Evidence map›Paper›PMID 35955011›Full record

ArticleInternational journal of environmental research and public health2022

Environmental Information Disclosure, Digital Transformation, and Total Factor Productivity: Evidence from Chinese Heavy Polluting Listed Companies.

Hongnan Liu, Weili Liu, Guangchun Chen

Abstract read
In one paragraph

Article in International journal of environmental research and public health, 2022. 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

3 authors.

Hongnan LiuChina Center for Special Economic Zone Research, Shenzhen University, Shenzhen 518060, China.ORCID 0000-0003-3095-6612
Weili LiuChina Center for Special Economic Zone Research, Shenzhen University, Shenzhen 518060, China.
Guangchun ChenSchool of Economics and Management, Guangdong Technology College, Zhaoqing 526100, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Environmental information disclosure, as a new environmental regulatory model, is important for achieving collaborative environmental pollution management and sustainable socioeconomic development. Based on the data of listed firms in China's A-share heavy pollution industry from 2009 to 2019, this paper empirically tested the impact of environmental information disclosure on the total factor productivity of enterprises and the contribution of digital transformation to this impact. An increase in the level of environmental information disclosure had a significant positive effect on the total factor productivity of enterprises. However, with the increase in digital transformation among enterprises, the effect of environmental information disclosure on total factor productivity improvement is gradually being replaced. The heterogeneity test results showed that the positive effect of environmental information disclosure on total factor productivity changed depending on property rights, firm size, and geographical location. The effect of environmental information disclosure was stronger for non-state firms, large firms, and firms located in the east-central region. Further mechanism tests showed that the effect was induced through innovation incentives and facilitated financing. The above results provide a valuable reference for a comprehensive understanding of the effect of environmental information disclosure on productivity and adjustment by the digital transformation of enterprises.

Indexed as

DisclosureEnvironmental PollutionChinaMetallurgyMotivationdigital transformationenvironmental information disclosurefinancing constraintsinnovation incentivetotal factor productivity

Identifiers

PMID35955011
PMCPMC9368149

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.