The objective of this five-year Disrupting Operations of Illicit Supply Networks (D-ISN) project is to map and characterize the volume of illicitly-sourced materials in energy-critical minerals (ECM) supply chains. ECMs are essential to renewable, nuclear, and fossil energy generation and are included in the US government’s list of 35 ‘critical materials’, yet their supply chains remain opaque and vulnerable to illicit activity. There are currently no global measurements of the licit-illicit composition of ECM trade flows or their evolution over time. To address the problem, this project seeks to map and model global ECM flows based on original research in several source, transit, and destination countries. The findings and tools developed under this study will improve discovery and traceability of illicitly sourced ECM, identify vulnerable points along several ECM supply chains, and generate predictive models of their dynamics in order to identify effective disruption strategies. The results will be informed by data drawn from open and proprietary datasets, as well as data gathered at national and subnational levels, and tested under extensive field research. The project will advance our national ability to identify vulnerabilities in the supply chains of energy-critical materials, catalyze game-changing innovative technological tools and multidisciplinary methodologies that enhance capacity for the detection and disruption of illicit activities affecting ECM supply chains. This is a key step for ensuring transparent, secure, and sustainable supply chains.
The project integrates methods from machine learning, remote sensing, and multilingual qualitative research to detect and disrupt illicit activities by characterizing the global illicit in several ECMs: Cobalt, Lithium, Niobium, Platinum Group Metals (PGM), Rare Earth Elements (REE), and Tantalum. Using complex systems theory within a telecoupling framework, the project will (1) characterize and model the processes through which licit and illicit ECM trade flows converge and analyze linked trade, policy, and land use dynamics since 2000 (the what); (2) identify sources of trade irregularities; (3) use remote sensing, human expert analysis, and AI to calculate mining occurring beyond official concessions (the where); (4) use human-in-the-loop machine learning to generate a predictive model of correlations between illicit international trade flows and local land use change, and then use qualitative methods from human geography to conduct a causal mechanism analysis to identify “the how and the ¬why†of changing illicit ECM trade flows. The project addresses a major gap and will generate actionable science by providing a global assessment of: the credibility of official trade data upon which supply chain analyses are based; the role of illicit commodities in sustaining global ECM flows, including their relative occurrence in specific destination markets, and; the level of illicit mining activity occurring within the mappable extent of mining activities. This project advances scientific theory through a convergence science approach that places social, physical, data and computer sciences on equal footing in order to form a complete picture of illicit ECM flows, and develops a first-of-its-kind multi-modal approach to identify links between illicit ECM trade flows and specific locations of illicit production on a global scale. The methods developed under this project will enable detection of illicitly sourced ECM, will be applicable to other mineral commodity flows, and will provide a strong foundation for continuing research on the complex dynamics of illicit mineral production and trade. This project is jointly funded by IIS and the Established Program to Stimulate Competitive Research (EPSCoR).
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
