Role of Banking Sector Performance in Renewable Energy Consumption: A Comparative Analysis of OECD Countries and the Western Balkans
Bibliographic record
Abstract
The paper explores the impact of banking sector performance on renewable energy consumption and focuses on a panel of 43 countries, including 38 OECD countries and five Western Balkan countries for the period 2010-2022.Recognizing the crucial role of the banking sector in financing renewable energy investments, the study evaluates the influence of five key banking sector performance indicators: the ratio of cost to revenue, asset quality, return on assets, financial stability, and return on equity.The research employs both random and fixed effects models to analyze panel data for the OECD and Western Balkan countries.The Hausman Test results indicated that the Random Effects model was the most suitable for the OECD data, as it efficiently uses within and between country variations without correlation issues.For the Western Balkans, the Fixed Effects model was preferred, as it controls for unobserved heterogeneity that could bias the results.This methodological choice ensures robust and accurate econometric analysis tailored to the specific data characteristics of each region.The key findings of the study reveal that within OECD countries, there is a positive relationship between financial stability, the ratio of cost to revenue, and the return on assets with renewable energy consumption.On the other hand, our analysis shows that in the Western Balkan countries, the quality of banking assets stands out as a key factor influencing renewable energy consumption.These findings emphasize the banking sector's critical role in achieving sustainable energy transitions and show that specific financial policies, such as providing green loans, offering tax incentives for renewable energy investments, and implementing more strict asset quality regulations, could improve the effectiveness of renewable energy investment across different regions.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".