Factors Affecting CO2 Emissions in the Developing Countries and Developed Countries
Bibliographic record
Abstract
This study investigates the impact of foreign direct investment (FDI), economic growth and energy consumption on carbon emissions in ten selected countries which the total carbon emissions in the world), including five developing countries (China, India, Brazil, Mexico and Indonesia) and five developed countries (European Union, the United States, Canada, the United Kingdom and Japan).This paper employs a panel quantile regression model that takes unobserved individual heterogeneity and distributional heterogeneity into consideration.Moreover, to avoid an omitted variable bias, certain related control variables are included in our model.Our empirical results show that the effect of the independent variables on carbon emissions is heterogeneous across quantiles.Specifically, the effect of FDI on carbon emissions is positive and significant for developed countries.Energy consumption increases carbon emissions, with the strongest effects occurring at middle quantiles for developed countries.Among the high-emissions countries, greater economic growth to reduce emissions.The results of the study also support the validity of the halo effect hypothesis in higher-emissions countries.However, we find little evidence in support of an inverted U-shaped curve in the developing countries.In addition, a higher level of trade openness can mitigate the increase in carbon emissions, especially in low-and high-emissions nations.Finally, the results of the study also provide policymakers with important policy recommendations.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".