NOW LET US – AI RAG SaaS Studio TP.HCM
NOW LET US
Digital Product Studio
Back to news
AGENTIC-SYSTEMS...1 min read

Assessing the Carbon Emissions and Energy Consumption of U.S. Hyperscale Data Centers

Share
NOW LET US Article – Assessing the Carbon Emissions and Energy Consumption of U.S. Hyperscale Data Centers

A new study analyzing 403 hyperscale data centers in the US reveals that the AI boom is driving electricity consumption and carbon emissions to alarming levels, with their carbon intensity averaging 48% higher than the national grid average.

Computer Science > Artificial Intelligence

Title:Assessing the Carbon Emissions and Energy Consumption of U.S. Hyperscale Data Centers

View PDF HTML (experimental)Abstract:The rapid proliferation of hyperscale data centers (HDCs) in the US, mainly driven by the adoption of artificial intelligence, has raised concerns about this industry's environmental footprint. We compiled facility-level information on 403 US hyperscale data centers operating between May 2024 and April 2025 and estimated their electricity consumption, electricity sources, and attributable CO2 emissions. Across different facility-load scenarios, these HDCs consumed approximately 68-99 TWh of electricity and were associated with about 37-54 million metric tons of CO2. Under the central scenario, HDC electricity demand corresponded to approximately 1.8% of total US electricity consumption, with roughly 54% of attributed generation supplied by fossil-fuel sources. The HDC electricity-weighted average carbon intensity was approximately 545 gCO2/kWh, about 48% above the contemporaneous US national grid-average carbon intensity of 370 gCO2/kWh. Our approach provides an attributional tool for assessing the environmental footprint of hyperscale data centers using the most recent EPA eGRID plant-level data.

Current browse context:

Bibliographic and Citation Tools

Code, Data and Media Associated with this Article

Demos

Recommenders and Search Tools

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.

© 2026 Now Let Us. All rights reserved.

Source: arXiv cs.AI Recent

Advertisement
Ad slot ready: 5887729102

More in this category

NOW LET US Related – An interpretable and trustworthy AI framework for large-scale longitudinal structure-pain association studies using data from the Osteoarthritis Initiative (OAI)

agentic-systems

An interpretable and trustworthy AI framework for large-scale longitudinal structure-pain association studies using data from the Osteoarthritis Initiative (OAI)

Researchers have developed an interpretable and trustworthy AI framework to study the relationship between knee joint structural abnormalities and pain progression. By combining deep learning with advanced statistical modeling, this framework significantly improves prediction accuracy and clinical reliability.

NOW LET US Related – Brick-Composer: Using MLLMs for Assembly with Diverse Bricks

agentic-systems

Brick-Composer: Using MLLMs for Assembly with Diverse Bricks

Researchers introduce Brick-Composer, a learning framework that equips multimodal large language models (MLLMs) with spatial reasoning and visual grounding capabilities for brick assembly, significantly improving their construction accuracy.

NOW LET US Related – Ten Headache Specialists versus Artificial Intelligence for Clinical Literature Summarization: A Critical Evaluation and Comparison

agentic-systems

Ten Headache Specialists versus Artificial Intelligence for Clinical Literature Summarization: A Critical Evaluation and Comparison

A new study compared three state-of-the-art LLMs (GPT-4o, Claude Sonnet, and Llama 3.1) against ten medical specialists in summarizing clinical literature. While expert-written summaries remain preferred, the study reveals that distinguishing between human- and AI-generated medical content is becoming increasingly difficult.

NOW LET US Related – Minimizing the Hidden Cost of Scales: Graph-Guided Ultra-Low-Bit Quantization for Large Language Models

agentic-systems

Minimizing the Hidden Cost of Scales: Graph-Guided Ultra-Low-Bit Quantization for Large Language Models

Researchers have proposed SAGE-PTQ, a novel ultra-low-bit post-training quantization framework for LLMs that minimizes hidden scaling overhead. It significantly reduces GPU memory usage and accelerates decoding speed while maintaining high accuracy compared to existing methods.

NOW LET US Related – A Motivational Architecture for Conversational AGI

agentic-systems

A Motivational Architecture for Conversational AGI

This paper proposes a conversational reinterpretation of the OpenPsi motivational lineage, coupled to MetaMo's higher-level motivational scaffold, for agents built on a modular execution substrate.

NOW LET US Related – Agents' Last Exam

agentic-systems

Agents' Last Exam

A new benchmark called "Agents' Last Exam" (ALE) has been introduced to evaluate AI agents on long-horizon, economically valuable, real-world tasks, revealing that current models achieve an average pass rate of just 2.6% on the hardest tier.

EXPLORE TOPICS

Discover All Categories

Deep dive into the specific technology sectors that matter most to you.