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• W2890328897 abstract "Accurate estimation of urban residential building-related energy consumption (URBEC) and energy intensity per unit floor area at the national level has significant implications for the analysis of carbon emission peaks. However, reliable data on China's building floor space (BFS) are lacking, resulting in unclear energy intensity levels. This study proposes a China BFS estimation method (CBFSEM) based on improved building stock turnover model. Using CBFSEM, it estimates the BFS of historic urban dwelling stock, the demolished and newly built dwelling from 2000 to 2015. It then estimates the corresponding energy consumption and intensity based on the obtained urban residential BFS data. Results showed that total URBEC in China increased dramatically from 217.1 Mtce in 2000 to 417.2 Mtce in 2015 with an average annual growth rate of 4.45%. China's total dwelling stock almost doubled, from 10.6 billion m2 in 2000 to 27.4 billion m2 in 2015 with an annual growth rate of 6.56%. The operational energy consumption accounted for approximately 70% of total URBEC and the building material production energy intensity was the highest in total URBEC, >60 kgce/m2. A comparison with the China Population Census showed that the deviations were well below 8%, which indicated the reliability of the CBFSEM and the estimated results. In general, this study fills the gap in available data and addresses the shortage of estimation methods for BFS and energy intensity. It also provides the government with technical support and scientific evidence to promote building energy efficiency." @default.
• W2890328897 created "2018-09-27" @default.
• W2890328897 creator A5008954135 @default.
• W2890328897 creator A5041381529 @default.
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• W2890328897 date "2019-02-01" @default.
• W2890328897 modified "2023-10-16" @default.
• W2890328897 title "Estimating urban residential building-related energy consumption and energy intensity in China based on improved building stock turnover model" @default.
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• W2890328897 doi "https://doi.org/10.1016/j.scitotenv.2018.09.008" @default.
• W2890328897 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/30199687" @default.
• W2890328897 hasPublicationYear "2019" @default.
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