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

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  • Xu, Guangyue
  • Wang, Weimin

Abstract

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.

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  • Xu, Guangyue & Wang, Weimin, 2020. "China’s energy consumption in construction and building sectors: An outlook to 2100," Energy, Elsevier, vol. 195(C).
  • Handle: RePEc:eee:energy:v:195:y:2020:i:c:s0360544220301523
    DOI: 10.1016/j.energy.2020.117045
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    Cited by:

    1. Xu, Bin & Lin, Boqiang, 2020. "Investigating drivers of CO2 emission in China’s heavy industry: A quantile regression analysis," Energy, Elsevier, vol. 206(C).
    2. Yi Yang & Xinwei Li & Huamin Li & Dongyin Li & Ruifu Yuan, 2020. "Deep Q-Network for Optimal Decision for Top-Coal Caving," Energies, MDPI, Open Access Journal, vol. 13(7), pages 1-14, April.
    3. Chi, Fang'ai & Xu, Liming & Pan, Jiajie & Wang, Ruonan & Tao, Yekang & Guo, Yuang & Peng, Changhai, 2020. "Prediction of the total day-round thermal load for residential buildings at various scales based on weather forecast data," Applied Energy, Elsevier, vol. 280(C).

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