Provenance record

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

arXiv cs.CY (Computers and Society) (tier 1, academic) 2026-10-26T04:00:00.000Z Original ↗

Source note: Preprints on societal impact.

Discourse valence

-50

Adverse

confidence 80% · 1 item

-100

-50

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Adverse readingFavourable reading

Single-model reading. This item did not meet the threshold for a multi-model panel.

Climate and energy Evaluation and measurement Governance and regulation

Excerpt as ingested

arXiv:2610.31739v1 Announce Type: new 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 e

Every model that read this

Model	Provider	Stage	Score	Conf.	Latency	Prompt	When
Llama 3.3 70B	Meta	analysis	-50	80%	4111ms	v1.0.0 / m1.0.1	2026-10-26 05:41

Llama 3.3 70B · reading

Rapid AI growth drives substantial electricity and resource demands.

evidence: primary horizon: n/a

Evidence extracted

AI will consume 6.7-12% of US electricity by 2028
AI-related water demand estimated at 4-6 billion m^3 annually by 2027

The chain

SOURCE     arXiv cs.CY (Computers and Society) (tier 1)
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DOCUMENT   a5230eed-8de3-48a3-9c54-f1181307a48c
           https://arxiv.org/abs/2610.31739
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EVIDENCE   2 extracted excerpts
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MODEL RUN  1 run, methodology 1.0.1
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SCORE      -50  (Adverse)
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CONFIDENCE 80%