cs.CYOct 22, 2026

Generalized green and red concepts for artificial intelligence, big data, and information and communication technologies: Multidimensional framework and countermeasures

Authors: Jinsong Wu, Yixin Xiao, Haowei Bi, Huamin Fang

Organizations: School of Artificial Intelligence, Guilin University of Electronic Technology, Guilin, China · Department of Electrical Engineering, University of Chile, Santiago, Chile

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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 m3^3 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.

Figures & tables

CategoryIndicatorRepresentative magnitude (example)
Energy & carbonData center electricity share (USA)4.4% (2023) projected to 6.7–12% by 2028 [ 14 ]
Energy & carbonTraining-related emissions (LLM example)One GPT-3 training run reported ∼\sim 1.21M lb CO2CO_{2} emissions [ 15 ]
WaterGlobal AI -related water demand4–6 billion m 3 per year projected by 2027 [ 16 ]
WaterOn-site cooling efficiency (WUE)Typical on-site WUE ∼\sim 0.2–1.8 L/kWh (site/climate dependent) [ 17 ]
Materials & wasteAI -driven e-waste growth (scenario)∼\sim 16 million tons by 2030 (model-based estimate) [ 18 ]
TABLE I: Representative footprint indicators for AI /LLM development (illustrative; varies by model, site, and grid).
ConceptScopePrimary objectiveTypical evaluation cues
RAIModel training/designAccuracy via brute-force scalingSOTA accuracy; larger models/data/compute
GAIModel training/designSimilar utility with less compute/powerFLOPs, time, energy, CO 2 emissions
GRAISystem / application / useAway from environmental, economic, social, and technical/operational sustainability goalsNet degradation in one or more dimensions (may appear efficient locally)
GGAISystem / application / useToward environmental, economic, social, and technical/operational sustainability goalsNet improvement in one or more dimensions under a stated boundary
TABLE II: Comparison between traditional (RAI/GAI) and generalized (GRAI/GGAI) concepts.
CategoryNear-termMedium-termLong-term
Metrics & accountabilityStandardize footprint disclosure (electricity/carbon/water/e-waste) for training and inference; report impacts for both services and uses.Audit-ready lifecycle accounting with region/time awareness; introduce threshold-based compliance and equity safeguards.Institutionalize multidimensional sustainability labeling and procurement standards; enable longitudinal benchmarking.
System design & demand shapingDeploy carbon-aware scheduling for non-urgent workloads; avoid peak-hour batch pipelines; reduce over-provisioned redundancy.Capacity planning guided by demand distributions (not peak-only design); introduce service-tiering and user-side incentives.Sustainability-by-design architectures for networks and data centers aligned with societal value and constraints.
Large-model developmentData curation/deduplication; selective invocation; pruning/distillation where acceptable.Training/inference co-optimization under resource constraints; systematic multi-objective evaluation beyond accuracy.Efficiency-first paradigms and incentives; shift away from brute-force scaling as the default development mode.
Energy & infrastructure transitionIncrease low-carbon electricity use where available; improve cooling and water stewardship; extend hardware lifetime and reuse.Integrate clean energy with storage and flexible loads; reduce embodied impacts via circular hardware supply chains.Deep decarbonization of power/material/manufacturing with robust water and waste governance at scale.
TABLE III: Roadmap of countermeasures (near-, medium-, and long-term) for generalized red artificial intelligence, red big data, and red information and communication technologies across service provision and downstream use.
 Green=(Environmentally/Economically/Socially/Technically) Sustainable\hfill∪ (Environmentally/Economically/Socially/Technically) Friendly\hfill\displaystyle\begin{gathered}{\text{Green}}=({\text{Environmentally/Economically/Socially/Technically) Sustainable}}\hfill\\ \cup{\text{ (Environmentally/Economically/Socially/Technically) Friendly}}\hfill\\ \end{gathered} 
Table 4
 Green=(Environmentally/Economically/Socially/Technically) Sustainable\hfill∩ (Environmentally/Economically/Socially/Technically) Friendly\hfill\displaystyle\begin{gathered}{\text{Green}}=({\text{Environmentally/Economically/Socially/Technically) Sustainable}}\hfill\\ \cap{\text{ (Environmentally/Economically/Socially/Technically) Friendly}}\hfill\\ \end{gathered} 
Table 5
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