The Fallacy of Sustainable Generative AI: Limitations in EU Environmental Regulation of Data Centres and Paths Forward
For EU policymakers and data centre operators, the paper highlights flaws in existing sustainability metrics and offers actionable policy proposals, though the analysis is largely conceptual.
The paper critiques current EU environmental regulations for data centres, arguing that metrics like PUE and WUE create an 'efficiency paradox' that masks true ecological costs. It proposes three policy interventions to improve transparency and accountability.
In the age of Artificial Intelligence (AI), Large Language Models, Generative AI and larger frontier AI models, data centres create a significant environmental burden on electricity grids and fresh water resources. Requiring data centre operators and Big Tech under the recast Energy Efficiency Directive (recast EED) to quantify, report and disclose the facility-level energy and water impacts seems to be a step into the right direction towards more transparency and accountability. Yet when two recast EED approved benchmarks - the Power Usage Effectiveness (PUE) and Water Usage Effectiveness (WUE) - can be skewed to create a false sense on efficiency gains, current EU policy pushing for sustainable hyperscale data centre expansion appears misplaced. This paper argues that current PUE and WUE reporting frameworks illustrate what we term the "efficiency paradox," according to which positive scores require retrofitting larger AI data centres at the expense of energy supply and people's water access. Countering this efficiency paradox requires a new strategy for data centre operators and the EU Commission to demonstrate the ecological gains of optimising for efficiency through individual reporting and additional policy interventions. We make three policy proposals to show how this strategy can be formalised in practice: (i) measures to reveal and certify efficiency improvements, (ii) documentation of trade-offs in PUE and WUE improvements and, (iii) a monitoring framework of their diminishing returns and countereffects over time. Implementing these measures will ensure that the recast EED common rating scheme is fit-for-purpose, balancing sustainability with AI innovation for local communities.