Analysis of Economic Growth on Carbon Dioxide Gas Emissions in G20 Countries
Bibliographic record
Abstract
Aims: The purpose of this research is to analyze the effect of GDP, GFCF, and urban population on carbon dioxide gas emissions. In this case, the member countries of the G20 are the group of countries responsible for 75% of the greenhouse gas emissions produced. The role of the G20 countries is needed in reducing the resulting carbon dioxide gas emissions, to prevent global warming or climate change. Study Design: This study used a quantitative descriptive method. Place and Duration of Study: The scope of this research is the member countries of the G20 such as Indonesia, South Africa, United States, Saudi Arabia, Argentina, Australia, Brazil, China, India, United Kingdom, Italy, Japan, Germany, Canada, South Korea, Mexico, France, Russia, and Turkiye, European Union with Time Period 2000-2019. Methodology: This study uses a descriptive method with a quantitative approach, namely to analyze and determine the effect of Gross Domestic Product (GDP), Gross Fixed Capital Formation (GFCF), and urban population (URB) on carbon dioxide gas emissions in the G20 countries. Furthermore, the data used is secondary data with a panel data regression model, namely a combination of time series data and cross sections starting from 2000-2019. Results: The results of this study indicate that GDP, GFCF and urban population have a positive and significant effect on increasing carbon dioxide gas emissions in G20 member countries. Conclusion: Based on the calculation results, it is found that the Gross Domestic Product (GDP), Gross Fixed Capital Formation (GFCF), and urban population (URB) in G20 member countries have a positive and significant effect on increasing carbon dioxide gas emissions, both in partial and simultaneous tests. So that the government's role in this case is needed to maintain a healthy environment with increasing economic growth, or in the sense of creating Sustainable Development Goals.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".