AI-Driven Modeling of the Energy Transition in the SPRING-F Group: A Hybrid Panel ARDL and Machine Learning Approach

This study analyses the dynamics of the energy transition within the SPRING-F group (Spain, Poland, Romania, Italy, the Netherlands, Germany, France) through a hybrid approach that combines econometric panel ARDL models with machine learning algorithms. The analysis is based on energy, economic, and technological indicators, including renewable energy consumption, energy intensity, emissions, GDP per capita, urbanization, trade openness, and R&D expenditure. The results of the exploratory analysis highlight the existence of clear structural differences between Western European and emerging Central and Eastern European economies. Based on the estimates made with the ARDL panel model, the long-term equilibrium relationships were confirmed. They indicated positive and significant effects of urbanization and economic growth on renewable energy consumption, as well as a negative impact of emissions. Regarding the short-term effects, the error correction coefficient suggests a moderate convergence towards equilibrium. Machine learning models highlight the superiority of nonlinear approaches, and SHAP analysis confirms the dominant role of emissions and the heterogeneity of national energy transition trajectories.