Causal analysis of Canada’s environment-growth nexus for inclusive development metrics
Bibliographic record
Abstract
Abstract Little is known about the relevance of alternative measures of growth in environmental and developmental economic analyses. In Canada, for example, no literature exists on whether there is a causal link between the level of environmental performance and alternative measures of economic progress (which are argued to better capture the overall economic wellbeing than the gross domestic product—GDP). As environmental policies may relate to overall economic wellbeing, we opine that understanding overall economic progress is essential for achieving sustainable development and emissions reduction targets. This paper addresses a knowledge gap by assessing the causal links and directions between Canada’s national-level greenhouse gas emissions (GHG—as an indicator of environmental performance) and three alternative measures of economic growth, namely, gross national disposable income (GNDI), human development index (HDI), and index of economic freedom (IEF); from 1995 to 2019. Our results indicate that causality exists between Canada’s GHG and the alternative growth measures. This implies that Canada’s GNDI, HDI, and IEF may be useful and complementary to GDP in forecasting the national-level total GHG emissions. The research provides insights to further consider the role of overall economic wellbeing in the quest for sustainable, lower-emissions, economic development in Canada and by extension in other nations.
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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 | 0.004 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 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".