The relationship between fuel consumption and carbon emissions in Canada using multiple regression analysis and recommendations for Vietnam
Bibliographic record
Abstract
Climate change has been one of the most severe issues nowadays. The overuse of greenhouse gases hurts us, leading to things such as contributing to respiratory disease, extreme weather, and food supply disruptions. This paper is the analysis of the relationship between fuel consumption and carbon emissions in Canada to emphasize on the importance of factors that affect climate change. We get the data from the Government of Canada website for Canada’s part and Macrotrends for Vietnam’s one. In this paper, the method is to use multiple regression analysis to determine the relationship between fuel consumption and carbon emissions. Multiple regression analysis allows to explicitly control for factors that simultaneously influence the dependent variable. The result is that vehicles, especially the more they are used, make a direct impact on and proportional to carbon dioxide emissions. Therefore, it is necessary to invest in cleaner transportation to reduce the carbon dioxide emissions and enhance people’s quality of life in the low-carbon economy. We have the recommendation for Vietnam, specifically, improving the public bus system is one of the suitable options in accordance with Vietnam’s infrastructure.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".