## 2 State of Art
### Comparative Analysis of CO2 Emission Prediction Models  
In ref. [3], [PERSON] and [PERSON] developed deep learning models to predict vehicle CO2 emissions using detailed Kaggle [3] data, covering seven years and including 7385 vehicle records in Canada. LSTM and BiLSTM models managed temporal dependencies and accurately predicted CO2 emissions. The model's performance was evaluated using statistical metrics such as MSE, RMSE, R%, and R2, with the BiLSTM model outperforming the LSTM in terms of accuracy and reliability. Table 1 summarizes the comparative analysis of existing artificial intelligence models for predicting CO2 emissions, including their datasets, methodologies, and performance metrics. In ref. [23], [PERSON] et al. (2024) applied three different artificial intelligence algorithms (multi-layer perceptron (MLP), extreme gradient boosting (XGBoost), and support vector machine (SVM)) to estimate CO\({}_{2}\) emissions in the transportation sector in Turkey. The input parameters considered are energy consumption (ENERGY), vehicle kilometers traveled (VK), population (POP), year (Y), and gross domestic product per capita (GDP). The algorithms were tested under four scenarios based on the effect of correlation: scenario 1 (ENERGY/VK/POP/Y/GDP), scenario 2 (ENERGY/VK/POP/Y), scenario 3 (ENERGY/VK/POP), and scenario 4 (ENERGY/VK). The models' performance was evaluated using statistical indicators (R\({}^{2}\), RMSE, MSE, and MAE), with the XGBoost algorithm offering the best performance in scenario 4 [23]. The AI algorithms effectively estimate CO\({}_{2}\) emissions, which has significant implications for policymakers and stakeholders. In refs. [16; 17; 18], our GreenNav model uses a dataset from multiple APIs to create a comprehensive database for our analysis. The data sources include OpenDataSoft and Google API. The data considered include street names and geolocation, road traffic flow, occupancy rate, date and hours for OpenDataSoft, road distance, average speed, and speed limits for Google API. Based on a CNN-LSTM architecture, the model was applied at a micro-scale, i.e., by street. The study was conducted in the city of Paris, and four months of data covered all the roads in the town, representing a total of 48 gigabytes of data, i.e., 8 million rows. The performance of the CNN-LSTM model was evaluated with a coefficient of determination (R\({}^{2}\)) of 0.91. In ref. [26], [PERSON] et al. (2023) developed a random forest model to predict CO\({}_{2}\) emissions in Chinese cities using data on population, road network density, and other urban governance elements. The data covered 1903 cities for 2010, 2012, and 2015. The model was compared to traditional approaches such as linear regressions. The random forest model performed better, with a coefficient of determination R\({}^{2}\) of 0.9431. In ref. [15], [PERSON] et al. (2024) developed an LSTM model to predict CO\({}_{2}\) emissions from light-duty diesel trucks (LDDTs) using PEMS and GPS data. The model considers vehicle-specific power (VSP), speed, road gradient, and acceleration. It achieved R\({}^{2}\) values between 0.986 and 0.990, with an RMSE between 0.165 and 0.167.