Enhancing nutrient recycling through expansion of biogas production - fact or fiction?
Bibliographic record
Abstract
Abstract Efficient recycling of digestate as fertilizers is crucial to achieve a rapid expansion of biogas production in Sweden and Europe. This study investigated if and to what extent the biogas and biofertilizer markets are connected and interdependent using the lens of sustainable market-shaping in a case study of the island Gotland, Sweden. We used interviews and document analyses to identify processes and connections that have impacted on how the biogas and biofertilizer markets have developed on the island. Using a framework that describes three phases of market development, we found that in recent years, the biogas system narrative has begun to incorporate the role of biofertilizers to enhance the biogas systems´ circularity. In contrast, the proving and the exchange practices of the market shaping processes are not adequately aligned to support the growth of a biofertilizer market. For the biogas market, growing demand for liquefied gas suggests that proximity to compressed gas markets will not be a determining factor for locating new biogas plants. However, spatial considerations are crucial for developing a biofertilizer market, especially if the aim is to distribute biofertilizers to agricultural areas that lack access to circular nutrients. Our results show that an expansion of the biogas market will not automatically shape a biofertilizer market or improve nutrient redistribution. Instead, a central actor needs to take responsibility for market shaping, possibly as part of a food security narrative where biogas producers could also take on the role of more actively promoting efficient use of their biofertilizers.
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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 | 0.003 | 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 | 0.004 | 0.013 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".