The Environmental Cost of AI's Energy Use: Carbon, Water and Land Footprints

The rapid global expansion of artificial intelligence is creating a new and largely underexamined sustainability challenge: the carbon, water and land footprints of the electricity required to power AI systems. While AI is increasingly presented as essential for innovation, economic growth, scientific discovery and climate action, its development depends on energy-intensive data centers, advanced chips, cooling systems, electricity grids, water resources, land and critical mineral supply chains that support AI hardware. This report shows that AI’s environmental costs depend not only on how much electricity is used, but also on where that electricity is generated and which energy sources power it. In 2025, data centers consumed an estimated 448 TWh of electricity, with AI workloads accounting for around 20% of this demand; by 2030, total data center electricity use could reach 945 TWh, while AI’s share could rise to 40%. Every kilowatt-hour used to train, deploy or operate AI carries carbon, water and land implications, and low-carbon electricity is not automatically low-water or low-land. As larger models, richer media outputs and everyday AI use scale rapidly, environmental burdens risk becoming concentrated in communities already facing water stress, land pressure, energy insecurity and limited governance capacity. Without transparent measurement, efficient design, lifecycle responsibility and environmental justice safeguards, the AI transition risks reproducing the uneven patterns of extraction and burden-shifting that have shaped earlier technological and energy transitions.