## 5 Conclusions  
To address the complex drivers of urban vegetation carbon sequestration (CS) changes, this study developed an innovative integrated attribution and influence analysis framework, GWR-RF-SEM. The results demonstrate that this framework effectively quantifies the contributions of various drivers to CS in densely urbanized areas and establishes a robust causal network structure. Compared to existing models, the GWR-RF model exhibits superior accuracy in measuring the contributions of different drivers to vegetation CS changes, successfully identifying and describing the main factors affecting CS across 197 counties. By utilizing SEM to analyze 14 driving indicators categorized into six groups, we elucidated the direct and indirect pathways influencing CS, providing a comprehensive causal structure diagram of urban CS dynamics. These findings significantly enhance our understanding of the mechanisms driving CS in the Yangtze River Delta Urban Agglomeration (YRDUA), China's most developed urban area, from 2001 to 2020. Notably, we observed significant differences in CS drivers between municipal districts and the outside districts, with the SEM module thoroughly characterizing these disparities and underscoring the need for targeted strategies to optimize CS efficiency. Key findings highlight that promoting vegetation photosynthesis, artificial afforestation, and optimizing urban green space structures are pivotal for enhancing urban CS, with photosynthesis emerging as the central determinant. Nitrogen deposition, soil nitrogen and phosphorus, and anthropogenic activity have significantly different effects on urban vegetation CS between municipal districts and the outside districts.  
In summary, the methodological approach developed in this study provides valuable insights into the mechanisms of urban vegetation CS, advancing the field of vegetation CS research, particularly in rapidly urbanizing areas. This advancement not only propels the field of vegetation CS studies forward, particularly in rapidly urbanizing areas, but also provides essential support for the enhancement of natural-based solutions depending on ecosystem services under urbanization and climate change.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/rs17122110/s1](https://www.mdpi.com/article/10.3390/rs17122110/s1)]([https://www.mdpi.com/article/10.3390/rs17122110/s1](https://www.mdpi.com/article/10.3390/rs17122110/s1)), Figure S1: Spatial distribution of the 2020 values of the 14 driver indicators of vegetation carbon sequestration in the YRDUA; Figure S2: GWR-RF modeling of vegetation carbon sequestration in the YRDUA local R2 in each county; Figure S3: Spatial distribution of driving contributions of 14 indicators to changes in vegetation carbon sequestration in the YRDUA, 2001-2020.  
Conceptualization, W.M.; methodology, W.M. and Y.Z.; software, W.M. and D.O.; validation, Y.S.; formal analysis, W.M. and N.W. ([PERSON]); data curation, Y.Z.; writing--original draft preparation, W.M. and Y.Z.; writing--review and editing, Y.C. and N.W. ([PERSON]); visualization, Y.C. and W.M.; supervision, H.L.; project administration, H.L.; funding acquisition, W.M. and H.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Basic Special Business Fund for R&D for Central Level Scientific Research Institutes with grants ZX2023 QT006 and GYZX220308, under the Nanjing Institute of Environmental Sciences, Ministry of Ecology and Environment, and was also supported by the National Key Plan for Research and Development of China (with grant no 2023 YC3804202).  
All the datasets used in this article can be found in Table 1.  
The authors declare no conflicts of interest.