Energy Efficiency

, Volume 12, Issue 4, pp 1027–1039 | Cite as

Exploring the drivers of energy consumption-related CO2 emissions in China: a multiscale analysis

  • Bangzhu ZhuEmail author
  • Shunxin Ye
  • Ping Wang
  • Kaijian He
  • Tao Zhang
  • Rui Xie
  • Yi-Ming Wei
Original Article


The exploration and modeling of the drivers of CO2 emissions can help make effective CO2 emission reduction policies. In this study, we examine the drivers of energy consumption-related CO2 emissions in China during 1978–2014 from a multiscale perspective. Firstly, we use the multivariate empirical mode decomposition model to simultaneously decompose the CO2 emissions and 17 drivers into several groups of intrinsic mode functions and one group of residues at different timescales. Secondly, we employ the stepwise regression analysis to explore and model the key drivers of CO2 emissions at different timescales without multicollinearity. The empirical results show that China’s CO2 emissions have obvious timescales of 6.17 years, 9.25 years, 18.5 years, 37.0 years, and long-term trend. At the short-term timescales, fuel structure and economic structure have significant impacts on CO2 emissions. At the medium-term timescales, urban population and fuel structure are the major contributors to CO2 emissions. At the long-term timescale, only per capita GDP has a positive effect on CO2 emissions. Finally, we propose the policy implications at the short, medium, and long timescales.


CO2 emissions Drivers Multivariate empirical mode decomposition Stepwise regression analysis China 


Funding information

This work is financially supported by the National Natural Science Foundation of China (71771105, 71473180, and 71303174), Guangdong Young Zhujiang Scholar (Yue Jiaoshi [2016]95), Natural Science Foundation for Distinguished Young Talents of Guangdong (2014A030306031), and Guangdong Key Base of Humanities and Social Science—Enterprise Development Research Institute.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.


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Copyright information

© Springer Nature B.V. 2018

Authors and Affiliations

  • Bangzhu Zhu
    • 1
    • 2
    Email author
  • Shunxin Ye
    • 2
  • Ping Wang
    • 1
  • Kaijian He
    • 3
  • Tao Zhang
    • 4
  • Rui Xie
    • 5
  • Yi-Ming Wei
    • 6
  1. 1.School of BusinessNanjing University of Information Science & TechnologyNanjingChina
  2. 2.School of ManagementJinan UniversityGuangzhouChina
  3. 3.Hunan Engineering Research Center for Industrial Big Data and Intelligent Decision MakingHunan University of Science and TechnologyXiangtanChina
  4. 4.Birmingham Business SchoolUniversity of BirminghamBirminghamUK
  5. 5.School of Economics and TradeHunan UniversityChangshaChina
  6. 6.Center for Energy and Environmental Policy ResearchBeijing Institute of TechnologyBeijingChina

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