The Effects of Environmental, Social and Governance Orientation: An International Empirical Literature Review
Bibliographic record
Abstract
In recent years, the ESG (Environmental, Social and Governance) criteria have become a central component in companies’ and investors’ economic-financial analysis and decision-making processes. This article provides a systematic and critical review of the scientific literature on this topic, exploring six main directions: (1) the relationship between ESG and financial performance; (2) the role of ESG factors in risk management; (3) the impact of ESG aspects on financial markets; (4) the interaction between corporate governance and ESG strategies; (5) the evolution of sustainability regulation; and (6) ESG measurement and rating issues. The literature results reveal a complex scenario: although much research documents a positive relationship between ESG practices and financial performance, numerous heterogeneities emerge related to the sectoral context, time horizon, data quality and materiality of the ESG factors considered. The increasing role of institutional investors and the regulatory framework in promoting transparency and accountability is also emphasized. Finally, the main open challenges in terms of methodological consistency, standardization of ESG ratings and combating greenwashing are identified. The paper concludes by highlighting the most promising future research perspectives, to support a more effective and informed integration of ESG factors into economic and financial decisions.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.021 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".