Evidence map›Paper›PMID 36131509›Full record

ArticleScience progress

Does tourism industry agglomeration improve China's energy and carbon emissions performance?

Yicheng Zhou, Boqiang Lin

Abstract read
In one paragraph

Article in Science progress. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. 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

2 authors.

Yicheng ZhouSchool of Management, China Institute for Studies in Energy Policy, Collaborative Innovation Center for Energy Economics and Energy Policy, 12466Xiamen University, Fujian, China.ORCID 0000-0002-4491-0327
Boqiang LinSchool of Management, China Institute for Studies in Energy Policy, Collaborative Innovation Center for Energy Economics and Energy Policy, 12466Xiamen University, Fujian, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The tourism industry is seen as having great potential, but tourism development and tourism activities may increase energy consumption and environmental pressure. Based on the provincial panel data of China from 2000 to 2017, we calculate the energy and carbon emission performance by using the non-radial distance function (NDDF) and further investigate the impact of tourism industry agglomeration on energy and carbon emission efficiency by combining the panel fixed effect model, mediation effect model and quantile regression. Our research results show that there is an inverted U-shaped relationship between tourism industry agglomeration and energy and carbon emission efficiency, and tourism industry agglomeration can improve energy and carbon emission efficiency at present. At the same time, the impact of tourism industry agglomeration on energy and carbon emission efficiency has regional heterogeneity. The industrial structure upgrading plays an important role in the process of tourism industry agglomeration. In addition, with the improvement of energy and carbon emission performance, the impact of tourism industry agglomeration is also different. These findings suggest that policymakers should promote tourism industry agglomeration to realize energy conservation and emission reduction. The Chinese government should focus on the tourism resources and advantages of different regions and formulate differentiated regional policies to improve ecological performance.

Indexed as

CarbonTourismCarbon DioxideChinaEfficiencyIndustryCarbonCarbon DioxideChinaenergy and carbon emission performanceindustrial structure upgradinginverted u-shaped relationshipTourism industry agglomeration

Identifiers

PMID36131509
PMCPMC10358587

What OpenQuestion holds

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LicenceCC BY-NC
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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.