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Topic Modeling Uncovers Shifts in Media Framing of the German Renewable Energy Act

Dehler-Holland, Joris ORCID iD icon 1; Schumacher, Kira 1; Fichtner, Wolf ORCID iD icon 1
1 Institut für Industriebetriebslehre und Industrielle Produktion (IIP), Karlsruher Institut für Technologie (KIT)


Renewable energy policies have been recognized as a cornerstone in the transition toward low-emission energy systems. Media reports are an important variable in the policy-making process, interrelating politicians and the public. To understand the changes in media framing of a pioneering renewable energy support act, we collected 6,645 articles from five Germany-wide newspapers between 2000 and 2017 on the German Renewable Energy Act. We developed a structural topic model based on a change-point analysis to assess the temporal patterns of newspaper coverage. We introduced the notion of topic sentiment to elucidate the emotional content of topics. The results show that after its enactment, optimism about renewable energies dominated the media agenda. After 2012, however, the Renewable Energy Act was more associated with its costs. Such shifts in renewable energy policy framing may limit political leverage to reach ambitious climate and energy targets.

Verlagsausgabe (Version 2) §
DOI: 10.5445/IR/1000128185/v2
Veröffentlicht am 28.01.2021
Verlagsausgabe (Version 1) §
DOI: 10.5445/IR/1000128185
Veröffentlicht am 08.01.2021
DOI: 10.1016/j.patter.2020.100169
Zitationen: 30
Zitationen: 30
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Industriebetriebslehre und Industrielle Produktion (IIP)
Publikationstyp Zeitschriftenaufsatz
Publikationsdatum 08.01.2021
Sprache Englisch
Identifikator ISSN: 2666-3899
KITopen-ID: 1000128185
Erschienen in Patterns
Verlag Elsevier
Band 2
Seiten 100169
Vorab online veröffentlicht am 22.12.2020
Schlagwörter renewable energy policy; German energy transition; attention cycle; newspaper content analysis; framing; structural topic model; text mining; natural language processing; sentiment analysis; time-series analysis
Nachgewiesen in Dimensions
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