Approches stratégiques des émissions CO2: Les cas de l'industrie cimentière et de l'industrie chimique (Strategic Approaches to CO2 Emissions: The Case of the Cement Industry and of the Chemical Industry)
Bibliographic record
Abstract
La capacite des entreprises a transformer une contrainte environnementale en source d’opportunite strategique est un sujet controverse dans la litterature. S’appuyant sur une etude comparative des strategies de lutte contre les emissions CO2 mises en place par les industries cimentiere et chimique, l’article demontre que la latitude des entreprises a adopter une approche proactive face au developpement durable est fortement contrainte par les caracteristiques du secteur en termes de dependance vis-a-vis des ressources naturelles, de flexibilite dans la composition du portefeuille d’activites et de structure du secteur aval. Cet article fut nomine en 2012 pour le prix du meilleur article par le Syntec dans la categorie “Management / Ressources Humaines/Organization.”The ability of firms to transform an environmental constraint into a strategic opportunity has been a controversial issue in the literature. Based on a comparative study of CO2 strategies in the cement and chemical industries, the article shows that the capacity of firms to be proactive regarding sustainable development is largely constrained by the characteristics of the sector in terms of dependence on natural resources, flexibility in the composition of activities portfolio and structure of the downstream sector.This paper was nominated in 2012 for the best paper by the Syntec in the category Management/Human Resources/Organization.
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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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".