Green Financing as a Bridge Between Green Banking Strategies and Environmental Performance in Punjab, India
Bibliographic record
Abstract
Green banking (GB), which aligns financial practices with environmental sustainability, is becoming increasingly important in solving modern global concerns such as climate change and resource conservation.This research examines the notion of GB and its significant consequences for the environmental performance of the banking sector in Punjab (India).The unique economic and environmental characteristics of Punjab makes it an essential area for investigating the implementation of GB strategies.The research focuses on the intricate relationship between operations-related GB strategies and a bank's environmental performance, particularly emphasizing the mediation of banks' green financing.Data for this study was meticulously gathered from public and private sector bank employees, comprising 290 participants.Structural Equation Modeling (SEM) method was utilised for the analysis and the findings demonstrate a statistically significant and positive relationship among the variables indicated above.Furthermore, the research reveals that green finance partially mediates this relationship.These results have far-reaching implications for the banking sector's various stakeholders.This research can help regulatory authorities and policymakers in the conceptualization of laws and regulations that encourage green banking adoption.Banking institutions, in turn, may use this knowledge to improve their strategies and operations, improving environmental performance while preserving financial stability.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".