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<p>Generative artificial intelligence (GenAI) is widely used in the gig economy and electronic markets. However, its progress incurs substantial environmental costs due to its high and constantly increasing energy consumption. As inference—the process by which GenAI generates output—accounts for a large part of this demand, individual usage patterns are becoming a key factor in understanding the overall energy footprint and a crucial prerequisite for developing effective educational strategies and policies. Drawing from the construal-level theory of psychological distance, this study explores individuals’ perceptions related to GenAI energy consumption. Based on a nationally representative sample of 1080 participants from the USA, we provide evidence that individuals are aware of GenAI-related energy consumption. Moreover, we show that, compared to household appliances, there is a diffusion of responsibility for GenAI’s energy consumption, with more emphasis placed on policy-makers and developers than on users. We also explore how individuals intend to implement pro-environmental strategies and shed light on different avenues suggested by individuals to lower energy consumption when using GenAI. We conclude with implications for theory, practice, and policymaking.</p>

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Public perceptions of AI energy consumption: Awareness, responsibility attribution, and strategies

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年6月25日

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  • Michael Klesel,
  • Uwe Messer

摘要

Generative artificial intelligence (GenAI) is widely used in the gig economy and electronic markets. However, its progress incurs substantial environmental costs due to its high and constantly increasing energy consumption. As inference—the process by which GenAI generates output—accounts for a large part of this demand, individual usage patterns are becoming a key factor in understanding the overall energy footprint and a crucial prerequisite for developing effective educational strategies and policies. Drawing from the construal-level theory of psychological distance, this study explores individuals’ perceptions related to GenAI energy consumption. Based on a nationally representative sample of 1080 participants from the USA, we provide evidence that individuals are aware of GenAI-related energy consumption. Moreover, we show that, compared to household appliances, there is a diffusion of responsibility for GenAI’s energy consumption, with more emphasis placed on policy-makers and developers than on users. We also explore how individuals intend to implement pro-environmental strategies and shed light on different avenues suggested by individuals to lower energy consumption when using GenAI. We conclude with implications for theory, practice, and policymaking.