@article{MuellerHansenCallaghanLeeetal.2021,
  author    = {M{\"u}ller-Hansen, Finn and Callaghan, Max W. and Lee, Yuan Ting and Leipprand, Anna and Flachsland, Christian and Minx, Jan C.},
  title     = {Who cares about coal? : Analyzing 70 years of German parliamentary debates on coal with dynamic topic modeling},
  journal   = {Energy research and social science},
  volume    = {72},
  doi       = {10.1016/j.erss.2020.101869},
  url       = {http://nbn-resolving.de/urn:nbn:de:bsz:wup4-opus-76902},
  year      = {2021},
  abstract  = {Despite Germany's Paris Agreement pledge and coal exit legislation, the political debate around carbon-intensive coal remains heated. Coal power and mining have played an important, yet changing role in the history of German politics. In this paper, we analyze the entire parliamentary debate on coal in the German parliament (Bundestag) from its inception in 1949 to 2019. For this purpose we extract the more than 870,000 parliamentary speeches from all protocols in the history of the Bundestag. We identify the 9167 speeches mentioning coal and apply dynamic topic modeling – an unsupervised machine learning technique that reveals the changing thematic structure of large document collections over time - to analyze changes in parliamentary debates on coal over the past 70 years. The trends in topics and their varying internal structure reflect how energy policy was discussed and legitimized over time: Initially, coal was framed as a driver of economic prosperity and guarantee of energy security. In recent years, the debate evolved towards energy transition, coal phase-out and renewable energy expansion. Germany’s smaller and younger parties, the Greens and the Left Party, debate coal more often in the context of the energy transition and climate protection than other parties. Our results reflect trends in other countries and other fields of energy policy. Methodologically, our study illustrates the potential of and need for computational methods to analyze vast corpora of text and to complement traditional social science methods.},
  language  = {en}
}