Comparative Analysis of the Financial Stability of Renewable-based Electricity Companies: The Case for Hydroelectric Organizations
Bibliographic record
Abstract
Hydroelectricity remains the dominate RES (Renewable Energy Source) and the most developed, reaching growth rate peaks in some countries in the 20th century. However, the share of it has fallen over the last few years, as other renewable sources have received rapid development. Despite this, growth for hydroelectricity has remained stable, with China, India, Japan, Russia, Turkey, France, Norway, Canada, USA and Brazil as market leaders. This article analyzes the key trends of development of the hydroelectricity market as a whole, as well as the financial stability of its organizations using bankruptcy likelihood prediction models. The Brazilian and Russian companies were chosen to assess as both countries are classified as developing markets. The bankruptcy prediction models indicate that overall, the financial stability of hydroelectricity giants of Brazil and Russia is at a high level, though profitability ratios are very low. During the COVID-19 pandemic, several financial support measures were implemented by governments, along with the already existing instruments for stimulating renewable energy growth. Authors’ forecasts show that current trends on the market indicate that net addition capacity growth in the next few years will not be enough to meet Net Zero goals for the renewables market.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| 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".