Strategic Roadmapping and Technology Portfolio Selection for Heating Decarbonization in Canada
Bibliographic record
Abstract
Heating systems contribute significantly to Canada’s greenhouse gas emissions, accounting for approximately 117 megatons of CO₂ equivalent. demanding urgent decarbonization to meet national climate targets. This thesis employs the Advanced Technology Roadmap Architecture framework, integrating strategic roadmapping and technology portfolio selection methodologies to evaluate pathways for transitioning Canada’s heating sector to net-zero emissions by 2050. By analyzing historical emissions, forecasting adoption trends for key technologies like heat pumps, and conducting stakeholder-driven scenario analysis, this research identifies critical barriers to scaling low-carbon solutions, including high upfront costs, infrastructural limitations, and regional climatic constraints. Seven representative heating architectures—air-source heat pumps, ground-source heat pumps, district heating, hydrogen-based systems, electric resistive heating, and conventional gas-fired furnaces—are evaluated comprehensively. Among these, district heating is particularly emphasized due to its potential for significant emissions reductions and minimal consumer-bearing initial cost of ownership, especially when strategically integrated with waste heat recovery from data centers. This integration utilizes otherwise wasted thermal energy, creating a robust symbiotic opportunity for urban and industrial decarbonization. To support the practical deployment of these architectures, the thesis establishes a targeted technology portfolio comprising essential enabling and supporting technologies. Enabling technologies include centralized supervisory control systems, urban-scale district heating networks, inverter-driven compressors, advanced refrigerants, ground heat exchangers, and circulation pumps with variable frequency drives. Critical supporting technologies identified encompass building information modeling integration kits, cybersecurity modules, digital permitting platforms, smart thermostats, and thermal energy storage systems, among others. This thesis further explores technology trade-offs, focusing on structural complexity, technology readiness, and associated risks of deployment. Through detailed modeling and stakeholder-informed scenario analysis, the thesis concludes that effective decarbonization of heating in Canada necessitates substantial policy interventions, robust financial incentives, targeted infrastructure investments, and region-specific strategies. The analysis indicates that a carefully allocated $8 billion catalyst investment could close approximately 60% of Canada’s heating emissions gap by 2050. Ultimately, district heating coupled with waste heat recovery emerges as a particularly promising strategic option, underscoring its transformative potential within a diversified approach to achieving Canada’s sustainable heating future.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".