Predicting China’s Provincial Carbon Peak: An Integrated Approach Using Extended STIRPAT and GA-BiLSTM Models

As China commits to reaching peak carbon emissions and achieving carbon neutrality, accurately predicting the provincial carbon peak year is vital for designing effective, region-specific policies. This study proposes an integrated approach based on extended STIRPAT and GA-BiLSTM models to predict China’s provincial carbon peak year. First, based on panel data across 30 provinces in China from 2000 to 2023, we construct a multidimensional indicator system that encompasses socioeconomic factors, energy consumption dynamics, and technological innovation using the extended STIRPAT model, which explains 87.42% of the variation in carbon emissions. Second, to improve prediction accuracy, a hybrid model combining GA-optimized BiLSTM networks is proposed, capturing temporal dynamics and optimizing parameters to address issues like overfitting. The GA-BiLSTM model achieves an R 2 of 0.9415, significantly outperforming benchmark models with lower error metrics. Third, based on the model constructed above, the peak years are projected for baseline, low-carbon, and high-carbon scenarios. In the low-carbon scenario, 19 provinces are projected to peak before 2030, which is 8 more than in the baseline scenario. Meanwhile, under the high-carbon scenario, some provinces such as Jiangsu and Hebei may fail to peak by 2040. Finally, based on the predicted carbon peak year, provinces are categorized into four pathways—early, recent, later, and non-peaking—to provide targeted policy recommendations. This integrated framework significantly enhances prediction precision and captures regional disparities, enabling tailored decarbonization strategies that support China’s dual carbon goals of balancing economic growth with environmental protection. The approach provides critical insights for region-specific low-carbon transitions and advances sustainable climate policy modeling.