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Rebound Effects in Methods of Artificial Intelligence

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Willenbacher, Martina, Torsten Hornauer, and Volker Wohlgemuth. 2022. “Rebound Effects in Methods of Artificial Intelligence.” In Advances and New Trends in Environmental Informatics, edited by Volker Wohlgemuth, Stefan Naumann, Grit Behrens, and Hans-Knud Arndt, 73–85. Cham: Springer International Publishing. https://doi.org/10.1007/978-3-030-88063-7_5.

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.

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