We selected a 288 km expressway in the western Sichuan Plateau mountainous area to reveal the factors influencing CO2 emissions from light-duty vehicles and establish a localized prediction model. Instantaneous CO2 emission data (9,381 sets) were obtained through real-vehicle emission tests. The influential characteristics of CO2 emission rates under different environmental characteristics, alignment conditions, and operational states were analyzed. CO2 emission prediction models based on random forest (RF) and model-agnostic meta-learning (MAML) algorithms were constructed, compared, and analyzed. The findings indicated that: (1) Vehicle-specific power (VSP) was the most important factor determining the CO2 emission rate. The feature importance of VSP in the upslope and downslope directions was 0.25 and 0.22, respectively; (2) The CO2 emission rate distribution patterns were approximated by a Gamma distribution within different grade and angle change rate intervals. At grades < -1%, between -1 and 1%, and > 1%, CO2 emission rates decreased, stabilized, and increased, respectively, with increasing angle change rate intervals; (3) The evaluation metrics for the MAML model outperformed those of the RF model, indicating higher adaptability to unknown tasks.