From Regulation to Results: Examining the Nexus of <scp>GDP</scp> , Energy Use, and Ozone‐Depleting Substances Emissions Post‐Montreal Protocol
Bibliographic record
Abstract
ABSTRACT After the Montreal Protocol was implemented in 1989, nations committed to reducing ozone‐depleting substances (ODS) emissions to improve environmental conditions and mitigate UV radiation effects. These regulations significantly influenced industrial production practices and energy use patterns in various countries. This study investigates the interconnected relationships among GDP, energy use, and ODS emissions. Robust estimates from the Generalised Method of Moments (GMM) are employed to design and analyse a panel Vector Autoregression (VAR) model on data from Latin America and the Caribbean from 1998 to 2018. The main conclusions show that there is an EKC for ODS emissions, a unidirectional causal relationship between GDP and ODS emissions, and a bidirectional relationship between GDP and energy use. These results suggest that continued enforcement of environmental regulations and the promotion of more efficient energy sources are essential to sustaining economic growth while mitigating environmental impacts. Enhanced international cooperation and investment in green energy are crucial for achieving long term sustainability. The research also explores the topic of laws and practical campaigns that try to solve these problems.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".