Adapting building performance simulation for climate resilience: accounting for urban microclimates and future climates
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Climate change intensifies extreme events, increasing risks to comfort, thermal stress, and occupant health. To respond, buildings must be designed and operated for climate resilience, which heavily depends on advancing building performance simulation (BPS) tools and practices. Traditional BPS primarily focuses on annual or seasonal performance using historical ‘typical’ or projected weather data, often limited to a single building or a single projection future scenario, and overlooking key factors affecting urban performance. This viewpoint paper critically examines how BPS needs to evolve to support climate resilience. We first identify the limitations of conventional BPS and emphasize the need to scale from individual buildings to neighbourhoods and urban districts, addressing a wide range of climates and extreme conditions. Next, we highlight the importance of advanced downscaling techniques, multi-year climate projections, advanced metrics, and microclimate analysis. In summary, achieving climate-resilient BPS requires broadening both spatial and temporal scales for future-ready building design.
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.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it