Customizing a sustainability evaluation framework for Infrastructure projects in developing countries: the case study of Iran
Bibliographic record
Abstract
Considering the profound role of infrastructure in the welfare of societies, it is important to invest in their sustainable development, particularly in developing countries. One of the main challenges, however, is the lack of a practical assessment framework and locally-proper criteria to rate the sustainability level. The purpose of this research is identifying proper context-specific sustainability criteria and introducing a sustainability assessment framework for developing countries like Iran, based on the customization of an existing comprehensive assessment framework (i.e., the Envision Rating System). Research data was collected through in-depth interviews with subject-matter experts and using an Analytic Hierarchy Process (AHP) approach to revise the parameters’ weights and points based on the context-specific conditions. Alongside the five newly added credits, the research’s findings on the weights of the main groups represent the higher importance of the social aspect of sustainability in Iran in contrast to the country where the Envision was developed. Also, credits reflecting water crisis and public health concerns in Iran, including ‘Preserve Water Resources’ and ‘Enhance Public Health and Safety’ were recognized as the most important credits in the customized framework, respectively. To validate the application of the customized framework, sustainability performance of a case was studied. This customized framework can meaningfully contribute to sustainable development by providing a new method and solution to appraise the sustainability of infrastructure projects in developing countries and help decision makers build higher-quality infrastructure to improve urban resilience.
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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 | 0.012 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".