Spatio-temporal evolution and future simulation of urban agglomeration expansion in the Guangdong–Hongkong–Macau Greater Bay Area

Abstract

AbstractUrban agglomerations are growth poles that promote regional economic development; however, rapid expansion exerts an increasingly negative influence on regional ecosystem security, restricting sustainable urban development. Therefore, predicting the impact of future expansion on regional ecological security can guide regionally coordinated development. In this study, we used land-use data to determine the spatiotemporal evolution of urbanization and land use in the Guangdong–Hong Kong–Macao Greater Bay Area (GBA) urban agglomeration from 1990 to 2020 and employed the artificial neural network–cellular automata (ANN–CA)–Markov model to simulate future expansion under three different scenarios for 2025 and 2035. From 1990 to 2020, the overall development level of the GBA urban agglomeration was relatively high, with the area of construction land following a pattern of first increasing rapidly and then gradually transitioning to a slower rate of growth. Land use was consistently dominated by forest land, which accounted for more than 50% of the total land area, followed by arable land (30.56%) and construction land (7.92%). The most intense expansion in the GBA occurred in the main urban areas of Guangzhou, Shenzhen, Foshan, Dongguan, and other cities. The expansion sources were mainly farmland, water, and other ecological land types. The spatial structure and characteristics of simulated future regional land-use changes in 2025 and 2035 differed significantly under the three different scenarios. Under the natural development scenario, urban expansion was unconstrained, and rapid growth occurred over a large area of ecological land. Conversely, the addition of ecological constraints effectively controlled the occupation of forest land and cultivated land. Under the economic development scenario, urban expansion incorporated more unused and cultivated land. The results of this study provide a reference for policy decision-making in regional planning, urban planning, and regional ecological protection under multiple future scenarios.