LAPSE:2023.15263
Published Article

LAPSE:2023.15263
Spatial Network Structure of China’s Provincial-Scale Tourism Eco-Efficiency: A Social Network Analysis
March 2, 2023
Abstract
While tourism eco-efficiency has been analyzed actively within tourism research, there is an extant dearth of research on the spatial network structure of provincial-scale tourism eco-efficiency. The Super-SBM was used to evaluate the tourism eco-efficiency of 30 provinces (excluding Tibet, Hong Kong, Macao and Taiwan). Then, social network analysis was employed to examine the evolution characteristics regarding the spatial network structure of tourism eco-efficiency. The main results are shown as follows. Firstly, tourism eco-efficiency of more than two thirds’ provinces witnessed an increasing trend. Secondly, the spatial network structure of tourism eco-efficiency was still loose and unstable during the sample period. Thirdly, there existed the multidimensional nested and fused spatial factions and condensed subsets in the spatial network structure of tourism eco-efficiency. However, there was still a lack of low-carbon tourism cooperation among second or third sub-groups. These conclusions can provide references for policymakers who expect to reduce carbon emissions from the tourism industry and to achieve sustainable tourism development.
While tourism eco-efficiency has been analyzed actively within tourism research, there is an extant dearth of research on the spatial network structure of provincial-scale tourism eco-efficiency. The Super-SBM was used to evaluate the tourism eco-efficiency of 30 provinces (excluding Tibet, Hong Kong, Macao and Taiwan). Then, social network analysis was employed to examine the evolution characteristics regarding the spatial network structure of tourism eco-efficiency. The main results are shown as follows. Firstly, tourism eco-efficiency of more than two thirds’ provinces witnessed an increasing trend. Secondly, the spatial network structure of tourism eco-efficiency was still loose and unstable during the sample period. Thirdly, there existed the multidimensional nested and fused spatial factions and condensed subsets in the spatial network structure of tourism eco-efficiency. However, there was still a lack of low-carbon tourism cooperation among second or third sub-groups. These conclusions can provide references for policymakers who expect to reduce carbon emissions from the tourism industry and to achieve sustainable tourism development.
Record ID
Keywords
low-carbon tourism, social network analysis, spatial network correlation, Super-SBM, tourism eco-efficiency
Subject
Suggested Citation
Liu Q, Song J, Dai T, Xu J, Li J, Wang E. Spatial Network Structure of China’s Provincial-Scale Tourism Eco-Efficiency: A Social Network Analysis. (2023). LAPSE:2023.15263
Author Affiliations
Liu Q: Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China [ORCID]
Song J: Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
Dai T: Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China [ORCID]
Xu J: School of Geographic Information and Tourism, Chuzhou University, Chuzhou 239099, China; Finnish Meteorological Institute, FI-00101 Helsinki, Finland
Li J: School of Geographic Information and Tourism, Chuzhou University, Chuzhou 239099, China
Wang E: Department of Geography & Geographic Information Science, University of North Dakota, Grand Forks, ND 58202, USA
Song J: Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
Dai T: Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China [ORCID]
Xu J: School of Geographic Information and Tourism, Chuzhou University, Chuzhou 239099, China; Finnish Meteorological Institute, FI-00101 Helsinki, Finland
Li J: School of Geographic Information and Tourism, Chuzhou University, Chuzhou 239099, China
Wang E: Department of Geography & Geographic Information Science, University of North Dakota, Grand Forks, ND 58202, USA
Journal Name
Energies
Volume
15
Issue
4
First Page
1324
Year
2022
Publication Date
2022-02-11
ISSN
1996-1073
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Original Submission
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PII: en15041324, Publication Type: Journal Article
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LAPSE:2023.15263
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https://doi.org/10.3390/en15041324
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