Bibliographic record
Abstract
The current stage of renewable energy (RE) development poses new challenges to this sector.The existing mechanisms of state stimulation of Renewable energy system are gradually exhausting its capacity.This requires the development of new methods to support the industry, or giving them up altogether.This article presents the results of the theoretical analysis of the systemic features of RE risk assessment at each stage of a project's life cycle.A sectoral approach to the risk assessment of energy projects is proposed.It is based on the well-known logit-model that studies a set of external and internal indicators.Based on this model, a study of the dynamics of the risk indicators of RE projects on three basic stages was conducted.Calculations were made for RE projects implemented in different countries of the world, including China, USA, Canada, Japan, India and a number of European countries.Initially, all projects were divided into three main groups depending on the types of state support: concessional lending, subsidies or the lack thereof.Based on the results of the calculations, the overall and average dynamics of risk by group and by project stage allowed for assessing the global effectiveness of state measures to support the sector, as well as for drawing appropriate conclusions in the context of individual countries.The results of the study are of practical importance and will be used in developing a new approach to risk assessment, taking into account the specifics of the RE market, as well as in enhancing the concept of competition in the global energy market.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".