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

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.

Automated summary

This paper measures the tourism carbon efficiency (TCE) in China by considering energy consumption and carbon dioxide (CO2) emissions, and explores the determinants of TCE with spatial spillover effects. The study applies a bootstrap slacks-based measure (SBM) model to assess TCE in 30 provincial-level administrative regions from 2008 to 2019. The results show that regional differences and spatial spillover effects impact China's TCE. Factors such as technology innovation, urbanization rate, and government support positively affect TCE, while economic growth has a negative impact. Educational attainment, green infrastructure, and government support have a negative spatial spillover effect on TCE, and transportation infrastructure has a negative total effect.