Assessing the Impact of Factors Driving Global Carbon Dioxide Emissions
Bibliographic record
Abstract
The aim of this study is to empirically investigate the causal relationship between global CO2 emissions and six of their potentially contributing factors (i.e., economic growth, energy consumption, population, trade openness, financial development and corruption), by using a panel data collected from 65 countries during 1995 to 2013. We developed a dynamic model and used a four-step testing procedures (i.e., panel unit root tests, panel cointegration tests, long-run estimates, i.e. FMOLS estimates and a Granger causality test). The results showed that the most important factors driving global CO2 emissions were economic growth, energy consumption, corruption and financial development. It is recommended that countries develop their own CO2 reducing policies by designing an appropriate combination/mix of policy tools, such as regulation, economic, voluntary and educational/ informational instruments to address their environmental pollution. Countries could consider all dimensions of well-being when they measure their economic development. Imposing pollution taxes on fossil fuel based energy supplies, developing emissions standards, strengthening anti-corruption strategies and educating people about the adverse effects of CO2 emissions on the natural environment and human health are potential policy measures.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".