Big Tech's Indirect Emissions Surged 150% In Three Years With Rise Of AI: UN Report
Carbon emissions from top AI systems are projected to reach up to 102.6 million tons of carbon dioxide annually

Indirect carbon emissions from major tech companies surged by an average of 150% from 2020-2023, driven by the growing energy demands of data centers, a United Nations report said on Thursday. The expanded use of artificial intelligence across the leading tech firms, such as Amazon, Microsoft, Alphabet and Meta, escalated their global indirect carbon emissions due to the vast amount of energy required for efficient data center operations, according to a report by the International Telecommunication Union (ITU). These emissions stem largely from electricity, steam, heating, and cooling used in the companies.
The report highlighted Amazon as a top emitter, with a 182% increase in 2023 compared to three years earlier, followed by Microsoft at 155%, Meta at 145% and Alphabet at 138%. Recently, ITU recorded the greenhouse gas emissions of 200 leading digital companies over the three-year report. In light of the estimated report, Amazon stated its commitment to environmentally sustainable operations through investments in carbon-free projects, including nuclear and renewable energy. Microsoft noted a doubling in power savings last year and a shift towards chip-level liquid cooling designs to replace traditional cooling systems. Moreover, Meta, which owns Facebook and WhatsApp, also confirmed their recent attempts to cut emissions and reduce consumption of water and other energies in data centers.
As investment in AI increases, carbon emissions from top AI systems are projected to reach up to 102.6 million tons of carbon dioxide annually, the report added. "The rapid growth of artificial intelligence is driving a sharp rise in global electricity demand, with electricity use by data centers increasing four times faster than the overall rise in electricity consumption," the report found. Although many companies have set emission targets, the implementations remain limited practically.
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