Bootstrapped DEA and Clustering Analysis of Eco-Efficiency in China’s Hotel Industry
Yang Li; An-Chi Liu; Yi-Ying Yu; Yueru Zhang; Yiting Zhan; Wen-Cheng Lin
As one of the world&rsquo:s largest and fastest growing industries, tourism is facing the challenge of balancing growth and eco-environmental protection. Taking tourism CO2 emissions as undesirable outputs, this research employs the bootstrapping data envelopment analysis (DEA) approach to measure the eco-efficiency of China&rsquo:s hotel industry. Using a dataset consisting of 31 provinces in the period 2016&ndash:2019, the bootstrapping-based test validates that the technology exhibits variable returns to scale. The partitioning around medoids (PAM) algorithm, based on the bootstrap samples of eco-efficiency, clusters China&rsquo:s hotel industry into two groups: Cluster 1 with Shandong as the representative medoid consists of half of the superior coastal provinces and half of the competitive inland provinces, while Cluster 2 is less efficient with Jiangsu as the representative medoid. Therefore, it is suggested that the China government conduct a survey of only Shandong and Jiangsu to approximately capture the key characteristics of the domestic hotel industry&rsquo:s eco-efficiency in order to formulate appropriate sustainable development policies. Lastly, biased upward eco-efficiencies may provide incorrect information and misguide managerial and/or policy implications.
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