Revue De La Littérature Récente Concernant Les Ilots De Chaleur Attribués Aux Data Centers
Table des matières
Revue de littérature concernant les ilots de chaleur attribuables aux data centers
| The Data Heat Island Effect: Quantifying the Impact of AI Data Centers in a Warming World | Data Center Waste Heat as an Emerging Urban Thermal Hazard: First Field Measurements of Neighborhood-Scale Air Temperature Impacts | Amezana Roboto’s investigation | |
|---|---|---|---|
| Authors | Marinoni et al. | Sailor et al. | Amezana Roboto |
| Year | 2026 | 2026 | 2026 |
| Peer reviewed | No (preprint) | Yes, published in ASME | No (investigation) |
| Methodology | Satellite and meteorological hitoric data, AI data centers locations | Vehicle based actual measurements around the data centers, crossed with meteorological data (wind) | Satellite and metorological data |
| Radius | Up to 10km, intensity decreasing, down to 30% at 7km | Up to 500m | 150m, then 300m with a decrease |
| Temperature difference | 2C average, up to 9C maximum difference recorded | 0.7 - 0.9C average, up to 2C in only one scenario | 0.3 C attributible to servers and cooling, 2C difference total combined with artificialization, baseline trend |
| Kind of Datacenters | AI Data centers (big dataset) | 36MW to 160 MW (4 datacenters) | 11 MW (only one datacenter) |
| Weaknesses | - doesn’t differentiate baseline trend, artificialization effect, from servers/cooling effect | - urban area measurements not allowing for a complete radius coverage | - not actual measurements - how about the modelization for separating artificialization from cooling system ? |
| Strength | - area covered | - actual measurements, accounting for wind pattern and velocity | - Satellite data resolution (30x higher than Marioni’s) - Separates artificialization effect, baseline trend, from actual servers/cooling effect - size of the dataset : 32000 temperature data points |
| Context | Lesser dense areas, far from cities | Dense urban areas | Urban area |
Data Center Waste Heat as an Emerging Urban Thermal Hazard: First Field Measurements of Neighborhood-Scale Air Temperature Impacts
Sailor D Abolhassani S Martin E
communication
2026
method:
- vehicle based traverse measurements of air temperature in residential neighborhoods
** in dense cities **
2c max, average of 0.7 - 0.9
up to 500m
The Data Heat Island Effect: Quantifying the Impact of AI Data Centers in a Warming World
Marinoni et al
** outside of cities **
preprint
method:
- satellite data crossed with data center locations
2026
• Quantification of the land surface temperature increase connected to the establishment of an AI hyperscalers; • Assessment of the region of influence of this increase; • Estimation of the population affected by the temperature increase.
2C average but (up to 9C max):
- doesn’t check if because of artificialization or really from the heat coming from servers and cooling system
- doesn’t separate from the general trend
- too large resolution to be precise and distinguish from the local area
Amezana Roboto
2026
33 times higher resolution than marioni’s
separates artificialization effect from the actual cooling effect
2c difference but : 17% only from cooling ~= 0.3 C
150m radius, different from other kind of facilities 300m with a decrease