An Open Access Journal
Method and approach | Study | Transport modes | Model name and/or characteristics | Application modal shift analysis | Remarks |
---|---|---|---|---|---|
Choice modellinga (micro) | Regmi and Hanaoka [37] | Road and rail (diesel only) | Binary mode choice model based on SP survey | Corridor between Loas and Thailand, 43 freight forwarders. | A 30.5% reduction in CO2 emissions due to a shift from 100% road to 56.8% road. |
Buhler and Jochem [5] | Road and rail | Binary mode choice model based on RP survey | 498 freight forwarders in Germany | A drop of 1% (if a road user charge applies) to 4% (due to increased rail speed) in CO2 emissions. | |
(Semi-) LCA (macro) | Kim and van Wee [21] | Road, rail (diesel and electricity), Short Sea Shipping (SSS) | Explicitly includes emissions from electricity production | Corridor between Western and Eastern Europe | Comparison of CO2 emissions for 7 unimodal/intermodal scenarios. |
Kim and van Wee [22] | Road and rail (diesel and electricity) | Explicitly includes emissions from electricity production | No specific area | Comparison of CO2 emissions for 5 unimodal/intermodal scenarios. | |
Nocera and Cavallaro [32] | Road and rail | Well-to-wheel principle | Transalpine corridors | Comparison of CO2 emissions in 2030 for 3 scenarios compared to baseline 2030. | |
Strategic freight transport network models (macro) | Nelldal and Andersson [30] | Road and rail | TRANSTOOLSb, strategic transport network model | European Union | Reduction of 20% of EU transport GHG emissions over land by 2050 compared to baseline. |
Jonkeren et al. [17] | Road, rail, Inland Waterways (IWW) | NODUS, GIS-based transport network model based on virtual network concept | The Rhine freight corridor | Increase of 1.1% of annual CO2 emissions due to modal shift from IWW to road. | |
Mostert et al. [28]. | Road, rail, IWW | Intermodal allocation model | Freight flows within, from and to Belgium (NUTS 3 level) | Study focuses on effect of modal shift on pollution rather than CO2. | |
Asuncion et al. [2] | Road, rail, SSS | GIS-based optimization model: New Zealand Intermodal Freight Network | Auckland-Wellington Auckland – Christchurch | Significant CO2 emission savings due to a modal shift | |
de Bok et al. [3] | Road, rail, IWW | BASGOED, strategic transport network model | Netherlands | Analyses effect of implementing CO2 pricing on modal split. | |
Macharis et al. [24] | Road, rail, IWW | LAMBIT model, GIS-based model for location analysis of Belgian intermodal terminals | Belgium | Analyses effect of internalization of external costs, among which CO2 on market area of intermodal transport. | |
Tavasszy and Meieren [40] | Road, rail, IWW | TRANSTOOLS, strategic transport network model | EU | Modal shift can cover 8% of the total reduction potential for CO2. | |
Tsamboulas et al. [42] | Road, rail, IWW, SSS | Macro-scan tool | Lerida – Karlsruhe Halkida – Ingolstadt | One of the applications is internalization of CO2 costs. | |
Zhang and Pel [43] | Road, rail, IWW (intermodal and synchromodal) | SynchroMO model | Rotterdam hinterland (Rhine river corridor until Duisburg. | Only container flows and for short-term analysis (24 h) | |
Decomposition analysis (macro) | Notteboom and Coeck [33] | Road, rail, IWW | Shift-share analysis | Belgian freight transport market | No effect on CO2 calculated in this report. Method used for analysis of change in intermodal competition. |
Other methods (mixed micro, macro) | Islam and Zunder [15] | Road, rail | Case studies based on interviews, questionnaires, company data and strategic transport network models. | 1) Dourges – Mataro 2) Mechelen – Zeebrugge 3) Amiens – Mechelen – Euskirchen 4) Rotterdam – Busto Arsizio | 2500 t CO2 saved per year in Corridors 1 and 2 jointly in 2008/2009. |