Bibliographic record
Abstract
In the context of tighter regulations by European Union institutions, sustainable finance allows companies and individuals to identify environmentally friendly ways to access loans according to their sustainability priorities. The financial sector, facing increasingly stringent regulatory requirements, is adapting existing processes and developing new management tools to address the evolving environmental context. This article examines green loans as a sustainable source of finance through a structural survey of eight experts from five major banks operating in Lithuania. The following study methods are employed: systematization and comparison of theoretical literature; questionnaire survey of experts; and analysis of interviews, involving an inductive approach adopting the Gioia Methodology. The survey was carried out in 2024, and its results show that, despite a high level of uncertainty in this area, all of the banks involved are making significant efforts to develop green loans. However, progress is more rapid in sectors where there is a clearer assessment of the greenness of that sector. The article concludes by analyzing green loans in two key areas: the sectors to which the loans are issued and the most significant challenges. The analysis highlights both strengths and points for improvement, such as the need for closer communication.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.020 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".