Co-creating Canada's path to net-zero: a stakeholder-driven modelling analysis
Bibliographic record
Abstract
Canada has pledged ambitious emission targets, aiming to achieve a reduction of at least 40–45% below 2005 levels by 2030 and net-zero emissions by 2050. Being amongst the major economies with high dependence on fossil fuels, however, this path is far from straightforward. This research employs NATEM, a TIMES-based regional energy system model for North America with explicit representation of Canada, as well as knowledge produced and shared by stakeholders during a targeted workshop dedicated to identifying decarbonisation bottlenecks, to compare the paths to net zero on the basis of whether stakeholder perceptions are considered or not. We find that the path to net-zero is technically feasible but critically entails the use of negative emissions technologies, like (bioenergy with) carbon capture and storage (CCS) and direct air capture, in addition to the large-scale deployment of a large range of mitigation options already available today. Based on the feedback received from the stakeholders, around both the use of CCS-based technologies and the potential of demand-side measures such as modal shifts in transportation and better urban planning, we impose a set of additional conditions and restrictions. We find that the co-created net-zero pathway is also technically feasible while relying less on technologies that may trigger bottlenecks prioritised by the stakeholders; notably, despite yielding a similar emissions trajectory, it entails significantly different sectoral and technological configurations to the non-co-created net-zero scenario, requiring an acceleration of near-term abatement measures, mainly through electrification and quicker rollout of renewable and other clean energy technologies.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".