Sustainability Practices and the Corporate Cost of Equity in Emerging and Developed Markets
Bibliographic record
Abstract
Sustainability practices have been attracting growing interest from firms and society in general. Their adoption generates the expectation of improving firms’ financial performance. However, empirical studies did not reach a consensus on the effects of these practices. This study investigates whether adopting sustainable practices negatively impacts firms’ cost of equity. In addition, it analyzes the moderating effect of the country’s development level on this relationship. We conducted a multilevel regression analysis using data from the Bloomberg, Capital IQ Pro, and World Bank databases covering the period from 2010 to 2022. The sample included 5,638 non-financial firms from developed countries (the United States, Japan, Germany, the United Kingdom, and France) and emerging countries (China, Indonesia, India, South Africa, and Brazil), considering three levels: time, firm, and country. The results revealed a negative relationship between sustainable practices and firms’ cost of equity. Furthermore, firms in developed countries that adopt sustainable practices tend to have a lower cost of equity than those in emerging countries. These findings contribute to the ongoing debate in academic literature and help to reduce investors’ uncertainty when allocating capital to sustainable firms. Finally, the results support regulators in confirming the effectiveness of sustainability-oriented policies.
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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 | 0.001 | 0.006 |
| 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 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".