The climate change impacts and responses index: quantifying disparities and guiding policies for collective resilience
Bibliographic record
Abstract
Identifying climate change risks, vulnerabilities of exposed populations, and implemented responses to climate change are crucial for developing effective strategies to mitigate climate hazards. However, existing climate change assessment indexes still have some limitations, such as insufficient consideration of policy response, limited coverage of countries, and lack of a multidimensional perspective. In this study, we developed a novel climate change impacts and responses (CCIR) index that incorporates comprehensive information on climate risks, disease burden, and mitigation actions in response to climate risks and damage. To our best knowledge, the CCIR index is the first of its kind that explores variations in climate risks, impacts, and responses across countries to identify vulnerabilities and find more targeted solutions. A positive correlation was found between the CCIR index and national Gross Domestic Product per capita, indicating that wealthier countries might allocate more resources toward mitigating climate impacts. Moreover, countries with better climate education tended to have a lower carbon footprint, highlighting the importance of climate education. Furthermore, countries with lower risks of emerging infectious diseases were more likely to consume more renewable energy. The results highlight the value of using a multidimensional CCIR framework to analyze the interactions among socioeconomic factors, environmental policies, and climate change risks in 158 countries. This comprehensive approach provides actionable insights to mitigate climate impacts and improve national climate resilience. It also streamlines monitoring efforts and promotes joint climate action across international boundaries. By identifying climate risks and opportunities, the CCIR index can help policymakers design, refine, and implement adaptation policies and measures to respond to the impacts of climate change.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".