China’s energy consumption in construction and building sectors: An outlook to 2100

As China takes great efforts to cap its total energy consumption, it is important to understand the future energy use in all sectors. This paper aims to present a long-term prediction of energy use in China’s construction and building sectors (CBS) up to the year 2100. A STIRPAT (Stochastic Impacts by Regression on Population, Affluence, and Technology) model is used to establish the relationship between six socioeconomic and technological factors and China’s CBS energy consumption. Based on the statistical data from 2000 to 2016, ridge regression is applied to derive the coefficients of the STIRPAT model to counter the impact of multicollinearity on regression results. The projections are performed for three scenarios: a benchmark scenario, an intensive scenario, and an extensive scenario. The results show that for all three scenarios, the overall trend of China’s CBS energy consumption is to continuously increase from the present, reach a peak in the range between 1155 and 1243 million tons of standard coal equivalent (Mtce) in 2050, and then decrease to 942–1116 Mtce in 2100. The above projection and the associated STIRPAT model are valuable for developing policies on construction and buildings to control the total energy use in China.

[1]  Zhaohua Wang,et al.  The peak of CO2 emissions in China: A new approach using survival models , 2019, Energy Economics.

[2]  Guangyue Xu,et al.  Determining China's CO2 emissions peak with a dynamic nonlinear artificial neural network approach and scenario analysis , 2019, Energy Policy.

[3]  Xi Ji,et al.  Assessing the energy-saving effect of urbanization in China based on stochastic impacts by regression on population, affluence and technology (STIRPAT) model , 2017 .

[4]  Jun Li,et al.  Towards a low-carbon future in China's building sector—A review of energy and climate models forecast , 2008 .

[5]  Stephanie Pincetl,et al.  Structural, geographic, and social factors in urban building energy use: Analysis of aggregated account-level consumption data in a megacity , 2016 .

[6]  Trevor Hastie,et al.  An Introduction to Statistical Learning , 2013, Springer Texts in Statistics.

[7]  Boqiang Lin,et al.  An application of a double bootstrap to investigate the effects of technological progress on total-factor energy consumption performance in China , 2017 .

[8]  Yongze Song,et al.  Analyzing the influence factors of the carbon emissions from China's building and construction industry from 2000 to 2015 , 2019, Journal of Cleaner Production.

[9]  Longyu Shi,et al.  Prediction of long-term energy consumption trends under the New National Urbanization Plan in China , 2017 .

[10]  Da Yan,et al.  Building energy use in China: Ceiling and scenario , 2015 .

[11]  W. Feng,et al.  Assessing the effects of technological progress on energy efficiency in the construction industry: A case of China , 2019, Journal of Cleaner Production.

[12]  Guglielmina Mutani,et al.  Chinese residential energy demand: Scenarios to 2030 and policies implication , 2015 .

[13]  Dezhi Li,et al.  Carbon emissions and policies in China's building and construction industry: Evidence from 1994 to 2012 , 2016 .

[14]  中華人民共和国国家統計局.  China statistical yearbook , 1988 .

[15]  Jianghua Liu,et al.  The impact of urbanization on China’s residential energy consumption , 2019, Structural Change and Economic Dynamics.

[16]  Nan Zhou,et al.  China's energy and emissions outlook to 2050: Perspectives from bottom-up energy end-use model , 2013 .

[17]  Qian Shi,et al.  Driving factors of the changes in the carbon emissions in the Chinese construction industry , 2017 .

[18]  Wei Feng,et al.  China's energy consumption in the building sector: A Statistical Yearbook-Energy Balance Sheet based splitting method , 2018, Journal of Cleaner Production.

[19]  Yujie Lu,et al.  Which activities contribute most to building energy consumption in China? A hybrid LMDI decomposition analysis from year 2007 to 2015 , 2017 .

[20]  L. Hao,et al.  Do different sizes of urban population matter differently to CO2 emission in different regions? Evidence from electricity consumption behavior of urban residents in China , 2019 .

[21]  Haiyang Li,et al.  Carbon emission and abatement potential outlook in China's building sector through 2050 , 2018, Energy Policy.

[22]  Laura Gabrielli,et al.  Analysis of building energy consumption through panel data: The role played by the economic drivers , 2017 .

[23]  Meredydd Evans,et al.  Scenarios of building energy demand for China with a detailed regional representation , 2014 .

[24]  G. Yun,et al.  Data-driven approach to prediction of residential energy consumption at urban scales in London , 2019, Energy.

[25]  M. Santamouris,et al.  Socio-economic status and residential energy consumption: A latent variable approach , 2019, Energy and Buildings.

[26]  Guoqin Zhang,et al.  The role of climate, construction quality, microclimate, and socio-economic conditions on carbon emissions from office buildings in China , 2018 .

[27]  E. Rosa,et al.  Effects of population and affluence on CO2 emissions. , 1997, Proceedings of the National Academy of Sciences of the United States of America.

[28]  Yiqun Pan,et al.  CO2 emissions in China's building sector through 2050: A scenario analysis based on a bottom-up model , 2017 .

[29]  Son H. Kim,et al.  China's building energy demand: Long-term implications from a detailed assessment , 2012 .