Biogas Upgrading: A Review of National Biomethane Strategies and Support Policies in Selected Countries
Bibliographic record
Abstract
Bioenergy contributes significantly towards the share of renewable energies, in Europe and worldwide. Besides solid and liquid biofuels, gaseous biofuels, such as biogas or upgraded biogas (biomethane), are an established renewable fuel in Europe. Although many studies consider biomethane technologies, feedstock potentials, or sustainability issues, the literature on the required legislative framework for market introduction is limited. Therefore, this research aims at identifying the market and legislative framework conditions in the three leading biomethane markets in Europe and compare them to the framework conditions of the top six non-European biomethane markets. This study shows the global status and national differences in promoting this renewable energy carrier. For the cross-country comparison, a systematic and iterative literature review is conducted. The results show the top three European biomethane markets (Germany, United Kingdom, Sweden) and the six non-European biomethane markets (Brazil, Canada, China, Japan, South Korea, and the United States of America), pursuing different promotion approaches and framework conditions. Noteworthy cross-national findings are the role of state-level incentives, the tendency to utilise biomethane as vehicular fuel and the focus on residues and waste as feedstock for biomethane production. Presenting a cross-country comparison, this study supports cross-country learning for the promotion of renewable energies like biomethane and gives a pertinent overview of the work.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.015 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".