AI & Environment Resource HubLi et al. · 2025-03-26
AI models have a significant yet overlooked water footprint, requiring transparency and holistic sustainability efforts alongside carbon reduction.
Li et al.. 2025-03-26. Making AI Less “Thirsty”: Uncovering and Addressing the Secret Water Footprint of AI Models. https://arxiv.org/pdf/2304.03271 (AI & Environment Resource Hub; record paper-007; collection snapshot 2026-09-15).
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Source sheet: Scientific Paper · Row 11 · Original ID: paper-007.
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Shared topics: Cross-cutting sustainability; Climate and greenhouse gases; Water; Oceans and coasts
This study presents a hybrid AI and mathematical modeling approach using deep learning and fractional-order equations to forecast water cycle dynamics and inform sustainable water and climate policy decisions.
Shared topics: Cross-cutting sustainability; Water; Oceans and coasts
We believe that innovation and technological advancement are key to building 21st-century economies in the region. We partner with diverse organizations to fostering economic growth, social progress, and environmental sustainability.
Shared topics: Climate and greenhouse gases; Water; Oceans and coasts
Severe flash floods in Charikar, Afghanistan, on August 26, 2020, were driven by extreme atmospheric instability, deep low-level convergence, and local topography, with climate change intensifying frontal activity and baroclinicity in the region.
Shared topics: Cross-cutting sustainability; Climate and greenhouse gases; Water
This paper examines the environmental impact of deploying generative AI models on cloud versus edge platforms, finding that edge deployment can achieve over 90% energy savings while reducing carbon emissions and water consumption by more than 80%.