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Bootstrapped DEA and Clustering Analysis of Eco-Efficiency in China's Hotel Industry
| Content Provider | MDPI |
|---|---|
| Author | Li, Yang Liu, An-Chi Yu, Yi-Ying Zhang, Yueru Zhan, Yiting Lin, Wen-Cheng |
| Copyright Year | 2022 |
| Description | 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. |
| Starting Page | 2925 |
| e-ISSN | 20711050 |
| DOI | 10.3390/su14052925 |
| Journal | Sustainability |
| Issue Number | 5 |
| Volume Number | 14 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2022-03-02 |
| Access Restriction | Open |
| Subject Keyword | Sustainability Tourism, Leisure, Sport and Hospitality Hotel Industry Data Envelopment Analysis Eco-efficiency Returns To Scale Bootstrap Method Cluster Analysis |
| Content Type | Text |
| Resource Type | Article |
