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Building Stock Models for Embodied Carbon Emissions—A Review of a Nascent Field

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  • Ming Hu

    (School of Architecture, University of Notre Dame, Notre Dame, IN 46556, USA)

  • Siavash Ghorbany

    (Department of Civil and Environmental Engineering and Earth Sciences, College of Engineering, University of Notre Dame, Notre Dame, IN 46556, USA)

Abstract

Building stock modeling emerges as a critical tool in the strategic reduction of embodied carbon emissions, which is pivotal in reshaping the evolving construction sector. This review provides an overall view of modern methodologies in building stock modeling, homing in on the nuances of embodied carbon analysis in construction. Examining 23 seminal papers, our study delineates two primary modeling paradigms—top-down and bottom-up—each further compartmentalized into five innovative methods. This study points out the challenges of data scarcity and computational demands, advocating for methodological advancements that promise to refine the precision of building stock models. A groundbreaking trend in recent research is the incorporation of machine learning algorithms, which have demonstrated remarkable capacity, improving stock classification accuracy by 25% and urban material quantification by 40%. Furthermore, the application of remote sensing has revolutionized data acquisition, enhancing data richness by a factor of five. This review offers a critical examination of current practices and charts a course toward an environmentally prudent future. It underscores the transformative impact of building stock modeling in driving ecological stewardship in the construction industry, positioning it as a cornerstone in the quest for sustainability and its significant contribution toward the grand vision of an eco-efficient built environment.

Suggested Citation

  • Ming Hu & Siavash Ghorbany, 2024. "Building Stock Models for Embodied Carbon Emissions—A Review of a Nascent Field," Sustainability, MDPI, vol. 16(5), pages 1-18, March.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:5:p:2089-:d:1350125
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    References listed on IDEAS

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    1. Zhou, Wei & Moncaster, Alice & O'Neill, Eoghan & Reiner, David M. & Wang, Xinke & Guthrie, Peter, 2022. "Modelling future trends of annual embodied energy of urban residential building stock in China," Energy Policy, Elsevier, vol. 165(C).
    2. Hietaharju, Petri & Pulkkinen, Jari & Ruusunen, Mika & Louis, Jean-Nicolas, 2021. "A stochastic dynamic building stock model for determining long-term district heating demand under future climate change," Applied Energy, Elsevier, vol. 295(C).
    3. Yang, Jingjing & Deng, Zhang & Guo, Siyue & Chen, Yixing, 2023. "Development of bottom-up model to estimate dynamic carbon emission for city-scale buildings," Applied Energy, Elsevier, vol. 331(C).
    4. Yamaguchi, Yohei & Kim, Bumjoon & Kitamura, Takuya & Akizawa, Kotone & Chen, Hemiao & Shimoda, Yoshiyuki, 2022. "Building stock energy modeling considering building system composition and long-term change for climate change mitigation of commercial building stocks," Applied Energy, Elsevier, vol. 306(PA).
    5. Swan, Lukas G. & Ugursal, V. Ismet, 2009. "Modeling of end-use energy consumption in the residential sector: A review of modeling techniques," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(8), pages 1819-1835, October.
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