– Example 2: Future vehicle fleet distributions in EURO classes are automatically forecasted based on the current city’s fleet distribution and matched with forecasts of the German national fleet.
• Parameter forecasts, which are the result of local phenomena such as the population increase or decrease and the travel behaviour, are extracted from reports
or discussed in BAU-Scenario workshops with the city and have to be adjusted
manually. MS Excel-based tools are provided to help with the effects of cross
assumptions.
• Parameters impacted by local politics, facilitated in projects, policy and regulation are discussed in BAU-Scenario workshops and have to be modelled
upfront in an iterative process, using levers, to determine input parameters.
• Example: If the city started constructing a new metro line, it is modelled in the
transportation model and the resulting modal split in future years is used as
inputs for the BAU-Scenario.
2.5 Target Setting
After the baseline is agreed in the form of a BAU scenario for the urban transportation system, the targets for different emissions at different target years are set.
Carbon emission targets are commonly published, whereas air pollutant emission
targets have to be retrieved from a separate process, converting limitations for
concentrations of air pollutants at hot spot measurement stations into emission
targets for transport-related emissions.
2.6 Levers
In order to reach their emission reduction targets, cities need to take measures.
These measures are modelled as scenarios in the transportation system model and
are referred to as levers. The levers aim at reducing transportation demand, shifting
transportation volume to less polluting modes (modal shift), shifting to other fuels,
increasing the energy/carbon efficiency of one or several modes of transport or
adding pollution control devices. The levers can be of different natures (see the
following list with examples):
• Technology based levers: advanced traffic light management
• Policy based: the implementation of a low emission zone
• A combination of policy and technology based: city tolling
• Behaviour change: eco-efficient driver training
• Generic: an X percent reduction of car use.
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• Parameter forecasts, which are the result of local phenomena such as the population increase or decrease and the travel behaviour, are extracted from reports
or discussed in BAU-Scenario workshops with the city and have to be adjusted
manually. MS Excel-based tools are provided to help with the effects of cross
assumptions.
• Parameters impacted by local politics, facilitated in projects, policy and regulation are discussed in BAU-Scenario workshops and have to be modelled
upfront in an iterative process, using levers, to determine input parameters.
• Example: If the city started constructing a new metro line, it is modelled in the
transportation model and the resulting modal split in future years is used as
inputs for the BAU-Scenario.
2.5 Target Setting
After the baseline is agreed in the form of a BAU scenario for the urban transportation system, the targets for different emissions at different target years are set.
Carbon emission targets are commonly published, whereas air pollutant emission
targets have to be retrieved from a separate process, converting limitations for
concentrations of air pollutants at hot spot measurement stations into emission
targets for transport-related emissions.
2.6 Levers
In order to reach their emission reduction targets, cities need to take measures.
These measures are modelled as scenarios in the transportation system model and
are referred to as levers. The levers aim at reducing transportation demand, shifting
transportation volume to less polluting modes (modal shift), shifting to other fuels,
increasing the energy/carbon efficiency of one or several modes of transport or
adding pollution control devices. The levers can be of different natures (see the
following list with examples):
• Technology based levers: advanced traffic light management
• Policy based: the implementation of a low emission zone
• A combination of policy and technology based: city tolling
• Behaviour change: eco-efficient driver training
• Generic: an X percent reduction of car use.
LCA in Strategic Decision Making for Long …
197