Machine learning-driven CO2 emission forecasting for light-duty vehicles in China

The research examines the carbon dioxide (CO2) emissions produced by light-duty vehicles (LDVs) utilizing a thorough dataset of 7,384 cars gathered by the Chinese government between 2018 and 2022. The research aims to attain a 40–45% decrease in CO2 emissions by 2030 by the application of advanced machine learning algorithms, specifically Catboost. The results reveal that Catboost, recognized for its data efficiency and capability to manage categorical information, surpasses other models in predictive accuracy, including support vector regression and ridge regression. It is particularly notable for its capability to estimate emissions using just a limited set of vehicle attributes. The research offers crucial insights into air pollution, providing vital suggestions for car owners and manufacturers to reduce their environmental effects. Future investigations should prioritize improving the precision of the model and broadening the datasets to enhance the quality of forecasts.