The urban heat island (UHI) effect has a serious negative impact on urban ecosystems and human well-being. Mitigating UHI through nature-based methods is highly recommended. The cooling effect of urban blue infrastructure (UBI) can significantly alleviate the effects of UHI. Revealing the crucial influencing factors of the cooling effect of UBI is of great significance for mitigating the UHI effect. In this study, the water-cooling intensity (WCI) and water-cooling range (WCR) were used to quantitatively analyze the cooling effect of UBI in Hefei city in summer. Furthermore, the influencing factors and their interactions with the cooling effect of UBI were investigated based on the Geodetector model. The results revealed that: (1) The surface thermal environment of the built-up area of Hefei presented obvious spatial differentiation characteristics. (2) There were nine influencing factors that significantly influenced the WCI variation, with the greatest influencing factor of road density. In contrast, only the landscape shape index had a significant effect on WCR variation. (3) The interaction of environmental characteristics, water body characteristics, and socioeconomic characteristics had a significant influence on the cooling effect of UBI, and the interaction relationship between the influencing factors was mutually enhanced. The findings from our research can provide a theoretical reference and practical guidance for the protection, restoration, and planning of UBI as a nature-based solution to improve the urban thermal environment.

Context: The relationships between ecosystem services (ES) and human well-being (HWB) have been found to be influenced by geographic locations and socioeconomic development, and vary from local to global scales. However, there is a lack of comparative analyses at fine administrative scales such as town and village scales. Objective: This study took the core region of the Yangtze River Delta (YRD) of China as the study area to examine the spatial characteristics of the values of ES and the subjective satisfaction scores of HWB and then compare their relationships at the town and village scales. Methods: The values of 9 ES indicators were quantified using the ecosystem service equivalent factor method, and the subjective satisfaction scores of 11 HWB indicators were investigated using the questionnaire survey. The ES-HWB relationships between 9 ES and 11 HWB measures in the study area were investigated using Spearman's correlation analysis. Results: The value of ES per unit area in the study area in 2020 was about 15,202.90 USD/ha, nearly three times the average level in China, but the per capita value was relatively low, at 322.11 USD/person. The satisfaction score of HWB was relatively high, especially for the dimensions of social relations (4.46), health (4.26), and safety (4.22), based on a 5-point Likert scale. As spatial scales decreased from town to village scales and thematic scales increased from secondary to primary indicators, the strength of the ES-HWB correlations diminished and their direction changed as well. According to secondary indicators, most of the ES-HWB relationships were positive at the town scale but became negative or nonexistent at the village scale (e.g. the Spearman correlation coefficient between the value of raw material supply and the satisfaction score of leisure and entertainment shifted from 0.9 at the town scale to -0.51 at the village scale). Conclusions: The correlation strength and direction of the ES-HWB relationships still changed with spatial and thematic scales at the town and village scales. Thus, better understanding the relationships requires studies at multiple and broader scales and calls for caution when using the aggregating indicators, because they can also lead to different ES-HWB relationships.