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.

中文翻译：

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评估缓解策略作为未来不确定性下风险管理的工具：多模型方法。

尽管世界了解气候变化未来可能带来的威胁，但在政策决策方面仍然存在严重障碍需要解决。气候变化政策中的科学和社会不确定性肯定是这一障碍的重要组成部分。《巴黎协定》签署后，世界进入下一阶段决定下一步行动。如果没有风险管理的观点，任何决策都将是“基于忽视替代方案”的行为。日本环境省设立了一个跨学科研究项目，名为综合气候评估——风险、不确定性和社会 (ICA-RUS)，由国家环境研究所 Seita Emori 博士主持。ICA-RUS由五个研究主题组成，即（1）全球气候风险综合，（2）针对气候风险的土地、水和生态系统优化，（3）关键气候风险分析，（4）气候评估风险管理方案；(5) 科学理性与社会理性之间的相互作用。我们参与的第四个主题是通过整合评估模型模拟来提供技术方案和政策措施的量化评估。我们采用多模型方法来处理技术、经济、土地利用变化等各个领域之间的复杂关系。第四个研究主题涉及四种不同类型的综合评估模型，即MARIA-14（森）、EMEDA（华盛顿）、GRAPE（黑泽）和AIM（增井）。这些模型通过提供两个信息类别为 ICA-RUS 做出贡献。首先，这些模型基于共享的社会经济路径情景和ICA-RUS第一个主题给出的共享的气候政策案例提供了共同的模拟结果，以了解评估的范围。其次，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、适应和不确定性下的决策。本文的目的是描述四个模型之间的多模型相互比较的概要和主要结果，重点是第一个模型并呈现主要结果。此外，在本研究中，我们引入了多模型模拟结果的统计元分析，以了解不同结构的模型是否提供了相互一致的发现。我们活动的主要发现如下：首先，在严格的气候目标下，模型之间的区域经济损失往往存在分歧，而全球总经济损失则没有。其次，碳捕获与封存（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，模型显示世界农作物总产量变化较小，而地区之间差异较大。第四，对多模型模拟结果的统计荟萃分析表明，这些模型在国内生产总值损失和缓解方案之间存在隐含但共同的关系，即使它们的结构和模拟结果不同。由于这项研究只不过是统计荟萃分析的初步练习，因此预计数据挖掘或机器学习等更复杂的方法可以应用于模拟数据库，以提取模型背后的隐含信息。