## 3 Model Development
### CO\({}_{2}\) Emissions and Energy Consumption Estimation
\[ER_{y}=BE_{y}-PE_{y}-LE_{y} \tag{2}\]  
where:  
\(ER_{y}=\) CO\({}_{2}\) emission (or energy consumption) reductions in year \"\(y\)\" (tonne/year);  
\(BE_{y}=\) Baseline CO\({}_{2}\) emissions (or energy consumption) in year \"\(y\)\" (tonne/year);  
\(PE_{y}=\) Project CO\({}_{2}\) emissions (or energy consumption) in year \"\(y\)\" (tonne/year);\(LE_{y}\) = Leakage CO\({}_{2}\) emissions (or energy consumption) in year \"\(y\)\" (tonne/year). For the CO\({}_{2}\) emissions and energy consumption computations, the emission factors and energy consumption rates for each vehicle type and each engine type were obtained from the SIRDC [48], the OTP [49], [PERSON] et al. [29], S6-5 [25], and [PERSON] et al. [45]. The vehicle types in the KKTP are typically classified into five categories: motorcycles (MCs), passenger cars (PCs), pick-up trucks (PUTs), buses (Bs), and trucks (Ts) [30]. CO\({}_{2}\) emissions and energy consumption at different driving speeds, obtained from the chassis dynamometer tests by the OTP [49], could formulate the relationships between CO\({}_{2}\) emissions and energy consumption with driving speeds for different types of vehicles tested, as shown in Table 3, with R\({}^{2}\) values ranging from 0.68 to 0.98. When vehicles travel at lower speeds, CO\({}_{2}\) emissions increase and fuel efficiency decreases for all types of vehicles, as shown by the equations. This trend reverses when vehicles travel at higher speeds. A similar trend could be noticed from the data obtained from the OTP [49]. For instance, a PC model (Toyota Altis Euro II) with a gasoline 91 engine emitted 218.6 g/km of CO\({}_{2}\) and generated fuel consumption of 10.7 km/liter at a speed of 14.6 km/h. On the other hand, it produced 135.1 g/km of CO\({}_{2}\) and achieved 17.2 km/liter fuel consumption at a speed of 73.9 km/h. In the LRT project (Scenario 1), the operation of the LRT system also required that the electricity needed to power the LRT vehicles be estimated using Equation (3) [30]. Notably, various factors (such as the number of coaches used, service frequency, and distance) contribute to the total amount of electricity consumed by all five LRT systems. The comprehensive details of scheduling the actual operations for each of the five LRT systems are beyond the scope of this study. Based on empirical studies [30; 50], the amount of electricity used for servicing all five LRT systems in Khon Kaen city was assumed to be solely dependent on the magnitude of the total estimated LRT ridership to be served. The CO\({}_{2}\) emission factors for electricity generation and distribution were obtained from the Energy Policy and Planning Office (EPPO), as shown in Figure 5. For the electricity used in the public transport (LRT) services (\(EC_{sky}\)), a time factor of 365 days was applied to convert daily transit ridership to annual transit ridership [30; 32; 49; 50]. Assuming that the leakage emission is zero, the net CO\({}_{2}\) emission reductions could be computed by subtracting the difference between the baseline (without project) emission (\(BE_{y}\)) and the project (with project) emissions (\(PE_{y}\)) using the direct project emission (\(DPE_{y}\)), as presented in Equation (3). \[DPE_{y}=EC_{sky}\times EF_{grid} \tag{3}\]  
where:  
\(DPE_{y}\) = Direct project emission in year \"\(y\)\" (tCO\({}_{2}\)/yr);  
\(EC_{sky}\) = Amount of electricity used for public transport operation in the year \"\(y\)\" = 0.39 MWh/rider \(\times\) number of transit ridership/day in year \"\(y\)\" \(\times\) 365 days (MWh/yr);