4 Interpretation
4.1 Scope, Structure and Parameterization
The scope of the model must be flexible enough to adapt to any city’s carbon
reporting guidelines or methodology. It creates trust in the model, if results turn out
similar to what the city expects to see for a carbon baseline. Based on this trust,
additional scopes can be included for lever comparison. Since the target of this
model isn’t to compare cities, but levers within a city, the majority of all cities
showed little reluctance to include additional scopes and therefore emissions.
The structural options of current LCA-Software provide limits to the complexity
of models. Since parameters, calculated in one process can’t be passed down to
processes of subordinate processes, any cross-functionality between processes has
to be modelled outside the processes on a project level, where it can’t be properly
managed. Since processes of urban transportation systems influence each other
inherently, an increasing size of a model will eventually make it unmanageable in
practice.
The overall transportation volume in pkm or tkm is not a KPI that is tracked by
most cities. The determinations of the very basis of any urban transportation system
balance, therefore becomes a tedious process.
4.2 Forecasting and Target Setting
The bottom up forecasting, based on commonly available emission databases, that
have to be imported into the LCA-software, provide a good indication how emissions will develop in the future. For carbon, this is sufficient for target setting for
lever selection and scenario definition, since carbon targets are expressed in annual
emissions. For Air quality, it is a good indication, but hard to draw a final conclusion. A distance to political target/threshold judgment can only be made in
combination with a contribution analysis of an individual air pollution measurement
station, allowing an emission to concentration conversion. Some cities have this
available already [5].
The contribution analysis of different time slices identifies which transportation
mode to address with levers at what time, to get the maximum benefit. As an
example: if a lever reduces the PM2.5 emissions of light trucks by 50%, the effect
for the overall transportation system related emissions will be almost twice as high
in 2015 than in 2025 (see Figs. 2 and 3).
202
F. A. Jaeger et al.
4.1 Scope, Structure and Parameterization
The scope of the model must be flexible enough to adapt to any city’s carbon
reporting guidelines or methodology. It creates trust in the model, if results turn out
similar to what the city expects to see for a carbon baseline. Based on this trust,
additional scopes can be included for lever comparison. Since the target of this
model isn’t to compare cities, but levers within a city, the majority of all cities
showed little reluctance to include additional scopes and therefore emissions.
The structural options of current LCA-Software provide limits to the complexity
of models. Since parameters, calculated in one process can’t be passed down to
processes of subordinate processes, any cross-functionality between processes has
to be modelled outside the processes on a project level, where it can’t be properly
managed. Since processes of urban transportation systems influence each other
inherently, an increasing size of a model will eventually make it unmanageable in
practice.
The overall transportation volume in pkm or tkm is not a KPI that is tracked by
most cities. The determinations of the very basis of any urban transportation system
balance, therefore becomes a tedious process.
4.2 Forecasting and Target Setting
The bottom up forecasting, based on commonly available emission databases, that
have to be imported into the LCA-software, provide a good indication how emissions will develop in the future. For carbon, this is sufficient for target setting for
lever selection and scenario definition, since carbon targets are expressed in annual
emissions. For Air quality, it is a good indication, but hard to draw a final conclusion. A distance to political target/threshold judgment can only be made in
combination with a contribution analysis of an individual air pollution measurement
station, allowing an emission to concentration conversion. Some cities have this
available already [5].
The contribution analysis of different time slices identifies which transportation
mode to address with levers at what time, to get the maximum benefit. As an
example: if a lever reduces the PM2.5 emissions of light trucks by 50%, the effect
for the overall transportation system related emissions will be almost twice as high
in 2015 than in 2025 (see Figs. 2 and 3).
202
F. A. Jaeger et al.