# Evaluation of Turkey's Road-Based Greenhouse Gas Inventory and Future Projections
## 3 Results
### Emission Estimates for Future Years  
This study used the polynomial regression approach to predict the number of vehicles up to the year 2050. The findings of this analysis are presented in Figure 5. The following equations were used to predict future outcomes:  
\[\mathrm{PC:y=-23542263.54~{}x^{1}+5963.80~{}x^{2}+23233610555.34~{}(R^{2}=0.996)} \tag{9}\]  
\[\mathrm{LCV:y=-10136215.80~{}x^{1}+2562.45~{}x^{2}+10023901731.90~{}(R^{2}=0.985)} \tag{10}\]  
\[\mathrm{HDV:y=-1077353.36~{}x^{1}+275.54~{}x^{2}+1053131321.99~{}(R^{2}=0.967)} \tag{11}\]  
where y is the dependent variable (number of vehicles in the future), and x is the independent variable (number of vehicles in the past). The registered vehicle count, which stood at 26,482,847 in 2022, is projected to have grown by 105%, reaching 50,701,935 by 2050. The projected figures indicate that there will be 34,855,102 PCs, 13,344,351 LCVs, and 2,502,482 HDVs. Figure 4: Distribution of emissions from vehicles traveling on state roads in Turkey ((**A**) 2010 and (**B**) 2020). There are different methods for estimation in the literature. [PERSON] and [PERSON] estimated the number of vehicles in Indonesia using the ARIMA (Auto Regressive Integrated Moving Average) method. The estimate for the next 11 years was based on historical data from 2001 to 2019. As a result of the research, the best estimation models were determined as ARIMA for cars and ARIMA for motorcycles. The article explains that the increasing number of automobiles generates various difficulties, including traffic congestion, air pollution, and traffic accidents, and emphasizes the need to predict future vehicle numbers to mitigate such problems [53]. [PERSON] et al. used machine learning (ML) and vehicle measurement data to estimate historical hourly traffic levels on Maryland's road network. The study was aimed at improving the correct prediction of hourly traffic volumes in areas with few sensors. In addition to the current profiling approach, a model was created using an artificial neural network (ANN). This technology produces 24% more accurate volume estimations than the profiling method utilized throughout the United States [54]. [PERSON] et al. conducted a comprehensive evaluation of machine learning (AI)-based approaches for traffic flow prediction. The study focuses on Intelligent Transportation Systems (ITSs) and the impact of machine learning (ML) and deep learning (DL) techniques used in these systems on traffic prediction. The difficulties observed in implementing these techniques were discussed in the paper [55]. [PERSON] et al. estimated greenhouse gas (GHG) emissions from the road transportation sector in North America using multivariate regression and double exponential smoothing (DES) models. The purpose of the research was to contribute to the development of strategic decisions to minimize GHG emissions by calculating existing and future emissions. The results revealed that kilometers traveled and the number of vehicles have a substantial impact on greenhouse gas emissions [56]. According to studies, the number of vehicles on the road is going to rise steadily. This means a rise in emissions. Turkey implemented the EURO 5 standards in October 2009 and the EURO 6 standards in January 2015 with the aim of decreasing pollution levels in newly manufactured vehicles. In 2021, an analysis of registered vehicles by age group revealed that 23.1% fall within the 0-15 years range, 24.6% fall within the 6-110 years range, 15.8% fall  
Figure 5: Polynomial regression vehicle projections for the future. within the 11-15 years range, 10.2% fall within the 16-120 years range, and 26.3% are older than 21 years [57]. Hence, while these norms will partially regulate the pace of emission growth, more is needed. Therefore, emissions predictions were also made based on the estimated number of vehicles up to 2050. The graphs are shown in Figure 6. Upon analyzing Figure 6, it becomes evident that each emission type has a consistent upward tendency as time passes.