Evidence map›Paper›PMID 35409437›Full record

ArticleInternational journal of environmental research and public health2022

Evaluation of Tourism Development Efficiency and Spatial Spillover Effect Based on EBM Model: The Case of Hainan Island, China.

Pengfei Zhang, Hu Yu, Mingzhe Shen, Wei Guo

Abstract read
In one paragraph

Article in International journal of environmental research and public health, 2022. 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

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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

4 citing papers in PubMed.

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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

4 authors.

Pengfei ZhangInstitute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China.
Hu YuInstitute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China.ORCID 0000-0003-3159-8183
Mingzhe ShenSchool of Economic and Management, Yanshan University, Qinhuangdao 066004, China.
Wei GuoSchool of Economic and Management, Yanshan University, Qinhuangdao 066004, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Tourism development efficiency is one of the key scales to measure the development quality of tourism destination. This study improves the existing input-output index system of tourism efficiency evaluation; knowledge innovation is introduced into the input index, and environmental health pressure is introduced into the output index. Based on the case of Hainan Island, we used the EBM model compatible with radial and non-radial data to evaluate the tourism development efficiency. In order to make up the deficiency of spatial effect analysis based on the geographical distance weight matrix, the spatial spillover effect of tourism development in Hainan Island was analyzed based on a geographical distance weight matrix and an economic distance weight matrix. The findings indicate that nearly 20 years of the Hainan tourism development efficiency mean value was 0.7435, represented by Sanya, and Haikou city of Hainan's tourism industry development level was higher. However, the spatial spillover effect of Hainan's overall tourism development is not good. In addition to Tunchang, Ledong city suggests that an appropriate increase in tourism elements, such as investment, expands the scale of the tourism industry, and most cities follow the law of diminishing marginal utility and inappropriate scale blindly. Especially in the face of knowledge innovation becoming the main factor hindering the efficiency of tourism development, we should pay more attention to technological innovation and management reform and coordinate the relationship between tourism development and ecological environment protection.

Indexed as

Economic DevelopmentTourismCitiesEfficiencyIndustrial DevelopmentHainanspatial spillover effectsustainable developmenttourism efficiency

Identifiers

PMID35409437
PMCPMC8997903

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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.