Regional variability and its impact on the decarbonization of emissions-intensive, trade-exposed industries in Canada
Notice bibliographique
Résumé
Emissions-intensive, trade-exposed (EITE) industries must decarbonize to limit global warming to 1.5°C. This study explores how policy stringency and regional variability impact EITE industrial decarbonization. It uses Canada as a case study due to its heterogeneous industrial sector and high regional resource variability. The study has two scenarios: one with global climate action where the world pushes to limit warming to 1.5°C, and one where Canada acts to achieve net-zero emissions by 2050 and the rest of the world lags. The scenarios differ in three ways: the global price of oil, the pace of technological change for low emission technologies, and domestic climate policy. In the global action scenario, a carbon price of $430USD<sub>2020</sub> was needed to achieve 75% decarbonization of EITE industries by 2050. In our global inaction scenario, EITE industries only decarbonize 25%, as domestic climate policy considered international competition and the risk of industrial shutdown. If competitiveness concerns persist as simulated in this scenario, Canada is highly unlikely to achieve deep industrial decarbonization by 2050. The results also show that regional variability plays a significant role in low emissions technology adoption. While all regions will need targeted innovation and commercialization support as well as market uptake mechanisms, we find that relative advantages and disadvantages in terms of resource availability and industrial mix play an important role in how regional decarbonization occurs. For instance, regions with inexpensive local fossil fuels and ready geology suitable for CO<sub>2</sub> storage have a high uptake of carbon capture and storage. Regions with access to abundant hydroelectricity rely more on electrification as a decarbonization pathway. Regions with no relative resource advantages are at greater risk for industrial shutdown due to the higher cost of decarbonization. Achieving national deep decarbonization of EITE industries in Canada or elsewhere is highly unlikely without global climate action due to competitiveness concerns.Policymakers interested in addressing competitiveness concerns might do so either through policy mechanisms that support the uptake of low emission technologies, mechanisms that penalize regions without decarbonization policy, or both.Policymakers should consider relative regional advantages and disadvantages to EITE decarbonization by designing policies that account for industrial heterogeneity and regional resource availability. Achieving national deep decarbonization of EITE industries in Canada or elsewhere is highly unlikely without global climate action due to competitiveness concerns. Policymakers interested in addressing competitiveness concerns might do so either through policy mechanisms that support the uptake of low emission technologies, mechanisms that penalize regions without decarbonization policy, or both. Policymakers should consider relative regional advantages and disadvantages to EITE decarbonization by designing policies that account for industrial heterogeneity and regional resource availability.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
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 tête enseignante, 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 ».