Neighborhood sustainability assessment tools and water system adaptation: a framework to analyse the adaptive capacity in the physical–social context
Bibliographic record
Abstract
The relationship between climate change and sustainable development has rarely been studied, particularly in the context of the built environment development assessment tools and adaptation to both short-and long-term climate change impacts. This research attempts to present a framework to investigate the capacity of three neighborhood sustainability assessment (NSA) tools to enable adaptation to climate change impacts, which are defined here in relation to both physical and social contexts. There are two sets of components that create the structure for the systematic framework. First, the need to address both short-term and long-term impact scenarios, in particular, temperature and precipitation, when analyzing the water sector. It is argued that the adaptive capacity should consider the supply, consumption, and disposal as physical characteristics, and governance and management as social characteristics. To operate this analysis framework the analysis, we argue secondly that both resilience and vulnerability are valuable in analysis of the adaptive capacity in order to identify points of adaptation and exposure. Finally, the resulting analytical framework is applied to three example NSAs, BREEAM COMMUNITIES, LEED-ND, and CASBEE-UD and compares their capacity to enable adaptive capacity. The paper concludes that the three tools have a higher capacity in adapting the physical components to the climate change impacts, than the social, where the latter have shown a noticeable vulnerability in covering issues such as stakeholders' governance, local community participation, and community management, despite the importance of such factors in addressing adaptive capacity to climate change, resulting from both short-and long-term risk scenarios.
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 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 | 0.000 | 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 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".