Longo, StefanoHospido Quintana, AlmudenaMauricio Iglesias, Miguel2023-11-102023-11-102023-07-18Journal of Environmental Management 344 (2023) 1186240301-4797http://hdl.handle.net/10347/31249Utilities produce and store vast amount of data related to urban wastewater management. Not yet fully exploited, proper data analysis would provide relevant process information and represents a great opportunity to improve the process performance. In the last years, several statistical tools and benchmarking methods that can extract useful information from data have been described to analyse wastewater treatment plant (WWTP) energy efficiency. Improving energy efficiency at WWTPs is however a complex task which involves several actors (both internal and external to the water utility), requires an exchange of different types of information which can be analysed by a broad selection of methods. Benchmarking method therefore must not only be selected based on whether they provide a clear identification of inefficient processes; it must also match the available data and the skills of those performing the assessment and objectives of stakeholders interpreting the results. Here, we have identified the requirements of the most common benchmarking methods in terms of data, resources, complexity of use, and information provided. To do that, inefficiency is decomposed so that the analyst, considering the objective of the study and the available data, can link each element to the appropriate method for quantification and benchmarking, and relate inefficiency components with root-causes in wastewater treatment. Finally, a framework for selecting the most suitable benchmarking method to improve energy efficiency in WWTPs is proposed to assist water sector stakeholders. By offering guidelines on how integrates and links data, methods and actors in the water sector, the outcomes of this article are expected to move WWTPs towards increasing energy efficiencyeng© 2023 The Authors. Published by Elsevier Ltd. This article is available under the Creative Commons CC-BY-NC license and permits non-commercial use, distribution and reproduction in any medium, provided the original work is properly citedAtribución-NoComercial 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc/4.0/Data envelopment analysisRegression analysisStochastic frontier analysisWastewater treatmentKey performance indicatorActor analysisjournal article10.1016/j.jenvman.2023.118624open access