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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 () and Wen-Cheng Lin ()
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Yang Li: Department of International Business, National Taiwan University, Taipei 10617, Taiwan
An-Chi Liu: College of Business Administration, Fujian Business University, Fuzhou 350016, China
Yi-Ying Yu: Department of International Business Administration, Chinese Culture University, Taipei 11114, Taiwan
Yueru Zhang: New Huadu Business School, Minjiang University, Fuzhou 350108, China
Yiting Zhan: New Huadu Business School, Minjiang University, Fuzhou 350108, China
Wen-Cheng Lin: School of Economics and Management, Guangdong University of Petrochemical Technology, Maoming 525000, China

Sustainability, 2022, vol. 14, issue 5, 1-16

Abstract: As one of the world’s largest and fastest growing industries, tourism is facing the challenge of balancing growth and eco-environmental protection. Taking tourism CO 2 emissions as undesirable outputs, this research employs the bootstrapping data envelopment analysis (DEA) approach to measure the eco-efficiency of China’s hotel industry. Using a dataset consisting of 31 provinces in the period 2016–2019, 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’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’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.

Keywords: hotel industry; data envelopment analysis; eco-efficiency; returns to scale; bootstrap method; cluster analysis (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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