Energy and Carbon Flows in Managed Biological Systems of Canada: Climate Change Implications
Bibliographic record
Abstract
Canadian agricultural and forestry systems are required to contribute to achieving net-zero greenhouse gas emissions by 2050. However, these systems focus on strategies that can only reduce a fraction of their emissions. In this thesis, a series of studies were conducted to obtain a holistic view of opportunities to better manage the flows of energy and carbon associated with the agri-food and forestry systems of Canada towards achieving the net-zero emission target. First, using data obtained from government sources and the literature, a comprehensive model was created to quantify and compare the flows of energy and carbon associated with the agri-food system and those associated with crude oil-refined petroleum products systems. Results of this study suggest that about 86% of energy and carbon associated with Canada’s agri-food system is embedded in wastes and residual biomass that are usually left to decompose, thereby returning carbon as carbon dioxide (CO2) into the atmosphere. The second study developed a methodology to calculate the 100-year global warming impact associated with diverting residual lignocellulosic biomass from business-as-usual scenarios (decomposition in agricultural, forestry, or landfill sites) into bioenergy use. The results showed that the CO2 released from bioenergy diversion has a global warming potential (GWPbio, units of kg CO2e/kg CO2) that is >0 and could be as high as 0.97 (the GWP for fossil CO2 emissions is 1.0) if the biomass decomposition is slow as in the case of wood in landfills. A third study added the biogenic CO2 analysis to an assessment of life cycle greenhouse emissions associated with co-firing lignocellulosic residual biomass with natural gas to produce one megagram of clinker in a cement plant. Although the co-firing scenario reduced natural gas emissions by 23%, it increased the total energy inputs and emissions associated with clinker production by about 18% and 10%, respectively, relative to the reference scenario. The thesis recommends that policy and investment decisions aimed at achieving net-zero emissions in the agri-food and forestry sectors should consider all anthropogenic flows of energy and carbon, and care should be taken if residual biomass is to be diverted for use as a bioenergy feedstock.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".