Potential paths to Canada's climate commitments through strategic solar photovoltaic deployment
Bibliographic record
Abstract
Canadian climate policy calling for a transition to renewable energy is not only a response to the increasing frequency and liability of climate-related disasters, but also a strategic move to mitigate fossil-fuel economic volatility. The easiest path to transition is using the lowest-cost source of energy, which is solar photovoltaics (PV). This study brings clarity to Canada’s efforts to achieve the net zero target quantifying growth rates of PV system development required to reach net zero. First, Canada’s net energy goal background and the recent solar PV-specific growth, markets, and policies are reviewed. Next, the methodology for achieving Canada’s climate goal with PV deployment is detailed. The results indicate to meet carbon emissions targets in 2030 (40 % and 45 % below 2005) and be net-zero by 2050 requires 666, 762, and 1847 GW of PV, respectively. The latter solar PV required increases to 2019 GW with the expected 7.5 % escalation in primary energy. The 2023 total PV capacity installed in Canada is 4.6 GW and the rate of growth is completely inadequate to achieve Canada’s goals. This study presents different approaches to achieve Canada’s emissions goals using PV and details deployment in terms of energy, investment, and employment pointing towards the need of new policies.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".