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Estimating Lifetimes and Stock Turnover Dynamics of Urban Residential Buildings in China

Author

Listed:
  • Wei Zhou

    (Department of Engineering, University of Cambridge)

  • Alice Moncaster

    (School of Engineering and Innovation, Open University)

  • David M Reiner

    (EPRG, CJBS, University of Cambridge)

  • Peter Guthrie

    (Department of Engineering, University of Cambridge)

Abstract

Building lifetime and stock turnover are both key determinants in modelling building energy and carbon. However in China, aside from anecdotal claims that urban residential buildings are generally short-lived, there are no recent official statistics, and empirical data are extremely limited. We present a system dynamics model where survival analysis is used to characterise the dynamic interplay between new construction, aging, and demolition of residential buildings in urban China. The uncertainties associated with building lifetime were represented using a Weibull distribution, whose shape and scale parameters were calibrated based on official statistics on floor area up to 2006. The calibrated Weibull lifetime distribution allowed us to estimate the dynamic stock turnover of Chinese urban residential buildings for 2007 to 2017. We find that the average lifetime of urban residential buildings was around 34 years, and the overall residential stock size reached 23.7 billion m 2 in 2017. The resultant age-specific sub-stocks provide a baseline for the overall stock, which—along with the calibrated Weibull lifetime distribution—can be used in further modelling and for analysis of policies to reduce the whole-life embodied and operational energy and CO 2 emissions in Chinese residential buildings.
(This abstract was borrowed from another version of this item.)

Suggested Citation

  • Wei Zhou & Alice Moncaster & David M Reiner & Peter Guthrie, 2019. "Estimating Lifetimes and Stock Turnover Dynamics of Urban Residential Buildings in China," Working Papers EPRG1923, Energy Policy Research Group, Cambridge Judge Business School, University of Cambridge.
    • Handle: RePEc:enp:wpaper:eprg1923
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    Cited by:

    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. Francesco Pomponi & Bernardino D’Amico, 2020. "Low Energy Architecture and Low Carbon Cities: Exploring Links, Scales, and Environmental Impacts," Sustainability, MDPI, vol. 12(21), pages 1-6, November.
    3. Langevin, J. & Reyna, J.L. & Ebrahimigharehbaghi, S. & Sandberg, N. & Fennell, P. & Nägeli, C. & Laverge, J. & Delghust, M. & Mata, É. & Van Hove, M. & Webster, J. & Federico, F. & Jakob, M. & Camaras, 2020. "Developing a common approach for classifying building stock energy models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 133(C).
    4. 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).
    5. Danyang Cheng & David M. Reiner & Fan Yang & Can Cui & Jing Meng & Yuli Shan & Yunhui Liu & Shu Tao & Dabo Guan, 2023. "Projecting future carbon emissions from cement production in developing countries," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
    6. Wei Zhou & Eoghan O'Neill & Alice Moncaster & David M Reiner & Peter Guthrie, 2019. "Applying Bayesian Model Averaging to Characterise Urban Residential Stock Turnover Dynamics," Working Papers EPRG1933, Energy Policy Research Group, Cambridge Judge Business School, University of Cambridge.
    7. 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.

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    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • O18 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Urban, Rural, Regional, and Transportation Analysis; Housing; Infrastructure
    • R21 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Household Analysis - - - Housing Demand

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