Exploring energy transition narratives through mayoral insights using artificial intelligence
Date
2024-12-21
Authors
Ahmed, Fatma
Ahmed, Rwan
Poelzer, Greg
Poelzer, Gregory
Söderberg, Charlotta
Zapata, Oscar
Guilmette, Elaina
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Elsevier
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Article
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Abstract
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.
Description
2214-6296/© 2024 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by nc-nd/4.0/).
Keywords
Energy transition, Mayors, Natural Language Processing (NLP), Sentiment analysis, Arctic, Theory of planned behavior (TPB)
Citation
Ahmed, F., Ahmed, R., Poelzer, G., Poelzer, G., Charlotta Söderberg, Zapata, O., & Guilmette, E. (2024). Exploring energy transition narratives through mayoral insights using artificial intelligence. Energy Research & Social Science, 120, 103902–103902. https://doi.org/10.1016/j.erss.2024.103902
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DOI
10.1016/j.erss.2024.103902