Leveraging Artificial Intelligence for Smarter Climate Policy and Strategic Government Planning
Bibliographic record
Abstract
The growing challenges posed by climate change require global strategies that exploit emerging technologies, in particular artificial intelligence (AI), to improve the analysis of climatic data and the planning of resilience. AI capabilities in the processing of vast data sets allow the identification of patterns, the forecasting of the impacts related to the climate and the development of targeted mitigation strategies, positioning it as a transformative tool in the fight against climate change (Al-Raeei, 2024). The integration of AI technologies in climatic initiatives not only promotes innovative research methodologies, but also supports decision makers in the formulation of evidence-based policies to navigate the complexities of climatic systems. The effective analysis of climatic data involves the collection, processing and interpretation of a wide range of sources of environmental data. Artificial intelligence techniques such as machine learning, predictive analysis and the processing of natural language allow the analysis of data in real time, which can improve early warning systems, optimize the allocation of resources and facilitate adaptive management in various sectors (Limón et al., 2025). This analytical power is essential to identify climatic risks and vulnerability, thus allowing governments to implement proactive resilience planning efforts.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 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 | 0.000 |
| 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".