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A Probabilistic Dynamic Material Flow Analysis Model for Chinese Urban Housing Stock

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Listed:
  • Zhi Cao
  • Lei Shen
  • Shuai Zhong
  • Litao Liu
  • Hanxiao Kong
  • Yanzhi Sun

Abstract

The stock†driven dynamic material flow analysis (MFA) model is one of the prevalent tools to investigate the evolution and related material metabolism of the building stock. There exists substantial uncertainty inherent to input parameters of the stock†driven dynamic building stock MFA model, which has not been comprehensively evaluated yet. In this study, a probabilistic, stock†driven dynamic MFA model is established and China's urban housing stock is selected as the empirical case. This probabilistic dynamic MFA model has the ability to depict the future evolution pathway of China's housing stock and capture uncertainties in its material stock, inflow, and outflow. By means of probabilistic methods, a detailed and transparent estimation of China's housing stock and its material metabolism behavior is presented. Under a scenario with a saturation level of the population, urbanization, and living space, the median value of the urban housing stock area, newly completed area, and demolished area would peak at around 49, 2.2, and 2.2 billion square meters, respectively. The corresponding material stock and flows are 79, 3.5, and 3.3 billion tonnes, respectively. Uncertainties regarding housing stock and its material stock and flows are non†negligible. Relative uncertainties of the material stock and flows are above 50%. The uncertainty importance analysis demonstrates that the material intensity and the total population are major contributions to the uncertainty. Policy makers in the housing sector should consider the material efficiency as an essential policy to mitigate material flows of the urban building stock and to lower the risk of policy failures.

Suggested Citation

  • Zhi Cao & Lei Shen & Shuai Zhong & Litao Liu & Hanxiao Kong & Yanzhi Sun, 2018. "A Probabilistic Dynamic Material Flow Analysis Model for Chinese Urban Housing Stock," Journal of Industrial Ecology, Yale University, vol. 22(2), pages 377-391, April.
    • Handle: RePEc:bla:inecol:v:22:y:2018:i:2:p:377-391
    DOI: 10.1111/jiec.12579
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    Citations

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    Cited by:

    1. Bradley Kloostra & Benjamin Makarchuk & Shoshanna Saxe, 2022. "Bottom‐up estimation of material stocks and flows in Toronto's road network," Journal of Industrial Ecology, Yale University, vol. 26(3), pages 875-890, June.
    2. Zhou, Wei & O'Neill, Eoghan & Moncaster, Alice & Reiner, David M. & Guthrie, Peter, 2020. "Forecasting urban residential stock turnover dynamics using system dynamics and Bayesian model averaging," Applied Energy, Elsevier, vol. 275(C).
    3. Ling Zhang & Qingqing Lu & Zengwei Yuan & Songyan Jiang & Huijun Wu, 2023. "A bottom‐up modeling of metabolism of the residential building system in China toward 2050," Journal of Industrial Ecology, Yale University, vol. 27(2), pages 587-600, April.
    4. Rafaela Tirado & Adélaïde Aublet & Sylvain Laurenceau & Mathieu Thorel & Mathilde Louërat & Guillaume Habert, 2021. "Component-Based Model for Building Material Stock and Waste-Flow Characterization: A Case in the Île-de-France Region," Sustainability, MDPI, vol. 13(23), pages 1-34, November.
    5. Qiance Liu & Litao Liu & Xiaojie Liu & Shenggong Li & Gang Liu, 2021. "Building stock dynamics and the impact of construction bubble and bust on employment in China," Journal of Industrial Ecology, Yale University, vol. 25(6), pages 1631-1643, December.
    6. 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).
    7. Liang Yuan & Weisheng Lu & Yijie Wu, 2023. "Characterizing the spatiotemporal evolution of building material stock in China's Greater Bay Area: A statistical regression method," Journal of Industrial Ecology, Yale University, vol. 27(6), pages 1553-1566, December.
    8. Zhu, Chen & Li, Xiaodong & Zhu, Weina & Gong, Wei, 2022. "Embodied carbon emissions and mitigation potential in China's building sector: An outlook to 2060," Energy Policy, Elsevier, vol. 170(C).
    9. Xaysackda Vilaysouk & Savath Saypadith & Seiji Hashimoto, 2022. "Semisupervised machine learning classification framework for material intensity parameters of residential buildings," Journal of Industrial Ecology, Yale University, vol. 26(1), pages 72-87, February.
    10. Ruirui Zhang & Jing Guo & Dong Yang & Hiroaki Shirakawa & Feng Shi & Hiroki Tanikawa, 2022. "What matters most to the material intensity coefficient of buildings? Random forest‐based evidence from China," Journal of Industrial Ecology, Yale University, vol. 26(5), pages 1809-1823, October.

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