Integrated environmental-economic analysis of different scenarios regarding forest-based bioenergy in Quebec
Bibliographic record
Abstract
This study provides valuable insights for decision-makers by examining the contribution of the forest sector in achieving deep decarbonization. It develops a detailed modeling approach that considers different forest-based bioenergy pathways within a techno-economic framework to determine the contribution of forest-based bioenergy in mitigating climate change. These pathways are incorporated into the NATEM-Québec, a detailed bottom-up energy model. The results demonstrate the importance of forest-based bioenergy as a key component of Quebec's decarbonization strategy and highlight its potential for other regions experiencing a decline in traditional forest products. Furthermore, the findings emphasize the challenges of decarbonizing the transportation and heavy industry sectors while also emphasizing the need for extensive electrification and increased bioenergy usage. This thesis also develops an integrated approach by integrating CBM-CFS3, a stand- and landscape-level modeling framework, with NATEM-Québec to eliminate the carbon neutrality assumption prevalent in previous literature, addressing potential accounting errors and biased decision-making. Integrating biogenic CO2 flows into the energy system model reveals the role of forest sequestration in mitigating emissions and the need to include biogenic emissions in nationally determined contributions. Accounting for biogenic emissions leads to reduced biomass and bioenergy usage in GHG scenarios. Therefore, assuming biogenic carbon neutrality may result in biased decision-making because it allows the model to use more biomass without being constrained by biogenic CO2 emissions. While immediate investment in bioenergy with carbon capture and storage, direct air capture, and other negative emission technologies may not be feasible, transitioning towards cost-effective forest management strategies can facilitate achieving net-zero emissions by 2050. Moreover, this research undertakes an extensive regionalized techno-economic analysis, focusing on various hydrogen pathways within the province of Quebec, Canada, following the developed integrated approach. The study highlights the significance of hydrogen, both blue and green, in achieving ambitious net-zero emission targets, particularly in difficult-to-decarbonize industrial sectors. The wider adoption of electrolysis is recommended for situations where electrification is not feasible, or energy storage is required. Overall, this research provides valuable insights and recommendations for policymakers and highlights the significance of forest-based bioenergy and hydrogen in the energy transition toward a sustainable, net-zero emission economy.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".