Exploring the Relationship between CO2 Emission, Economic Growth, and Energy Consumption at Aggregate Level: A Panel Data Analysis
Bibliographic record
Abstract
This article explores the relationship between CO2 emissions, economic growth, energy consumption, and, other control factors for the eight highest CO2-emitting countries (USA, Russia, Iran, China, Germany, India, Japan, and Canada) in 1995-2023. The m objective of the study is to determine how economic growth (GDP), energy consumption (EC), industrial production (IP), and other macroeconomic factors influence CO2 emissions. Using panel data analysis, this study applies various econometric techniques, including fixed and random effects models, Multicollinearity tests, heteroscedasticity tests, and cross-sectional dependence tests. Independent variables include GDP, Total energy use, Industrial output, Density of the population, and Trade while the dependent variable is CO2 emission. The results suggest that energy consumption positively correlates with CO2 emission, where, for each unit of energy consumption, CO2 emission increases by 0.0024 units. The outcomes also reveal a negative correlation of international trade with CO2 emissions implying that trade inhibits emissions. However, the relationship between GDP and, carbon emissions was formed to be statistically insignificant. Population density and industrial production have mixed effects on emissions, with industrial production showing a positive impact. The study emphasizes the importance of adopting cleaner energy sources, enhancing energy efficiency, and considering trade policies that might reduce emissions. The findings suggest that transitioning from coal and oil to cleaner energy sources, such as natural gas, could be an effective strategy for reducing carbon emissions without significantly hindering economic growth. The study provides valuable insights for policymakers in high-emission countries aiming to balance economic development with environmental sustainability.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".