Environmental Management Activity toward Financial Performance in Indonesian Mining Companies
Bibliographic record
Abstract
The objective of this study is to determine the influence of environmentalmanagement activity based on Indonesia’s statement of financial accountingstandards number 33, namely accounting for mining towards the financial performanceof Indonesian mining companies. The measurement of environmentalactivity was proxied by three environmental activity. They are disclosure of strippingcosts in the production phase, exploration and evaluation of assets and environmentalmanagement on general mining.There are 41 samples of this research consisting of all mining companies in Indonesiathat have fulfilled the sample criteria from 2011 until 2013. The data on thisresearch was tested by multiple linear regression. The result of this researchshowed that the stripping costs in the production phase and environmental managementon general mining had significantly positive effects on financial performance,while exploration and evaluation assets had significantly negative effectson financial performance.This study shows that the cost to acquire the best technology that companies usewhen performing exfoliating ground at the beginning of production activitybrings a positive performance for the company. Similarly, environmental managementimplemented in the company also had a positive impact for the survivalof the company. These results indicate that the company implemented best act inthe management of the environment, increasing the company's performance. Theconsequence of all this is the sustainability of the company is increasingly assured
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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 | 0.004 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".