›› 2016, Vol. 28 ›› Issue (12): 116-126.

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Study on the Multilayer Airport System in China Based on Evaluation of Airport Competitiveness

Liang Xiaozhen1, Lai Kinkeung2, Liu John3, Qiao Han4, Yin Hongxia4   

  1. 1. School of Management, Shanghai University, Shanghai 200444;
    2. Department of Management Sciences, City University of Hong Kong, Hong Kong 999077;
    3. Centre for Transport, Trade and Financial Studies, City University of Hong Kong, Hong Kong 999077;
    4. School of Economics and Management, University of Chinese Academy of Sciences, Beijing 100190
  • Received:2015-11-14 Online:2016-12-28 Published:2017-03-15

Abstract:

In order to study the multilayer airport system in China, this paper designs a systematic evaluation methodology. Firstly, we establish an evaluation index system of airport competitiveness, which includes 30 indicators and mainly covers two aspects:self-development of the airport and its location conditions. Then based on the index system, we apply entropy weight method, grey relational analysis and principal component analysis, to evaluate the competitiveness of 42 major airports in China for the years 2006, 2008, 2010 and 2012 respectively. The consistency of these three methods is examined by Mann-Kendall test. A combined evaluation is conducted and reasons for the great variation in rankings for some airports during the period of 2006-2012 are analyzed. Finally, we also give the rankings of the 42 airports and the factors affecting their respective competitiveness to investigate the advantages and disadvantages of each airport, and divide all 42 airports into four groups by applying hierarchical clustering. According to the results of evaluation, as well as the development features of Chinese airport industry, the competitive advantages of each airport, and differences between them, this paper puts forward some policy proposals for the construction of multilayer airport system in China.

Key words: airport system, combined evaluation, entropy weight method, grey relational analysis, principal component analysis, clus-ter analysis