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Exploring the Application of Principal Component Analysis in Assessing International Trade Competitiveness

By: Ruonan Zhang 1, Fengfei Sun 2
1 Suzhou Vocational University, Suzhou, Jiangsu, 215000, China
2Jiangsu Botao Intelligent Thermal Engineering Co., Ltd., Suzhou, Jiangsu, 215562, China

Abstract

The position and influence of countries in the global economic system are increasingly dependent on their market competitiveness. Assessing the competitiveness of a country or region in international trade can provide policy makers with a basis for strategic decision-making. This paper constructs a model for comprehensively assessing the competitiveness of international trade market through principal component analysis and cluster analysis, and empirically analyzes the competitiveness of China’s new energy automobile industry in the international market. The study first constructs an evaluation index system for competitiveness in international trade market, covering four major areas: policy, environment, production and technology. By standardizing the data, the main components were extracted using principal component analysis, and the competitiveness of each province and city was scored based on the variance contribution ratio (77.22%) of the first two principal components. The results show that Guangdong Province is far ahead in the international competitiveness of the new energy automobile industry, with a score of 6.67, ranking first in the country; Beijing and Shanghai rank second and third respectively, with scores of 4.17 and 2.26. Cluster analysis shows that the competitiveness of the new energy automobile industry can be categorized into five echelons, of which Guangdong, Zhejiang, and Jiangsu belong to the strongest competitiveness echelon, and Jilin, Heilongjiang, Guizhou and other provinces are relatively weak. The competitiveness of Jilin, Heilongjiang, Guizhou and other provinces is relatively weak. The study shows that the international market competitiveness of new energy automobile industry is affected by multiple factors, among which technological innovation, policy support and production cost are important factors determining competitiveness. The model can provide data support and theoretical basis for the government and enterprises in formulating relevant policies and strategies.