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How does carbon price change? Evidences from EU ETS

Author

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

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

Abstract

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
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    References listed on IDEAS

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    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.
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    8. Wang, Jiqian & Guo, Xiaozhu & Tan, Xueping & Chevallier, Julien & Ma, Feng, 2023. "Which exogenous driver is informative in forecasting European carbon volatility: Bond, commodity, stock or uncertainty?," Energy Economics, Elsevier, vol. 117(C).
    9. 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.
    10. 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.
    11. 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, vol. 12(5), pages 1-22, March.
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    14. Wang, Xiao-Qing & Su, Chi-Wei & Lobonţ, Oana-Ramona & Li, Hao & Nicoleta-Claudia, Moldovan, 2022. "Is China's carbon trading market efficient? Evidence from emissions trading scheme pilots," Energy, Elsevier, vol. 245(C).

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    More about this item

    Keywords

    carbon price; Ensemble Empirical Mode Decomposition; variance ratio; price volatility; temperature sensitivity;
    All these keywords.

    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

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