[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-416":3,"related-416":63},{"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":39,"search_phrases":58,"slug":61,"view_count":21,"doi":8,"paper":8,"created_at":62},416,"数字经济发展对农业碳排放的影响:来自规模效应与效率效应的证据","https:\u002F\u002Fwww.ebiotrade.com\u002Fnewsf\u002F2026-8\u002F20260807000145187.htm","发表于《Frontiers in Sustainable Food Systems》。基于中国2011-2022年省级面板数据,从规模效应(scale effect)与效率效应(efficiency effect)双视角考察农业数字化如何影响农业碳排放与碳排放效率。以双向固定效应模型为主分析方法,研究表明农业数字化显著改善农业碳绩效:农业数字化水平每提升1%,农业碳排放总量下降15.72%,碳排放效率提升3.97%,规模效应与效率效应同时成立。机制证据表明,现阶段减排主要通过增量创新而非突破式创新实现;南方地区与数字化水平较高省份减排效应更强,且数字化存在空间溢出效应。结论提示加快农业数字化、强化区域差异化支持与创新激励可助推低碳农业转型。",null,"数字经济发展对农业碳排放的影响：来自规模效应与效率效应的证据\n\n《Frontiers in Sustainable Food Systems》：Impact of digital economy development on agricultural carbon emissions: evidence from scale and efficiency effects\n\n_【字体： 大 中 小 】_ _时间：2026年08月07日_ _来源：Frontiers in Sustainable Food Systems 4.1_\n\n**编辑推荐：**\n\n农业数字化日益被视为农业现代化与低碳转型的关键路径。研究人员基于中国2011—2022年省级面板数据，从规模效应（scale effect）与效率效应（efficiency effect）双视角考察农业数字化如何影响农业碳排放（agricultural car\n\n农业数字化日益被视为农业现代化与低碳转型的关键路径。研究人员基于中国2011—2022年省级面板数据，从规模效应（scale effect）与效率效应（efficiency effect）双视角考察农业数字化如何影响农业碳排放（agricultural carbon emissions）与碳排放效率（carbon emission efficiency）。研究以双向固定效应模型（two-way fixed-effects model）为主分析方法，结果表明农业数字化显著改善农业碳绩效：农业数字化水平每提升1%，农业碳排放总量下降15.72%，碳排放效率提升3.97%，规模效应与效率效应同时成立。机制证据表明，现阶段减排主要通过增量创新（incremental innovation）而非突破式创新（breakthrough innovation）实现；南方地区与数字化水平较高省份减排效应更强，且数字化存在空间溢出效应（spatial spillover effects）。结论提示，加快农业数字化、强化区域差异化支持与创新激励，可助推低碳农业转型。\n\n研究背景方面，全球气候变化背景下农业作为温室气体重要源，其排放具分散源、工艺复杂、技术替代受限等特征，在保粮食安全前提下减排难度大。既有文献对数字化与农业碳排放关系存在两派观点：一派认为精准投入与绿色技术扩散降低强度，另一派认为市场连通与规模机械化扩张推高能源需求。已有研究多孤立看排放总量或效率，难以判断是外延扩张还是内涵改进驱动转型。为此，研究人员将农业数字化作为核心解释变量，同时以碳排放总量与碳排放效率（基于Super-SBM模型测度，含非期望产出）刻画碳绩效，并区分规模效应（改变产出Y与高碳投入）与效率效应（改变单位产出强度θ），从增量创新与突破式创新异质性、阈值非线性、空间溢出三方面展开。\n\n样本与数据来自2011—2022年中国31个省级行政区平衡面板，融合?《中国统计年鉴》?《中国农村统计年鉴》、专利数据库、进出口统计、区域投入产出表及Ran等（2025）突破式创新测度；农业数字化（DVI）以农业数字经济增加值占农业增加值比为主指标，辅以农村宽带\u002F电脑\u002F手机普及率等七指标变异系数法合成DVI1；农业碳排放按IPCC因子法聚合水稻甲烷、耕地N 2 O、化肥农药农膜、灌溉用电、农机柴油及猪牛羊肠道发酵与粪管甲烷\u002FN 2 O；控制变量含人均GDP及其平方、人口、城镇化、农业开放度、财政支农、种植结构、生产集聚等。关键技术方法包括：Super-SBM（含非期望产出、VRS）测度碳效率；双向固定效应面板回归；Tobit机制检验；面板阈值模型；空间自回归（SAR）\u002F空间误差（SEM）\u002F空间杜宾（SDM）模型配地理邻接权重并做经济距离稳健。\n\n研究结果部分，理论分析提出四组假设：H1指数字化通过要素配置优化提升效率但总量方向不确定；H2指增量创新主作用于效率、突破式创新兼影响规模与效率；H3指存在数字化阈值，过阈后减排显化；H4指空间溢出经效率扩散与规模再配置两通道，净效应符号不定。实证主回归显示DVI对lnC负向显著（弹性-0.1572）、对lnCE正向显著（弹性0.0397），即规模与效率双效应成立。机制检验中DVI显著提升增量创新（ln(1+人均绿色农业专利)）而未显著撬动突破式创新占比，印证现阶段靠增量创新减排。阈值模型估计出单\u002F多阈值，低于阈时减排不显著，跨阈后负向弹性增强。空间SDM表明本地数字化降本地排放、提升本地效率，且对邻省效率有正间接效应、对邻省总量排放呈负向间接效应，支持效率扩散主导而非规模虹吸。异质性上南方、高数字基础省弹性更大。\n\n讨论与结论翻译：农业数字化通过优化生产要素配置、生产决策与过程控制改善农业碳效率；其对总排放的影响在理论上不确定，因效率提升可与生产规模扩张并存。数字化经异质创新渠道作用，增量创新主增效率收益，突破式创新可同时影响效率与规模。数字化对碳排放的影响具阈值特征，跨临界值后减排方显。数字化不仅影响本地碳绩效，亦经效率扩散与规模再配置影响邻域，净溢出符号因变量而异。研究人员建议加快农业数字化并配区域差异化支持与创新激励，以推进低碳农业转型。该文发表于《Frontiers in Sustainable Food Systems》。\n\n相关新闻\n\n![Image 1](https:\u002F\u002Fnews.ebiotrade.com\u002FQR_ebiotrade.jpg)生物通微信公众号\n\n![Image 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3](https:\u002F\u002Fwww.ebiotrade.com\u002Fimages\u002Fservice_com.gif)\n\n粤ICP备09063491号","Frontiers in Sustainable Food Systems 2026-08-07","2026-08-07T00:00:00Z","论文",10,false,74,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},18,22,19,12,3,1,"基于省级面板数据的实证研究，揭示农业数字化减排机制，对政策制定有参考价值，但时效性稍差。",[25],{"name":10,"url":6},[27,28,29,30,31],"数字经济","低碳农业","农业碳排放","规模效应","效率效应",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},"H4","carbon","农业碳与绿色低碳","\u002Ftopic\u002Fenvironment\u002Fcarbon","H",[],{"status":40,"locked":14,"version":41,"method":42,"evidence":43},"classified","2026-10-22.2","rules",{"candidates":44,"agricultural_evidence":47},[45,53],{"code":33,"score":46,"supported":47,"title":48,"abstract":51,"tags":52},24,true,[49,50],"碳排放","农业碳",[49,50],[28,29],{"code":54,"score":22,"supported":14,"title":55,"abstract":56,"tags":57},"I6",[],[],[27],[59,60],"农业碳排放 低碳农业 效率效应 数字经济","农业碳排放 低碳农业","农业碳排放低碳农业效率效应数字经济-416","2026-08-11T23:57:03.209009Z",{"total":64,"page":22,"page_size":64,"items":65},6,[66,109,183,230,283,340],{"id":67,"title":68,"url":69,"summary":70,"summary_zh":8,"content":71,"source_name":72,"source_url":8,"published_at":73,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":74,"score_detail":75,"sources":78,"tags":80,"primary_topic":83,"secondary_topics":84,"classification":85,"search_phrases":103,"slug":106,"view_count":20,"doi":107,"paper":8,"created_at":108},270,"Impact of digital economy development on agricultural carbon emissions: evidence from scale and efficiency effects","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1820332\u002Fabstract","2026年8月6日Frontiers in Sustainable Food Systems正式发表的研究,基于中国省级面板数据,系统考察数字经济发展对农业碳排放的影响及其规模效应与效率效应。研究发现,数字经济发展通过规模效应与效率效应两条路径影响农业碳排放:一方面,数字化通过降低交易成本、扩大市场半径,推动农业产业规模扩张,可能带来碳排放总量增长;另一方面,数字化通过精准农业、智能装备、生产要素优化配置,显著提升农业生产效率,降低单位产出碳排放强度,推动农业低碳转型。研究通过门槛回归与中介效应模型识别两类效应的临界条件与转化机制,发现效率效应在数字经济发展达到一定阈值后逐步占主导,推动数字经济发展从增碳向减碳转变,为制定差异化农业碳减排政策提供实证依据。","## Abstract\n\nAgricultural digitalization is increasingly viewed as a key pathway to modernizing agriculture and advancing low-carbon transformation. Using provincial panel data from China spanning 2011–2022, this study examines how agricultural digitalization affects agricultural carbon emissions and carbon emission efficiency from the perspectives of scale effect and efficiency effect. A two-way fixed-effects model is employed as the main analytical approach. The results show that agricultural digitalization significantly improves agricultural carbon performance. Specifically, a 1% increase in the level of agricultural digitalization leads to a 15.72% reduction in total agricultural carbon emissions and a 3.97% improvement in carbon emission efficiency, indicating that both scale and efficiency effects are operative. Mechanism evidence suggests that, at the current stage, the mitigation effect primarily works through incremental innovation rather than breakthrough innovation. The carbon-reduction effect is stronger in southern China and in provinces with more advanced digital development, and digitalization also exhibits spatial spillover effects. These findings suggest that accelerating agricultural digitalization, while strengthening region-specific support and innovation incentives, can contribute to the transition toward low-carbon agriculture.\n\n## 1 Introduction\n\nIn the context of intensifying global climate change and strengthened low-carbon transition objectives, agriculture, as a major source of greenhouse gas emissions, has become a central topic in research on mitigation pathways and green development strategies. According to the Intergovernmental Panel on Climate Change (IPCC), agricultural production activities constitute a major source of anthropogenic greenhouse gas emissions (), the global food system accounts for more than one third of total anthropogenic greenhouse gas emissions worldwide (). The United Nations Environment Programme reports that approximately 40% of anthropogenic CH 4 emissions originate from agricultural production activities. Moreover, the greenhouse effect of CH 4 is more than 26 times that of CO 2 (; ). As one of the world's major agricultural producers, China's agricultural production activities constitute the largest anthropogenic greenhouse gas emission source aside from energy use and industrial processes (). Compared with the industrial sector, agricultural carbon emissions are characterized by dispersed sources, complex production processes, and strong constraints on technological substitution. Accordingly, achieving agricultural emission-reduction targets under the constraints of safeguarding food security and ensuring a stable supply of agricultural products remains a formidable practical challenge.\n\nIn recent years, the digital economy, represented by big data, cloud computing, artificial intelligence, and the platform economy, has developed rapidly and profoundly reshaped agricultural factor allocation patterns, industrial organization structures, and production decision-making mechanisms. Existing research on the impact of digitalization on agricultural carbon emissions has yielded two distinct sets of findings. One perspective argues that digitalization can reduce carbon emission intensity by promoting the precise allocation of inputs, intelligent management, and the diffusion of green technologies (; ; ). Another perspective emphasizes expansion mechanisms, arguing that digitalization leads to stronger market connectivity, large-scale mechanization, and rapid expansion of digital infrastructure, which may stimulate output growth and energy demand, thereby increasing carbon emissions under certain conditions (; ). However, the key issue in existing research is not whether digitalization has a positive or negative impact on agricultural carbon emissions, but rather that digitalization may operate through different pathways and exert varying effects on agricultural carbon emissions.\n\nTo systematically elucidate the differentiated mechanisms through which digitalization affects agricultural carbon emissions, it is first necessary to clearly define two core concepts: the scale effect and the efficiency effect. The scale effect refers to how digitalization alters total agricultural carbon emissions by influencing output scale, production structure, and the demand for high-carbon inputs at the extensive margin. Efficiency effects refer to how digitalization alters the carbon emission efficiency per unit of agricultural output by improving the allocation and utilization of input factors such as land, labor, machinery, fertilizers, pesticides, and irrigation at the intensive margin. In the agricultural production process of a given region, it is possible for carbon emissions per unit of output to decrease while total carbon emissions ultimately rise due to production expansion; conversely, if the production structure changes, total agricultural carbon emissions may decrease even if improvements in carbon emission efficiency are limited. Unfortunately, most existing literature examines total agricultural carbon emissions (; ) and agricultural carbon emission efficiency () in isolation, making it difficult to determine whether the observed trends are driving a genuine low-carbon transformation in agriculture or merely shifting the balance between extensive expansion and intensive improvements.\n\nAccordingly, this study takes the level of agricultural digitalization as the core explanatory variable and characterizes agricultural carbon emission performance from two dimensions: total agricultural carbon emissions and agricultural carbon emission efficiency. From a differentiated innovation perspective, the paper applies threshold models, spatial models, and other methods to systematically examine the impact of agricultural digitalization on agricultural carbon emissions and the associated mechanisms. The primary contributions of this paper can be summarized in three aspects. First, unlike existing studies that largely focus on general tests of the correlation between digitalization and agricultural carbon emissions, this paper systematically examines the differentiated impacts of digitalization on agricultural carbon performance from the dual perspectives of total carbon emissions and carbon emission efficiency. It thereby reveals whether digitalization drives the low-carbon transition in agriculture through adjustments in the scale of emissions at the extensive margin or through efficiency improvements at the intensive margin. Second, this study incorporates both the phased characteristics of digitalization and regional interdependencies into the agricultural carbon performance analysis framework, systematically examining the nonlinear effects of agricultural digitalization on total carbon emissions and carbon emission efficiency, as well as its spatial spillover effects. Unlike existing studies that discuss threshold effects and spatial effects in isolation, this paper not only examines the differential roles of digitalization in agricultural low-carbon transformation across different development stages but also analyzes whether the local low-carbon benefits it generates can spread to surrounding areas, thereby identifying the stage-specific conditions and spatial boundaries under which digitalization delivers environmental performance. Third, unlike existing studies that treat innovation as a single transmission channel, this paper distinguishes between incremental innovation and breakthrough innovation. It examines the differentiated roles of these innovation types in the process by which agricultural digitalization influences carbon performance, thereby providing a deeper understanding of the underlying mechanisms through which digitalization empowers low-carbon transformation in agriculture.\n\nThe structure of the paper is organized as follows. Section 2 develops the theoretical framework and formulates the research hypotheses. Section 3 introduces the research design, data sources, and the measurement of variables. Section 4 presents the empirical findings, robustness tests, and mechanism analyses. Section 5 provides further analysis of threshold effects and spatial effects. Section 6 concludes the study and offers relevant policy implications.\n\n## 2 Theoretical analysis and research hypotheses\n\n### 2.1 Analytical framework\n\nFrom the perspective of production function theory, agricultural digitization can be understood as a technology-enhancing factor that not only shifts the production frontier but also influences the generation of agricultural carbon emissions. In other words, since agricultural output (_Y_) is jointly determined by land (_L_), labor (_N_), capital and machinery (_K_), intermediate inputs such as fertilizers and pesticides (_M_), energy inputs (_E_), and the level of digitization (_D_), the agricultural production process can be expressed as:\n\n$Y = A \\left(\\right. D \\left.\\right) \\cdot F \\left(\\right. L , \\textrm{ } N , \\textrm{ } K , \\textrm{ } M , \\textrm{ } E \\left.\\right)$\n\nIn this context, _A_(_D_) represents the technology-enhancing effect resulting from agricultural digitization. As the level of digitization increases, improvements are made in information acquisition, factor allocation, timing of input application, and management practices during the production process, thereby driving growth in agricultural output and altering the mix of inputs.\n\nAgricultural carbon emissions, as an unintended byproduct, are the aggregate result of emissions from various agricultural subsectors at the provincial level. These include emissions from crop production activities, emissions from major livestock and poultry farming activities, as well as emissions associated with the use of agricultural inputs.\n\n$C = C^{\\text{crop}} + C^{\\text{livestock}} + C^{\\text{input}}$\n\nSpecifically, _C_ crop refers to carbon emissions from crop production, _C_ livestock refers to emissions from livestock and poultry farming, and _C_ input refers to carbon emissions associated with agricultural inputs.\n\nThe impact of digitalization on agricultural carbon emissions does not necessarily operate through the same micro-level mechanisms across all subsectors; rather, it can exert its influence through sector-specific channels and manifest as a comprehensive net effect at the provincial level. Specifically, in crop production, digitalization primarily affects input allocation and emission intensity through precision fertilization, smart irrigation, optimized scheduling of agricultural machinery, and crop condition monitoring; in livestock farming, digitalization primarily influences methane and nitrous oxide emissions through precision feeding, monitoring of rearing environments, disease early warning systems, and digital manure management. Therefore, the digitalization indicators used in this study are best understood as measures of the level of digital development in agriculture or the digital enablement environment, rather than as a uniform adoption rate of a single production technology.\n\nAt the same time, agricultural carbon emissions depend not only on the scale of production itself but also on the emission intensity per unit of output. Let the total agricultural carbon emissions be _C_; then, this can be further expressed as:\n\n$C = Y \\cdot \\theta \\left(\\right. D , \\textrm{ } Z \\left.\\right)$\n\nHere, θ(_D, Z_) represents the carbon emission intensity per unit of output, and _Z_ denotes other conditional variables that influence emission intensity, such as agricultural structure, resource endowments, institutional environment, and technological adoption capacity. Digitalization may reduce carbon emission intensity per unit of output that is, decrease θ through precision fertilization, smart irrigation, and optimized scheduling of agricultural machinery; alternatively, it may increase total output _Y_ by expanding operational scale, enhancing market connectivity, and increasing the intensity of machinery and energy use. Therefore, the impact of digitalization on total agricultural carbon emissions depends on the combined direction and relative magnitude of its effects on output scale and emission intensity. Consequently, the analysis of agricultural carbon performance can be divided into two interrelated yet distinct theoretical dimensions.\n\nThe scale effect refers to the impact of digitalization on total agricultural carbon emissions by altering the scale of agricultural output, the organization of production, and the absolute volume of carbon-related inputs. As shown in the above equation, when digitalization significantly increases the scale of agricultural output _Y_, total carbon emissions _C_ may still rise even if the emission intensity per unit of output θ decreases. For example, if digitalization expands market access, leads to larger-scale operations, and increases the use of machinery, electricity, cold chain logistics, and other energy-intensive infrastructure, total carbon emissions may rise. However, scale expansion does not necessarily imply an increase in carbon emissions per unit of output, as economies of scale, improved organizational methods, and more efficient coordination mechanisms may also reduce emission intensity. Therefore, the scale effect focuses on the marginal impact on carbon performance that is, whether digitalization ultimately expands or curbs total carbon emissions.\n\nThe efficiency effect refers to how digitalization influences agricultural carbon emission efficiency at the intensive margin by altering the allocation of factors and the generation of emissions within the production function. If agricultural carbon emission efficiency is defined as the output level corresponding to a unit of carbon emissions, or more generally understood as the ability to control unwanted outputs under given input-output conditions, then digitalization through the use of precision monitoring technologies, intelligent decision support systems, and smart equipment can improve the allocation and timing of inputs such as fertilizers, pesticides, irrigation, machinery, and energy. This reduces redundant inputs and lowers carbon emissions per unit of output, i.e., reduces θ (; ; ). In the empirical analysis that follows, the efficiency dimension is represented by agricultural carbon emission efficiency as a proxy variable. This indicator reflects performance determined by both desired and undesired outputs under given input conditions, thereby corresponding to the concept of intensive margins in the aforementioned theory. It should be noted that improved efficiency does not necessarily imply a reduction in total carbon emissions, as the same efficiency gains may stimulate output expansion or lead to structural adjustments. Conversely, even with limited efficiency gains, total carbon emissions may still decrease if digitalization reduces production scale or drives a shift toward low-carbon-intensive activities. Therefore, total carbon emissions and carbon emission efficiency do not represent a simple substitution relationship in terms of variable structure; rather, they respectively characterize different theoretical dimensions of carbon performance.\n\nFurthermore, taking the logarithm of the above equation and evaluating the partial derivative yields:\n\n$\\frac{\\partial ln \\textrm{ } C}{\\partial D} = \\frac{\\partial ln \\textrm{ } Y}{\\partial D} + \\frac{\\partial ln \\theta}{\\partial D}$\n\nThis equation indicates that the impact of digitalization on total carbon emissions can be broken down into two components: one is the change in output scale resulting from digitalization, which reflects the scale effect; the other is the change in emissions intensity per unit of output resulting from digitalization, which reflects the efficiency effect. When the emission reduction effect of the former exceeds that of the latter, total agricultural carbon emissions may increase; conversely, they may decrease. Consequently, the theoretical impact of digitalization on total agricultural carbon emissions is uncertain, whereas its effect on improving the efficiency of agricultural carbon emissions has a clearer direction.\n\nIn addition to crop production, agricultural digitalization may also affect emissions from livestock production. Through precision feeding, environmental monitoring, disease surveillance, and digital manure management, digital technologies can improve feed use efficiency, reduce waste, and mitigate methane and nitrous oxide emissions from animal husbandry. Therefore, the carbon consequences of digitalization are not limited to crop-related inputs, but may also extend to livestock-related emission sources.\n\nBased on the above analysis, this study proposes the following hypothesis:\n\n**Hypothesis 1**. Agricultural digitization is expected to improve the carbon efficiency of agriculture by optimizing the allocation of factors of production, production decisions, and process control; however, its impact on total agricultural carbon emissions remains theoretically uncertain, as efficiency gains may occur simultaneously with an expansion in production scale.\n\n### 2.2 Innovation heterogeneity and carbon transfer mechanisms\n\nInnovation is a key mechanism through which digitalization influences carbon performance, but different types of innovation affect economies of scale and efficiency in distinct ways. Digitalization can reduce information costs, strengthen data feedback, promote collaboration, and improve the allocation of innovation resources, thereby stimulating green technological innovation in agriculture. However, the impact of innovation on agricultural carbon emissions varies depending on whether it manifests primarily as incremental innovation or breakthrough innovation. Incremental innovation refers to improvements made within existing technological frameworks, such as more precise fertilization, optimized water-saving irrigation, and smarter agricultural machinery scheduling. The impact of incremental innovation on carbon emissions lies in improving agricultural carbon emissions by reducing emission intensity within the existing production system. Breakthrough innovation, on the other hand, may reshape the production frontier through new organizational forms, service models, or production technologies. Consequently, breakthrough innovation may influence carbon emission intensity not only by pushing the technological frontier outward but also by altering production organization, market coverage, and the feasible boundaries of agricultural operations (; ). Consequently, if digitalization primarily induces incremental innovation, its impact on agricultural carbon emissions is more likely to manifest as optimization within the existing system that is, improved carbon efficiency. If, however, digitalization induces breakthrough innovation, its impact on agricultural carbon emissions may reshape production methods, significantly enhancing efficiency while potentially giving rise to new production models, thereby simultaneously influencing both economies of scale and efficiency gains.\n\nBased on the above analysis, this study proposes the following hypothesis:\n\n**Hypothesis 2**. Agricultural digitization influences agricultural carbon emissions through heterogeneous innovation channels; incremental innovations primarily enhance efficiency gains, while breakthrough innovations may simultaneously influence both efficiency gains and scale effects.\n\n### 2.3 Threshold effects of agricultural digitalization on carbon emissions\n\nThe impact of agricultural digitalization on agricultural carbon emissions may exhibit nonlinear and threshold-dependent characteristics. This can be attributed to the early stage of agricultural digitalization, during which inadequate digital infrastructure and low technology adoption rates limit its impact on agricultural carbon emissions. Once agricultural digi","Frontiers in Sustainable Food Systems 2026-08-06","2026-08-06T00:00:00Z",78,{"impact":17,"substance":18,"depth":17,"authority":20,"freshness":76,"relevant":22,"comment":77},8,"基于省级面板数据，实证分析农业数字化对碳排放的影响，结论可靠，对政策制定有参考价值。",[79],{"name":72,"url":69},[81,27,29,30,31,82],"Frontiers","门槛效应",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":86},{"candidates":87,"agricultural_evidence":47},[88,93,99],{"code":33,"score":89,"supported":47,"title":90,"abstract":91,"tags":92},7,[],[49,50],[29],{"code":94,"score":21,"supported":47,"title":95,"abstract":96,"tags":98},"I1",[],[97],"精准农业",[],{"code":54,"score":22,"supported":14,"title":100,"abstract":101,"tags":102},[],[],[27],[104,105],"农业碳排放 效率效应 数字经济 规模效应","农业碳排放 效率效应","农业碳排放效率效应数字经济规模效应-270","10.3389\u002Ffsufs.2026.1820332\u002Fabstract","2026-08-07T00:18:31.300729Z",{"id":110,"title":111,"url":112,"summary":113,"summary_zh":114,"content":8,"source_name":115,"source_url":112,"published_at":116,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":117,"score_detail":118,"sources":121,"tags":123,"primary_topic":126,"secondary_topics":127,"classification":128,"search_phrases":162,"slug":165,"view_count":119,"doi":166,"paper":167,"created_at":182},1518,"Do new agricultural business entities reduce the marginal abatement cost of agricultural carbon emissions? Evidence from China","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1922569","Introduction The development of new agricultural business entities has become an important driver of agricultural modernization and rural transformation. Their growing role in promoting green agricultural development and carbon mitigation highlights the need to assess not only their emission-reduction potential but also the economic costs of carbon mitigation. However, whether these entities can reduce the marginal abatement cost of agricultural carbon emissions remains unclear. Methods Using panel data from 30 provinces in China from 2011 to 2023, this study empirically examines the impact of new agricultural business entities on the marginal abatement cost of agricultural carbon emissions and its underlying mechanisms by employing a two-way fixed effects model, mediation analysis, and spatial econometric models. Results The results show that the development of new agricultural business entities significantly reduces the marginal abatement cost of agricultural carbon emissions. Further analysis indicates that farmer cooperatives and family farms play a particularly important role in reducing carbon abatement costs, with stronger effects in major grain-producing regions and areas with greater governance capacity. Mechanism tests suggest that new agricultural business entities reduce marginal abatement costs by easing agricultural credit constraints, improving land allocation efficiency, and promoting agricultural technological progress. In addition, the spatial econometric results reveal significant spatial spillover effects, indicating that the development of new agricultural business entities in neighboring regions also contributes to lower local agricultural carbon abatement costs. Discussion These findings provide new evidence on the economic mechanisms through which new agricultural business entities contribute to agricultural carbon mitigation and offer policy implications for promoting the low-carbon transformation of agriculture in China and other developing countries.","引言 新型农业经营主体的发展已成为推动农业现代化与农村转型的重要动力。其在促进农业绿色发展与碳减排方面日益凸显的作用，要求我们不仅评估其减排潜力，还需考量碳减排的经济成本。然而，这些主体能否降低农业碳排放的边际减排成本，目前尚不明确。方法 基于2011至2023年中国30个省份的面板数据，本研究运用双向固定效应模型、中介效应分析及空间计量模型，实证考察了新型农业经营主体对农业碳排放边际减排成本的影响及其内在机制。结果 研究结果显示，新型农业经营主体的发展显著降低了农业碳排放的边际减排成本。进一步分析表明，农民专业合作社与家庭农场在降低碳减排成本方面发挥着尤为重要的作用，且在粮食主产区及治理能力较强的地区效果更为显著。机制检验提示，新型农业经营主体通过缓解农业信贷约束、改善土地配置效率及促进农业技术进步等途径，降低了边际减排成本。此外，空间计量结果揭示了显著的空间溢出效应，表明邻近地区新型农业经营主体的发展亦有助于降低本地区农业碳减排成本。讨论 上述发现为新型农业经营主体助力农业碳减排的经济机制提供了新的证据，并对中国及其他发展中国家推动农业低碳转型具有政策启示意义。","Frontiers in Sustainable Food Systems","2026-09-03T00:00:00Z",72,{"impact":17,"substance":18,"depth":17,"authority":20,"freshness":119,"relevant":22,"comment":120},2,"研究新型农业经营主体对农业碳排放边际减排成本的影响，数据详实，结论具有政策参考价值。",[122],{"name":115,"url":112},[124,28,125,29],"农业经济","新型农业经营主体",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":129},{"candidates":130,"agricultural_evidence":47},[131,135,142,149,156],{"code":33,"score":76,"supported":47,"title":132,"abstract":133,"tags":134},[],[49,50],[28,29],{"code":136,"score":64,"supported":47,"title":137,"abstract":138,"tags":141},"G3",[],[139,140],"农业信贷","agricultural credit",[],{"code":143,"score":64,"supported":47,"title":144,"abstract":145,"tags":148},"J2",[],[146,147],"农业现代化","agricultural modernization",[],{"code":150,"score":151,"supported":47,"title":152,"abstract":153,"tags":155},"G2",4,[],[154],"家庭农场",[125],{"code":157,"score":21,"supported":47,"title":158,"abstract":159,"tags":161},"I4",[],[160],"空间计量",[],[163,164],"新型农业经营主体 农业碳排放 低碳农业 农业经济","新型农业经营主体 农业碳排放","新型农业经营主体农业碳排放低碳农业农业经济-1518","10.3389\u002Ffsufs.2026.1922569",{"doi":166,"openalex_id":168,"authors":169,"venue":115,"cited_by_count":174,"oa_url":112,"card":175,"direction":179,"ingested_from":181},"W7206169038",[170,172],{"name":171,"orcid":8},"Juan Pu",{"name":173,"orcid":8},"Li Mingliang",0,{"tldr":176,"method":177,"finding":178,"direction":179,"opportunity":180},"研究新型农业经营主体对农业碳排放边际减排成本的影响及机制。","使用2011-2023年中国省级面板数据，采用双向固定效应、中介效应和空间计量模","新型农业经营主体显著降低边际减排成本，存在空间溢出效应，合作社和家庭农场作用更明显。","农业绿色发展与碳","可进一步研究新型经营主体降低减排成本的具体技术路径，或在不同区域尺度下的差异化政策设计。","openalex","2026-09-03T23:30:07.743142Z",{"id":184,"title":185,"url":186,"summary":187,"summary_zh":8,"content":8,"source_name":188,"source_url":8,"published_at":8,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":74,"score_detail":189,"sources":192,"tags":194,"primary_topic":199,"secondary_topics":200,"classification":201,"search_phrases":217,"slug":220,"view_count":174,"doi":8,"paper":221,"created_at":229},5360,"国家农业绿色发展试点区对农业碳排放的影响:基于 284 城市 2013-2023 年面板数据","https:\u002F\u002Fm2.mtmt.hu\u002Fapi\u002Fpublication\u002F37540958","研究基于 284 个城市 2013—2023 年面板数据,以国家农业绿色发展试点区(NAGDPZ)为准自然实验,采用双重差分法(DID)系统评估政策对农业碳排放的影响。研究发现:(1)NAGDPZ 政策显著降低农业碳排放,系数为 -0.008(10% 显著);(2)机制分析表明减排效应主要通过农业生产效率提升、数字普惠金融发展和农村创新创业推动;(3)异质性分析显示南方、非粮食主产区、农业金融支持水平较高地区的碳减排效应更显著,系数分别为 -0.008、-.016、-0.015。","《中国环境科学》46(1):555-567 2026",{"impact":18,"substance":18,"depth":17,"authority":190,"freshness":119,"relevant":22,"comment":191},14,"基于284城市十年面板数据的政策评估，方法规范、结论有新意，但发表时间较早、时效性弱，适合作为研究型精选而非快讯。",[193],{"name":188,"url":186},[195,196,197,29,198],"农业绿色发展","数字普惠金融","双重差分","绿色试点区",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":202},{"candidates":203,"agricultural_evidence":47},[204,208,212],{"code":33,"score":46,"supported":47,"title":205,"abstract":206,"tags":207},[49,50],[49,50],[29,195],{"code":136,"score":22,"supported":14,"title":209,"abstract":210,"tags":211},[],[],[196],{"code":213,"score":22,"supported":14,"title":214,"abstract":215,"tags":216},"H8",[],[],[195],[218,219],"国家农业绿色发展试点区 农业碳排放","284城市 面板数据 碳减排","国家农业绿色发展试点区农业碳排放-5360",{"doi":8,"openalex_id":8,"authors":222,"venue":8,"cited_by_count":174,"oa_url":8,"card":223,"direction":179,"ingested_from":228},[],{"tldr":224,"method":225,"finding":226,"direction":179,"opportunity":227},"基于284城市面板数据，用DID评估国家农业绿色发展试点区对农业碳排放的影响。","284城市2013-2023年面板数据，双重差分法（DID）准自然实验。","试点区政策显著降低农业碳排放，主要通过生产效率、数字普惠金融和农村创新创业实现。","可探究数字普惠金融与绿色试点政策的协同减排机制，及非粮食主产区的差异化政策设计。","agent","2026-10-20T02:47:54.150914Z",{"id":231,"title":232,"url":233,"summary":234,"summary_zh":8,"content":8,"source_name":235,"source_url":8,"published_at":236,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":237,"score_detail":238,"sources":242,"tags":244,"primary_topic":249,"secondary_topics":250,"classification":251,"search_phrases":271,"slug":274,"view_count":22,"doi":8,"paper":275,"created_at":282},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":18,"substance":239,"depth":17,"authority":240,"freshness":76,"relevant":22,"comment":241},23,13,"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。",[243],{"name":235,"url":233},[245,246,247,29,248],"数字乡村","智慧农业","农业数字化","绿色创新",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":252},{"candidates":253,"agricultural_evidence":47},[254,258,262,267],{"code":33,"score":151,"supported":47,"title":255,"abstract":256,"tags":257},[],[49],[29],{"code":94,"score":119,"supported":14,"title":259,"abstract":260,"tags":261},[],[],[247,246],{"code":263,"score":22,"supported":14,"title":264,"abstract":265,"tags":266},"I",[],[],[247],{"code":54,"score":22,"supported":14,"title":268,"abstract":269,"tags":270},[],[],[245],[272,273],"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":276,"venue":8,"cited_by_count":174,"oa_url":8,"card":277,"direction":179,"ingested_from":228},[],{"tldr":278,"method":279,"finding":280,"direction":179,"opportunity":281},"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":284,"title":285,"url":286,"summary":287,"summary_zh":288,"content":8,"source_name":115,"source_url":286,"published_at":289,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":117,"score_detail":290,"sources":295,"tags":297,"primary_topic":302,"secondary_topics":303,"classification":304,"search_phrases":311,"slug":314,"view_count":174,"doi":315,"paper":316,"created_at":339},4870,"Association of energy poverty, import and export diversification with agricultural sector performance in South Asia","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1749155","This paper examines the association between energy poverty, import diversification, export diversification, and agricultural sector performance in South Asia, while controlling for carbon dioxide emissions from agriculture and employment in agriculture. Using panel data for eight South Asian countries—Afghanistan, Bangladesh, Bhutan, India, Maldives, Nepal, Pakistan, and Sri Lanka—covering the period from 2000 to 2023, the study constructs an Energy Poverty Index through Principal Component Analysis based on six energy-related indicators. The Feasible Generalized Least Squares results show that better energy conditions and lower energy poverty are positively associated with agricultural sector performance. In contrast, import diversification and carbon dioxide emissions from agriculture are negatively associated with agricultural sector performance, while export diversification is positively associated with it. Employment in agriculture shows a positive but statistically insignificant association. These findings remain robust when Panel-Corrected Standard Errors and Driscoll–Kraay standard error estimators are applied. Overall, the results suggest that improved energy access, environmentally sustainable agricultural practices, and strategic trade diversification are closely linked to agricultural sector performance in South Asia.","本文考察了南亚地区能源贫困、进口多样化、出口多样化与农业部门绩效之间的关联，同时控制了农业二氧化碳排放和农业就业。研究使用八个南亚国家——阿富汗、孟加拉国、不丹、印度、马尔代夫、尼泊尔、巴基斯坦和斯里兰卡——2000年至2023年的面板数据，基于六项能源相关指标，通过主成分分析构建了能源贫困指数。可行广义最小二乘法（Feasible Generalized Least Squares）结果显示，更好的能源条件和更低的能源贫困与农业部门绩效呈正相关。相比之下，进口多样化和农业二氧化碳排放与农业部门绩效呈负相关，而出口多样化则与其呈正相关。农业就业显示出正相关，但在统计上不显著。在应用面板校正标准误（Panel-Corrected Standard Errors）和Driscoll–Kraay标准误估计量后，这些发现仍然稳健。总体而言，结果表明，改善能源获取、环境可持续的农业实践以及战略性贸易多样化与南亚农业部门绩效密切相关。","2026-10-13T00:00:00Z",{"impact":20,"substance":291,"depth":292,"authority":240,"freshness":293,"relevant":22,"comment":294},21,17,9,"基于南亚八国2000—2023年面板数据的实证研究，方法规范、结论有新意，但属区域学术成果，公共政策影响有限。",[296],{"name":115,"url":286},[29,298,299,300,301],"面板数据","南亚农业","能源贫困","农业贸易",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":305},{"candidates":306,"agricultural_evidence":47},[307],{"code":33,"score":151,"supported":47,"title":308,"abstract":309,"tags":310},[],[49],[29],[312,313],"南亚 能源贫困","南亚 农产品 出口多元化","南亚能源贫困-4870","10.3389\u002Ffsufs.2026.1749155",{"doi":315,"openalex_id":317,"authors":318,"venue":115,"cited_by_count":174,"oa_url":286,"card":334,"direction":179,"ingested_from":181},"W7221619650",[319,322,324,326,328,330,332],{"name":320,"orcid":321},"Abdullah‐Al Mamun","https:\u002F\u002Forcid.org\u002F0000-0002-3251-4271",{"name":323,"orcid":8},"Md. Golam Kibria",{"name":325,"orcid":8},"Farian Tahrim",{"name":327,"orcid":8},"Jeremy Ko",{"name":329,"orcid":8},"Chun Kai Leung",{"name":331,"orcid":8},"Mohammad Ridwan",{"name":333,"orcid":8},"Wai-kit Ming",{"tldr":335,"method":336,"finding":337,"direction":179,"opportunity":338},"研究南亚八国能源贫困、进出口多样化与农业表现的关系。","2000-2023年面板数据，主成分分析构建能源贫困指数，FGLS回归。","能源改善和出口多样化正向关联农业表现，进口多样化和农业碳排放负向关联。","可深入探究能源贫困缓解与农业碳减排的协同机制及南亚区域异质性。","2026-10-14T23:30:06.945702Z",{"id":341,"title":342,"url":343,"summary":344,"summary_zh":345,"content":8,"source_name":346,"source_url":343,"published_at":347,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":348,"score_detail":349,"sources":351,"tags":353,"primary_topic":356,"secondary_topics":357,"classification":358,"search_phrases":375,"slug":378,"view_count":151,"doi":379,"paper":380,"created_at":395},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":17,"substance":18,"depth":17,"authority":240,"freshness":293,"relevant":22,"comment":350},"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。",[352],{"name":346,"url":343},[245,246,354,29,355],"农业韧性","农业产业集聚",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":359},{"candidates":360,"agricultural_evidence":47},[361,366,371],{"code":33,"score":17,"supported":47,"title":362,"abstract":364,"tags":365},[363],"carbon emission",[49,50,363],[29],{"code":54,"score":89,"supported":47,"title":367,"abstract":368,"tags":370},[],[245,369],"digital village",[245],{"code":94,"score":22,"supported":14,"title":372,"abstract":373,"tags":374},[],[],[246],[376,377],"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":379,"openalex_id":381,"authors":382,"venue":346,"cited_by_count":174,"oa_url":343,"card":389,"direction":394,"ingested_from":181},"W7220924322",[383,385,387],{"name":384,"orcid":8},"Jinghui Hu",{"name":386,"orcid":8},"Jun He",{"name":388,"orcid":8},"Junzhong He",{"tldr":390,"method":391,"finding":392,"direction":179,"opportunity":393},"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。","数字乡村与农业信息化","2026-10-09T23:30:32.435467Z"]