Value Creation Through Environmental, Social, and Governance (ESG) Disclosures
Bibliographic record
Abstract
This study investigates the impact of environmental, social, and governance (ESG) disclosure on value creation in a balanced panel of 100 non-financial Sharia-compliant firms listed on the Saudi Stock Exchange over the period 2014–2023. The analysis employs a combination of econometric techniques, including fixed effects models with Driscoll–Kraay standard errors, Pooled Ordinary Least Squares (POLS) with Driscoll–Kraay standard errors and industry and year dummies, and two-step system generalized method of moments (GMM) estimation to address potential endogeneity and omitted variable bias. Value creation is measured using Tobin’s Q (TBQ), Return on Assets (ROA), and Return on Equity (ROE). The models also control for firm-specific variables such as firm size, leverage, asset tangibility, firm age, growth opportunities, and market capitalization. The findings reveal that ESG disclosure has a positive and statistically significant effect on firm value across all three performance measures. Furthermore, firm size significantly moderates this relationship, with larger Sharia-compliant firms experiencing greater value gains from ESG practices. These results align with agency, stakeholder, and signaling theories, emphasizing the role of ESG in enhancing transparency, reducing information asymmetry, and strengthening stakeholder trust. The study provides empirical evidence relevant to policymakers, investors, and firms striving to achieve Saudi Arabia’s Vision 2030 sustainability goals.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".