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TRUVACE RECORD VERSION
record: TRV-2026-0279
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-07-19T01:18:32.581685Z
status: published
lens: g_space
sector: climate
headline: Challenges of Artificial Intelligence Development in the Context of Energy Consumption and Impact on Climate Change
dek: With accelerating climate change and rising global energy consumption, the application of artificial intelligence (AI) and machine learning (ML) has emerged as a crucial tool for enhancing energy efficiency and mitigating the impacts of climate change. However, their implementation has a dual character: on one hand, AI facilitates sustainable solutions, including energy optimization, renewable energy integration and carbon reduction; on the other hand, the training and operation of large language models (LLMs) e…
gain_title: AI and ML are being applied to improve energy efficiency and support climate mitigation through energy optimization, renewable integration, and carbon reduction.
problem_title: (none)
trace_subject: (none)
gain_reading: AI and ML are being applied to improve energy efficiency and support climate mitigation through energy optimization, renewable integration, and carbon reduction.
gain_evidence: AI facilitates sustainable solutions, including energy optimization, renewable energy integration and carbon reduction | application of artificial intelligence (AI) and machine learning (ML) has emerged as a crucial tool for enhancing energy efficiency and mitigating the impacts of climate change
problem_reading: (none)
problem_evidence: (none)
quick_read: By late 2024, a review of 237 publications from 2010 to 2024 found AI and ML increasingly studied as tools for energy efficiency and climate mitigation, with over 60% of papers appearing in the last two years and focus areas including sustainable construction and climate forecasting.

The dual character matters because efficiency benefits could be offset if large-model training and operation continue to demand high energy, raising questions about net carbon impact. The review points to unresolved needs for more energy-efficient AI architectures and improved energy management to align AI growth with carbon neutrality goals.
limitation: 
tag: Evidence-backed gain
key_points: Review analyzed 237 scientific publications from 2010 to 2024 on AI and ML for energy efficiency and climate. | Over 60% of the documents have been published in the last two years, with sustainable construction and climate change forecasting attracting most interest. | 58% of research is concentrated in three areas: engineering, computer science and energy, with leading contributions from China, India, the UK and the USA.
rundown: The source is a review of 237 publications from 2010 to 2024 examining AI and ML adoption across construction, transportation, industry, energy and households. It reports that interest has grown sharply, with over 60% of documents published in the last two years.

It notes geographic concentration, with 283 articles each from researchers in China, India, the UK and the USA, more than twice the number from elsewhere, and disciplinary concentration in engineering, computer science and energy. It concludes by pointing to development of more energy-efficient AI architectures and new methods of energy management as future research directions.
sources:
- peer_reviewed | Energies | https://doi.org/10.3390/en17235965 | 2024-11-27
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