Assessing the Drivers of Corporate Sustainability Performance Disclosures Using the Global Reporting Initiative (GRI) G4 Framework
Bibliographic record
Abstract
The primary objective of this study is to analyze the factors influencing the corporate sustainability performance disclosures of companies listed on the Bombay Stock Exchange (BSE) using the Global Reporting Initiative (GRI) G4 framework. This research is based on a sample of 434 firms listed on the BSE from 2017 to 2022. According to the content analysis method, the disclosure score of 434 non-financial companies is 79% (approximately), suggesting that, on an average, the sample companies have revealed 79% of the GRI-specified elements in their sustainability reports. The outcomes of the regression models indicate that profitability, firm size, innovation, board size, gender diversity, sustainability committee, and industry type are major drivers of corporate sustainability performance disclosure. Furthermore, research identified significant differences in the determinants of such practices between high-polluting and low-polluting companies. This research aims to elucidate the intricate dynamics affecting corporate sustainability performance by examining a diverse array of concerns. It employs meticulous data analysis to identify critical elements influencing sustainability disclosure. These findings may assist corporate managers, investors, policymakers, and stakeholders in comprehending the critical aspects to consider when formulating strategies that promote sustainability and enhance long-term value maximization.
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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 | 0.015 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".