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Extreme heat is the leading cause of heat-related mortality around the world. Extracting heat vulnerability information from the urban complexity system is crucial for urban health studies. Using heat vulnerability index (HVI) is the most common approach for urban planners to locate the places with high vulnerability for intervention and protection. Previous studies have demonstrated that HVI can play a vital role in determining which areas are at risk of heat-related deaths. Both equal weight approach (EWA) and principal component analysis (PCA) are the conventional methods to aggregate indicators to HVI. However, seldom studies have compared the differences between these two approaches in estimating HVI. In this paper, we evaluated the HVIs in Hangzhou in 2013, employing EWA and PCA, and assessed the accuracies of these two HVIs by using heat-related deaths. Our results show that both HVI maps showed that areas with high vulnerability are located in the central area while those with low vulnerability are located in the suburban area. The comparison between HVIEWA and HVIPCA shows significantly different spatial distributions, which is caused by the various weight factors in EWA and PCA. The relationship between HVIEWA and heat-related deaths performs better than the relationship between HVIPCA and deaths, implying EWA could be a better method to evaluate heat vulnerability than PCA. The HVIEWA can provide a spatial distribution of heat vulnerability at intracity to direct heat adaptation and emergency capacity planning.

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The study of tradeoffs/synergies among ecosystem services (ESs) is highly significant for land-use planning and regional ecosystem optimization. Land-use change and topographic factors have important implications for ESs. Strengthening the comparative analysis of the capacity of ESs provided by different land-use types in specific regions, studying the topographic gradient effects of ecosystem service trade-offs/synergies with slope changes, and identifying the dominate trade-off/synergy relationship among ESs will help us to carry out ecosystem regulation according to local conditions through land-use layout optimization at a fine scale. Our research site was located in the Dabie Mountains of western Anhui Province, China (DBM), where, based on the InVEST software, R language, self-organizing maps (SOM), and GeoDA, the temporal and spatial variations of five typical ESs, including food supply, soil retention, water yield, carbon storage, and biodiversity maintenance from 2005 to 2020, were analyzed, and spatial distributions of the different ESs clusters were also recognized by using the SOM method. Moreover, the impacts of land-use type and slope on ESs, and the characteristics of trade-offs/synergies among the five ESs, were discussed. Results showed, firstly, that the total values of ESs showed a changing trend of “three increases and two decreases” from 2005 to 2020. Among the ESs, food supply, soil retention and water yield showed upward trends, with annual growth rates of 2.83%, 6.50% and 2.98%, respectively, whereas carbon storage and biodiversity maintenance showed downward trends, with annual decline rates of 0.03% and 0.07%, respectively. Second, the results showed that the Moran’s I index of the total ESs was 0.3995 in 2005 and 0.4305 in 2020, respectively, indicating that they had significant spatial clustering characteristics. The Low-Low clustering regions with reduced changes were mainly in the central and northern parts of the study area, whereas the High-High clustering regions with increased changes were found distributed mainly in the south of the study area. Thirdly, it was found that cropland and woodland were the main contributors to the total amounts of ESs, but the supply capacity of ESs per unit area of woodland was the largest, constituting nearly 1/3 of the total supply capacity of the ESs. Last, the slope effect on trade-offs and synergies was significant between typical ecosystem service pairs in the study area; trade-offs were the main relationships between the pairs of ESs in the study area, which accounted for nearly 60% of all types of trade-offs/synergies during the 15 years. In addition, the spatial distributions of the trade-offs/synergies between ESs pairs were visualized clearly, and the six ES bundles were identified by using the SOM method at the township administrative scale. The identification of ecosystem service bundles is of great significance for the division of ecological functional zones and ecological regulation in the DBM.