Abstract. Atmospheric carbon dioxide (CO2) is the most significant greenhouse gas, and its concentration is continuously increasing, mainly as a
consequence of anthropogenic activities. Accurate quantification of CO2 is critical for addressing the global challenge of climate change
and for designing mitigation strategies aimed at stabilizing CO2 emissions. Satellites provide the most effective way to monitor the
concentration of CO2 in the atmosphere. In this study, we utilized the concentration of the column-averaged dry-air mole fraction of
CO2, i.e., XCO2 retrieved from a CO2 monitoring satellite, the Orbiting Carbon Observatory-2 (OCO-2), and the net
primary productivity (NPP) provided by the Moderate Resolution Imaging Spectroradiometer (MODIS) to estimate the anthropogenic CO2
emissions using the Generalized Regression Neural Network (GRNN) over East and West Asia. OCO-2 XCO2, MODIS NPP, and the Open-Data Inventory
for Anthropogenic Carbon dioxide (ODIAC) CO2 emission datasets for a period of 5 years (2015–2019) were used in this study. The annual
XCO2 anomalies were calculated from the OCO-2 retrievals for each year to remove the larger background CO2 concentrations and
seasonal variability. The XCO2 anomaly, NPP, and ODIAC emission datasets from 2015 to 2018 were then used to train the GRNN model, and,
finally, the anthropogenic CO2 emissions were estimated for 2019 based on the NPP and XCO2 anomalies derived for the same
year. The estimated and the ODIAC CO2 emissions were compared, and the results showed good agreement in terms of spatial
distribution. The CO2 emissions were estimated separately over East and West Asia. In addition, correlations between the ODIAC emissions
and XCO2 anomalies were also determined separately for East and West Asia, and East Asia exhibited relatively better results. The results
showed that satellite-based XCO2 retrievals can be used to estimate the regional-scale anthropogenic CO2 emissions, and the
accuracy of the results can be enhanced by further improvement of the GRNN model with the addition of more CO2 emission and concentration
datasets.