A multivariable regression tool for embodied carbon footprint prediction in housing habitat. (April 2016)

• Record Type: Journal Article
• Authors: Gardezi, Syed Shujaa Safdar; Shafiq, Nasir; Zawawi, Noor Amila Wan Abdullah; Khamidi, Muhd Faris; Farhan, Syed Ahmad
• Is Part Of: Habitat international. Volume 53(2016)
• Page Start: 292
• Page End: 300
• Publication Date: 2016-04
• Subjects: Embodied carbon footprint -- Life cycle assessment -- Multivariable regression -- Prediction tool -- Housing construction; Human settlements -- Periodicals; 307
• DOI: 10.1016/j.habitatint.2015.11.005
• Languages: English
• ISSNs: 0197-3975

Abstract

Abstract: A novel embodied carbon prediction tool has been developed for conventionally constructed housing units. Single and double storey terraced, semi-detached and detached housing projects were evaluated by adoption of partial life cycle assessment (LCA) framework. The statistical technique of multivariable regression analysis was merged with LCA and building information modeling (BIM) for prediction of such environmental issue in housing sector. The assessment was limited to pre-use phase with LCA boundary of "cradle to site". The criteria and requirements for a statistically consistent and efficient prediction tool were successfully satisfied with an acceptable average prediction error of less than ±5%. Based on very basic explanatory variables, the tool also helped to manage the barrier of huge data requirements for such environmental studies. The study is expected to act as a milestone and help the researchers and industry professionals for quick, effective and sustainable environmental assessment, decision making and solutions. Highlights: Significant contributions of CO2 from Malaysian housing sector. Carbon footprint predictions, a concern for sustainable housing construction. Developed a novel embodied carbon footprint prediction tool for housing sector. Successful compliance to criteria of consistent/efficient prediction tool. Study of this type conducted for the first time in Malaysian tropical climate.