## 3 CO2 Emissions in Motor Vehicles in the Context of Selected Factors
Engine startup is a transient state, and its emission levels depend on many factors, including ambient temperature and the length of time the vehicle has been idle before starting [28]. Urban driving involves operating the vehicle in city traffic conditions, characterized by frequent stops and starts, leading to higher fuel consumption and CO2 emissions compared to highway driving. On highways, velocity limits are generally higher, and the distance covered in onego is longer. This results in lower fuel consumption and lower CO2 emissions with respect to urban driving [29]. In turn, satellite navigation systems (GNSS--Global Navigation Satellite Systems), by providing precise location data, also contribute to optimizing vehicle velocity and reducing CO2 emissions in road transport, regardless of the driving mode [30]. Another important issue is the accuracy of CO2 emission measurements, which are crucial for assessing the environmental impact of motor vehicles. The current testing procedure in Europe is the WLTP (Worldwide Harmonized Light Vehicles Test Procedure), introduced in 2017, which replaced the older NEDC (New European Driving Cycle) standard [20]. WLTP takes into account various vehicle configurations that affect mass and aerodynamics. The test lasts about 30 min, covering a distance of approximately 23 km [31]. However, this is a laboratory method. In contrast, for real-world measurements, RDE (Real Driving Emissions) testing is performed using PEMS (Portable Emissions Measurement Systems) mounted on vehicles [32]. These devices enable emissions measurement during normal vehicle operation in various road and climatic conditions. They allow for the identification of discrepancies between laboratory results and actual emissions [32]. In addition, the studies described in [33,34] provide important data on road transport emissions from real case studies and can be used in the modeling of CO2 emissions from combustion engine vehicles. Furthermore, the analysis of emission reductions during the COVID-19 lockdown presented in [35] can in turn provide a basis for assessing the impact of different road transport scenarios on GHG emissions. It is also important to consider the tools available for analyzing CO2 emission levels. Classification algorithms based on machine learning algorithms are frequently used tools in the analysis of CO2 emission data. The most frequently mentioned algorithms used for this purpose are XGBoost, Decision Trees, Random Forest, KNN, SVM, Stochastic gradient descent, and Naive Bayes [36,37]. The algorithms that stand out in terms of accuracy are Random Forest, KNN, and SVM, which in [36] show the best results of goodness of fit indicators. In turn, in the case of the research conducted in [37], the XGBoost algorithm showed the highest accuracy among the methods tested. Decision trees were used in [38] to identify various factors (demographic, socioeconomic, car or bicycle ownership, etc.) affecting CO2 emissions. In [39], a random forest model was employed to predict CO2 emissions and fuel consumption by vehicles in developing urban areas. Furthermore, the application of SVMs for predicting CO2 emissions in cars using geographic information systems was described in [40,41]. Another method used for this purpose is logistic regression, which is rarely mentioned in this series mainly due to differences in the mathematical model of the method. Algorithms with a more complex mathematical model, e.g., SVM or XGBoost, ultimately enable the analysis of large datasets, which are often given directly, and often without any data aggregation. The innovative approach proposed in this paper is to aggregate data into three subsets of driving cycles of the vehicle containing characteristic emission features and then use logistic regression to classify the data grouped in this way. This approach allows for the use of a less complex (in terms of the mathematical model) classification method, which has a number of significant advantages over other algorithms for this purpose.