## 3 Results  
### Spatial-Temporal Distribution of Carbon Sequestration and Potential Drivers  
#### 3.1.1 Attributes of Carbon Sequestration  
The data revealed that the total CS at the county level within the YRDUA experienced growth from 487.68 \(\times\) 10\({}^{7}\) t in 2001 to 519.67 \(\times\) 10\({}^{7}\) t in 2020, marking an increase of over 6.55%. The peak value during this period was 538.89 \(\times\) 10\({}^{7}\) t in 2014. Within this period, CS increased in 148 counties, representing 70.14% of the total, and decreased in 63 counties. Structurally, in 2020, the total CS of counties within and outside municipal districts was 147.76 \(\times\) 10\({}^{7}\) t and 371.91 \(\times\) 10\({}^{7}\) t, respectively, constituting 28.43% and 71.57% of the total. The data (refer to Figure 3) indicated that in 2001, the average CS in municipal districts was 1.28 \(\times\) 10\({}^{7}\) t, which was below the overall county average of 2.31 \(\times\) 10\({}^{7}\) t, whereas counties outside municipal districts averaged 3.44 \(\times\) 10\({}^{7}\) t. By 2020, CS had escalated in both categories by 4.69% and 6.98%, respectively. From 2001 to 2020, 36 municipal districts exhibited a decline in CS. Predominantly, the municipal districts, characterized by dense transportation networks and building clusters, presented a lower CS backdrop, highlighting the ecological space's encroachment. The fluctuating CS patterns in these districts were complex, reflecting the nonlinear and intricate influences of urbanization on CS. A similar, albeit less intense, a pattern was observed in counties outside the municipal districts. The one-way ANOVA test affirmed a very significant difference (\(p\) < 0.001) in the CS trends between municipal districts and outer counties for the years 2001, 2020, and the entire period from 2001 to 2020 (Figure 3). This underscores the likely variance in the driving mechanisms of CS alterations between the two regions.  
Figure 2: Conceptual SEM model of CS changes in the YRDUA. The black arrows indicate the direction of causality, with the cause at the point where the arrow departs and the effect at the point where the arrow points. Different colors represent different types of influences, mentioned in Table 1.  
From 2001 to 2020, spatial distribution analysis revealed that the highest mean CS values, in the ranges of ((5.54, 9.59) \(\times\) 10\({}^{7}\) t) and ((3.37, 5.54) \(\times\) 10\({}^{7}\) t), were predominantly located in the southern and western regions of the YRDUA. These areas, characterized by dense forest cover, consistently exhibited elevated CS values (Figure 4a). Outside of the YRDUA's southern mountainous zones, regions with high baseline CS values demonstrated a general upward trend. In contrast, municipal districts, which had lower baseline CS values, displayed variable CS dynamics (Figure 4b), often experiencing simultaneous increases and decreases in CS. In contrast, areas outside the districts showed more pronounced rising trends.  
A spatial analysis of hot and cold spots using Gi* statistics revealed distinct clustering patterns in CS changes, aligning with [PERSON]'s slope trend analysis (Figure 4b,c). Hot spots, with a 99% confidence level, included 14 counties across the coastal regions of Jiangsu Province, western and northern Anhui Province, and areas adjoining Anhui, Zhejiang, and Jiangsu. Cold spots, identified with over 95% confidence, encompassed 20 counties, primarily in coastal zones.  
Figure 4: CS spatial distribution characteristics in YRDUA from 2001 to 2020: (**a**) mean CS; (**b**) [PERSON]’s slope of CS; (**c**) hot spot analysis (Getis-Ord Gi*).  
Figure 3: Box plots of CS in all counties, municipal districts, and cities outside the districts in the YRDUA. The values for the years 2001 and 2020 represent the mean CS for each district, while the values for the period 2001–2020 represent the change in CS for each district.