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Record W4414163332 · doi:10.1007/s10668-025-06749-5

Enhancing nutrient recycling through expansion of biogas production - fact or fiction?

2025· article· en· W4414163332 on OpenAlexaff
Madeleine Larsson, Nancy Brett, Geneviève S. Metson, Karin Tonderski

Bibliographic record

VenueEnvironment Development and Sustainability · 2025
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicBioeconomy and Sustainability Development
Canadian institutionsWestern University
FundersEnergimyndighetenSveriges LantbruksuniversitetLinköpings Universitet
KeywordsBiofertilizerBiogasDigestateProduction (economics)Food securitySustainable developmentBioenergyAgriculture

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.574
Threshold uncertainty score0.403

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.012
GPT teacher head0.225
Teacher spread0.213 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2025
Admission routes1
Has abstractyes

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