EVOLUTION AND INFLUENCING FACTORS OF THE GREEN DEVELOPMENT SPATIAL ASSOCIATION NETWORK IN THE GUANGDONG-HONG KONG-MACAO GREATER BAY AREA
Authors:
Open Access
- 23 March 2022
- journal article
- research article
- Published by Vilnius Gediminas Technical University in Technological and Economic Development of Economy
- Vol. 28 (3) , 716-742
Abstract
Accurately grasping the structural characteristics and influencing factors of green development spatial association are significant for green coordinated development and ecological civilization construction in the Guangdong-Hong Kong-Macao Greater Bay Area (GBA). This study evaluates the GBA’s green development performance from 2015 to 2019 based on duality theory, and uses social network analysis to explore the structural characteristics and evolution of the green development spatial association network, and then uses the exponential random graph model to reveal the influencing factors of network formation. we find that: (1) the GBA’s green development is steady. Its spatial association network became increasingly complex, and tends to be tight. (2) As important hubs, Guangzhou, Shenzhen, and Hong Kong have the dominant positions in the GBA’s green development spatial association network. Huizhou, Jiangmen, Zhaoqing, and Macao are at the edge of the network, and their interoperability with other cities is relatively weak. (3) Four subgroups exist in the GBA during different periods, with obvious gradient characteristics between them, and the multilevel transmission mechanism of the green development network gradually forms. (4) Economic development and urbanization level, ecological environment endowment, and geographical, institutional, and industrial proximity all have significant impacts on the formation of the GBA’s green development spatial association network. First published online 23 March 2022 Keyword : Guangdong-Hong Kong-Macao Greater Bay Area, green development, spatial association network, social network analysis, duality theory, exponential random graph modelKeywords
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