CO 2 emissions in China's building sector through 2050: A scenario analysis based on a bottom-up model

Yang, Tao; Pan, Yiqun; Yang, Yikun; Lin, Meishun; Qin, Bingyue; Xu, Peng; Huang, Zhizhong

doi:10.1016/j.energy.2017.03.098

Cited by 111 publications

(34 citation statements)

References 32 publications

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“…individual end-use energy consumption from bottom to up (ERI, 2003;Zhou et al, 2003). And there are other bottom-up models (Yang et al, 2017). The advantages and disadvantages of these three kinds of BEC estimation methods are listed in Table 1.…”

Section: Different Estimation Methods and Data Sources Of Building Enmentioning

confidence: 99%

China's energy consumption in the building sector: A Statistical Yearbook-Energy Balance Sheet based splitting method

Huo

Hong

Zhang

et al. 2018

Journal of Cleaner Production

232

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China's energy consumption in the building sector (BEC) is not counted as a separate type of energy consumption, but divided and mixed in other sectors in China's statistical system. This led to the lack of historical data on China's BEC. Moreover, previous researches' shortages such as unsystematic research on BEC, various estimation methods with complex calculation process, and difficulties in data acquisition resulted in "heterogeneous" of current BEC in China. Aiming to these deficiencies, this study proposes a set of China building energy consumption calculation method (CBECM) by splitting out the building related energy consumption mixed in other sectors in the composition of China Statistical Yearbook-Energy Balance Sheet. Then, China's BEC from 2000 to 2014 are estimated using CBECM and compared with other studies. Results show that, from 2000 to 2014, China's BEC increased 1.7 times, rising from 301 to 814 million tons of standard coal consumed, with the BEC percentage of total energy consumption stayed relatively stable between 17.7% and 20.3%. By comparison, we find that our results are reliable and the CBECM has the following advantages over other methods: data source is authoritative, calculation process is concise, and it is easy to obtain time series data on BEC etc. The CBECM is particularly suitable for the provincial government to calculate the local BEC, even in the circumstance with statistical yearbook available only.

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Section: Different Estimation Methods and Data Sources Of Building Enmentioning

confidence: 99%

China's energy consumption in the building sector: A Statistical Yearbook-Energy Balance Sheet based splitting method

Huo

Hong

Zhang

et al. 2018

Journal of Cleaner Production

232

View full text Add to dashboard Cite

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“…Along with other drivers of energy demand in their model, the historical per-capita floor area published in the Yearbooks was a key variable. Similarly, to explore various scenarios of carbon emissions from Chinese buildings through 2050, Yang et al [45] applied a grey modelling technique based on the historical per-capita floor area data published in past China Statistical Yearbooks to forecast the future trend of building stock size for the period up to 2020 and scenario analysis for the period beyond 2020.…”

Section: Literature Reviewmentioning

confidence: 99%

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

et al. 2019

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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.

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“…As for China's building stock, Hu et al adopted dynamic MFA model to calculate and predict the urban and rural housing stock of China, but did not identify the commercial BFS (Dong et al, 2017;Hu et al, 2010aHu et al, , 2010b2010c, 2010d. Yang et al studied the statistic method for China building energy consumption, and they directly used BFS data from the China Statistical Yearbook to calculate the energy intensity (Yang et al, 2017). Zhou et al developed a bottom-up Long-range Energy Alternatives Planning (LEAP) model to project future energy consumption of end-use sectors in China, including the building sector (Zhou and Lin, 2008).…”

Section: Review On Building Floor Space Related Researchmentioning

confidence: 99%

“…Current scholars adopted the BFS as the driving force to predict future energy consumption and material demand of China's buildings (Dong et al, 2017;Hu et al, 2010a;Zhou and Lin, 2008). They mainly obtained the base year BFS directly from China Statistical Yearbook, and then predicted the future BFS according to some assumptions and the national development plan (Hong et al, 2016;McNeil et al, 2016;Yang et al, 2017;Zhou et al, 2013). Obtaining BFS data directly from the China Statistical Yearbook is highly problematic, because there exist deficiencies in China's statistical system, such as the change of the statistical range and change of the statistical caliber over time, together with the incompleteness of time series data and other factors.…”

Section: Introductionmentioning

confidence: 99%

China's building stock estimation and energy intensity analysis

Huo

Cai

Hong

et al. 2019

Journal of Cleaner Production

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Reliable and objective data regarding building stock is essential for predicting and analyzing energy demand and carbon emission. However, China's building stock data is lacking. This study proposes a set of China building floor space estimation method (CBFSM) based on the improved building stock turnover model. Then it measures China's building stocks by vintage and type from 2000 to 2015, as well as building energy intensity (national level and provincial level) and energy-efficient buildings. Results showed that total building stocks increased significantly, rising from 35.2 billion m 2 in 2000 to 63.6 billion m 2 in 2015, with the average growth rate 4.0%. The deviations were well below 10% by comparing with China Population Census, which validated the reliability of CBFSM and the results. As for energy intensity, urban dwellings and rural dwellings showed relatively stable and increasing trend respectively. The commercial building energy intensity saw a downward trend during "12th Five Year Plan" period. This indicated the effectiveness of building energy efficiency work for commercial buildings since 2005.38.6 billion m 2 residential dwellings and 5.7 billion m 2 commercial buildings still need to be retrofitted in future. CBFSM can overcome shortages in previous studies. It can also provide Chinese government with technical support and data evidence to promote the building energy efficiency work.

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CO 2 emissions in China's building sector through 2050: A scenario analysis based on a bottom-up model

Cited by 111 publications

References 32 publications

China's energy consumption in the building sector: A Statistical Yearbook-Energy Balance Sheet based splitting method

China's energy consumption in the building sector: A Statistical Yearbook-Energy Balance Sheet based splitting method

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

China's building stock estimation and energy intensity analysis

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