IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v16y2024i19p8324-d1485055.html
   My bibliography  Save this article

A Sustainable Model for Forecasting Carbon Emission Trading Prices

Author

Listed:
  • Jiaqing Chen

    (School of Economics and Finance, Xi’an Jiao tong University, Xi’an 710061, China
    Xiamen Tiandi Development and Construction Group Co., Ltd., Xiamen 361013, China)

  • Dongpeng Peng

    (School of Internet Economics and Business, Fujian University of Technology, Fuzhou 350014, China)

  • Zhiwei Liu

    (School of Internet Economics and Business, Fujian University of Technology, Fuzhou 350014, China)

  • Lingzhi Wu

    (School of Internet Economics and Business, Fujian University of Technology, Fuzhou 350014, China)

  • Ming Jiang

    (School of Internet Economics and Business, Fujian University of Technology, Fuzhou 350014, China)

Abstract

Carbon trading has garnered considerable attention as a pivotal policy instrument for advancing carbon peaking and carbon neutrality, which are essential components of sustainable development. The capacity to precisely anticipate the cost of carbon trading has significant implications for the optimal deployment of market mechanisms, the economic advancement of technological innovations in corporate emissions reduction, and the facilitation of international energy policy adjustments. To this end, this paper proposes a novel and sustainable trading price prediction tool that employs a four-step process: decomposition, reconstruction, prediction, and integration. This innovative approach first utilizes the Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN), then reconstructs the decomposition set using multi-scale entropy (MSE), and finally uses the Long Short-Term Memory neural network model (LSTM) enhanced by the Grey Wolf Optimizer (GWO) to predict the carbon emission trading price. The experimental results demonstrate that the tool achieves high accuracy for both the EU carbon price series and the carbon price series of China’s seven major carbon trading markets, with accuracy rates of 99.10% and 99.60% in Hubei and the EU carbon trading markets, respectively. This represents an improvement of approximately 3.1% over the ICEEMDAN-LSTM model and 0.91% over the ICEEMDAN-MSE-LSTM model, thereby contributing to more sustainable and efficient carbon trading practices.

Suggested Citation

  • Jiaqing Chen & Dongpeng Peng & Zhiwei Liu & Lingzhi Wu & Ming Jiang, 2024. "A Sustainable Model for Forecasting Carbon Emission Trading Prices," Sustainability, MDPI, vol. 16(19), pages 1-16, September.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:19:p:8324-:d:1485055
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/16/19/8324/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/16/19/8324/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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. Kakade, Kshitij & Jain, Ishan & Mishra, Aswini Kumar, 2022. "Value-at-Risk forecasting: A hybrid ensemble learning GARCH-LSTM based approach," Resources Policy, Elsevier, vol. 78(C).
    3. Ye, Jing & Xue, Minggao, 2021. "Influences of sentiment from news articles on EU carbon prices," Energy Economics, Elsevier, vol. 101(C).
    4. 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.
    5. Balcılar, Mehmet & Demirer, Rıza & Hammoudeh, Shawkat & Nguyen, Duc Khuong, 2016. "Risk spillovers across the energy and carbon markets and hedging strategies for carbon risk," Energy Economics, Elsevier, vol. 54(C), pages 159-172.
    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. Wang, Jujie & Cui, Quan & He, Maolin, 2022. "Hybrid intelligent framework for carbon price prediction using improved variational mode decomposition and optimal extreme learning machine," Chaos, Solitons & Fractals, Elsevier, vol. 156(C).
    8. Zahraee, Seyed Mojib & Shiwakoti, Nirajan & Stasinopoulos, Peter, 2022. "Application of geographical information system and agent-based modeling to estimate particle-gaseous pollutantemissions and transportation cost of woody biomass supply chain," Applied Energy, Elsevier, vol. 309(C).
    9. Batten, Jonathan A. & Maddox, Grace E. & Young, Martin R., 2021. "Does weather, or energy prices, affect carbon prices?," Energy Economics, Elsevier, vol. 96(C).
    10. María Inmaculada Rodríguez-García & María Gema Carrasco-García & Javier González-Enrique & Juan Jesús Ruiz-Aguilar & Ignacio J. Turias, 2023. "Long Short-Term Memory Approach for Short-Term Air Quality Forecasting in the Bay of Algeciras (Spain)," Sustainability, MDPI, vol. 15(6), pages 1-20, March.
    Full references (including those not matched with items on IDEAS)

    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. 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).
    2. Huang, Wenyang & Zhao, Jianyu & Wang, Xiaokang, 2024. "Model-driven multimodal LSTM-CNN for unbiased structural forecasting of European Union allowances open-high-low-close price," Energy Economics, Elsevier, vol. 132(C).
    3. Shanglei Chai & Zixuan Zhang & Zhen Zhang, 2025. "Carbon price prediction for China's ETS pilots using variational mode decomposition and optimized extreme learning machine," Annals of Operations Research, Springer, vol. 345(2), pages 809-830, February.
    4. Wang, Jujie & Xu, Shulian & Shu, Shuqin, 2024. "An optimal weight heterogeneous integrated carbon price prediction model based on temporal information extraction and specific comprehensive feature selection," Energy, Elsevier, vol. 312(C).
    5. Zhang, Fang & Xia, Yan, 2022. "Carbon price prediction models based on online news information analytics," Finance Research Letters, Elsevier, vol. 46(PA).
    6. Gao, Feng & Shao, Xueyan, 2022. "A novel interval decomposition ensemble model for interval carbon price forecasting," Energy, Elsevier, vol. 243(C).
    7. 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.
    8. Bai, Yun & Deng, Shuyun & Pu, Ziqiang & Li, Chuan, 2024. "Carbon price forecasting using leaky integrator echo state networks with the framework of decomposition-reconstruction-integration," Energy, Elsevier, vol. 305(C).
    9. Xu, Yingying & Dai, Yifan & Guo, Lingling & Chen, Jingjing, 2024. "Leveraging machine learning to forecast carbon returns: Factors from energy markets," Applied Energy, Elsevier, vol. 357(C).
    10. Chen, Weidong & Xiong, Shi & Chen, Quanyu, 2022. "Characterizing the dynamic evolutionary behavior of multivariate price movement fluctuation in the carbon-fuel energy markets system from complex network perspective," Energy, Elsevier, vol. 239(PA).
    11. Wu, Han & Du, Pei, 2024. "Dual-stream transformer-attention fusion network for short-term carbon price prediction," Energy, Elsevier, vol. 311(C).
    12. Liu, Shuihan & Xie, Gang & Wang, Zhengzhong & Wang, Shouyang, 2024. "A secondary decomposition-ensemble framework for interval carbon price forecasting," Applied Energy, Elsevier, vol. 359(C).
    13. Zeyu Zhang & Xiaoqian Liu & Xiling Zhang & Zhishan Yang & Jian Yao, 2024. "Carbon Price Forecasting Using Optimized Sliding Window Empirical Wavelet Transform and Gated Recurrent Unit Network to Mitigate Data Leakage," Energies, MDPI, vol. 17(17), pages 1-22, August.
    14. 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.
    15. Man, Yuanyuan & Zhang, Sunpei & He, Yongda, 2024. "Dynamic risk spillover and hedging efficacy of China’s carbon-energy-finance markets: Economic policy uncertainty and investor sentiment non-linear causal effects," International Review of Economics & Finance, Elsevier, vol. 93(PA), pages 1397-1416.
    16. Wang, Jujie & Zhuang, Zhenzhen & Gao, Dongming, 2023. "An enhanced hybrid model based on multiple influencing factors and divide-conquer strategy for carbon price prediction," Omega, Elsevier, vol. 120(C).
    17. Chen, Linfei & Zhao, Xuefeng, 2024. "A multiscale and multivariable differentiated learning for carbon price forecasting," Energy Economics, Elsevier, vol. 131(C).
    18. Fansheng Meng & Rong Dou, 2024. "Prophet-LSTM-BP Ensemble Carbon Trading Price Prediction Model," Computational Economics, Springer;Society for Computational Economics, vol. 63(5), pages 1805-1825, May.
    19. Xinyu Wu & Xuebao Yin & Xueting Mei, 2022. "Forecasting the Volatility of European Union Allowance Futures with Climate Policy Uncertainty Using the EGARCH-MIDAS Model," Sustainability, MDPI, vol. 14(7), pages 1-13, April.
    20. Na Fu & Liyan Geng & Junhai Ma & Xue Ding, 2023. "Price, Complexity, and Mathematical Model," Mathematics, MDPI, vol. 11(13), pages 1-30, June.

    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:jsusta:v:16:y:2024:i:19:p:8324-:d:1485055. 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.