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Links with Other Policy Formulation Tools
In principle, scenarios have close links with other policy formulation tools, especially those to assess potential impacts of policy options, like modelling, cost-benefit analysis (CBA) (see Chapter 7, this volume), cost-effectiveness analysis (CEA) and trade-off analysis. In fact, these tools arguably become more policy relevant when based on futures studies, as their outcomes greatly depend on underlying assumptions about present and future circumstances.
Exploratory scenarios are largely based on multivariate systems analysis and cause-effect models. Normative forecasting relies more on Bayesian statistics, linear and dynamic programming. For both exploratory and normative approaches, dynamic modelling is very relevant to identify the feedback mechanisms. Modelling (see Chapter 5, this volume) is intrinsically linked to the use of scenarios because models provide artificial experiments to explore system behaviour in the future where facts are not freely available (Matthews et al. 2007). Models help assess the complex interactions between system components and therefore support the development of quantitative pathways. This is the reason why model-based scenarios are often prescribed in ex ante assessments of policies (see Chapter 5, this volume; Bennett et al. 2003; Rounsevell et al. 2006; Helming and Pérez-Soba 2011). 'Story-And-Simulation' is the state-of-the-art of linking scenario narratives and models, thus enabling interaction between scientists and a range of other stakeholders (see Chapter 2, this volume). The framework is on the one hand flexible enough to use in conjunction with additional tools, and on the other sufficiently strict to separate clearly the roles of stakeholders and scientists and allow for co-production of knowledge (Kok et al. 2011). Most studies use a traditional 'Story-And-Simulation' approach coupling qualitative stories with (spatially explicit) mathematical models. More recently, the addition of other tools such as conceptual models and Fuzzy-Sets has shown their potential in facilitating the quantification of stakeholder input, for example directly obtaining estimates for model parameters. The potential for using these (and other related tools) has barely been touched upon in the literature.
Uncertainty management is another tool that is intrinsically linked to the credibility of scenarios. If continuity in trends can be assumed, uncertainties for investment decisions can be assessed in a quantitative way by attaching probabilities to different quantitative forecasts in order to calculate pay-off periods under different assumptions. Decisions can be optimized and project risks can be included in the required discount rate for an investment. For government policy, robustness can be increased by assessing whether a measure is still effective in meeting a policy target when scenario assumptions are changed. Policymakers could choose to limit the policy to no-regret measures (saving money and accepting the risks of non-compliance with the policy targets) or extend the policy strategy with additional measures to ensure that targets will be met under different scenario assumptions (the precautionary approach).
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