Participants to be invited to the workshops should reflect a broad range of stakeholders from the city administration, political parties, industry and economy (such as the chamber of commerce), practitioners, science, and civil society. All relevant departments of the city or regional administrations should be represented. Please refer to the catalogue of stakeholders for guidance and as inspiration on the types of stakeholders who might be relevant to your situation. The SPROUT project has also generated a template, as well as the accompanying instructions for organizers, for conducting scenario-building workshops. 

Drivers are factors that are likely to have a significant direct or indirect effect on the system under examination – in the SPROUT case the urban mobility systems of the partner cities. Drivers are factors that are beyond the influence of the city (which distinguishes them from policies and policy measures). They might include technological innovations, economic development, or demography. Please refer to the section on drivers for more information.

In the SPROUT project, 1^st– and 2^nd – layer cities were asked to rate the level of importance of all urban mobility transition drivers identified on a pre-defined 5-point scale, ranging from ‘not important’ to ‘very important’. The Rating of the drivers through the SPROUT 1st layer cities can be found in D3.1, on page 15/16.

For each selected driver, possible future states are determined. Future states are a limited number of qualitatively or quantitively described states. The potential future states can be expressed

• quantitatively (numeric);
• as a scale (for example ‘increase – no change – decrease’)
• nominal (for example urban pattern: dense – sprawling – polycentric).

The respective response options (variants) should cover possible future states for the individual drivers. For all variables, SPROUT used a two-step scale (for example: ‘moderate growth– strong growth’; or ‘decrease – increase’), but more granular scales are also possible.

As a next step, the direct mutual influence of the drivers and their assumed states are cross-assessed based on qualitative experts judgements. This analysis is the basis for identifying coherent scenarios in the next step.

Experts asked to assess whether and how a certain state of one driver directly impacts on the state of another driver; for example, an increase of the state of driver ‘A’ leads to a decrease of the state of driver ‘B’; or driver ‘A’ and driver ‘B’ are unconnected. This process is repeated for all pairs of drivers and in both directions (A ➡ B; B ➡ A). The assessment builds on an integer scale, ranging from negative to neutral and positive values. SPROUT used a 2-stages Likert scale with a scale ranging from –2 to +2. A value of ‘0’ denotes ‘no influence’:

For example: one could assume that ‘increased tourism’ can have a weakly promoting influence on the ‘growth of new business models’, but that ‘growth of new business models’ does not influence ‘tourism’.

In SPROUT, several individual expert assessments of all pair-interactions were compiled into a ‘cross-impact matrix’. Scenarios are more consistent the higher the score of positive influences (promoting direct influence) is, and the fewer restrictive influences there are between their elements (drivers and their values). This macro-matrix exercise was completed in SPROUT by a team of experts from the Vrije Universiteit Brussel (VUB), including specialists on urban mobility, electric mobility, urban logistics and ecommerce. Details are provided in SPROUT D3.1.

The results of the SPROUT cross-impact assessment can be seen in the SPROUT cross-impact table. This general table can be adopted and validated in the city-specific context.

Example of a cross-impact table from a SPROUT city

Each possible combination of all driver states presents one scenario, which leads to an unmanageable number of scenarios. Consequently, the first step is to cut down this number by eliminating internally inconsistent scenarios. Only consistent scenarios, that are based on mutually supportive assumptions, and that avoid contradicting causal relations are further considered.

For example, one could consider the combination of “increasing urban sprawl” and an “decreasing valuation of car ownership” inconsistent.

The results of the city-specific impact table can then be analysed using tools such as ScenarioWizard, a free software for applying cross impact balance analysis. Based on this input in SPROUT, the software generated between 1 and 7 consistent scenarios, which were then the basis for selecting the final set of the most “consistent” scenarios.

In total, three scenarios have been developed for each SPROUT pilot city: two are based on the outcome of the cross-impact balance analysis performed in Task 3.1, and the third is the result of the developments deemed most likely by local stakeholder.

Based on the results of the scenarios’ sustainability assessment, as well as their policy-related assessment, the final city-specific narrative scenarios were created, putting together all the previous elements and adding visualizations. The scenarios served as a basis for the pilots’ implementation and the development of alternative policy responses for all the cities. 

The quantitative output of the cross-impact analysis is enhanced by a city-specific narrative description. These narrative descriptions can be obtained through workshops with local stakeholders. Finally, these narrative scenarios can be translated into visual representations, which would be useful in communicating the message to the relevant stakeholders, and the public at large. The example visual representation for the SPROUT cities are found here: 

• Budapest
• Kalisz
• Padua
• Tel Aviv
• Valencia

In the following video presentation, Sara Tori from Vrije Universiteit Brussel presents the SPROUT methodology for developing narrative and visual scenarios for cities.