Integrated Smart Urban Systems and Resource Efficiency: A Structural Equation Modelling Study in Saudi Arabia

This study investigates how integrated smart urban systems enhance resource efficiency in Saudi Arabia’s rapidly urbanising cities. Despite growing global interest in smart cities, there remains a critical gap in empirical research that simultaneously examines smart energy, water, and waste systems within a unified analytical framework, particularly in emerging urban contexts, while the mediating role of governance efficiency remains underexplored. Adopting a quantitative survey design, this study employs Structural Equation Modelling (SEM) to analyse 384 valid data from 384 stakeholders across selected urban areas. A stratified sampling approach ensures representation of policymakers, urban planners, and infrastructure managers, and measurement constructs are adapted from validated scales to ensure reliability and validity. The study examines relationships between smart energy systems, smart water management, smart waste monitoring, governance efficiency, and resource efficiency outcomes, with governance efficiency conceptualised as a mediating variable enhancing the effectiveness of smart urban systems. The study hypothesises that integrated smart technologies significantly improve resource efficiency, with smart infrastructure as a key predictor; structural model robustness is assessed using standard SEM fit indices, with CFI (0.93), TLI (0.91), and RMSEA (0.052). This research contributes to theory by integrating smart urban systems and governance into a unified framework, extending smart city and circular economy literature, while offering practical insights aligned with Saudi Vision 2030 to support sustainable urban development. It is recommended that policymakers prioritise integrated smart infrastructure, strengthen institutional frameworks, and promote public awareness to maximise resource optimisation. Future research should adopt longitudinal designs, expand across multiple cities, and incorporate behavioural and policy variables to enhance generalizability and deepen insights into smart urban resource efficiency.