Brownfields to Brightfields: Re-Purposing Alberta’s Unreclaimed Oil and Gas Sites for Solar Photovoltaics
Bibliographic record
Abstract
A portion of Alberta’s unreclaimed ‘brownfield’ oil and gas sites could be re-purposed as ‘brightfields’ with solar photovoltaic installations supporting provincial objectives to reduce carbon emissions from electricity generation, address brownfield liabilities and mitigate cumulative effects of development. Elemental Energy (Alberta 2003) Inc. has initiated a repurposing pilot project however formal institutions, such as policies and regulations, and informal institutions, such as norms and values, may influence expansion of this sustainable endeavour. Through interviews and document analysis, this research investigated, “What are the opportunities and barriers to developing solar photovoltaic infrastructure on Alberta’s unreclaimed oil and gas sites?” The findings suggest that existing institutions support re-purposing a subset of brownfields with micro-generation systems, however policy and regulatory ambiguity hinder broader expansion by affecting the economic feasibility of distributed generation projects, limiting the number of re-purposing candidate sites and reinforcing constraining mindsets in the power generation and oil and gas industries.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".