Retraining investment for Alberta’s oil and gas workers for green jobs in the solar industry
Bibliographic record
Abstract
Abstract If oil sands are to be eliminated from the energy market to protect the global environment, human health and long-term economic welfare, a significant number of workers will be displaced in the transition to renewable energy technologies. This study outlines a cost-effective and convenient path for oil and gas workers in Alberta to be retrained in the burgeoning solar photovoltaic (PV) industry. Many oil and gas workers would be able to transfer fields with no additional training required. This study examines retraining options for the remainder of workers using the most closely matching skill equivalent PV job to minimize retraining time. The costs for retraining all oil sands workers are quantified and aggregated. The results show the total costs for retaining all oil sands workers in Alberta for the PV industry ranges between CAD$91.5 m and CAD$276.2 m. Thus, only 2–6% of federal, provincial, and territorial oil and gas subsidies for a single year would need to be reallocated to provide oil and gas workers with a new career of approximately equivalent pay. The results of this study clearly show that a rapid transition to sustainable energy production is feasible as costs of retraining oil and gas workers are far from prohibitive.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 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".