The data heat island effect: quantifying the impact of AI data centers in a warming world
Authors: Andrea Marinoni, Erik Cambria, Weisi Lin, Mauro Dalla Mura, Jocelyn Chanussot, Edoardo Ragusa, Chi Yan Tso, Yihao Zhu, Benjamin Horton
Organizations: Department of Computer Science and Technology, University of Cambridge, 15 JJ Thomson Avenue, Cambridge, CB3 0FD, UK. · Glitch Analytics Ltd., The Old School House, Church Lane, Oving, HP22 4HL, UK. · College of Computing and Data Science, Nanyang Technological University, 50 Nanyang Avenue, 639798, Singapore. · GIPSA-lab, Grenoble-INP UGA, 11 rue des Mathématiques, Grenoble Campus BP46, Saint-Martin-d’Hères Cedex, 38402, France. · INRIA, 655 Avenue de l’Europe, CS 90051, Montbonnot Cedex, 38334, France. · Dipartimento di Ingegneria Navale, Elettrica, Elettronica e delle Telecomunicazioni - DITEN, University of Genoa, via alla Opera Pia 11a, Genoa, 16145, Italy. · School of Energy and Environment, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong. · Department of Management, College of Business, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong.
The strong and continuous increase of AI-based services leads to the steady proliferation of AI data centres worldwide with the unavoidable escalation of their power consumption. It is unknown how this energy demand for computational purposes will impact the surrounding environment. Here, we focus our attention on the heat dissipation of AI hyperscalers. Taking advantage of land surface temperature measurements acquired by remote sensing platforms over the last decades, we are able to obtain a robust assessment of the temperature increase recorded in the areas surrounding AI data centres globally. We estimate that the land surface temperature increases by 2°C on average after the start of operations of an AI data centre, inducing local microclimate zones, which we call the data heat island effect. We assess the impact on the communities, quantifying that more than 340 million people could be affected by this temperature increase. Our results show that the data heat island effect could have a remarkable influence on communities and regional welfare in the future, hence becoming part of the conversation around environmentally sustainable AI worldwide.
Figures & tables
Figure 1: Graphical abstract of this work: the proposed multiscale multimodal architecture for data analysis integrates records of land surface temperature trends from year 2004 to 2024, gridded population maps, and AI data centres locations worldwide, to achieve a thorough understanding of the impact of data heat islands in time, space and over communities.
Figure 2: Temperature increase through time over the AI hyperscalers locations centred around the time of start of operations ( i=0 ), according to the procedure described in Section 3 - equation ( 1 ). The aggregate average of the LST difference is shown in red solid line. The shaded areas show the interval between the maximum and minimum value of LST increase that has been recorded across the considered AI hyperscalers. The bar across the average line identifies the limit of the 95th percentile of the distribution we compute.
Δ0(k)
k=12
k=24
k=36
k=120
average
2.03
2.05
2.06
2.12
minimum
0.30
0.31
0.32
0.37
maximum
9.02
9.09
9.14
9.24
Table 1: Temperature increase (in °C) as defined in Equation ( 1 ) at AI hyperscalers start of operation (i=0) as a function of k .
Figure 3: Temperature increase through space as a function of the distance from the AI hyperscalers locations, according to the procedure described in Section 2 - equation ( 2 ). The same color policy as in Figure 2 applies here.
Figure 4: Distribution of population as a function of the LST increase they are affected by within r=10 km radius from the AI hyperscalers considered in this study with respect to the LST trend of the 5 years prior to the start of operations of the data centres, i.e., Δ010(60) in ( 2 ) in Section 2 .
The rapid rise of generative artificial intelligence (AI) is driving unprecedented growth in global computational demand, placing increasing pressure on electricity systems. This study introduces an AI-energy coupling framework that combines large language models (LLMs)-based analysis of corporate, policy, and media data with quantitative energy-system modeling to forecast the electricity footprint of AI-driven data centers from 2025 to 2030. Results show that the new AI infrastructure is highly concentrated in North America, Western Europe, and the Asia-Pacific, which together account for more than 90% of projected compute capacity. Aggregate electricity consumption by the six leading firms is projected to increase from roughly 118 TWh in 2024 to between 239 TWh and 295 TWh by 2030, equivalent to about 1% of global power demand. Regions such as Oregon, Virginia, and Ireland may experience high Power Stress Index (PSI) values exceeding 0.25, indicating local grid vulnerability, whereas diversified systems such as those in Texas and Japan can absorb new loads more effectively. These findings demonstrate that AI infrastructure is evolving from a marginal digital service into a structural component of power-system dynamics, underscoring the need for anticipatory planning that aligns computational growth with renewable expansion and grid resilience.
Danbo Chen, Zijun Zhou, Yongyang Cai +3
The Ohio State University, Columbus, OH 43210, USA. · Meta Platforms Inc., Menlo Park, CA 94025, USA. · Zhejiang A&F University, Hangzhou, Zhejiang 311300, China.
The emerging of large language models (LLMs) has posed significant challenges to the thermal management of data center. Intense GPU computation for LLMs results in localized hotspots. Moreover, spiking thermal loads during training and inference bursts make real-time cooling response more difficult to predict and control. Thermal-aware capacity planning of data center requires massive expensive high-fidelity CFD simulations. AI models can perform real-time prediction for unseen designs. However, existing works either have large prediction error, or have over-simplified assumptions for data center operations. This work presents an AI-driven framework that can perform thermal-aware capacity planning for a real-world data center in seconds. The embedded AI model learns from numerous key parameters (rack power, server power, server placement, HVAC settings etc.), and provides temperature prediction within milliseconds. This AI model is tested against high-fidelity CFD simulations, and results show that for unseen data center designs, model can achieve high accuracy with 10000X speedup. Driven by the AI model, the authors design the thermal-aware capacity planning framework. This framework can help data center designers and operators instantaneously optimize both workload distribution and HVAC cooling efficiency.
The sudden growth in AI data centers is raising numerous environmental concerns. Low-frequency noise pollution is a concern for local residents, that may not just be an annoyance but also carry significant health impacts. An FDTD model of sound propagation over the topography near an AI data center is presented. The topography plays a large role in the level of noise pollution, with areas of sudden elevation on either side of the river near the AI data center more susceptible to high noise levels, while areas further afield largely protected from noise pollution by the topography. Understanding the role that topography plays in noise pollution, and capturing the noise pollution in specific topographies, can help identify areas where noise mitigation strategies might be best employed.
Gavin Buxton
Science Department, Robert Morris University, Moon Township, PA 15108, US.