Spatial spillover and determinants of tourism efficiency: A low carbon emission perspective

Hongwei Liu; Chenchen Gao; Henry Tsai

doi:10.1177/13548166231167045

Spatial spillover and determinants of tourism efficiency: A low carbon emission perspective

Hongwei Liu
, Chenchen Gao
, Henry Tsai

Research output: Journal article publication › Journal article › Academic research › peer-review

29 Citations (Scopus)

Abstract

This paper measures tourism carbon efficiency (TCE) in China by incorporating energy consumption and carbon dioxide (CO₂) emissions into an efficiency assessment framework, and to further investigate the determinants of TCE by considering the spatial spillover effects. To do this, a bootstrap slacks-based measure (SBM) model was applied to assess the TCE in 30 provincial-level administrative regions of China from 2008 to 2019. Next, the Moran’s index and spatial Durbin model (SDM) were adopted to explore the spatial distribution and determinants of TCE. The results indicate that regional differences affect the level of China’s TCE, as do spatial spillover effects. In addition, technology innovation, urbanization rate, and government support positively affect TCE. In contrast, economic growth negatively affects TCE. Educational attainment, green infrastructure, and government support have a negative spatial spillover effect on TCE. Transportation infrastructure has a negative total effect on TCE.

Original language	English
Pages (from-to)	543-566
Number of pages	24
Journal	Tourism Economics
Volume	30
Issue number	3
Early online date	31 Mar 2023
DOIs	https://doi.org/10.1177/13548166231167045
Publication status	Published - May 2024

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy
SDG 8 Decent Work and Economic Growth

Keywords

bootstrap slacks-based measure model
carbon emission
spatial Durbin model
spatial spillover effect
tourism carbon efficiency

ASJC Scopus subject areas

Geography, Planning and Development
Tourism, Leisure and Hospitality Management

More information

10.1177/13548166231167045

http://hdl.handle.net/10397/99268

Cite this

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title = "Spatial spillover and determinants of tourism efficiency: A low carbon emission perspective",

abstract = "This paper measures tourism carbon efficiency (TCE) in China by incorporating energy consumption and carbon dioxide (CO2) emissions into an efficiency assessment framework, and to further investigate the determinants of TCE by considering the spatial spillover effects. To do this, a bootstrap slacks-based measure (SBM) model was applied to assess the TCE in 30 provincial-level administrative regions of China from 2008 to 2019. Next, the Moran{\textquoteright}s index and spatial Durbin model (SDM) were adopted to explore the spatial distribution and determinants of TCE. The results indicate that regional differences affect the level of China{\textquoteright}s TCE, as do spatial spillover effects. In addition, technology innovation, urbanization rate, and government support positively affect TCE. In contrast, economic growth negatively affects TCE. Educational attainment, green infrastructure, and government support have a negative spatial spillover effect on TCE. Transportation infrastructure has a negative total effect on TCE.",

keywords = "bootstrap slacks-based measure model, carbon emission, spatial Durbin model, spatial spillover effect, tourism carbon efficiency",

author = "Hongwei Liu and Chenchen Gao and Henry Tsai",

note = "Funding Information: The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the National Natural Science Foundation of China (72271001, 71801001); Key project of Anhui Philosophy and Social Science Planning Fund (AHSKZ2022D10); the Research Grants Council of the Hong Kong Special Administrative Region, China (Project No. 15504820); Research Fund for Innovation Development of Anhui Social Science (2021CX070); Research Fund for University Postgraduate (YJS20210073). Publisher Copyright: {\textcopyright} The Author(s) 2023.",

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AU - Gao, Chenchen

AU - Tsai, Henry

N1 - Funding Information: The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the National Natural Science Foundation of China (72271001, 71801001); Key project of Anhui Philosophy and Social Science Planning Fund (AHSKZ2022D10); the Research Grants Council of the Hong Kong Special Administrative Region, China (Project No. 15504820); Research Fund for Innovation Development of Anhui Social Science (2021CX070); Research Fund for University Postgraduate (YJS20210073). Publisher Copyright: © The Author(s) 2023.

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N2 - This paper measures tourism carbon efficiency (TCE) in China by incorporating energy consumption and carbon dioxide (CO2) emissions into an efficiency assessment framework, and to further investigate the determinants of TCE by considering the spatial spillover effects. To do this, a bootstrap slacks-based measure (SBM) model was applied to assess the TCE in 30 provincial-level administrative regions of China from 2008 to 2019. Next, the Moran’s index and spatial Durbin model (SDM) were adopted to explore the spatial distribution and determinants of TCE. The results indicate that regional differences affect the level of China’s TCE, as do spatial spillover effects. In addition, technology innovation, urbanization rate, and government support positively affect TCE. In contrast, economic growth negatively affects TCE. Educational attainment, green infrastructure, and government support have a negative spatial spillover effect on TCE. Transportation infrastructure has a negative total effect on TCE.

AB - This paper measures tourism carbon efficiency (TCE) in China by incorporating energy consumption and carbon dioxide (CO2) emissions into an efficiency assessment framework, and to further investigate the determinants of TCE by considering the spatial spillover effects. To do this, a bootstrap slacks-based measure (SBM) model was applied to assess the TCE in 30 provincial-level administrative regions of China from 2008 to 2019. Next, the Moran’s index and spatial Durbin model (SDM) were adopted to explore the spatial distribution and determinants of TCE. The results indicate that regional differences affect the level of China’s TCE, as do spatial spillover effects. In addition, technology innovation, urbanization rate, and government support positively affect TCE. In contrast, economic growth negatively affects TCE. Educational attainment, green infrastructure, and government support have a negative spatial spillover effect on TCE. Transportation infrastructure has a negative total effect on TCE.

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DO - 10.1177/13548166231167045

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Spatial spillover and determinants of tourism efficiency: A low carbon emission perspective

Abstract

UN SDGs

Keywords

ASJC Scopus subject areas

More information

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