*Names in bold indicate Presenter
These dynamics are at odds with the goals of prominent federal programs (such as the U.S. Department of Energy’s SunShot initiative) that seek practical strategies to speed overall rates of solar PV adoption. We show how this agent-based model can be used to suggest levers for governmental intervention. Depending on levels of network segregation observed in the system, it is possible to predict when governmental interventions will be most useful for promoting a sustained increase in solar PV adoption. These dynamics—including predictions about the effectiveness of government programs meant to speed overall solar adoption trends—are illustrated in empirical application of our model to approximately 50,000 solar installations in southern California.
Overall, this research helps to build emerging theories of social learning and network segregation—understanding these processes is critically important as we seek practical strategies that may be used to enhance pro-environmental behaviors and move towards a sustainability transition. This research illustrates how agent-based modeling approaches may be used to test fundamental theories of environmental behavior that are often not feasible to test using traditional methods of statistical analysis. Moreover, we illustrate how agent-based modeling frameworks may be used to evaluate and experiment with policy interventions in a virtual space, which enhances the scientific basis of environmental policymaking.