Beyond Compliance: How ESG Reporting Influences the Cost of Capital in UK Firms
Bibliographic record
Abstract
This research examines the effect of ESG disclosure on the cost of capital for non-financial firms in the UK, indexed by the FTSE All-Share Index, during the period from 2014 to 2018. Using multivariate analysis with ordinary least squares (OLS), fixed effects, robust regression, and Tobit models, this research assesses the effect of ESG reporting, governance, and the cost of capital, including robustness checks using an alternative ESG indicator, the environmental pillar score. Contrary to expectations, ESG reporting is positively associated with the cost of capital. However, corporate governance moderates this relationship, weakening the positive correlation and reversing it to a negative association for firms with strong governance practices, consistent with the hypotheses. This research also finds that firm size, liquidity, profitability, and leverage, positively affect the cost of capital, while board size, independent board composition, audit committee independence, and auditor type do not significantly influence it. Notably, non-executive directors on the audit committee have a significant negative effect on the cost of capital. These findings are valuable for investors, companies, regulators, auditors, policymakers, and the academic and research community. Specifically, for investors, this study provides insights into how ESG disclosures can influence investment risks and returns, highlighting the importance of robust corporate governance. Companies can leverage these insights to enhance their governance practices and optimize their capital costs. Regulators and policymakers can use the findings to develop guidelines that encourage transparent ESG reporting and strong governance frameworks, thereby improving market stability and investor confidence. Auditors can utilize the results to better understand the effect of non-financial reporting on financial metrics, helping to provide more accurate audits and assessments. These findings inform investors, companies, regulators, auditors, and academia, in fostering a more sustainable and transparent financial environment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".