Evidence map›Paper›PMID 41481628›Full record

ArticlePloS one2026

Research on the impact of population mobility on green total factor productivity: A perspective from sustainable development.

Zuogong Wang, Xueli Yang, Linhai Wang, Zhuangfei Han

Abstract read
In one paragraph

Article in PloS one, 2026. 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
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0citing papers in PubMed
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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

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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

4 authors.

Zuogong WangWestern China Green Development Strategy Research Institute, Guizhou Ufniversity of Finance and Economics, Guiyang, China.ORCID https://orcid.org/0000-0002-5835-3883
Xueli YangSchool of Economics, Guizhou University of Finance and Economics, Guiyang, China.
Linhai WangDepartment of Mathematics, University of Illinois Urbana-Champaign, Champaign, Illinois, United States of America.
Zhuangfei HanHenan University School of Economics, Henan University, Kaifeng, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The optimal allocation of the labor force through population mobility is a critical determinant of the synergistic economy, society, and environment, with substantial implications for Green Total Factor Productivity (GTFP). This study employs panel data from 2011 to 2020 encompassing 30 Chinese provinces, to measure the GTFP using the SBM-DDF model. We then investigated the network association characteristics of GTFP across provinces through social network analysis. Furthermore, fixed- and moderating-effects models are applied to empirically examine the impact of population mobility on GTFP, as well as the moderating roles of technological progress, financial development, and digital inclusive finance. These results indicate that China's GTFP exhibits spatial clustering and regional disparities, with western and eastern regions being the primary sources and destinations of green resource flows, respectively. Population mobility enhances national GTFP, a finding that remains robust across several endogeneity and robustness tests. However, this effect is heterogeneous; it is significant in destination regions, eastern and western regions, and the post-industrialization stage, but negligible elsewhere. Furthermore, the moderating effect analysis shows that technological progress amplifies this relationship both nationally and in destination areas. Additionally, financial development strengthens the effect in destination regions, whereas digital inclusive finance strengthens the effect in origin regions. Recognizing population mobility as a globally pervasive phenomenon, this study highlights its functional role and advances our understanding of its relationship with sustainable development.

Indexed as

Population DynamicsSustainable DevelopmentChinaEconomic DevelopmentEfficiencyHumans

Identifiers

PMID41481628
PMCPMC12758790

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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.