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Life Cycle Assessment of building stocks from urban to transnational scales: A review

Journal

RENEWABLE & SUSTAINABLE ENERGY REVIEWS
Volume 74, Issue -, Pages 316-332

Publisher

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.rser.2017.02.060

Keywords

Building stocks; Energy consumption; Bottom-up; LCA; GIS; Decision support

Funding

  1. National Research Fund, Luxembourg [AFR - 7579115]

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Buildings are responsible for a large share of the energy consumption and environmental impacts worldwide. An assessment of the energy demand and environmental performance of building stocks at large spatial scales is increasingly needed for decision support in sustainable planning and policy making. While current bottom-up building stock models mainly focus on the operational energy use, a new research trend has introduced Life Cycle Assessment (LCA) for a more holistic environmental performance assessment including several stages of the building life cycle. This paper reviews a set of selected bottom-up LCA studies evaluating the environmental impact of building stocks at several scales, from urban to transnational. The selected studies were analyzed according to three common elements: building stock aggregation model, energy analysis and LCA. Two main stock aggregation approaches, archetypes and building-by-building, are applied to model individual buildings and extrapolate results at the stock level. Bottom-up energy models, either statistical or engineering-based, provide energy profiles for the operational phase of buildings. LCA is subsequently performed to assess the building environmental performance and support policy decisions. Current limitations and opportunities for the improvement of the LCA of large building stocks are finally addressed and discussed to support future works. Building stock aggregation models may be enhanced by addressing model calibration, integration with Geographic Information Systems (GIS) and stock dynamics models. Uncertainty propagation, sensitivity analysis and model simplification are recommended for energy models and LCA to bridge data gaps and reduce computationally intensive models. Future research is encouraged on the inclusion of climate change, evolution of the energy supply and integrated modeling for the LCA of building stocks.

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