The carbon tax policy effect on energy intensity in Canada
Bibliographic record
Abstract
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".