Unleashing Local Capital In Greening Alberta’s Grid: An Evaluation Of A Co-operative Investment Model To Stimulate Renewable Energy Development In Support Of Alberta’s Climate Leadership
Bibliographic record
Abstract
Never before has a co-operative business model been attempted in Alberta in stimulating community economic development using renewable electricity generation and local capital. Given Alberta’s depressed power prices and economy, and the political challenges of an energy transition, the success of a renewable energy investment co-operative is unknown. The Alberta Solar Co-op (ASC) was incorporated as an Opportunity Development Co-operative in February, 2016, to provide Albertans direct investment opportunities in Alberta’s renewable energy future. The co-op model is well suited for sustainable development, including electricity. While many jurisdictions world-wide have demonstrated this, Alberta lags. The ASC is developing Alberta’s first community owned, 2-megawatt solar farm. This study finds that government support in the form of a $2 million grant and an energy credit approximating $90/megawatt-hour may provide member-investors a positive financial return on their investment – and provincial and municipal governments, a social, economic, and environmental return on theirs.
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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".