A multi-hazard risk assessment for buildings in Ireland due to climate change impacts
Bibliographic record
Abstract
Climate change significantly impacts both the natural and the built environment, necessitating a comprehensive understanding of the risk due to current and future climate-related threats. This study presents a multi-hazard risk assessment framework for buildings in Ireland, serving as an essential first step in developing effective climate adaptation strategies.The framework is constructed based on three typical components of disaster risk assessment: hazard, vulnerability, and exposure analysis. It provides a comprehensive evaluation of climate-related hazards, including heatwaves, wildfires, heavy precipitation, extreme temperatures, landslides, and strong winds. By incorporating various datasets, the methodology employs a systematic and standardized indicator-based approach to evaluate multiple hazards, offering a holistic risk profile.The study demonstrates the framework's application through a case study of Dublin, Ireland. This practical implementation illustrates how the methodology can be used to identify potential climate change risk hotspots in urban environments. The approach allows for a high-level risk assessment, which is crucial before commencing any detailed analysis.By providing a clear and replicable methodology, this research contributes to the global effort to safeguard the built environment against climate change impacts. The framework serves as a valuable tool for policymakers and urban planners, enabling them to prioritize areas for intervention and develop targeted adaptation strategies. This study underscores the importance of proactive risk assessment in enhancing urban resilience to climate change.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".