# An Assessment of Anthropogenic CO\({}_{2}\) Emissions by Satellite-Based Observations in China
## 4 Conclusions  
In this paper, to support the verification of bottom-up inventory of anthropogenic emission, an anthropogenic CO\({}_{2}\) emission estimation method using a machine learning technique is applied to the gap-filled ACOS XCO\({}_{2}\) dataset over the mainland of China derived from GOSAT observations. The annual emission signatures, indicated by dXCO\({}_{2}\), is enhanced by removing the background XCO\({}_{2}\) from the 2010 to 2015 XCO\({}_{2}\) data. We then apply the annual averaged dXCO\({}_{2}\) from 2010 to 2014 to build an estimating model of anthropogenic emission using an artificial network approach. The model is verified by estimating results in 2015 and comparing with the ODIAC emissions. Lastly, we quantify the correlation between the annual dXCO\({}_{2}\) and the magnitude of anthropogenic emission. Our result indicate that the anthropogenic emission can be estimated at regional scale by the changing magnitude of XCO\({}_{2}\) especially for those regions with larger emissions. However, it has relatively higher uncertainty to grasp the CO\({}_{2}\) signals of the low or without anthropogenic emission areas and  
Figure 8: **(a)** the mean of dXCO\({}_{2}\) from the annual dXCO\({}_{2}\) during 2010 to 2015 overlaid with CARMA power plants locations; **(b)** the correlation between emission of CARMA power plants within \(1^{\circ}\times 1^{\circ}\) grid which are binned by every 0.3 t/yr of lgE and the corresponding mean dXCO\({}_{2}\), different color dots represent different segment of CARMA power plants emissions. Figure 7: **(a)** Segment of ODIAC emissions, where the data are binned by every 0.3 t/yr of lgE using mean emission calculated from annual emission during 2010–2015; **(b)** correlation between mean ODIAC CO\({}_{2}\) emissions and mean dXCO\({}_{2}\) calculated from annual dXCO\({}_{2}\) during 2010–2015 for each segment, where red line is the regression line between dXCO\({}_{2}\) and ODIAC CO\({}_{2}\) emissions with emission layer than \(10^{4}\) t/yr. point emitting sources. The CO\({}_{2}\) uptake of biosphere and fluxes of wind field affect the estimation when using the annual dXCO\({}_{2}\). The observation mode of GOSAT satellite in space and time and fast mixing of atmospheric CO\({}_{2}\) also affect the detection of point emitting sources. Our study demonstrates that the XCO\({}_{2}\) derived from satellite observation can effectively provide a way to reveal the spatial patterns of underlying anthropogenic emissions. It is expected that the estimation of anthropogenic emission could be greatly improved by using more and more XCO\({}_{2}\) data from multi-satellite such as OCO-2, OCO-3, GOSAT-2, and TanSat in future. Moreover, we can combine the ancillary data related with CO\({}_{2}\) uptake and emission which can be obtained by satellite remote sensing observations at the same time, such as gross primary production (GPP), industrial heat source from VIIRS (Visible infrared Imaging Radiometer) Night fire product for point sources, Night light from Defense Meteorological Satellite Program/Operational Linescan System (DMSP/OLS), to constrain the estimating model developed in this study. This data-driven approach based on satellite-based observations can offer the possibility of rapid updates for anthropogenic CO\({}_{2}\) emissions, and provide a new way of investigating anthropogenic emissions to support the implement of regional reduction of carbon emissions. [PERSON] and [PERSON] conceived and designed the experiments; [PERSON] performed the experiments; [PERSON] and [PERSON] analyzed the data; [PERSON], [PERSON] and [PERSON] contributed analysis tools; [PERSON] and [PERSON] wrote the paper. All authors proofread the manuscript. This research were funded by Key Program of the Chinese Academy of Sciences (Grand No.ZDRW-ZS-2019-1) and CAS Earth Big Data Science Project: \"Global Medium and Low Resolution Time Series Spatial Information Products\" (Grant No.XDA19080303). And the APC was funded by Key Program of the Chinese Academy of Sciences.