Smart Water Management: Governance Innovation, Technological Integration, and Policy Pathways Toward Economic and Ecological Sustainability

Smart water management (SWM) represents a transformative shift in urban water governance, integrating advanced digital technologies—including the Internet of Things (IoT), Artificial Intelligence (AI), big data analytics, and digital twin modeling—to enable real-time monitoring, predictive analytics, and adaptive decision-making. While drawing extensively on a structured literature review to build its theoretical foundation, this manuscript is primarily presented as a research paper that combines conceptual analysis with empirical insights derived from comparative case studies, rather than a standalone comprehensive review. A five-layer system architecture—encompassing data sensing, transmission, processing, intelligent analysis, and decision support—is introduced to evaluate how technological components interact across operational layers. The model is applied to two representative cases: Singapore’s Smart Water Grid and selected pilot programs in Chinese cities (Shenzhen, Hangzhou, Beijing). These cases are analyzed for their level of digital integration, policy alignment, and performance outcomes, offering insights into both mature and emerging smart water implementations. Findings indicate that the transition from manual to intelligent governance significantly enhances system performance and robustness, particularly in response to climate-induced disruptions. Despite benefits such as reduced non-revenue water and improved pollution control, challenges including high initial investment, data interoperability issues, and cybersecurity risks remain critical barriers to widespread adoption. Policy recommendations focus on establishing national standards, promoting cross-sectoral data sharing, encouraging public–private partnerships, and investing in workforce development to support the long-term sustainability and scalability of smart water initiatives.