Climate Change Research ›› 2019, Vol. 15 ›› Issue (6): 660-669.doi: 10.12006/j.issn.1673-1719.2019.027

• Greenhouse Gas Emissions • Previous Articles     Next Articles

CO2 reduction potential of shifting containers from road to rail for green transport development

Xue-Zong TAO,Qin WU,Chuan-Zhong YIN   

  1. College of Transport and Communications, Shanghai Maritime University, Shanghai 201306, China
  • Received:2019-01-27 Revised:2019-03-13 Online:2019-11-30 Published:2019-11-30


An assessment framework was established to improve the estimation accuracy of emission reduction potential of shifting containers from road to rail (SRR), based on the analysis of SRR’s emission reduction principle. To this end, the activity-modal structure-energy intensity-emission factor (ASIF) methodology was revised by integrating the parameters of activity type, emission category, equipment structure, and energies’ life-cycle emission factor, which considered the emissions from main haulage, loading and unloading, operations by electric vehicles and equipment, pre- and post-haulage, transshipment between road and rail for the door-to-door transport chain of both empty and loaded container movement. Then, the export container transportation between Yiwu and Ningbo Port Area was taken for case study, and the input data was collected by use of face-to-face interview and published literature. The results showed that the emission reduction rate of CO2 for one TEU SRR of transport demand would be overestimated by 0.50-36.73 percentage points, when necessary factors are ignored. In the best-case SRR scenario, the CO2 emissions will be reduced by 34.2 kt with a reduction rate of 13.58%. The findings could provide theoretical support for the relevant government departments to objectively evaluate SRR’s emission reduction potential and to make efficient SRR policy measures.

Key words: Container transportation, Freight modal shift, Activity-modal structure-energy intensity-emission factor (ASIF) methodology, CO2 reduction, Transport chain

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