Beauty in the Eye of the Beholder: A Comparison of ‘Green Power’ Certification Programs in Australia, Canada, the United Kingdom and the United States
Bibliographic record
Abstract
With growing concerns about air quality issues and the continuing restructuring of electricity supply industries world-wide, the prospects for ‘green power’ - that is, environmentally-friendly electricity - are probably better now than they have ever been. What kinds of power are deemed to be ‘green’, however, is the subject of much debate. In an effort to standardise the definition of green power, certification programs are being developed independently by different countries at the national or sub-national level. Effectively, these are the mechanisms by which green power is currently being defined. This article compares the attributes of four certification programs for green power-those in Australia, Canada, the United Kingdom and the United States. The goal is to determine the ways in which these programs are similar, and the extent to which they are different. Though there is no definitive effort to determine which certification program is ‘best’, the key debates surrounding the most contentious parts of green power certification programs are identified and investigated. Accordingly, the policy discussion about the relative merits of different approaches is informed and stimulated.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".