Evidence map›Paper›PMID 38355820›Full record

ArticleScientific reports2024

The carbon emissions of writing and illustrating are lower for AI than for humans.

Bill Tomlinson, Rebecca W Black, Donald J Patterson, Andrew W Torrance

Abstract read
In one paragraph

Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

Who cites it

7 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Can Generative AI improve social science?Proceedings of the National Academy of Sciences of the United States of America · 2024
    Article
  6. Article
  7. Review
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.

Bill TomlinsonDepartment of Informatics, University of California, Irvine, Irvine, CA, 92697, USA. wmt@uci.edu.
Rebecca W BlackDepartment of Informatics, University of California, Irvine, Irvine, CA, 92697, USA.
Donald J PattersonDepartment of Informatics, University of California, Irvine, Irvine, CA, 92697, USA.
Andrew W TorranceSchool of Law, University of Kansas, Lawrence, KS, 66045, USA.

Funding

National Science Foundation DUE-2121572
6 · The paper itself

Abstract

As AI systems proliferate, their greenhouse gas emissions are an increasingly important concern for human societies. In this article, we present a comparative analysis of the carbon emissions associated with AI systems (ChatGPT, BLOOM, DALL-E2, Midjourney) and human individuals performing equivalent writing and illustrating tasks. Our findings reveal that AI systems emit between 130 and 1500 times less CO2e per page of text generated compared to human writers, while AI illustration systems emit between 310 and 2900 times less CO2e per image than their human counterparts. Emissions analyses do not account for social impacts such as professional displacement, legality, and rebound effects. In addition, AI is not a substitute for all human tasks. Nevertheless, at present, the use of AI holds the potential to carry out several major activities at much lower emission levels than can humans.

Indexed as

CarbonGreenhouse GasesHumansCarbonGreenhouse Gases

Identifiers

PMID38355820
PMCPMC10867074

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.