The managerial and practical implications of this thesis lead to the improvement of strategic decision-making skills of managers and key decision-makers in the energy industry as well as governments to build macroeconomic predictions for a sustainable future. The managerial and practical implications of this thesis lead to the improvement of strategic decision-making skills of managers and key decision-makers in the energy industry as well as governments to build macroeconomic predictions for a sustainable future.
STATE OF THE ART OF ENERGY
- E NERGY D EMAND S TRUCTURE , S TAKEHOLDERS AND R ISK P ROFILE
- B ACKGROUND OF R ENEWABLE E NERGY
- B ACKGROUND OF N ON -R ENEWABLE E NERGY
- L ITERATURE R EVIEW OF E NERGY S CENARIOS
- R ESEARCH P ROBLEM AND R ESEARCH Q UESTIONS
- S UMMARY OF C HAPTER 1
It was initially developed in the military sphere (Whiteman, 1998), but recently it has also been adopted in the business world (Bennett & Lemoine, 2014). In the United States, the most common type of coal for electricity generation is bituminous (US Department of the Interior, n.d.). Another issue with modeling and scenario building in the energy realm is the background motivation of the modelers.
In the narrow context of energy consumption modeling, a heterogeneous set of variables has already been established (Camarero et al., 2015).
METHODOLOGY
M ETA -A NALYSIS A PPROACH WITH E NERGY S CENARIOS
The existence of such a unique and comprehensive collection can improve future energy modeling scenarios and sharpen the understanding of the status quo within the energy modeling field. The publication period of the selected articles is limited to the past 13 years up to (inclusive) 2007. In addition, a certain type of gray literature is included in the analysis, as some of the most comprehensive scenario modeling has been performed by organizations such as the International Energy Agency (IEA) , which are recognized as having a certain bias, as previously described.
Their research still forms an important part of the conversation about the energy transition and the measures to consider. In recognition of different quality standards and the lack of peer review, a marker is placed during data collection to easily distinguish peer-reviewed scientific papers from industry reports. Then, all identified variables will be examined and combined to combine similar or closely related variables in order to increase the comparability and clarity of the results.
They will be named with double letters of the corresponding category and numbered again in chronological order (EE1, EE2,. Additionally, a Multiple Correspondence Analysis (MCA) will be performed to detect patterns in the recorded data .This is important, as data entry will be performed using X for the first variable entry, O for any mention of the subsequent variable, and a blank cell for those not mentioned.
L IMITATIONS
S UMMARY OF C HAPTER 2
EMPIRICAL STUDY OF VARIABLES IN ENERGY SCENARIO
D ATA C OLLECTION
Both of the latter were used and applied to keep track of research progress and also to determine the point where marginal returns in the form of additional variables to labor reached zero. The thirteen randomly selected papers from the predefined research set are a diverse set of twelve scientific papers and one industrial report, which were published between 2009 and 2019. Lesperance Sustainable Development Scenario, etc. major oil producer.
South Africa Scientific article 2009 7 Some future scenarios for renewable energy Sadorsky, Perry Global scientific article 2011 16 Scenarios for sustainable energy in Scotland Child et al. The future of the European electricity system and the impact of fluctuating renewable energy – a scenario analysis. As for the structure of the scenario models, already during the selection for the research group, the focus was on clear outlines of the scenarios, as this was important in identifying the basic premises.
Sadorsky (2011) puts, for example, a lot of effort in designing and outlining all the premises important to the model, giving the reader not only an idea of which technologies can be adopted or abandoned, but even going so far as to describe the overall feel. towards certain measures and policies: "In this scenario, discussions about greater use of renewable energy are stuck in criticisms and comments about what cannot be done, instead of focusing on what has been done and the more that could done." (Sadorsky, 2011, p. 1097). Although the style of documents differs, there are assumptions in each resource to enable predictions about the future evolution of the energy mix. Due to the nature of the procedure, the order of variables is chronological and follows the reading order.
V ARIABLE D ESCRIPTION
Besides classification and numbering, the most important part of variable notation is the descriptive naming scheme. On the other hand, in the case of Saudi Arabia, it is recognized that "(...) the relative lack of any financial stimulus. TT2 is one of the biggest characters presented in the discussion of renewable energy solutions in the context of being feasible in terms of cost, but also when looking at production stability.
However, in general it can be said that in the context of this variable in energy models it should be seen as a reduction of losses. In the case of French Guiana, it is stated that "(...) the thermal part will represent only 16%." TT18 is the mention of the importance of hydropower in the future that includes the production of conventional hydropower plants but also pumped hydropower plants.
As already mentioned, large percentages of renewable energy sources can potentially create fluctuations in the energy supply that must be stretched out. To overcome these problems and give renewable energy technology a chance in the market, financial support can be issued (Variable GG3) to support. GDP growth is represented by variable EE12, which in the past has been closely related to energy consumption (IEA, 2019d).
In the case of coal, “(…) transport costs depend on access to the sea. Although in the context of the environmental impacts of fossil fuels, the primary focus is climate.
S TATISTICAL D ATA A NALYSIS AND D ISCUSSION
Nevertheless, the World Energy Outlook probably acts as one of the most important energy industry reports and consequently covers a lot of ground on the content side. The most commonly referenced variable of the analysis, mentioned in all thirteen resources, is "VV1 Greenhouse Gas Emission Reduction" (Figure 13). Understandably, this topic, often linked to man-made climate change, is the variable most mentioned in the analysis.
UN Secretary-General Antonio Guterres, among others, has described climate change as “the most systematic threat to humanity” (Sengupta, 2018) and therefore calls on UN members to reduce greenhouse gas emissions. Also, the development of energy demand is understandably one of the most important factors when considering the future development of the energy system and has been found in both highly technical and highly qualitative resources. The multiple correspondence analysis paints an interesting picture of the relationship of all analyzed resources to each other, using the scenario variables as statistical variables.
This multidimensional approach using dummy variables for included (Y) and not included (N) variables shows a grouping (Figure 14) of some of the analyzed papers suggesting similarities in the context of used variables across scenario models. It is the most diverse resource of the pool that provides the most in-depth analysis on a very broad scale. Al-Saleh's (2009) energy scenario model on Saudi Arabia (No. 3) also finds itself separated from the majority of the papers, indicating a different type of variables used in the model.
S UMMARY OF C HAPTER 3
MCA shows that the single most influential contributor (Source 1) (IEA, 2019d) in the field of energy scenario modeling and another very extensive body of research in terms of variable volume (Source 7) (Sadorsky, 2011 ) appears to be the "most different" of all the analyzed papers. Furthermore, regarding the relationships between the variables and their interconnected presentation in the sources, Figure 15 presents the complex correlation relationship of the variables. It is clear that most of the variables are clustered in one part, which corresponds to Figure 14, revealing a certain degree of similarity between the papers and their use of variables.
Visible in this chart is again the upper right group, which represents variables that are exclusive to Resource 1, World Energy Outlook 2019.
SCIENTIFIC CONTRIBUTION AND MANAGERIAL
S CIENTIFIC C ONTRIBUTION
The total number of variables is also higher by a factor of between 2.8 and 1.7 in favor of technical variables (Figure 11). Although the root cause of this is unknown, it can be speculated that technical variables are simply much easier to convert into numbers, which are then easier to incorporate into a mathematical energy model. On the other hand, the effects of qualitative variables such as "VV6 awareness of environmental issues" are much more difficult to predict and model.
Nevertheless, it has been shown that especially those non-technical factors can have a major impact on the development of energy markets (GHG emission movement, fear of nuclear energy, etc.). A scientific continuation of this thesis could therefore include the cause and effect of an overrepresentation of technical variables as well as a comparison of energy model predictions and their underlying input variables.
M ANAGERIAL I MPLICATIONS
However, a few months after the announcement, the Fukushima nuclear disaster occurred and after a public outcry, mass protests and political leaders expressing reservations against nuclear energy, a 180 degree shift occurred, modifying the initial plan that "( ...) the role assigned to nuclear power in the energy concept was reconsidered (...)” (BMWi, 2011, p. 1) which ultimately led to the progressive phasing out of nuclear power plants all together in the following years. As previously shown, some estimates have been consistently misleading in favor of non-renewable energy sources (Breyer et al., 2017; Creutzig et al., 2017), which can serve as a motivation to maintain the status quo of the energy mix as long as possible Finally, it can be said that the sphere of energy scenario models is highly diverse and covers many different aspects of the possible future development of energy markets around the world.
Another research finding is the discovery of multiple styles of energy scenario models. Smart Energy Europe: Technical and economic impact of a potential 100% renewable energy scenario for the European Union. Retrieved February 8, 2020, from https://www.elsevier.com/solutions/scopus/how-scopus-works/content Endt, C., & Witzenberger, B.
Hominid use of fire in the Lower and Middle Pleistocene: A review of the evidence [and comments and responses]. Retrieved January 19, 2020, from https://www.usgs.gov/faqs/what-are-types-coal?qt-news_science_products=0#qt-news_science_products. Evaluation of renewable energy-based rural electrification program in western China: Emerging issues and possible scenarios.
7 Some Future Scenarios for Renewable Energy Sadorsky, Perry 2011 Global Science Paper 8 European Renewable Energy Target for 2030. Analysis of Japan's Long-Term Energy Outlook Considering Mass Deployment of Variable Renewable Energy by Energy Scenario nuclear.
