Sustainable Feedstock Planning for Renewable Natural Gas (RNG) Production via Anaerobic Digestion (AD): An Environmental Perspective with Economic Considerations
Bibliographic record
Abstract
Unsustainable organic waste (OW) management significantly contributed to the global climate change crisis. Landfilling is the most widely used method to manage OW globally and in Canada as well which contributes to 2% of the country’s GHG emissions. As a response to these issues, Canada has imposed federal and provincial OW diversion targets, food waste landfilling bans, and regulations such as Clean Fuel Regulations (CFR). There is an urgent need to develop know-how and resources to aid sustainable waste management decision-making. Anaerobic digestion (AD) is a hybrid solution for two burning issues: landfilling of OW and GHG emissions from fossil fuels. Life cycle carbon intensity (CI) assessment is a tool that can be used to evaluate the environmental sustainability of RNG production. Recently registered CFR in Canada has proposed a consistent approach for life cycle assessment on clean fuels produced in Canada. This research developed feedstock planning methods for RNG production using AD in the Canadian context from a life cycle thinking-based approach. In phase 1 of the study, the environmental and economic performance of RNG production focusing on the feedstock planning stage has been conducted. According to the life cycle CI assessment results, the environmental impacts of each feedstock type were monetized using carbon credits. The net revenue was calculated considering the RNG sales, carbon credits, tipping fee revenue, and feedstock price. SSO (200 CAD/t) gives the highest net revenue followed by GHW, and WC. Feedstocks that have low energy yield (DM & SS) showed considerable credit revenue potential which will be a good incentive for them. In phase 2, a feedstock prioritizing framework was proposed for the RNG industry to rank different feedstocks for RNG production. The feedstock ranking results indicated SSO as the preferred feedstock followed by GHW and WC. The findings of this research will aid RNG producers in identifying optimal feedstocks, thereby enhancing production efficiency. Additionally, the outcomes will contribute to Canada’s commitment to achieving the United Nations Sustainable Development Goals (UNSDGs) and advancing the Federal Sustainable Development Strategy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".