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Research On Club Convergence Of China's Tourism Eco-efficiency And Its Influence Factors

Posted on:2021-04-23Degree:MasterType:Thesis
Country:ChinaCandidate:Q XuFull Text:PDF
GTID:2439330611961204Subject:Tourism geography
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Since the reform and opening up,China's rapid development of tourism has also caused a series of environmental degradation problems.As an important means to measure the sustainable development level of tourism,the precise measurement and comprehensive evaluation of tourism ecological efficiency are of great value to understand China's tourism ecological level.Tourism factors flow flexibly between regions,and there are inevitable convergence and radiation effects of tourism ecological efficiency in adjacent or similar regions.Therefore,club convergence of tourism ecological efficiency from the perspective of spatial effect is a reliable and valuable attempt.It is beneficial to narrow the regional efficiency gap,enhance regional tourism governance cooperation,and realize the overall,coordinated and healthy development of China's tourism ecological efficiency.In this paper,based on literature review,adopted the SBM model to measure the tourism ecological efficiency of China's 31 provinces(municipalities and autonomous regions)from 2007 to 2017,and depicted its spatio-temporal differentiation characteristics.Secondly,based on the convergence theory,Markov chain is adopted to further explore the club convergence characteristics of China's tourism ecological efficiency and summarize its spatio-temporal evolution trend.Furthermore,by means of the geographical detector model,detecting the influence factors for the convergence of China's tourism eco-efficiency clubs.Finally,put forward targeted suggestions to promote the steady improvement of China's tourism ecological efficiency and the sustainable development of tourism.The main conclusions of this study are as follows: 1)in terms of time dimension,the level of ecological efficiency of tourism in China is average from 2007 to 2017,among which the eastern part is the highest,the western part is the second,and the central part is the lowest.However,the efficiency of tourism ecological efficiency in the eastern region showed an undulating downward trend,while that in the western region was in line with the national average,with a strong increase in the later period,while that in the central region showed a slow increase.2)spatial dimension: although the spatial distribution of tourism ecological efficiency in China's provinces is unbalanced from 2007 to 2017,the distribution is relatively concentrated,and the efficiency level shows a decreasing trend from southeast to northwest.The evolution pattern of efficiency space is not large,among which the southwest and eastern provinces and cities are active with high efficiency,while the central and western provinces and cities fall into the trap of ‘low efficiency'.3)under any time and space lag,the tourism ecological efficiency of China's shows significant club convergence effect.The longer the time,the lower the degree of club convergence,and the closer the efficiency types of the clubs,the easier the transfer.The efficiency level of the neighboring regions has an obvious influence on the efficiency transfer of the provinces.Most provinces keep the same direction of the efficiency transfer of the field,showing a club convergence distribution of ‘be a rascal among rascal'.4)in terms of the influence factors,the influence of traditional economic factors on tourism eco-efficiency was weakened during the study period,while the influence of policies,industries and other factors was enhanced.Considering the significance and influence comprehensively,the factors influencing the eco-efficiency of China's tourism are from major to minor: tourism specialization level,the ratio of tourists,environmental regulation,tourism development level,industrial structure,scientific and technological innovation,and the degree of opening to the outside world.
Keywords/Search Tags:tourism ecological efficiency, club convergence, spatiotemporal characteristics, influence factors, Markov chain
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