In order to mitigate carbon dioxide (CO₂) emissions, policy action that addresses vehicle emissions is essential. While many previous studies have focused on light-duty vehicles (LDV), little is known about medium- and heavy-duty vehicles (MHDV). This study lays the groundwork for future MHDV investigations in the Republic of Korea by developing an MHDV CO₂ emissions inventory. The bottom-up approach was used to calculate national CO₂ emissions. Simulation methods that calculated the CO₂ emissions of each vehicle and statistical data, such as vehicle miles traveled (VMT) and the number of registered vehicles were used to predict CO₂ emissions. The validity of this simulation model was examined by comparing it with the chassis dynamometer test results. The results of this study showed that the CO₂ emissions of MHDV in 2015 were 24.47 million tons, which was 25.5% of the total road transportation CO₂ emissions, despite only comprising 4.2% of the total vehicles. Trucks emitted 69.6% and buses emitted 30.4% of the total MHDV CO₂ emissions. Using the results between 2012 and 2015, the level of business-as-usual (BAU) CO₂ emissions will be 25.37 million tons in 2020.

The present paper suggests fuel consumption modeling for HDVs based on the code from the Japanese Ministry of the Environment. Two interpolation models (inversed distance weighted (IDW) and Hermite) and three types of fuel efficiency maps (coarse, medium, and dense) were adopted to determine the most appropriate combination for further studies. Finally, sensitivity analysis studies were conducted to determine which parameters greatly impact the fuel efficiency prediction results for HDVs. While vitiating each parameter at specific percentages (±1%, ±3%, ±5%, ±10%), the change rate of the fuel efficiency results was analyzed, and the main factors affecting fuel efficiency were summarized. As a result, the Japanese transformation algorithm program showed good agreement with slightly increased prediction accuracy for the fuel efficiency test results when applying the Hermite interpolation method compared to IDW interpolation. The prediction accuracy of fuel efficiency remained unchanged regardless of the chosen fuel efficiency map data density. According to the sensitivity analysis study, three parameters (fuel consumption map data, driving force, and gross vehicle weight) have the greatest impact on fuel efficiency (±5% to ±10% changes).