Forecasting Transportation CO2 Emissions in China: A Rolling-Origin Comparison of Econometric, Machine-Learning, and Deep-Learning Models

Transportation-related CO2 emissions remain an important challenge for sustainable mobility and the energy transition in rapidly developing economies. This study examines transportation-related CO2 emissions in China using annual data for 1990–2024 within a STIRPAT-informed forecasting framework. Urban population share, GDP per capita, the fossil-energy share, and physical freight activity are used as theory-guided predictors. The analysis compares naïve persistence, a dynamic OLS benchmark, Support Vector Regression (SVR), XGBoost, and a compact Long Short-Term Memory (LSTM) model using chronological rolling-origin evaluation. One-year-ahead forecasts are assessed for 2010–2024 using RMSE, MAE, MSE, out-of-sample R2, and Harvey–Leybourne–Newbold-adjusted Diebold–Mariano tests, with a direct two-year-ahead experiment used as a robustness check. In the primary experiment, the LSTM records the lowest observed RMSE (45.254) and MAE (35.956), closely followed by the dynamic OLS benchmark in RMSE and XGBoost in MAE. At the two-year horizon, XGBoost records the lowest RMSE (45.537) and MAE (34.426). Most pairwise forecast-accuracy differences are not statistically significant at the 5% level, and the LSTM–XGBoost difference is not significant under either squared or absolute loss. Rolling-origin SHAP analysis identifies GDP as the largest source of incremental predictive information for XGBoost, followed by freight activity, while strong collinearity limits structural interpretation. Conditional sensitivity experiments further show that the fitted forecasting model should not be treated as a causal policy simulator. Overall, the findings demonstrate that temporal validation, conventional benchmarks, and robustness analysis are essential when evaluating complex forecasting models on short annual emissions series.