Evidence map›Paper›PMID 41772020›Full record

ArticleScientific reports2026

Types identification, patterns characterize and pathway optimization of synergistic development between urbanization and pollution control in Chinese urban agglomerations.

Yushu Qin, Hongtao Li

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.

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0citing papers in PubMed
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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

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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.

Yushu QinSchool of Economics, Guangxi Minzu University, Nanning, 530006, China.
Hongtao LiSchool of Politics and Public Administration, Guangxi Minzu University, Nanning, 530006, China. lihongtao@gxmzu.edu.cn.

Funding

National Social Science Fund of China 25CGL167
6 · The paper itself

Abstract

Against the backdrop of China's carbon peaking and neutrality goals, the way urban agglomerations coordinate rapid urbanization with pollution control has direct implications for energy use and the low-carbon transition. Yet evidence at the urban-agglomeration scale remains limited: synergy is often reduced to an overall score or coarse category, within-agglomeration spatiotemporal patterns are rarely integrated, and policy implications stay generic because outcomes are seldom linked to subsystem-level drivers. To address these gaps, this study examines nineteen major urban agglomerations in China and develops a unified evaluation and diagnostic framework that supports comparable city-level type identification, agglomeration-scale spatiotemporal mapping, and subsystem-based explanation of coupling outcomes. Using a consistent indicator system, we first apply the entropy-TOPSIS method to measure city-level urbanization and pollution control, then construct a coupling-coordination typology and track the spatiotemporal evolution of coordination types within and across agglomerations. We further decompose subsystem influences to identify binding constraints and derive agglomeration-specific optimization directions. The results show that: (1) most cities are concentrated in medium-level development patterns, with clear within-agglomeration heterogeneity and core-periphery gradients; (2) coupling coordination improves over time but remains generally weak, with progress concentrated in core cities and slow diffusion to surrounding cities, so maladjustment persists widely; and (3) coordination outcomes are constrained by asymmetric subsystem influences, meaning that at least one subsystem in most agglomerations persistently acts as a bottleneck. Overall, the study provides a reproducible basis for diagnosing where and why synergy remains weak and for designing differentiated, subsystem-targeted pathways to support greener and lower-carbon development in China's urban agglomerations.

Indexed as

Low-carbon transitionOptimization pathwaysSynergistic developmentUrban agglomerations

Identifiers

PMID41772020
PMCPMC13056994

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.