A Probabilistic Dynamic Material Flow Analysis Model for Chinese Urban Housing Stock

The stock†driven dynamic material flow analysis (MFA) model is one of the prevalent tools to investigate the evolution and related material metabolism of the building stock. There exists substantial uncertainty inherent to input parameters of the stock†driven dynamic building stock MFA model, which has not been comprehensively evaluated yet. In this study, a probabilistic, stock†driven dynamic MFA model is established and China's urban housing stock is selected as the empirical case. This probabilistic dynamic MFA model has the ability to depict the future evolution pathway of China's housing stock and capture uncertainties in its material stock, inflow, and outflow. By means of probabilistic methods, a detailed and transparent estimation of China's housing stock and its material metabolism behavior is presented. Under a scenario with a saturation level of the population, urbanization, and living space, the median value of the urban housing stock area, newly completed area, and demolished area would peak at around 49, 2.2, and 2.2 billion square meters, respectively. The corresponding material stock and flows are 79, 3.5, and 3.3 billion tonnes, respectively. Uncertainties regarding housing stock and its material stock and flows are non†negligible. Relative uncertainties of the material stock and flows are above 50%. The uncertainty importance analysis demonstrates that the material intensity and the total population are major contributions to the uncertainty. Policy makers in the housing sector should consider the material efficiency as an essential policy to mitigate material flows of the urban building stock and to lower the risk of policy failures.

[1]  John E. Fernández,et al.  Resource Consumption of New Urban Construction in China , 2007 .

[2]  Wei Yang,et al.  Simulation of the evolution of the Chinese building and infrastructure stock , 2008 .

[3]  W. Haas,et al.  Maintenance and Expansion: Modeling Material Stocks and Flows for Residential Buildings and Transportation Networks in the EU25 , 2015, Journal of industrial ecology.

[4]  Daniel Müller,et al.  Dynamic material flow analysis for Norway's dwelling stock , 2007 .

[5]  G. Huppes,et al.  Dynamic Material Flow Analysis for Strategic Construction and Demolition Waste Management in Beijing , 2010 .

[6]  A. Raftery,et al.  Probabilistic Projections of the Total Fertility Rate for All Countries , 2011, Demography.

[7]  Håvard Bergsdal,et al.  Dynamic material flow analysis for PCBs in the Norwegian building stock , 2014 .

[8]  Helge Brattebø,et al.  Dynamic building stock modelling: General algorithm and exemplification for Norway , 2016 .

[9]  Wenjia Cai,et al.  Short-Lived Buildings in China: Impacts on Water, Energy, and Carbon Emissions. , 2015, Environmental science & technology.

[10]  T. Astrup,et al.  Systematic Evaluation of Uncertainty in Material Flow Analysis , 2014 .

[11]  Gregory A. Keoleian,et al.  Dynamic Modeling of In‐Use Cement Stocks in the United States , 2008 .

[12]  Gjalt Huppes,et al.  Iron and steel in Chinese residential buildings: A dynamic analysis , 2010 .

[13]  Wei Yang,et al.  Long-term management of building stocks , 2007 .

[14]  Daniel B. Müller,et al.  Stock dynamics for forecasting material flows—Case study for housing in The Netherlands , 2006 .

[15]  Rolf Widmer,et al.  Modeling metal stocks and flows: a review of dynamic material flow analysis methods. , 2014, Environmental science & technology.

[16]  Seiji Hashimoto,et al.  Concrete transformation of buildings in China and implications for the steel cycle , 2015 .

[17]  R. Kleijn,et al.  Dynamic substance flow analysis: the delaying mechanism of stocks, with the case of PVC in Sweden , 2000 .

[18]  Wenying Chen,et al.  Modelling building’s decarbonization with application of China TIMES model☆ , 2016 .

[19]  Daniel B. Müller,et al.  Dynamics of urban and rural housing stocks in China , 2010 .

[20]  Tomer Fishman,et al.  Stochastic Analysis and Forecasts of the Patterns of Speed, Acceleration, and Levels of Material Stock Accumulation in Society. , 2016, Environmental science & technology.

[21]  Yuichi Moriguchi,et al.  Where will large amounts of materials accumulated within the economy go?--A material flow analysis of construction minerals for Japan. , 2007, Waste management.

[22]  N. Kohler,et al.  Methodology for the survival analysis of urban building stocks , 2007 .

[23]  Håvard Bergsdal,et al.  Historical energy analysis of the Norwegian dwelling stock , 2011 .

[24]  Tao Huang,et al.  Toward a Low Carbon–Dematerialization Society , 2012 .

[25]  Daniel B. Müller,et al.  The Role of In-Use Stocks in the Social Metabolism and in Climate Change Mitigation , 2013 .

[26]  Thomas D. Gautheir.  Detecting Trends Using Spearman's Rank Correlation Coefficient , 2001 .

[27]  Guido Sonnemann,et al.  Uncertainty assessment by a Monte Carlo simulation in a life cycle inventory of electricity produced by a waste incinerator , 2003 .

[28]  H Christopher Frey,et al.  Quantification of Variability and Uncertainty in Lawn and Garden Equipment NOx and Total Hydrocarbon Emission Factors , 2002, Journal of the Air & Waste Management Association.

[29]  Helge Brattebø,et al.  Explaining the historical energy use in dwelling stocks with a segmented dynamic model: case study of Norway 1960-2015 , 2016 .

[30]  H. Brattebø,et al.  Analysis of energy and carbon flows in the future Norwegian dwelling stock , 2012 .

[31]  René Kleijn,et al.  Predicting future emissions based on characteristics of stocks , 2002 .

[32]  Helge Brattebø,et al.  Sensitivity analysis in long-term dynamic building stock modeling—Exploring the importance of uncertainty of input parameters in Norwegian segmented dwelling stock model , 2014 .

[33]  David J. Spiegelhalter,et al.  Handling uncertainty in housing stock models , 2012 .

[34]  Wei Feng,et al.  Building stock dynamics and its impacts on materials and energy demand in China , 2016 .

[35]  M. Delignette-Muller,et al.  fitdistrplus: An R Package for Fitting Distributions , 2015 .

[36]  Stefan Pauliuk,et al.  Transforming the Norwegian Dwelling Stock to Reach the 2 Degrees Celsius Climate Target , 2013 .

[37]  Adrian E Raftery,et al.  bayesTFR: An R Package for Probabilistic Projections of the Total Fertility Rate. , 2011, Journal of statistical software.

[38]  D. Hu,et al.  Input, stocks and output flows of urban residential building system in Beijing city, China from 1949 to 2008 , 2010 .

[39]  A. Raftery,et al.  Bayesian Probabilistic Projections of Life Expectancy for All Countries , 2013, Demography.

[40]  Helge Brattebø,et al.  Using a dynamic segmented model to examine future renovation activities in the Norwegian dwelling stock , 2014 .

[41]  Nan Li,et al.  Bayesian probabilistic population projections for all countries , 2012, Proceedings of the National Academy of Sciences.

[42]  Helmut Rechberger,et al.  Applying Fuzzy and Probabilistic Uncertainty Concepts to the Material Flow Analysis of Palladium in Austria , 2015 .

[43]  Daniel B. Müller,et al.  Exploring built environment stock metabolism and sustainability by systems analysis approaches , 2009 .

[44]  Maria Cecilia P. Moura,et al.  120 Years of U.S. Residential Housing Stock and Floor Space , 2015, PloS one.

[45]  Helge Brattebø,et al.  Dynamic-MFA examination of Chilean housing stock: long-term changes and earthquake damage , 2014 .

[46]  Hiroki Tanikawa,et al.  Materials demand and environmental impact of buildings construction and demolition in China based on dynamic material flow analysis , 2013 .

[47]  Kiyotaka Tahara,et al.  Using decomposition analysis to forecast metal usage in the building stock , 2016 .

[48]  E. Dascalaki,et al.  Dynamic building stock modelling: Application to 11 European countries to support the energy efficiency and retrofit ambitions of the EU , 2016 .

[49]  中华人民共和国国家统计局人口和就业统计司.  中国人口和就业统计年鉴 = China population and employment statistics yearbook , 2007 .

[50]  Daniel Müller,et al.  Dynamic type-cohort-time approach for the analysis of energy reductions strategies in the building stock , 2016 .

[51]  Daniel B. Müller,et al.  Stock dynamics and emission pathways of the global aluminium cycle , 2013 .

[52]  Xiubing Li,et al.  Scenario Analysis on Urbanization and Rural-Urban Migration in China , 2003 .

[53]  Lei Shen,et al.  Toward a better practice for estimating the CO2 emission factors of cement production: An experience from China , 2016 .