[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2028":3,"related-2028":107},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":23,"tags":25,"primary_topic":31,"secondary_topics":37,"classification":38,"search_phrases":82,"slug":85,"view_count":77,"doi":86,"paper":87,"created_at":106},2028,"Carbon trading and agricultural green total factor productivity: evidence from China’s pilot carbon market and the mediating role of mechanization","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1922103","Agriculture is both a major source of greenhouse gas emissions and highly vulnerable to climate-related risks, yet the implications of carbon pricing for agricultural sustainability remain insufficiently understood. This study examines the effect of China’s carbon trading pilot policy (CTPP) on agricultural green total factor productivity (AGTFP) using a difference-in-differences (DID) model and panel data covering 30 provinces from 2005 to 2022. AGTFP is measured using the SBM–GML index, with agricultural carbon emissions treated as an undesirable output. The baseline results show that the CTPP increases AGTFP by 0.1351 units, equivalent to 18.3% of the sample mean. Given the staggered adoption of the policy, the robustness of this finding is further assessed using the heterogeneity-robust Callaway–Sant’Anna estimator. The resulting estimate remains positive and statistically significant, although cohort-specific pre-treatment variation warrants cautious interpretation. The mediation results suggest that agricultural mechanization, measured by the comprehensive mechanization rate of crop plowing, sowing, and harvesting (AMR), may constitute a potential transmission channel. The Sobel test provides marginal evidence of an indirect effect at the 10% significance level. Regional estimates are positive and statistically significant in eastern and western China but statistically insignificant in central China. These findings provide new evidence that carbon pricing directed at industrial emitters may generate spillovers to agricultural productivity and highlight the importance of coordinating carbon-market development with sustainable agricultural policy.","农业既是温室气体排放的主要来源，又高度易受气候相关风险的影响，然而碳定价对农业可持续性的影响仍缺乏充分认识。本研究利用双重差分（DID）模型和2005—2022年中国30个省份的面板数据，考察中国碳交易试点政策（CTPP）对农业绿色全要素生产率（AGTFP）的影响。AGTFP采用SBM–GML指数测算，并将农业碳排放作为非期望产出处理。基准结果显示，CTPP使AGTFP提高0.1351个单位，相当于样本均值的18.3%。鉴于该政策为交错实施，本文进一步使用对异质性稳健的Callaway–Sant’Anna估计量评估上述发现的稳健性。所得估计值仍为正且具有统计显著性，但各队列处理前差异需谨慎解读。中介结果表明，以农作物耕种收综合机械化率（AMR）衡量的农业机械化可能构成潜在传导渠道。Sobel检验在10%显著性水平上提供了间接效应存在的边际证据。分区域估计显示，东部和西部地区效应为正且具有统计显著性，中部地区则不具统计显著性。上述发现提供了新证据，表明针对工业排放主体的碳定价可能对农业生产率产生溢出效应，并凸显了协调碳市场发展与可持续农业政策的重要性。",null,"Frontiers in Sustainable Food Systems","2026-09-09T00:00:00Z","论文",10,true,82,{"impact":17,"substance":17,"depth":18,"authority":19,"freshness":20,"relevant":21,"comment":22},22,17,13,8,1,"基于中国碳交易试点与30省面板数据的DID研究，证实碳定价对农业绿色全要素生产率存在正向溢出效应，方法规范、结论有新意，对农业绿色低碳政策有参考价值。",[24],{"name":10,"url":6},[26,27,28,29,30],"农业机械化","农业绿色发展","农业碳排放","绿色全要素生产率","碳交易",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"H4","carbon","农业碳与绿色低碳","\u002Ftopic\u002Fenvironment\u002Fcarbon","H",[],{"status":39,"locked":40,"version":41,"method":42,"evidence":43},"classified",false,"2026-10-22.2","rules",{"candidates":44,"agricultural_evidence":14},[45,52,60,68,75],{"code":32,"score":46,"supported":14,"title":47,"abstract":48,"tags":51},9,[],[49,50],"碳排放","农业碳",[28,27,30,29],{"code":53,"score":54,"supported":14,"title":55,"abstract":56,"tags":59},"F5",7,[],[57,58],"农业机械","机械化",[26],{"code":61,"score":62,"supported":14,"title":63,"abstract":64,"tags":67},"B7",6,[],[65,66],"温室","greenhouse",[],{"code":69,"score":62,"supported":14,"title":70,"abstract":71,"tags":74},"J1",[],[72,73],"农业政策","agricultural policy",[],{"code":76,"score":77,"supported":14,"title":78,"abstract":79,"tags":81},"H8",4,[],[80],"可持续农业",[27],[83,84],"绿色全要素生产率 农业绿色发展 农业机械化 农业碳排放","绿色全要素生产率 农业绿色发展","绿色全要素生产率农业绿色发展农业机械化农业碳排放-2028","10.3389\u002Ffsufs.2026.1922103",{"doi":86,"openalex_id":88,"authors":89,"venue":10,"cited_by_count":98,"oa_url":6,"card":99,"direction":103,"ingested_from":105},"W7211993243",[90,92,95],{"name":91,"orcid":9},"Jiayi Hou",{"name":93,"orcid":94},"Alessandra Castellini","https:\u002F\u002Forcid.org\u002F0000-0002-1750-4817",{"name":96,"orcid":97},"Fujiang Huang","https:\u002F\u002Forcid.org\u002F0000-0001-8567-6867",0,{"tldr":100,"method":101,"finding":102,"direction":103,"opportunity":104},"评估中国碳交易试点政策对农业绿色全要素生产率的影响及其机械化中介机制。","2005-2022年30省面板数据，SBM-GML测度AGTFP，DID与Cal","碳交易试点使AGTFP提高0.1351单位（约18.3%），机械化是潜在中介，东部和西部显著、中部不","农业绿色发展与碳","可深入探究碳市场对农业的溢出机制，并检验机械化之外的其他中介路径及中部失效原因。","openalex","2026-09-10T23:30:07.179345Z",{"total":62,"page":21,"page_size":62,"items":108},[109,159,277,330,386,442],{"id":110,"title":111,"url":112,"summary":113,"summary_zh":9,"content":9,"source_name":114,"source_url":9,"published_at":9,"category":12,"cover_url":9,"hotness":13,"is_selected":40,"score":115,"score_detail":116,"sources":121,"tags":123,"primary_topic":127,"secondary_topics":128,"classification":129,"search_phrases":146,"slug":149,"view_count":98,"doi":9,"paper":150,"created_at":158},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",78,{"impact":17,"substance":17,"depth":117,"authority":118,"freshness":119,"relevant":21,"comment":120},18,14,2,"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。",[122],{"name":114,"url":112},[27,124,125,28,126],"数字普惠金融","双重差分","绿色试点区",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":40,"version":41,"method":42,"evidence":130},{"candidates":131,"agricultural_evidence":14},[132,137,142],{"code":32,"score":133,"supported":14,"title":134,"abstract":135,"tags":136},24,[49,50],[49,50],[28,27],{"code":138,"score":21,"supported":40,"title":139,"abstract":140,"tags":141},"G3",[],[],[124],{"code":76,"score":21,"supported":40,"title":143,"abstract":144,"tags":145},[],[],[27],[147,148],"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":9,"openalex_id":9,"authors":151,"venue":9,"cited_by_count":98,"oa_url":9,"card":152,"direction":103,"ingested_from":157},[],{"tldr":153,"method":154,"finding":155,"direction":103,"opportunity":156},"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":160,"title":161,"url":162,"summary":163,"summary_zh":164,"content":9,"source_name":10,"source_url":162,"published_at":165,"category":12,"cover_url":9,"hotness":13,"is_selected":40,"score":166,"score_detail":167,"sources":172,"tags":174,"primary_topic":179,"secondary_topics":184,"classification":191,"search_phrases":250,"slug":253,"view_count":98,"doi":254,"paper":255,"created_at":276},5472,"Turning dirt into gold: the business case of soil carbon for agrarian pathways in arid climate","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1966265","Introduction Agricultural carbon sequestration offers considerable potential for mitigating anthropogenic climate change, particularly in the low-organic-carbon soils of the Middle East. This study presents a foundational scenario-based assessment of soil carbon sequestration and economic feasibility. Materials and methods Crop production and market datasets from Jordanian statistics (1998–2023) were used to estimate potential soil carbon sequestration and economic performance. Using benchmarks from literature in comparable agroecological regions, four soil carbon management practices were evaluated across multiple crops for their carbon storage potential using Monte Carlo uncertainty and sensitivity analysis. Sensitivity of economic performance was assessed under optimistic\u002Fpessimistic scenarios. Results Among the evaluated practices, the highest carbon sequestration rate was achieved through increased Soil Organic Carbon (SOC), reaching a maximum of 27.44 metric tons of CO₂ per hectare per year (tCO₂\u002Fha\u002Fyear) under the high scenario for cucumbers. In contrast, barley showed the highest sequestration rate at 0.45 tCO₂\u002Fha\u002Fyear under increased SOC in the high scenario. Sensitivity analysis showed that ±20% uncertainty in the management coefficients resulted in practice-specific changes of 1.11 tCO₂\u002Fha\u002Fyear for increased SOC and 0.46 tCO₂\u002Fha\u002Fyear for improved cropland management. Horticultural crops, particularly cucumbers and lemons, exhibited the highest sequestration rates and strongest responses to management interventions. Field crops such as wheat, barley, and olives demonstrated lower, more stable sequestration rates. Economic analysis revealed that cucumbers, grapes, potatoes, and Open-field farming (OFF) tomatoes achieved the highest tri-pillar performance (net and adjusted profits and break-even cost) alongside effective carbon sequestration. These findings highlight that crop choice is a primary determinant of both crop profitability and sequestrated carbon potential and feasibility, with significant implications for climate-smart agriculture, carbon credit mechanisms, and intensification strategies in data-scarce regions. Study limitations While empirical field validation remains a future priority, this model-based study provides a screening-level theoretical baseline to guide targeted research and investment in arid agricultural systems.","引言 农业碳封存为缓解人为气候变化提供了可观潜力，尤其是在中东低有机碳土壤中。本研究提出了一项基于情景的土壤碳封存与经济学可行性基础评估。材料与方法 利用约旦统计数据（1998—2023年）中的作物生产和市场数据集，估算潜在的土壤碳封存及经济表现。参考可比农业生态区域文献中的基准值，采用蒙特卡洛不确定性分析和敏感性分析，评估了四种土壤碳管理措施在多种作物上的碳储存潜力。在乐观\u002F悲观情景下评估了经济表现的敏感性。结果 在评估的各项措施中，通过增加土壤有机碳（SOC）实现的碳封存速率最高，在黄瓜的高情景下达到最高27.44公吨二氧化碳\u002F公顷\u002F年（tCO₂\u002Fha\u002Fyear）。相比之下，大麦在增加SOC的高情景下表现出最高的封存速率，为0.45 tCO₂\u002Fha\u002Fyear。敏感性分析表明，管理系数±20%的不确定性导致增加SOC和改善农田管理分别产生1.11 tCO₂\u002Fha\u002Fyear和0.46 tCO₂\u002Fha\u002Fyear的措施特异性变化。园艺作物，尤其是黄瓜和柠檬，表现出最高的封存速率和对管理干预的最强响应。小麦、大麦和橄榄等大田作物表现出较低但更稳定的封存速率。经济分析显示，黄瓜、葡萄、马铃薯和露地栽培（OFF）番茄在有效碳封存的同时实现了最高的三支柱表现（净利润、调整后利润和盈亏平衡成本）。这些发现突出表明，作物选择是作物盈利能力和封存碳潜力及可行性的主要决定因素，对气候智慧型农业、碳信用机制和数据稀缺地区的集约化策略具有重要意义。研究局限性 尽管田间实证验证仍是未来的优先事项，但这项基于模型的研究提供了筛选层面的理论基线，以指导干旱农业系统中的针对性研究和投资。","2026-10-20T00:00:00Z",70,{"impact":168,"substance":169,"depth":170,"authority":19,"freshness":46,"relevant":21,"comment":171},12,20,16,"基于约旦1998–2023年统计数据的模型情景研究，量化了不同作物固碳速率与经济收益，对干旱区农业碳汇与碳信用机制有参考价值，但属区域性模拟研究，尚未经田间验证。",[173],{"name":10,"url":162},[175,30,176,177,178],"气候智慧农业","干旱农业","土壤碳汇","作物选择",{"code":180,"slug":181,"name":182,"path":183,"parent_code":36},"H2","soil","土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil",[185,190],{"code":186,"slug":187,"name":188,"path":189,"parent_code":36},"H7","climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":40,"version":41,"method":42,"evidence":192},{"candidates":193,"agricultural_evidence":14},[194,199,206,211,218,225,232,238,244],{"code":180,"score":118,"supported":14,"title":195,"abstract":196,"tags":198},[181],[197,181],"土壤",[],{"code":186,"score":13,"supported":14,"title":200,"abstract":201,"tags":205},[],[202,203,204],"气候变化","干旱","climate change",[175],{"code":32,"score":20,"supported":14,"title":207,"abstract":208,"tags":210},[],[50,209],"carbon sequestration",[177,30],{"code":212,"score":62,"supported":14,"title":213,"abstract":214,"tags":217},"A2",[],[215,216],"小麦","wheat",[],{"code":219,"score":62,"supported":14,"title":220,"abstract":221,"tags":224},"B1",[],[222,223],"番茄","黄瓜",[],{"code":226,"score":227,"supported":14,"title":228,"abstract":229,"tags":231},"A5",3,[],[230],"马铃薯",[],{"code":233,"score":227,"supported":14,"title":234,"abstract":235,"tags":237},"B2",[],[236],"葡萄",[],{"code":239,"score":227,"supported":14,"title":240,"abstract":241,"tags":243},"H6",[],[242],"农业生态",[],{"code":245,"score":227,"supported":14,"title":246,"abstract":247,"tags":249},"I1",[],[248],"smart agriculture",[],[251,252],"约旦 土壤碳汇 黄瓜","土壤固碳 经济可行性","约旦土壤碳汇黄瓜-5472","10.3389\u002Ffsufs.2026.1966265",{"doi":254,"openalex_id":256,"authors":257,"venue":10,"cited_by_count":98,"oa_url":162,"card":270,"direction":275,"ingested_from":105},"W7223064172",[258,260,262,265,267],{"name":259,"orcid":9},"Tasnim Althalaj",{"name":261,"orcid":9},"Ross. M. Thompson",{"name":263,"orcid":264},"Tala H. Qtaishat","https:\u002F\u002Forcid.org\u002F0000-0002-2700-6686",{"name":266,"orcid":9},"Dan Bumblauskas",{"name":268,"orcid":269},"Fayha Al-Shibli","https:\u002F\u002Forcid.org\u002F0000-0002-5628-0975",{"tldr":271,"method":272,"finding":273,"direction":103,"opportunity":274},"基于约旦1998–2023年数据，评估干旱区土壤固碳潜力与经济可行性。","用蒙特卡洛不确定性与敏感性分析，评估四种土壤碳管理措施在多作物下的表现。","黄瓜等园艺作物固碳率最高，作物选择是固碳与盈利的关键决定因素。","数据稀缺干旱区可结合田间验证与碳信用机制，研究作物级固碳经济激励设计。","智慧农业 \u002F 农业物联网","2026-10-21T23:30:07.383006Z",{"id":278,"title":279,"url":280,"summary":281,"summary_zh":9,"content":9,"source_name":282,"source_url":9,"published_at":283,"category":12,"cover_url":9,"hotness":13,"is_selected":40,"score":284,"score_detail":285,"sources":288,"tags":290,"primary_topic":295,"secondary_topics":296,"classification":297,"search_phrases":318,"slug":321,"view_count":21,"doi":9,"paper":322,"created_at":329},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":286,"depth":117,"authority":19,"freshness":20,"relevant":21,"comment":287},23,"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。",[289],{"name":282,"url":280},[291,292,293,28,294],"数字乡村","智慧农业","农业数字化","绿色创新",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":40,"version":41,"method":42,"evidence":298},{"candidates":299,"agricultural_evidence":14},[300,304,308,313],{"code":32,"score":77,"supported":14,"title":301,"abstract":302,"tags":303},[],[49],[28],{"code":245,"score":119,"supported":40,"title":305,"abstract":306,"tags":307},[],[],[293,292],{"code":309,"score":21,"supported":40,"title":310,"abstract":311,"tags":312},"I",[],[],[293],{"code":314,"score":21,"supported":40,"title":315,"abstract":316,"tags":317},"I6",[],[],[291],[319,320],"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":9,"openalex_id":9,"authors":323,"venue":9,"cited_by_count":98,"oa_url":9,"card":324,"direction":103,"ingested_from":157},[],{"tldr":325,"method":326,"finding":327,"direction":103,"opportunity":328},"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":331,"title":332,"url":333,"summary":334,"summary_zh":335,"content":9,"source_name":10,"source_url":333,"published_at":336,"category":12,"cover_url":9,"hotness":13,"is_selected":40,"score":337,"score_detail":338,"sources":341,"tags":343,"primary_topic":348,"secondary_topics":349,"classification":350,"search_phrases":357,"slug":360,"view_count":98,"doi":361,"paper":362,"created_at":385},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",72,{"impact":168,"substance":339,"depth":18,"authority":19,"freshness":46,"relevant":21,"comment":340},21,"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。",[342],{"name":10,"url":333},[28,344,345,346,347],"面板数据","南亚农业","能源贫困","农业贸易",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":40,"version":41,"method":42,"evidence":351},{"candidates":352,"agricultural_evidence":14},[353],{"code":32,"score":77,"supported":14,"title":354,"abstract":355,"tags":356},[],[49],[28],[358,359],"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":361,"openalex_id":363,"authors":364,"venue":10,"cited_by_count":98,"oa_url":333,"card":380,"direction":103,"ingested_from":105},"W7221619650",[365,368,370,372,374,376,378],{"name":366,"orcid":367},"Abdullah‐Al Mamun","https:\u002F\u002Forcid.org\u002F0000-0002-3251-4271",{"name":369,"orcid":9},"Md. Golam Kibria",{"name":371,"orcid":9},"Farian Tahrim",{"name":373,"orcid":9},"Jeremy Ko",{"name":375,"orcid":9},"Chun Kai Leung",{"name":377,"orcid":9},"Mohammad Ridwan",{"name":379,"orcid":9},"Wai-kit Ming",{"tldr":381,"method":382,"finding":383,"direction":103,"opportunity":384},"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":387,"title":388,"url":389,"summary":390,"summary_zh":391,"content":9,"source_name":392,"source_url":389,"published_at":393,"category":12,"cover_url":9,"hotness":13,"is_selected":40,"score":394,"score_detail":395,"sources":397,"tags":399,"primary_topic":402,"secondary_topics":403,"classification":404,"search_phrases":421,"slug":424,"view_count":77,"doi":425,"paper":426,"created_at":441},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":117,"substance":17,"depth":117,"authority":19,"freshness":46,"relevant":21,"comment":396},"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。",[398],{"name":392,"url":389},[291,292,400,28,401],"农业韧性","农业产业集聚",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":40,"version":41,"method":42,"evidence":405},{"candidates":406,"agricultural_evidence":14},[407,412,417],{"code":32,"score":117,"supported":14,"title":408,"abstract":410,"tags":411},[409],"carbon emission",[49,50,409],[28],{"code":314,"score":54,"supported":14,"title":413,"abstract":414,"tags":416},[],[291,415],"digital village",[291],{"code":245,"score":21,"supported":40,"title":418,"abstract":419,"tags":420},[],[],[292],[422,423],"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":425,"openalex_id":427,"authors":428,"venue":392,"cited_by_count":98,"oa_url":389,"card":435,"direction":440,"ingested_from":105},"W7220924322",[429,431,433],{"name":430,"orcid":9},"Jinghui Hu",{"name":432,"orcid":9},"Jun He",{"name":434,"orcid":9},"Junzhong He",{"tldr":436,"method":437,"finding":438,"direction":103,"opportunity":439},"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",{"id":443,"title":444,"url":445,"summary":446,"summary_zh":447,"content":9,"source_name":10,"source_url":445,"published_at":448,"category":12,"cover_url":9,"hotness":449,"is_selected":40,"score":394,"score_detail":450,"sources":452,"tags":457,"primary_topic":460,"secondary_topics":465,"classification":467,"search_phrases":495,"slug":498,"view_count":98,"doi":499,"paper":500,"created_at":517},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。","2026-10-07T00:00:00Z",25,{"impact":117,"substance":17,"depth":117,"authority":19,"freshness":46,"relevant":21,"comment":451},"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。",[453,454],{"name":10,"url":445},{"name":455,"url":456},"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",[291,292,458,28,459],"玉米","区块链",{"code":461,"slug":462,"name":458,"path":463,"parent_code":464},"A3","maize","\u002Ftopic\u002Ffield-crops\u002Fmaize","A",[466],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":40,"version":41,"method":42,"evidence":468},{"candidates":469,"agricultural_evidence":14},[470,475,479,485,491],{"code":461,"score":471,"supported":14,"title":472,"abstract":473,"tags":474},15,[462],[458,462],[458],{"code":32,"score":168,"supported":14,"title":476,"abstract":477,"tags":478},[409],[49],[28],{"code":245,"score":54,"supported":14,"title":480,"abstract":481,"tags":484},[],[482,483],"数字农业","digital agriculture",[292],{"code":486,"score":227,"supported":14,"title":487,"abstract":488,"tags":490},"G2",[],[489],"社会化服务",[],{"code":314,"score":21,"supported":40,"title":492,"abstract":493,"tags":494},[],[],[291],[496,497],"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":499,"openalex_id":501,"authors":502,"venue":10,"cited_by_count":98,"oa_url":511,"card":512,"direction":275,"ingested_from":105},"W7220869193",[503,506,508],{"name":504,"orcid":505},"Pengfei Zhou","https:\u002F\u002Forcid.org\u002F0000-0003-2117-7967",{"name":507,"orcid":9},"Zhiyi Hu",{"name":509,"orcid":510},"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":513,"method":514,"finding":515,"direction":103,"opportunity":516},"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。","2026-10-08T23:30:07.668939Z"]