The carbon tax policy effect on energy intensity in Canada
Notice bibliographique
Résumé
Purpose This study aims to analyze the impacts of carbon policy on energy intensity across Canadian provinces and industries. This detailed analysis allows for a nuanced understanding of how distinct provinces, categorized as either energy-rich or less energy-endowed, respond differently to policy and economic shifts. Design/methodology/approach We use a decomposition method to investigate the changes in energy intensity arising from changes in the composition of economic activities and efficiency. We also estimate the impact of various socioeconomic factors on energy intensity and its components using a panel data regression method at the provincial and industry levels. Findings Despite an overall increase in energy consumption, the country has seen a decline in energy intensity of about 1.24% per year since 1997. The national and provincial decomposition results suggest that much of the reduction in the intensity index is attributed to efficiency improvements rather than shifts in economic activities. The decline in energy intensity has continued following the initial implementation of carbon policies in provinces. Industry decomposition reveals that industries like agriculture, manufacturing and transportation have decreased their energy intensity, primarily driven by shifts toward less energy-intensive activities. However, mining and construction industries have seen an increase in energy intensity, primarily due to a decline in efficiency. Provincial panel regression results indicate that energy intensity tends to be higher in provinces with increased investment, a higher capital-labor ratio and colder climates. Conversely, energy intensity is lower in provinces with higher energy prices and higher population growth. Carbon taxes have also contributed to decreasing energy intensity, but the effect varies across provinces. Research limitations/implications The decomposition results may overstate the efficiency factor, particularly when dealing with aggregated data. Additionally, the selected timeframe may fail to encompass long-term trends or adequately reflect the full impact of policies implemented towards the study period’s conclusion. Practical implications Our provincial and industry analyses indicate that while efficiency improvements contribute to an overall reduction in Canada’s energy intensity, increased activity in energy-intensive industries, such as the mining industry, counteracts some of the efficiency gains. The carbon policy will be more effective if it accounts for heterogeneous responses of provinces according to their economic structures. Social implications Overall, the policy may not have been effective in improving efficiency due to either its lack of significant impact on people’s living standards or uncertainty surrounding its future. Our findings on the heterogeneous impacts of the carbon tax policy across Canadian provinces and industries will help design strategies that promote both reduced energy intensity and sustainable economic development across Canada’s diverse provinces. Originality/value In this study, we conduct a thorough analysis of the recent energy intensity trends in Canada, examining the impact of various socioeconomic factors and carbon policies at the national, provincial and industry levels from 1997 to 2019. This study contributes to the existing literature on energy intensity by incorporating the carbon tax policy effects across Canadian provinces and industries with the most recent available data. This detailed analysis allows for a nuanced understanding of how distinct provinces, categorized as either energy-rich or less energy-endowed, respond differently to policy and economic shifts.
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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 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| 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) | 0,000 | 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 ».