Carbon debt repayment or carbon sequestration parity? Lessons from a forest bioenergy case study in Ontario, Canada
Bibliographic record
Abstract
Abstract Forest bioenergy can contribute to climate change mitigation by reducing greenhouse gas (GHG) emissions associated with energy production. We assessed changes in GHG emissions resulting from displacement of coal with wood pellets for the Atikokan Generating Station located in Northwestern Ontario, Canada. Two contrasting biomass sources were considered for continuous wood pellet production: harvest residue from current harvest operations (residue scenario) and fibre from expanded harvest of standing live trees (stemwood scenario). For the stemwood scenario, two metrics were used to assess the effects of displacing coal with forest biomass on GHG emissions: (i) time to carbon sequestration parity, defined as the time from the beginning of harvest to when the combined GHG benefit of displacing coal with biomass and the amount of carbon in regenerating forest equalled the amount of forest carbon without harvest for energy production; and (ii) time to carbon debt repayment, defined as the time from the beginning of harvest to when the combined GHG benefit of displacing coal with biomass and the amount of carbon in the regenerating forest equalled forest carbon at the time of harvest. Only time to carbon sequestration parity was used for the residue scenario. In the residue scenario, carbon sequestration parity was achieved within 1 year. In the stemwood scenario, times to carbon sequestration parity and carbon debt repayment were 91 and 112 years, respectively. Sensitivity analysis showed that estimates were robust when parameter values were varied. Modelling experiments showed that increasing growth rates for regenerating stands in the stemwood scenario could substantially reduce time to carbon sequestration parity. We discuss the use of the two metrics (time to carbon sequestration parity and time to carbon debt repayment) for assessing the effects of forest bioenergy projects on GHG emissions and make recommendations on terminology and methodologies for forest bioenergy studies.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 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".