Urban climate modeling is essential for resilient city planning but remains constrained by high computational costs and expert dependence. We introduce KLIMASCANNER, an AI-based QGIS plugin for rapid microclimate predictions, and employ Multi-Attribute Utility Theory (MAUT) to compare it against conventional case-by-case (CBC) modeling across five dimensions: energy efficiency, speed, transparency, cost, and accuracy. Monte Carlo analysis (N=10,000) with three uncertainty sources shows KLIMASCANNER is preferred in 94–99.9% of scenarios depending on stakeholder profile, with break-even at approximately 40 simulations. Sensitivity analyses confirm robustness to cost assumptions and weight configurations, though CBC remains competitive when transparency is prioritized. AI-assisted modeling can democratize urban climate assessment for smaller municipalities, while numerical outputs alone cannot fully capture decision-making complexity in climate-resilient planning. • KLIMASCANNER is an AI-based QGIS plugin that predicts how urban developments affect local temperature, wind fields, cold air corridors, and thermal comfort indices at 5 m resolution. • Multi-Attribute Utility Theory (MAUT) with Monte Carlo uncertainty analysis (N=10,000) provides a rigorous framework to compare AI-based and physics-based climate modeling approaches. • KLIMASCANNER is preferred in 94–99.9% of simulated scenarios, depending on stakeholder profile, with citizens showing the highest uncertainty due to transparency considerations. • Break-even analysis indicates AI-based tools become cost-effective after approximately 40 simulations, democratizing climate assessment for smaller municipalities.
Gaël Kermarrec (Sun,) studied this question.