Rebound Effects in Methods of Artificial Intelligence

Martina Willenbacher (), Torsten Hornauer and Volker Wohlgemuth
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Martina Willenbacher: Leuphana University Lüneburg, Institute of Environmental Communication
Torsten Hornauer: University of Applied Sciences HTW Berlin
Volker Wohlgemuth: University of Applied Sciences HTW Berlin

A chapter in Advances and New Trends in Environmental Informatics, 2022, pp 73-85 from Springer

Abstract: 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.

Keywords: Artificial intelligence; Rebound-effect; Resource and energy efficiency (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:spr:prochp:978-3-030-88063-7_5

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DOI: 10.1007/978-3-030-88063-7_5

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