长江流域资源与环境 >> 2018, Vol. 27 >> Issue (01): 36-.doi: 10.11870/cjlyzyyhj201801007

• 区域可持续发展 • 上一篇    下一篇

基于DEAMalmquist指数二次分解模型的江苏省旅游产业效率时空演变及影响因素

申鹏鹏1,周年兴1*,张允翔1,王  坤2,李在军1   

  1. (1.南京师范大学地理科学学院,江苏 南京  210023;2.贵州大学旅游与文化产业学院,贵州 贵阳 550025)
  • 出版日期:2018-01-20

SpatioTemporal Evolution and Influencing Factors of Tourism Industry Efficiency in Jiangsu Province Based on DeaMalmquist Index Secondary Decomposition Model#br#

SHEN Pengpeng1,ZHOU Nianxing1,ZHANG Yunxiang1, WANG Kun2,LI Zaijun1#br#   

  1. (1.College of Geographical Science, Nanjing Normal University, Nanjing 210023,China; 2.College of Tourism and Cultural industry, Guizhou University,Guiyang 550025,China)
  • Online:2018-01-20

摘要:  旅游产业生产效率是衡量旅游资源合理利用与旅游经济发展水平的重要依据。基于DEAMalmquist指数二次分解模型,对2000~2015年江苏13个地市旅游产业效率的时空演变特征及影响因素进行探究。结果显示:(1)2000~2015年全省旅游全要素生产率总体上有所上升,但上升幅度不大,年平均值为1020;空间分布上,旅游全要素生产率水平呈现明显的地区差异性, 南部城市普遍高于北部城市,东部城市优于西部城市。(2)各地市旅游全要素生产率增长主要归功于技术进步变化,其中,规模技术进步变化是影响各地市旅游全要素变化的最主要因素,纯技术进步变化次之,纯技术效率变化和规模效率变化对旅游全要素的影响最小。(3)面板回归结果表明,产业结构、对外开放水平、经济发展程度、交通、劳动力是各地市旅游产业生产效率变动的主要影响因素。因此,不同地区应采取不同的旅游发展政策,推进旅游产业技术进步,大力提升旅游产业生产效率。
关键词: DEAMalmquist指数;二次分解;旅游产业效率;江苏省

Abstract: The productive efficiency of tourism is an important basis to evaluate the reasonable use of the tourism resources and development level of tourism economy. First based on the DEAMalmquist index secondary decomposition model, this paper reveals the spatiotemporal evolution characteristics and influencing factors of the tourism productive efficiency of 13 municipalities in Jiangsu Province .The results show that: (1) the total factors productivity levels increased moderately, and the annual average of total factors productivity during 2000-2015 in Jinagsu Province is 1020. Regarding the spatial distribution, the total factor productivity of tourism shows obvious regional differences that the southern cities generally displayed a higher total factor productivity than northern cities, while the eastern cities are also higher than western cities. (2) The total factors productivity growth is mainly attributed to the technological progresses, among which the change of scale technological progress is the dominant factor, followed by the change of pure technological progress, while the change of pure technical efficiency and the change of scale efficiency has the least influence on the total factors of tourism. (3) Through the panel regression model, some factors such as industrial structure, the openingup level, economic development, traffic and labor also affect the productive efficiency of tourism in Jiangsu Province, which can lead to the efficiency changes and regional imbalances. The paper puts forward that different regions should adopt different tourism development policies, promote the technological progress of tourism industry and improve the production efficiency of tourism industry.

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