Carbon emissions are the most common way to measure AI’s environmental footprint. That makes sense because they’re standardized, comparable across industries, and relatively well understood.<\/p>
But lifecycle research is showing that climate-related impacts account for roughly a third of the total picture.<\/p>
The rest, including mineral depletion, particulate matter, and freshwater quality, depends heavily on infrastructure decisions that most sustainability conversations haven’t caught up with yet.<\/p>
Understanding what falls outside carbon-only accounting is becoming a practical question for anyone building or choosing AI infrastructure.<\/p>
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