Technique for quantification of embodied carbon footprint of construction projects using probabilistic emission factor estimators. (15th April 2016)

• Record Type: Journal Article
• Authors: Yeo, Zhiquan; Ng, Ruisheng; Song, Bin
• Is Part Of: Journal of cleaner production. Volume 119(2016:Apr.)
• Page Start: 135
• Page End: 151
• Publication Date: 2016-04-15
• Subjects: Carbon footprint -- Life-cycle assessment -- Embodied emissions -- Green building -- Computational tools; Factory and trade waste -- Management -- Periodicals; Manufactures -- Environmental aspects -- Periodicals; Déchets industriels -- Gestion -- Périodiques; Usines -- Aspect de l'environnement -- Périodiques; 628.5
• DOI: 10.1016/j.jclepro.2016.01.076
• Languages: English
• ISSNs: 0959-6526

Abstract

Abstract: Buildings consume a significant proportion of the world's resources, mostly in the form of materials usage. In addition, with the ongoing efforts focusing on reducing the operational energy consumption of buildings, the embodied emissions of buildings are expected to increase in proportion in the future. With the increasing focus placed on the embodied emissions of buildings, organizations in the construction industry will face requirements to report information regarding the embodied emissions of buildings. However, in order to obtain the embodied emission of buildings, tremendous efforts have to be placed as the process is data intensive. In order to overcome this challenge, a streamlined technique is proposed to minimize the efforts required by practitioner to obtain the embodied carbon footprint. The proposed technique comprises a probabilistic model of emission factor estimators used to estimate the required embodied emissions. Based on the four projects presented as case study for the proposed technique, the practitioner would be required to manually match the appropriate emission factors to the activity data for between 12 and 21% of the data points. This is in contrast to the current traditional technique, which requires the manual matching of 100% of data points. The resultant deviation of the computed embodied carbon footprint from the proposed technique and the current technique was between −0.25% and +3.65%.