Evidence map›Paper›PMID 38285701›Full record

ArticlePloS one2024

Research on the development strategy selection of the new energy vehicle industry from the perspective of green credit-Based on the foursquare evolutionary game analysis.

Jinlong Wang, Xiangbin Liu

Open access · goldAbstract read
In one paragraph

Article in PloS one, 2024. 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
1.0field-weighted citation impact, top 29% of its field
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, 10 citations in OpenAlex.

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 at 2 institutions in 1 country.

Jinlong WangSchool of finance, Harbin University of Commerce, Harbin, Heilongjiang, China.ORCID 0000-0001-6221-2680
Xiangbin LiuSchool of finance, Harbin University of Commerce, Harbin, Heilongjiang, China.
Harbin University · CNHarbin University of Commerce · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Developing new energy vehicles is vital to promote green development and the harmonious coexistence of humans and nature. It is also the only way to help China move from a significant automobile country to a powerful automobile country. Based on the background of the "recession" of government subsidies and considering the importance of green credit in promoting green and low-carbon transformation, this paper constructs a four-party evolutionary game model that includes government, automotive companies, banks, and consumers to analyze the stability of the strategic choices of various parties in the development process of the new energy vehicle industry. It uses MATLAB simulation tools to analyze the impact of relevant factors on system stability. The research shows that: (1) The government's subsidy mechanism significantly promotes the development of the new energy vehicle industry. Still, there is a subsidy threshold, beyond which the effect will weaken and quickly bring financial pressure. (2) With the gradual decline of government subsidies, the bank's green credit policy has a specific policy complementary effect on the decline of government subsidies. (3) Considering that costs and benefits are the main influencing factors for automotive companies and consumers' strategic choices, the impact of factors such as the punishment of violations, adjustment of subsidy policies, and consumers' environmental awareness must also be paid attention to.

Indexed as

AutomobilesBiological EvolutionCarbonChinaComputer SimulationGovernmentHumansCarbon

Identifiers

PMID38285701
PMCPMC10824455
OpenAlexW4391324281

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.