TY - JOUR
T1 - Spatial spillover and determinants of tourism efficiency: A low carbon emission perspective
AU - Liu, Hongwei
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.
PY - 2024/5
Y1 - 2024/5
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.
KW - bootstrap slacks-based measure model
KW - carbon emission
KW - spatial Durbin model
KW - spatial spillover effect
KW - tourism carbon efficiency
UR - http://www.scopus.com/inward/record.url?scp=85152284921&partnerID=8YFLogxK
U2 - 10.1177/13548166231167045
DO - 10.1177/13548166231167045
M3 - Journal article
AN - SCOPUS:85152284921
SN - 1354-8166
VL - 30
SP - 543
EP - 566
JO - Tourism Economics
JF - Tourism Economics
IS - 3
ER -