Measuring climate change from an actuarial perspective: A survey of insurance applications
Bibliographic record
Abstract
Abstract Climate change refers to persistent alterations to global Earth's climate, such as a rise in global temperatures, which have reached unprecedented peaks in recent years. At the same time, global mean ocean‐and‐sea levels are on an upward trajectory. These climatic shifts significantly influence the frequency, intensity, and duration of extreme weather events, such as heatwaves, heavy precipitations, droughts, floods, and tropical cyclones, which represent substantial risks and challenges for the insurance industry. This paper delves into the profound impact of climate change on the insurance sector, with a particular focus on the agriculture, property, health, and life insurance industries. Our scientific approach consists in measuring climate change through an index composed of a basket of climate and weather‐related extremes, such as the Actuarial Climate Index™ (ACI) defined in and for North America, and its European counterparts, the Iberian ACI (IACI) and French ACI (FACI) climate indices. We discuss how these indices help quantify the impact of climate change on the balance sheets of insurance companies and, therefore, its impact on the sustainability of the insurance business. The paper underscores the pressing need for the insurance industry to adapt and strategically plan for the increasing risks associated with climate change.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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 | 0.001 |
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".