Aiming for Adaptation: Developing a Quantitative Framework for Building Resilience in Response to Climate Change-Induced Grid Outages
Bibliographic record
Abstract
Climate change intensifies the occurrence of extreme weather events, resulting in prolonged power outages and raising concerns about the resilience of buildings. This research addresses critical challenges in resilience development, including the lack of flexible definitions, cost-effective solutions, and financial evaluation metrics. A comprehensive framework was developed to assess building resilience through energy simulations, risk-based financial modeling, and occupant surveys on willingness to pay (WTP) for resilience improvements. The findings highlight a 70% enhancement in resilience metrics with proposed upgrades compared to code-compliant designs and an 88% reduction in financial risks under extreme weather conditions. Occupant surveys revealed a low WTP for resilience measures, steering the study towards cost-effective strategies. Pre-conditioning techniques improved thermal resilience by 18%, while integrating Phase Change Materials (PCMs) achieved up to a 50% combined improvement. This work contributes to the field by proposing a quantitative resilience framework with flexible Key Performance Indicators (KPIs) for building performance evaluation during outages. It advances modeling methodologies for resilience assessment and offers a multi-objective framework to integrate financial risks into decision-making. The research underscores the gap between WTP and the required investments, identifying scalable low-cost solutions through passive and active strategies. These solutions enhance thermal resilience and economic feasibility, supporting decision-makers in addressing climate change impacts effectively.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".