Recalibrating global artificial intelligence e-waste estimates

• By 2030, AI servers could generate 131.0–224.8 kilotons of e-waste per year. • AI systems may contribute less to global e-waste than previously anticipated. • The gap highlights the need for supply-chain data and realistic AI server lifespans. • 2030 AI e-waste could still match Denmark, Norway, or Austria’s 2022 e-waste. • Substantial AI e-waste persists, underscoring the need for data center transparency.

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Publication Details

Journal
Resources Conservation and Recycling
Published
2026-02-24
DOI
https://doi.org/10.1016/j.resconrec.2026.108872
Primary Topic
Recycling and Waste Management Techniques
Type
article
Field-Weighted Citation Impact
0.00
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article

Recalibrating global artificial intelligence e-waste estimates

Alex de Vries-Gao
Resources Conservation and Recycling
Recycling and Waste Management Techniques
article

Recalibrating global artificial intelligence e-waste estimates

Alex de Vries-Gao
article en

Abstract

• By 2030, AI servers could generate 131.0–224.8 kilotons of e-waste per year. • AI systems may contribute less to global e-waste than previously anticipated. • The gap highlights the need for supply-chain data and realistic AI server lifespans. • 2030 AI e-waste could still match Denmark, Norway, or Austria’s 2022 e-waste. • Substantial AI e-waste persists, underscoring the need for data center transparency.

Resources Conservation and RecyclingVol. 229
Flevoziekenhuis (NL), Vrije Universiteit Amsterdam (NL), De Nederlandsche Bank (NL)
Openalex Percentile: Top 42%
Recycling and Waste Management Techniques
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