Economic opportunities and challenges in biojet production: A literature review and analysis
Bibliographic record
Abstract
Biojet has emerged for pursuing reduced emissions targets. However, global uptake of biojet has remained negligible. Sources have speculated on economic reasons for biojet's lack of commercial success, but there have been few reviews across experts that have identified key causes. We compile the views of experts through a comprehensive literature review (covering over 200 sources between 2003 and 2021) that explores opportunities and challenges (OACs) for the emerging biojet industry through an economic lens. We categorize OACs into identified factors (e.g., high costs of production) and track the number of times each factor is mentioned in the literature. We use these counts to rank OAC factors and associate these with concepts of demand and supply, and their impacts on investments in biojet. We also examine how citations of key opportunities and challenges have changed over time. The highest ranked opportunities are associated with demand-side factors (e.g., increasing demand for reduced emissions), while the highest ranked challenges are associated with supply-side factors (e.g., high costs of production). Policy considerations, which could affect demand and/or supply, are also highly ranked, but like many factors, are viewed as both opportunities and challenges. Overall, the literature is optimistic towards future demand, but pessimistic towards future supply, with the bottom line indicating few prospects for current investments in biojet. Given the ongoing confidence in demand for biojet, this situation could potentially change with future investments in research to reduce costs and uncertainty, along with clear and consistent policies that could incentivize biojet 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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.024 | 0.031 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".