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In the digital transformation era, the widespread adoption of AI demands increased computational power, leading to higher energy consumption and CO2 emissions, particularly evident in Germany’s energy sector, which contributes to one-third of industrial CO2 emissions. To address this, AI algorithms must become more sustainable and efficient. This paper’s systematic literature analysis identifies key criteria for sustainable algorithms, including transparency, explainable AI (XAI), efficiency, privacy, and sustainable strategy management. By adopting sustainable AI frameworks, companies can optimize energy use and resources. Enterprises must adapt their AI infrastructures, clarify AI’s role, raise awareness, and establish structured responsibility within C-level management and development teams to leverage AI as a strategic business asset.

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AI at any Price? Systematic Derivation of Quality Criteria to Design AI Algorithms more Sustainably and Efficiently for Economic Processes Illustrated by the German Energy Sector

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  • 发表日期：

    2025年

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  • Daniel Kölzer,
  • Vanessa Just

摘要

In the digital transformation era, the widespread adoption of AI demands increased computational power, leading to higher energy consumption and CO2 emissions, particularly evident in Germany’s energy sector, which contributes to one-third of industrial CO2 emissions. To address this, AI algorithms must become more sustainable and efficient. This paper’s systematic literature analysis identifies key criteria for sustainable algorithms, including transparency, explainable AI (XAI), efficiency, privacy, and sustainable strategy management. By adopting sustainable AI frameworks, companies can optimize energy use and resources. Enterprises must adapt their AI infrastructures, clarify AI’s role, raise awareness, and establish structured responsibility within C-level management and development teams to leverage AI as a strategic business asset.