CROSS-COMPARING OECD COUNTRIES ON CARBON EMISSION POLICY IMPLEMENTATION GAP
Bibliographic record
Abstract
The world is facing devastation from climate change as countries are not implementingeffective and efficient carbon emission policies quickly enough to decrease the effects of climatechange. Countries have supposedly tried to decrease their contribution to worsening climatechange but are not meeting their climate goals set by the Paris Climate Change Agreement. Thispaper examines the factors leading to the policy implementation gap in carbon emissions andtheir policy implications. It looks at how the policies implemented based on internationalorganizations fall short of meeting their carbon emission reduction goals. I assess this policyimplementation gap in four Organization for Economic Co-operation and Development (OECD)countries: Australia, Canada, Norway, and Türkiye. I argue that varying levels of politicalwillingness to meet climate goals, the differing types of multilevel governance, and varyingresources devoted to climate issues significantly impact the level of policy implementation gapin carbon emissions. The first type of cross-comparison is to compare each country to each otherindividually in their efforts to decrease the policy implementation gap in carbon emissions. Thesecond cross-comparison is between the two federal systems (Australia and Canada) and twounitary systems (Norway and Türkiye), which may provide patterns of behavior based on thegovernance system. These layers of cross-comparison showcase how political willingness,differing multilevel governance, and varying resources devoted to climate issues affect the policyimplementation gap in carbon 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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".