AI laws overlook environmental damage – here’s what needs to change

2026-04-02 · Source: Artificial intelligence (AI) – The Conversation · Field: Legal & Regulatory — Regulatory Affairs & Government Relations, Environmental Law & Policy · Depth: Novice, short

Summary

The environmental impact of Artificial Intelligence (AI) is a significant concern, often overlooked by current regulatory frameworks. AI is an energy-intensive and water-thirsty industry, contributing to greenhouse gas emissions, pollution, and nature loss throughout its lifecycle, from hardware manufacturing to model training, deployment, and disposal. For instance, training OpenAI's GPT-3 in 2020 consumed an estimated 700,000 litres of freshwater. While AI models are becoming more energy-efficient, the proliferation and increasing size of models lead to rising overall energy consumption and emissions, with AI use vastly outweighing training in energy demand. Existing regulations, such as the EU's AI Act, which came into force on August 1, 2024, acknowledge some environmental consequences and require disclosure of energy consumption data upon request, but lack compulsory measures for sustainability. The UK's approach, outlined in its 2023 white paper, explicitly excludes sustainability from its proposed AI regulatory framework.

Key takeaway

For CTOs and VPs of Engineering evaluating AI adoption, recognize that the environmental costs of AI are substantial and largely unregulated. Your teams should prioritize transparency in AI development by tracking and disclosing energy, water, and carbon footprints. Advocate for and implement internal "energy star" rating systems for AI models to inform deployment decisions, potentially leveraging tax or funding incentives for more sustainable practices, rather than waiting for comprehensive external regulation.

Key insights

AI's substantial environmental footprint is largely unaddressed by current global regulatory efforts.

Principles

• AI lifecycle impacts environment
• Transparency enables sustainability
• Regulation shapes AI's footprint

Method

A proposed method involves mandatory disclosure of AI's energy, water, carbon, and material consumption, establishing baselines, setting targets, and implementing labelling systems and incentives for sustainable AI development and use.

In practice

• Train AI on less carbon-intensive grids
• Use less water-intensive data centers
• Implement AI "energy star" ratings

Topics

• AI Environmental Impact
• AI Regulatory Frameworks
• EU AI Act
• Data Center Energy Consumption
• Water Footprint

Best for: CTO, VP of Engineering/Data, Director of AI/ML, Policy Maker, Legal Professional, Consultant

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial intelligence (AI) – The Conversation.