Accurate carbon price forecasting is vital for risk management but is hindered by high volatility and sensitivity to external shocks. Existing multivariate models typically overlook unstructured news sentiment, failing to capture irrational fluctuations driven by market public opinion. To address this, this paper proposes VBN-Net, a hybrid model integrating carbon-specific news sentiment with Variational Mode Decomposition (VMD). Two core innovations are presented: First, a multi-modal input mechanism combines structured financial data with unstructured carbon news sentiment to effectively capture policy-driven shocks. Second, a Sequential Beluga Whale Optimization strategy is designed to adaptively optimize feature engineering in steps. Unlike conventional approaches, the VBN-Net first employs VMD for denoising and frequency decomposition, and then optimizes the fusion weights of news sentiment across different frequency components derived from multi-source news. This strategy effectively overcomes the subjectivity of manual parameter selection, providing high-quality features for a fixed CNN-BiLSTM backbone. By integrating VMD-based denoising with optimized multi-source news fusion, the model achieves consistent performance improvements across multiple evaluation metrics. The empirical findings validate the effectiveness of the proposed model in enhancing forecasting performance, thereby providing a reliable analytical tool for participants in the carbon market.
Yang et al. (Fri,) studied this question.