The Role of Sustainable Environmental Indicators in Urban Happiness: A Study of Al-Amir Neighborhood in Al-Najaf City, Iraq
Bibliographic record
Abstract
Happy Cities aims to enhance sustainable well-being by creating a sustainable, balanced urban setting.This research seeks to examine the influence of sustainable environmental indicators on the happiness of locals.The Al-Amir neighborhood of the city of Najaf was chosen as a case study due to its high population density, rapid urban expansion, and status as one of Najaf's most significant urban communities.Additionally, there are obvious environmental imbalances that affect people's comfort, pleasure, and satisfaction both directly and indirectly.The research demonstrates its importance through an in-depth analysis of the role of five key sustainable environmental indicators-air quality, green spaces, noise, thermal comfort, and waste management-on residents' happiness.Based on a field analytical technique, the study evaluated the relationship between sustainable environmental indicators and population satisfaction utilizing a population survey, field measurement devices, and Pearson correlation coefficient analysis (P).According to the findings, noise and air quality are the next most important sustainable environmental factors that affect population satisfaction, followed by green spaces and thermal comfort levels, while the impact of waste management was comparatively small.Copenhagen was examined as a cutting-edge worldwide model that has successfully incorporated sustainable environmental indicators into urban design, hence enhancing quality of life, to demonstrate the effect of environmental planning on raising population happiness.The research recommends adopting the Copenhagen experience to develop and improve the environment in Al-Amir neighborhood, particularly by increasing green spaces, improving thermal comfort and ventilation, reducing noise sources, and developing waste management, which contributes to achieving a comfortable, sustainable, and happier urban environment.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".