This phrase goes back to Kemp-Benedict (2012).
As discussed in Section 2 (Subsection 2.3), two methods of storyline construction were mainly used in our sample: IL is the traditional approach, while CIB recently emerged as an alternative. Its proponents expect from CIB a more systematic and comprehensive storyline generation. IL builds on the intuitive capabilities of expert groups to design meaningful images of the future after discussing the relevant facts and trends (Huss and Honton 1987; Wilson 1998; Alcamo 2008). In contrast to IL, CIB requires experts to describe and code their insights about the system interdependencies on an integer scale and then proceeds with an algorithmic construction of qualitative scenarios or storylines.
CIB analyzes a discrete configuration space defined by a set of system variables (“descriptors,” typically 10–20) and a discrete set of alternative futures for each descriptor (typically 2–4). The alternative futures can be defined qualitatively, quantitatively, or a mix of both. This “morphological box” opens a space of typically thousands to billions of configurations or more, depending on the number of descriptors and assigned alternative futures. As a database for judging the internal consistency of configurations, a “cross-impact matrix” is built, answering the question of how a certain future of one descriptor would promote or restrict the development of a certain future of another descriptor. The impact usually is rated on a 7-point integer scale running from − 3 (strongly restricting) to + 3 (strongly promoting). Sources for the ratings can be literature review or expert/stakeholder judgments. The configuration space is searched, and only few configurations satisfying a self-consistency criterion are accepted to be “consistent scenarios.” Detailed descriptions are delivered by Weimer-Jehle (2006), Schweizer and Kriegler (2012), Weimer-Jehle (2018), and others. A short demonstration of CIB is included in the Supplementary Materials.
Given the diversity of motivations and application cases we found in Section 2, no storyline construction method can claim to be an ideal choice for each case. Well-informed choices presuppose a clear picture about the strength and challenges of the options, however. IL as a partner to environmental or energy modeling has long been applied and broadly discussed in literature (cf. Section 2). Consistent with the status of CIB as a relatively new technique, few compilations of the experiences of its use in socio-technical energy scenario construction are available. Hence, in this article, we focus on the discussion of CIB in the context of Story-and-Simulation applications and beyond, and collect and systematize statements found in literature pertaining to the strengths and challenges of CIB as a storyline generator. To allow a balanced view on the experiences that the scholars gathered from method applications and methodological research, we describe seven categories of strengths (Chapter 3.1) and seven categories of challenges (Chapter 3.2).
Deriving storylines of higher quality is a frequent motivation for recommending or employing CIB. The most common are references to narrative coherence and consistency (e.g., Girod et al. 2009; Schweizer and Kriegler 2012; Weimer-Jehle et al. 2016), claiming that CIB has advantages in “… ensuring internal consistency [of storylines] in the face of complexity …” (Lloyd and Schweizer 2014:2064). Scholars also refer to the advantage of providing formal consistency safeguarding in a way that keeps the process comprehensible (Wachsmuth 2015) and feasible to experts and non-experts, or as Lloyd and Schweizer put it: “… the [CIB] scoring algorithm takes pressure off any expert brains contemplating specific socioeconomic futures to test consistency, and puts it on computers that effortlessly check large numbers of scenarios for the desired consistency” (Lloyd and Schweizer 2014:2066). Kemp-Benedict concluded that the “… CIB method addresses this problem [storyline consistency], and Schweizer and Kriegler’s application of the method shows that even the best narrative-writing teams can benefit from this guidance” (Kemp-Benedict 2012:1).
Comprehensiveness of storyline sets
Storyline sets in exploratory scenario studies are expected to give an overview on the space of possible futures. However, several researchers have expressed concerns that intuitive approaches could easily miss important parts of the space, with potentially dramatic consequences in the case of high-stake decisions, while CIB can be expected to deliver more comprehensive sets of possible futures (Schweizer 2020; Weimer-Jehle et al. 2016). Lloyd and Schweizer (2014:2064) noted: “[While IL is] under-sampling the vast space of possible futures […] CIB can explore further than Intuitive Logics by uncovering unexpected combinations of possible outcomes.” A few existing case studies comparing IL and CIB scenario sets seem to confirm that CIB regularly generates futures overseen by IL (Schweizer and Kriegler 2012; CfWI 2014; Kurniawan 2018). Comprehensiveness is supported further by the option to generate large storyline sets, if required (Schweizer and O’Neill 2014; Pregger et al. 2019).
Other researchers reported added value from the opposite side of the problem. When Vögele et al. (2019) observed that “… without a CIB analysis, the range of possible futures might be misjudged,” they referred to a case where a purely combinatorial approach for putting together storylines would have produced misleading conclusions. The role of CIB in this case was not to extend, but rather to restrict the range of analyzed storylines to a meaningful set.
Transparency and other scientific criteria
Scholars comparing CIB to IL from an epistemological perspective point out that constructing scenarios by CIB is more transparent, traceable, reproducible, revisable, and, overall, more objective than the alternative IL approach (Lloyd and Schweizer 2014), making CIB more suitable for scientific development and assessment of socio-economic scenarios (ibid.). Analyzing the effects of CIB on Storyline-and-Simulation exercises, Kosow (2016:327) found that “[t] he central benefit for the participating experts is that the approach supports them in better analyzing, structuring and reflecting their assumptions, knowledge and ideas on possible future developments of our interdependent societies and environments.” Transparency, however, is not only an issue for scenario constructors: “For the external recipient users of the scenarios […], the central expected benefit [of using CIB] is that the assumptions on future uncertainty and complexity underlying different qualitative and quantitative or integrated scenarios become more accessible and critiquable. This is a prerequisite for credible and usable information—and might support the potential impact of combined scenarios in policy-advice” (ibid.).
Reduction of storyline subjectivity
Subjectivity is a natural ingredient of storylines if they are designed to express stakeholder visions. However, it presents a bias if storylines are expected to provide scientific analysis. Consistently, reducing (but not eliminating) subjectivity and increasing objectivity in storyline construction are a recurring argument in literature for applying or recommending CIB as a construction technique for qualitative scenarios and storylines (e.g., Wachsmuth 2015). Carlsen et al. (2017:613) noted that CIB, together with scenario diversity analysis and scenario discovery, “… can help scenario builders to be more scientific and neutral …” by providing well-defined step-by-step procedures and greater transparency, and by recognizing and systematically exploring uncertainty. Lloyd and Schweizer (2014:2085) summarize: “From a purely philosophical perspective, the CIB method clearly promotes an increase of objectivity ….” One way that subjectivity can express itself in storylines is wishful thinking, and some authors point to the capacity of CIB to confine this problem (Musch and von Streit 2017; Schweizer 2020).
Enabling additional insights
The prospects of additional insights conveyed by Storyline-and-Simulation approaches depend on the ability of the storyline construction method to perform a “qualitative systems analysis,” including aspects beyond the horizon of the models. CIB helps to take both qualitative and quantitative factors into consideration, which directly and indirectly influence developments (Venjakob et al. 2017; Vögele et al. 2019). Consequently, in Storyline-and-Simulation applications, CIB was found to be able to demonstrate “… significant interdependencies between societal developments and the energy transition” (Pregger et al. 2019). CIB-based storylines and their analysis by energy models “… also provide [ …] indications of possible drivers and risks related to the energy transition from which societal, economic, technological and structural conditions can interact in an amplifying or inhibiting manner” (ibid.).
CIB as a knowledge container
Scenarios or storylines are not the only product resulting from a CIB analysis. Its database, the cross-impact matrix, is a valuable result in itself because it makes the mental models of experts explicit and promotes focus on the most relevant factors and relationships, including those never considered before (Musch and von Streit 2017). The benefits of the database can be significantly enhanced by documenting the explanations of the experts/stakeholders about their cross-impact judgments (Weimer-Jehle et al. 2016). Verbal explanations enable analysts and recipients to decide on the soundness of the expert/stakeholder judgments and to understand the logics of the storylines.
CIB as a facilitator of inter- and transdisciplinary discourses
Socio-technical storylines are expected to represent relevant aspects and interdependencies beyond the traditional scope of energy modeling. To this aim, they should reflect an interdisciplinary or even transdisciplinary perspective, and the construction method should be able to handle the contributions of a broad range of experts and non-experts. IL clearly delivers on this scale. However, practitioners confirm that CIB, with skillful application, can also facilitate inter- and transdisciplinary discourses. Kemp-Benedict (2015:2) judged: “The CIB-method is well-suited to a workshop setting with participants who are knowledgeable, but not necessarily skilled at using models” and Pregger et al. (2019) reported: “[CIB] allows for close interdisciplinary collaboration between societal experts and energy modellers, leading to the identification of important new methodological insights and experiences regarding integrated scenario building ….” Venjakob et al. (2017:27–28) summarized: “[CIB’s] strength is to bring together inter- and transdisciplinary actors and enable them to design a shared and consistent picture of the future despite their different perspectives and topical foci [ …] CIB workshops [ …] convey possible future developments in a vivid way and offer an easy access to application also for laypersons.”
These advantages do not come without burdens and limitations. Method researchers and practitioners also reported challenges connected with CIB that should be considered when choosing and employing it for storyline construction.
Expenditure of time
Well-done CIB exercises require considerable time resources, which smaller scenario projects might find difficult to accommodate. Several authors comment on the trade-off between analytical added value and time resources: “While the CIB method is time-consuming, it provides valuable insights, and strengthens the study results” (Kemp-Benedict 2015:4), or “We may wish to say that this level of detail is actually the strength of the [CIB] approach; nevertheless, that does not change the fact that it is costly to achieve” (Lloyd and Schweizer 2014:2067).
Limited descriptor numbers
The typical size of CIB analyses means that analysis is performed on a highly aggregated level and system elements (“descriptors”) of secondary, yet not negligible, importance are excluded. Both issues limit the power of the descriptors to mutually explain their behavior and unskillful selection of descriptors and their alternative futures can compromise the results. Descriptor selection can also generate pressure on the expert consultation process, since the limitation of feasible descriptor numbers can lead to lengthy discussions among the experts about the best way to reduce complexity (Musch and von Streit, 2017). Furthermore, the limitation of feasible descriptor numbers means that CIB does not cure the general problem of socio-technical scenario exercises, i.e., the problem that storylines usually do not provide sufficient information to define the complete data set needed for model runs. Instead, they still require the modelers to make interpretations: Storylines, CIB-based or not, cannot determine all parameters of an energy model in a direct way. Parameters not explicitly addressed in the storylines have to be defined by the modelers before a model run can be started. Therefore, modelers “… must set such model parameters in accordance with the modeller’s interpretation of the ‘spirit’ of the context scenario” (Pregger et al. 2019). This can lead to uncertainties in the coupling of storylines and model analysis.
Consistency as a construction principle
CIB may exclude scenarios for good reasons because they presuppose internal inconsistent driver constellations. Yet their discussion—at least as possible visions—might have been fruitful and their exclusion may narrow down the cognitive range of the discourse about the future (Musch and von Streit 2017). To avoid this negative effect on vision discourses, participants can be encouraged to formulate visions beyond consistency considerations and CIB can be applied for identifying implementation obstacles (Weimer-Jehle et al. 2011:31–35).
Subjectivity not completely removed
Storyline construction, regardless of the method used, frequently makes use of data from a broad range of sources. Data may be based, in part, on objective findings or “subjective” expert assessment may be the only accessible source. In contrast to IL, CIB processes data with “… an air of rigour …” (Drakes et al. 2017:7) that can give rise to misinterpretations. A careful interpretation of the results is crucial. Kemp-Benedict (2012:2) expressed similar concerns when he wrote: “[T] he CIB method is [ …] 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.” Different worldviews may play out in diverging assessments of interdependencies, and asking an expert group for a consensus judgment may sometimes be merely a majority vote or a result of social power relationships between the group members rather than genuine knowledge integration (Musch and von Streit 2017). The additional effort of performing separated analysis of dissenting worldviews, as practiced, for instance, by Wachsmuth (2015), may be required in those cases.
Need to be aware of the limitations of the approach
The role of CIB in a Story-and-Simulation approach is processing qualitative information unfit for quantitative modeling. However, “… qualitative methods, like CIB, cannot substitute quantitative analysis” (Venjakob et al. 2017:28). CIB should not be used for analyses that can be performed better by models. Further, CIB builds on the analysis of interdependencies of pairs of factors. Therefore, it is challenging to include highly complex relationships in the analysis (e.g., third factors intervening in the relationship between two other factors; Pregger et al. 2019) and it needs substantial experience to properly address such interdependencies.
Experience and preparation matter
Generally speaking, a lack of experience with the method can give rise to quality risks in every working step of a CIB analysis. This applies not only to the researchers but also to the knowledge contributors. If experts or stakeholder participants are expected to collaborate through the scenario construction process without sufficient preparation on the purpose and workings of the method, improper assessments (Kosow 2016) or even resistance by some participants may occur (Drakes et al. 2017). A mixed reception to the method in expert groups was also reported by other researchers (Venjakob et al. 2017), who worked with two expert groups: one easily adapting to the method and one that was reluctant. Yet, overall, those experiences seem to be exceptions, with most application reports that provide a description of the expert elicitation process not mentioning such difficulties.
Need of combining CIB with other methods
CIB should not be misinterpreted as an “all-in-one” method organizing the complete process of storyline preparation. Rather, it is a tool for organizing the core of the process—the actual storyline construction. This implies that there is demand for supplementary methods supporting the process before and after construction, for instance, descriptor sampling (e.g., Biß et al. 2017), conversion, or diversity techniques when selecting scenarios in cases where CIB delivers a large solution set. Selecting appropriate method combinations can be key for developing full virtues of CIB. In the words of Kemp-Benedict (2012:2): “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.”