重要鉱物供給リスク評価：チリ・アルゼンチン・中国を対象としたオープンデータと再現可能なDSS計算

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

🔬研究者:公開データと再現可能な計算手順で重要鉱物リスク評価の透明なベンチマークを提供する。

🏢実務担当者:調達先国の供給リスクを定量化し、サプライチェーン開示や調達戦略の根拠に活用できる。

🏛政策担当者:重要鉱物の供給途絶リスクを国別に可視化し、資源安全保障政策の優先順位付けに資する。

This dataset provides open statistical data, model parameters, and reproducible calculations supporting the research “An Intelligent Decision Support System for Assessing Critical Mineral Supply Risks in the Energy Transition.” The dataset demonstrates an intelligent decision support system (DSS) for assessing critical mineral supply risks at the country–mineral level, using lithium supply from Chile, Argentina, and China as an experimental case study. The proposed methodology integrates three risk assessment components: Extraction Risk Model (K E): Nine quantitative criteria organised into four groups covering resource availability, production stability, environmental and climate constraints, and infrastructure and energy conditions. International Supply Risk Model (KS): Ten quantitative criteria organised into three groups covering international trade flows, transport and logistics, and institutional conditions. Expert-Based Situational Risk Assessment (RT): Six simulated expert judgements per country, representing additional situational supply risks. The quantitative indicators are derived from publicly accessible sources, including the U.S. Geological Survey (USGS), World Mining Data, World Bank WDI, WITS/UN Comtrade, Logistics Performance Index (LPI), Worldwide Governance Indicators (WGI), and ND-GAIN. All 19 quantitative criteria are normalised to a common 0–10 risk scale and aggregated into two component risk scores. A three-dimensional cone-shaped fuzzy membership function integrates these scores with the expert-based assessment to generate an overall risk score ranging from 0 to 1. The Excel workbook contains original input values, derived indicators, normalised risk scores, model calculations, configurable parameters, expert assessment scenarios, final risk classifications, sensitivity analysis, and decision-support recommendations. A dedicated Legend worksheet explains the variables, criteria, units, calculation procedures, and data limitations, facilitating independent reproduction and adaptation of the experiment. Experimental results: The integrated risk scores are 0.345 for Chile (Low risk), 0.475 for Argentina (Moderate risk), and 0.493 for China (Moderate risk). Data transparency and limitations: The expert judgements are synthetic and were generated exclusively to demonstrate the computational methodology; they do not represent actual expert surveys. International trade indicators refer specifically to lithium carbonates (HS 283691), rather than the entire lithium supply chain. Argentina's export volatility indicator is calculated from three available observations, and trade-partner concentration is approximated using grouped partner data. Normalisation bounds and weighting assumptions are configurable and illustrative. Consequently, the reported results demonstrate computational feasibility and reproducibility, not empirically validated probabilities of supply disruption. Reuse and reproducibility: The dataset can be used to reproduce the published computational experiment, examine the sensitivity of model outputs, modify assessment parameters, and extend the proposed methodology to other countries and critical mineral resources, subject to the availability and comparability of relevant statistical data.

• Zenodo https://zenodo.org/records/24100845first seen 2026-11-11 04:14:46