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A text-based framework for carbon price forecasting via multivariate temporal graph neural network

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Abstract

Accurate forecasting of carbon prices is essential for both policymakers and market participants. However, the inherent volatility of carbon prices, driven by numerous external factors, poses significant predictive challenges. This study proposes a carbon price forecasting framework based on graph neural network, which effectively harnesses online news headline data. First, external information from online news headlines is integrated into the carbon price forecasting model. Second, to address the temporal variability of news impact, a method for textual information extraction that accounts for time-related changes is proposed. Specifically, thematic information is distilled from news across different periods using a dynamic topic model (DTM), and sentiment information is conducted using SnowNLP, which is enhanced with an accumulative decay factor. Subsequently, due to the complex relationship between carbon price and textual information, a multivariate temporal graph neural network (MtemGNN) is constructed to automatically learn the correlation between features, thus improving predictive performance. Finally, using Shenzhen, Guangzhou, and Fujian carbon markets as case studies, the experimental results demonstrate that extracting thematic and sentiment information using the proposed textual information extraction method can enhance carbon price forecasting effectiveness. Furthermore, the proposed MtemGNN exhibits higher forecasting precision compared to other baseline models.

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Data availability

The datasets analyzed during the current study are available from the first author on reasonable request.

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Funding

This work was supported by the National Natural Science Foundation of China (No. 71971089, No. 72001083, No. 72471097 and No. 72401101), the Natural Science Foundation of Guangdong Province (No. 2022A1515011612 and No. 2024A1515010941), the Guangdong Province Philosophy and Social Sciences Planning Projecte (No. GD23XGL097), and the Guangdong Basic and Applied Basic Research Foundation (No. 2023A1515110580).

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Authors and Affiliations

  1. College of Mathematics and Information, South China Agricultural University, Guangzhou, 510642, China

    Dabin Zhang, Zehui Yu, Boting Zhang, Ruibin Lin & Huanling Hu

  2. College of Management, Shenzhen University, Shenzhen, 518060, China

    Zhimei Zeng

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Contributions

DZ was contributed to conceptualization, supervision, funding acquisition, writing—review and editing. ZY was contributed to methodology, software, formal analysis, investigation, visualization and writing – original draft. ZZ was contributed to software, visualization, writing—review and editing. BZ was contributed to data curation, writing—review and editing. RL was contributed to methodology, writing—review and editing. HH was contributed to supervision, funding acquisition, writing—review and editing.

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Correspondence to Huanling Hu.

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Zhang, D., Yu, Z., Zeng, Z. et al. A text-based framework for carbon price forecasting via multivariate temporal graph neural network. J Supercomput 81, 488 (2025). https://doi.org/10.1007/s11227-025-06974-9

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