Climate Change Adaptation and Mitigation Strategies in Future Smart Cities
Bibliographic record
Abstract
Urban areas, central to human activity, face significant challenges from climate change and development, necessitating urgent action. The rise of smart cities, utilizing technology to enhance sustainability and quality of life, presents an opportunity to address these issues. This paper examines how future smart cities can leverage innovative technologies, stakeholder collaboration, and sustainable design to build resilient systems. These systems mitigate and adapt to climate challenges, promoting ecological balance and social equity. Key strategies include Vertical Green Structures (VGSs), renewable energy, and energy-efficient prefabricated housing. These homes feature radiative cooling coatings and Phase Change Materials (PCMs), reducing cooling loads by <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$7-17 \%$</tex> across diverse Indian climates. The study underscores the importance of integrating technology and community engagement. This fosters sustainable urban growth amid climate unpredictability.
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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 | 0.000 | 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".