Evidence map›Paper›PMID 39616251›Full record

ArticleScientific reports2024

Impact of computing infrastructure on carbon emissions in China.

Fengfu Mao, Yafei Wei, Yuanfan Wang

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. 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

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

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

3 authors.

Fengfu MaoSchool of Economics, Zhejiang Gongshang University, No. 18, Xuezheng Street, Hangzhou, 310018, Zhejiang, China.
Yafei WeiSchool of Economics, Zhejiang Gongshang University, No. 18, Xuezheng Street, Hangzhou, 310018, Zhejiang, China.
Yuanfan WangSchool of Economics, Zhejiang Gongshang University, No. 18, Xuezheng Street, Hangzhou, 310018, Zhejiang, China. wangyfa11@126.com.

Funding

the Annual General Project of the Graduate Scientific Research and Innovation Fund of Zhejiang Gongshang University YBXM2024041the Ministry of education of Humanities and Social Science Project 24YJA790043the Natural Science Foundation of Zhejiang Province Z24G030005
6 · The paper itself

Abstract

The development of computing infrastructure has brought about increased productivity, but it has also brought about energy consumption and carbon emissions. Based on the panel data and business enterprise registration data of 279 prefecture-level cities from 2008 to 2021, using the econometric model system, this study investigates the relationship between computing infrastructure and carbon emission intensity, yielding several findings: First, our result finds that there is an inverse "U-shaped" pattern in the association between carbon emission intensity and computing infrastructure. According to several robustness tests, such as using IV method and PCSD model, the research conclusion still holds. Second, heterogeneity analysis indicates that our findings are particularly pronounced in central regions, hub cities and moderately digitally developed cities. Third, mechanism analysis shows that carbon emission intensity is influenced by computing infrastructure through energy consumption, green technological innovation, and servitization of the economic structure, with energy consumption also following an inverted "U-shaped" pattern. These findings contribute to understanding the environmental impact of digital infrastructure and offer insights for promoting sustainable development.

Indexed as

Carbon emission intensityComputing infrastructureEconomic structure servitizationEnergy consumptionGreen technological innovation

Identifiers

PMID39616251
PMCPMC11608359

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.