The first goal of this study is to construct a dataset for Korea''s transportation sector to be used in the GCAM (Global Change Assessment Model). This study endeavored to update and improve pre-existing data for the base year, especially in terms of service demand, energy consumption, load factors, and fuel efficiency of the transportation sector. The updated information was obtained on the basis of relationships expressed through the transportation service demand identity. Although the decomposed identity developed here is not immune to the “rebound effect” problem endemic to similar such studies, it nevertheless yields fruitful insight into individual component trends in the transportation sector.
On the basis of this re-worked data, this study conducted simulations in GCAM whose results were then compared to results obtained from running the standard, unaltered GCAM model. This study’s simulations do appear to yield results that are more plausible than those provided by the standard GCAM model, with the exception being in the rail sector. While this study and the standard GCAM model yield similar results with respect to the rail freight subsector, for the rail passenger subsector, the standard GCAM model does appear to provide more plausible results.
The second major goal of this study was to evaluate the competiveness of “green” cars, including hybrid vehicles (“hybrids”), natural gas vehicles (“NGVs”), and electric vehicles based on either battery (“BEV”) or fuel cell (“FCEV”) technologies. The four technology scenarios assume that current vehicle technologies are gradually replaced by one of the four technology types ? hybrid, NGV, BEV, or FCEV. In addition to considering each vehicle technology in turn, we also considered them in combination (“ALL”). To facilitate comparison, the reference case assumes the existence of no green cars in Korea’s transportation sector. The penetration year for green cars is assumed to be 2020. Assumptions regarding the green cars'' fuel efficiency ratings are based on currently available technology. The assumptions we employed regarding improvements in fuel efficiency are the same as those used in the standard GCAM. To consider CO2 emissions from BEV and FCEV, a carbon coefficient rate is applied.
Overall, the results indicate that if the carbon coefficient is ignored, BEV and FCEV represent the most promising technologies for reducing CO2 emissions. However, when carbon coefficients are applied, the results are altered such that the ALL scenario emerges as the best scenario in terms of reducing CO2 emissions.
To extend this study further it would be desirable to include updated information regarding transportation costs, to use more nuanced assumptions regarding rates of improvement in fuel efficiency, and to employ an even more detailed breakdown of technological options.