Assessing carbon and pollutant efficiency in wastewater treatment plants: A data-driven benchmarking framework using latent class stochastic frontier analysis

Abstract

Achieving carbon neutrality in wastewater treatment plants (WWTPs) presents a significant challenge, as reducing greenhouse gas (GHG) emissions must not compromise pollutant removal efficiency. This study in- troduces a novel Carbon and Pollutant Efficiency Index (CPEI) to benchmark the environmental performance of WWTPs by integrating GHG emissions and pollutant removal efficiency into a single metric. To address limi- tations in previous studies, which primarily relied on Data Envelopment Analysis (DEA), this research employs Latent Class Stochastic Frontier Analysis within a metafrontier framework to account for unobservable hetero- geneities among WWTPs. A case study of 109 WWTPs in Spain identified three distinct operational classes, with average CPEI scores of 0.595, 0.586, and 0.506 for Classes 1, 2, and 3, respectively. Results indicate that chemical oxygen demand (COD) removal is the primary driver of GHG emissions, reflecting the energy-intensive nature of aerobic treatment processes. None of the WWTPs achieved full efficiency (CPEI = 1), suggesting substantial room for improvement. Policy implications include the need for tailored regulatory strategies that balance carbon reduction with pollutant removal targets. The proposed CPEI provides a robust decision-support tool for benchmarking WWTPs and guiding policies towards sustainable and decarbonized wastewater management