Simulation-based methods using urban climate data to inform policy
This presentation focuses on methods to harness city climate data to develop policy solutions.While emerging AI methods are exciting, often for policy analysis, we need to design scenariosthat ask how specific policy levers might impact social, economic, and climate metrics in ourcities. To conduct such analysis, we need models of how shifts in policy levers change thedecisions of key actors, and how these decisions interact to drive outcomes of interest. From ascience viewpoint, these are often referred to as 鈥減rocess-based models.鈥 Such models cancomplement, and build on, pattern-based AI and other statistical models. In this presentation, wewill offer a brief introduction to several process-based simulation models that can be applied toclimate challenges in cities: systems dynamics, cellular automaton, and agent-based models.