Evidence map›Paper›PMID 36767989›Full record

ArticleInternational journal of environmental research and public health2023

The Impact of Foreign Direct Investment on the Forestry Industry Structure Upgrading: The Moderating Effect on Labor Migration.

Fangmiao Hou, Xiaoyi Li, Chengliang Wu, Yufei Yin, Hui Xiao

Abstract read
In one paragraph

Article in International journal of environmental research and public health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

5 authors.

Fangmiao HouSchool of Economics and Management, Beijing Forestry University, Beijing 100083, China.
Xiaoyi LiSchool of Economics and Management, Beijing Forestry University, Beijing 100083, China.
Chengliang WuSchool of Economics and Management, Beijing Forestry University, Beijing 100083, China.
Yufei YinSchool of Economics and Management, Beijing Forestry University, Beijing 100083, China.
Hui XiaoSchool of Economics and Management, Beijing Forestry University, Beijing 100083, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study examines the impact of foreign direct investment in forestry, a prominent phenomenon in China, on forestry industry structure upgrading, using comprehensive economic theory and the panel data of 27 provinces from 2003 to 2019 in China. We used fixed and moderating effect models, and regional heterogeneity tests were conducted. Our results indicate that, at the national level, foreign direct investment in forestry and labor migration promotes forestry industry structural upgrading. In addition, our results indicate that labor migration as a moderating variable weakly promotes forestry industry structural upgrading via foreign direct investment in forestry, and these effects have regional heterogeneity. Finally, different control variables also have influence on forestry industry structural upgrading, such as the number of forestry stations. Based on these empirical results, we provide an explanation and give policy implications, such as developing secondary and tertiary forestry industries, building forestry infrastructure, and improving the efficiency of forestry foreign investment utilization to promote the optimization of the forestry industry structure in China.

Indexed as

Emigration and ImmigrationForestryChinaEconomic DevelopmentHumansIndustryInvestmentsforestry foreign direct investmentforestry industry structure upgradinglabor migrationmoderating effect model

Identifiers

PMID36767989
PMCPMC9915711

What OpenQuestion holds

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LicenceCC BY
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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.