Beyond CO2 Emissions: The Overlooked Impact of Water Consumption of Information Retrieval Models
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Abstract (arXiv)
As in other fields of artificial intelligence, the information retrieval community has grown interested in investigating the power consumption associated with neural models, particularly models of search. This interest has become particularly relevant as the energy consumption of information retrieval models has risen with new neural models based on large language models, leading to an associated increase of CO2 emissions, albeit relatively low compared to fields such as natural language processing.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). First posted on arXiv 29 Jun 2023.
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Citing papers per year: 2024: 4; 2025: 1; 2026: 1.
6 papers.
- 2024An Investigation of Prompt Variations for Zero-shot LLM-based Rankerscited by 7
- 2024Sociotechnical Implications of Generative Artificial Intelligence for Information Accesscited by 6
- 2024AI, Climate, and Regulation: From Data Centers to the AI Actcited by 4
- 2025Not All Water Consumption Is Equal: A Water Stress Weighted Metric for Sustainable Computingcited by 4
- 2024Quality Prediction of AI Generated Images and Videos: Emerging Trends and Opportunitiescited by 3
- 2026GaiaFlow: Semantic-Guided Diffusion Tuning for Carbon-Frugal Searchcited by 0
- 2026GaiaFlow: Semantic-Guided Diffusion Tuning for Carbon-Frugal Search17 Feb 2026
- 2025Not All Water Consumption Is Equal: A Water Stress Weighted Metric for Sustainable Computing28 Jun 2025
- 2024Quality Prediction of AI Generated Images and Videos: Emerging Trends and Opportunities11 Oct 2024
- 2024AI, Climate, and Regulation: From Data Centers to the AI Act9 Oct 2024
- 2024An Investigation of Prompt Variations for Zero-shot LLM-based Rankers20 Jun 2024
- 2024Sociotechnical Implications of Generative Artificial Intelligence for Information Access19 May 2024