Study on the Impact of Population and Economic Activities on Environmental Indicators through Pca DOI Creative Commons
Junchao Zhang,

Junmeng Yan,

Xin Li

et al.

Transactions on Economics Business and Management Research, Journal Year: 2024, Volume and Issue: 5, P. 228 - 237

Published: March 31, 2024

Principal Component Analysis (PCA) is applied in environmental policy planning to effectively identify and assess key factors affecting carbon peaking emissions reduction. By analysing multidimensional data covering GDP, population, average temperature, oil consumption price, CO2 emissions, PCA reveals complex correlations of challenges. Preliminary regression analyses expose relationships between variables, with a particular focus on anomalous for 2020, emphasising the importance precise analytical methods.The highlights significant impacts economic activity, population oil-related indicators by simplifying high-dimensional data, retaining information, extracting principal components highest variance.The KMO Bartlett test confirms applicability PCA, variance interpretation dendrograms guiding component selection ensure adequate retention information. This approach enabled identification indicators, construction weighted composite model quantitatively analyse yearly basis, strengthened understanding overall role activities environment. Overall, application its refining clarifying drivers energy consumption, development targeted policies, enriching toolkit demonstrating contribution statistical techniques governance.

Language: Английский

Study on the Impact of Population and Economic Activities on Environmental Indicators through Pca DOI Creative Commons
Junchao Zhang,

Junmeng Yan,

Xin Li

et al.

Transactions on Economics Business and Management Research, Journal Year: 2024, Volume and Issue: 5, P. 228 - 237

Published: March 31, 2024

Principal Component Analysis (PCA) is applied in environmental policy planning to effectively identify and assess key factors affecting carbon peaking emissions reduction. By analysing multidimensional data covering GDP, population, average temperature, oil consumption price, CO2 emissions, PCA reveals complex correlations of challenges. Preliminary regression analyses expose relationships between variables, with a particular focus on anomalous for 2020, emphasising the importance precise analytical methods.The highlights significant impacts economic activity, population oil-related indicators by simplifying high-dimensional data, retaining information, extracting principal components highest variance.The KMO Bartlett test confirms applicability PCA, variance interpretation dendrograms guiding component selection ensure adequate retention information. This approach enabled identification indicators, construction weighted composite model quantitatively analyse yearly basis, strengthened understanding overall role activities environment. Overall, application its refining clarifying drivers energy consumption, development targeted policies, enriching toolkit demonstrating contribution statistical techniques governance.

Language: Английский

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