Spatial Distribution Characteristics and Driving Factors of Little Giant Enterprises in China’s Megacity Clusters Based on Random Forest and MGWR

Jianshu Duan, Zhengxu Zhao, Youheng Xu, Xiangting You, Feifan Yang and Gang Chen ()
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Jianshu Duan: Department of Geographic Information Science, School of Geographic and Oceanographic Sciences, Nanjing University, Nanjing 210023, China
Zhengxu Zhao: Department of Geographic Information Science, School of Geographic and Oceanographic Sciences, Nanjing University, Nanjing 210023, China
Youheng Xu: Department of Geographic Information Science, School of Geographic and Oceanographic Sciences, Nanjing University, Nanjing 210023, China
Xiangting You: Department of Geographic Information Science, School of Geographic and Oceanographic Sciences, Nanjing University, Nanjing 210023, China
Feifan Yang: Department of Geographic Information Science, School of Geographic and Oceanographic Sciences, Nanjing University, Nanjing 210023, China
Gang Chen: Department of Geographic Information Science, School of Geographic and Oceanographic Sciences, Nanjing University, Nanjing 210023, China

Land, 2024, vol. 13, issue 7, 1-25

Abstract: As a representative of potential “hidden champions”, a concept originating in Germany, specialized and innovative Little Giant Enterprises (LGEs) have become exemplary models for small and medium-sized enterprises (SMEs) in China. These enterprises are regarded as crucial support for realizing the strategy of building a strong manufacturing country and addressing the weaknesses in key industrial areas. This paper begins by examining urban agglomerations, which serve as the main spatial carriers for industrial restructuring and high-quality development in manufacturing. Based on data from LGEs in the Yangtze River Delta (YRD) and Pearl River Delta (PRD) urban agglomerations from 2019 to 2023, the study employs the Random Forest (RF) and Multi-scale Geographically Weighted Regression (MGWR) methods to conduct a comparative analysis of their spatial patterns and influencing factors. The results are as follows: (1) LGEs exhibit spatial clustering in both the YRD and PRD regions. Enterprises in the YRD form a “one-axis-three-core” pattern within a distance of 65 km, while enterprises in the PRD present a “single-axis” pattern within a distance of 30 km, with overall high clustering intensity. (2) The YRD is dominated by traditional manufacturing and supplemented by high-tech services. In contrast, the PRD has a balanced development of high-tech manufacturing and services. Enterprises in different industries are generally characterized by a “multi-point clustering” characteristic, of which the YRD displays a multi-patch distribution and the PRD a point–pole distribution. (3) Factors such as industrial structure, industrial platforms, and logistics levels significantly affect enterprise clustering and exhibit scale effects differences between the two urban clusters. Factors such as industrial platforms, logistics levels, and dependence on foreign trade show positive impacts, while government fiscal expenditure shows a negative impact. Natural geographical location factors exhibit opposite effects in the two regions but are not the primary determinants of enterprise distribution. Each region should leverage its own strengths, improve urban coordination and communication mechanisms within the urban cluster, strengthen the coordination and linkage of the manufacturing industry chain upstream and downstream, and promote high-tech industries, thereby enhancing economic resilience and regional competitiveness.

Keywords: specialized and innovative little giant enterprises (LGEs); urban agglomeration; random forest; multiple geographically weighted regression; YRD; PRD (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2024
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