LOW-CARBON TRANSITION
MODEL-BASED ANALYSES OF UPCOMING CHALLENGES AND OPPORTUNITIES
vorgelegt von M.Sc.
Konstantin Emanuel Löffler ORCiD: 0000-0002-5435-1880
an der Fakultät VII – Wirtschaft und Management der Technischen Universität Berlin
zur Erlangung des akademischen Grades Doktor der Wirtschaftswissenschaften
– Dr. rer. oec. – genehmigte Dissertation
Promotionsausschuss:
Vorsitzender: Prof. Dr. Joachim Müller-Kirchenbauer
Gutachter: Prof. Dr. Pao-Yu Oei (Europa-Universität Flensburg) Gutachter: Prof. Dr. Christian von Hirschhausen
Gutachter: Priv.-Doz. Dr. Hans Auer (TU Wien) Tag der wissenschaftlichen Aussprache: 11. Juni 2021
Berlin 2021
This dissertation uses the Global Energy System Model (GENeSYS-MOD) to analyze va- rious effects of the global low-carbon transition. The focus of the work is mainly on develo- pments in the energy system, but also branches out to analyze and discuss socio-economic issues that are connected to this technical transformation (in the form of employment ef- fects and social cost analyses). The dissertation tries to answer region-specific questions for Europe, India, China, and Germany, while also bridging back to a global perspecti- ve, and giving an excursus into the lessons learned from modeling 100% renewable energy scenarios.
Part I of the dissertation presents various regional analyses, focusing on the energy transi- tion and its related effects. For Europe, the issue of asset stranding is examined by intro- ducing non-perfect foresight into the energy system model computation. The analysis of the Indian energy system focuses on the difficult task of combining rapid demand growth with emission reduction targets, and taking a closer look at the employment effects of the transformation of the local energy system. A case study on China implements new model features, trying to improve the result details, and specifically targets the developments in the Chinese industrial and coal sectors in the context of the Paris Climate Agreement. The first part is concluded by an explorative sensitivity analysis for the GermanEnergiewende, aimed at identifying the key chances and barriers that it could face. Part II then focuses on the current state and future possibilities for energy system models by giving a discussion about the use of discounting in cost-optimizing energy system models, specifically focusing on social discounting (to ensure intergenerational equality) and social costs of carbon (to include all negative external effects of greenhouse-gas emissions). The chapter provides a point of discussion by highlighting the immense effect on model results and by pointing out the lack of literature and awareness on the topic. The final chapter of Part II then concludes my dissertation by giving insights into the lessons learned from modeling 100%
renewable energy systems, providing both a retrospective about important model settings, as well as an outlook for further research in the field.
By providing multiple perspectives on the challenges and opportunities that the global low- carbon transition brings with it, this dissertation is able to offer valuable insights, as well as policy recommendations and inputs into the energy modeling community. The aim is to contribute to both the discussion on current topics, but also highlight possibilities for further research in the field.
Keywords: Energy system modeling, decarbonization pathways, low-carbon transition, energy system
Die vorliegende Arbeit nutzt das offene, multi-sektorelle EnergiesystemmodellGlobal Ener- gy System Model(GENeSYS-MOD) um diverse Effekte der globalen Energietransformation zu analysieren. Der Fokus der Arbeit ist hauptsächlich auf Entwicklungen des Energiesys- tems, gibt aber auch Ausblicke in sozio-ökonomische Aspekte die mit der technischen Trans- formation einhergehen. Die Dissertation versucht regionen-spezifische Fragen für Europa, Indien, China, und Deutschland zu beantworten, wobei gleichzeitig eine Brücke zurück zum globalen Kontext geschlagen wird.
Der erste Teil der Dissertation präsentiert mehrere regionale Analysen, die auf die Energie- transformation und ihre einhergehenden Effekte eingehen. Für Europa wird die Thematik von versunkenen Investitionen analysiert, indem reduzierte Voraussicht in die Modellierung mit aufgenommen wird. Die Analyse des indischen Energiesystems betrachtet den schwieri- gen Zwiespalt zwischen starkem Zuwachs an Energienachfrage, sowie der Notwendigkeit für Emissionsreduktionen und gibt dabei Ausblicke in die damit einhergehende Entwicklung des lokalen Arbeitsmarkets. Eine Fallstudie zu Chinas Entwicklung im Kontext des Pariser Klimaabkommens implementiert neue Modellfunktionen, die den Detailgrad der Ergebnisse, speziell im Hinblick auf den Industriesektor, verbessern sollen. Teil I wird abgeschlossen mit einer explorativen Sensitivitätsanalyse der deutschen Energiewende, welche darauf abzielt, deren größten Chancen und Hindernisse zu quantifizieren. Der zweite Teil der Arbeit gibt Einblicke in den aktuellen Stand und zukünftige Möglichkeiten im Feld der Energiesystem- modellierung, indem spezifisch die Problematik der Diskontierung im Klimakontext sowie die Einbringung sozialer Kostenwerte (zur Wahrung von intergenerationeller Gleichheit und Internalisierung von Umweltkosten in die Modellergebnisse) diskutiert werden. Das letzte Kapitel des zweiten Teils schließt die Dissertation mit Lehren aus der Modellierung 100%
erneuerbarer Energiesysteme ab. Es gibt hierbei sowohl eine Restrospektive über wichtige Modell-Annahmen, als auch einen Ausblick auf zukünftige Forschungsmöglichkeiten.
Durch die Darlegung verschiedenster Perspektiven über die Chancen und Hindernisse, die die globale Energietransformation mit sich bringt, gewährt diese Dissertation wertvolle Er- kenntnisse, sowie Politikvorschläge und Inputs in die Welt der Energiesystemmodellierung.
Das Ziel ist es, zur Diskussion über aktuelle Forschungsthemen beizutragen, sowie Möglich- keiten für zukünftige Forschung in dem Feld aufzuzeigen.
Schlüsselwörter:Energiesystemmodellierung, Dekarbonisierungspfade, Energiewende, Transformation des
After working on a project for such a long time, there are always many people involved beyond the actual author. As such, I have many people to thank for where I am today and for enabling me to finish my dissertation. It is extremely hard to name all of you and to find both the right words, as well as an order of any sorts, but I will try nonetheless. Even though I am not able to name every single person that has influenced and supported me in my path, I want you to know that I think of every single one of you and am deeply grateful for everything.
I want to thank Prof. Dr. Pao-Yu Oei and Prof. Dr. Christian von Hirschhausen for their tremendous support over the last few years, without which this thesis would never have come to life. I am extremely grateful for the great opportunities that I got at theWorkgroup for Infrastructure Policyat TU Berlin and the departmentEnergy, Transport, Environment at DIW Berlin, led by Prof. Dr. Claudia Kemfert. I find it an exceptional how much trust you put in me and how many great experiences and prospects you offered me, even when I was still in my Master’s studies. Another great thanks goes to my third supervisor and referee of my dissertation, Priv.-Doz. Dr. Hans Auer, for his great feedback and constructive critcism throughout many stages of my work.
I also want to thank Dr. Roman Mendelevitch, who co-hosted the study research project in 2016 with Prof. Dr. Oei - which gave me my first experience in academic research and got me interested in pursuing my doctorate. This is also where I met my valued colleagues (some would even call us ’triplets’) Thorsten Burandt and Karlo Hainsch, without which writing my dissertation would not only have been a lot less fun, but likely would never have happened. It is very rare to find such great colleagues and friends, and I have wholeheartedly enjoyed working with you over the last few years. I believe that the way that we complement each other in our different perspective and way of thinking is one of the main drivers behind our research ideas. Also, I want to say thanks to all my colleagues at theWIP at TU Berlin - it has been a pleasure to work in such an open and energetic environment.
Finally, I want to thank my friends and family for always being there for me and giving me energy on this journey throughout the years. I am extremely grateful to have you in my life, able to rely on you throughout both the good, but also the difficult times - especially in the recent COVID-era. A special thanks goes to Anita for her amazing support and the great times we were able to have over the last years, giving me much-needed respite, even in the most challenging times.
Thank you.
Hiermit versichere ich, dass ich die vorliegende Dissertation selbstständig und ohne unzu- lässige Hilfsmittel verfasst habe. Die verwendeten Quellen sind vollständig im Literatur- verzeichnis angegeben. Die Arbeit wurde noch keiner Prüfungsbehörde in gleicher oder ähnlicher Form vorgelegt.
Konstantin Löffler Berlin, 1. August 2021
1 Introduction 1
I Regional Analyses using the Global Energy System Model
(GENeSYS-MOD) 37
2 A quantitative assessment of the stranded assets problem in Europe 39
3 Effects of the low-carbon transition on the Indian energy system 57
4 Decarbonization pathways for the energy system in China 83
5 Chances and barriers for Germany’s Energiewende 111
II The State of Energy System Models: Current Best Prac- tices and Future Expansion Possibilities 139
6 Social discounting, social costs of carbon, and their use in energy system
models 141
7 Lessons from modeling 100% renewable scenarios using GENeSYS-MOD155
III Appendices for individual chapters 175
A Appendix to Chapter 1: Introduction 177
B Appendix to Chapter 2: A quantitative assessment of the stranded
assets problem in Europe 179
C Appendix to Chapter 3: Effects of the low-carbon transition on the
Indian energy system 187
D Appendix to Chapter 4: Decarbonization pathways for the energy sys-
tem in China 195
E Appendix to Chapter 5: Chances and barriers for Germany’s
Energiewende 205
References 213
1 Introduction 1
1.1 Motivation . . . 2
1.2 Chances and barriers for the global low-carbon transition . . . 4
1.2.1 Renewable energy potentials & integration . . . 5
1.2.2 Sector-coupling . . . 8
1.2.3 Hydrogen . . . 9
1.2.4 Energy- and climate policy . . . 11
1.2.5 Energy demands . . . 14
1.2.6 Socio-economic drivers . . . 15
1.2.7 Open science . . . 17
1.3 The Global Energy System Model - GENeSYS-MOD . . . 18
1.4 Outline of the dissertation . . . 21
1.4.1 Part I - Regional Analyses using the Global Energy System Model (GENeSYS-MOD) . . . 23
1.4.2 Part II - The State of Energy System Models: Current Best Practices and Future Expansion Possibilities . . . 28
1.4.3 Chapter origins and own contributions . . . 31
1.5 Research outlook . . . 32
I Regional Analyses using the Global Energy System Model (GENeSYS-MOD) 37
2 A quantitative assessment of the stranded assets problem in Europe 39 2.1 Introduction and literature review . . . 402.2 Status quo . . . 43
2.2.1 The current status of the energy system . . . 43
2.2.2 Current political landscape . . . 44
2.3 Model and data . . . 46
2.4 Results . . . 49
2.5 Conclusion . . . 55
3 Effects of the low-carbon transition on the Indian energy system 57 3.1 Introduction . . . 58
3.2 Status quo of the Indian energy sector and climate policy . . . 59
3.3 Overview over relevant literature . . . 62
3.4 Methodology . . . 65
3.4.1 Model description and data . . . 65
3.4.2 Model validation . . . 68
3.4.3 Scenarios . . . 68
3.4.4 Calculation of employment effects . . . 68
3.5 Development of the Indian energy system under varying carbon dioxide (CO2) constraints . . . 69
3.5.1 Cross-sectoral analysis and sector-coupling . . . 70
3.5.2 Effects on the electricity sector . . . 71
3.5.3 India’s climate ambitions in context . . . 74
3.6 Possible chances and barriers for the low-carbon transition in India . . . 76
3.6.1 The transmission grid and decentrality . . . 76
3.6.2 Employment effects . . . 78
3.7 Discussion of model results . . . 79
3.8 Conclusion . . . 81
4 Decarbonization pathways for the energy system in China 83 4.1 Introduction . . . 84
4.2 Characterization of the Chinese climate and energy policy . . . 85
4.3 Status quo of relevant literature . . . 87
4.4 Methodology . . . 91
4.4.1 Key assumptions and data . . . 94
4.4.2 Scenario analysis . . . 95
4.4.3 Model calibration and validation . . . 96
4.4.4 Model characterization and limitation . . . 96
4.5 Impact of CO2 budgets on the Chinese energy system . . . 98
4.6 Barriers for a decarbonization of the Chinese energy system . . . 106
4.7 Recommendations . . . 107
4.8 Conclusion . . . 109
5 Chances and barriers for Germany’s Energiewende 111 5.1 Introduction . . . 112
5.1.1 Literature review . . . 113
5.1.2 Status quo of Germany’s Energiewende . . . 115
5.2 Materials and Methods . . . 116
5.2.1 Model description . . . 116
5.2.2 Exploring uncertainty via sensitivity analysis . . . 118
5.2.3 Chosen base case scenario . . . 119
5.2.4 Sensitivities analyzed in this study . . . 120
5.3 Results . . . 124
5.3.2 Demands . . . 127
5.3.3 Carbon price . . . 130
5.3.4 Hydrogen . . . 132
5.3.5 Renewable energy sources . . . 133
5.4 Conclusion . . . 136
II The State of Energy System Models: Current Best Prac- tices and Future Expansion Possibilities 139
6 Social discounting, social costs of carbon, and their use in energy system models 141 6.1 Introduction . . . 1426.2 Discounting, social costs of carbon, and energy system models: An overview 143 6.3 Implementing social discounting in GENeSYS-MOD . . . 145
6.4 Results . . . 147
6.5 Implications and discussion . . . 151
6.6 Conclusion . . . 152
7 Lessons from modeling 100% renewable scenarios using GENeSYS-MOD155 7.1 Introduction . . . 155
7.1.1 The origin of 100% renewable scenarios . . . 155
7.1.2 Research focus . . . 157
7.2 Methodology . . . 158
7.2.1 Description of the Global Energy System Model (GENeSYS-MOD) . 158 7.2.2 Data assumptions . . . 159
7.3 Choosing the best spatial resolution . . . 160
7.3.1 The devil lies within the detail: differences of a continental, national and regional Investigation . . . 160
7.3.2 The energy transition can result in the shift of energy supply centers 161 7.4 Temporal aspects of modeling . . . 163
7.4.1 Improving the time resolution . . . 163
7.4.2 Effects of reduced foresight on energy pathways . . . 165
7.5 More detailed analysis of sectoral transitions . . . 167
7.5.1 Examining the industry sector more closely . . . 167
7.5.2 What is an optimal share of renewables for each sector . . . 168
7.5.3 Examining the employment potential of the energy system transition 170 7.6 Conclusion . . . 171
III Appendices for individual chapters 175
A Appendix to Chapter 1: Introduction 177
B Appendix to Chapter 2: A quantitative assessment of the stranded
assets problem in Europe 179
B.1 Model description . . . 179
B.2 Emission budget . . . 181
B.3 Validation of model results . . . 181
B.4 Model data . . . 183
C Appendix to Chapter 3: Effects of the low-carbon transition on the Indian energy system 187 C.1 Model description . . . 187
C.2 Model data . . . 189
C.3 Additional model results . . . 192
C.4 Supplementary material . . . 193
D Appendix to Chapter 4: Decarbonization pathways for the energy sys- tem in China 195 D.1 Data . . . 195
D.2 GENeSYS-MOD: blocks of functionality . . . 198
E Appendix to Chapter 5: Chances and barriers for Germany’s Energiewende 205 E.1 Model description . . . 205
E.2 Selected input data . . . 207
E.3 German federal states . . . 210
E.4 Base case results . . . 211
E.5 Supplementary material . . . 212
References 213
1.1 Indicators for global climate change. . . 4
1.2 Levels of mitigation potentials. . . 7
1.3 Survey results on socio-economic drivers in energy system models. . . 16
1.4 Simplified graph of GENeSYS-MOD. . . 19
1.5 Improvements and additions across versions of GENeSYS-MOD. . . 20
1.6 Regional studies conducted with GENeSYS-MOD. . . 21
1.7 Outline of the dissertation. . . 23
2.1 Installed natural gas capacities and their yearly load factor for Germany, Italy, UK, and the Netherlands. . . 44
2.2 Model structure of GENeSYS-MOD v2.0. . . 47
2.3 Computational process of the reduced foresight scenarios (RED & POL). . . 48
2.4 Primary energy supply for the years 2020, 2030, 2040, and 2050, both rela- tive, as well as total amount in Exajoule (EJ). . . 50
2.5 Total stranded assets for coal- and gas-fueled power generation per region in the year 2035. . . 50
2.6 Total amount of unused capacities for coal-based power plants. . . 51
2.7 Gas power plant capacities and load factor for Germany, Italy, UK, and the Netherlands. . . 52
2.8 Emission differences between scenarios for the sectors electricity, heat, and transportation in Mt CO2 in comparison to the Base scenario. . . 53
2.9 Levelized cost analysis for key technologies (Average across the modeled re- gions). . . 54
3.1 Overview of available coal reserves and installed capacities of onshore wind and solar power. . . 60
3.2 Overview of electricity demand in 2015, electricity generation in 2015, and onshore wind and utility-scale solar average capacity factors. . . 67
3.3 Primary energy in India. . . 69
3.4 Sector-wise energy generation in TWh per scenario. . . 70
3.5 Power generation (positive values) and use per sector (negative values) in TWh. . . 72
3.6 Power generation - Map for all regions in 2050. . . 73
3.7 Electricity dispatch in theLimited Emissionsscenario in 2050. . . 74
3.8 Climate targets. . . 75
3.9 Net electricity trade per region and rooftop solar installations in comparison. 77 3.10 Employment per energy carrier in million jobs. . . 79
4.1 Overview of available coal reserves (in EJ) as well as solar radiation (in kWh/d) and final energy demand (in EJ) per Chinese province. . . 86 4.2 Comparison of yearly power generation by technology in TWh in different
decarbonization pathways. . . 98 4.3 Yearly industrial heat generation in PJ per technology and scenario. . . 99 4.4 Shares of consumed energy in percent of total consumption in different sec-
tors and scenarios. . . 100 4.5 Coal consumption in TWh per sector in the Limited Effort pathway with
coal consumption of other pathways as comparison. . . 101 4.6 Regional power production shares in 2050 in the different pathways. Provinces
with high demand are highlighted with darker background color. . . 102 4.7 Difference of power production per year and sensitivity for an artificially
aggregated Chinese power system compared to the base scenario. . . 104 4.8 Yearly dispatch in different sensitivity assessments. . . 105 5.1 Structure of Global Energy System Model (GENeSYS-MOD) including its
main technologies and the respective connections. . . 117 5.2 Spread of emission reductions compared to 1990 across all tested sensitivities
(left) and spread of accumulated emissions in 2050 across all sensitivities.
The range for the emission budgets is derived from the IPCC SR1.5 with a share for Germany based on its population. . . 125 5.3 Spread of the share of renewables in electricity generation across all tested
sensitivities. . . 126 5.4 Average generation costs for electricity across all tested sensitivities. The
top graph shows the development over time for Germany as a whole, the bottom graph shows the spread of electricity generation costs in 2050 per federal state. The costs are displayed in € per MWh and do not factor in infrastructure costs. . . 127 5.5 Changes in electrification rates by varying electricity demand in building,
industry and transport sector (figure above). Changes in electrification rates by varying total energy demand in building, industry and transport sector (figure below). . . 128 5.6 Effects of demand development sensitivities on electricity generation (in TWh).130 5.7 Effects of emission price sensitivities on the electrification rate across differ-
ent sectors. . . 131 5.8 Usage of hydrogen per sector (top) and usage of hydrogen per federal state
in 2050 (bottom) by varying costs for breakthrough technologies. . . 132
5.9 Development of average generation costs for electricity (top) and net trade of electricity in 2050 (bottom). Electricity generation costs in € per MWh,
net trade in TWh. . . 134
5.10 Electricity generation per federal state for offshore, onshore, and solar rela- tive to the base case. Red color indicates less production than in the base case, yellow indicates no change, and green indicates an increase in generation.135 6.1 Graphical representation of effects of chosen annual discount rates on net present value of investments over time. . . 147
6.2 Comparison of cumulative CO2 emissions over the modeled period in Mt CO2.148 6.3 Electricity mix in the year 2050 for different choices of social discount rate, for both regular carbon price and social costs of carbon sensitivity. . . 149
6.4 Electricity use per sector in TWh per sector in the year 2050. . . 150
6.5 Installed capacities in the year 2050 for different choices of social discount rate and carbon price sensitivities in GENeSYS-MOD Europe. . . 151
7.1 Description of GENeSYS-MOD. . . 158
7.2 Scaling down 100% Renewable scenarios - for the World, Europe and Germany.161 7.3 Change of regional power production in South Africa. . . 163
7.4 Effects of more detailed temporal resolution in comparison to better technical representation of ramping. . . 165
7.5 Primary energy supply, both relative, as well as total amount in Exajoule (EJ) for Europe. . . 166
7.6 Total stranded assets for coal- and gas-fueled power generation in the year 2035 across Europe. . . 167
7.7 Decarbonization of industrial heat in China. . . 168
7.8 Calculating an optimal renewable share for Mexico. . . 169
7.9 Employment effects for 100% renewable scenarios in Colombia. . . 171
B.1 Model structure of the GENeSYS-MOD implementation used in this study. . 179
B.2 Emission budget calculations. . . 181
B.3 Comparison of 2015 model results vs. historical numbers. . . 182
C.1 Model structure of the GENeSYS-MOD implementation used in this study. . 188
C.2 Presentation of the yearly average capacity factors for onshore wind and solar PV per data point in a 40x40km grid. . . 191
C.3 Created jobs per job type in India for theDelayed ActionandLimited Emis- sions scenarios. . . 193
D.1 Presentation of the yearly average capacity factors for onshore wind and solar PV per data point in a 50x50km grid . . . 197 D.2 Simplified block structure of OSeMOSYS and GENeSYS-MOD . . . 198 E.1 Model structure of the GENeSYS-MOD implementation used in this study. . 206 E.2 Power generation per year and technology in the base-case. . . 211 E.3 Primary energy demand per year and fuel in the base-case. . . 211 E.4 Emissions per sector and year in the base-case. . . 212
1.1 Chapter origins . . . 31
3.1 Changes in input demand assumptions and primary energy demand per sce- nario, relative to 2015 values. . . 70
4.1 Capacity in GW and yearly generation in TWh of main electricity generation technologies in China . . . 108
5.1 Analyzed sensitivities in this study, including quantity and value ranges for each chosen parameter. . . 123
6.1 Choice of social discount rate parameter options. . . 147
A.1 Further publications with GENeSYS-MOD . . . 177
C.1 Capital costs of main electricity generating technologies in M€/GW . . . 189
C.2 Capital costs of main electricity generating technologies in M€/GW . . . 190
C.3 Import fossil fuel cost in M€/PJ and domestic costs of hard-coal in primary coal-exporting provinces . . . 190
C.4 Sector-specific demands for India . . . 190
C.5 Resulting installed capacities for theDelayed Action andLimited Emissions scenarios. . . 192
D.1 Capital costs of main electricity generating technologies in M€/GW . . . 195
D.2 Capital costs of main electricity generating technologies in M€/GW . . . 196
D.3 Import fossil fuel cost in M€/PJ and domestic costs of hard-coal in primary coal-exporting provinces . . . 196
D.4 Sector-specific demands . . . 196
E.1 Acronyms for German federal states. . . 210
BEV battery-electric vehicle.
C celsius.
CCS carbon capture and storage.
CCTS carbon capture, transport, and storage.
CGE computable general equilibrium.
CHP combined heat and power.
CO2 carbon dioxide.
DME dimethyl ether.
EMP-E Energy Modeling Platform for Europe.
EU European Union.
FYP five-year plan.
GDP gross domestic product.
GENeSYS-MOD Global Energy System Model.
GHG Greenhouse gas.
Gt gigaton.
GW gigawatt.
H2 hydrogen.
ICE internal combustion engine.
IEA International Energy Agency.
IIASA International Institute for Applied Systems Analysis.
IPCC Intergovernmental Panel on Climate Change.
NDC nationally determined contribution.
NEP National Electricity Plan.
NET negative emission technology.
O&M operation and maintenance.
OECD Organization for Economic Co-operation and Develop- ment.
OSeMOSYS Open Source Energy Modelling System.
POL political boundaries scenario.
PV photovoltaics.
R&D research and development.
RED reduced foresight scenario.
RES renewable energy sources.
SDG sustainable development goal.
SMR steam methane reforming.
UBA German Environment Agency.
UN United Nations.
USA United Stated of America.
WEO World Energy Outlook.
Introduction
1.1 Motivation
Since my early childhood, I have been interested in energy-related topics. My grandfa- ther was an engineer working on the Proton-Synchrotron particle accelerator at CERN in Geneva, Switzerland, a prior version and part of the famous Large-Hadron-Collider. As such, he is a strong believer in nuclear fusion and alternative ways to generate electricity. I remember getting a small solar-powered fuel-cell-electric-vehicle for my 12th birthday and being blown away by the fact that ”science” was able to make the small vehicle go without any (obvious) external energy source. Following these experiences, I decided to study eco- nomics at TU Berlin, being especially interested in energy-, environmental-, and resource economics, as well as their interlinkages. After taking various classes on the topics, I realized more and more how the ways we generate and use energy affects the general environment, specifically in regard to man-made climate change. In 2016, I joined a student research project on the topic of ”designing an energy system model based on 100% renewables”
led by Professors Christian von Hirschhausen and Pao-Yu Oei, where I met my colleagues Thorsten Burandt and Karlo Hainsch, with whom I co-developed GENeSYS-MOD, an en- ergy system based on the Open-Source Energy Modeling FrameworkOSeMOSYS (Howells et al. 2011a). This study project was my first interaction with real-world research and turned out to be the perfect mixture of both my personal interests, as well as a practi- cal application of all the methods and concepts that I had acquired in my prior years of studying. To my great surprise, the research project sparked a lot of attention and support by our supervisors, leading to my first talks at academic conferences at the Trans-Atlantic Infraday in Washington, D.C. in November 2016, and the Internationale Energiewirtschaft- stagung (IEWT) in Vienna in February 2017 - while actually still ”only” being enrolled as a regular Masters-level student. At the time, I still did not even envisage myself remaining at the university, less doing my doctorate.
However, one thing that I learned early on when I began working in the field of quantitative modeling, is, that as the modeler, you are neverreally satisfied with the generated results, as there would always be some limitation to your model. Thus, after finishing up our first student research project and publishing the results in the peer-reviewed journal Energies (Löffler, Hainsch, Burandt, Oei, Kemfert, et al. 2017), it was clear that more work was needed on the topic. Since most modeling efforts at the time focused only on one single sector of the energy system, I felt that our approach of cross-sectoral system-wide analysis was able to answer many open questions. In our 2017 paper, we found that a global energy system based on 100% renewables for 2050 was technically feasible, but the grand questions ofhow exactly to get there, and what this would mean for the different regions of our global model, remained. This sparked my interest for more and more new analyses,
What frequently came up in comments and discussions at my talks at academic conferences were questions about region-specific issues, renewable potentials, and issues that the power system would face under high amounts of electrification and high shares of variable renew- ables. My colleagues and I quickly realized that we needed to take a closer look at various regions and technical aspects of our global model application to address these shortcom- ings. Thus, we continuously improved the model formulation (Hainsch et al. 2018; Burandt, Löffler, and Hainsch 2018; Burandt et al. 2019) and applied it to a range of new regions, including Europe, India, China, Mexico (Löffler, Hainsch, Burandt, Oei, and Hirschhausen 2017; Lawrenz et al. 2018; Löffler et al. 2019; Bartholdsen et al. 2019; Sarmiento et al. 2019;
Burandt et al. 2019; Auer et al. 2020). My goal in continuing my research and writing my dissertation has always been in improving upon our model and giving valuable insights for both academia and policy makers regarding the transformation of the energy system, which I regard as one of the major challenges for today’s society in the fight against global warm- ing. Making our model, data, and publications freely available to the public by publishing open access and freely distributing our model source code has thus always been a high priority of mine, since I believe that these questions can only be solved with transparent research that enables a fruitful discourse on upcoming decisions. I was therefore humbled to be able to contribute to the 1.5 Degree Special Assessment Report of the IPCC, where we submitted our global model runs to the public database and were added to the official scenario explorer (Huppmann et al. 2018).
In summation, I believe that by using cross-sectoral energy system models, many possible chances and barriers for the global low-carbon transition,1 both on a global as well as on a regional level, can be quantified and analyzed. While no model is able to perfectly predict the future, the goal of this dissertation is to give some level of insight into possible aspects of the transformation of the energy system and the transition away from fossil fuels in key regions of the world. Or as Charles F. Kettering famously put it: ”Research means that you don’t know, but are willing to find out.”
The remainder of the introduction continues with a short introduction in the energy system model that has been used and developed for this dissertation, followed by an overview of the chances and barriers that the global low-carbon transition is facing, going into detail on some of the key influential factors. I will then give an outline of the individual chapters and lists their pre-publications, as well my own contributions. The introduction concludes with an outlook for future research.
1Throughout this dissertation, the terms transition and transformation will frequently be used. I hereby adhere to the definition of Child and Breyer (2017), which describe transformation as change in physical aspects of energy systems (e.g., ”the transformation of the energy system”, referring to the technological changes required), while transition describes change across the entire socio-technical system (e.g., ”the energy transition”, encompassing also the changes in social structures, behavior, or political thinking).
1.2 Chances and barriers for the global low-carbon transition - an energy system modelers perspective
”Warming of the climate system is unequivocal, and since the 1950s, many of the observed changes are unprecedented over decades to millennia” (IPCC 2013, 2014b). This dramatic change in climate (see Figure 1.1) is mostly man-made, since the emission of Greenhouse Gases (GHGs) is responsible for the greenhouse effect which slowly increases the planet’s temperature by trapping radiation within the atmosphere (IPCC 2014b; EIA 2018). If the concentration of these GHGs is not reduced significantly within the next decades, irreversible and severe consequences for humans and natural systems are the consequence (McMichael, Woodruff, and Hales 2006; Caesar et al. 2018). Extreme weather events (e.g., heat waves, coastal flooding) will become more common as well as a rising risk of destroying unique ecosystems such as the dying of coral reefs (IPCC 2014b).
One of the biggest contributors of GHG emissions is the energy sector, accounting for more than two thirds of the global emissions (IEA 2016; IPCC 2015). The most important greenhouse gas is CO2, which is responsible for more than 80% of the emissions in the energy sector (Foster and Bedrosyan 2014). Therefore, various challenges arise for different countries when it comes to decarbonizing their energy systems. On the one hand, emerging countries face the problem of an often substantial increase in energy demands (due to economic & population growth), but have to manage environmental concerns and the need of emission reductions at the same time. On the other hand, already developed countries and regions have to step up for their historic obligations and take a leading role in the energy transition, which is often seen as being coupled with higher costs and more uncertainty (IEA 2015).
In recent years, the focus was heavily set on decarbonizing the electricity sector. However, in a fully decarbonized energy system, the heating, industry, and transportation sectors deserve just as much, if not more attention. A high degree of electrification, thus coupling the heating, industry, and transportation sectors with the electricity sector, is predicted in future scenarios, which implicitly affects the power sector (IEA 2015; Fraunhofer IWES et al. 2015; Wietschel et al. 2018).
Traditionally, energy system models relied on the trinity of fossil fuels with carbon capture, nuclear energy, and renewables to meet climate targets, the two former ones providing backup capacity in case of no wind and no sun. This pattern is now increasingly challenged by the availability of low-cost storage technologies and other flexibility options, such as demand-side management, high-voltage grid interconnections, etc, providing the necessary flexibility to balance intermittent renewables (Hirth, Ueckerdt, and Edenhofer 2015). At the same time, carbon capture, transport, and storage (CCTS) seems highly unlikely as a reliable option (C. v. Hirschhausen, Herold, and Oei 2012), with nuclear energy being no cost-efficient alternative either (Kemfert et al. 2017).
1.2.1 Renewable energy potentials & integration
Energy systems based on high shares of renewable energy sources frequently face criticism regarding their feasibility. The points of contention are usually evolving around the insuf- ficient potentials for renewable energy sources (RES) (Trainer 2013; Loftus et al. 2015) and their intermittent nature, causing problems for the electricity grid (Trainer 2010, 2012). These claims are disputed however, and have been directly rebutted in various studies (Delucchi and Jacobson 2012; Jacobson et al. 2016; Jacobson, Delucchi, Bauer, et al. 2017; Diesendorf and Elliston 2018). These discussions finally culminated in the academic exchange between Heard et al. (2017) and Brown et al. (2018): According to
Heard et al. (2017), their studied range of 100% renewable energy scenarios had significant shortcomings in their representation of renewables, namely in the areas of reliability (e.g., that periods of both very low wind and solar are not appropriately addressed - a topic that comes up quite often as the Dunkelflaute in German policy discussions), as well as in grid balancing issues (e.g., voltage regulation). Brown et al. (2018), however, argued that there are indeed differences between (technical) feasibility and (economic) viability, but also underlined that all of the raised issues can be overcome with a well-planned capac- ity mix of renewable energy sources, including a wide range of flexibility options such as storages, which can provide the desired balancing and voltage regulating effects. What is clear, however, is that a ”paradigm shift” in how the current electricity markets operate is necessary. This includes a transformation from the current, inflexible energy system where supply is reacting on demand, to a more balanced approach, where smart systems and consumer participation improve the ways that we generate and consume energy (Auer and Haas 2016).
In their 2001 report, the Intergovernmental Panel on Climate Change (IPCC) referred to multiple levels of potentials regarding a roll-out of RES (IPCC 2001), shown in Figure 1.2.
They distinguish between a physical potential, representing the theoretical upper bound of any mitigation measure, and their technological potential, socioeconomic potential, eco- nomic potential, and market potential, each ranging lower than their predecessor and each facing its own barriers. However, as also depicted in the graph, all of the latter potentials will grow over time, as social acceptance, technological progress, and cost decreases lead to improvements in their categories, and those barriers can be overcome. The IPCC lists community involvement in policy-making, education, technology research & development, and subsidy reforms as possible actions to overcome the barriers in the various categories (IPCC 2001).
Verbruggen et al. (2010) expand the IPCC framework and add the category ofsustainable development potential, stating that the previous categorization was not putting enough emphasis on sustainability goals, which also factor in planetary boundaries. Considering the developments over the last 20 years since the inception of this graph, this inclusion seems quite sensible, as sustainability issues have become increasingly relevant in today’s discourse as shown in the United Nation’s sustainable development goal (SDG) or the European Green Deal (United Nations 2015; European Commission 2019).
Figure 1.2: IPCC Levels of mitigation potentials. Source: IPCC (2001).
Summarizing, renewable energy sources are the cornerstones of the energy transition. Only by moving towards renewable energy sources can we meet carbon emission targets and therefore combat global warming. They provide one of the key opportunities for change, however, their usage also comes with some limitations that need to be solved. While many studies have shown that pathways using 100% renewable energy are feasible, both on a global, as well as on a regional scale (Bogdanov et al. 2019; Ram et al. 2017; Löf- fler, Hainsch, Burandt, Oei, Kemfert, et al. 2017; Jacobson, Delucchi, Bauer, et al. 2017;
Henning and Palzer 2012; Bartholdsen et al. 2019; Sarmiento et al. 2019; Kojima 2012;
Gulagi et al. 2017; Bogdanov and Breyer 2016; Jacobson et al. 2015; Bogdanov et al. 2021), there are still challenges where feasibility and viability might diverge (see e.g. Chapters 1.2.2,1.2.3, and 1.5). It is important to address these issues by improving communication of research, educating the public, informing policy makers, expanding technology research, and prioritizing clear, long-term goals over short-term targets.
1.2.2 Sector-coupling
Sector-coupling as a concept describes the coupling of the power sector with traditionally non-electric sectors such as heating, cooling, transportation, and industry (Lund et al. 2010;
Mathiesen et al. 2015; Robinius et al. 2017; Nuffel et al. 2018; Erbach 2019). This is important, since renewable, carbon free generation of electricity is comparatively cheap and easy to obtain, while the conventional technologies in other sectors mostly rely on the burning of fossil fuels, with limited alternatives available. This means that electrification is a major trend in most future energy scenarios. The link between the power- and other sectors can be established either directly, or indirectly. Direct electrification would mean that technologies use electricity as an input fuel to generate either heat (e.g., in power-to- heat applications like resistance heaters), cooling (e.g., via heat pumps, using electricity to move ambient heat), or kinetic energy (e.g., by powering electric motors).
Additionally, electricity could be used to generate hydrogen in the process of electrolysis, commonly referred to as power-to-gas. This hydrogen can then either be used directly (e.g., in fuel cells for re-electrification, or burned directly in hydrogen heaters) or further transformed into synthetic natural gas via methanation, so that it could be burned in regular gas turbines. Also, by adding further steps to the process chain, there is also the possibility to create so-called ”powerfuels” in the process of power-to-liquid (Dieterich et al. 2020). This process chain would be able to create a wide variety of liquid fuels (e.g., methanol, dimethyl ether (DME), or hydrocarbons to create gasoline or kerosene) to be used as an energy source or feedstock in industrial applications. This would offer a potential replacement for conventional fossil fuels without requiring a change in the consuming technologies, albeit with a hefty loss in energy efficiency due to conversion steps.
Direct electrification is often one of the most efficient ways of sector-coupling. Not only are the electric technologies quite efficient themselves,2 but especially with electricity gen- erated from renewable energy sources, the primary energy requirements can be reduced
2Take for example an internal combustion engine (ICE) vehicle only reaching∼12-30% efficiency compared
significantly. This is due to the fact that electricity generation using fossil fuels is also rather inefficient, therefore increasing the total amount of energy input needed. However, the current direct electrification technologies often come with comparatively higher costs to their conventional counterparts. These costs will most likely decrease due to technol- ogy learning curves in the future, however, they are currently posing a certain barrier for entry.
Although sector-coupling is picking up as a topic in energy research, only less than 60%
of studies looking at 100% RES in the energy system currently include a multi-sectoral perspective (Hansen, Breyer, and Lund 2019). This is a clear shortcoming and needs to be addressed in the future, as a holistic view across all sectors is needed to capture cross- sectoral effects (e.g., regarding the limited availability of biomass and where it should be utilized).
Especially when climate targets are considered, sector-coupling is an important step to reduce emissions in otherwise difficult-to-decarbonize sectors. By using a mixture of direct and indirect electrification, emission reductions by 100% would be possible, even given today’s technology options. Since renewable electricity is cheap and abundantly available, sector-coupling is a great opportunity for the deep decarbonization of the energy sector, at least from a system perspective. In practice, issues like financing and economic viability need to be ensured, which can be a problem if the used technologies only run at a low number of full-load hours (Ajanovic and Haas 2018). It is important that these issues are addressed in the energy markets to ensure a proper implementation of the possible sector-coupling potentials.
1.2.3 Hydrogen
The potential role of hydrogen in the transformation of the energy system currently receives increasing interest. In July 2020, the European Commission published a European hydrogen strategy, presenting a roadmap of actions for the coming years and identifying challenges to overcome in the process of deploying hydrogen at large scale (European Commission 2020).
Simultaneously, many other country governments have also announced their own hydrogen roadmaps (COAG Energy Council 2019; Council 2019; U.S. Department of Energy 2020).
These strategies identify hydrogen as a valuable fuel in the decarbonization of the energy system. The main idea is to produce hydrogen (H2) from renewable energy sources, i.e., without emitting CO2, and to subsequently use it in consumption areas where electricity- based solutions are less suitable. Examples of these applications can be found across all sectors: In the power sector, hydrogen can serve as storage medium which is produced
in times of high renewable power generation and used in hours of lower generation. In the transportation sector, hydrogen-fueled fuel-cell electric vehicles may offer advantages compared to battery electric vehicles, especially for heavy duty transport (road-based, shipping and aviation). Moreover, various processes in the industry sector require very high temperature levels, which currently are achieved by burning fossil fuels, but could also be based on hydrogen, in addition to it’s potential use as a feed-stock for industrial processes, e.g. in steel-making or chemistry (Otto et al. 2017; Ausfelder and Bazzanella 2016). In sum, there are many potential applications for hydrogen in a decarbonized energy system. Importantly, hydrogen has similar properties as fossil fuels, being a burnable, gaseous energy carrier. This means that pre-existing conventional generation capacities (and trade networks) could be retrofitted, which would therefore increase the lifetime of the technologies that would otherwise be defunct, or provide decarbonization options where it would otherwise be difficult to achieve, e.g. in the buildings sector (Nastasi and Di Matteo 2017). Hydrogen, being a gas, also offers advantages compared to electricity regarding storage and transportation of energy.
However, the production of renewable hydrogen currently is hardly economic, since it re- quires costly conversion technologies that also include noteworthy energy losses. Therefore, hydrogen production at a commercial scale is currently only done via steam methane re- forming (SMR), a process that converts fossil natural gas to hydrogen, implying additional emissions and removing the core benefit that hydrogen offers if generated via the use of RES. However, even in the long run, the question of economic viability and availability of enough cheap renewable electricity remains (Ajanovic and Haas 2018; Momirlan and Veziroglu 2002). Not only does the power-to-gas conversion require investments into elec- trolyzers, but due to the efficiency of only roughly 64-70%3 (International Energy Agency 2015; Mazloomi and Sulaiman 2012), about 50% more electricity - and therefore renewable capacities - would be required. Also, one issue is that of low full-load hours of most vari- able renewables, posing issues for the business case of such electrolyzer setups (Ajanovic and Haas 2018; Ball and Weeda 2015). Using electrolysis as an easy flexibility method for storing excess electricity production is great in the perspective of the energy system model, but for power-to-gas plants to be economically viable, they would most likely be needed to be coupled to a steady supply of electricity to ensure higher capacity factors for the technology (e.g., by coupling it to the general electricity grid, or by coupling it with a technology with high full-load hours such as offshore wind, or hydropower). In this context, the usage of nuclear energy for the generation of ”carbon free” hydrogen has also been discussed in the last decades (Bauer and Jansen 1982; Verfondern 2007), but has generally failed due to the uneconomic nature of nuclear (even without accounting for the
long-term costs of decomissining and permanent storage), which would be even worsened by the efficiency losses from the conversion steps (Hirschhausen 2017; Kemfert et al. 2017).
Thus, there is an inherent importance to determine the most cost-efficient use cases for renewable hydrogen and the optimal sourcing of hydrogen (both with regards towhen and wherethis renewable hydrogen would be generated).
On the other hand, in today’s world, hydrogen is often used as an excuse by incumbent actors to keep existing gas infrastructure alive by promising a switch to hydrogen at a later stage. This is a dangerous aspect that needs to be properly evaluated, as unnecessary additional investments into natural-gas-based infrastructure might create unwanted path dependencies (Hirschhausen, Praeger, and Kemfert 2020; Brauers, Braunger, and Jewell 2021). Especially with regard to technology investments, it needs to be ensured that this fuel switch can actually realistically happen without the need for additional retrofitting costs (Brauers, Braunger, and Jewell 2021; Van de Graaf et al. 2020). Summing up, hydrogencan be an important cornerstone of the low-carbon transition, providing difficult- to-decarbonize sectors a much needed option for emission reductions. However, research into hydrogen technologies, especially regarding renewable hydrogen generation, is still an ongoing process and adoption is only in the early stages. To ensure a successful decar- bonization of all aspects of the energy system, it is important to properly evaluate its use cases and carefully decide on investment decisions.
1.2.4 Energy- and climate policy
While technological progress is a major driver for future energy pathways, it is dangerous to simply take it as a ”given” - as a sort of exogenous law of nature. Instead, it is important to analyze why and how technological progress and innovation happens, and how innovation can be fostered and accelerated. Indeed, Clark, Robert, and Hampton (2016) have found that the perception of constant ”automatic” advances in technology can drive excessive optimism and therefore lead to a less perceived urgency for action. Therefore, the field of
”innovation policy” emerged in the 1990s to properly analyze these issue of innovation speed, especially in the context of public policy (Edler and Fagerberg 2017; Archibugi et al. 1999;
Edquist 2002; Borrás and Edquist 2013). Edler and Fagerberg (2017) find that policy actions have significant implications for innovation and technology development, aiming to guide developments in research and development (R&D) (e.g., by trying to address or reduce market failures). Also, from the viewpoint of the companies themselves, having a clear set of policy goals in which they can operate is very beneficial for the future strategy of a company, especially given the amount of uncertainty that the energy transition brings.
If, for instance, no clear targets for emission reductions or limits on environmental damages
are announced, companies might either delay investments or, in the worst case, invest into fossil infrastructure that quickly becomes obsolete (creating so-called stranded assets).4 The same goes for laws, which need to follow the policy guidelines and enable market actors to act appropriately (e.g., in the case of charging infrastructure for electric vehicles, clear policy guidelines and laws are needed to find private investors).
Energy- and climate policy has tried to follow the overarching goal of emission reductions and the achievement of the Paris Agreement, where 195 nations decided to set the goal of limiting global warming to well below two degrees Celsius (C) (UNFCCC 2015a). How- ever, current policies still lack behind the agreed-upon goal, also due to the approach to let every country decide on it’s own contribution to the goal (the so-called nationally de- termined contributions (NDCs) without a global cross-check on its consistency (Climate Action Tracker 2020). This means that even with all the pledges being factored in, the Paris goal of ”well below 2 ℃” would be vastly surpassed (Climate Action Tracker 2020).
Additionally, there has been some turmoil around the United States of America (USA), with President Donald J. Trump temporarily withdrawing from the agreement (Tollefson 2019), an act that has only recently be undone via the new Biden administration (U.S.
Department of State 2021). In fact, even with the Paris Agreement in place since 2015, global emissions have continued to rise, from 35.2 gigatons (Gt) of CO2 in 2015 to 36.4 GtCO2in 2019 (Friedlingstein et al. 2020). Therefore, there is a strong need for additional policy intervention, using more and more effective policy instruments, if global warming is to be limited in a meaningful way (Climate Action Tracker 2020).
There is, however, an ongoing debate on the choice of policy options and their concrete implementation and effectiveness to reach set targets (Enzensberger, Wietschel, and Rentz 2002; Oikonomou and Jepma 2007; Fouquet 2013). The general idea of policy intervention is to solve market failures (e.g., imperfect information, no internalization of all external costs, monopolies, etc.) and therefore ensure a more sustainable supply of energy services.
To achieve these goals, the policy makers have the opportunity to use various policy instru- ments such as subsidies (e.g., for incentivizing more energy efficient technologies), taxes (e.g., on CO2 to internalize the negative environmental effects), the definition of standards (e.g., by setting certain standards for fuel consumption in passenger vehicles), regulation (e.g., by regulating prices for natural monopolies such as electricity grids), emission trading, or feed-in tariffs. The concrete choice of chosen measures is discussed in research and policy alike (Oikonomou and Jepma 2007; Safarzadeh, Rasti-Barzoki, and Hejazi 2020). Still, it has to be noted that national climate plants are not always consistent with the implemented
4Throughout this dissertation, I use the definition of stranded assets proposed by Caldecott, Howarth,
policies and therefore need to be revised to actually be aligned with set targets (Kranzl et al. 2013).
Another widely discussed topic in climate policy is that ofenergy justice. In order to ensure co-operation of all parties for the global low-carbon transition, it is important that equality concerns are also addressed - facilitating a so-calledjust transition. Such a just transition must take into consideration questions of energy justice, which also entails distributional issues (McCauley et al. 2019). Since the Global South5is both more affected by the negative consequences of man-made climate change, but also economically disfavored, they are facing especially challenging times (Eckstein, Künzel, and Schäfer 2021). In fact - seven out of the ten most affected countries are among the least developed (Eckstein, Künzel, and Schäfer 2021). A large part of this problem is also financing, since local communities in developing countries usually rely on outside funding for their energy infrastructure (Bhattacharyya 2013). Therefore, a global accord, taking these issues into account, needs to be found.
While the Paris Agreement is a good first step, further joint action needs to take place moving forward. Also, an aspect of energy justice is that of intergenerational equality, a concept that promotes the equal weighting of costs and benefits for future generations. The issue here is that most energy system models heavily discount investments, meaning that expenditures, and therefore meaningful emission reductions, only occur in the far future instead of today (García-Gusano et al. 2016; Steinbach and Staniaszek 2015). This is due to the fact that high discount rates of usually around 5% would weigh the damages that occur 35 years into the future only at 16% of the rate that they would have today.
There is therefore a debate on the choice of discount rates in energy system models and whether discount rates taking these intergenerational equality concerns into account (so- called ”social discount rates”) should be applied instead (García-Gusano et al. 2016).
The energy transition can have positive effects on politically desirable goals such as energy access, energy security, and energy independence. By generating renewable electricity with local RES capacities, a country can reduce it’s import dependency, especially with regard to fossil fuels (Valentine 2011). By moving supply closer to demand and enabling local access to energy sources, even in traditionally resource-sparse countries, a more ”democratic”
energy system emerges (Vanegas Cantarero 2020). This shows that there is actually a large incentive for national policy makers to move to renewable energy sources, as they allow to (i) establish new access to electricity, even in remote areas (Longe, Oluwajobi, and Omowole 2013; Moner-Girona et al. 2016; Madriz-Vargas, Bruce, and Watt 2018), (ii) provide energy security since they are more available, affordable, and resilient (Valentine
5The University of Virginia defines the term Global South as a reference ”to economically disad- vantaged nation-states and as a post-cold war alternative to “Third World.””, as well as a term ”to address spaces and peoples negatively impacted by contemporary capitalist globalization.”;
https://globalsouthstudies.as.virginia.edu/what-is-global-south
2011), and (iii) improve the energy independence of a region by providing locally sourced energy (Vanegas Cantarero 2020).
1.2.5 Energy demands
One aspect of the energy transition is also that of energy demands, both for the final energy demand side (representing the actual energy carriers or energy proxies that are demanded by the different sectors, e.g., thermal energy for space heating, or mobility as a proxy for energy), as well as for the primary energy demand (representing the total amount of energy that is required from a supply side to ensure the proper fulfillment of the final energy demands). Especially for a successful energy transition and emission reductions of up to 100%, lowering energy demands plays a crucial role in the fulfillment of these goals (Kesicki and Anandarajah 2011). In this regard, renewable energy sources and sector- coupling provide great ways to reduce primary energy demand by removing additional transformation steps (e.g., by generating electricity directly without any additional input, instead of burning fossil fuels in thermal power plants to generate electricity, usually by using steam turbines with a maximal efficiency of around 40-55%). With additional energy efficiency improvements within these technology options, the primary energy demand can be reduced even further.
However, managing the final energy demand is just as important - if not even more so. Final energy demands are usually taken as an exogenous input into linear, cost-optimizing energy system models, driven by scenario assumptions. In the past, it has often been assumed that gross domestic product (GDP) and energy demands are closely interlinked and have a close correlation with one another (Dincer and Dost 1997). Concepts like the Environmental Kuznets Curve state that this is especially the case for growing economies, where energy intensity and pollution rise the most in developing countries (Bongers 2020). However, these concepts are now increasingly challenged, and discussions about the necessity of
”green growth” - economic growth without a rise in resource or energy requirements, and thus without increasing the environmental footprint of the economy, or even de-growth tendencies, promoting no to negative growth, have gained traction. Energy consumption and economic growth need to be decoupled going forward, as even renewable energy sources require materials and resources to be constructed, and therefore have an environmental footprint (Ward et al. 2016; Wu, Zhu, and Zhu 2018; Akizu-Gardoki et al. 2018). It is important for the energy system to stay within the environmental boundaries (a concept that is explained by Raworth (2017)), which means that (economic) development can only continue to happen, if resource and energy efficiencies are improved, therefore reducing
be sufficiency, namely a willful decrease of the amount of consumption of energy services.
Decreasing the primary and final energy demands are thus key factors in the low-carbon transition (Kesicki and Anandarajah 2011). Hence, they deserve special attention from both research and policy - especially because of the strong interlinkage of large-scale macro- economic effects with behavioral phenomena. One example for such phenomena would be the rebound effect, which states that an increase in energy efficiency can lead to a higher consumption of the service, counteracting or even overcompensating the efficiency gain under certain circumstances (e.g., if price signals or policies are not set appropriately) (Berkhout, Muskens, and W. Velthuijsen 2000). Only by factoring in an fostering both efficiencyandsufficiency, a timely transition towards a more sustainable future, and a 100%
renewable energy system, can be made possible.
1.2.6 Socio-economic drivers
Another important topic for the global low-carbon transition is that of its inherent inter- disciplinarity (Schuitema and D. Sintov 2017; Cohen et al. 2021). To reach the ambitious climate goals of limiting global warming to well below 2 ℃, not only technological, but also societal changes need to happen, spanning across many disciplines of research. During the recent years, there has been a strong trend to include socio-economic aspects into technical models, since in order to allow for the necessary technology change, the societal change associated with it requires the same, if not more attention (Shaman et al. 2013). To foster citizen engagement and gain acceptance in the population, techno-economic research does not only need to be properly communicated, but also put into context with societal issues. Many assumptions of optimization models (e.g., the assumption of a benevolent social planner, that of perfect information and foresight, or perfect competition in energy markets) do not hold up in real life and therefore need to be checked against reality. In fact, at the Energy Modeling Platform for Europe (EMP-E) conference in 2020, a survey was given to modelers, asking about their opinion on the inclusion of socio-economic effects into energy system models. The results, shown in Figure 1.3, underline the importance that experts in the field see in the topic.
Response to the question: Do you think that social impacts should be more integrated in energy models?, N=38
Which three social aspects would you like to see integrated into energy models? (multiple choices, up to 3), N=38
Figure 1.3: Results from a survey of modeling experts on the topic of socio-economic effects in energy models at the EMP-E in October 2020. Source: Süsser (2020).
The survey results show that a majority of the asked modelers thought that social impacts should be better integrated into energy models, and the topics social acceptance of technolo- gies and infrastructure, social drivers and barriers of innovation diffusion, energy poverty, and consumer behavior were perceived as the most important points to be included (Süsser 2020).
Markkanen and Anger-Kraavi (2019) conclude in their paper that ”social impacts [should be] taken into consideration in all stages of policy making, including policy planning, de- velopment and implementation”. Making sure that the low-carbon transition can actually succeed requires careful planning and multi-stakeholder engagement, as well as a wider view of research (Markkanen and Anger-Kraavi 2019; Schuitema and D. Sintov 2017;
Cohen et al. 2021). Therefore, socio-economic effects such as active engagement of the public and change of behavioral patterns can be a major chance in the fight against climate change (Stoll-Kleemann, O’Riordan, and Jaeger 2001). However, this is not always easy to achieve. While many people state support for the energy transition and view renewables
involved (e.g., by phenomena like the ”not-in-my-back-yard” effect - where people that gen- erally support renewable energy sources still oppose them when they are to be located close to their living area (Wolsink 2012)). This means that if societal concerns are overlooked and/or policies are executed poorly (e.g., by sending the wrong price signals), additional barriers in the form of rising inequality and discontent, lowering acceptance rates, could arise.
1.2.7 Open science
Open science is a term that gains more and more traction in the current academic discourse.
Vicente-Saez and Martinez-Fuentes (2018) define Open science as ”transparent and accessi- ble knowledge that is shared and developed through collaborative networks”. That means that the term ”open science” encompasses multiple aspects of openness and combines them into one holistic statement. These issues include ”open access” (which is usually referred to in the context of academic publications that are freely accessible), ”open source” (which is commonly used as a term in software development, meaning that the source code is openly available) or ”open data” (referring to accessible and reproducible data that is free from strict licensing clauses). But also the way that knowledge is created and shared is encom- passed in the term ”open science” (Fecher and Friesike 2014). The idea behind open science is that by making all knowledge openly accessible, a fruitful discourse can be provided that not only generates better and more robust results in a shorter time frame (a fact that is for example clearly demonstrated in the world of software development, where Paulson, Succi, and Eberlein (2004) have shown that bugs in open source software are found and fixed faster than in their closed-source counterparts), but also improve upon the general transparency and validity of the resulting research. This combined is also very beneficial for the researchers involved, leading to more citations, media attention, potential collabo- rators, job opportunities and funding opportunities (McKiernan et al. 2016). In fact, the European Commission now has a clear statement published on the topic of open science, making it a necessity for future funding (European Commission 2021).
This is also true for the field of energy system modeling. Very few detailed multi-sectoral energy system models currently exist, and most of them are either closed-source (since they are being developed commercially), do not cover all sectors, or are mere frameworks, without any provided data. However, the increasing public debate on these important issues underlines the necessity for open-source, open-data models, which can offer significant contributions to international research efforts and open debates (Pfenninger et al. 2017;
Müller, Weibezahn, and Wiese 2018). More and more people from academia, politics, but also the general public are interested in the backgrounds of the generated research and
model results and want to be able to understand and contribute to these issues, a fact that is clearly highlighted by world-wide movements like Fridays For Future. The importance of open science in all fields, including that of energy system analysis, cannot be understated, since the necessary speed and scope of change requires support across all parts of society.
1.3 The Global Energy System Model - GENeSYS-MOD
The energy system model used for the analyses in this dissertation is the Global Energy System Model. It is an open-source, multi-sectoral energy system model, based on the Open Source Energy Modelling System (OSeMOSYS) by Howells et al. (2011a). Mathematically, GENeSYS-MOD is a cost-optimizing linear program and thus falls in the classification of bottom-up energy system models (Herbst et al. 2012). It belongs to the same group as the widely known MARKAL-TIMES energy system models, with MARKAL being nerly identical to OSeMOSYS in its structure. As the naming suggests, the first application of GENeSYS-MOD was that of a global energy system model designed to reach 100%
renewable energy sources in 2050 (Löffler, Hainsch, Burandt, Oei, Kemfert, et al. 2017).
Nevertheless, the general formulation and framework is quite flexible and also allows for any level of spatial resolution, ranging from macro-regions to country-level, or even regional analyses. In fact, even analyses at an urban scale would be entirely possible, as showcased by Leibowicz et al. (2018) and Brozynski and Leibowicz (2018) in their studies, using an OSeMOSYS-based model for the urban area of Austin, Texas.
GENeSYS-MOD features a plethora of technologies across the sectors electricity, heat, in- dustry, and transportation. The model decides on the optimal investments into capacity expansion, energy dispatch and storage, and sector-coupling, trying to minimize total sys- tem costs (z) over the modeled time period, for every region (r), technology (t), and year (y) (see Equation 1.1).
minz=∑
r
∑
t
∑
y
TotalDiscountedCostr,t,y+∑
r
∑
y
TotalDiscountedTradeCostsr,y (1.1)
Equation 1.1: Objective function of GENeSYS-MOD.
Meanwhile, it also needs to satisfy various other constraints such as emission limits, re- newable potentials, sector change rates, or other policy-related targets (e.g. phase-outs of
