Synergizing Spatial and Temporal Dynamics for Carbon Price Forecasting: A Heterogeneous Ensemble Approach

Accurately forecasting European carbon prices is essential for effective climate policy and market risk management yet remains challenging due to the coexistence of exogenous macroeconomic shocks, endogenous market momentum, and pronounced spatial and temporal dependencies across related markets. Conventional forecasting models often fail to accommodate such spatial–temporal heterogeneity, leading to unstable predictive performance and limited economic interpretability. To address these challenges, we introduce a heterogeneous ensemble framework that integrates the strengths of variable selection, frequency‐domain decomposition, and graph‐based spatial learning. Using 41 variables, we employ LASSO to extract endogenous market momentum, implement the MEMD‐ARIMAX‐mLSTM framework to model temporal dynamics, and adopt GWnet‐attn to capture spatial interdependencies. Through the ensemble approach, the framework effectively combines these distinct market dynamics. Empirical results demonstrate that the proposed model significantly outperforms recent state‐of‐the‐art benchmarks. Ablation studies and robustness tests demonstrate that integrating spatial and temporal information substantially reduces forecasting errors and confers strong robustness, while highlighting the criticalness of the energy–carbon nexus, offering critical insights for market design and risk management.