GHG Emissions and Economic Growth in the European Union, Norway, and Iceland: A Validated Time-Series Approach Based on a Small Number of Observations
Bibliographic record
Abstract
This research aims to ensure methodological conformance and to test the validity of its empirical application. To do so, the study analysed differentiation of the development patterns of four time-series variables. The relationships between greenhouse gas (GHG) emissions, employment, inflation, and gross domestic product (GDP) at constant prices were analysed, comparing the European Union (EU-27) and two European Free Trade Association countries. The study period covers twelve years of monthly and quarterly data from the beginning of 2010 to mid-2021, where the highest frequency of data was 138 observations. The methodology used included unit root testing and the vector autoregressive model (VAR). The study’s main results show that GDP at constant prices significantly affected GHG emissions in the EU-27 countries. Meanwhile, the lag between inflation and employment did not have a considerable impact. This finding shows that inflation was not a stable variable and had a strong autocorrelation. Variable employment did not follow a normal distribution. It was necessary for this research to adopt a suitable model for the technical procedure.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".