":38,"score":20,"supported":13,"title":304,"abstract":305,"tags":306},[],[],[25],[308,309],"农业数字化 碳排放 U型","数字技术 农业碳减排","农业数字化碳排放U型-5227",{"doi":8,"openalex_id":8,"authors":312,"venue":8,"cited_by_count":79,"oa_url":8,"card":313,"direction":84,"ingested_from":86},[],{"tldr":314,"method":315,"finding":316,"direction":84,"opportunity":317},"基于中国31省2010—2023年面板数据，发现农业数字化与碳排放呈U型非线性关系。","双向固定效应面板模型与SEM中介模型，31省平衡面板数据。","数字化通过精准管理和绿色创新降碳，但存在0.540的U型转折点。","可探究转折点后数字化增碳的机制，及劳动生产率等调节因素如何推迟拐点。","2026-10-18T00:05:30.735414Z",{"id":320,"title":321,"url":322,"summary":323,"summary_zh":324,"content":8,"source_name":325,"source_url":322,"published_at":326,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":327,"score_detail":328,"sources":330,"tags":332,"primary_topic":335,"secondary_topics":336,"classification":337,"search_phrases":353,"slug":356,"view_count":147,"doi":357,"paper":358,"created_at":372},4520,"Agricultural Industry Agglomeration and Carbon Emission Intensity: A Systems Perspective on Resilience and Digital Development","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fsystems14101263","Decarbonizing agriculture depends not only on the efficiency of individual production activities but also on coordination among production, processing, and service activities. From a systems perspective, this study treats provincial agriculture as an interconnected industrial system and examines the relationship between agricultural industry agglomeration and agricultural carbon emission intensity. Moving beyond conventional measures of production concentration, agricultural industry agglomeration is measured as coordination between horizontal agglomeration in agricultural production and vertical agglomeration across production, processing, and agricultural services. Based on a balanced panel of 390 observations across 30 Chinese provinces from 2011 to 2023, the study uses province and year fixed effects, mediation and moderation analyses, robustness checks, and an endogeneity check. Results show that agricultural industry agglomeration is significantly associated with lower agricultural carbon emission intensity. The mediation results are consistent with a partial intermediary role for agricultural industry resilience in this relationship. The interaction and simple slope results show that the positive association between agricultural industry agglomeration and agricultural industry resilience is larger at higher levels of digital village development. These findings connect subsystem coordination, adaptive capacity, digital conditions, and carbon performance, thereby extending research on agricultural transformation toward lower carbon intensity from a systems perspective.","农业脱碳不仅取决于单个生产活动的效率，还取决于生产、加工和服务活动之间的协调。本研究从系统视角出发，将省级农业视为一个相互关联的产业系统，考察农业产业集聚与农业碳排放强度之间的关系。本研究超越传统的生产集中度衡量方式，将农业产业集聚界定为农业生产横向集聚与生产、加工及农业服务纵向集聚之间的协调。基于2011年至2023年中国30个省份390个观测值的平衡面板数据，本研究采用省份和年份固定效应、中介与调节分析、稳健性检验以及内生性检验。结果表明，农业产业集聚与较低的农业碳排放强度显著相关。中介结果表明，农业产业韧性在这一关系中发挥部分中介作用。交互效应和简单斜率结果表明，在数字乡村发展水平较高时，农业产业集聚与农业产业韧性之间的正向关联更强。这些发现将子系统协调、适应能力、数字条件与碳绩效联系起来，从而从系统视角拓展了农业向低碳强度转型的研究。","Systems","2026-10-08T00:00:00Z",80,{"impact":16,"substance":17,"depth":16,"authority":18,"freshness":103,"relevant":20,"comment":329},"基于30省2011-2023年面板数据，从系统视角揭示农业产业集聚通过韧性降低碳排放强度，并受数字乡村发展调节，方法规范、结论有政策参考价值。",[331],{"name":325,"url":322},[25,26,333,27,334],"农业韧性","农业产业集聚",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":44,"locked":13,"version":45,"method":46,"evidence":338},{"candidates":339,"agricultural_evidence":51},[340,344,349],{"code":31,"score":16,"supported":51,"title":341,"abstract":342,"tags":343},[194],[53,54,194],[27],{"code":38,"score":345,"supported":51,"title":346,"abstract":347,"tags":348},7,[],[25,188],[25],{"code":68,"score":20,"supported":13,"title":350,"abstract":351,"tags":352},[],[],[26],[354,355],"农业产业集聚 碳排放强度","数字乡村 农业韧性","农业产业集聚碳排放强度-4520","10.3390\u002Fsystems14101263",{"doi":357,"openalex_id":359,"authors":360,"venue":325,"cited_by_count":79,"oa_url":322,"card":367,"direction":154,"ingested_from":226},"W7220924322",[361,363,365],{"name":362,"orcid":8},"Jinghui Hu",{"name":364,"orcid":8},"Jun He",{"name":366,"orcid":8},"Junzhong He",{"tldr":368,"method":369,"finding":370,"direction":84,"opportunity":371},"从系统视角研究中国省级农业产业集聚对农业碳排放强度的影响及韧性与数字化的作用。","30省2011-2023年面板数据，固定效应、中介与调节分析。","农业产业集聚显著降低碳排放强度，产业韧性部分中介，数字乡村发展正向调节。","可探究产业集聚不同模式与数字化协同对碳减排的非线性及空间溢出效应。","2026-10-09T23:30:32.435467Z",{"id":374,"title":375,"url":376,"summary":377,"summary_zh":378,"content":8,"source_name":379,"source_url":376,"published_at":380,"category":11,"cover_url":8,"hotness":165,"is_selected":13,"score":327,"score_detail":381,"sources":383,"tags":388,"primary_topic":391,"secondary_topics":395,"classification":397,"search_phrases":422,"slug":425,"view_count":79,"doi":426,"paper":427,"created_at":445},4416,"Agricultural blockchain readiness environment and carbon emission intensity in maize production: evidence from provincial panel data in major maize-producing areas of China","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1942225","China’s major maize-producing regions face the joint challenge of sustaining food production and reducing agricultural emissions. Existing research on digital agriculture often uses broad digitalization measures and therefore does not distinguish the regional conditions that may enable information management, traceability, and verification applications associated with blockchain. This study constructs an Agricultural Blockchain Readiness Environment Index (ABRE Index, block_index) using entropy weighting across digital infrastructure, the agricultural industrial base, and policy support. The index measures regional readiness and enabling conditions rather than realized blockchain adoption. Using 180 province-year observations for 12 major maize-producing provinces from 2010 to 2024, we estimate two-way fixed-effects models with province-clustered inference and conduct supplementary robustness analyses. The ABRE Index is negatively associated with carbon emissions per unit of maize-sown area, with the baseline coefficient statistically significant at the 10% level under conventional clustered inference and supported by supplementary inference procedures. The coefficient remains negative in all 12 leave-one-out specifications, is statistically significant at the 10% level or better in 10 specifications, and the wild cluster bootstrap test yields p = 0.0415. The index is positively correlated with blockchain-related patent activity ( r = 0.1883, p = 0.0114), although this correlation does not establish that the index measures realized adoption. The mechanism analysis is consistent with an organizational pathway through agricultural socialized services: the ABRE Index is positively associated with service_ratio (0.1099, p \u003C 0.01), and agricultural socialized services are negatively associated with ce_area (−2.2765, p \u003C 0.05). The interaction with the high-development indicator is negative but does not reach the conventional 10% significance threshold (−0.1499, p = 0.103). Overall, the evidence supports an associational relationship between blockchain-readiness conditions and maize-production carbon-emission intensity; it does not establish a strictly causal effect of blockchain adoption.","中国主要玉米产区面临保障粮食生产与减少农业排放的双重挑战。现有数字农业研究多采用宽泛的数字化指标，因而未能区分可能支撑与区块链相关的信息管理、追溯和验证应用的区域条件。本研究基于数字基础设施、农业产业基础和政策支持三个维度，采用熵权法构建了农业区块链就绪环境指数（ABRE Index，block_index）。该指数衡量的是区域就绪程度和使能条件，而非已实现的区块链采用。利用2010年至2024年12个主要玉米生产省份的180个省份—年份观测值，我们估计了双向固定效应模型，采用省份聚类推断，并进行了补充稳健性分析。ABRE指数与单位玉米播种面积碳排放呈负相关，基准系数在常规聚类推断下于10%水平上统计显著，并得到补充推断程序的支持。在全部12个逐一剔除（leave-one-out）设定中，该系数均保持为负，其中10个设定在10%或更优水平上统计显著，野生聚类自举检验（wild cluster bootstrap test）得出p = 0.0415。该指数与区块链相关专利活动呈正相关（r = 0.1883，p = 0.0114），尽管这一相关性并不能证明该指数衡量的是已实现的采用。机制分析与通过农业社会化服务发挥作用的组织路径相一致：ABRE指数与service_ratio呈正相关（0.1099，p \u003C 0.01），农业社会化服务与ce_area呈负相关（−2.2765，p \u003C 0.05）。与高发展指标的交互项为负，但未达到常规10%显著性阈值（−0.1499，p = 0.103）。总体而言，证据支持区块链就绪条件与玉米生产碳排放强度之间存在关联关系；但并未确立区块链采用的严格因果效应。","Frontiers in Sustainable Food Systems","2026-10-07T00:00:00Z",{"impact":16,"substance":17,"depth":16,"authority":18,"freshness":103,"relevant":20,"comment":382},"基于12个玉米主产省2010—2024年面板数据构建农业区块链就绪环境指数，发现其与玉米碳排放强度负相关，方法规范、数据规模可观，但结论仅为关联性而非因果，属细分领域实证进展。",[384,385],{"name":379,"url":376},{"name":386,"url":387},"Frontiers in Sustainable Food Systems 2026 年 10 月 07 日 Volume 10 Sec. Land, Livelihoods and Food Security","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1942225\u002Ffull",[25,26,389,27,390],"玉米","区块链",{"code":392,"slug":393,"name":389,"path":394,"parent_code":116},"A3","maize","\u002Ftopic\u002Ffield-crops\u002Fmaize",[396],{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},{"status":44,"locked":13,"version":45,"method":46,"evidence":398},{"candidates":399,"agricultural_evidence":51},[400,404,408,414,418],{"code":392,"score":186,"supported":51,"title":401,"abstract":402,"tags":403},[393],[389,393],[389],{"code":31,"score":58,"supported":51,"title":405,"abstract":406,"tags":407},[194],[53],[27],{"code":68,"score":345,"supported":51,"title":409,"abstract":410,"tags":413},[],[411,412],"数字农业","digital agriculture",[26],{"code":63,"score":138,"supported":51,"title":415,"abstract":416,"tags":417},[],[199],[],{"code":38,"score":20,"supported":13,"title":419,"abstract":420,"tags":421},[],[],[25],[423,424],"农业区块链 玉米 碳排放","玉米主产区 区块链 碳强度","农业区块链玉米碳排放-4416","10.3389\u002Ffsufs.2026.1942225",{"doi":426,"openalex_id":428,"authors":429,"venue":379,"cited_by_count":79,"oa_url":438,"card":439,"direction":444,"ingested_from":226},"W7220869193",[430,433,435],{"name":431,"orcid":432},"Pengfei Zhou","https:\u002F\u002Forcid.org\u002F0000-0003-2117-7967",{"name":434,"orcid":8},"Zhiyi Hu",{"name":436,"orcid":437},"Yang Shen","https:\u002F\u002Forcid.org\u002F0000-0002-6781-6915","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1942225\u002Fpdf",{"tldr":440,"method":441,"finding":442,"direction":84,"opportunity":443},"构建农业区块链就绪环境指数，分析其与玉米生产碳排放强度的关联。","熵权法构建ABRE指数，用2010-2024年12省面板数据做双向固定效应模型。","ABRE指数与玉米单位播种面积碳排放负相关，机制可能通过农业社会化服务实现。","可探究区块链就绪环境通过社会化服务减碳的因果机制，并拓展到其他主粮作物或区域。","智慧农业 \u002F 农业物联网","2026-10-08T23:30:07.668939Z"]