To address the growing complexity between the built environment and urban life, modern urban planning has increasingly relied on digital technologies for decision-making, including City Information Modeling (CIM). While CIM has matured from standards and data integration to real-time analytics and digital twins, most initiatives optimize physical systems while struggling to incorporate “intangible” citizen-generated data. This paper addresses this gap by presenting a replicable methodology to embed a customized Environment, Social, and Governance (ESG) framework with a CIM workflow by joining both tangible and intangible city layers to inform planning. The general methodology includes: (1) identifying a French priority neighborhood (QPV) and prioritizing local ESG indicators with a focus group, (2) conceptualizing and designing a data model that integrates urban geometry with subjective human perceptions, and (3) prototyping and developing the CIM model. This approach allows for a multi-scale assessment of socio-environmental sustainability, accounting for uncertainty in human perception. The novelty of this research lies in creating a replicable CIM method that converts non-geocoded citizen perceptions into spatially weighted and probabilistic indicators. Future work will complete the CIM workflow, detailing the integration of the ESG indicators in a practical CIM tool designed to inform decisions through reporting and recommendations.
