Abstract

Water pumping stations are critical components of water transmission systems, particularly in the Gulf region, where potable water supply is heavily dependent on energy-intensive desalination processes. Despite their importance, pumping station efficiency is often assessed using single-dimensional indicators that fail to capture operational complexity, scale effects, and environmental impacts. This study develops a Data Envelopment Analysis (DEA) framework to evaluate the performance of 16 water pumping stations in Dubai, each treated as a decision-making unit (DMU), by incorporating both operational and sustainability dimensions. A multi-model approach was applied, integrating input-oriented Charnes, Cooper, and Rhodes (CCR) and Banker, Charnes, and Cooper (BCC) models with Slack-Based Measure (SBM) models that treat energy consumption as an undesirable output. The results reveal substantial variation in efficiency, with average scores of 0.73 under CCR, 0.95 under BCC, 0.62 under SBM Constant Returns to Scale (CRS), and 0.86 under SBM Variable Returns to Scale (VRS). The gap between CCR and BCC results indicates that inefficiencies are primarily driven by scale rather than managerial performance, while the lower SBM scores highlight the significant impact of energy consumption on overall efficiency. Peer analysis confirms the robustness of the findings, identifying Decision-Making Unit (DMU) 16 as the primary benchmark, appearing 37 times across the models. Slack analysis further reveals critical inefficiencies, particularly in DMU 03 and DMU 06.

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