Which Members of OECD are Inactive or Laissez-Faire at Reducing Greenhouse Gas Emissions
Bibliographic record
Abstract
Global climate change has become a global challenge. Greenhouse gases are one of the leading causes of climate change, especially the emission of carbon dioxide and other greenhouse gases. The emissions of these gases mainly come from human activities such as energy production and use, industrial activities, transportation, and agriculture. The international community has adopted various agreements to reduce global greenhouse gas emissions and achieve climate goals in response to climate change. However, achieving greenhouse gas targets is about more than just reducing overall emissions. Economic efficiency must also be considered. Economic efficiency refers to the maximum effect achieved in achieving a specific goal: the maximum greenhouse gas reduction effect at the least cost. This study analyzed the economic and greenhouse gas emission reduction efficiency of OECD member countries through the two-stage data envelopment analysis method. And, using quartiles, the OECD member countries' comprehensive efficiency grouping to distinguish which countries are inactive in greenhouse gas emission reduction or countries that are laissez-faire. Finally, the study found that Iceland, Luxembourg, and Ireland chose not to curb greenhouse gas emissions to pursue economic development, while Latvia engaged to do both. Meanwhile, Australia, Canada, and the United States have adopted a laissez-faire approach, making no effort to rein in greenhouse gas emissions and boost national economic growth. The results of this study will provide the United Nations and international organizations with a policy reference to promote the reduction of global greenhouse gas emissions.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".