Measuring AI’s Energy and Environmental Footprint: Urgent Need for Standardized Metrics
Key Insights
The rapid expansion of AI is driving significant increases in data center energy consumption, water use, and carbon emissions, yet current metrics like PUE fail to capture the full environmental impact.
A single hyperscale AI data center can consume hundreds of thousands of gallons of water daily and generate massive e-waste, but only a quarter of operators track hardware disposal.
Proposed federal actions aim to establish standardized metrics for AI’s energy and environmental footprint, involving DOE, NIST, and EPA to improve transparency and accountability.
Without accurate measurement, AI’s growing power demand could strain grids and undermine climate goals, with some utilities resorting to coal plants to meet data center loads.
The rapid expansion of artificial intelligence (AI) is driving a surge in data center energy consumption, water use, carbon emissions, and electronic waste—yet these environmental impacts remain largely opaque. Without standardized metrics and reporting, policymakers and grid operators cannot accurately track or manage AI’s growing resource footprint. Companies often rely on outdated measures like Power Usage Effectiveness (PUE) and renewable energy credits, masking true emissions that may be up to 662% higher than reported. Hyperscale AI data centers consume hundreds of thousands of gallons of water daily and contribute to significant e-waste, yet only about 25% of operators track retired hardware.
A policy memo proposes congressional and federal executive actions to establish comprehensive, standardized metrics for AI’s energy and environmental impacts. The plan calls for the Department of Energy (DOE) and the National Institute of Standards and Technology (NIST) to design and monitor uniform data on AI’s footprint, with coordination by the White House Office of Science and Technology Policy (OSTP). Steps include developing metrics (led by DOE, NIST, and EPA), implementing data reporting (with EIA and NTIA), and integrating these metrics into grid planning (via DOE and FERC). Standardization is critical to managing AI’s growing power demand while maintaining U.S. leadership in the field.
AI’s opaque footprint poses a significant challenge. Generative AI and large-scale cloud computing are driving unprecedented energy demand, with data centers consuming an estimated 415 TWh in 2024—1.5% of global power demand. The International Energy Agency (IEA) forecasts this could more than double to 945 TWh by 2030, comparable to powering a country like Germany. Divergent estimates stem from varying assumptions about AI query volume, hardware supply, and efficiency gains, leaving grid planners unprepared for potential surges in demand.
The surge threatens climate progress, as utilities unable to supply clean energy may restart coal plants to meet data center loads. Google’s carbon emissions rose 48% over five years, and Microsoft’s by 23.4% since 2020, largely due to AI. Yet, emissions data is often obscured by renewable credits, with actual local emissions going unreported. For example, Meta’s 2022 data center operations reported 273 metric tons of CO₂ using credits but over 3.8 million metric tons under actual grid mix calculations.
Legacy metrics like PUE are insufficient for AI workloads, failing to account for water consumption, hardware manufacturing, and e-waste. Only 28% of operators track hardware beyond its use, and just 25% measure e-waste. This lack of transparency hinders accountability and smart policymaking.
Standardizing metrics presents a win-win opportunity. Better data can incentivize efficiency innovations and target grid investments. Efforts like the Artificial Intelligence Environmental Impacts Act and the EU’s AI Act are paving the way, but the U.S. must act swiftly to avoid grid strain and climate setbacks.