The current insurance practices employed to manage flood risk in the global built environment and their applicability to Sri Lanka
Bibliographic record
Abstract
Purpose This study aims to examin the challenges and issues surrounding flood insurance in Sri Lanka, which can provide valuable insights into how such programs can be effectively implemented in developing countries. It is essential to consider the role of insurance companies, the potential financial burdens and how these practices can be integrated into urban planning to create a more resilient built environment. Design/methodology/approach Published literature was referred to collect information on insurance schemes practiced worldwide. Semi-structured interview transcripts were analyzed, followed by a thematic analysis of the case study. Findings The findings from this study show that most private insurers in Sri 3Lanka are reluctant to provide insurance coverage to high-risk flood-affected communities. Additionally, the flood insurance program introduced by the government in 2016 and 2017 is not functioning anymore because of the insufficient funds allocated to execute such assistance programs. As a remedial measure, developing a national flood insurance scheme is proposed, supported by a strong aid program from international donors and public–private partnerships with government and private insurance companies. As New Zealand, Canada and Indonesia practiced, cross-subsidization and offering discounts for premiums for policyholders who have taken some mitigatory measures are the main findings from the comprehensive literature review. Originality/value One of this study’s main contributions is to identify the key areas that need to be developed in the existing nature of flood insurance in developing countries, including Sri Lanka. These key areas were identified from the feedback of private insurers and the literature on current insurance practices.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".