European CO2 emissions persistence Analysis. A comparative IPCC contributor study with fractional integration
Bibliographic record
Abstract
This paper investigates the persistence of CO 2 emissions in the largest European economies (Germany, France, Italy, Spain, and the Netherlands) from 1970 to 2023 by using a fractional integration framework. With this purpose, we contribute to the existing literature by investigating two research questions. First, to assess persistence in the specific subsectors, organized by Intergovernmental Panel on Climate Change (IPCC) standard categories; and second, to study their cross-country and cross-sectoral long-term and short-term relationships. The main findings suggest clear evidence of persistent patterns in emissions and their associated components, with a significant negative trend in all cases except France. Regarding the relationship between crossed components, we find evidence that transportation and industry demonstrate a high degree of correlation, yet no evidence of cointegration is observed. Conversely, waste shows a high level of cointegration across countries but no correlation. We find different patterns for the remaining components, with no discernible relationship observed across sectors within a single country or across different countries for the same sector. These findings suggest that despite the EU's substantial commitment to reducing carbon emissions, there appears to be no coordinated strategy across the different countries to fully implement these policies. • CO2 emission persistence study in largest EU economies according to IPCC categories. • Clear evidence of persistent patterns in emissions and their associated components. • Significant negative trend in all cases except France. • Results unable to identify any clear patterns across sectors within a single country. • Findings show lack of coordination across EU countries to implement emission policies.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".