The Uneven Distribution of AI’s Environmental Impacts

How companies can responsibly manage the growing water and energy demands of their data centers across the world.

July 15, 2024

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The training process for a single AI model, such as an LLM, can consume thousands of megawatt hours of electricity and emit hundreds of tons of carbon. AI model training can also lead to the evaporation of an astonishing amount of freshwater into the atmosphere for data center heat rejection, potentially exacerbating stress on our already limited freshwater resources. These environmental impacts are expected to escalate considerably, and there remains a widening disparity in how different regions and communities are affected. The ability to flexibly deploy and manage AI computing across a network of geographically distributed data centers offers substantial opportunities to tackle AI’s environmental inequality by prioritizing disadvantaged regions and equitably distributing the overall negative environmental impact.

The adoption of artificial intelligence has been rapidly accelerating across all parts of society, bringing the potential to address shared global challenges such as climate change and drought mitigation. Yet underlying the excitement surrounding AI’s transformative potential are increasingly large and energy-intensive deep neural networks. And the growing demands of these complex models are raising concerns about AI’s environmental impact.

Shaolei Ren is an associate professor of electrical and computer engineering at the University of California, Riverside. He has taught and researched computational sustainability for more than a decade. His work on sustainable AI has been featured in many international AI governance and ethics guidelines, contributed to K-12 education materials, led to industry innovations like real-time water footprint reporting tools, and gained worldwide media coverage.

Adam Wierman is the Carl F. Braun Professor in the Department of Computing and Mathematical Sciences at Caltech. His research strives to make the networked systems that govern our world sustainable and resilient. He is best known for his work spearheading the design of algorithms for sustainable data centers, which has seen significant industry adoption, as well as his work on heavy tails, including his coauthored book, The Fundamentals of Heavy Tails.

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