Environmental, Social, and Governance (ESG) Integration in Bangladesh: Revealing the Role of Corporate Governance Nexus
Bibliographic record
Abstract
Environmental, social, and governance (ESG) considerations have become increasingly integral to the modern business environment as stakeholders place greater emphasis on accountability and sustainability. This study examines the interaction between ESG integration and corporate governance structures in an emerging market, focusing on firms listed on the Dhaka Stock Exchange (DSE) that operate in environmentally sensitive industries during the period 2014–2021. Employing content analysis of annual and sustainability reports, we constructed an ESG disclosure index grounded in the Global Reporting Initiative (GRI) standards. The results indicate that larger boards, the inclusion of foreign directors, and the existence of audit committees are positively associated with enhanced ESG practices, whereas frequent board meetings are negatively related to ESG disclosure. Furthermore, the enforcement of corporate governance guidelines by the Bangladesh Securities and Exchange Commission has strengthened ESG reporting, although the proportion of firms engaging in such disclosure remains limited. These findings highlight the need for stakeholders—including regulators, policymakers, and academics—to promote the consistent integration of ESG principles within corporate governance frameworks.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.002 |
| 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".