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The Selection and Design of Scenarios as a Policy Formulation Tool
The Dutch Scientific Council for Policy (WRR) argues that since the future is fundamentally unpredictable and not every imaginable future is possible, policies should not be based on a single, surprise-free futures study (WRR 2010). Every futures study should really start with two critical questions (see Figure 3.2). Answering these two questions leads to different types of futures studies:
1. Is it wise to assume stability and continuity of the system? If not, uncertainty should be central in the study and one surprise-free forecast will be insufficient;
2. Is it sensible to assume normative consensus about what future is desirable? If the answer is yes, different scenarios should grasp the uncertainty range. If the answer is no, divergent normative perspectives on the future are needed.
According to the WRR, there is often a blind spot for developing divergent normative perspectives, which present a range of policy choices with explicit indications for whom these choices are desirable.
Figure 3.2 A decision tree that considers the degree of future uncertainty and normative consensus
In developing scenarios, we can distinguish different phases in any policy formulation process (Schwartz and Ogilvy 1998; de Jouvenel 2004; Metzgeret al. 2010):
1. Problem characterization
A specific scenario exercise will have to start with the definition of the policy issue at stake, for example, energy security, climate change, and so on, and, related to that, the system boundaries, that is, what is the spatial scale of the subject and the relevant time horizon? For example, when developing scenarios for city planning, global scenarios for the next 100 years will not be necessary, although they can give input to the process in defining relevant exogenous factors.
2. Problem conceptualization
This phase identifies the drivers that impact the system under analysis. The drivers can be exogenous/external (for example, technological developments or oil prices), exogenous/internal (for example, policy choices) or endogenous factors (drivers that are dependent on other drivers, for example, energy demand as the result of traffic development or energy saving). Literature surveys, analyses of statistical trends, surveys with Delphi methods, and stakeholder workshops can all produce inputs for a scenario development. A morphological analysis of relevant factors and relationships, a scheme with causes and effects, such as the Drivers-Pressures-State-Impacts-Responses (DPSIR) scheme (EEA 1999) is one means to frame the problem. Workshops and (qualitative) modelling or systems analysis techniques can help to create a common understanding or find out where views on how the world works differ. The result of this phase is the identification of key drivers that affect the subject directly or indirectly.
3. Scenario framing
In this phase, the logic of the scenarios is defined. The certainty of future development of the key drivers is identified. Can continuity be assumed and trends extrapolated (for example, on energy use)? Alternatively, for which exogenous drivers are contrasting scenarios needed because the uncertainty range is large or discontinuities cannot be excluded (for example, the oil-price development, or new European regulation on electric vehicles)? If so, what are the main drivers and do these need contrasting scenarios? If there are many uncertain drivers, the number of possible scenarios can become quite large and this would lead to a set of scenarios that becomes incomprehensible to users. In such cases, a tree structure can be used to create some order. For example, a high versus low economic growth scenario can be assumed, each split into a fossil fuel and renewable energy scenario. All four scenarios can be further split into a high or low oil price variant, and so on.
In order to limit the total number of scenarios to a manageable number, the main drivers have to be selected, or assumptions made about different drivers with a high mutual dependency can be merged into a set of contrasting coherent scenarios (for example, combining high oil prices with fast technological developments). The latter approach requires the development of a credible storyline or narrative.
Triangles, scenario-axes or pentagons can be used to explain the contrasts in such coherent scenarios. Triangles and pentagons can be used to illustrate that scenarios have been designed from a certain perspective (economy, society or environment; or from a citizen, public or private company perspective). This can assist in identifying trade-offs and looking for compromises. Axes can be used when two dominant drivers (or groups of drivers) have been identified that are independent of each other. Use of the deregulation-regulation axis versus the globalization-regionalization axis is quite common. In this phase, it is also good to consider the inertia in the system and to check if the chosen time horizon is still valid.
4. Scenario description
Here, each scenario comes to life, that is, it is described in a credible and salient way, for example, using figures, images, narratives and metaphors. According to van der Heijden (2005), a scenario that will actually be used in policy formulation is internally consistent, links historic events with hypothetical ones in the future, carries storylines that can be expressed in simple diagrams, is as plausible as other scenarios, reflects elements that are already determined, and identifies indicators or 'signposts' that show that the scenario is already occurring. The narrative should not only be written in scientific or economic terms; it should also be based on different 'ways of knowing' (Lejano et al. 2013) and include memorable metaphors (Wack 1985). Participatory approaches can help to enrich the plausibility of the scenarios, and increase the acceptance for use in the policy process.
5. Scenario assessments
In this final phase, potential policy options are identified and assessed. Many questions typically emerge in this phase. What, for example, is the impact of policy options in each scenario? What trade-offs do policymakers have to face? Can no-regrets options (in other words, measures that are right in all scenarios) be defined? How can the cost-effectiveness of policies be optimized? Numerical models can be an important tool to use, but in the last few years (serious) gaming has often been used as an option to better understand the attitudes of key players in a scenario and to define robust policy recommendations.
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