A novel climate and health decision support platform: Approach, outputs, and policy considerations

被引:1
作者
Hess, Jeremy J. [1 ,2 ,3 ,4 ]
Sheehan, Timothy J. [1 ,3 ]
Miller, Alyssa [1 ,3 ]
Cunningham, Rad [5 ]
Errett, Nicole A. [1 ,3 ]
Isaksen, Tania Busch [1 ,3 ]
Vogel, Jason [6 ]
Ebi, Kristie L. [1 ,3 ,4 ]
机构
[1] Univ Washington, Ctr Hlth & Global Environm, Seattle, WA 98195 USA
[2] Univ Washington, Sch Med, Dept Emergency Med, Seattle, WA USA
[3] Univ Washington, Sch Publ Hlth, Dept Environm & Occupat Hlth Sci, Seattle, WA USA
[4] Univ Washington, Sch Med & Publ Hlth, Dept Global Hlth, Seattle, WA USA
[5] Washington State Dept Hlth, Olympia, WA USA
[6] Univ Washington, Coll Environm, Climate Impacts Grp, Seattle, WA USA
关键词
Heat; Climate change; Climate change adaptation; Decision support; Risk; Risk assessment; Risk factors; Protective factors; Risk management; Hazard mapping; Vulnerability mapping; Environment and public health; EXTREME-HEAT EXPOSURE; FUZZY-LOGIC MODEL; ADAPTIVE MANAGEMENT; CHANGE ADAPTATION; KING COUNTY; WASHINGTON; RISK; VULNERABILITY; FRAMEWORK; ILLNESS;
D O I
10.1016/j.envres.2023.116530
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Background: The adverse health impacts of climate change are increasingly apparent and the need for adaptation activities is pressing. Risks, drivers, and decision contexts vary significantly by location, and high-resolution, place-based information is needed to support decision analysis and risk reduction efforts at scale. Methods: Using the Intergovernmental Panel on Climate Change (IPCC) risk framework, we developed a causal pathway linking heat with a composite outcome of heat-related morbidity and mortality. We used an existing systematic literature review to identify variables for inclusion and the authors' expert judgment to determine variable combinations in a hierarchical model. We parameterized the model for Washington state using obser-vational (1991-2020 and June 2021 extreme heat event) and scenario-driven temperature projections (2036-2065), compared outputs against relevant existing indices, and analyzed sensitivity to model structure and variable parameterization. We used descriptive statistics, maps, visualizations and correlation analyses to present results.Results: The Climate and Health Risk Tool (CHaRT) heat risk model contains 25 primary hazard, exposure, and vulnerability variables and multiple levels of variable combinations. The model estimates population-weighted and unweighted heat health risk for selected periods and displays estimates on an online visualization plat-form. Population-weighted risk is historically moderate and primarily limited by hazard, increasing significantly during extreme heat events. Unweighted risk is helpful in identifying lower population areas that have high vulnerability and hazard. Model vulnerability correlate well with existing vulnerability and environmental justice indices.Discussion: The tool provides location-specific insights into risk drivers and prioritization of risk reduction in-terventions including population-specific behavioral interventions and built environment modifications. Insights from causal pathways linking climate-sensitive hazards and adverse health impacts can be used to generate hazard-specific models to support adaptation planning.
引用
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页数:15
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