Evidence map›Paper›PMID 38295023›Full record

ArticlePloS one2024

Evolutionary game analysis of carbon emission reduction of Internet enterprises under multiple constraints.

Maochun Zhou, Lei Qian

Abstract 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
–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.

Maochun ZhouSchool of Business Administration, Liaoning Technical University, Huludao City, Liaoning Province, China.
Lei QianSchool of Business Administration, Liaoning Technical University, Huludao City, Liaoning Province, China.ORCID 0009-0007-5985-5328

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

With the rapid development of information technology, internet enterprises have sprung up, bringing about huge power consumption due to constantly expanding enterprise scale, which in turn leads to significant carbon emissions. Additionally, the large influence of internet enterprises on the public and other businesses makes it particularly necessary to pay attention to their carbon emission reduction efforts. To explore the evolution path and patterns of carbon emission reduction among internet enterprises under the carbon neutrality goal, this paper constructs an evolutionary game model for internet enterprises to enter the carbon emissions trading market based on Externality and Sustainable Development Theories, while considering constraints from the carbon market, financial institutions and the public. The model utilizes Python 3.8.2 software for numerical simulations, aiming to push internet enterprises towards low-carbon development. The research findings indicate that: (1) Carbon emission reduction behavior of internet enterprises exhibits significant externality, and when constraints are weak or incentives are not evident, the motivation for enterprises to reduce carbon emissions is insufficient. (2) The carbon market can effectively promote carbon emission reduction among internet enterprises, and the strategy of entering the carbon market becomes the preferred option for these enterprises gradually. (3) Multiple constraints, including emission reduction costs, penalty for non-compliance, government subsidies, financing costs, opportunity losses, and reputation losses, can force internet enterprises towards low-carbon development.

Indexed as

CarbonCommerceChinaInformation ScienceInformation TechnologyInternetCarbon

Identifiers

PMID38295023
PMCPMC10830028

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.