Investigating the Relationship Between Canada’s Environmental Quality and GDP-Alternative Measures: An Error Correction Approach
Bibliographic record
Abstract
Abstract This paper investigates the long-run relationship between Canada’s total greenhouse gas emissions (as an indicator of environmental quality) and economic development captured by gross domestic product (GDP) and GDP-alternative measures (which are argued to be more representative of the wider-scale economic progress, Rani & Mandal, 2020). The three GDP-alternative measures assessed were gross national disposable income (GNDI), human development index (HDI), and index of economic freedom (IEF). Time series properties of per capita greenhouse gas emissions (GHGpc) were evaluated. Augmented Dickey Fuller stationarity test was performed for GHGpc, after which, Johansen tests were performed to evaluate cointegration between GHGpc and the economic growth measures. Error correction models were run to evaluate the long-run behavior of GHGpc with per capita GDP and GNDI (GDPpc and GNDIpc, respectively), HDI, and IEF. GHGpc was found to be cointegrated with both GDPpc and all the GDP-alternative indicators. The paper contributes to the existing literature by demonstrating that Canada’s per capita GHG emission has a long-run relationship with both GDP and GDP-alternative indicators. This study represents the first assessment in the body of knowledge of the relationship between Canada’s national-level total GHG emissions and GDP-alternative measures. Keywords: Greenhouse gas, Stationarity, Cointegration, Error correction, GDP-alternatives.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".