4.7 Article

An analytical method for evaluating and visualizing embodied carbon emissions of buildings

期刊

BUILDING AND ENVIRONMENT
卷 168, 期 -, 页码 -

出版社

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.buildenv.2019.106476

关键词

Life cycle inventory; Building materials; Replacements; Technological developments; Zero emission; Low-carbon building code regulations

资金

  1. Norwegian University of Science and Technology

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Greenhouse gas emissions associated with buildings constitute a large part of global emissions, where building materials and associated processes make up a significant fraction. These emissions are complicated to evaluate with current methodologies due to, amongst others, the lack of a link between the material inventory data and the aggregated results. This paper presents a method for evaluating and visualizing embodied emission (EE) data of building material production and transport, including replacements, from building life cycle assessments (LCAs). The method introduces a set of metrics that simultaneously serve as a breakdown of the EE results and as an aggregation of the building's inventory data. Furthermore, future emission reductions due to technological improvements are modeled and captured in technological factors for material production and material transport. The material inventory is divided into building subparts for high-resolution analysis of the EE. The metrics and technological factors are calculated separately for each subpart, which can then be evaluated in relation to the rest of the building and be compared to results from other buildings. Two methods for evaluating and visualizing the results are presented to illustrate the method's usefulness in the design process. A case study is used to demonstrate the methods. Key driving factors of EE are identified together with effective mitigation strategies. The inclusion of technological improvements shows a significant reduction in EE ( 11.5%), reducing the importance of replacements. Furthermore, the method lays the foundation for further applications throughout the project phases by combining case-specific data with statistical data.

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