Unpacking Environmental, Social, and Governance Score Disparity: A Study of Indonesian Palm Oil Companies
Bibliographic record
Abstract
This study investigates the inconsistencies in ESG scores assigned by different rating agencies. Focusing on two Indonesian palm oil companies, this paper examines the link between their reported sustainability performance and the resulting ESG scores. This study employs content analysis to assess how the companies disclose information around double materiality, stakeholder engagement, and certifications. Additionally, the methodologies used by two rating agencies are reviewed to identify potential misalignments. The analysis reveals discrepancies in the ratings, suggesting factors like differences in the level of engagement with each company and scoring methodologies might be at play. This highlights the need for standardized sustainability reporting and more transparent rating methodologies within the palm oil industry. While limited to two companies and two agencies, the findings can inform efforts to improve transparency both in sustainability practices and scoring methodologies. This would ultimately lead to more reliable ESG scores, benefiting all related stakeholders. To goal of this study is to promote responsible practices in the palm oil industry by emphasizing the impact of reporting practices.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".