面向 AI、大数据与信息通信技术的广义绿色与红色概念：多维框架与对策
Generalized green and red concepts for artificial intelligence, big data, and information and communication technologies: Multidimensional framework and countermeasures
AI 导读
一篇 arXiv 论文提出广义绿色 AI（GGAI）与广义红色 AI（GRAI），并将分类扩展至红色大数据、红色信息通信技术及红色电子电气与数字技术，纳入统一的环境-经济-社会-技术/运营框架。
正文 · 原文
Abstract:The rapid expansions of artificial intelligence (AI), big data (BD), and information and communication technologies (ICT) are driving substantial growth in electricity, water, and material demands, with data centers projected to consume 6.7--12\% of U.S. electricity by 2028 (up from 4.4\% in 2023) and AI-related water demand estimated at 4--6 billion m$^3$ annually by 2027. Existing ``green AI'' and ``red AI'' discussions have largely emphasized computational efficiency and carbon emissions, providing limited guidance for lifecycle trade-offs, service-versus-use impacts, and broader sustainability externalities beyond energy. To bridge this gap, this paper proposes generalized green AI (GGAI) and generalized red AI (GRAI) and extends the taxonomy to red BD (RBD) and red ICT (RICT) as well as red electronic and electrical engineering (REEE) \& red digital technologies (RDT) under a unified Environmental-Economic-Social-Technical/Operational (E/Ec/S/T) framework. Based on representative footprint indicators in the large-model era (electricity, carbon, water, and e-waste), we analyze how risks propagate across AI-BD-ICT stacks and across service-use boundaries. We further organize actionable countermeasures into a four-tier taxonomy aligned with the Environmental-Economic-Social-Technical/Operational framework: metric-level reform for accountability, revision of information and communication technologies design principles and demand shaping, efficiency-oriented LLM development practices (data, model, training, and scaling), and energy-infrastructure transition. The proposed framework operationalizes the Environmental-Economic-Social-Technical/Operational-aligned assessment and intervention prioritization across the AI-BD-ICT stacks, enabling sustainability-by-design without sacrificing utility.
| Comments: | This was submitted February 2026 and was reviewed on hold and then approved after April 2026, the ticket number was MOD-85400 |
| Subjects: | Computers and Society (cs.CY); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.31739 [cs.CY] |
| (or arXiv:2610.31739v1 [cs.CY] for this version) | |
| https://doi.org/10.48550/arXiv.2610.31739 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Jinsong Wu [view email]
[v1]
Thu, 22 Oct 2026 17:06:36 UTC (131 KB)
来源：arXiv cs.AI · arxiv.org