The Global Landscape of Environmental AI Regulation: From the Cost of Reasoning to a Right to Green AI

2026-02-10

arXiv.org

AI Summary

This study addresses the growing environmental costs of generative artificial intelligence, particularly the lack of transparency surrounding model-level energy consumption and the ecological impacts of inference—a critical gap in current regulatory frameworks. Through a cross-jurisdictional policy comparison, empirical assessment of environmental footprints, regulatory text analysis, and policy modeling, this work provides the first systematic account of the cumulative environmental burden incurred during the inference phase of generative AI. It introduces the novel concept of a “right to green AI” and proposes a three-pillar policy framework centered on model transparency, user rights to environmentally sustainable choices, and international regulatory coordination. The framework informs targeted amendments to key legislation, including the EU AI Act, offering a practical, transnational legislative template for environmentally responsible AI governance.

Abstract

Artificial intelligence (AI) systems impose substantial and growing environmental costs, yet transparency about these impacts has declined even as their deployment has accelerated. This paper makes three contributions. First, we collate empirical evidence that generative Web search and reasoning models - which have proliferated in 2025 - come with much higher cumulative environmental impacts than previous generations of AI approaches. Second, we map the global regulatory landscape across eleven jurisdictions and find that the manner in which environmental governance operates (predominantly at the facility-level rather than the model-level, with a focus on training rather than inference, with limited AI-specific energy disclosure requirements outside the EU) limits its applicability. Third, to address this, we propose a three-pronged policy response: mandatory model-level transparency that covers inference consumption, benchmarks, and compute locations; user rights to opt out of unnecessary generative AI integration and to select environmentally optimized models; and international coordination to prevent regulatory arbitrage. We conclude with concrete legislative proposals - including amendments to the EU AI Act, Consumer Rights Directive, and Digital Services Act - that could serve as templates for other jurisdictions.

Problem

• Environmental AI
• AI regulation
• Generative AI
• Inference consumption
• Model-level transparency

Innovation

• environmental AI regulation
• model-level transparency
• green AI
• regulatory arbitrage
• inference energy consumption