The definition of a French actuarial climate index; one more step towards a European index
Bibliographic record
Abstract
Climate change is defined often as the long-term fluctuations in climate patterns affecting the planet globally. Its main observed consequences are a rise in average temperatures in many parts of the globe, and an increase in the frequency and severity of extreme weather events, such as floods, droughts, or wind storms. Climate change is also associated with a rise in sea levels, more frequent and more severe wildfires, a loss of biodiversity, as well as other disrupting events that can have a serious economic impact.These new climate risks are increasingly affecting the frequency and the severity of claims in different insurance branches. In order to help insurance companies predict and manage climate risks, North-American actuaries have defined the Actuaries Climate Index™ (ACI), that combines information from several important weather variables in the historical records of United States and Canada. The ACI shows a significantly increasing trend over recent years. Now that the ACI provides a factual and objective climate risk measure for North-America, the need arises to test if a similar tool can measure the impact of climate change in other parts of the planet, and if the change is similar or not. Despite the observed global nature of climate change, different regions and countries can be affected in different ways. As a first step it is important to check if the ACI methodology is useful to assess climate risk even outside the United States and Canada. This paper proposes the use of the same ACI methodology to calculate an actuarial climate index with the climate data of France, which we call the French Actuarial Climate Index (FACI). The paper reviews the methodology and the data used to obtain the FACI, and with it studies the impact of climate change in France, including high-resolution analyses, per component, season and region. Together with the recent indices calculated for Spain and Portugal, this FACI represents one more step towards the definition of a European index.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".