The riddle of the sands: C02 emissions reduction and California's renewables portfolio
Bibliographic record
Abstract
Development of nuclear energy in northern Alberta has been proposed as a means of reducing the environmental costs of oilsands extraction; rather than open-pit mining, steam from nuclear power plants would be used for in situ extraction of petroleum. Such development could be facilitated by the export of electricity to California, thereby facilitating achievement of the State's legislative target that 60 % of electricity come from renewable sources by 2030 and 100 % by 2045. Using a policy-oriented, grid allocation model and projections of future power requirements in California, this study determines whether there is indeed potential for Alberta to export carbon-free electricity to California to the benefit of both jurisdictions. We find that doing so could reduce California's CO 2 emissions in the electricity sector by some 70 to 85 %. However, if California decided to rely more on in-house generation of nuclear power, the market available to Alberta would be constrained by the extent to which the State exploits nuclear capacity. It is also constrained by the extent to which the load profile can be altered and the ability to exploit wind and solar regimes that differ from those currently used to generate power. • Environmental damage from oilsands extraction can be reduced using nuclear energy. • Projected 157 % increase in California electric load due to EVs, AI and data centers. • Eschewing nuclear and fossil-fuel energy will require imports of non-carbon power. • Alberta nuclear energy can help California achieve climate targets for electricity. • Electricity demand from EVs will challenge California carbon-neutral policies.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".