Corporate Governance: Driving Climate Change Disclosure and Advancing SDGs
Bibliographic record
Abstract
Climate change presents a critical challenge to achieving the 2030 Sustainable Development Goals (SDGs), particularly SDG 13 on Climate Action. This study examined the effect of corporate governance on carbon emission disclosure and carbon performance among 150 non-financial firms listed on the Indonesia Stock Exchange (IDX) from 2016 to 2022. Drawing on stakeholder, legitimacy, agency, and resource dependence theories, the study utilized panel data comprising 468 firm-year observations and employed ordinary least squares (OLS) regression to assess both direct and moderating effects. The findings indicate that governance attributes covering board size, board gender diversity, foreign ownership, and the presence of a CSR committee had a positive effect on carbon emission disclosure and carbon performance. Moreover, these governance factors enhanced the correlation between disclosure and performance, suggesting that robust governance could strengthen the environmental impact of transparency. However, board independence exhibited a negative or statistically insignificant effect, highlighting a potential disconnect between governance expectations and environmental oversight in emerging markets. Despite increasing awareness, the levels of carbon disclosure and performance in Indonesia remained low, averaging only 27.8% and 6.6%, respectively. This study provides policy recommendations to strengthen ESG regulations, encourages firms to institutionalize sustainability practices, and calls for cross-country comparative research to improve generalizability.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".