7. Theoretical Models, Case Studies and Simulation Games as Knowledge and Decision Tools

The scientific literature on climate mitigation and low-carbon transformation relies heavily on the use of scenario methods. The complexity as well the temporal dimension of relevant processes and outcomes require decision-makers to use decision tools to structure decision-making. For example, the reduction of emissions will require years if not decades before substantial effects will be significant. As such, present decision-makers and their constituents will most likely not personally experience the outcomes of their decisions. Similarly, some positive effects might take place in other areas leading to these effects becoming intangible for those who would need to accept present burdens of actions. Nevertheless, as it is assumed that delaying decisions or even avoiding making decisions will lead to more ‘painful’ and, therefore, unacceptable costs, decisions are still to be made. Because decision outcomes can be theoretical at best, while actions are practical, tools are needed to bridge the gaps between these two analytical levels. Inevitably, decision-makers are required to formulate visions or goals that would frame decisions, or even set or change paradigms. Goals have a temporal dimension as they imply achieving a specific status in the future. Therefore, formulating policy goals will require tools that address the need to ‘prepare’ for the future.

Scenarios are constructed to investigate alternative future developments under a set of assumed or controlled conditions. Scenarios are instruments to help decision-makers cope with problem cases that are complex and uncertain. As Nick Hughes (2009) observes, recent years have seen a proliferation in the use of scenario methods to define low-carbon futures. ‘Energy in a Finite World’ of the International Institute of Applied Systems Analysis is one of the first global studies that have used scenarios as a tool to assess future CO₂ emissions in the late 1970s (Häfele et al. 1981). The IPCC has used emissions and climate scenarios as a central component of its work as an institution that assesses climate change research. Working with authors and modelers, it has developed emission scenarios to be used for driving global circulation models to develop climate scenarios. The SA90 scenarios were introduced by Working Group 1 of the IPCC in 1990 (IPCC 1990).

Some questions that a scenario developer needs to address include how scenarios can provide insights not only into the integrated scope or description of the system being studied, but also how the system may have evolved into its current state and more importantly, how the system might continue to evolve, taking into consideration changing motivations and functionalities. What are the possible systemic effects of changes in the motivation and behavior of actors? This will most likely invoke the re-evaluation of various elements of the scenario, asking whether and how the actors could have decided to resolve these changes in a different manner. Asking these questions implies that scenarios have a normative undertone.

There are limitations on decisions based on scenarios. Scenarios often do not envisage changes in an actor’s preferences, as well as in relations between actors. Therefore, scenarios are often limited to providing insights on behavioral change and paradigm shifts that are highly valuable in any analysis of transformation processes. Because choices will depend on temporal and spatial circumstances, humans can change their decisions when circumstances change. As Michel Godet (2000) argues, humans can be both rational and driven by the emotional faculties. While scenarios are often not able to capture these changes, they are also often designed assuming the rationality of decisions and actions. In addition, as circumstances change, human interactions also change. Iterative actor interactions imply changing coalitions, behavior and identities. Scenarios, therefore, need to be complemented by tools that support the anticipation of changes in an actor’s preferences and relationships.

Furthermore, scenarios are often not able to properly address the dynamics of social, technological, and economic change. As social, technological, and economic changes unfold, new issues may arise, or old issues may gain a different meaning and significance, prompting the need to revise previous decisions. For example, the dynamics of change may imply a new importance on certain conflict cleavages that will touch on differences between actors in terms of their values and even ideologies. Because of this, the agreements and other outcomes of previously conducted bargaining games will require renegotiation and re-elaboration. In some cases, renegotiations will need to start by default. In other cases, particularly when the procedures of renegotiations were included in previous agreements, bargaining over new issues will be met by amendments or additional protocols to existing agreements.

One scenario is a collection of conditions and factors that jointly express a specific storyline. The storyline is heuristic in the sense that it employs an approach to problem-solving that does not guarantee or pursue ‘optimality.’ A scenario is an analytical instrument through which assumptions or ‘stories’ are made about the system under the conditions of limited knowledge and information. Therefore, a scenario inevitably pertains to a deviation from the ‘optimal solution’, as optimality presumes complete information. Nevertheless, it is not the objective of scenarios to present the optimal solution or the ‘perfect story’, but rather to look for a simple set of conditions sufficient to achieve immediate goals. This storyline serves as the structure in decision-making, limiting the number of possible decisions, thus, reducing indecisiveness. For example, the storyline of a scenario may highlight the necessity to adhere to human rights in present and future actions. It encourages decisions and actions by providing the scope of necessary actions and eliminating possible decisions and actions that do not correspond with the storyline. Nevertheless, the storyline, which is often a result of consensus-building processes, needs, as a normative approach, to be inclusive and should support the orchestration of various negotiation processes.

An ideal type can be regarded as a distinct type of model or scenario. One major difference between an ideal type and a model or scenario lies in its methodological implication. When a model or scenario depicts a historical or trajectory-based process, as is the case with the low-carbon transformation of energy systems, an understanding of purposes and the related causal interdependencies becomes necessary. Can properly identified indicators be adequately measured and interpreted? Are the benchmarks being fulfilled in order to achieve the predetermined purposes as the process unfolds? Is there room for tolerance and the adjustment of targets? Are there relevant self-driving dynamics that need to be addressed? The usage of ideal types allows researchers to focus on the individuality of actors, structures, issues, processes, and outcomes, without undermining the scientific validity of the results of analyses. It allows the ‘systematic’ management of indicators and benchmarks.

Similar to scenarios, ideal types can be identified as analytical instruments designed to strategically facilitate decision-making. Ideal types formulate hypotheses that are verifiable through the observation of empirical proceedings, providing resources for understanding patterns of actions, world views (Weltbilder), structural order and lines of development or trajectories. Ideal types are composed of basic concepts that define a “constructed world of ideas” or “idea constructs” (Gedankenbilder), with a distinct analytical functionality—to compare. Later, ideal types compel researchers to conduct a comparison between the constructed ideal type and the ‘historical case.’

The ideal type is a normative and heuristic model or scenario that does not need to emerge from the experiences of a successful case (e.g., developed countries). It does not need to include optimally technical solutions, because it can also refer to a case where conditions are merely tolerated for pragmatic reasons. As George Jellinek (1900) suggests, ideal types can only be manifested as incomplete as they disregard one or more parts of reality, limiting the complex characteristics of the case. The divergence between the ideal type and the historical case serves as leverage point for formulating questions and constituting explanations.

As a method of scientific inquiry and research, case studies have long assumed a well-established and prominent status in many academic disciplines (see McKeown 1999; Maiden 2004; George & Bennett 2007). While it is often thought that Frederic Le Play first introduced the case-study method into social science in 1829 (see Healy 1947), similar forms of case-study methods can be traced to earlier historical times, even to as early as Ancient Greece (see Lloyd 1992). Case study analysis involves a method that uses the up-close, in-depth, and detailed examination of an object of study (the case) in a specific spatial, temporal, cultural, social, political, and economic context. The usage of case studies in social science has often been chosen, particularly because of its descriptive value in explaining the outcomes or effects of variables. Nevertheless, the systematic further development of case study methods for the cumulative building of theories from various disciplines of social science is often neglected (see George & Bennett 2007).

Simulation as a method of scientific inquiry gained popularity in the 1990s as computer simulations were used to simplify and mimic complex systems. Considered the third pillar of empirical research, along with experiment and theory (see Drösser 1994), simulations are recognized as allowing the manipulation of complex systems that would be impossible, too costly or unethical to do so in natural systems (Peck 2004). Simulation can add to theory development and testing, can assist in formulating hypotheses and can limit the scope of which data are most important to gather (see Peck 2004; Gilbert & Troitzsch 2005).

This book does not intend to do computer simulations, but rather a so-called ‘role-play’ simulation. Particularly because this book highlights the aspects pertaining to functional, institutional, and bargaining interactions, a simulation was conducted using the role-pay format, where the participants were brought together to mimic interactions under conditions and parameters set by the game designer. For simplicity, when referring to simulations in this book, role-play simulations will be meant. In this book, more than 15 postgraduate students from the University of Technology in Kingston, Jamaica, participated in a role-play simulation. While the main goal of the simulation was educational, the role-play simulation was also designed to provide useful empirical insights into the analysis of sustainable, low-carbon transformation.

The knowledge and decisions tools introduced in this chapter can be particularly helpful in identifying and assessing obvious and latent stumbling blocks to effective collective decision-making. These tools can also provide impulses for how these stumbling blocks can be addressed to allow actors to cope with them. Major challenges and barriers to low-carbon system transformation are diverse. The first step of bridging the gaps between theory and practice is to ensure the fluidity of the relationship between the two. Theory generation should be dynamic, as reality becomes more complex with the emergence of new issues that require multi- and interdisciplinary as well as an integrated outlook to grasp their essence and meaning. As challenges and barriers are identified by policy-makers (covered here by the case studies) and academes (covered by the theoretical models), their perspectives combine, and new insights can be found that can allow more effective measures to facilitate the transformation process.