Evidence map›Paper›PMID 34184215›Full record

ArticleEnvironmental science and pollution research international2021

How does industrial policy affect manufacturing carbon emission? Evidence from Chinese provincial sub-sectoral data.

Li Song, Xiaoliang Zhou

Abstract read
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In one paragraph

Article in Environmental science and pollution research international, 2021. 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. 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

2 authors.

Li SongSchool of Economics and Management, Fuzhou University, No. 2 Wulongjiang North Avenue, Daxue New District, Fuzhou, 350108, Fujian, China. songdm520@hotmail.com.ORCID http://orcid.org/0000-0003-1607-3231
Xiaoliang ZhouSchool of Economics and Management, Fuzhou University, No. 2 Wulongjiang North Avenue, Daxue New District, Fuzhou, 350108, Fujian, China.

Funding

Major project funding for social science research base in Fujian province social science planning FJ2020MJD2015
6 · The paper itself

Abstract

Assessing the environmental impact of industrial policy is of great importance to China's industrial green transformation and high-quality development, while this topic receives little attention in the literature. Based on the panel data of 29 manufacturing industrial sectors in China's 30 provinces from 2006 to 2015, using a model that includes the year, province, and industry fixed-effect, this paper fills the gap by exploring whether and how industrial policy affects carbon emission in Chinese manufacturing. The result shows that industrial policy significantly increases carbon emission in supported industries by 10.3%, which is achieved by increasing industrial energy consumption, relaxing government environmental regulation, and encouraging enterprises to invest more in fixed assets. Further, the effects of industrial policy on carbon emission are heterogeneous in many aspects. In the central and western regions, technology-intensive industries, industries with stronger government intervention, industries with higher exports, industries supported by both central and local governments, and during the 12th Five-Year Plan, the industrial policy will increase carbon emissions more. Our findings for the first time indicate the negative impact of industrial policies on carbon emission reduction in the Chinese manufacturing industry, which implies that traditional selective industrial policies in developing countries like China need a paradigm shift to achieve green development.

Indexed as

CarbonCarbon DioxideChinaIndustryPolicyCarbonCarbon DioxideCarbon emissionChinaIndustrial policyManufacturing

Identifiers

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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.