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Benchmarking energy efficiency in Europe: An integrated two-stage framework using machine learning and decision-making approaches

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  • Lo, Huai-Wei
  • Deveci, Muhammet
  • Lin, Sheng-Wei

Abstract

This study introduces an innovative two-stage framework for evaluating energy efficiency across European nations by integrating Data Envelopment Analysis (DEA), Ordinary Least Squares (OLS), Random Forest (RF), and a hybrid Multi-Criteria Decision-Making (MCDM) approach combining Stepwise Weight Assessment Ratio Analysis (SWARA) and Cumulative Prospect Theory (CPT). The framework addresses the limitations of traditional efficiency evaluation methods by synthesizing parametric and non-parametric techniques with machine learning algorithms to manage uncertainties in efficiency assessments effectively. We analyze data from 14 European countries from 2013 to 2022, incorporating key indicators including energy consumption, renewable energy production, energy intensity, and CO2 emissions. The empirical analysis reveals significant regional variations in energy efficiency performance, with Nordic countries demonstrating superior performance through established sustainable practices and robust environmental policies. Eastern European nations show remarkable progress through infrastructure modernization and effective utilization of EU (European Union) structural funds. In contrast, major Western European economies face challenges in optimizing efficiency due to aging infrastructure and complex industrial transitions. The SWARA-CPT methodology provides novel insights by integrating temporal dynamics and decision-makers risk preferences, resulting in a comprehensive ranking that positions Norway and Sweden at the forefront of energy efficiency performance. This study contributes to the literature by developing a sophisticated analytical framework that enhances understanding spatiotemporal efficiency patterns and provides actionable policy recommendations for improving energy efficiency across Europe. The findings underscore the importance of tailored regional approaches and cross-border knowledge transfer in advancing energy efficiency objectives while supporting broader climate change mitigation efforts.

Suggested Citation

  • Lo, Huai-Wei & Deveci, Muhammet & Lin, Sheng-Wei, 2025. "Benchmarking energy efficiency in Europe: An integrated two-stage framework using machine learning and decision-making approaches," Applied Energy, Elsevier, vol. 392(C).
    • Handle: RePEc:eee:appene:v:392:y:2025:i:c:s0306261925007743
    DOI: 10.1016/j.apenergy.2025.126044
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