Year: 2025
Type: conference-paper
Abstract: This research presents a groundbreaking automated workflow for lifecycle carbon emissions assessment, seamlessly integrating Building Information Modeling (BIM), Geographic Information Systems (GIS), and Internet of Things (IoT) data into a unified framework. Addressing the complexities of carbon management in the construction sector, the workflow leverages Python to automate the Extract-Transform-Load (ETL) process and Neo4j for dynamic visualization and analysis. By streamlining data processing, the methodology achieves up to 80% reduction in processing time compared to traditional methods, significantly enhancing accuracy and efficiency. A case study at NTU campus validates the workflow's robustness and practical utility, identifying critical emission hotspots and enabling targeted sustainability interventions. The system surpasses BS EN 15978 standard requirements by providing real-time, scalable insights into carbon management across embodied, operational, and end-of-life stages. Its adaptability and replicability make it a valuable tool for institutions seeking to enhance sustainability practices. This innovative automation integration and data-driven analysis establishes a new benchmark for lifecycle carbon assessment, contributing to global efforts toward carbon neutrality in the built environment.
Source: CIB Conferences
Authors: Chien-Pu Huang, Shang-Hsien Hsieh
Institution: National Taiwan University
Language: English
Subfield: Building and Construction
Field: Engineering
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W4411472647