Towards Sustainable Environment in G7 Nations: The Role of Renewable Energy Consumption, Eco-innovation and Trade Openness
Post-publication record
Source: Retraction Watch, joined by DOI. OpenAlex records retraction as is_retracted, a boolean over a state space with at least four values, so it cannot express an expression of concern, a correction or a reinstatement; it reports them as false, which reads as “fine”.
Bibliographic record
Abstract
Some of the globe’s most economically advanced nations make up the G7 (Canada, Japan, France, Germany, Italy, United States and United Kingdom). Nevertheless, in tandem with such strong economic growth, the environmental conditions in these nations have deteriorated, raising serious issues among stakeholders. Therefore, we examine the effect of eco-innovation and trade openness on CO2 emissions in G7 nations. We also take into account the role of renewable energy, economic growth and nonrenewable energy use using a dataset covering the period from 1990–2019. We employed recent econometric techniques such as slope heterogeneity (SH) and cross-sectional dependence (CSD), Westerlund cointegration, fully modified ordinary least square (FMOLS), dynamic ordinary least square (DOLS), panel quantile regression and panel causality tests to assess these associations. The outcomes of the CSD and SH tests disclosed that using a first-generation unit root test will produce biase outcomes. Furthermore, the outcomes of the Westerlund cointegration disclosed support long-run association between CO2 and its drivers. In addition, the results of the long-run estimators (FMOLS and DOLS) unveiled that nonrenewable energy and trade openness contribute to the damage to the environment while economic expansion, renewable energy and eco-innovation enhance the quality of the environment. Furthermore, the outcomes of GDP, REC and ECO curb CO2 while NREC energy and TO surge CO2. Finally, the outcomes of the panel causality test unveiled that CO2 emissions can be predicted by all the exogenous variables.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".