Carbon Emissions from Model Training
模型訓練碳排放
Inception
Risk Description
When the energy consumption and carbon emissions from training large models are considerable and grow rapidly with model scale; due to unmitigated control gaps, under decarbonization pressure, an organization's AI-related carbon footprint faces increasing scrutiny from investors, customers, and regulators, yet is often excluded from existing environmental disclosure scope, triggering compliance exposure and operational reputational costs.
Framework Mappings
NIST AI 600-1Environmental Impacts
ISO/IEC 42005影響評估流程
ISO/IEC 5338啟動
MIT AI Risk RepositoryDomain 6
Risk Treatment & Implementation Guidance
Include AI-related compute carbon emissions in inventory scope with a reliable footprint quantification method; Optimize energy efficiency to reduce training and inference consumption; Procure green power and set reduction plans to support credible sustainability disclosure