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.

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