AI Governance
Data Centers
Drones
Climate
Privacy
2026 Maine Senate
Bob Dylan
AI’s Climate and Energy Footprint
Coverage from MIT Sloan School of Management, Grist, and others
Expanding AI requires data centers, chips, storage, and cooling, adding electricity demand whose climate impact depends on efficiency and the power grid.
The International Energy Agency estimated data centers used about 415 terawatt-hours of electricity worldwide in 2024. Research also examines emissions enabled when AI boosts oil and gas production, alongside potential benefits from clean-energy applications; the net effect is uncertain and depends on how these technologies are deployed. Choices about new generation, grid investment, corporate emissions accounting, and climate policy help determine both the environmental costs and who bears them.
The story now gives greater weight to data centers’ water and local infrastructure impacts, alongside electricity use. Investor attention to firm power and infrastructure, and proposals for disclosure of AI-related resource use and enabled emissions, add market and accountability dimensions.
The current version more explicitly says Google, Microsoft, Amazon, and Meta have reported emissions increases despite growing clean-energy purchases. This strengthens the corporate-emissions framing but does not materially change the story’s overall direction.
New estimates put a sharper scale on AI infrastructure’s resource demands: global data-center electricity use could nearly double by 2030, with water use also highlighted. Google’s reported 37% electricity increase adds a company-specific measure of the pressure.
Expanding AI use is increasing electricity demand from data centers and adding emissions across technology supply chains, complicating major firms’ climate commitments. AI tools are also being applied to fossil-fuel exploration and production, extending the potential climate impact beyond the power needed to run data centers. Clean-energy procurement and efficiency improvements are growing, but their effects must be considered alongside rising overall demand and emissions; the material also includes more limited examples of AI used for climate planning and adaptation.