Governance in multi-system transitions: A new methodological approach for actor involvement in policy making processes

Aslı Ateş, Karoline S. Rogge and Katherine Lovell

Energy Policy, 2024, vol. 195, issue C

Abstract: Multi-system interactions associated with the decarbonisation of energy and mobility systems represent a complex phenomenon in the acceleration phase of net-zero transitions. In this paper, we present a novel methodological approach to examine actor involvement in the governance of multi-system transitions, with a focus on the UK's net-zero energy-mobility transitions from 2008 to 2021. Utilising Named Entity Recognition (NER), a natural language processing technique, we systematically map actors and their interactions within policy consultations and how these have changed over time. Our analysis differentiates between single-system and multi-system policy making processes; identifies weak and strong links among actors as two types of multi-system interactions; categorises actors into business, policy, academia, and society groups; and examines the evolution of engagement across multiple governance levels. Our findings indicate an increasing trend of multi-system interactions, suggesting the UK's progression towards the acceleration phase of net-zero transitions. Our analysis further reveals the predominance of policy actors, particularly from the national level, in governing such multi-system transitions processes, followed by business actors. Despite some limitations, our approach offers a scalable method for analysing large volumes of text, providing valuable insights into the governance dynamics of multi-system transitions. We conclude with implications for policy making and offer suggestions for future research, emphasising the importance of understanding actor involvement and political contestations around net-zero trajectories for ensuring the achievement of sustainability goals.

Keywords: Actors; Energy; Mobility; Transport; e-mobility; Socio-technical transitions; Natural language processing; Named entity recognition; Machine learning; UK; Policy consultation; Multi-system governance (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:enepol:v:195:y:2024:i:c:s0301421524003331

DOI: 10.1016/j.enpol.2024.114313

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