发表机构
Badji Mokhtar University; Chadli Bendjedid University（巴吉·穆赫塔尔大学; 沙德利·本杰迪德大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文系统分析生成式与智能体AI全生命周期的能耗、碳排放、水耗和电子废弃物，提出生命周期分类法、SAFIA评估框架及政策建议，揭示推理能耗可超训练、智能体放大能耗等问题，呼吁智能体感知测量与强制披露。
AI 中文摘要
生成式和智能体人工智能（AI）系统的激增引入了巨大的计算需求，其环境后果虽重大却未得到充分审视。本文考察了现代AI系统在能源消耗、碳排放、水资源使用以及电子废弃物方面的环境足迹，覆盖大型语言模型、多模态基础模型和智能体工作流的全生命周期，从硬件制造和训练，经过微调和推理，直至报废处理。本研究提供了一种概念性分析，利用基于已发表数据的数量级估算，未进行原始物理测量。我们贡献了一个生命周期分类法，将生命周期阶段与五个影响维度和排放范围交叉结合；一个包含九项指标的AI系统可持续性评估框架（SAFIA）；对传统AI、生成式AI和智能体AI的比较分析；七个开放挑战；针对监管机构、云提供商、硬件制造商和AI开发者的政策建议；以及一份至2035年的研究路线图。分析表明，在部署生命周期内，推理能耗可与训练能耗相媲美甚至超过后者；智能体工作流可将等效单次推理的能耗乘以一至几个数量级，具体取决于模型和工具调用的次数；间接用水和隐含碳被系统性低估；测量工具、披露实践和监管尚未跟上智能体部署的步伐。这些发现呼吁采用智能体感知的能源测量、基于生命周期的碳和水核算，以及对大规模AI训练和部署的强制性披露。
英文摘要
The proliferation of generative and agentic artificial intelligence (AI) systems has introduced computational demands whose environmental consequences are substantial yet underexamined. This paper examines the environmental footprint of modern AI systems across energy consumption, carbon emissions, water usage, and electronic waste over the full lifecycle of large language models, multimodal foundation models, and agentic workflows, from hardware fabrication and training through fine-tuning and inference to end-of-life disposal. This work provides a conceptual analysis, utilizing order-of-magnitude estimations based on published data, without conducting original physical measurements. We contribute a lifecycle taxonomy that crosses lifecycle phases with five impact dimensions and emission scopes; a Sustainability Assessment Framework for AI Systems (SAFIA) comprising nine indicators; a comparative analysis of traditional, generative, and agentic AI; seven open challenges; policy recommendations for regulators, cloud providers, hardware manufacturers, and AI developers; and a research roadmap to 2035. The analysis indicates that inference can rival or exceed training energy over a deployment lifetime, that agentic workflows can multiply the energy of equivalent single-pass inference by one to several orders of magnitude depending on the number of model and tool calls, that indirect water use and embodied carbon are systematically underreported, and that measurement tools, disclosure practices, and regulation have not kept pace with agentic deployment. These findings call for agent-aware energy measurement, lifecycle-based carbon and water accounting, and mandatory disclosure for large-scale AI training and deployment.
