IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v250y2025ics0960148125009905.html
   My bibliography  Save this article

Bioenergy market predictions using AI: Integrating climate change and green finance

Author

Listed:
  • Guo, Lili
  • Cheng, Quanfeixue
  • He, Xiangyi
  • Su, Mengying
  • Li, Houjian

Abstract

This study aims to improve the accuracy of bioenergy return prediction by applying advanced deep learning models. The volatility of the bioenergy market and its susceptibility to multiple influencing factors necessitate the use of advanced methods to improve prediction accuracy and reliability. Therefore, this study employs 1965 daily data points sourced from the Bloomberg database, covering the period from April 1, 2015, to October 19, 2022. We utilized six hybrid deep learning models: LSTM, GRU, CNN-BiLSTM, CNN-BiLSTM-Attention, INFO-CNN-BiLSTM, and NRBO-BiLSTM-Attention. These models were applied to predict global bioenergy returns, using the Global Climate Policy Uncertainty Index, Global Green Bond Index Returns, Arctic Oscillation Index, and the Nasdaq BioClean Fuel Index as inputs. The forecast horizons were set to 5 days, 10 days, and 15 days. Model performance was assessed using Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and MCS testing. Our findings indicate the following: (1) Although the INFO-CNN-BiLSTM model exhibited the smallest prediction error, the NRBO-CNN-BiLSTM-Attention model's predictions were the closest to the actual values, and Multiple Comparison Testing confirmed its superior performance in predicting bioenergy returns; (2) Across the 15-day, 10-day, and 5-day horizons, the NRBO-CNN-BiLSTM-Attention model consistently improved prediction accuracy and overall performance. These findings contribute to the optimization of global energy structures and the promotion of sustainable energy development and application.

Suggested Citation

  • Guo, Lili & Cheng, Quanfeixue & He, Xiangyi & Su, Mengying & Li, Houjian, 2025. "Bioenergy market predictions using AI: Integrating climate change and green finance," Renewable Energy, Elsevier, vol. 250(C).
  • Handle: RePEc:eee:renene:v:250:y:2025:i:c:s0960148125009905
    DOI: 10.1016/j.renene.2025.123328
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148125009905
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.renene.2025.123328?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.

    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:renene:v:250:y:2025:i:c:s0960148125009905. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/renewable-energy .

    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.