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

arXiv: https://arxiv.org/abs/2603.00068 · HTML: https://mlsanity.com/paper/2603.00068/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 20 of its 121 references that are in the map.
- First posted on arXiv 10 Feb 2026.

## Builds on (most cited first, 20 of 20)

- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Energy and Policy Considerations for Deep Learning in NLP](https://mlsanity.com/paper/1906.02243/) (2019)
- [Green AI](https://mlsanity.com/paper/1907.10597/) (2019)
- [Estimating the Carbon Footprint of BLOOM, a 176B Parameter Language Model](https://mlsanity.com/paper/2211.02001/) (2022)
- [Power Hungry Processing: Watts Driving the Cost of AI Deployment?](https://mlsanity.com/paper/2311.16863/) (2023)
- [A Systematic Review of Green AI](https://mlsanity.com/paper/2301.11047/) (2023)
- [Hype, Sustainability, and the Price of the Bigger-is-Better Paradigm in AI](https://mlsanity.com/paper/2409.14160/) (2024)
- [Measuring the environmental impact of delivering AI at Google Scale](https://mlsanity.com/paper/2508.15734/) (2025)
- [From Efficiency Gains to Rebound Effects: The Problem of Jevons' Paradox in AI's Polarized Environmental Debate](https://mlsanity.com/paper/2501.16548/) (2025)
- [An Ontology of Dark Patterns Knowledge: Foundations, Definitions, and a Pathway for Shared Knowledge-Building](https://mlsanity.com/paper/2309.09640/) (2023)
- [Algorithms as Social-Ecological-Technological Systems: an Environmental Justice Lens on Algorithmic Audits](https://mlsanity.com/paper/2305.05733/) (2023)
- [KV Cache Compression for Inference Efficiency in LLMs: A Review](https://mlsanity.com/paper/2508.06297/) (2025)
- [Towards Sustainable NLP: Insights from Benchmarking Inference Energy in Large Language Models](https://mlsanity.com/paper/2502.05610/) (2025)
- [More than Carbon: Cradle-to-Grave environmental impacts of GenAI training on the Nvidia A100 GPU](https://mlsanity.com/paper/2509.00093/) (2025)
- [AI, Climate, and Regulation: From Data Centers to the AI Act](https://mlsanity.com/paper/2410.06681/) (2024)
- [Understanding Efficiency: Quantization, Batching, and Serving Strategies in LLM Energy Use](https://mlsanity.com/paper/2601.22362/) (2026)
- [Video Killed the Energy Budget: Characterizing the Latency and Power Regimes of Open Text-to-Video Models](https://mlsanity.com/paper/2509.19222/) (2025)
- [Imposing AI: Deceptive design patterns against sustainability](https://mlsanity.com/paper/2508.08672/) (2025)
- [From FLOPs to Footprints: The Resource Cost of Artificial Intelligence](https://mlsanity.com/paper/2512.04142/) (2025)
- [On the Effectiveness of Proposed Techniques to Reduce Energy Consumption in RAG Systems: A Controlled Experiment](https://mlsanity.com/paper/2601.02522/) (2026)

## Abstract (arXiv)

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.