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A new carbon price prediction model

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  • Li, Guohui
  • Ning, Zhiyuan
  • Yang, Hong
  • Gao, Lipeng

Abstract

The excessive emission of carbon is one of the important factors causing environmental pollution, and the prediction of carbon trading market price is an important mean of emission reduction. In order to accurately predict the carbon price, a new carbon price prediction model is proposed in this paper. Firstly, the data is decomposed into multiple intrinsic mode functions (IMFs) by optimized variational mode decomposition (OVMD). Secondly, the complexity of IMFs is analyzed by spatial-dependence recurrence sample entropy (SdrSampEn). Thirdly, the IMFs with higher complexity are integrated and decomposed by complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) to get high complexity IMFs. Then, particle swarm optimized extreme learning machine (PSOELM) is used to predict the high complexity IMFs, and extreme learning machine (ELM) is used to predict other. Finally, the predicted value is reconstructed to complete the prediction. In this paper, OVMD is proposed to solve the selection of decomposition layers K by variational mode decomposition (VMD) from the perspective of variance contribution rate. Through the experimental results, the effectiveness of the proposed model is verified, and it can be used to predict the supply and demand of carbon market and evaluate the effectiveness of current carbon trading policies.

Suggested Citation

  • Li, Guohui & Ning, Zhiyuan & Yang, Hong & Gao, Lipeng, 2022. "A new carbon price prediction model," Energy, Elsevier, vol. 239(PD).
    • Handle: RePEc:eee:energy:v:239:y:2022:i:pd:s036054422102572x
    DOI: 10.1016/j.energy.2021.122324
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    as
    1. Ma, Qiang & Murshed, Muntasir & Khan, Zeeshan, 2021. "The nexuses between energy investments, technological innovations, emission taxes, and carbon emissions in China," Energy Policy, Elsevier, vol. 155(C).
    2. 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.
    3. 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).
    4. Teixidó, Jordi & Verde, Stefano F. & Nicolli, Francesco, 2019. "The impact of the EU Emissions Trading System on low-carbon technological change: The empirical evidence," Ecological Economics, Elsevier, vol. 164(C), pages 1-1.
    5. Rickels Wilfried & Görlich Dennis & Peterson Sonja, 2015. "Explaining European Emission Allowance Price Dynamics: Evidence from Phase II," German Economic Review, De Gruyter, vol. 16(2), pages 181-202, May.
    6. 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.
    7. Zeeshan Khan & Muntasir Murshed & Kangyin Dong & Siqun Yang, 2021. "The roles of export diversification and composite country risks in carbon emissions abatement: evidence from the signatories of the regional comprehensive economic partnership agreement," Applied Economics, Taylor & Francis Journals, vol. 53(41), pages 4769-4787, September.
    8. 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.
    9. Zhang, Yagang & Pan, Guifang & Chen, Bing & Han, Jingyi & Zhao, Yuan & Zhang, Chenhong, 2020. "Short-term wind speed prediction model based on GA-ANN improved by VMD," Renewable Energy, Elsevier, vol. 156(C), pages 1373-1388.
    10. Hong Yang & Lipeng Gao & Guohui Li, 2020. "Underwater Acoustic Signal Prediction Based on MVMD and Optimized Kernel Extreme Learning Machine," Complexity, Hindawi, vol. 2020, pages 1-17, April.
    11. 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.
    12. Chunguang Sheng & Guangyu Wang & Yude Geng & Lirong Chen, 2020. "The Correlation Analysis of Futures Pricing Mechanism in China’s Carbon Financial Market," Sustainability, MDPI, vol. 12(18), pages 1-20, September.
    13. Zhu, Bangzhu & Wei, Yiming, 2013. "Carbon price forecasting with a novel hybrid ARIMA and least squares support vector machines methodology," Omega, Elsevier, vol. 41(3), pages 517-524.
    14. Dutta, Anupam & Bouri, Elie & Noor, Md Hasib, 2018. "Return and volatility linkages between CO2 emission and clean energy stock prices," Energy, Elsevier, vol. 164(C), pages 803-810.
    15. Wei Sun & Ming Duan, 2019. "Analysis and Forecasting of the Carbon Price in China’s Regional Carbon Markets Based on Fast Ensemble Empirical Mode Decomposition, Phase Space Reconstruction, and an Improved Extreme Learning Machin," Energies, MDPI, vol. 12(2), pages 1-27, January.
    16. Sun, Wei & Zhang, Chongchong, 2018. "Analysis and forecasting of the carbon price using multi—resolution singular value decomposition and extreme learning machine optimized by adaptive whale optimization algorithm," Applied Energy, Elsevier, vol. 231(C), pages 1354-1371.
    17. Sen Wang & Yonghui Sun & Yan Zhou & Rabea Jamil Mahfoud & Dongchen Hou, 2019. "A New Hybrid Short-Term Interval Forecasting of PV Output Power Based on EEMD-SE-RVM," Energies, MDPI, vol. 13(1), pages 1-17, December.
    18. Xu, Hua & Wang, Minggang & Jiang, Shumin & Yang, Weiguo, 2020. "Carbon price forecasting with complex network and extreme learning machine," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 545(C).
    19. Khan, Zeeshan & Ali, Muhsin & Jinyu, Liu & Shahbaz, Muhammad & Siqun, Yang, 2020. "Consumption-based carbon emissions and trade nexus: Evidence from nine oil exporting countries," Energy Economics, Elsevier, vol. 89(C).
    20. Song, Yazhi & Liu, Tiansen & Liang, Dapeng & Li, Yin & Song, Xiaoqiu, 2019. "A Fuzzy Stochastic Model for Carbon Price Prediction Under the Effect of Demand-related Policy in China's Carbon Market," Ecological Economics, Elsevier, vol. 157(C), pages 253-265.
    21. Pham, Tuan D. & Yan, Hong, 2018. "Spatial-dependence recurrence sample entropy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 494(C), pages 581-590.
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    3. Kumar, Tharun Roshan & Beiron, Johanna & Biermann, Maximilian & Harvey, Simon & Thunman, Henrik, 2023. "Plant and system-level performance of combined heat and power plants equipped with different carbon capture technologies," Applied Energy, Elsevier, vol. 338(C).
    4. 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.
    5. 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).
    6. Dan Wang & Juheng Yang, 2022. "Carbon Neutrality Strategies for Chinese International Oil Company Based on the Rapid Development of Global Carbon Market," Sustainability, MDPI, vol. 14(18), pages 1-16, September.
    7. 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).
    8. Hao, Xinyu & Sun, Wen & Zhang, Xiaoling, 2023. "How does a scarcer allowance remake the carbon market? An evolutionary game analysis from the perspective of stakeholders," Energy, Elsevier, vol. 280(C).

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