@article{He2025, 
author = {Zixiang He and Rupeng Wang and Jifeng Wang and Honglin Chen and Shiyu Zhang and Ke Wang and Junjiang Lai and Nanqi Ren and Shih-Hsin Ho},
title = {Water conservation strategies reduce greenhouse gas emission from wastewater treatment plants: A domino effect},
year = {2025},
journal = {Environmental Science and Ecotechnology},
volume = {26},
keywords = {Machine learning, Water management, Wastewater treatment, Greenhouse gas emission reduction, Quantifying synergy},
url = {https://www.sciopen.com/article/10.1016/j.ese.2025.100574},
doi = {10.1016/j.ese.2025.100574},
abstract = {Wastewater-treatment plants (WWTPs) enable urban water reclamation but are significant sources of greenhouse-gas (GHG) emissions. Because GHG output scales with the volume and pollutant load of influent sewage, city-wide water-use patterns offer a direct yet under-examined lever for decarbonizing WWTP operations. The feedbacks linking demand-side water conservation to plant emissions remain poorly understood, obscuring important mitigation co-benefits. Here we show a domino-effect feedback between urban water-use patterns with WWTP carbon emissions. Our analysis demonstrates that optimized water management can improve average WWTP eco-efficiency by up to 189%, leading to an annual reduction in water consumption of 48.3 billion m3 and a decrease in GHG emissions by 1.67 million tons CO2-equivalent. Under this synergistic water-carbon management scenario, the wastewater sector could achieve carbon neutrality by 2037, seven years ahead of schedules based solely on technological advancements. Our findings present a novel and replicable framework that simultaneously addresses water scarcity and climate change. Unlike costly and slow-to-implement technological innovations, leveraging cross-sectoral synergies in water-intensive industries such as agriculture and manufacturing offers a pragmatic pathway to meeting critical carbon-reduction targets.}
}