Topic Modeling Uncovers Shifts in Media Framing of the German Renewable Energy Act
| Autor: | Wolf Fichtner, Joris Dehler-Holland, Kira Schumacher |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Topic model
German energy transition Q42 H23 Economics attention cycle General Decision Sciences O3 Q48 text mining Article Newspaper German C1 Leverage (negotiation) Framing (construction) Political science ddc:330 L50 C8 natural language processing lcsh:Computer software framing O33 business.industry Sentiment analysis Cornerstone newspaper content analysis O13 language.human_language Renewable energy O38 renewable energy policy lcsh:QA76.75-76.765 structural topic model sentiment analysis language time-series analysis Economic system business |
| Zdroj: | Patterns, Vol 2, Iss 1, Pp 100169-(2021) Patterns, 2, 100169 Patterns |
| ISSN: | 2666-3899 |
| DOI: | 10.5445/ir/1000128185 |
| Popis: | Summary 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. Graphical Abstract Highlights • We assess coverage of the German Renewable Energy Act in newspapers over 18 years • Change-point analysis enables structural topic modeling to capture temporal dynamics • We introduce the notion of topic sentiment to assess the emotional content of topics • Positive accounts of the renewable industry shift to costs imposed on society The Bigger Picture Worldwide, policymakers push for a faster adoption of renewable energy technologies to mitigate climate change. Although policies that support the adoption of new technologies often have positive effects on innovation and job creation in an industry, they also involve costs borne by society. Media representations often have effects on public opinion on a policy. To understand how media reports on the German Renewable Energy Act developed over time, we developed advanced text mining models. We find that media coverage has shifted from positive accounts of the renewable energy industry toward the costs that the Renewable Energy Act imposes on society. If such patterns generalize, then public support and long-term renewable goals might be endangered. We propose that policies could be designed so that new innovative technologies, such as batteries or power-to-gas, and the optimism created by new technologies rub off onto "old" renewables to maintain broad public support. A structural topic model is developed to assess the temporal dynamics of topic prevalence and sentiment in newspaper coverage of the German Renewable Energy Act. The results show that coverage followed a pattern similar to issue-attention cycles. Newspapers predominantly reported on the renewable energy industry until, in 2012, framing changed, and from then on, costs dominated the agenda. The shift in framing can affect political leverage in reaching more ambitious renewable energy targets. |
| Databáze: | OpenAIRE |
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