Development and application of an integrated smart city model
Bibliographic record
Abstract
This study presents an innovative integrated approach for smart cities, aimed at promoting environmentally sustainable economies through novel technological and socio-economic transitions. The proposed model determines the smart city index (SCI) by aggregating 32 distinct performance indicators that significantly transform the environment, economy, energy, social, governance, and transportation sectors. This model is inherently multidisciplinary and is methodologically processed using multi-criteria decision analysis, which is aggregated using four distinct weighting schemes. The model results reveal that based on the equal weighting scheme, Sydney emerges as the city with the highest SCI score of 0.72, whereas Lima is identified as the city with the lowest SCI score of 0.26. On the other hand, based on the sustainability triad scheme, Toronto tops the list with an SCI score of 0.77, whereas Abuja scores the lowest with an SCI score of 0.31. Interestingly, Toronto, Vancouver, and Montreal continue to maintain their position among the top 5 cities across all three schemes: equal weighting, sustainability triad, and energy-focused schemes. Furthermore, the energy-focused scheme identifies Montreal as the top-performing city, scoring 0.7, followed by Oshawa at 0.67, and four Canadian cities top the SCI scores in this scheme. In contrast, Lima still remains at the bottom of the list with an SCI score of 0.27. Finally, based on a smart health-focused scheme, Sydney, Osaka, and Hämeenlinna rank highest in SCI scores. Overall, the proposed approach and model provide valuable insights and guidelines for policy-makers and urban planners to design and implement smart city initiatives that can significantly enhance sustainable development and improve quality of life in urban settings.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".