The Impact of Climate Change on the Insurance Industry: Perceptions of Industry Experts and Corporate Responses
Bibliographic record
Abstract
The impact of climate change is posing substantial risks for contemporary businesses and individuals. In response, insurance companies are adapting old and adopting new strategies and practices. This study aims to identify operational and structural changes that insurance companies implement in response to risks posed by climate change. The overarching goal of this study is to understand the perceptions of industry experts about how climate change impacts the insurance industry, and identify corporate responses to the pressures stemming from climate change and the rising societal awareness of its impact. Using qualitative research methods, we gathered primary data from eight interviews with senior executives involved in sustainability initiatives within the insurance industry, along with secondary data on Singapore’s three largest insurance companies. Our findings indicate that industry experts view climate change as a significant external force influencing corporate strategies and operational frameworks. Further, insurance companies are investing in environmentally friendly businesses, changing product portfolios, and developing collaboration with administrative and regulatory bodies. Implications of these findings for managers and policymakers are discussed, along with directions for future research.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".