Evidence map›Paper›PMID 39870898›Full record

ArticleScientific reports2025

Impact of policy adjustments on low carbon transition strategies in construction using evolutionary game theory.

Song Wang, Dongliang Zhu, Jiachen Wang

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

3 authors.

Song WangCollege of Architecture and Civil Engineering, Xinyang Normal University, Xinyang, 464000, China.
Dongliang ZhuCollege of Architecture and Civil Engineering, Xinyang Normal University, Xinyang, 464000, China. zdl18075343973@163.com.
Jiachen WangCollege of Architecture and Civil Engineering, Xinyang Normal University, Xinyang, 464000, China.

Funding

Soft Science Research Program of Henan Province 232400410083Xinyang Normal University Postgraduate Research Innovation Fund 2022KYJJ089
6 · The paper itself

Abstract

The construction industry is generally characterized by high emissions, making its transition to low-carbon practices essential for achieving a low-carbon economy. However, due to information asymmetry, there remains a gap in research regarding the strategic interactions and reward/punishment mechanisms between governments and firms throughout this transition. This paper addresses this gap by investigating probabilistic and static reward and punishment evolutionary games. The findings indicate that (1) Probabilistic rewards and penalties policies are more effective during the initial stages of the transition, whereas static mechanisms are more conducive to ensuring long-term stability. (2) The maximum values of rewards and penalties significantly influence the evolution of the low-carbon transition, with higher incentives enhancing motivation and more significant penalties imposing stricter constraints. (3) An increase in the cost of government involvement facilitates the low-carbon transition. (4) The benefits to both government and enterprises are critical in determining the application of static versus probabilistic rewards and penalties. The government may decide to cap probabilistic rewards and penalties by the magnitude of the benefits or adopt static rewards and penalties. This study offers theoretical support and a decision-making framework for developing effective low-carbon policies.

Indexed as

Evolutionary gameLow-carbon economyLow-carbon transitionProbabilistic rewards and punishmentsStatic rewards and punishments

Identifiers

PMID39870898
PMCPMC11772619

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.