European Union Allowance price forecasting with Multidimensional Uncertainties: A TCN‐iTransformer Approach for Interval Estimation

In response to the research demand for forecasting European Union Allowance (EUA) prices, this paper proposes a probabilistic forecasting framework based on a spatiotemporal convolutional neural network. This framework innovatively integrates multidimensional external uncertainty indicators, captures the long‐term dependencies of carbon prices through a spatiotemporal convolutional structure, and combines quantile regression with conformal prediction to effectively estimate prediction intervals. Empirical studies demonstrate that the proposed TCN‐iTransformer model outperforms existing methods in both point prediction and interval prediction, exhibiting excellent prediction interval coverage probability and normalized average width at different confidence intervals. The Diebold–Mariano (DM) test and ordinary least squares (OLS) regression analysis further validate the predictive advantages of the proposed model. Furthermore, SHAP analysis reveals that the U.S. Treasury yield spread has the most significant impact on EUA price forecasting, while geopolitical risks predominantly exert negative effects. The research findings provide important references for constructing risk mitigation strategies in the European Union carbon emissions market under complex market environments.