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Emission inspection of motor vehicles (emission inspection) is a crucial player in solving the problem of motor vehicle exhaust pollution, and research on the features affecting emission inspection results and their importance is a basis for optimizing the environmental management of motor vehicles. However, there is no study on the multi-feature impact analysis of the emission inspection results. This hinders the emission inspection from playing a better guiding role in the policy formulation of motor vehicle management. In this paper, the ensemble learning algorithm and interpretable machine learning theory are used. Nineteen feature indicators and over 400,000 vehicle mass analysis system (VMAS) detection data in Chengdu were selected from the emission inspection database to construct prediction models for emission inspection results. Moreover, the factors affecting emission inspection results and their ranks by importance were also obtained. The results revealed that the environment has a strong influence on the outcomes from emission inspections (accounting for about one-third of the total effect). Besides, the following eight feature indicators displayed great effects on emission inspection results in sequence: emission inspection agency (18.38%), world manufacturer code (15.01%), vehicle usage days (9.60%), transmission type (9.41%), accumulated mileage (9.21%), emission standard (5.82%), temperature (5.54%), and driving mode (5.50%). In this study, prediction models for emission inspection results are established, and the results are interpreted based on the interpretable machine learning theory. It is considered that more attention should be paid to the effect of inspection differences among emission inspection agencies on fairness, as well as the effects of differences in world manufacturer and transmission type on vehicle deterioration in future research. The supervision of emission inspection agencies, training of inspectors, elimination of obsolete vehicles, and government-guided purchase should be strengthened. This study provides empirical support for optimizing the formulation of motor vehicle environmental management policies. Implications:  Emission inspection of motor vehicles (emission inspection) is a crucial player in solving the problem of motor vehicle exhaust pollution. In this work, prediction models for emission of motor vehicles inspection results are established. The results revealed that following eight feature indicators displayed great effects on emission inspection results in sequence: emission inspection agency (18.38%), world manufacturer code (15.01%), vehicle usage days (9.60%), transmission type (9.41%), accumulated mileage (9.21%), emission standard (5.82%), temperature (5.54%), and driving mode (5.50%). It is considered that more attention should be paid to the effect of inspection differences among emission inspection agencies on fairness, as well as the effects of differences in world manufacturer and transmission type on vehicle deterioration in future research. The supervision of emission inspection agencies, training of inspectors, elimination of obsolete vehicles, and government-guided purchase should be strengthened. This study provides empirical support for optimizing the formulation of motor vehicle environmental management policies.

Influential factors for the emission inspection results of urban in-use vehicles: From an ensemble learning perspective