Evidence map›Paper›PMID 42310429›Full record

ArticleScientific reports2026

Artificial intelligence facilitates urban green transition in the Yangtze River Delta urban agglomeration.

Weiming Yu, Xiaoyan Yang, Linlin Zheng, Qingshan Wang

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

4 authors.

Weiming Yu *School of Economics and Management, Beibu Gulf University, Qinzhou, 535000, China. lluzmprotonmail@163.com.
Xiaoyan Yang *School of Petroleum and Chemical Engineering, Beibu Gulf University, Qinzhou, 535000, China. lindazll163@163.com.
Linlin Zheng *School of Economics, Shanghai University, Shanghai, 201900, China.
Qingshan WangSchool of Economics, Shanghai University, Shanghai, 201900, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study examines the pivotal role of artificial intelligence (AI) in advancing the urban green transition (UGT), with a particular focus on the Yangtze River Delta (YRD) Urban Agglomeration. Drawing on panel data from 40 cities in the YRD between 2012 and 2024, the research utilizes benchmark regression, non-linear effect analysis, and spatial econometric models to investigate how AI influences UGT through production, consumption, and agglomeration channels. The main findings are as follows: (1) AI exerts a significant positive effect on UGT. (2) UGT exhibits strong spatial autocorrelation, whereas AI's impact is characterized by robust local promotion but limited spatial spillover. (3) The influence of AI displays marked heterogeneity, varying by urban hierarchy, geographic location, resource endowment, and levels of institutional and market development. (4) The effects of AI on UGT are mediated by fixed capital stock, industrial structure upgrading, and consumption levels. Additionally, digital infrastructure, green consumption awareness, and digital industry agglomeration serve as moderating factors. AI's influence also exhibits nonlinear diminishing marginal effects at specific thresholds in productive services and agricultural agglomeration. These results highlight the multifaceted mechanisms through which AI drives sustainable urban development and offer policy implications for fostering green transformation in metropolitan regions.

Indexed as

Agglomeration-sideArtificial intelligenceConsumption-sideDigital economyProduction-sideUrban green transition

Identifiers

PMID42310429
PMCPMC13424368

What OpenQuestion holds

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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.