Evidence map›Paper›PMID 33374555›Full record

ArticleInternational journal of environmental research and public health2020

Redistribution of China's Green Credit Policy among Environment-Friendly Manufacturing Firms of Various Sizes: Do Banks Value Small and Medium-Sized Enterprises?

Yuming Zhang, Chao Xing, David Tripe

Abstract read
In one paragraph

Article in International journal of environmental research and public health, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Are Clean Technologies More Effective Than End-of-Pipe Technologies? Evidence from Chinese Manufacturing.International journal of environmental research and public health · 2021
    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

3 authors.

Yuming ZhangSchool of Management, Shandong University, Jinan 250100, China.
Chao XingSchool of Management, Shandong University, Jinan 250100, China.ORCID 0000-0003-3774-2684
David TripeSchool of Economics and Finance, Massey University, Palmerston North 4442, New Zealand.ORCID 0000-0003-1461-0451

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

According to previous studies, China's green credit policy, which was launched in 2012, increases environment-friendly manufacturing enterprises' loan amounts. In this paper, we focus on a redistribution mechanism among environment-friendly manufacturing firms, namely, we determine whether the effects of the green credit policy differ between small and medium-sized environment-friendly manufacturing enterprises (SMEMEs) and large environment-friendly manufacturing enterprises (LEMEs). Using a difference in difference model (DID) and a difference in difference in difference model (DDD), we find that SMEMEs obtain more loans than LEMEs due to the green credit policy. We further analyze three potential foundations of this redistribution mechanism: information asymmetry, financial development, and government environmental investment. The results demonstrate that the redistribution effect occurs in both low and high information asymmetry conditions but only in regions with satisfactory financial development and with lower government environmental investment. Our findings enrich the literature on green credit, sustainable finance, and small finance, and they provide references for enterprises, banks, and governments.

Indexed as

CommerceInvestmentsChinaGovernmentIndustryPolicybanks loansChinaenvironment-friendly manufacturing enterprisesgreen creditsmall and medium-sized enterprises

Identifiers

PMID33374555
PMCPMC7793140

What OpenQuestion holds

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LicenceCC BY
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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.