Climate Mitigation Innovations From National Legislation Under Risk Conditions
Bibliographic record
Abstract
Most nations across the globe have already embraced climate legislation to tackle the challenge of climate change. This article considers the role of country risk (i.e., economic risk, financial risk, political risk, and climate physical risk) in affecting the relationship between climate mitigation legislation (CML) on climate mitigation innovations (CMIs) using a panel of 130 countries from 1995 to 2022. The findings show that CML generally promotes CMI. However, moderating effects reveal that country risk can weaken the positive impacts of CML on CMI, underscoring the importance of integrating risk management into legislative frameworks to drive CMI. Asymmetry checks show that the direct and moderating effects are more pronounced in countries with greater CMI, suggesting that greater CMI requires stronger risk mitigation. Heterogeneity analysis reveals the moderating effect of risks on the impact of CML on CMI differs significantly between developed and developing countries, with developing countries facing a more urgent need for climate risk management.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".