Factors Influencing Energy Consumption from China’s Tourist Attractions: A Structural Decomposition Analysis with LMDI and K-Means Clustering-Reference-Cited by-同舟云学术

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Factors Influencing Energy Consumption from China’s Tourist Attractions: A Structural Decomposition Analysis with LMDI and K-Means Clustering

Published:2023-05-10 Issue: Volume: Page:
ISSN:1420-2026
Container-title:Environmental Modeling & Assessment
language:en
Short-container-title:Environ Model Assess

Author:

Zhao Erlong,Wu Jing,Wang Shubin,Sun Shaolong,Wang Shouyang

Funder

National Key Research and Development Program of China

National Natural Science Foundation of China

Publisher

Springer Science and Business Media LLC

Subject

General Environmental Science

Link

https://link.springer.com/content/pdf/10.1007/s10666-023-09898-x.pdf

Reference42 articles.

1. Correia, A., & Kozak, M. (2016). Tourists’ shopping experiences at street markets: Cross-country research. Tourism Management, 56, 85–95. https://doi.org/10.1016/j.tourman.2016.03.026

2. Lenzen, M., Sun, Y. Y., Faturay, F., Ting, Y. P., Geschke, A., & Malik, A. (2018). The carbon footprint of global tourism. Nature Climate Change, 8(6), 522–528. https://doi.org/10.1038/s41558-018-0141-x

3. Zhao, E., Du, P., & Sun, S. (2022). Historical pattern recognition with trajectory similarity for daily tourist arrivals forecasting. Expert Systems with Applications, 203, 117427. https://doi.org/10.1016/j.eswa.2022.117427

4. Hwang, E., Kim, J., Lee, J. C., & Kim, S. (2019). To do or to have, now or later, in travel: Consumption order preference of material and experiential travel Activities. Journal of Travel Research, 58(6), 961–976. https://doi.org/10.1177/0047287518790416

5. Bano, S., Liu, L., & Khan, A. (2022). Dynamic influence of aging, industrial innovations, and ICT on tourism development and renewable energy consumption in BRICS economies. Renewable Energy, 192, 431–442. https://doi.org/10.1016/j.renene.2022.04.134

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