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A multiscale examination of heat health risk inequality and its drivers in mega-urban agglomeration: A case study in the Yangtze River Delta, China
Journal of Cleaner Production ( IF 11.1 ) Pub Date : 2024-05-09 , DOI: 10.1016/j.jclepro.2024.142528 Hanyi Wu , Chuanwu Zhao , Yu Zhu , Yaozhong Pan
Journal of Cleaner Production ( IF 11.1 ) Pub Date : 2024-05-09 , DOI: 10.1016/j.jclepro.2024.142528 Hanyi Wu , Chuanwu Zhao , Yu Zhu , Yaozhong Pan
Increased frequency of extreme urban heat and its exposure to urban populations is one of the challenges presented by climate change, especially in urban clusters. Due to the rapid but unequal development, heat exposure disproportionately increased in the underdeveloped regions compared to the developed regions in urban agglomeration. To address this issue, it is crucial to clarify the spatial pattern of heat health risk (HHR) inequality for urban heat resilience. However, analyses for the disparity of HHR inequality often used a single scale, neglecting important spatial context effects at other scales. Moreover, the rationale of HHR inequality remains unclear. Here, we took the well-developed and highly urbanized Yangtze River Delta (YRD) region as a case study and employed multiscale approaches to examine how and why the HHR inequality varied at and within the regional scale. We first assessed HHR using a comprehensive assessment framework at a 1 km grid level. Then, we quantified the inequality between regions using local Moran's I and KS distance. Therefore, we utilized the Gini coefficient and Bayes quantile regression to quantify inequality and identify its drivers within the regional scale. Finally, we proposed a conceptual framework to inform policymaking in regions with different patterns of multiscale equality. Our results found that the HHR in YRD exhibited significant spatial inequality at the regional scale (Moran's I = 0.562, P < 0.001) and within the regional scale (Gini coefficient: 0.27–0.54). Higher population concentrations and building densities often led to higher HHR. In high HHR areas, intra-regional inequality was often lower due to high and coordinated socioeconomic levels (Gini coefficient: 0.27–0.34). Additionally, in areas with low and medium levels of risk, healthcare resource availability and local temperatures had a greater impact on intra-regional inequities, which varied at different levels of inequality. This study contributes to a better understanding of multiscale HHR inequality, which helps optimize heat risk management strategies and regional sustainable development.
更新日期：2024-05-09
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