<i>(Keynote)</i> Green Ammonia: Promise and Environmental Tradeoffs across Agriculture and Energy Sectors
Bibliographic record
Abstract
The invention of the Haber–Bosch process, which converts inert dinitrogen gas into ammonia, revolutionized agriculture by enabling the large-scale production of nitrogen (N) fertilizers. This innovation has powered global increases in crop production and is essential for feeding a growing population. However, conventional ammonia production is heavily reliant on fossil fuels, contributing approximately 1% of global annual greenhouse gas emissions. Green ammonia—ammonia produced using renewable energy—has emerged as a transformative alternative with significant potential to decarbonize both agriculture and the energy sector. Yet, this shift also brings potential unintended environmental consequences. In the transportation sector, green ammonia is a viable and promising option to decarbonize marine shipping. One projection finds that substituting green ammonia for 44% of fossil fuels in marine shipping would reduce CO 2 emissions by up to 0.38 Gt CO 2 -eq yr -1 but would require an increase in new N synthesis of 212 Tg N yr -1 . Even a modest leakage of un-combusted ammonia or unintended end products, such as nitrogen oxides (NO x ) and nitrous oxide (N 2 O), could exacerbate coastal pollution, disrupt oceanic N cycling processes, and increase emissions of N 2 O, which is the third most important greenhouse gas and the most abundantly emitted stratospheric ozone depleting substance. In the agricultural sector, green ammonia technology could lead to decentralization of fertilizer production, which stands in contrast to the current centralized, carbon-intensive production methods. This shift could enhance fertilizer use and bolster food production in countries where N fertilizer accessibility has been limited, thus improving crop production, economic prosperity, nutrition and food security. However, the current end products of distributed green ammonia production facilities are limited to a few types, such as anhydrous ammonia and aqueous ammonia, which are not widely used in crop production due to concerns for safety and machinery requirements. Ongoing innovation may enable farmers to improve the timing and dosing of fertilizer to better match crop needs and thereby reduce N losses. However, cheap and abundant N fertilizer could also exacerbate the current severe environmental problems of N losses to air and water from overuse and inefficient use of N fertilizers. Overall, many environmental impacts of green ammonia are still largely unknown and poorly quantified. This presentation will offer a framework and initial quantification of these environmental impacts. Along with the pursuit of decarbonizing the economy with green ammonia, it is critical to improve our understanding of its environmental impacts and establish necessary monitoring networks to ensure positive outcomes from its production and utilization.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.123 | 0.058 |
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".