The Determinants of Carbon Emission Disclosures with Proper Rating as a Mediating Variable in Non-Financial Companies in Indonesia
Bibliographic record
Abstract
The results of previous studies vary regarding the effect of company size and financial performance on disclosure of carbon emissions.This article aims to find empirical evidence of the effect of company size and financial performance on disclosure of carbon emissions by adding PROPER rating as a mediating variable as the novelty of this study.The population is 144 non-financial companies listed on the IDX in 2015-2019.The results show that company size affects PROPER rating and disclosure of carbon emissions.Meanwhile, financial performance has no effect on disclosure of carbon emissions with a PROPER rating.PROPER rating can mediate the effect of company size on disclosure of carbon emissions, but PROPER rating is not able to mediate ROA on Carbon Emissions.The implications of the findings of this research, companies, governments, investors and stakeholders in decision making related to Carbon Emission Disclosure.For example choosing a company that has a greater level of relationship with the environment or including a high-profile company as a place to invest.In addition, academics can develop models and replace financial performance proxies with other proxies related to leverage, liquidity and solvency.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".