Participatory scenario development


Lead Editor of this section is Kaspar Kok.

Short Version

Scenario development is an important feature of IA since it allows for the formulation of assumptions on future developments in one connected storyline. According to Van Notten (2006), "Scenarios are consistent and coherent descriptions of alternative hypothetical futures that reflect different perspectives on past, present, and future developments, which can serve as a basis for action". Scenarios can be described qualitatively or quantitatively, often both approaches will be combined. Scenarios are for instance used to integrate the qualitative storylines regarding future development of complex systems with the quantitative formulations applied in formal modeling. Participatory scenario development allows for the integration of stakeholders’ views on the key influencing factors of future developments and the embedding of scenario assumptions in a specific regional context.


van Notten, P. 2004. Writing on the wall – Scenario development in times of discontinuity. Amsterdam.

Extended Version

(Text provided by the SustainabilityA-Test Project: Simon, K.-H., Matovelle, A., Pereira, A. G., Pedrosa,T.)


The term ‘scenario analysis’ can be used in a general way, including the phases of defining scenarios, developing scenarios and interpreting the results.

A manifold of definitions exists in practice and in the literature on what ‘scenario analysis’ is. However, most scenario developers would agree that scenarios are constructed especially to assist in the understanding of possible future developments of complex systems. Scenarios are also assumed to provide some form of future perspective to an integrated assessment. Scenarios are made up of a set of explicit ‘if−then’ propositions that explore the consequences of a range of driving force assumptions (i.e. each scenario should include a set of driving forces as well as a representation of resulting  pressures, states, impact and/or responses). A scenario can take many forms including an image, a graphic, a table, or text.

Role of scenario analysis tools in an integrated assessment

Scenario analysis tools are powerful tools in integrated assessments especially for identifying policy options. They are mainly used in the phase of problem definition, describing policy options, and impact analysis of an integrated assessment. In any situation where scenario analysis tools are used, they are used in conjunction with other tools, especially participatory tools and modelling tools.

Problem definition – providing future perspectives to problem framing – scenario analysis contributes to the building of knowledge about the problem and its cause and effect relationships. Suitable scenario tools are capable of helping to get over narrow views on the problem area, like brainstorming, workshops (like ‘future conferences’) and other participatory scenario−building exercises.

Finding options – scenario analysis plays an important role in visioning futures and setting the objectives. Scenarios are used to elucidate visions on sustainable futures and pathways, including policy interventions and variation of framework conditions. At this stage of the integrated assessment, the main drivers are identified and a broad spectrum of possible future developments, or possible pathways to certain objectives, is described. An important role for scenario application in this phase is a first clustering of promising policy options that can be further investigated in Phase III (analysis).

Impact analysis – further steps of analysis are based upon the results of the scenario definition processes. Scenario calculation often provide the data series needed by the more analytical tools to calculate expected impacts, costs and benefits of various policy options.

In the follow−up phase of an IA there is no particular role for scenario analysis tools. Existing projections and scenarios might be used for ex−post evaluation of implemented policies.

Choosing between different scenario analysis tools

There are various ways to develop scenarios, and various reasons for doing so. Probably the most important factor determining what method to use is reason for which the scenario is.

Three typical reasons for developing scenarios can be distinguished, which more or less link to the different phases of integrated assessment. These are (Westhoek et al., 2006):

Strategic orientation, to answer questions such as what alternative ‘worlds’ can be expected, what preparations are needed, what if current assumptions are wrong, and what would be robust strategies?

1. Advocacy/ vision building, to answer questions such as what are the positive changes that are needed (e.g. structural changes, value changes, etc.)?

2. Policy optimisation, to answer questions such as what policy variant is most effective, cost−efficient, fast, acceptable, etc?

3. There are many additional considerations for choosing one scenario development tool over the other.


Four of these are discussed in further detail below:

1. Type of desired scenario – participatory vs. non−participatory and qualitative vs. quantitative (or hybrid);

2. Problem content – the nature and scope of the issue to be addressed (Steyaert and Lisoir, 2005);

3. Scenario outcome – types of outcomes that the approach is good producing (Involve, 2005);

4. Whether or not the tool requires specific (scenario) expertise to be applied.

Each criterion is explained in more detail below, followed by an overview Table mapping a few tools for scenario development to the criteria.

Type of scenarios

There are several types of scenarios that can be produce by a tool or combination of tools. Several scenario characteristics were already mentioned in this chapter from which two are strongly affected by the type of tools used:

1. participatory vs. non−participatory scenarios with respect to inclusion of stakeholders

2. qualitative vs. quantitative (or hybrid) scenarios with respect to knowledge used.

If the scenario aims to explore more the values of the issues at stake, it is important to use tools that allow participation of stakeholders. On the other hand, if the scenario has as main objective to support the assessment with data series, it is probably more appropriate to use quantitative tools.

Problem Content

The nature and scope of the issue to be addressed can be regarded based on four aspects (Steyaert and Lisoir, 2005):

1. Knowledge – to what extent does the society already possess a general knowledge of the subject?

2. Maturity – to what extent has the society already developed opinions or even legislation on the subject? Do strong views exist or is the issue so emergent that norms have not become established?

3. Complexity – is the subject highly complex, such that a great deal of (technical) information is required?

4. Controversy – is the issue highly controversial and has the debate become polarised, such that consensus is difficult to reach?

Scenario Outcome

Different tools produce different types of outcomes that can go from simple gather of information to the

production of a scenario. Four types of outcomes that can be produced by tools are considered (Involve, 2005):

  1. Information – some methods are good for gathering the information necessary to characterise a scenario;
  2. Organise information – some methods are good to organise the data to be used in the scenario’s building;
  3. Produce a scenario – methods that involve the development of a complete scenario;
  4. Hybrid – some methods are good to gather information and produce a new scenario.


Table 1. Selection criteria for scenario analysis tools


*) Note that when a method is said to be useful in terms of the criteria mentioned here, denoted by ‘V ’, this is based on the typical application of the method. An empty cells means that a method is not particularly good for that specific situation.

**) Participatory methods are those tools that can help with building a scenario such as Focus Group, Delphi survey and in−depth interviews. These tools are described in the Participatory tools chapter.

***) With respect to contents, the “+”−sign denotes high suitability, the “–”−sign low suitability and the “m” medium suitability of a tool for situations where there is a high level of common knowledge, a high level of maturity (most participants have formed their opinion), high complexity and a highly controversial issue. “±” denotes that the tool can be used for all cases.

An additional important differentiation between approaches for developing scenario analysis is the one that of backcasting and forecasting scenario analysis. While the forecasting approach starts with current situations and explores possible development alternatives, in backcasting a desired future situation is taken as the analytical starting point and possible different strategies or strands of development that lead to that situation are described and evaluated.

As the Forecasting scenarios is the most expanded in the scenario analysis, a short description is given in the webbook.



Involve (2005), People & participation – How to put citizens at the heart of decision−making, Beacon Press.

Steyaert, S and Lisoir, H (2005) Participatory methods toolkit – A practitioner’s manual, King Baudouin Foundation and Flemish Institute for Science and Technology Assessment.


Scenario Workshop

Scenarios are specially constructed stories about the future. Every scenario represents a different but plausible world. One of the objective of scenario planning is to show how different forces can manipulate the future towards opposite directions. Scenarios enrich our mental maps and increase the number of options to act on coming events.

A complexity reduction of systems, based on secure information, is a typical outcome of a scenario planning exercise. A workshop therefore cannot substitute a longer process of information gathering. However, scenario workshops do utilize the collective consciousness of a large group of different stakeholders. The higher the diversity, the better the results of the workshop.

There is no blueprint approach for a scenario workshop. However, it has been shown that a series of steps can bring good results in a short time. When applied to visioning, elements from future search can be included as well. The individual steps can be organized in different ways.




Practice Examples / Software


ProVision is an interactive mapping and impact assessment tool that can be used to develop land use change scenarios and conduct a stakeholder‐based SIA. The tool has a mode to upload available land use maps, to modify and interactively change them as a basis for the scenario development. It includes a simple rule set up that either allows or restricts for land use changes during the scenario development. The impact assessment is based on the concept of land use functions (LUFs). In this case LUFs are directly linked to a certain land use type and not to the region. Here, each land use type contributes a certain value (benefit) to each LUF that needs to be assigned by regional experts. ProVision can serve local stakeholder workshops to support sustainability impact assessments of land use changes.


QUICKScan is a decision support tool developed by Alterra, supported by modelling software to visualize quantitative and value-based modelling in the decision process. The tool enables the creation of alternative storylines for policy questions by the stakeholders, and translates these in-situ into a model by combining tacit expert knowledge with available spatial explicit monitoring- and statistical-data. QUICKScan builds on concepts from Participatory Modelling and Participatory GIS. The tool is designed to calculate fast, and therefor perform multiple iterations of a modelling exercise during a workshop.

Verweij, P., Winograd, M., Perez-Soba, M., Knapen, R., van Randen, Y. (2012).  QUICKScan: a pragmatic approach to decision support. International Environmental Modelling and Software Society (iEMSs) 2012 International Congress on Environmental Modelling and Software Managing Resources of a Limited Planet, Sixth Biennial Meeting, Leipzig, Germany. (23 Nov 2013)


Interactice Backcasting

Definition & Objectives

Already in 1976, Lovins introduced the ‘backwards−looking−analysis’ in his exploration of long−term energy policy in the United States (Lovins, 1976). In 1982, Robinson elaborated on this approach and developed the method of backcasting as an alternative approach to traditional forecasting and planning methods (Robinson, 1982). It has been especially designed as a tool for exploring sustainable policies.

Backcasting is based on an epistemological and pragmatic critique on predictive forecasting. This critique is based on the notion that science is a social process, which may involve power and the exclusion of minority views. This inter alia means that one should avoid that the covered experts’ assumptions determine research outcomes. Dreborg (1996) claims that based on dominant trends one might overlook solutions that would presuppose the breaking of trends. So, backcasting should deliberately seek for possibilities to breaking with dominant trends.



For interactive backcasting, as for other participatory tools, the starting point is to carefully define the settings of the assessment (Van de Kerkhof, 2004). The settings include:

1. The formulation of a problem and scope for the assessment, which may be rather broad, e.g. the identification of sustainable development strategies for a particular region, or rather specific, e.g. the assessment of the possibilities for reducing GHG emissions in a particular sector by 80% in the year 2050. A generally accepted starting point is that backcasting takes mostly a time frame of 25 − 50 years.

2. The identification and recruitment of participants, which may be interested companies, governments, NGOs and citizens. A choice should be made as to whether the participants will constitute a more homogeneous group or a more heterogeneous group. In order to enhance interactive learning it might be recommendable if most participants do not know one another from the outset.

3. The identification of the science to be involved and how. Scientists can contribute to the backcasting in different roles. The Canadian experience shows probably the best−documented record in involving modeling tools as to assist participants in the construction of future images. But scientific assistance may also be provided by written input or oral presentations in order to provide participants with state of the art knowledge.

The procedure of the actual assessment will basically take two major steps. The first step is the definition of future images or desirable end−states, the second step is analyzing backwards from the future into the present, thereby identifying a time path for major decisions and change.

Combination with other methods

Backcasting can probably be combined with a huge amount of tools for assessing or promoting sustainability. The Canadian approach is a combination of computer−aided models, scenarios and participatory processes (e.g. Tansey et al., 2002). Robert et al. (2002) have brought together a number of tools into a ‘systems model for sustainable development, e.g. backcasting, upstream thinking, ecological footprinting, and LCA. Höjer (1998) reports of a survey which combined backcasting and conventional Delphi. Such a combination might be worthwhile in an interactive setting as well, provided that conventional Delphi be replaced by interactive policy Delphi. In a policy Delphi, scientific experts, policy makers and other stakeholders do not aim for convergence, but seek to generate the strongest possible opposing views on the potential solutions to a major policy problem (Van de Kerkhof, 2004).

Strengths & weaknesses

(+) Developing future visions and implementation pathways fosters creativity and learning among those involved. It may enhance an integrated picture of the possibilities and bottlenecks to reduce

emissions; and it may provide insights in the working of and opportunities for institutions and organizations. (Robinson, 2003: 853).

(+) Dreborg (1996: 819) considers backcasting primarily as a means to enhance creative thinking about long−term developments and policy.

(+) In order to induce sustainability, a systemic change may in certain cases be preferable to an incremental improvement. Backcasting may support the policy process by exploring the critical

decisions and investments to be made. This, in its turn, may lead to a more efficient spending of scarce resources (Roth and Käberger, 2002).

(+) Backcasting can result in the interactive development of a long−term strategy. Implementing such a strategy can provide a company or a country with a competitive advantage (Roth and Käberger 2002).

(+) The capacity of the backcasting approach to combine analytical methods and participatory methods, i.e. the role of computer models as mediating between scientific and civic cultures, (Robinson, 2003).

(+) Backcasting is easy to handle and fun for the participants. It manages to establish a learning effect, especially at the technical and cognitive level of analysis (Van de Kerkhof, 2004).


( - ) The function of the future image is not always clear. If not immediately linked to backward looking analysis, the tool may become somewhat utopian in character (Van de kerkhof, 2004; Hisschemöller and Mol, 2001).

( - ) The role of scientific models and scientific support in general.

( - ) Potential for discussing conflicting views is limited (Van de Kerkhof, 2004)


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Dortmans, P.J. (2004, in press). Forecasting, backcasting, migration landscapes and strategic planning maps. In: Futures 36: 1 – 13.

Dreborg, K.H. (1996). The essence of backcasting. In: Futures 28 (9): 813 – 828.

Hisschemöller, M. en M. van de Kerkhof (eds.) (2001). Climate OptiOns for the Long term – Nationale Dialoog. Deel B – Eindrapport. IVM E−01/05. Amsterdam

Höjer, M. (1998). Transport telematics in urban systems – a backcasting Delphi study. In Transpn Rec−D 3(6): 445 – 463.

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Lovins, A.B. (1976). Energy strategy: the road not taken? In: Foreign Affairs 55 (1): 63 – 96.

Robert, M. (2003). Backcasting and econometrics for sustainable planning – Information technology and individual preferences of travel. Journal of cleaner production.

Robinson, J. (2003). Future subjunctive: Backcasting as social learning. In Futures 35: 839−856.

Robinson, J. (1982). Energy backcasting: a proposed method of policy analysis. In Energy Policy 10 (4): 337– 344.

Van de Kerkhof, M., M. Hisschemöller and M. Spanjersberg (2002). Shaping diversity in participatory foresight studies. Experiences with interactive backcasting in a stakeholder dialogue on long−term climate policy in the Netherlands. In Greener Management International 37 (1): 85−99.

Van de Kerkhof, M., M. Spanjersberg en M. Hisschemöller (2001). Evaluation of the National Dialogue. In: Hisschemöller, M. en A.P.C. Mol (eds.). Evaluating the COOL Dialogues. Climate OptiOns for the Long term. Final report – volume E. Wageningen / Amsterdam


Derived from Hisschemöller, M. 2006: Interactive Backcasting.
This introduction to the method was developed as part of the FP6 project SustainabilityA-Test. coordinated by IVM (Insitute for Environmental Studies).