# Not All Water Consumption Is Equal: A Water Stress Weighted Metric for Sustainable Computing

arXiv: https://arxiv.org/abs/2506.22773 · HTML: https://mlsanity.com/paper/2506.22773/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 48 references that are in the map.
- First posted on arXiv 28 Jun 2025.

## Builds on (most cited first, 6 of 6)

- [Efficient Memory Management for Large Language Model Serving with PagedAttention](https://mlsanity.com/paper/2309.06180/) (2023)
- [Holistically Evaluating the Environmental Impact of Creating Language Models](https://mlsanity.com/paper/2503.05804/) (2025)
- [Towards Environmentally Equitable AI via Geographical Load Balancing](https://mlsanity.com/paper/2307.05494/) (2023)
- [Beyond CO2 Emissions: The Overlooked Impact of Water Consumption of Information Retrieval Models](https://mlsanity.com/paper/2306.16668/) (2023)
- [A Dataset for Research on Water Sustainability](https://mlsanity.com/paper/2405.17469/) (2024)
- [WaterWise: Co-optimizing Carbon- and Water-Footprint Toward Environmentally Sustainable Cloud Computing](https://mlsanity.com/paper/2501.17944/) (2025)

## Built on by (most cited first, 4 of 4)

- [Systematic Characterization of LLM Quantization: A Performance, Energy, and Quality Perspective](https://mlsanity.com/paper/2508.16712/) (2025)
- [BIRDS: Characterizing and Understanding Biodiversity Impact of Large Language Model Serving](https://mlsanity.com/paper/2605.27480/) (2026)
- [Beyond PUE: A Local Impact Audit Framework for Data Center Environmental Accountability](https://mlsanity.com/paper/2609.23421/) (2026)
- [Beyond Energy: When Sustainability Dimensions Reshape LLM Serving Decisions](https://mlsanity.com/paper/2609.35569/) (2026)

## Abstract (arXiv)

Water consumption is an increasingly critical dimension of computing sustainability, especially as AI workloads rapidly scale. However, current water impact assessment often overlooks where and when water stress is more severe. To fill in this gap, we present SCARF, the first general framework that evaluates water impact of computing by factoring in both spatial and temporal variations in water stress. SCARF calculates an Adjusted Water Impact (AWI) metric that considers both consumption volume and local water stress over time. Through three case studies on LLM serving, datacenters, and semiconductor fabrication plants, we show the hidden opportunities for reducing water impact by optimizing location and time choices, paving the way for water-sustainable computing. The code is available at https://github.com/jojacola/SCARF.