Telling better stories: strengthening the story in story and simulation

Abstract

The scenarios of the IPCC Special Report on Emissions Scenarios (SRES) (Nakicenovic and Swart 2000) are both widely cited and widely criticized. This combination of censure and regard reflects their importance, as they provide both a point of reference and a point of departure for those wishing to understand the long-term implications of policies and human activities for the climate and adaptive capacity. The paper by Schweizer and Kriegler in this issue (Schweizer and Kriegler 2012) reports a unique and interesting critique of the SRES scenarios. The authors find several results, including that the path the world may now be on (labeled by them ‘coal-powered growth’) is under-represented in the SRES scenarios. While such post-hoc critiques are easy to dismiss, Schweizer and Kriegler were careful to use only the information available to the SRES authors, and they applied a technique that (if it had been available) could have been carried out at that time. In this way they demonstrate that not only was coal-powered growth a clearly discernible possible future at the time of the SRES, but variants on the theme dominate the handful of highly consistent and robust scenarios as identified by their method. Their paper is well-timed because a new round of climate scenarios is now under development (Kriegler et al 2012, van Vuuren et al 2012), and it could learn from evaluations of the SRES process and scenarios. Schweizer and Kriegler (2012) construct a consistent scenario logic using a relatively new foresight technique, cross-impact balances (CIB) (Weimer-Jehle 2006). As explained above, to sharpen their critique and properly evaluate the method, they apply CIB to the information that the authors of the SRES had at their disposal at the time they constructed their scenarios. Their study is therefore anachronistic, in that the CIB method was not published when the SRES was released, but historically faithful in that Schweizer and Kriegler limit themselves to the information available at that time, based on statements that appear in the SRES itself. The CIB method is a technique for constructing internally consistent qualitative scenarios. Global-scale scenario exercises, in particular climate scenarios, typically include both qualitative (narrative) and quantitative (model) elements. As noted by Schweizer and Kriegler, the dominant method for such studies, which Alcamo (2001, 2008) formalized and named the ‘story and simulation’ (SAS) approach, relies at least in part on quantitative modeling to ensure consistency. Schweizer and Kriegler rightly criticize the idea that models alone can ensure consistency of a scenario narrative. By itself, this critique is not new. Indeed, if asked, both Alcamo and Raskin et al (Raskin et al 2005), whom Schweizer and Kriegler (2012) cite, would probably agree with them; both sources emphasize the need for qualitative storylines that go beyond what models can provide. However, Schweizer and Kriegler correctly point out that these sources provide little or no guidance to those responsible for the narratives beyond a dialog with the model outputs. The CIB method addresses this problem, and Schweizer and Kriegler’s application of the method shows that even the best narrative-writing teams can benefit from this guidance. While the paper of Schweizer and Kriegler makes a compelling argument for using CIB in global scenarios, it should be used in combination with other methods. A scenario exercise has several aims, of which consistency is one. Another important goal is diversity: given a set of internally consistent scenarios, a diverse set covers the space of possibilities, and thereby helps users of the scenarios avoid underestimating or overestimating the potential for change in one or another key factor (e.g., see (Carlsen 2009)). From this point of view, the SRES authors could legitimately respond to Schweizer and Kriegler’s finding that the SRES scenarios excluded interesting variants on coal-fueled growth by arguing that they did include some variants, and to include more would have conflicted with a legitimate goal of breadth. In this imagined dialog, Schweizer and Kriegler could concede the point, but then point out that several of the SRES scenarios were revealed to be either marginally or very inconsistent by their exercise. Thus, CIB and a technique that helps ensure breadth can usefully complement one another. The CIB method is also liable to a form of specification error, in that the worldviews of the people filling in the cross-impact table influence the results. This is a problem with many foresight techniques, but it is masked by the formalism of CIB, and there is a danger it will go unnoticed. For example, Schweizer and Kriegler’s paper suggests that the A1T2 scenario is (marginally) internally consistent. It has relatively low carbon emissions, low rates of population growth, very high GDP per capita growth rates, low primary energy intensity, very low carbon intensity, high fossil-fuel availability, global economic policy focus, and mixed global and regional energy policy focus. It has been argued by Jackson (2009) and Victor (2008), among others, that the evidence is slim that we ever will decouple carbon emissions from GDP to any meaningful extent. Thus, they would presumably argue that this is an inconsistent scenario, and might very well have done so at the time the SRES was written. That is not by itself a reason to reject the scenario, but it suggests that a CIB exercise could be run assuming the qualitative models implied by different worldviews, and the results contrasted. Such an exercise would go beyond the sensitivity analysis that Schweizer and Kriegler report in their paper. The cross-impact balance method should be a useful tool for constructing the next round of climate scenarios. It will be even more useful if combined with techniques that ensure a diversity of scenarios. This could include formal techniques such as ‘scenario diversity analysis’, which maximizes a quantitative measure of the spread of a set of qualitative scenarios defined by states of driving forces (Carlsen 2009). It could also include a survey of different worldviews, and the qualitative models that they imply, such as that carried out by Sunderlin (Sunderlin 2003). Futures studies has moved forward from the time the SRES was published, and new techniques are now available that can help us to tell better stories of the future. 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