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

Abstract

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.

Regional Economic Development in the AI Era: Methods, Opportunities, and Challenges

“…They can be broadly grouped into several thematic clusters:  Deep Learning for Economic Analysis: Bai et al [17] and Cheng and Huang [9] both utilized deep learning models for evaluating regional eco-nomic development, focusing on university development levels and regional economic growth factors, respectively.  Machine Learning Applications: Bertoletti et al [8] and Du and Ji [18] utilized machine learning and econometric methods to examine regional economic development in relation to higher education systems and high-tech industrial development zones, respectively. In [19], a simulated intelligent environment based on machine learning is used to examine the influence of industrial agglomeration on the regional economy.…”

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

“…The PLOS ONE Editors retract this article [ 1 ] because it was identified as one of a series of submissions for which we have concerns about peer review integrity and similarities across articles. These concerns call into question the validity and provenance of the reported results.…”