Evidence map›Paper›PMID 34444204›Full record

ArticleInternational journal of environmental research and public health2021

Modeling the Dynamic Linkage between Tourism Development, Technological Innovation, Urbanization and Environmental Quality: Provincial Data Analysis of China.

Zhang Chenghu, Muhammad Arif, Khurram Shehzad, Mahmood Ahmad, Judit Oláh

Abstract read
In one paragraph

Article in International journal of environmental research and public health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Spatial-Temporal Characteristics and Driving Factors of the Eco-Efficiency of Tourist Hotels in China.International journal of environmental research and public health · 2022
    Article
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  3. Article
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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.

Zhang ChenghuSchool of Economics and Finance, Xi'an Jiaotong University, Xi'an 710054, China.
Muhammad ArifSchool of Economics and Finance, Xi'an Jiaotong University, Xi'an 710054, China.
Khurram ShehzadSchool of Economics and Management, Southeast University, Nanjing 210096, China.ORCID 0000-0001-7835-5264
Mahmood AhmadBusiness School, Shandong University of Technology, Zibo 255000, China.ORCID 0000-0002-9974-0150
Judit OláhDepartment of Management, Faculty of Applied Sciences, WSB University, 41300 Dabrowa Górnicza, Poland.ORCID 0000-0003-2247-1711

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study investigates the linkage between tourism development, technological innovation, urbanization and environmental degradation across 30 provinces of China. Based on data from 2001 to 2018, the study used an advanced economic methodology for the long-run estimate, the Augmented Mean Group (AMG) estimator, which accounts for heterogeneity in slope parameters and dependencies across countries. The empirical results show that tourism development degrades environmental quality, while technological innovation mitigates carbon emissions. Further, findings show that urbanization increases carbon emissions, while an inverted U-shaped relationship exists between economic growth and environmental degradation, implying the existence of EKC in China. Further, the Dumitrescu-Hurlin panel causality test shows that any policy aimed at tourism development or technological innovation would substantially contribute to environmental degradation, but not the other way round.

Indexed as

InventionsUrbanizationCarbon DioxideChinaData AnalysisEconomic DevelopmentTourismCarbon DioxideCO2 emissionGDPGDP squaretechnological innovationtourism developmenturbanization

Identifiers

PMID34444204
PMCPMC8394915

What OpenQuestion holds

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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.