IDEAS home Printed from
   My bibliography  Save this paper

How does carbon price change? Evidences from EU ETS


  • Zhen-Hua Feng
  • Chun-Feng Liu
  • Yi-Ming Wei

    () (Center for Energy and Environmental Policy Research (CEEP), Beijing Institute of Technology)


By proposing the hypotheses for carbon price volatility, this paper uses variance ratio and Ensemble Empirical mode decomposition (EEMD) to analyze the carbon price. Results show that carbon price is influenced by temperature, market mechanism and heterogeneous environment. Carbon market is temperature-sensitive, affected by seasonal changes, which presents a style of movement amplitude; Carbon price is affected by the market mechanism at a high frequency, with the duration being less than 15 weeks and amplitudes less than 5 euros. Heterogeneity environment has an impact on carbon price at a low frequency, the duration lasting more than 34 weeks or even more and amplitudes more than 10 euros or higher. Meanwhile, the analysis for historical carbon price change shows the long term trend declines gradually since 2005 from 18 to 16 euro per ton. The continuing declining trend agrees with special events by time. Our research explores the reasons of carbon price volatility and some recommendations are given trying to regulate carbon market.

Suggested Citation

  • Zhen-Hua Feng & Chun-Feng Liu & Yi-Ming Wei, 2010. "How does carbon price change? Evidences from EU ETS," CEEP-BIT Working Papers 11, Center for Energy and Environmental Policy Research (CEEP), Beijing Institute of Technology.
  • Handle: RePEc:biw:wpaper:11

    Download full text from publisher

    File URL:
    Download Restriction: no

    Other versions of this item:


    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.

    Cited by:

    1. Chang, Kai & Chen, Rongda & Chevallier, Julien, 2018. "Market fragmentation, liquidity measures and improvement perspectives from China's emissions trading scheme pilots," Energy Economics, Elsevier, vol. 75(C), pages 249-260.
    2. Chaton, Corinne & Creti, Anna & Peluchon, Benoît, 2015. "Banking and back-loading emission permits," Energy Policy, Elsevier, vol. 82(C), pages 332-341.
    3. Zhu, Jiaming & Wu, Peng & Chen, Huayou & Liu, Jinpei & Zhou, Ligang, 2019. "Carbon price forecasting with variational mode decomposition and optimal combined model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 519(C), pages 140-158.
    4. Gong, Xu & Lin, Boqiang, 2019. "Modeling stock market volatility using new HAR-type models," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 516(C), pages 194-211.
    5. Feng, Zhen-Hua & Wei, Yi-Ming & Wang, Kai, 2012. "Estimating risk for the carbon market via extreme value theory: An empirical analysis of the EU ETS," Applied Energy, Elsevier, vol. 99(C), pages 97-108.
    6. Xu, Jia & Tan, Xiujie & He, Gang & Liu, Yu, 2019. "Disentangling the drivers of carbon prices in China's ETS pilots — An EEMD approach," Technological Forecasting and Social Change, Elsevier, vol. 139(C), pages 1-9.
    7. Jianguo Zhou & Xuejing Huo & Xiaolei Xu & Yushuo Li, 2019. "Forecasting the Carbon Price Using Extreme-Point Symmetric Mode Decomposition and Extreme Learning Machine Optimized by the Grey Wolf Optimizer Algorithm," Energies, MDPI, Open Access Journal, vol. 12(5), pages 1-22, March.
    8. Meng, Ming & Niu, Dongxiao, 2012. "Three-dimensional decomposition models for carbon productivity," Energy, Elsevier, vol. 46(1), pages 179-187.
    9. Zhou, Kaile & Yang, Shanlin & Shao, Zhen, 2016. "Energy Internet: The business perspective," Applied Energy, Elsevier, vol. 178(C), pages 212-222.
    10. Jianguo Zhou & Xuechao Yu & Xiaolei Yuan, 2018. "Predicting the Carbon Price Sequence in the Shenzhen Emissions Exchange Using a Multiscale Ensemble Forecasting Model Based on Ensemble Empirical Mode Decomposition," Energies, MDPI, Open Access Journal, vol. 11(7), pages 1-17, July.

    More about this item


    carbon price; Ensemble Empirical Mode Decomposition; variance ratio; price volatility; temperature sensitivity;

    JEL classification:

    • Q54 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Climate; Natural Disasters and their Management; Global Warming
    • C02 - Mathematical and Quantitative Methods - - General - - - Mathematical Economics


    Access and download statistics


    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:biw:wpaper:11. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Zhi-Fu Mi). General contact details of provider: .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.