The Global Centre for Risk and Innovation (GCRI)
Data centres and supercomputers consume electricity for computing and cooling, and many use water for cooling directly or through power generation. As demand grows, they affect grids, water resources and emissions in the places they are built. This page sets out how computing uses energy and water, how efficiency is measured with standard metrics, and what evidence serves the operators, regulators and planners concerned.
GCRI · In development · service level R0
Data centre energy and water use is the electricity and water consumed by computing facilities for processing and cooling. Cooling choices trade water against energy, clusters of facilities can strain local grids and water, and waste heat can be reused. Standard metrics such as PUE and WUE describe efficiency within stated boundaries.
For compute energy water, in GCRI's spatial-intelligence reading, the map opens on night lights, true-colour imagery and population density with a past-year temperature and sunshine chart at the centre, framed on the contiguous United States; every other NASA layer, live feed and forecast waits in the layer list.
The interactive map needs JavaScript; the layer list and sources are below.
Layers 3
Water now, for this page
Keyboard, with the map focused
Hold Ctrl or ⌘ and scroll to zoom
Latest available
NASA GIBS / EOSDIS · ASTER GDEM (METI, NASA) · Blue Marble (NASA) · © OpenStreetMap contributors (ODbL) · Natural Earth
Loading the latest NASA imagery.
Drag to move. Ctrl or ⌘ and scroll, pinch or the buttons to zoom; hover for the values under the pointer; reset returns to the region.
For compute energy water, in GCRI's spatial-intelligence reading, the map opens on night lights, true-colour imagery and population density with a past-year temperature and sunshine chart at the centre, framed on the contiguous United States; every other NASA layer, live feed and forecast waits in the layer list.Satellite and model products as NASA publishes them, each with its own date; indicators, not assessments of any place; claim ceiling INTEGRATED.
Layers and dates
The working screen behind this subject: its structure, terms and states. Names are placeholders and values illustrative.
Compute energy water · HPC facility · Month 12
| Climate ensemble | 724 | 985 | Near quota |
| Flood modelling | 437 | 587 | Within |
| Genomics pipeline | 282 | 224 | Near quota |
Infrastructure impacts and sustainability. The dossier below sets out how the risk develops, what is measured, where the data come from, how it is modelled and who decides.
Servers, storage and networking draw power continuously, and cooling adds overhead. Total demand depends on workload growth and efficiency gains.
Evaporative cooling uses less electricity but consumes water; air and closed-loop liquid cooling use less water but may use more energy.
Clusters of data centres in one region can strain local grids and water supplies even when national totals look modest.
Waste heat from computing can supply district heating where networks exist nearby, turning a loss into a resource.
Each term as this page uses it. Where a standard-setting body defines the term, the wording follows that body.
| Term | Definition |
|---|---|
| Water usage effectiveness | Site water use divided by IT equipment energy, a standard data centre metric. |
| Evaporative cooling | Cooling by evaporating water, which saves energy but consumes water. |
| Waste heat reuse | Using heat from computing for other purposes, such as district heating. |
Each indicator carries its unit, its cadence, the kind of source behind it and the evidence label a value would carry. No values are shown: a programme supplies them under its own authority.
| Indicator | Unit | Cadence | Source type | Label |
|---|---|---|---|---|
| Power usage effectiveness | ratio of total facility to IT energy | annual | Operator reporting under ISO/IEC 30134-2 | DECLARED |
| Water usage effectiveness | litres per kilowatt hour of IT energy | annual | Operator reporting | DECLARED |
| Data centre electricity consumption | terawatt hours | annual | Energy agency estimates | ESTIMATED |
| Renewable energy share | percent of electricity | annual | Operator reporting | DECLARED |
| Waste heat reused | megawatt hours | annual | Operator reporting | DECLARED |
| Data centre share of regional electricity demand | percent | annual | Grid operator data | ESTIMATED |
The topic as a computational graph: named sources feed the methods, an evidence review gates the outputs, and the decisions stay with the authorities that hold them.
Fig. 02Nodes carry their type, two metric rows and a state; connectors show what feeds what; dashed frames name the owners; the timeline is the cadence. Design of the pipeline, not a record of a run.
Named public datasets and institutional records, each with its licence class. Sources by agreement are used only under their terms and never republished.
| Source | What it gives | Licence class |
|---|---|---|
| EU data centre reporting under the Energy Efficiency Directive | Energy, water and efficiency data from EU data centres | Public summaries, EU terms (to check) |
| IEA data centre electricity estimates | Global and regional demand | Licence per product (to check) |
| Uptime Institute global data centre survey | Industry practices and efficiency trends | Public summaries, terms to check |
| Green500 list | Energy efficiency of supercomputers | Public, TOP500 terms |
| Grid operator load forecasts | Expected data centre load | Public records, terms per operator |
Data and Compute prepares evidence. The decisions below are taken by the authorities named, under their own rules, and never by the platform.
| Who decides | The question | Evidence this page serves |
|---|---|---|
| Energy regulators and grid operators | How to plan connections and supply for data centres | Regional impact analyses |
| Water authorities | Whether proposed cooling water use is sustainable | Water footprint evidence |
| Operators and public computing centres | Which cooling and heat reuse options to adopt | Footprint methods; heat reuse maps |
Nearly all the electricity a computer uses becomes heat that must be removed. Cooling methods range from air cooling to liquid cooling and evaporative systems. Evaporative cooling uses water efficiently for energy but consumes water; dry cooling saves water but uses more electricity, especially in hot weather. The choice is a local trade-off between energy and water.
Large data centres are concentrated, steady loads that can require new transmission and generation. Their growth can compete with housing, industry and electrification for grid connections. Heatwaves stress both the grid and cooling systems at the same time, a common-cause risk for continuity. Some workloads, such as model training or batch analysis, can be scheduled flexibly; others, such as real-time services, cannot.
Ratios of total facility energy to IT energy, and water use per unit of IT energy. Limits: they say nothing about whether the computing itself is efficient or useful.
Projecting data centre demand for grid planning, with power systems engineering. Limits: announced projects often exceed what is built, making forecasts uncertain.
Accounting for manufacturing, operation and disposal, drawing on energy technology. Limits: data on hardware manufacturing is limited.
Assessing how much load could shift. Limits: depends on workload types and contracts.
The current service level is R0: information and enquiries. An enquiry receives a reference and names the next step; applications, payments, compute allocations and registrations open only when their team, terms and review capacity exist.