This study proposes a text-based framework for predicting EU carbon prices. Using weekly data from 2020 to 2024, we construct a multivariate dataset combining financial indicators, commodity prices, Google Trends measures, and news-based sentiment extracted using FinBERT. To address potential noise and redundancy among predictors, principal component analysis (PCA) is applied for dimensionality reduction. The empirical results suggest that deep learning models generally achieve lower forecast errors than traditional models. In particular, the CNN-LSTM model with PCA-based inputs achieves the best predictive performance, reducing RMSE by approximately 33% relative to the corresponding multivariate CNN-LSTM specification. These findings provide evidence that combining deep learning architectures with dimensionality-reduction techniques can improve forecasting performance in carbon markets.
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