A Stacking-Based Multi-Step LSTM and Policy-Enhanced SVR Method for Carbon Emission Prediction

China’s “dual-carbon” targets require more scientifically precise methods for carbon emission forecasting. Existing methods mainly rely on time series or regression models: the former captures temporal trends but lacks interpretability, while the latter provides explanatory power but struggles with nonlinear patterns. To overcome these limitations, this paper applies a multi-step LSTM with transfer learning to capture nonlinear temporal dynamics of carbon emissions, incorporates an SVR with added policy variables to improve accuracy, and finally employs a stacking model to integrate above advantages. Predictions are then aggregated via linear regression to leverage complementary strengths. The proposed model is trained on 1960–2004 data and tested on 2005–2019, 2023 and 2024 data. Results show that the optimized LSTM and SVR improve prediction accuracy, while the Stacking-based ensemble surpasses individual models in accuracy and robustness. Based on the integrated model, predictions for 2023–2050 indicate that if policies are strengthened in 2025, China’s carbon emissions will peak in 2024 and subsequently decline to about 8175 Mt CO2 by 2050; if policies are not strengthened in 2025, emissions will peak in 2026 and subsequently decline to about 6983 Mt CO2.