Evidence map›Paper›PMID 37319257›Full record

ArticlePloS one2023

Environmental protection tax and green innovation of heavily polluting enterprises: A quasi-natural experiment based on the implementation of China's environmental protection tax law.

Juqiu Deng, Jiayu Yang, Zhenyu Liu, Qingyang Tan

Erratum issuedAbstract read
In one paragraph

Article in PloS one, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. 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

4 authors.

Juqiu DengSchool of Economics, Sichuan University, Chengdu, Sichuan, China.
Jiayu YangSchool of Economics, Sichuan University, Chengdu, Sichuan, China.ORCID 0000-0003-0102-7734
Zhenyu LiuSchool of Economics, Sichuan University, Chengdu, Sichuan, China.
Qingyang TanSchool of Economics, Sichuan University, Chengdu, Sichuan, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Environmental protection tax is an important tool for directing environmentally friendly growth in heavily polluting enterprises, but existing research has yet to provide consistent conclusions on whether and how environmental protection tax can promote green innovation in heavily polluting industries. The paper uses a double difference model based on data from Chinese listed companies in heavily polluting industries from 2012 to 2021 to empirically investigate whether environmental protection tax drives green innovation behavior of heavily polluting enterprises. The findings show that the environmental protection tax increases the degree of green innovation in heavily polluting enterprises, primarily through the anti-driving effect, in which an increase in environmental management expenses forces firms to increase their R&D investment, which improves the degree of green technical innovation. Furthermore, the environmental protection tax has a strong promotion effect on heavy polluters' green innovation for state-owned enterprises and those in growing period or located in high marketization regions. However, this promotion effect is insignificant for non-state-owned enterprises and those in recession period, and environmental protection tax hinders green innovation of enterprises in mature period and those located in low marketization regions. Accordingly, it is suggested to improve preferential tax policies, increase investment in corporate green innovation and strengthen the supervision of environmental tax.

Indexed as

Conservation of Natural ResourcesChinaEnvironmental PolicyIndustryInvestments

Identifiers

PMID37319257
PMCPMC10270568

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

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.