Enhancing carbon price robust forecasting: A text-driven method utilizing weighted interval-joint quadratic support vector regression

Rui Luo, Jinpei Liu, Peipei Chen and Jian Luo

Energy Economics, 2025, vol. 148, issue C

Abstract: Accurate forecasting of carbon prices can effectively promote the low-carbon transition and has received increasing attention in recent years. Interval carbon prices forecasting incorporating online texts has emerged as a promising approach to improve the prediction accuracy and timeliness. However, existing forecasting models struggle with extracting correlation features from interval prices. The significant data noise and outliers in input variables further complicates the accuracy and stability of prediction results. Therefore, in this study we propose a novel text-driven method for interval carbon price forecasting based on weighted interval-joint quadratic support vector regression (WIJQSVR). First, we incorporate news text reflecting investor sentiment into the prediction factors, providing a more comprehensive consideration of factors influencing carbon prices. Second, we design a weight algorithm to evaluate the relative importance of each training point, thereby reducing the interference of outliers. Furthermore, a kernel-free WIJQSVR model is proposed by designing an inverse cumulative distribution function following the triangular distribution for representing interval carbon price. The model effectively handles the noisy data and learns the underlying interval interrelation of input values by generating the interval-joint quadratic hypersurface. To verify the performance of the proposed method, extensive computational experiments on Guangdong and Hubei interval carbon price forecasting are conducted. Results clearly support that the proposed method is superior to other benchmark methods in both forecasting accuracy and robustness, which will be an effective tool for carbon price forecasting.

Keywords: Interval carbon prices forecasting; Robust forecasting; Support vector regression; Weight algorithm; Sentiment analysis (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0140988325004098
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:eneeco:v:148:y:2025:i:c:s0140988325004098

DOI: 10.1016/j.eneco.2025.108585

Access Statistics for this article

Energy Economics is currently edited by R. S. J. Tol, Beng Ang, Lance Bachmeier, Perry Sadorsky, Ugur Soytas and J. P. Weyant

More articles in Energy Economics from Elsevier
Bibliographic data for series maintained by Catherine Liu ().