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Influencing factors and fluctuation characteristics of China’s carbon emission trading price

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  • Zhou, Kaile
  • Li, Yiwen

Abstract

The environmental deterioration and resulting climate change have become one of the major challenges that human has faced in recent years. Carbon emission trading, as an effective economic tool to deal with climate change issues, has attracted widespread attention. As a major carbon emitter, China plays an important role in combating global climate change. Based on the carbon emission trading price data of China’s Hubei Emission Exchange, a Vector Auto-Regressive (VAR)-Vector Error Correction (VEC) model is first used to investigate the dynamic relationship between energy price, macroeconomic indicators, air quality, and carbon emission trading price. The results show that there is a long-term equilibrium relationship between carbon emission trading price and these indicators. When the carbon emission price is too high and deviates from the long-term equilibrium value, it will slowly decline to reach the long-term equilibrium value. The price of carbon emission trading is largely affected by macroeconomic indicators among all these influencing factors. In addition, a Generalized Auto-Regressive Conditional Heteroskedasticity (GARCH) model is used to explore the fluctuation characteristics of China’s carbon emission trading price. It is found that the return series of carbon emission price are consistent with the characteristics of financial time series, such as fluctuation aggregates, spikes and thick tails, and non-normal distribution. There is a positive leverage effect for the fluctuation of China’s carbon emission price. It is further found that external bad news has a greater impact on the fluctuation of China’s carbon emission trading price than good news.

Suggested Citation

  • Zhou, Kaile & Li, Yiwen, 2019. "Influencing factors and fluctuation characteristics of China’s carbon emission trading price," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 524(C), pages 459-474.
    • Handle: RePEc:eee:phsmap:v:524:y:2019:i:c:p:459-474
    DOI: 10.1016/j.physa.2019.04.249
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    Cited by:

    1. Yuanfeng Hu & Yixiang Tian & Luping Zhang, 2023. "Green Bond Pricing and Optimization Based on Carbon Emission Trading and Subsidies: From the Perspective of Externalities," Sustainability, MDPI, vol. 15(10), pages 1-20, May.
    2. Xie, Qiwei & Hao, Jingjing & Li, Jingyu & Zheng, Xiaolong, 2022. "Carbon price prediction considering climate change: A text-based framework," Economic Analysis and Policy, Elsevier, vol. 74(C), pages 382-401.
    3. Liang Shen & Xiaodi Wang & Qinqin Liu & Yuyan Wang & Lingxue Lv & Rongyun Tang, 2021. "Carbon Trading Mechanism, Low-Carbon E-Commerce Supply Chain and Sustainable Development," Mathematics, MDPI, vol. 9(15), pages 1-26, July.
    4. Wenjun Chu & Shanglei Chai & Xi Chen & Mo Du, 2020. "Does the Impact of Carbon Price Determinants Change with the Different Quantiles of Carbon Prices? Evidence from China ETS Pilots," Sustainability, MDPI, vol. 12(14), pages 1-19, July.
    5. Xiaohua Song & Wen Zhang & Zeqi Ge & Siqi Huang & Yamin Huang & Sijia Xiong, 2022. "A Study of the Influencing Factors on the Carbon Emission Trading Price in China Based on the Improved Gray Relational Analysis Model," Sustainability, MDPI, vol. 14(13), pages 1-27, June.
    6. Yan, Wan-Lin & Cheung, Adrian (Wai Kong), 2023. "The dynamic spillover effects of climate policy uncertainty and coal price on carbon price: Evidence from China," Finance Research Letters, Elsevier, vol. 53(C).
    7. Jianguo Zhou & Dongfeng Chen, 2021. "Carbon Price Forecasting Based on Improved CEEMDAN and Extreme Learning Machine Optimized by Sparrow Search Algorithm," Sustainability, MDPI, vol. 13(9), pages 1-20, April.
    8. Chun Jiang & Yi-Fan Wu & Xiao-Lin Li & Xin Li, 2020. "Time-frequency Connectedness between Coal Market Prices, New Energy Stock Prices and CO 2 Emissions Trading Prices in China," Sustainability, MDPI, vol. 12(7), pages 1-17, April.
    9. Wen, Fenghua & Zhao, Haocen & Zhao, Lili & Yin, Hua, 2022. "What drive carbon price dynamics in China?," International Review of Financial Analysis, Elsevier, vol. 79(C).
    10. Liu, Zhibin & Huang, Shan, 2021. "Carbon option price forecasting based on modified fractional Brownian motion optimized by GARCH model in carbon emission trading," The North American Journal of Economics and Finance, Elsevier, vol. 55(C).
    11. Liu, Qingchen & Li, Hongchang & Shang, Wen-long & Wang, Kun, 2022. "Spatio-temporal distribution of Chinese cities’ air quality and the impact of high-speed rail," Renewable and Sustainable Energy Reviews, Elsevier, vol. 170(C).
    12. Jiaojiao Sun & Feng Dong, 2023. "Optimal reduction and equilibrium carbon allowance price for the thermal power industry under China’s peak carbon emissions target," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-27, December.
    13. Zhang, Wen & Wu, Zhibin & Zeng, Xiaojun & Zhu, Changhui, 2023. "An ensemble dynamic self-learning model for multiscale carbon price forecasting," Energy, Elsevier, vol. 263(PC).

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