Cities often have dozens of potential climate actions, but lack a way to prioritize them. They don't know which will reduce the most emissions or improve resilience in the most effective way.

This lightweight tool ranks climate actions based on their potential impact — using a mix of expert scoring, local conditions, and AI reasoning.

We started by reviewing action libraries from C40, CDP, and national plans. I mapped common decision factors and user behaviors, then prototyped a ranking experience that feels more like a prioritization workshop than a spreadsheet.

I integrated an LLM to help cities generate custom scenarios or justify choices. I designed the interface to be modular and explainable — so users could tweak assumptions and immediately see how the ranking changed.