Chinese residential energy demand: Scenarios to 2030 and policies implication

Delmastro, Chiara; Lavagno, Evasio; Mutani, Guglielmina

doi:10.1016/j.enbuild.2014.12.004

Cited by 54 publications

(14 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The engineering method is based on end-uses' engineering characteristics, such as power ratings and heat transfer relationship etc. Representative bottom-up models are: LEAP model (Zhou, 2011), CBEM model (Yang, 2009), Energy for Buildings (EFB) model (Delmastro et al, 2015) and other models. In ERI's method, the building sector is categorized into residential and commercial building, and these buildings are then sub-categorized into individual end-uses.…”

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

230

View full text Add to dashboard Cite

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.

show abstract

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

230

View full text Add to dashboard Cite

show abstract

“…In their model, Yearbook data on per-capita floor area were used to calibrate the modelling of floor area expansion before 2010, and it was assumed that buildings would retire at an annual rate of 1/30 of the remaining stock. Delmastro et al [44] developed the Energy for Buildings (EfB) model as part of the Energy Demand Projection Model for China (EDPM-CN) to analyse the residential energy consumption trends up to 2030 under various technological and policy scenarios. Along with other drivers of energy demand in their model, the historical per-capita floor area published in the Yearbooks was a key variable.…”

Section: Literature Reviewmentioning

confidence: 99%

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

et al. 2019

View full text Add to dashboard Cite

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.

show abstract

“…Chinese residential energy consumption (CREC) is a typical type of building energy consumption and accounts for over 80% of Chinese national building energy consumption in the current stage [2]. If the growth trend of CREC continues, then CREC is expected to exceed 1.2 billion tons of standard coal equivalent (tce) in 2030 [3]; this condition can lead to severe environmental pollution and hinder China's sustainable development strategy. Therefore, energy savings in Chinese residential buildings (ESCRB), including energy savings in Chinese urban residential buildings (ESCURB) and energy savings in Chinese rural residential buildings (ESCRRB), has aroused public concern.…”

Section: Introductionmentioning

confidence: 99%

Estimating energy savings in Chinese residential buildings from 2001 to 2015: A decomposition analysis

Yan¹,

Ma²,

Pan³

2017

JESTR

View full text Add to dashboard Cite

Evaluating energy savings in Chinese residential buildings (ESCRB) plays an important role in Chinese building energyefficiency work. However, the said work is currently challenged by the lack of effective method for estimating ESCRB data by summarizing all the quantifiable and unquantifiable impact factors. To overcome this problem, this study employed the equation of Human Impact, Population, Affluence, and Technology (IPAT), and the index decomposition of Logarithmic Mean Divisia Index (LMDI) to establish an effective ESCRB estimation method, and then calculated ESCRB data during the period of 2001-2015. Results of this study reflect that ESCRB has significantly accumulated with the rapid development of Chinese building energy-efficiency work in the past 15 years. In particular, ESCRB data

show abstract

Chinese residential energy demand: Scenarios to 2030 and policies implication

Cited by 54 publications

References 12 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

Estimating energy savings in Chinese residential buildings from 2001 to 2015: A decomposition analysis

Contact Info

Product

Resources

About