A Governance Framework of Urban Digital Twins for Smart City Management in Iran: A Hybrid Methodological Approach and A Multi-Layered Design

The growing complexity of urban infrastructure and the demand for sustainability are propelling the development of digitalization in cities. Urban digital twins (UDTs) are an emerging digital transformation solution for smart city management; however, there is limited knowledge about the empirical use of UDTs in developing countries. In this research, the first three stages of descriptive statistical analysis, multi-criteria decision making, and structural equation modeling are used for designing and validating a multi-layered governance structure for the implementation of UDTs in Iranian smart cities. A structured questionnaire, grounded on a horizontal digital twin architecture based on five layers, Technological Infrastructure (TI), Connectivity and Access (CA), Data and Processing (DP), Governance and Operational Management (GOM), and Innovation Enablement (IE), was used to conduct a quantitative expert-based survey. 22 experts in the civil engineering, construction, safety management, and digital technology fields rated the 12 components on the five-point Likert scale. All the components were significantly higher than the neutral value (3.0), with a range of means from 3.59 – 4.36 (Hedges’ g = 0.52 – 2.23), showing a high level of consensus among all components. Phase 2 applied the Best-Worst Method, assigning the highest priority to TI (0.284), followed by DP (0.246), GOM (0.221), CA (0.152), and IE (0.097); all consistency ratios were below 0.10. Structural equation modeling (SEM) was used in phase 3. The sample size (n = 22) was expanded by Monte Carlo data augmentation, resulting in 80 observations, which had the multivariate dependence retained by Cholesky decomposition. The statistical equivalence was checked by comparing with the Frobenius norm (2.070) and the analysis of the eigenvalue spectrum. The model demonstrated excellent fit (χ²/df = 1.87; CFI = 0.96; TLI = 0.95; RMSEA = 0.063; SRMR = 0.071). The direct influence on TI was greatest for Public–Private Partnership (PPP) (β = 0.74, p < 0.001). The results highlight the importance of scalable, federated infrastructure, open data policies, national standards, and IoT governance to support the development of UDT implementation in Iran.