AI 中文总结
研究指出欧盟数据中心环境监管中PUE和WUE基准存在“效率悖论”，提出通过个体报告和政策干预展示生态效益的策略，并给出揭示认证效率提升、记录权衡及监测收益递减等三项政策建议，以平衡可持续性与人工智能创新。
AI 中文摘要
在人工智能时代，数据中心给电网和淡水资源带来巨大环境负担。欧盟重新制定的能源效率指令要求数据中心运营商量化、报告和披露设施层面的能源和水影响，这看似朝着提高透明度和问责制迈出了正确一步。然而，两个经重新制定的能源效率指令批准的基准——能源使用效率（PUE）和水使用效率（WUE）——可能会产生偏差，给人一种效率提升的错觉，当前欧盟推动可持续超大规模数据中心扩张的政策似乎有误。本文认为当前的PUE和WUE报告框架存在“效率悖论”，即积极得分需要以牺牲能源供应和人们的用水权为代价对大型人工智能数据中心进行改造。应对这一悖论需要数据中心运营商和欧盟委员会制定新策略，通过个体报告和额外政策干预来展示优化效率带来的生态效益。本文提出三项政策建议：揭示和认证效率提升的措施；记录PUE和WUE提升中的权衡；监测其随时间的收益递减和反作用的框架。实施这些措施将确保重新制定的能源效率指令共同评级计划符合目的，在可持续性与人工智能创新之间为当地社区取得平衡。
英文摘要
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.
