Climate Risk, Global Shocks and Ecological Footprint: Policy Uncertainty on CO2 Emissions
Bibliographic record
Abstract
Global climate goals aim to reduce greenhouse gas emissions, slow down climate change and bequeath a more sustainable environment to future generations. This has prompted research on a wide range of factors aimed at increasing knowledge on reducing emissions. However, one key factor that has been neglected is climate risk or climate policy uncertainty. This study investigates the direct and moderating roles of climate policy uncertainty and global shocks, on the ecological footprint of South Africa. Employing a novel climate policy uncertainty index dataset, and rigorous econometric techniques, the study finds that climate policy uncertainty has a reducing effect on carbon emissions while reinforcing the positive effects of income and foreign direct investment on carbon emissions. On the other hand, global uncertainty such as COVID-19 can dampen the positive effects of income and Foreign Direct Investments. The study recommends prioritization of production efficiency and environmentally friendly input to slow down emissions.
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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".