A Comparative Framework for Building Life Cycle Embodied Carbon Emissions Databases and Its Application for Public Databases
Chen, Shi Yu
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CitationChen, Shi Yu. 2022. A Comparative Framework for Building Life Cycle Embodied Carbon Emissions Databases and Its Application for Public Databases. Master's thesis, Harvard Graduate School of Design.
AbstractData availability and accuracy are some of the main obstacles to calculating the life-cycle embodied carbon emissions in buildings. There have been several studies to assess life cycle assessment (LCA) databases in the past. These database studies often rely heavily on commercial databases, and most studies only evaluate a single data point for each material in the building life cycle inventory. Comparing databases in this manner can be potentially biased, not representative as a whole, and lacking a systematic approach. This study proposes a systematic comparative framework as an addition to existing methods to aid the comparison of construction-material embodied carbon¬ databases, which comprise a part of LCA. The framework identifies the underlying issues and difficulties in comparing embodied carbon databases. It then provides a fair method for data comparison across the databases. Finally, it assists the understanding of data availability and data homogeneity within and across the databases. The framework's applicability is demonstrated by comparing three publicly available databases: EC3, the ICE Database, and the ÖKOBAUDAT Database. Life cycle embodied carbon assessments (LCECA) on a single-family house are performed using the aggregate data from the three public databases and the commercial database Gabi Database within the LCA tool Tally. The embodied carbon study suggests that the materials' median embodied carbon factors value from the aggregated public database provides a reasonable embodied carbon assessment compared to the commercial data. However, the heterogeneity of possible results from the public dataset highlights the potential errors and consequences of single material data selection.
Citable link to this pagehttps://nrs.harvard.edu/URN-3:HUL.INSTREPOS:37372338