Safe ends with just means: Charting a course to a fossil fuel free economy for Canada and beyond
Bibliographic record
Abstract
In this dissertation, I bring together a broad area of research in climate science and policy, ethics, ecological economics, political economy, and environmental sciences. I aim to cut through complexity and present clear conclusions that follow from decades of scholarship that has not yet made the impression on policymakers or the public that it deserves. This dissertation comprises three papers: 1) a critical overview of fair shares and decarbonization scenarios, and a way to reconcile what should be done with what experts think can be done (with a Canadian case study); 2) a framework for climate testing proposed fossil fuel infrastructure that can be used to evaluate an individual project's compatibility with global or domestic emissions reduction targets (with a case study of Canadian gas); and 3) an analysis of the potential for a shift towards services to mitigate GHG emissions and other environmental impacts. The results of paper 1 show that under a relatively ambitious but still insufficient decarbonization program, Canada is projected to accrue an emissions debt of 6 to 52 GtCO2e by 2050, which could be valued at $0.8 trillion to $6.5 trillion using the best estimate for the social cost of carbon dioxide. Paper 2 concludes current plans to extract Canadian gas are unequivocally at odds with national and global climate efforts, and that even climate action in line with an inequitable share of ~2–3°C of warming necessitates an immediate and rapid phase out of gas extraction within years. Papers 1 and 2 contribute policy tools needed for Canada and similar wealthy fossil fuel producing nations to do their fair share of a global energy transition, even when a domestic energy transition is limited by political or technological constraints. Paper 3 shows that when counting household consumption of people as part of the sectors that employ them, supposedly ‘clean’ sectors like services are just as harmful as ‘dirty’ ones. This exposes the limited potential to reduce environmental impacts by growing the service sector, refuting claims made by advocates of green growth via an explosion of the knowledge economy. These findings may be used to inform policymaking, so that appropriate emphasis is placed on behavioural, technological, and structural changes to the economy. Together, realizations from this dissertation can be used to craft fair and practical policy for a just transition for Canada, through integrated domestic and foreign policy, which also could serve as a model for other affluent nations.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.028 | 0.018 |
| Scholarly communication | 0.020 | 0.009 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.011 | 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".