| ID | 196977 |
| Title Proper | Governance in multi-system transitions |
| Other Title Information | a new methodological approach for actor involvement in policy making processes |
| Language | ENG |
| Author | Ateş, Aslı |
| Summary / Abstract (Note) | 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. |
| `In' analytical Note | Energy Policy Vol.195; Dec 2024: p.114313 |
| Journal Source | Energy Policy 2024-12 195 |
| Key Words | Energy ; Mobility ; UK ; Actors ; Socio-Technical Transitions ; Machine learning ; Natural Language Processing ; Transporte-mobility ; Named entity recognition ; Policy consultation ; Multi-system governance |