Evidence map›Paper›PMID 35329414›Full record

ArticleInternational journal of environmental research and public health2022

Impact of Industrial Structure Upgrading on Green Total Factor Productivity in the Yangtze River Economic Belt.

Jinhua Sun, Decai Tang, Haojia Kong, Valentina Boamah

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 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

9 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. The Impact of Foreign Direct Investment on Urban Green Total Factor Productivity and the Mechanism Test.International journal of environmental research and public health · 2022
    Article
  6. Article
  7. Article
  8. Article
  9. 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

4 authors.

Jinhua SunSchool of Law and Business, Sanjiang University, Nanjing 210012, China.
Decai TangSchool of Law and Business, Sanjiang University, Nanjing 210012, China.ORCID 0000-0003-4447-2619
Haojia KongSchool of Economics and Management, Nanjing University of Science & Technology, Nanjing 210000, China.ORCID 0000-0002-1348-5401
Valentina BoamahSchool of Management Science and Engineering, Nanjing University of Information Science & Technology, Nanjing 210044, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The Yangtze River economic belt is an inland river economic belt with international influence composed of 11 provinces and municipalities in the Yangtze River Basin. This paper uses the super-efficiency model to calculate the green total factor productivity of 11 provinces and municipalities in the Yangtze River economic belt (YREB). Then we establish a model to study the impact of industrial structure upgrading, industrial structure rationalization, and environmental regulation on green total factor productivity (GTFP). Empirical analysis shows that the industrial structure upgrading and environmental regulation have a significant impact on GTFP and show regional characteristics. The more developed the economy and the higher the industrial structure, the greater the impact of upgrading and environmental regulation on GTFP. Compared with other control variables, the urbanization rate impacts GTFP, followed by regional economic development.

Indexed as

Economic DevelopmentRiversChinaCitiesEfficiencyIndustryenvironmental regulationgreen total factor productivityindustrial structureYangtze River economic belt

Identifiers

PMID35329414
PMCPMC8954668

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.