# Decoupling the Impacts of Climate Change and Human Activities on Terrestrial Vegetation Carbon Sink
## 3 Results  
### Spatiotemporal Changes in NEP in CCZ  
The average NEP in CCZ from 2000 to 2020 was 492.61 gC m\({}^{-2}\) yr\({}^{-1}\). NEP across coastal zones from north to south shows an obvious spatial heterogeneity (Figure 2). Combined with the statistical results of multi-year average NEP (Figure 3), Zhejiang Province was identified as the dividing line of this north-south difference. Overall, NEP in CCZ exhibits a pattern of higher values in the area south of Zhejiang Province and lower values in the north. Western Liaoning Province and most regions of the Beijing-Tianjin \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Slope (NEP\({}_{obs}\))**} & \multicolumn{2}{c}{**Classification Criterion**} & \multirow{2}{*}{**Driver Type**} & \multicolumn{2}{c}{**Contribution Rate (\%)**} \\ \cline{2-2} \cline{4-6}  & **Slope (NEP\({}_{CC}\))** & & & **CC** & **HA** \\ \hline \multirow{6}{*}{\(>\)0} & \multirow{2}{*}{\(>\)0} & \multirow{2}{*}{\(>\)0} & double-driven improvement & Slope (\(NEP_{CC}\)) & Slope (\(NEP_{HA}\)) \\ \cline{3-6}  & & & \(>\)0 & \(<\)0 & climate-driven improvement & 100 & 0 \\ \cline{2-6}  & & \(<\)0 & \(>\)0 & \(\begin{array}{c}\text{human-driven}\\ \text{improvement}\end{array}\) & 0 & 100 \\ \cline{2-6}  & & \(<\)0 & \(<\)0 & \(\begin{array}{c}\text{double-driven}\\ \text{degradation}\end{array}\) & \(\begin{array}{c}\text{double-driven}\\ \text{Slope (\(NEP_{GC}\))}\end{array}\) & \(\begin{array}{c}\text{double-driven}\\ \text{Slope (\(NEP_{HA}\))}\end{array}\) \\ \cline{2-6}  & & \(<\)0 & \(>\)0 & \(\begin{array}{c}\text{climate-driven}\\ \text{degradation}\end{array}\) & 100 & 0 \\ \cline{2-6}  & & \(>\)0 & \(<\)0 & \(\begin{array}{c}\text{human-driven}\\ \text{degradation}\end{array}\) & 0 & 100 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Classification criterion and contribution rate calculation of the drivers. Hebei region witnessed an obvious increase in NEP. The multi-year mean value of NEP in the area south of Zhejiang Province was 711.62 gC m\({}^{-2}\) yr\({}^{-1}\), while that in the area north of Zhejiang Province was only 285.36 gC m\({}^{-2}\) yr\({}^{-1}\). Figure 3: Average NEP changes in the main provincial-level administrative regions from 2000 to 2020. Figure 2: Spatiotemporal dynamics of NEP in CCZ. As shown in Figure 4, at the provincial scale, the hotspot cluster of NEP in CCZ also shows a distinct north-south difference. The high-value cluster of NEP is mainly observed in the provinces to the south of Zhejiang, including three cities in Fujian, five cities in Guangdong, four cities in Guangxi, as well as the majority of regions in Taiwan and Hainan. In contrast, the low-value cluster is mainly composed of most cities in the north of Zhejiang Province, including five cities in Liaoning, twelve cities in the Beijing-Tianjin-Hebei region, eleven cities in Shandong, nine cities in Jiangsu, and two cities in Zhejiang. Overall, the cold cluster in the north is relatively concentrated and covers a wider area, while the hot cluster in the south is relatively dispersed. As shown in Figure 5, based on the MK test, the area with an upward trend of NEP was much larger than that with a downward trend. Specifically, the trend types of NEP in CCZ over the past 21 years were dominated by significant and insignificant increases, accounting for 46.12% and 23.95% of the total area, respectively, followed by an insignificant decrease (17.82%), a slight increase (4.74%), a significant decrease (4.58%), and a slight decrease (1.67%). At the provincial scale, the areas demonstrating an increasing trend in NEP are larger than those showing a decreasing trend in each provincial region. But in Shanghai, Fujian, Taiwan, and Hong Kong, the area that demonstrates an upward trend in NEP is smaller than that showing a downward trend in NEP. Notably, in the Beijing-Tianjin-Hebei region and Shandong Province, the increased area of NEP exceeded 50% of the respective area in each region.