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Article type: Research Article
Authors: Chen, Huia | Tian, Guixianb; *
Affiliations: [a] School of Engineering Management and Logistics, Shaanxi Railway Institute, Weinan, Shaanxi, China | [b] School of Business, Pingxiang University, Pingxiang, Jiangxi, China
Correspondence: [*] Corresponding author: Guixian Tian, School of Business, Pingxiang University, Pingxiang, Jiangxi 337055; China. E-mail: [email protected].
Abstract: In order to improve the economic growth efficiency of industrial and commercial enterprises in coastal cities and realize the GDP growth of coastal cities, this paper studies the economic growth factors of industrial and commercial enterprises in coastal cities based on the unexpected super efficiency model. Based on the research and analysis of the previous economic growth theories, this paper finds out the main factors that affect the economic growth of industrial and commercial enterprises in coastal cities, and uses the advanced econometric method to establish the relevant test model to analyze the correlation between the time series of economic growth factors and the time series of coastal cities, so as to realize the economic growth factors of industrial and commercial enterprises in coastal cities Element study. The empirical results show that the main factors affecting the economic growth of industrial and commercial enterprises in coastal cities are capital and labor force, with labor force as the main body; Technical and institutional factors also contribute to the GDP of industrial and commercial enterprises in coastal cities, but the impact is not significant and needs further improvement. In general, these factors can promote the economic growth of industrial and commercial enterprises in coastal cities. The time series and time series of each factor variable are first-order non-stationary series with long-term cointegration relationship.
Keywords: Unexpected super efficiency model, coastal cities, industrial and commercial enterprises, economic growth, influencing factors, time series data
DOI: 10.3233/JCM-226852
Journal: Journal of Computational Methods in Sciences and Engineering, vol. 23, no. 5, pp. 2795-2809, 2023
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