AI & Environment Resource HubUC Riverside · Publication date unknown
[Water Consumption] Leading researcher on the Water Footprint of AI, quantifying usage for data center cooling.
UC Riverside. Date unknown. Dr. Shaolei Ren. https://www.linkedin.com/in/shaolei-ren-68557415/ (AI & Environment Resource Hub; record atlas-c85b6c11d850; collection snapshot 2026-09-15).
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Source sheet: People of Interest · Row 5 · Original ID: Not supplied.
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Shared topics: Water; Biodiversity and ecosystems; Built environment and infrastructure
This JustWaterFutures February 2026 seminar was titled AI and Water Justice: Data Centres as Sites of Struggle and explored the ways in which communities across the Americas are rising up against data centers, their failure to consult directly with affected communities, and the massive ecological footprints they leave behind.
Shared topics: Water; Biodiversity and ecosystems; Built environment and infrastructure
Join Nature Forward and AI expert Masheika Allgood for a timely webinar on how data centers affect the health of our local water systems. While data center conversations have focused on the staggering amounts of water that go into the facilities, there hasn't been a study on how the water that is discharged from these facilities impacts overall water system health, until now. Ms. Allgood will share findings from her case study research in Atlanta, GA, revealing what pollutants show up in wastewater discharged from data centers and what those results mean for surrounding communities. You'll learn practical ways to advocate for stronger protections for our water and the people who rely on it.
Shared topics: Water; Biodiversity and ecosystems; Built environment and infrastructure
Matthew Riemland examines how translation professionals can confront the environmental costs of AI, including carbon emissions, water consumption, and rare mineral extraction, through 'eco-translation practice.' He argues these harms stem from structural power imbalances rather than individual choices, and proposes vocational and structural strategies of resistance, from supporting data center activism to demanding transparency from AI developers.
Shared topics: Water; Biodiversity and ecosystems; Built environment and infrastructure
This study demonstrates that machine learning models, particularly Random Forest Classifiers, can effectively simulate and classify precipitation and extreme weather patterns across North Indian states, offering valuable insights for disaster preparedness and water resource management.