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Rebound Effects in Methods of Artificial Intelligence

Research output: Contributions to collected editions/works › Chapter

Standard

Rebound Effects in Methods of Artificial Intelligence. / Willenbacher, Martina; Hornauer, Torsten; Wohlgemuth, Volker.
Advances and New Trends in Environmental Informatics: A Bogeyman or Saviour for the UN Sustainability Goals?. ed. / Volker Wohlgemuth; Stefan Naumann; Grit Behrens; Hans-Knud Arndt. Cham: Springer Schweiz, 2021. p. 73-85.

Research output: Contributions to collected editions/works › Chapter

Harvard

Willenbacher, M, Hornauer, T & Wohlgemuth, V 2021, Rebound Effects in Methods of Artificial Intelligence. in V Wohlgemuth, S Naumann, G Behrens & H-K Arndt (eds), Advances and New Trends in Environmental Informatics: A Bogeyman or Saviour for the UN Sustainability Goals?. Springer Schweiz, Cham, pp. 73-85. https://doi.org/10.1007/978-3-030-88063-7_5

APA

Willenbacher, M., Hornauer, T., & Wohlgemuth, V. (2021). Rebound Effects in Methods of Artificial Intelligence. In V. Wohlgemuth, S. Naumann, G. Behrens, & H.-K. Arndt (Eds.), Advances and New Trends in Environmental Informatics: A Bogeyman or Saviour for the UN Sustainability Goals? (pp. 73-85). Springer Schweiz. https://doi.org/10.1007/978-3-030-88063-7_5

Vancouver

Willenbacher M, Hornauer T, Wohlgemuth V. Rebound Effects in Methods of Artificial Intelligence. In Wohlgemuth V, Naumann S, Behrens G, Arndt HK, editors, Advances and New Trends in Environmental Informatics: A Bogeyman or Saviour for the UN Sustainability Goals?. Cham: Springer Schweiz. 2021. p. 73-85 doi: 10.1007/978-3-030-88063-7_5

Bibtex

@inbook{9c919d665ee849fdbb97434c4d4cb5ac,
title = "Rebound Effects in Methods of Artificial Intelligence",
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 = "Sustainability sciences, Communication, Artificial intelligence, Rebound-effect, Resource and energy efficiency",
author = "Martina Willenbacher and Torsten Hornauer and Volker Wohlgemuth",
year = "2021",
doi = "10.1007/978-3-030-88063-7_5",
language = "English",
isbn = "978-3-030-88062-0",
pages = "73--85",
editor = "Volker Wohlgemuth and Stefan Naumann and Grit Behrens and Hans-Knud Arndt",
booktitle = "Advances and New Trends in Environmental Informatics",
publisher = "Springer Schweiz",
address = "Switzerland",

}

RIS

TY - CHAP

T1 - Rebound Effects in Methods of Artificial Intelligence

AU - Willenbacher, Martina

AU - Hornauer, Torsten

AU - Wohlgemuth, Volker

PY - 2021

Y1 - 2021

N2 - 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.

AB - 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.

KW - Sustainability sciences, Communication

KW - Artificial intelligence

KW - Rebound-effect

KW - Resource and energy efficiency

U2 - 10.1007/978-3-030-88063-7_5

DO - 10.1007/978-3-030-88063-7_5

M3 - Chapter

SN - 978-3-030-88062-0

SN - 978-3-030-88065-1

SP - 73

EP - 85

BT - Advances and New Trends in Environmental Informatics

A2 - Wohlgemuth, Volker

A2 - Naumann, Stefan

A2 - Behrens, Grit

A2 - Arndt, Hans-Knud

PB - Springer Schweiz

CY - Cham

ER -

DOI

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