Evidence map›Paper›PMID 37234613›Full record

ArticleHeliyon2023

The evolution of renewable energy environments utilizing artificial intelligence to enhance energy efficiency and finance.

Fengge Yao, Zenan Qin, Xiaomei Wang, Mengyao Chen, Adeeb Noor, Shubham Sharma, Jagpreet Singh, Dražan Kozak, Anica Hunjet

RetractedAbstract readRetracted Publication
In one paragraph

Article in Heliyon, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It has been retracted, and should not be counted. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

9 authors.

Fengge YaoFinance of School, Harbin University of Commerce, Harbin, China.
Zenan QinFinance of School, Harbin University of Commerce, Harbin, China.
Xiaomei WangSchool of Tourism and Culinary Arts, Harbin University of Commerce, Harbin, China.
Mengyao ChenSchool of Media, NingboTech University, Ningbo, 315000, China.
Adeeb NoorDepartment of Information Technology, King Abdulaziz University, Jeddah, 80221, Saudi Arabia.
Shubham SharmaMechanical Engineering Department, University Centre for Research and Development, Chandigarh University, Mohali, Punjab, 140413, India.
Jagpreet SinghDepartment of Computer Science and Engineering, IK Gujral Punjab Technical University, SAS Nagar, Punjab, 160055, India.
Dražan KozakUniversity of Slavonski Brod, Mechanical Engineering Faculty in Slavonski Brod, Trg Ivane Brlić-Mažuranić 2, HR-35000, Slavonski Brod, Croatia.
Anica HunjetUniversity Center Varaždin, University North 104. Brigade 3, HR-42 000, Varaždin, Croatia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The development of a country is inseparable from the material guarantee mainly based on energy, but energy is limited, which may restrict the sustainable development of the country. It is very necessary to accelerate the adoption of programs aimed at switching non-renewable energy sources to ones that are, and giving priority to improving renewable energy consumption and storage capabilities. From the experience of the G7 economies, the development of renewable energy (RE) is inevitable and urgent. The China Banking Regulatory Commission has recently issued a number of directives, such as the "Directives for Green Credit" and "Instructions for Granting Credit to Support Energy Conservation and Emission Reduction," to help businesses that use "renewable energy expand". This article firstly discussed the definition of the "green institutional environment" (GIE) and the construction of the index system. Then, on the basis of clarifying the relationship between the GIE, and RE investment theory, a semi-parametric regression model was constructed to empirically analyze the mode and effect of the GIE. Considering the balance between improving model accuracy and reducing computational complexity, the number of hidden nodes opted in this study is 300 so as to lower the time needed to predict the model. Finally, from the perspective of enterprise scale, the level of GIE played a significant role in promoting RE investment in small and medium-sized enterprises, with a coefficient of 1.8276, while the impact on RE investment in large enterprises had not passed the significance test. Based on the conclusions, the government should focus on building a GIE dominated by green regulatory systems, supplemented by green disclosure and supervision systems, and green accounting systems, and should make reasonable plans for releasing various policy directives. At the same time, while offering full play to the guiding role of the policy, its rationality should also be paid attention to, and the excessive implementation of the policy should be avoided, so that an orderly, and good GIE can be created.

Indexed as

Artificial intelligenceEnergy efficiency financingG7 economiesRenewable energy

Identifiers

PMID37234613
PMCPMC10208837

What Socratic 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 Socratic graph.