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Developing qualitative scenario storylines for environmental change assessment Mark D. A. Rounsevell∗ and Marc J. Metzger This article reviews the historic evolution of qualitative scenario storylines and the various methods used in their development and application in environmental change assessment. The scenario method largely emerged from military strategy and war planning, with the techniques being adopted and advanced further by the business sector. Scenario storylines became widely applied to environmental problems from the 1970s and since then a number of new studies have been developed at both global and regional scales. Many different methods are used in scenario storyline development although most examples applied to environmental change assessment are exploratory and defined through a matrix logic that reflects different dimensions of environmental change drivers. This article discusses several development techniques for scenario storylines, provides examples of existing scenario storylines, discusses the differences between them, and highlights a number of limitations in the current scenario storyline development methods. The credibility, legitimacy, and saliency of future scenario storylines are discussed with respect to personal beliefs, the equifinality of alternative development pathways, the validation and uncertainty of assumptions, stakeholder engagement in visions development, and participatory methods. 2010 John Wiley & Sons, Ltd. WIREs Clim Change 2010 1 606–619
H
erbert Kahn’s phrase ‘The most likely future isn’t’ succinctly lays out the motivation and imperative for methods that allow us to explore the future. The phrase embodies the notion that what we think will happen in the future probably will not because the basis of our thought process is itself flawed: our thinking being limited by personal experience, prejudice, and other forms of bias. Scenario analysis has emerged as a means of characterizing the future and its uncertainties through structured, but imaginative thinking as a process that pushes us beyond the axioms and norms that are the constraints of conventional wisdom. Scenarios have been defined as1 : ‘. . . plausible and often simplified descriptions of how the future may develop based on a coherent and internally consistent set of assumptions about key driving forces and relationships’. Scenarios can be qualitative or quantitative or a mix of ∗ Correspondence to:
[email protected] School of Geosciences, University of Edinburgh, Edinburgh EH8 9XP, UK
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these. Storylines are the qualitative and descriptive component of a scenario, which create images of future worlds. They can reflect the assumptions within scenarios about the drivers of change, or they can describe the consequences or outcomes of a scenario. Although most storylines are based on written narratives, other forms of communicating images of the future are possible. This includes telling stories through imagery and animation,2 filmmaking (e.g., The Age of Stupida ), and model simulations or conceptual trend mapping.3 In the subsequent discussion we use the term ‘scenario storyline’ to refer to the qualitative component of scenarios. Scenario storylines have an important role to play when we have limited understanding of the causal relationships within a system that prevents quantification of these relationships in models. Although scenario storylines attempt to open our eyes to different ways of perceiving our world, they are not predictions and they do not seek truth. What they do try to achieve is to stimulate, provoke, and communicate visions of what the future could hold for us. They aim for creativity, rigor, internal coherence, and plausibility.
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As a tool, scenario storylines are more useful the further into the future we explore as uncertainties also increase and predictions become unsound. Scenario storylines are used increasingly in environmental change assessment as a means of exploring uncertainties about the consequence of human actions on the environment and the response of society to environmental change. Much of this effort has been driven by concern about climate change, as an exemplar of human–environment interactions with the work of the Intergovernmental Panel on Climate Change (IPCC)4 notable in refining the role of scenario storylines in environmental change assessment. In this article, we explore a wide range of methods for the development of qualitative, scenario storylines and their application in environmental impact, adaptation, and vulnerability assessments. In particular, we discuss how scenario storylines are used to make regional interpretations of global drivers, how they assist in highlighting uncertainties and in communicating complex transitions in future worlds. We focus on the three main challenges for the development of scenario storylines in environmental studies, notably their saliency (are the scenarios relevant to information needs?), credibility (are the scenarios scientifically sound?), and legitimacy (who developed the scenarios and how?).5
HISTORIC CONTEXT OF SCENARIOS TO EXPLORE GLOBAL CHANGE Human interest in the future has a long history that can be traced back to the writings of early philosophers, such as Plato’s description of his ideal Republic and visionaries from Thomas More to George Orwell.6 However, formal scenario techniques were first developed by military strategists and the first documented outlines of what we now regard as scenarios can be found in the 19th century writing of the Prussians Von Clausewitz and Von Moltke.7 Modern day scenario techniques emerged in the post-war period. In order to guide the development of new weapons systems in the emerging cold war, Herman Kahn and colleagues developed scenarios for the US Air Defence System Missile Command at the Rand Corporation in the 1950s.7 Their methods, referred to as Intuitive-Logic Models,6 have since dominated scenario development. In the 1960s, Kahn began to apply his scenario methodology to social forecasting and public policy, publishing his book The Year 2000: A Framework for Speculation on the Next Thirty-Three Years in 1967.8 It has become a landmark in the field of scenario planning9 by providing one of the earliest definitions of scenarios, demonstrating their use as a Volume 1, July/August 2010
tool for exploring complex and uncertain domains, and generating controversy that spawned numerous counter studies,6 including the Club of Rome Reports The Limits to Growth10 and Mankind at the Turning Point.11 Although initially in the domain of public policy planning, it was not long before the business community adopted these techniques, notably Royal Dutch Shell. Its profitable navigation through the 1970s oil crisis has become a classic story of the usefulness of scenario planning.6,12,13 Strategic planners at Shell used scenarios to integrate disperse investigations of the global oil markets and concluded that at some volume of production oil was more valuable kept in the ground than sold. This led to one scenario in which a coalition of oil-producing countries was able to limit production, causing oil prices to rise. This scenario was considered at the time to be radical, but plausible. The scenario exercise led Shell to adjust its business management and hedge against a potential for high oil prices by increasing the efficiency of its refining and shipping operations. Consequently, Shell was able to adapt to expensive oil much faster than its competitors.12,13 By the 1980s, scenario planning was a well-established planning tool in large corporations, with 75% of the Fortune 100 companies reporting using scenario techniques in 1981.14 Meanwhile, scenarios remained an important tool in public policy planning. The most profound example is perhaps the role scenarios played in shaping the debate and influencing the agenda for political change in South Africa. The 1984 scenarios developed for the Anglo American Corporation, a global mining company, explored future uncertainties that would affect its South African operations. Its domestic scenarios illustrated how a bleak ‘low-road’ scenario of escalating violence could propel South Africa into an economic wasteland, while appropriate action could result in a ‘high-road’ scenario with democratic welfare and economic growth.15,16 Consequently, Anglo executives decided to share their views and insights via ‘road show’ lectures reaching ‘25,000 to 30,000 people from all walks of life’, as well as a book17 that became a national best seller. Two further scenario studies for South Africa followed in the 1990s: the business-led Old Mutual/Nedcors scenarios and the much discussed Mont Fleur scenarios. The latter were based on a series of joint workshops with a diverse group of leaders from South Africa’s business, political, and civil society coordinated by the University of Cape Town and facilitators from Shell.15,16 Although it is difficult to assess the real contribution of these scenario studies to socioeconomic reform in South Africa, they have helped in breaking prevailing
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paradigms and in stimulating discussion about change, in both government and society.16 Emerging concerns about the earth’s long-term carrying capacity led to the first wave of global environmental scenarios, including the first mathematical models developed by the Club of Rome10,11 as well as speculative narratives.18 After a brief lull, a second wave of integrated global analyses followed the Brundtland Report19 and the 1992 Rio World Conference on Environment and Development. The Limits to Growth was updated20 and the first integrated models of climate change impacts were developed.21,22 Although there were advances in these new models, the credibility of numerical models remains limited to simulating well-understood systems over sufficiently short times. Conversely, new narrative scans of alternative futures23,24 provide saliency, but lacked the structure and rigor of the quantitative approaches. The Global Scenario Group (GSG), convened in 1995, realized that complementing quantitative modeling techniques with qualitative scenario exploration would provide a broader perspective than is possible from mathematical modeling alone.25–27 The GSG scenarios have since been adopted for a range of global environmental assessments including the UNEP Global Environmental Outlook.28 Furthermore, combining rich qualitative storylines with quantitative modeling techniques, known as the storyline and simulation approach,29,30 has become the accepted method for integrated environmental assessment and has been used in all major assessments including those by the IPCC,4 Millennium Ecosystem Assessment,1,31 and many regional and national studies.29,32 All the scenarios discussed above followed the Intuitive-Logic model, initially developed by Kahn.8 Although this forms the most widely used method, even referred to as the ‘gold standard of scenario development’,33 there are many other techniques.34–36 An early contrasting school of scenario development formed the La Prospective model, stemming from the French philosopher Gaston Berger’s work in the 1950s. It focused on developing positive (or normative) images of the future that would provide a guiding vision for policy makers. While the intuitivelogic model aims to provide understanding of broad trends and processes, La Prospective focuses on specific strategic decisions and tactical plans.6 However, the foremost difference between both scenario models is in the use of probability. La Prospective identifies the most probable scenario as well as illustrating less probable upper and lower limit scenarios using numerical analysis of a broad range of potentially relevant factors based on experts’ views.37 Although these methods have proved successful in strategic decision 608
making in public policy and business,38 they may be less appropriate for global environmental change science due to their narrow scope.
METHODS IN SCENARIO STORYLINE DEVELOPMENT Types of Scenarios The literature contains a large number of different and at times conflicting definitions, characteristics, principles, and methodological ideas about scenarios,6 which has led to the development of many scenario typologies.6,39,40 The environmental sciences tend to apply one of three different concepts in scenario storyline development, which are termed here exploratory, normative, and business-as-usual (BAU). Exploratory scenario storylines relate most closely to the intuitive-logic model discussed above. They describe plausible, but alternative socioeconomic development pathways that allow scenario analysts to compare across a range of different situations, generally from 20 to 100 years into the future. Exploratory scenario storylines typically adopt a coevolutionary stance41,42 in which multiple assumptions about different development pathways lead to potentially very different outcomes over long-term time horizons. Although this is the most common use of exploratory scenario storylines, they can also be used to identify different development pathways that lead to similar or converging scenario outcomes. This effect, known as equifinality, can provide valuable insight into the robustness of a given future with respect to alternative scenario storyline assumptions. Equifinal scenario outcomes are rarely discussed in the literature. An important example of exploratory scenario storylines in environmental assessment is the IPCC Special Report on Emissions Scenarios (SRES).4 Normative scenario storylines are framed around desired futures or outcomes and in this sense have parallels with La Prospective School of scenario thinking. The storyline itself is a description of the series of events and causal relationships that lead from the current world condition to the desired future world. Inherent to this way of thinking is that very different pathways may exist that converge on the same desired outcome. Policy scenarios are often normative, for example, the Convention on Biological Diversity target of achieving a significant reduction in the current rate of biodiversity loss at the global, regional, and national level by 2010 (www.cbd.int/2010target), the Millennium Development Goals by 2015 (www.un.org/millenniumgoals), or the European Union’s target of 20% renewable energy consumption
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FIGURE 1 | The five generic stages in scenario development.46
by 2020 (ec.europa.eu/agriculture/bioenergy/index en. htm). This reflects the needs of policy to achieve desired outcomes over relatively short-time horizons within a decision process that is often mediated by the political context. BAU scenario storylines (sometimes termed conventional wisdom or extrapolations) are typically used in short-term policy analysis to explore the consequences of relatively well-known, near-term changes in regulatory contexts.3 They assume that broader trends will have little influence on future worlds because over short-time horizons the policy effects are thought to dominate. The value and plausibility of these scenario storylines decrease through time into the future, although they are commonly used to define a reference case against which other types of exploratory or normative scenario storylines are compared.3,43 Although business management textbooks discuss scenario methods44,45 and provide an in-depth overview of the practice of environmental scenario analysis,32 explicit descriptions of the methods are surprisingly rare in the scientific literature and can lead to confusion, especially when concepts are also poorly defined. Figure 1 provides a summary of five generic Volume 1, July/August 2010
stages in scenario development46 as used consistently throughout this article.
Drivers and Scenario Logics in Exploratory Scenarios In exploratory scenarios, storylines describe the qualitative assumptions about drivers. Drivers are the underlying causes of change, which derive from a number of broad categories sometimes referred to as STEEP: Social, Technological, Economic, Environmental, and Policy governance.6 Storyline assumptions and the relationships between different drivers are structured and described within what is commonly termed the ‘scenario logic’. The scenario logic provides order to a range of potentially divergent issues and in doing so allows comparison across different narratives. Importantly, the scenario logic seeks to establish internal consistency between the various statements and assumptions that underpin a storyline. The most commonly used method for the construction of scenario logics in environmental change assessment is the matrix approach, with the IPCC SRES storylines4 being perhaps the best known and widely used example. The SRES storylines are framed around
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two axes that reflect alternative future world orientations. One axis describes regional versus global strategies that reflect alternative visions of global connectivity and attitudes to geopolitical cooperation. The other axis describes a world based on policy liberalization and free markets in contrast to a world that attaches high importance to environmental and equity issues. The axes reflect both a continuum of possibilities along each axis, but also an enormous number of possible scenarios that derive from different combinations of the two axes within the scenario space created by the matrix. In practice, the axes create four quadrants that reflect scenarios ‘families’ with similar, but variable attributes and these families are often used in subsequent analyses. Given the inordinate number of scenario possibilities that exist within each family, ‘marker’ scenarios are commonly used as an exemplar of a scenario family. The four SRES marker scenarios are labeled: A1 (global economic), A2 (regional economic), B1 (global environmental and equitable), and B2 (regional environmental and equitable). One of the major criticisms of a matrix approach is that the axes create polarization in thinking about concepts that are not necessarily mutually exclusive. For example, the economy–environment axis of the SRES storylines excludes worlds that apply free market solutions to environmental problems or environmentally orientated worlds that support strong economic growth. Although being the commonly applied method, the matrix approach is not a prerequisite for the creation of exploratory scenarios. Several examples exist of storylines that simply reflect alternative world visions based on specific themes for which there is no cross-referencing as with the matrix dimensions.1,2,47,48 Such approaches commonly involve participatory approaches that seek to avoid the constraints that a matrix would impose on the imagination of participating stakeholders. Participatory approaches may also be used, however, to identify the key uncertainties to be explored in a matrix approach.49
Scale Issues A wide range of global scenario storylines have been developed for environmental assessment in different application domains (see Appendix 1 for a list of the principal examples). Global storylines are used increasingly to define the boundary conditions for regional environmental change assessments in which regional narratives are interpreted from the global storylines. The global to regional interpretation process commonly involves a description of drivers that 610
are more pertinent to the scale level of analysis. This reflects that different human and physical processes occur at different scale levels and that stakeholder interests also vary across scales.50 Examples of regional storyline interpretations from global scenarios include the ATEAM51 , EURuralis52 , and ACCELERATES53,54 scenarios, which were all to some extent based on interpretations of the SRES narratives. These interpretations tend to disaggregate the global scenario storyline assumptions in geographic terms (all the above examples were undertaken for Europe), but also in thematic terms, for example, regional studies may focus on specific sectors such as agriculture53,55 or health,56 or disciplinary domains such as demography or sustainable development.57–59 Appendix 2 provides a summary of published, regional scale scenario development exercises. These examples are striking in highlighting the regional disparities in scenario development with most studies undertaken in Europe and Africa, some in Latin America, but nothing for the rest of the world.
Participatory Approaches Although most scenario storylines use the expert judgment of scenario analysts, participatory approaches based on stakeholder elicitation seek to broaden the knowledge sources that contribute to storyline development.60–63 The EEA Prelude scenarios, for example, used a facilitated stakeholder process in which the participants themselves had complete control over the storyline development process.2,61 Careful facilitation and planning are crucial44,45,64 and can lead to important and surprising insights into the storylines that contribute to the design of policies better suited to serve the needs of those concerned.60 These social learning methods can thus provide immense saliency and richness to scenario storylines, and become a vehicle for consensus building and problem solving.65,66 It also enhances the legitimacy of the resulting scenarios in the eyes of participating stakeholders.61,65–67 The credibility of participatory approaches is, however, limited by a potential lack of diversity among participating stakeholder groups,65 differences in epistemologies or knowledge systems and thus in perceptions between stakeholders,67 and problems with internal consistency. Participating stakeholders do not always have a complete mental model of the system that is being described within a scenario storyline, and this limits understanding of the system interrelationships and feedbacks. As internal consistency in storyline assumptions is fundamentally important in scenario development,44 novel approaches have been proposed
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to improve the consistency of narrative storylines that use participatory expert judgment. Attempts have been made68 to introduce rigor into the interpretation of regional scenario storylines from global scenario storylines using a pairwise comparison approach69 to maintain the internal consistency of storyline assumptions across scenarios. The approach was based on an inconsistency analysis using Saaty rankings to ensure that the individual qualitative statements of participating experts were consistent with all other statements made by the same expert in describing a scenario storyline. Fuzzy cognitive mapping (FCM) has also been used as an aid in developing more structured narratives that better incorporate important feedbacks as well as making more explicit the system level understanding of the storyline developer.70 Both pairwise comparison and FCM also support the translation of narratives into model input parameters for subsequent scenario quantification.
Normative Scenarios and Bayesian Belief Networks There are several examples of normative scenario storylines applied to environmental assessment.3,71 Normative scenarios use ‘backcasting’ techniques that identify the events leading from the current situation to a desired future outcome, and techniques that attach probabilities to these events have been developed using Bayesian belief networks (BBNs).72,73 BBNs provide a framework for graphically representing the logical relationships between variables and for quantifying the strength of these relationships using conditional probabilities.74 BBNs have been used in a range of land use change studies focusing on both biophysical75,76 and coupled social–ecological systems,77,78 providing an appropriate framework for stakeholder consultation and decision support systems by explicitly quantifying parameter uncertainty.77 BBNs are, however, acyclic so that temporal dynamics and feedback loops cannot be easily implemented.73 To do this, separate networks are required for each future time slice.79 Furthermore, BBNs work best with discrete variables, requiring the difficult task of discretization of continuous environmental data,79 although Bayesian hierarchical simulation-based modeling can provide a solution to this problem.73 Recent work has nevertheless demonstrated the potential of BBNs in the construction of sustainability scenario storylines71 that facilitated stakeholder elicitation and communication, using non-deterministic relationships derived from expert judgment and model parameterizations based on probability distributions rather than single values.71 Volume 1, July/August 2010
Exploratory Probabilistic Futures Although Bayesian methods are explicitly probabilistic, most exploratory scenarios and storylines are deterministic. They define absolute values for each scenario parameter assumed from an interpretation of a single storyline description. Uncertainty is reflected in different parameter values between scenarios, but not in the parameter assumptions made for a given scenario storyline. In practice, however, great uncertainty surrounds any qualitative assumptions about the future. The method of conditional probabilistic futures has emerged as a means of quantifying uncertainty ranges in scenario storyline assumptions. Examples of this method have been reported for demography and greenhouse gas emissions80,81 and the method is being applied to climate change scenarios. Conditional probabilistic futures are explicit about the uncertainty surrounding a scenario parameter using probability distributions functions (PDFs). Technically, various Monte Carlo based sampling techniques may be used to generate quantitative scenarios of different variables from the PDFs using models, but the theoretical foundation for the definition of uncertainty ranges reflected in PDFs remains tenuous. At best, these are expert judgments derived from scenario storyline interpretations; at worst, speculation. Most environmental scenario storylines, whether exploratory, normative, or BAU reflect trends in drivers that steadily evolve into the future, despite discussion of the importance of surprise events in the literature, dating back to 1967.8 They seldom account for shocks that might create bifurcations or path dependency in future socioeconomic trajectories, although the MA does discuss potential catastrophic changes and following responses within the scenario context.1 Shocks reflect what are potentially high impact events, but with a low probability of occurrence. Some scenario storyline studies have attempted to address shocks, notably the scenario storylines developed by the ALARM project.47,82 These exercises are framed around thought experiments of what might happen to human behavior when confronted with extreme changes in environmental conditions.
CHALLENGES AND LIMITS Differences Between Existing Scenario Storylines Many of the existing global and regional scenario storylines map onto one another on the basis of similarities in assumptions and the dimensions of the matrix axes. However, there is often a large divergence between studies even within the same scenario
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storyline group.83 In some cases, even the direction of change varies for the same scenario storyline83 and clearly this has implications for the credibility and legitimacy of these studies. It is worth noting, however, that the divergence between individual studies often appears to depend most on whether the scenario storylines were constructed at the global or regional scales. Regional scenarios are often consistent with one another, whereas global studies are often more divergent.83 These differences are likely to arise because the assumptions that underpin the interpretation of regional scenario storylines are different from those for global narratives. Uncertainties are also exacerbated by the use of different models to generate quantitative scenario outcomes. Which of these sources of uncertainty is the more important is difficult to gauge. However, it is apparent that storyline assumptions, as interpretations based on judgments, are highly dependent on the subjectivity of different scenario analysts. This can reflect different levels of knowledge, different world views and perspectives, or semantics.
The Limits to Knowledge Scenario storyline assumptions are limited by absolute knowledge uncertainties. There are simply environmental change processes that we know little or nothing about. There are also issues of relative knowledge uncertainties between different scenario analysts. In practice the level of knowledge as well as the experience of each individual within a scenario storyline development process will affect the outcomes of the exercise. These differences may reflect simple knowledge gaps or a broader issue of the social–cultural context. Some individuals may simply be unaware of different issues or refuse to believe them. The subjective nature of such relative differences is in practice impossible to avoid, but can be mitigated by scenario developers being transparent in expressing and reporting scenario storyline assumptions. Scenario analysts are also often subject to ‘axiomatic preconceptions’ and norms in which assumptions become accepted as ‘truths’. An example of an axiomatic preconception is the commonly cited relationship between political liberalization and innovation. Within the SRES storylines, for example, free markets (as in the A1 family) are assumed to lead to more rapid rates of technological development. There is certainly some evidence for this in reality, but other examples where the relationship does not appear to stand up to scrutiny, e.g., the French car industry, have been highly innovative, but also subsidized.84 Again, axiomatic preconceptions depend strongly on the experiences of the individual scenario analyst and their sociocultural context. 612
Semantics also influence the subjective nature of scenario storyline interpretations. For example, the SRES storylines distinguish between global and regional scales, but is a region a continent, a country, or a sub-national territory? This definition will lead to very different interpretations of what ‘regionally orientated’ means within an SRES storyline.
Validating Scenario Assumptions Validating scenario assumptions is problematic. Returning to the above example: How is it possible to validate the effect of free markets on the rates of technological development? Traditionally, scientific methods involve testing a hypothesis against observations, and quantitative models follow this procedure by validating model outputs against observed data. However, many scenario assumptions refer to worlds that might exist in the future (plausibly), but which have never existed in the past. Thus there is no empirical data against which these assumptions can be tested. A simple example of this is the plausible scenario of dismantling the support mechanisms provided to European agriculture by the Common Agricultural Policy. Such a free market situation has not existed in Europe (at least not over the period for which observed data exist) and against which the consequences of a free market scenario could be tested. Moreover, the past (as the source of observation) is just one realization of many pasts that could, but did not, occur. So, how can we verify our assumptions about future worlds that are very different from anything we have observed in the past? One possibility is to derive validation data sets from geographic analogs instead of the traditional temporal analogs. By finding more than one region with similar characteristics, but differences in a key driver (e.g., the policy context) it should be possible to understand the effects this driver has on specific environmental outcomes. In practice, however, it is difficult to find appropriate geographic analogs with sufficiently similar characteristics.
Judgment in Scenario Development
Traditional scenario methods1,51,85 work well when the processes that are influenced by the storylines are well understood and can be adequately quantified. However, when processes are less well understood, as is the case for social processes and policy implications, interpretation and judgment become increasingly important.46 Here, set paradigms and ideologies have an influence and personal values and beliefs that affect scenario outcomes should be made explicit, especially where scenario storylines are used to inform policy evaluations.86 A consistent analysis of the influence
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of personal or political judgment in scenario interpretation is possible when alternative scenario storylines are interpreted from contrasting perspectives through explicit ‘belief ranges’.46 Identifying belief ranges can help to pinpoint the underlying processes leading to alternative scenario storyline outcomes. The challenge here is also to develop quantitative modeling techniques that are sufficiently flexible to be able to quantify judgment-related variations within alternative scenarios. There is also a strong need for greater transparency in reporting scenarios that outlines and reflects on important assumptions that may be open to political interpretation.87 This is currently rarely done.
Scenarios in Support of Policy Scenarios can be seen either as ‘products’ that may be the basis of decision making or as learning processes88 and both approaches have value in support of policy. Scenario-based exercises are useful in providing the potential for policy makers to visualize future worlds and to help guide and develop adaptive strategies.87,89 Scenario storylines can support public policy in much the same way that Shell and other private sector companies have used scenarios to explore the implications of alternative business strategies. The process of raising awareness and promoting creative and imaginative thinking about more distant futures can help plan robust
and flexible policy measures to deal with change.89 Exploratory scenario storylines, however, tend to be static representations of alternative worlds that are poor at dealing with the response (adaptation) of people to changing circumstances. The IPCC SRES scenarios, for example, do not include climate policy as a driving force and consequently are of limited value for exploring the effects of alternative policy options in support of climate mitigation. A number of scenario methods are being used by policy advisors and there is a role for scenario exercises in planning and communications work to develop imaginative thinking.89 There are, however, key barriers to the use of scenario storylines in policy arenas, including: the short-term nature of policy cycles and timescale, the difficulty that individuals have in imagining different futures, limited documentation of the scenario storyline development process, a lack of clarity about the purpose of a scenario exercise, and limited relevance (or saliency) to specific policy details.89
COMPARATIVE SUMMARY OF SCENARIO STORYLINE METHODS The scenario qualities of saliency, credibility, and legitimacy5 provide an appropriate framework for the comparison of various scenario storyline development methods discussed above (Table 1). There can be
TABLE 1 Comparison of Scenario Methods Regarding Their Saliency, Credibility, and Legitimacy, Based on Definitions in Ref 5 Scenario Storyline Method
Saliency (Relevance for Decision Makers’ Needs)
Credibility (Scientific Adequacy of the Methods and Evidence)
Legitimacy (Unbiased Incorporation of Divergent Values)
Exploratory
Medium. Compromised when the focus has low policy relevance
Medium. The most applied method, described in a vast body of research
Low-Medium. Strongly dependent on the beliefs of the scenario analysts involved
Normative
High. A focus on specific desired futures
Low-Medium. Difficult to address uncertainties in trajectories toward the desired future
Low-Medium. Strongly dependent on the visions of the scenario analysts involved.
Business-as-usual
High. Usually directly policy relevant and based on extrapolation
Low. Based on current processes without alternatives and uncertain developments
Medium-High. Based on current values and beliefs
Participatory
High. Shaped by relevant stakeholders
Low-Medium. Limited by the lack of stakeholder mental models and internal consistency
Medium-High. Stakeholders consulted, but dependent on those individuals
Probabilistic
Low-Medium. Increases complexity leading to communication difficulties
High. Based on formal representation of uncertainty
Low-Medium. May be affected by communication issues due to complexity
Scaling methods
High. Increasing the spatial and thematic resolution enhances the relevance to local stakeholders
Low-High. Depends on whether scaling introduces new process information or is simply a graphical representation
Low-Medium. Depends on stakeholder acceptance of methods
The low, medium high classification reflects the opinions of the authors; the scenario storyline methods given in this table are not necessarily mutually exclusive, but follow the structure of the discussion above.
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1. The influence of personal beliefs such as political ideologies should be made more explicit in scenario development and reported transparently to improve credibility; 2. Alternative pathways that result in the same (equifinal) future outcomes should be discussed and compared; 3. Better methods to validate scenario assumptions such as the use of geographic and temporal analogs would enhance the credibility of scenario storylines; 4. Further consideration needs to be given to the uncertainty that surrounds storyline assumptions and the implications of this for quantitative scenarios, e.g., conditional probabilistic futures and Bayesian approaches; 5. Stakeholder engagement should be used to better define normative visions of future worlds and the alternative development pathways to achieve these visions; 6. Participatory methods, with their high saliency and legitimacy, merit wider application, but should be developed further to increase their credibility.
trade-offs between these attributions (e.g., increasing scientific credibility can reduce the saliency for policy), and multiple audiences can have different views on the scenario development process and outcomes. Nevertheless, Table 1 summarizes the basic characteristics of the scenario storyline methods that were discussed previously using these attributes. It demonstrates that no perfect scenario storyline method exists for all situations and this in part explains the diversity of methods used in environmental change studies. Each method has its strengths and weaknesses and the choice of a particular method depends very much on the context and aims of a particular study.
CONCLUSIONS There is a long history of the use of scenarios and storylines across a range of sectors, but only a relatively short history of their use in environmental change assessment, notably since the 1970s, but more so since the 1990s. Environmental studies have tended to focus on exploratory scenarios using the matrix logic approach. This does not use the full diversity of storyline development methods. Environmental change assessment would benefit greatly from the methodological experiences of other sectors, principally business. In conclusion, we have identified the following gaps in scenario storyline development methods, which if addressed could enhance the credibility, legitimacy, and saliency of future environmental change assessment:
NOTE aA
2008 film by Franny Armstrong about a man living alone in a devastated future world in 2055, looking at old footage from 2008 and asking: why didn’t we stop climate change when we had the chance?
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5. Cash DW, Clark WC, Alcock F, Dickson NM, ¨ Eckley N, Guston DH, Jager J, Mitchell RB. Knowledge systems for sustainable development PNAS 2003, 100:8086–8091. 6. Bradfield R, Wright G, Burt G, Cairns GV, der Heijden K. The origins and evolution of scenario techniques in long range business planning. Futures 2005, 37: 795–812. 7. von Reibnitz U. Scenario Techniques. Hamburg: McGraw-Hill GmbH; 1988. 8. Kahn H, Wiener AJ. The Year 2000: A Framework for Speculation on the Next Thirty-three Years. New York: Macmillan; 1967. 9. Raubitschek R. Multiple scenario analysis and business planning In: Lamb R, Shrivastava P, eds. Advances in Strategic Management. Vol. 5. London: JAI Press Inc.; 1988.
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Available at: http://www.gsg.org. (Accessed March 22, 2010). 28. UNEP (United Nations Environment Programme). Global Environmental Outlook. London: Earthscan; 2002. 29. EEA. Scenarios as Tool for International Environmental Assessments. Environmental issue report No. 24. Copenhagen: European Environment Agency; 2001, 31. 30. Alcamo J. The SAS approach: combing qualitative and quantitative knowledge in environmental scenarios. In: Alcamo J, ed. Environmental Futures: The Practice of Environmental Scenario Analysis. Amsterdam, The Netherlands: Elsevier; 2008, 123–150. 31. Carpenter SR, DeFries R, Dietz T, Mooney HA, Polasky S, Reid WV, Scholes RJ. Millennium ecosystem assessment: research needs. Science 2006, 314: 257–258. 32. Alcamo J, ed. Environmental Futures: The Practice of Environmental Scenario Analysis. Amsterdam: Elsevier; 2008, 224 pp. 33. Millett S. The future of scenarios: challenges and opportunities. Strategy Leadersh 2003, 31:16–24. 34. Bishop P, Hines A, Collins T. The current state of scenario development: an overview of techniques. Foresight 2007, 9:5–25. 35. Borjeson L, Hojer M, Dreborg I, Ekvall T, Finnveden G. Scenario types and techniques: towards a user’s guide. Futures 2006, 38:723–739. 36. Van Notten PWF, Sleegers AM, van Asselt BMA. The future shocks: on discontinuity and scenario development. Technol Forecast Soc Change 2005, 72:175–194. 37. Godet M, Roubelat F. Creating the future: the use and misuse of scenarios. Long Range Plann 1996, 29: 164–171. 38. Godet M. Integration of scenarios and strategic management: using relevant, consistent and likely scenarios. Futures 1990, 22:730–739. 39. Van Notten PWF, Rotmans J, Van Asselt MBA, Rothman DS. An updated scenario typology. Futures 2003, 35:423–443. 40. Wilkinson A, Eidinow E. Evolving practices in environmental scenarios: a new scenario typology. Environ Res Lett 2008, 3:045016. 41. Lorenzoni I, Jordan A, Hulme M, Turner RK, O’Riordan T. A co-evolutionary approach to climate change impact assessment. Part I. Integrating socioeconomic and climate change scenarios. Glob Environ Change 2000, 10:57–68. 42. Lorenzoni I, Jordan A, O’Riordan T, Turner RK, Hulme M. A co-evolutionary approach to climate change impact assessment. Part II. A scenario-based case study in East Anglia (UK). Glob Environ Change 2000, 10:145–155.
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43. Kuhlman T. Scenarios: driving forces and policies. Helming K, Perez-Soba M, Tabbusch P, eds. Sustainability Impact Assessment of Land Use Changes. Berlin, Germany: Springer; 2008, 131–157. 44. Schwartz P. The Art of the Long View, Planning for the Future in an Uncertain World. Chichester: John Wiley & Sons; 1998. 45. Van der Heijden K. Scenarios: The Art of Strategic Conversation. 2nd ed. Chichester, UK: John Wiley & Sons; 2005. 46. Metzger MJ, Rounsevell MDA, Van den Heiligenberg HARM, Perez-Soba M, Soto Hardiman P. How personal judgement influences scenario development: an example for future rural development in Europe. Ecol Soc 2010. Available at: http://www.ecologyandsociety. org/vol15/iss2/art5/. 47. Spangenberg JH. Integrated scenarios for assessing biodiversity risks. Sustainable Dev 2007, 15:343–356. 48. Reginster I, Rounsevell MDA, Riguelle F, Carter TR, Fronzek S, Omann I, Spangenberg JH, Stocker A, Bondeau A, Hickler T. The effects of alternative socioeconomic and environmental policies on European land use from 2006 to 2080. Land Use Policy. In press. 49. Foran T, Lebel L. Informed and fair? Water and trade futures in the border regions of mainland southeast Asia. USER Working Paper WP-2007-02 Unit for Social and Environmental Research, Chiang Mai University, Chiang Mai, 2007. Available at: http://www.mpowernet.org/download pubdoc.php? doc=3730. (Accessed March 22, 2010). 50. Zurek M, Henrichs T. Linking scenarios across geographical scales in international environmental assessments. Technol Forecast Soc Change 2007, 74: 1282–1295. ¨ ´ 51. Schroter D, Cramer W, Leemans R, Prentice IC, Araujo MB, Arnell NW, Bondeau A, Bugmann H, Carter TR, Garcia CA, et al. Ecosystem service supply and human vulnerability to global change in Europe. Science 2005, 310:1333–1337. 52. Westhoek HJ, van den Berg M, Bakkes JA. Scenario development to explore the future of Europe’s rural areas. Agric Ecosyst Environ 2006, 114:7–20. 53. Audsley E, Pearn KR, Simota C, Cojocaru G, Koutsidou E, Rounsevell MDA, Trnka M, Alexandrov V. What can scenario modelling tell us about future European scale land use, and what not? Environ Sci Policy 2006, 9:148–162. 54. Berry PM, Rounsevell MDA, Harrison PA, Audsley E. Assessing the vulnerability of agricultural land use and species to climate change and the role of policy in facilitating adaptation. Environ Sci Policy 2006, 9:189–204. 55. Rounsevell MDA, Ewert F, Reginster I, Leemans R, Carter TR. Future scenarios of European agricultural land use. II: projecting changes in cropland and grassland. Agric Ecosyst Environ 2005, 107:117–135. 616
56. UNAIDS. Aids in Africa: three scenarios to 2025. Available at: http://aidsscenarios.unaids.org/scenarios/, 2005. (Accessed March 22, 2010). 57. WBCSD (World Business Council for Sustainable Development). Exploring Sustainable Development. Summary brochure. Geneva: WBCSD; 1997. Available at: http://www.wbcsd.org/newscenter/reports/1997/ exploringscenarios.pdf. (Accessed March 22, 2010). 58. WBCSD (World Business Council for Sustainable Development). Energy 2050: Risky Business. Geneva: WBCSD; 1999. Available at: http:// www.wbcsd.org/. (Accessed March 22, 2010). 59. WBCSD (World Business Council for Sustainable Development). Biotechnology Scenarios 2000–2050. Geneva: WBCSD; 2000. Available at: http://www.wbcsd.org/. (Accessed March 22, 2010). 60. Patel M, Kok K, Rothman DS. Participatory scenario construction in land use analysis: an insight into the experiences created by stakeholder involvement in the Northern Mediterranean. Land Use Policy 2007, 24: 546–561. 61. Volkery A, Ribeiro T, Henrichs T, Hoogeveen Y. Your vision or my model? Lessons from participatory land use scenario development on a European scale. Syst Pract Action Res 2008, 21:459–477. 62. Lebel L, Bennett E. Participation in building scenarios of regional development. In: Norberg J, Cumming GS, eds. Complexity Theory for a Sustainable Future. New York: Columbia University Press; 2008, 207–222. 63. Lebel L. Multi-level scenarios for exploring alternative futures for upper tributary watersheds in mainland Southeast Asia. Mountain Res Dev 2006, 26:263–273. 64. Kok K, Patel M, Rothman DS, Quaranta G. Multi-scale narratives from an IA perspective: part II. Participatory local scenario development. Futures 2006, 38:285–311. 65. Andersen I-E, Jaeger B. Scenario workshops and consensus conferences: towards more democratic decisionmaking. Sci Public Policy 1999, 26:331–340. 66. Wollenberg E, Edmunds D, Buck L. Using scenarios to make decisions about the future: anticipatory learning for the adaptive co-management of community forests. Landsc Urban Plan 2000, 47:65–77. 67. Kok K, Biggs R, Zurek M. Methods for developing multiscale participatory scenarios: insights from Southern Africa and Europe. Ecol Soc 2007, 13:8. 68. Abildtrup J, Audsley E, Fekete-Farkas M, Giupponi C, Gylling M, Rosato P, Rounsevell MDA. Socio-economic scenario development for the assessment of climate change impacts on agricultural land use. Environ Sci Policy 2006, 9:101–115. 69. Saaty TL. The Analytic Hierarchy Process. New York: McGraw Hill; 1980. 70. Kok K. The potential of Fuzzy Cognitive Maps for semiquantitative scenario development, with an example from Brazil. Glob Environ Change 2009, 19:122–133.
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71. Bringezu S, Saurat M, Haines-Young R, Rollet A, Svensson M. FORESCENE Final Report. Available at: http://www.forescene.net/Resources.htm, 2009. (Accessed March 22, 2010). 72. Charniak E. Bayesian networks without tears. AI Mag 1991, 12:50–63. 73. Uusitalo L. Advantages and challenges of Bayesian networks in environmental modelling. Ecol Model 2007, 203:312–318. 74. Castelletti A, Soncini-Sessa R. Bayesian Networks and participatory modelling in water resource management. Environ Model Softw 2007, 22:1075–1088. 75. Aspinall R. An inductive modelling procedure based on Bayes’ theorem for analysis of pattern in spatial data. Int J Geogr Inf Syst 1992, 6:105–121. 76. Tucker K, Rushton SP, Sanderson RA, Martin EB, Blaiklock J. Modelling bird distributions—a combined GIS and Bayesian rule based approach. Landsc Ecol 1997, 12:77–93. 77. Bacon PJ, Cain JD, Howard DC. Belief network models of land manager decisions and land-use change. J Environ Manage 2002, 65:1–23. 78. Aalders I. Modelling land-use decision behaviour with Bayesian belief networks. Ecol Soc 2008, 13:16. 79. Jensen FV, Nielsen TD. Bayesian Networks and Decision Graphs. 2nd ed. IEEE Computer Society, Springer, New York; 2007, 463. 80. O’Neill BC. Conditional probabilistic population projections: an application to climate change. Int Stat Rev 2004, 72:167–184. 81. O’Neill BC. Population scenarios based on probabilistic projections: an application for the Millennium. Ecosyst Assess Popul Environ 2005, 26:229–254. 82. Settele J, Hammen V, Hulme P, Karlson U, Klotz S, Kotarac M, Kunin W, Marion G, O’Connor M, Petanidou T, et al. ALARM: assessing Large-scale environmental risks for biodiversity with tested methods. Gaia 2005, 14:69–72. 83. Busch G. Future European agricultural landscapesWhat can we learn from existing quantitative land use scenario studies? Agric Ecosyst Environ 2006, 114: 121–140. ´ MB, Carter TR, 84. Rounsevell MDA, Reginster I, Araujo Dendoncker N, Ewert F, House JI, Kankaanpa¨ a¨ S, Leemans R, Metzger MJ, et al. A coherent set of future land use change scenarios for Europe. Agric Ecosyst Environ 2006, 114:57–68. 85. Carter TR, Jones RN, Lu X, Bhadwal S, Conde C, Mearns LO, O’Neill BC, Rounsevell MDA, Zurek MB.
New assessment methods and the characterisation of future conditions. In: Parry ML, Canziani OF, Palutikof JP, van der Linden PJ, Hanson CE, eds. Climate Change 2007: Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge, UK: Cambridge University Press; 2007, 133–171. 86. De Vries JM, Petersen AC. Conceptualizing sustainable development. An assessment methodology connecting values, knowledge, worldviews and scenarios. Ecol Econ 2009, 68:1006–1019. 87. Parson EA. Useful global change scenarios: current issues and challenges. Environ Res Lett 2008, 3:045016. 88. Hulme M, Dessai S. Predicting, deciding, learning: can one evaluate the ‘‘success’’ of national climate scenarios? Environ Res Lett 2008, 3:045013. 89. Bryson J, Piper J, Rounsevell MDA. Experiences of using scenarios in three European environmental policy institutions. Environ Policy Governance. In press. 90. Rashkin PD. Global scenarios: background review for the millennium ecosystem assessment. Ecosystems 2005, 8:133–142. 91. Rothman D. Environmental scenarios—a survey. In: Alcamo J, ed. Environmental Futures: The Practice of Environmental Scenario Analysis. Amsterdam, The Netherlands: Elsevier; 2008, 37–65. 92. UNEP (United Nations Environment Programme). Global Environmental Outlook 4. Valletta, Malta: Progress Press Ltd.; 2007. Available at: http://www. unep.org/geo/geo4/. (Accessed March 22, 2010). 93. Gallop´ın GC, Rijsberman F. Three global water scenarios. Int J Water 2000, 1:16–40. 94. UNEP (United Nations Environment Programme). Africa Environment Outlook 2: Our Environment, Our Wealth. Valletta, Malta: Progress Press Ltd.; 2006. Available at: http://www.unep.org/dewa/africa/ aeo2 launch. (Accessed March 22, 2010). 95. UNEP (United Nations Environment Programme). Latin America and the Caribbean Environmental Outlook. Available at: http://www.unep.org/geo/pdfs/GEO lac2003English.pdf, 2003. (Accessed March 22, 2010). 96. Scholes and Biggs Ecosystem services in Southern Africa: a regional assessment. A contribution to the Millennium Ecosystem Assessment, prepared by the regional-scale team of the Southern African Millennium Ecosystem Assessment, 2004.
FURTHER READING Bruce-Briggs B. Supergenius: The Mega Worlds of Herman Kahn. New York: North American Policy Press; 2001.
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APPENDIX 1: RECENT GLOBAL STORYLINES (ADAPTED AND EXPANDED FROM REF 90). A MORE DETAILED SUMMARY IS PROVIDED IN REF 91 Study
Horizon
Global Scenario
Group27
2050
Focus
Storylines
Environment, development
1. Conventional worlds: Gradual convergence in incomes and culture toward dominant market model (a) Market forces: Market-driven globalization, trade liberalization, institutional modernization (b) Policy reform: Strong policy focus on meeting sustainability goals through technology 2. Barbarization: Social and environmental problems overwhelm market and policy response (a) Breakdown: Unbridled conflict, institutional disintegration, and economic collapse (b) Fortress world: Authoritarian rule with elites in ‘fortresses’, poverty, and repression outside 3. Great transitions: Fundamental changes in values, lifestyles, and institutions (a) Eco-communalism: Local focus and a bioregional perspective (b) New sustainability paradigm: Sustainable globalization, changing industrial society
Global Environmental Outlook 492
2050
Environment, development
Markets First, Policy First, Security First, Sustainability First [correspond, respectively, to 1(a), 1(b), 2(b), and 3(b) above]
IPCC-SRES4
2100
Climate change
A1: Rapid market-driven growth, with convergence in incomes and culture; rapid technological change A2: Self-reliance and preservation of local identities; fragmented development B1: Similar to A1, but emphasizes global solutions to sustainability, relying heavily on technology B2: Local technological and policy solutions to economic, social, and environmental sustainability
World Business Council on Sustainable Development57–59
2050
Business and sustainability
FROG!: Market-driven growth, economic globalization, and rapid technological change GEOpolity: Top-down approach to sustainability, with emphasis on technology Jazz: Bottom-up approach to sustainability, ad hoc alliances, innovation
World Water
Vision93
2025
Fresh water crisis Business-as-usual: Current water policies continue, high inequity Technology, economics, and the private sector: Market-based mechanisms; better technology Values and lifestyles: Less water-intensive activities; ecological preservation
Millennium Ecosystem Assessment1
2100
Ecosystems, sustainability
Global orchestration: A globally connected world with well-developed global markets and supranational institutions to deal with global environmental problems and inequity Order of strength: A fragmented world concerned with security and protection of regional markets and with little attention for common goods Adapting mosaic: A fragmented world resulting from discredited global institutions leads to the rise of local and regional initiatives supporting common goods TechnoGarden: A globally connected world relying strongly on technology, also for solving environmental problems and global inequity
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APPENDIX 2: RECENT REGIONAL STORYLINES Study
Region
Horizon
Africa Environmental Outlook 294
Africa
2025
Latin America and the Caribbean Environmental Outlook95
Latin America
2032
Focus Environment, development
Storylines Market forces: Market-driven globalization, trade liberalization, institutional modernization Policy reform: Strong policy focus on meeting sustainability goals through technology Fortress world: Authoritarian rule with elites in ‘fortresses’, poverty, and repression outside Great transitions: Fundamental changes in values, lifestyles, and institutions; sustainable globalization
Southern Africa MA96
Southern Africa
2030
Ecosystems, sustainability
African patchwork: Regional fragmentation, ineffective governance, little investment in health and education, localized military conflicts African partnership: Regional cooperation with strong central governance, political stability and economic growth, significant development
SRES-based scenarios :
Europe
A1: Rapid market-driven growth; convergence in incomes and culture; rapid technological change
ATEAM55,84
2080
Global change vulnerability
A2: Self-reliance and preservation of local identities; fragmented development
EURuralis52
2030
Rural regions
B1: Global solutions to sustainability; heavy reliance on technology
ACCELERATES53,54
2080
Agricultural land use
B2: Local technological and policy solutions to sustainability
2035
Environment
Great escape: Contrasts between urban centers and rural regions
PRELUDE2
Europe
Evolved society: Harmony between environmental awareness and policy intervention Clustered network: Social coordination with expanded infrastructure and compact cities Lettuce surprise U: Innovation leading to environmental friendly solutions Big crisis: Cohesion with a strong government focusing on sustainability ALARM48,82
Europe
2080
Biodiversity
GRAS: Growth applied strategy with a focus on economic development BAMBU: Business as might be usual SEDGE: Focus on achieving the sustainable European development goal
FARO-EU46
Europe
2030
Rural development
Muskateer: A strong belief that the public sector must intervene to solve social, economic, and environmental problems Marketeer: A strong belief that market liberalization will achieve solutions to social, economic, and environmental problems
ESPON3
Europe
2030
Spatial planning
Competitiveness oriented future with market liberalization and reduced policy intervention Cohesion oriented future with increased policy support to rural regions and the environment
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