AI, Energy, Water, and Climate
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AI’s compute footprint is a growing environmental and policy issue. This sub-article expands Section 1.5 of Hardware & Software and Economic & Social Impacts.
Energy Demand
- Training frontier models consumes megawatt-scale power.
- Inference at scale adds continuous load; data-center demand is rising.
Water and Cooling
- Liquid cooling and evaporative systems consume large amounts of water.
- Drought-prone regions face tension between compute growth and water supply.
Efficiency and Carbon
- Efficiency research (quantization, distillation, sparse attention) cuts cost per token.
- Renewable energy and carbon accounting are emerging priorities.
Policy and Transparency
- Some jurisdictions require reporting of energy/water use.
- Compute governance debates include environmental criteria.
Why It Matters
AI’s growth is colliding with climate goals; efficiency and transparency are essential.
Sources
- International Energy Agency. AI and energy reports.
- Data-center water studies (e.g., Li et al., 2024).
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