Assessing the affordability and independence of building-integrated household green hydrogen systems in Canadian urban households under climate change
Bibliographic record
Abstract
Climate change will impact the affordability and independence of household green hydrogen systems due to shifting climate patterns and more frequent extreme events. However, quantifying these impacts remains challenging because of the complex interactions among climate, building characteristics, and energy systems in urban environments. This study presents an integrated modeling platform that couples regional climate projections, building energy performance simulations, and energy system optimization to assess long-term climate impacts across four representative Canadian cities from 2010 to 2090. The results indicate that cooling-dominated cities may face up to a 50 % increase in energy costs and an 20 % rise in grid dependency, whereas heating-dominated cities may experience cost reductions of up to 20 % and a 35 % decrease in grid reliance. Although climate-aligned system designs cannot fully mitigate climate-induced performance variations, they influence levelized cost of energy, increasing it by up to 60 % in cooling-dominated cities but improving it by over 5 % in heating-dominated ones. These findings suggest that enhancing grid connectivity may be a more effective strategy than modifying system designs in cooling-dominated regions, whereas adaptive design strategies offer greater benefits in heating-dominated areas.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".