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Applying Bayesian Model Averaging to Characterise Urban Residential Stock Turnover Dynamics

Author

Listed:
  • Wei Zhou

    (Department of Engineering, University of Cambridge)

  • Eoghan O'Neill

    (Department of Engineering, University of Cambridge)

  • Alice Moncaster

    (Department of Engineering, University of Cambridge)

  • David M Reiner

    (EPRG, CJBS, University of Cambridge)

  • Peter Guthrie

    (Department of Engineering, University of Cambridge)

Abstract

Building stock is a key determinant in building energy and China is the largest producer of CO2 emissions and the largest consumer of energy and building energy, so any effective energy and climate policy will need to address this key driver of energy use. However, official statistics on total floor area of urban residential stock in China only exist up to 2006. Previous studies estimating Chinese urban residential stock size and energy use made various questionable methodological assumptions and only produced deterministic results. We present a Bayesian approach to characterise the stock turnover dynamics and estimate stock size uncertainties. Firstly, a probabilistic dynamic building stock turnover model is developed to describe the building aging and demolition process governed by a hazard function specified by a parametric survival model. Secondly, using five candidate parametric survival models, the building stock turnover model is simulated through Markov Chain Monte Carlo (MCMC) to obtain posterior distributions of model-specific parameters, estimate marginal likelihood, and make predictions on stock size. Finally, Bayesian Model Averaging (BMA) is applied to create a model ensemble that combines the model-specific posterior predictive distributions of the stock evolution pathway in proportion to posterior model probabilities. This Bayesian modelling framework and its results in the form of probability distributions of annual total stock and age-specific substocks, can provide a solid basis for further modelling and analysis of policy trade-offs across embodied-versus-operational energy consumption and carbon emissions of buildings in the context of sector-wide transitions aimed at decarbonising buildings.
(This abstract was borrowed from another version of this item.)

Suggested Citation

  • 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.
  • Handle: RePEc:enp:wpaper:eprg1933
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    References listed on IDEAS

    as
    1. Tiago M. Fragoso & Wesley Bertoli & Francisco Louzada, 2018. "Bayesian Model Averaging: A Systematic Review and Conceptual Classification," International Statistical Review, International Statistical Institute, vol. 86(1), pages 1-28, April.
    2. Wei Zhou & Alice Moncaster & David M Reiner & Peter Guthrie, 2019. "Estimating Lifetimes and Stock Turnover Dynamics of Urban Residential Buildings in China," Sustainability, MDPI, vol. 11(13), pages 1-18, July.
    3. Soetaert, Karline & Petzoldt, Thomas, 2010. "Inverse Modelling, Sensitivity and Monte Carlo Analysis in R Using Package FME," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 33(i03).
    4. Li, Gong & Shi, Jing, 2010. "Application of Bayesian model averaging in modeling long-term wind speed distributions," Renewable Energy, Elsevier, vol. 35(6), pages 1192-1202.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    building stock; lifetime distribution; Bayesian Model Averaging; Markov Chain Monte Carlo; embodied energy; operational energy; China;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • 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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