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Record W4413854862 · doi:10.54728/jfmg.202409.00081

Environmental, Social, and Governance (ESG) Integration in Bangladesh: Revealing the Role of Corporate Governance Nexus

2025· article· en· W4413854862 on OpenAlexaff
Imran Khan, Anup Kumar Saha, Md. Anisul Islam Sajib, Md. Mahbubul Alam Siddiqui

Bibliographic record

VenueJournal of Financial Markets and Governance · 2025
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicSustainable Finance and Green Bonds
Canadian institutionsOptech (Canada)
Fundersnot available
KeywordsNexus (standard)Corporate governanceBusinessCorporate social responsibilityAccountingPolitical sciencePublic relationsFinance

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.528
Threshold uncertainty score0.849

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.011
GPT teacher head0.197
Teacher spread0.186 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

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