Carbon Trading Price Forecasting Based on Multidimensional News Text and Decomposition–Ensemble Model: The Case Study of China’s Pilot Regions

Accurately predicting carbon trading price is challenging due to pronounced nonlinearity, non-stationarity, and sensitivity to diverse factors, including macroeconomic conditions, market sentiment, and climate policy. This study proposes a novel hybrid forecasting framework that integrates multidimensional news text analysis, ICEEMDAN (Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise) decomposition, and machine learning to predict carbon prices in China’s pilot trading prices. We first extract a market sentiment index from news texts in the WiseSearch News Database using a customized Chinese carbon-market dictionary. In addition, a price trend index and topic intensity index are derived using Latent Dirichlet Allocation (LDA) and Convolutional Neural Networks (CNN), respectively. All feature sequences are subsequently decomposed and reconstructed using sample-entropy-based ICEEMDAN approach. The resulting multi-frequency components were then used as inputs for a range of machine-learning models to evaluate predictive performance. The empirical results demonstrate that the incorporation of multidimensional text information on China’s carbon market, combined with financial features, yields a substantial gain in prediction accuracy. Our integrated decomposition-ensemble framework achieves optimal performance by employing dedicated models—BiGRU, XGBoost, and BiLSTM for the high-frequency, low-frequency, and trend components, respectively. This approach provides policymakers, regulators, and investors with a more reliable tool for forecasting carbon prices and supports more informed decision-making, offering a promising pathway for effective carbon-price prediction.