Embodied GHG emissions of buildings – Critical reflection of benchmark comparison and in-depth analysis of drivers. Issue 3 (November 2020)

• Record Type: Journal Article
• Authors: Röck, Martin; Balouktsi, Maria; Mendes Saade, Marcella Ruschi; Rasmussen, Freja Nygaard; Hoxha, Endrit; Birgisdottir, Harpa; Frischknecht, Rolf; Habert, Guillaume; Passer, Alexander; Lützkendorf, Thomas
• Is Part Of: IOP conference series. Volume 588: Issue 3(2020)
• Publication Date: 2020-11
• Subjects: Earth sciences -- Periodicals; Environmental sciences -- Congresses; Environmental sciences -- Periodicals; 550.5
• DOI: 10.1088/1755-1315/588/3/032048
• Languages: English
• ISSNs: 1755-1307

Abstract

Abstract: In the face of the unfolding climate crisis, the role and importance of reducing Greenhouse gas (GHG) emissions from the building sector is increasing. This study investigates the global trends of GHG emissions occurring across the life cycle of buildings by systematically compiling life cycle assessment (LCA) studies and analysing more than 650 building cases. Based on the data extracted from these LCA studies, the influence of features related to LCA methodology and building design is analysed. Results show that embodied GHG emissions, which mainly arise from manufacturing and processing of building materials, are dominating life cycle emissions of new, advanced buildings. Analysis of GHG emissions at the time of occurrence, shows the upfront 'carbon spike' and emphasises the need to address and reduce the GHG 'investment' for new buildings. Comparing the results with existing life cycle-related benchmarks, we find only a small number of cases meeting the benchmark. Critically reflecting on the benchmark comparison, an in-depth analysis reveals different reasons for cases achieving the benchmark. While one would expect that different building design strategies and material choices lead to high or low embodied GHG emissions, the results mainly correlate with decisions related to LCA methodology, i.e. the scope of the assessments. The results emphasize the strong need for transparency in the reporting of LCA studies as well as need for consistency when applying environmental benchmarks. Furthermore, the paper opens up the discussion on the potential of utilizing big data and machine learning for analysis and prediction of environmental performance of buildings.