## 2 Materials and Methods
### Data Sources
#### 2.2.2 Carbon Sequestration Driving Factors  
A total of 14 potential driving factors were selected to explore the driving mechanisms behind CS changes in urbanized areas. These encompass a range of factors including anthropogenic activities, climatic change, atmospheric CO\({}_{2}\) concentration, nitrogen deposition, soil nutrient cycles, and vegetation photosynthesis. The use of a multi-faceted data approach aims to elucidate the complex interplay between healthy vegetation growth and the myriad factors influencing CS. These 14 indicators in six categories are described below, and the data sources used in this study are detailed in Table 1.  
Anthropogenic activity: Five indicators were selected to represent anthropogenic activities: nighttime lights (NTLs), population density (POP), annual area of afforestation (AAF), green area ratio (GAR), and wetland coverage ratio (WCR). NTLs, as an effective proxy for detecting human economic activity, may have a particular influence on CS through affecting vegetation phenology and ecophysiology, which remains inadequately quantified [43]. The NTL data produced by [PERSON] et al. (2021) [44] were refined through a cross-sensor nighttime light data correction scheme, which captures urban illumination more accurately in the long term. POP data were also employed to explore the impact of urbanization on the CS capacity of vegetation. The AAF was chosen to represent human activities in afforestation and reforestation, aiming to reflect the long-term ecological benefits of afforestation [45]. The GAR and WCR were calculated from the GlobeLand30 dataset [46]. The GAR is defined as the ratio of arable land, forest land, grassland, shrubs, and wetlands in the total area, which was chosen to assess the impact of urban land use pattern transformations on the quantity of carbon sequestered by vegetation. Due to the wide distribution of wetlands in the YRDUA, the WCR was selected to investigate the relationship between changes in wetland patterns and factors influencing vegetation CS during urbanization, as highlighted in [47]. Considering the high level of regional urbanization, changes in both GAR and WCR are strongly influenced by anthropogenic factors. Consequently, this study regards \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  
\end{table} 
Table 1: Carbon sequestration and its driving data in YRDUA. the two indicators as a concentrated manifestation of human impacts on spatial patterns and classifies them into the category of Anthropogenic Activity. Climate change: The climate change data were derived from the ERA5-Land dataset [48]. This comprehensive dataset merges model data with global observations. Three indicators were selected: 2 m surface temperature (T2m), surface solar radiation downwards (SSRD), and total precipitation (TP). Atmospheric CO\({}_{2}\) concentration: CO\({}_{2}\) fertilization and nitrogen deposition are two significant factors contributing to the increase in vegetation CS. However, the specific impact of CO\({}_{2}\) fertilization on the CS of vegetation in urban areas with high CO\({}_{2}\) concentrations, where the effect is possibly nearing saturation [49], remains unclear. Global spatial CO\({}_{2}\) concentration data from CarbonTracker were used [50] in this study. Monthly CO\({}_{2}\) concentration values were calculated by summing fossil fuel emissions, land biosphere (excluding fire), wildfire emissions, and air-sea exchange for each month. Annual averages were then derived from these monthly values. Nitrogen deposition: Similar to atmospheric CO\({}_{2}\) concentration, the effects of nitrogen deposition on urban vegetation CS are also unclear [51; 52; 53; 54], especially the effects from different types of nitrogen deposition in rapidly urbanizing areas [55; 56]. The spatial and temporal pattern datasets of dry deposition of atmospheric inorganic nitrogen (DAN) [57] and wet deposition of atmospheric inorganic nitrogen (WAN) [58] were selected for analysis.