An Integrated SSA-LSTM-Transformer Model for Identifying and Predicting Driving Factors of Provincial Carbon Emissions in China

To support China’s dual-carbon goals and sustainability-oriented mitigation planning, this study develops an SSA–LSTM–Transformer framework for provincial carbon emission forecasting and interpretable driver analysis. Using panel data for 30 provinces from 2005 to 2022, SSA is employed for adaptive hyperparameter optimization, while the LSTM captures local temporal memory and the Transformer models long-range dependencies. Ablation tests and benchmarking against eight widely used models demonstrate that the proposed framework achieves the best overall performance on the held-out test set, with R 2 = 0.9911 and NRMSE = 0.0192. SHAP analysis indicates that a more carbon-intensive energy structure is associated with higher predicted emissions, whereas stronger technological innovation is associated with lower predicted emissions, and feature-importance patterns vary across development-stage groups. Forecast trajectories diverge during 2025–2035 and show a convergence tendency by 2050 under the model assumptions, informing differentiated near-term mitigation pathways and longer-term cross-regional coordination and technology diffusion. The results provide an interpretable evidence base for sustainability-oriented provincial decarbonization policies.