The Role of Critical Minerals Demand in Advancing the Sustainable Development Goals (<scp>SDGs</scp>) in Latin America
Bibliographic record
Abstract
ABSTRACT This study examines the projected demand for critical minerals in Latin America, such as copper, cobalt, lithium, and graphite, and evaluates their contribution to the region's sustainable development goals (SDGs) using the novel Sequential Interactive Modelling for Urban Systems (SIMUS) methodology. As Latin America undergoes an energy transition, these minerals play a vital role in technologies supporting clean energy, urban infrastructure, and sustainable industrial practices. The study ranks these minerals based on their unweighted and SDG‐weighted contributions, identifying copper and nickel as particularly significant for goals like affordable energy (SDG 7), climate action (SDG 13), and sustainable cities (SDG 11). The analysis also highlights the importance of sustainable mining and resilient supply chains to meet the growing demand, especially for lithium, which is crucial for energy storage and electric vehicles. The study's findings underscore how minerals interrelate in achieving SDGs, demonstrating how copper, for example, addresses energy poverty by enabling affordable electricity access. The SIMUS framework provides insights into strategic resource prioritization, enabling policymakers to align mineral demand with economic and environmental sustainability goals in Latin America.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".