# Beyond CO2 Emissions: The Overlooked Impact of Water Consumption of Information Retrieval Models

arXiv: https://arxiv.org/abs/2306.16668 · HTML: https://mlsanity.com/paper/2306.16668/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Jun 2023.

## Built on by (most cited first, 6 of 6)

- [An Investigation of Prompt Variations for Zero-shot LLM-based Rankers](https://mlsanity.com/paper/2406.14117/) (2024)
- [Sociotechnical Implications of Generative Artificial Intelligence for Information Access](https://mlsanity.com/paper/2405.11612/) (2024)
- [AI, Climate, and Regulation: From Data Centers to the AI Act](https://mlsanity.com/paper/2410.06681/) (2024)
- [Not All Water Consumption Is Equal: A Water Stress Weighted Metric for Sustainable Computing](https://mlsanity.com/paper/2506.22773/) (2025)
- [Quality Prediction of AI Generated Images and Videos: Emerging Trends and Opportunities](https://mlsanity.com/paper/2410.08534/) (2024)
- [GaiaFlow: Semantic-Guided Diffusion Tuning for Carbon-Frugal Search](https://mlsanity.com/paper/2602.15423/) (2026)

## 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.