Estimation of CO ₂ Emissions in Fault Systems at a Global Scale

Abstract Influenced by tectonic, geophysical, and environmental aspects, the release of carbon dioxide (CO 2 ) in fault systems is a fundamental component of Earth's carbon cycle. Appreciating their contribution to natural greenhouse gas flow depends on knowing these emissions. We integrated 867 degassing records with harmonized raster variables to provide a predictive framework utilizing machine learning to estimate CO 2 fluxes in tectonically active areas. Grid search cross‐validation trained and optimized five regression models: Support Vector Regression, K‐Nearest Neighbors, Decision Tree, Random Forest, and Gradient Boosting. Then, a stacking regressor was used, which achieved an R 2 of 0.677, surpassing the individual models. To avoid extrapolation into tectonically stable regions, the model was applied pixel by pixel inside an active‐tectonic mask that defines the applicability domain of the model, using only globally available raster variables such as Bouguer anomaly, heat flow, peak ground acceler