IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v19y2022i10p6217-d819951.html
   My bibliography  Save this article

Forecasting Carbon Price in China: A Multimodel Comparison

Author

Listed:
  • Houjian Li

    (College of Economics, Sichuan Agricultural University, Chengdu 611130, China)

  • Xinya Huang

    (College of Economics, Sichuan Agricultural University, Chengdu 611130, China)

  • Deheng Zhou

    (College of Economics, Sichuan Agricultural University, Chengdu 611130, China)

  • Andi Cao

    (College of Economics, Sichuan Agricultural University, Chengdu 611130, China)

  • Mengying Su

    (College of Economics, Guangxi University for Nationalities, Nanning 530006, China)

  • Yufeng Wang

    (College of Economics, Sichuan Agricultural University, Chengdu 611130, China)

  • Lili Guo

    (College of Economics, Sichuan Agricultural University, Chengdu 611130, China)

Abstract

With the global concern for carbon dioxide, the carbon emission trading market is becoming more and more important. An accurate forecast of carbon price plays a significant role in understanding the dynamics of the carbon trading market and achieving national emission reduction targets. Carbon prices are influenced by many factors, which makes carbon price forecasting a complicated problem. In recent years, deep learning models are widely used in price forecasting, because they have high forecasting accuracy when dealing with nonlinear time series data. In this paper, Multivariate Long Short-Term Memory (LSTM) in deep learning is used to forecast carbon prices in China, which takes into account the factors affecting the carbon price. The historical time series data of carbon prices in Hubei (HBEA) and Guangdong (GDEA) and three traditional energy prices affecting carbon prices from 5 May 2014 to 22 July 2021 are collected to form two data sets. To prove the forecast effect of our model, this paper not only uses Multivariate LSTM, Multilayer Perceptron (MLP), Support Vector Regression (SVR), and Recurrent Neural Network (RNN) to forecast the same data, but also compares the forecast results of Multivariate LSTM with the existing research on HBEA and GDEA forecast based on deep learning recently. The results show that the MAE, MSE, and RMSE obtained by the Multivariate LSTM are all smaller than other prediction models, which proves that the model is more suitable for carbon price forecast and offers a new approach to carbon prices forecast. This research conclusion also provides some policy implications.

Suggested Citation

  • Houjian Li & Xinya Huang & Deheng Zhou & Andi Cao & Mengying Su & Yufeng Wang & Lili Guo, 2022. "Forecasting Carbon Price in China: A Multimodel Comparison," IJERPH, MDPI, vol. 19(10), pages 1-16, May.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:10:p:6217-:d:819951
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/19/10/6217/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/19/10/6217/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Hamilton, James D., 1996. "This is what happened to the oil price-macroeconomy relationship," Journal of Monetary Economics, Elsevier, vol. 38(2), pages 215-220, October.
    2. Keppler, Jan Horst & Mansanet-Bataller, Maria, 2010. "Causalities between CO2, electricity, and other energy variables during phase I and phase II of the EU ETS," Energy Policy, Elsevier, vol. 38(7), pages 3329-3341, July.
    3. Holtsmark, Bjart & Maestad, Ottar, 2002. "Emission trading under the Kyoto Protocol--effects on fossil fuel markets under alternative regimes," Energy Policy, Elsevier, vol. 30(3), pages 207-218, February.
    4. Considine, Timothy J., 2000. "The impacts of weather variations on energy demand and carbon emissions," Resource and Energy Economics, Elsevier, vol. 22(4), pages 295-314, October.
    5. Byun, Suk Joon & Cho, Hangjun, 2013. "Forecasting carbon futures volatility using GARCH models with energy volatilities," Energy Economics, Elsevier, vol. 40(C), pages 207-221.
    6. 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).
    7. Alameer, Zakaria & Fathalla, Ahmed & Li, Kenli & Ye, Haiwang & Jianhua, Zhang, 2020. "Multistep-ahead forecasting of coal prices using a hybrid deep learning model," Resources Policy, Elsevier, vol. 65(C).
    8. Wu, Yu-Xi & Wu, Qing-Biao & Zhu, Jia-Qi, 2019. "Improved EEMD-based crude oil price forecasting using LSTM networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 516(C), pages 114-124.
    9. 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.
    10. repec:dau:papers:123456789/5269 is not listed on IDEAS
    11. Cen, Zhongpei & Wang, Jun, 2019. "Crude oil price prediction model with long short term memory deep learning based on prior knowledge data transfer," Energy, Elsevier, vol. 169(C), pages 160-171.
    12. Paolella, Marc S. & Taschini, Luca, 2008. "An econometric analysis of emission allowance prices," Journal of Banking & Finance, Elsevier, vol. 32(10), pages 2022-2032, October.
    13. Zhu, Bangzhu & Han, Dong & Wang, Ping & Wu, Zhanchi & Zhang, Tao & Wei, Yi-Ming, 2017. "Forecasting carbon price using empirical mode decomposition and evolutionary least squares support vector regression," Applied Energy, Elsevier, vol. 191(C), pages 521-530.
    14. 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.
    15. Shenghua Xiong & Chunfeng Wang & Zhenming Fang & Dan Ma, 2019. "Multi-Step-Ahead Carbon Price Forecasting Based on Variational Mode Decomposition and Fast Multi-Output Relevance Vector Regression Optimized by the Multi-Objective Whale Optimization Algorithm," Energies, MDPI, vol. 12(1), pages 1-21, January.
    16. Guoqiang Sun & Tong Chen & Zhinong Wei & Yonghui Sun & Haixiang Zang & Sheng Chen, 2016. "A Carbon Price Forecasting Model Based on Variational Mode Decomposition and Spiking Neural Networks," Energies, MDPI, vol. 9(1), pages 1-16, January.
    17. Wang, Huai-zhi & Li, Gang-qiang & Wang, Gui-bin & Peng, Jian-chun & Jiang, Hui & Liu, Yi-tao, 2017. "Deep learning based ensemble approach for probabilistic wind power forecasting," Applied Energy, Elsevier, vol. 188(C), pages 56-70.
    18. Hafeez, Ghulam & Alimgeer, Khurram Saleem & Khan, Imran, 2020. "Electric load forecasting based on deep learning and optimized by heuristic algorithm in smart grid," Applied Energy, Elsevier, vol. 269(C).
    19. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Jakubek, Dariusz & Ocłoń, Paweł & Nowak-Ocłoń, Marzena & Sułowicz, Maciej & Varbanov, Petar Sabev & Klemeš, Jiří Jaromír, 2023. "Mathematical modelling and model validation of the heat losses in district heating networks," Energy, Elsevier, vol. 267(C).
    2. Li, Jingmiao & Liu, Dehong, 2023. "Carbon price forecasting based on secondary decomposition and feature screening," Energy, Elsevier, vol. 278(PA).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Huang, Yumeng & Dai, Xingyu & Wang, Qunwei & Zhou, Dequn, 2021. "A hybrid model for carbon price forecastingusing GARCH and long short-term memory network," Applied Energy, Elsevier, vol. 285(C).
    2. Gao, Feng & Shao, Xueyan, 2022. "A novel interval decomposition ensemble model for interval carbon price forecasting," Energy, Elsevier, vol. 243(C).
    3. Peng Ye & Yong Li & Abu Bakkar Siddik, 2023. "Forecasting the Return of Carbon Price in the Chinese Market Based on an Improved Stacking Ensemble Algorithm," Energies, MDPI, vol. 16(11), pages 1-39, June.
    4. Wen Zhang & Zhibin Wu, 2022. "Optimal hybrid framework for carbon price forecasting using time series analysis and least squares support vector machine," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(3), pages 615-632, April.
    5. Yumin Li & Ruiqi Yang & Xiaoman Wang & Jiaming Zhu & Nan Song, 2023. "Carbon Price Combination Forecasting Model Based on Lasso Regression and Optimal Integration," Sustainability, MDPI, vol. 15(12), pages 1-26, June.
    6. Kaijian He & Qian Yang & Lei Ji & Jingcheng Pan & Yingchao Zou, 2023. "Financial Time Series Forecasting with the Deep Learning Ensemble Model," Mathematics, MDPI, vol. 11(4), pages 1-15, February.
    7. 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.
    8. 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.
    9. 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.
    10. Shenghua Xiong & Chunfeng Wang & Zhenming Fang & Dan Ma, 2019. "Multi-Step-Ahead Carbon Price Forecasting Based on Variational Mode Decomposition and Fast Multi-Output Relevance Vector Regression Optimized by the Multi-Objective Whale Optimization Algorithm," Energies, MDPI, vol. 12(1), pages 1-21, January.
    11. Katarzyna Rudnik & Anna Hnydiuk-Stefan & Aneta Kucińska-Landwójtowicz & Łukasz Mach, 2022. "Forecasting Day-Ahead Carbon Price by Modelling Its Determinants Using the PCA-Based Approach," Energies, MDPI, vol. 15(21), pages 1-23, October.
    12. 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).
    13. 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.
    14. Po Yun & Chen Zhang & Yaqi Wu & Yu Yang, 2022. "Forecasting Carbon Dioxide Price Using a Time-Varying High-Order Moment Hybrid Model of NAGARCHSK and Gated Recurrent Unit Network," IJERPH, MDPI, vol. 19(2), pages 1-19, January.
    15. 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.
    16. Peng Chen & Andrew Vivian & Cheng Ye, 2022. "Forecasting carbon futures price: a hybrid method incorporating fuzzy entropy and extreme learning machine," Annals of Operations Research, Springer, vol. 313(1), pages 559-601, June.
    17. Jianguo Zhou & Qiqi Wang, 2021. "Forecasting Carbon Price with Secondary Decomposition Algorithm and Optimized Extreme Learning Machine," Sustainability, MDPI, vol. 13(15), pages 1-17, July.
    18. Wang, Minggang & Zhu, Mengrui & Tian, Lixin, 2022. "A novel framework for carbon price forecasting with uncertainties," Energy Economics, Elsevier, vol. 112(C).
    19. 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.
    20. Xian, Sidong & Feng, Miaomiao & Cheng, Yue, 2023. "Incremental nonlinear trend fuzzy granulation for carbon trading time series forecast," Applied Energy, Elsevier, vol. 352(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijerp:v:19:y:2022:i:10:p:6217-:d:819951. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.