Enhancing Urban Climate Resistance Through the Application of Selected Strategies and Technologies
Bibliographic record
Abstract
Adapting cities for climate resilience is crucial as climate change increases the frequency and severity of extreme weather events. This study outlines a comprehensive set of resilience strategies aimed at enhancing urban resilience across four key domains: water, food, shelter, and energy. These strategies, applicable to both new and existing neighborhoods, range from simple, short-term measures to complex, long-term initiatives. A three-pronged evaluation framework, consisting of three platforms, is introduced to assess these strategies where criteria are initially selected based on their impact on strategy adoption and implementation. This framework employs hypothetical scores and weights that can be adjusted for specific urban contexts through detailed studies. Key outcomes of the evaluation conducted in the first platform include a systematic method to rank strategies based on six criteria: cost, infrastructure impact, scalability, regulatory and zoning challenges, community acceptance, and maintenance needs. For example, community gardens and rainwater harvesting systems are highly scalable and accepted, whereas green roofs require more investment and maintenance. The second and third platform of the framework facilitate the identification of strategies that enhance resilience across each of the resilience domains, as well as across several domains. The results highlight the top-performing strategies under different weighted scenarios. Strategies like green roofs strategy scores high in domains like water management, due to its capacity to absorb and manage stormwater, and energy, by providing natural insulation that reduces heating and cooling demands. Additionally, green roofs contribute to food production when utilized for urban agriculture and enhance shelter by improving building durability and increasing biodiversity This data-driven framework supports the strategic prioritization of resilience strategies, enhancing urban planning and investment decisions globally. Its modularity ensures adaptability to diverse urban settings and climatic issues.
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 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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.000 |
| 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".