面向人工智能、大数据与信息通信技术的广义绿色与红色概念：多维框架及对策
Generalized green and red concepts for artificial intelligence, big data, and information and communication technologies: Multidimensional framework and countermeasures
Jinsong Wu · Yixin Xiao · Haowei Bi · Huamin Fang
中文摘要
人工智能(AI)、大数据(BD)与信息通信技术(ICT)的快速扩张，正显著增加电力、水和材料需求；预计到2028年，数据中心将消耗美国电力的6.7--12%（2023年为4.4%），到2027年，AI相关年用水量将达到4--6 billion m³。现有“绿色AI”与“红色AI”讨论主要聚焦计算效率和碳排放，对生命周期权衡、服务与使用阶段的影响，以及能源之外的更广泛可持续性外部性指导有限。为弥补这一缺口，论文提出广义绿色AI(GGAI)与广义红色AI(GRAI)，并在统一的环境-经济-社会-技术/运营(E/Ec/S/T)框架下，将分类扩展至红色BD(RBD)、红色ICT(RICT)、红色电子与电气工程(REEE)及红色数字技术(RDT)。研究以大模型时代的代表性足迹指标——电力、碳、水和电子废弃物——为基础，分析风险如何在AI-BD-ICT技术栈及服务—使用边界间传播，并将可行对策归纳为四级体系：面向问责的指标层面改革、信息通信技术设计原则修订与需求塑形、以效率为导向的大语言模型(LLM)开发实践（数据、模型、训练与扩展），以及能源基础设施转型。该框架支持对AI-BD-ICT技术栈进行评估和干预优先级排序，从而在不牺牲效用的情况下实现可持续性设计。
关键要点
- 01现有绿色AI与红色AI讨论偏重计算效率和碳排放，对生命周期及更广泛外部性关注不足。
- 02提出广义绿色AI(GGAI)和广义红色AI(GRAI)，并统一扩展至BD、ICT及相关数字技术。
- 03以电力、碳、水和电子废弃物为指标，分析风险在AI-BD-ICT技术栈和服务—使用边界中的传播。
- 04形成四级对策体系，涵盖指标改革、技术设计、LLM高效开发及能源基础设施转型。
- 05框架旨在兼顾技术效用，支持跨AI-BD-ICT技术栈的评估、干预排序与可持续性设计。
解读
尚无解读。
原始英文摘要
arXiv:2610.31739v1 Announce Type: cross 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.