A Review of Sustainable Urban Development Frameworks in Developing Countries
Bibliographic record
Abstract
Cities are considered the engines of economic prosperity and responsible for a substantial amount of the world’s CO2 environmental pollution. Urban sustainable development frameworks have become common to manage these challenges. However, new frameworks are required as existing global frameworks are not sufficient to meet current challenges nor do they consider future requirements in developing countries. These frameworks currently focus on planning and engineering aspects and lack the flexibility to incorporate local issues factoring in views of stakeholders. Examples of this are issues such as political instability, degradation of public services and utilities and damage to the infrastructure and economic deterioration caused by conflict. This paper reviews and analyses existing sustainable urban development frameworks, it identifies that they have focused on urban sustainable development assessment tools by electing several urban sustainable development factors with rating systems. Through this review, this paper finds several strategies have not been addressed in previous research related to locality specific issues. These include (a) consideration of the views of the public, (b) future urban planning requirements, (c) future domestic property requirements of occupants, and (d) achieving a reduction in domestic energy consumption. Thus, this paper proposes that future frameworks should be designed based on considering stakeholder feedback, experts’ consultation, and validation stage. This is a challenging proposition, however it does, provide significant advantages in highlighting and addressing the community’s priorities for solving problems in the local context, experts’ views, in order to combatting the gaps found between decision-maker’s opinions and public priorities.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".