Evidence map›Paper›PMID 36767265›Full record

ArticleInternational journal of environmental research and public health2023

Spatial-Temporal Variation and Influencing Factors of Regional Tourism Carbon Emission Efficiency in China Based on Calculating Tourism Value Added.

Jun Liu, Fanfan Deng, Ding Wen, Qian Zhang, Ye Lin

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. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Article
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.

Jun LiuSchool of Tourism, Hubei University, Wuhan 430062, China.ORCID 0000-0003-0493-6474
Fanfan DengSchool of Tourism, Hubei University, Wuhan 430062, China.
Ding WenSouth China Institute of Environmental Sciences, Ministry of Ecology and Environment, Guangzhou 510530, China.ORCID 0000-0003-0975-8179
Qian ZhangSchool of Tourism, Hubei University, Wuhan 430062, China.
Ye LinSchool of Business, Hubei University, Wuhan 430062, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Tourism-related carbon emission efficiency is an important indicator that reflects the sustainable development of tourism and can better balance the relationship between negative environmental impact and economic value. According to panel data of 30 provincial regions, "the tourism value added coefficient" (not including the Tibet Autonomous Region) in mainland China from 2000 to 2019, we estimate the tourism of each provincial administrative unit carbon emissions, measure the tourism carbon efficiency value, and analyze the measurement results of the change trend, spatial differentiation characteristics, and influencing factors. The results show that (1) the carbon emission efficiency of regional tourism in China increased significantly from 2000 to 2019, but there was a significant difference in the carbon emission efficiency of tourism among regions, and the sustainable development level of regional tourism was still unbalanced. (2) The spatial pattern of provincial administrative units in China has the adjacent characteristics of High-High agglomeration and Low-Low agglomeration, the difference in the tourism eco-efficiency development level among regions gradually decreases with time, and there is a dynamic convergence characteristic. (3) The

Indexed as

CarbonTourismChinaEconomic DevelopmentEfficiencyIndustrial DevelopmentUrbanizationCarbongeographical detectorinfluencing factorsspatial patterntourism carbon emission efficiencytourism value added

Identifiers

PMID36767265
PMCPMC9915264

What OpenQuestion holds

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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.