A systematic methodology for the robust quantification of energy efficiency at wastewater treatment plants featuring Data Envelopment Analysis

© 2018. This is the accepted manuscript of the following article: Longo, S., Hospido, A., Lema, J., & Mauricio-Iglesias, M. (2018). A systematic methodology for the robust quantification of energy efficiency at wastewater treatment plants featuring Data Envelopment Analysis. Water Research, 141, 317-328. doi: 10.1016/j.watres.2018.04.067This article examines the potential benefits of using Data Envelopment Analysis (DEA) for conducting energy-efficiency assessment of wastewater treatment plants (WWTPs). WWTPs are characteristically heterogeneous (in size, technology, climate, function ...) which limits the correct application of DEA. This paper proposes and describes the Robust Energy Efficiency DEA (REED) in its various stages, a systematic state-of-the-art methodology aimed at including exogenous variables in nonparametric frontier models and especially designed for WWTP operation. In particular, the methodology systematizes the modelling process by presenting an integrated framework for selecting the correct variables and appropriate models, possibly tackling the effect of exogenous factors. As a result, the application of REED improves the quality of the efficiency estimates and hence the significance of benchmarking. For the reader's convenience, this article is presented as a step-by-step guideline to guide the user in the determination of WWTPs energy efficiency from beginning to end. The application and benefits of the developed methodology are demonstrated by a case study related to the comparison of the energy efficiency of a set of 399 WWTPs operating in different countries and under heterogeneous environmental conditionsThe authors belong to the Galician Competitive Research GroupGRC2013-032 and the CRETUS strategic partnership (AGRUP2015/02), co-funded by FEDER (EU). Besides, they are supported by ‘ENERWATER’ Coordination Support Action that has received founding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 649819S