Mapping Chinese urban carbon emissions with a graph-based multi-branch deep learning framework
文献类型：期刊论文
| 作者 | Shao, Wei1,2; Tu, Yue5,6; Yue, Tianxiang1,2; Zhao, Na1,2; Zhou, Haowei4; Zhang, Liqiang3 |
| 刊名 | GISCIENCE & REMOTE SENSING
 |
| 出版日期 | 2026-12-31 |
| 卷号 | 63期号:1页码:2631837 |
| 关键词 | Carbon dioxide emissions graph neural network deep learning economic-emission coupling influencing factors |
| ISSN号 | 1548-1603 |
| DOI | 10.1080/15481603.2026.2631837 |
| 产权排序 | 1 |
| 文献子类 | Article |
| 英文摘要 | Urban carbon emissions have emerged as a central challenge for sustainable development in China. Existing statistical and machine learning approaches, however, face limitations in capturing spatial heterogeneity and historical dynamics of urban emissions. To overcome these challenges, this study developed Carbon Graph Multi-branch Network (CGMN), a graph-based deep learning framework that integrates spatial relationships, temporal dependencies, and multi-source urban features for urban carbon emissions estimation. Given the scarcity of labeled city-level emissions, a composite loss framework is designed to incorporate both strong and weak supervision, enhancing the model's consistency, generalization, and robustness. We also developed a spatial mismatch index between emissions and Gross Domestic Product (GDP) to investigate the evolving relationship between urban carbon emissions and economic activity. The proposed CGMN achieves robust predictive performance (R & sup2; = 0.79; RMSE = 10.63; MAE = 8.31; WAPE = 27.51), demonstrating its capability to capture the contributions of key driving factors. Feature importance analysis reveals that economic structure is the dominant determinant of urban carbon emissions, accounting for 32% of the total feature contribution. From 2000 to 2021, China's urban carbon emissions increased rapidly until around 2012 before stabilizing, with an average annual growth rate of 5.7%. The spatial mismatch analysis reveals pronounced regional disparities: low-mismatch cities decreased by 53%, mainly in western and central regions, while high-mismatch cities increased by 35%, concentrated in eastern coastal areas. These results provide a scientific basis for understanding the spatiotemporal evolution of China's urban carbon emissions and offer insights for promoting coordinated economic and low-carbon development. |
| URL标识 | 查看原文 |
| WOS关键词 | CO2 EMISSIONS ; CITY-LEVEL ; GAS ; CONSUMPTION ; COMBUSTION ; IMAGERY ; CITIES |
| WOS研究方向 | Physical Geography ; Remote Sensing |
| 语种 | 英语 |
| WOS记录号 | WOS:001691587100001 |
| 出版者 | TAYLOR & FRANCIS LTD |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/220937]  |
| 专题 | 资源与环境信息系统国家重点实验室_外文论文 |
| 通讯作者 | Yue, Tianxiang |
| 作者单位 | 1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing, Peoples R China; 2.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing, Peoples R China; 3.Beijing Normal Univ, Fac Geog Sci, State Key Lab Remote Sensing Sci, Beijing, Peoples R China 4.Univ Southampton, Sch Geog & Environm Sci, Southampton, England; 5.East China Normal Univ, Sch Geog Sci, Shanghai, Peoples R China; 6.East China Normal Univ, Minist Educ, Key Lab Geog Informat Sci, Shanghai, Peoples R China; |
| 推荐引用方式 GB/T 7714 | Shao, Wei,Tu, Yue,Yue, Tianxiang,et al. Mapping Chinese urban carbon emissions with a graph-based multi-branch deep learning framework[J]. GISCIENCE & REMOTE SENSING,2026,63(1):2631837. |
| APA | Shao, Wei,Tu, Yue,Yue, Tianxiang,Zhao, Na,Zhou, Haowei,&Zhang, Liqiang.(2026).Mapping Chinese urban carbon emissions with a graph-based multi-branch deep learning framework.GISCIENCE & REMOTE SENSING,63(1),2631837. |
| MLA | Shao, Wei,et al."Mapping Chinese urban carbon emissions with a graph-based multi-branch deep learning framework".GISCIENCE & REMOTE SENSING 63.1(2026):2631837. |
入库方式： OAI收割
来源：地理科学与资源研究所
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