How Global City Intelligence scores affordability, air quality, energy readiness, and resilience with transparent source-backed logic.
Scores are intentionally practical: a city is healthier to live in when access, exposure, affordability, and resilience work together.
| Metric | Value | Context |
|---|---|---|
| Affordability | 25% | Housing pressure, daily essentials, transport dependency, and service access. |
| Air quality | 25% | Health-oriented interpretation of PM2.5, PM10, nitrogen dioxide, ozone, and monitoring confidence. |
| Energy readiness | 25% | Clean-energy transition capacity, grid resilience, climate stress, and renewable-resource context. |
| Urban resilience | 25% | Climate adaptation, institutional capacity, infrastructure reliability, and daily-life continuity. |
The platform avoids thin generated pages by pairing each score with visible context, tables, source blocks, and internal links. Scores are useful only when users can understand what is being rewarded, what is being penalized, and which data category drives the interpretation.
The mock data is production-shaped. Each city, module, ranking, and source is typed so future ingestion can replace static values while preserving crawlable pages and unique metadata.
These pages use trusted institutional references for methodology and context. Mock values are typed and ready to be replaced by API-backed city datasets without changing route structure.
Used as a policy and methodology reference for urban exposure and resilience signals.
Used to normalize air-quality indicators toward health-protective benchmarks.
Used as an energy-resource and weather-normalization reference.
Used to explain urban climate vulnerability and adaptation scoring logic.