Assessment of mitigation strategies as tools for risk management under future uncertainties: a multi-model approach
Open accessAbstract
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.
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ML has the potential to help with sensitivity and uncertainty analysis, with finding numerical solutions for computationally expensive submodels, and assessing the validity of models.
“ML also has the potential to help with sensitivity and uncertainty analysis [386], with finding numerical solutions for computational expensive submodels [206,714], and assessing the validity of the models [556].”
From Tackling climate change with machine learning (2023) · cites this paper as [556]
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- Authors
- Mori, S., Washida, T., Kurosawa, A., & Masui, T.
- Affiliations
- Tokyo University of Science (JP)
- Sophia University (JP)
- Institute of Applied Energy (JP)
- National Institute for Environmental Studies (JP)
- Corresponding author
- Mori, Shunsuke
- Venue
- Sustainability Science
- Published
- 2018-01-08
- Volume
- 13
- Issue
- 2
- Pages
- 329-349
- ISSN
- 1862-4057 (ISSN-L)
- 1862-4065
- PMC
- PMC6086289
- OpenAlex
- W2783139298
- Cited by
- 14
- References
- 24
- Citations by year
- 2026: 2 · 2025: 2 · 2023: 1 · 2022: 3 · 2020: 2 · 2019: 1 · 2018: 3
- FWCI
- 2.28
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Sustainability Science (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- Ministry of the Environment (JP) (Environment Research and Technology Development Fund S10-4)
- Topics
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Climate Change Policy and Economics
- Physical Sciences › Environmental Science › Global and Planetary Change › Climate Change and Geoengineering
- Physical Sciences › Environmental Science › Global and Planetary Change › Sustainability and Climate Change Governance
- Keywords
- climate risk management, climate change mitigation, integrated assessment models, grapes, AIM, Shared Socioeconomic Pathways, climate policy scenarios, economic losses, carbon capture and storage, bioenergy with carbon capture and storage, geoengineering, crop production, land use change, decision making under uncertainty
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