Work

Modeling Absolute CO2–GDP Decoupling in the Context of the Global Energy Transition: Evidence from Econometrics and Explainable Machine Learning

Year: 2026

Type: article

Abstract: 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.

Source: Sustainability

DOI: 10.3390/su18020758

Authors: Ricardo Teruel-Gutiérrez, Pedro Fernandes da Anunciação, Ricardo Teruel-Sánchez

Institutions: Universidad Politécnica de Cartagena, Universidade Politécnica de Setúbal

Language: English

Topic: Energy, Environment, Economic Growth

Subfield: Economics and Econometrics

Field: Economics, Econometrics and Finance

Domain: Social Sciences

SDGs: Climate action, Affordable and clean energy

Open Access status: gold

Estimated APC paid: $2,896