Identifying Challenges in Implementing Sustainable Practices in a Developing Nation
Bibliographic record
Abstract
The South Pars Special Economic Energy Zone (SPSEEZ) is the largest petroleum zone in Iran and the second biggest gas producer in the world. It is now one of the world’s most important eco-industrial poles. Despite the rapid development and activists’ calls to sustainable path, there is little systematic effort in the assessment of industrial zones sustainability in developing countries. Iran, a nation that has ratified the Rio Declaration pact, has moved forward in order to achieve sustainable development. There have always been controversial debates due to its success. This paper employs survey, interview as well as observation to explore the perception of people on planning and sustainable development efforts and to identify the most important challenges at SPSEEZ. The result shows that the major impediment against sustainability is the lack of involvement from urban planners and the public during decision-making process. Finally, the paper contributes to the identification of the most urgent problems in SPSEEZ and the functions of different stakeholders as a reference for better sustainable development planning.
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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 | 0.017 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".