How to analyze and compare scenarios? Evaluation of scenarios dealing with the future of our energy system: DESERTEC, EU-Roadmap 2050, Greenpeace [R]evolution, World Energy Outlook & Shell Energy Scenarios

Table of Contents

List of figures

List of abbreviations

1 Introduction

2 Scenarios
2.1 History
2.2 Scenario Development: A General Framework
2.2.1 Purpose
2.2.2 System Analysis
2.2.3 Modelling
2.2.4 Selecting
2.2.5 Communication
2.2.6 Application
2.3 Characteristics of Scenarios
2.3.1 Variations in Purpose
2.3.2 Variations in System Analysis
2.3.3 Variations in Modelling
2.3.4 Variations in Selecting
2.3.5 Variations in Communication
2.3.6 Variations in Application
2.3.7 Scenario typology
2.4 Energy Scenarios
2.4.1 DESERTEC
2.4.2 EU-Roadmap 2050
2.4.3 Greenpeace [R]evolution
2.4.4 World Energy Outlook 2011
2.4.5 Shell Energy Scenarios

3 Conclusion

References

List of figures

Figure 1: Peer-reviewed articles on scenarios

Figure 2: The general framework of the Scenario Development Process

Figure 3: System Thinking Iceberg Model

Figure 4: CLD representing the underlying structure of the price of renewable energy

Figure 5: Relationship of predetermines and interpretations in scenarios

Figure 6: The normative approach

Figure 7: The explorative approach

Figure 8: Inductive Scenario Building Approach

Figure 9: Deductive Scenario Building Approach

Figure 10: The Scenario Typology

Figure 11: Electricity generated in MENA and Europe

Figure 12: DESERTEC Typology

Figure 13: The EU-Roadmap 2050 typology

Figure 14: Development of Primary Energy Demand in Energy [R]evolution Scenario

Figure 15: The Energy [R]evolution typology

Figure 16: Changes in primary energy demand in New Policy Scenario

Figure 17: The World Energy Outlook typology

Figure 18: Primary energy by source in Scramble scenario

Figure 19: Primary energy by source in Blueprints scenario

Figure 20: The Shell Energy sceneario typology

List of abbreviations

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1 Introduction

At the moment our energy system is highly dependent on fossil sources, which amount for about 80% of the total primary energy demand with crude oil being the biggest part.^[1] This dependency leads to two major problems. Firstly, fossil energy is stored energy from the sun that took millions of years to be developed. Since the long time frames of development, fossil sources are called non-renewable. Since decades pessimists are predicting the end of oil supply, which was postponed in the past because of new discoveries and technological progress. Still crude oil is the scarcest fossil energy source, since the known reserves are enough to last for about 40 years with current depletion. However the current depletion is not enough to supply the increasing energy demand, which is rising by 6,7% p.a., leading to an increase of 50% until 2030. If there are no major changes in the total primary energy demand, it is unavoidable that a lower supply of crude oil will clash with higher demand. A switch to coal seems meaningful since it is the only fossil source that is still abundant. The problem of coal is that it is the dirtiest fossil source in regard to green house gases, which leads directly to the second problem: Climate change. Changes in the climate have occurred ever since in the past, but this time it is the first time that it is caused by residents of the world – human beings. Before the industrial revolution the concentration of carbon dioxide, the chief greenhouse gas that results from human activities and causes global warming amounted 280 ppm, which increased since then to the current amount of 390 ppm.^[2] This is the highest concentration of carbon dioxide in the atmosphere in the last 400.000 years of the earth’s history. The exact effects of the increased carbon dioxide concentrations on the climate are not known, but there is a scientific consensus that in order to prevent a devastating climate change the emissions have to be dramatically reduced. Since the energy sector is responsible for the bulk of greenhouse gas emissions a switch from oil to coal without technical progress to limit carbon dioxide is not meaningful.^[3]

The alternatives to fossil energy sources are on the one hand nuclear energy and on the other hand renewable energy sources. Nuclear energy amounts at the moment for about 5% of the total primary energy demand. The process of nuclear energy is fascinating, since the energy outcome of one kilogram of uranium equals the stored energy of about 3.000 tons of coal. With an increase of nuclear energy the problem of climate change can be reduced, since the process is carbon neutral. But because of the reason that uranium deposits are also scarce, as well as other problems as, as nuclear proliferation, incidents and the unsolved problem of waste disposal it is be doubted that it will be of great importance in the future.^[4]

Renewable energy sources on the other hand have been the major primary energy source before the era of the black gold. Today they amount for about 15% of the total primary energy demand, with biomass and hydro-power having with 10% and 5% the biggest shares. All the other renewable energy sources, as wind power, solar heat and photovoltaic, play a minor role since they don’t even cover 1% of the primary energy demand. Renewable energy sources could solve both major problems of the energy sector, since they are carbon neutral and by definition renewable. The drawbacks are that at the moment most renewable sources are not competitive and need to be subsidized by governments and the technical challenge of storing the produced energy.^[5]

It is easy to understand that the future of the energy sector is highly uncertain and that tools that are extrapolating past trends are of no use under these circumstances. In the last years several energy scenarios have been conducted, trying to display the major uncertainties. Not surprisingly the results are strongly varying, leading to question of the characteristics and the quality of scenarios. However there has been no analysis of the characteristics and quality of energy scenarios, which is being done in this bachelor thesis.

After a short introduction of the history of scenarios in chapter 2.1, a general framework of the scenario development process is developed in chapter 2.2 and its possible variations are listed in chapter 2.3. The result is a scenario typology consisting of 15 variables that can be used to examine the characteristics and the quality of scenarios. The scenario typology is then applied at a total of 5 scenarios at chapter 2.4. The sample of scenarios was chosen to display the broad range of different scenario developers. It consists of the DESERTEC scenario of a charitable trust, the EU-Roadmap 2050 of a political institution, the Greenpeace [R]evolution of an NGO, the World Energy Outlook of an intergovernmental institution and the Shell Energy scenarios of a company dealing in the energy sector.

2 Scenarios

The last half of the 20th century is characterized by remarkable economic and political cooperation caused by increased globalisation. The world population doubled and the world income quadrupled in 50 years. These large increases in the well being of more people are going hand in hand with more responsibilities, since environmental and resource constraints are becoming more obvious. For the first time the current generation needs to take into account the impact for several generations ahead. This background of complex environments, together with the recent developments of computing power and tools for simulation of large and complex systems are building the basis for the increased interest in scenarios, which are in general methods in the strategic planning process that are based on the development and analysis of possible developments in the future.^[6]

illustration not visible in this excerpt

Figure 1: Peer-reviewed articles on scenarios

Source: Ramírez R../ Selsky, J./ Van der Heijden K. (2009): Causal Texture Theories of Turbulence & the Growth and Role of Scenario Practices, Working Paper, University of Oxford, Liverpool, P.6.

2.1 History

The history of scenarios starts in the 1960s in the field of social psychology and the development of the Casual Texture theory. The theory deals with systems that are trying to survive in a sustainable way in the outer environment. The system and the environment are connected with each other, leading to a process of co-evolution, in which the inner system and the outer environment systematically influence each other. If there are connections within the outer environment, that the inner system can’t influence the contextual environment becomes a source of instability. Under these turbulent circumstances scenarios are helpful for decision makers to understand how the contextual environment may shape in future. This theory influenced the scientific discourse of organisations trying to find strategies fitting to its uncertain business environment.^[7]

About the same time, the futurist Hermann Kahn and his colleagues at the RAND Corporation and the Hudson Institute introduced the term scenario planning in the field of social and business sciences.^{[8] [9]} His research was strongly influenced by the cold war, which led to publications of probable, possible and worst-case scenarios of the world, including nuclear war scenarios.^[10] Olaf Helmer, who was also employed at the RAND Corporation, focused his scientific work on methodical problems. Together with Norman Dalkey he developed the DELPHI-method, which is still one of the basic tools of futurism.^[11]

One of the best known publications of futurism is the book “Limits to Growth”, in which the authors analyzed the possible future trends of five variables, which are world population, industrialization, pollution, food production and resource depletion. The main conclusion of the book is that an extrapolation of current trends will lead to fast and unstoppable decline of the world population, food production and industrialization. The reasons for that doomsday are environmental destruction and depletion of natural resources.^[12] Although the book was heavily criticized, it is considered to be a pioneer in the field sustainable development and is therefore also of great influence in the energy sector.^{[13] [14]}

The work of futurists and social psychologists influenced the theory and practice of strategy design. Pierre Wack, the former executive of Royal Dutch Shell, is considered among the first to bring these methods and theories to business strategy. He developed multiple, but equally possible scenarios of the future business environment of the company. The use of scenarios led to a huge advantage at the beginning of the oil crises in 1973 and 1979, since Royal Dutch Shell had already worked out a suitable strategy in the case of a crisis scenario and was able to interpret signals for its unfolding correctly, leading to an outperformance of the rest of the oil industry.^[15] Since the success of Royal Dutch Shell, the use of scenario planning as a strategic planning tool is increasing, especially in organisations that are operating in instable business environments.^[16]

The use of scenarios in the field of public planning is still in its infancy, but becoming more important because of two effects. Firstly, the actions of traditional governance often end up with unintended side effects.^[17] An example in the field of energy policy can be found in Spain, where the feed-in legislation had the intension to increase the share and technological progress of renewable energy sources. The unexpected success of the policy led to serious decreases of coal-fired power generation. Since this clashed with the policy of the protection of coal-mining employment, the government intervened to protect this industry.^[18] Secondly, the importance of regional integration and globalisation is increasing. This means that many actions by one national government have an impact above their own boundaries, leading to more complex environments. Examples are environmental pollution, resource management and energy policy.^[19]

2.2 Scenario Development: A General Framework

A description of a general valid scenario development process is a challenging task, since scenario planning is considered to be a meta-method, in which different methods contribute to the content and the quality of the various aspects.^[20] This means that no single method of scenario analysis exists, nor can the same method be used in all cases. The structure for creating scenarios has to be flexible to fit to its various purposes.^[21]

However, given the observed diversity of scenarios, one can only analyze and compare scenarios in a credible and consistent manner when there is a shared understanding of the commonalties of the scenario development process.^[22] Figure 2 shows the general framework of the scenario development process, which will be described in detail in this chapter. Deviations from the general framework and variations within the framework will be presented in chapter 2.3. Both the general framework and the deviations and variations are essential for the analysis of different energy scenarios.

illustration not visible in this excerpt

Figure 2: The general framework of the Scenario Development Process

Source: Cf. Zürni, S. (2004): Möglichkeiten und Grenzen der Szenarioanalyse – Eine Analyse am Beispiel der Schweizer Energieplanung, Stuttgart and Berlin, P. 223.

2.2.1 Purpose

The starting point of every scenario development process is the definition of the purpose of the analysis. Furthermore the methods, the time frame and the system boundaries of the system under investigation have to be determined.^[23]

The purpose is of great importance since it influences all the other stages of the general framework, meaning that an unclear purpose will lead to poor results.^[24]

2.2.2 System Analysis

In this step the real existing system and its properties are examined. The most challenging part is to gain accurate data that effect the future development of the system. Not surprisingly, this step is exposed to large number of possible variations, depending on the defined purpose, as well as the availability and form of data. Nevertheless a general procedure can be described.^[25]

In order to identify and collect data, which is at least relevant for the defined purpose, the scenario developer has to choose between a broad variety of methods. The integration of experts via interviews or workshops is most commonly used. The gathered data is then divided into two classes. Firstly internal data, which has the characteristic, that the scenario user has an impact on the future development of it. Secondly external data, that can’t be influenced by the scenario user. In this stage the focus is on the external data, which is of the form that it is representing the external environment of the system. The next step is to evaluate what parts of the external environment are predictable and which are uncertain. Predetermines, where there are no different interpretations of what is happening and therefore not exposed to uncertainty are to be neglected in this stage, but included in the process later as described in chapter 2.2.5. In this stage, the attention is on external data that is uncertain, meaning that it is impossible to exactly describe the future outcome or even the existing state with certainty. Because of simplification it is usual that the uncertainties are reduced to those factors with the largest effect on the purpose.^[26]

2.2.3 Modelling

The aim of this step is to model the events, patterns, trends and of most importance the underlying structure of the system under investigation, which are illustrated in the “System Thinking Iceberg Model” in Figure 3. The upper part of the iceberg is the event level, which is visible and can be observed. It is the level that describes how human beings perceive and describe the world. An example in the field of energy is the availability of energy at any time on demand.

The second level of the iceberg model is the level of patterns and trends, which are changes in the events that occur over time. For an examination of patterns the relationships between various events have to be found and described. As shown in Figure 3 this level is under water, metaphorically meaning that it is challenging but possible to see the patterns and trends. An example of a trend in the field of energy is the increase in the oil price since the millennium change.

The last level is the structure. The structure supports and creates the patterns and trends, which are leading to events that can easily be seen. Structures are composed by the relationship of patterns and trends. The description of structure is difficult, because as metaphorically shown in Figure 3, it is impossible to see it.^[27]

illustration not visible in this excerpt

Figure 3: System Thinking Iceberg Model

Source: Cf. Senge, P. (1992): The Fifth Discipline. The Art and Practice of the Learning Organization, London, P. 93-114.

The data that was collected in the step of the System analysis represents the visible part of the iceberg metaphor. In order to derive the patterns and trends as well as the underlying structure of the system the scenario developer has to start with finding logic clusters about the uncertain environmental factors. Those clusters are then modelled and studied in depth in order to derive the driving forces that are the main reason of the trends and patterns.^[28]

In the praxis of the scenario development process, it is usual to start with the most important key events and develop simple causal loop diagrams, which visualise how the variables are affecting one another. The relationships are represented by arrows that are labelled positive or negative. A positive label means that an increase of the variable leads to an increase of the dependent variable, whereas a negative label means that an increase of the variable leads to a decrease of the dependent one. The main task is to identify driving forces, which are the main reason for the changes in the future behaviour of the environment and which are interdependent of other examined variables. The modelling process is finished when the model sufficiently represents the underlying structure of the system.^[29]

Figure 4 represents a simple causal loop diagram, where the price of renewable energy is the key event and the price of fossil energy and governmental funding are regarded to be the two main driving forces.

illustration not visible in this excerpt

Figure 4: CLD representing the underlying structure of the price of renewable energy

Source: Own illustration

As a final step theories about the future behaviour of the driving forces have to be conducted. Since the driving forces have a big impact on the event level through causal links it is then possible to examine plausible developments of the visible part of the system.^[30]

2.2.4 Selecting

Scenarios are formed through combination of possible future behaviours of the various driving forces. Because many combinations can be meaningful, the problem of selecting the most suitable ones occurs.^[31] The usual number of selected scenarios is between two and four, since more than four scenarios are proven to be organisationally impractical and only one cannot reflect uncertainty.^[32]

Again there is not the one-fits-all method of selecting scenarios, but all methods are based on the following requirements of the chosen ones:^{[33] [34] [35]}

- Consistency: The cause-effect chains of the scenarios are internally consistent.
- Plausibility: The scenarios are realistically plausible.
- Relevance: The scenarios lead to useful insights of the defined purpose.
- Differentiation: The scenarios are structurally or qualitatively different.
- Challenging: The scenario challenges mental models and leads to new and original perspectives.

2.2.5 Communication

In this step the chosen scenarios are communicated to a more or less broad public. The communication process consists of two steps. Firstly, each scenario is to be named. Most authors argue in favour for a short memorable descriptive title, that is eliciting a rich imagery. Secondly a storyline has to be developed, which links the past, present and the possible future in a narrative way. The different interpretations of the driving forces are transparently worked out, whereas the predetermines are reflected in each storyline, as shown in Figure 5.^{[36] [37]}

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Figure 5: Relationship of predetermines and interpretations in scenarios

Source: Cf. Van der Heijden K. (2010): Scenarios: The Art of Strategic Conversation, 2.ed., Chichester, P. 92.

2.2.6 Application

The application of scenarios is the link between insights and action. The output of the previous steps as such is not leading to concrete action, but the understanding of the underlying structure and its driving forces are usually leading to opportunities of leverage that have been unknown before. The scenario user is therefore searching for suitable actions in the case of the unfolding of the communicated scenarios.^[38]

This stage is again exposed to a large number of possible variations, depending on the defined purpose and the possible influence of the scenario user on the system. It is even possible, that scenarios are solely conducted for a learning process and that there is no implicit application at all. Nevertheless in this paper it is assumed that those scenarios could be applied by third parties.

2.3 Characteristics of Scenarios

As already mentioned in chapter 2.2 there exists no one-fits-all method of scenario analysis, nor can the same method be used in all cases. The structure for creating scenarios has to be flexible to fit to its various purposes.^[39]

In order to improve the analysis and the comparison of differently developed scenarios a scenario typology is described in the following chapter, representing all deviations from the general framework and variations within it. There have been many descriptions of typologies in the scientific literature of scenarios. However, older studies^{[40] [41]} have focused on characteristics of a specific field, as an industry sector. Unsurprisingly there have been huge differences in the proposed typologies. The biggest drawback of those typologies is that it is impossible to compare scenarios from different fields. In contrast to the older ones, are newer studies^{[42] [43] [44]} aiming to capture the diversity of scenarios to be able to integrate the broad varieties in the scenario development process in the typology. Those studies led to similar results, although they have been conducted independently from each other.

2.3.1 Variations in Purpose

In this chapter variations in the purpose of the scenario are presented. Since the purpose has effects on all the other stages, variations presented in these chapters also influence all the following steps of the scenario development process.

2.3.1.1 Time Scale

Depending on the temporal or time scale, scenarios are classified in long-term and short-term scenarios:^{[45] [46] [47]}

- Long term scenarios are having time horizons until far in the future. Scenarios that have time scales of 25 years or more are classified as long termed.
- Short term scenarios have a shorter time horizon than 25 years, usually about 3 to 10 years. Short term scenarios are especially useful in fields that are characterized by highly complex and uncertain system, as for example the growth of GDP.

2.3.1.2 Vantage Point

Depending on the vantage point and the inclusion of norms and values, scenarios are classified in normative and explorative scenarios:^{[48] [49] [50]}

- Normative scenarios have consciously included norms and values. Normative scenarios start with the description of preferable and desirable future situation and are scrutinizing what developments are needed in order for them to occur. Since normative scenarios are starting from the future this approach is often referred as backcasting.

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Figure 6: The normative approach

Source: Own illustration

- Explorative or descriptive scenarios do not consciously include norms and values. Starting from the present situation and the analysis of the development of driving forces future situations are described, independent of their desirability. These scenarios usually refer to possible or probable scenarios. Since even explorative scenarios are unable to perfectly exclude values and interests of the scenario development team they cannot be regarded as value-free.

illustration not visible in this excerpt

Figure 7: The explorative approach

Source: Own Illustration

2.3.1.3 Subject

Scenarios also differ according the subject of the purpose, where a classification in issue-based, area based and institution-based scenarios is meaningful. Overlaps between the three categories are possible:^[51]

- Issue-based scenarios have a defined issue or topic of the study, as for example the future of energy.
- Area based scenarios describe possible futures of a particular geographical area, such as a country or a city.
- Institution based scenarios are based on the future development of the environment of an institution, as a company.

2.3.1.4 Temporal nature

The temporal nature, which distinguishes between process and situation scenarios, should not be confused with the time scale:^{[52] [53] [54]}

- Process scenarios include the descriptions of pathways that are leading from the current situation to a certain future situation.
- Situation scenarios focus on the description of the future situations, thereby neglecting or only implicitly addressing the pathway that result in the end-state. Situation scenarios are a deviation from the general framework and it has to be scrutinized if the description of the future state is a scenario or rather a vision.

2.3.1.5 Spatial scales

The spatial scale of scenarios ranges from the global level to supranational areas, down to national and regional areas. A simplification is to use only the following two categories:^[55]

- Global and Supranational scenarios take into considerations big spatial levels as the whole world and supranational entities, as the European Union.
- National and local scenarios focus on a smaller spatial level, as countries, areas and regions.

2.3.2 Variations in System Analysis

The system analysis, where data is collected that is representing the real existing system mainly differs in the way how data is collected and according the nature of the data.

2.3.2.1 Method of data collection

The data and information needed for scenarios can be collected in the following two ways:^{[56] [57]}

- Desk research means that the data is collected without any participatory process. This includes amongst other methods the collection and analysis of data through scientific journals and computer simulations.
- The participatory approach uses certain processes between individuals for the gathering of information. This approach might draw on experts in the field, decision-makers and all kind of stakeholders. There are various organisational structures of participatory processes as workshops and Future Conferences.

2.3.2.2 Nature of data

Depending on the nature of data scenarios can be characterised as qualitative or quantitative scenarios. Since overlaps of both are possible and common in the scenario development process the third category, which is including both qualitative and quantitative is meaningful:^{[58] [59] [60]}

- Qualitative Scenarios are dealing with data that is nominal or ordinal scaled. Ordinal scaled means that the different values of the data cannot be compared or be put in any order. Ordinal scaled data have the characteristics that the values are of different intensity and can be put in order.^[61] The use of qualitative data is appropriate if the analysed system is of high complexity and when the information cannot be quantified. In the absence of quantitative data the scenario process usually relies on soft methods as the judgement and intuition of persons.^[62]
- Quantitative Scenarios are dealing with data that are interval or ratio scaled. In interval scaled data the values can be put in an order and the distance between the values can be interpreted meaningfully. In addition to that ratio scaled data have an absolute zero.^[63] With the availability of quantitative data, the use of mathematical tools and computer models is meaningful.^[64]
- Scenarios with both qualitative and quantitative data are using data on all scale measurements in order to make the scenario analysis more consistent and robust. The drawback of this approach is that the fusion of qualitative and quantitative data is a methodical challenge.

2.3.3 Variations in Modelling

The step of modelling, where the underlying structure of the system is modelled and the driving forces for future developments are derived mainly differs according the number of driving forces and the nature of dynamics.

2.3.3.1 Number of driving forces

This characteristic addresses the number of examined driving forces in the scenario development process and is an indicator for the complexity and uncertainty of the model.^[65]

- Small amount refers to scenarios with less than 10 examined driving forces, in the model building.
- Medium amount means that in the scenario analysis 10 to 20 driving forces have been examined. A reduction of the driving forces is meaningful.
- Large amount addresses to scenarios that are using more than 20 driving forces in the modelling process. Because of the resulting complexity a reduction of the driving forces is usually conducted.

2.3.3.2 The nature of the dynamics

Depending on the nature of dynamics scenarios are distinguished in peripheral and surprise-free trend scenarios:^{[66] [67] [68]}

- Peripheral scenarios, which are often also called extreme or contrast scenarios describe a discontinuous pathway to the future, including unlikely and extreme trends. Those scenarios describe possible situations in which there is only a short period of time of leverage that requires decisive actions.
- Trend scenarios are probable pathways to the futures, extrapolating from existing trends. Trend scenarios have to be scrutinized if the analysis is a scenario or rather a forecast. Usually trend scenarios are building the basis of peripheral scenarios, in order to show the deviation from the trend and the extreme outcome.

[...]

^[1] Cf. Quaschning V. (2010): Erneuerbare Energien und Klimaschutz – Hintergründe, Techniken, Anlagenplanung, Wirtschaftlichkeit, Munich, P. 26.

^[2] Cf. Forster, P., et al. (2007): Changes in Atmospheric Constituents and in Radiative Forcing. in: Solomon, S. et al. (eds.): Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge and New York, P. 129 -234, P.135.

^[3] Cf. McKibben, B . (2009): Our Energy Challenge – Essay by Bill McKibben, in National Geography, Vol.215, Issue 6, P. 1-96, P.24-27.

^[4] Cf. Quaschning V. (2010): Erneuerbare Energien und Klimaschutz – Hintergründe, Techniken, Anlagenplanung, Wirtschaftlichkeit, Munich, P. 21-36.

^[5] Cf. Quaschning V. (2010): Erneuerbare Energien und Klimaschutz – Hintergründe, Techniken, Anlagenplanung, Wirtschaftlichkeit, Munich, P. 14-35.

^[6] Cf. Ramírez R./Selsky, J. /Van der Heijden K. (2010a): Preface, in: Ramírez R. (Ed.)/Selsky, J. (Ed.)/Van der Heijden K. (Ed.): Business Planning for Turbulent Times: New Methods for Applying Scenarios, 2nd ed, London, P. XVI-XX, P.XVI-XVII.

^[7] Cf. Ramírez R./Selsky, J./Van der Heijden K. (2010b): Conceptual and Historical Overview, in: Ramírez R. (Ed.)/Selsky, J. (Ed.)/Van der Heijden K. (Ed.): Business Planning for Turbulent Times: New Methods for Applying Scenarios, 2nd ed, London, P.17-30, P.17-18.

^[8] Cf. Van der Heijden, K. (2010): Scenarios: The Art of Strategic Conversation, 2.ed., Chichester, P. 3.

^[9] Cf. Fink, A. (1999): Szenariogestützte Führung industrieller Produktionsunternehmen, Diss., Paderborn, P. 14-15.

^[10] Cf. Kahn H./Wiener A. (1967): The Year 2000 – A Framework for Speculation on the Next Thirty-Three Years, New York, P. 248-358.

^[11] Cf. Dalkey, N. (1963): An Experimental Application of the Delphi Method to the Use of Experts, in: Management Science, Vol. 9, Issue 4, P. 458-467, P. 460-467.

^[12] Cf. Meadows, D. et al. (1972): Die Grenzen des Wachstums – Bericht des Club of Rome zur Lage der Menschheit, Stuttgart, P. 110-113.

^[13] Cf. Rao, P. (2000): Sustainable development: Economics and Policy, Malden, P. 8.

^[14] Cf. Rogers, P./Jalal, K./Boyd, J. (2008): An Introduction to Sustainable Development, London, P. 20.

^[15] Cf. Van der Heijden, K. (2010): Scenarios: The Art of Strategic Conversation, 2.ed., Chichester, P. 3-10.

^[16] Cf. Fink, A. (1999): Szenariogestützte Führung industrieller Produktionsunternehmen, Diss., Paderborn, P. 16.

^[17] Cf. Arvidsson N. (2010): Designing More Effective Political Governance of Turbulent Fields: The Case of Health Care, in: Ramírez R. (Ed.)/Selsky, J. (Ed.)/Van der Heijden K. (Ed.): Business Planning for Turbulent Times: New Methods for Applying Scenarios, 2nd ed, London, P.131 -146, P.133-134.

^[18] Cf. BP (ed.) (2011): BP Statistical Review of World Energy June 2011, http://bp.com/statisticalreview, October 5th 2011.

^[19] Cf. Arvidsson N. (2010): Designing More Effective Political Governance of Turbulent Fields: The Case of Health Care, in: Ramírez R. (Ed.)/Selsky, J. (Ed.)/Van der Heijden K. (Ed.): Business Planning for Turbulent Times: New Methods for Applying Scenarios, 2nd ed, London, P.131 -146, P.133-134.

^[20] Cf. Fink, A. (1999): Szenariogestützte Führung industrieller Produktionsunternehmen, Diss., Paderborn, P. 96.

^[21] Cf. Masini, E/Vasques J. (2000): Scenarios as Seen from a Human and Social Perspective, in: Technological Forecasting and Social Change, Vol. 5, Issue 1, P. 49-66, P.66.

^[22] Cf. Van Notten, P. et al. (2003): An updated scenario typology, in Futures, Vol.35, Issue 5, P. 423-443, P.423.

^[23] Cf. Zürni, S. (2004): Möglichkeiten und Grenzen der Szenarioanalyse – Eine Analyse am Beispiel der Schweizer Energieplanung, Stuttgart and Berlin, P. 224.

^[24] Cf. Lindgren, M./Brandhold, H. (2009): Scenario Planning : The Link between Future and Strategy, rev. and updated ed., Basingstoke, P. 56.

^[25] Cf. Zürni, S. (2004): Möglichkeiten und Grenzen der Szenarioanalyse – Eine Analyse am Beispiel der Schweizer Energieplanung, Stuttgart and Berlin, P. 224.

^[26] Cf. Van der Heijden, K. (2010): Scenarios: The Art of Strategic Conversation, 2.ed., Chichester, P. 228-230.

^[27] Cf. Senge, P. (1992): The Fifth Discipline. The Art & Practice of The Learning Organization, London, P. 93-114.

^[28] Cf. Van der Heijden, K. (2010): Scenarios: The Art of Strategic Conversation, 2.ed., Chichester, P. 230.

^[29] Cf. Van der Heijden, K. (2010): Scenarios: The Art of Strategic Conversation, 2.ed., Chichester, P. 230- 235.

^[30] Cf. Zürni, S. (2004): Möglichkeiten und Grenzen der Szenarioanalyse – Eine Analyse am Beispiel der Schweizer Energieplanung, Stuttgart and Berlin, P. 225.

^[31] Cf. Zürni, S. (2004): Möglichkeiten und Grenzen der Szenarioanalyse – Eine Analyse am Beispiel der Schweizer Energieplanung, Stuttgart and Berlin, P. 225.

^[32] Cf. Van der Heijden, K. (2010): Scenarios: The Art of Strategic Conversation, 2.ed., Chichester, P. 225.

^[33] Cf. Zürni, S. (2004): Möglichkeiten und Grenzen der Szenarioanalyse – Eine Analyse am Beispiel der Schweizer Energieplanung, Stuttgart and Berlin, P. 225.

^[34] Cf. Lindgren, M./Brandhold, H. (2009): Scenario Planning : The Link between Future and Strategy, rev. and updated ed., Basingstoke, P. 33-34.

^[35] Cf. Van der Heijden, K. (2010): Scenarios: The Art of Strategic Conversation, 2.ed., Chichester, P.225-226.

^[36] Cf. Van der Heijden, K. (2010): Scenarios: The Art of Strategic Conversation, 2.ed., Chichester, P. 259-261.

^[37] Cf. Lindgren, M./Brandhold, H. (2009): Scenario Planning : The Link between Future and Strategy, rev. and updated ed., Basingstoke, P. 74-82.

^[38] Cf. Van der Heijden, K. (2010): Scenarios: The Art of Strategic Conversation, 2.ed., Chichester, P.273.

^[39] Cf. Masini, E/Vasques J. (2000): Scenarios as Seen from a Human and Social Perspective, in: Technological Forecasting and Social Change, Vol. 5, Issue 1, P. 49-66, P.66.

^[40] Cf. Gobet M./Rubelat F.(1996): The Use and Misuse of Scenarios, in: Long Range Planning, Vol. 29, Issue 2, P. 164-171, P.166-170.

^[41] Cf. Heugens P./Van Oosterhout J. (2001): To boldly go where no man has gone before: integrating cognitive and physical features in scenario studies, in Futures, Vol. 33, Issue 10, P. 861-872, P. 862-871.

^[42] Cf. Van Notten, P. et al. (2003): An updated scenario typology, in Futures, Vol.35, Issue 5, P. 423-443, P.423-434.

^[43] Cf. Zürni, S. (2004): Möglichkeiten und Grenzen der Szenarioanalyse – Eine Analyse am Beispiel der Schweizer Energieplanung, Stuttgart and Berlin, P. 243-256.

^[44] Cf. Fink, A. (1999): Szenariogestützte Führung industrieller Produktionsunternehmen, Diss., Paderborn, P. 29-45.

^[45] Cf. Van Notten, P. et al. (2003): An updated scenario typology, in Futures, Vol.35, Issue 5, P. 423-443, P.430-431.

^[46] Cf. Fink, A. (1999): Szenariogestützte Führung industrieller Produktionsunternehmen, Diss., Paderborn, P. 34-39.

^[47] Cf. Zürni, S. (2004): Möglichkeiten und Grenzen der Szenarioanalyse – Eine Analyse am Beispiel der Schweizer Energieplanung, Stuttgart and Berlin, P. 247.

^[48] Cf. Van Notten, P. et al. (2003): An updated scenario typology, in Futures, Vol.35, Issue 5, P. 423-443, P.429..

^[49] Cf. Fink, A. (1999): Szenariogestützte Führung industrieller Produktionsunternehmen, Diss., Paderborn, P. 34-39.

^[50] Cf. Zürni, S. (2004): Möglichkeiten und Grenzen der Szenarioanalyse – Eine Analyse am Beispiel der Schweizer Energieplanung, Stuttgart and Berlin, P. 248.

^[51] Cf. Van Notten, P. et al. (2003): An updated scenario typology, in Futures, Vol.35, Issue 5, P. 423-443, P.429.

^[52] Cf. Van Notten, P. et al. (2003): An updated scenario typology, in Futures, Vol.35, Issue 5, P. 423-443, P.433.

^[53] Cf. Fink, A. (1999): Szenariogestützte Führung industrieller Produktionsunternehmen, Diss., Paderborn, P. 39-45.

^[54] Cf. Zürni, S. (2004): Möglichkeiten und Grenzen der Szenarioanalyse – Eine Analyse am Beispiel der Schweizer Energieplanung, Stuttgart and Berlin, P. 248.

^[55] Cf. Van Notten, P. et al. (2003): An updated scenario typology, in Futures, Vol.35, Issue 5, P. 423-443, P.431.

^[56] Cf. Van Notten, P. et al. (2003): An updated scenario typology, in Futures, Vol.35, Issue 5, P. 423-443, P.432.

^[57] Cf. Fink, A. (1999): Szenariogestützte Führung industrieller Produktionsunternehmen, Diss., Paderborn, P. 39-45.

^[58] Cf. Van Notten, P. et al. (2003): An updated scenario typology, in Futures, Vol.35, Issue 5, P. 423-443, P.431-432.

^[59] Cf. Fink, A. (1999): Szenariogestützte Führung industrieller Produktionsunternehmen, Diss., Paderborn, P. 31-34.

^[60] Cf. Zürni, S. (2004): Möglichkeiten und Grenzen der Szenarioanalyse – Eine Analyse am Beispiel der Schweizer Energieplanung, Stuttgart and Berlin, P. 249-250.

^[61] Cf. Hartung J./Elpelt B./Klösener K. (2005): Statistik – Lehr und Handbuch der angewandten Statistik, 14.ed., München, P. 16-17.

^[62] Cf. Fink, A. (1999): Szenariogestützte Führung industrieller Produktionsunternehmen, Diss., Paderborn, P. 34-39.

^[63] Cf. Hartung J./Elpelt B./Klösener K. (2005): Statistik – Lehr und Handbuch der angewandten Statistik, 14.ed., München, P. 17.

^[64] Cf. Fink, A. (1999): Szenariogestützte Führung industrieller Produktionsunternehmen, Diss., Paderborn, P. 34-39.

^[65] Cf. Zürni, S. (2004): Möglichkeiten und Grenzen der Szenarioanalyse – Eine Analyse am Beispiel der Schweizer Energieplanung, Stuttgart and Berlin, P. 250.

^[66] Cf. Van Notten, P. et al. (2003): An updated scenario typology, in Futures, Vol.35, Issue 5, P. 423-443, P.433.

^[67] Cf. Fink, A. (1999): Szenariogestützte Führung industrieller Produktionsunternehmen, Diss., Paderborn, P. 31-34.

^[68] Cf. Zürni, S. (2004): Möglichkeiten und Grenzen der Szenarioanalyse – Eine Analyse am Beispiel der Schweizer Energieplanung, Stuttgart and Berlin, P. 248.