# XCO\({}_{2}\) Super-Resolution Reconstruction Based on Spatial Extreme Random Trees
## 3 Results
### Spatiotemporal Variations in XCO\({}_{2}\) in China  
The results of China's 1 km-resolution XCO\({}_{2}\) spatiotemporal dataset reconstructed using spatial extreme random trees from 2016 to 2020 are shown in Figure 6, indicating notable spatial variations in the distribution of XCO\({}_{2}\). Overall, consistent with the existing research results, the carbon dioxide concentration tends to be relatively lower in western China and higher in eastern China, a pattern that aligns with the level of urbanization and economic development in China [74]. The rapid urbanization in China from 2016 to 2020 has led to significant differences in XCO\({}_{2}\) between developed eastern regions and underdeveloped western regions. The eastern regions of China are typically characterized by economic prosperity, dense population, and intensive energy and industrial activities, which may result in higher carbon dioxide concentrations. Cities such as Beijing, Shanghai, and parts of Jiangsu province often exhibit higher population densities, traffic volumes, and industrial activities, leading to higher carbon dioxide concentrations in urban areas. In contrast, the western and northeastern regions of China may be more influenced by natural factors. Regions with high vegetation coverage, such as Tibet, Inner Mongolia, and Heilongjiang, absorb more carbon dioxide through plant photosynthesis, resulting in lower carbon dioxide concentrations. Additionally, the western regions have lower population densities and are gradually transitioning from traditional to new energy sources (such as wind and solar energy), thus reducing carbon emissions. The economy in the southwestern and southern regions is relatively developed, with extensive human activities. However, because of the warm and humid climate prevailing in these regions, vegetation is lush, resulting in the higher efficiency of carbon fixation through photosynthesis. Therefore, the XCO\({}_{2}\) level is moderate [75]. Further, optimizing the energy structure and improving  
Figure 5: Density scatter plot of spatial extreme random trees model fitting results from 2016 to 2020 (the black dashed line represents the 1:1 line). energy efficiency to decrease carbon intensity, as well as increasing vegetation coverage to enhance carbon sequestration, are effective approaches for reducing or mitigating XCO\({}_{2}\). The 1 km-resolution XCO\({}_{2}\) data reconstructed via spatial extreme random trees can achieve comprehensive coverage with high resolution in China, thereby enabling more effective monitoring of carbon sources and sinks. The spatial distribution of XCO\({}_{2}\) trends in China from 2016 to 2020, as shown in Figure 7, is consistent with the global trend, with an overall increase in XCO\({}_{2}\) levels nationwide each year [76]. Specifically, all regions exhibit significant increments, with an average annual rise of over 1.88 ppm/year over the past five years. The rate of XCO\({}_{2}\) growth varies across regions. In the northwest, northeast, and northern parts of China, the concentration of carbon dioxide in the atmosphere has increased rapidly, with XCO\({}_{2}\) levels higher than the five-year average. This could be attributed to accelerated urbanization in China, intensive human activities, and increased energy consumption that have led to higher carbon emissions. In contrast, although XCO\({}_{2}\) levels in southwest, central, eastern, and southern China have increased annually, the growth rates have been relatively slow. For example, XCO\({}_{2}\) levels increased more slowly near cities such as Chengdu, Chongqing, Changsha, and Wuhan. This may be due to the rapid economic development, high population density, and high level of urbanization in these regions, which emit more carbon dioxide, resulting in a large carbon dioxide concentration base and in a lower growth  
Figure 6: The 1 km-resolution XCO\({}_{2}\) results reconstructed using spatial extreme random trees from 2016 to 2020.  
rate in these regions.