[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2662":3},{"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,"view_count":31,"doi":32,"paper":33,"created_at":52},2662,"Annual Gridded Anthropogenic CH4 Emissions Estimation in China (2019–2025) Integrating Multisource Data: SHAP-Based Driver Attribution and Spatio-Temporal Patterns","https:\u002F\u002Fdoi.org\u002F10.3390\u002Frs18183168","Accurately quantifying the spatiotemporal dynamics and driving mechanisms of anthropogenic methane (CH4) emissions (MEs) is of great significance for achieving regional “dual-carbon” goals and global climate collaborative governance. However, existing ME inventories and macro-inversion models generally face bottlenecks such as coarse spatial resolution, lack of data update timeliness, and the inability of traditional static emission factors to capture non-linear responses. To address these issues, this study proposes an annual ME inventory enhancement framework integrating multi-source geographic and remote sensing data. This framework evaluates four advanced machine learning (ML) algorithms, including Random Forest (RF), Categorical Boosting (CB), Extreme Gradient Boosting (XGB), and Light Gradient Boosting Machine (LGBM), to construct a 0.1° high-resolution spatial grid of anthropogenic ME in China from 2019 to 2025. Furthermore, it introduces the SHapley Additive exPlanations (SHAP) framework and multi-scale spatial autocorrelation analysis to parse the driving mechanisms and clustering patterns. The results show the following: (1) LGBM exhibits the optimal comprehensive estimation accuracy (R2 = 0.938, RMSE = 3.707 Kt) and robust capability in capturing extreme ME sources (RTop2 = 0.929). (2) SHAP attribution reveals that coal mining and nighttime light (NTL) represent the primary contributing features to ME predictions (with a cumulative contribution of 65.60%), followed by agricultural and pastoral activities (24.98%), and all factors exhibit significant non-linear threshold and step-response characteristics. (3) Regarding spatiotemporal evolution, China’s total anthropogenic ME shows a trend of initial slow increase followed by high-level stabilization; spatially, it presents a “hot in the north, cold in the south” pattern, with extreme high values highly clustered in the Shanxi–Shaanxi–Inner Mongolia energy triangle and its peripheral expansion nodes. This study provides scientific references for formulating tailored, multi-scale, and refined CH4 mitigation strategies.","准确量化人为甲烷（CH₄）排放（MEs）的时空动态与驱动机制，对实现区域“双碳”目标及全球气候协同治理具有重要意义。然而，现有ME清单与宏观反演模型普遍面临空间分辨率粗、数据更新时效性不足、传统静态排放因子难以刻画非线性响应等瓶颈。针对上述问题，本研究提出了一种融合多源地理与遥感数据的年度ME清单增强框架。该框架评估了四种先进机器学习（ML）算法，包括随机森林（RF）、类别提升（CB）、极端梯度提升（XGB）和轻量梯度提升机（LGBM），构建了2019—2025年中国0.1°高分辨率人为ME空间网格。此外，引入SHapley加性解释（SHAP）框架与多尺度空间自相关分析，解析驱动机制与集聚模式。结果表明：（1）LGBM具有最优的综合估算精度（R² = 0.938，RMSE = 3.707 Kt），且在捕捉极端ME源方面表现稳健（RTop2 = 0.929）。（2）SHAP归因揭示，煤炭开采与夜间灯光（NTL）是ME预测的主要贡献特征（累计贡献率65.60%），其次为农牧活动（24.98%），且所有因子均呈现显著的非线性阈值与阶跃响应特征。（3）在时空演变方面，中国人为ME总量呈先缓慢增长后高位稳定的趋势；空间上表现为“北热南冷”格局，极端高值高度集聚于晋陕蒙能源金三角及其外围扩展节点。本研究为制定因地制宜、多尺度、精细化CH₄减排策略提供了科学参考。",null,"Remote Sensing","2026-09-15T00:00:00Z","论文",10,false,81,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},18,22,14,9,1,"基于多源遥感与机器学习构建中国0.1°人为甲烷排放网格清单，方法新颖、数据规模大，对农业源排放核算与双碳治理有参考价值，但偏学术、非直接农业应用。",[24],{"name":10,"url":6},[26,27,28,29,30],"农业信息化","机器学习","遥感","甲烷排放","双碳目标",0,"10.3390\u002Frs18183168",{"doi":32,"openalex_id":34,"authors":35,"venue":10,"cited_by_count":31,"oa_url":6,"card":44,"direction":50,"ingested_from":51},"W7213229152",[36,39,42],{"name":37,"orcid":38},"Chaokang He","https:\u002F\u002Forcid.org\u002F0009-0005-8014-434X",{"name":40,"orcid":41},"Qinjun Wang","https:\u002F\u002Forcid.org\u002F0000-0001-6084-1889",{"name":43,"orcid":9},"Wenyue Xie",{"tldr":45,"method":46,"finding":47,"direction":48,"opportunity":49},"融合多源数据与机器学习构建中国2019-2025年0.1°人为甲烷排放网格，并用SHAP解析驱动机制","随机森林、CatBoost、XGBoost、LightGBM对比，结合SHAP归","LGBM精度最优（R²=0.938），煤炭开采与夜间灯光贡献65.6%，排放呈北热南冷格局。","农业绿色发展与碳","可延伸至农业源甲烷排放的精细网格化与非线性驱动阈值研究，支撑分区减排。","农业遥感与作物表型","openalex","2026-09-16T23:30:28.864720Z"]