这项研究为欧洲能源规划者提供了AI数据中心扩张的量化风险图，做电力系统或气候政策的团队可以直接参考其情景分析来调整基础设施投资策略。
一项研究利用空间显式优化模型，模拟了21种AI增长情景下欧洲数据中心的能源需求。结果显示，到2050年AI可能带来73-723太瓦时的额外电力需求，并在2030-2050年间导致67-181百万吨二氧化碳的累积排放超调。研究指出，2030年后AI基础设施选址将更受稳定电源和系统灵活性的影响，而非仅依赖清洁能源丰富度。在中等情景下，AI需要额外200小时的稳定发电，使关键枢纽的平准化度电成本增加35欧元/兆瓦时。即使悲观情景下，现有基础设施也需要新增70吉瓦容量，而管理增长路径下可能达到226吉瓦。研究认为，虽然2050年净零目标可能实现，但中期排放风险显著，欧盟需调整政策以应对数字化转型。
Powering the Future of AI: Navigating the Trade-offs for Europe's Energy Transition and Net-Zero Goals
The rapid expansion of AI globally has led to the proliferation of energy-intensive hyperscale data centres (DCs), making them as a structurally challenging component in power system planning and operation. Using a spatially explicit optimisation model of Europe across 21 AI growth scenarios, we systematically quantify additional demand, capacity requirements, emissions, and operational impacts of DCs. Results indicate that AI could drive 73-723 TWh of extra demand by 2050, risking cumulative emissions overshoots of 67-181 MtCO2 between 2030 and 2050. Our analysis indicates that after 2030, the geography of AI infrastructure will be shaped more by firm power and system flexibility than by the mere abundance of clean energy. In moderate scenarios, AI requires an additional of 200 hours of firm generation, which increases LCOE by 35 EUR/MWh in key hubs. We show that even under the pessimistic scenarios, existing infrastructure would require 70 GW additional capacity, while under managed growth pathways, this expansion could reach 226 GW. We further find DCs workload dynamics strongly shape energy dispatch, system flexibility, and emissions, while improved efficiency significantly reduces capacity needs, and system peaks. While our findings suggest that net-zero targets for 2050 may be achieved, critical emission risks may appear in the intermediate years, and the EU may compromise its carbon-neutral goals unless policies adapt to this accelerating digital transformation.