Not All Water Consumption Is Equal: A Water Stress Weighted Metric for Sustainable Computing
Wu, Yanran, Hua, Inez, Ding, Yi
–arXiv.org Artificial Intelligence
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.
arXiv.org Artificial Intelligence
Jul-2-2025
- Country:
- Asia
- Middle East > Jordan (0.04)
- Taiwan > Taiwan Province
- Taipei (0.04)
- Europe > United Kingdom
- England > Cambridgeshire > Cambridge (0.04)
- North America > United States
- New York > New York County
- New York City (0.04)
- Wyoming (0.04)
- District of Columbia > Washington (0.04)
- Illinois (0.04)
- New Mexico (0.04)
- Iowa (0.04)
- California > Los Angeles County
- Los Angeles (0.04)
- Massachusetts > Middlesex County
- Cambridge (0.15)
- Oregon (0.05)
- Arizona (0.05)
- Texas (0.04)
- Indiana > Tippecanoe County
- Lafayette (0.04)
- West Lafayette (0.04)
- Nevada > Clark County
- Las Vegas (0.04)
- New York > New York County
- Asia
- Genre:
- Research Report (0.82)
- Industry:
- Education > Health & Safety
- School Nutrition (0.69)
- Energy > Power Industry (0.69)
- Government > Regional Government
- Information Technology (0.96)
- Education > Health & Safety
- Technology: