Intergenerational Green Artificial Intelligence Governance: Operationalising Environmental Accountability in Sustainable Artificial Intelligence Systems

Abstract

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 integrated into legal and regulatory frameworks. This article doctrinally examines Green AI through Edith Brown Weiss's tripartite framework of intergenerational equity comprising conservation of options, conservation of quality and conservation of access. It argues that current AI governance frameworks remain incomplete because they focus primarily on safety, bias, transparency and human oversight while treating environmental sustainability as a secondary or voluntary concern. The article develops an Intergenerational Green AI Accountability Framework that translates intergenerational equity into practical governance indicators, including FLOPs per task, CO 2 e per training run, CO 2 e per inference, kWh per inference, water usage effectiveness, renewable‐energy share, embodied hardware carbon, e‐waste destination and equitable access to green compute. It further proposes a polycentric governance architecture, such as model‐level environmental disclosure, lifecycle assessment, environmental model cards, independent audits, sustainable public procurement and equity‐sensitive international cooperation. The article concludes that AI should not be regarded as sustainable merely because it is deployed for climate or development objectives. It should be considered sustainable only where its own environmental costs are measured, disclosed, reduced and fairly governed.