In the heart of Iași Municipality, Romania, a team of researchers has uncovered a critical insight that could reshape how cities approach the energy and environmental footprint of their drinking water supply systems. Led by Madalina Elena Abalasei from the “Cristofor Simionescu” Faculty of Chemical Engineering and Environmental Protection at the “Gheorghe Asahi” Technical University of Iași, the study introduces a novel framework that combines three powerful tools—ECAM, Entropy Weight Method (EWM), and TOPSIS—to evaluate and rank the energy consumption and greenhouse gas (GHG) emissions of water supply operations.
The findings are stark: water distribution is the most energy- and carbon-intensive stage in the supply chain, accounting for over half of the total electricity consumption and GHG emissions. “This isn’t just about numbers; it’s about actionable intelligence,” Abalasei explains. “By identifying where the heaviest burdens lie, utilities can prioritize interventions where they’ll have the greatest impact.” The study estimates that the municipality’s system consumes 10.35 GWh of electricity annually, emitting 4.71 million kg CO2-equivalent GHGs. Distribution alone guzzles 0.308 kWh per cubic meter of water—a figure that dwarfs the treatment stage’s much lower impact.
The framework itself is a game-changer for the energy sector. ECAM, the Energy Performance and Carbon Emissions Assessment and Monitoring tool, quantifies energy use and emissions across abstraction, treatment, and distribution. EWM then objectively weights the criteria—specific energy consumption and annual GHG emissions—while TOPSIS ranks the stages based on performance. The result? A clear hierarchy: treatment ranks highest (Pi = 1.000), distribution lowest (Pi = 0.000). “This isn’t just academic,” says Abalasei. “Utilities can use this methodology to benchmark their systems, set realistic targets, and allocate resources where they’re needed most.”
For the energy sector, the commercial implications are immediate. Water utilities, already under pressure to decarbonize, now have a data-driven roadmap to reduce costs and emissions. The study’s sensitivity analysis further strengthens its reliability, ensuring that rankings hold even when input criteria shift. Published in *Frontiers in Environmental Science*, this research could become a cornerstone for future policy and investment decisions in sustainable water infrastructure. The question now isn’t whether utilities will adopt such tools—but how quickly they can act.