ABSTRACT
This study analyzes the modal shift from road to rail transport and resulting CO2 emissions along the Laem Chabang Port–Thanaleng corridor. A stated preference experiment is conducted to obtain feedback from freight forwarders, and a mode choice model is developed to estimate the freight mode share offered by the development of a dry port. A bottom-up approach is used to assess CO2 emissions from freight transport operations. The results show that in the base-case scenario, a 43% mode shift to rail from road and a 30% reduction in CO2 emissions can be expected compared with the business-as-usual scenario.
Acknowledgements
This research was supported by the Environment Research and Technology Development Fund (S-6) of the Ministry of the Environment of Japan. The authors acknowledge the assistance provided by the Laos International Freight Forwarders Association, Laos and Laem Chabang Port Authority, Thailand, for data collection and arranging meetings with stakeholders. We thank three anonymous reviewers for their useful comments and suggestions on an earlier version of the paper. The views expressed herein are those of the authors and do not necessarily reflect the views of the United Nations.
Notes
1Total transport time includes time taken from seaport (LCP) to Thanaleng including that for loading, unloading, driving, handling at dry port, and border crossing processes.
2Total transport cost includes that of vehicle operation, fuel, driver, labor, loading, unloading, and handling at ports and dry ports; in the case of rail, it also includes the track access fee, handling of containers, and return of empty containers.
3Reliability in terms of punctuality of delivery within scheduled time is considered.
6Full factorial choices = (No. of Levels)(No. of Alternatives×No. of attributes).
7It shows that 43% do not have executive authority to make mode choice decisions; however, they can make recommendations that need to gain approval.
8Use of the generic model by omitting the nonsignificant mode-specific constant for truck estimated a 58.6% share for trucks and a 41.4% share for trains in the base-case scenario. This is similar to the mode share estimated by the model with mode-specific parameters.
9Data provided by the Petroleum Authority of Thailand.
10 http://www.cn.ca/en/greenhouse-gas-calculator-emission-factors.htm (accessed 24 July 2012).
Color versions of one or more of the figures in the article can be found online at www.tandfonline.com/ujst.