The Impact of Green Banking Activities on Environmental Performance: A Youth-Driven Perception Study in Indonesian Financial Institutions
Bibliographic record
Abstract
Green banking is a significant financial strategy for balancing environmental sustainability with economic progress. Banks can help address Indonesia’s environmental concerns by promoting sustainable behavior, financing green projects, and implementing environmentally friendly regulations. This study investigates how green banking practices affect perceived environmental performance and financial sustainability, with a particular emphasis on the involvement of young Indonesian bankers. A structured questionnaire was issued to 314 young bankers from various parts of Indonesia, using Likert-scale measures of three domains: banks’ perceived environmental performance, green banking activities, and sources of green finance. The findings show high perceived links between green banking operations and banks’ environmental performance, with green financing serving as a crucial mediator. Specific methods, such as paper reduction, internet banking, and supporting sustainable initiatives, were thought to improve bank performance. The findings underline the importance of younger generations in supporting and carrying out green activities, emphasizing their role in encouraging long-term change. Using Structural Equation Modelling (SEM), the study demonstrates that green finance improves perceived environmental performance and promotes sustainable banking practices. These findings emphasize the importance of incorporating green principles into banking strategy in order to achieve both financial and environmental sustainability in developing countries.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".