Environmental Disclosure Modelling in a Developing Economy: Does Corporate Governance Matter? A Double Hurdle Regression Approach
Bibliographic record
Abstract
The paper examines environmental Disclosure Modelling in a Developing Economy using the Craigg double hurdle model and controlling for the role of corporate governance. This study employs the ex-post research design and investigates firm’s environmental disclosures in Nigeria, by controlling for corporate governance characteristics. The study employs a sample of 35 non-financial firms listed on the Nigerian Stock Exchange using the simple random sampling technique. Secondary data retrieved from the financial statements of the selected companies was used for the study. Both the Tobit and double-hurdle models were estimated but based on the Bayesian and Akaike’s information criteria for model selection, the double-hurdle model is preferred. The result reveals that though Board size is not a significant determinant of probability to disclose environmental information in annual reports (-0.0408, p=0.175), it is a significant determinant of the extent of environmental disclosure reports (0.1943, p=0.00) given that a firm has decided to disclose. Board independence is a significant determinant of both probability to disclose environmental information and extent of disclosure (-2.2373, p=0.00) with a negative coefficient. The Board gender diversity is not a significant determinant of probability to disclose environmental information in annual reports (-0.60076, p=0.461), it is a nevertheless a significant determinant of the extent of environmental disclosure reports (-3.5913, p=0.00) when firms then decide to disclose. Institutional ownership turns out to be a significant determinant of both the probability to disclose environmental information and extent of disclosure (0.0273, p=0.00) when firms choose to disclose. Finally, the truncated model results also reveals that though managerial ownership is not a significant determinant of probability to disclose environmental information in annual reports (-0.01352, p=0.148), it is nevertheless a significant determinant of the extent of environmental disclosure reports (-0.0206, p=0.001) when firms then decide to disclose.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".