Environmental Impact of AI Training
environmental_impact_of_training · AI-governance topic
Environmental Impact of AI Training is energy consumption, water usage, carbon emissions, and resource demands of large-model training + inference. EU AIA Recital 142 + Art. 95 voluntary codes; NIST AI 600-1 Environmental Impacts (named risk category); G7 Hiroshima Code §6 sustainable AI; emerging French ARCEP + Spanish AI Bill obligations; SDG-linked references in UN + AU + ASEAN frameworks. Across 29 tracked AI-governance instruments, 1 address this topic explicitly, 8 via general principles, and 20 are silent.
Definition and scope
Energy consumption, water usage, carbon emissions, and resource demands of large-model training + inference. EU AIA Recital 142 + Art. 95 voluntary codes; NIST AI 600-1 Environmental Impacts (named risk category); G7 Hiroshima Code §6 sustainable AI; emerging French ARCEP + Spanish AI Bill obligations; SDG-linked references in UN + AU + ASEAN frameworks.
The cross-jurisdiction picture below shows how each of 29 tracked instruments treats this topic. The patterns vary substantially — and 20 regimes are silent, leaving gaps that future policy work could address.
Historical primacy & cross-jurisdiction tension
First addressed by OECD AI Principles (Recommendation) on (implicit). Subsequent regimes have either codified, diverged from, or remained silent on this baseline.
Cross-jurisdiction coverage
At a glance — same legend as the hub matrix
Silent regimes — gap signal
Instruments that do not address Environmental Impact of AI Training — candidates for future policy work.
- Executive Order 14179 — Removing Barriers to American Leadership in AIUS
- UK Pro-Innovation Approach to AI Regulation (White Paper)UK
- Interim Measures for Generative AI Service ManagementCN
- NIST AI Risk Management FrameworkUS
- Bletchley Declaration on AI Safetyglobal
- Seoul Declaration on Safe, Innovative and Inclusive AIglobal
- California SB-1047: Safe and Secure Innovation for Frontier AI Models ActUS
- India Digital Personal Data Protection Act + AI Advisory (MEITY)IN
- Brazil AI Bill (PL 2338/2023)BR
- Anthropic Responsible Scaling Policy (RSP) v2US
- OpenAI Preparedness FrameworkUS
- Google DeepMind Frontier Safety FrameworkUS
- Meta Frontier AI FrameworkUS
- UK-US AI Safety Institute Memorandum of Understandingglobal
- White House Voluntary AI CommitmentsUS
- Singapore Model AI Governance Framework for Generative AISG
- Japan METI AI Guidelines for BusinessJP
- NYC Local Law 144 of 2021 (Automated Employment Decision Tools)US
- Colorado Artificial Intelligence Act (SB 24-205)US
- Illinois HB 3773 / Public Act 103-0804 (AI Employment Discrimination)US
References
- EU-AIA-2024: Art. 95 voluntary codes of conduct include environmental sustainability; Recital 142 references energy efficiency reporting for GPAI
- US-EO-14110: §5.2 directs environmental-review consideration; §4.2 reporting includes some energy data
- G7-HIROSHIMA: Code §6 references sustainable AI development; not detailed obligation
- OECD-AI-PRIN: Principle 1.1 inclusive growth + sustainable development; addresses environment implicitly
- COE-AI-CONV: Art. 7 sustainability principle; environmental impact subsumed
- UN-RES-2024: Preamble references SDGs which include climate goals
- NIST-AI-RMF-GENAI: NIST AI 600-1 — Environmental Impacts is one of 12 named GenAI risk categories
- ASEAN-AI-GUIDE-2024: Guide references sustainable AI principles; not operationalised
- AU-AI-STRATEGY-2024: Continental strategy includes sustainability themes; not operationalised
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9 instruments tracked.