Sustainable, safe, smart, and connected building management to reduce greenhouse gas emission

Abstract

Climate change is a critical global challenge, and the building sector accounts for nearly 30% of global greenhouse gas (GHG) emissions, remaining a key target for mitigation. Indoor environments contribute significantly to GHG emissions, primarily through heating, cooling, lighting, and occupant-driven energy use. Indoor mapping, serving as the foundation for Digital Twins (DTs), provides a spatiotemporal framework that integrates sensor data with Building Information Modelling (BIM), Geographic Information Systems (GIS), and Internet of Things (IoT) to support energy-efficient, low-carbon building operations. However, many small- to medium-sized organizations lack practical frameworks to integrate indoor spatial data with operational and energy management systems. Therefore, this study aimed to develop an integrated solution (a DT-based indoor mapping system) to support sustainable and smart building management practices for the office space of Four Rivers Environmental Service Group in Thunder Bay, Ontario. Architectural plans, occupancy records, and energy data were combined with newly acquired spatial information from the laser distance meter and Light Detection And Ranging (LiDAR) to generate accurate 2D CAD drawings and 3D building models. These datasets were imported into ArcGIS Pro software and processed using ArcGIS Indoors tools to create a comprehensive indoor mapping environment that incorporates spatial features, room geometries, and building hierarchies. The resulting DT facilitates indoor navigation, space optimization, and asset management, illustrating the practical benefits of integrating structural, functional, and organizational data within a single platform. This framework provides a foundation for informed planning, operational efficiency, and sustainable management practices in small- to medium-sized organizational contexts. However, the study was limited by the exclusion of AI-driven predictive analytics and confidentiality constraints regarding direct energy metrics; therefore, future research should prioritize longitudinal studies correlating real-time positioning with utility smart-metering to empirically quantify energy demand reduction.