Scenario analysis has more than a half-century of history behind it, and a wide range of scenario methods and techniques are now available. While the term “scenario” refers to a story about the future – that is, a narrative – many scenario exercises include a quantitative analysis. This is particularly true in the environmental realm, and recent important examples include the Special Report on Emissions Scenarios for the Intergovernmental Panel on Climate Change, the United Nations Environment Programme’s Global Environment Outlook, the Millennium Ecosystem Assessment and the Comprehensive Assessment of Water Management in Agriculture.
Capturing some of the lessons provided by certain of these exercises, a methodology has been developed for combining qualitative and quantitative components of a scenario, the story-and-simulation (SAS) approach. However, the SAS methodology appears to have been designed with a particular kind of scenario exercise in mind, in which one or more models already exist, and the goal of the exercise is to match the assumptions driving the model with the storyline developed by a team of scenario narrative writers. While this is the dominant approach at the global level, at smaller scales, another type of scenario-development process predominates, in which a quantitative analysis is to be carried out, but the model does not already exist, or at any rate will not be identified prior to the scenario narrative development.
This chapter presents an approach to quantifying a scenario narrative for the second type of exercise. The particular focus in this chapter is on scenarios for sustainability assessment, and so the system being studied is a socioecological system. Scenario modelling for socioecological systems will always have an element of art to it, so that an automatic procedure is unlikely to ever be achieved. Nevertheless, a process can be put in place that makes the quantification exercise more coherent and manageable. The approach presented in this chapter has been developed over time in the course of their scenario modelling work by staff of the Stockholm Environment Institute and is called “indicator-driven development”.
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