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Carbon reduction technology pathways for existing buildings in eight cities

Author

Listed:
  • Yu Qian Ang

    (Massachusetts Institute of Technology)

  • Zachary Michael Berzolla

    (Massachusetts Institute of Technology)

  • Samuel Letellier-Duchesne

    (Massachusetts Institute of Technology)

  • Christoph F. Reinhart

    (Massachusetts Institute of Technology)

Abstract

We work with policymakers in eight cities worldwide to identify technology pathways toward their near- and long-term carbon emissions reduction targets for existing buildings. Based on policymakers’ interests, we define city-specific shallow and deep retrofitting packages along with onsite photovoltaic generation potential. Without further grid decarbonization measures, stock-wide implementation of these retrofits in the investigated neighborhoods reduces energy use and carbon emissions by up to 66% and 84%, respectively, helping Braga, Dublin, Florianopolis, Middlebury, and Singapore to meet their 2030 goals. With projected grid decarbonization, Florianopolis and Singapore will reach their 2050 goals. The remaining emissions stem from municipalities not planning to electrify heating and/or domestic hot water use. Different climates and construction practices lead to varying retrofit packages, suggesting that comparable technology pathway analyses should be conducted for municipalities worldwide. Twenty months after the project ended, seven cities have implemented policy measures or expanded the analysis across their building stock.

Suggested Citation

  • Yu Qian Ang & Zachary Michael Berzolla & Samuel Letellier-Duchesne & Christoph F. Reinhart, 2023. "Carbon reduction technology pathways for existing buildings in eight cities," Nature Communications, Nature, vol. 14(1), pages 1-16, December.
    • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-37131-6
    DOI: 10.1038/s41467-023-37131-6
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    References listed on IDEAS

    as
    1. Ang, Yu Qian & Berzolla, Zachary Michael & Reinhart, Christoph F., 2020. "From concept to application: A review of use cases in urban building energy modeling," Applied Energy, Elsevier, vol. 279(C).
    2. Ang, Yu Qian & Polly, Allison & Kulkarni, Aparna & Chambi, Gloria Bahl & Hernandez, Matthew & Haji, Maha N., 2022. "Multi-objective optimization of hybrid renewable energy systems with urban building energy modeling for a prototypical coastal community," Renewable Energy, Elsevier, vol. 201(P1), pages 72-84.
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    Cited by:

    1. Alina Galimshina & Maliki Moustapha & Alexander Hollberg & Sébastien Lasvaux & Bruno Sudret & Guillaume Habert, 2024. "Strategies for robust renovation of residential buildings in Switzerland," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
    2. Ramon Elias Weber & Caitlin Mueller & Christoph Reinhart, 2024. "A hypergraph model shows the carbon reduction potential of effective space use in housing," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
    3. Sheng Yang & Hong-Yi Shi & Jia Liu & Yang-Yan Lai & Özgür Bayer & Li-Wu Fan, 2024. "Supercooled erythritol for high-performance seasonal thermal energy storage," Nature Communications, Nature, vol. 15(1), pages 1-13, December.
    4. Chen, Qi & Kuang, Zhonghong & Liu, Xiaohua & Zhang, Tao, 2024. "Application-oriented assessment of grid-connected PV-battery system with deep reinforcement learning in buildings considering electricity price dynamics," Applied Energy, Elsevier, vol. 364(C).

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