Sustainable Environmental Governance and Corporate Environmental Performance: Empirical Evidence from Saudi Companies
Bibliographic record
Abstract
This study investigates the internal governance mechanisms of sustainability and their influence on corporate environmental performance (CEP) from 2014 to 2021 in Saudi Arabia. The analysis centers on three primary mechanisms: the presence of a sustainability committee, issuance of sustainability reports, and external assurance of these reports. Utilizing a sample of 188 firm-year observations from publicly listed companies, we evaluate each mechanism and their combined effect as predictors of CEP through ordinary least squares (OLS) regression analysis. We constructed a composite index of sustainability governance practices to assess the overall governance strength of the firm, referred to as the sustainability index. Our findings indicate that, while sustainability external assurance is positively associated with CEP, it is statistically insignificant. By contrast, the positive correlations of sustainability committees and sustainability reports with CEP were significant. The overall sustainable mechanisms’ composite index in the regression positively influences corporate environmental performance. This suggests that the composite sustainability index is more effective than individual sustainability mechanisms because of its complementary functions. This study aims to advance theories related to emerging markets in which institutional arrangements and stakeholder demands differ from those in developed countries. These results emphasize on the importance of corporate boards and policymakers in establishing dedicated sustainability committees, enhancing reporting quality, and integrating various governance systems to improve environmental performance which, in turn, promote responsible corporate behavior and ensure accountability towards environmental protection and sustainable development. This aligns with Saudi Arabia’s Vision 2030 and the Sustainable Development Goals, specifically Goals 12 and 13.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".