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An abundant literature on the advantages and drawbacks of different auction design elements exists. However, their comparative effects on market concentration have not been explored, despite the increasing concern of governments about this issue. This article covers this gap in the literature. It follows an exploratory approach to provide a better understanding of the relationship between auctions, design elements and market concentration. An analytical framework is developed to assess the effects of auction and auction design elements on two aspects of market concentration (number and diversity of firms). Then, some research proposals are inferred from this framework and illustrated with a case study of wind power in Spain. Data have been collected through an expert elicitation survey and a Multi-criteria Analysis has been performed. The results show that, compared to administratively-set remuneration, auctions increase market concentration. Furthermore, their impact on market concentration is mediated by the choice of specific design elements (the existence of a schedule, the remuneration type and stringent prequalification requirements). However, those effects of auctions and their design elements are relatively low compared to other context factors. Therefore, if the aim is to reduce market concentration, policy makers should improve these context conditions, followed by auction design.

Auctions are currently the dominant and most rapidly expanding form of support for renewable energy project deployment, capturing the interests of policy-makers and academics alike. This article provides a systematic review of the relatively recent but already abundant literature on auctions for renewable energy and proposes a future research agenda on the topic. Major themes, gaps in knowledge, and trends in the literature are highlighted. More research efforts will be needed on the institutional capacity and arrangements, coordination between auctions and other procedures and appropriate inclusion of auctions in policy mixes. The trade-offs between different criteria should be the focus of more rigorous and quantitative marginal analysis, identifying the extent to which improving a given criterion worsens another. The existence, causes and consequences of underbidding, the implications of the just energy transition for the performance of auctions and auction design, the marginal impact of the level of design elements and the interactions between design element choices in “design element packages” merit more attention. There should be a greater focus on the micro-level in the assessment of the success of auctions and auction design. At a methodological level, assessments of auctions with case studies should coexist with more quantitative studies on the topic, using a wide range of methodologies. However, the transferability of lessons on the functioning of design elements across different contexts will be necessarily limited. Finally, a plea is made to national governments and international institutions to facilitate the access to auction data.

An increase in market concentration (MC), which refers to a large share of few and large firms in a given sector, is a main concern for governments all over the world since it is believed to affect competition levels and, thus, lead to inefficiencies. It has also captured the attention of researchers in the past. A trend towards MC has been observed in many sectors, as they progress through different stages (i.e., what we call “consolidation effect”). This can also be the case in the renewable energy (RE) sectors. On the other hand, the features of RE policy may also influence the entry and exit of firms in RE sectors and, thus, MC, as they affect the revenues, costs and risks of its players (i.e., what we call the “policy effect”). However, despite its academic and policy relevance, the literature on MC in the RE sectors is very tiny and the few analyses have been undertaken without a systematic and comprehensive analytical framework. The aim of this paper is to provide an analytical framework to assess the impact of deployment support on market concentration in the RE sectors. This framework is based on several approaches, i.e. industrial organisation, industry life cycle theory and the literature on renewable energy support. It considers the aforementioned two effects on market concentration in the project ownership stage of the value chain. The empirical analysis illustrates the viability of this framework with a case study of wind energy in Spain, and suggests that it can be applied to other countries and RE sectors. Using primary and secondary data sources, the paper shows that there has been an increase in market concentration, although this has not been monotonic, suggesting the influence of the aforementioned two main effects.

Innovation in general, and innovation in renewable electricity technologies (RETs) in particular, are a main element of the clean energy transition. Traditionally, policy measures to spur innovation have fallen into two main categories: supply-push and demand-pull. The latter category includes auctions, which are now the most popular scheme to support renewable energy worldwide. Although renewable energy auctions have been assessed according to several criteria (e.g., effectiveness and efficiency), their impact on innovation has not received much attention in the past. Since auctions will continue to be the instrument of choice in many countries, it is crucial to identify their effects on innovation. This paper aims to identify and discuss the comparative innovation effects of renewable energy instruments, focusing on auctions, with the help of an in-depth literature review of demand-pull (deployment) instruments. The results suggest that auctions do not score well in encouraging innovation, although the evidence for that is limited. In contrast, there is a broad agreement that administratively-set feed-in tariffs have played a much stronger role in this sense, with consistent (and more abundant) evidence that they have promoted innovation. Some reasons which explain this result are provided, taking into account different innovation mechanisms discussed in the literature. The findings of this paper are deemed policy-relevant. If the demand-pull side of innovation processes is missing due to the implementation of an instrument (auctions) which does not generate such demand-pull, then the RET innovation processes which are needed in the clean energy transition may be put at risk.

Auctions have become the main instrument of choice to support renewable electricity around the world. This is probably due to their alleged virtues in terms of efficiency. However, whether auctions will meet their expectations and be successful will depend on the choice of design elements in particular settings. Although the analysis of the advantages and drawbacks of different design elements has received considerable attention in the literature, this is not the case with the real-world adoption of different design elements across different regions and renewable electricity technologies and overtime. The aim of this paper is to cover this gap in the literature. Using a database of 90 renewable electricity auctions from around the world, built by the authors, this article analyzes different patterns of adoption of design elements overtime, across continents and technologies. The results of the analysis show that, indeed, large differences across regions and overtime can be observed for some design elements. Regarding regional differences, this is the case for geographical diversity, local content requirements, remuneration form, auction form and disclosure of ceiling prices. Some design elements clearly show a distinct pattern over time: volume metric, size diversity, selection criteria, auction format, auction type, pricing rule and ceiling prices. In contrast, the differences across technologies are less marked and are circumscribed to geographical diversity, auction format and remuneration form. Several possible explanations for the patterns and trends in auction design are proposed.

The use of the European Union cooperation mechanisms for concentrated solar power (CSP) projects could kill two birds with one stone. First, CSP electricity can cover demand when variable renewables cannot generate. Second, CSP projects deployed under the cooperation mechanisms could contribute to a European-wide optimization of resource use and grid management. This paper analyzes whether the dispatchable nature of CSP is a main driver to the use of the cooperation mechanism for this technology. Based on an expert elicitation and a survey to different types of stakeholders, our results show that, indeed, dispatchability will be the main driver to the use of the cooperation mechanisms for CSP projects in the future. The findings suggest that two types of policy interventions will be required to encourage the use of these mechanisms for CSP. Some policy measures should be directed at the technology itself, whereas other policies should target the cooperation mechanisms.

The circular economy (CE) and eco-innovation (EI) are two concepts deemed instru-mental in achieving a sustainable transition. They have been proposed in the aca-demic literature and by practitioners and have acquired very high public policyrelevance, being endorsed by policymakers and ultimately leading to regulationssupporting them. It has been argued that both concepts are compatible and interre-lated and that EI is instrumental in achieving the CE. However, little is known abouthow different EI features contribute to the CE at the microlevel. This article tries tocover this gap. Its aim is to assess and quantify the causal relationship betweendifferent EI features and the CE with the help of a unique dataset of small- andmedium-sized firms in Spain and an econometric analysis. Our results show that onlysystemic EIs contribute to a global CE, whereas other EI types such as componentadditions or small changes in existing production processes could even be barriers tohigh levels of circularity. It is found out that technological novelty is not relevant forreaching the CE. The results support the understanding of how EIs enable a transitionto the CE. Care should be taken not to promote incremental EIs that do not onlyachieve low (or no) circularity but that effectively lock-in the economic system insolutions that entail a barrier to the achievement of high-level circularity.

We investigate whether political ideology has an observable effect on decarbonization ambition, renewable power aims, and preferences for power system balancing technologies in four European countries. Based on the Energy Logics framework, we identify ideologically different transition strategies (state-centered, market-centered, grassroots-centered) contained in government policies and opposition party programs valid in 2019. We compare these policies and programs with citizen poll data. We find that ideology has a small effect: governments and political parties across the spectrum have similar, and relatively ambitious, decarbonization and renewables targets. This mirrors citizens’ strong support for ambitious action regardless of their ideological self-description. However, whereas political positions on phasing out fossil fuel power are clear across the policy space, positions on phasing in new flexibility options to balance intermittent renewables are vague or non-existent. As parties and citizens agree on strong climate and renewable power aims, the policy ambition is likely to remain high, even if governments change.

Firms are influenced by internal factors (resources and capabilities) and external factors (e.g., regulation) when taking the decision to eco‐innovate. However, the analysis of the internal factors has received much less attention than the external ones. This paper aims to fill this gap in the literature by analyzing the role of resources, competences, and dynamic capabilities (RCCs) as determinants (drivers and barriers) of different eco‐innovation (EI) types. Those EI types contribute differently to the sustainable transition of the economy and society, that is, towards the circular economy. The statistical analyses reveal that RCCs are quite relevant as determinants of EIs, and that different RCCs are more or less relevant for different EI types. In particular, the determinants of systemic and radical EIs substantially differ from those for continuous improvements. Our results suggest that physical RCCs, involvement in green supply chains, an EI‐friendly corporate culture, technology‐push and market‐pull, and internal financing resources represent drivers to these EIs, whereas cooperation, organizational learning, an ISO (International Organization for Standardization) ecological certification, and technological path dependency are barriers. The results may guide firms to pursue competitive and sustainable advantage by innovating through certain EI types corresponding to available and dedicated RCCs. They may also be useful to policy makers who are willing to promote specific EI types.

Obesity and type 2 diabetes mellitus are global emergencies and long noncoding RNAs (lncRNAs) are regulatory transcripts with elusive functions in metabolism. Here we show that a high fraction of lncRNAs, but not protein-coding mRNAs, are repressed during diet-induced obesity (DIO) and refeeding, whilst nutrient deprivation induced lncRNAs in mouse liver. Similarly, lncRNAs are lost in diabetic humans. LncRNA promoter analyses, global cistrome and gain-of-function analyses confirm that increased MAFG signaling during DIO curbs lncRNA expression. Silencing Mafg in mouse hepatocytes and obese mice elicits a fasting-like gene expression profile, improves glucose metabolism, de-represses lncRNAs and impairs mammalian target of rapamycin (mTOR) activation. We find that obesity-repressed LincIRS2 is controlled by MAFG and observe that genetic and RNAi-mediated LincIRS2 loss causes elevated blood glucose, insulin resistance and aberrant glucose output in lean mice. Taken together, we identify a MAFG-lncRNA axis controlling hepatic glucose metabolism in health and metabolic disease. Despite widespread transcription of LncRNA in mammalian systems, their contribution to metabolic homeostasis at the cellular and tissue level remains elusive. Here Pradas-Juni et al. describe a transcription factor-LncRNA pathway that couples hepatocyte nutrient sensing to regulation of glucose metabolism in mice.

Obesity and type 2 diabetes mellitus are global emergencies and long noncoding RNAs (lncRNAs) are regulatory transcripts with elusive functions in metabolism. Here we show that a high fraction of lncRNAs, but not protein-coding mRNAs, are repressed during diet-induced obesity (DIO) and refeeding, whilst nutrient deprivation induced lncRNAs in mouse liver. Similarly, lncRNAs are lost in diabetic humans. LncRNA promoter analyses, global cistrome and gain-of-function analyses confirm that increased MAFG signaling during DIO curbs lncRNA expression. Silencing Mafg in mouse hepatocytes and obese mice elicits a fasting-like gene expression profile, improves glucose metabolism, de-represses lncRNAs and impairs mammalian target of rapamycin (mTOR) activation. We find that obesity-repressed LincIRS2 is controlled by MAFG and observe that genetic and RNAi-mediated LincIRS2 loss causes elevated blood glucose, insulin resistance and aberrant glucose output in lean mice. Taken together, we identify a MAFG-lncRNA axis controlling hepatic glucose metabolism in health and metabolic disease. Despite widespread transcription of LncRNA in mammalian systems, their contribution to metabolic homeostasis at the cellular and tissue level remains elusive. Here Pradas-Juni et al. describe a transcription factor-LncRNA pathway that couples hepatocyte nutrient sensing to regulation of glucose metabolism in mice.

The aim of this paper is to empirically identify and rank the drivers and barriers to the deployment of concentrated solar power (CSP) in the EU in the past and the future at two different levels of analysis: System/grid or macro level and project/investment or micro level. An expert elicitation and an investors’ survey were carried out for this purpose. The results differ across the two levels (experts and investors), time frames and CSP designs. Specifically, deployment support, policy framework conditions and a proven technology have been major drivers of CSP deployment in the past, according to the expert elicitation. Dispatchability is regarded as the main future driver of the technology, followed by policy framework conditions and complementarity with PV. The survey of investors highlights the relevance of dispatchability, key technology and investors’ features as drivers, and stress the importance of administrative processes, construction permits and grid connection as barriers. The results suggest the need to combine different policies in order to activate the drivers and/or mitigate the barriers.

The need for a sustainability transition is undisputed. Ecological innovation (eco-innovation) is a possible mechanism with which to reach a fully sustainable economy and society. Several types of eco-innovation exist, that is, end-of-pipe, efficiency or effectiveness related. While all contribute to the sustainability transition, they have different impacts, ranging from local optimization to systemic redesign, that is, for the circular economy. In that sense, past research has brought forward many definitions and conceptualizations. The aim of this chapter is to go a step further and contribute to a universal taxonomy by characterizing eco-innovations departing from the resource-based view of the firm. Therefore, four dimensions of eco-innovations are contrasted against six groups of firm resources. It is found that, depending on eco-innovation types and dimensions, the respective antecedents are different, that is, certain resources facilitate local optimization while systemic eco-innovations are the result of cooperation (joint efforts and resource bases) of several innovators. This chapter provides a precise conceptualization of the interaction between firm resources and eco-innovations.

[EN] Eco-innovations, or innovations that reduce the environmental impacts of production and consumption activities, are considered crucial for sustainability transitions and a key element of a Circular Economy. Although previous contributions have acknowledged the existence of different types of eco-innovations (e.g., product vs. service or incremental vs. radical), a precise conceptualization of eco-innovation types, which takes into account its multifaceted character, is missing. Yet such a conceptualization is crucial in order to understand how eco-innovations contribute to a sustainable transition, how policy makers can promote different eco-innovation types, and how business practitioners can develop eco-innovations. This article covers this gap in the literature. Its aim is twofold: 1) to develop a quantitative method to categorise different eco-innovation types in a particular setting, taking into account their distinct features and dimensions; 2) to apply this method in a given sector and country, building a taxonomy of eco-innovation types. It draws on a survey of 197 Spanish industrial small and medium size enterprises (SMEs) which developed or adopted an eco-innovation between 2012 and 2013. The statistical analyses reveal the existence of a taxonomy of five eco-innovation types: systemic, externally driven, continuous improvement, radical (technology-push initiated) and eco-efficient. They differ in their techno-economic configurations, contribution to environmental sustainability and corporate goals and required changes in the firms. Specific policy and managerial implications are deducted.

Despite a high academic interest in eco-innovation, a clearly defined common understanding of the characteristics of eco-innovations is largely missing. Existing research on this topic is still mostly qualitative, fragmented, difficult to compare or aggregate and generally specialised on certain aspects. Quantitative research is deemed necessary to improve the knowledge base and measurement of essential aspects regarding the characteristics of eco-innovation. The aim of this study is to quantitatively explore the underlying structure of the eco-innovation concept based on the current knowledge of those characteristics and to advance on the quantification of a four-dimensional framework proposed in the past (Carrillo-Hermosilla et al., 2010). Industrial small and medium-size enterprises in Spain were asked to quantify a set of variables according to the perceived relevance for the firm of a realized eco-innovation. Factor Analyses were conducted on 197 collected data sets. Our statistical results reveal how the identified characteristics shape an underlying structure of eco-innovations along the four dimensions (design, user, product-service and governance) proposed in that article. The analysis identifies the factors which make up these dimensions, allowing a characterization of eco-innovations with considerably less complexity. The final impact of eco-innovation on the environment goes in tandem with and is usually mediated by considerable impacts at the company level (including internal management and organizational practices) which lead to changes in products and processes. Furthermore, our results stress the critical role played by users and clients' engagement and acceptance and cooperation with other stakeholders in the eco-innovation process. The eco-innovation may entail radical, path-breaking changes in existing relations between the firm and its production network. This article contributes to advance the understanding of the phenomenon by providing a compr

Exploiting the complex structure of relational data enables to build better models by taking into account the additional information provided by the links between objects. We extend this idea to the Semantic Web by introducing our novel SPARQL-ML approach to perform data mining for Semantic Web data. Our approach is based on traditional SPARQL and statistical relational learning methods, such as Relational Probability Trees and Relational Bayesian Classifiers. We analyze our approach thoroughly conducting four sets of experiments on synthetic as well as real-world data sets. Our analytical results show that our ap- proach can be used for almost any Semantic Web data set to perform instance-based learning and classification. A comparison to kernel methods used in Support Vector Machines even shows that our approach is superior in terms of classification accuracy.