How Do Green Banking Practices Impact Banks’ Profitability? A Meta-Analysis
Bibliographic record
Abstract
In light of the growing global emphasis on sustainability, understanding the nexus between green banking practices and banks’ profitability is essential and timely. The main aim of this study was to conduct a meta-analysis examining the link between green banking practices and banks’ profitability. Based on 28 proxy relationships between green banking and green financing activities on banks profitability, a random-effects meta-analytic model was used to examine the corresponding effect sizes. An overall positive statistically insignificant effect size between green financing and green banking activities on banks profitability was established, implying that green banking activities do not consistently translate into financial benefits. However, this study established considerable heterogeneity of the results due to the application of different methodologies in diverse geographical contexts and varying green financing proxies. The study strongly recommends banks and policymakers adopt tailor-made, evidence-based green financing strategies to align their sustainability initiatives with market realities, regulatory frameworks, and institutional capacities. Such strategies promote the pursuit of both financial performance and environmental responsibility.
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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 | 0.026 | 0.063 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.045 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| 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".