Abstract
Reducing carbon emissions is crucial to the sustainable development of tourism. However, there are no consistent conclusions about the nexus between tourism and carbon emissions. Considering the possible nonlinear and spatial effects of tourism on carbon emissions, this paper employed spatial econometric models combined with quadratic terms of explanatory variables to explore the nexus between them using Chinese provincial panel data from 2003 to 2016. The main results are as follows: (1) There is a significant inverse U-shaped relationship between tourism development and carbon emissions. In the provinces whose tourism receipts are relatively low, the effects of tourism on carbon emissions are positive but decrease gradually as the tourism receipts increase and then shifts to negative and continues decreasing gradually when the tourism receipts beyond the critical value. (2) For the geographical proximity and industrial relevance, one province’s tourism development not only affects its carbon emissions but also affects its neighbors’ carbon emissions through spatial lag effect (indirect effect) which is also inverse U-shaped. (3) Carbon reduction policies, sustainable education, and transportation infrastructure all have significant moderating effects on the relationship between tourism and carbon emissions, but the moderating effect of the management efficiency of tourism is not statistically significant. Furthermore, improvements to the sustainable education and transportation infrastructure not only strengthen the direct negative effect of tourism on carbon emissions but also strengthen the indirect negative effect of tourism on carbon emissions. This study not only advances the existing literature but is also of considerable interest to policymakers.
Highlights
Tourism is highly vulnerable to climate change, in addition to contributing to it
Because the spatial Durbin model (SDM) model and the spatial Durbin error model (SDEM) model are both nested in the general nested spatial model (GNSM) model, we compared these three models using the likelihood ratio test (LR test)
The likely ratio test (LR) test was used to test whether the SDEM model should be reduced to the spatial error model (SEM), which does not contain the spatial lags of explanatory variables compared to the SDEM model
Summary
Tourism is highly vulnerable to climate change, in addition to contributing to it. Tourism is one of the key drivers to the anthropogenic component of climate change [1,2], which is predicted to contribute approximately. Reducing carbon emissions from tourism helps to offset global warming but is conducive to the sustainable development of the tourism industry. The nexus between tourism and carbon emissions has been widely studied over time, a lack of tourism statistics and materials makes it difficult to quantify carbon emissions from tourism [4]. Tourism is not a traditional sector in the System of National Accounts, and as a result, the statistics of carbon emissions of the tourism industry on a national or regional scale is difficult to calculate. It is challenging to assess the other two kinds of
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