Exploring energy transition narratives through mayoral insights using artificial intelligenceShow others and affiliations
2025 (English)In: Energy Research & Social Science, ISSN 2214-6296, E-ISSN 2214-6326, Vol. 120, article id 103902Article in journal (Refereed) Published
Abstract [en]
This paper explores energy transition dynamics in three Arctic cities: Luleå (Sweden), Fairbanks (Alaska), and Yellowknife (Canada), with a focus on sustainable urban development. Semi-structured interviews with the mayors of these cities provide insights into their decision-making processes and strategies regarding energy transitions. Using Natural Language Processing (NLP) for semantic analysis, the study uncovers implicit priorities, challenges, and aspirations from the qualitative data. The analysis is guided by the theory of planned behavior, which helps to explain the underlying motivations, attitudes, and perceived behavioral control that influence policy decisions. Results reveal common themes such as balancing environmental goals with economic and social concerns, while also highlighting context-specific challenges in each city. This research contributes to the understanding the role of municipal leadership in energy transitions and demonstrates the effectiveness of NLP techniques in extracting meaningful insights from interviews. The findings aim to inform policymakers and urban planners on fostering sustainable energy transitions in Arctic regions.
Place, publisher, year, edition, pages
Elsevier, 2025. Vol. 120, article id 103902
Keywords [en]
Energy transition, Mayors, Natural Language Processing (NLP), Sentiment analysis, Arctic, Theory of planned behavior (TPB)
National Category
Peace and Conflict Studies Other Social Sciences not elsewhere specified
Research subject
Political Science
Identifiers
URN: urn:nbn:se:ltu:diva-111149DOI: 10.1016/j.erss.2024.103902ISI: 001393915600001Scopus ID: 2-s2.0-85212620797OAI: oai:DiVA.org:ltu-111149DiVA, id: diva2:1923375
Note
Validerad;2025;Nivå 2;2025-01-01 (joosat);
Funder: National Science Foundation [grant number 2127364];
Full text: CC BY-NC-ND license
2024-12-232024-12-232025-10-21Bibliographically approved