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An interval-valued carbon price forecasting method based on web search data and social media sentiment

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Abstract

Accurate carbon price prediction is a crucial task for the carbon trading market. Previous studies have ignored the impact of online data and are limited to point predictions, which brings challenges to the accurate forecasting of carbon prices. To address those issues, this paper proposes an interval-valued carbon price forecasting method based on web search data and social media sentiment. First, we collect web search data and social media sentiment to improve prediction performance by synthesizing multiple types of data information. Second, we employ principal component analysis (PCA) to preprocess high-dimensional web search data, and utilize BosonNLP for quantifying social media information, thereby enhancing the predictability of the dataset. Subsequently, a variational mode decomposition (VMD) is applied to the carbon price and online data, followed by utilizing particle swarm optimization support vector regression (PSO-SVR) to predict each sub-modes and summing them up to obtain the ultimate forecasting outcome. Finally, using carbon prices in Guangdong and Hubei provinces as case studies, the experimental results demonstrate that web search data and social media sentiment significantly enhance the predictive accuracy of interval-valued carbon prices. Furthermore, the proposed VMD-PSO-SVR outperforms other comparative models in the accuracy and reliability of interval-valued forecasting.

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Data availability

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

Notes

  1. http://www.cnemission.com/

  2. https://www.hbets.cn/

  3. https://index.baidu.com/

  4. http://www.tanpaifang.com/

  5. https://www.wind.com.cn/

  6. https://cn.investing.com/

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Funding

The work was supported by the National Natural Science Foundation of China (Nos. 72071001, 71901001, 72001001, 71871001), Humanities and Social Sciences Planning Project of the Ministry of Education (Nos. 20YJAZH066, 21YJCZH148), Natural Science Foundation of Anhui Province (Nos. 2008085MG226, 2008085QG334, 2008085QG333), and Excellent Young Talent Project of in Colleges and Universities of Anhui Province (No. gxyqZD2022001).

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Authors and Affiliations

  1. School of Business, Anhui University, Hefei, 230601, Anhui, China

    Jinpei Liu & Xue Li

  2. Center for Applied Mathematics, Anhui University, Hefei, 230601, China

    Jinpei Liu & Huayou Chen

  3. School of Big Data and Statistics, Anhui University, Hefei, 230601, China

    Piao Wang & Huayou Chen

  4. School of Mathematical Sciences, Anhui University, Hefei, 230601, Anhui, China

    Huayou Chen

  5. School of Internet, Anhui University, Hefei, 230601, Anhui, China

    Jiaming Zhu

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  1. Jinpei Liu
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  2. Xue Li
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  4. Huayou Chen
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  5. Jiaming Zhu
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Contributions

All authors contributed to the study conception and design. Xue Li: material preparation, data collection, and writing—original draft. Jinpei Liu: conceptualization and writing — review and editing. Piao Wang: supervision and writing — review and editing. Huayou Chen: methodology. Jiaming Zhu: supervision and writing — review and editing. All authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Piao Wang.

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Liu, J., Li, X., Wang, P. et al. An interval-valued carbon price forecasting method based on web search data and social media sentiment. Environ Sci Pollut Res 30, 95840–95859 (2023). https://doi.org/10.1007/s11356-023-29028-0

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