A global outlook on increasing wildfire risk: Current policy situation and future pathways
Bibliographic record
Abstract
to understand how wildfire risk policies are designed to mitigate1 the impacts of wildfires. Wildfires are a growing threat in many parts of the world, posing significant risks to human life, and the environment. In recent years, wildfires have increased, driven largely by climate change, human activity, and changes in land-use patterns. Wildfire risk adaptation and mitigation measures vary widely between countries and regions around the world. Therefore, it is essential to develop a comprehensive policy approach to mitigate wildfire risks and promote sustainable forest and land management practices. This article aims to provide insight into wildfire policies, implementation actions, and their effectiveness by describing wildfire policies centered mainly on exclusion and wildfire risk mitigation. the article examines existing wildfire-related policies and relevant literature based on 10 systematic factors. Further exploring how these policies can be enhanced to meet the challenges of the coming years for six European countries (Cyprus, France, Greece, Italy, Portugal, UK) as well as Australia, Canada, USA, and South Africa. The status quo, perceived strengths, weaknesses, and recommendations from key-informants were presented to enhance wildfire policies in each country. The article analyses current wildfire policies in fire-prone countries, highlighting regional variations and the need for an integrated management strategy. It offers country-specific recommendations based on the participants viewpoints, for coordinated efforts to mitigate wildfire risks and promote sustainable forest management.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".