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Rebound Effects in Methods of Artificial Intelligence

In: Advances and New Trends in Environmental Informatics

Author

Listed:
  • Martina Willenbacher

    (Leuphana University Lüneburg, Institute of Environmental Communication
    University of Applied Sciences HTW Berlin)

  • Torsten Hornauer

    (University of Applied Sciences HTW Berlin)

  • Volker Wohlgemuth

    (University of Applied Sciences HTW Berlin)

Abstract

Artificial intelligence (AI) is one of the pioneering driving forces of the digital revolution in terms of the areas of application that already exist and those that are emerging as potential. On the technical side, this paper deals with the energy requirements of artificial intelligence processes. It also identifies efficiency approaches in this sector. Increases in productivity often lead to an increased demand for energy, which is contrary to sustainability in terms of reducing CO2 emissions. Therefore, it will be examined to what extent rebound effects can reduce the savings potential for energy in relation to methods of artificial intelligence and what the main factors of CO2 emissions are.

Suggested Citation

  • Martina Willenbacher & Torsten Hornauer & Volker Wohlgemuth, 2022. "Rebound Effects in Methods of Artificial Intelligence," Progress in IS, in: Volker Wohlgemuth & Stefan Naumann & Grit Behrens & Hans-Knud Arndt (ed.), Advances and New Trends in Environmental Informatics, pages 73-85, Springer.
    • Handle: RePEc:spr:prochp:978-3-030-88063-7_5
    DOI: 10.1007/978-3-030-88063-7_5
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