Data-driven prediction of cost overruns in critical mineral projects in the U.S.A

Authors: Ebo A. Quansah, Abass Aliu

DOI: https://doi.org/10.37082/IJIRMPS.v14.i2.232955

Short DOI: https://doi.org/hbxhbq

Country: United States

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Abstract: Cost overruns in U.S. critical-mineral projects such as lithium, cobalt, and rare earth elements are frequently driven by geological uncertainty, complex permitting processes, market volatility, and operational inefficiencies. Data-driven approaches, including machine learning, artificial intelligence, and probabilistic modeling, are increasingly recognized for their ability to improve cost-prediction accuracy and reveal nonlinear risk patterns that traditional methods often miss. Across recent studies, three key insights consistently emerge. First, advanced analytical techniques provide stronger predictive performance and greater adaptability to changing project conditions. Second, systemic factors such as ore-grade variability, regulatory delays, supply-chain disruptions, and contractor performance issues remain central contributors to cost escalation in mineral development. Third, there is growing momentum toward integrating predictive analytics into U.S. project governance to enhance transparency, risk monitoring, and strategic planning. Despite these advancements, notable gaps persist, including the absence of standardized national datasets, limited long-term validation of predictive models, and insufficient incorporation of socio-economic and policy variables. Overall, improving cost-overrun prediction for U.S. critical-mineral projects will require both technical progress in modeling and institutional reforms that promote coordinated data sharing, governance modernization, and evidence-based decision-making. Strengthened data-driven analytics across the project lifecycle will be essential for cost-efficient, sustainable, and secure mineral-supply development.

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Paper Id: 232955

Published On: 2026-04-17

Published In: Volume 14, Issue 2, March-April 2026

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