Chinese National Scenic Areas’ Tourism Efficiency: Multi-scale Fluctuation, Prediction and Optimization

Abstract

The tourism efficiency in scenic areas is influenced by multi-factor joined effects, presenting multi-scale fluctuation characteristics in temporal terms and regional differences in spatial terms. This paper, by adopting the data envelopment analysis (DEA) method and Bootstrap-DEA model, calculates their tourism and decomposition efficiency. Then, based on methods of empirical mode decomposition, wavelet decomposition and Morlet wavelet multi-scale decomposition, the paper reveals the fluctuation characteristics, process and its spatial differentiation of tourism efficiency change in scenic areas from the perspective of the time–frequency domain, quantitatively investigating the important factors influencing its dynamic change, so as to summarize and refine the dynamic evolution models of tourism efficiency. Lastly, the mean generating function method is applied to conduct statistical prediction for scenic areas’ tourism efficiency in different regions, combined with the principal component method for regional integration, which provides an effective way for studying temporal–spatial differentiation characteristics of tourism efficiency change in scenic areas.