Analyzing the regional economic changes in a high-tech industrial development zone using machine learning algorithms.

The aims are to improve the efficiency in analyzing the regional economic changes in China's high-tech industrial development zones (IDZs), ensure the industrial structural integrity, and comprehensively understand the roles of capital, technology, and talents in regional economic structural ch...

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Autores principales:	ErLe Du, Meng Ji
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/4cfe7e9e37424f1b9fcc1db6a12585c2
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spelling	oai:doaj.org-article:4cfe7e9e37424f1b9fcc1db6a12585c22021-12-02T20:03:51ZAnalyzing the regional economic changes in a high-tech industrial development zone using machine learning algorithms.1932-620310.1371/journal.pone.0250802https://doaj.org/article/4cfe7e9e37424f1b9fcc1db6a12585c22021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0250802https://doaj.org/toc/1932-6203The aims are to improve the efficiency in analyzing the regional economic changes in China's high-tech industrial development zones (IDZs), ensure the industrial structural integrity, and comprehensively understand the roles of capital, technology, and talents in regional economic structural changes. According to previous works, the economic efficiency and impact mechanism of China's high-tech IDZ are analyzed profoundly. The machine learning (ML)-based Data Envelopment Analysis (DEA) and Malmquist index measurement algorithms are adopted to analyze the dynamic and static characteristics of high-tech IDZ's economic data from 2009 to 2019. Furthermore, a high-tech IDZ economic efficiency influencing factor model is built. Based on the detailed data of a high-tech IDZ, the regional economic changes are analyzed from the following dimensions: economic environment, economic structure, number of talents, capital investment, and high-tech IDZ's regional scale, which verifies the effectiveness of the proposed model further. Results demonstrate that the comprehensive economic efficiency of all national high-tech IDZs in China is relatively high. However, there are huge differences among different regions. The economic efficiency of the eastern region is significantly lower than the national average. The economic structure, number of talents, capital investment, and economic efficiency of the high-tech IDZs show a significant positive correlation. The economic changes in high-tech IDZs can be improved through the secondary industry, employee value, and funding input. The ML technology applied can make data processing more efficient, providing proper suggestions for developing China's high-tech industrial parks.ErLe DuMeng JiPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 6, p e0250802 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q ErLe Du Meng Ji Analyzing the regional economic changes in a high-tech industrial development zone using machine learning algorithms.
description	The aims are to improve the efficiency in analyzing the regional economic changes in China's high-tech industrial development zones (IDZs), ensure the industrial structural integrity, and comprehensively understand the roles of capital, technology, and talents in regional economic structural changes. According to previous works, the economic efficiency and impact mechanism of China's high-tech IDZ are analyzed profoundly. The machine learning (ML)-based Data Envelopment Analysis (DEA) and Malmquist index measurement algorithms are adopted to analyze the dynamic and static characteristics of high-tech IDZ's economic data from 2009 to 2019. Furthermore, a high-tech IDZ economic efficiency influencing factor model is built. Based on the detailed data of a high-tech IDZ, the regional economic changes are analyzed from the following dimensions: economic environment, economic structure, number of talents, capital investment, and high-tech IDZ's regional scale, which verifies the effectiveness of the proposed model further. Results demonstrate that the comprehensive economic efficiency of all national high-tech IDZs in China is relatively high. However, there are huge differences among different regions. The economic efficiency of the eastern region is significantly lower than the national average. The economic structure, number of talents, capital investment, and economic efficiency of the high-tech IDZs show a significant positive correlation. The economic changes in high-tech IDZs can be improved through the secondary industry, employee value, and funding input. The ML technology applied can make data processing more efficient, providing proper suggestions for developing China's high-tech industrial parks.
format	article
author	ErLe Du Meng Ji
author_facet	ErLe Du Meng Ji
author_sort	ErLe Du
title	Analyzing the regional economic changes in a high-tech industrial development zone using machine learning algorithms.
title_short	Analyzing the regional economic changes in a high-tech industrial development zone using machine learning algorithms.
title_full	Analyzing the regional economic changes in a high-tech industrial development zone using machine learning algorithms.
title_fullStr	Analyzing the regional economic changes in a high-tech industrial development zone using machine learning algorithms.
title_full_unstemmed	Analyzing the regional economic changes in a high-tech industrial development zone using machine learning algorithms.
title_sort	analyzing the regional economic changes in a high-tech industrial development zone using machine learning algorithms.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/4cfe7e9e37424f1b9fcc1db6a12585c2
work_keys_str_mv	AT erledu analyzingtheregionaleconomicchangesinahightechindustrialdevelopmentzoneusingmachinelearningalgorithms AT mengji analyzingtheregionaleconomicchangesinahightechindustrialdevelopmentzoneusingmachinelearningalgorithms
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Analyzing the regional economic changes in a high-tech industrial development zone using machine learning algorithms.

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