Explained: Generative AI’s environmental impact"Rapid development and deployment of powerful generative AI models comes with environmental consequences, including increased electricity demand and water consumption."
Adam Zewe | MIT News
Publication Date:January 17, 2025
The AI-environment paradox: Unraveling the impact of artificial intelligence (AI) adoption on pro-environmental behavior through work overload and self-efficacy in AI learningThis study examines the complex relationships among artificial intelligence (AI) adoption in organizations, employee work overload, and pro-environmental behavior at work (PEBW), while examining the moderating role of self-efficacy in AI learning. Drawing on several theories, we developed and tested a moderated mediation model utilizing a 3-wave time-lagged survey of 416 employees from diverse South Korean corporations. Our findings reveal that the link between AI adoption and PEBW is fully mediated by work overload, with AI adoption positively influencing work overload, which in turn negatively affects PEBW. Importantly, self-efficacy in AI learning moderates the AI adoption-work overload link, such that the positive influence is weaker for members with higher levels of self-efficacy. These results highlight the unintended consequences of AI adoption on environmental behaviors and underscore the significance of individual differences in shaping responses to technological change. The current research contributes to the literature by elucidating the mechanisms through which AI adoption influences PEBW and by identifying factors that can mitigate potential negative effects. The findings offer meaningful perspectives for organizations aiming to balance technological advancement with environmental sustainability goals, emphasizing the need for strategies that enhance members’ self-efficacy in AI learning and manage workload effectively. This paper advances our knowledge of the complex interplay between technological adoption, work experiences, and pro-environmental behaviors in contemporary organizational settings. •AI adoption indirectly affects pro-environmental behavior at work via work overload.•Work overload fully mediates AI adoption-PEBW relationship.•Self-efficacy in AI learning moderates AI adoption's effect on work overload.•Three-wave time-lagged study with 416 South Korean employees.•Integrates social cognitive theory, job demands-resources, and context-attitudes-behavior frameworks.
Unraveling the Hidden Environmental Impacts of AI Solutions for Environment Life Cycle Assessment of AI Solutions"In the past ten years, artificial intelligence has encountered such dramatic progress that it is now seen as a tool of choice to solve environmental issues and, in the first place, greenhouse gas emissions (GHG). At the same time, the deep learning community began to realize that training models with more and more parameters require a lot of energy and, as a consequence, GHG emissions. To our knowledge, questioning the complete net environmental impacts of AI solutions for the environment (AI for Green) and not only GHG, has never been addressed directly. In this article, we propose to study the possible negative impacts of AI for Green. First, we review the different types of AI impacts; then, we present the different methodologies used to assess those impacts and show how to apply life cycle assessment to AI services. Finally, we discuss how to assess the environmental usefulness of a general AI service and point out the limitations of existing work in AI for Green."
