The Nexus between Green Banking Initiatives and Environmental Performance: Examining the Moderating Effect of Environmental Risk Management
Bibliographic record
Abstract
This study investigates the relationships among green banking initiatives, green innovation, environmental risk management, and environmental performance in the Vietnamese banking industry. The research model is developed based on the existing literature and tested using structural equation modeling (SEM) on a sample of 459 mid-level managers from 36 banks in Vietnam. The findings reveal that green lending, green investment, and green internal operations have significant positive effects on green innovation, which in turn has a significant positive effect on environmental performance. Moreover, environmental risk management positively moderates the effects of green banking initiatives on green innovation, as well as the effect of green innovation on environmental performance. The robustness tests, including alternative model specification, subgroup comparisons, control variable analyses, and triangulation with secondary data and literature, provide consistent and complementary evidence for the hypothesized relationships. The study makes several important contributions to the literature on green banking, sustainability, and innovation in Vietnam. It develops and tests a comprehensive theoretical model, uses a large sample of mid-level managers from multiple banks, employs rigorous statistical methods and robustness tests, and highlights the critical role of environmental risk management in the effective implementation of green banking and innovation strategies. The findings offer valuable insights and practical implications for bank managers, regulators, and policymakers in Vietnam, as the country strives to promote sustainable finance and address pressing environmental challenges.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".