4.4 Article

The application of life cycle assessment in buildings: challenges, and directions for future research

期刊

出版社

SPRINGER HEIDELBERG
DOI: 10.1007/s11367-022-02058-5

关键词

Life cycle assessment (LCA); Building information modeling (BIM); Dynamic data; Semantic models; Machine learning (ML)

资金

  1. Engineering and Physical Sciences Research Council (EPSRC) in the UK
  2. Fond National de la Recherche (FNR) in Luxembourg [EP/T019514/1, INTER/UKRI/19/14106247]

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This paper reviews the current research in life cycle assessment (LCA) applied to buildings, focusing on trends and identifying gaps for future research. A systematic literature review was conducted to identify current research and applications of LCA in buildings. The paper argues for the development of new generation LCA methods that can continuously learn from real-time data and inform effective operation and management strategies for buildings. It also emphasizes the importance of considering the time dimension in product system modeling and the combination of life cycle impact assessment models for more comprehensive and reliable LCA results. The paper promotes the concept of semantic-based dynamic LCA to achieve cradle-to-grave-to-reincarnation environmental sustainability capability and highlights the need to leverage digital building resources for accurate and reliable environmental assessments.
Purpose This paper reviews the state-of-the art research in life cycle assessment (LCA) applied to buildings. It focuses on current research trends, and elaborates on gaps and directions for future research. Methods A systematic literature review was conducted to identify current research and applications of LCA in buildings. The proposed review methodology includes (i) identifying recent authoritative research publications using established search engines, (ii) screening and retaining relevant publications, and (iii) extracting relevant LCA applications for buildings and analyzing their underpinning research. Subsequently, several research gaps and limitations were identified, which have informed our proposed future research directions. Results and discussions This paper argues that humans can attenuate and positively control the impact of their buildings on the environment, and as such mitigate the effects of climate change. This can be achieved by a new generation of LCA methods and tools that are model based and continuously learn from real-time data, while informing effective operation and management strategies of buildings and districts. Therefore, the consideration of the time dimension in product system modeling is becoming essential to understand the resulting pollutant emissions and resource consumption. This time dimension is currently missing in life cycle inventory databases. A further combination of life cycle impact assessment (LCIA) models using time-dependent characterization factors can lead to more comprehensive and reliable LCA results. Conclusions and recommendations This paper promotes the concept of semantic-based dynamic (real-time) LCA, which addresses temporal and spatial variations in the local built and environmental ecosystem, and thus more effectively promotes a cradle-to-grave-to-reincarnation environmental sustainability capability. Furthermore, it is critical to leverage digital building resources (e.g., connected objects, semantic models, and artificial intelligence) to deliver accurate and reliable environmental assessments.

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