How are decarbonization policies in the US and Canada shaping low-carbon ammonia production strategies?
Bibliographic record
Abstract
Abstract The US and Canada contribute to 11% (22 million tons (Mt) per year) of global ammonia production, with an additional 42 Mt of production capacity currently planned or under construction. The distinct decarbonization policies adopted by these two countries—namely production tax credits in the US and carbon taxes in Canada—lead to significantly different outcomes and implications for decarbonized ammonia production strategies. This study evaluates facility-specific production strategies for low-carbon ammonia, considering the decarbonization policies of both countries. We assess the most cost-effective strategy for low-carbon ammonia production at each facility, both with and without the influence of these policies. Our results indicate that Canada’s carbon tax incentivizes the adoption of carbon capture and storage (CCS), while the US production tax credits promote the use of wind energy and biomass coupled with CCS, to produce hydrogen for ammonia synthesis. These findings highlight a dichotomy between the impacts of tax credits and carbon taxes: production tax credits facilitate the transition to low-carbon production methods, whereas carbon taxes incentivize existing facilities to upgrade with CCS technology. These insights underscore the effectiveness of tailored policy approaches and provide a comprehensive blueprint for other regions globally seeking to transition towards low-carbon ammonia 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".