Decarbonization Pathways for Canada’s Federated Energy System Using a Subnational Integrated Assessment Model
Bibliographic record
Abstract
<title>Abstract</title> Amid growing climate risks and energy security challenges, Canada's path to Net Zero emissions by 2050 hinges on regionally differentiated transformations across its energy system. This study presents a detailed scenario-based analysis using MESSAGEix-Canada, the country's first open-source, sub-national integrated assessment model. We explore how energy system transitions evolve across provinces and sectors under varying policy pathways.Results from the Net Zero scenario indicate a 65% reduction in fossil fuel extraction, an eight-fold increase in electricity supply, and a tenfold growth in low-emissions hydrogen, achieved without significantly increasing total energy system investments relative to the Legislated pathway. Instead, capital shifts away from oil and gas production toward renewables, storage, and grid expansion. Electrification of end-use sectors, alongside carbon capture and clean hydrogen deployment, drives emissions reductions. Spatial analysis reveals Alberta, Saskatchewan, and Newfoundland and Labrador face steep structural changes in resource extraction, while provinces like Ontario and Quebec become hubs of electrification and clean energy infrastructure.The analysis highlights that achieving Net Zero is technically feasible, but demands urgent, coordinated, and province-specific strategies. Policymakers in resource-intensive provinces must plan for a managed fossil phase-out and support economic diversification. In contrast, electricity-rich provinces must scale transmission and hydrogen capacity to meet cross-sector demand. MESSAGEix-Canada provides a transparent and flexible platform to co-design such transitions with stakeholders—supporting policy alignment, investment targeting, and just transition planning within Canada's federated climate governance landscape.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".