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

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 m2 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 CO2 emissions in Chinese residential buildings.

Forecasting urban residential stock turnover dynamics using system dynamics and Bayesian model averaging

“…Using the same method, Cao et al [28] estimated the Chinese building lifetime, the size of the building stock, and the associated stock and flow of building materials, such as cement, steel, wood, brick, etc. Zhou et al [29] used Weibull distribution to represent the lifetime distribution of urban residential buildings. In addition to calibrating parameters and estimating stock size, Zhou's aging chain modelling produced a detailed distribution of age-specific sub-stocks, i.e.…”

“…Miatto et al [44] tested various PDFs and found that the lognormal distribution offered the best fit to lifespans of buildings in Nagoya and Wakayama, Japan, where buildings were short-lived, with average lifespans shorter than 30 years. Zhou et al [29] applied the Weibull distribution to approximate lifetime uncertainties of Chinese urban residential buildings. From an economic perspective, buildings can be regarded as a type of capital asset, and accordingly building stock can be regarded as a type of capital asset, and accordingly building stock can be regarded as capital stock [42,43].…”

Low Energy Architecture and Low Carbon Cities: Exploring Links, Scales, and Environmental Impacts

“…China is also the focus of the article by Zhou et al [27]. They analyze building lifetime and stock turnover as the key determinants in modelling energy demand and emissions of building stocks.…”

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“…Table 6-10 Global region assignment to the "Long Lifetime", "Medium Lifetime" and "Short Lifetime" groups For China, the average household lifetime greatly depends on the period they were built and are much shorter than in developed countries (Zhou et al, 2019). Average lifetimes were estimated by (Hu et al, 2010) at 20 years for rural houses built before 1978 (poor housing conditions), at 50 years for urban houses built before 1966 and at 15 years for urban houses built during 1966-1971 (China's cultural revolution), while after 1978 the lifetimes are considered to have greatly increased.…”

“…A combination of various reasons is responsible for the short lifetimes, e.g. the use of inferior quality of building materials, poor design standards, and the inappropriate fast demolition of buildings as a result of rapid urbanization and city rebuilding (Zhou et al, 2019). In this analysis, although a higher average lifetime is considered for China (group "Long Lifetime") the results on future annual consumption are not considered to have been greatly affected since old stocks in the past were low.…”