尽管全世界都知道未来气候变化可能带来的威胁,在政策决定方面仍有严重的障碍有待解决。气候变化政策中的科学和社会不确定性必须是这一障碍的很大一部分。根据《巴黎协定》,世界进入下一阶段,决定下一步的行动。没有风险管理的观点,任何决定都将是“基于忽略替代方案”的行为。环境部,日本建立了跨学科研究项目,称为综合气候评估-风险,不确定性,和社会(ICA-RUS)由SeitaEmori博士主持,国家环境研究所。ICA-RUS由五个研究主题组成,即,(1)全球气候风险综合,(2)优化用地,水,和气候风险的生态系统,(3)关键气候风险分析;(4)气候风险管理方案评价,(5)科学理性和社会理性之间的相互作用。我们参加了第四个主题,通过整合评估模型模拟,对技术选择和政策措施进行定量评估。我们采用多模型方法来处理技术等各个领域之间的复杂关系,经济学,和土地利用变化。四种不同类型的综合评估模型,即,MARIA-14(Mori),EMEDA(Washida),葡萄(黑泽明),和AIM(Masui),参与第四个研究主题。这些模型通过提供两个信息类别来为ICA-RUS做出贡献。首先,这些模型基于ICA-RUS第一个主题给出的共享社会经济途径情景和共享气候政策案例提供了共同的模拟结果,以查看评估范围.第二,每个模型还提供特定于模型的结果来回答特殊主题,例如,地球工程,部门贸易,适应,和不确定因素下的决策。本文的目的是描述四个模型之间多模型相互比较的概述和主要结果,重点放在第一个模型上,并介绍主要结果。此外,在这项研究中,我们引入了多模型模拟结果的统计荟萃分析,以查看不同结构的模型是否提供了相互一致的结果.我们活动的主要发现如下:第一,在严格的气候目标中,模型之间的区域经济损失趋于分歧,而全球总经济损失没有。第二,碳捕获和封存(CCS)以及BECCS对于提供严格的气候目标的可行性至关重要,即使部署潜力因模型而异。第三,这些模型显示了世界总产量中作物产量的微小变化,而地区之间出现了很大的差异。第四,多模型模拟结果的统计荟萃分析表明,即使这些模型的结构和模拟结果不同,它们在国内生产总值损失和缓解方案之间也存在隐含但共同的关系。由于这项研究只不过是统计荟萃分析的初步练习,预计更复杂的方法,如数据挖掘或机器学习可以适用于仿真数据库,以提取模型背后的隐含信息。
Although the world understands the possible threat of the future of climate changes, there remain serious barriers to be resolved in terms of policy decisions. The scientific and the societal uncertainties in the climate change policies must be the large part of this barrier. Following the Paris Agreement, the world comes to the next stage to decide the next actions. Without a view of risk management, any decision will be \"based on neglecting alternatives\" behavior. The Ministry of the Environment, Japan has established an inter-disciplinary research project, called Integrated Climate Assessment-Risks, Uncertainties, and Society (ICA-RUS) conducted by Dr. Seita Emori, National Institute for Environmental Studies. ICA-RUS consists of five research themes, i.e., (1) synthesis of global climate risks, (2) optimization of land, water, and ecosystem for climate risks, (3) analysis of critical climate risks, (4) evaluation of climate risk management options, and (5) interactions between scientific and social rationalities. We participated in the fourth theme to provide the quantitative assessment of technology options and policy measures by integrating assessment model simulations. We employ the multi-model approach to deal with the complex relationships among various fields such as technology, economics, and land use changes. Four different types of integrated assessment models, i.e., MARIA-14 (Mori), EMEDA (Washida), GRAPE (Kurosawa), and AIM (Masui), participate in the fourth research theme. These models contribute to the ICA-RUS by providing two information categories. First, these models provide common simulation results based on shared socioeconomic pathway scenarios and the shared climate policy cases given by the first theme of ICA-RUS to see the ranges of the evaluation. Second, each model also provides model-specific outcomes to answer special topics, e.g., geoengineering, sectoral trade, adaptation, and decision making under uncertainties. The purpose of this paper is to describe the outline and the main outcomes of the multi-model inter-comparison among the four models with a focus upon the first and to present the main outcomes. Furthermore, in this study, we introduce a statistical meta-analysis of the multi-model simulation results to see whether the differently structured models provide the inter-consistent findings. The major findings of our activities are as follows: First, in the stringent climate target, the regional economic losses among models tend to diverge, whereas global total economic loss does not. Second, both carbon capture and storage (CCS) as well as BECCS are essential for providing the feasibility of stringent climate targets even if the deployment potential varies among models. Third, the models show small changes in the crop production in world total, whereas large differences appear between regions. Fourth, the statistical meta-analysis of the multi-model simulation results suggests that the models would have an implicit but common relationship between gross domestic product losses and mitigation options even if their structures and simulation results are different. Since this study is no more than a preliminary exercise of the statistical meta-analysis, it is expected that more sophisticated methods such as data mining or machine learning could be applicable to the simulation database to extract the implicit information behind the models.