Does Government Environmental Expenditure Reduce Residential Energy Consumption in Canada? Evidence from Provincial Panel Data
Bibliographic record
Abstract
The residential sector has the potential to significantly impact overall energy consumption and emissions due to the long lifespan of residential buildings and the difficulty of outsourcing residential emissions to other nations. This research primarily aimed to investigate the impact of government expenditure on environmental protection in reducing household energy consumption, thus contributing to Canada’s environmental quality. We utilized panel data from ten Canadian provinces covering the period from 1995 to 2020. To estimate household energy demand, both conceptual and empirical models were developed. Advanced second-generation econometric techniques were applied, including appropriate unit root and co-integration tests. Long-term relationships were analyzed using the fully modified ordinary least squares (FMOLS) and dynamic ordinary least squares (DOLS) methodologies. The long-term estimates indicate that all explanatory variables align with theoretical expectations and are highly significant. The findings reveal that government funding for environmental protection, along with province-specific environmental policies, contributes to reducing per capita residential energy consumption, which is essential for enhancing energy efficiency. Additionally, factors such as weather, income, demographic shifts, and energy prices are found to influence household energy consumption trends. These outcomes highlight the importance of increased government funding for environmental protection and underscore the need for provinces to implement context-specific environmental policies. This approach is essential in the effort to reduce household energy consumption across Canada.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".