Oil, Transitions, and the Blue Economy in Canada
Bibliographic record
Abstract
Decisions about whether to include oil in blue economy plans can be controversial but also fundamental to the ability of these plans to transform (or not) business-as-usual in the oceans. This paper examines (a) how oil is sometimes included and justified in blue economy planning when its development is at odds with climate commitments and the need for just transitions away from fossil fuels, and (b) how oil could be included in blue economy planning, or transitions to blue economies and just energy transitions away from oil. We examine how tensions between sustainability/climate commitments and oil development impacts are resolved in practice, specifically by analyzing a particular approach to the blue economy that focuses on technology and innovation. The overlap of oil with renewable energy, specifically through technology, has become an important part of recent ocean and blue economy narratives in oil-producing nations and illustrates the contradictions inherent in ocean development discourse. We draw specifically on the case of Newfoundland and Labrador (NL), the only province in Canada with a mature offshore oil industry and thus the region most potentially impacted by decisions about whether to include oil in Canada’s blue economy. We argue that the blue economy approach to ocean governance being enacted in NL is currently being used as a form of legitimation for continuing the development of oil with no real transition plan away from it. Furthermore, we argue that blue economy plans must not only envision transitions to renewables but also explicitly and actively transitions away from oil to minimize environmental and social justice and equity issues at multiple scales. We end by highlighting some necessary conditions for how ocean economies that include oil can transition to sustainable and equitable blue economies.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.027 | 0.010 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".