SLiCE: An Open Data Model for Scalable High-Definition Life Cycle Engineering, Hotspot Analysis and Dynamic Assessment of Buildings.

Abstract Building construction and operation are responsible for around 40% of global energy-related greenhouse gas emissions. To identify emissions reduction and removal potentials as well as wider environmental impacts, researchers, policy, and decision makers need comprehensive life cycle sustainability assessment insights on individual buildings and building stocks at large. This article proposes a data model for scalable, high-definition life cycle analysis of building – the SLiCE data model – as a promising solution to overcome the limitations identified for existing models. The article conceptualizes the problem within the Space-Time-Indicator Nexus; presents the proposed SLiCE data structure; and showcases practical uses of SLiCE data for environmental hotspot analysis as well as for dynamic assessment of climate impacts. The open SLiCE data model and SLiCE hotspot analysis tool are henceforth available for implementation within life cycle assessment of building and building stocks, enabling comprehensive insights on buildings’ environmental impacts across spatiotemporal scales. Software and data availability The SLiCE building data model as well as the presented implementation in the SLiCE hotspot analysis prototype are open source and available with this article. The SLiCE hotspot analysis, implemented as an IPython Jupyer Notebook with interactive widgets, tool is available on Github (https://github.com/mroeck/slice_hotspots/), with the submission pre-release published via Zenodo (https://zenodo.org/badge/latestdoi/645859866). All items are published under a GNU General Public License v3.0. We encourage you to review, reuse, and refine the model and scripts and share-alike. Preprint (not peer-reviewed) Röck M, Passer A, Allacker K. “SLiCE: An Open Data Model for Scalable High-Definition Life Cycle Engineering, Hotspot Analysis and Dynamic Assessment of Buildings.” 2023, Preprint DOI: https://doi.org/10.5281/zenodo.836924