A Study of the Impact of Green Banking Practices on Bank’s Environmental Performance: An Indian Perspective
Bibliographic record
Abstract
Global climate change has become an evident threat to the economy and is affecting the dayto-day operations of humans in every way alike.This issue of climate change has also impacted the traditional banking system.It has made way for Green Banking (GB), which ensures the balance of sustainability and growth in the final sector.The study's main aim is to quantify the effect that the various practices that relate to GB are having on the environmental performance of banks while also understanding the mediating impact of green finance.This study uses a survey-based approach to sample the data of 274 bank employees in India's Northern Capital Region (NCR).To quantify the relation between the various variables and highlight the statistical significance of the relationship, the study uses structural equation modelling (SEM) to quantify the measurement and the structural model.The study's findings reveal that the various factors of operations, consumer focus, employee training, and policy practices in the aspect of GB affect the environmental performance of a bank in some manner or another.It discusses the empirical and policy implications for future research directions in the concerned area.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| 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".