IDEAS home Printed from https://ideas.repec.org/a/eee/chsofr/v188y2024ics0960077924010853.html
   My bibliography  Save this article

An optimized and interpretable carbon price prediction: Explainable deep learning model

Author

Listed:
  • Sayed, Gehad Ismail
  • Abd El-Latif, Eman I.
  • Darwish, Ashraf
  • Snasel, Vaclav
  • Hassanien, Aboul Ella

Abstract

Accurate prediction of carbon prices is crucial for a stable market, enabling informed decision-making and strategic planning. Over the years, several models for predicting carbon prices have been proposed to enhance accuracy. However, previous research has primarily focused on improving accuracy, often neglecting the importance of making the findings understandable and meaningful. This paper aims to bridge that gap by not only improving prediction accuracy but also ensuring that the results are transparent and comprehensible, thus contributing to more effective and informed decision-making in the carbon market. An optimized Long Short-Term Memory (LSTM) network enhanced with the modified light spectrum optimizer (MLSO) is proposed to improve carbon price prediction accuracy. Additionally, the paper incorporates Explainable AI (XAI) techniques to interpret the results, bridging the gap between accuracy and interpretability. The proposed model is evaluated on carbon price historical transaction data acquired from Investing.com and tested on eight other benchmark datasets with different characteristics. The proposed model achieved 0.66 root mean square error (RMSE), 0.99 R2, 0.37 mean absolute error (MAE), 0.15 mean absolute percentage error (MAPE), and 0.44 mean square error (MSE). The results showed that low price, high price, and open price features have the highest significance in driving the model's predictions in comparison to other features like date, volume, and price change features. Additionally, the results indicate that the year, day, and month do not significantly influence the carbon price. The proposed model outperforms state-of-the-art models and other well-known machine learning algorithms according to the experimental results. Moreover, the results indicate that the predictive capability of the proposed model serves as a valuable tool for investors and carbon traders to understand the factors influencing price changes, optimize their strategies, and minimize risk.

Suggested Citation

  • Sayed, Gehad Ismail & Abd El-Latif, Eman I. & Darwish, Ashraf & Snasel, Vaclav & Hassanien, Aboul Ella, 2024. "An optimized and interpretable carbon price prediction: Explainable deep learning model," Chaos, Solitons & Fractals, Elsevier, vol. 188(C).
    • Handle: RePEc:eee:chsofr:v:188:y:2024:i:c:s0960077924010853
    DOI: 10.1016/j.chaos.2024.115533
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960077924010853
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.chaos.2024.115533?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Bangzhu Zhu & Jingyi Zhang & Chunzhuo Wan & Julien Chevallier & Ping Wang, 2023. "An evolutionary cost‐sensitive support vector machine for carbon price trend forecasting," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(4), pages 741-755, July.
    2. Bangzhu Zhu & Jingyi Zhang & Chunzhuo Wan & Julien Chevallier & Ping Wang, 2023. "An evolutionary cost‐sensitive support vector machine for carbon price trend forecasting," Post-Print halshs-04250346, HAL.
    3. Wen-Wen Zhang & Bin Zhao & Yue-Qi Jiang & Yong-You Nie & Basil Sharp & Yu Gu & Shi-Chun Xu & Yun Zhu & Jia Xing & Shu-Xiao Wang & He He, 2024. "Co-benefits of regionally-differentiated carbon pricing policies across China," Climate Policy, Taylor & Francis Journals, vol. 24(1), pages 57-70, January.
    4. 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.
    5. Mohamed Abdel-Basset & Reda Mohamed & Karam M. Sallam & Ripon K. Chakrabortty, 2022. "Light Spectrum Optimizer: A Novel Physics-Inspired Metaheuristic Optimization Algorithm," Mathematics, MDPI, vol. 10(19), pages 1-63, September.
    6. Cheng, Hui-Ching & Shu, Ming-Hung & Huang, Jui-Chan, 2024. "Economic strategies for efficient use of natural resources: The impact of carbon taxation and fiscal policy," Resources Policy, Elsevier, vol. 92(C).
    7. 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.
    8. 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.
    9. 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.
    10. Yang, Cai & Zhang, Hongwei & Weng, Futian, 2024. "Effects of COVID-19 vaccination programs on EU carbon price forecasts: Evidence from explainable machine learning," International Review of Financial Analysis, Elsevier, vol. 91(C).
    11. Ajay K. Dhamija & Surendra S. Yadav & PK Jain, 2017. "Forecasting volatility of carbon under EU ETS: a multi-phase study," Environmental Economics and Policy Studies, Springer;Society for Environmental Economics and Policy Studies - SEEPS, vol. 19(2), pages 299-335, April.
    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. 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).
    2. 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.
    3. 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).
    4. Huang, Wenyang & Wang, Huiwen & Wei, Yigang, 2023. "Identifying the determinants of European carbon allowances prices: A novel robust partial least squares method for open-high-low-close data," International Review of Financial Analysis, Elsevier, vol. 90(C).
    5. Xiangjun Cai & Dagang Li & Li Feng, 2024. "Enhanced Carbon Price Forecasting Using Extended Sliding Window Decomposition with LSTM and SVR," Mathematics, MDPI, vol. 12(23), pages 1-20, November.
    6. Huang, Wenyang & Wang, Huiwen & Qin, Haotong & Wei, Yigang & Chevallier, Julien, 2022. "Convolutional neural network forecasting of European Union allowances futures using a novel unconstrained transformation method," Energy Economics, Elsevier, vol. 110(C).
    7. Qingjie Zhou & Panpan Zhu & Yinpeng Zhang, 2023. "Contagion Spillover from Bitcoin to Carbon Futures Pricing: Perspective from Investor Attention," Energies, MDPI, vol. 16(2), pages 1-22, January.
    8. Yan, Kai & Zhang, Wei & Shen, Dehua, 2020. "Stylized facts of the carbon emission market in China," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 555(C).
    9. Chen, Linfei & Zhao, Xuefeng, 2024. "A multiscale and multivariable differentiated learning for carbon price forecasting," Energy Economics, Elsevier, vol. 131(C).
    10. 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.
    11. 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.
    12. 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.
    13. Xu Gong & Mengjie Li & Keqin Guan & Chuanwang Sun, 2023. "Climate change attention and carbon futures return prediction," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 43(9), pages 1261-1288, September.
    14. Lei, Heng & Xue, Minggao & Liu, Huiling, 2022. "Probability distribution forecasting of carbon allowance prices: A hybrid model considering multiple influencing factors," Energy Economics, Elsevier, vol. 113(C).
    15. Jujie Wang & Zhenzhen Zhuang, 2023. "A novel cluster based multi-index nonlinear ensemble framework for carbon price forecasting," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(7), pages 6225-6247, July.
    16. Xiangjun Chen & Bo Yan, 2024. "Research on jumps and volatility in China’s carbon market," Economic Change and Restructuring, Springer, vol. 57(1), pages 1-43, February.
    17. 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.
    18. Tan, Xueping & Sirichand, Kavita & Vivian, Andrew & Wang, Xinyu, 2022. "Forecasting European carbon returns using dimension reduction techniques: Commodity versus financial fundamentals," International Journal of Forecasting, Elsevier, vol. 38(3), pages 944-969.
    19. John Foster & Liam Wagner & Phil Wild & Junhua Zhao & Lucas Skoofa & Craig Froome, 2011. "Market and Economic Modelling of the Intelligent Grid: End of Year Report 2009," Energy Economics and Management Group Working Papers 09, School of Economics, University of Queensland, Australia.
    20. Rafał Weron, 2009. "Heavy-tails and regime-switching in electricity prices," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 69(3), pages 457-473, July.

    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:eee:chsofr:v:188:y:2024:i:c:s0960077924010853. 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: Thayer, Thomas R. (email available below). General contact details of provider: https://www.journals.elsevier.com/chaos-solitons-and-fractals .

    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.