Achieving Carbon Neutrality for A Future Large Greenhouse Gas Emitter in Quebec, Canada: A Case Study
Bibliographic record
Abstract
To reach the Paris Agreement targets of holding the global temperature increase below 2 °C above the preindustrial levels, every human activity will need to be carbon neutral by 2050. Feasible means for industries to achieve carbon neutrality must be developed and assessed economically. Herein we present a case study on available solutions to achieve net-zero carbon from the get-go for a planned liquefied natural gas (LNG) plant in Quebec, which would classify as a large Canadian greenhouse gas (GHG) emitter. From a literature review, available options were prioritized with the promoter. Each prioritized potential solution is discussed in light of its feasibility and the associated economic opportunities and challenges. Although net-zero carbon is feasible from the get-go, results show that the promoter should identify opportunities to reduce as much as possible emissions at source, cooperate with other industries for CO2 capture and utilization, replace natural gas from fossil sources by renewable sources and offset the remaining emissions by planting trees and/or buying offsets on the compliance and voluntary markets. As some of these solutions are still to be developed, to ensure timely net-zero pledge for the lifespan of the LNG plant, a portfolio and progressive approach to combine offsets and other options is preferable.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".