估算AI使用产生的温室气体排放：企业级测量框架
Estimating GHG Emissions from AI Use: Framework for Corporate-Level Measurement
AI总结：
针对企业AI排放核算缺乏公认方法的问题，提出标准化企业级AI排放核算框架，以支撑减排目标设定与脱碳决策。
AI中文摘要：
数据中心的电力需求预计将从2025年占美国总用电量的约5%增长到2030年的9%至17%，企业级人工智能（AI）的使用也遵循类似轨迹，涵盖员工生产力助手、直接访问大语言模型（LLM）以及嵌入企业软件的AI功能。当前AI排放在许多企业的碳足迹中占比很小，但这一占比不太可能长期保持低位。若缺乏合理估算，随着排放增长，企业将无法设定减排目标或确定有效的脱碳杠杆。企业、监管机构和审计师正要求经得住审查的排放估算，但目前尚无广泛认可的方法。已发表的按查询量估算的结果可能相差几个数量级，具体取决于统计范围、所测量的供应商以及对电力使用和电网结构的假设。本白皮书提出了一种企业级AI排放核算的标准化框架，该框架旨在在当前数据约束下具备合理性，按企业数据情况分层设计，明确披露其假设，可随供应商披露信息的完善而更新，且旨在服务于行动而非仅用于披露。由于AI排放核算仍处于初期阶段，它有机会从一开始就设计为具备可操作性，从而使测量在AI快速发展过程中激励负责任的选择。
英文摘要：
Electricity demand from data centers is expected to grow from roughly 5% of U.S. consumption in 2025 to between 9% and 17% by 2030, and corporate artificial intelligence (AI) use is following a similar trajectory, spanning employee productivity assistants, direct access to large language models (LLMs), and AI features embedded in enterprise software. AI emissions today are a small share of footprints for many enterprises, but that share is unlikely to remain small for long. Without reasonable estimates, companies cannot set reduction targets or identify effective decarbonization levers as emissions grow. Companies, regulators, and auditors are asking for emissions estimates that withstand scrutiny, but no widely accepted methodology exists today. Published per-query estimates can differ by several orders of magnitude depending on what is counted, which provider is measured, and what assumptions are made about electricity use and the grid mix. This white paper proposes a standardized framework for corporate-level AI emissions accounting. The framework is designed to be defensible with current data constraints, tiered to meet companies where their data are, transparent about its assumptions, updatable as provider disclosure matures, and built for action rather than disclosure alone. Since AI emissions accounting is still nascent, it has the opportunity to design for actionability from the outset, so that measurement incentivizes responsible choices during AI's rapid buildout.
