The Evolution of Data Envelopment Analysis Models for Circular Economy Performance Assessment

Data Envelopment Analysis (DEA) has emerged as a major non-parametric technique for measuring efficiency in sustainability and environmental economics because it can handle multiple inputs and outputs without making explicit functional assumptions. DEA allows the simultaneous consideration of economic performance, resource utilization, environmental impacts, and recycling results in the evaluation of the circular economy (CE). This review investigates the evolution of DEA models in the last years and the variables used to measure the CE performance. We analyze 209 peer-reviewed articles to systematically explore the evolution of DEA applications from conventional single-stage efficiency models to advanced network-based structures that better reflect the intricacy of circular systems. The review discusses the most advanced DEA approaches to date in the literature on CE assessment and uncovers specific factors that affect the choice of models in empirical studies. Finally, it points to promising directions of future research by showing interest in the development of comprehensive DEA models adapted to the specificity of the CE systems.