From object-centred conservation to urban heritage digital twins: a scalable socio-technical framework for smart heritage governance

Urban heritage conservation faces growing pressure from climate change, redevelopment, infrastructure expansion, tourism and uneven institutional capacity. Digital documentation, heritage building information modelling (HBIM), geographic information systems (GIS), Internet of Things (IoT) sensing and digital twins have improved recording and monitoring, but much current work remains asset-centred and technically fragmented. This paper develops a scalable socio-technical framework for digital twin-enabled smart urban heritage governance. An integrative conceptual review informed by design science synthesises 30 scholarly and technical publications published between 2020 and 2026, together with six international policy and guidance documents. The framework defines a heritage digital twin as a governed decision system rather than a static three- dimensional model. It integrates seven layers: physical heritage and risk; data acquisition; semantic knowledge; analytical intelligence; governance and accountability; community participation; and feedback and learning. Translation gates connect asset, district, city and regional scales while addressing uncertainty, value conflict, privacy, institutional mandate and public communication. Three illustrative stress tests demonstrate how the framework can be adapted to data-rich, data-scarce and rapidly redeveloping urban contexts. The study provides a structured basis for empirical testing, policy design and context-sensitive implementation. AI-Enabled Evolution of Material Intelligence: From Passive Properties to