Assessing heat vulnerability risk of Jinan and Guangzhou's older populations based on multisource remote sensing data

As climate change intensifies, understanding heat vulnerability - defined as the susceptibility of populations to the adverse effects of heat waves - becomes increasingly critical for effective adaptation and management strategies. This study uses an "Exposure-Sensitivity-Adaptation" framework combined with GeoDetector to assess heat vulnerability in Jinan and Guangzhou. The analysis incorporates Land Surface Temperature (LST), Normalized Difference Vegetation Index (NDVI), population density, Points of Interest (POI), and socioeconomic data. These factors are used to construct a comprehensive index system and a vulnerability measurement model focused on assessing the heat vulnerability of older populations. This study identifies vulnerable spatial regions and reveals the key factors driving heat vulnerability, offering valuable insights into its spatial dynamics and underlying causes in urban areas. The results show significant spatial disparities in heat vulnerability between and within the two cities. In Guangzhou, the core urban districts, such as Tianhe and Yuexiu, exhibit lower vulnerability due to robust infrastructure, higher disposable incomes, and extensive green space coverage. In contrast, peripheral districts, including Conghua and Zengcheng, face higher vulnerability due to uneven resource distribution and slower urbanisation. In Jinan, the urban core, including Shizhong and Licheng districts, demonstrates lower vulnerability supported by modern infrastructure and medical resources, while peripheral areas, such as Shanghe County and parts of Zhangqiu, show heightened vulnerability driven by ageing populations, limited healthcare, and lower economic development. At the community level, high-risk areas are clustered in economically disadvantaged neighbourhoods and regions with dense older populations. In Guangzhou, high-vulnerability communities (e.g., Beijing, Binjiang, and Datang) are in peripheral districts or underdeveloped areas with limited resources. Similarly, in Jinan, high-risk communities (e.g., Guanzhaying, Hongjialou, and Huanghe) are concentrated in peripheral counties and older urban neighbourhoods with ageing populations. To mitigate heat vulnerability, the study recommends enhancing urban green spaces, retrofitting ageing infrastructure, and implementing community-specific education campaigns. These findings provide actionable insights into tailoring urban planning and climate adaptation strategies to improve heat resilience, particularly in rapidly urbanising cities with ageing populations and socio-economic inequalities.