[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-197":3,"related-197":77},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":8,"published_at":11,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":24,"tags":26,"primary_topic":32,"secondary_topics":38,"classification":45,"search_phrases":71,"slug":74,"view_count":75,"doi":8,"paper":8,"created_at":76},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。",null,"## 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","论文",10,false,74,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},20,22,18,12,2,1,"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。",[25],{"name":10,"url":6},[27,28,29,30,31],"数字乡村","农业碳减排","省级面板","双向固定效应","可持续发展",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},"I6","digital-village","数字乡村与数据要素","\u002Ftopic\u002Fdigital-agriculture\u002Fdigital-village","I",[39],{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},"H4","carbon","农业碳与绿色低碳","\u002Ftopic\u002Fenvironment\u002Fcarbon","H",{"status":46,"locked":14,"version":47,"method":48,"evidence":49},"classified","2026-10-22.2","rules",{"candidates":50,"agricultural_evidence":52},[51,57,64],{"code":33,"score":17,"supported":52,"title":53,"abstract":55,"tags":56},true,[27,54],"digital village",[27],[27],{"code":40,"score":58,"supported":52,"title":59,"abstract":61,"tags":63},15,[60],"农业碳",[62,60],"碳排放",[28],{"code":65,"score":66,"supported":52,"title":67,"abstract":68,"tags":70},"E5",3,[],[69],"农村电商",[],[72,73],"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",8,"2026-08-04T01:15:47.389860Z",{"total":78,"page":22,"page_size":78,"items":79},6,[80,149,203,271,320,368],{"id":81,"title":82,"url":83,"summary":84,"summary_zh":8,"content":8,"source_name":85,"source_url":8,"published_at":86,"category":12,"cover_url":8,"hotness":13,"is_selected":52,"score":87,"score_detail":88,"sources":94,"tags":96,"primary_topic":101,"secondary_topics":107,"classification":110,"search_phrases":133,"slug":136,"view_count":137,"doi":8,"paper":138,"created_at":148},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":18,"substance":89,"depth":90,"authority":91,"freshness":92,"relevant":22,"comment":93},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。",[95],{"name":85,"url":83},[27,97,98,99,100],"数字基础设施","粮食安全","农业碳排放","农户数字化",{"code":102,"slug":103,"name":104,"path":105,"parent_code":106},"A8","yield-food-security","单产提升与粮食安全","\u002Ftopic\u002Ffield-crops\u002Fyield-food-security","A",[108,109],{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":46,"locked":14,"version":47,"method":48,"evidence":111},{"candidates":112,"agricultural_evidence":52},[113,118,123,127],{"code":40,"score":19,"supported":52,"title":114,"abstract":115,"tags":117},[60],[62,60,116],"碳足迹",[99],{"code":33,"score":119,"supported":52,"title":120,"abstract":121,"tags":122},13,[27],[27],[27,97],{"code":102,"score":92,"supported":52,"title":124,"abstract":125,"tags":126},[98],[],[98],{"code":128,"score":66,"supported":52,"title":129,"abstract":130,"tags":132},"F4",[],[131],"农药",[],[134,135],"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":139,"venue":8,"cited_by_count":140,"oa_url":8,"card":141,"direction":145,"ingested_from":147},[],0,{"tldr":142,"method":143,"finding":144,"direction":145,"opportunity":146},"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",{"id":150,"title":151,"url":152,"summary":153,"summary_zh":8,"content":8,"source_name":154,"source_url":8,"published_at":155,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":156,"score_detail":157,"sources":159,"tags":161,"primary_topic":165,"secondary_topics":166,"classification":168,"search_phrases":190,"slug":193,"view_count":21,"doi":8,"paper":194,"created_at":202},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":19,"substance":18,"depth":19,"authority":119,"freshness":75,"relevant":22,"comment":158},"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。",[160],{"name":154,"url":152},[27,162,99,163,164],"智慧农业","农业社会化服务","农业绿色低碳",{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},[167],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":46,"locked":14,"version":47,"method":48,"evidence":169},{"candidates":170,"agricultural_evidence":52},[171,176,180,185],{"code":40,"score":172,"supported":52,"title":173,"abstract":174,"tags":175},23,[62,60],[62,60],[99],{"code":33,"score":20,"supported":52,"title":177,"abstract":178,"tags":179},[27],[27],[27],{"code":181,"score":22,"supported":14,"title":182,"abstract":183,"tags":184},"G2",[],[],[163],{"code":186,"score":22,"supported":14,"title":187,"abstract":188,"tags":189},"I1",[],[],[162],[191,192],"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",{"doi":8,"openalex_id":8,"authors":195,"venue":8,"cited_by_count":140,"oa_url":8,"card":196,"direction":200,"ingested_from":147},[],{"tldr":197,"method":198,"finding":199,"direction":200,"opportunity":201},"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":204,"title":205,"url":206,"summary":207,"summary_zh":208,"content":8,"source_name":209,"source_url":206,"published_at":210,"category":12,"cover_url":8,"hotness":211,"is_selected":14,"score":212,"score_detail":213,"sources":215,"tags":220,"primary_topic":224,"secondary_topics":225,"classification":227,"search_phrases":244,"slug":247,"view_count":22,"doi":248,"paper":249,"created_at":270},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":17,"substance":18,"depth":19,"authority":20,"freshness":78,"relevant":22,"comment":214},"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。",[216,217],{"name":209,"url":206},{"name":218,"url":219},"MDPI Applied Sciences 16(17):8752","https:\u002F\u002Fwww.mdpi.com\u002F2076-3417\u002F16\u002F17\u002F8752",[27,221,222,223],"低碳农业","农业科技","碳排放效率",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[226],{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},{"status":46,"locked":14,"version":47,"method":48,"evidence":228},{"candidates":229,"agricultural_evidence":52},[230,234,239],{"code":33,"score":58,"supported":52,"title":231,"abstract":232,"tags":233},[54],[27,54],[27],{"code":40,"score":13,"supported":52,"title":235,"abstract":236,"tags":238},[],[62,60,237],"carbon emission",[221],{"code":181,"score":66,"supported":52,"title":240,"abstract":241,"tags":243},[],[242],"社会化服务",[],[245,246],"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":248,"openalex_id":250,"authors":251,"venue":209,"cited_by_count":140,"oa_url":206,"card":264,"direction":145,"ingested_from":269},"W7207843203",[252,254,256,259,262],{"name":253,"orcid":8},"Yuming Li",{"name":255,"orcid":8},"Liudi Li",{"name":257,"orcid":258},"Xiuguang Bai","https:\u002F\u002Forcid.org\u002F0000-0003-0140-208X",{"name":260,"orcid":261},"Bo Wei","https:\u002F\u002Forcid.org\u002F0000-0002-1058-5888",{"name":263,"orcid":8},"Jing Zhang",{"tldr":265,"method":266,"finding":267,"direction":200,"opportunity":268},"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":272,"title":273,"url":274,"summary":275,"summary_zh":8,"content":8,"source_name":276,"source_url":8,"published_at":277,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":212,"score_detail":278,"sources":280,"tags":282,"primary_topic":286,"secondary_topics":287,"classification":288,"search_phrases":308,"slug":311,"view_count":21,"doi":8,"paper":312,"created_at":319},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",{"impact":19,"substance":18,"depth":19,"authority":91,"freshness":78,"relevant":22,"comment":279},"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。",[281],{"name":276,"url":274},[27,28,283,284,285],"农业数字化","省级面板数据","绿色技术创新",{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},[],{"status":46,"locked":14,"version":47,"method":48,"evidence":289},{"candidates":290,"agricultural_evidence":52},[291,296,300,304],{"code":40,"score":292,"supported":52,"title":293,"abstract":294,"tags":295},7,[],[62,60],[28],{"code":37,"score":22,"supported":14,"title":297,"abstract":298,"tags":299},[],[],[283],{"code":186,"score":22,"supported":14,"title":301,"abstract":302,"tags":303},[],[],[283],{"code":33,"score":22,"supported":14,"title":305,"abstract":306,"tags":307},[],[],[27],[309,310],"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":8,"openalex_id":8,"authors":313,"venue":8,"cited_by_count":140,"oa_url":8,"card":314,"direction":200,"ingested_from":147},[],{"tldr":315,"method":316,"finding":317,"direction":200,"opportunity":318},"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。","2026-09-25T00:09:34.643179Z",{"id":321,"title":322,"url":323,"summary":324,"summary_zh":8,"content":8,"source_name":325,"source_url":8,"published_at":277,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":326,"score_detail":327,"sources":330,"tags":332,"primary_topic":335,"secondary_topics":336,"classification":337,"search_phrases":356,"slug":359,"view_count":137,"doi":8,"paper":360,"created_at":367},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":328,"substance":18,"depth":19,"authority":91,"freshness":78,"relevant":22,"comment":329},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。",[331],{"name":325,"url":323},[27,28,283,333,334],"河南农业","fsQCA",{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},[],{"status":46,"locked":14,"version":47,"method":48,"evidence":338},{"candidates":339,"agricultural_evidence":52},[340,344,348,352],{"code":40,"score":58,"supported":52,"title":341,"abstract":342,"tags":343},[60],[62,60],[28],{"code":37,"score":22,"supported":14,"title":345,"abstract":346,"tags":347},[],[],[283],{"code":186,"score":22,"supported":14,"title":349,"abstract":350,"tags":351},[],[],[283],{"code":33,"score":22,"supported":14,"title":353,"abstract":354,"tags":355},[],[],[27],[357,358],"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":8,"openalex_id":8,"authors":361,"venue":8,"cited_by_count":140,"oa_url":8,"card":362,"direction":200,"ingested_from":147},[],{"tldr":363,"method":364,"finding":365,"direction":200,"opportunity":366},"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":369,"title":370,"url":371,"summary":372,"summary_zh":8,"content":373,"source_name":374,"source_url":8,"published_at":375,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":326,"score_detail":376,"sources":378,"tags":380,"primary_topic":386,"secondary_topics":387,"classification":388,"search_phrases":405,"slug":408,"view_count":137,"doi":8,"paper":409,"created_at":411},1033,"数字乡村建设对农业新质生产力的影响效应与作用机制——基于2012—2023年30省份面板数据","http:\u002F\u002Fwww.qikanvip.com\u002Fqkml\u002F127584.html","原文标题:数字乡村建设对农业新质生产力的影响效应与作用机制。作者:杜一鸣、贾慧静、王强(西安交通大学马克思主义学院等)。基于2012—2023年中国30个省份的面板数据,采用双向固定效应模型,实证检验数字乡村建设对农业新质生产力的影响,并引入中介效应模型和调节效应模型考察农村创新创业活跃度的中介作用以及农村产业融合的调节效应。研究结果表明:数字乡村建设可以显著促进农业新质生产力提升,且这种促进作用在'胡焕庸线'以东地区、非粮食主产区及农业新质生产力水平较高的地区更为显著;农村创新创业活跃度发挥了部分中介作用;农村产业融合对数字乡村建设与农业新质生产力之间的关系具有显著的正向调节作用。","0\n\n发表咨询在线!\n\n###### 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[统计与决策2026年第01期](http:\u002F\u002Fwww.qikanvip.com\u002Fqkml\u002F2026T01T15982\u002F)\n3.   [数字乡村建设对农业新质生产力的影响效应与作用机制](http:\u002F\u002Fwww.qikanvip.com\u002Fqkml\u002F127584.html)\n\n# 统计与决策2026年第01期：数字乡村建设对农业新质生产力的影响效应与作用机制\n\n来源：[期刊VIP网](http:\u002F\u002Fwww.qikanvip.com\u002F)时间： 2026年8月28日 星期五\n\n* * *\n\n作者：杜一鸣;贾慧静;王强;\n\n单位：西安交通大学马克思主义学院;延安大学政法与公共管理学院;温州理工学院经济与管理学院;\n\n**摘要：**在加快培育新质生产力的背景下，数字乡村建设已成为推动农业现代化和农业生产力提升的重要路径，但其对农业新质生产力的作用路径与内在机制仍有待系统考察。文章基于2012—2023年中国30个省份的面板数据，采用双向固定效应模型，实证检验数字乡村建设对农业新质生产力的影响，并进一步引入中介效应模型和调节效应模型，考察农村创新创业活跃度的中介作用以及农村产业融合的调节效应。研究结果表明：数字乡村建设可以显著促进农业新质生产力提升，且这种促进作用在“胡焕庸线”以东地区、非粮食主产区及农业新质生产力水平较高的地区更为显著。从作用机制看，农村创新创业活跃度在数字乡村建设与农业新质生产力之间发挥了部分中介作用，数字要素投入通过激发农村创新创业活动加速了新质劳动者、新质劳动资料与新质劳动对象的形成与升级。此外，农村产业融合对数字乡村建设与农业新质生产力之间的关系具有显著的正向调节作用，农村产业融合水平越高，数字乡村建设对农业新质生产力的赋能作用越强。\n\n**关键词：**数字乡村建设;;农业新质生产力;;农村创新创业活跃度;;产业融合\n\n**基金资助：**浙江省哲学社会科学规划“省市合作”课题（25SSHZ079YB）\n\n##### 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