Environmental Cost of Artificial Intelligence: Carbon, Water, and Land Footprints

This article is part of an ongoing series looking at AI in KM, and KM in AI.

• Public debate has rightly focused on AI risks such as bias, privacy, disinformation, labor disruption, and inequity. Yet, one of the most consequential dimensions of AI that remains comparatively underexamined is its environmental footprint and the justice implications that follow from where and how AI infrastructure expands.
• AI is not “just code”; it also involves physical infrastructure and supply chains, including data centers, chips, electricity generation, cooling systems, water withdrawals, land occupation, critical minerals, and eventual e-waste.
• On current trajectories, data center electricity demand could roughly double to 945 TWh by 2030, nearly triple the combined annual electricity use of Pakistan, Bangladesh, and Nigeria, together home to more than 650 million people.
• Producing that much electricity would have a carbon footprint of 399 million tonnes CO2e, requiring 6.7 billion trees grown over 10 years to offset – roughly twice the number of trees in the United Kingdom.
• The associated water footprint of 9.3 trillion liters would be equivalent to the annual domestic water needs of all 1.3 billion residents of Sub-Saharan Africa.
• The land footprint associated with this electricity would exceed 14,500 km², nearly 10 times the size of Mexico City.
• AI’s environmental impacts are shaped not only by data center growth and electricity supply mixes, but also by the escalating cost of building ever-larger models.
• In response, the report calls for a responsible AI ecosystem grounded in six operational principles: transparency; efficiency by design; equity and environmental justice; lifecycle responsibility; global cooperation; and sustainable use.
• The report identifies priority implications for key stakeholders: governments, users and deploying organizations, and communities and civil society.

Header image source: Adapted from Robert Scoble on Flickr, CC BY 2.0.

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