Data Envelopment analysis applications for energy efficiency in power generation: A comprehensive review with bibliometric and content analysis

As global electricity demand continues to rise, concerns over greenhouse gas emissions and climate change have intensified, making sustainability and efficiency in power generation critical priorities. Data Envelopment Analysis (DEA) has emerged as a powerful tool for assessing and improving energy efficiency while integrating environmental considerations. This study provides a comprehensive review of DEA applications in power generation, employing bibliometric and content analysis to identify key research trends, methodological advancements, and influential contributions. Analyzing 461 publications, our findings reveal DEA's growing role in balancing efficiency with sustainability. The review highlights significant progress in advanced DEA models (including Network DEA, Fuzzy DEA, and hybrid approaches with methods such as the Malmquist Index and Stochastic Frontier Analysis) that enhance eco-efficiency assessment and inform sustainable energy transitions. International collaborations, particularly those led by China and the United States, underscore the global momentum in applying DEA to support emissions reduction, renewable energy integration, and carbon tax policy. Beyond efficiency benchmarking, DEA has evolved into a decision-support framework that links operational performance with broader sustainability outcomes, guiding policy interventions such as carbon pricing and renewable subsidy allocation. While sustainability remains a central focus, there is a need to explore the integration of DEA with emerging technologies, such as artificial intelligence and machine learning, to enhance predictive capabilities and efficiency assessments.