Quantifying conflict risks in raw material supply using the INFORM risk index
Bibliographic record
Abstract
Regulations and initiatives focused on responsible sourcing of raw materials have grown in the last decades. So-called “conflict minerals”— typically tin, tantalum, tungsten, and gold (3TG) associated with Democratic Republic of Congo — have been a target for responsible sourcing since 2008. As production of most raw materials has increased, there is need to pay attention to sourcing from conflict-affected or high-risk areas which poses various risks and consequences. To support sustainable and responsible use, companies seek to identify and manage potential risks. Existing tools and initiatives are limited in supporting companies' sustainability goals, as many are generic or are not publicly accessible. This article introduces an open-access tool that provides a product focus, the ConflictRisk method, which assesses the risks associated with sourcing raw materials from conflict-affected and high-risk areas at the country level using publicly available data. Building upon the existing GeoPolRisk framework, this method replaces the governance indicator with the INFORM Risk Index, which provides a direct measure of armed conflict. The ConflictRisk method was demonstrated in a case study considering 52 raw materials imported into the United States, and revealing risks even for materials sourced from outside traditionally recognized high-risk areas. Differences between the ConflictRisk method and existing frameworks, such as the European Union's conflict mineral regulations, underscore the value of a quantitative assessment. The article also presents a framework for integrating the ConflictRisk method into Life Cycle Sustainability Assessment to characterize the impacts of conflict risks. Limitations of the method include its reliance on national-level data, which can restrict the granularity of conflict risk evaluations at the sub-national level. The study also emphasizes the need for comprehensive supply chain analysis and further research to refine the integration of conflict risk into broader sustainability assessments. • The ConflictRisk method offers a product-focused method for companies to quantify risk of conflict in raw material sourcing at the country-level. • Based on the GeoPolRisk framework, the ConflictRisk method replaces a governance indicator with the INFORM Risk Index indicator. • A case study on 43 raw materials imported into the U.S. shows conflict risks for a broad range of raw materials. • The ConflictRisk method integrates with Life Cycle Sustainability Assessment to align sustainability with responsible sourcing.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".