Benchmarking energy efficiency of water treatment plants: Effects of data variability.

Abstract

This study evaluates, for the first time, the energy efficiency of a sample of drinking water treatment plants (DWTPs) using the data envelopment analysis (DEA) tolerance method, which is based on the simulation of scenarios to integrate data variations. The integration of data uncertainty in energy efficiency estimation changes drastically results for approximately one-third of the DWTPs evaluated. The results showed that, even in the best-case scenario, most of the DWTPs evaluated are inefficient and may therefore, be able to reduce the energy used to treat raw water. From a policy perspective, the findings of this study reveal that omitting data variability in benchmarking would involve critical repercussions when efficiency scores are used by regulators to set water tariffs. Omitting the degree of data uncertainty is likely to result in biased conclusions; in the scenarios evaluated, the inclusion of this information altered the rankings of some energy-efficient DWTPs.