Notice bibliographique
Résumé
In 2015, greenhouse gas (GHG) emissions were 722 Mt CO2e (million tonnes CO2 equivalent) in Canada.1 Canada's GHG emissions goals from the Paris Agreement include reducing emissions to 523 Mt CO2e/year by 2030 and 149 Mt CO2e/year by 2050.2 This paper considers four areas of potential emissions reductions, based on known technology and methods that are currently in use globally. Essentially, what can we do easily? Road transportation, sour gas plants, residential buildings, and commercial and institutional buildings are studied to determine recommendations for GHG emissions reductions. A series of quantitative metrics are laid out to test key aspects of each type of emissions reduction considered, including the economic, environmental and social feasibility in Canada, offering comparative analysis and recommendations. Based on this analysis, with known technology in the four areas noted above, an estimated reduction of 68 Mt CO2e/year or a 9% reduction relative to the 2015 baseline can be achieved. Of the areas considered, the most significant potential GHG reductions are in 2 |passenger and freight vehicles. The retrofitting of existing sour gas plants offers a concentrated CO2 stream and therefore an efficient location in which to capture CO2 with known technology. However, the total volume is relatively small and the costs are concentrated on a small stakeholder group. Strong economic incentives may be required in order to proceed with this measure. In addition to GHG emissions reductions, the vehicle and residential retrofits considered here have a modest operating cost savings incentive for consumers due to the associated reduction in energy usage. The proposed emissions abatement projects outlined here can potentially help Canada reduce emissions by 9%, not huge but a start toward Canada's Paris Agreement goals. However, achievement of these reductions will take time. Policy choices are key to accelerating timing and level of uptake of these abatement methods. In addition, policies must consider Canadian preferences and performance over the last twenty-five years. Lower GHG emissions measures have been in the marketplace for years, but consumer preferences do not always follow even when there are economic incentives. This paper also explores the quandary of why Canadians demonstrate two behaviours: they support carbon reduction initiatives in the forms of carbon tax and participation in the Paris Agreement, while at the same time steadfastly owning larger vehicles and living in larger homes. Evidence suggests Canadians embrace current policies and financial incentives to lower their emissions, but in general do not search out how they can reduce emissions further. In fact, their personal preferences align with economic success and growth, rather than policy ideals.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,003 | 0,004 |
| Études des sciences et des technologies | 0,004 | 0,003 |
| Communication savante | 0,005 | 0,003 |
| Science ouverte | 0,003 | 0,003 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,013 | 0,002 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».