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In the context of the progressive decarbonization of transport, it is becoming crucial to develop tools that enable individual users to reduce CO₂ emissions. This paper proposes a multivariate regression model for assessing carbon dioxide emissions for a single passenger vehicle. The research was based on experimental data obtained from the OBD diagnostic interface, documenting a series of test runs with a passenger vehicle equipped with a gasoline-powered internal combustion engine. On the basis of the analysis of the acquired data, three subsets related to engine operation were identified: starting and low-speed driving mode, urban driving mode and extra-urban driving mode. Multivariate regression models were constructed for the analyzed subsets. The analysis made it possible to identify the variables with the greatest impact on CO₂ emissions and to formulate final conclusions on the key factors determining emissions. The results of the study provide important information for optimizing emission reduction strategies from the point of view of a single user.