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Single Measures

The GHG-TransPoRD project commenced its quantitative analysis by estimating GHG reduction potentials of  single measures for each mode. Measures could be a new technology as well as a national or urban transport policy. Against a common energy framework a long list of measures was estimated for each mode as well as for alternative fuels. Short lists of promising measures were generated according to their  GHG reduction potential. The analysed measures were classified by four broad categories of how they would reduce GHG emissions (ASIF approach):

  1.  Activity reduction: means that the measure would reduce transport demand. Usually valid for demand management measures (i.e. transport policy).
  2. Modal shift: means that a measure would affect the modal distribution of transport demand such that low carbon modes increase their modal share. Usually valid for demand management measures (i.e. transport policy) and infrastructure policy.
  3. Energy intensity: means that a measure improves energy efficiency: Usually valid for technical measures in vehicles (e.g. engine efficiency, rolling resistance).
  4. Carbon intensity of fuels: means that a measure reduces the carbon emissions per unit of fuel consumed. Usually that would mean to use alternative fuels i.e. non-fossil or low carbon fossil (e.g. biofuels, CNG).
The elaborated shortlists contained 19 bundles developed from more than 60 measures concerning road technologies differentiated into car and truck measures, 26 measures related to urban and (national) road policies, 11 air measures, 11 rail measures and 10 shipping measures each classified by the four categories of the ASIF approach. Based on these short lists that largely neglect the interaction between different measures the medium and long-term theoretical reduction potentials by mode against the reference of the energy framework compiled by GHG-TransPoRD have been estimated. The results of the analysis of single measures are summarized in Table 1 providing the theoretical technical reduction potentials, which constitute the highest potentials compared with the economic potentials and the potentials  that finally were estimated by our scenario analysis.

Table 1: Theoretical technical reduction potentials by mode based on aggregation of potentials of single measures (see D2.1)

[%-relative reduction to reference]
Technical cars*
-40 to -45%
-60 to -68%
Technical trucks
-30 to -36%
-57 to -63%
Urban measures**
National policies***
Technology non-urban traffic
Technology urban traffic
Technology & policy
Technology & policy
-20 to -25%

Source: GHG-TransPoRD.

* Potentials are calculated using the reference energy mix for electricity. Potentials can be higher if electricity would be produced carbon free, as then upstream emissions of electric vehicles would become zero.
** Taking into account most relevant and compatible urban measures.
*** Assuming reasonable combinations of national policies.
**** Not considering the impacts of land use changes. The economically realizable potential for reductions by use of biofuels is significantly smaller than the theoretical technical potential. And it strongly depends on external factors like the price of fossil fuels.