local impact due to the fact that they are emitted in very densely populated locations. Cities have strict local targets for these pollutants. Reporting on Scope 1–3 air
pollution emissions is misleading in an urban context. In some cities for example in
Austria, an increased share of photovoltaic (PV) power in the forecasted electricity
mix lead to higher overall particulate matter emissions. These Scope 3 particulate
matter emissions from PV panel production are of little relevance for the city where
the panels are installed, since the emissions occur far away in much less densely
populated areas and have little global impact.
3.2 Structure and Parameterization
The hierarchical structure of the model in combination with the strong parameterization involves difficulties in modelling practice, since a parameter calculated in
a process is not passed down to processes in the lower hierarchies (upstream
processes) in LCA-Software. Any parameter, used by more than one process,
therefore has to be calculated on a project level (global parameter). LCA-software
does not provide a folder structure on this level.
The most difficult parameters to derive are the overall transportation volume,
especially for freight, and passenger kilometre or ton kilometre based modal split
data. All cities were able to provide journey shares. But this data can’t be used to
create a passenger kilometre-based inventory without additional assumptions and
data. Central databases such as Urban Audit asses KPIs, which are only a cutout of
the overall transportation system. A typical example is the “[s]hare of journeys to
work by car −%” [4].
3.3 Forecasting & Target Setting
The first important information, the modelling delivers the cities, is how the overall
sum of future emissions will develop over time in the BAU-Scenario. This serves as
the baseline. Each lever calculation is compared with this BAU-Scenario to provide
relative saving potentials. This is most relevant for NO 2 (Fig. 1). It implies, that the
distance to the city’s target is significantly reduced before any lever is applied.
Secondly, the model delivers a contribution analysis in time slices. For PM2.5,
emission shares of private vehicles increase compared to those of trucks (Fig. 2 vs.
Fig. 3).
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pollution emissions is misleading in an urban context. In some cities for example in
Austria, an increased share of photovoltaic (PV) power in the forecasted electricity
mix lead to higher overall particulate matter emissions. These Scope 3 particulate
matter emissions from PV panel production are of little relevance for the city where
the panels are installed, since the emissions occur far away in much less densely
populated areas and have little global impact.
3.2 Structure and Parameterization
The hierarchical structure of the model in combination with the strong parameterization involves difficulties in modelling practice, since a parameter calculated in
a process is not passed down to processes in the lower hierarchies (upstream
processes) in LCA-Software. Any parameter, used by more than one process,
therefore has to be calculated on a project level (global parameter). LCA-software
does not provide a folder structure on this level.
The most difficult parameters to derive are the overall transportation volume,
especially for freight, and passenger kilometre or ton kilometre based modal split
data. All cities were able to provide journey shares. But this data can’t be used to
create a passenger kilometre-based inventory without additional assumptions and
data. Central databases such as Urban Audit asses KPIs, which are only a cutout of
the overall transportation system. A typical example is the “[s]hare of journeys to
work by car −%” [4].
3.3 Forecasting & Target Setting
The first important information, the modelling delivers the cities, is how the overall
sum of future emissions will develop over time in the BAU-Scenario. This serves as
the baseline. Each lever calculation is compared with this BAU-Scenario to provide
relative saving potentials. This is most relevant for NO 2 (Fig. 1). It implies, that the
distance to the city’s target is significantly reduced before any lever is applied.
Secondly, the model delivers a contribution analysis in time slices. For PM2.5,
emission shares of private vehicles increase compared to those of trucks (Fig. 2 vs.
Fig. 3).
LCA in Strategic Decision Making for Long …
199