Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Factors Influencing Energy Consumption from China’s Tourist Attractions: A Structural Decomposition Analysis with LMDI and K-Means Clustering
Tourism has become a major driver of China’s economic growth and consumes much energy causing environmental pollution problems. This paper combines the LMDI (logarithmic mean Divisia index) method and K-means clustering to analyze the factors influencing tourism energy consumption in seven Chinese provinces and discusses strategies for energy consumption in tourism. Specifically, firstly, this paper decomposes the tourism energy consumption factors in each province into six factors and identifies the driving forces of different factors on energy consumption. Secondly, K-means clustering method is used to classify different provinces into three categories using the latest dynamic influencing factors as clustering factors and provincial targeted suggestions are made according to the characteristics of different categories. This paper combines the LMDI model with cluster analysis to find targeted energy optimization strategies for the energy consumption of the Chinese tourism industry.
Factors Influencing Energy Consumption from China’s Tourist Attractions: A Structural Decomposition Analysis with LMDI and K-Means Clustering
Tourism has become a major driver of China’s economic growth and consumes much energy causing environmental pollution problems. This paper combines the LMDI (logarithmic mean Divisia index) method and K-means clustering to analyze the factors influencing tourism energy consumption in seven Chinese provinces and discusses strategies for energy consumption in tourism. Specifically, firstly, this paper decomposes the tourism energy consumption factors in each province into six factors and identifies the driving forces of different factors on energy consumption. Secondly, K-means clustering method is used to classify different provinces into three categories using the latest dynamic influencing factors as clustering factors and provincial targeted suggestions are made according to the characteristics of different categories. This paper combines the LMDI model with cluster analysis to find targeted energy optimization strategies for the energy consumption of the Chinese tourism industry.
Factors Influencing Energy Consumption from China’s Tourist Attractions: A Structural Decomposition Analysis with LMDI and K-Means Clustering
Environ Model Assess
Zhao, Erlong (Autor:in) / Wu, Jing (Autor:in) / Wang, Shubin (Autor:in) / Sun, Shaolong (Autor:in) / Wang, Shouyang (Autor:in)
Environmental Modeling & Assessment ; 29 ; 569-587
01.06.2024
19 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
Tourist attractions , Energy consumption , Environmental pollution , LMDI method , Tourism influence factors , K-means Environment , Math. Appl. in Environmental Science , Mathematical Modeling and Industrial Mathematics , Operations Research/Decision Theory , Applications of Mathematics , Earth and Environmental Science
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