Common data environment for digital twins from building to city levels

Digital twin (DT) technology is pivotal for advancing sustainable, liveable, and resilient smart cities. As DTs scale from building to infrastructure and city levels, data management remains a key challenge due to increasing data heterogeneity. This paper addresses this gap by defining a common data environment (CDE) that connects physical and virtual spaces with three enablers: data sources, data management with functional components (FCs), and data consumers. A systematic literature review (SLR) of 264 papers (from 14,532) analyses these enablers, identifying knowledge gaps and future directions. A prospective DT data ecosystem model is proposed to support city-level DTs (CDT) and federated sub-DTs, integrating informational, technological, functional, organisational, and user-centred features. The paper highlights the immaturity of current data environments in managing heterogeneous data for comprehensive DT applications. It provides state-of-the-art insights and practical recommendations to researchers, practitioners, and policymakers to enhance data management in diverse smart city scenarios. • Data management challenges are highlighted for DTs from building to city levels. • CDE is defined with 3 enablers: data sources, data management FCs, data consumers. • The total of 264 DT case studies are identified and reviewed systematically. • The state-of-the-art, knowledge gaps and future directions are revealed. • A prospective DT data ecosystem model is proposed.