Factors Affecting Return on Assets in the Renewable Energy Sector during Supply Chain Disruptions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it