Advancing hydrogen sustainability in Alberta: Life cycle sustainability assessment of hydrogen production pathways
Bibliographic record
Abstract
This study conducts a Life Cycle Sustainability Assessment (LCSA) of hydrogen production pathways in Alberta, Canada, evaluating environmental, economic, and social dimensions. Eight pathways are analyzed: steam methane reforming (SMR) with and without carbon capture and storage (CCS), autothermal reforming (ATR) with and without CCS and with and without grid electricity, as well as alkaline electrolysis using grid and wind electricity. While alkaline electrolysis with wind electricity shows the best performance under the climate change and ozone depletion categories, ATR + CCS (CO 2 capture rate of 91 %, no-grid electricity) demonstrates the strongest performance in seven of nine environmental impact categories, being the worst performer in none, and having the lowest social risks. Economically, SMR and ATR without CCS exhibit the lowest levelized cost of hydrogen, followed by ATR + CCS (CO 2 capture rate of 91 %, no-grid electricity). ATR + CCS (CO 2 capture rate of 91 %, no-grid electricity) emerges as a promising pathway offering an overall balance of sustainability under the current study's assumptions. The results suggest that a blue hydrogen to electricity scenario, where ATR + CCS with 100 % on-site hydrogen-fueled power generation replaces grid electricity, may be the most suitable pathway for hydrogen production in Alberta. Key recommendations include optimizing environmental performance in climate change and ozone depletion impacts, reducing costs, and mitigating social risks in ATR pathways. This LCSA supports policies and investments to advance hydrogen's role in Alberta's decarbonization and energy transition. • LCSA to assess sustainability impacts of hydrogen pathways in Alberta, CA. • Sustainability assessment reveals key trade-offs between the three dimensions. • ATR + CCS 91 % - no grid electricity offers most suitable sustainability balance.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".