A Systematic Review of International Standards and Codes Towards a Unified Framework for Fire Financial Losses
Bibliographic record
Abstract
Abstract In England, fire incidents resulted in an estimated £12 billion in losses in 2022, affecting lives, properties, businesses, and communities. However, current standards and codes for evaluating fire costs remain incomplete. The developed research aims to enhance a unified model for assessing direct and indirect fire losses, and fire protection costs based on a systematic review conducted screening 91 national and international standards from the UK, USA, Canada, Australia, New Zealand, China, Taiwan, as well as the European Union and the International Organization for Standardization with 27 relevant documents selected for analysis. Findings reveal that for property damage, standards often use formula-based approaches such as multiplying the average loss per square meter by construction cost or component-specific calculations. Detailed methodologies for indirect costs remain limited. Additionally, active and passive fire protection measures are estimated as the sum of installation and maintenance expenses. The research demonstrates the critical need for a standardised framework to accurately assess fire financial losses, enhancing decision-making for fire protection investments and mitigating the economic burden of fire incidents.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.165 | 0.396 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.015 |
| Bibliometrics | 0.062 | 0.038 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.007 | 0.009 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".