Evidence map›Paper›PMID 39224311›Full record

ArticleHeliyon2024

Analyzing nonlinear and asymmetric effects of green finance and renewable energy on energy efficiency amidst technological innovation in E7 countries.

Mingda Shi, Jing Yu

Abstract read
In one paragraph

Article in Heliyon, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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.

Mingda ShiSocial Hub, Fintech Thrust, Hong Kong University of Science and Technology Guangzhou, Guangzhou, Guangdong Province, 511458, China.
Jing YuHarbin University, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The study aims to explore the relationship between green funding, green energy, and energy efficiency in E7 countries, guided by the SDG-7 guidelines recommended by the United Nations General Assembly. Methodologically, the study employs the Nonlinear Autoregressive Distributed Lag (NARDL) and Two-Stage Least Squares (2SLS) techniques on data collected between 1988 and 2022. The rationale for this approach lies in its ability to capture both short-term and long-term dynamics in the relationship between green funding, green energy, and energy efficiency. Analysis of the data reveals varying stages of green funding growth among E7 countries, with China (1.52), Brazil (1.44), India (1.35), Indonesia (1.94), Mexico (1.73), and Russia (1.93) exhibiting different levels of progress. Russia and Turkey are identified as having the highest Gini coefficients in 2019, indicating disparities in green funding distribution within these countries. The empirical findings underscore the critical role of investment in the energy sector by both corporations and the public sector to enhance access to electricity, bolster energy security, and foster environmentally sustainable economic development. However, the study identifies insufficient investment as a fundamental obstacle hindering progress in green energy efficiency in E7 nations. Despite the potential for implementing energy efficiency measures and renewable energy sources in E7 countries, the future remains uncertain due to existing obstacles in green financing and regulatory frameworks. Consequently, the paper emphasizes the imperative of addressing these obstacles to unlock the full funding potential for energy efficiency initiatives in E7 nations.

Indexed as

E7 countriesEnergy efficiencyGreen financeRenewable energy

Identifiers

PMID39224311
PMCPMC11367541

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.