Determining the affordability of a green energy transition in British Columbia
Bibliographic record
Abstract
Canada has an almost notorious reputation for being environmentally unfriendly in the global context. From being the first nation to withdraw from the Kyoto protocol in 2011, to ranking last out of 27 wealthy countries in environmental protection, Canada has partially lost its all-round likable status. This has made numerous environmentalists eager to actualize a program where renewables would become the dominant energy source. However, cost is always at the top of the list of issues. The purpose of this paper is to determine whether it is practical to duplicate an energy transition in British Columbia, such that is currently being practiced in Germany. More specifically, would such a transition be affordable to BCʼs government, to home owners, and to power companies, and would it also create jobs? Through research of scholarly sources, this study finds that it is impractical for such an energy transition to be executed; however, if only the financial aspect is analyzed, it is possible for an affordable green energy transition that also creates jobs to be implemented in BC. Some recommendations if the results of this paper is to be acted upon, are to first test the program in a medium-sized city, to reduce consumption while also increasing dependence on renewables, and to learn from Ontarioʼs mistakes and resist high initial feed-in tariff rates.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".