Net-Zero Water Infrastructure: AI and CIRx for Intelligent Stormwater and Wastewater Management

Abstract

Water and wastewater utilities are among the largest municipal consumers of electricity, with pumping, aeration, and treatment processes accounting for a substantial share of a city’s operational carbon footprint, placing utilities squarely within the scope of municipal net-zero commitments even though water infrastructure is often treated as a secondary consideration relative to buildings and transportation in climate action planning. This paper examines how artificial intelligence (AI) and the Condition-Index Rx (CIRx) composite scoring methodology can be extended beyond conventional structural and hydraulic prioritization to explicitly incorporate energy and emissions considerations into stormwater and wastewater infrastructure management, supporting utilities pursuing net-zero operational targets. We propose an energy-aware extension to the CIRx framework in which asset rehabilitation and operational decisions are evaluated not only against structural condition, hydraulic performance, and consequence of failure, but also against their expected impact on pumping energy demand, aeration efficiency, and inflow-and-infiltration-driven treatment load, all of which carry direct energy and emissions consequences. We discuss machine learning applications for pump and aeration energy optimization, inflow-and-infiltration reduction targeting, and renewable energy integration at treatment facilities, and we examine how AI-driven decision support can help utilities identify rehabilitation investments that deliver combined structural, hydraulic, and carbon-reduction benefits rather than treating emissions reduction as a separate initiative. The paper closes with a discussion of data and governance considerations specific to energy-aware infrastructure decision-making and directions for future research on integrating climate and emissions data into infrastructure asset management.