Climate change adaptation and mitigation strategies at future smart cities
Bibliographic record
Abstract
The increasing impacts of climate change, rising sea levels, unpredictable weather conditions, unforeseen natural events, natural disasters, extinction of biodiversity, and the vulnerability of ecosystems are causing concerns across the globe. Amidst the environmental vulnerabilities and unique socio-economic contexts, cities worldwide face severe challenges in ensuring sustainable development. Therefore, climate change adaptation and mitigation are critical components of planning and developing future smart cities. Smart cities leverage technology and data to improve urban infrastructure, enhance quality of life, and reduce environmental impacts. Here are some key strategies for adaptation and mitigation in smart cities. It is crucial to stress the equal need for adaptation and mitigation strategies to ensure effective climate change adaptation. Adaptation strategies include developing resilient infrastructure, providing early warning systems, effective water management, and public health initiatives. Mitigation strategies include but are not limited to integrating renewable energy technologies, energy efficiency measures, sustainable transportation systems, increasing urban greenery, implementing carbon capture, implementing storage technologies, and Implementing practices that reduce waste, promote recycling, and encourage the reuse of materials. This paper highlights that by integrating these strategies, future smart cities can effectively address the challenges posed by climate change, enhancing resilience and sustainability while improving the quality of life for their residents. A few initiatives to integrate technology and community engagement are also discussed, underlining the crucial role of community participation in the fight against climate change.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".