Sustainable and Resilient Architecture: Prioritizing Climate Change Adaptation
Bibliographic record
Abstract
The purpose of this paper is to emphasize the need for architects to prioritize sustainability and resilience in building design, especially in the face of an increase in the frequency and intensity of natural disasters caused by climate change. Sustainable, climate-adapted, and resilient architecture can reduce greenhouse gas emissions, promote resource efficiency, and improve people's quality of life. This paper explores the design aspects of the top ten COTE projects for 2023 recognized by the American Institute of Architects. These projects emphasize sustainable performance, stormwater and energy reduction strategies, and design-for-change principles. The main objective is to identify how sustainable project design adapts to climate change and supports resilient recovery from disasters. The methodology involves identifying and reviewing design criteria for sustainable performance. It also involves analysing stormwater runoff and energy reduction strategies. It investigates the futuristic vision for design for change, and highlights design innovations accomplished through the selected projects. This paper provides valuable insights into how projects approach adaptive and resilient design through sustainability. Architects can benefit from this holistic approach to designing spaces that adapt to the ever-changing climate and promote sustainable design innovation.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".