Evidence map›Paper›PMID 41006412›Full record

ArticleScientific reports2025

A study on synergistic development of urban digitalization and greening under the diffusion of low-carbon pilot policies.

Jingyuan Zhang, Jinguang Guo

Abstract read
In one paragraph

Article in Scientific reports, 2025. 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

2 authors.

Jingyuan ZhangSchool of Public Administration, Dongbei University of Finance and Economics, Dalian, 116025, Liaoning, China.
Jinguang GuoSchool of Public Administration, Dongbei University of Finance and Economics, Dalian, 116025, Liaoning, China. dl_bright@163.com.

Funding

Jingmei Gao 24YJA630022Jinguang Guo 72274029
6 · The paper itself

Abstract

Low-carbon pilot policies, as comprehensive environmental strategies, provide a valuable perspective for examining the integrated development of urban digitalization and environmental sustainability. However, from the perspective of an integrated "pilot-diffusion" approach, the varying effects of pilot policies on the synergistic development of urban digitalization and greening, under different diffusion modes, are still not well clear. This study uses panel data from 278 Chinese cities between 2005 and 2022 and employs a double machine learning model to effectively control for high-dimensional confounders, reducing biases commonly associated with traditional methodologies. The results indicate that low-carbon city pilot policies effectively promote the synergistic development of urban digitalization and greening, with hierarchical diffusion demonstrating stronger policy effects compared to horizontal diffusion. Additionally, the upgrading of industrial structures and green technological innovation were found to regulate the effects of the policies, and the promotional effect of low-carbon pilot policies on the urban digitalization and greening development development of cities exhibits heterogeneity across different regions and city types. Through the analysis of policy diffusion patterns, this paper clarifies how different diffusion mechanisms influence policy outcomes, providing targeted policy recommendations and theoretical insights to support the coordinated progress of urban digitalization and greening. To enhance future low-carbon pilot policies, this paper emphasizes the need to adapt policy designs to specific diffusion modes and regional contexts, encouraging targeted and differentiated strategies to foster sustainable urban development.

Indexed as

Double machine learningLow-carbon pilot policiesPolicy diffusionUrban digitalization and greening synergies

Identifiers

PMID41006412
PMCPMC12475201

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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.