Evidence map›Paper›PMID 41027956›Full record

ArticleScientific reports2025

A stochastic evolutionary game of boosting urban low-carbon development in China.

Rongjiang Cai, Edmund Hoi Ngan Loi, Xi Wang, Shufang Zhao, Tao Zhang

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
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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

5 authors.

Rongjiang CaiSchool of Economics and Management, Ningbo University of Technology, Ningbo, China.
Edmund Hoi Ngan LoiFaculty of Humanities and Social Sciences, Macao Polytechnic University, Macao, China.
Xi WangFaculty of Humanities and Social Sciences, Macao Polytechnic University, Macao, China.
Shufang ZhaoFaculty of Humanities and Social Sciences, Macao Polytechnic University, Macao, China.
Tao ZhangFaculty of Humanities and Social Sciences, Macao Polytechnic University, Macao, China. taozhang@mpu.edu.mo.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

China has made notable strides in developing low-carbon cities through policy formulation, pilot programs, and technological innovation. However, significant challenges remain. This study investigates the strategic choices and interaction mechanisms among enterprises, governments, and the public in the context of urban low-carbon development under environmental uncertainty. Using stochastic evolutionary game theory, we introduce Gaussian white noise into a tripartite evolutionary game model to more accurately simulate the influence of external environmental uncertainties on stakeholder decision-making. Numerical simulation yields several key findings: (1) Active public participation can partially substitute for government regulation; (2) Government subsidy mechanisms exhibit heterogeneity, with excessively high subsidies potentially discouraging public participation; (3) Enterprise behavior is highly sensitive to social losses, and minimizing these losses strongly incentivizes low-carbon initiatives; (4) Public participation is most responsive to enterprise compensation mechanisms; and (5) The effectiveness of government regulation is positively correlated with penalty intensity. To promote urban low-carbon development, it is essential to optimize subsidy structures to reduce heterogeneity, enhance enterprise compensation to increase public engagement, and establish reasonable penalty mechanisms to improve regulatory effectiveness. This study provides a theoretical foundation and policy recommendations to support urban low-carbon development. While the model parameters are primarily based on Chinese cases, future research should incorporate international case studies to enrich empirical data and offer broader insights into the global low-carbon transition.

Indexed as

Evolutionary game systemLow-carbon city constructionStochastic tripartite gameSustainable development

Identifiers

PMID41027956
PMCPMC12484589

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.