A computational analysis of the media coverage of the European Parliament's 'green' Designation on sustainable energy and climate change

Energy is pivotal to the sustainable development agenda for 2030. In this context, nuclear energy emerges as a potential solution for renewable energy development and carbon dioxide mitigation. However, the Russian invasion of Ukraine in 2022 exacerbated global energy concerns, leading to a surge in...

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Detalles Bibliográficos
Autores: Zeler, Ileana Lis|||0000-0002-5550-1000, Rodríguez Amat, Joan Ramon|||0000-0001-8391-3638, Muhammad Amir, Riasat|||0009-0009-8696-7469
Tipo de recurso: artículo
Fecha de publicación:2025
País:España
Institución:Universitat Autònoma de Barcelona
Repositorio:Dipòsit Digital de Documents de la UAB
Idioma:inglés
OAI Identifier:oai:ddd.uab.cat:311522
Acceso en línea:https://ddd.uab.cat/record/311522
https://dx.doi.org/urn:doi:10.1016/j.enpol.2025.114592
Access Level:acceso abierto
Palabra clave:Nuclear energy
Sustainability
Energy policy
European union
News media
Topic modelling
Descripción
Sumario:Energy is pivotal to the sustainable development agenda for 2030. In this context, nuclear energy emerges as a potential solution for renewable energy development and carbon dioxide mitigation. However, the Russian invasion of Ukraine in 2022 exacerbated global energy concerns, leading to a surge in fuel prices and an economic slowdown, prompting the European Parliament to designate nuclear energy investments as "green." Despite the potential of nuclear energy in advancing sustainability goals, public perception, influenced by historical negative narratives and media coverage, remains a challenge. This article examines the impact of the European Parliament's decision on the news flow and coverage about sustainability, climate change, and nuclear energy in the global media. By analysing seven months of global news coverage, a total of 7822 news stories in English were selected. Through descriptive analysis and unsupervised topic modelling Latent Dirichlet Allocation (LDA) machine learning techniques, this study explores trends in news content and coverage, shedding light on the intersection of energy policies, and media coverage.