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A component based bottom-up building stock model for comprehensive environmental impact assessment and target control

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  • Heeren, Niko
  • Jakob, Martin
  • Martius, Gregor
  • Gross, Nadja
  • Wallbaum, Holger

Abstract

The building stock is one of the most important energy consumers worldwide. Therefore, a number of energy reduction targets and regulations exist for the construction sector. Different building stock models have been developed in order to investigate the potentials of energy-efficiency and changes in energy source in the building stock. However, these models often have important shortcomings, since they are single-issued and do not include the life cycle of buildings. Thus, we propose an innovative assessment methodology in the form of a life cycle-based building stock model (LC-Build). The building stock is clustered in building cohorts of similar construction and equipment characteristics in terms of type, construction period and building technology systems. The most important building components are assigned specific thermal transmittance values. Figures for diffusion and retrofit rate describe the development of the building stock fabric. Additionally, environmental impact from the energy supply side is taken into account. This approach facilitates the evaluation of the effectiveness of measures and their dynamics on the building stock, such as newer and more efficient technologies and practices related to energy policies and prices. Furthermore, the model has a direct relationship to the construction activity (energy-efficiency measures, substitution of fossil energy based heating systems) and fosters the comprehension of material flows, related environmental impact, and costs. The practicality of this approach is demonstrated by means of a case study in the city of Zurich in Switzerland. The results suggest that Zurich has a remarkable potential to reduce its greenhouse gas emissions from the building sector: 85% by 2050. The case study highlights the advantages of the proposed modeling approach. The LC-Build is a valuable tool to identify and test sustainable energy targets for building stocks, such as the European 20–20–20 target.

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  • Heeren, Niko & Jakob, Martin & Martius, Gregor & Gross, Nadja & Wallbaum, Holger, 2013. "A component based bottom-up building stock model for comprehensive environmental impact assessment and target control," Renewable and Sustainable Energy Reviews, Elsevier, vol. 20(C), pages 45-56.
    • Handle: RePEc:eee:rensus:v:20:y:2013:i:c:p:45-56
    DOI: 10.1016/j.rser.2012.11.064
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    5. Röck, Martin & Baldereschi, Elena & Verellen, Evelien & Passer, Alexander & Sala, Serenella & Allacker, Karen, 2021. "Environmental modelling of building stocks – An integrated review of life cycle-based assessment models to support EU policy making," Renewable and Sustainable Energy Reviews, Elsevier, vol. 151(C).
    6. Yang, Xining & Hu, Mingming & Heeren, Niko & Zhang, Chunbo & Verhagen, Teun & Tukker, Arnold & Steubing, Bernhard, 2020. "A combined GIS-archetype approach to model residential space heating energy: A case study for the Netherlands including validation," Applied Energy, Elsevier, vol. 280(C).
    7. Niko Heeren & Stefanie Hellweg, 2019. "Tracking Construction Material over Space and Time: Prospective and Geo‐referenced Modeling of Building Stocks and Construction Material Flows," Journal of Industrial Ecology, Yale University, vol. 23(1), pages 253-267, February.
    8. Mastrucci, Alessio & Marvuglia, Antonino & Leopold, Ulrich & Benetto, Enrico, 2017. "Life Cycle Assessment of building stocks from urban to transnational scales: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 74(C), pages 316-332.
    9. Anderson, John E. & Wulfhorst, Gebhard & Lang, Werner, 2015. "Energy analysis of the built environment—A review and outlook," Renewable and Sustainable Energy Reviews, Elsevier, vol. 44(C), pages 149-158.
    10. Marina Economidou & Paolo Zangheri & Andreas Müller & Lukas Kranzl, 2018. "Financing the Renovation of the Cypriot Building Stock: An Assessment of the Energy Saving Potential of Different Policy Scenarios Based on the Invert/EE-Lab Model," Energies, MDPI, vol. 11(11), pages 1-25, November.
    11. Andreas Froemelt & René Buffat & Stefanie Hellweg, 2020. "Machine learning based modeling of households: A regionalized bottom‐up approach to investigate consumption‐induced environmental impacts," Journal of Industrial Ecology, Yale University, vol. 24(3), pages 639-652, June.
    12. Shi, Qian & Lai, Xiaodong & Xie, Xin & Zuo, Jian, 2014. "Assessment of green building policies – A fuzzy impact matrix approach," Renewable and Sustainable Energy Reviews, Elsevier, vol. 36(C), pages 203-211.
    13. Nägeli, Claudio & Jakob, Martin & Catenazzi, Giacomo & Ostermeyer, York, 2020. "Policies to decarbonize the Swiss residential building stock: An agent-based building stock modeling assessment," Energy Policy, Elsevier, vol. 146(C).
    14. Carine Lausselet & Johana Paola Forero Urrego & Eirik Resch & Helge Brattebø, 2021. "Temporal analysis of the material flows and embodied greenhouse gas emissions of a neighborhood building stock," Journal of Industrial Ecology, Yale University, vol. 25(2), pages 419-434, April.
    15. Yang, Xining & Hu, Mingming & Tukker, Arnold & Zhang, Chunbo & Huo, Tengfei & Steubing, Bernhard, 2022. "A bottom-up dynamic building stock model for residential energy transition: A case study for the Netherlands," Applied Energy, Elsevier, vol. 306(PA).

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