Comparative Analysis of Direct Ammonia and Hydrogen Utilization: A Holistic Sustainability Perspective for Decarbonizing Transport and Power Sectors
Bibliographic record
Abstract
Green hydrogen is pivotal for decarbonizing the transport and power sectors, but it faces significant challenges related to long-term storage and distribution. Green ammonia, a promising hydrogen carrier, offers logistical advantages; however, its end-use viability remains largely unexamined. This study compares two pathways: (1) hydrogen produced from ammonia cracking, used at refueling stations for hydrogen fuel cell vehicles and in stationary power plants; and (2) direct use of ammonia at fueling stations for direct ammonia fuel cell (DAFC) vehicles and in DAFC-based power plants. A techno-economic analysis was conducted to evaluate the levelized cost of fuel (LCOF), levelized cost of electricity (LCOE), and carbon abatement cost (CAC) for all scenarios. Results indicate that for transport hydrogen (Case 1) achieves an LCOF of 0.078 $/km, outperforming ammonia at 0.133 $/km, owing to its higher energy density. For power generation, hydrogen’s LCOE (0.49 $/kWh) is lower than ammonia (0.93 $/kWh), though both remain above the conventional pathway owing to lower fuel cell efficiencies and higher capital costs. The CAC analysis indicates that decarbonizing the transport sector is more economical with hydrogen (353 $/tCO 2 ) or ammonia (595 $/tCO 2 ) compared to the power sector, where CAC rises to 1525 $/tCO 2 for hydrogen and 4510 $/tCO 2 for ammonia. Forecast analysis suggests that by 2030, both hydrogen and ammonia with LCOF of 0.064 and 0.055 $/km, respectively, can outperform conventional gasoline (0.07 $/km), with ammonia becoming the most cost-effective option for transportation. This study highlights that hydrogen and ammonia, each offering distinct advantages depending on end-use application and time frame, are mutually enabling solutions for decarbonized energy systems. These findings support the need for policy mechanisms such as carbon tax and targeted technological innovation to bridge cost gaps and address barriers in the energy transition.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".