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The unseen carbon cost of AI workforce: A behavioral theory perspective of environmental scalability

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  • Lv, David
  • Cho, Erin

Abstract

As artificial intelligence (AI) technologies become increasingly integrated across various sectors, understanding their environmental implications is crucial for achieving sustainable development goals (SDGs). While the direct energy consumption and emissions from computationally intensive AI systems have been explored, the broader indirect environmental effects remain largely unexplored. This study proposes a comprehensive scenario-based framework to assess the potential direct and indirect impacts of widespread AI adoption on emissions and resource consumption. Using a behavioral theory perspective, we highlight the paradoxical risk of unbridled AI growth inadvertently exacerbating resource depletion and emissions, which undermine sustainability objectives. To address this, we conceptualize an environmentally scalable model of AI development and deployment, wherein technological advancement and ecological sustainability are synergistic, generating net positive environmental benefits.

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  • Lv, David & Cho, Erin, 2025. "The unseen carbon cost of AI workforce: A behavioral theory perspective of environmental scalability," Business Horizons, Elsevier, vol. 68(6), pages 759-776.
    • Handle: RePEc:eee:bushor:v:68:y:2025:i:6:p:759-776
    DOI: 10.1016/j.bushor.2025.07.005
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