## 1 Introduction
### Scenario Development  
To deal with the climate change issues, the individual and integrated scenarios developed in this research study utilized the Comparative Study on Urban Transport and the Environment (CUTE) framework [41], including three main strategies: avoid (A) (reduce unnecessary travel), shift (S) (change travel to lower CO\({}_{2}\) emission modes), and improve (I) (improve the CO\({}_{2}\) emission reduction performance of transport technologies). Furthermore,  
Figure 3: Trends in (**a**) gross provincial product (GPP), (**b**) employment, (**c**) population, and (**d**) registered vehicles for Khon Kaen city, 2006–2022, reprinted from Refs. [32; 38]. three strategies were associated with four policy instruments, including technology (T), regulation (R), information (I), and economy (E). Three individual scenarios and two integrated scenarios were proposed and compared to the baseline (without project) scenarios, in terms of the CO2 emissions and energy consumption performance, in the designated years 2026 and 2046. These scenarios are briefly discussed below and are summarized in Table 1.  
**Scenario 0--**without a project, i.e., baseline case: This scenario assumes that the existing road network and transport system characteristics will remain constant during 2016 and 2046, except for the inclusion of some previously committed government projects, as shown in Figure 4. Future socioeconomic data (e.g., population, students, employment, economic situation, etc.) in specific years were extrapolated based on time series trends. The impacts of megarojects under Thailand's transport infrastructure development plans (e.g., the high-speed train project, double-track railway project, motorway project, etc.) could then be estimated accordingly. Based on the historical trends in the growth rates of population and employment in the study area and the future potential trends of those growth rates (as a result of Thai national transport infrastructure development projects, such as high-speed train projects) and Khon Kaen's status as an educational, medical, transport, and logistics hub [32], the growth rates of population and employment in the study area were assumed to be 2.75% in 2016 and 3.25% in 2046. In contrast, based on historical census statistics [32], the number of students was assumed to decline by 1.8 percent per year from 2016 to 2026 and be constant from 2026 to 2046 because of the expected impact of the future potential trends described previously. **Scenario 1--**light rail transit (LRT) measure: This scenario falls under the shift (S) strategy, and technology (T) policy instruments. Scenario 1 assumes that all five LRT lines, the associated four P&R facilities, several feeder systems, the intelligent transport system (ITS) signalized intersections, and other facilities will be completely developed and fully implemented by 2026 [32], as shown in Figure 4. In addition, the existing paratransit systems (i.e., \"Songtheow\" modified pick-up trucks, public vans, and shuttle buses) will be reorganized as the feeder systems for all LRT service lines. **Scenario 2--**transit-oriented development (TOD) plus light rail transit (LRT) measure: This scenario fits both the avoid (A) and shift (S) strategies and technology (T) policy instruments. Scenario 2 assumes that all five LRT lines and their associated infrastructure and facilities (Scenario 1), as well as the TOD development, will be in place by 2026 [32], as shown in Figure 4. TOD development can reduce CO2 emissions and energy consumption by increasing urban densities and mixed land use to minimize unnecessary travel and encourage shorter trips, active transport modes (walking and cycling), and the use of LRT services. Three potentially integrated TOD sites were selected. For the TOD site with a radius of approximately 500 m around the LRT station, the average densities of housing surrounding the station were predicted to be 80 and 135 housing units/hectare [32] in 2026 and 2046, respectively.