The Influence of Environmental, Social, and Governance Disclosure on Capital Structure: An Investigation of Leverage and WACC
Bibliographic record
Abstract
This paper seeks to examine the extent to which environmental, social, and governance (ESG) disclosure affects capital structure and cost of capital for non-financial Fortune 500 firms. With a sample period from 2007 to 2022 and a system (Generalized Method of Moments) GMM estimation method, we investigate the linkage between ESG disclosure scores and both leverage and the weighted average cost of capital (WACC). Thus, we find that firms with stronger ESG performance have higher ESG disclosure and lower leverage ratios and WACC, highlighting that firms with good ESG outcomes have better equity financing facilities and are perceived to be less risky. We also find the moderation effect where the effects of ESG disclosure depend on the level of ESG disclosure. The empirical results thus show that the environmental and social factors have significant influences on leverage and WACC than the governance factors. Furthermore, we show that firm size affects these relationships in that larger firms are more affected by the variables. These findings extend the literature on ESG, and provide relevant information for corporate financial managers, investors, and policymakers about the financial effects of ESG disclosure. This paper therefore provides evidence of the relevance of ESG factors in decisions on capital structure and cost of capital especially for large firms.
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 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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".