Methodology
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 data layer is production-shaped. Each city, module, ranking, and source is typed so verified datasets can be integrated continuously without changing crawlable routes, metadata, or structured data.
4 institutional references inform this view, listed below with reliability notes. Structured indicators on this page are directional and intended for orientation; verified datasets are being integrated and official sources should be used for critical decisions.
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.