Work package Sustainable Futures

The work package will be innovative in that it combines futures studies methodologies, specifically backcasting, with systems dynamics.

Aim and research questions

The aim of this WP is to explore what a sustainable transport system for the Stockholm region might look like in a future with more digitally supported mobility and accessibility services. The systems- dynamics models and backcasting images of the future produced within the WP will be designed to explore possible future tensions which could appear through relying on digitalization and platform- based services to support the development of a sustainable transport system based upon the principles of Avoid, Shift and Improve. The following four sources of tension have been identified as a preliminary step:

• A risk that the new mobility and accessibility services will have indirect effects that counteract goals of climate-neutrality and social justice.
• Tensions between social and climate goals promoted by policy on the one hand, and the economic goals of enterprises on the other, specifically platform companies. Here, tensions in terms of social justice may be the most obvious.
• Tensions between new services and existing low-emitting transport modes (i.e. walking, cycling, public transport), i.e. the risk that new mobility and accessibility services does not reduce demand for car traffic, even if it adds new alternatives.
• Tensions that appear when technological development and the development of new business models among large global companies is so fast that the legal system and public institutions cannot keep up with the pace.

The point of focusing on these tensions is to create a basis for further policy development, and to identify risks so that social targets can be prioritized when problems caused by the transport system need to be addressed.

Content

The whole of this WP aims to illustrate the measures required to achieve a sustainable transport system in the Stockholm region. Since input from other parts of the program will be important, seminars and workshops with participants from other WPs will be a key part of this work (supported by the seminars and workshops arranged in WP S, see further below). The workshops will be both a way of collecting results and ideas from other work packages, and a way of coordinating the level of effort required from different actors if sustainability targets are to be reached. A main focus is likely to involve investigating how the avoid strategy can be implemented in the future, since preliminary findings from the futures-oriented WP in the current stage of Mistra SAMS indicate that other measures will not be enough. Thus, backcasting in this phase takes the work from the first phase one step further, and this work package will also focus more heavily on the Stockholm region.

Work package leader

The leader of this work package is Mattias Höjer, KTH

Tasks

Task SF1: Causal-loop diagram

Aim

The aim of this task is to identify and analyze the most important causal relations affecting the environmental sustainability with a specific focus on energy-use and greenhouse gas emissions.

Content and scientific context

Previous work in the Mistra-SAMS program and the other work packages form the starting points for this section. Results from WP C, WP P, and WP M will be especially important, but results from the living labs in Phase 1 also provide relevant input. A workshop with the other program participants (supported by WP S) will be held early in the project to transfer knowledge from the other WPs. The results of this workshop will then be used to develop a causal-loop diagram. Input from the workshop will be complemented mainly by literature studies.

Deliverables

1 internal workshop report (Q2 2021)

1 conference presentation (Q4 2021)

1 peer review paper presenting a causal-loop diagram as an important element (Q2 2021)

Task leader:  Mattias Höjer, KTH

Task SF2: Systems-dynamics modeling

Aim

The aim of this task is to explore the transport system in the region through systems-dynamics modeling, using Vensim software (preliminary choice).

Content and scientific context

The systems-dynamics modeling from Task 1 will be the starting point for modeling in task SF 2. The avoid-shift-improve framework will be used to explore developments toward a sustainable transport system for the Stockholm region, supported by mobility and accessibility services. The framework will be used to identify potential ways of reducing energy use and GHG emissions.

Deliverables

1 internal workshop report (Q2 2022)

1 conference presentation (Q4 2022)

1 peer review paper presenting the model (Q2 2023)

1 PhD thesis (Q4 2023)

Task leader:  Anna Pernestål, KTH

Task SF3: Target-fulfilling images of the future

Aim

The aim of this WP is to develop target-fulfilling images of the future which highlight important opportunities and risks connected to relevant possible developments. Topics under discussion will revolve around the question “How can future platform-based transport systems fulfill targets of climate neutrality and social justice?”.

Content and scientific context

The task will take a backcasting approach, but will be innovative in the sense that it continues where the systems-dynamics modeling stopped. It is therefore innovative in terms of the methodology used. The target-fulfilling images of the future will include relations between e.g. traffic volumes, vehicle technology and transport modes, and will pay particular attention to issues that are investigated in other WPs, but typically take them a few steps further, i.e. place them in a future context. The systems-dynamics model from task SF2 will be an important stepping stone for the backcasting images of the future. Again, the avoid-shift-improve framework will provide a structure for communicating changes in images of the future.

Deliverables

1 internal workshop report (Q2 2024)

1 conference presentation (Q2 2024)

1 peer review paper presenting the backcasting and systems dynamics for a sustainable platform-based transport system model (Q4 2024)

Task leader: Mattias Höjer, KTH