Achieving environmental quality through stringent environmental policies: Comparative evidence from G7 countries by multiple environmental indicators
Bibliographic record
Abstract
Compatible with the increasing public interest on climate change, countries have taken measures to combat climate change and support environmental sustainability. Considering this fact, this study investigates whether environmental measures, proxied by the environmental policy stringency (EPS) index, are efficient in achieving sustainability of environment in G7 countries as the leading economies; uses multiple environmental sustainability indicators, and applies quantile methods from 1991/Q1 to through 2020/Q4. The results show that (i) EPS curbs carbon dioxide emissions in France and the United States across all quantiles. Also, it has a declining effect in Germany and Italy at lower quantiles and in Canada at lower and higher quantiles; (ii) EPS declines ecological footprint in United States across all quantiles, while it curbs in Canada and Germany at lower quantiles as well as in Italy and United Kingdom at higher quantiles; (iii) EPS stimulates load capacity factor in France, United Kingdom, and United States across all quantiles and in Canada at higher quantiles; (iv) causal effect of EPS on the environment varies throughout quantiles; (v) the robustness of the results by quantile regression method is verified. Overall, the results reveal that the effect of EPS on environmental sustainability differentiates across environmental indicators, countries, and quantiles. In ensuring environmental quality, EPS is completely helpful in the United States, fully inefficient in Japan, and has a mixed effect in remaining G7 countries.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".