FINANCIAL STABILITY OF ELECTRICITY COMPANIES IN THE CONTEXT OF THE MACROECONOMIC INSTABILITY AND THE COVID-19 PANDEMIC
Bibliographic record
Abstract
The electricity sector is an important part of any country's economy as it holds a cross-sectoral importance and produces a socially significant product for residents and industries. Economically, the sector is less vulnerable during world crises, receiving many variations of the state support. Both world electricity consumption and electricity generation have grown steadily over 2007-2019, with China, USA, India, Russia, Japan, Canada, South Korea, Germany, Brazil and France being world market leaders. This article analyzes the current state and the main trends of the development of the electricity industry as a whole and the financial stability of its companies. The United States and Russia, with similar functioning market models, were chosen to assess. The analysis of the financial stability of PJSC Inter RAO and Exelon Corp, two electricity giants in Russia and in the United States, has shown that they demonstrate stable results: Exelon Corp is more profitable while PJSC Inter RAO is less dependent on financing from creditors. Overall, electricity companies and the industry as a whole should not suffer much from the COVID-19 pandemic: many financial support measures have been developed in both countries, helping the sector to recover to 2019 levels by 2021.Keywords: energy sector, electricity industry, economic and financial crisis, coronavirus pandemic (COVID-19), low-carbon economy, financial stability.JEL Classifications: G30, L94, Q43, Q48DOI: https://doi.org/10.32479/ijeep.11575
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 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".