Assessment of Energy-demand based GHG Mitigation Options for the Pulp and Paper Sector
Bibliographic record
Abstract
The pulp and paper industry plays a vital role in Canada’s economy, and Alberta’s pulp and paper industry has a 10% production share in Canada. Alberta’s pulp and paper industry is the third largest energy consumer in the province’s industrial sector, and there is significant potential to reduce energy demand and associated greenhouse gas (GHG) emissions. In this research, a bottom-up energy demand tree is developed for Alberta’s pulp and paper industry to understand the energy intensities of various types of equipment associated with different end uses. This demand tree is further used to simulate an integrated resource planning model, the Long-range Energy Alternative Planning (LEAP) system model. Based on expected growth in the pulp and paper industry, a business-as-usual (BAU) scenario is developed for the years 2010 to 2050 to project the energy demand and GHG emissions of Alberta’s pulp and paper mills. Twenty-eight GHG mitigation scenarios are developed for Alberta’s pulp and paper mills, and energy and emissions reductions are estimated with respect to the BAU scenario. The scenarios are also analyzed in terms of the cost-benefit aspects by developing a GHG abatement cost curve. The GHG abatement cost curves compare the scenarios in terms of net GHG mitigation achievable in each scenario and GHG abatement cost ($/tonne of CO2 equivalent mitigation) compared to the business-as-usual case. The energy demand (electricity and natural gas) of Alberta’s pulp and paper mills is expected to decrease from 20.37 PJ in 2010 to 19.46 PJ in 2050 in the BAU scenario. Twenty-eight scenarios were evaluated with the aim of reducing energy demand and mitigating emissions. These scenarios were developed for planning horizons of 2010-2030 and 2010-2050. Implementing the integrated scenarios can reduce emissions by 8.26 MT of CO2 eq. collectively for the years 2010-2050 compared to the BAU scenario.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".