Uncovering CO 2 Drivers with Machine Learning in High- and Upper-Middle-Income Countries

Cosimo Magazzino (), Umberto Monarca, Ernesto Cassetta, Alberto Costantiello and Tulia Gattone
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Cosimo Magazzino: Economic Research Center, Western Caspian University, AZ1001 Baku, Azerbaijan
Umberto Monarca: Department of Law, University of Foggia, 71121 Foggia, Italy
Ernesto Cassetta: Department of Economics and Statistics, University of Udine, 33100 Udine, Italy
Alberto Costantiello: Department of Management, Technology and Finance, LUM University “Giuseppe Degennaro”, 70010 Bari, Italy
Tulia Gattone: Department of Economics, John Cabot University, 00165 Rome, Italy

Energies, 2025, vol. 18, issue 21, 1-21

Abstract: Rapid decarbonization relies on knowing which structural and energy factors affect national carbon dioxide emissions. Much of the literature leans on linear and additive assumptions, which may gloss over curvature and interactions in this energy–emissions link. Unlike previous studies, we take a different approach. Using a panel of 80 high- and upper-middle-income countries from 2011 to 2020, we model emissions as a function of fossil fuel energy consumption, methane, the food production index, renewable electricity output, gross domestic product (GDP), and trade measured as trade over GDP. Our contribution is twofold. First, we evaluate how different modeling strategies, from a traditional Generalized Linear Model to more flexible approaches such as Support Vector Machine regression and Random Forest (RF), influence the identification of emission drivers. Second, we use Double Machine Learning (DML) to estimate the incremental effect of fossil fuel consumption while controlling for other variables, offering a more careful interpretation of its likely causal role. Across models, a clear pattern emerges: GDP dominates; fossil fuel energy consumption and methane follow. Renewable electricity output and trade contribute, but to a moderate degree. The food production index adds little in this aggregate, cross-country setting. To probe the mechanism rather than the prediction, we estimate the incremental role of fossil fuel energy consumption using DML with RF nuisance functions. The partial effect remains positive after conditioning on the other covariates. Taken together, the results suggest that economic scale and the fuel mix are the primary levers for near-term emissions profiles, while renewables and trade matter, just less than is often assumed and in ways that may depend on context.

Keywords: CO 2 emissions; fossil fuels; support vector machine; random forest; double machine learning (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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