Spatial heterogeneity of driving factors for urban heat health risk in Chongqing, China: A new identification method and proposal of planning response framework

被引:13
|
作者
Huang, Haijing [1 ,2 ]
Ma, Jinhui [1 ]
Yang, Yufei [3 ]
机构
[1] Chongqing Univ, Sch Architecture & Urban Planning, Chongqing, Peoples R China
[2] Chongqing Univ, Key Lab New Technol Construct Cities Mt Area, Chongqing, Peoples R China
[3] CDHT Pk City Construct Bur, Chengdu, Peoples R China
基金
中国国家自然科学基金;
关键词
Urban heat; Heat health risk assessment; Driving factors; Spatial heterogeneity; Planning response; CLIMATE-CHANGE; VULNERABILITY INDEX; WAVE VULNERABILITY; MORTALITY; POPULATION; STRESS; IMPACT; ISLAND; SCALE; COOL;
D O I
10.1016/j.ecolind.2023.110449
中图分类号
X176 [生物多样性保护];
学科分类号
090705 ;
摘要
With urban heat challenges increasingly severe, assessing heat health risk has become crucial for human settlements. Unfortunately, previous studies have not accurately identified the driving factors, posing a significant obstacle to translating assessment results into policymaking. Particularly, potential spatial heterogeneity of driving factors at the indicator-level may exist. Therefore, this study developed a systematic method to examine the spatial heterogeneity of driving factors for heat health risk in Chongqing. Based on this heterogeneity, an integrated framework linking heat health risk, driving factors, and response strategies was proposed, supporting specific solutions for different cities. The results indicate that driving factors exhibit strong heterogeneity at the indicator-level. Even within the same prevention zone and urban functional areas, the maximum differences in the number of driving factors and combination categories can reach four and five, respectively. Moreover, relying solely on the driving factors obtained through traditional methods to develop cooling measures is unreasonable. When these driving factors are consistent, there are still, on average, six combinations of driving factors at the indicator-level, and each combination includes an average of 2.9 indicators. The higher the level of the risk prevention zone, the more driving factors it contains. The average number of driving factors above the moderate risk level is 3.9, higher than the 1.1 and 2 found in moderate and below moderate risk levels. Overall, this study provides a reference for understanding spatial heterogeneity of driving factors for heat health risk and offers an approach to assist policymakers in formulating guided cooling strategies.
引用
收藏
页数:18
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