The embeddedness of companies in regional energy transitions

ABSTRACT

Both place-specific context conditions and the interplay of a variety of actors influence regional energy transitions. Yet, the role of regional lead companies and how they are embedded in regional transitions has not been systematically analysed, even though a lack of embeddedness in their geographical context could possibly impede transitions. In our contribution, we expand transition studies with insights from economic geography and sociology to grasp the embeddedness of regional lead companies in regional transitions along ten indicators. We analyse how intra-organizational and regional factors influence these indicators and the overall degree of embeddedness. Finally, we reflect on how the (dis)embeddedness of companies affects regional energy transitions. Empirically, our analysis is based on a most different case study design, comparing a wind turbine manufacturer and its detached relation to the energy transition around a city in Central Germany with a wind project developer deeply embedded in a rural Northern German district. These findings are mirrored not only in different embeddedness degrees, but also in different types of embeddedness that we term ‘transactional’ and ‘transformational’. By systematically describing the interrelation between organizations and regions, our contribution shows how multifaceted embeddedness is and how closely inner-organizational factors are intertwined with regional transitions.

KEYWORDS:

• Embeddedness
• sustainability transitions
• onshore wind energy
• geography of transitions
• companies and transitions
• regional change

Acknowledgements

This paper was written in the context of the project REENEA (Regional energy transition as a social process) conducted at the University of Oldenburg from 2018–2023. We thank the DFG for funding this research and our interview partners for their time and openness. Particular thanks go to Camilla Chlebna for conducting one of the empirical case studies and repeatedly discussing possible interpretations with us. We also thank the participants of the GeoInno 2022, the anonymous reviewers, the editor and several colleagues of our group for helpful comments on previous versions of this paper.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Correction Statement

This article has been corrected with minor changes. These changes do not impact the academic content of the article.

Notes

1 The term ‘regional lead companies’ is inspired by Global Production Networks (GPN) literature, where it usually covers lead companies centrally positioned in global value chains (Lund and Steen 2020). This literature also analyses “regional development as a process of strategic coupling between regional assets and the strategic needs of a lead firm”. However, regional development in this approach is usually reduced “to the outcome of firm strategies” on a narrow set of economic indicators (Dawley, MacKinnon, and Pollock 2019, 853)

2 An exception is the Triple Embeddedness Framework by Geels (2014). It explains how incumbents react to gradually increasing environmental selection pressures with strategic responses. This framework also addresses that companies are embedded into industries and value chains and hence not only in regions (cf. chapter 6). However, this framework does not conceptualize embeddedness as a bi-directional relationship or explicate the spatial and regional conditions of embeddedness.

3 Additionally, Annex A includes a comprehensive table with definitions of the embeddedness indicators and extended, application-oriented specifications which emerged from coding the empirical material.

4 The case studies were conducted in the research project REENEA on regional energy transitions at the University of Oldenburg. In this context, regions are viewed as a socially constructed territory situated between the level of municipalities and federal states. For more information on how regions were selected and regional borders were drawn, refer to Rohe (2021).

Additional information

Funding

This work was supported by Deutsche Forschungsgemeinschaft: [grant number 316848319].