Quantifying the environmental synergistic effect of cooling-air purification-carbon sequestration from urban forest in China

Abstract

Urban forest is considered nature-based solution for mitigating the adverse impacts of climate change. However, large-scale quantification of urban forest synergistic effect is still limited. This study integrated multi-source remote sensing data, machine learning, and geospatial methods to quantify the synergistic effect (i.e., spatial interaction) of urban forest on urban heat island, PM2.5 concentration, and carbon stock and its driving mechanism in China. Results showed that urban forest explained 60–71% of the synergistic effect, which was greater than on the individual environmental factors, but decreased by 15.62% from 2000 to 2020. Moreover, urban forest synergistic effect exhibited an obvious spatial-temporal patterns of north-south heterogeneity and was dominated by anthropogenic factors. We further identified the “evolutionary gene” of urban forest, i.e., the similarity rule in the synergistic effect among cities during their development process. This knowledge contributes to the sustainable resource development of climate change adaptation strategies.