The impact of carbon emissions on country risk: Evidence from the G7 economies
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper empirically investigates the effect of carbon emissions on sovereign risk? To answer this question, we use fixed effects model by using annual data from G7 advanced economies, which includes Canada, France, Germany, Italy, Japan, UK and USA, for the period from 1996 to 2014. We employ a novel extreme value theory to measure sovereign risk. The results indicate that climate change (carbon emissions) are likely to increase sovereign risk significantly. We also expand our analysis to some specific sectors, as some of the sectors emit more carbon than others. Specifically, we take top three polluting sectors namely: transportation, electricity and industry and show that they are more likely to increase the sovereign risk. Our results are robust to change in risk measures, estimation in differences and dynamic version of econometric models. Therefore, we have robust consideration that the carbon emissions significantly explain the sovereign risk.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it