Evidence map›Paper›PMID 41922733›Full record

ArticleScientific reports2026

Exploring the nonlinear relationship between robotics manufacturing and urban carbon emissions.

Jie Lin, Yizhi Xie, Jianfu Shen

Abstract read
In one paragraph

Article in Scientific reports, 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

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.

Jie LinBusiness School, Shantou University, 5 Cuifeng Road, Shantou, 515821, Guangdong, China. linjie@stu.edu.cn.
Yizhi XieDepartment of Social Science and Policy Studies, Education University of Hong Kong, Ting Kok, Hong Kong, China.
Jianfu ShenDepartment of Building and Real Estate, Hong Kong Polytechnic University, Hung Hom, Hong Kong, China.

Funding

Guangdong Provincial Basic and Applied Basic Research Fund Committee 2024A1515110058
6 · The paper itself

Abstract

Robotics is widely regarded as a key driver of digital transformation and green industrial upgrading, yet its environmental implications remain ambiguous. Existing studies primarily focus on the emission effects of robot applications, while largely neglecting robotics manufacturing as an energy- and resource-intensive industrial activity. Using panel data from 277 Chinese prefecture-level cities from 2008 to 2019, this study examines the relationship between robotics manufacturing development and urban carbon emissions. We find a robust inverted U-shaped relationship: carbon emissions initially increase with the expansion of robotics manufacturing but decline once the industry reaches a moderate scale. Mechanism analysis reveals a stage-dependent sequential pathway, whereby mature robotics manufacturing promotes robot adoption, improves urban energy efficiency, and ultimately reduces carbon emissions, while this channel is inactive at early stages. Heterogeneity analysis shows that carbon-mitigation effects are more pronounced in the central region than in the eastern region, suggesting a latecomer advantage in green industrialization. Subsector analysis further indicates that system integration delivers earlier and stronger carbon-reduction effects than ontology manufacturing. These findings highlight the importance of considering industrial life-cycle stages and value-chain positions when designing policies to align high-tech industrial development with carbon-reduction goals.

Indexed as

Carbon emissionsEnergy efficiencyInverted U-shaped relationshipRobotics manufacturing industry

Identifiers

PMID41922733
PMCPMC13187413

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.