Estimating global anthropogenic CO2 emissions through satellite observations

Tracking human-induced CO₂ emissions accurately is fundamental for comprehending the carbon cycle and formulating effective mitigation strategies for achieving carbon neutrality. Observations from satellites present a potentially unbiased and efficient solution compared to the prevailing self-reporting approach. This study presents a novel approach for estimating anthropogenic emissions using satellite-based CO₂ measurements and the geographically and temporally weighted regression (GTWR) model. The GTWR model innovatively integrates both spatial and temporal variations to better capture the spatial heterogeneity and temporal dynamics of CO₂ emissions. Column-averaged CO₂ (XCO₂) measurements from the Greenhouse Gases Observing Satellite (GOSAT) and several environmental parameters are used as predictors in this model. Validation against the Open-source Data Inventory for Anthropogenic CO₂ (ODIAC) data shows high correlation (R² = 0.929), highlighting the significant potential of direct satellite estimations in enhancing emissions tracking. Comparisons with ODIAC, the Emission Database for Global Atmospheric Research (EDGAR), and co-emitted NO₂ from OMI/Aura reveal consistent patterns and trends, demonstrating the reliability of this approach. This consistency further corroborates the effectiveness of satellite-based estimations in quantifying anthropogenic CO₂ emissions, which is essential for monitoring carbon cycle and developing mitigation policies. The satellite-based estimations demonstrate high correlation and consistency, providing a robust foundation for objectively assessing global carbon emission mitigation policies. Future enhancements in spatial resolution and reduced dependence on existing inventories will further strengthen the reliability of these estimations.