Environmental, Social, and Governance ( <scp>ESG</scp> ) Research: A Systematic Review of Recent Trends (2020–2024)
Bibliographic record
Abstract
ABSTRACT The 2020–2024 period marks a pivotal era in sustainable development, characterized by significant regulatory developments, including the EU Corporate Sustainability Reporting Directive (CSRD), the International Sustainability Standards Board standards, and the introduction of mandatory ESG reporting requirements worldwide. This review examines how recent policy changes have reshaped the linkages between ESG and sustainability. Following the PRISMA 2020 guidelines, we conducted a systematic review examining empirical studies of ESG–sustainability performance relationships published between 2020 and 2024. Studies examined the quantitative relationships between ESG sustainability performance and publicly traded corporations. Quality assessment employed the adapted Newcastle–Ottawa Scale with sustainability‐specific criteria. From 2847 screened records, 89 quantitative studies covering 126,000 firms in 67 countries met the inclusion criteria. Overall, 56% reported positive ESG–performance links, 38% mixed/neutral, and 6% negative. Asia‐Pacific showed the strongest positive share (67%), followed by multiregional (61%), Europe (56%), and North America (40%). Manufacturing (74%) and financial services (70%) outperformed technology (33%). This systematic review demonstrates strengthened positive associations and reveals Asia‐Pacific's emergence as a regional leader, providing timely evidence for environmental managers and policymakers navigating post‐2020 regulatory frameworks.
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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 | 0.015 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.016 | 0.020 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".