Study on the Impact of Population and Economic Activities on Environmental Indicators through Pca

Abstract

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