[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"topic-LMDI":3},{"name":4,"kind":5,"tokens":6,"total":7,"page":7,"page_size":8,"items":9},"LMDI","tag",[4],1,100,[10],{"id":11,"title":12,"url":13,"summary":14,"summary_zh":15,"content":15,"source_name":16,"source_url":15,"published_at":17,"category":18,"cover_url":15,"hotness":19,"is_selected":20,"score":21,"score_detail":22,"sources":29,"tags":31,"primary_topic":36,"secondary_topics":42,"classification":43,"search_phrases":75,"slug":78,"view_count":27,"doi":79,"paper":80,"created_at":90},1365,"基于数字化水平评估的贵州省农业碳排放影响因素分解","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fenvironmental-science\u002Farticles\u002F10.3389\u002Ffenvs.2026.1901851\u002Fabstract","基于 2013—2022 年贵州省面板数据，使用 LMDI 与 VAR 模型分解数字化对农业碳排放的驱动效应。结果显示：数字化对碳排放由强促增转为弱促增；能源强度与数字化引致的能源效率合计减排 3168 万吨；预测 2023—2030 年可净减排 925 万吨；数字化并非自动等同于减排，需以精准投入管理、畜禽粪污处理和清洁能源为支撑。",null,"Frontiers in Environmental Science · 2026-09","2026-08-31T16:00:00Z","论文",10,false,67,{"impact":23,"substance":24,"depth":25,"authority":26,"freshness":27,"relevant":7,"comment":28},15,20,18,12,2,"基于省级面板数据的实证研究，揭示数字化对农业碳排放的阶段性影响，为精准减排提供依据。",[30],{"name":16,"url":13},[32,33,34,4,35],"数字农业","农业碳排放","贵州","能源效率",{"code":37,"slug":38,"name":39,"path":40,"parent_code":41},"H4","carbon","农业碳与绿色低碳","\u002Ftopic\u002Fenvironment\u002Fcarbon","H",[],{"status":44,"locked":20,"version":45,"method":46,"evidence":47},"classified","2026-10-22.2","rules",{"candidates":48,"agricultural_evidence":51},[49,57,64,70],{"code":37,"score":50,"supported":51,"title":52,"abstract":55,"tags":56},23,true,[53,54],"碳排放","农业碳",[53,54],[33],{"code":58,"score":59,"supported":51,"title":60,"abstract":61,"tags":63},"C",3,[],[62],"畜禽",[],{"code":65,"score":59,"supported":51,"title":66,"abstract":67,"tags":69},"C6",[],[68],"畜禽粪污",[],{"code":71,"score":7,"supported":20,"title":72,"abstract":73,"tags":74},"I1",[],[],[32],[76,77],"农业碳排放 数字农业 能源效率 贵州","农业碳排放 数字农业","农业碳排放数字农业能源效率贵州-1365","10.3389\u002Ffenvs.2026.1901851\u002Fabstract",{"doi":79,"openalex_id":15,"authors":81,"venue":15,"cited_by_count":82,"oa_url":15,"card":83,"direction":87,"ingested_from":89},[],0,{"tldr":84,"method":85,"finding":86,"direction":87,"opportunity":88},"评估数字化水平对贵州农业碳排放的影响，预测未来减排潜力。","LMDI分解与VAR模型，基于2013-2022年面板数据。","数字化减排效应由强转弱，需精准管理等措施支撑。","农业绿色发展与碳","可研究数字化与农业减排的耦合机制，探索不同数字化水平下的最优减排路径。","agent","2026-09-02T00:05:06.917066Z"]