Characterising Chinese Urban Residential Stock Turnover Dynamics using Bayesian Model Averagi
Building stock turnover is a key determinant in building energy modelling. In turn, building lifetime is integral to the dynamics of stock turnover. The building stock of China, as evidence for building energy policies, is a strategically important but under-researched area. Despite anecdotal claims that urban residential buildings are generally short-lived, there are no official statistics on building lifetime, and empirical data is extremely limited. Moreover, 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 have made various questionable methodological assumptions and only produced deterministic results. This paper presents a Bayesian approach to characterise the stock turnover dynamics and estimate stock size uncertainties for the period from 2007 to 2017. Firstly, a probabilistic dynamic 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, with each of five candidate parametric survival models and using official statistics up to 2006, the dynamic 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 of stock size. Finally, Bayesian Model Averaging (BMA) is applied to create a model ensemble that combines the model-specific posterior predictive distributions of the 2007-2017 stock evolution pathway in proportion to posterior model probabilities. The distribution of building lifetime, unconditional on the survival models and the model-specific parameters, is obtained. This study is a first-of-its-kind use of a full Bayesian approach to investigate model and parameter uncertainties that were not taken account of by limited existing models targeting Chinese building stock. The Bayesian modelling approach and the results can serve as a baseline for further studies on forecasting building stock development trajectory and analysing energy and carbon impacts. This will have particular relevance for modelling and analysing policy scenarios to investigate the trade-offs across embodied-versus-operational energy and carbon emissions facing Chinese residential buildings. This information will be critical for sector-wide transformation towards low-carbon buildings, as the Chinese Government pledges to peak its overall emissions by 2030.
周维，英国剑桥大学工程系博士候选人、剑桥大学商学院能源政策研究组(EPRG)成员；英国牛津大学能源研究所(OIES)访问研究员；美国麻省理工学院能源与环境政策研究中心(CEEPR)访问博士生。在气候融资、清洁能源、可持续交通、建筑节能等领域有15年工作经验。目前为国际多边金融机构温室气体排放核算方法学工作组(IFIs TWG on GHG Accounting) 成员，亚洲开发银行(ADB)项目咨询专家。曾常驻亚洲开发银行总部（菲律宾马尼拉）任长期咨询专家，参与大量清洁能源、交通、市政、农业领域气候融资、碳融资项目，涉及项目所在国包括亚洲地区的中国、菲律宾、泰国、越南、印度尼西亚、斯里兰卡、孟加拉、巴基斯坦、印度、尼泊尔、蒙古等国，以及太平洋地区的斐济、巴布亚新几内亚、萨摩亚、库克群岛、瓦努阿图等国。早年曾分别供职于英国可再生能源有限公司，负责多个行业及领域的碳交易项目开发，以及奥雅纳(Arup)香港办公室建筑物理组，参与中国大陆及香港澳门地区的可持续建筑项目开发，典型项目包括北京南站、广州西塔、澳门美高梅酒店等。本科毕业于清华大学，后获新加坡国立大学硕士和英国剑桥大学硕士。