RETRACTED: Does political stability contribute to environmental sustainability? Evidence from the most politically stable economies
Post-publication record
OpenAlex flags this work as retracted, but it carries no matching Retraction Watch record in this frame.
Bibliographic record
Abstract
The study evaluates the effect of political risk on CO 2 emission in the top 10 most politically stable economies (Australia, Canada, Germany, Finland, Denmark, Norway, Netherlands, New Zealand, Sweden, and Switzerland) from 1991/Q1 and 2019/Q4. To the investigators' understanding, this is the first empirical analysis that inspects the effect of political risk on CO 2 emissions in the top 10 most politically stable economies. Therefore, the current paper fills a gap in the existing literature. Innovative quantile-on-quantile regression and quantile causality approaches are applied to explore this nexus. The quantile-on-quantile regression results reveal that in the majority of the quantiles, political risk enhances environmental quality for the case of Norway, Sweden, Canada, and Switzerland. Moreover, political risk degrades the quality of the environment in Australia, Germany, and Denmark, while the outcomes were mixed for the rest. Since political stability has encouraged international corporations to invest. As a result, guaranteeing political stability will attract more foreign investment, pressuring the governments of these countries to treat the climate catastrophe more urgently. Moreover, reforms should be aimed at sustaining existing environmental policies related to the green economy, while local and international firms should vigorously pursue investments in renewable energy sources and energy-saving-efficient technologies.
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 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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".