Exploring Regional Carbon Emission Factors and Peak Prediction: A Case Study of Hubei Province

Thorough analysis of regional carbon emissions is essential, given their substantial contribution to overall carbon emissions, as it underpins the formulation of targeted low-carbon development strategies. This study investigates whether Hubei Province can achieve carbon peaking by 2030 and explores feasible emission reduction pathways. To address the limitations of existing regional carbon emission studies—particularly the direct use of decomposition factors in prediction models and the lack of logical separation between mechanism analysis and forecasting—a hybrid analytical-predictive framework is proposed. Specifically, the logarithmic mean Divisia index (LMDI) method is first employed to decompose historical carbon emissions and identify the driving forces, while the STIRPAT model combined with the Lasso regression is subsequently used to screen key influencing factors for emission prediction, thereby avoiding the direct use of decomposition factors in forecasting. Based on the selected factors, a genetic algorithm–optimized backpropagation neural network (GA-BP) is developed to predict carbon emissions in Hubei Province from 2024 to 2035. The predictive performance of the GA-BP model is validated using three statistical indicators (R 2 , MAPE, and RMSE) and compared with Extreme Learning Machine (ELM), Support Vector Regression (SVR), and conventional BP models. Furthermore, six development scenarios are designed in accordance with provincial policy objectives to assess the feasibility of carbon peaking. The results indicate the following: (1) Based on the results of the LMDI decomposition, Lasso–STIRPAT analysis, and model sensitivity analysis, per capita GDP is identified as the primary driving factor of carbon emissions in Hubei Province. (2) The GA-BP model demonstrates superior predictive accuracy compared with benchmark models and (3) carbon peaking by 2030 can only be achieved under Scenario 6, highlighting the necessity of coordinated structural and technological interventions. Based on these findings, targeted policy recommendations for carbon emission reduction are proposed.