Spatially and temporally differentiated characterization factors for supply risk of abiotic resources in life cycle assessment
Bibliographic record
Abstract
Life cycle assessment, a comprehensive tool to evaluate environmental impacts across a product's life cycle, traditionally focuses on "inside-out" impacts caused by the product on the environment, emphasizing resource use, global warming, and other environmental impacts. In contrast, the "outside-in" perspective considers resource availability and accessibility to industry. This second perspective was developed as a way to integrate raw material criticality assessment into LCA. The GeoPolRisk method assesses the supply risk based on global production concentration, import shares, political stability scores, and the average price of the commodity. This article introduces a characterization model for the GeoPolRisk method and calculates the Geopolitical Supply Risk Potential of using 46 raw materials across different countries in multiple years. The characterization factors show the highest values for precious metals, like platinum group metals (PGMs), reflecting their high market prices and concentrated production in geopolitically unstable regions. The results emphasize the significance of spatial and temporal variations in characterization factors, providing a nuanced assessment of supply risk of raw materials associated with the product system. Despite data limitations, the characterization factors offer a good estimate of the supply risk of raw materials available for use in product systems. A case study on photovoltaic laminate production highlights gallium, copper, and tin as significant contributors to supply risk. From an "outside-in" perspective, the case study demonstrates how the GeoPolRisk method complements traditional environmental indicators such as global warming, making it a valuable tool for assessing mineral resource supply risk in Life Cycle Assessment.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".