The transition to a neutral carbon and sustainable urban water cycle requires improving eco-efficiency in wastewater treatment processes. To support decision-making based on eco-efficiency evaluations, reliable estimations are fundamental. In this study, the eco-efficiency of a sample of 109 WWTPs was evaluated using efficiency analysis tree method. It combines machine learning and linear programming techniques and therefore, overcomes overfitting limitations of non-parametric methods used by past research on this topic. Results from the case study revealed that optimal costs and greenhouse gas emissions depend on the quantity of organic matter and suspended solids removed from wastewater. The estimated average eco-efficiency is 0.373 which involves that the assessed WWTPs could save 0.32 €/m3 and 0.11 kg of CO2 equivalent/m3. Moreover, only 4 out of 109 WWTPs are identified as eco-efficient which implies that the majority of the evaluated facilities can achieve substantial savings in operational costs and greenhouse gas emissions.
Keywords: Eco-efficiency; Economics; Greenhouse gas emissions savings; Linear programming; Regression trees; Wastewater treatment plants.
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