文章基本信息

标题：Estimating option values of solar radiation management assuming that climate sensitivity is uncertain
本地全文：下载
作者：Yosuke Arino ; Keigo Akimoto ; Fuminori Sano 等
期刊名称：Proceedings of the National Academy of Sciences
印刷版ISSN：0027-8424
电子版ISSN：1091-6490
出版年度：2016
卷号：113
期号：21
页码：5886-5891
DOI：10.1073/pnas.1520795113
语种：English
出版社：The National Academy of Sciences of the United States of America
摘要：Although solar radiation management (SRM) might play a role as an emergency geoengineering measure, its potential risks remain uncertain, and hence there are ethical and governance issues in the face of SRM’s actual deployment. By using an integrated assessment model, we first present one possible methodology for evaluating the value arising from retaining an SRM option given the uncertainty of climate sensitivity, and also examine sensitivities of the option value to SRM’s side effects (damages). Reflecting the governance challenges on immediate SRM deployment, we assume scenarios in which SRM could only be deployed with a limited degree of cooling (0.5 °C) only after 2050, when climate sensitivity uncertainty is assumed to be resolved and only when the sensitivity is found to be high (T2x = 4 °C). We conduct a cost-effectiveness analysis with constraining temperature rise as the objective. The SRM option value is originated from its rapid cooling capability that would alleviate the mitigation requirement under climate sensitivity uncertainty and thereby reduce mitigation costs. According to our estimates, the option value during 1990–2049 for a +2.4 °C target (the lowest temperature target level for which there were feasible solutions in this model study) relative to preindustrial levels were in the range between $2.5 and $5.9 trillion, taking into account the maximum level of side effects shown in the existing literature. The result indicates that lower limits of the option values for temperature targets below +2.4 °C would be greater than $2.5 trillion.
关键词：solar radiation management ; option value ; climate sensitivity ; uncertainty ; decision tree analysis