This commentary argues that the artificial intelligence (AI) boom is not immaterial but relies on energy- and resource-intensive infrastructures. While “Green AI” scholarship has advanced model-level efficiency metrics and reporting, it has largely overlooked the material circuits that enable these models. We explain how efficiency-first framings are reinforced by an ideological blend of cybertarianism and techno-nationalism, which together reframe environmental and labor externalities as acceptable costs of competition. We propose three priorities for a more adequate agenda: (1) adopt the “trash metaphor” to highlight the afterlives and externalities of AI infrastructures; (2) implement structural solutions that oversee and regulate the entire material circuits of AI; and (3) foster green citizenship through public information rights, participatory siting, and ongoing civic oversight to advocate for and sustain the necessary structural changes. Overall, we call for aligning AI's claimed climate benefits with demonstrable compliance to ecological budgets rather than aspirational efficiency narratives.
This paper addresses the “AI-Green Paradox,” wherein opaque or biased algorithms can inadvertently undermine environmental, social, and governance (ESG) outcomes, and provides a highly specific, condition-aware “Monday Morning Checklist” for C-suite executives and policymakers.
Zhiyin Xiao, Guangfei Wu· Strategy & Leadership· 0 citations
An integrated governance framework that incorporates MacIntyrean categories of practices, institutions, and traditions while explicitly addressing the environmental, socio-cultural, economic, and organizational dimensions of the common good is developed, illustrating how virtues can guide the responsible design, deploy...
Dulce M. Redín, M. C. Ames· Journal of Business Ethics· 0 citations
Artificial intelligence (AI) is now being used as a core technology in global climate change mitigation efforts, with evidence showing that AI data-center cooling optimization reduced energy use by 40 percent in data centers, the share of renewables in the grid increased from around 27 to 29 percent between 2019 and 20...
Hamid Raza Malik, Abdul Basıt, N. Nawaz et al.· International Journal of Inn...· 0 citations
Artificial intelligence is increasingly promoted as a tool for sustainable development, climate governance and resource optimisation. Nevertheless, the environmental footprint of AI systems, including energy consumption, carbon emissions, water use, hardware production and electronic waste, remains insufficiently i...
Parul Shukla, J. S. Magoge, Ishita Chatterjee et al.· Applied AI Letters· 0 citations
This paper argues that most responsible AI (RAI) governance frameworks do not meaningfully address AI’s environmental and social costs, and proposes the concept of critical AI governance, which rests on three overarching principles: critically questioning dominant ideologies and economic imperatives, prioritizing socia...
Paul Schuetze, B. Brevini· AI & SOCIETY· 0 citations
A three-pillared holistic and intentional policy approach that actively pushes for the adoption of cooperative technologies in existing and emerging systems, sets up collective institutional structures to sustain and maintain them in the long-term, and introduces regulatory proposals that limit private entities from di...
Abhineet Nayyar, M. Knodel, Kimberlee Weatherall et al.· 0 citations
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