Optimization of Urban-Scale Sustainable Energy Strategies to Improve Citizens’ Health
Bibliographic record
Abstract
Sustainable energy strategies have been a critical subject for sustainable development, especially in cities. Citizens, as an integral part of the urban environment, play a significant role in urban spaces, as does their health. An accurate understanding of citizens’ mental, social, and physical health in urban settings is required to design and plan better cities. This study aims to assess the level of alignment with health factors in Mahabad, a major medium-sized city in Iran. Previous studies indicate that the built environment can influence health dimensions. Health factors depend to a great extent on how well the environment is formed and how it is put together. This research is a descriptive, analytical, cross-sectional study that analyzes the environment’s psychological elements and physical and mental health factors of Mahabad’s citizens. According to the Cochran model, 384 questionnaires were distributed among households. For data analysis, SPSS 12 and Arc GIS software were used. The main results of this research show that five factors, “Environmental quality”, “Identity and social relationships”, and “Readability”, have the most impact on the physical and mental health of citizens (respondents). These issues are much more pronounced in the downtown neighborhoods. This study showed that urban experts can understand different levels of public health by knowing the historical, social, cultural, and economic factors and characteristics. The result will help decision makers, city authorities, designers, and urban planners to be more informed about citizens’ health and the ways to improve it.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".