Evaluating ecosystem impacts of biogas pathways using life cycle assessment and ecosystem service models
Bibliographic record
Abstract
The regional energy transition from fossil fuels to alternatives with lesser environmental impact has become significant in recent years. The UN and the Sustainable Development Goals (SDGs) emphasize the importance of cleaner, safer, and more modern energy production to support environmental and climate protection. Renewable energy-based agricultural feedstock is considered a better substitute; however, its increasing share among alternative technologies powered by biomass sources still requires comprehensive environmental impact assessments. This study employs Life Cycle Assessment (LCA) modeling to evaluate the environmental impacts of biogas pathways, focusing on maize silage production for biogas in Alberta, Canada. Using openLCA software and Eco-invent data, the analysis covers the entire supply chain from upstream to downstream, assessing emissions, land use, and climate change impacts. The methodology involved consulting literature reviews, utilizing databases such as Eco-Indicator 99, ReCiPe, and the Tool for the Reduction and Assessment of Chemical and Other Environmental Impacts (TRACI). Key findings indicate that nitrogen fertilizer use (above 120 kg/ha in the maize farm) significantly contributes to eutrophication. Additionally, drying maize silage with natural gas poses a high climate change potential. These insights suggest that while biogas from maize silage is not entirely environmentally benign, improvements are achievable through optimized practices.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| 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".