The environmental Kuznets curve reexamined for CO₂ emissions in Canadian manufacturing industries /
Bibliographic record
Abstract
Recent studies of the environmental Kuznets curve raise questions regarding the relationship between environmental indicators and GDP and the fundamental reasons that explain this relationship. In response, this thesis presents one-sector and two-sector models to analyze the alternative causal relationships between an environmental indicator and GDP at different stages of economic development. These models analyze how economic scale, technology, preferences, and economic structure influence the causality and shape of the relationship. These theoretical studies are followed by two empirical studies. The first tests the causal relationship between CO2 emissions and GDP in Canadian manufacturing industries. The second explores several factors as the fundamental causes that influence the CO2 emissions in the same industries. Factors, such as economic scale, preferences, technological progress, structural change, and energy input, are found to be crucial in the determination of CO2 emissions. The empirical results are positive, but there are data limitations. The empirical studies can be re-evaluated as more data becomes available.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".