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Title
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A probabilistic dynamic material flow analysis model for Chinese urban housing stock
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Author
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Abstract
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| 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. |
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Language
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English
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Source (journal)
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Journal of industrial ecology. - Cambridge, Mass.
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Publication
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Cambridge, Mass.
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2018
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ISSN
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1088-1980
[print]
1530-9290
[online]
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DOI
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10.1111/JIEC.12579
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Volume/pages
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22
:2
(2018)
, p. 377-391
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ISI
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000429716700011
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Full text (Publisher's DOI)
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Full text (publisher's version - intranet only)
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