Sustainability analysis of electrolysis based green hydrogen production pathways: A life cycle perspective
Bibliographic record
Abstract
As the global transition towards clean and sustainable energy systems, hydrogen has emerged as a key energy carrier in the pursuit of a carbon-neutral future. This study presents a comprehensive life cycle assessment to evaluate the long-term environmental impacts of hydrogen production via water electrolysis in the province of Ontario, Canada, with a particular focus on effects on resource depletion, ecosystem quality, and human health. Using a cradle-to-gate framework, the assessment compares different electrolysis technologies and electricity sources through the application of the SimaPro life cycle modeling software. The results indicate that the choice of electricity source plays a critical role in determining the overall environmental performance of hydrogen production pathways. Among the evaluated options, electrolysis powered by hydropower consistently shows the lowest impact on human health, with environmental scores of 56 and 61 millipoints for proton exchange membrane and alkaline electrolyzers, respectively. In contrast, systems powered by solar, wind, and nuclear energy nearly double the impact. For resource depletion, hydropower-based hydrogen production also exhibits the lowest environmental burden, with a resource depletion score of 0.1 millipoints. This is five times lower than wind and nuclear-powered systems, while solar shows the highest impact at 1.8 millipoints. The findings highlight that, within the context of Ontario, Canada, hydropower-driven electrolysis using proton exchange membrane technology presents the most environmentally favorable option for green hydrogen production.
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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 | 0.002 | 0.002 |
| 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".