Monitoring greenhouse gas (GHG) emissions is crucial for developing effective mitigation strategies. Recent advances in satellite remote-sensing measurements allow us to ack greenhouse gas emissions globally. This study assesses CO2 emissions from various point/local sources, particularly power plants in India, using eight years of concurrent high-resolution OCO-2 satellite measurements. Gaussian plume (GP) model was used to evaluate the power plant emissions reported in the Carbon Brief (CB) database. In total (39 cases), 42 different power plant CO2 emissions were assessed, with 26 of them being assessed more than once. The estimated power plant CO2 emissions were within ± 25% of the emissions reported in the CB database in 11 out of 39 cases and within ± 50% in 18 cases. To evaluate the EDGAR and ODIAC CO2 emission inventories in terms of missing or highly underestimated sources, we estimated the cross-sectional (CS) CO2 emission flux for 47 cases. We identified the possible omission of power plant emissions in three cases for both inventories. Furthermore, we also showed 21 cases in which CO2 emissions from unknown (non-power plant) sources were highly underestimated in the EDGAR and ODIAC CO2 emission inventories. Due to the simplicity of the employed approaches and their lower computational requirements compared to other methods, they can be applied to large datasets over extended time periods. This enables the acquisition of initial emission estimates for various sources, including those that are unknown or underestimated.