In recent years, measures proposed to address urban flooding caused by extreme rainfall often demand substantial investment, restricting their broad implementation. This study quantitatively assessed the inundation situations of 138 capital cities under both normal and extreme rainfall conditions. Using machine learning techniques, we found that grey infrastructure—closely commensurate with a city’s economic development—dominates flood reduction during normal rainfall events. However, during extreme precipitation, as rainfall intensity rises, the marginal effectiveness of grey infrastructure declines markedly. In contrast, green infrastructure and topography—less commensurate with economic development—play increasingly critical roles in mitigating urban flooding. These findings suggest that economic development has a limited impact on urban flooding during extreme rainfall events. Rationally utilizing topography and enhancing green spaces provides a cost-effective nature-based solution, which is particularly important for urban planning in low- and middle-income countries undergoing rapid urbanization.

Revealing the characteristics of spatial–temporal dynamics and influencing factors is important for optimizing the spatial distribution of tea production. Taking prefecture-level cities as the basic spatial unit, this study uses the Herfindahl index and exploratory spatial data analysis to reveal the spatial–temporal dynamics of China’s tea production from 2000 to 2015. A theoretical analysis framework is established and a spatial econometric model is used to explore its influencing factors. The results show a U-shaped trend in the degree of tea spatial agglomeration, which gradually declined during 2000–2010, and rapidly increased during 2011–2015. The proportion of tea production shifted from the eastern region to the central and western regions, and spatial distribution coverage expanded to the north. Tea production had significant spatial correlation, and spatial agglomeration characteristics were exhibited for similar values (high or low). Tea production had a significant spatial spillover effect. Natural resources, labor cost, specialized production, and policies all affected the spatial–temporal dynamics of tea production somewhat, but the effects of traffic conditions and technological level were insignificant. Finally, this study proposed optimizing four aspects of the tea spatial layout: regional cooperation, comprehensive suitability evaluation of tea cultivation, spatial agglomeration, and distinctive local brands.