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Estimating the Health Effects of Greenhouse Gas Mitigation Strategies: Addressing Parametric Model and Valuation Challenges

机译:估算温室气体减排策略对健康的影响:应对参数模型和评估挑战

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摘要

Background: Policy decisions regarding climate change mitigation are increasingly incorporating the beneficial and adverse health impacts of greenhouse gas emission reduction strategies. Studies of such co-benefits and co-harms involve modeling approaches requiring a range of analytic decisions that affect the model output.Objective: Our objective was to assess analytic decisions regarding model framework, structure, choice of parameters, and handling of uncertainty when modeling health co-benefits, and to make recommendations for improvements that could increase policy uptake.Methods: We describe the assumptions and analytic decisions underlying models of mitigation co-benefits, examining their effects on modeling outputs, and consider tools for quantifying uncertainty.Discussion: There is considerable variation in approaches to valuation metrics, discounting methods, uncertainty characterization and propagation, and assessment of low-probability/high-impact events. There is also variable inclusion of adverse impacts of mitigation policies, and limited extension of modeling domains to include implementation considerations. Going forward, co-benefits modeling efforts should be carried out in collaboration with policy makers; these efforts should include the full range of positive and negative impacts and critical uncertainties, as well as a range of discount rates, and should explicitly characterize uncertainty. We make recommendations to improve the rigor and consistency of modeling of health co-benefits.Conclusion: Modeling health co-benefits requires systematic consideration of the suitability of model assumptions, of what should be included and excluded from the model framework, and how uncertainty should be treated. Increased attention to these and other analytic decisions has the potential to increase the policy relevance and application of co-benefits modeling studies, potentially helping policy makers to maximize mitigation potential while simultaneously improving health.Citation: Remais JV, Hess JJ, Ebi KL, Markandya A, Balbus JM, Wilkinson P, Haines A, Chalabi Z. 2014. Estimating the health effects of greenhouse gas mitigation strategies: addressing parametric, model, and valuation challenges. Environ Health Perspect 122:447–455; 
机译:背景:有关缓解气候变化的政策决定越来越多地纳入减少温室气体排放战略的有益和不利健康影响。对此类共生和共害的研究涉及需要一系列影响模型输出的分析决策的建模方法。目的:我们的目标是评估有关模型框架,结构,参数选择以及建模时不确定性处理的分析决策方法:我们描述了缓解共生效益模型基础的假设和分析决策,研究了其对建模产出的影响,并考虑了量化不确定性的工具。在评估指标,折现方法,不确定性特征和传播以及对低概率/高影响事件的评估方面,方法存在很大差异。缓解政策的不利影响也存在各种变数,并且建模域的扩展范围有限,无法包含实施注意事项。展望未来,应与决策者合作开展共同效益建模工作;这些努力应包括正面和负面影响以及关键的不确定性的全部范围,以及一系列的折现率,并应明确说明不确定性的特征。我们提出一些建议,以改善健康共同利益建模的严密性和一致性。结论:对健康共同利益进行建模需要系统地考虑模型假设的适用性,应从模型框架中包括和排除哪些内容以及应如何确定不确定性。被处理。对这些及其他分析性决策的更多关注可能会增加政策的相关性和共同效益模型研究的应用,从而有可能帮助决策者在最大程度地缓解风险的同时改善健康。引用文献:Remais JV,Hess JJ,Ebi KL,Markandya A,Balbus JM,Wilkinson P,Haines A,Chalabi Z.2014。《估算温室气体缓解策略对健康的影响:应对参数,模型和估值挑战。环境健康观察122:447–455;

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