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 acceleration, mean annual temperature, vegetation cover, soil properties, porosity, and permeability. The resulting map highlights coherent high‐flux zones along the Pacific Ring of Fire, the Andes, and other active orogens, confirming that geodynamic and geothermal processes strongly control CO 2 degassing. The given method provides a repeatable approach for subsequent research on subsurface degassing and its role in the global carbon cycle, thereby improving the capacity to define natural CO 2 emissions in tectonically active areas.
Betancourt et al. (Wed,) studied this question.