Acta Geographica Sinica ›› 2017, Vol. 72 ›› Issue (2): 242-255.doi: 10.11821/dlxb201702005

• Urban and Regional Development • Previous Articles     Next Articles

Evaluation of tourism competitiveness of Chinesesmart tourism city

Song HUANG1(), Yanlin LI2(), Pingjuan DAI1   

  1. 1.College of Historical Culture and Tourism, Guangxi Normal University, Guilin 541001, Guangxi, China
    2. College of Vocation-Technical Teachers, Guangxi Normal University, Guilin 541004, Guangxi, China
  • Received:2016-09-10 Revised:2016-11-28 Online:2017-02-15 Published:2017-04-28
  • Supported by:
    National Natural Science Foundation of China, No.41361019;National Social Science Foundation of China, No.08BMZ041;Program for New Century Excellent Talents in University, No.NCET-12-0652

Abstract:

To enhance tourism competitiveness is the core objective of the construction of a smart tourism city. On the basis of the previous studies, the research builds an index system on the tourism competitiveness evaluation of smart tourism city, including 5 indexes of first grade, 14 of second grade and 33 of third grade, selects the first 12 pilot cities of national smart tourism such as Beijing, Nanjing, Wuhan, Chengdu, Dalian and Xiamen, constructs the BP neural network model of tourism competitiveness evaluation of smart tourism city through the simulation computation, and applies the model to evaluate and analyze the tourism competitiveness of the smart tourism cities mentioned above. The results indicated that the scientific and technological innovation of the smart tourism city is the most important first grade index which impacts the tourism competitiveness. The other first grade indexes are economic development of smart tourism city, support capability of tourism development, tourism development potentiality and environmental support capability ranked according to their importance; the overall level of tourism competitiveness of Chinese smart tourism cities is not high and extremely uneven and the smart tourism cities are divided into five categories according to their own evaluation level and competition situation. The first category includes Beijing, which is the benchmark of tourism competitiveness of Chinese smart tourism cities. All the five first grade indexes of Beijing run ahead of other smart tourism cities. Its comprehensively-evaluated value reaches 0.887, rated AA level, and its competition situation is superior. The second category includes Nanjing, whose evaluation value of the environmental support capability of smart tourism city is tied for the first place with Beijing. Its evaluation values of economic development power, scientific and technological innovation, and support capability of development are all ranked 2nd. The comprehensively-evaluated value of Nanjing is 0.536, rated BB level, which is significantly higher than that of other smart tourism cities except Beijing and its competition situation is better. The third category cities, including Wuhan, Dalian, Chengdu, Wenzhou, Xiamen, Zhenjiang and Yantai, are representatives of the present general level of tourism competitiveness of Chinese smart tourism cities, whose 5 first grade index values are all low and there are no remarkable gaps between them. Their comprehensively-evaluated values are between 0.3 and 0.4, rated B level and their competition situation is on the average. The fourth category includes Fuzhou and Luoyang, the vast majority of its first index values are lower. Their comprehensively-evaluated values are between 0.2 and 0.3, rated CC level and their competition situation is weak. The fifth category includes Huangshan, whose comprehensively-evaluated value is just 0.176, rated C level and its competition situation is weaker. The BP neural network model of tourism competitiveness evaluation of smart tourism city is of better scientificity, universality and operability. Judging the tourism competitiveness of smart tourism cities according to their evaluation values, comparing it with the index system to find and bridge the gaps, and monitoring the evaluation index dynamically will be an important direction of tourism competitiveness research of smart tourism cities.

Key words: smart tourism city, tourism competitiveness evaluation, BP neural network model, China