Insurance and Climate Change Risk Management: Rescaling to Look Beyond the Horizon
Bibliographic record
Abstract
Abstract Climate change represents a significant financial risk to the insurance industry, but research has yet to assess whether the industry is managing this risk. Through the application of scale as a vertically nested hierarchy of relationships, this paper seeks to evaluate whether insurers are ‘rescaling’ risk management practices to accommodate the temporal and spatial uncertainty associated with climate change. This framework is applied to a content analysis of 178 (183) firm responses to the 2012 (2015) U.S. National Association of Insurance Commissioners Climate Risk Disclosure Survey to detect evidence of rescaling through climate change risk management (CCRM). The results reveal that the majority of companies do not integrate climate change into their risk management practices, but reinsurers are rescaling in a greater proportion than primary insurers. This finding confirms that a nested spatial and temporal scale in the insurance industry creates resistance to CCRM. The use of scale contributes to emerging scholarship on organizations and climate change by offering a framework for measuring organizational responses and justifying a research agenda on rescaling strategies as a means of risk 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 | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".