Forecasting urban residential stock turnover dynamics using system dynamics and Bayesian model averaging
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DOI: 10.1016/j.apenergy.2020.115388
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- Wei Zhou & Eoghan O’Neill & Alice Moncaster & David Reiner & Peter Guthrie, 2020. "Forecasting Urban Residential Stock Turnover Dynamics using System Dynamics and Bayesian Model Averaging," Working Papers EPRG2016, Energy Policy Research Group, Cambridge Judge Business School, University of Cambridge.
- Zhou, W. & O’Neill, E. & Moncaster, A. & Reiner D. & Guthrie, P., 2020. "Forecasting Urban Residential Stock Turnover Dynamics using System Dynamics and Bayesian Model Averaging," Cambridge Working Papers in Economics 2054, Faculty of Economics, University of Cambridge.
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- Wei Zhou & Alice Moncaster & Eoghan O’Neill & David M Reiner & Xinke Wang & Peter Guthrie, 2022. "Modelling future trends of annual embodied energy of urban residential building stock in China," Working Papers EPRG2209, Energy Policy Research Group, Cambridge Judge Business School, University of Cambridge.
- Zhou, W. & Moncaster, A. & O’Neill, E. & Reiner, D. & Wang, X. & Guthrie, P., 2022. "Modelling future trends of annual embodied energy of urban residential building stock in China," Cambridge Working Papers in Economics 2223, Faculty of Economics, University of Cambridge.
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More about this item
Keywords
Building stock; Lifetime distribution; System dynamics; 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
- 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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