生成式AI环境成本飙升，亟需模型级透明与全球协同监管
The Global Landscape of Environmental AI Regulation: From the Cost of Reasoning to a Right to Green AI
- 分析2025年生成式搜索与推理模型的环境影响，揭示其累积排放远超前代
- 全球11个司法管辖区监管多聚焦训练阶段，缺乏对推理能耗的明确要求
- 提出模型级透明、用户绿色选择权及国际协调三策，可借鉴欧盟立法模板
人工智能系统带来日益增长的环境代价，但对其影响的透明度却在下降。本文有三项贡献：首先，整理实证证据表明，2025年流行的生成式网络搜索与推理模型相较前代具有更高的累积环境影响；其次，对11个司法管辖区的全球监管格局进行映射，发现当前环境治理主要集中在设施层面而非模型层面，侧重训练阶段而非推理阶段，且除欧盟外普遍缺乏针对AI的能源披露要求，适用性受限；第三，为应对该问题，提出三方面政策建议：强制模型级透明（涵盖推理能耗、基准测试与算力位置）、用户有权拒绝非必要生成式AI集成并选择环境友好型模型、推动国际协调以防止监管套利。最后提出具体立法提案，包括对欧盟《人工智能法案》《消费者权利指令》和《数字服务法》的修订，可作为其他地区参考模板。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。