Carbon Emissions Prediction for Sustainable Regional Transportation Based on a Hybrid Deep Learning Model

As passenger travel and freight transport demand continue to rise, transportation remains a major source of global carbon emissions. Predicting transportation carbon emissions helps support sustainable transportation planning and the development of scientifically sound emission reduction policies. However, there are currently no unified standards for accounting for transportation carbon emissions, and different approaches yield widely varying predictions, creating challenges for formulating carbon emission policies. In this study, a top–down approach based on China’s energy statistical data is adopted to calculate regional transportation carbon emissions (TCE). Twenty factors influencing carbon emissions are selected, and Spearman correlation analysis is used to examine the correlations among these factors. Lasso regression and the STIRPAT model are employed to quantitatively analyze the influence of each factor. Subsequently, an improved hybrid deep learning model, GA–CNN–LSTM–Attention, is proposed to predict carbon emissions under baseline, restriction, and control scenarios. Finally, an empirical analysis is grounded in a case study of Fujian Province, China. The quantitative analysis results show that, of all the factors, the urbanization rate has the greatest impact on transportation carbon emissions in the region, and the GA–CNN–LSTM–Attention prediction model achieves higher forecasting accuracy. Under the first two scenarios, carbon emissions in the region show a year-by-year increasing trend from 2022 to 2035, with no emissions peak observed. Under the controlled scenario, carbon emissions are projected to peak in 2030, reaching approximately 70.28 million tons of CO2. The prediction results provide a scientific basis for sustainable transportation development and government policymaking on carbon reduction.