A Fuzzy-Machine Learning Framework for Energy Efficiency Optimization and Smart Transition Analysis in European Economies

This study aims to identify and interpret latent energy-economic typologies across European economies and to assess whether their energy transition paths exhibit convergence or persistent structural divergence. To achieve this objective, the paper investigates the energy–economic structure of thirteen European economies between 2000 and 2024 using an integrated fuzzy–machine learning framework. Eight indicators related to renewable energy, energy efficiency, emissions, electricity use, digitalization, investment, urbanization and economic development were analyzed to identify structural typologies across countries. Using the Fuzzy C-Means algorithm, four distinct clusters were identified: (i) moderately developed economies with balanced renewable adoption and energy efficiency, (ii) structurally integrated economies with medium energy intensity and stable economic performance, (iii) an emerging economy with persistent structural constraints, and (iv) advanced high-performance economies engaged in accelerated energy transition. To validate the fuzzy classification, Random Forest and XGBoost models were trained based on the same indicators, achieving high predictive accuracy (94% and 92%, respectively). Feature importance analysis reveals that CO2 emissions, energy efficiency and urbanization play the most significant roles in differentiating country profiles. The proposed framework provides a comprehensive approach for understanding energy transition heterogeneity, structural convergence and the drivers shaping the evolution of European energy–economic systems.