Resource and Pathways Analysis for Decarbonizing the Pulp and Paper Sector in Quebec
Bibliographic record
Abstract
Decarbonizing industries could significantly increase electricity demand, necessitating strategic grid expansion. This study evaluates the impact of decarbonizing the Pulp and Paper Sector under four 2050 scenarios: carbon capture, biomass-based, direct electrification, and indirect electrification. A bottom-up approach is employed to estimate 2020 final energy demand by heat grade and subsector. Both final and primary energy demand systems are modeled, accounting for the efficiencies of end-use technologies and primary energy transformation processes. The analysis compares primary renewable energy demand (electricity and biomass) normalized per ton of equivalent CO2 avoided against a business-as-usual scenario. It also considers the requirements for wood residues, organic waste, and CO2 storage. The carbon capture scenario, while low in electricity demand, requires significant organic waste for renewable natural gas production and 2.6 Mt of CO2 storage to offset direct and indirect emissions, making it the least feasible due to uncertainties around carbon storage in Quebec. Among the remaining scenarios, the direct electrification stands out by offering the lowest primary energy demand. It combines heat pumps with electric boilers for steam production and lime kilns are converted to a plasma-based solution. The study also includes a sensitivity analysis highlighting the potential of energy efficiency measures to ease the burden of decarbonization.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".