Factors Affecting Return on Assets in the Renewable Energy Sector during Supply Chain Disruptions
Bibliographic record
Abstract
Return on assets (ROA) is a critical metric in assessing a company’s sustainability, especially in light of supply chain disruptions. Within the renewable energy sector, such disruptions often lead to a decline in ROA. Through the utilization of a within-between random model, this study uncovers the necessity for distinct strategies both prior to and during supply chain disruptions to maintain a high ROA. Pre-disruption, emphasis should be placed on securing additional funding for research and development (R&D) initiatives and expanding market reach. However, amid disruptions, sustaining a high ROA demands a strategic pivot. Specifically, renewable energy firms should scale back expansion efforts, redirect cash toward R&D, and exercise caution when venturing into new international markets, particularly in the absence of substantial government subsidies. Notably, this paper focuses solely on large-scale listed companies, overlooking potential innovative strategies employed by smaller-scale companies—an area ripe for future investigation. Despite this limitation, our findings offer valuable insights into enhancing sustainable performance within the renewable energy sector.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".