## 3 Results and Discussion
### Spatial Analysis of Carbon Sinks in Natural Semic Areas  
Using the point-of-interest (POI) data of national A-level-and-above tourist attractions, ArcGIS 10.2 was employed to conduct kernel density analysis of the attractions. Density clusters were extracted to determine the spatial scope for subsequent carbon sink calculations (Figure 2). A-level-and-above scenic areas in China generally exhibit an agglomerative distribution, with a spatial pattern characterized by more in the southeast, \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & **Touce in 2019** & **Touce in 2020** & & **Touce in 2019** & **Touce in 2020** \\ \hline Sightsee & PCCs & 0.464 ** & \(-\)0.03 & Toua & PCCs & 0.768 ** & 0.502 ** \\  & Sig. & 0.01 & 0.86 & & Sig. & 0.00 & 0.00 \\ Lei Vac & PCCs & 0.464 ** & \(-\)0.03 & RLY & PCCs & 0.393 * & 0.15 \\  & Sig. & 0.01 & 0.86 & & Sig. & 0.03 & 0.41 \\ BT & PCCs & 0.464 ** & \(-\)0.03 & HWY & PCCs & 0.658 ** & 0.432 * \\  & Sig. & 0.01 & 0.86 & & Sig. & 0.00 & 0.02 \\ VRF & PCCs & 0.464 ** & \(-\)0.03 & WTWY & PCCs & 0.491 ** & 0.361 * \\  & Sig. & 0.01 & 0.86 & & Sig. & 0.01 & 0.05 \\ Others & PCCs & 0.464 ** & \(-\)0.03 & CV Avn & PCCs & 0.783 ** & 0.982 ** \\  & Sig. & 0.01 & 0.86 & & Sig. & 0.00 & 0.00 \\ \hline \hline \end{tabular} Note: Correlation levels of 0.8\(-\)1.0 indicate a robust correlation, 0.6\(-\)0.8 indicate a strong correlation, 0.4\(-\)0.6 indicate a moderate correlation, 0.2\(-\)0.4 indicate a weak correlation, and 0.0\(-\)0.2 indicate a very weak or no correlation. In the Pearson correlation coefficient analysis using IBM SPSS Statistics 26, ** indicates significance at the 0.01 level (two-tailed), indicating a significant correlation, and * indicates significance at the 0.05 level (two-tailed), also indicating a significant correlation.  
\end{table} 
Table 4: Analysis of various indicators of carbon emissions in tourism from 2019 to 2020.  
less in the northwest. These scenic areas are mainly concentrated in central China's eastern coastal economic belt and economically developed regions. In terms of quantity, the number of A-level-and-above scenic areas in eastern China far exceeds that in the western region, and they are primarily distributed in Beijing, Shanghai, Shandong, the Yangtze River Delta, and the Pearl River Delta, which are areas with rich historical and cultural heritage. The northeastern region has a relatively larger number of scenic areas, with small-scale high-density clusters observed in Liaoning Province. In contrast, other areas have fewer scenic areas and moderate clustering. The southern, central, and southwestern provinces generally have a relatively even distribution of scenic areas, with a moderate to moderate-to-general degree of clustering. However, Guangdong, Shaanxi, Henan, Chongqing, and Sichuan have higher-density cluster areas related to regional economic development and historical and cultural factors. Xinjiang, Tibet, and Qinghai have a small number of scenic areas and exhibit large low-density clusters. Based on the spatial analysis of tourist attractions, this study categorizes highly concentrated, moderately concentrated, moderately concentrated, and generally concentrated areas as \"cluster areas.\" It calculates the vegetation carbon sinks within these areas to represent the overall vegetation carbon sinks of scenic areas nationwide. Areas with low clustering are classified as \"non-cluster areas\" and are not included in the carbon sink calculation. The overall distribution pattern of vegetation carbon sinks in scenic areas across provinces in China in 2019-2020 exhibits similar regional variations (Figures 3 and 4). The spatial distribution of vegetation carbon sinks in scenic areas is closely related to climate, showing a \"more in the southeast, less in the northwest\" pattern. Regarding total carbon sink quantity, southern provinces have a higher number of carbon sinks in their scenic areas.