Sustainability Impact Assessment of Forest Bioenergy Value Chains in Quebec (Canada)—A ToSIA Approach
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Forest bioenergy value chains can offer attractive opportunities to promote economic development and mitigate climate change. However, implementing profitable and efficient forest biomass value chains requires overcoming barriers that continue to hinder the development of bioenergy systems in several jurisdictions. The objective of this study was to compare the economic, social, and environmental sustainability of various potential configurations of forest bioenergy value chains, including forest biomass supply and bioenergy production chains, in the Capitale-Nationale region of Quebec (Canada), which is a jurisdiction that has considerable forest resources but makes little use of bioenergy. We based our study on the ToSIA model parameterization and compared various policy measures, biomass supply, and logistics scenarios for 2008 and 2030. Our results showed that wood chip and pellet value chains in the Capitale-Nationale region would positively contribute to the regional economy in 2030, even in the absence of subsidies. Moreover, actions to increase biomass feedstock mobilization in 2030 would lead to an increase in gross value added, employment, and energy production in the region compared with 2008 and a greater increase than other considered policy or logistical measures. However, increased biomass feedstock mobilization would also mean higher relative GHG emissions and more fossil fuel energy input per unit of bioenergy than in the other scenarios. Conversely, optimizing biomass feedstock and combustion technologies could help minimize the fossil fuel energy input needed and GHG and some non-GHG pollutant emissions. Overall, our study suggested that implementing policy and logistical measures for forest biomass value chains could make the significant mobilization of forest bioenergy attainable and, in turn, Quebec’s 2030 bioenergy target of 17 petajoules realistic.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it