Identifying drivers affecting air quality in metropolitan areas of developing countries: evidence from Tehran metropolitan area
Bibliographic record
Abstract
Environmental issues and the significant reduction of air quality in the metropolitan areas of developing countries have become chronic challenges. While the impacts of many reasons such as the rapid trend of urbanization, lacking sustainable thinking in urban planning approaches, and urban sprawl have been explored in previous literature, the role of political economy, especially the structure based on the rentier economy, in the change of air quality as an environmental challenge in the metropolitan areas of developing countries has received little attention. To fill this gap, this study focuses on the role of the rentier economy and identifies the drivers based on it that have a tremendous impact on the air quality in Tehran metropolitan area, Iran. To this end, using the Grounded Theory (GT) foundation database and two-round Delphi survey, the opinions of 19 experts were used to identify and explain major drivers that impact air quality in Tehran. Our findings revealed that nine major drivers have an increasing impact on the air quality in the metropolitan area of Tehran. These drivers considering the dominance of the rentier economy are interpreted as the lack of powerful local governance, the rental economy, centralized structure of government, unsustainable economic development, institutional conflicts, a faulty planning system, financial unsustainability of municipalities, unfair distribution of power, and inefficient urban development policies. Among the drivers, the impacts of institutional conflicts and lack of powerful local governance on air quality are more considerable. This study highlights the role of the rentier economy as a major obstacle to resilient responses and constructive actions against chronic environmental challenges such as drastic changes in air quality in metropolitan areas of developing countries.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".