# An Assessment of Anthropogenic CO\({}_{2}\) Emissions by Satellite-Based Observations in China
## 3 Results and Discussion
### Discussion of Correlation between Retrieved \(X_{co2}\) and Anthropogenic Emissions  
It has been indicated that the cluster of XCO\({}_{2}\) changes derived from GOSAT observations shows a correlating coefficient of 0.5 with anthropogenic emission. This correlation is more significant than a single grid of XCO\({}_{2}\) as the atmospheric CO\({}_{2}\) measurement is an instantaneous snapshot of the realistic atmosphere [33]. Its clustering analysis is derived from original XCO\({}_{2}\) data.  
We segment the ODIAC emissions which are binned according to every 0.3 t/yr of lgE (Figure 7a) using mean emission calculated from annual emission during 2010-2015, and then make correlation analysis between the mean of emission and mean of dXCO\({}_{2}\) within binned regions. It is found that the segmental mean of dXCO\({}_{2}\) demonstrate a significant and positive correlation with ODIAC emissions in which the determined coefficient (R\({}^{2}\)) for all data is up to 0.82 (Figure 7b) and the dXCO\({}_{2}\) demonstrate strong positive linear correlation with emission starting from 10\({}^{4}\) t/yr where R\({}^{2}\) is up to 0.95 (red line in Figure 7b). The dXCO\({}_{2}\) is almost unchanged in the region with emission lower than 10\({}^{4}\) t/yr. These results imply that satellite observations of atmospheric CO\({}_{2}\) could be used to estimate regional anthropogenic emissions for those regions with larger magnitude of anthropogenic CO\({}_{2}\) emissions. Additionally, we overlay the CARMA power plants dataset on the mean dXCO\({}_{2}\) from the annual dXCO\({}_{2}\) during 2010 to 2015 (Figure 8a). It can be seen that the high dXCO\({}_{2}\) are corresponding to high-density power plants, especially in northeast China. We accumulate the magnitude of emissions of power plants within one grid of mapping XCO\({}_{2}\) dataset, then we segment emissions of power plants which are binned according to every 0.3 t of lgE, and take correlation analysis between the mean of power plants emission and the mean of dXCO\({}_{2}\) within binned regions (Figure 8a). dXCO\({}_{2}\) demonstrate strong positive linear correlation with power plants emission starting from 10\({}^{6}\) t (blue dots). The grids they represent are distributed consistently with high dXCO\({}_{2}\) area. The result demonstrates a R\({}^{2}\) of 0.59 which is less than regional statistics. Power plants emission lower than 10\({}^{5.5}\) t demonstrate weak linear correlation with dXCO\({}_{2}\) because the influence of CO\({}_{2}\) uptake of biosphere.  
Figure 6: Scatterplot between estimated CO\({}_{2}\) emission using mapped-XCO\({}_{2}\) data and the actual ODIAC emission in 2015, in which red line is the regression line between estimated emission and ODIAC emission, and black dotted line is the 1:1 line.  
From Figure 8a, it can be found that the dXCO\({}_{2}\) in western area, the desert area of Xinjiang, shows high values even if there are much less anthropogenic emission over this area as shown in Figure 4b. This is likely resulted in the uncertainty of ACOS XCO\({}_{2}\) retrievals in desert which has been indicated by [PERSON] et al. [34].