ESG ratings in the mining industry: Factors and implications
Bibliographic record
Abstract
• Larger mining firms have more favorable ESG ratings than smaller ones. • ESG-rated mining firms are bigger than unrated firms. • Mining firms with high unmanaged ESG risk have lower ESG scores. • There is no correlation between financial indicators and ESG ratings. While previous research explores the relationship between ESG ratings, firm size, and financial outcomes, there is a lack of comprehensive analysis comparing multiple ESG ratings within the mining industry. This gap is crucial given the increasing focus on ESG in mining operations and its potential impact on company performance. Based on proprietary financial and ESG ratings data from 200 mining companies, this study investigates the relationship between two different ESG ratings and firm characteristics. We compare ESG-rated firms with unrated firms in terms of firm size, and financial performance indicators, and explore country-level patterns in ESG ratings. Findings reveal that ESG-rated mining companies are generally larger than unrated firms but neither more profitable nor face lower debt costs. The results also show that, among rated firms, larger mining firms have more favorable ESG ratings than smaller ones. However, we fail to find a correlation between ESG ratings and financial performance. Finally, the evidence suggests that mining companies rated high for unmanaged ESG risk are likely to have lower ESG scores. This research contributes to understanding whether and how ESG ratings could impact investment decisions and risk management strategies in the mining sector.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".