The Role of Sustainability Resources of Large Greenhouse Gas Emitters: The Case of Corporations in Alberta, Canada
Bibliographic record
Abstract
With the global challenge of climate change, it becomes crucial to understand the factors that can guide carbon intensive companies to comply with environmental regulations through significant reductions in greenhouse gas (GHG) emissions. Using the natural-resource-based view, the argument in this paper is that focusing on sustainability-driven resources by companies is a way to meet environmental compliance and reduce GHG emissions while gaining differential competitive benefits. A specific analysis on Alberta case has discussed large GHG emitters’ environmental compliance mechanisms in the context of their sustainability resources. The aim is examining if large GHG emitters in Alberta related to corporations having sustainability resources are complying with the Specified Gas Emitters Regulation (SGER) reduction requirement through cleaner-production driven internal mechanisms. The paper examines the existence of the sustainability resources in the reporting companies related to large GHG emitters responsible for 86% of total GHG reported by facilities with emissions above the threshold of 100 kilotonnes of GHG per year under SGER in Alberta. Corporations are found not using their sustainability resource potential to achieve internal reductions in GHG emissions throughout their facilities. Thus, some recommendations are presented for Alberta case as well as for environmental regulations in other jurisdictions that can potentially help policy makers improve their climate change regulations and achieve their global targets and enable companies to gain competitive advantage while meeting GHG reduction compliance.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.004 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".