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Modeling absolute CO2–GDP decoupling in the context of the global energy transition: evidence from econometrics and explainable machine learning
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Orientador(es)
Resumo(s)
This study investigates the feasibility of absolute decoupling—where economies expand while CO2 (Carbon Dioxide) emissions decline in absolute terms—by identifying its key macro–energy drivers across 79 countries (2000–2025). We construct a comprehensive panel of energy-system indicators and estimate the probability of decoupling using two complementary classifiers: a penalized logistic regression and a gradient-boosted decision tree model (GBM). The non-parametric GBM significantly outperforms the linear baseline (ROC–AUC ~0.80 vs. 0.67), revealing complex non-linearities in the transition process. Explainable AI analysis (SHAP) demonstrates that decoupling is not driven by GDP growth rates alone, but primarily by sharp reductions in energy intensity and the active displacement of fossil fuels. Crucially, our results indicate that increasing renewable capacity is insufficient for absolute decoupling if the fossil fuel share does not simultaneously decline. These findings challenge passive “green growth” narratives, suggesting that current policies are inadequate; achieving climate targets requires targeted mechanisms for active fossil fuel phase-out rather than merely relying on renewable additions or economic modernization.
Descrição
Palavras-chave
Absolute decoupling CO2 emissions Energy intensity Green growth Sustainability transitions
Contexto Educativo
Citação
Teruel-Gutiérrez, R., Fernandes da Anunciação, P., & Teruel-Sánchez, R. (2026). Modeling absolute CO2–GDP decoupling in the context of the global energy transition: Evidence from econometrics and explainable machine learning. Sustainability, 18(2), 758. https://doi.org/10.3390/su18020758
Editora
MDPI