A Megacities Review: Comparing Indicator-Based Evaluations of Sustainable Development and Urban Resilience
Bibliographic record
Abstract
Urbanization is defining global change, and megacities are fast becoming a hallmark of the Anthropocene. Humanity’s pursuit toward sustainability is reliant on the successful management of these massive urban centers and their progression into sustainable and resilient settlements. Indicators and indices are applied assessment and surveillance tools used to measure, monitor, and gauge the sustainable development and urban resilience of megacities. Unknown is how indicator-based evaluations of sustainable development and urban resilience of the world’s largest 43 cities compare. In response, this review paper used the PRISMA reporting protocol, governed by 33 established and 10 emerging megacities, to compare and contrast evaluations of sustainable development and urban resilience. Results reveal that applied assessments of sustainable development of megacities appeared earlier in time and were more abundant than those of urban resilience. Geographically, China dominated other nations in affiliations to scientific research for both sustainable development and urban resilience of megacities. Among the 100 most recurrent terms, three distinct key term clusters formed for sustainable development; seven budding key term clusters formed for urban resilience suggesting breadth in contrast to sustainable development depth. The most cited assessments of sustainable development emphasize topics of energy, methodological approaches, and statistical modeling. The most cited assessments of urban resilience emphasize topics of flooding, transit networks, and disaster risk resilience. Megacities research is dominated by few countries, suggesting a need for inclusion and international partnerships. Lastly, as the world’s people become increasingly urbanized, sustainable development and urban resilience of megacities will serve as a key barometer for humanity’s progress toward sustainability.
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 | 0.018 | 0.093 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.022 | 0.029 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".