A comprehensive life cycle impact evaluation of hydrogen production processes for cleaner applications
Bibliographic record
Abstract
The worldwide energy demands have greatly increased with urbanization and population growth. Air pollution, acid rain, greenhouse gas emissions, global warming originating from CO2 emissions, depletion of energy supplies, and environmental degradation resulting from climate change are all consequences of using non-renewable fossil fuel-based energy infrastructure. To minimize emissions, renewable energy-based alternative energy sources must be investigated. In this regard, hydrogen (H2) has emerged as a promising fuel to meet energy requirements, and green H2 production with net-zero emissions has gained significant interest in recent years. Therefore, this study uses the life cycle assessment approach to evaluate the atmospheric emissions and environmental impact parameters of the gasification, electrolysis, and dark fermentation-microbial electrolysis hybrid process and assess their sustainability levels, considering the sustainable development goals. Among the studied H2 production processes, the maximum CO2 emission originates from the coal gasification process, accounting for 18.6 kg-CO2/kg-H2, while the alkaline electrolysis process provides the lowest total CO2 emission of 6.39 kg-CO2/kg-H2. Furthermore, the biological-based dark fermentation-microbial electrolysis cell process is a promising option owing to its highest negative biogenic CO2 emission of -68.69 kg-CO2/kg-H2. The environmental impact parameters of the studied processes are calculated considering the emissions, and the highest global warming potential of 21.75 kgCO2-eq./kg-H2 is obtained for the coal gasification process, considering the life cycle assessment coefficients. Overall, the lowest atmospheric emissions and environmental impacts are obtained for the electrolysis process. Consequently, these results revealed that switching from the fossil fuel resources used in the conventional H2 production methods to fully sustainable sources, such as renewables, can make energy production methods entirely sustainable from an environmental point of view.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".