Abstract: Under the strategic background of carbon peaking and carbon neutrality, gasoline, as a member of the high carbon emission ranks, faces the challenge of emission reduction. Based on the gasoline molecular composition data obtained by gas chromatography and the gasoline CO₂ emission data obtained by the New European Driving Cycle,a priori model for the relationship between gasoline and the CO₂ emission per kilometer by categorizing the gasoline components was established according to the PONA composition, the number of carbon atoms and the number of substituents, and using hierarchical clustering method to cluster the gasoline molecular composition data, and dividing the training set and test set according to the clustering result, in order to provide data support for the production of low carbon emission gasoline. The priori model of gasoline and CO₂ emission per kilometer was established, aiming to provide data support for the production of low-carbon emission gasoline. Due to the small and concentrated range of data samples, the priori model has poor applicability in predicting CO₂ emissions. Therefore, the multi-distribution overall trend diffusion technique based on radius nearest neighbor classification (RNC-MD-MTD) was proposed and virtual samples were generated by this method. The calculation results showed that the prediction accuracy of the model was effectively improved with the addition of virtual samples generated by the RNC-MD-MTD method, which proved the validity of the method, and the final prediction model for CO₂ emission running per kilometer had a decision coefficient of 0.98, a mean absolute percentage error of 0.29% and a root-mean-square error of 792.6 mg/km.

Key words: gasoline components, carbon dioxide emission, virtual sample, radius neighbor classifier