## 3 Results
### Contributions of Carbon Sequestration on County Scale
#### 3.2.2 Driver Contributions  
Based on the GWR-RF model coefficients, we estimated the relative contribution of six major categories (Figure 7a-f) and 14 subdivisions of driving factors (Figure S3) to CS changes from 2001 to 2020 for each county. Among them, the driving factors whose absolute value of driving contribution exceeds 50% and whose number exceeds 20 counties include AAF, GAR, T2m, SSRD, CO\({}_{2}\), CSIF, and SoilN (Figure S3). The differences in CLIMATE's driving contributions to CS changes are significant, but they are mainly concentrated in coastal counties (Figure 7b). The positive effects of T2m and SSRD in the southern coastal counties of the YRUDA offset the negative effects of TP to a certain extent (Figure S3f-h); in the central region of the study area, the negative effects of T2m offset the positive effects of SSRD (Figure S3g,h). CO\({}_{2}\) has a positive impact on Anhui and northern Jiangsu and some coastal areas of Zhejiang. Among them, 21 counties have a positive contribution of more than 50% (Figure 7c). The CO\({}_{2}\) fertilization effect is suppressed within urban areas where high concentrations of CO\({}_{2}\) are found. In some regions, negative values were observed, likely due to the combined effects of other factors or data inaccuracies, which reflected the saturation effect of CO\({}_{2}\) fertilization. The decrease in WAN (Figure 5k) resulted in a negative effect on CS changes in the counties in the western YRDUA, while the simultaneous decrease in WAN and DAN (Figure 5i,k) resulted in a positive driving contribution to CS changes in the counties in the southern YRDUA (Figure 7d); this shows that the impact of changes in nitrogen deposition on CS has reached a relatively sensitive level. CSIF had a positive effect on 154 counties but still inhibited some municipal districts (Figure 7e), which is related to the decrease in photosynthesis intensity in these areas (Figure 5l). \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Evaluation Indicator** & **OLS** & **GWR** & **RF** & **GWR-RF** \\ \hline RSS & 66.34 & 32.31 & 18.01 & 14.77 \\ AIC & 388.68 & 286.24 & 206.44 & 162.64 \\ AICc & 393.81 & 331.16 & 263.16 & 210.11 \\ \(R^{2}\) & 0.69 & 0.84 & 0.90 & 0.93 \\ _Adj._\(R^{2}\) & 0.66 & 0.81 & 0.87 & 0.91 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Model accuracy of CS attribution in YRDUA. Figure 6: Scatterplot of GWR-RF model fit and importance ranking of drivers for changes in CS from 2001 to 2020 in the YRDUA: (**a**) scatterplot, with city districts and urban areas outside city districts marked in different colors, and the blue shadow represents the confidence interval of the linear fitting; (**b**) importance ranking of drivers, where the significance levels are as follows: * denotes \(p<0.05\) and ** denotes \(p<0.01\), respectively. The driving factor with the largest absolute contribution rate was identified as the dominant factor in CS changes. CSIF, AAF, WAN, SoilN, SSRD, and GAR, respectively, dominated the CS increase in 33, 14, 9, 7, 7, and 6 counties (Figure 7g); at the same time, CSIF, WAN, AAF, and T2m dominated the CS decrease in 47, 22, 14, and 8 counties (Figure 7h). Finally, through partitioning the absolute values of driver contribution by category, it can be revealed that CSIF and HUMAN emerged as the predominant determinants of CS changes in 74 and 70 counties, respectively. These factors accounted for the majority of cases, followed by N deposition (30 districts), CLIMATE (14 districts), SoilNP (7 districts), and CO\({}_{2}\) (2 districts) (Figure 7i). However, the number of counties where anthropogenic activities (AAF) led to an increase in CS was lower than the number of counties where AAF led to a decrease in CS. As shown in Figure 5c and Figure 53c, the continuous increase in urban afforestation does not necessarily lead to increased CS by vegetation, though afforestation certainly plays a positive role.