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In this, the paper explores two key dimensions: legal instruments to make AI greener; and methods to render AI regulation, and the law itself, more sustainable:

I try to make three concrete points:

(1) First, regulating for more sustainable AI: we need to go beyond mere transparency. I consider co-regulation strategies; sustainability by design; restrictions on training data; and consumption caps. One important strategy going forward may be: sustainability impact assessments.

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(2) This regulatory toolkit may then, in a second step, serve as a blueprint for other information technologies and infrastructures facing significant sustainability challenges due to their high GHG emissions, for example: blockchain; Metaverse applications; and data centers.
(3) Most importantly, certain rights we have come to take for granted, such as the right to erasure (Article 17 GDPR), may have to be limited due to sustainability considerations.

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For example, the subjective right to erasure, in some situations, has to be balanced against the collective interest in mitigating climate change. And if retraining an AI model to comply with the right to be forgotten entails the annual energy costs of a small town, we should perhaps refrain from granting that right – or at least factor these sustainability costs into the equation.