Simulation-based methods using urban climate data to inform policy
Dawn Parker & Rodrigo Costa
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 scenarios
that ask how specific policy levers might impact social, economic, and climate metrics in our
cities. To conduct such analysis, we need models of how shifts in policy levers change the
decisions of key actors, and how these decisions interact to drive outcomes of interest. From a
science viewpoint, these are often referred to as “process-based models.” Such models can
complement, and build on, pattern-based AI and other statistical models. In this presentation, we
will offer a brief introduction to several process-based simulation models that can be applied to
climate challenges in cities: systems dynamics, cellular automaton, and agent-based models.
