Spatial and temporal correlation between green space landscape pattern and carbon emission—Three major coastal urban agglomerations in China

Abstract

Urban green spaces, including parks, gardens, and tree-lined streets, can play a crucial role in mitigating atmospheric CO2 levels. Understanding the distribution and dynamics of these green spaces is essential for their effective incorporation into urban planning to reduce carbon emissions. However, previous literatures have largely overlooked the integration of green space patterns in urban planning, thereby constraining our capacity for effective carbon mitigation. This study utilizes an enhanced Long Short-Term Memory network with a Self-Attention Mechanism to estimate carbon emissions and evaluates the influence of urban green spaces. Results from the Bohai Rim (CBS), the Pearl River Delta (PRD), and the Yangtze River Delta (YRD) reveal spatial clustering of carbon emissions radiating outward from core cities. Additionally, the analysis demonstrates that the number, density, shape complexity, and spatial aggregation of green spaces can significantly impact carbon emissions. Specifically, the quantity and concentration of green spaces help reduce emissions, while greater shape complexity and spatial aggregation tend to have the opposite effect. Based on these findings, the study offers insights for optimizing urban green space planning to support carbon emission reduction strategies.