Reducing Carbon Emission Through Corporate Sustainability
Bibliographic record
Abstract
Decarbonization efforts by the Canadian government has put conflict between profit and sustainability in the manufacturing industry in the country. Affected sectors are energy companies, iron and steel makers, chemical producers, and other manufacturing companies that involve burning or altering an element in their process to create new compound. Company's profitability, production levels and competitiveness are all aspects that is being challenged, and finding effective strategies to reduce carbon footprint is the main focus of manufacturing companies today. The purpose of this research is to explore and understand the impact of carbon tax and whether it is an effective policy that will help mitigate climate change. Using a qualitative method, we will collect our initial data by interviewing five executives from five manufacturing companies in Canada. The initial data collection will be combined with case studies and research. Additional data will come from monitoring the progress of decarbonization strategies in a span of five years. We will examine how these strategies have developed over time and the progress they have made in reducing carbon emissions, and test the hypothesis that, carbon tax policy incentivizes manufacturers to reduce their carbon emissions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".