全球能源需求和人工智能碳足迹的基于场景的预测
Berke M Turkay1, Ipek Pehlivan2, Nuri C Onat3
1Mitchell E. Daniels, Jr. School of Business, Purdue University, West Lafayette, Indiana, United States of America.
PloS one
|March 11, 2026
概括
人工智能 (AI) 的能源使用可能会在2050年之前使全球需求增加三倍. 将高效的AI模型与清洁能源相结合,可以将排放量减少40%,减轻AI日益增长的碳足迹.
科学领域:
- 环境科学环境科学
- 计算机科学 计算机科学
- 能源政策 能源政策
背景情况:
- 人工智能 (AI) 是一个快速发展的技术,具有显著的,但不够量化的,长期的能源和排放影响.
- 全球电力消耗日益受到人工智能的驱动,需要更深入地了解其对环境的影响.
研究的目的:
- 开发一个基于场景的建模框架,模拟到2050年的人工智能的全球能源使用和碳排放.
- 量化不同人工智能开发途径和电力组合对未来能源需求和排放的影响.
主要方法:
- 一个基于模拟的建模框架,将AI计算需求与生命周期碳核算联系起来.
- 考虑模型规模,部署结构和电网组成的替代开发途径的评估.
- 情景分析预测到2050年的人工智能能源使用和排放.
主要成果:
- 到2050年,人工智能对电力的需求可能会增加一个数量级,可能达到全球需求的30%,并每年造成超过8千亿的二氧化碳相当的排放.
- 硬件和算法的效率提升减少了每次操作的能量,但不足以抵消总需求的增长.
- 乐观的效率场景仍然显示,人工智能电力需求比2024年的水平高出六倍以上.
结论:
- 巩固人工智能模型和向低碳电力来源过渡可以将人工智能相关的排放量减少高达40%,仅仅超过效率增长.
- 关于人工智能部署和电力脱碳的政策选择对于管理计算的碳强度至关重要.
- 人工智能的环境影响预计将存在显著的区域差异,这强调了有针对性的政策干预的必要性.
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