Impact of Environmental, Social, and Governance (ESG) and Enterprise Risk Management (ERM) on Business Performance in IDX Energy Listed Company Period ( 2020 -2024)
Bibliographic record
Abstract
This study investigates the impact of Environmental, Social, and Governance (ESG) practices and Enterprise Risk Management (ERM) implementation on the financial and non-financial performance of energy companies listed on the Indonesia Stock Exchange (IDX) during the 2020–2024 period. Using a quantitative explanatory design and multiple regression analysis, the research explores the relationships between ESG, ERM, and key performance indicators including Return on Assets (ROA), Return on Equity (ROE), Debt-to-Equity Ratio (DER), and Price-to-Book Value (PBV). The results indicate that ESG has no significant influence on either financial or non-financial performance, suggesting that sustainability initiatives in the energy sector are still developing. Conversely, ERM demonstrates a significant effect on ROE and DER, highlighting its role in enhancing profitability and investor confidence. However, ERM does not significantly affect ROA or PBV, and no significant relationship is found between ESG and ERM. These findings emphasize the importance of integrating sustainability and risk management strategies to improve financial resilience and long-term firm value. The study provides insights for policymakers, investors, and practitioners in strengthening ESG–ERM alignment within Indonesia’s energy industry
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 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".