Enhancing Data Envelopment Analysis (DEA) for Incorporating Energy Recycling in Circular Economy

This study expands the mathematical modeling of Data Envelopment Analysis (DEA) to incorporate recyclable and undesirable outputs, a significant advancement in performance evaluation literature with practical applications. The proposed structure is customizable and includes many processes that employ input variables to generate desirable outcomes, which may then be used as inputs in future processes. The study examines scenarios in which energy expended in water treatment can be recovered through hydropower generators within distribution networks, and electricity produced from biogas emissions during wastewater treatment can be similarly reclaimed. The DEA input-output architecture is being revised, and the associated mathematical programming is being expanded to align with the fundamental concepts. The resulting linear model offers an optimal answer. This update transforms DEA into a comprehensive tool for the circular economy, which is rapidly evolving. The productivity of Iran's provincial water and wastewater sectors, is evaluated to demonstrate the efficacy of the proposed modeling approach. The results identified four decision-making units out of 33 with a more sustainable use of recyclable inputs. Moreover, by clustering the DMUs under study based on their productivity measure, there are four clusters which exactly discriminate between the units in terms of their achievements in recycling outputs as well as other performance measures. These findings have important management implications, as they highlight the specific actions taken by these units that contributed to their sustainability success. By analyzing these practices, other units can adopt similar strategies to improve their own sustainability performance. This study allows for a comprehensive productivity assessment and offers valuable managerial insights, thereby influencing decision-making in real-world scenarios.