Ranking Indian Companies on Sustainability Disclosures Using the GRI-G4 Framework and MCDM Techniques
Bibliographic record
Abstract
In this research paper, the researcher attempts to calculate the level and quality of corporate sustainability reporting practices of companies in India that use the Global Reporting Initiative (GRI).The study also seeks to measure the relative reporting performance of these companies and rank them based on sustainability disclosure criteria-namely economic, environmental, social, and governance parameters-as outlined in the Global Reporting Initiative Guidelines (GRI-G4).A Multi-Criteria Decision Making technique is employed to assess improvements in the sustainability reporting practices of GRI-based reporting companies in India.The present study uses a content analysis technique to examine the level and quality of sustainability disclosure based on the GRI-G4 reporting framework.A binary coding system is applied to measure the level of corporate sustainability reporting (CSR), wherein '1' indicates that the item is disclosed and '0' indicates otherwise.To calculate the quality of sustainability disclosure, a four-point scale (ranging from '0' to '3') is used.Furthermore, the Multi-Criteria Decision Making (MCDM) technique, such as Entropy, is used to calculate criteria weight, and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is used for ranking.The findings of this study are useful for various stakeholders, including potential investors, asset managers, rating agencies, NGOs, customers, academics, students, and policy makers like the Securities and Exchange Board of India (SEBI) and the Ministry of Corporate Affairs, as they make more informed 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 | 0.010 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".