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How does carbon price change? Evidences from EU ETS

Author

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

Abstract

By proposing the hypotheses for carbon price volatility, this paper uses variance ratio and Ensemble Empirical Mode Decomposition (EEMD) to analyse the carbon price. Results show that 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, historical carbon price change shows the long-term trend declines gradually since 2005 from 18 to 16 euros 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, 2011. "How does carbon price change? Evidences from EU ETS," International Journal of Global Energy Issues, Inderscience Enterprises Ltd, vol. 35(2/3/4), pages 132-144.
    • Handle: RePEc:ids:ijgeni:v:35:y:2011:i:2/3/4:p:132-144
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    Cited by:

    1. Jiaxin Han & Enkhjargal Dalaibaatar, 2023. "A Study on the Influencing Factors of China’s Ecological Footprint Based on EEMD–GeoDetector," Sustainability, MDPI, vol. 15(8), pages 1-15, April.
    2. 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, vol. 11(7), pages 1-17, July.
    3. 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.
    4. 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.
    5. Meng, Ming & Niu, Dongxiao, 2012. "Three-dimensional decomposition models for carbon productivity," Energy, Elsevier, vol. 46(1), pages 179-187.
    6. 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.
    7. Fan, Xia & Li, Chuanju, 2025. "Credit-pool policy and new energy vehicle industrial innovation from the perspective of market regulation," Energy, Elsevier, vol. 322(C).
    8. Marco Quatrosi, 2024. "Emission trading in a high dimensional context: to what extent are carbon markets integrated with the broader system?," Economia Politica: Journal of Analytical and Institutional Economics, Springer;Fondazione Edison, vol. 41(3), pages 793-814, October.
    9. Yu, Hui & Li, Ying & Wang, Wei, 2023. "Optimal innovation strategies of automakers with market competition under the dual-credit policy," Energy, Elsevier, vol. 283(C).
    10. 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.
    11. Zhou, Kaile & Yang, Shanlin & Shao, Zhen, 2016. "Energy Internet: The business perspective," Applied Energy, Elsevier, vol. 178(C), pages 212-222.
    12. Ali Ben Mrad & Amine Lahiani & Salma Mefteh‐Wali & Nada Mselmi, 2025. "Forecasting Carbon Prices: What Is the Role of Technology?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 44(6), pages 1867-1883, September.
    13. 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).
    14. Chaton, Corinne & Creti, Anna & Peluchon, Benoît, 2015. "Banking and back-loading emission permits," Energy Policy, Elsevier, vol. 82(C), pages 332-341.
    15. 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.
    16. 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.
    17. Zhang, Xin & Wang, Jujie & He, Xuecheng, 2025. "An optimal multi-scale ensemble transformer for carbon emission allowance price prediction based on time series patching and two-stage stabilization," Energy, Elsevier, vol. 328(C).
    18. 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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    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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