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Economic Synergistic Development of Guangdong-Hong Kong-Macao Greater Bay Area Urban Agglomeration: Based on Composite System.
- Source :
- Computational Intelligence & Neuroscience; 9/13/2022, Vol. 2022, p1-10, 10p
- Publication Year :
- 2022
-
Abstract
- Synergistic development is the only way which must be passed and a key point to achieve high-quality economic development. This paper regards regional synergetic development as a composite system, builds up the evaluation indicator system, and calculates the level of economic synergetic development of Guangdong-Hong Kong-Macao Greater Bay Area Urban Agglomeration, by using the collaborative degree model of composite system. The results show that each subsystem of the composite system has a high degree of order from 2007 to 2019, but compared with Beijing-Tianjin-Hebei urban agglomeration and Yangtze River Delta urban agglomeration, the level of economic collaborative development of Guangdong-Hong Kong-Macao Greater Bay Area urban agglomeration in 2008–2019 is relatively low and has large spatial differences. The main reason is that under the background of "one country, two systems" policy, the institutional differences between Guangdong, Hong Kong, and Macao have not been effectively linked up and synergetic, Greater Bay Area urban agglomeration has not yet formed an organic whole, and the synergy effect of mutual support and promotion is relatively weak. Based on this, we should seize the great historical opportunity of the construction of Guangdong-Hong Kong-Macao Greater Bay Area, accelerate the construction of the mechanism for the synergetic economic development of the three areas, accelerate the establishment of an integrated market, build a reasonable division of labor system and collaborative innovation system, and jointly promote the synergetic economic development of Greater Bay Area. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 16875265
- Volume :
- 2022
- Database :
- Complementary Index
- Journal :
- Computational Intelligence & Neuroscience
- Publication Type :
- Academic Journal
- Accession number :
- 159483270
- Full Text :
- https://doi.org/10.1155/2022/7677188