| In order to cope with global climate change,in 2020,China aims to have carbon dioxide emission peak before 2030 and achieve carbon neutrality before 2060.The tourism industry is not a “smokeless industry” in the traditional perception.It occupies a certain proportion of global greenhouse gas emissions.As early as 2013,the tourism industry's greenhouse gas emissions had reached 8% of the global total,and this proportion continues to grow.As a major tourism country in the world,the growth rate of Chinese travel demand has far surpassed the industry's decarbonization technology.While the tourism industry promotes local economic growth,the carbon emissions problems brought by it should not be underestimated.The tourism of China has received6.155 billion in 2019.Such large-scale of transportation,accommodation and activities are driving the carbon emissions continue to rise to high levels.Therefore,reducing tourism carbon emissions is of great significance to promoting carbon emission reduction in China.The goal of tourism carbon emission efficiency is to maximize the economic benefits of regional tourism through the efficient allocation of tourism resources,capital,labor,and energy in the region,while minimizing carbon emissions,which can fully reflect the low-carbon transition of China's tourism economy.With the development of tourism economy in various regions and the increasing spatial correlation effect of energy utilization,the temporal and spatial changes of tourism carbon emission efficiency have become more dynamic,complicated and diversified.From the perspective of spatial linkage,conducting research on the spatial correlation network and influencing factors of China's tourism carbon emission efficiency is of great significance for reducing tourism carbon emissions,narrowing regional differences,and promoting low-carbon and efficient development of tourism and the formulation and adjustment of regional emission reduction policies.In view of this,this article systematically sorts out the current status and problems of China's tourism carbon emissions.then based on efficiency theory,this article constructs a theoretical analysis framework and indicator system for China's tourism carbon emissions efficiency,and use the DEA-EBM model to measure the carbon emission efficiency of tourism in 30 provinces and regions in China from 2000 to 2019.and Its in Internal influencing factors through the methods of GML index and MGML index;then,this article is based on the regional difference of China's tourism carbon emission,using the coefficient of variation and Theil index method to reveal the trend and the reasons of the difference,and through the kernel density function,Markov transition matrix and convergence test method to analyze the dynamic evolution process and evolution trend of efficiency in detail.Then,in order to further explore the transmission mechanism of tourism carbon emission efficiency between provinces,the study uses gravity model and social network analysis method to construct China's provincial tourism carbon emission efficiency spatial correlation network,from the network form,structure relationship,attribute function and Spatial spillover and other aspects reveal the provincial linkage and interaction of China's tourism carbon emission efficiency,and analyze the main influencing factors of its spatial correlation network through the QAP method.Finally,the study constructed a panel Tobit model and a spatial measurement model to conduct an empirical analysis on the factors affecting the carbon emission efficiency of China's tourism industry,and based on the above analysis results,draw the conclusions and recommendations of this article.The main conclusions of this article are as follows:(1)From 2000 to 2019,the overall carbon emission efficiency of China's tourism industry showed a fluctuating upward trend,and its efficiency increased from 0.417 to0.642,which was at a medium to high level;during the study period,provinces with high efficiency values gradually spread to the east and south of China,Showing the characteristics of small-scale agglomeration in space.The GML index,MGML and their decomposition results show that the national efficiency growth is mainly driven by technological progress.The efficiency growth of the eastern and western regions mainly comes from the effect of technological innovation,while the central region is dominated by the technological leadership effect.In most years,there is no obvious "efficiency catch-up" effect in China.(2)From 2000 to 2019,the value of the coefficient of variation of carbon emission efficiency of tourism across the country and the eastern and western regions gradually decreased,and there was a phenomenon of ? convergence.After 2008,the phenomenon of ? convergence weakened and disappeared in the central region.From the perspective of efficiency gap decomposition,the gap within the group occupies an absolute dominant position in the overall gap.The difference in efficiency levels in the eastern region is the largest,followed by the western region,and the smallest in the central region.The nuclear density curve results show that the carbon emission efficiency of China's tourism industry has the characteristics of "club convergence",and some highefficiency provinces and low-efficiency provinces agglomerated in space.The results of the Markov transition matrix show that the carbon emission efficiency of the tourism industry has significant characteristics of solidification,and its efficiency type has the greatest probability of maintaining the "low" and "high" types.After 2010,the probability of its efficiency "upward" transfer is much higher than that of " Probability of"downward" transfer.From the perspective of efficiency evolution trends,there is an absolute ? convergence phenomenon in whole country and the three major regions.The eastern and central regions have a faster convergence rate than the western region;the efficiency levels of the country and the eastern region have conditions for ? convergence,and the two will eventually move towards their respective stability.At the same time,there is no condition for ? convergence in the central and western regions.(3)The spatial spillover and correlation effects of tourism carbon emission efficiency between provinces are very obvious,showing a more complex network structure,the overall network is relatively stable,and it is gradually developing towards a balanced development.The QAP regression results show that the difference in urbanization,opening to the outside world,and the expansion of adjacency are the main forces that promote the formation of the spatial network structure of the carbon emission efficiency of the provincial tourism industry,and the reduction of the difference in the level of tourism consumption will benefit the tourism industry cooperation.(4)Panel Tobit regression results show that the improvement of economic development level,industrial structure upgrade,tourism agglomeration,innovation level,transportation level,marketization level and tourism resource agglomeration level can significantly promote the improvement of tourism carbon emission efficiency,and expand the scale of tourism reception,increasing the level of opening up,and increasing the proportion of carbon emissions from tourism transportation will have a significant inhibitory effect on the efficiency of tourism carbon emissions.The regression results of influencing factors by region show that the effects of economic development level,industrial agglomeration,and industrial reception scale are consistent with those of the whole country,while the effects of other influencing factors have regional differences.(5)The regression results of the spatial measurement model show that China's provincial tourism carbon emission efficiency spatial spillover effect is significant.Under the considering the spatial spillover effect,the direction of the estimated coefficients of industrial structure upgrade,transportation level,and innovation level is the same as the Tobit regression result.But they are not significant,and the results of other influencing factors are consistent with the results of Tobit regression.From the perspective of effect decomposition,the direct effect of economic development,industrial agglomeration,and tourism reception scale on the efficiency of tourism carbon emissions is positive.While the carbon emission structure of the tourism industry,the level of opening up,the level of agglomeration of tourism resources and the level of marketization have a negative direct effect on the efficiency of tourism carbon emissions;the improvement of the level of economic development and the upgrading of the tourism carbon emission structure have a negative effect on the carbon emission efficiency of the tourism industry in neighboring areas,and the indirect effect of other factors on the efficiency is not significant;the level of economic transportation and innovation The level has a significant positive effect on the carbon emission efficiency of tourism in this district and neighboring areas,but the industrial structure is not significant neither.(6)Based on the research conclusions,this article puts forward policy recommendations for improving the carbon emission efficiency of China's tourism industry: First,clarify the key areas and key links of tourism emission reduction,and narrow the carbon emission gap;second,strengthen the “high-efficiency” agglomeration and fully stimulate efficiency the internal driving force for growth;the third is to strengthen the linkages of the tourism industry,enhance spatial spillover effects;the fourth is to improve the tourism transportation network to promote the structure of the tourism carbon emission;fifth is to improve the level of tourism industry agglomeration and promote the quality of tourism economy;sixth is to base on different facts,adjust measures to local conditions,and implement classified policies;seventh is to enhance the market regulation mechanism and strict government supervision level;eighth is to popularize the concept of low-carbon tourism and promote the effective implementation of low-carbon consumption. |
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