Just energy transitions to low carbon economies: Coal mining areas and welfare policies

1 PROGRAMA DE DOCTORADO EN ECONOMÍA TESIS DOCTORAL: JUST ENERGY TRANSITIONS TO LOW CARBON ECONOMIES: COAL MINING AREAS AND WELFARE POLICIES Presentada por Pablo García García para optar al grado de Doctor por la Universidad de Valladolid Dirigida por: Prof. Dr. D. Óscar Carpintero Redondo Prof. Dr. D. Luis Buendía García 2 GENERAL INDEX INDEX OF FIGURES ........................................................................................................... 4 INDEX OF TABLES ............................................................................................................. 6 LIST OF ABBREVIATIONS AND COUNTRY CODES ............................................................ 7 ABSTRACT ...................................................................................................................... 11 ACKNOWLEDGEMENTS .................................................................................................. 13 CHAPTER 1 INTRODUCTION ........................................................................................... 14 1.1. Motivations of this research .......................................................................... 14 1.2. Goals ................................................................................................................ 17 1.3. Hypotheses ...................................................................................................... 19 1.4. Theoretical framework .................................................................................. 20 1.4.1. Ecological Economics .................................................................................. 20 1.4.2. Sustainability transitions and just energy transitions ................................... 24 1.4.3. Welfare States and the environment............................................................. 34 1.5. Methodological framework ........................................................................... 42 1.6. Structure of this thesis ................................................................................... 43 CHAPTER 2 JUST ENERGY TRANSITIONS TO LOW CARBON ECONOMIES: A REVIEW OF THE CONCEPT AND ITS EFFECTS ON LABOUR AND INCOME ........................................... 47 2.1. Preliminary bibliometrics ............................................................................. 48 2.2. Methodology ................................................................................................... 50 2.3. Methodological state-of-the-art .................................................................... 51 2.4. Survey of the empirical effects ...................................................................... 54 2.4.1. Effects on employment................................................................................. 55 2.4.2. Effects on income distribution ..................................................................... 60 2.4.3. Institutional and financial barriers................................................................ 62 2.4.4. Classification of reviewed papers considering methodology and results .... 64 2.5. Future developments ..................................................................................... 76 2.5.1. Niches ........................................................................................................... 76 2.5.2. Research agenda ........................................................................................... 77 2.6. Conclusions ..................................................................................................... 79 CHAPTER 3 THE JUST ENERGY TRANSITION TO RENEWABLES IN MINING AREAS: A LOCAL SYSTEM DYNAMICS APPROACH .......................................................................... 81 3.1. Contextualisation ........................................................................................... 82 3.1.1. Political-normative context .......................................................................... 82 3.1.2. Socioeconomic context ................................................................................ 86 3 3.2. Local strategical advantages to foster a just energy transition ................. 89 3.2.1. Climate and orography ................................................................................. 89 3.2.2. Technological capacity and infrastructures .................................................. 92 3.2.3. Education and skills ..................................................................................... 93 3.3. Methodology ................................................................................................... 95 3.3.1. Modelling precedents ................................................................................... 96 3.3.2. Modelling strategy and sources of information............................................ 98 3.4. Scenarios and results of the simulation ...................................................... 104 3.5. Limitations of the processes and further corrections ............................... 109 3.5.1. Concept and design .................................................................................... 109 3.5.2. Diagnosis .................................................................................................... 112 3.5.3. Processes of public participation ................................................................ 118 3.6. Conclusions and policy implications .......................................................... 119 CHAPTER 4 WELFARE REGIMES AS ENABLERS OF JUST ENERGY TRANSITIONS: REVISITING AND TESTING THE HYPOTHESIS OF SYNERGY FOR EUROPE .................... 123 4.1. Theoretical-empirical alignment ................................................................ 124 4.2. Solutions to the shortcomings of indicators and previous omissions ...... 130 4.3. Data and methodology ................................................................................. 138 4.4. Results ........................................................................................................... 144 4.5. Discussion: fostering synergy through Sustainable Welfare ................... 152 4.5.1. Potentiality and essential matters of UBI ................................................... 155 4.5.2. A local derivation: CBI, between UBI and PES ........................................ 157 4.5.3. Potentiality and essential matters of UBS .................................................. 160 4.5.4. A comparison of UBI and UBS and their (not so) conflicting nature ........ 161 4.5.5. Downscaling Sustainable Welfare tools: A proposal for joint application of CBI+S ......................................................................................................... 165 4.6. Conclusions ................................................................................................... 168 CHAPTER 5 CONCLUSIONS ........................................................................................... 171 REFERENCES ................................................................................................................ 184 4 INDEX OF FIGURES Figure 1-1 The conception of the economy from the viewpoint of Ecological Economics ........................................................................................................................................ 22 Figure 1-2 Complex interrelations in Ecological Economics ......................................... 23 Figure 1-3 Conceptualisation of a just energy transition ................................................ 25 Figure 1-4 Just energy transition evolution timeline ...................................................... 27 Figure 1-5 Approaches to the concept of just energy transition ..................................... 29 Figure 1-6 Theoretical effects of energy transition on labour and income from the ILO perspective ...................................................................................................................... 32 Figure 1-7 Diagram of influences based on the theorisations of the ILO: direct relations in green and indirect relations in red .............................................................................. 33 Figure 1-8 Welfare regimes according to decommodification and social stratification. 36 Figure 1-9 Structure of the thesis ................................................................................... 46 Figure 2-1 Number of retrieved papers about just energy transitions to low carbon economies per year, 2006-2021 ...................................................................................... 48 Figure 2-2 Bibliometric network and clusters of just energy transitions ....................... 49 Figure 2-3 Conceptual density heatmap of the bibliographic network of just energy transitions ....................................................................................................................... 49 Figure 2-4 Review process and reduction of references (number of retrieved papers in circles) ............................................................................................................................ 50 Figure 2-5 Categorical classification of models ............................................................. 53 Figure 2-6 Methodological composition of studies about the impacts of the energy transition on employment and income distribution ........................................................ 54 Figure 2-7 Sign of the effects concluded by studies about employment (Panel A) and income distribution (Panel B) ......................................................................................... 54 Figure 2-8 Methodological niches in the field of just energy transitions ....................... 77 Figure 3-1 Diagram of decision for the inclusion of municipalities in the CTJ ............. 85 Figure 3-2 Location of the province of León in Spain and the areas of just transition in the province .................................................................................................................... 87 Figure 3-3 Direct and diffuse monthly irradiance (KWh/m2) in León (Panel A). Monthly deviation of the total irradiance in León referenced to the community average (KWh/m2) (Panel B) ....................................................................................................... 90 Figure 3-4 Average wind density (W/m2) at 100 meters in the peninsular North-West (Panel A). Localisation and capacity (MW) of hydropower plants (Panel B). Potential availability of residual biomass, from forests (green-yellow) and agriculture (yelloworange) (Panel C) ............................................................................................................ 91 Figure 3-5 Wind power intermediaries and enterprises in north-western Spain ........... 92 5 Figure 3-6 New enrolments in essential undergraduate studies for the transition, schoolyears 2016/2017-2019/2020 ................................................................................. 95 Figure 3-7 Modelling process ......................................................................................... 99 Figure 3-8 Diagram of influences of the Leonese case .................................................. 99 Figure 3-9 Forrester diagram of the Leonese case ....................................................... 101 Figure 4-1 A: Classification of the variables proposed in the literature, excluding variables unlinked to drivers compared with B: this proposal under the theoretical causal rationale ............................................................................................................. 138 Figure 4-2 Historical evolution of the variables considered to test synergy in the sample ...................................................................................................................................... 145 Figure 4-3 Optimal number of clusters by year under Thorndike’s criterium ............. 146 Figure 4-4 Annual dendrograms in the first calculation stage in 2009 (Panel A) and 2013 (Panel B) .............................................................................................................. 146 6 INDEX OF TABLES Table 1-1 Opportunities and challenges of a just energy transition in the ILO framework ........................................................................................................................................ 30 Table 1-2 Preliminary classification of welfare regimes ................................................ 36 Table 2-1 Classification of the reviewed empirical studies ............................................ 65 Table 3-1 Main indicators of the socioeconomic context in comparison with the province and the autonomous community ...................................................................... 88 Table 3-2 Education centres in EQF 3-5 in the affected areas ....................................... 94 Table 3-3 Available in-person places by essential vocational training branch for the transition and CTJ/area in schoolyear 2020/2021 .......................................................... 94 Table 3-4 Data sources to feed the local model............................................................ 102 Table 3-5 Scenarios about the Leonese transition ........................................................ 105 Table 3-6 Current employment factors of wind and photovoltaic power in León and estimates in the literature .............................................................................................. 105 Table 3-7 Results of the simulations of the Leonese case, SCEN 1-4 ......................... 106 Table 3-8 Results of the simulation of the Leonese case, SCEN 3 under the unequal distribution of tenders to balance negative land impacts (SCEN uneq) ....................... 108 Table 3-9 Initial application of the criterium of coverage in the inclusion of municipalities by impact ............................................................................................... 111 Table 3-10 Percentage of coincidence among the statements of the SWOT diagnosis by CTJ, initial (Panel A) and after the revision (Panel B) ................................................. 114 Table 3-11 Percentage of redundancy of SWOT statements, initial (Panel A) and after the revision (Panel B) ................................................................................................... 117 Table 4-1 Fields of synergy and conflict between WS and ES corresponding to theoretical drivers of synergy ....................................................................................... 126 Table 4-2 Variables used in the literature and associated drivers of synergy .............. 133 Table 4-3 Variables and data sources ........................................................................... 139 Table 4-4 Correlation matrix ........................................................................................ 142 Table 4-5 Concurrence matrix, without generosity index, 2008-2016 ......................... 147 Table 4-6 Persistent concurrences and relative profiling ............................................. 148 Table 4-7 Variations in concurrences caused by the inclusion of generosity, 2008-2010 ...................................................................................................................................... 151 Table 4-8 Comparison of UBI (CBI) and UBS ............................................................ 162 7 LIST OF ABBREVIATIONS AND COUNTRY CODES AEE Asociación Empresarial Eólica (Wind Business Association) AL Available land al. Others Avebiom Asociación Española de Valorización Energética de la Biomasa (Spanish Association for Energy Valorisation of Biomass) AW Available qualified workers B Biomass Bac. Baccalaureate BAU Business As Usual BDEW Bundesverband der Energieund Wasserwirtschaft (German Association of Energy and Water Industries) C Concurrence matrix CARF Carbon footprint CBI Conservation Basic Income CBI+S Conservation Basic Income and Services CBS Conservation Basic Services CES Constant Elasticity of Substitution CGE Computable General Equilibrium (model) CIEMAT Centro de Investigaciones Energéticas, Medioambientales y Tecnológicas (Centre of Energy, Environmental, and Technological Research) CLD Causal Loop Diagram CO2 Carbon dioxide COMF Potential competition for public funding COVID Coronavirus disease CTJ Convenio de Transición Justa (Spanish Agreement of Just Transition) Cum. Aff. W. Cumulative Affected Workers Decommodif. Decommodification DEGURBA Degree of urbanisation DMC Domestic Material Consumption DTU Danmarks Tekniske Universitet (Tecnical University of Denmark) e.g. Exempli gratia (for example) EPI Environmental Performance Index EPPE Environmental public expenditure EPS Environmental Policy Stringency EQF European Qualifications Framework ES Environmental State ESS European Social Survey EU European Union EUR Euro currency GDP Gross Domestic Product GHG Greenhouse Gases GIS Geographic Information Systems GU Graduates in universities GV Graduates in vocational training 8 i.e. Id est (that is) IAE Impuesto sobre Actividades Económicas (Spanish tax on economic activities) IAM Integrated Assessment Model IBI Impuesto sobre Bienes Inmuebles (Spanish property tax) IEA International Energy Agency ILO International Labour Organisation INE Instituto Nacional de Estadística (Spanish National Institute of Statistics) IO Input-Output IOA Input-Output Analysis IOTs Input-Output Tables IPCC Intergovernmental Panel on Climate Change IRENA International Renewable Energy Agency ISTAS Instituto Sindical de Trabajo Ambiente y Salud (Spanish Trade Union Institute of Work, Environment and Health) Km2 Square kilometre KWh Kilowatt-hour LOCE Local public expenditure LOCR Local public revenues LTUN Long-term unemployment rate M Millions m2 Square metre MATF Material footprint MITECO Ministerio para la Transición Ecológica y Reto Demográfico (Spanish Ministry of Ecological Transition and Demographic Challenge) MLP Multi-Level Perspective MRIO Multi Regional Input-Output Mt Megatonne MW Megawatt n Employment factor Observations (sampling) N Direct labour demand NN Net employment NR Jobs at risk OCAW Oil, Chemical and Atomic Workers International Union OECD Organisation for Economic Co-operation and Development p Standard power Educated working-age population PCI Projects of Common Interest PES Payment for Ecosystem Services PPF Production Possibility Frontier PPP Purchasing Power Parity PV Photovoltaic r Land requirements R&D Research and Development REE Red Eléctrica de España (Spanish National Electricity Network) RENE Renewable energy share 9 RES Renewable Energy Sources RL Required land RRMW Recycling rate of municipal waste SARS-CoV-2 Severe Acute Respiratory Syndrome Coronavirus 2 SAU Sociedad Anónima Unipersonal (Spanish single-shareholder corporation) SCEN Scenario SD System Dynamics SDGs Sustainable Development Goals Sec. Secondary Education SEPE Servicio Público de Empleo Estatal (Spanish Public Employment Service) SFD Stocks and Flows Diagram SINC Income share SNM Strategic Niche Management STA Spatial Transition Analysis Strat. Stratification SW Sustainable Welfare SWOT Strengths, Weaknesses, Opportunities and Threats T Tonnes t time UBI Universal Basic Income UBS Universal Basic Services ULe University of León UN United Nations Unemployment rate UNED Universidad Nacional de Educación a Distancia (Spanish National University of Distance Education) Uneq Unequal UNU-WIDER United Nations University World Institute for Development Economics Research USD United States Dollar currency Voc. Vocational training W Wind density Wind W. A. Pop. Working-age population WEFF Welfare effort WS Welfare States 16 polarisation. Likewise, it has had an important environmental effect derived from the abandonment of installations and exploitations and a fiscal effect based on the reduction of local public revenues through property taxes (IBI in Spanish) and taxes over economic activities (IAE in Spanish), which has motivated the loss of public services. In this context, previous attempts to promote alternative activities in the areas resulted unsuccessfully. Even if the Leonese energy transition began in the 1990s, the Spanish Ministry of Ecological Transition and Demographic Challenge, MITECO in Spanish, is currently leading processes of just transition in Montaña Central-La Robla (Ministry of Ecological Transition and Demographic Challenge, 2020l, 2020e) and El Bierzo-Laciana, with four priority areas: Fabero-Sil (Ministry of Ecological Transition and Demographic Challenge, 2020c, 2020j), Bierzo Alto (Ministry of Ecological Transition and Demographic Challenge, 2020a, 2020h), Laciana-Alto Sil (Ministry of Ecological Transition and Demographic Challenge, 2020d, 2020k), and Cubillos del Sil-Ponferrada (Ministry of Ecological Transition and Demographic Challenge, 2020b, 2020i), therefore congregating five of the seven active interventions at a regional scale. These interventions under the specific umbrella of just energy transition coincided with the beginning of this research. As empirical works have gradually begun to find some undesirable social consequences of the energy transitions, not only in Spain, but in other European and nonEuropean contexts, wide social sectors claim for parallel public intervention to redirect the restructuring and, therefore, new questions have emerged on the side of the policies. Specifically, there is a need to determine how governments can compensate for the negative impacts of the transition while potentiating the positive outcomes. In this regard, an increasing number of research outcomes are asking to define the role of current Welfare States (WS) to face the transition and explore emerging fields such as Sustainable Welfare (SW). The most notable discussion in this regard revolves around the inherent traits of Social-democratic welfare regimes in the sense of Esping-Andersen (1990) to streamline the establishment of a state that is socially and environmentally conscious, the Green State, Eco State or Environmental State (ES). This notion is central to the unfolding process of just energy transition, although it appears more frequently attached to the study of transitions towards sustainability in general, and commonly receives the denomination “hypothesis of synergy” (Fritz & Koch, 2019; Koch & Fritz, 2014; Koch, Gullberg, Schoyen, & Hvinden, 2016). According to the hypothesis of synergy, the levels of decommodification and low social stratification offered by Social-democratic regimes lay the ground for a more effective transition in social and environmental terms. If accepted, well-known public welfare policies that have been active since the 1970s and in which 17 current governments have acquired relevant experience could represent an immediate solution to the negative impacts and side-effects of the restructuring. If rejected, more research is needed to determine the shaping of precise political actions. The theorisations of this hypothesis were formulated in the literature some years ago (Dryzek, 2008; Gough et al., 2008; Meadowcroft, 2008). However, the empirical proposals to test it are more recent (Fritz & Koch, 2019; Jakobsson, Muttarak, & Schoyen, 2018; Koch & Fritz, 2014; Otto & Gugushvili, 2020; Zimmermann & Graziano, 2020). As we dived into the theoretical and empirical discussion, we discovered the existence of contradicting results and the underlying need to reinforce the methodologies used to test them. The discussions around the hypothesis and their implications for the field of just energy transitions are solely in a preliminary stage in which much remains to be said. The work that is presented in the following Chapters embraces these matters, doubts, and discussions. It seeks to contribute to the conceptualisation of just energy transitions, translation of justice into the empirical study, determination of social effects, and definition of margins of policy action through welfare regimes. 1.2. Goals The main goal of this research is to determine the socioeconomic effects of energy transitions to RES (Renewable Energy Sources) and suggest potential courses of action to ease the process through welfare regimes, considering the insights offered by Ecological Economics about the social-environmental conjunction. In compliance with this main goal, this research pays special attention to two aspects: First, the conclusions of previous incipient studies and reports about justice in energy transitions (Balibar, 2017; D’Alessandro, Luzzati, & Morroni, 2010; Fernandes, 2017; Fragkos & Paroussos, 2018; ILO, 2018; Just Transition Research Collaborative, 2018; Kemfert, 2017; Kjaer, 2013; Markandya, Arto, González-Eguino, & Román, 2016; Solomon & Krishna, 2011; Williams & Doyon, 2019) and the cases of energy restructuring and transition that are closer to the author’s experience: the Leonese mining and thermoelectric areas. Second, the role played by the public sector in the management of the process of transition, by minimising the negative social impacts and maximising the positive impacts through public policies, particularly welfare policies (Bouzarovski & Tirado Herrero, 2017; Westholm & Beland Lindahl, 2012). To foster the main goal, this research proposes three secondary goals: First, to revise the literature about the socioeconomic effects of the transitions to sustainable energy models and the framework of just transitions (Gambhir, Green, & Pearson, 2018; Heffron & McCauley, 2018; ILO, 2015; Jasanoff, 2018; Newell & 18 Mulvaney, 2013; Poschen, 2017; UNRISD, 2019) . The revision must determine how the concept has been studied by researchers addressing the effects on labour and income, the methods that have been employed, and the results reached. Subsequently, the analysis must determine to what extent the empirical results are conditioned by the applied methodologies, and whether the studies are consistent or not with each other. Finally, it must assess these efforts and detect their strengths and weaknesses to propose a research agenda. Second, to analyse the most recent proposals for a just transition of the areas under study (Ministry of Ecological Transition and Demographic Challenge, 2020f; Spanish Institute of Just Transition, 2021) and determine the strengths and weaknesses of public plans, as well as to offer quantitative insights into the development of the restructuring to find the priorities of future political agendas and stakeholders’ action. This determination of the socioeconomic impacts of the energy transition in the terms described in the main goal seeks to contribute to the discussion about the translation of the idea of justice into the practical ground and the determination of the precise effects of the process. Third, to explore the capability of welfare regimes to ease the transition and provide a sustainable and just future (Bäckstrand & Kronsell, 2015; Koch, 2019; Koch & Fritz, 2014; Meadowcroft, 2008), i.e., revisit and test the hypothesis of synergy, so public welfare policies can be aligned with the best strategies to satisfy this aim. These goals are jointly meaningful for this research because of four circumstances: First, the current proposals for an energy transition have originated certain reticence due to the potential socioeconomic consequences of the process. This is especially observed in areas like León, with high specialisation and dependence on the exploitation of fossil sources that have rapidly declined during the past decades, consequently concentrating the harshest socioeconomic deterioration. It is, therefore, necessary to jointly analyse the environmental concerns and socioeconomic deterioration in the same theoretical-empirical corpus. Second, there is a call to provide more accurate visions that focus on the socioeconomic challenges, restrictions, and dilemmas of the transition, both in the scholarly and political discussion, consequently detecting and broadening new horizons in the frame of public policies and welfare. Third, proposals of transition gather highly ambitious political goals. There is a need for studies to assess critically the plausibility of these plans, particularly regarding the socioeconomic restrictions that they are facing. 19 Finally, this proposal contributes to promoting an underdeveloped area of the literature, the conjunction of energy transitions and social effects, and offers innovative insights into the case of León, derived from the incursion of this thesis on a scale that previous research has neglected: the local rural level. In summary, our proposal aspires to jointly apply quantitative techniques and qualitative assessments orientated by the literature to assess justice in the energy transition, as well as to dive into the theoretical discussion to further adjusted political agendas. 1.3. Hypotheses Once we have determined our goals, we are able to disclose the hypothesis that this research work aims at testing. Hypothesis 1 The energy transition is considered an opportunity to provide jobs. The proliferation of RES requires economic activities such as construction, installation, manufacturing, operation, and maintenance to the extent of seeing RES as more labourintensive sources than traditional ones. As the energy transition progress, considering the framework theorised by the ILO (Figure 1-7), we ought to expect a displacement of investments from conventional to green sectors, a subsequent labour-demanding deployment of infrastructures, an injection of subsidies to fund these technologies, as well as education programmes to facilitate the transition. In consequence, the demand for workers is expected to increase in the energy sector. A positive impact in the energy sector is likely to increase disposable income and arouse a sensation of economic security among workers, with a subsequent positive impact on the rest of the economy. Meanwhile, negotiation would serve to improve the quality of jobs, including the level of wages. As income increases, the revenues that the public sector can collect also grow so that governments can tackle inequalities and poverty with greater supporting resources under the form of defensive policies. Provided that the transition to RES is expected to have a positive impact, it is considered a relevant opportunity for declining fossil-dependent areas in developed countries under intense processes of deindustrialisation, which have experienced a sharp socioeconomic erosion during the past decades. Although some alternatives have been formulated for them in diverse contexts, the most immediate option is a reconversion towards RES. This option is adequate, given that the needed energy infrastructures to transport energy are still in place and human capital is highly specialised in technical skills because of past experiences in mining and thermoelectric production. 20 Coherently with this vision, we hypothesise that the impact of the energy transition on employment and income is positive for the energy sector (direct) and this positive result echoes in the rest of the economy (indirect and induced). Hypothesis 2 In the process of transition, WS are increasingly seen as necessary to compensate for the potential negative effects and boost positive impacts, hence reinforcing justice. Social-democratic regimes, i.e., those with high decommodification and low stratification, are thought to be in an advantageous position to perform a just transition. On the one hand, decommodification alleviates environmental pressure by providing independence from markets, so that individuals can have a prosperous life without incurring environmentally damaging activities. On the other hand, a low stratification enables individuals to share power and increase democratic practices, so that procedural justice is regarded. Therefore, we propose the hypothesis of eco-social synergy as our second statement to test: Social-democratic welfare regimes display better environmental performance and are closer to the notion of ES, hence reinforcing the just transition. 1.4. Theoretical framework This Section discloses the theoretical framework in which this thesis is rooted. It consists of three parts: Ecological Economics, sustainability transitions and just energy transitions, and Welfare States and the environment. 1.4.1. Ecological Economics Ecology and Economy share a common root: οἶκος, the “house”. Both disciplines are, therefore, related and solely differentiated by λóγος and νόμος, i.e., the “logic” or “reasoning” (understood as a field or discipline) and the “rules” or “norms” (understood as the administration), respectively. Etymologically, Ecology is “the study of the house” and Economics is “the management of the house”, hence understanding “house” as the commonplace for humanity: the world as a whole or a region of it (Costanza, 2019). This etymological parallelism, nonetheless, has remained far from the course of action regarding the separate historical evolution of Ecology and Economics in recent times. Modern Economics has pictured the economic functioning and the evolution of societies as a subject that is unaffiliated with the environmental dimension, or even superior to it. In recent times, Economics has been closer to the Aristotelian censure of chrematistics than to “the management of the house”. As a result of the evolution of disciplines, both converged again to reclaim their common origin. This convergence materialised in the field of Ecological Economics, whose precedents can be detected in the 17th century and proper origins are traced to the 21 late 19th and early 20th centuries. At that moment, Geddes, Soddy, and Popper-Lynkeus, none of them economists, supported a biophysical approach to the economy (MartinezAlier & Schlüpmann, 1987). More recent, advanced works in the 1960s and 1970s mark its more commonly considered appearance, although Martinez-Alier (2015) delays the consideration of Ecological Economics as a school until the 1980s when the first books, journals, and journal issues with a concordant title appeared. Particularly in this period, Boulding proposed a transition from “frontier” to “spaceship” Economics, i.e., from a social paradigm of welfare based on material consumption to a paradigm in which welfare has decoupled from the material dimension (Boulding, 1966). Daly subsequently assimilated Economics into a life science (Daly, 1968) and jointly with Boulding and the first authors in this alternative tradition, some economists and some ecologists, suggested a shift from markets towards biophysics. Furthermore, Daly proposed a steady state in an early stage (Daly, 1973). Diving into the issue, Georgescu-Roegen (Carpintero Redondo, 2006), influenced by the studies on human energy uses by Lotka, examined the law of entropy and the economic process to conclude that, from a thermodynamical point of view, unlimited growth is unsustainable (Georgescu-Roegen, 1971) so degrowth is prescriptive (Georgescu-Roegen, 1979), and wrote critiques to the incipient trends in the field (Georgescu-Roegen, 1977). Parallel works by institutionalists, such as Kapp and von Ciriacy-Wantrup, also contributed to shaping the emerging school. Due to the diversity of its founders, Costanza (2019) describes Ecological Economics as a “meta paradigm”. It recognises the complexity of the socialenvironmental conjunction, and thus, proposes holistic and pluralistic discussions. Similarly, Ecological Economics is defined as a transdisciplinary field of study that considers the economy as a subsystem of a larger, limited and global ecosystem, hence inferior or subordinate to it, so that the economy is an open system embedded in the ecosystem (Carpintero, 2009; Martinez-Alier, 2015), as the canonical representation of the discipline reflects (Figure 1-1) (Passet, 1979). 22 Source: Own elaboration based on Passet (1979). Passet’s (1979) notion is of uttermost relevance for this framework, whose argumentation provides a key point. The reproduction of each sphere in Figure 1-1 is dependent on the others, so the economy and society cannot survive without the support of nature. Passet emphasises the order of relations so that the elements of the economic domain belong to the biosphere and follow its principles, but the biosphere does not belong to the economic sphere nor follow its rules. Hence, two major implications can be derived. First, the existence of limits imposed by superior spheres. The evolution of the economy and society is hierarchically constrained by the scope of the biosphere. The economy cannot surpass it and must adhere to the principles that govern it. Second, a violation of the limits of the biosphere triggers an ecological crisis. To foster a sustainable relation between economy and biosphere, the subsystem ought to observe its metabolism, i.e., how it takes energy and materials from the superior system. The adequate coexistence suggests using renewable sources and closing material cycles through recycling. In consequence, the energy transition as an area of the broader transition to sustainability is in essence a transition from one socio-metabolic regime to another (Krausmann, Fischer-Kowalski, Schandl, & Eisenmenger, 2008), i.e., a socioecological transition (Fischer-Kowalski et al., 2012). The latter can be defined as the change from a model of interaction between societies and natural systems (socioecological regime) to another model that faces social and environmental deterioration (Fischer-Kowalski & Haberl, 2007). This vision, in combination with Daly’s proposal for a steady state or GeorgescuRoegen’s insights into the unfeasibility of unlimited growth based on entropy, contrasted with the economic theory predominant in this period: Keynesianism, i.e., a focus on shortFigure 1-1 The conception of the economy from the viewpoint of Ecological Economics 23 term and continuous, frequently irreversible, expansions through demand. However, a common interest motivated a progressive conciliation of ideas to the extent of setting up a Macroeconomic corpus without growth, including Keynesian postulates (Daly & Farley, 2010; Holt, Pressman, & Spash, 2009; Jackson, 2009). Such progress without economic expansion is interlinked with the notion illustrated by Easterlin’s Paradox, remarkably known in the Economics of Happiness (Easterlin, 1974; Easterlin, McVey, Switek, Sawangfa, & Zweig, 2010). In practice, this conception of happiness or prosperity leads to promoting a shift towards less intense and productive jobs, universal incomes, and restoration of ecosystems, as Jackson (2009) recommended to the Government of the United Kingdom, thus claiming for the greater intervention of the public sector regarding welfare. According to Costanza (2019), the goals of the school are threefold and hierarchical. First, to determine the extent of human activities and reconcile them with the ecological limits of the biosphere. Second, to promote a just distribution of resources. And third, to subsequently allocate resources, marketed or not, in an efficient way provided the status of the biosphere and the social situation of justice (Figure 1-2). From the viewpoint of the cited author, nature and society are similar as they are subject to limits in a context of uncertainty and complexity, and both belong to the domain of sustainability, with biophysical and justice limits respectively. Source: Own elaboration based on Costanza (2019). A distinctive feature of Ecological Economics as a meta paradigm, open and transdisciplinary, beyond initial disputes on its focus is incommensurability (Carpintero Redondo, 1999; Martinez-Alier, Munda, & O’Neill, 1998). Although monetary valuations are sometimes used, notably for communicative purposes with other schools of thought or individuals that are external to Economics, there is a widespread consensus Figure 1-2 Complex interrelations in Ecological Economics 24 about the unsuitability of prices to make future decisions (Vatn & Bromley, 1994). Assessments with a strict preference for physical valuations and indexes of sustainability that catch the social metabolism (material and energy flows, ecological footprints, inter alia) replace prices and market equilibriums (Martinez-Alier, 2015), while participation, deliberation or willingness to pay methods replace cost-benefit analysis (Costanza, 2019; Munda, 2008; Spash, 2011; Zografos & Howarth, 2008). As far as the physical valuation is concerned, Ecological Economics points to the need for spatial analysis of human activity and its guidance in the allocation of resources. Spatial analysis is aimed at determining the dynamics, in combination with time and subject to uncertainty, of ecosystem services in the satisfaction of individual and collective preferences, as well as in the provision of well-being (Costanza, 2019). In this regard, and following the participatory approach beyond commensurability, modelling techniques that can represent the spatial-temporal liaisons between the complex coevolution of the economy and the ecosystem have found a relevant place in the school. The introduction of spatial analysis in economic studies is major progress. Traditionally, space has been a foreign domain for Economics, in a reductionist and artificial attempt to stem the tide. To “manage the house”, we ought to know and consider the location and spatial challenges to which resources are subject. The transversality and versatility of Ecological Economics, incommensurability, an underlying interest for space, and remarkably, the limits derived from the superior hierarchy of the biosphere and the need for sustainability to conciliate the spheres of the seminal conceptions of Ecological Economics through socio-ecological transitions based on metabolic shifts, constitute the ground in which this thesis is rooted and represent a structure to lead our focus: just energy transitions. 1.4.2. Sustainability transitions and just energy transitions Given the social-environmental status and the reconciliation of disciplines that study our common house, during the past few decades there has been growing interest in the notion of socio-ecological transition, as determined above. The energy concerns that are linked to the current socio-ecological regime, which fuel our motivation to perform this research as commented in Section 1.1, suggest the need to focus on the subsequent energy transition to a low carbon paradigm. Such an energy transition implies changes in energy generation, distribution, storage, and usage (IPCC, 2011, 2019), but also a complex set of interconnected social, political and economic rearrangements (Miller, Iles, & Jones, 2013). In the convergence of energy transitions and socioeconomic concerns the concept of a “just energy transition” arises. Even though this idea has a considerable grounding in contemporary history, it has experienced a revival and evolution in recent years (ILO, 2015; Just Transition Research 25 Collaborative, 2018) motivated by the said dual crisis (Markandya et al., 2016; Renner, Sweeney, & Kubit, 2008) and the uncertainty about the social effects of the energy transition, notably regarding labour and income distribution. Historically, while past energy transitions were involuntary, the present transition is a complex policy-led process with time goals (European Commission, 2019; UN, 2015a, 2015b). A set of public policies is aimed at fostering the sustainable energy transition while, at the same time, managing the resulting consequences for natural and human systems (Newell & Mulvaney, 2013), since it is thought that markets cannot be trusted (Fay, Hallegatte, & Vogt-Schilb, 2013) to compensate for the negative side effects. The concept of “just energy transition”, in the spotlight of this thesis due to the energy concerns associated with the current twofold crisis, is a part of broader concepts that we have to address first (Figure 1-3). Three layers can be distinguished. In the first layer, there is the idea of a socio-ecological transition. A second layer would be the concept of the energy transition. In a third and last layer, we can find the notion of just energy transition, which arises as one of the diverse possibilities of shaping an energy transition. Figure 1-3 Conceptualisation of a just energy transition Source: Own elaboration. In the first layer, the transitions to a sustainable regime are a broad category that alludes to a process of shift from one socio-ecological regime to a more sustainable regime (Behrens et al., 2014; Cambridge Econometrics, 2013; Carpintero & Riechmann, 2013; Fouquet, 2016; Haberl, Fischer-Kowalski, Krausmann, Martinez-Alier, & Winiwarter, 2011; Rockström et al., 2009; Sempere Carreras, 2014). 32 Figure 1-6 Theoretical effects of energy transition on labour and income from the ILO perspective Source: Own elaboration. 33 Figure 1-7 Diagram of influences based on the theorisations of the ILO: direct relations in green and indirect relations in red Source: Own elaboration. As a concluding point to clarify our theoretical framework, we propose a definition by combining the previous partial definitions and the elements revised in this Section. A “just energy transition towards a low carbon economy” can be defined as a longterm technological and socio-economic process of structural shift (Iychettira, Hakvoort, & Linares, 2017; Miller et al., 2013; O’Connor, 2010) that affects the generation, distribution, storage and use of energy (Hirsh & Jones, 2014; IPCC, 2011; Miller et al., 2015) and causes rearrangements at the micro (innovation), meso (social networks, rules and technical elements) and macro (exogenous environment) levels (Geels, 2004; Geels & Schot, 2010; Haxeltine et al., 2008; Solomon & Krishna, 2011; Sovacool & Geels, 2016), while also ensuring that the desired socioeconomic functions can be accomplished through decarbonised and renewable means of energy production and consumption (Smil, 2005, 2016), safeguarding social justice, equity and welfare. From a practical perspective, the transition is a public matter of instrument choice (to foster the substitution of energy sources) and instrument change (to adapt the policy 34 scheme in coherence with the multidimensional challenges that must be faced) (UNUWIDER, 2017). Scholars have not agreed upon the principles of how to analyse a just energy transition, apart from the need to contrast the net effects of all alternatives (Jasanoff, 2018). Notwithstanding, a group of demands and features for a just energy transition are recurrent. They can be called the “must-have” principles of a just energy transition: • The transition must be flexible enough to cope with uncertainty and social complexity (Smil, 2005). • The costs derived from the transition are not the only matter to contemplate. The collectives that have barriers to accessing the benefits of the transition, generally low-income households (Damette, Delacote, & Del Lo, 2018; McCabe, Pojani, & Broese van Groenou, 2018), must have their barriers removed and benefit in equal conditions. • Policies must consider and respect the rights of local communities and solve the historical injustices caused by the diversity of perspectives among stakeholders. Scholars propose community-based participatory decision-making and increasing cross-border activism supported by research (Finley-Brook & Holloman, 2016). • The transition policies must ensure qualified jobs and human resources, enable retraining, focus on education, health and creativity, provide certainty to industries in transition and facilitate new businesses (Sievers, Breitschopf, Pfaff, & Schaffer, 2019). • Policies must be consistent, driven by a long-term vision and fuelled by cooperation between stakeholders, with special emphasis on the participation of workers (Gambhir et al., 2018). Beyond this conceptualisation, both notional and historical, the recent impulse by the ILO, and the incipient methodological debates that we observed at the beginning of this research work, in 2018, many issues must be addressed to contribute to enabling just energy transitions worldwide. Especial attention deserves the correction of negative effects and the potentiation of the positive ones, particularly regarding the role of current welfare structures in the process. 1.4.3. Welfare States and the environment The reality of WS is complex and requires a definition and conception to enable subsequent analysis. In this thesis, we define WS as a series of institutions of a social, organisational and normative nature that assume the direct provision of social services, regulate private activities to shape the economy and provide cash benefits (Corlet Walker, 35 Druckman, & Jackson, 2021; Gough, 1979), to achieve economic security and reduce inequalities as social relations become increasingly commodified (Titmuss, 1968). Other features like public-sector entrepreneurship and innovation can also be attached to the WS in Western Continental Europe (Millward, 2011), but we do not consider them in our definition provided their contextual specificity. The need to analyse WS amid this complexity enabled the possibility to focus on this aspect or the other of it, but given the broad literature related to the taxonomies of WS, and that they are a good synthesis of their potentialities in the provision of social protection, it makes sense to delve into the classification of regimes according to their differential traits. These traits are a reflection of the differential institutional configuration that supported the emergence of WS and can be seen as combinations of four main institutions that operate the provision of social protection and welfare: the family, the community, the state, and the market (Muñoz de Bustillo, 2019). To this extent, we start from the classification suggested by the seminal work of Gøsta Esping-Andersen (EspingAndersen, 1990), as well as the later literature that drew from it (Arts & Gelissen, 2002; Ferrera, 1996; Koch & Fritz, 2014). Esping-Andersen (1990) proposed a categorisation under two indicators: decommodification and social stratification. Decommodification is the strong likelihood of reaching a satisfactory standard of living and well-being independently of the level of market implication. Social stratification is the difference between individuals or groups of individuals based on characteristics that reflect their comparative living conditions, frequently income. Under this dimensionality, WS are classifiable into three groups: Social-democratic, Conservative and Liberal (Figure 1-8). Social-democratic regimes combine high decommodification and low social stratification. Conservative WS show a medium level of decommodification and stratification. Finally, Liberal typologies display low decommodification and high stratification. 36 Source: Own elaboration. Any taxonomy is useful to label realities for identification and analytical purposes. However, it is also limited given the complex nature of such realities. Coherently, to balance complexity and simplifications, scholars have also proposed some alternatives to Esping-Andersen’s taxonomy (Arts & Gelissen, 2002; Ferrera, 1996). Ferrera (1996) focused on Europe, as we do in this thesis, with a higher contextual specificity than that pictured by Esping-Andersen, and distinguished between Anglo-Saxon, Bismarckian, Scandinavian, and Southern European regimes. In contrast, subsequent works that mixed the basis of Esping-Andersen and a near notion of the spirit behind Ferrera’s proposal determined the existence of Social-democratic, Conservative, Liberal, Mediterranean, and Eastern regimes (Koch & Fritz, 2014). As the works of Koch and Fritz have inspired our empirical analysis, we suggest their categorisation for comparative purposes (Table 1-2). Table 1-2 Preliminary classification of welfare regimes Regime Distinctive traits Countries Decommodif. Social strat. Context Socialdemocratic Similar: High Similar: Low Diverse Austria, Belgium, Denmark, Iceland, the Netherlands, Figure 1-8 Welfare regimes according to decommodification and social stratification 37 Norway and Sweden Conservative Similar: Medium Similar: Medium Diverse Finland, France, Germany, Italy, Japan and Switzerland Liberal Similar: Low Similar: High Diverse Australia, Canada, Ireland, New Zealand, United Kingdom, United States of America Mediterranean Diverse Diverse Similar: Area, History Greece, Portugal, Spain and Turkey Eastern Diverse Diverse Similar: Area, History Czech Republic, Estonia, Hungary, Poland, Slovakia and Slovenia Source: Own elaboration based on Koch & Fritz (2014). Notwithstanding, this classification should be observed with caution. Apart from the mentioned limitations of any taxonomy, as commented previously, we consider that some “labels” are subject to discussion. Particularly, there is a growing consensus on the Social-democratic nature of the Finnish welfare regime, as well as on the Mediterranean adequacy of the Italian case, despite some Conservative traits (del Pino Matute & Rubio Lara, 2013). In this respect, we reiterate that our adhesion to this classification is related to the need of ensuring comparability between works. As the work of Koch & Fritz (2014) is a relevant source of inspiration for this research, we consider their taxonomy to enable a comparison with our outcomes. However, as we indicate in our methodological framework in Section 1.5 and our procedures in Section 4.3, the underlying classification 38 of WS does not determine our results: the optimal typology of eco-social conjunctions to test synergy is strictly based on data. The classifications based on Esping-Andersen’s taxonomy have remained meaningful despite the events that occurred over the last decades in the socioeconomic dimension of WS and said limitations. These events consist of a transformation of Fordism motivated by successive crises, reconfigurations of economic thought and globalisation (Rueda, 2012), and can be summarised as follows (Gómez Serrano & Buendía, 2014): • A more moderate economic growth, which has limited the capacity of WS to secure public revenues and subsequently, public expenditures, and is concomitant with an increasing fiscal fraud, financialisation, and, ultimately, globalisation. Globalisation is essential to understand the evolution, as this phenomenon is responsible for conditioning fiscal and labour rules, limiting the capacity to fund public policies through fiscal competence, and causing tensions in the demand for greater protection support (Muñoz de Bustillo, 2019). • The momentum of liberal schools of thought and their followers in policy design potentiated privatisation and commodification (Korpi & Palme, 2003). • The demographic transformation, in which concur ageing, a reduction of fertility, a considerable increase of life expectancy, the consequent increase of retired population, and a reduction of the relation between active and dependent population (Esping-Andersen, 2000). • Finally, regarding the labour market, the proliferation of part-time and temporary jobs, unemployment in the young population, the scarce remuneration in low-skilled positions, the delay in the access to the first job connected with the demand for education and higher qualifications, the instability of family as a social institution, and the worsening of labour quality and social protection. As Gómez Serrano & Buendía (2014) showed, these reconfigurations, although generalised, have generated specific impacts across typologies that have modified their initial scopes. Conservative regimes have faced a more notable downgrading of the social pact, which materialised as legislative restrictions, reductions in key social spending like retirement, and erosion of universality. 39 Liberal regimes traditionally presented a low relevance of informal social institutions, a high contribution of the market in the provision of welfare services, elevated confidence in individuals to satisfy their needs, and a marginal universality of public welfare. They showed the most notable transformations in the past decades (Pierson, 1994) until the financial crisis of 2008, which in contrast did not alter the previous evolution. Said transformations reinforced the inherent traits of these regimes and consisted of downgrading the role of unions, cutting the generosity of retirement pensions, reducing the generosity and extension of unemployment benefits, and introducing further privatisations. Though, they simultaneously promoted a frontal boarding of poverty through complementary incomes and the emergence of public-private agreements to provide social services, with the only exception of health services, which remained largely universal (Gómez Serrano & Buendía, 2014). Social-democratic regimes have persistently been considered the truest to their original principles, as they have not undergone sharp variations. On the contrary, they have built up over their primary results in the 1970s (Moreno, 2013; Rubio Lara, 2013; Rueda, 2012). On the one hand, a high-skilled labour supply, the personalisation of job search, and flexicurity enable full employment. On the other hand, low wage dispersion, high activity rates, work-life balance, high generosity, and universality of benefits are based on an equally elevated fiscal pressure, hence promoting low dependence on informal social institutions and between individuals. Even if Social-democratic WS seem to remain more attached to their core principles, we can observe a certain level of denaturalisation of some traits or retrenchment (Buendía, 2015; Buendia & Palazuelos, 2014). In the case of the Nordic countries, and particularly Sweden, this phenomenon is reflected in the promotion of public-private management of pensions, the introduction of actuarial techniques for the calculation of benefits (Belfrage & Ryner, 2009), the increasing participation of markets in the provision of social services (Buendía García, 2012) and a slight limitation of universality (Moreno, 2013). Regarding Mediterranean regimes, the evolution of this derivation has been more ambiguous (Moreno & Mari-Klose, 2013): as WS were attempting to close the breaches of universality and generosity in comparison with more developed regimes, they were also facing some minimalist trends. Minimalism has been significantly associated with lower fiscal pressure, the prevailing traditional role of informal social institutions, notably family, in constant evolution during the last decades, globalisation, and the fiscal rules imposed to enter the Euro community (Navarro, 2000). The crisis in 2008 was a determinant factor for Mediterranean regimes, as they suffered its consequences more harshly, particularly increasing unemployment levels, decreasing the quality of jobs and the level of wages, reinforcing gender inequality and promoting part-time jobs (Rodríguez 40 Cabrero, 2011). When the pandemic started, in 2020, Mediterranean regimes were still recovering from the previous collapse. Despite these nuances, we observe four trends in public welfare that have marked the two first decades of the 21st century (Gómez Serrano & Buendía, 2014): • The individualisation of risks, contrary to the collectivisation of risks, which moved forward the foundational basis of WS. • The emergence of new social agents to provide social protection, such as religious institutions and other non-governmental organisations, therefore representing a return to the social mechanisms of welfare before the establishment of WS. These institutions have a limited range of action in comparison with formal governmental agents and have restrictions regarding infrastructure, capacity, and resources. • The increasingly relevant role of informal social institutions, notably family. In parallel with the emergence of new social agents, there is a reemergence of traditional institutions of social support. • The deterioration of fiscal basis, because of the mentioned dynamics on the side of the economy, in combination with a proliferation of indirect taxation on the side of the public sector. Hence, public revenues are increasingly attached to economic cycles and WS are more limited to acting in case of crises than in previous designs, in which direct taxation represented a higher fiscal basis. In this evolutive context, whether denaturalisation or potentiation of the original features of welfare regimes, the current developments in the field of just energy transitions, and socio-ecological transitions to sustainability in general, are increasingly demanding WS interventions to ease the process. The role of WS in the transition is critical, as they could be facilitators in the process and recipients of the environmental impacts. As recipients, WS are subject to evolving technological, environmental, and demographic dynamics, but the environmental evolution would be the determinant factor to explain the future of welfare (Muñoz De Bustillo, 2020). As facilitators, although the environmental status is a global issue, the role of the contemporary state should not be overlooked (Bäckstrand & Kronsell, 2015; Duit, Feindt, & Meadowcroft, 2016). In the context of climate change, the states are demanded to finance adaptation and mitigation projects, face more notable and frequent natural disasters, and compensate for the fiscal regressivity of mitigation policies (Bailey, 2015; Johansson, Khan, & Hildingsson, 2016). A central element of the relations between WS and the environment, and subsequently the process of energy transition, can be found in the Nordic models 41 (Westholm & Beland Lindahl, 2012). The energy transition of Sweden, particularly, could be a non-intentional side effect of the welfare regime and parallel model of competence that developed during the 1980s. The WS generated the requisites of a decentralised energy model by establishing a uniform, standardised, and powerful local administrative structure. Simultaneously, the echoes of the rising energy prices in the 1970s motivated the emergence of a model of competence in which the Swedish regions mobilised their local resources. The ultimate detonator was the downgrading of the interventionism of the central state, which became a supervisor of decentralised actions. Nevertheless, the Swedish case is particular and the configuration of the WS is not an immediate guarantee of positive environmental performance. Even if environmental interventions flow through the structures of the welfare regimes, social and environmental goals and interventions can be conflicting (Dryzek, 2008; Koch & Fritz, 2014). As Dryzek (2008) emphasises, environmental policies can be seen as social policies with the sole exception of their scope. While social policies face individually unpredictable, but collectively predictable risks, environmental policies address collectively unpredictable risks. Some of the cited works and related theories (Bäckstrand & Kronsell, 2015; Bomberg, 2015; Borgnäs, Eskelinen, Perkiö, & Warlenius, 2015; Dryzek, Downes, Hunhold, Schlosberg, & Hernes., 2003; Duit et al., 2016; Eklind Kloo, 2015; Jakobsson et al., 2018; Meadowcroft, 2008; Sommerer & Lim, 2016; Vilella-Vila, 2012) analyse the establishment of ES, beyond WS. ES aim at managing the environment and its social interactions through continuous political activity (Duit et al., 2016) to achieve a sustainable future domestically and globally (Bomberg, 2015). In the construction of the incipient ES, not only limited to Western countries (Sommerer & Lim, 2016), we can distinguish two phases (Meadowcroft, 2012). First, the establishment of central environmental agencies as an attempt to control pollution and elaborate environmental agendas. Second, the dilution of these attempts in a systemic and long-term ambition, in which environmental decision-making joins economic issues, through a diversity of political instruments and international cooperation. From a practical viewpoint, WS and ES are not significantly differing structures (Meadowcroft, 2008). Both are political responses to long-term social changes concerning industrialisation, urbanisation, and democratisation, which theoretically cannot be faced through markets and voluntary actions. Furthermore, both structures impact normal social interaction while facing significant limitations in the political and economic spheres. In contrast to the well-established trajectory, theorisations, and empirical efforts around WS, much remains to be said concerning ES, as a relatively recent proposal that has not constituted a close reality yet. Consequently, by adhering to this theoretical framework of WS, we seek to contribute to further the idea of ES in Chapter 4, through 48 in the topic and elaborating a research agenda accordingly. Finally, Section 2.6 gathers the main conclusions. 2.1. Preliminary bibliometrics A preliminary search for the concept of just energy transition applied to low carbon economies in bibliographic databases such as ScienceDirect and Web of Science reveals significant traits of the topic that motivates this research. The first papers that study explicitly this issue can be found as soon as 2006. Greater interest, yet relatively modest, can be progressively detected since 2010. This appeal adopts the form of an exponential evolution from 2015 to the present moment, probably fuelled by the UN’s SDGs (Figure 2-1). Figure 2-1 Number of retrieved papers about just energy transitions to low carbon economies per year, 2006-2021 Source: Own elaboration. The processing of these papers through text mining techniques and linguistic algorithms (Figures 2-2 and 2-3) (van Eck & Waltman, 2018) reveals relevant bibliometric insights that match with the results of the literature review, as presented in the following Sections: First, the predominance of sectorial studies of impacts at a country level, mostly in Europe and particularly in Germany, through models of economic growth, with a focus on employment and income (Figure 2-2). Second, the analysis of energy systems and climate change has congregated greater attention than energy justice, agents’ practices, and policies (Figure 2-3). 49 Figure 2-2 Bibliometric network and clusters of just energy transitions Source: Own elaboration through VOSviewer. Figure 2-3 Conceptual density heatmap of the bibliographic network of just energy transitions Source: Own elaboration through VOSviewer. These preliminary insights provide an initial context for the review. Yet, a proper systematic review in the terms described in the methodology is required to obtain a detailed state-of-the-art. 50 2.2. Methodology The method used to elaborate the state-of-the-art consists of an unweighted systematic review (Sovacool et al., 2018) structured in the following phases (Figure 2-4): Phase 1. Contextualisation. A revision of reports and working papers from international organisations is done, as well as a review of the most commonly referenced articles in related academic projects. This leads us to conclude that a review on just energy transitions must focus on labour and income. Phase 2. Searching in scientific databases and screening the results. Diverse search strategies are launched to retrieve papers containing the different conceptual combinations detected in the previous phase, e.g., “just energy transition”, intitle: “energy transition” AND employment OR jobs OR income. Once the total number of articles found in such databases has been gathered, they are classified to determine their interest in this research by relying on topicality and conciseness. The effects on labour and income especially cover the case of developed countries, due to the relative scarcity of studies based on data from less developed areas. Phase 3. Broadening references. From the total number of selected papers, a search for more references is done by checking the bibliography their authors used. The recovered references are processed as done in the previous phases. Phase 4. Synthesis. In the final phase, we extract the meaningful information from the review in a summarised format. Figure 2-4 Review process and reduction of references (number of retrieved papers in circles) Source: Own elaboration. 51 This methodology shares the same limitations as other systematic reviews in the field of energy research (Sovacool et al., 2018), i.e., it is a research strategy intensive in resources, narrowly focused on restrictive questions, with an implicit preference for quantitative research and led by an additive approach. To avoid these inherent limitations, we have taken the precaution of including the conceptual and institutional overview of a qualitative nature presented in the form of a theoretical framework (Section 1.4.2) and a Section of barriers (Section 2.4.3) to compensate for the possible quantitative bias and the merely additive narratives derived from the review of methodologies. The results of the application of this method are presented in the following Sections. 2.3. Methodological state-of-the-art The predominant method to analyse the topic under study is the elaboration and application of models. The modelling of just energy transitions emerged from the attempt to integrate a growing environmental awareness with the Keynesian and Post-Keynesian approaches. This branch has started to develop models to assess economic inequality and job markets in connection with environmental limitations and alternative economic paradigms (Hardt & O’Neill, 2017), contributing to filling a gap in the research into transitions (Köhler et al., 2019). As the models are diverse, they are classified into groups and subgroups (Hardt & O’Neill, 2017; Rosebud Lambert & Pereira Silva, 2012). The most widespread classification is also the most categorical. It considers the existence of numerical and analytical models according to their base; while earth system models, Integrated Assessment Models (IAMs), and Computable General Equilibrium models (CGE) are considered according to their level of detail (Stehfest, van Vuuren, Bouwman, & Kram, 2014). Numerical models usually apply IOA (Input-Output Analysis), which is recurring in the literature (Lehr, Lutz, & Edler, 2012) and consists of a set of tables that show the flows of goods and services between intermediate units and final units (Markandya et al., 2016). They are useful as transformations propagated through supply chains, but they also present methodological limitations (Fragkos & Paroussos, 2018; Markandya et al., 2016): the difficulty of considering time lags, measuring feedbacks between prices and quantities, the linearity and invariance of the coefficients, the generalisations of sectors, the homogeneity of outputs, and the lack of economies of scale. What is more, their reliability is inherited from primary data sources and they tend to overestimate job creation. Input-Output Tables (IOTs) are also scaled on a national level, so it is difficult to use them in a regional or local perspective (Rosebud Lambert & Pereira Silva, 2012). 52 Analytical methods are commonly used for regional or local studies instead. They are more transparent, but cannot predict indirect or induced jobs. Regarding the perspective of modelling, there are two main, widely used approaches: the top-down approach and the bottom-up approach. When applied separately, these approaches have significantly demonstrated weaknesses (Bacon & Kojima, 2011; Kammen, Kapadia, & Fripp, 2006). The top-down approach relies on a static Input-Output (IO) methodology that cannot offer an accurate level of detail in sectorial estimates. Moreover, it is not appropriate to capture the shifts caused by investment variables and offers other inconveniences attached to the timing of the model. The bottom-up approach configures limited analytical models dependent on accounting techniques that do not provide the big picture. It would therefore be useful to combine both approaches; benefitting from their strengths and avoiding their weaknesses. This combined approach is called “hybrid” and has been increasingly applied in recent research (Crespo del Granado, van Nieuwkoop, Kardakos, & Schaffner, 2018). Apart from this classification, and according to the level of detail (Stehfest et al., 2014), earth system models contemplate a simple economic framework and a complex environmental background; while CGE rely on a complex economic frame despite a simpler environmental background. IAMs balance the complexity of environmental and economic systems. In methodological terms, increasing attention has been paid to the use of IAMs, such as IMAGE (Stehfest et al., 2014), ReMIND-R (Luderer, Leimbach, Bauer, & Kriegler, 2011) and EPPA (Wilkerson, Leibowicz, Turner, & Weyant, 2015), to the point of becoming the primary method for studying the transition. While this classification gathers the most common terminology (Figure 2-5), it should be used with caution and an open perspective, since the empirical state-of-the-art is surpassing such restrictive classifications, as the existence of methodological intersections and hybrid approaches proves. 53 Source: Own elaboration. Apart from this caution, we have found three increasingly generalised claims to remark. First, the growing call to allow Social Sciences to enrich this discussion (Sonetti, Arrobbio, Lombardi, Lami, & Monaci, 2020; Sovacool, 2014), besides the encouragement of the use of case studies (Ge & Zhi, 2016; Sovacool, 2014), the application of systemic thinking (Finley-Brook & Holloman, 2016) and the cooperation between fields (Jenkins et al., 2016) through transdisciplinary analysis (Heffron & McCauley, 2017). Second, the increasing interest in socio-metabolic approaches (Behrens et al., 2014; Fischer-Kowalski et al., 2012; Rodríguez-Huerta, Rosas-Casals, & Sorman, 2017) to understand the paths of societies and provide thorough comparisons, reinforcing the use of case studies (Ramos-Martin & Canellas-i-Boltà, 2008). Third, the explicit argumentations in favour of demand approaches, based on the risk of limiting the capacity of the models when it comes to the caption of social problems and impacts on welfare with full employment assumptions (Taylor, Rezai, & Foley, 2016). Concurrently, most of the published works are still based on growth paradigms. A lack of studies focusing on structural and transformative approaches had already been noticed long ago (Haxeltine et al., 2008), even though it is a recurring alternative found in theoretical studies, as stated in the theoretical framework. In summary, the methodological composition of the state-of-the-art is presented in Figure 2-6: Figure 2-5 Categorical classification of models 54 Figure 2-6 Methodological composition of studies about the impacts of the energy transition on employment and income distribution Source: Own elaboration. Once this methodological framework has been set up, we are in a position to delve into the empirical effects set out by works in the field, as described in the following Section. 2.4. Survey of the empirical effects By classifying studies in terms of the sign of the impact, we observe that most of them point to positive, yet small, effects on employment and notable negative effects on income distribution (Figure 2-7). Source: Own elaboration. Figure 2-7 Sign of the effects concluded by studies about employment (Panel A) and income distribution (Panel B) 55 This overview is developed in detail in the next Sections. The effects here summarised and gathered below come from studies before the SARS-CoV-2 pandemic causing COVID. Therefore, they do not contemplate its energy and socioeconomic impacts. Nonetheless, despite uncertainty, there are pieces of evidence that suggest that, even if the pandemic has deeply affected economic, social, energy and environmental dimensions, the effects of the transition do not differ significantly from those described in the absence of such perturbation (Guidehouse & Cambridge Econometrics, 2020). 2.4.1. Effects on employment Most studies estimate the effects of the energy transition by calculating the variation in the number of direct, indirect, and/or induced jobs (Cambridge Econometrics, 2013). According to their geographical context, we have gathered them into three groups: supranational, national, and regional studies. Regarding studies at the European supranational level, the following can be highlighted: Cambridge Econometrics (2013) used the model E3ME, considering the EU 2020 objectives, and found that this strategy would lead to a small creation of jobs via an increase in the investment variable. However, it was unable to offer a detailed disaggregation of job flows between sectors. The Green Jobs study by Cambridge Econometrics also focused on the 2020 horizon, but data were taken at a moment of economic recession and did not consider major changes in the labour market or technologies. The study concluded that the variation in the number of jobs in Europe would be small and the most significant problems would be the consequences of a negative impact of labour mobility on aged workers and skill shortages in certain sectors. The role of labour mobility is a key to determining whether the effects are positive or negative. Focusing on the 2030 horizon, both for the EU and other areas, E3ME has been updated and linked with the Warwick Labour Market Extension model (Lewney, Alexandri, Storrie, & Antón Pérez, 2019). The results reinforce those obtained in the previous application of E3ME (Cambridge Econometrics, 2013): again a positive outcome is found in employment due to investment, especially in energy, construction and related materials sectors. Cambridge Econometrics (Cambridge Econometrics, 2018) additionally provides the impacts of a shift to low-carbon transport following the energy transition through FEF (E3ME model) and ELAB: they concluded that a potentiation of hybrid vehicles would have beneficial results, while a proliferation of battery electric vehicles would destroy jobs. NEMESIS and ASTRA models have also been applied to the 2030 horizon (Ragwitz et al., 2009). NEMESIS projected more optimistic conclusions, highlighting the 56 benefits of an accelerated deployment scenario. ASTRA was more pessimistic due to the differences attached to the investment variable. At this moment, the focus is on 2030, but especially on 2050, according to action plans (European Commission, 2019; IRENA, 2018), or even on 2100, to deepen the research ambition (Petit, 2017). Nevertheless, the methodological approaches and the results of the cited studies are revealing and must be considered. Towards the 2050 horizon, the E3ME and GEM-E3 models have been used (Cambridge Econometrics, 2013), as well as the LUT model (Ram, Aghahosseini, & Breyer, 2019). The E3ME estimates a net increase in employment equal to 1.2%, due to the fall in oil prices caused by the decrease in fossil fuel supplies, the increase in investments, the use of public revenues, the loss of jobs in polluting and intensive energy sectors, and the rise in electricity prices. The GEM-E3 also concludes that a net positive, yet small, impact is likely to be expected, despite the scenario. Notwithstanding, skill shortages push wages up and negatively affect the level of employment. In contrast, LUT predicts a high positive result, since renewables would contribute to 80% of employment by 2050 and the transition would potentiate economic growth, productivity, and efficiency. Concerning the GEM-E3 model, Fragkos & Paroussos follow a hybrid approach, relying on a more remarkable Neoclassical background, thus prioritising supply rather than demand (Fragkos & Paroussos, 2018). As a result, they estimate creation and reallocation of jobs of around 1% of the European labour force. Again, a positive yet insignificant outcome is found. Skill shortages are not so important in this update, probably because of the methodological background. Additional studies in the EU conclude that the transition has an especially positive effect on the energy sector. However, the measures implemented cause a rise in prices that negatively impact production and employment in the rest of the sectors. According to their authors, the negative effects can be partially tackled through market integrations (Creutzig et al., 2014). In contrast with the ex-ante nature of these works, studies that look at the past through counterfactual methods can also be found (Markandya et al., 2016). Markandya et al. (2016) use the IO method applied to past data on electricity and gas from a multiregional perspective to find spill-over effects caused by changes in the energy system. Its conclusion is coherent with the preceding ex-ante studies: an increase in employment can be detected after the implementation of sustainable energy measures. Besides the European supranational level, there are also abundant studies applied to national and regional contexts that most often emphasise the uneven distribution of impacts across them (Gambhir et al., 2018). Among these studies, certain cases are 57 presented as paradigmatic: Germany is the most analysed and cited case (Pegels & Lütkenhorst, 2014) under a diverse set of methods referenced below; the Netherlands, using the CGEM ThreeME model (Bulavskaya & Reynès, 2018); Italy, with a LotkaVolterra growth model (Bernardo & D’Alessandro, 2016); France, using directly the IO methodology (Perrier & Quirion, 2017) and the EUROGREEN model (D’Alessandro, Cieplinski, Distefano, & Dittmer, 2020); and Catalonia, using the MuSIASEM model (Rodríguez-Huerta et al., 2017). Germany has generated more literature than any other country, in most cases to point out the negative effects of the German energy strategy (Energiewende), especially on employment and equity (Fischer, Hake, Kuckshinrichs, Schröder, & Venghaus, 2016). However, these studies are frequently conceived from an exclusive point of view and do not aspire to observing the big picture, since they only focus on electricity (mainly as a reflection of the national strategy, which is entirely focused on electricity) (Unnerstall, 2017). Notwithstanding, the German case has served to clarify some transcendent conclusions. Fischer et al. (Fischer et al., 2016) offer two important facts. First, the key to analysing the job variations caused by the transition is not gross employment, but net employment: gross employment shows a positive trend as long as renewables create new job positions; however, net employment measures whether the destruction of jobs on older, conventional sources are replaced or not by the creation of jobs in renewables. Second, the macroeconomic effects of the transition are deeply determined by the speed of its implementation. The speed is a political variable that can be used to influence the outcome, determining the level of macroeconomic and environmental costs assumed by society. Many studies support a fast transition based on Research and Development (R&D), the backing of social agents, and the development of cost-competitive renewable technologies (Bromley, 2016), apart from the context of globalisation (Kern & Rogge, 2016). In contrast, other authors are sceptical in regard to speeding up the process (Smil, 2010b), or they are even sceptical about the full concept (UNU-WIDER, 2017). Apart from the speed, the outcome depends on the situation of the economy regarding the production possibilities frontier, the prices of primary factors, the conditions of flexibility in the job market and the labour intensity (Fragkos & Paroussos, 2018). Focusing on solar and wind energy, the two strategic sources for the transition; while most employment is created by investment in installations, a growing number of jobs are generated in maintenance and operation services. Although the importance of investment is clear, export markets are also a key variable, in line with other studies that complain about recurrently omitting it (Markandya et al., 2016). Markandya et al. (2016) find, through their counterfactual method, that employment creation follows geographic patterns and is more dispersed than expected. In this vein, and looking to future 64 Finally, from the financial point of view, one of the biggest concerns is related to the financing of the grid development by electricity consumers (Schlesewsky & Winter, 2018). Under an adaptive market hypothesis, there would be room to identify more structural barriers, pricing mechanisms, market design influence, and behavioural issues in the elaboration of transition policies (Hall et al., 2017). 2.4.4. Classification of reviewed papers considering methodology and results In Table 2-1, the main empirical studies found to be relevant for this review are classified according to their analytical unit, methodological traits, results, and timing. 65 Table 2-1 Classification of the reviewed empirical studies Units Methods Methodological traits Results Time span References World regions IAM A human and an earth system are interlinked through land and emissions. It is triggered by such drivers as population, economy, policies, technology, lifestyle and resources to calculate impacts on the environment and development. Limitations: inaccurate monetary feedbacks, calibration distortions in the period 2010-2020, lack of specificity at local and national scales, impossibility of testing specific policies and absence of a governance system. As labour supply and income disparity via the Gini index are drivers of the model, relying on the definition of scenarios, the interest of this model is the joint consideration of socioeconomic and ecological dimensions in an integrated approach. 19702050/2100 Stehfest et al. (2014) (Stehfest et al., 2014) IAM It hard-links a macroeconomic (Ramsey optimal growth with equilibrium constraints), an energy and a climate module. Capital, labour and energy are inputs to the model. It includes international trade and investment dynamics. Limitations: lack of detail in processes happening inside world regions, renewable supply intermittence is not detailed, difficulty in capturing efficiency potentials, missing technological spill-overs and stocks of knowledge, lack of constraints in bioenergy. Since labour is an input, the relevance of the model is the parallel consideration of energy-economyclimate relations in an integrated model with trade and investment dynamics. 2005-2100 Luderer et al. (2011) (Luderer et al., 2011) Employment factors The total employment is calculated as the addition of key jobs during the transition. Energy system transition Considerable positive effects: renewables contribute to 80% of jobs by 2050 and widely exceed the rate of 2015-2050 Ram et al. (2019) (Ram et al., 2019) 66 model: simulation applied to cost optimality in five-year periods. Innovation: inclusion of energy storage effects. lost jobs. Higher efficiency, productivity and economic growth. Different impacts across regions, but with a general growth potential. Methodological combinations As it is a wide-picture report, it combines different methodologies for each topic under focus. It is the only reviewed piece that provides an advanced understanding of gender impacts and an estimation of the effects of climate change combined with the impacts of the energy transition. The relation between decoupling and employment is studied through econometric logarithmic regressions. The resource intensity of employment is calculated by distributing resource consumption equally among all registered employment. The impact of human-induced disasters on labour is estimated by adapting Noy’s benchmarking index. The effects of heat stress on workers are calculated by applying grid cell data to population cohorts and employment distribution by sector. The employment balance by sectors, wages, emissions, skills, and gender is estimated with multiregional IOTs. Net positive effects: declining energy activities will lose 6M jobs, while greening energy activities will generate 24M worldwide. Gender inequality is exacerbated because new jobs are created in industries with higher male-related employment: construction, energy, and manufacturing. Climate change-related natural disasters caused the loss of 0.8% of working time globally between 2010 and 2015. Heat stress will suppose a reduction of 2% in working hours globally by 2030. 1995-2030 ILO (2018) (ILO, 2018) World countries IRENA database indicators Analysis of IRENAS’ database historical indicators on renewable energy-related employment by volume, technology, product, gender, and In 2018, 11M jobs were registered in renewable energy (7% interannual increase), with approximately 33% of them in solar photovoltaic power, 19% 2018 IRENA (2019) (IRENA, 2019) 67 country. Thus, focused on directly observed records for the energy sector solely. in hydro, 19% in liquid biofuels and 11% in wind power. 39% of global employment is in China, followed by the EU with an 11% share. By gender, 32% of jobs in renewables are carried out by women. Global South country sample Indicators Analysis of statistics and estimations from national governments and international organisations. The goal is to study the compliance with SDGs and the potentiality of a just energy transition in a sample of Global South countries. The South African government estimates 300,000 new jobs in renewables in 2020, but lacks a net perspective. India created 400,000 jobs in 2015 and could create 1M by 2022 relying on wind and solar sources. Rural Vietnam is thought to benefit from decentralised energy technologies (no data provided). Philippines reached 9,700 jobs in bioenergy and 100,000 in the construction of renewable infrastructure in 2015. Morocco has greater exposure to the consequences of climate change. The ratio of employment in renewable vs. conventional plants is 5:1 (no overall data provided). Jamaica created 525 jobs between 2015 and 2017 and could reach 40,000 jobs if performs a complete transition to renewables. Indicators until 2017 and projections to 2020/2030/ 2050 Hirsch et al. (2017) (Hirsch et al., 2017) US IAM Comparison between models. GCAM is a simulation model of partial equilibrium focused on energy and land use that runs in five-year periods. The interest lies in the comparison of models to strengthen the design and adoption of measures by policymakers. Main insight derived from the 1990-2100 Wilkerson et al. (2015) (Wilkerson et al., 2015) 68 MERGE is an intertemporal optimisation model of general equilibrium based on economic growth and investment, whose time step is ten years. EPPA is a simulation model of general equilibrium centred on the nexus between energy and economy with a five-year step. comparison: carbon intensities are the source of disparity in comparisons between models. Lack of clear evidence in labour and distributional matters. Regressions Logistic and Poisson regressions with fixed effects and temporal lags applied to sociodemographic and economic variables regarding the effects of wind energy. Wind energy has not caused significant injustices in income or ethnicity, but on younger people, with lower levels of qualifications and labour force participation in rural areas. 2008-2017 Mueller & Brooks (2020) (Mueller & Brooks, 2020) Econometric model Parameters are determined by statistical techniques applied to time series. Keynesian background. Demand assumes a higher relevance. Electricity prices are used to generate industrial prices and thus consumption. Employment is a result of output, which is pictured by observing consumption, international trade, efficiency, fuel consumption, and investment. Income is derived from employment and contrasted with consumption. It contemplates the possibility of nonoptimality and imbalances in markets. Investment is not totally linked to savings since a stock of capital is available. Only detailed at a European level: the rest of the world is assumed in the 2013 edition (2019 update also includes areas like the US). Limitation: The application of E3ME to the US context has generated considerably negative impacts of -1.6% in employment and -3.4% in GDP. 2009/2050, 2011/2020, 2009/2050 Lewney et al. (2019) (Lewney et al., 2019) EU Small creation of jobs via increasing investment. Biggest positive impact on construction and energy efficiency materials sectors. Jobs will change scope without major changes in skill distribution. Marginal overall changes in jobs, labour rigidity and skill shortages. Labour mobility as a key factor to determine the net effects. Employment net increase of 1.2% due to the fall in oil prices, the increase in investment, the use of public revenues, the loss of jobs in polluting and intensive sectors, and the rise in electricity prices. Cambridge Econometrics (2013), Lewney et al. (2019) (Lewney et al., 2019) (Cambridge Econometrics, 2013) 69 not all roadmap technologies were considered. In the 2019 update, the result is coherent with previous results: an increase of 0.5% is expected in employment and 1.1% in GDP. Investment is again the main cause of this positive result. Econometric model Macro-sectoral model based on CES production functions. Neo-Keynesian effects have a greater influence. Core model plus four modules. Energy cost affects exports, and thus consumption via competitiveness. Optimistic consequences. Benefits of an accelerated deployment scenario: 410,000 new jobs and an associated increase of GDP equal to 0.24% in the EU resulting from the total achievement of the 20% renewable energy goal. More ambitious policies could grant a 0.4% increase in the GDP and up to 545,000 jobs in 2030. 1990-2030 Ragwitz et al. (2009) (Ragwitz et al., 2009) IAM Built on SD. Neoclassical background complemented by demand-side structural change. SD: Nine modules and a connection between demand and supply. Foreign trade is considered and covered. Energy cost is compensated through shifts in marginal consumption that avoids sectors with higher labour intensities. Pessimistic consequences due to the investment variable and the enhanced impacts of energy cost compensations on labour-intensive sectors: energy costs are compensated for through shifts in households’ marginal consumption, subsequently reinforcing the demand for less labourintensive goods. CGE Hybrid model: parameters coming from related literature. Neoclassical background: focus on supply and optimality leading to market clearing via prices. Investment is closely attached to savings. Alternative options for users, who can choose some variables. A small net positive impact is expected despite the scenario. Wage problematic. Results are more pessimistic than in the E3ME model. Substitution and reallocation of jobs constituting around 1% of the total labour force. Final effects depend on the PPF, the prices of primary factors, 2009-2050, 2015-2050 Cambridge Econometrics (2013), Fragkos & Paroussos (2018) (Fragkos & Paroussos, 2018) (Cambridge Econometrics, 2013) 70 labour flexibility and intensity. Skill shortages are less important. Indicators Focused on the energy sector. Contrasts the EU North with the EU South. Positive effect on energy sector employment found in the literature and up to 1% increase in GDP, favourable to Southern Europe. Rise in prices that affect production and employment in the remaining sectors. Interconnectivity is highly recommendable. -2050 Creutzig et al. (2014) (Creutzig et al., 2014) IO Multiregional to find spill-over effects. No scenarios. Counterfactual method. Increase in employment after the implementation of sustainability measures (530,000 net jobs, 0.24% in 2009). Exports as a key factor. 1995-2009 Markandya et al. (2016) (Markandya et al., 2016) Germany Review, workshops & surveys Qualitative methodology focused on net employment. The macroeconomic effects are determined by the speed of implementation. Negative effects of the German energy strategy, especially on employment and equity. Negative impacts on employment and higher electricity prices affecting equity receive more attention from the industry, unions, and governments than from the general population. 2009-2016 and instant data from 2016 Fischer et al. (2016) (Fischer et al., 2016) Cost-benefit analysis Cost-benefit analysis and cost assessment. Solar and wind contrasted. Qualitative, reliant on market argumentation. Focused on electricity. Most employment is created from investment in installations, but a growing number of jobs are generated in maintenance and services. Employment creation follows geographical patterns and is dispersed. Export markets as a key variable. The distributive effects are unfair from a social perspective (prices have doubled, affecting low-income households, and public subsidies for 2000-2013, 1998-2013, 1990/20102030, 2010 vs 2016, 2000-2015, 1998-2015 Andor et al. (2015), Frondel et al. (2010), Gawel et al. (2015), Heindl et al. (2014), Pegels & Lütkenhorst (2014), Schlesewsky & Winter (2018) (Schlesewsky & Winter, 2018) (Andor et al., 2015) 71 renewables tend to benefit wealthy citizens). Inequality has been increased by network changes, causing a loss of welfare. Regressive effects could lead to social conflict. Failures in the design of the German transition drive some authors to propose greater trust in market mechanisms, although it is not a generalised claim and most of them propose better policies. Those who believe in market mechanisms perceive that previous studies show a dramatisation of facts and price rises are explained because of beneficial quality increases. (Frondel et al., 2010) (Gawel et al., 2015) (Heindl et al., 2014) (Pegels & Lütkenhorst, 2014) Econometrics Econometric analysis of massive surveys. Perceived distributional effects are key, as are the real effects. Support for the statement which says that those who pollute the most, pay the most. Real burdens are not as high as perceived and are manipulated by elites. Instant data from 2015 Groh & Ziegler (2018) (Groh & Ziegler, 2018) Economic impact assessment Economic activities connected to physical consumption and energy are unidirectionally soft-linked for each scenario to a macroeconomic model with regional distributions and federal structures. Overall positive impact on growth and employment, but a problematic geographical distribution of effects. Negative impact on the rise in prices. The construction sector is the winner. 2010 vs 2030 Sievers et al. (2019) (Sievers et al., 2019) IO and Econometrics Neo-Keynesian background considering nominal rigidities, adjustment delays and dynamic error correction. Top-down approach. It contemplates governmental activities Positive net effect on economic growth boosted by investment. Effects on employment depend on labour flexibility. The restructuration requires a qualification improvement. 2000-2030 Blazejczak et al. (2014) (Blazejczak et al., 2014) 72 and international investment and trade. The model for Germany has been expanded with the use of IO with the specific aim of calculating sectorial impacts. Employment levels are calculated transitioning from gross output to working hours through prices and productivities. IO and Econometrics Energy-economy-climate econometric simulation model based on IOTs. Bottom-up approach. Limited rationality and non-clearing markets. It considers distribution and redistribution of income, as well as financial agents, and acknowledges international dependencies. It runs in one-year periods. Net employment will increase, especially from 2020. 150,000 net employments can be created by 2030. Import/export flows are key to determining the result: under decreased levels of renewable energy exports, net levels of employment could be negative (i.e., greater job destruction). The future effect on gross employment will not be as fast as in the past. 2000-2030 Lehr et al. (2012) (Lehr et al., 2012) Netherlan ds CGE Neo-Keynesian background. Endogenous capital stock and slow adjustment in prices and quantities. It recognises disequilibrium situations. Country-generic. Positive impact: 50,000 new jobs in 2030 and 1% increase in GDP. 2010-2030 Bulavskaya & Reynès (2018) (Bulavskaya & Reynès, 2018) Italy System Dynamics Lotka-Volterra growth model. Wages and employment are endogenous variables. Long-term horizons are studied based on emissions and GDP variations, while the short-term is analysed in terms of employment and inequalities. 1.2M increase in the number of jobs, pointing to the relevance of the investment variable. GDP falls slightly due to pressure on wages, investment and capital accumulation. It emphasises the link between employment and income distribution and the advantages of System 1970-2030 Bernardo & D'Alessandro (2016)(Bernardo & D’Alessandro, 2016) 73 Dynamics. The result is determined by wage flexibility. France IO Data is not updated and missing important events. Inapplicable for several branches. Results are compatible with those at the European level. The different results between branches are caused by wage levels and the share of labour in added value. 2010 Perrier & Quirion (2017) (Perrier & Quirion, 2017) IO and System Dynamics Country-specific dynamic macroeconomic simulation model. Combination of Post-Keynesian and Ecological Economics backgrounds. Nexus energy-economy-environment. It models an open economy by assuming the rest of the world. Supply is defined through IOTs with endogenous change and technological progress. There are financial constraints for firms. It contemplates three levels of skills, the variation of wages and working time, as well as the effects of automatisation. Inequality is analysed via the Gini index. Households’ consumption considers financial variables besides wages and social transfers. It models the public budget balance and recognises the existence of budgetary restrictions. Scenarios: green growth, social equity, and degrowth. Limitations: invariance of technical coefficients of non-energy industries, simplified policy framework, lack of specific technologies for renewable Under a green growth scenario, unemployment increases 3% by 2050, the Gini index increments by 2.5 points in 30 years, GDP grows by 1%, and emissions equal to 23% of those registered in 1990. The deficit declines and reaches 1.5% in 2050. Concerning policies for social equality scenario, unemployment falls 2% in 2050, the Gini index declines more than 4.5 points and stabilises by 2040, the variation of the GDP and the reduction of emissions are similar to the green growth scenario. Deficit increases up to 4% in 2050 (3% EU limit exceeded). In degrowth, unemployment falls 7% by 2050, the Gini index drops approximately between 6 and 9 points, GDP falls 0.7% by 2050, and emissions equal to 17.8%. Deficit dramatically hikes to 6,5% in 2050 (EU limit notably exceeded). 2014-2050 D’Alessandro et al. (2020) (D’Alessandro et al., 2020) 80 multidimensional and multidirectional phenomenon. Likewise, many studies have put their efforts into estimating direct gross impacts on employment levels, when the focus to assess the just transition should be on direct, indirect and induced net volumes. Even if this misconception of employment balances has been increasingly prevented, most studies are blind to impacts beyond volumes, such as the quality of jobs, the variations in working hours and the gender balance of the transition. These absences evince the disconnection between the institutional approach, reflected by the ILO, and the academic approach, and also between scholars’ theoretical considerations and their final empirical applications. Third, the key variables identified in the literature to determine the outcome of the transition are the timing/speed of policy implementation, investment, international trade, the production possibilities frontier, the prices of primary factors, labour flexibility and intensity, and the elasticity of wage levels. Studies generally find a positive, yet relatively small, impact on employment levels, led by the construction, manufacturing, and energy sectors, in conjunction with a negative distributional effect caused by rising electricity prices as a result of network developments and the regressive side effects of public subsidies. Neoclassical models have reached less optimistic results than other methodological backgrounds and tend to emphasise the role of investment and wage levels, while relegating skill shortages. Methodologies relying on surveys and workshops highlight the negative distributional effects with more intensity. This result points to the necessity to consider perceptions alongside real data in the assessment of the transition. In the light of these conclusions, we propose a research agenda that underlines the need to make greater efforts in the study of income dynamics, the effects on job quality and working hours and the role of supporting such policies as compensatory fiscal mechanisms, specifically considering the precise impact of the transition pathways on women. There is also a need to introduce post-growth scenarios in the models to complement green growth strategies and to broaden the geographic scope being researched: instead of Germany, that monopolises the available literature, it would be enriching to consider the Nordic countries, especially Sweden and Denmark, due to their institutional backgrounds, as well as other scales like the local ones. As the methods, results and research gaps in the literature have been disclosed, we proceed to analyse our case study in the next Chapter. 81 Chapter 3 THE JUST ENERGY TRANSITION TO RENEWABLES IN MINING AREAS: A LOCAL SYSTEM DYNAMICS APPROACH The literature review in the previous Chapter indicates a prevalence of quantitative approaches, notably modelling, applied to global and national scales. The prevalence is probably justified based on the need to quantify impacts, the global nature of environmental matters, and the leadership of national scales in fixing energy and climate targets. This prevalence, which has contributed to shaping the state-of-the-art and providing the commented insights, has relegated local scales to a secondary position. Following the common structure, studies consider that any sub-national scale is local. Considering the territorial structure of our close context, local can refer to the regional entities, the provinces, and even the municipalities. Among them, the smallest local realities have been particularly neglected, specially from the viewpoint of quantitative assessments due to some methodological barriers that seem uncrossable. This gap is clearly relevant in the Spanish case. The central government has launched public interventions under the precise umbrella of the just energy transition that has coincided with the development of this research work. In the Spanish strategy, municipalities are the basic unit of governmental coverage and intervention. Most of the municipalities are rural, as happens in León, which congregates most of the interventions at a regional and national scale. Hence, given the near experience with just energy transition that is currently unfolding, this thesis suggests analysing the process to extract lessons to correct the ongoing processes and inspire other processes in declining fossil-dependent areas. As far as the gap in quantitative approaches is concerned, we aim at contributing to designing intuitive and affordable tools for a vast diversity of stakeholders and minimising barriers such as data availability. In consequence, this Chapter aligns with the second secondary goal of the research, “to analyse the most recent proposals for a just transition of the areas under study and determine the strengths and weaknesses of public plans, as well as to offer quantitative insights into the development of the restructuring to find the priorities of future political agendas and stakeholders’ action”. Furthermore, it is addressed to complete the information required to test the first hypothesis, i.e., determine the sign of 82 the impacts of the energy restructuring, in combination with the insights derived from the review. The following Section 3.1 draws a contextualisation in political-normative and socioeconomic terms of the case of León. Section 3.2 presents the strategical advantages that the Leonese case offer to capitalise on the just energy transition, regarding climate and orography, technology and infrastructures, as well as education and human skills. Section 3.3 introduces the quantitative methodology in detail, attending to both the modelling precedents, our proposal, and data sources. Section 3.4 discloses the scenarios and results of the simulations. To complete the case study, Section 3.5 introduces a commentary on the political plans in the light of the insights derived from the study of just energy transitions along the research. The commentary follows three lines: concept and design of the plans, diagnosis, and processes of public participation. Section 3.6 derives the conclusions. 3.1. Contextualisation This brief contextualisation covers the political-normative context and the socioeconomic context. Both aspects are needed to design a realistic model. 3.1.1. Political-normative context The processes of just energy transition in León are built over a wide politicalnormative framework, in which every administrative level has provided its own goals and regulations to develop the global core principles of the SDGs and the ILO. At a European level, there is the 2030 Climate & Energy Framework (European Commission, 2014), updated with the European Green Deal in 2020 (European Commission, 2020a) and the Mechanism of Just Transition (European Commission, 2020d). The updated Framework, which receives the main consideration in national strategies, fixes the three main European goals: a 55% reduction of GHG (referenced to 1990), the presence of at least 32% of renewable sources in the energy mixes and the improvement of energy efficiency by 32.5%. The Mechanism of Just Transition is aimed at alleviating the socioeconomic impact of the energy rearrangements in the most affected regions through the investment of EUR 150 M between 2021 and 2027 (European Commission, 2020c) based on three pillars: the Just Transition Fund, InvestEU “Just Transition” and the loans of the European Investment Bank. At a national level, Spain has elaborated the Strategical Framework of Energy and Climate, which consists of three elements: the National Integrated Plan of Energy and Climate, the Law of Climate Change and Energy Transition and the Strategy of Just Transition. Regarding this Strategy, the government has set up a Plan for Urgent Action in Coal Mining Municipalities and Centrals in Closure 2019-2021, as well as a system of 83 Agreements of Just Transition (“Convenio” in Spanish or “CTJ”) (Ministry of Ecological Transition and Demographic Challenge, 2020f). CTJ are the main tool to accomplish the Strategy in Spain. They are focused on the zones that are affected by the cease of mining, thermoelectric or electronuclear activities and pursue the creation of jobs through the use of local resources and the attraction of investments. Their elaboration begins with a report of “Delimitation, Characterisation and Diagnosis”, which determines both the territorial borders of the CTJ and the sociodemographic and economic situation of the area and proposes an analysis of Strengths, Weaknesses, Opportunities and Threats (SWOT). This diagnosis is then sent to the stakeholders of the area with a questionnaire to evaluate it and is also reviewed by external auditors. Afterwards, the Institute elaborates a second report including the answers obtained in the questionnaires and organises a technical conference, i.e., a group of workshops in which stakeholders are asked to participate to present and discuss the conclusions. With this information, the initial report is revised to concretise the lines of action. CTJ take the municipality as the basic territorial unit and the affected employment as the main criterium of inclusion in the coverage of actions, quantified in the worst-case scenario and subject to additional corrections of coherence and territorial cohesion (Ministry of Ecological Transition and Demographic Challenge, 2020i, 2020j, 2020h, 2020l, 2020k). To decide the inclusion or exclusion of a municipality in the coverage of a CTJ, the Institute applies the following steps (Figure 3-1): • First, there is the need to identify the affected installations and the municipality in which they are located. These are immediately included in the area of coverage. • Afterwards, there is a quantification of the affected workers, both own and outsourced, a determination of the municipalities where they live and a calculation of the impact of their unemployment referenced to the local working-age population. If the impact on the municipality is greater than the mean of all municipalities where the affected workers live, the municipality is included in the coverage zone, as long as it belongs to the autonomous community of the CTJ. At the end of this process, at least 85% of the affected workers must be gathered by the executed municipal selection. After the phase of public participation and audit, the Institute has additionally introduced the impact on labour income, as an analogous criterium to that of impact over employment (Ministry of Ecological 84 Transition and Demographic Challenge, 2020e, 2020d, 2020a, 2020c, 2020b). • Finally, there is the application of the criteria of territorial coherence and cohesion, which are motivated by three requisites: geographical continuity, respect for the sub-regional (in Spanish “comarcal”) structure and belonging to the groups of rural development. The first establishes that the selected municipalities must be adjoining. The second and the third determine the inclusion in the CTJ of a sub-region (or group of rural development) if the population of the municipalities that have been selected under the criteria of impact overflows 70% of the population of the sub-region (or group). Again, after the phase of public participation, two additional criteria have been introduced. First, the inclusion of those municipalities where at least two miners of coal were present in 2011. Second, the inclusion of those strictly rural municipalities (DEGURBA 3) that belong to the mining basin where mining workers were present in 2001. 85 Figure 3-1 Diagram of decision for the inclusion of municipalities in the CTJ Source: Own elaboration. 86 Additionally, the MITECO and the Ministry of Labour and Social Economy have set up a dialogue with the mining sections of unions and business associations, called the “Tripartite Social Dialogue” (Pérez Díaz & María-Tomé Gil, 2020). As a result, they have launched an “Agreement for a Just Transition of the Coal Mining and a Sustainable Development of the Mining Subregions, 2019-2027” and an “Agreement for a Just Energy Transition for Thermoelectric Plants in Risk of Closure: Employment, Industry and Territories”. In parallel, the two Ministries provide the capacity of the Public Employment Service (SEPE in Spanish) to educate and ease the insertion of the affected workers, through complete education, upskilling or reskilling programmes. For its part, business associations have compromised to elaborate proposals of substitution of the mining activities, and unions have compromised to follow the progress made, accelerate education, promote occupational safety and disseminate the projects of transition. At a regional level, the Council of Castille and León is elaborating the Law of Climate Change and Energy Transition, with a Strategy of Renewable Thermal Energy and Energy Efficiency. Likewise, there is a Plan under execution to Dynamise the Economy of Mining Municipalities through an investment of EUR 3.6 M to facilitate the employment of affected workers in León (and also in Palencia) who have a special difficulty being hired due to their sociodemographic profile or status, therefore generating an impact of 340 jobs. 3.1.2. Socioeconomic context The diagnosis carried out by the CTJ in León draws its particular socioeconomic status based on seven indicators: depopulation, dependency, ageing, working-age population, registered businesses, jobs at risk at the closure of plants, and the local budgetary effect of the cease of activities through the corresponding taxes on property (IBI) and economic activities (IAE) (Ministry of Ecological Transition and Demographic Challenge, 2020i, 2020j, 2020h, 2020l, 2020k). After the process of revision, the Institute has included pyramids of population, with wider ratios (from 2002 instead of 2009) of childhood, youth, ageing and masculinity, as well as the dispersion of the population, the gross annual income, the disposable gross annual income, land uses, the register of facilities for tourism, the availability of communication technologies, protected natural locations, geographical indications and traditional specialities (Ministry of Ecological Transition and Demographic Challenge, 2020e, 2020d, 2020a, 2020c, 2020b). This diagnosis was elaborated before the COVID pandemic, with a notable demographic and economic impact. Consequently, we have updated the available indicators by the end of 2020 (INE, 2021b, 2021a) for the areas of just transition (Figure 3-2) in Table 3-1. 87 Figure 3-2 Location of the province of León in Spain and the areas of just transition in the province Source: Own elaboration. 88 Table 3-1 Main indicators of the socioeconomic context in comparison with the province and the autonomous community Source: Own elaboration based on INE (2021 a, b) and MITECO (2020 a, b, c, d, e, f, g, h, i, j, k, l). The councils that serve as references for the local public budget are: Páramo del Sil in Fabero-Sil, Cubillos del Sil in Cubillos Sil-Ponferrada, and La Robla in Montaña Central-La Robla. Effects are calculated in fiscal years 2017/2017, 2017/2017 and 2018/2020, respectively. Fabero-Sil Bierzo Alto Laciana-Alto Sil Cubillos Sil-Ponferrada Var. population (1996-2020) -4,53% -11,75% -31,54% -29,49% -43,83% 5,01% -34,47% Var. dispersed population (2000-2019) - -20,77% -10,84% -23,99% -13,95% -9,15% -22,27% Index of chilhood (2019) 11,86% 10,59% 6,90% 8,16% 7,28% 11,97% 7,13% Index of youth (2019) 13,09% 12,30% 12,06% 12,59% 11,56% 13,13% 10,86% Index of ageing (2019) 213,84% 255,37% 418,49% 352,75% 392,36% 196,86% 461,07% Var. gross income per capita (2013-2017) 5,03% 3,42% 2,64% 3,75% 1,92% 6,47% 5,39% Var. disposable income per capita (2013-2017) 6,15% 4,44% 4,14% 4,73% 3,90% 6,96% 6,84% Var. working age population (2009-2018) -4,19% -3,69% -3,36% -3,70% -2,42% -4,61% -2,90% Unemployment rate (2019) 11,19% 12,99% 17,85% 14,01% 11,56% 17,23% 14,34% Var. registered businesses (2012-2020) -2,91% -4,78% -4,59% -8,16% -3,47% -5,54% -12,18% Direct impact over working age population (2020) - - 2,45% 0,41% 0,53% 0,50% 1,63% Affected local public budget - - 46% - - 61% 31% Indicator C&L León CTJ El Bierzo-Laciana CTJ Montaña Central-La Robla 89 These indicators illustrate that the cease of coal has aggravated locally the processes of depopulation, ageing and dependency that the province and the community also suffer. The evident exception to this behaviour is located in the area Cubillos del SilPonferrada, which largely improves the situation of the province due to the influence of Ponferrada, a diversified and larger town that has acted as a pole of attraction of population during the decline of mining. Montaña Central-La Robla displays the greatest reduction of registered businesses. In contrast, Laciana-Alto Sil has destroyed the industrial network at a slower pace in comparison with the province. Regarding the jobs at risk at the moment of closure, the impact is more intense in Montaña Central-La Robla and Fabero-Sil. This effect from a fiscal perspective is notable in the case of Cubillos del Sil, where 61% of the public budget is lost. 3.2. Local strategical advantages to foster a just energy transition To tackle this situation of socioeconomic deterioration, León presents some elements of strategic relevance to secure the success of the just transition, due to its climate and orography, its technological capacity and availability of resources, with special mention given to professional skills. 3.2.1. Climate and orography Recent studies point to León as a strategic location for solar photovoltaic (PV) as it would be the most profitable province in the community for recouping the investment in this technology (5.4 years) (Pérez Díaz & María-Tomé Gil, 2020; Sotysolar, 2020). This result is however negligible to our analysis for generalising to the province the average insolation (2,727 hours), including southern and eastern areas, plainer and lower, which generate a climate of transition. The areas of the CTJ present higher cloudiness and rainfall because of the proximity of the Atlantic Ocean combined with the altitude, which generates oceanic and high-mountain climates. Besides this generalisation, there is another constricting element: a solar irradiance (Figure 3-3 A) below the community average, notably by the end of summer and the beginning of autumn, although such a divergence softens by the end of the spring and the beginning of summer (Figure 3-3 B) (Sancho Ávila et al., 2012). 96 3.3.1. Modelling precedents The scarcer literature about local scales tends to circumscribe to qualitative approaches, to the extent of considering local studies as strictly initiative based learning literature (Turnheim et al., 2015). Selvakkumaran & Ahlgren (2018b) demonstrated the relevance of quantitative techniques in local cases, yet dominated by the socio-technical theories of Strategic Niche Management or SNM (Coenen, Raven, & Verbong, 2010; Hoppe, Graf, Warbroek, Lammers, & Lepping, 2015; Seyfang, Hielscher, Hargreaves, Martiskainen, & Smith, 2014) and Multi-Level Perspective or MLP (Beermann & Tews, 2017; Fallde & Eklund, 2015; Fudge, Peters, & Woodman, 2016). Regarding SNM, Coenen, Raven & Verbong (2010) performed a qualitative dissertation, while Hoppe, et al. (2015) settled this theory through a quantitative analysis based on qualitative data from interviews to illustrate the relevance of the mentioned theory in combination with the leadership of public officials and community trust. Seyfang et al. (2014) insisted on the role of mutual trust after exploring the functionality of networking and intermediary organisations in the British community energy sector. Among the works aligned with MLP, Beermann & Tews (2017) showcased an empirical analysis, both based on objective indicators and results from surveys, to analyse the new role of decentralised renewable capacities after institutional shifts and the progress of the transition, therefore pointing to the need of greater systemic coordination. Fallde & Eklund (2015) captured the intertwines between the support from national levels and the leadership of local actors in the development of sustainable projects in municipal transportation. For their part, Fudge, Peters & Woodman (2016) identified through qualitative data from interviews possible specific factors to link local and macro goals in the British context. These papers, as observed by Selvakkumaran & Ahlgren (2018b) in their review, tend to explain and theorise past events as an extension of general transitions, a posteriori, without the needed specificity and emphasis on complex dynamics. This specificity of local energy transitions lies on five issues according to these authors: spatial scale, ownership of the transition, differing priorities among stakeholders, different institutional structures, and situative governance issues. Regarding the range of quantitative methods, local cases have increasingly applied SD to model transitions because of its ability to allow systemic thinking, connect these specificities and bridge complex multidisciplinary issues (Selvakkumaran & Ahlgren, 2020). As disclosed in the literature review, systemic thinking is a widespread claim in just transition studies (Finley-Brook & Holloman, 2016), a research gap, and consequently, one of our proposals in the research agenda (Section 2.5). Such methodology has been more frequently employed to analyse local energy transitions than any other local socio-technical transformation. It has covered the electricity sector itself (Agnew, Smith, & Dargusch, 2018; Capelo, Ferreira Dias, & Pereira, 2018; Castaneda, 97 Franco, & Dyner, 2017; Kubli & Ulli-Beer, 2016; Liu et al., 2018; Pruyt & Thissen, 2007; Selvakkumaran & Ahlgren, 2018a) or in combination with other sectors (Blumberga, Timma, & Blumberga, 2015; Hollmann & Voss, 2005; Matthew, Nuttall, Mestel, & Dooley, 2017; Pruyt, 2011), as well as the full energy sector and its relationships with non-energy sectors (Brouwer et al., 2018; Zhao et al., 2018). Yet, these local approaches based on SD have little to do with the field of just transitions. Agnew, Smith & Dargusch (2018) and Blumberga, Timma & Blumberga (2015) focused on the adoption of balancing tools in local renewable systems; Capelo, Ferreira Dias & Pereira (2018), Castaneda, Franco & Dyner (2017) and Kubli & UlliBeer (2016) modelled the impacts of policies and technologies on energy activities; Liu et al. (2018), Selvakkumaran & Ahlgren (2018a), Matthew, Nuttall, Mestel & Dooley (2017) and Brouwer et al. (2018) analysed the effects of policy support or other social factors in the deployment of renewable technologies, endogenous electricity demand or resource efficiency; Hollmann & Voss (2005) modelled the decentralisation of energy supply; Pruyt & Thissen (2007) studied the European electricity sector from a local viewpoint; Pruyt (2011) opted for the smart transition management; and finally, Zhao et al. (2018) modelled the sub-national implications of carbon trading mechanisms. In the frame of local scales, rural energy transitions have documented the power of sparsely populated areas to attract renewable facilities, notably wind farms (Rudolph & Kirkegaard, 2019), and the subsequent possibility to reinforce entrepreneurship (Morrison & Ramsey, 2019) and foster innovation (Dawley, 2014). Nonetheless, this body of literature has not documented so frequently the social contestation against said rural renewable facilities (Naumann & Rudolph, 2020; Phadke, 2011; Rudolph & Kirkegaard, 2019; Shamsuzzoha, Grant, & Clarke, 2012; Woods, 2003). Papers about rural energy transitions have also eluded quantitative techniques, probably because of the scarcity of data at a small scale. However, the literature about local SD points to the suitability of this technique in contexts of data shortages, as it allows the integration of data directly from local stakeholders as primary sources of information (Selvakkumaran & Ahlgren, 2020). Simultaneously, the literature about rural areas demands greater systemic thinking and more precisions to ensure certainty (Naumann & Rudolph, 2020). Thus, rural studies claim for a tool that has a growing background in local studies, though at a greater scale. It is therefore desirable in our novel contribution about the case of León to take advantage of the strengths of SD, like holistic thinking and the possibility to include data from stakeholders on the ground, to present an aprioristic analysis. Hence, we propose looking to the future while navigating the methodological difficulties of the rural scale, as explained below. 98 3.3.2. Modelling strategy and sources of information Accounting for these challenges and limitations, SD inspires the design of a simple and affordable dynamic modelling exercise that captures the trade-offs of the Leonese transition and could well illustrate the situation of other fossil-dependent areas in developed contexts. The phases to build this model are the following (Figure 3-7): • Revision of current political plans. The first step is to revise the plans of intervention that have been developed by the Spanish Institute of Just Transition at the MITECO. This revision serves to determine the priority areas of intervention and the status of the process, delimitation, and analysis. • Determination of the main socioeconomic dynamics and missing points. Based on the analysis, we determine the main fronts of the process of restructuring and detect the absent elements in the diagnosis made by the public organism in coherence with the theoretical framework of the just energy transitions and the results of the literature review. • Translation of socioeconomic dynamics and missing points into a stock and flow rationale based on SD. Once the potential elements of the model have been detected, we translate them into a modelling language that enables subsequent empirical work. As the basis is SD, we have looked for variables that are suitable to picture the most immediate socioeconomic dynamics of the case, hence determining if they are stock, flow or auxiliary variables, and depict the relations among them. As a result, we generate the diagram of influences and the stock and flow diagram (Forrester) that allows simulation. • Selection of data. As we are working at such a reduced scale, the selection of variables in previous stages has been done in coherence with data availability and capitalising on the advantages of SD to include stakeholders’ information directly from the diagnosis of political plans. We combine energy, environmental and human data. • Design of scenarios. The lack of certainty in political plans does not allow to derive scenarios that are immediately aligned with future actions. Consequently, scenarios in this modelling exercise are orientated to illustrate the conjunction of energy, environmental and social dynamics. The focus point is, therefore, how the key elements of the local transition interact, with a special interest in spatial dynamics. 99 • Simulation. This final phase consists of the introduction of data into the stock and flow diagram and the execution of subsequent runs to obtain the results that motivate the discussion of the conclusions and policy implications of the case, which could be useful for stakeholders and political managers, in combination with the detected missing points. Source: Own elaboration. The selection of variables is based on three criteria: simplicity, relevance, and availability. Simplicity is mandatory provided that just transitions are public participatory processes that gather diverse social agents. Simple choices promote an intuitive and achievable mutual understanding, therefore incentivising transparency and vivid participation. Relevance secures a fit between analytical choices and the hotspots, as reported by local stakeholders through the CTJ. Last, but not least given the context under study, variables must be publicly available at a local level in rural areas, as well as in other possibly similar zones, to ensure replicability and methodological significance. The relations between the selected variables under these criteria are shown in Figure 3-8. Figure 3-8 Diagram of influences of the Leonese case Source: Own elaboration. The model runs in subsequent stages, simultaneously in the two areas of just transition: Figure 3-7 Modelling process 100 • First, we introduce the scenarios of new installed capacity in wind (W), solar photovoltaic (PV) and biomass (B) power, as they are the most relevant technologies for the transition as found in the literature review (Chapter 2) and the CTJs. • Second, these new capacities are translated into required land (RL) by applying the standard commercial power of wind and photovoltaic infrastructures (p) and their associated use of land through the projection of elevations to the ground as observed in satellite images of local similar installations (r). Land requirements are the unitary demand for land of each turbine and panel so that they progressively reduce the stock of available land (AL). 𝑅𝐿𝑡=𝑊𝑡 𝑝𝑤𝑟𝑤+𝑃𝑉𝑡 𝑝𝑝𝑣 𝑟𝑝𝑣 𝐴𝐿𝑡=𝐴𝐿0−∑𝑅𝐿𝑡 𝑡 1 • Concurrently, capacities are translated through technology-specific employment factors (n) into direct labour demand (N), which increases the stock of net employment in energy activities (NN), also reduced by the destruction of jobs in risk in coal activities (NR) from a net employment perspective. Employment factors are the ratios between registered local workers in renewable energy activities and the installed renewable capacity on the basis of the direct impact (Cameron & Van Der Zwaan, 2015) and have been specially calculated for this case study to prevent misleading reuse of generalised or outdated factors in compliance with the precaution formulated in the niches of the literature review and research agenda (Section 2.5). The availability of qualified workers (AW) is crosschecked with the educated working-age population (P) and the new graduates in the local vocational training centres (GV) and Universities (GU) in key technical fields for a just transition. 𝑁𝑡=𝑊𝑡 𝑛𝑤+𝑃𝐻𝑡 𝑛𝑝𝑣 +𝐵𝑡 𝑛𝑏 𝑁𝑁𝑡=−𝑁𝑅0+∑𝑁𝑡 𝑡 1 𝐴𝑊𝑡=𝑃0+∑𝐺𝑉𝑡+ 𝑡 1∑𝐺𝑈𝑡−∑𝑁𝑡 𝑡 1 𝑡 1 101 The SD structure of the model following this rationale and notation is presented in Figure 3-9. Figure 3-9 Forrester diagram of the Leonese case Source: Own elaboration. Selvakkumaran & Ahlgren (2020) came up with a classification of models about local transitions through SD that is based on six criteria: sectorial focus, type of transition, modelling depth, objective, justification for the use of SD and the level of interaction with the local scale. Following this classification, we can place this exercise in the state of the art. Our methodology presents a focus on energy, precisely on the mining and the electricity sector amid the energy transition, but also derives conclusions for agriculture and other non-energy sectors. Even if we provide systemic thinking arguments and a Causal Loop Diagram (CLD) (Figure 3-8), the core of the model is a Stocks and Flows Diagram (SFD) used for simulation (Figure 3-9). Our goal is both prescriptive and evaluative, as we are aimed at exploring solutions and providing a tool to assess potential projects. As stated in the Section of precedents, the reason to apply SD is the need to encompass energy, land and human variables in the same modelling framework, a motive that falls between systemic thinking and the necessity to bridge said issues. Finally, the level of interaction with the local scale is strict: we do not appeal to landscape factors beyond the local focus and integrate information from local stakeholders through the CTJ, both qualitative to determine the worries and relationships of the transition (Figure 3-8) and quantitative to obtain parameters for the simulation (Table 3-4). Accordingly, the data to execute the simulation come from a variety of sources (Table 3-4). 102 Table 3-4 Data sources to feed the local model Category Variable Sub element Source Reference Public registry (R), local stakeholder (S) or userdefined (U) Energy New renewable capacity (W, PV, B) Current tenders Secondary source: ISTAS (Pérez Díaz & María-Tomé Gil, 2020). Primary source: local energy authorities. S Territorial distribution Scenarios U Standard power of renewable infrastructure (p) Wind turbines Industrial standards U Photovoltaic panels Biomass Own estimate based on current registers at a province level Land Available land (AL) Municipal surface Council of León (Council of León, 2021) R Urban land Land registry (Spanish Land Registry, 2021) R Woodland Forest Inventory of the Castile and León Regional Council (Castile and León Regional Council, 2021) R Land requirements (r) Satellite estimates based on similar local facilities U Human Qualified workingage population (AW) New vocational training graduates National Registry of Teaching Centres, Spanish Ministry of Education (Ministry of Education and Vocational Training, 2021) R 103 & Castile and León Regional Council (Castile and León Regional Council, 2020) New University graduates Statistics and Transparency Bureaus of the local Universities (National University of Distance Education, 2021; University of León, 2021) R Educated working-age population Agreements, Institute of Just Transition (Ministry of Ecological Transition and Demographic Challenge, 2020h, 2020i, 2020j, 2020k, 2020l) R Employment factors of renewables (n) Own estimate based on: Secondary source: ISTAS (Pérez Díaz & María-Tomé Gil, 2020). Primary source: Business registry (Registradores, 2020). R Jobs at risk in fossil activities (NR) Secondary source: Agreements, Institute of Just Transition (Ministry of Ecological Transition and Demographic Challenge, 2020h, 2020i, 2020j, 2020k, 2020l). Primary source: affected local companies. S Source: Own elaboration. 104 The simulation relies on parameters r, p and n to translate new energy capacity into new jobs and estimate the land requirements for renewable technologies. These parameters are grounded on data and assumptions. Concerning employment factors (n), we take the ratio between the current number of employees in companies of renewable energy and the current capacity. This calculation is performed specifically for the Leonese case based on currently registered data, to prevent the inadequate reuse of employment factors detected in the literature review in Chapter 2. Based on current data, only the factors of solar PV, wind and biomass power can be estimated: jobs in hydropower are marginal and appear mixed up with other categories in the registries. Regarding the land requirements of renewables (r), we focus on wind and solar power. Based on satellite images, we suppose that turbines with a diameter of 90 m will constitute wind power facilities with distances of 400 m between generators and solar facilities will rely on standard panels of 13x8 m with 20 m of distance. Under these dimensions, considering industrial standards, turbines would add (p) 2 MW and panels, 0.0184 MW. 3.4. Scenarios and results of the simulation Once the CTJ have been critically analysed, we disclose the results obtained under our suggested quantitative framework. We propose the simulation of four scenarios (Table 3-5). In the first scenario (SCEN 1), we install preferentially in the affected areas 50% of the current renewable tenders at a province level (769 MW of wind and 3,647 MW of solar PV) and maintain the current biomass potential. The second scenario (SCEN 2) calculates the effects of a more realistic situation in which 80% of wind tenders and 30% of PV tenders are placed in the affected areas due to their comparative advantage in wind speeds. As in SCEN 1, the biomass potential is constant. In the third scenario (SCEN 3), we replicate SCEN 2 with a year-on-year increase of 5% in the biomass potential. Finally, SCEN 4 replicates SCEN 3 under the employment factors that gradually converge with their average in the scholarly literature (Cameron & Van Der Zwaan, 2015; Fragkos & Paroussos, 2018; Ortega, del Río, Ruiz, & Thiel, 2015; Rosebud Lambert & Pereira Silva, 2012; Rutovitz, Dominish, & Downes, 2015). This average value is calculated by adding direct jobs in Construction, Installation, Manufacture, Operation and Maintenance (Table 3-6). 105 Table 3-5 Scenarios about the Leonese transition Scenario Proportion of current tenders installed in the affected areas New annual biomass capacity Employment factors Wind Solar PV SCEN 1 50% 50% 0% Current SCEN 2 80% 30% 0% Current SCEN 3 80% 30% 5% Current SCEN 4 80% 30% 5% Current (2021) to average in literature (2030) Source: Own elaboration. Table 3-6 Current employment factors of wind and photovoltaic power in León and estimates in the literature Renewable technology Employment factor (jobs/MW) Currently in León Average value in literature Wind 3.563 7.092 Solar PV 2.447 18.857 Source: Own elaboration. León registers employment factors notably below the average of the literature. However, such averages come from papers mostly focused on a national level. Given this discordance, we take these literature estimates in SCEN 4 ceteris paribus as a maximum or optimistic value to cover the range of the possible developments during the decade, so that the expected level of employment could be between the results of SCEN 1-3 and SCEN 4. Essentially, SCEN 1-3 is a set of BAU scenarios concerning employment factors. Following the main worries of the just transition in León, we focus on the three stock variables of the model: available qualified workers (AW), net employment (NN) and land availability (AL) (Table 3-7). 112 For its part, the criterium of territorial coherence has procured coverage to municipalities with a lower impact than those included, e.g., Folgoso de la Ribera has been included with a 0.3% score while Priaranza del Bierzo has been excluded with a 0.66% score. This problem has been solved narrowly through the criterium of territorial cohesion. Given that, at the end of the process, the population of the selected municipalities represents 70.42% of the population of the sub-region, El Bierzo has entered as a whole in the coverage of the CTJ. In absence of such a modest margin, the described contradictions would have persisted. As happened with the criteria of barrier, it may be questioned the operativity of this criterium of territorial cohesion in circumstances close to the 70% limit. 3.5.2. Diagnosis In the case of the diagnosis, the main limitations derive from the selection of indicators and the SWOT analysis. Regarding indicators, there is a need to highlight the limitations of sociodemographic and income variables. Sociodemographic variables, notably those related to the level of education, have been gathered in the census in 2011 as the sole available source. Hence, they register a lag of more than 10 years, a decade that has resulted decisive in the decline of the mining and thermoelectric generation, and also reflects the impact of COVID. Contrary to this limitation, to design the model, we recur to data provided by current public registries, like the regional education authorities. Likewise, CTJ take the average labour income of the municipalities despite such income being only calculated for those municipalities of higher size or relevance, therefore biasing the diagnosis, and subsequently, the delimitation through this new criterium. These incomes are presented in current monetary units; hence, their variations could hide a “monetary illusion” in relatively wide and fluctuant periods as the ones analysed in the CTJ. The limitations of diagnosis through these indicators motivate a reflection on the need for a data strategy before the strategy of transition, in which availability and quality of statistical information are tested and improved. Regarding the SWOT analyses, there is a lack of specificity in the analyses, which have been reused in the previous process of diagnosis at an autonomous community level, and redundancy of statements. The most affected areas necessarily present common traits due to their shared problems and the same socioeconomic context, so some degree of coincidence in diagnosis is expected. In the case of León, we have detected significantly high rates of coincidence, hence suggesting a lack of specificity in diagnosis or an unnecessary 113 disaggregation of the SWOT analysis in the priority areas. This limitation can be observed in Table 3-10. 114 Table 3-10 Percentage of coincidence among the statements of the SWOT diagnosis by CTJ, initial (Panel A) and after the revision (Panel B) 115 Source: Own elaboration based on the CTJ. 116 Initially (Table 3-10 A), the highest percentages of coincidence appear in the determination of weaknesses (Ministry of Ecological Transition and Demographic Challenge, 2020i, 2020j, 2020h, 2020l, 2020k). CTJ share more than 30% of the statements. Bierzo Alto and Laciana-Alto Sil show a 68% coincidence; Fabero-Sil and Laciana-Alto Sil, Bierzo Alto and Cubillos del Sil-Ponferrada, and Montaña Central-La Robla, Bierzo Alto and Laciana-Alto Sil record a 52% coincidence. CTJ in Bierzo Alto and Laciana-Alto Sil again show the highest coincidence scores in the section of threats (54%) and opportunities (27%), as well as a high percentage, although in a medium range, regarding strengths (26%). Cubillos del Sil-Ponferrada and Bierzo Alto have the most elevated concordance score concerning strengths (33%). The delimitation of opportunities presents a lower coincidence and range than the rest of the sections. In the phase of revision (Table 3-10 B), the coincidences are attenuated (Ministry of Ecological Transition and Demographic Challenge, 2020e, 2020d, 2020a, 2020c, 2020b). Indeed, additional elements have been introduced in the SWOT analyses with a slight increase of specificity: for instance, there is a citation to the ski resort in Leitariegos in the CTJ Laciana-Alto Sil, the PCI in Cúa in Fabero-Sil (even if the PCI of Bierzo Alto has been omitted), inter alia. In addition to this coincidence, redundancy is abundant in the Leonese SWOT analyses. These redundancies consist of the repetition of a statement by analytical category with an identical implication, but a different expression, and thus, they do not provide additional valuable information and disperse the conclusions of the diagnosis. As far as the weaknesses are concerned, we can observe the concurrence of “Absence of alternatives to mining”, “Specialisation in mining”, and “Lack of diversification”, which essentially share the same meaning. We can also find “Unemployment” and “Imbalance between labour supply and demand” or “Self-supply smallholding agriculture” and “Smallholding agricultural sector”. Among the threats, there are “Proximity of dynamic nucleus of population” and “Emigration towards urban areas” or “Absence of innovative activity” and “Loss of innovative activity”. Regarding strengths, the redundant statements are “Industrial and mining patrimony” and “Historical and cultural patrimony” together with “Patrimonial richness”; “Tourism and leisure potential” and “Potential resources for tourism”; and “Renowned agri-food products” with “Prestigious production of meat”. Finally, among opportunities, we can find “Potentiation of hydro resources, hunting and fishing” with “Exploitation of the own resources” (which should not be considered as a proper opportunity given that it is the primary goal of the CTJ, as happens with “Potentiation of alternative activities to mining” or “Reorientation towards renewable energy sources”); “Existence of a market for quality products and agricultural 117 professionalisation” with “Traditional products”; and “Institutional support for an alternative development and IT” with “Support from the regional Council for education”. This way, percentages of redundancy in the SWOT have been calculated per CTJ (Table 3-11). Table 3-11 Percentage of redundancy of SWOT statements, initial (Panel A) and after the revision (Panel B) Source: Own elaboration based on the CTJ. The CTJ of Fabero-Sil presents the highest redundancies, both initially (Table 311 A) (Ministry of Ecological Transition and Demographic Challenge, 2020j) and after the revision (Table 3-11 B) (Ministry of Ecological Transition and Demographic Challenge, 2020c). Surprisingly, after the revision redundancy has increased: 45% of weaknesses, 22% of threats and 42% of strengths are repetitive statements. The same applies to strengths (from 18% to 36%) and opportunities (from 0% to 29%) in Bierzo Alto (Ministry of Ecological Transition and Demographic Challenge, 2020a, 2020h) and the strengths in Laciana-Alto Sil (from 20% to 22%) (Ministry of Ecological Transition and Demographic Challenge, 2020d, 2020k). In the comparative, we can detect an attempt to reduce these redundancies, as proves the remaining quadrants, but this attempt has been frustrated with the inclusion of new specific elements: for example, in Fabero-Sil, the “Lack of quality in road and communication infrastructures” has been included simultaneously with “Insufficiency of telecommunication services”; or the “Presence of biosphere reserves” when a similar statement “Biosphere reserves and biodiversity” was already present. With the phase of revision, there are new redundancies, not only regarding SWOT categories but also among them: for instance, the existence of bad communications as a weakness and a threat or the ski resort in Leitariegos as a strength and an opportunity. Both phenomena, coincidence and redundancy, configure diagnoses that are more limited and dispersed than expected in the case of a proposal for a just transition with a high specificity between areas. 118 Beyond these limitations, it is worthy of note the consideration of some elements in the diagnosis and their unjustified changes during the phase of revision. In the CTJ in Bierzo Alto, it is considered a weakness the presence of “High levels of income and the sale of properties” (Ministry of Ecological Transition and Demographic Challenge, 2020a). Similarly, there is an explicit mention of the unfavourable situation of workers in connection with the goals of gender equality, but this mention has not been included in all the CTJ, just in Bierzo Alto (Ministry of Ecological Transition and Demographic Challenge, 2020a) and Montaña Central-La Robla (Ministry of Ecological Transition and Demographic Challenge, 2020e). Regarding unjustified changes, the CTJ in Laciana-Alto Sil is a notable case: initially, it had a “business sector compromised with the territory” (Ministry of Ecological Transition and Demographic Challenge, 2020k), but after the revision, it has a “business sector not compromised with the territory” (Ministry of Ecological Transition and Demographic Challenge, 2020d) without an explicit argumentation at this regard. These issues point to a dysfunctional application of the SWOT analysis, considering the end that is presented in the CTJ. Consequently, it is highly recommendable that the Institute and the stakeholders explore alternative techniques of diagnosis and correct this lack of specificity and the dispersion that the Leonese CTJ show. 3.5.3. Processes of public participation From a procedural viewpoint, channelled in the CTJ through questionnaires and technical meetings (Ministry of Ecological Transition and Demographic Challenge, 2020e, 2020d, 2020a, 2020c, 2020b), apart from the mentioned doubts about gender equality that have been previously disclosed due to their conceptual nature, diagnoses have not included the worries of the growing movements of opposition to the installation of renewables in the affected areas. The opposition in León is based on three main conflicts: the displacement of farmers, the management of the communal woodlands and the environmental-landscape impacts of the installations. In the affected areas and many other locations in the province and the community, the installation of renewable infrastructures is motivating a change in land uses. Currently, many smallholdings that are suitable for the renewable generation of electricity are abandoned or rented to farmers. The massive and profitable deployment of renewable technologies during the transition has led energy corporations to offer greater renting proposals than those agreed with the lessee farmers. The highest profitability of renting for renewables in comparison with the primary renting involves the expulsion of farmers, incapable of equalising or improving the corporate offers. Those affected see their sustenance at risk while, in the best of situations, they are offered non-satisfactory jobs in the new renewable installations. 119 Simultaneously, there are frictions between the demand for land and the management of the traditional woodlands. Even if the impacts on these collectively managed woodlands have serious implications regarding justice and affect rural agroecological development, their precise case has not been addressed in a specific way in the processes of transition. This omission can be related to two issues: one connected with the design, already mentioned, and the other distinctly procedural. At first, CTJ are of an eminently social and economic nature, and relegate the environmental impacts, including land uses, despite their implicit socioeconomic character. Secondly, from a procedural point of view, the model of communal management is sufficiently specific at a local scale to be overlooked in absence of inclusive participatory processes. Again, this analysis reveals a lack of specificity linked with a possible omission of relevant stakeholders. Aligned with the changes in land uses, the environmental-landscape issue is linked to the need of releasing the territory from biodiverse uses or uses with an aesthetic value to enable the installation of generating technologies. Strategies to tackle this matter remain to be specified in the CTJ. In brief, the procedural problem of the Leonese CTJ is rooted in the implicit socioeconomic and environmental effects of changes in land use, not so much in labour issues, widely covered, despite the indicated limitations. After presenting the insights provided by modelling and further suggestions to correct the processes, in the following Chapter, we dive into welfare regimes and policies, and their potential for eco-social synergy to ease the restructuring. 3.6. Conclusions and policy implications In the field of just energy transitions, national scales, and more recently regional scales, lead the available literature. Conversely, local cases have maintained a secondary position, frequently limited to qualitative methods aligned with socio-technical theories far from the proper framework of just transitions, under a posteriori techniques and without the needed specificity and the inclusion of complex dynamics. Regarding quantitative methods, local cases are paying increasing attention to System Dynamics because of its holistic ability, the possibility to increase specificity and integrate data from stakeholders to alleviate data shortages at such a small scale. Regarding local contexts, research about rural energy transitions tends to elude quantitative techniques, probably because of the scarcity of data at a small scale, while demanding greater systemic thinking and more precisions to ensure certainty. Thus, rural studies claim for a tool that has a growing background in local studies, though at a greater scale. We gather these insights and claims to contribute to the quantitative study of proper just energy transitions with the case of restructuring of the rural mining areas of León 120 (Spain) towards renewable energy sources through an intuitive, scalable and easily adaptable modelling exercise based on System Dynamics. These areas have suffered deep negative impacts because of the decline of coal extraction and thermoelectric production during the past three decades, such as unemployment, local migrations, depopulation, ageing and increasing dependence, as well as environmental impacts. The need for a just transition in León gains momentum and congregates growing institutional support under the Institute of Just Transition. Yet, stronger opposition movements complain about the social and ecological impact of these technologies, which are threatening to displace lessee farmers from their cultivations and hinder new uses for tourism or agroindustry. This Chapter models the two fronts of the just transition in León, net employment and land requirements, through SD to combine local energy, land and human systems, given the suitability of this methodology. The model is designed under the premises of simplicity, to promote equal social participation, relevance, to face the hotspots of the transition, and restricted availability of information, to cover local studies in rural contexts with data scarcity. Such scarcity is extreme in the Leonese case as we are working at a strictly local scale through the aggregation of mining municipalities differentiated into two areas. Previous local approaches with SD did not face such a challenge, as they adopted a wide conception of the local scale, mainly with a regional and not necessarily rural focus. With the resultant tool, we simulate four incremental scenarios based on ceteris paribus observation. These scenarios account for the potentiation of wind power tenders due to the comparative advantage of the affected areas in wind generation, the impact of the potentiation of cumulative biomass capacity and the convergence of below-average techno-specific employment factors to the mean factors of scholarly literature. We conclude that the current tenders have the potential to compensate the jobs at risk by 2022-2023 so social agents should immediately implement upskilling and reskilling programmes to ease the process and benefit workers in the short term. In the most optimistic scenario, the transition is unable to absorb the flows of newly qualified workers in the most dynamic area of El Bierzo-Laciana and causes a shortage of workers in the less dynamic Montaña Central-La Robla. More realistic scenarios project the incapacity of renewables to keep a young, qualified population in the areas. Alternatives like rural tourism, agroindustry and a “silver economy” with augmented social capacities and care are highly recommendable but equally limited. Besides, the increase of local wind tenders is the most adequate option to maximise renewability and generate employment while minimising conflicts related to land uses. Even if the model detects a significant decrease in land availability by 2030, 121 there are not any reasons to interfere with farming zones if the tenders follow the wind potentiation strategy. However, the areas will face sensitive trade-offs between negative impacts on land use and positive impacts on employment. If such trade-offs are mismanaged, they could result in comparatively unfair outcomes in the areas and reinforce public mistrust. Future works should apply GIS and STA to crosscheck the optimal locations of renewable infrastructures and land uses, considering local preferences and conflicts. Based on these findings, the just transition of the Leonese energy sector will be partially just with the mere strategical management of the current renewable tenders and the implementation of upskilling and reskilling programmes. However, a proper and full just transition is three decades late. The mining areas will not meet again the brilliant times of the 1980s but will probably inspire modest developments in a more sustainable near future. These results and the proposed methodology point to some policy implications that apply to other rural mining areas in developed countries and under intense deindustrialisation during the last decades. Also, these implications appeal to a reform of the qualitative dimension of the CTJ, analysed in Section 3.5. First, just energy transition plans usually have a narrow focus on net direct employment in the energy sector. As shown, a just energy transition requires a crosssectorial approach because the phase-out of mining and thermoelectric production has indirect and induced effects on the local economies (Table 3-1) and renewable technologies are unable to provide significant shares of employment in such reduced scales (Table 3-7). Parallelly, just energy transition plans tend to deal strictly with the socioeconomic effects of restructuring and neglect the environmental effects, despite their implicit socioeconomic consequences. The environmental impacts of new jobs, their quality, and their implications for land use, together with the landscape effects of the phase-out of coal, are perceived as a separate dimension, even if stakeholders appreciate the potentiality for biodiversity and rural tourism, as happens in León. It is impossible to isolate environmental impacts from socioeconomic effects: human and land dynamics constitute the system of the just energy transition (Figures 3-8 and 3-9) and have simultaneous reactions with implications for justice and social contestation (Table 3-7). The inspection of environmental effects, and particularly land dynamics, is of uttermost relevance to promote justice and tackle social contestation in near areas that undergo the process of transition simultaneously under similar socioeconomic situations, like El Bierzo-Laciana and Montaña Central-La Robla. Potential asymmetries (Table 38) will immediately erode social trust and must be an element of further reflection in 128 hypothesis. The authors also analyse whether different institutional configurations determine the attitudes of citizens regarding social and environmental policies. By clusters, but focusing on attitudes, Conservative WS show the best environmental results. Accordingly, they reject the hypothesis but cannot completely discard that Socialdemocratic WS have contributed to the development of ES as the nexus between WS and ES is more complex than expected. Another work explores public support for environmental and welfare policies as a facilitator of Eco-social States (Jakobsson et al., 2018) and finds no evidence of synergy: Conservative and Liberal WS perform similarly to the Social-democratic ones. As stated by its authors, the weaknesses of focusing on polls are two: attitudes do not reflect real policies nor real welfare, and data come directly from individuals, therefore introducing subjectivity. More recently, Fritz & Koch have revisited the hypothesis (Fritz & Koch, 2019). Insisting in a correspondence analysis over perceptions, the Social-democratic States combine higher rates of support for climate and welfare policies with poorer environmental performances. Together with Sweden, Conservative WS like Germany and Switzerland arise as supporters of environmental measures. Another work devoted to mapping Eco-welfare States through hierarchical clustering (Zimmermann & Graziano, 2020) observes that the Nordic States perform above average both in social and environmental terms, therefore considering the hypothesis verified. Nonetheless, since the characterisation of countries is posterior to the clustering, the authors warn that this result is descriptive and limited. Likewise, the mechanisms of synergy remain unknown. A final work relies on polls and descriptive variables to perform a multinomial regression model (Otto & Gugushvili, 2020). It coincides with previous analyses in pointing to the Nordic States as places of concurrence of elevated support for climate and social policies. From these works, we extract four conclusions: First, they try to cluster countries to test the correspondence between social and environmental dimensions. Second, analyses mostly focus on the study of individual attitudes. Objective indicators, i.e., those that aspire to measure real welfare and environmental performances instead of attitudes or opinions, have only been used in two works (Koch & Fritz, 2014; Zimmermann & Graziano, 2020). Third, regarding WS, the classification proposed by Esping-Andersen, or a near notion of it, lies beneath most of the studies and overlaps with other classifications about 129 the relationship between contemporary capitalisms and the environment (Cahen-Fourot, 2020; Wood et al., 2020). Fourth, works propose diverse variables (Table 4-2), notably attached to GDP, but avoid a discussion about such a selection and its implications. Previous papers perceive the drivers of synergy as merely contextualising ideas, instead of precise elements that should be aligned with their methodological choices, and select their own variables to test it. These works do not explain the reasons behind such differing choices nor interpret their results conditioned to them. Since those drivers were previously determined in this Section, we can establish a correspondence with the variables or set of variables proposed to study it (Table 4-2). To simplify, we call “set of variables” to collections of variables related to opinion polls, given their extension and lesser relevance to this work, as argued in the following paragraphs. Table 4-2 reveals that previous studies do not cover all the drivers of synergy, but analyse variables that are not linked with them. Such drivers as the local administration, the parallelisms WS-ES and the assimilation of environmental and social policies have never been used to define variables. Such variables as GDP, attitudes and public opinions, sociodemographic variables, union density, population and poverty do not correspond to a driver. The drivers can be tested through objective indicators, without relying on opinion polls, which present the abovementioned limitations (Jakobsson et al., 2018). To be coherent, we exclude all subjective variables. Nonetheless, we have detected one exception: the inclination to pay for environmental protection relates to ecological modernisation: if citizens perceive environmental protection and modernisation as mutually reinforcing, they may accept higher environmental taxes. This disarrangement is probably caused by the clustering rationale proposed in the cited contributions, which approach synergy as a correspondence between social and environmental dimensions, given the listed variables (Table 4-2). Yet, theoretical developments follow a causal chain based on the traits of WS that facilitate ES. If we follow the latter, the causes of synergy originate in the social dimension under a WS. These would deliver social results that concurrently serve as pre-conditions, i.e., causes, of the ES. These secondary causes would deliver environmental results. Coherently, we can identify variables related to causes and results, as well as intermediate variables (Figure 4-1). The latter represent a nexus between dimensions and constitute an approximation to the mechanisms of the synergy, because causal and result variables merely contextualise the initial and final socio-environmental status. By identifying intermediate variables, we could further the knowledge of such mechanisms, which 130 remain unclear, as concluded by Fritz & Koch (2014) and Zimmermann & Graziano (2020). This review has determined the drivers of synergy, the variables selected to verify it in empirical works and the disarrangement between the two visions, hence providing an initial screening of variables (Table 4-2). 4.2. Solutions to the shortcomings of indicators and previous omissions Regarding the remaining variables (Figure 4-1 A), we can still refine them by identifying additional shortcomings: First, provided that decommodification is a driver, referencing magnitudes to GDP is contradictory. As the dimension of the market itself, its inclusion is against the possibility of individuals to live a good life regardless of their implication therein. Furthermore, there is a vast discussion about the shortcomings of GDP as a measurement of notions other than economic production (Bergh, 2009; Kalimeris, Bithas, Richardson, & Nijkamp, 2020) and the barriers to alternative calculations (Hoff, Rasmussen, & Sørensen, 2020). The GDP is strictly the monetary measurement of final goods and services produced within a country. Thus, it does not reflect real welfare. We suggest focusing on the generosity index, which provides a measure of the institutional provisions of national welfare policies that is systematically comparable between countries in long periods (Scruggs, 2014; Scruggs, Detlef, & Kuitto, 2017), and maintain the welfare effort, i.e., the social spending as a percentage of the GDP, as a mere indicator of public services. Welfare effort, which can be also problematic given that it is a ratio over the GDP, is used here as a supporting measure due to the restricted availability of the generosity index in recent periods (Section 4.3). For its part, the share of green taxes over GDP as a proxy for environmental regulation is avoidable, as the energy renewability already approaches strictness and the prerequisite of regulation. Equally, it serves to avoid the subjectivity introduced by the inclination to pay for environmental protection connected with ecological modernisation. As the deployment of renewables combines environmental concerns and technology, it is also a proxy for such a driver. Second, the Gini index is applied as an indicator of stratification (Koch & Fritz, 2014; Zimmermann & Graziano, 2020), but it does not offer greater information regarding income strata, solely about the overall situation of inequality. Conversely, income ratios, like the 80/20 share or the Palma ratio, get closer to this notion as they picture the situation of the tails of the income distribution, i.e., of the most favoured and the least favoured individuals, where the relevant dynamics of inequality take place (Palma, 2014). Besides, we have identified the level of protection of workers as an additional variable that also affects democracy in the same sense that stratification operates: if workers, who are among the most affected stakeholders of the transition 131 (Gambhir et al., 2018), are protected, they can participate and shape decision-making in equal conditions. To simplify, given this coincidence, we consider that income ratios approach cohesion and justice in the sense that the driver of strong democracy suggests (Jakobsson et al., 2018; Ramalho et al., 2018; Thombs, 2019). Third, the inclusion of the ratio services/industry is misaligned with its corresponding driver, which requires the proliferation of low-intensity services promoted by WS. Conversely, services in general, both high-intensity and low-intensity as inputted in the ratio, include such intensive activities as transportation and housing and their indirect activities, like the deployment of infrastructures (Fix, 2019). Assuming that public provisions are of low environmental intensity is inaccurate. Public services and investments are responsible for notable emissions and employments of resources in current WS because of the high intensity that some of their activities require (Ottelin, Heinonen, & Junnila, 2018). We suggest sticking to indicators of emissions and materials, since this driver focuses on the decarbonisation and dematerialisation of the economy, including the direct, indirect and induced impacts of public activities. Fourth, the strictness of the environmental policy is difficult to quantify accurately and subject to four challenges (Botta & Koźluk, 2014). First, the multidimensionality of environmental regulations, policy instruments and administrative levels. Second, the difficulty of sampling to quantify it through perceptions in surveys, similarly to the issue of subjectivity in the tests of synergy. Third, the identification of the effects of these measures in a sea of policies and institutional configurations. Fourth, the limited availability of comparable data. Botta & Koźluk (2014) discussed the different methods to tackle these challenges and subsequently created a composed indicator of Environmental Policy Stringency (EPS), which was used afterwards by Zimmermann & Graziano (2020) to test synergy. The EPS evaluates the presence of taxes over GHG, trading schemes, feed-in tariffs, deposit-refund schemes, standards of emissions and public subsidies for research about renewables. Despite its contribution to this discussion, the EPS is subject to limitations, as recognised by its creators. It is sensitive to the weight of its mentioned integrating factors, simplifies multidimensionality and has a narrow focus on the energy sector and few political instruments. Moreover, it is not available for all countries, not even in the OECD, as the large time-series required for observation. Furthermore, it does not measure the resultant environmental outcomes, solely the effect of the limited policies that it observes. Consequently, we support again the inclusion of indicators of emissions and materials, as direct evidence of such environmental outcomes. The seats in the national Parliament obtained by green parties, also proposed by Zimmermann & Graziano (2020) in this sense, is scarcely meaningful for two reasons. 132 Firstly, all political parties have an environmental ideology and considering that of the greens to be the most representative in the final political outcome is a limited assumption. Green parties are of recent creation (Carter, 2015). In general, Social Democracies set up the WS under study. Also, other parties share positions with them, as proven by the government alliances in many countries, e.g., Sweden. Secondly, a driver of synergy lies in the local administration (Westholm & Beland Lindahl, 2012), so national Parliaments provide little information. Fifth, the use of the Environmental Performance Index (EPI) under the time series format required by analyses is not recommended. The goal of the EPI is to provide a national score and a country ranking about the establishment of environmental targets through the combination of environmental performance indicators. The score is dependent on the number, typology and updating of its component indicators, which have ranged from 25 in 2008 to 20 in 2016 to 32 in 2020, for instance. These methodological rearrangements impede the assembly of data to generate time series and panels (Yale Center for Environmental Law & Policy, 2020). Sixth, the Domestic Material Consumption (DMC) and GHG emissions pale in comparison with the material (Wiedmann et al., 2015) and carbon footprints (Hertwich & Peters, 2009), respectively, especially recommended by Ecological Economics and degrowth literature (Weiss & Cattaneo, 2017). The DMC measures the raw materials apparently, not finally, consumed per year within a country and is subject to the omission of upstream international transactions of raw materials and products. For its part, the GHG focuses on the aggregation of emissions by emitting economic activities in a country. The advantage of footprints is the detection of relevant material requirements (Zhang, Chen, Liu, & Zhu, 2017) and CO2 residues of international trade (Xu, Dietzenbacher, & Los, 2020). Hence, picturing the environmental situation also from the point of view of consumption and capturing the behaviour of countries beyond their borders. ES reduce their footprints by increasing energy renewability, but also by closing the material cycles through recycling, hitherto omitted despite this relevance. Thus, we suggest the introduction of recycling rates of municipal waste as an approximation, given that long series of recycling rates for key materials are unavailable. 133 Table 4-2 Variables used in the literature and associated drivers of synergy Variable Reference Dimension Driver Unrelated to the drivers Social Environmental Decommodification Stratification Democracy Local admin. Ecological modernisation Parallelisms Services Assimilation Contextualising Unlinked Welfare effort Koch & Fritz (2014) X X (Long-term) Unemployment rate Zimmermann & Graziano (2020) X X Gini index Koch & Fritz (2014), Zimmermann & Graziano (2020) X X Protection of employees Zimmermann & Graziano (2020) X X X Inclination to pay for environmental protection Jakobsson et al. (2018) X X Ratio services/industry Zimmermann & Graziano (2020) X X Ecological footprint Koch & Fritz (2014) X X CO2 or GHG emissions Koch & Fritz (2014), Otto & Gugushvili (2020), X X 134 Jakobsson et al. (2018) Renewability of energy mix Koch & Fritz (2014), Zimmermann & Graziano (2020) X X Share of green taxes over GDP Koch & Fritz (2014) X X EPI Zimmermann & Graziano (2020) X X DMC Zimmermann & Graziano (2020) X X Strictness of environmental policy Zimmermann & Graziano (2020) X X Seats in the national Parliament obtained by green parties Zimmermann & Graziano (2020) X X GDP and GDP PPP Jakobsson et al. (2018), Zimmermann & Graziano (2020), Koch & Fritz (2014), Otto & X X 135 Gugushvili (2020) Acceptance of statements about state intervention and voluntary frugality Koch & Fritz (2014) X X Attitudes regarding income distribution Jakobsson et al. (2018), Otto & Gugushvili (2020) X X Sociodemographic variables Jakobsson et al. (2018), Otto & Gugushvili (2020) X X Public opinions from the European Social Survey (ESS) Fritz & Koch (2019), Otto & Gugushvili (2020) X X Union density Zimmermann & Graziano (2020) X X Population Jakobsson et al. (2018) X X 136 Poverty rate Otto & Gugushvili (2020) X X Source: Own elaboration. 137 Finally, we must cover the drivers not studied in previous analyses: To measure the local administration, we propose the decentralisation of public expenditure, i.e., the share of public local expenditure over the total administrative expenditure, and its analogue: the decentralisation of tax collection. These variables reflect relatively the capacity of local administrations to collect their financial resources and spend them in their political programmes, thus indicating their proportional capacities against the other government levels. To introduce the parallelisms WS-ES, we suggest the share of public environmental spending over total public expenditure, provided that environmental expenditure is the most representative environmental measure that flows through WS. In addition, we have designed a categorical binary variable to measure potential competition for funding. It is equal to 1 when social and environmental expenditure evolves interannually in a contrary sense, or if the increase (decrease) in social spending is higher (lower) than the increase (decrease) in environmental spending. Regarding the assimilation of environmental and social policies, the two previous variables also match this driver. On the one hand, public environmental spending can be perceived as social spending in ES, as indicated in Section 4.1. On the other hand, the assimilation of environmental and social policies should prevent fiscal competition, potentially detected by the proposed binary variable. Consequently, our proposal is shaped in Figure 4-1 B. 144 4.4. Results Concurrences prove that clusters are not stable, therefore reinforcing the need for dynamic observation. The number of optimal clusters increased during the crisis and stabilised afterwards (Figure 4-3). After the shock, the cyclical variables (welfare effort, unemployment rates and potential competition for funding) immediately reacted and generated increasing distances between countries, i.e., fewer clusters and lower concurrence scores corresponding to differential evolutions during the crisis (Figure 4-2). 145 Figure 4-2 Historical evolution of the variables considered to test synergy in the sample Source: Own elaboration. 146 Figure 4-3 Optimal number of clusters by year under Thorndike’s criterium Source: Own elaboration. To illustrate the instability of concurrences, we present the dendrograms of two selected moments: 2009 and 2013 (Figure 4-4). Figure 4-4 A shows the sharpest variation of concurrences due to the crisis, which manifests as fewer countries per cluster (12). Figure 4-4 B displays a situation of stabilisation after the shock, with larger clusters (6). Figure 4-4 Annual dendrograms in the first calculation stage in 2009 (Panel A) and 2013 (Panel B) Source: Own elaboration. After transferring this information provided by the annual dendrograms into the concurrence matrix, it results as follows in stage one (Table 4-5). 147 Table 4-5 Concurrence matrix, without generosity index, 2008-2016 Source: Own elaboration. ID AT BE CZ DK EE FI FR DE GR HU IS IE IT NL NO PL PT SK SI ES SE CH GB AT 100% 33% 11% 0% 11% 0% 44% 22% 11% 22% 25% 44% 22% 0% 22% 22% 22% 0% 0% 22% 0% 11% 33% BE 33% 100% 33% 0% 11% 0% 56% 33% 33% 44% 0% 33% 44% 22% 0% 33% 33% 33% 33% 22% 0% 0% 33% CZ 11% 33% 100% 0% 11% 0% 22% 22% 11% 44% 0% 11% 11% 33% 22% 22% 11% 67% 56% 0% 0% 11% 11% DK 0% 0% 0% 100% 0% 89% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 78% 0% 0% EE 11% 11% 11% 0% 100% 0% 0% 22% 22% 0% 0% 22% 11% 22% 22% 22% 11% 22% 22% 22% 0% 22% 44% FI 0% 0% 0% 89% 0% 100% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 89% 0% 0% FR 44% 56% 22% 0% 0% 0% 100% 56% 11% 33% 0% 33% 56% 11% 11% 44% 44% 11% 22% 22% 0% 0% 33% DE 22% 33% 22% 0% 22% 0% 56% 100% 22% 56% 0% 44% 67% 11% 0% 78% 56% 22% 44% 22% 0% 0% 56% GR 11% 33% 11% 0% 22% 0% 11% 22% 100% 22% 0% 22% 22% 0% 0% 22% 22% 11% 22% 56% 0% 0% 22% HU 22% 44% 44% 0% 11% 0% 33% 56% 22% 100% 0% 44% 44% 22% 0% 56% 44% 56% 44% 33% 0% 0% 44% IS 25% 0% 0% 0% 0% 0% 0% 0% 0% 0% 100% 0% 0% 0% 75% 0% 0% 0% 0% 0% 0% 75% 0% IE 44% 33% 11% 0% 22% 0% 33% 44% 33% 44% 0% 100% 44% 11% 0% 44% 44% 11% 22% 33% 0% 11% 56% IT 22% 44% 11% 0% 11% 0% 56% 67% 11% 44% 0% 33% 100% 11% 0% 56% 78% 11% 33% 56% 0% 0% 44% NL 0% 22% 33% 0% 22% 0% 11% 11% 0% 22% 0% 11% 11% 100% 33% 11% 11% 44% 33% 0% 0% 33% 11% NO 22% 0% 22% 0% 22% 0% 11% 0% 0% 0% 75% 0% 0% 33% 100% 0% 0% 11% 11% 0% 0% 67% 0% PL 22% 33% 22% 0% 22% 0% 44% 78% 22% 56% 0% 44% 56% 11% 0% 100% 56% 22% 44% 33% 0% 0% 78% PT 22% 33% 11% 0% 11% 0% 44% 56% 11% 44% 0% 33% 78% 11% 0% 56% 100% 11% 33% 67% 0% 0% 44% SK 0% 33% 67% 0% 22% 0% 11% 22% 11% 56% 0% 11% 11% 44% 11% 22% 11% 100% 56% 0% 0% 22% 11% SI 0% 33% 56% 0% 22% 0% 22% 44% 22% 44% 0% 22% 33% 33% 11% 44% 33% 56% 100% 11% 0% 11% 22% ES 22% 22% 0% 0% 22% 0% 22% 33% 56% 33% 0% 33% 56% 0% 0% 33% 67% 0% 11% 100% 0% 0% 33% SE 0% 0% 0% 78% 0% 89% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 100% 0% 0% CH 11% 0% 11% 0% 22% 0% 0% 0% 0% 0% 75% 11% 0% 33% 67% 0% 0% 22% 11% 0% 0% 100% 0% GB 33% 33% 11% 0% 44% 0% 33% 56% 22% 44% 0% 56% 33% 11% 0% 78% 44% 11% 22% 33% 0% 0% 100% 148 Regarding stable concurrences, the Nordic countries are the most exclusive. Denmark, Finland and Sweden only generate liaisons with each other. In contrast, Norway is the least selective. This extreme exclusivity implies that Sweden, Denmark and Finland are sufficiently far from other countries (including Norway and Iceland) but close to each other, despite shocks. Conversely, there are very unspecific countries, such as Austria, Estonia and the Netherlands, which establish links with a diversity of countries. We have elaborated the set of persistent concurrences and their profiles according to the average concurrence values for each variable since the described methodology minimises internal mean differences inside the concurrences. As synergy implies a comparative, we have applied conditional formatting based on the distance to the mean (yellow for average, greener for above average and redder for below average in positive traits, like the welfare effort, and on the contrary sense in negative traits, like the footprints) in order to observe how they compare at a glance (Table 4-6). Table 4-6 Persistent concurrences and relative profiling Source: Own elaboration. WEFF – Welfare effort; LTUN – Long-term unemployment rate; UN – Unemployment rate; SINC – S80/S20 quintile income ratio; LOCE – Local government expenditure as a percentage of general government expenditure; LOCR – Local government revenues as a percentage of general government revenues; EPPE – General government expenditure on environmental protection as a percentage of public WEFF LTUN UN SINC LOCE LOCR EPPE COMF RENE RRMW CARF MATF DK FI SE IS NO CH CZ SK SI 4NL 17.62 38.17 5.71 3.79 31.35 9.62 3.29 0.78 4.47 50.13 15.08 26.33 GR ES 6EE 17.07 41.72 9.69 5.51 24.46 4.88 1.38 0.78 14.91 23.04 14.50 26.35 BE FR 8AT 27.24 26.21 5.17 4.15 14.73 6.18 0.91 0.78 28.89 58.60 14.61 30.72 IT PT IE GB DE PL HU 0.78 9.94 9.76 17.11 14.59 23.66 11 22.51 41.99 7.81 4.51 22.24 12.75 1.42 17.81 7.73 1.79 0.83 5.69 36.10 30.46 0.67 18.61 9.51 19.34 10 21.36 41.92 9.43 4.89 39.03 9 25.86 52.73 11.37 5.80 21.21 14.65 1.52 16.72 11.59 2.06 24.36 0.78 10.99 12.86 26.40 7 29.90 44.29 8.72 4.15 45.80 10.88 22.12 0.78 6.95 5 24.84 49.61 20.15 6.27 9.24 6.83 2.17 20.86 12.26 1.98 39.52 0.70 50.08 18.64 33.14 3 20.01 50.58 8.82 3.54 22.91 10.97 32.36 0.74 10.57 2 17.96 24.64 4.58 3.47 27.08 19.85 1.64 50.04 29.97 0.72 Concurrence Countries Social Intermediate Environmental 1 27.63 20.54 7.50 3.91 42.98 13.10 27.99 0.89 29.23 149 expenditure; COMF – Potential competition for funding (1 if yes); RENE – Renewable energy as a percentage of the national energy mix; RRMW – Recycling rate of municipal waste; CARF – Carbon footprint per capita (T CO2 per capita); MATF – Material footprint per capita (T per capita). We derive the following insights: First, contrary to previous research, we have not found a compact group as “the Nordics”, but two well-differentiated groups: Denmark, Finland and Sweden on the one hand and Iceland and Norway on the other. The latter form a concurrence with Switzerland. Second, concurrences do not follow the Esping-Andersen typology or its adapted versions (Koch & Fritz, 2014), e.g., the mentioned case of Switzerland (Conservative) with Norway and Iceland (Social-democratic), Belgium (Social-democratic) and France (Conservative), Portugal (Mediterranean) and Italy (Conservative), and Germany (Conservative) with Poland and Hungary (Eastern). This lack of alignment implies that the environmental dimension is unrelated to the typologies of WS. The classifications of WS are exclusively related to the social dimension and do not match the obtained ecosocial typologies and the underlying performances of the environmental dimension or the behaviour of intermediate variables. Table 4-6 suggests that there is not an unambiguous approach, but diverse profiles that support the use of these eco-social concurrences instead of the former categorical WS classifications. Third, the Nordics are socially paradigmatic, but the worst positioned in environmental terms: despite their above-average share of renewable energy (29.23% and 50.08%), they generate the greatest carbon (13.10 and 18.64) and material footprints (33.14) through average recycling rates (42.98% and 39.52%). Denmark, Finland and Sweden display the most remarkable local administration (local spending represents 50% of total spending), the highest level of potential fiscal competition for funding (89% of the years) and the lowest environmental spending (0.72%). Austria and the Netherlands, considered Social-democratic according to an updated version of Esping-Andersen’s classification (Koch & Fritz, 2014) although Conservative in the original (EspingAndersen, 1990), constitute individual cases but present an analogous behaviour, except for an average fiscal competition (78%) and a modest local administration, notably in Austria (14.73%). Nonetheless, the Netherlands does so through a welfare effort (17.62%) and energy renewability (4.47%) notably below the average, but with the greatest environmental expenditure (3.29%) and the second biggest recycling rate (50.13%). Belgium and France are responsible for the greatest welfare effort (29.90%). Yet they reach an average social performance and a better environmental profile. France is the main country responsible for this profile and behaves slightly better, even if 150 Belgium has moderately lower unemployment rates (7.92%) and stratifications (3.89). They also rely on a local administration with below-average importance (16.72%). We conclude that the Social-democratic regimes present the best social situations and the worst environmental results. Fourth, the concurrence of the Czech Republic, Slovakia and Slovenia introduces an interesting profile. They show poor social performances, except for one of the lowest stratifications (3.54). Likewise, they register average renewability (10.57%) and the lowest recycling rate (22.91%) with one of the lowest carbon footprints (10.97) and one of the highest material footprints (32.36). The country responsible for this behaviour is Slovakia, an outlier regarding the disarray of footprints. Fifth, the South shows the worst social performances despite an above-average welfare effort. As happened with the Nordics, there is not a single South, but two differentiated groups. While Greece and Spain display an average environmental performance, Italy and Portugal exceed, regardless of slightly above-average renewability (18.61%) and a below-average recycling rate (30.46%). In contrast, Greece-Spain registers the lowest local relevance (9.24%), while in Italy-Portugal it is average. As happened with the French-Belgium duo, high welfare efforts (24.84% in Greece-Spain and 25.86% in Italy-Portugal) appear with low social performances. Regarding the last individual case, Estonia combines one of the poorest social performances, closely following Greece-Spain, and average environmental performance with well below-average renewability and recycling rates. Sixth, the Liberal regimes from Ireland and the United Kingdom combine average and slightly below-average social performances, significant low renewability (5.69%), average recycling, but better footprints, notably regarding materials (23.66). They also register the second greatest competition for funding (83%). Finally, the unexpected Conservative-Eastern concurrence of Germany, Poland and Hungary is the average social performer, but displays the second-lowest carbon footprint (9.76) and the lowest material footprint (17.11). The East is not isolated and approaches the Conservative regimes. The inclusion of the generosity index (not correlated with the welfare effort) has caused few variations for the countries for which it is calculated from 2008 to 2010 ceteris paribus (Table 4-7): some Social-democratic regimes increase the concurrence with Conservatives and Liberals (Austria with France and Ireland, Belgium with Ireland, the Netherlands and Switzerland), Conservatives with Liberals (Germany with Ireland, Switzerland with Ireland) and Mediterranean with Liberals (Greece and the United Kingdom). Conversely, it increases the gap between Ireland and Greece. In consequence, this secondary exercise does not clarify concurrences. 151 Table 4-7 Variations in concurrences caused by the inclusion of generosity, 2008-2010 Source: Own elaboration. Thus, we reject the hypothesis of synergy for two major reasons: First, the Social-democratic regimes present the best social and the worst environmental performances. The socially paradigmatic Nordic countries combine the greatest shares of renewable energy and average recycling rates with the greatest carbon and material footprints. Nordics are unable to translate the mobilisation of domestic resources and renewability into decarbonisation and dematerialisation. Public services represent relevant shares of such footprints, e.g., in Finland 19% and 38% respectively, mostly related to construction and infrastructures (Ottelin et al., 2018). A similar warning was already pointed out by Koch & Fritz (2014), but they concluded that Sweden (and Austria) verified the hypothesis. Even if Sweden is the best environmental performer in the first Nordic cluster, its footprints do not respond to renewability and recycling: France-Belgium registers similar carbon and material footprints (around 11 MtCO2 and 23 T per capita, respectively) with a considerably lower renewable share (6.95% versus 35.67% in Sweden) and a slightly lower recycling rate (45.80% versus 47.77%). Likewise, the verification in Austria is rejected too. Additionally, Denmark, Finland and Sweden do not potentiate the parallelisms between WS and ES, as they register the most remarkable potential competition for funding and the lowest public environmental expenditure. Contrary to Jakobsson et al. (2018) and Zimmermann & Graziano (2020), Conservatives and Liberals do not perform similarly to the Nordics, but better in average environmental terms, as Koch & Fritz (2014) noted. ID AT BE DK FI FR DE GR IE IT NL NO PT ES SE CH GB AT 0% 0% 0% 0% 33% 0% 0% 33% 0% 0% 0% 0% 0% 0% 0% 0% BE 0% 0% 0% 0% 0% -33% 0% 33% 0% 0% 0% 0% 0% 0% 0% 0% DK 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% FI 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% FR 33% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% DE 0% -33% 0% 0% 0% 0% 0% 33% 0% 0% 0% 0% 0% 0% 0% 0% GR 0% 0% 0% 0% 0% 0% 0% -67% 0% 0% 0% 0% 0% 0% 0% 33% IE 33% 33% 0% 0% 0% 33% -67% 0% 0% 0% 0% 0% 0% 0% 33% 0% IT 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% NL 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 33% 0% NO 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% PT 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% ES 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% SE 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% CH 0% 0% 0% 0% 0% 0% 0% 33% 0% 33% 0% 0% 0% 0% 0% 0% GB 0% 0% 0% 0% 0% 0% 33% 0% 0% 0% 0% 0% 0% 0% 0% 0% 152 Second, as concurrences do not match previous WS classifications, the results reflect a diversity of profiles and, as shown in Table 4-6, social and environmental dimensions are unrelated. There is not a single, univocal connection between the WS regimes, the intermediate variables and the situation of the environmental dimension. Instead, we find diversified portraits that require further discussion. 4.5. Discussion: fostering synergy through Sustainable Welfare In light of these results, we want to acknowledge the limitations of our research, which points to some methodological and political fronts, and open the scene to future research of this topic. The first clarification is that we have rejected synergy in this sample, with the cited variables applied to the feasible period. Improvements in data availability could enrich the interpretation of results, notably from now on, as data started to be fully available from 2008. Second, our contribution is the alignment of the empirical-theoretical considerations and subsequent screenings of variables, about which few discussions have been proposed. To obtain comparable results, we have mimicked previous methods, while avoiding the assumptions concerning the classifications of regimes. In this vein, the road unfolds in two different but parallel ways. Regarding clustering, there is a need to surpass categorical classifications as reality proves more complex. Additionally, synergy is a process of coevolution and, consequently, the discrete analysis provides little information. Besides, synergy as formulated in previous contributions is a matter of comparison. Nevertheless, synergy could also be defined in feasible social-environmental terms based on the profiles of countries or persistent concurrencies, as each of them displays a unique behaviour and a different panoply of possibilities, weaknesses, strengths, and threats. Beyond Social-democratic WS, and with a cautionary approach given the diversity, we can observe some behaviours related to other concurrences. A Conservative WS like Germany joined by some Eastern regimes like Poland and Hungary present the best environmental performance with an average social performance. This a priori unexpected combination is unavailable in other concurrences: Mediterranean WS also have positive environmental situations but combined with the poorest social profiles, and Liberal WS maintain the average social performances close to the Conservative WS with slightly positive to average environmental circumstances. Furthermore, the GermanPolish-Hungarian concurrence is unique in comparison with their typologies: not all Conservative and Eastern WS behave in this way. Worthy of mention among the Eastern regimes is the poor performance of the concurrence between the Czech Republic, Slovakia, and Slovenia. Future research should clarify this unique behaviour. 153 On the other hand, there is room for other methodologies. Simulation tools and systemic modelling have much to say in this respect. Yet, while looking at the future, research cannot forget the past. Improvements in the availability of metrics beyond GDP, such as the generosity index, beyond Gini, as with income shares and the Palma ratio, and footprints including biophysical-economic dynamics, could illustrate the joint evolution of social and environmental dimensions during past decades. Third, quantitative contributions cannot relegate the qualitative-institutional discussion about the roles of the state (Vatn, 2020). Diversity suggests complex nonunidirectional ways of approaching synergies and conflicts. From this viewpoint, the potential mechanisms of synergy, i.e., the intermediate variables, must be more varied than those theorised. Anyway, the central element in a conflict between WS and ES is the sustainability of economic growth, responsible for environmental deterioration and welfare support. The absence of synergy fuels the debate about transitions between green growth and postgrowth, to simplify despite variety (Drews, Savin, & van den Bergh, 2019), and could well provide arguments to both sides. If a decoupled green growth was possible, the conflict would be immediately deactivated. Conversely, in a post-growth scenario, current WS will be placed on the edge of a precipice: as income decreases or stabilises, there is a need to strengthen the coverage of WS through public expenditure supported by revenues calculated over a decreasing or steady income, apart from other specific barriers (Strunz & Schindler, 2018). To face this, some streams suggest redistributing wealth apart from income (Koch, 2020). In parallel, degrowth demands strong democratic support to such challenging measures as the limitation of private property and the redistribution of working time (Cieplinski, D’Alessandro, & Guarnieri, 2021; Nieto, Carpintero, Lobejón, & Miguel, 2020). When scholarship denies decoupling and realises the challenges of degrowth, it arrives at a crossroads that motivates the increasing momentum of SW to explore the environmental implications of WS. SW fosters the satisfaction of human needs (not preferences) within ecological limits from an intergenerational and global perspective (Koch et al., 2016) and unveils the internal contradictions of current WS to deal with the transition to ES (Hirvilammi, 2020). Its main tools are universal basic incomes, services and bonds, as well as job guarantee proposals, to increase freedom to determine one’s own lifestyle, decommodify society and motivate transformation (Bohnenberger, 2020). Our conclusions do not limit to synergy-facilitating SW based on the institutional characteristics of Social-democratic WS. In this regard, Universal Basic Services (UBS), which guarantee a socially agreed decent standard of living as a human right, could serve this goal (Coote & Percy, 2020). UBS are inspired by current universal services like