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Efficiency of China’s carbon market: A case study of Hubei pilot market

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  • Chen, Yingqi
  • Ba, Shusong
  • Yang, Qing
  • Yuan, Tian
  • Zhao, Haibo
  • Zhou, Ming
  • Bartocci, Pietro
  • Fantozzi, Francesco

Abstract

A better understanding of the carbon market can guide further reforms to improve its functionality. Market efficiency is a key indicator to uncover its current performance. Previous studies have revealed passed carbon market efficiency; however, given the dynamics of the market, it is worthy to track the up-to-date status. This paper, specifically, studies the Hubei pilot carbon market, which is quite interesting, considering its market scale, as well as the COVID-19 pandemic context. Wild bootstrapping Variance Ratio test is implemented to detect the market efficiency with the most recent and abundant data. Results show that the market efficiency in the period of 2014–2020 is around 0.3951, less than 1, suggesting a weak form of efficiency. Observing the sub-sample periods, the efficiency shows to be quite volatile: it climbes from 0.3621 to 0.4027 and finally drops to 0.3985. Furthermore, the market efficiency soares after the COVID-19, which echoes the smooth local reopening thanks to supporting policies. To some extent, this study enlarged the analysis of COVID-19 impact on the industrial sector and for this reason it provides important reference for further research. The unique contribution of this paper is to provide the more updated evidence on the efficiency of China’s pilot carbon market, as well as proofs of soaring market efficiency, after the pandemic.

Suggested Citation

  • Chen, Yingqi & Ba, Shusong & Yang, Qing & Yuan, Tian & Zhao, Haibo & Zhou, Ming & Bartocci, Pietro & Fantozzi, Francesco, 2021. "Efficiency of China’s carbon market: A case study of Hubei pilot market," Energy, Elsevier, vol. 222(C).
    • Handle: RePEc:eee:energy:v:222:y:2021:i:c:s036054422100195x
    DOI: 10.1016/j.energy.2021.119946
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    as
    1. Charles, Amélie & Darné, Olivier, 2009. "The efficiency of the crude oil markets: Evidence from variance ratio tests," Energy Policy, Elsevier, vol. 37(11), pages 4267-4272, November.
    2. Lo, Andrew W. & MacKinlay, A. Craig, 1989. "The size and power of the variance ratio test in finite samples : A Monte Carlo investigation," Journal of Econometrics, Elsevier, vol. 40(2), pages 203-238, February.
    3. Han, Meng & Ding, Lili & Zhao, Xin & Kang, Wanglin, 2019. "Forecasting carbon prices in the Shenzhen market, China: The role of mixed-frequency factors," Energy, Elsevier, vol. 171(C), pages 69-76.
    4. Charles, Amélie & Darné, Olivier & Fouilloux, Jessica, 2013. "Market efficiency in the European carbon markets," Energy Policy, Elsevier, vol. 60(C), pages 785-792.
    5. Krishnamurti, Chandrasekhar & Hoque, Ariful, 2011. "Efficiency of European emissions markets: Lessons and implications," Energy Policy, Elsevier, vol. 39(10), pages 6575-6582, October.
    6. Feng, Zhen-Hua & Zou, Le-Le & Wei, Yi-Ming, 2011. "Carbon price volatility: Evidence from EU ETS," Applied Energy, Elsevier, vol. 88(3), pages 590-598, March.
    7. Sun, Wei & Huang, Chenchen, 2020. "A novel carbon price prediction model combines the secondary decomposition algorithm and the long short-term memory network," Energy, Elsevier, vol. 207(C).
    8. Zhao, Xin-gang & Wu, Lei & Li, Ang, 2017. "Research on the efficiency of carbon trading market in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 79(C), pages 1-8.
    9. Huang, Qian & Xu, Jiuping, 2020. "Bi-level multi-objective programming approach for carbon emission quota allocation towards co-combustion of coal and sewage sludge," Energy, Elsevier, vol. 211(C).
    10. Omid Sabbaghi & Navid Sabbaghi, 2018. "Market efficiency and the global financial crisis: evidence from developed markets," Studies in Economics and Finance, Emerald Group Publishing Limited, vol. 35(3), pages 362-385, June.
    11. Zhao, Xin-gang & Jiang, Gui-wu & Nie, Dan & Chen, Hao, 2016. "How to improve the market efficiency of carbon trading: A perspective of China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 59(C), pages 1229-1245.
    12. Fama, Eugene F, 1970. "Efficient Capital Markets: A Review of Theory and Empirical Work," Journal of Finance, American Finance Association, vol. 25(2), pages 383-417, May.
    13. Kim, Jae H., 2009. "Automatic variance ratio test under conditional heteroskedasticity," Finance Research Letters, Elsevier, vol. 6(3), pages 179-185, September.
    14. Andrew W. Lo, A. Craig MacKinlay, 1988. "Stock Market Prices do not Follow Random Walks: Evidence from a Simple Specification Test," The Review of Financial Studies, Society for Financial Studies, vol. 1(1), pages 41-66.
    15. Omid Sabbaghi & Navid Sabbaghi, 2017. "The Chicago Climate Exchange and market efficiency: an empirical analysis," Environmental Economics and Policy Studies, Springer;Society for Environmental Economics and Policy Studies - SEEPS, vol. 19(4), pages 711-734, October.
    16. Boya, Christophe M., 2019. "From efficient markets to adaptive markets: Evidence from the French stock exchange," Research in International Business and Finance, Elsevier, vol. 49(C), pages 156-165.
    17. repec:dau:papers:123456789/4222 is not listed on IDEAS
    18. Zhang, Wei & Li, Jing & Li, Guoxiang & Guo, Shucen, 2020. "Emission reduction effect and carbon market efficiency of carbon emissions trading policy in China," Energy, Elsevier, vol. 196(C).
    19. Kim, Jae H., 2006. "Wild bootstrapping variance ratio tests," Economics Letters, Elsevier, vol. 92(1), pages 38-43, July.
    20. Song, Yazhi & Liu, Tiansen & Ye, Bin & Li, Yin, 2020. "Linking carbon market and electricity market for promoting the grid parity of photovoltaic electricity in China," Energy, Elsevier, vol. 211(C).
    21. Fan, Xinghua & Lv, Xiangxiang & Yin, Jiuli & Tian, Lixin & Liang, Jiaochen, 2019. "Multifractality and market efficiency of carbon emission trading market: Analysis using the multifractal detrended fluctuation technique," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    22. Cong, Ren & Lo, Alex Y., 2017. "Emission trading and carbon market performance in Shenzhen, China," Applied Energy, Elsevier, vol. 193(C), pages 414-425.
    23. Alberola, Emilie & Chevallier, Julien & Cheze, Benoi^t, 2008. "Price drivers and structural breaks in European carbon prices 2005-2007," Energy Policy, Elsevier, vol. 36(2), pages 787-797, February.
    24. Xia, Yan & Tang, Zhipeng, 2017. "The impacts of emissions accounting methods on an imperfect competitive carbon trading market," Energy, Elsevier, vol. 119(C), pages 67-76.
    25. Mishra, Vinod & Smyth, Russell, 2014. "Is monthly US natural gas consumption stationary? New evidence from a GARCH unit root test with structural breaks," Energy Policy, Elsevier, vol. 69(C), pages 258-262.
    26. Bredin, Don & Hyde, Stuart & Muckley, Cal, 2014. "A microstructure analysis of the carbon finance market," International Review of Financial Analysis, Elsevier, vol. 34(C), pages 222-234.
    27. Ramede Sodsai & Karoon Suksonghong, 2018. "Does market capitalisation matters? Tests of weak-form efficient market hypothesis for Thai stock market," International Journal of Monetary Economics and Finance, Inderscience Enterprises Ltd, vol. 11(3), pages 235-242.
    28. Montagnoli, Alberto & de Vries, Frans P., 2010. "Carbon trading thickness and market efficiency," Energy Economics, Elsevier, vol. 32(6), pages 1331-1336, November.
    29. Qu, Kaiping & Yu, Tao & Huang, Linni & Yang, Bo & Zhang, Xiaoshun, 2018. "Decentralized optimal multi-energy flow of large-scale integrated energy systems in a carbon trading market," Energy, Elsevier, vol. 149(C), pages 779-791.
    30. Khediri, Karim Ben & Charfeddine, Lanouar, 2015. "Evolving efficiency of spot and futures energy markets: A rolling sample approach," Journal of Behavioral and Experimental Finance, Elsevier, vol. 6(C), pages 67-79.
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    6. Ren, Xiaohang & Li, Yiying & Qi, Yinshu & Duan, Kun, 2022. "Asymmetric effects of decomposed oil-price shocks on the EU carbon market dynamics," Energy, Elsevier, vol. 254(PB).

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