Economic Resilience of German Lignite Regions in Transition

Stognief, Nora; Walk, Paula; Schöttker, Oliver; Oei, Pao-Yu Article — Published Version Economic Resilience of German Lignite Regions in Transition sustainability Provided in Cooperation with: German Institute for Economic Research (DIW Berlin) Suggested Citation: Stognief, Nora; Walk, Paula; Schöttker, Oliver; Oei, Pao-Yu (2019) : Economic Resilience of German Lignite Regions in Transition, sustainability, ISSN 2071-1050, MDPI, Basel, Vol. 11, Iss. 21, https://doi.org/10.3390/su11215991 , https://www.mdpi.com/2071-1050/11/21/5991 This Version is available at: https://hdl.handle.net/10419/223232 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ sustainability Article Economic Resilience of German Lignite Regions in Transition Nora Stognief 1,2,3,*, Paula Walk 1, Oliver Schöttker 3and Pao-Yu Oei 1,2 1Workgroup for Economic and Infrastructure Policy (WIP), TU Berlin. Straße des 17. Juni 135, 10623 Berlin, Germany; [email protected] (P.W.); [email protected] (P.-Y.O.) 2 Department of Energy, Transportation, Environment, DIW Berlin. Mohrenstraße 58, 10117 Berlin, Germany 3Department of Economics, in particular environmental economics, Institute of Environmental Sciences, Brandenburg University of Technology Cottbus—Senftenberg. Erich-Weinert-Straße 1, Building 10, 03046 Cottbus, Germany; oliver[email protected] *Correspondence: [email protected] Received: 7 October 2019; Accepted: 24 October 2019; Published: 28 October 2019   Abstract: This paper recalls the development of the German lignite regions Rhineland and Lusatia since 1945 to allow for a better understanding of their situation in 2019. We analyze their economic resilience, defined as adaptive capacity, using Holling’s adaptive cycle model. We find that the Rhineland is currently in the conservation phase, while Lusatia experiences a reorganization phase following the economic shock of the German reunification. Key policy recommendations for the upcoming coal phase-out are to foster innovation within the Rhineland’s infrastructures to avoid overconnection, and to expand digital and transportation infrastructure in Lusatia so that the structurally weak region can enter the exploitation phase. Future policymaking should take into consideration the differences between the two regions in order to enable a just and timely transition during which lasting adaptive capacity can be built. Keywords: coal phase-out; energy transition; coal transition; sustainability transition; Energiewende; just transition; structural change; regional economic resilience; adaptive cycle model; Germany 1. Introduction A global decline of the use of fossil fuels is crucial for reaching the 1.5 ◦ C goal of the Paris climate agreement. At the same time, history has shown that past coal transitions have often had severe negative socioeconomic consequences on the affected regions due to poor management [ 1 ]. Consequently, literature has put an increasing focus on the just transition towards sustainable social-ecological systems [2–5]. In this context, the concept of resilience, especially the evolutionary perspective using the adaptive cycle model (AC) [ 6 ], has inspired useful insights on how regional economies withstand major disturbances [ 7 , 8 ]. We use this concept to address the situation of the two major German lignite mining regions that are currently undergoing sustainability transitions: the Rhineland in the western German state of North-Rhine-Westphalia (NRW), and Lusatia in eastern Germany in the former German Democratic Republic (GDR) (for the location of the regions see Figure 1). To analyze the coal transition in the two regions, it is important to understand the current state of the energy transition in Germany. Climate protection advocates have long been calling for a coal phase-out, a claim which has since gained the support of the majority of the population [ 9 ]. As an attempt to comply with the Paris Agreement as well as a reaction to public opinion, the German federal government implemented a commission for growth, structural change, and employment (the so-called “coal commission”) in 2018 with the mandate to develop a roadmap for the phase-out of coal, including Sustainability 2019,11, 5991; doi:10.3390/su11215991 www.mdpi.com/journal/sustainability Sustainability 2019,11, 5991 2 of 17 a fixed end-date. Its recommendation of phasing out coal by 2035–2038 [ 10 ] is a compromise between claims to phase out coal by 2030 to meet the Paris Agreement [ 11 ] and concerns of representatives of the lignite regions asking for sufficient time to manage the structural change. As of September 2019, the recommendations of the commission are currently being transferred and specified in the so-called Structural Enhancement Act (Strukturstärkungsgesetz), to be passed by the end of 2019. Sustainability 2019, 11, x FOR PEER REVIEW 2 of 17 so-called “coal commission”) in 2018 with the mandate to develop a roadmap for the phase-out of coal, including a fixed end-date. Its recommendation of phasing out coal by 2035–2038 [10] is a compromise between claims to phase out coal by 2030 to meet the Paris Agreement [11] and concerns of representatives of the lignite regions asking for sufficient time to manage the structural change. As of September 2019, the recommendations of the commission are currently being transferred and specified in the so-called Structural Enhancement Act (Strukturstärkungsgesetz), to be passed by the end of 2019. Figure 1. Location of the study regions Rhineland and Lusatia. Germany already has some experience with coal transitions, namely from the former hard coal mining regions in the Ruhr area and the Saarland in the western part of the country. While past structural change in those regions has not been without difficulties, they can generally serve as positive examples in comparison to other international experiences [12–14]. Other regions that have yet to face similar transition can learn from the experiences already made, the good ones as well as the bad ones. However, the differences between individual coal regions usually do not allow for direct transfer of successful strategies from one region to another [13–15]. Instead, an investigation of individual prerequisites and conditions is required for each region in order to derive meaningful policy recommendations. To facilitate this process, we synthesize existing theories and frameworks on sustainability transitions and regional economic resilience in Section 2. In Section 3, we explain the methodology of the adaptive cycle model and its application to our specific case. We analyze our case study, the past development of German lignite regions, from a perspective of regional economic resilience in Sections 4.1 and 4.2. In Section 4.3, we then identify possible pathways towards a sustainable and resilient transition. We conclude with a discussion of the results in Section 5. 2. Sustainability Transitions and Regional Economic Resilience Why is it so difficult for carbon-intensive regions to reduce their dependence on fossil fuels even if alternatives are available? Old industrial areas tend to be subject to two phenomena: path dependence and carbon lock-in. Path dependence occurs when past events, such as an important invention based on fossil fuels like the steam engine, lead to further carbon-based developments and Figure 1. Location of the study regions Rhineland and Lusatia. Germany already has some experience with coal transitions, namely from the former hard coal mining regions in the Ruhr area and the Saarland in the western part of the country. While past structural change in those regions has not been without difficulties, they can generally serve as positive examples in comparison to other international experiences [ 12 – 14 ]. Other regions that have yet to face similar transition can learn from the experiences already made, the good ones as well as the bad ones. However, the differences between individual coal regions usually do not allow for direct transfer of successful strategies from one region to another [ 13 – 15 ]. Instead, an investigation of individual prerequisites and conditions is required for each region in order to derive meaningful policy recommendations. To facilitate this process, we synthesize existing theories and frameworks on sustainability transitions and regional economic resilience in Section 2. In Section 3, we explain the methodology of the adaptive cycle model and its application to our specific case. We analyze our case study, the past development of German lignite regions, from a perspective of regional economic resilience in Sections 4.1 and 4.2. In Section 4.3, we then identify possible pathways towards a sustainable and resilient transition. We conclude with a discussion of the results in Section 5. 2. Sustainability Transitions and Regional Economic Resilience Why is it so difficult for carbon-intensive regions to reduce their dependence on fossil fuels even if alternatives are available? Old industrial areas tend to be subject to two phenomena: path dependence and carbon lock-in. Path dependence occurs when past events, such as an important invention based on fossil fuels like the steam engine, lead to further carbon-based developments and inventions [ 16 , 17 ]. Sustainability 2019,11, 5991 3 of 17 The concept of carbon lock-in is closely related; it denotes a situation where self-reinforcing mechanisms consolidate a society’s dependence on carbon, seemingly creating increasing returns while putting up barriers for sustainable alternatives [18]. The research field of ‘just transition’ analyzes the question of how regions depending on fossil fuels can transform towards a low carbon economy. Research questions evolve around political economy questions of “who wins, who loses, how and why” [ 2 ]. Researchers analyze the existing distribution of energy, ask who lives with the side effects of energy extraction, production, and generation, and who will bear the social costs of decarbonizing energy systems and economies [2,3,19]. A distinction is made between two types of approaches to sustainability transitions: transition management and adaptive management. While both are closely related, they differ in their primary objectives: the former strives to steer change with a focus on creative capacity, while the latter aims to build resilience, focusing on adaptive capacity [ 20 , 21 ]. From a transition management perspective, Berlo et al. [ 22 ], Vögele et al. [ 23 ], and Leipprand and Flachsland [ 24 ] have analyzed the energy transition in Germany using the multi-level perspective (MLP) by Geels [ 25 ]. Foxon, Reed, and Stringer [ 20 ] propose to use both approaches, transition management and adaptive management, in a complementary way, combining the objectives of both. They propose that in the face of uncertainty, building resilience may help sustain the pathways developed in transition management. In adaptive management, evolutionary interpretations of resilience are the most prevalent, which means that most studies reject the idea of equilibria from engineering and ecological resilience. By contrast to the latter two interpretations, where systems always strive to reach steady states, evolutionary approaches focus on the long-term capacity of the system to adapt to changing conditions [26]. This approach is widely used in the study of regions [ 7 , 8 , 26 – 28 ]. Boschma [ 26 ] proposes an evolutionary approach to regional resilience, where regions are resilient when they are able to overcome a trade-offbetween adaptation and adaptability in a situation of structural change. The more a region adapts to specific conditions, the more it has to forfeit its capacity to react to shocks or disturbances. Fath et al. [ 29 ] define resilience as the capacity of a system to successfully navigate all stages of the AC. From a regional economic perspective, Courvisanosae et al. [ 8 ] define adaptable resilient regions as demonstrating “change in the nature of industry over time without significant reduction in employment or income despite shocks (or perturbations)”. A widely used evolutionary model of resilience is Holling’s [ 6 ] adaptive cycle (AC) model. According to the AC, systems pass four sequential phases determined by the variation of three parameters: potential, connectedness, and resilience. High resilience is equivalent to high adaptive capacity, this means the ability to flexibly respond to disturbances [ 7 ]. Originating from ecology, the AC can also be transferred to human systems, including regional economies [7,8,27,30,31]. The AC has since established itself to become a popular tool in resilience research, not only in its original field, but also in the study of human systems. Fath et al. [ 29 ] have further explored the AC in the context of the resilience of social systems, identifying key features for the success of a system as well as investigating the typical pathologies systems can experience in each phase of the cycle. Slight et al. [ 31 ] have identified leverage points for each phase of the AC that can help regional economies building adaptive capacity and successfully navigate the AC. Rogov and Rozenblat [ 32 ] propose an approach that determines urban resilience by the interplay of adaptive cycles on three different spatial scales. 3. Materials and Methods We chose to focus on regional economic resilience because it offers a new and relatively un-researched perspective on the issues surrounding the structural change in regions experiencing coal transitions. This paper applies an evolutionary understanding of resilience using the AC, since we consider equilibrium interpretations of resilience insufficient to describe the complex dynamics of regional economies. We transfer Simmie and Martin’s [ 7 ] framework of regional economic resilience to Sustainability 2019,11, 5991 4 of 17 our specific case. The results then serve as the basis for developing future policy recommendations concerning the management of structural change in the two major German lignite regions. Sinceitisnotanempirical, butageneralmodel, theACisintendedtobeusedmainlymetaphorically. The results of its application are supposed to encourage further research [ 30 ]. Therefore, this paper applies the AC as a complement to existing approaches using related methods such as the MLP. The AC (Figure 2and Table 1) is based on three key dimensions that determine the system’s response to disturbances and other events: (1) potential for change, determining the range of possible options within the system; (2) internal connectedness, measuring system rigidity; and (3) resilience, determining system steadfastness to unexpected or unpredictable disturbances. The periodic change of these variables constitutes the four phases of the AC: the reorganization phase ( α ), the exploitation phase ( r ), the conservation phase ( K ), and the release phase ( Ω ). Systems have two different, sequential objectives, depending on the current stage of the AC: either the maximization of production and accumulation, or the maximization of invention and re-assortment [30]. Sustainability 2019, 11, x FOR PEER REVIEW 4 of 17 to our specific case. The results then serve as the basis for developing future policy recommendations concerning the management of structural change in the two major German lignite regions. Since it is not an empirical, but a general model, the AC is intended to be used mainly metaphorically. The results of its application are supposed to encourage further research [30]. Therefore, this paper applies the AC as a complement to existing approaches using related methods such as the MLP. The AC (Figure 2 and Table 1) is based on three key dimensions that determine the system’s response to disturbances and other events: (1) potential for change, determining the range of possible options within the system; (2) internal connectedness, measuring system rigidity; and (3) resilience, determining system steadfastness to unexpected or unpredictable disturbances. The periodic change of these variables constitutes the four phases of the AC: the reorganization phase (𝛼), the exploitation phase (𝑟), the conservation phase (𝐾), and the release phase (Ω). Systems have two different, sequential objectives, depending on the current stage of the AC: either the maximization of production and accumulation, or the maximization of invention and re-assortment [30]. Figure 2. The adaptive cycle. Adapted from [30]. Table 1. Four phases of the adaptive cycle in a regional economy, the poverty trap, and the rigidity trap. Adapted from [6,7]. Characterized by Potential Connectedness Resilience Reorganization phase (𝛼) innovation and restructuring high low increasing Poverty trap low low low Exploitation phase (𝑟) growth and seizing of opportunities low increasing high Conservation phase (𝐾) stability and increasing rigidity very high high decreasing Rigidity trap high high high Release phase (Ω) decline and destruction low decreasing increasing Holling [6] describes the two most important dangers in the AC in case the system is lacking sufficient adaptive capacity: (1) the poverty trap and (2) the rigidity trap. Both are deviations from the original path of the AC. In the poverty trap, potential, connectedness, and resilience are low, keeping the system from progressing to the exploitation phase. By contrast, in the rigidity trap, potential, connectedness, and resilience are high. This state of overconnection may hinder beneficial innovations, which cannot establish themselves without prior destabilization of the existing regime Figure 2. The adaptive cycle. Adapted from [30]. Table 1. Four phases of the adaptive cycle in a regional economy, the poverty trap, and the rigidity trap. Adapted from [6,7]. Characterized by Potential Connectedness Resilience Reorganization phase ( α ) innovation and restructuring high low increasing Poverty trap low low low Exploitation phase (r)growth and seizing of opportunities low increasing high Conservation phase (K)stability and increasing rigidity very high high decreasing Rigidity trap high high high Release phase (Ω) decline and destruction low decreasing increasing Holling [ 6 ] describes the two most important dangers in the AC in case the system is lacking sufficient adaptive capacity: (1) the poverty trap and (2) the rigidity trap. Both are deviations from the original path of the AC. In the poverty trap, potential, connectedness, and resilience are low, keeping Sustainability 2019,11, 5991 5 of 17 the system from progressing to the exploitation phase. By contrast, in the rigidity trap, potential, connectedness, and resilience are high. This state of overconnection may hinder beneficial innovations, which cannot establish themselves without prior destabilization of the existing regime [ 33 ]. Moreover, the rigidity trap is fraught with an increasing risk of catastrophic breakdown in case of a disturbance affecting the higher-level system that the rigid system has adapted to [ 6 ]. An example could be the disappearance of a central actor that other actors depend on. Systems in the rigidity trap often try to avoid the release phase by unhealthy means such as a loss of outside connections and exhausting their internal resources in order to maintain the status quo [29]. To transfer the model to regional economies, Simmie and Martin [ 7 ] have translated the key attributes of the AC (see Table 2). In this work, we analyze the economies of the lignite mining regions in Lusatia and the Rhineland in a similar fashion to gain new insights about their resilience in the face of a disturbance, i.e., the impending coal phase-out. A result of this research shall be a conceptual framework applicable to other regions that are experiencing sustainability transitions. Table 2. Characteristics of the three parameters potential, connectedness, and resilience of the AC [ 6 , 7 ]. Holling [6] Simmie & Martin [7]Application to Our Study Regions Potential Inherent potential for change, determines the range of future options (the “wealth” of the system) Internal competences of firms, skills of workers, institutional forms and arrangements, different types of infrastructures Competences and skills in sustainable sectors (e.g., renewable energy), demographic structure Connectedness Internal controllability of a system, reflects the degree of rigidity of controlling variables and processes Patterns of interdependencies among firms in the region Diversity of regional economy, patterns of dependency on certain (esp. non-sustainable) industries Resilience Adaptive capacity, opposite of the vulnerability of the system to unexpected disturbances Capacity for innovation among firms and institutions, actors’ general inclination towards entrepreneurship and the formation of new firms, available investment or venture capital, willingness of workers to re-skill Capacity for sustainable innovation, R&D expenditures, intensity and personnel We apply this framework to a comparative case study of the German lignite regions, the Rhineland and Lusatia, drawing from existing research and descriptive statistics. The data and findings stem from available statistical information about the regional economy and industry, a review of academic and grey literature, and observations within the period from 2012 to 2019 that were made as part of three research projects, several visits of the regions, and interviews of involved actors. The period between the end of World War II and the final report of the coal commission in January 2019 was considered for the analysis since the developments during this period are the most relevant for understanding the current situation. We present a timeline comparing both regions’ economic development and put it in the context of major events and disturbances. In addition, regional economic and demographic data is considered. This information then provides the basis for aligning each region’s development to the different phases of the AC in order to investigate their regional economic resilience. We assess the risk of falling into the poverty or rigidity trap of the AC and derive policy recommendations for successfully navigating the AC against the background of structural change that is likely to come with the “Energiewende” and shut-down of the coal industry. Sustainability 2019,11, 5991 6 of 17 4. Results 4.1. Adaptive Cycles from 1945 until Present Figure 3shows the economic development of the two regions over time, featuring notable events and categorizing them into the four phases of the AC. The underlying data is presented in Table 3. For both regions, the end of World War II marked the beginning of a reorganization phase, which according to the AC is characterized by an increasing level of resilience, but also high uncertainty. In the post-war period, both German states made rebuilding their industries a priority. In the German Democratic Republic (GDR), lignite was the only domestic energy source available. While the Federal Republic commanded other resources such as hard coal, the lignite industry in the Rhineland remained important nonetheless [13]. Sustainability 2019, 11, x FOR PEER REVIEW 6 of 17 Democratic Republic (GDR), lignite was the only domestic energy source available. While the Federal Republic commanded other resources such as hard coal, the lignite industry in the Rhineland remained important nonetheless [13]. Figure 3. Adaptive cycles of the Rhineland and Lusatia (1945–2019), including a timeline and lignite industry data (until 2001: employment excluding power plants). Data source: [34–37]. Sustainability 2019,11, 5991 7 of 17 Table 3. Characteristics of the regional economies of the study regions and their correspondence to the three parameters potential, connectedness, and resilience of the AC. Data sources: [ 34 , 36 , 38 – 44 ], partly own calculations based on data. Lusatia 1Rhineland 2Germany Potential Competences and skills in sustainable sectors (e.g., renewable energy), demographic structure Low Very high Settlement structure, centrality Rural, most districts are peripheral Urban, all districts are very central Total population (2017) 1,157,609 2,440,995 Population density Sparsely populated Situated in the densely populated state of NRW Population development Reduction by almost 10% since 2000; aging population Relatively stable population numbers; average age structure Unemployment rate in % (2018) 6.7 6.4 5.2 GDP in % p.a. (2005–2015) 3.2 2.6 2.8 Regional GDP in €p.c. (2015) 28,434 32,769 37,128 Trade tax revenue in 1000€p.c. (2015) 332 251 560 Employment market Few large employers except for LEAG and public sector RWE largest employer; branches of many large firms Labor productivity in €per working hour (2015) 41.47 53.26 51.50 Available income in €p.c. (2015) 18,722 20,961 21,583 Number of STEM-employees per 1000 employees (2013) 37 45 37 Share of employees with an academic degree in % (June 2016) 12.1 13.1 15 Share of school leavers with a university entrance qualification in % (2011–2013) 31.1 48.1 35.3 Share of the population with access to public transport in % (2018) 383.3 95.4 489.7 Broadband supply in % (2016) 52 87 75 LTE availability in % (2019) 67.7 76.1 77 Connectedness Diversity of regional economy, patterns of dependency on certain (esp., non-sustainable) industries Low to Moderate Moderate to High Lignite operator LEAG (since 2016) RWE Direct employment in the lignite industry (2017) 8,639 9,739 Direct, indirect and induced employment (2016) 13,245 14,338 Share of employees in the lignite sector among all employees subject to social security contribution (SSC) in % (2016) 2.03 1.13 0.06 Share of employees in the lignite sector (including indirect and induced employment) subject to SSC in % (2016) 3.3 1.8 Sustainability 2019,11, 5991 8 of 17 Table 3. Cont. Lusatia 1Rhineland 2Germany Resilience Capacity for sustainable innovation, Research and Development (R&D) expenditures, intensity, and personnel Low but increasing High but decreasing Research intensity in % of GDP (2015) 0.5 1.04 2.01 Change of research intensity in % p.a. (2005–2015) 7.5 −1.5 1.8 R&D personnel intensity (share of employees subject to social security contribution) in % (2015) 0.34 0.86 1.32 Change of R&D personnel intensity in % p.a. (2005–2015) 4.5 −0.1 1.2 Company start-ups per 10,000 persons fit for work (2009–2012) 25.8 32.7 36.6 Share of high-tech start-ups (2009–2012) 5.7 6.9 7.0 Current phase in the AC Reorganization Conservation Number of AC phases since 1945 5 (one entire cycle; start of a new cycle) 3 1 Consisting of the administrative districts (Landkreise/NUTS-3 units) Dahme-Spreewald, Elbe-Elster, Spree-Neiße, Oberspreewald-Lausitz, Bautzen, Görlitz, and the city of Cottbus; 2 Consisting of Städteregion Aachen, Düren, Euskirchen, Heinsberg, Rhein-Erft-Kreis, Rhein-Kreis Neuss, and the city of Mönchengladbach; 3 Residence within a proximity of 600m (bus) or 1,200m (train) to a stop with a minimum of 20 departures daily; 4 Data for the whole of NRW (due to lack of data availability on district level). In the 1950s, the Federal Republic experienced a period of unexpectedly strong economic growth—the “Wirtschaftswunder”. In the AC, the “Wirtschaftswunder” happened alongside the exploitation phase, a time of great development opportunities and growth as well as a high level of resilience. Until 1973, the yearly growth of the Federal Republic’s GDP was at least 5% [ 45 ]. Areas with a traditionally strong industrial sector, such as the Rhineland in the federal state of North-Rhine-Westphalia (NRW), especially benefited from this development. Inthe 1950sand1960s, the remaininglignite firmsinthe regionmergedwithRWE, amajor company in the energy and mining sector, partly owned by local municipalities. Consequently, RWE became the most significant producer of lignite in West Germany. The academic sector grew as well: among others, the research institute in Jülich was founded in 1956 and in 1966, the Rheinisch-Westfälische Technische Hochschule (RWTH) Aachen expanded to become a full university. By contrast, due to the planned economy of the GDR, the situation in Lusatia was much different. The standard of living was generally much lower than in West Germany. Nevertheless, the mid-1960s marked the start of the exploitation phase with significant growth of the lignite industry in terms of employment and lignite output (see Figure 3). Lignite mining and generation were (and still are) the most significant industries. In the Federal Republic of Germany, the 1973 oil crisis abruptly halted the period of strong growth, initiating the conservation phase. The Rhineland, however, was able to keep its status as an attractive industrial region. While this is indicative of high potential, internal connectedness also increased and resilience started to decrease. As is typical for the conservation phase, most economic power is concentrated in certain industries that have shown a competitive advantage in the previous phases and cycles. In case of the Rhineland, this includes the lignite industry, but also the chemical, aluminum, automotive, and food industries. Their primary aim was now to increase their efficiency in order to achieve higher returns. Figure 3shows that lignite production increased during the 1960s and 1970s, albeit not as strongly as in Lusatia [34]. For the GDR, the year 1971 marked a paradigm shift in economic policy due to the political change of power. The new government’s first objective was to increase the standard of living, initially at the Sustainability 2019,11, 5991 15 of 17 Author Contributions: Conceptualization, N.S.; Methodology, N.S. and P.W.; Formal analysis, N.S.; Resources, N.S. and P.W.; Writing—original draft preparation, N.S.; Writing—review and editing, N.S., P.W., O.S. and P.-Y.O.; Supervision, O.S. and P.-Y.O. Funding: This work was supported by the German Ministry for Education and Research (BMBF) under grant number 01LN1704A for the research group CoalExit. We acknowledge support by the German Research Foundation and the Open Access Publication Fund of TU Berlin. Acknowledgments: We thank Christian Hauenstein, Felipe Corral Montoya, and Felix Wejda for useful discussions and suggestions. Conflicts of Interest: The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results. References 1. Spencer, T.; Colombier, M.; Sartor, O.; Garg, A.; Tiwari, V.; Burton, J.; Caetano, T.; Green, F.; Teng, F.; Wiseman, J. The 1.5 ◦ C target and coal sector transition: At the limits of societal feasibility. Clim. Policy 2018 , 18, 335–351. [CrossRef] 2. Newell, P.; Mulvaney, D. The political economy of the ‘just transition’: The political economy of the ‘just transition’. Geogr. J. 2013,179, 132–140. [CrossRef] 3. Stevis, D.; Felli, R. Global labour unions and just transition to a green economy. Int. Environ. Agreem. Politics Law Econ. 2015,15, 29–43. [CrossRef] 4. 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