Spatiotemporal Simulation of Green Space by Considering Socioeconomic Impacts Based on A SD-CA Model

Abstract

Green space is an important part of composite urban spatial systems. Therefore, reasonable planning strategies based on scientifically sound predictions of temporal and spatial changes in green space are critical for maintaining urban ecological environments, ensuring the health of residents, and maintaining social stability. However, existing forecasting models discount the impacts of urban social economy on green space. To address this gap, we constructed a system dynamics and cellular automata (SD-CA) coupling model that integrated the socioeconomic system and generated multiple scenarios. The results showed that at the current pace of socioeconomic development, Beijing’s central district will experience an overall reduction in green space and a decline in its integrity and diversity by 2035. If the population of this area reaches 9.29 million by 2035 and the GDP maintains an average growth rate of 6.1%, the areas of various land types will exhibit little change by 2035, and green space will be optimized to a certain extent. However, if the study area’s population decreases to 8.59 million by 2035 and the average GDP growth rate drops to 4.9%, the fragmentation, connectivity, and diversity index of green space will all increase significantly by 2035, and green space will be clearly optimized. We propose scientifically grounded strategies for maximizing the ecological functions and economic benefits of green space through optimized green space patterns, considered from a policy-oriented perspective of promoting socioeconomic development.