Evidence map›Paper›PMID 36735660›Full record

ArticlePloS one2023

Environmental protection subsidies, green technology innovation and environmental performance: Evidence from China's heavy-polluting listed firms.

Chunyan Du, Qiang Zhang, Dekai Huang

Abstract 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. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

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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.

Chunyan DuFinancial Planning Department, China West Normal University, Nanchong, Sichuan, China.ORCID 0000-0003-4326-4930
Qiang ZhangBusiness School, Yunnan University of Finance and Economics, Kunming, Yunnan, China.
Dekai HuangYibin Vocational and Technical College, Yibin, Sichuan, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The heavy-polluting industry is inexorably to responsible for the deterioration of the environment. Improving environmental performance is an unavoidable decision for heavy-polluting firms to ensure sustainable development under the policy framework of the carbon peak target. This study provides theoretical and empirical evidence for the effect of environmental protection subsidies on environmental performance. This study constructs basic and mediating effect models to measure how environmental protection subsidies affect environmental performance using panel data of China's heavy-polluting listed firms from 2008 to 2019. This is an important outcome of industrial green transformation in environmental governance and provides a scientific basis for government departments to formulate environmental policies. The results of the empirical analysis show that environmental protection subsidies can improve the environmental performance of heavy-polluting listed firms. After receiving environmental protection subsidies, firms engaged in clean and green production through green technology innovation, thereby reducing external environmental pollution and improving their environmental performance. The mediating role of green technology innovation in the relationship between environmental protection subsidies and environmental performance is significant only in state-owned firms and firms in Eastern China. The research results may further guide the direction of green development of heavy-polluting industries, and thus promote harmonious development between the environment and the economy.

Indexed as

Conservation of Natural ResourcesEnvironmental PolicyChinaEnvironmental PollutionInventionsMetallurgy

Identifiers

PMID36735660
PMCPMC9897542

What OpenQuestion holds

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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.