Kualitas Climate-Related Disclosures atas Rekomendasi TCFD pada Perusahaan Multinasional Periode 2020-2022
Bibliographic record
Abstract
The phenomenon of climate change has attracted world attention, as related cases, such as greenwashing, have been carried out by various entities in the world. TCFD is present to provide guidelines for disclosing risks and opportunities, providing stakeholders with information regarding strategy, the carbon emissions in the sector and the company's financial system. The purpose of this research is to analyze the quality of company’s climate-related disclosures and provide comparison between sectors and countries. The objects of research are banking and mining sector companies from China, the United States, and Canada which issue climate-related disclosures for the 2020-2022 period. Data is analyzed using content analysis method and quantitative and qualitative scoring. The research show that quality of “average-high” results, with governance category ranked first quantitatively while metrics and targets category qualitatively. Comparison between countries in order from highest to lowest value is the United States, Canada and finally China. The banking sector shows higher quality of climate-related disclosures than the mining sector.
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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".