Operational, Embodied and Whole Life Cycle Assessment Credits in Green Building Certification Systems: Desktop Analysis and Natural Language Processing Approach
Globally, the issue of climate change is becoming increasingly significant due to the rapid change in weather
conditions, and the construction industry contributes significantly to this. Green building certification systems
(GBCS) are vital for ensuring sustainable practices in the construction industry. As a result, it is essential to
guarantee the effectiveness of the GBCS to capture adequate information on environmental impacts throughout
the building life cycle and ensure best practices. However, limited works have holistically studied the operational,
embodied and whole life cycle assessment (OEW) credits in GBCS. Therefore, this current study seeks to
address the gap by critically assessing the OEW credits in notable GBCS to determine areas of strengths and
weaknesses. This study applied desktop analysis and document similarity techniques of natural language processing
to assess the technical manuals of the GBCS. Five GBCS (LEED, BREEAM, Green Star NZ, LOTUS and
GREENSL) were selected from developed and developing countries, and the newly developed GBCS (IGBT and
BSAM) were selected to have both perspectives. The analysis revealed that operational credits were given more
attention compared to embodied credits. It is observed that waste-related credits are not prioritised. In addition,
the concept of circular economy is yet to gain attention in the existing GBCS. Also, the document similarity
among the GBCS indicates that the GBCS have some level of similarity. However, the LOTUS and BSAM certification
systems were observed to have low similarity compared to other GBCS. The research proposed an
improvement framework to enhance the effectiveness of the GBCS.
History
Preferred citation
Olanrewaju, O. I., Enegbuma, W. I. & Donn, M. (2024). Operational, Embodied and Whole Life Cycle Assessment Credits in Green Building Certification Systems: Desktop Analysis and Natural Language Processing Approach. Building and Environment, 258, 111569-111569. https://doi.org/10.1016/j.buildenv.2024.111569