Understanding the impacts of net-zero electricity in Canada by 2035
Bibliographic record
Abstract
Canada is committed to eliminating greenhouse gas emissions from its electricity system. The Clean Electricity Standard (CES) policy intends to enforce a net-zero electricity system by 2035 in which all thermal generators must be ceased. While many parts of Canada are already there, many are not. And the need to grow electricity supply to meet new electrical demand will add to the pressure. The purpose of this paper is to present for the first time in Canada a Python-based generator-level model that will be used to illustrate how 2035 electricity decarbonization will affect thermal capacity, generation, and carbon dioxide emissions. In this thesis, we review electricity markets across Canada and the clean electricity policies in each province. We explore what the 2035 CES policy will mean for the electricity sector in terms of how the policy will truncate the installed capacity and energy produced from thermal generators, and the amount of CO2 released. We discuss what we call the spatial impacts of clean energy policies in terms of where the impacts will most be felt on a regional level in terms of jobs affected. Finally, we discuss opportunities to reduce the adverse community effects of this policy. For our main results, we find that relative to letting thermal power plants live out their current expected lives, the CES policy will truncate 22 percent of fossil capacity-years, reduce 17 percent of CO2 emissions (68 megatonnes from 2035 onwards) and put at risk 21.7 percent of jobs at thermal power plants. Knowing when and where a facility closure will take place can allow targeted deployment of training resources for those in need, long-term budgeting that includes tax revenue losses, and advance planning for transitioning individuals to local jobs in environmental remediation of fossil facilities and new clean electricity generation. The ability of coal mines and other fuel supply to operate at efficient scales in order to stockpile fuel while serving out existing contracts will also be facilitated by clear expectations as to when and where generator retirements will occur.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".