LCA in Strategic Decision Making
for Long Term Urban Transportation
System Transformation
Florian Ansgar Jaeger, Katrin Müller, Cornelia Petermann
and Eric Lesage
Abstract The paper provides an overview of how Siemens uses LCA methodology
and tools to support cities in the decision-making process to promote sustainable
urban transportation systems. It focuses on GHGs and local air pollution.
Determining the cause of GHG emissions and air pollution requires flexible scopes
and a highly parameterized, hierarchical model, which can be adapted to any city’s
transportation system. Emission forecasting capabilities are very important since
motorized transportation modes quickly change properties over time. The model
screens a large set of infrastructure improvement measures by the click of a button
and analyses their impact on KPIs for different years. Applicability, challenges and
limits of LCA to the specific application of urban transportation system modelling
are discussed.
1 Introduction
LCA methodology and tools have proven useful for assessing products or even
companies in organizational LCA. Having used LCA-based methods and tools to
model transport systems in more than 20 cities, we can say that this applies to
mobility related infrastructure solutions in cities as well. This report shows how it
was done and that the variety of indicators and flexibility LCA offers, not only
matters to different products and product categories, but is also crucial for cities.
The cities in the world have different focuses on environmental, economic or social
F. A. Jaeger (&) Á K. Müller Á C. Petermann
Research in Energy and Electronics, Energy Systems,
Sustainable Life Cycle Management and Environmental
Performance Management, Siemens Corporate Technology,
13629 Berlin, Germany
e-mail: florian_ansgar.jaeger@siemens.com
E. Lesage
Business Development and Strategy, Mobility Consulting,
Siemens Mobility, Berlin, Germany
© The Author(s) 2018
E. Benetto et al. (eds.), Designing Sustainable Technologies,
Products and Policies, https://doi.org/10.1007/978-3-319-66981-6_22
193
for Long Term Urban Transportation
System Transformation
Florian Ansgar Jaeger, Katrin Müller, Cornelia Petermann
and Eric Lesage
Abstract The paper provides an overview of how Siemens uses LCA methodology
and tools to support cities in the decision-making process to promote sustainable
urban transportation systems. It focuses on GHGs and local air pollution.
Determining the cause of GHG emissions and air pollution requires flexible scopes
and a highly parameterized, hierarchical model, which can be adapted to any city’s
transportation system. Emission forecasting capabilities are very important since
motorized transportation modes quickly change properties over time. The model
screens a large set of infrastructure improvement measures by the click of a button
and analyses their impact on KPIs for different years. Applicability, challenges and
limits of LCA to the specific application of urban transportation system modelling
are discussed.
1 Introduction
LCA methodology and tools have proven useful for assessing products or even
companies in organizational LCA. Having used LCA-based methods and tools to
model transport systems in more than 20 cities, we can say that this applies to
mobility related infrastructure solutions in cities as well. This report shows how it
was done and that the variety of indicators and flexibility LCA offers, not only
matters to different products and product categories, but is also crucial for cities.
The cities in the world have different focuses on environmental, economic or social
F. A. Jaeger (&) Á K. Müller Á C. Petermann
Research in Energy and Electronics, Energy Systems,
Sustainable Life Cycle Management and Environmental
Performance Management, Siemens Corporate Technology,
13629 Berlin, Germany
e-mail: florian_ansgar.jaeger@siemens.com
E. Lesage
Business Development and Strategy, Mobility Consulting,
Siemens Mobility, Berlin, Germany
© The Author(s) 2018
E. Benetto et al. (eds.), Designing Sustainable Technologies,
Products and Policies, https://doi.org/10.1007/978-3-319-66981-6_22
193