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Historic Buildings as Urban Sensors: Multiscale Diagnostics for Climate-Resilient Cities

Submitted:

11 March 2026

Posted:

12 March 2026

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Abstract
Built heritage is increasingly affected by climate-driven processes, yet its capacity to inform broader understandings of urban environmental change remains insufficiently explored. Here, we synthesize recent literature (2020–2024) on the application of the Historic Urban Landscape (HUL) approach to the integrated management of cultural heritage under climate risk, reframing the historic built environment as a multiscale diagnostic medium for climate–urban interactions. We analyze the steps and tools employed to support decision-making across territorial planning, risk assessment, and heritage governance in the papers selected from Web of Science, Science Direct, and Scopus databases. Results show that the approach is a flexible analytical framework that allows the integration of heterogeneous data, multi-criteria evaluations, and diverse stakeholder perspectives across spatial and temporal scales. Information modelling tools are shown to play a central role in structuring territorial knowledge, identifying patterns of vulnerability, and supporting comparative analyses across urban contexts. Nonetheless, significant challenges persist, including limited quantification of climate-induced degradation mechanisms, uncertainties in linking vulnerability assessments to predictive models, structural constraints on participatory implementation, and a tendency to apply the approach as a checklist due to inadequate understanding of its holistic dimensions. Overall, the HUL approach emerges as a scalable and transferable framework for embedding cultural heritage within climate research, advancing the conceptual integration of built heritage into resilience science and sustainability-oriented urban systems.
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Copyright: This open access article is published under a Creative Commons CC BY 4.0 license, which permit the free download, distribution, and reuse, provided that the author and preprint are cited in any reuse.
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