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Non-linear effects of corporate global value chain participation on pollution emissions: evidence from China

Overview of attention for article published in Frontiers in Energy Research, November 2023
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1 X user

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6 Mendeley
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Title
Non-linear effects of corporate global value chain participation on pollution emissions: evidence from China
Published in
Frontiers in Energy Research, November 2023
DOI 10.3389/fenrg.2023.1211179
Authors

Bo Wang, Xuyang Shao, Honghai Xu

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X Demographics

The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 6 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 6 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 2 33%
Unspecified 1 17%
Student > Doctoral Student 1 17%
Unknown 2 33%
Readers by discipline Count As %
Economics, Econometrics and Finance 2 33%
Environmental Science 1 17%
Unspecified 1 17%
Unknown 2 33%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 28 November 2023.
All research outputs
#22,298,544
of 24,889,544 outputs
Outputs from Frontiers in Energy Research
#1,440
of 4,280 outputs
Outputs of similar age
#130,386
of 165,843 outputs
Outputs of similar age from Frontiers in Energy Research
#7
of 159 outputs
Altmetric has tracked 24,889,544 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 4,280 research outputs from this source. They receive a mean Attention Score of 1.6. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 165,843 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 159 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.