Comparison Of Return On Investment (roi) And Energy Return On Investment (eroi) For Optimization Of Electricity Generation In Alberta
Bibliographic record
Abstract
The Government of Alberta, in the Provincial Energy Strategy, refers to electricity as the “facilitator of prosperity.” Increases in demand project a supply gap of 23,733 GWh in 2017 and 26,927 GWh in 2027 above 2010 installed generation capacity. In this report, operations research techniques were used to find optimized mixes of energy sources to fill the supply gap. Two optimization approaches were done to identify the mix with the highest financial return on investment (ROI) and the highest energy return on investment (EROI), respectively. ROI optimization would result in $724 million in 2017 and $1.5 billion in 2027 on total electricity generation costs in Alberta compared the business-as-usual case. EROI optimization would result in 3,0002 GWh savings on total energy input into electricity generation in 2017 and 4,989 GWh of total energy input in 2027. Both optimization approaches would also significantly reduce greenhouse gas emissions, pollution, and water consumption.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".