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The data heat island effect: quantifying the impact of AI data centers in a warming world

Andrea Marinoni, Pietro Lio', Erik Cambria, Luca Dal Zilio, Weisi Lin, Mauro Dalla Mura, Jocelyn Chanussot, Edoardo Ragusa, Gianmarco Mengaldo, Chi Yan Tso, Yihao Zhu, Benjamin Horton

Abstract

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.

The data heat island effect: quantifying the impact of AI data centers in a warming world

Abstract

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.
Paper Structure (7 sections, 2 equations, 4 figures, 1 table)

This paper contains 7 sections, 2 equations, 4 figures, 1 table.

Table of Contents

  1. Introduction
  2. Data and methods
  3. Results and discussion
  4. A way forward
  5. Software-based solutions
  6. Hardware-based solutions
  7. Conclusions

Figures (4)

  • 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 \ref{['sec:results']} - equation (\ref{['eq_DeltaT']}). 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.
  • Figure 3: Temperature increase through space as a function of the distance from the AI hyperscalers locations, according to the procedure described in Section \ref{['sec:methods']} - equation (\ref{['eq_DeltaT_space']}). The same color policy as in Figure \ref{['fig_TempIncrease']} 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., $\Delta_0^{10}(60)$ in (\ref{['eq_DeltaT_space']}) in Section \ref{['sec:methods']}.