Techno-economic analysis of nuclear-powered urea production with commercial greenhouse
Bibliographic record
Abstract
This study investigates the potential of integrating Small Modular Reactors (SMRs) into greenhouse operations and urea production to tackle rising energy demands and environmental concerns stemming from emissions in the food production sector. The research offers a comprehensive techno-economic assessment of a system utilizing SMRs to supply both heat and electricity to a greenhouse while generating sustainable urea fertilizer. The evaluation includes key metrics such as Levelized Cost of Urea (LCOU), Payback Period (PBT), Discounted Payback Period (DPB), and Internal Rate of Return (IRR). The analysis indicates a total capital expenditure of approximately 400 million USD, with the SMR representing 88 % of the cost. The LCOU is estimated at USD 1394 per metric ton, which is significantly higher than conventional market prices, leading to a prolonged PBT of 15.4 years and a lower IRR of 4.1 %. Sensitivity analyses demonstrate that fluctuations in urea prices and SMR capital costs significantly affect the system's financial viability. Despite the high initial costs, the SMR-powered system has the potential to reduce natural gas consumption and greenhouse gas emissions, thereby promoting long-term sustainability in agriculture. These findings emphasize how SMRs can deliver a cleaner, more sustainable energy solution for greenhouse heating and nitrogen fertilizer production, which are vital for supporting agricultural growth while minimizing environmental impact.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".