Market prospects for biochar production and application in California
Bibliographic record
Abstract
Abstract The state of California could play an important role in emerging markets for biochar, due in part to the availability of low‐value biomass resources and their potential for use in agriculture sector. In this study, we assess the scale of production and use, and comment on potential markets for biochar in California. We explore various sectors for the application of biochar produced from local biomass using surveys and a market‐sizing approach. A market‐oriented approach for biochar innovation and the ecosystem around a biochar producer is also discussed. Next, we identify barriers to biochar market success in the present and the near future based on a survey of local producers. Among the barriers analyzed, access to capital investment for scale‐up is the biggest barrier experienced by a majority of producers, followed by market and demand. When grouped under different categories, the extent of barriers decreased in the order: market > scale‐up > technical > socio‐political > environmental. Most producers anticipate that revenues from carbon offset credits would help them scale up their facilities and expand the biochar market. In the near future, soil‐based applications of biochar could be the most likely market for biochar, followed by filtration, livestock feed, and manure management. As the industry evolves, rewarding carbon credits, increasing awareness and improving production processes are expected to help commercialize biochar. Finally, we offer recommendations to promote the growth of biochar in California. © 2021 Society of Chemical Industry and John Wiley & Sons, Ltd
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".