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Prediction of Regional Carbon Price in China Based on Secondary Decomposition and Nonlinear Error Correction

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  • Beibei Hu

    (School of Economics and Management, Anhui University of Science and Technology, Huainan 232001, China)

  • Yunhe Cheng

    (School of Economics and Management, Anhui University of Science and Technology, Huainan 232001, China)

Abstract

Effective prediction of carbon prices matters a great deal for risk management in the carbon financial market. This article designs a blended approach incorporating secondary decomposition and nonlinear error-correction technology to predict the regional carbon price in China. Firstly, the variational mode decomposition (VMD) method is used to decompose the carbon price, and then, the time-varying filter-based empirical mode decomposition (TVFEMD) is introduced to decompose the residual term generated by VMD, and the multiple kernel-based extreme learning machine (MKELM) optimized by the sparrow search algorithm (SSA) is innovatively built to forecast the carbon subsequences. Finally, in order to mine the hidden information contained in the forecasted error, the nonlinear error-correction method based on the SSA-MKELM model is introduced to correct the initial prediction of carbon price. The empirical results show that the proposed model improves the prediction accuracy of carbon prices, with RMSE, MAE, MAPE, and DS up to 0.1363, 0.1160, 0.0015, and 0.9231 in Guangdong, respectively. In the case of the Hubei market, the model also performs best. This research innovatively expands the prediction theory and method of China’s regional carbon price.

Suggested Citation

  • Beibei Hu & Yunhe Cheng, 2023. "Prediction of Regional Carbon Price in China Based on Secondary Decomposition and Nonlinear Error Correction," Energies, MDPI, vol. 16(11), pages 1-22, May.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:11:p:4444-:d:1160515
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    as
    1. Wu, Qingyang & Wang, Yanying, 2022. "How does carbon emission price stimulate enterprises' total factor productivity? Insights from China's emission trading scheme pilots," Energy Economics, Elsevier, vol. 109(C).
    2. Razzaq, Asif & Sharif, Arshian & An, Hui & Aloui, Chaker, 2022. "Testing the directional predictability between carbon trading and sectoral stocks in China: New insights using cross-quantilogram and rolling window causality approaches," Technological Forecasting and Social Change, Elsevier, vol. 182(C).
    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. Lin, Yu & Lu, Qin & Tan, Bin & Yu, Yuanyuan, 2022. "Forecasting energy prices using a novel hybrid model with variational mode decomposition," Energy, Elsevier, vol. 246(C).
    5. Yu, Min & Niu, Dongxiao & Gao, Tian & Wang, Keke & Sun, Lijie & Li, Mingyu & Xu, Xiaomin, 2023. "A novel framework for ultra-short-term interval wind power prediction based on RF-WOA-VMD and BiGRU optimized by the attention mechanism," Energy, Elsevier, vol. 269(C).
    6. Li, Dan & Li, Yijun & Wang, Chaoqun & Chen, Min & Wu, Qi, 2023. "Forecasting carbon prices based on real-time decomposition and causal temporal convolutional networks," Applied Energy, Elsevier, vol. 331(C).
    7. Yang, Lu, 2022. "Idiosyncratic information spillover and connectedness network between the electricity and carbon markets in Europe," Journal of Commodity Markets, Elsevier, vol. 25(C).
    8. 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).
    9. Li, Guohui & Ning, Zhiyuan & Yang, Hong & Gao, Lipeng, 2022. "A new carbon price prediction model," Energy, Elsevier, vol. 239(PD).
    10. Wu, Kai & Zhu, Jingran & Xu, Mingli & Yang, Lu, 2020. "Can crude oil drive the co-movement in the international stock market? Evidence from partial wavelet coherence analysis," The North American Journal of Economics and Finance, Elsevier, vol. 53(C).
    11. Sun, Wei & Zhang, Junjian, 2022. "A novel carbon price prediction model based on optimized least square support vector machine combining characteristic-scale decomposition and phase space reconstruction," Energy, Elsevier, vol. 253(C).
    12. 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.
    13. Cong, Ren & Lo, Alex Y., 2017. "Emission trading and carbon market performance in Shenzhen, China," Applied Energy, Elsevier, vol. 193(C), pages 414-425.
    14. Chen, Xuejun & Yang, Yongming & Cui, Zhixin & Shen, Jun, 2019. "Vibration fault diagnosis of wind turbines based on variational mode decomposition and energy entropy," Energy, Elsevier, vol. 174(C), pages 1100-1109.
    15. Yang, Lu & Hamori, Shigeyuki, 2021. "The role of the carbon market in relation to the cryptocurrency market: Only diversification or more?," International Review of Financial Analysis, Elsevier, vol. 77(C).
    16. Yunhe Cheng & Beibei Hu, 2022. "Forecasting Regional Carbon Prices in China Based on Secondary Decomposition and a Hybrid Kernel-Based Extreme Learning Machine," Energies, MDPI, vol. 15(10), pages 1-18, May.
    17. Zeng, Shihong & Nan, Xin & Liu, Chao & Chen, Jiuying, 2017. "The response of the Beijing carbon emissions allowance price (BJC) to macroeconomic and energy price indices," Energy Policy, Elsevier, vol. 106(C), pages 111-121.
    18. Jianguo Zhou & Shiguo Wang, 2021. "A Carbon Price Prediction Model Based on the Secondary Decomposition Algorithm and Influencing Factors," Energies, MDPI, vol. 14(5), pages 1-20, March.
    19. Wang, Piao & Tao, Zhifu & Liu, Jinpei & Chen, Huayou, 2023. "Improving the forecasting accuracy of interval-valued carbon price from a novel multi-scale framework with outliers detection: An improved interval-valued time series analysis mode," Energy Economics, Elsevier, vol. 118(C).
    20. 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.
    21. Baoliu Liu & Zhenqing Sun & Huanhuan Li, 2021. "Can Carbon Trading Policies Promote Regional Green Innovation Efficiency? Empirical Data from Pilot Regions in China," Sustainability, MDPI, vol. 13(5), pages 1-15, March.
    22. Zhu, Bangzhu & Ye, Shunxin & Wang, Ping & He, Kaijian & Zhang, Tao & Wei, Yi-Ming, 2018. "A novel multiscale nonlinear ensemble leaning paradigm for carbon price forecasting," Energy Economics, Elsevier, vol. 70(C), pages 143-157.
    23. Zhang, Chu & Ma, Huixin & Hua, Lei & Sun, Wei & Nazir, Muhammad Shahzad & Peng, Tian, 2022. "An evolutionary deep learning model based on TVFEMD, improved sine cosine algorithm, CNN and BiLSTM for wind speed prediction," Energy, Elsevier, vol. 254(PA).
    24. Zhou, Feite & Huang, Zhehao & Zhang, Changhong, 2022. "Carbon price forecasting based on CEEMDAN and LSTM," Applied Energy, Elsevier, vol. 311(C).
    25. 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).
    26. Bangzhu Zhu, 2012. "A Novel Multiscale Ensemble Carbon Price Prediction Model Integrating Empirical Mode Decomposition, Genetic Algorithm and Artificial Neural Network," Energies, MDPI, vol. 5(2), pages 1-16, February.
    27. Li, Jinchao & Wu, Qianqian & Tian, Yu & Fan, Liguo, 2021. "Monthly Henry Hub natural gas spot prices forecasting using variational mode decomposition and deep belief network," Energy, Elsevier, vol. 227(C).
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