Carbon trading price forecasting in digitalization social change era using an explainable machine learning approach: The case of China as emerging country evidence

Carbon trading prices are considered important reference indicators for policy formulation and enterprise decision-making, and play an important role in low-carbon development. However, predictive approaches that balance the accuracy and interpretability of carbon trading prices remain limited. This study proposes a hybrid approach that integrates group variable selection regularization and uncertainty inference in Bayesian neural networks (BNN) with inherent interpretability to predict carbon trading prices. We also identify effective predictive indicators in an emerging market context. Based on the carbon data of emerging markets, a dataset of price information was constructed for model training. The results indicate that under the same regularized dataset, all BNN results surpass other artificial neural networks (ANNs). The group smoothly clipped absolute deviation-BNN performs best in all models and has interpretability. The model effectively identified new influencing factors for predicting carbon trading prices in emerging countries, including coal prices and blockchain-related information. The proposed new prediction approach provides a new basis for the prediction and evaluation of carbon trading prices and provides the necessary references for policy formulation in the digitalization era of social change.