Carbon emission disclosure and PROPER: Are they attractive to foreign investors?
Bibliographic record
Abstract
This study examines the effect of carbon emission disclosure and the Public Disclosure Program for Environmental Compliance (PROPER) on foreign ownership of manufacturing companies in Indonesia from 2018 to 2022 that publish annual reports or sustainability reports, registered with the Ministry of Environment and Forestry PROPER, and carbon emissions disclosure in the annual report and/or sustainability report. The analysis method uses the ordinary least squared (OLS) approach to study 28 PROPER manufacturing companies used as samples. The control variable used is return on equity (ROE), this is based on the idea that foreign investors are willing to invest in other countries to get a return on the equity or capital channeled to the company. The results show that carbon emissions disclosure has a significant positive effect on foreign ownership. Conversely, PROPER did not have any significant positive effect on foreign ownership. The results show interesting results that foreign investors are proven to consider environmental aspects in making decisions to invest in companies in Indonesia. On the other hand, the Indonesian government needs to ensure that its PROPER ratings can also provide benefits to foreign investors to attract their investment decisions.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".