ASSESSING THE ENVIRONMENTAL IMPACT OF MONETARY POLICY IN THE WORLD LARGEST CARBON EMITTERS: AN EMPIRICAL APPROACH
Bibliographic record
Abstract
The study examines the effect of monetary policy on carbon emissions of the top ten carbon-emitting countries in the world, including China, USA, India, Russia, Japan, Germany, South Korea, Iran, Canada, and Saudi Arabia.For empirical analysis, data is collected from 1992 to 2020, and the panel cointegration technique is applied for estimation.The independent variables included are income, interest rate, urbanization, energy consumption, trade openness, and government fiscal expansion.The estimated results reveal the long-term negative influence of high interest rates on carbon emissions, suggesting that contractionary monetary policy could potentially serve as a mean to mitigate environmental degradation.Economic growth is found to have a nonlinear influence on carbon emissions, validating the existence of the Environmental Kuznet Curve in these countries.It implies that carbon emissions first rise in tandem with economic growth, but subsequently start to decrease as economic growth continues to rise.Energy consumption, urbanization, and government fiscal expansion are found to deteriorate environmental degradation through increased carbon emissions, while trade upgrades the environment by decreasing emissions.These results are robust with alternative equation specifications and estimation technique.These results have important policy implications.
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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 | 0.001 | 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".