AHP-TOPSIS-Based Evaluation of the Relative Performance of Multiple Neighborhood Renewal Projects: A Case Study in Nanjing, China
Bibliographic record
Abstract
With the rapid development of urbanization worldwide, there is a large volume of neighborhoods that need to be renewed with various problems such as poor building performance, few public facilities, congested road traffic, unequal living standards, disappearing community culture, and deprived environments. Performance evaluations are considered to be useful tools for ensuring the outcomes of sustainable renewal. Although many research works have assessed the performances of urban renewal projects, evaluations, especially for neighborhood renewal projects, are often overlooked. Besides, it is also hard to find a general standard that is suitable for evaluating the performance of any neighborhood renewal project with a lack of related regulations or codes. Thus, this paper intends to build a framework to assess the relative performances of multiple neighborhood renewal projects through a hybrid AHP-TOPSIS method. A case study in Nanjing, China, is used to show how this framework could be applied to decision-making in order to pursue sustainable neighborhood renewal. The results are expected to provide references for sustainable renewal in each neighborhood. Suggestions related to the findings are proposed to further improve the performances of neighborhood renewal projects, such as establishing a multiple principle–agent framework, providing a sustainable funding system from both the public and private sector, and implementing multiprogram management measures.
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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 | 0.007 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".