First-order estimates of the costs, input-output energy analysis, and energy returns on investment of conventional and emerging biofuels feedstocks
Bibliographic record
Abstract
Here we report on a static, algebraic, spreadsheet-implemented modeling approach to estimate the costs, energy inputs and outputs, and global warming potential of biomass feedstocks. Inputs to the model included literature sourced data for: environmental factors, crop physiological-parameters such as radiation use efficiency and water use efficiency, and crop cost components. Using an energy-input-output life-cycle-assessment approach, we calculated the energy associated with each cost component, allowing an estimate of the total energy required to produce the crop and fuel alongside the energy return on investment. We did this for crop scenarios in the upper Midwest US and Far West US (for algae). Our results suggested that algae are capable of the highest areal biomass production rates of 120 MG/(ha·a), ten times greater than Maize. Algal fuel systems had the highest costs, ranging from 28 to 65 US $/GJ, compared to 17 US $/GJ for Maize ethanol. Algal fuel systems had the lowest energy returns on investment, nearly 0, compared to 25 for Switchgrass to ethanol. The carbon equivalent emissions associated with the production schemes predictions ranged from 40 (Maize) to 180 (algae PBR) CO2eq/GJnet. The promise of low cost fuel and carbon neutrality from algae is demonstrated here to be extremely challenging for fundamental reasons related to the capital-intensive nature of the cultivation system.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".