Safe ends with just means: Charting a course to a fossil fuel free economy for Canada and beyond
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it