Evaluating Urban Heat Island Mitigation Strategies in Rajshahi, Using ENVI-Met: A Remote Sensing Approach
Bibliographic record
Abstract
The Urban Heat Island (UHI) effect is a critical environmental challenge in the 21st century, intensified by rapid urbanization and industrialization. This study focuses on Rajshahi, a rapidly urbanizing city in Bangladesh, where the UHI effect has already begun to manifest significantly. Utilizing ENVI-met software, a comprehensive analysis was conducted to evaluate the effectiveness of urban vegetation strategies, such as green roofs and street planting, in mitigating local temperatures and improving outdoor thermal comfort in Rajshahi's Central Business District. The findings reveal that these mitigation strategies can reduce air temperatures by up to 10 Kelvin, providing substantial cooling benefits. This research highlights the importance of integrating green infrastructure into urban planning to combat the UHI effect, enhance sustainability, and improve the overall livability of urban environments. The study offers valuable insights and practical recommendations for urban planners and policymakers, aiming to foster resilient and sustainable urban development in rapidly growing cities like Rajshahi.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".