Long-run electricity consumption in computing: Exponential growth followed by stabilization due to efficiency gains.

Ricardo Pinto, Paul E Brockway, Tiago Domingos, Tânia Sousa

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In one paragraph

Article in iScience, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

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0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Ricardo PintoMARETEC-Marine, Environment and Technology Center, LARSyS - Laboratory for Robotics and Engineering Systems, Instituto Superior Técnico, Universidade de Lisboa, 1049-001 Lisboa, Portugal.

Paul E BrockwaySustainability Research Institute, School of Earth, Environment and Sustainability, University of Leeds, Leeds LS2 9JT, UK.

Tiago DomingosMARETEC-Marine, Environment and Technology Center, LARSyS - Laboratory for Robotics and Engineering Systems, Instituto Superior Técnico, Universidade de Lisboa, 1049-001 Lisboa, Portugal.

Tânia SousaMARETEC-Marine, Environment and Technology Center, LARSyS - Laboratory for Robotics and Engineering Systems, Instituto Superior Técnico, Universidade de Lisboa, 1049-001 Lisboa, Portugal.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Published projections suggest that information and communication technologies could account for up to 20% of global electricity use by 2030, yet these estimates are often based on short (<5 years) historical periods. Here, we present the first global long-run (1975-2022) analysis that jointly estimates electricity consumption, processed information, and efficiency. We find that these increased by 4, 11, and 7 orders of magnitude, respectively. However, after an initial exponential growth, the share of computing devices in world electricity consumption peaked at 2.5% in 2013, then decreased and stabilized at 1.8% since 2018. The stabilisation was due to the massive increases in information processing being offset by efficiency gains associated with the growing amount of computation in large datacenters and the shift from desktop computers to laptops and, more recently, to smartphones. These results indicate that concerns about the future electricity demand of computing may be overstated.

Indexed as

electricityenergy managementinformation and communication technologies

Identifiers

PMID41782823

DOI10.1016/j.isci.2026.114876

PMCPMC12955200

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