Assesses energy performance and health impacts in households, suggesting policy improvements.
The upgrading of energy-inefficient buildings is a critical part of the energy transition. Holistic analyses that foster informed and equitable policy interventions require interoperable data. We apply a principled approach that leverages The World Avatar to create a virtual knowledge graph underpinned by machine-understandable data representations. This approach provides a common terminology to integrate heterogeneous data sources to support multi-scale analysis of building energy retrofit options. We consider a case study in the UK based on the holistic analysis of household-level energy performance data, public health statistics and socio-economic metrics across geographic hierarchies. The analysis identifies regions with critical retrofit necessities, revealing disparities between these imperatives and extant policy levers. Granular retrofit targets are proposed to optimise resource allocation to the most vulnerable areas. Bespoke retrofit strategies are developed for 14.4 million households in the UK, providing actionable insights to support the targeted application of ‘fabric-first’ or ‘system-led’ retrofit pathways.
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Chen et al. (2025) studied this question.
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