Estimating Global Anthropogenic CO 2 Gridded Emissions Using a Data-Driven Stacked Random Forest Regression Model.

The accurate estimation of anthropogenic carbon emissions is of great significance for understanding the global carbon cycle and guides the setting and implementation of global climate policy and CO₂ emission-reduction goals. This study built a data-driven stacked random forest regression model for estimating gridded global fossil fuel CO₂ emissions. The driving variables include the annual features of column-averaged CO₂ dry-air mole fraction (XCO₂) anomalies based on their ecofloristic zone, night-time light data from the Visible Infrared Imaging Radiometer Suite (VIIRS), terrestrial carbon fluxes, and vegetation parameters. A two-layer stacked random forest regression model was built to fit 1° gridded inventory of open-source data inventory for anthropogenic CO₂ (ODIAC). Then, the model was trained using the 2014–2018 dataset to estimate emissions in 2019, which provided a higher accuracy compared with a single-layer model with an R² of 0.766 and an RMSE of 0.359. The predicted gridded emissions are consistent with Global Carbon Grid at 1° scale with an R² of 0.665, and the national total emissions provided a higher R² at 0.977 with the Global Carbon Project (GCP) data, as compared to the ODIAC (R² = 0.956) data, in European countries. This study demonstrates that data-driven random forest regression models are capable of estimating anthropogenic CO₂ emissions at a grid scale. [ABSTRACT FROM AUTHOR]