Does Domestic Investment Have a Role in An Economy? An Analysis of the Connection Between CO2 Energy and Growth in the US and Canada
Bibliographic record
Abstract
This study examines the relationship between domestic investment, economic growth, and carbon dioxide (CO2) emissions in the United States and Canada from 1990 to 2021 using ARDL bound testing. The findings reveal a long-run relationship between the variables in both countries. In the US, short-run results show a positive link between domestic investment and CO2 emissions, while economic growth has no significant impact. In the long run, only domestic investment positively influences CO2 emissions. Other factors such as trade openness exhibit positive short-run effects but long-run negative relationships with CO2 emissions, while fossil fuel consumption positively impacts emissions. Granger causality tests reveal bidirectional links between fossil fuel consumption and CO2 emissions, and a unidirectional causality from trade openness to CO2 emissions in the US economy. In Canada, positive short- and long-term connections exist between domestic investment and CO2 emissions. FDI and economic growth exhibit mixed short-run effects on emissions but positive long-term impacts. Fossil fuel consumption negatively affects emissions in both the short and long term, while trade openness shows a positive long-term effect. The findings stress the need for targeted policies to balance economic growth, investments, and environmental sustainability, including promoting renewable energy, sustainable trade, and clean technologies.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".